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Chapter 8. Economic and Other Impacts

8.1 Framework for Benefits and Costs

This Chapter presents the costs, benefits and other economic impacts of the proposed 

Phase 2 standards. It is important to note that NHTSA’s proposed fuel consumption standards 

and EPA’s proposed GHG standards would both be in effect, and each would lead to average 

fuel efficiency increases and GHG emission reductions. 

The net benefits of the proposed Phase 2 standards consist of the effects of the program 

on: 

 the vehicle program costs (costs of complying with the vehicle CO2 and fuel consumption 

standards),

 changes in fuel expenditures associated with reduced fuel use resulting from more 

efficient vehicles and increased fuel use associated with the “rebound” effect, both of 

which result from the program,

 the economic value of reductions in GHGs, 

 the economic value of reductions in other non-GHG pollutants, 

 costs associated with increases in noise, congestion, and accidents resulting from 

increased vehicle use,

 savings in drivers’ time from less frequent refueling,

 benefits of increased vehicle use associated with the “rebound” effect,

 the economic value of improvements in U.S. energy security. 

The benefits and costs of these rules are analyzed using 3 percent and 7 percent discount 

rates, consistent with current OMB guidance.AThese rates are intended to represent consumers’ 

preference for current over future consumption (3 percent), and the real rate of return on private 

investment (7 percent) which indicates the opportunity cost of capital. However, neither of these 

rates necessarily represents the discount rate that individual decision-makers use. 

The program may also have other economic effects that are not included here. In 

particular, as discussed in Chapter 2 of the draft RIA, the technology cost estimates developed 

here take into account the costs to hold other vehicle attributes, such as size and performance, 

constant. With these assumptions, and because welfare losses represent monetary estimates of 

how much buyers would have to be compensated to be made as well off as they would have been 

in the absence of this regulation,Bprice increases for new vehicles measure the welfare losses to 

 

A The range of Social Cost of Carbon (SCC) values uses several discount rates because the literature shows that the 

SCC is quite sensitive to assumptions about the discount rate, and because no consensus exists on the appropriate 

rate to use in an intergenerational context (where costs and benefits are incurred by different generations). Refer to 

Section F.1 for more information.

B This approach describes the economic concept of compensating variation, a payment of money after a change that 

would make a consumer as well off after the change as before it. A related concept, equivalent variation, estimates 
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the vehicle buyers.

C

If the full technology cost gets passed along to the buyer as an increase in 

price, the technology cost thus measures the primary welfare loss of the standards, including 

impacts on buyers. Increasing fuel efficiency would have to lead to other changes in the vehicles 

that buyers find undesirable for there to be additional welfare losses that are not included in the 

technology costs. 

As the 2012-2016 and 2017-2025 light-duty GHG/CAFE rules discussed, if other vehicle 

attributes are not held constant, then the technology cost estimates do not capture the losses to 

vehicle buyers associated with these changes.1 The light-duty rules also discussed other potential 

issues that could affect the calculation of the welfare impacts of these types of changes, such as 

aspects of buyers’ behavior that might affect the demand for technology investments, uncertainty 

in buyers’ investment horizons, and the rate at which truck owners trade off higher vehicle 

purchase price against future fuel savings. The agencies seek comments, including supporting 

data and quantitative analyses, of any additional impacts of the proposed standards on vehicle 

attributes and performance, or other potential aspects that could positively or negatively affect 

the welfare implications of this proposed rulemaking. 

Where possible, we identify the uncertain aspects of these economic impacts and attempt 

to quantify them (e.g., sensitivity ranges associated with quantified and monetized GHG impacts; 

range of dollar-per-ton values to monetize non-GHG health benefits; uncertainty with respect to 

learning and markups). For HD pickups and vans, the agencies explicitly analyzed the 

uncertainty surrounding its estimates of the economic impacts from requiring higher fuel 

efficiency in Chapter 7. The agencies have also examined the sensitivity of our estimates of 

savings in fuel expenditures to alternative assumptions about future fuel prices; results of this 

sensitivity analysis can be found in Chapter 8.12 of this draft RIA. NHTSA’s draft EIS also 

characterizes the uncertainty in economic impacts associated with the HD national program. For 

other impacts, however, there is inadequate information to inform a thorough, quantitative 

assessment of uncertainty. EPA and NHTSA continue to work toward developing a 

comprehensive strategy for characterizing the aggregate impact of uncertainty in key elements of 

its analyses and we will continue to work to refine these uncertainty analyses in the future as 

time and resources permit. 

This and other chapters of the draft RIA address Section 317 of the Clean Air Act on 

economic analysis. Chapter 8.11 addresses Section 321 of the Clean Air Act on employment 

analysis. The total monetized benefits and costs of the program are summarized in Section 8.10 

for the preferred alternative and in Chapter 9 for all alternatives. 

 

the income change that would be an alternative to the change taking place. The difference between them is whether 

the consumer’s point of reference is her welfare before the change (compensating variation) or after the change 

(equivalent variation). In practice, these two measures are typically very close together. 

C

Indeed, it is likely to be an overestimate of the loss to the consumer, because the buyer has choices other than 

buying the same vehicle with a higher price; she could choose a different vehicle, or decide not to buy a new 

vehicle. The buyer would choose one of those options only if the alternative involves less loss than paying the 

higher price. Thus, the increase in price that the buyer faces would be the upper bound of loss of consumer welfare, 

unless there are other changes to the vehicle due to the fuel efficiency improvements that make the vehicle less 

desirable to consumers.
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8.2 Conceptual Framework for Evaluating Impacts

The HD Phase 2 proposed standards would implement both the 2007 Energy

Independence and Security Act requirement that NHTSA establish fuel efficiency standards for 

medium- and heavy-duty vehicles and the Clean Air Act requirement that EPA adopt 

technology-based standards to control pollutant emissions from motor vehicles and engines 

contributing to air pollution that endangers public health and welfare. NHTSA’s statutory

mandate is intended to further the agency’s long-standing goals of reducing U.S. consumption 

and imports of petroleum energy to improve the nation’s energy security. 

From an economics perspective, government actions to improve our nation’s energy

security and to protect our nation from the potential threats of climate change address 

“externalities,” or economic consequences of decisions by individuals and businesses that extend 

beyond those who make these decisions. For example, users of transportation fuels increase the 

entire U.S. economy’s risk of having to make costly adjustments due to rapid increases in oil 

prices, but these users generally do not consider such costs when they decide to consume more

fuel. 

Similarly, consuming transportation fuel also increases emissions of greenhouse gases 

and other more localized air pollutants that occur when fuel is refined, distributed, and 

consumed. Some of these emissions increase the likelihood and severity of potential climaterelated economic damages, and others cause economic damages by adversely affecting human 

health. The need to address these external costs and other adverse effects provides a wellestablished economic rationale that supports the statutory direction given to government agencies 

to establish regulatory programs that reduce the magnitude of these adverse effects at reasonable 

costs. 

The proposed Phase 2 standards would require manufacturers of new heavy-duty

vehicles, including trailers (HDVs), to improve the fuel efficiency of the products that they

produce. As HDV users purchase and operate these new vehicles, they would consume 

significantly less fuel, in turn reducing U.S. petroleum consumption and imports as well as 

emissions of GHGs and other air pollutants. Thus as a consequence of the agencies’ efforts to 

meet NHTSA statutory obligations to improve U.S. energy security and EPA’s obligation to 

issue standards “to regulate emissions of the deleterious pollutant... from motor vehicles” that 

endangers public health and welfare,

2

the proposed fuel efficiency and GHG emission standards 

would also reduce HDV operators’ outlays for fuel purchases. These fuel savings are one 

measure of the proposed rule’s effectiveness in promoting NHTSA’s statutory goal of conserving 

energy, as well as EPA’s obligation to assess the cost of standards under section 202 (a) (1) and 

(2) of the Clean Air Act. Although these savings are not the agencies’ primary motivation for 

adopting higher fuel efficiency standards, these substantial fuel savings represent significant 

additional economic benefits of this proposal.

Potential savings in fuel costs would appear to offer HDV buyers strong incentives to pay

higher prices for vehicles that feature technology or equipment that reduces fuel consumption. 

These potential savings would also appear to offer HDV manufacturers similarly strong 

incentives to produce more fuel-efficient vehicles. Economic theory suggests that interactions 

between vehicle buyers and sellers in a normally-functioning competitive market would lead 
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HDV manufacturers to incorporate all technologies that contribute to lower net costs into the 

vehicles they offer, and buyers to purchase them willingly. Nevertheless, many readily available 

technologies that appear to offer cost-effective increases in HDV fuel efficiency (when evaluated 

over their expected lifetimes using conventional discount rates) have not been widely adopted, 

despite their potential to repay buyers’ initial investments rapidly. 

This economic situation is commonly known as the “energy efficiency gap” or “energy

paradox.” This situation is perhaps more challenging to understand with respect to the heavyduty sector versus the light-duty vehicle sector. Unlike light-duty vehicles – which are purchased 

and used mainly by individuals and households – the vast majority of HDVs are purchased and 

operated by profit-seeking businesses for which fuel costs represent a substantial operating 

expense. Nevertheless, on the basis of evidence reviewed below, the agencies believe that a 

significant number of fuel efficiency improving technologies would remain far less widely 

adopted in the absence of these proposed standards. 

Economic research offers several possible explanations for why the prospect of these 

apparent savings might not lead HDV manufacturers and buyers to adopt technologies that 

would be expected to reduce HDV operating costs. Some of these explanations involve failures 

of the HDV market for reasons other than the externalities caused by producing and consuming 

fuel. These include situations where information about the performance of fuel economy 

technologies is incomplete, costly to obtain, or available only to one party to a transaction (or 

“asymmetrical”), as well as behavioral rigidities in either the HDV manufacturing or HDVoperating industries, such as standardized or inflexibly administered operating procedures, or 

requirements of other regulations on HDVs. Other explanations for the limited use of apparently 

cost-effective technologies that do not involve market failures include HDV operators’ concerns 

about the performance, reliability, or maintenance requirements of new technology under the 

demands of everyday use, uncertainty about the fuel savings they will actually realize, and 

questions about possible effects on carrying capacity or other aspects of HDVs’ utility.

In the HD Phase 1 rulemaking (which, in contrast to these proposed standards, did not 

apply to trailers), the agencies raised five hypotheses that might explain this energy efficiency

gap or paradox: 

 Imperfect information in the new vehicle market: information available to prospective 

buyers about the effectiveness of some fuel-saving technologies for new vehicles may

be inadequate or unreliable. If reliable information on their effectiveness in reducing 

fuel consumption is unavailable or difficult to obtain, HDV buyers will 

understandably be reluctant to pay higher prices to purchase vehicles equipped with 

unproven technologies.

 Imperfect information in the resale market: buyers in the used vehicle market may not 

be willing to pay adequate premiums for more fuel efficient vehicles when they are 

offered for resale to ensure that buyers of new vehicles can recover the remaining 

value of their original investment in higher fuel efficiency. The prospect of an 

inadequate return on their original owners’ investments in higher fuel efficiency may

contribute to the short payback periods that buyers of new vehicles appear to 

demand.3 
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 Principal-agent problems causing split incentives: an HDV buyer may not be directly 

responsible for its future fuel costs, or the individual who will be responsible for fuel 

costs may not participate in the HDV purchase decision. In these cases, the signal to 

invest in higher fuel efficiency normally provided by savings in fuel costs may not be 

transmitted effectively to HDV buyers, and the incentives of HDV buyers and fuel 

buyers will diverge, or be “split.” The trailers towed by heavy-duty tractors, which 

are typically not supplied by the tractor manufacturer or seller, present an obvious 

potential situation of split incentives that was not addressed in the HD Phase 1 

rulemaking, but it may apply in this rulemaking. If there is inadequate pass-through 

of price signals from trailer users to their buyers, then low adoption of fuel-saving 

technologies may result.

 Uncertainty about future fuel cost savings: HDV buyers may be uncertain about 

future fuel prices, or about maintenance costs and reliability of some fuel efficiency

technologies. Buyers may react to this uncertainty by implicitly discounting potential 

future savings at rates above discount rates used in this analysis. In contrast, the costs 

of fuel-saving or maintenance-reducing technologies are immediate and thus not 

subject to discounting. In this situation, potential variability about buyers’ expected 

returns on capital investments to achieve higher fuel efficiency may shorten the 

payback period – the time required to repay those investments – they demand in order 

to make them.

 Adjustment and transactions costs: potential resistance to new technologies –

stemming, for example, from drivers’ reluctance or slowness to adjust to changes in 

the way vehicles operate – may slow or inhibit new technology adoption. If a 

conservative approach to new technologies leads HDV buyers to adopt them slowly, 

then successful new technologies would be adopted over time without market 

intervention, but only with potentially significant delays in achieving the fuel saving, 

environmental, and energy security benefits they offer. There also may be costs 

associated with training drivers to realize potential fuel savings enabled by new 

technologies, or with accelerating fleet operators’ scheduled fleet turnover and 

replacement to hasten their acquisition of vehicles equipped with these technologies. 

Some of these explanations imply failures in the private market for fuel-saving 

technology beyond the externalities caused by producing and consuming fuel, while others 

suggest that complications in valuing or adapting to technologies that reduce fuel consumption 

may partly explain buyers’ hesitance to purchase more fuel-efficient vehicles. In either case,

adopting this proposed rule would provide regulatory certainty and thus generate important 

economic benefits in addition to reducing externalities. 

Since the HD Phase 1 rulemaking, new research has provided further insight into 

potential barriers to adoption of fuel-saving technologies. Several studies utilized focus groups 

and interviews involving small numbers of participants, who were people with time and 

inclination to join such studies, rather than selected at random.4 As a result, the information

from these groups is not necessarily representative of the industry as a whole. While these 

studies cannot provide conclusive evidence about how all HDV buyers make their decisions, 

they do describe issues that arise for those that participated.
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One common theme that emerges from these studies is the inability of HDV buyers to 

obtain reliable information about the fuel savings, reliability, and maintenance costs of 

technologies that improve fuel efficiency. In many product markets, such as consumer 

electronics, credible reviews and tests of product performance are readily available to potential 

buyers. In the trucking industry, however, the performance of fuel-saving technology is likely to 

depend on many firm-specific attributes, including the intensity of HDV use, the typical distance

and routing of HDV trips, driver characteristics, road conditions, regional geography and traffic 

patterns.

As a result, businesses that operate HDVs have strong preferences for testing fuel-saving 

technologies “in-house” because they are concerned that their patterns of vehicle use may lead to 

different results from those reported in published information. Businesses with less capability to 

do in-house testing often seek information from peers, yet often remain skeptical of its 

applicability due to differences in the nature of their operations. One source of imperfect 

information is the lack of availability of certain technologies from preferred suppliers. HDV 

buyers often prefer to have technology or equipment installed by their favored original 

equipment manufacturers. However, some technologies may not be available through these 

preferred sources, or may be available only as after-market installations from third parties 

(Aarnink et al. 2012, Roeth et al. 2013).

Although these studies appear to show that information in the new HDV market is often 

limited or viewed as unreliable, the evidence for imperfect information in the market for used 

HDVs is mixed. On the one hand, some studies noted that fuel-saving technology is often not 

valued or demanded in the used vehicle market, because of imperfect information about its 

benefits, or greater mistrust of its performance among buyers in the used vehicle market than 

among buyers of new vehicles. The lack of demand might also be due to the intended use of the 

used HDV, which may not require or reward the presence of certain fuel-saving technologies. In 

other cases, however, fuel-saving technology can lead to a premium in the used market, as for 

instance to meet the more stringent requirements for HDVs operating in California.

All of the recent research identifies split incentives, or principal-agent problems, as a 

potential barrier to technology adoption. These occur when those responsible for investment 

decisions are different from the main beneficiaries of the technology. For instance, businesses

that own and lease trailers to HDV operators may not have an incentive to invest in trailerspecific fuel-saving technology, since they do not collect the savings from the lower fuel costs 

that result. Vernon and Meier (2012) estimate that 23 percent of trailers may be exposed to this 

kind of principal-agent problem, although they do not quantify its financial significance.5

Split incentives can also exist when the HDV driver is not responsible for paying fuel 

costs. Some technologies require additional effort, training, or changes in driving behavior to 

achieve their promised fuel savings; drivers who do not pay for fuel may be reluctant to 

undertake those changes, thus reducing the fuel-saving benefits from the perspective of the 

individual or company paying for the fuel. For instance, drivers might not consistently deploy 
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boat-tails equipped on trailers to improve vehicle aerodynamics.D Vernon and Meier also 

calculate that 91 percent of HDV fuel use is subject to this form of principal-agent problem, 

although they do not estimate how much it might reduce fuel savings to those who are paying for 

the fuel.

The studies based on focus groups and interviews (Klemick et al. 2013, Aarnink et al. 

2012, Roeth et al. 2013) provide mixed evidence on the severity of the split-incentive problem. 

Focus groups often do identify diverging incentives between drivers and the decision-makers 

responsible for purchasing vehicles, and economics literature recognizes that this split incentive 

can be a barrier to adopting new technology. Aarnink et al. (2012) and Roeth et al. (2013) cite 

examples of split incentives involving trailers and fuel surcharges, although the latter also cites 

other examples where these same issues do not lead to split incentives. 

In an effort to minimize problems that can arise form split incentives, many businesses 

that operate HDVs also train drivers in the use of specific technologies or to modify their driving 

behavior in order to improve fuel efficiency, while some also offer financial incentives to their 

drivers to conserve fuel. All of these options can help to reduce the split incentive problem, 

although they may not be effective where it arises from different ownership of combination 

tractors and trailers.

Uncertainty about future costs for fuel and maintenance, or about the reliability of new 

technology, also appears to be a significant obstacle that can slow the adoption of fuel-saving 

technologies. These examples illustrate the problem of uncertain or unreliable information about 

the actual performance of fuel efficiency technology discussed above. In addition, businesses

that operate HDVs may be concerned about how reliable new technologies will prove to be on 

the road, and whether significant additional maintenance costs or equipment malfunctions that 

result in costly downtime could occur. Roeth et al. (2013) and Klemick et al. (2013) both 

document the short payback periods that HDV buyers require on their investments -- usually

about 2 years -- which may be partly attributable to these uncertainties. 

These studies also provide some support for the view that adjustment and transactions 

costs may impede HDV buyers from investing in higher fuel efficiency. As discussed above, 

several studies note that HDV buyers are less likely to select new technology when it is not 

available from their preferred manufacturers. Some technologies are only available as aftermarket additions, which can add other costs to adopting them. 

Some studies also cite driver acceptance of new equipment or technologies as a barrier to 

their adoption. HDV driver turnover is high in the U.S., and businesses that operate HDVs are 

concerned about retaining their best drivers. Therefore, they may avoid technologies that require 

significant new training or adjustments in driver behavior. For some technologies that can be 

used to meet the proposed standards, such as automatic tire inflation systems, training costs are 

likely to be minimal. Other technologies such as stop-start systems, however, may require 

 

D

Some boat-tails are being developed with technology to open them automatically when the trailer reaches a 

suitable speed, to reduce this problem.
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drivers to adjust their expectations about vehicle operation, and it is difficult for the agencies to 

anticipate how drivers will respond to such changes.E

In addition to these factors, the studies considered other possible explanations for HDV 

buyers’ apparent reluctance or slowness to invest in fuel-saving equipment or technology. 

Financial constraints – access to lending sources willing to finance purchases of more expensive 

vehicles – do not appear to be a problem for the medium- and large-sized businesses

participating in Klemick et al.’s (2013) study. However, Roeth et al. (2013) noted that access to 

capital can be a significant challenge to smaller or independent businesses, and that price is 

always a concern to buyers. In general, businesses that operate HDVs face a range of competing 

uses for available capital other than investing in fuel-saving technologies, and may assign higher 

priority to these other uses, even when investing in higher fuel efficiency HDVs appears to 

promise adequate financial returns. 

Other potentially important barriers to the adoption of measures that improve fuel 

efficiency may arise from “network externalities,” where the benefits to new users of a 

technology depend on how many others have already adopted it. One example where network 

externalities seem likely to arise is the market for natural gas-fueled HDVs: the limited 

availability of refueling stations may reduce potential buyers’ willingness to purchase natural 

gas-fueled HDVs, while the small number of such HDVs in-use does not provide sufficient 

economic incentive to construct more natural gas refueling stations. 

Some businesses that operate HDVs may also be concerned about the difficulty in 

locating repair facilities or replacement parts, such as single-wide tires, wherever their vehicles 

operate. When a technology has been widely adopted, then it is likely to be serviceable even in 

remote or rural places, but until it becomes widely available, its early adopters may face 

difficulties with repairs or replacements. By accelerating the widespread adoption of these 

technologies, the proposed standards may assist in overcoming these difficulties.

As discussed previously, the lack of availability of fuel-saving technologies from 

preferred manufactures can also be a significant barrier to adoption (Roeth et al. 2013). 

Manufacturers may be hesitant to offer technologies for which there is not strong demand, 

especially if the technologies require significant research and development expenses and other 

costs of bringing the technology to a market of uncertain demand. 

Roeth et al. (2013) also noted that it can take years, and sometimes as much as a decade, 

for a specific technology to become available from all manufacturers. Many manufacturers 

prefer to observe the market and follow other manufacturers rather than be the first to market 

with a specific technology. The “first-mover disadvantage” has been recognized in other 

research where the “first-mover” pays a higher proportion of the costs of developing technology, 

but loses the long-term advantage when other businesses follow quickly.

6

 In this way, there may

 

E The distinction between simply requiring drivers (or mechanics) to adjust their expectations and compromises in 

vehicle performance or utility is subtle. While the former may not impose significant compliance costs in the long 

run, the latter would represent additional economic costs of complying with the standard.
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be barriers to innovation on the supply side that result in lower adoption rates of fuel-efficiency

technology than would be optimal. 

In summary, the agencies recognize that businesses that operate HDVs are under 

competitive pressure to reduce operating costs, which should compel HDV buyers to identify and 

rapidly adopt cost-effective fuel-saving technologies. Outlays for labor and fuel generally 

constitute the two largest shares of HDV operating costs, depending on the price of fuel, distance 

traveled, type of HDV, and commodity transported (if any), so businesses that operate HDVs 

face strong incentives to reduce these costs.7,8

However, the short payback periods that buyers of new HDVs appear to require suggest 

that some combination of uncertainty about future cost savings, transactions costs, and 

imperfectly functioning markets impedes this process. Markets for both new and used HDVs

may face these problems, although it is difficult to assess empirically the degree to which they 

actually do. Even if the benefits from widespread adoption of fuel-saving technologies exceed 

their costs, their use may remain limited or spread slowly because their early adopters bear a 

disproportionate share of those costs. In this case, the proposed standards may help to overcome 

such barriers by ensuring that these measures would be widely adopted.

Providing information about fuel-saving technologies, offering incentives for their 

adoption, and sharing HDV operators’ real-world experiences with their performance through 

voluntary programs such as EPA’s SmartWay Transport Partnership should assist in the adoption 

of new cost-saving technologies. Nevertheless, other barriers that impede the diffusion of new 

technologies are likely to remain. Buyers who are willing to experiment with new technologies 

expect to find cost savings, but those savings may be difficult to verify or replicate. As noted 

previously, because benefits from employing these technologies are likely to vary with the 

characteristics of individual routes and traffic patterns, buyers of new HDVs may find it difficult 

to identify or verify the effects of fuel-saving technologies in their operations. Risk-averse 

buyers may also avoid new technologies out of concerns over the possibility of inadequate 

returns on their investments, or with other possible adverse impacts. 

Some HDV manufacturers may delay in investing in the development and production of 

new technologies, instead waiting for other manufacturers to bear the risks of those investments 

first. Competitive pressures in the HDV freight transport industry can provide a strong incentive 

to reduce fuel consumption and improve environmental performance. However, not every HDV 

operator has the requisite ability or interest to access and utilize the technical information, or the 

resources necessary to evaluate this information within the context of his or her own operations.

As discussed previously, whether the technologies available to improve HDVs’ fuel 

efficiency would be adopted widely in the absence of the program is challenging to assess. To 

the extent that these technologies would be adopted in its absence, neither their costs nor their 

benefits would be attributed to the program. To account for this possibility, the agencies 

analyzed the proposed standards and the regulatory alternatives against two reference cases, or 

baselines, as described in Section X.

The first case uses a baseline that projects some improvement in fuel efficiency for new 

trailers, but no improvement in fuel efficiency for other vehicle segments in the absence of new 
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Phase 2 standards. This first case is referred to as the less dynamic baseline, or Alternative 1a. 

The second case uses a baseline that projects some improvement in vehicle fuel efficiency for 

tractors, trailers, pickup trucks, and vans but not for vocational vehicles. This second case is 

referred to as the more dynamic baseline, or Alternative 1b.

The agencies will continue to explore reasons for the slow adoption of readily available 

and apparently cost-effective technologies for improving fuel efficiency. We also will review 

any comments we receive on our hypotheses about its causes, as well as data or other 

information that can inform our understanding of why this situation seems to persist. 

8.3 Analysis of the Rebound Effect

The “rebound effect” has been defined a number of ways in the literature, and one 

common definition states that the rebound effect is the increase in demand for an energy service 

when the cost of the energy service is reduced due to efficiency improvements.

9,10,11 In the 

context of heavy-duty vehicles (HDVs), this can be interpreted as an increase in HDV fuel 

consumption resulting from more intensive vehicle use in response to increased vehicle fuel 

efficiency.

F Although much of this vehicle use increase is likely to take the form of increases in 

the number of miles vehicles are driven, it can also take the form of increases in the loaded 

weight at which vehicles operate or changes in traffic and road conditions vehicles encounter as 

operators alter their routes and schedules in response to improved fuel efficiency. Because this 

more intensive use consumes fuel and generates emissions, it reduces the fuel savings and 

avoided emissions that would otherwise be expected to result from the increases in fuel 

efficiency this rulemaking proposes. 

Unlike the light-duty vehicle (LDV) rebound effect, the HDV rebound effect has not 

been extensively studied. According to a 2010 HDV report published by the National Research 

Council of the National Academies (NRC)12, it is “not possible to provide a confident measure of 

the rebound effect,” yet NRC concluded that a HDV rebound effect probably exists and that,

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

rebound effect is not considered.” Although we believe the HDV rebound effect needs to be 

studied in more detail, we have nevertheless attempted to capture its potential effect in our 

analysis of these proposed rules, rather than to await further study. We have elected to do so 

because the magnitude of the rebound effect is an important determinant of the actual fuel 

savings and emission reductions that are likely to result from adopting stricter fuel efficiency and 

GHG emission standards. 

In our analysis and discussion below, we focus on one widely-used metric to estimate the 

rebound effect associated with all types of more intensive vehicle use, the increase in vehicle 

miles traveled (VMT) that results from improved fuel efficiency. VMT can often provide a 

 

F We discuss other potential rebound effects in section 8.3.3, such as the indirect and economy-wide rebound effects. 

Note also that there is more than one way to measure HDV energy services and vehicle use. The agencies’ analyses 

use VMT as a measure (as discussed below); other potential measures include ton-miles, cube-miles, and fuel 

consumption.
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reasonable approximation for all types of more intensive vehicle use. For simplicity, we refer to 

this as “the VMT rebound effect” or “VMT rebound” throughout this section, although we 

acknowledge that it is an approximation to the rebound effect associated with all types of more 

intensive vehicle use. The agencies use our VMT rebound estimates to generate VMT inputs 

that are then entered into the EPA MOVES national emissions inventory model and the Volpe 

Center’s HD CAFE model. Both of these models use these inputs along with many others to 

generate projected emissions and fuel consumption changes resulting from each of the regulatory 

alternatives analyzed.

Using VMT rebound to approximate the fuel consumption impact from all types of more 

intensive vehicle use may not be completely accurate. Many factors other than distance traveled 

– for example, a vehicle’s loaded weight – play a role in determining its fuel consumption, so it 

is also important to consider how changes in these factors are correlated with variation in vehicle 

miles traveled. Empirical estimates of the effect of weight on HDV fuel consumption vary, but 

universally show that loaded weight has some effect on fuel consumption that is independent of 

distance traveled. Therefore, the product of vehicle payload and miles traveled, which typically

is expressed in units of “ton-miles” or “ton-kilometers”, has also been considered as a metric to 

approximate the rebound effect. Because this metric’s value depends on both payload and 

distance, it is important to note that changes in these two variables can have different impacts on

HDV fuel consumption. This is because the fuel consumed by HDV freight transport is 

determined by several vehicle attributes including engine and accessory efficiencies, 

aerodynamic characteristics, tire rolling resistance and total vehicle mass—including payload

carried, if any.

Other factors such as vehicle route and traffic patterns can also affect how each of these

vehicle attributes contributes to the overall fuel consumption of a vehicle. While it seems 

intuitive that if all of these other conditions remain constant, a vehicle driving the same route and 

distance twice will consume twice as much fuel as driving that same route once. However,

because of the other vehicle attributes, it is less intuitive how a change in vehicle payload would 

affect vehicle fuel consumption. 

Because the factors influencing HDV VMT rebound are generally different from those 

affecting LDV VMT rebound, much of the research on the LDV sector is likely to not apply to 

the HDV sector. For example, the owners and operators of LDVs may respond to the costs and 

benefits associated with changes in their personal vehicle’s fuel efficiency very differently than a 

HDV fleet owner or operator would view the costs and benefits (e.g., profits, offering more 

competitive prices for services) associated with changes in their HDVs’ fuel efficiency. To the 

extent the response differs, such differences may be smaller for HD pickups and vans, which 

share some similarities with LDVs. As discussed in the 2010 NRC HD report, one difference 

from the LDV case is that when calculating the change in HDV costs that causes the rebound 

effect, it is more important to consider all components of HDV operating costs. The costs of 

labor and fuel generally constitute the two largest shares of HDV operating costs, depending on 

the price of petroleum, distance traveled, type of vehicle, and commodity transported (if 

any).13,14 Equipment depreciation costs associated with the purchase or lease of an HDV are 

another significant component of total operating costs (Figure 8-1). Even when HDV purchases 

involve upfront, one-time payments, HDV operators must recover the depreciation in the value 
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of their vehicles resulting from their use, so this is likely to be considered as an operating cost 

they will attempt to pass on to final consumers of HDV operator services. 

Figure 8-1 Average Truck Operation Costs

Estimates of the impact of fuel efficiency standards on HDV VMT, and hence fuel 

consumption, should account for changes in all of these components of HDV operating costs. 

The higher the net savings in total operating costs is, the higher the expected rebound effect 

would be. Conversely, if higher HDV purchase costs outweigh future cost savings and total 

operating costs increase, HDV costs could rise, which would likely result in a decrease in HDV

VMT. In theory, other cost changes resulting from any requirement to achieve higher fuel 

efficiency, such as changes in maintenance costs or insurance rates, should also be taken into 

account, although information on these elements of HDV operating costs is extremely limited. 

In this analysis, the agencies adapt estimates of the VMT rebound effect to project the response 

of HDV use to the estimated changes in total operating costs that result from the proposed Phase 

2 standards. 
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Since businesses are profit-driven, one would expect their decisions to be based on the 

costs and benefits of different operating decisions, both in the near-term and long-term. 

Specifically, one would expect commercial HDV operators to take into account changes in 

overall operating costs per mile when making decisions about HDV use and setting rates they

charge for their services. If demand for those services is sensitive to the rates HDV operators

charge, HDV VMT could change in response to the effect of higher fuel efficiency on the rates 

HDV operators charge. If demand for HDV services is insensitive to price (e.g., due to lack of 

good substitutes), however, or if changes in HDV operating costs due to the proposed standards 

are not passed on to final consumers of HDV operator services, the proposed standards may have 

a limited impact on HDV VMT.

The following sections describe the factors affecting the magnitude of HDV VMT

rebound; review the econometric and other evidence related to HDV VMT rebound; and 

summarize how we estimated the HDV rebound effect for this proposal. 

8.3.1 Factors Affecting the Magnitude of HDV VMT Rebound 

The magnitude and timing of HDV VMT rebound result from the interaction of many 

different factors.15 Fuel savings resulting from fuel efficiency standards may cause HDV

operators and their customers to change their patterns of HDV use and fuel consumption in a 

variety of ways. For example, HDV operators may pass on the fuel cost savings to their 

customers by decreasing prices for shipping products or providing services, which in turn could 

stimulate more demand for those products and services (e.g., increases in freight output), and 

result in higher VMT. As discussed later in this section, HDV VMT rebound estimates 

determined via other proxy elasticities vary widely, but in no case has there been an estimate that 

fully offsets the fuel saved due to efficiency improvements (i.e., no rebound effect greater than or 

equal to 100 percent). 

If fuel cost savings are passed on to the HDV operators’ customers (e.g., logistics 

businesses, manufacturers, retailers, municipalities, utilities consumers), those customers might

reorganize their logistics and distribution networks over time to take advantage of lower 

operating costs. For example, customers might order more frequent shipments or choose

products that entail longer shipping distances, while freight carriers might divert some shipments

to trucks from other shipping modes such as rail, barge or air. In addition, customers might

choose to reduce their number of warehouses, reduce shipment rates or make smaller but more 

frequent shipments, all of which could lead to an increase in HDV VMT. Ultimately, fuel cost 

savings could ripple through the entire economy, thus increasing demand for goods and services 

shipped by trucks, and therefore increase HDV VMT due to increased gross domestic product 

(GDP).

Conversely, if fuel efficiency standards lead to net increases in the total costs of HDV 

operation because fuel cost savings do not fully offset the increase in HDV purchase prices and 

associated depreciation costs, then the price of HDV services could rise. This is likely to spur a 

decrease in HDV VMT, and perhaps a shift to alternative shipping modes. These effects could 

also ripple through the economy and affect GDP. Note, however, that we project fuel cost 

savings will offset technology costs in our analysis supporting our proposed standards.
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It is also important to note that any increase in VMT on HDVs impacted by our proposed 

standards may be offset, to some extent, by a decrease in VMT on older HDVs. This may occur 

if lower fuel costs resulting from our standards cause multi-vehicle fleet operators to shift VMT 

to newer, more efficient HDVs in their fleet or cause operators with newer, more efficient HDVs 

to be more successful at winning contracts than operators with older HDVs.

Also, as discussed in Chapter 8.3.3 of this Draft RIA, the magnitude of the rebound effect 

is likely to be influenced by the extent of any market failures that affect the demand for more 

fuel efficient HDVs, as well as by HDV operators’ responses to their perception of the tradeoff 

between higher upfront HDV purchase costs versus lower but uncertain future expenditures on 

fuel.

8.3.2 Econometric and Other Evidence Related to HDV VMT Rebound

As discussed above, HDV VMT rebound is defined as the change in HDV VMT that 

occurs in response to an increase in HDV fuel efficiency. We are not aware of any studies that 

directly estimate this elasticityGfor the U.S. This section discusses econometric analyses of 

other related elasticities that could potentially be used as a proxy for measuring HDV VMT 

rebound, as well as other analyses that may provide insight into the magnitude of HDV VMT

rebound. 

One of the challenges to developing robust econometric analyses of HDV VMT rebound

in the U.S. is data limitations. For example, the main source of time-series HDV fuel efficiency

data in the U.S. is derived from aggregate fuel consumption and HDV VMT data. This may

introduce interdependence or “simultaneity” between measures of HDV VMT and HDV fuel 

efficiency, because estimates of HDV fuel efficiency are derived partly from HDV VMT. This 

mutual interdependence makes it difficult to isolate the causal effect of HDV fuel efficiency on 

HDV VMT and to measure the response of HDV VMT to changes in HDV fuel efficiency. 

Data on other important determinants of HDV VMT, such as freight shipping rates, 

shipment sizes, HDV payloads, and congestion levels on key HDV routes is also limited, of 

questionable reliability, or unavailable. Additionally, data on HDVs and their use is usually only 

available at an aggregate level, making it difficult to evaluate potential differences in 

determinants of VMT for different types of HDV operations (e.g., long-haul freight vs. regional 

delivery operations) or vehicle sub-classes (e.g., utility vehicles vs. school buses). 

Another challenge inherent in using econometric techniques to measure the response of 

HDV VMT to HDV fuel efficiency is developing model specifications that incorporate the 

mathematical form and range of explanatory variables necessary to produce reliable estimates of 

 

G Elasticity is the measurement of how responsive an economic variable is to a change in another. For example:

price elasticity of demand is a measure used in economics to show the responsiveness, or elasticity, of the quantity 

demanded of a good or service to a change in its price. More precisely, it gives the percentage change in quantity 

demanded in response to a one percent change in price.
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HDV VMT rebound. Many different factors can influence HDV VMT, and the complex 

relationships among those factors should be considered when measuring the rebound effect.16 

In practice, however, most studies have employed simplified models. Many use price 

variables (e.g., price per gallon of fuel, or fuel cost per mile driven) and some measure of 

aggregate economic activity, such as GDP. However, some of these studies exclude potentially 

important variables such as the amount of road capacity (which affects travel speeds and may be 

related to other important characteristics of highway infrastructure), or the price or availability of 

competing forms of freight transport such as rail or barge (i.e., characteristics of the overall 

freight transport network). 

8.3.2.1 Fuel Price and Fuel Cost Elasticities

This sub-section reviews econometric analyses of the change in HDV use (measured in 

VMT, ton-mile, or fuel consumption) in response to changes in fuel price ($/gallon) or fuel cost 

($/mile or $/ton-mile). The studies presented below attempt to estimate these elasticities in the 

HDV sector using varying approaches and data sources. 

Gately (1990) employed an econometric analysis of U.S. data for the years 1966 – 1988 

to examine the relationship between HDV VMT and average fuel cost per mile, real Gross 

National Product (GNP), and variables capturing the effects of fuel shortages in 1974 and 1979.17

The study found no statistically significant relationship between HDV VMT and fuel cost per 

mile. Gately’s estimates of the elasticity of HDV VMT with respect to fuel cost per mile were -

0.035 with and -0.029 without the fuel shortage variables, but both estimates had large standard 

errors. However, Gately’s study was beset by numerous statistical problems, which raise serious 

questions about the reliability of its results.H

More recently, Matos and Silva (2011) analyzed road freight transportation sector data 

for the years 1987 – 2006 in Portugal to identify the determinants of demand for HDV freight 

transportation.

18 Using a reduced-form equation relating HDV use (measured in ton-km) to 

economic activity (GDP) and the energy cost of HDV use (measured in fuel cost per ton-km 

carried), these authors estimated the elasticity of HDV ton-km with respect to energy costs to be 

-0.241. An important strength of Matos and Silva’s study is that it also estimated this same 

elasticity using a procedure that accounted for the effect of potential mutual causality between 

HDV ton-km and energy costs, and arrived at an identical value. 

Differences between HDV use and the level of highway service in Portugal and in the 

U.S. might limit the applicability of Matos and Silva’s result to the U.S. The volume and mix of 

commodities could differ between the two nations, as could the levels of congestion on their 

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

 

H The most important of these problems – similar historical time trends in the model’s dependent variable and the 

measures used to explain its historical variation – can lead to “spurious regressions,” or the appearance of behavioral 

relationships that are simply artifacts of the similarity (or correlation) in historical trends among the model’s 

variables.
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characteristics of HDVs themselves. HDVs also operate over a more limited highway network 

in Portugal than in the United States. Unfortunately, it is difficult to anticipate how these 

differences might cause Matos and Silva’s elasticity estimates to differ from what we might find 

in the U.S. Finally, their analysis focused on HDV freight transport and did not consider nonfreight uses of HDVs, which somewhat limits its usefulness in the analysis of this proposed 

rulemaking. 

De Borger and Mulalic (2012) examined the determinants of fuel use in the Denmark 

HDV freight transport sector for the years 1980 – 2007. The authors developed a system of 

equations that capture linkages among the demand for HDV freight transport, HDV fleet 

characteristics, and HDV fuel consumption.19 As De Borger and Mulalic state, “we precisely

define and estimate a rebound effect of improvements in fuel efficiency in the trucking industry: 

behavioral adjustments in the industry imply that an exogenous improvement in fuel efficiency

reduces fuel use less than proportionately. Our best estimate of this effect is approximately 10 

percent in the short run and 17 percent in the long run, so that a 1 percent improvement in fuel 

efficiency reduces fuel use by 0.90 percent (short-run) to 0.83 percent (long-run).”

While De Borger and Mulalic capture a number of important responses that contribute to 

the rebound effect, some caution is appropriate when using their results to estimate the VMT

rebound effect for this proposal. Like the Matos and Silva study, this study examined HDV 

activity in another country, Denmark, which has a less-developed highway system, lower levels 

of freight railroad service than the U.S., and is also likely to have a different composition of 

freight shipping activity. Although the effect of some of these differences is unclear, greater 

competition from rail shipping in the U.S. and the resulting potential for lower trucking costs to 

divert some rail freight to truck could cause the VMT rebound effect to be larger in the U.S. than 

De Borger and Mulalic’s estimate for Denmark. 

On the other hand, if freight networks are denser and commodity types are more 

homogenous in Denmark than the U.S., then shippers may have wider freight trucking options.

If this is the case, shippers in Denmark might be more sensitive to changes in freight costs, 

which could cause the rebound effect in Denmark to be larger than the U.S. Like the Matos and 

Silva study, this analysis also focuses on freight trucking and does not consider non-freight 

HDVs (e.g. vocational vehicles). We have been unable to identify adequate data to employ De 

Borger and Mulalic’s model for the U.S. (mainly because time-series data on freight carriage by 

trucks, driver wages, and vehicle prices in the U.S. are limited).

The Volpe National Transportation Systems Center previously has developed a series of 

travel forecasting models for the Federal Highway Administration (FHWA).20 Work conducted 

by the Volpe Center during 2009-2011 to develop the original version of FHWA’s forecasting 

model was presented in the Regulatory Impact Analysis for the HD GHG Phase 1 rule (see Table 

9-2 in that document: “Range of Rebound Effect Estimates from NHTSA Econometric 

Analysis”).21 In the analysis for the Phase 1 rule, Volpe estimated both state-level and national 

aggregate models to forecast HDV single unit and combination truck VMT that included fuel 
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cost per mile as an explanatory variable. This analysis used data from 1970 – 2008 for its 

national aggregate model, and data for the 50 individual states from 1994 – 2008 for its statelevel model.I, J

Volpe analysts tested a large number of different specifications for its national and state 

level models that incorporated the effects of factors such as aggregate economic activity and its 

composition, the volume of U.S. exports and imports, and factors affecting the cost of producing 

trucking services (e.g., driver wage rates, truck purchase prices, and fuel costs), and the extent 

and capacity of the U.S. and states’ highway networks. Table 8-1 summarizes Volpe’s Phase 1 

estimates of the elasticity of truck VMT with respect to fuel cost per mile.K As it indicates, these

estimates vary widely, and the estimates based on state-level and national data differ 

substantially. 

Table 8-1 Summary of Volpe Center Estimates of Elasticity of Truck VMT with Respect to Fuel Cost per 

Mile

Truck 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%

Volpe staff conducted additional analysis of the models that yielded the estimates of the 

elasticity of truck VMT with respect to fuel cost per mile reported in Table 8-1, using updated 

information on fuel costs and other variables appearing in these models, together with revised 

historical data on truck VMT provided by DOT’s Federal Highway Administration. The newlyavailable data, statistical procedures employed in conducting this additional analysis, and its 

results are summarized in materials that can be found in the docket for this rulemaking. This 

new Volpe analysis was not available at the time the agencies selected the values of the rebound 

effect for this proposal, but the agencies will consider this work and any other new work that 

becomes available in the final rule.

 

I Combination trucks are defined as “all [Class 7/8] trucks designed to be used in combination with one or more 

trailers with a gross vehicle weight rating over 26,000 lbs.” (AFDC, 2014; ORNL, 2013c). Single-unit trucks are 

defined as “single frame trucks that have 2–axles and at least 6 tires or a gross vehicle weight rating exceeding 

10,000 lbs.” (FHWA, 2013).

J The national-level and functional class VMT forecasting models utilize aggregate time-series data for the nation as 

a whole, so that only a single measure of each variable is available during each time period (i.e., year). In contrast, 

the state-level VMT models have an additional data dimension, since both their dependent variable (VMT) and most 

explanatory variables have 51 separate observations available for each time period (one for each of the 50 states as 

well as Washington, DC). In this context, the states represent a “cross-section,” and a continuous annual sequence of 

these cross-sections is available.

K One drawback of the fuel cost measure employed in Volpe’s models is that it is based on estimates of fuel 

economy derived from truck VMT and fuel consumption, which introduces the potential for mutual causality (or 

“simultaneity”) between VMT and the fuel cost measure and makes the effect of the latter difficult to isolate. This 

may cause their estimates of the sensitivity of truck VMT to fuel costs to be inaccurate, although the direction of any 

resulting bias is difficult to anticipate.
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Finally, EPA has contracted with Energy and Environmental Research Associates 

(EERA), LLC to analyze the HDV rebound effect for regulatory assessment purposes. Excerpts 

of EERA’s initial report to EPA are included in the docket and contain detailed qualitative 

discussions of the rebound effect as well as data sources that could be used in quantitative 

analysis.23 EERA also conducted follow-on quantitative analyses focused on estimating the 

impact of fuel prices on VMT and fuel consumption. We have included a working paper in the 

docket on this work.

24 Note that EERA’s working paper was not available at the time the 

agencies conducted the analysis of the rebound effect for this proposal, but the agencies will 

consider this work and any other work in the final rule.

There are reasons to be cautious about interpreting the elasticities from the studies

reviewed in this section as a measure of VMT rebound resulting from our proposed standards. 

For example, vehicle capacity and loaded weight can vary dynamically in the HDV sector –

possibly in response to changes in fuel price and fuel efficiency – and data on these measures are 

limited. This makes it difficult to confidently infer a direct relationship between trucking output 

(e.g., ton-miles carried) and VMT assuming a constant average payload. 

In addition, fuel cost per mile – calculated by multiplying fuel price per gallon by fuel 

efficiency in gallons per mile – and fuel price may be imprecise proxies for an improvement in 

fuel efficiency, because the response of VMT to these variables may differ. For example, if 

truck operators are more attentive to variation in fuel prices than to changes in fuel efficiency, 

then fuel price or fuel cost elasticities may overstate the true magnitude of the rebound effect. 

Similarly, there is some evidence in the literature that demand for crude petroleum and

refined fuels is more responsive to increases than to decreases in their prices, although this 

research is not specific to the HDV sector.

25 Since improved fuel efficiency typically causes fuel 

costs for HDVs to fall (and assuming fuel costs are not fully offset by increases in vehicle 

purchase prices), fuel price or cost elasticities derived from historical periods when fuel prices 

were increasing or fuel efficiency was declining may also overstate the magnitude of the rebound 

effect. An additional unknown is that HDV operators may factor fuel prices and fuel costs into 

their decision-making about rates to charge for their service differently from the way they 

incorporate initial vehicle purchase costs. 

Despite these limitations, elasticities with respect to fuel price and fuel cost can provide 

some insight into the magnitude of the HDV VMT rebound effect. 

8.3.2.2 Freight Price Elasticities

Freight price elasticities measure the percent change in demand for freight in response to 

a percent change in freight prices, controlling for other variables that may influence freight 

demand such as GDP, the extent that goods are traded internationally, and road supply and 

capacity. This type of elasticity is only applicable to the HDV subcategory of freight trucks (i.e., 

combination tractors and vocational vehicles that transport freight). One desirable attribute of 
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such measures for purposes of this analysis is that they show the response of freight trucking 

activity to changes to trucking rates, including changes that result from fuel cost savings as well 

as increases in HDV technology costs.M 

Freight price elasticities, however, are imperfect proxies for the rebound effect in freight 

trucks for a number of reasons.26 For example, in order to apply these elasticities we must 

assume that our proposed rule’s impact on fuel and vehicle costs is fully reflected in freight rates. 

This may not be the case if truck operators adjust their profit margins or other operational 

practices (e.g., loading practices, truck driver’s wages) instead of freight rates. It is not well 

understood how trucking firms respond to different types of cost changes (e.g., changes to fuel 

costs versus labor costs).

Freight price elasticity estimates in the literature typically measure freight activity in tons 

or ton-miles, rather than VMT. As discussed in the previous section, average truck capacity and 

payload in the HDV sector varies dynamically – possibly in response to changes in fuel price and 

fuel efficiency – and data on these measures are limited. This makes it difficult to confidently

infer a direct relationship between ton-miles and VMT by assuming a constant average payload.

Inferring a direct relationship between tons and VMT is even less straightforward. Additionally, 

there are significant limitations on national freight rate and freight truck ton-mile data in the 

U.S., making it difficult to confidently measure the impact of a change in freight rates on tonmiles.27

 

 

M Note however that a percent change in freight activity in response to a percent change in freight rates should 

theoretically be larger than a percent change in freight activity in response to a percent change in fuel efficiency 

because fuel efficiency only impacts a portion of freight operating costs (e.g., fuel and vehicle costs, but not likely 

driver wages or highway tolls).
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Table 8-2 An Illustration of the Impact of Various Factors on the Elasticity of Demand for HDV Freight Services With Respect to the Price of Those 

Services

FACTOR OF 

VARIABILITY 

(1)

SOURCE(2) LEAST 

ELASTIC 

(3)

FACTORS OF 

VARIABILITY FOR 

LEAST ELASTIC 

VALUE (4)

MOST 

ELASTIC 

(5)

FACTORS OF 

VARIABILITY FOR 

MOST ELASTIC 

VALUE (6)

REGION 

(7) 

DEMAND 

MEASURE (8) 

COMMODITY 

(9)

Commodity 

shipped

Abdelwahab (1998) -0.75 Construction -1.40 Textile products US A Mode choice Varies

Friedlaender & Spady (1980) -1.00 Food Products -3.55 Electrical machinery USA Ton-miles Varies

Oum et al (1990) -0.41 Metallic products -1.07 Fuel oil Canada Ton-miles Varies

Winston (1981) -0.14 Lumber, wood and 

Furniture

-2.96 Transport Equipment USA Tons Varies

Friedlaender & Spady (1980) -0.15 Wood/wood products -5.06 Electrical machinery Southern 

USA

Ton-miles Varies

Friedlaender and Spady 

(1981)

-0.59 Petroleum products -1.72 Wood USA tonne-km Various

Campisi and Gastaldi (1996) -0.27 Petroleum products -1.37 Minerals Italy tonnes Varies

Li et al. (2011) -1.09 Other -1.30 Nature resource USA, Italy 

& India

tonne-km Varies

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

Distance 

shipped

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

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

Christidis and Leduc (2009) -0.21 < 800 km -1.15 > 1500 km EU Tons All

Competing 

mode

Rich et al (2011) -0.08 All O-D pairs -0.11 O-D Pairs w/alternatives Scandinavia tonne-km Agricultural 

products

Demand 

Measure

Beuthe et al. (2001) -0.58 Tonnes -1.06 tonne-km Belgium -- Aggregate

Li et al. (2011) -1.02 Tonnes -1.30 tonne-km USA, Italy 

& India

-- Natural 

Resources

Region Abdelwahab (1998) -0.80 USA -2.18 Southwestern USA Various U.S. Mode Choice Metal products

Friedlaender & Spady (1980) -1.66 Mountain-Pacific USA -5.06 Southern USA Various U.S. Ton-miles Electrical 

Machinery

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

Resources

Model Form Oum et al. (1992) -0.69 Translog 1.34 Log-linear Various All Aggregate

Source: J.J. Winebrake et al./Energy Policy 48 (2012) 252-259
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Finally, freight price elasticity estimates in the literature vary significantly based on 

commodity type, length of haul, region, availability of alternative modes (discussed further in 

Section 8.3.2.3 below), and functional form of the model (i.e., log-linear, linear, translog) 

making it difficult to confidently apply any single estimate reported in the literature to 

nationwide freight activity (Table 8-2). For example, elasticity estimates for longer trips tend to 

be larger in magnitude than those for shorter trips, while demand to ship bulk commodities tends 

to be less elastic than for non-bulk commodities. 

Although these factors explain some of the differences among reported estimates, much 

of the observed variation cannot be explained quantitatively. For example, one study that 

controlled for mode, commodity class, demand elasticity measure (i.e., tons or ton-miles), model 

estimation form, country, and temporal nature of data only accounted for about half of the 

observed variation.28

8.3.2.3 Mode Shift Case Study

Although the total demand for freight transport is generally determined by economic 

activity, there is often the choice of shipping freight on modes other than HDVs. This is because 

the United States has extensive rail, waterway, pipeline, and air transport networks in addition to 

an extensive highway network; these networks often closely parallel each other and are often 

viable choices for freight transport for many long-distance shipping routes within the continental 

U.S. If rates for one mode decline, demand for that mode is likely to increase, and some of this 

new demand could represent shifts from other modes.N The “cross-price elasticity of demand,” 

which measures the percentage change in demand for shipping by another mode (e.g., rail) given 

a percentage change in the price of HDV freight transport services, provides a measure of the 

importance of such mode shifting. Aggregate estimates of cross-price elasticities vary widely

29

,

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

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

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

shipments greater than 500 miles, which weigh between 50,000 and 80,000 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 HDV and rail modes, 

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

to the best of our knowledge, the total amount of freight that could potentially be subject to mode 

shifting has not been studied extensively. 

In order to explore the potential for HDV fuel efficiency standards to produce economic 

conditions that favor a mode shift from rail to HDVs, EPA commissioned GIFT Solutions, LLC 

to perform case studies on the HD GHG Phase 1 rule using a number of data sources, including 

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

 

N Rail lines in parts of the U.S. are thought to be currently oversubscribed. If that is the case, and new freight 

demand is already being satisfied by trucks, then this would limit the potential for intermodal freight shifts between 

trucks and rail as the result of this proposed rule. 
Freight Transportation (GIFT) model developed by Winebrake and Corbett, which includes 

information on infrastructure and other route characteristics in the U.S.30,31 

A central assumption in the case studies was that economic conditions would favor a shift 

from rail to HDVs if either the price per ton-mile to ship a commodity by HDV, or the price to 

ship a given quantity of a commodity by HDV, became lower relative to rail transport options 

post-regulation. The results of the case studies indicate that the HD Phase 1 rule would not seem 

to create obvious economic conditions that lead to a mode shift from rail to truck, but there are a 

number of limitations and caveats to this analysis, which are discussed in the final report to EPA 

by GIFT.32,33 For example, even if trucking did not become less expensive than rail postregulation, a relative decrease in the truck versus rail rates might be enough to produce a shift, 

given that other factors could influence shippers’ decisions on modal choice. The study did not, 

however, consider these other factors such as time-of-delivery and modal capacity. As another 

example, the analysis assumes all fuel cost savings and incremental vehicle costs from the HD 

Phase 1 rule would be passed on to shippers via changes in freight rates, even though the analysis 

found some evidence that this might not occur (in two cases, the charges for shipping a truckload 

over a given route and distance were the same despite differences in payloads that should have 

been reflected in their fuel costs). Given these limitations, more work is needed in this area to 

explore the potential for mode shift in response to HD fuel efficiency standards.

8.3.2.4 Case Study Using Freight Price Elasticities

Cambridge Systematics, Inc. (CSI) employed a case study approach using freight price 

elasticity estimates in the literature to show several examples of the magnitude of the HDV 

rebound effect.38 In their unpublished paper commissioned by the National Research Council of 

the National Academies in support of its 2010 HDV report, CSI estimated the effect on HDV 

VMT from a net decrease in operating costs associated with fuel efficiency improvements, using 

two different technology cost and fuel savings scenarios for Class 8 combination tractors. 

Scenario 1 increased average fuel efficiency of the tractor from 5.59 miles per gallon to 6.8 miles 

per gallon, with an additional cost of $22,930 for purchasing the improved tractor. Scenario 2 

increased the average fuel efficiency to 9.1 miles per gallon, at an incremental cost of $71,630 

per tractor. Both of these scenarios were based on the technologies and targets from a report 

authored by the Northeast States Center for a Clean Air Future (NESCCAF) and International 

Council on Clean Transportation (ICCT).39

The CSI estimates were based on a range of direct (or “own-price”) freight elasticities (-

0.5 to -1.5)40 and cross-price freight elasticities (0.35 to 0.59) 41 obtained from the literature.42 In

their calculations, CSI assumed 142,706 million miles of tractor VMT and 1,852 billion tonmiles were affected. The tractor VMT was based on the Bureau of Transportation Statistics’ 

(BTS) estimate of highway miles for combination tractors in 2006, and the rail ton-miles were 

based on the BTS estimate of total railroad miles during 2006. This assumption is likely to 

overstate the rebound effect, since not all freight shipments occur on routes where tractors and 

rail service shipments compete directly. Nevertheless, this assumption appears to be reasonable 

in the absence of more detailed information on the percentage of total miles and ton-miles that 

are subject to potential mode shifting. 
For CSI’s calculations, all costs except fuel costs and vehicle costs were taken from a 

2008 ATRI study.

43

 It is not clear from the report how the new vehicle costs were incorporated 

into CSI’s calculations of per-mile tractor operating costs. For example, neither the ATRI report 

nor the CSI report discusses assumptions about depreciation, useful lifetimes of tractors, and the 

opportunity cost of capital. 

Based on these two scenarios, CSI estimated the change in tractor VMT in response to a 

net decrease in operating costs (i.e., accounting for fuel cost and changes in tractor purchase 

costs) associated with fuel efficiency improvement of 11-31 percent for Scenario 1 and 5-16 

percent for Scenario 2, without accounting for any fuel savings from reduced rail service. When 

the fuel savings from reduced rail usage were included in the calculations, they estimated the 

change in tractor VMT in response to a net decrease in operating costs associated with fuel 

efficiency improvement would be 9-30 percent for Scenario 1, and 3-15 percent for Scenario 2. 

Note that these estimates reflect changes to tractor VMT with respect to total operating 

costs, so they should theoretically be larger than a percent change in tractor VMT with respect to 

a percent change in fuel efficiency because fuel efficiency only impacts a portion of truck 

operating costs (e.g., fuel and vehicle costs, but not likely driver wages or highway tolls).

CSI included caveats associated with these calculations. For example, their report states 

that freight price elasticity estimates derived from the literature are “heavily reliant on factors 

including the type of demand measures analyzed (vehicle-miles of travel, ton-miles, or tons), 

geography, trip lengths, markets served, and commodities transported.” These factors can 

increase variability in the results. Also, estimates in CSI’s study have the limitation of using 

freight price elasticities to estimate the HDV rebound effect discussed previously in Section 

IV.D.2.b. 

8.3.2.5 Simulation Model Study Using Freight Price Elasticities

Guerrero (2014) constructs a freight simulation model of the California trucking sector to 

measure the impact of fuel saving investments and fleet management on GHG emissions.44

Rather than estimating these impacts using econometric analysis of raw data, the study uses 

values from the existing literature. Guerrero determines that “...improving the performance of 

trucking also increases the number of trips demanded because the market price also decreases. 

This ‘rebound’ effect offsets around 40-50 percent of these vehicle efficiency emission 

reductions, with 9-14 percent of the effect coming from increased pavement deterioration and 

31-36 percent coming from increased fuel combustion.” Note that to the extent that trip lengths 

also vary in response to improvements in HDV fuel efficiency, changes in the number of HDV 

trips may not exactly reflect changes in the total number of miles the vehicles are operated. 

However, these findings are based on freight price elasticities, which – as we discuss in 

Section IV.D.2.b and in the context of the CSI study above – have significant limitations. The 

study also simulates only one state’s freight network (California), which may not be a good 

representation of national activity.
8.3.3 How the Agencies Estimated the HDV Rebound Effect for this Proposal

8.3.3.1 Values Used in the Phase 1 Analysis

At the time the agencies conducted their analysis of the Phase 1 fuel efficiency and GHG 

emissions standards, the only evidence on the HDV rebound effect were the previously described 

studies from CSI and the Volpe Center.P On the basis of this evidence, the agencies chose 

rebound effects of 15 percent for vocational vehicles and 5 percent for combination tractors, both 

of which were toward the lower end of the range of values from these studies. The agencies 

found no evidence on the rebound effect for HD pickup trucks and vans, but concluded it would 

be inappropriate to use the values selected for vocational vehicles or combination tractors for 

those vehicles. Because the usage patterns of HD pickup trucks and vans can more closely 

resemble those of large light-duty vehicles, the agencies used the 10 percent rebound effect we

had employed in our most recent light-duty rulemaking to analyze the Phase 1 standards for 2b/3 

vehicles.

8.3.3.2 How the Agencies Analyzed VMT Rebound in this Proposal

After considering the new evidence that has become available since the HD Phase 1 final 

rule, the agencies elected to continue using the rebound effect estimates we used previously in 

the HD Phase 1 rule in our analysis of Phase 2 proposed standards. In arriving at this decision, 

the agencies considered the shortcomings and limitations of the newly-available studies 

described previously, particularly the limited applicability of the two published studies using 

data from European nations to the U.S. context. After weighing these attributes of the more 

recent studies, the agencies concluded that we had insufficient evidence to justify revising the 

rebound effect values that were used in the Phase 1 analysis.

In our assessment, we do not differentiate between short-run and long-run rebound 

effects, although these effects may differ. The vocational and combination truck estimates are 

based on the Volpe Center analysis presented in the HD Phase 1 rule and the case study from 

CSI. As with the HD Phase 1 rule, we did not find any literature specifically examining the HD 

pickup and truck sector. Since these vehicles are used for very different purposes than 

combination tractors and vocational vehicles, and they are more similar in use to large light-duty

vehicles, we have chosen the light-duty rebound effect of 10 percent used in the final rule 

establishing fuel economy and GHG standards for MYs 2017-2025 light-duty vehicles in our 

analysis of HD pickup trucks and vans. 

While for this proposal, the agencies have selected to use these rebound effect values, we 

acknowledge the literature shows a wide range of rebound effect estimates. Therefore, we will 

review and consider revising these estimates in the final rule, taking into consideration all

available data and analysis, including submissions from public commenters and new research on 

the rebound effect. 

 

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

the VMT impact from our proposed standards with respect to changes in the fuel cost per mile 

driven. As described previously, the HDV rebound effect should ideally be a measure of the 

change in fuel consumed with respect to the change in overall operating costs due to a change in 

HDV fuel efficiency. Such a measure would incorporate all impacts from our proposal, 

including those from incremental increases in vehicle prices that reflect costs for improving their 

fuel efficiency. Therefore, VMT rebound estimates with respect to fuel costs per mile must be

“scaled” to apply to total operating costs, by dividing them by the fraction of total operating 

costs accounted for by fuel. 

The agencies scaled the VMT rebound calculations to total operating costs using the most 

recent information from the American Transportation Research Institute (ATRI).45 ATRI 

estimates that the average motor carrier cost per mile is $1.633 for 2012. Other elements of the 

total costs are listed below in Table 8-3.

Table 8-3 Elements of the Operating Costs per Mile

OPERATING COST PER MILE ATRI

Fuel Cost $0.641

New Vehicle Cost $0.174

Maintenance & Repair Cost $0.138

All Other (labor, insurance, etc.) $0.680

Total Motor Carrier Costs $1.633

The agencies made simplifying assumptions in the VMT rebound analysis for this 

proposal, similar to the approach taken during the development of the HD GHG Phase 1 final 

rule. However, for the HD Phase 2 final rulemaking, we plan to use a more comprehensive 

approach. Due to timing constraints during the development of this proposal, the agencies did 

not have the technology package costs for each of the alternatives prior to the need to conduct 

the inventory analysis, except for the pickup truck and van category in analysis Method A. 

Therefore, the same "overall" VMT rebound values were used for Alternatives 2 through 5 (as 

discussed in Chapter 8.3.3 of this Draft RIA and analyzed in Chapter 6 of the Draft RIA), despite 

the fact that each alternative results in a different change in incremental technology and fuel 

costs. For the final rulemaking, we plan to determine VMT rebound separately for each HDV 

category and for each alternative. Tables 64 through 66 in Chapter 7 of the Draft RIA present 

VMT rebound for each HDV sector that we estimated for the preferred alternative. These VMT 

impacts are reflected in the estimates of total fuel savings and reductions in emissions of GHG 

and other air pollutants presented in Section VI and VII of this preamble for all categories.

For the purposes of this proposal, we made several additional simplifying assumptions 

when 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., $125,000 for the reference case 

Class 8 combination tractor, $40,000 for the reference case HD pickups, and $70,000 for the 

vocational vehicles) 46 divided by the total lifetime number of expected vehicle miles (e.g., 1.53 

million miles for a Class 8 combination tractor, 265,869 miles for 2b/3 trucks, and 306,457 miles 

for vocational vehicles). 47 We recognize that this calculation implicitly 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. 

The proposal assumes an “average” incremental technology cost for the alternatives, as 

shown in Table 8-4. The technology cost of the combination tractor category is based on the HD 

GHG Phase 1 technology package cost, plus $2,368 for the trailer technology cost.48 The 

technology cost for HD pickup and vans is also based on the HD GHG Phase 1 technology

package cost.49 The agencies developed a unique vocational vehicle technology cost estimate 

because the HD GHG Phase 1 only represents the impact of tire and engine technologies. 

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

VEHICLE CATEGORY TECHNOLOGY COST

Combination Tractors $8,372

HD Pickup & Vans $985

Vocational Vehicles $1,000

The fuel costs per mile in the analysis were calculated using EIA’s Annual Energy

Outlook 2014’s projections for diesel fuel price.50 The average fuel economy for each category

was determined using MOVES2014. The combination tractor-trailer fuel economy used was 

6.03 mpg, the vocational vehicle category was 9.84 mpg, and the HD pickup category was 13 

mpg. The technology effectiveness of the alternatives in the proposal was assumed to be 20 

percent for combination tractors and 15 percent for HD pickups and vocational vehicles.

The operating costs calculated based on all of these inputs are shown below in Table 8-5. 

Table 8-5 Operating Costs for the Reference and Alternatives

OPERATING COST PER MILE REFERENCE CASE ALTERNATIVES

Fuel Cost $0.620 $0.517

New Vehicle Cost $0.082 $0.087

Maintenance & Repair Cost $0.138 $0.138

All Other (labor, insurance, etc.) $0.680 $0.680

Total Motor Carrier Costs $1.520 $1.422

Other simplifying assumptions include the use of an average cost rather than a marginal 

cost. Some trucking firms may use a marginal cost to determine whether to increase their fuel 

usage, however we do not have any data on when firms might use a marginal cost calculation 

rather than an average cost calculation. Although using a marginal cost might be more 

appropriate for calculating the rebound effect, we do not have a methodology for calculating the 

marginal cost.51 

In the costs and benefits summarized in preamble Section IX.K, we have not explicitly

taken into account any potential fuel savings or GHG emission reductions from the rail, air or 

water-borne shipping sectors due to mode shifting because estimates of this effect seem too 

speculative at this time. Likewise we have not taken into account any fuel savings or GHG 

emissions reductions from the potential shift in VMT from older HDVs to newer, more efficient 
HDVs. As discussed in the preamble at Section IX.E, we have found limited evidence of the 

impact of HDV fuel efficiency standards on mode shifting and no evidence on shifting activity 

away from older HDVs to newer HDVs. 

In addition, we have not attempted to capture the extent of how current market failures 

might impact the rebound effect. The direction and magnitude of the rebound effect in the HD 

truck market are expected to vary depending on the existence and types of market failures 

affecting the fuel efficiency of the trucking fleet. If firms are already accurately accounting for 

the costs and benefits of these technologies and fuel savings, then these regulations would 

increase their net costs, because trucks would already include all cost-effective fuel saving 

technologies. As a result, the rebound effect would 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 IX.A or RIA Chapter 8.2 for further discussion), it is 

more likely that truck VMT would increase. If firms recognize their lower net costs as a result of 

these regulations and pass those costs along to their customers, then the rebound effect would 

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

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

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 IX.A and RIA Chapter 8.2, if there are 

market failures such as split incentives, estimating the rebound effect may depend on the nature 

of the failures. For example, if the 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. 

Note that while we focus on the VMT rebound effect in our analysis of this proposed 

rule, there are at least two other types of rebound effects discussed in the economics literature. 

In addition to VMT rebound effects, there are “indirect” rebound effects, which refers to the 

purchase of other goods or services (that consume energy) with the costs savings from energy

efficiency improvements; and “economy-wide” rebound effects, which refers to the increased 

demand for energy throughout the economy in response to the reduced market price of energy 

that happens as a result of energy efficiency improvements.
Research on indirect and economy-wide rebound effects is nascent, and we have not 

identified any that attempts to quantify indirect or economy-wide rebound effects for HDVs. In 

particular, the agencies are not aware of any data to indicate that the magnitude of indirect or 

economy-wide rebound effects, if any, would be significant for this proposed rule.RTherefore, 

we rely the same analysis of vehicle miles traveled to estimate the rebound effect in this proposal 

that we did for the HD Phase 1 rule, where we attempted to quantify only rebound effects from 

our rule that impact HDV VMT. We will review any comments received as well as any new 

work in this area that helps to assess and quantify different rebound effects that could result from 

improvements in HDV efficiency, including different types of more intensive truck usage that 

affect fuel consumption but not VMT such as loaded weight, truck routing, and scheduling.

In order to test the effect of alternative assumptions about the rebound effect, NHTSA 

examined the sensitivity of its estimates of benefits and costs of the Phase 2 Preferred 

Alternative for HD pickups and vans to alternative assumptions about the rebound effect. While 

the main analysis for pickups and vans assumes a 10 percent rebound effect, the sensitivity

analysis estimates the benefits and costs of the proposed standards under the assumptions of 5, 

15, and 20 percent rebound effects. 

Alternative values of the rebound effect change the estimates of benefits and costs from 

the proposed standards in three ways. First, higher values of the rebound effect increase the 

amount of additional VMT that results from improved fuel efficiency; this increases costs 

associated with additional congestion, accidents, and noise, thus increasing total costs associated 

with the proposed standards. Conversely, smaller values of the rebound effect reduce costs from 

additional congestion, accidents, and noise, so they reduce total costs of the proposed standards. 

Second, larger increases in VMT associated with higher values of the rebound effect reduce the 

value of fuel savings and related benefits (such as reductions in GHG emissions) by

progressively larger amounts, while smaller values of the rebound effect cause smaller 

reductions in these benefits. At the same time, however, a higher rebound effect generates larger 

benefits from increased vehicle use, while a smaller rebound effect reduces these benefits 

compared to the base case. Thus the impact of alternative values of the rebound effect on total 

benefits from the proposed standards depends on the exact magnitudes of these latter two effects. 

On balance, these three effects can cause net benefits to increase or decrease for alternative 

values of the rebound effect. 

 

R One entity sought reconsideration of the Phase 1 rule on the grounds that indirect rebound effects had not been 

considered by the agencies and could negate all of the benefits of the standards. This assertion rested on an 

unsupported affidavit lacking any peer review or other indicia of objectivity. This affidavit cited only one published 

study. The study cited did not deal with vehicle efficiency, has methodological limitations (many of them 

acknowledged), and otherwise was not pertinent. EPA and NHTSA thus declined to reconsider the Phase 1 rule 

based on these speculative assertions. See generally 77 FR 51703-04 (Aug. 27, 2012) and 77 FR 51502-03 (Aug. 

24, 2012).
Table 8-6 Sensitivity of Preferred Alternative Impacts under Different Assumptions about Rebound Effect 

for Pickups and Vans, using 3% Discount Rate

HD PICKUPS AND VANS REBOUND EFFECT

Main 

Analysis

Sensitivity Cases Using 

Alternative Rebound 

Assumptions

10 % 5 % 15 % 20 %

Fuel Reductions (Billion Gallons) 7.8 8.2 7.5 7.1

GHG Reductions (MMT CO2 eq) 94.1 95.7 87.2 83.0

Total Costs ($ billion) 5.5 5.0 6.5 7.2

Total Benefits ($ billion) 23.5 23.0 22.9 22.8

Net Benefits ($ billion) 18.0 18.0 16.4 15.5

Table 8-6 summarizes the impact of these alternative assumptions on fuel and GHG 

emissions savings, total costs, total benefits, and net benefits. As it indicates, using a 5 percent

value for the rebound effect reduces benefits and costs of the proposed standards by identical 

amounts, leaving net benefits unaffected. Values of the rebound effect above 10 percent increase 

costs and reduce benefits from their values in the main analysis, thus reducing net benefits of the 

proposed standards. Nevertheless, the preferred alternative has significant net benefits under 

each alternative assumption about the magnitude of the rebound effect for HD pickups and vans. 

Thus, these alternative values of the rebound effect would not have affected the agencies’ 

selection of the preferred alternative, as that selection is based on NHTSA’s assessment of the 

maximum feasible fuel efficiency standards and EPA’s selection of appropriate GHG standards.

8.4 Impact on Class Shifting, Fleet Turnover, and Sales

The agencies considered two additional potential indirect effects which may lead to 

unintended consequences of the program to improve the fuel efficiency and reduce GHG 

emissions from HD trucks. The next sections cover the agencies’ qualitative discussions on 

potential class shifting and fleet turnover effects.

8.4.1 Class Shifting

Heavy-duty vehicles are typically configured and purchased to perform a function. For 

example, a concrete mixer truck is purchased to transport concrete, a combination tractor is 

purchased to move freight with the use of a trailer, and a Class 3 pickup truck could be 

purchased by a landscape company to pull a trailer carrying lawnmowers. The purchaser makes 

decisions based on many attributes of the vehicle, including the gross vehicle weight rating of the 

vehicle, which in part determines the amount of freight or equipment that can be carried. If the 

proposed Phase 2 standards impact either the performance of the vehicle or the marginal cost of 

the vehicle relative to the other vehicle classes, then consumers may choose to purchase a 

different vehicle, resulting in the unintended consequence of increased fuel consumption and 

GHG emissions in-use.
The agencies, along with the NAS panel, found that there is little or no literature which 

evaluates class shifting between trucks.52 NHTSA and EPA qualitatively evaluated the proposed 

rules in light of potential class shifting. The agencies looked at four potential cases of shifting: -

from light-duty pickup trucks to heavy-duty pickup trucks; from sleeper cabs to day cabs; from 

combination tractors to vocational vehicles; and within vocational vehicles.

Light-duty pickup trucks, those with a GVWR of less than 8,500 pounds, are currently 

regulated under the existing GHG/CAFE Phase 1 program and will meet GHG/CAFE Phase 2

emission standards beginning in 2017. The increased stringency of the light-duty 2017-2025 

MY vehicle rule has led some to speculate that vehicle consumers may choose to purchase

heavy-duty pickup trucks that are currently regulated under the HD Phase 1 program if the cost 

of the light-duty regulation is high relative to the cost to buy the larger heavy-duty pickup trucks. 

Since fuel consumption and GHG emissions rise significantly with vehicle mass, a shift from 

light-duty trucks to heavy-duty trucks would likely lead to higher fuel consumption and GHG 

emissions, an untended consequence of the regulations. Given the significant price premium of a 

heavy-duty truck (often five to ten thousand dollars more than a light-duty pickup), we believe 

that such a class shift would be unlikely even absent this program. These proposed rules would 

continue to diminish any incentive for such a class shift because they would narrow the GHG 

and fuel efficiency performance gap between light-duty and heavy-duty pickup trucks. The 

proposed regulations for the HD pickup trucks, and similarly for vans, are based on similar 

technologies and therefore reflect a similar expected increase in cost when compared to the lightduty GHG regulation. Hence, the combination of the two regulations provides little incentive for 

a shift from light-duty trucks to HD trucks. To the extent that our proposed regulation of heavyduty pickups and vans could conceivably encourage a class shift towards lighter pickups, this 

unintended consequence would in fact be expected to lead to lower fuel consumption and GHG 

emissions as the smaller light-duty pickups have significantly better fuel economy ratings than 

heavy-duty pickup trucks.

The projected cost increases for this proposed action differ between Class 8 day cabs and 

Class 8 sleeper cabs, reflecting our expectation that compliance with the proposed standards 

would lead truck consumers to specify sleeper cabs equipped with APUs while day cab 

consumers would not. Since Class 8 day cab and sleeper cab trucks perform essentially the same 

function when hauling a trailer, this raises the possibility that the higher cost for an APU 

equipped sleeper cab could lead to a shift from sleeper cab to day cab trucks. We do not believe 

that such an intended consequence would occur for the following reasons. The addition of a 

sleeper berth to a tractor cab is not a consumer-selectable attribute in quite the same way as other 

vehicle features. The sleeper cab provides a utility that long-distance trucking fleets need to 

conduct their operations -- an on-board sleeping berth that lets a driver comply with federallymandated rest periods, as required by the Department of Transportation Federal Motor Carrier 

Safety Administration's hours-of-service regulations. The cost of sleeper trucks is already higher 

than the cost of day cabs, yet the fleets that need this utility purchase them.53 A day cab simply 

cannot provide this utility with a single driver. The need for this utility would not be changed 
even if the additional costs to reduce greenhouse gas emissions from sleeper cabs exceed those 

reducing greenhouse gas emissions from day cabs.S

A trucking fleet could instead decide to put its drivers in hotels in lieu of using sleeper 

berths, and switch to day cabs. However, this is unlikely to occur in any great number, since the 

added cost for the hotel stays would far overwhelm differences in the marginal cost between day

and sleeper cabs. Even if some fleets do opt to buy hotel rooms and switch to day cabs, they

would be highly unlikely to purchase a day cab that was aerodynamically worse than the sleeper 

cab they replaced, since the need for features optimized for long-distance hauling would not have 

changed. So in practice, there would likely be little difference to the environment for any 

switching that might occur. Further, while our projected costs assume the purchase of an APU 

for compliance, in fact our regulatory structure would allow compliance using a near zero cost 

software utility that eliminates tractor idling after five minutes. Using this compliance approach, 

the cost difference between a Class 8 sleeper cab and day cab due to our proposed regulations is 

small. We are proposing this alternative compliance approach reflecting that some sleeper cabs 

are used in team driving situations where one driver sleeps while the other drives. In that 

situation, an APU is unnecessary since the tractor is continually being driven when occupied. 

When it is parked, it would automatically eliminate any additional idling through the shutdown 

software. If trucking businesses choose this option, then costs based on purchase of APUs may

overestimate the costs of this program to this sector.

Class shifting from combination tractors to vocational vehicles may occur if a customer 

deems the additional marginal cost of tractors due to the regulation to be greater than the utility

provided by the tractor. The agencies initially considered this issue when deciding whether to 

include Class 7 tractors with the Class 8 tractors or regulate them as vocational vehicles. The 

agencies’ evaluation of the combined vehicle weight rating of the Class 7 shows that if these 

vehicles were treated significantly differently from the Class 8 tractors, then they could be easily

substituted for Class 8 tractors. Therefore, the agencies are proposing to continue to include both 

classes in the tractor category. The agencies believe that a shift from tractors to vocational 

vehicles would be limited because of the ability of tractors to pick up and drop off trailers at 

locations which cannot be done by vocational vehicles.

The agencies do not envision that the proposed regulatory program would cause class 

shifting within the vocational vehicle class. The marginal cost difference due to the regulation of 

vocational vehicles is minimal. The cost of LRR tires on a per tire basis is the same for all 

vocational vehicles so the only difference in marginal cost of the vehicles is due to the number of 

axles. The agencies believe that the utility gained from the additional load carrying capability of 

the additional axle would outweigh the additional cost for heavier vehicles.T

 

S The average marginal cost difference between sleeper cabs and day cabs in the proposal is roughly $2,500.

T The proposed rule projects the difference in costs between the HHD and MHD vocational vehicle technologies is 

approximately $30.
In conclusion, NHTSA and EPA believe that the proposed regulatory structure for HD 

trucks would not significantly change the current competitive and market factors that determine 

purchaser preferences among truck types. Furthermore, even if a small amount of shifting would 

occur, any resulting GHG impacts would likely to be negligible because any vehicle class that 

sees an uptick in sales is also being regulated for fuel efficiency. Therefore, the agencies did not 

include an impact of class shifting on the vehicle populations used to assess the benefits of the 

proposed program. 

8.4.2 Fleet Turnover and Sales Effects

A regulation that affects the cost to purchase and/or operate trucks could affect whether a 

consumer decides to purchase a new truck and the timing of that purchase. The term pre-buy

refers to the idea that truck purchases may occur earlier than otherwise planned to avoid the 

additional costs associated with a new regulatory requirement. Slower fleet turnover, or lowbuys, may occur when owners opt to keep their existing truck rather than purchase a new truck 

due to the incremental cost of the regulation. 

The 2010 NAS HD Report discussed the topics associated with HD truck fleet turnover. 

NAS noted that there is some empirical evidence of pre-buy behavior in response to the 2004 and 

2007 heavy-duty engine emission standards, with larger impacts occurring in response to higher 

costs.54 However, those regulations increased upfront costs to firms without any offsetting future 

cost savings from reduced fuel purchases. In summary, NAS stated that

...during periods of stable or growing demand in the freight sector, pre-buy behavior may

have significant impact on purchase patterns, especially for larger fleets with better 

access to capital and financing. Under these same conditions, smaller operators may

simply elect to keep their current equipment on the road longer, all the more likely given 

continued improvements in diesel engine durability over time. On the other hand, to the 

extent that fuel economy improvements can offset incremental purchase costs, these 

impacts will be lessened. Nevertheless, when it comes to efficiency investments, most 

heavy-duty fleet operators require relatively quick payback periods, on the order of two 

to three years.55 

The proposed regulations are projected to return fuel savings to the truck owners that 

offset the cost of the regulation within a few years. The effects of the regulation on purchasing 

behavior and sales will depend on the nature of the market failures and the extent to which firms 

consider the projected future fuel savings in their purchasing decisions. 

If trucking firms account for the rapid payback, they are unlikely to strategically 

accelerate or delay their purchase plans at additional cost in capital to avoid a regulation that will 

lower their overall operating costs. As discussed in Chapter 8.2, this scenario may occur if this 

proposed program reduces uncertainty about fuel-saving technologies. More reliable 

information about ways to reduce fuel consumption allows truck purchasers to evaluate better the 

benefits and costs of additional fuel savings, primarily in the original vehicle market, but 

possibly in the resale market as well. In addition, the proposed standards are expected to lead 

manufacturers to install more fuel-saving technologies and promote their purchase; the increased 

availability and promotion may encourage sales. 
Other market failures may leave open the possibility of some pre-buy or delayed 

purchasing behavior. Firms may not consider the full value of the future fuel savings for several 

reasons. For instance, truck purchasers may not want to invest in fuel efficiency because of 

uncertainty about fuel prices. Another explanation is that the resale market may not fully 

recognize the value of fuel savings, due to lack of trust of new technologies or changes in the 

uses of the vehicles. Lack of coordination (also called split incentives—see Chapter 8.2) 

between truck purchasers (who may emphasize the up-front costs of the trucks) and truck 

operators, who would like the fuel savings, can also lead to pre-buy or delayed purchasing 

behavior. If these market failures prevent firms from fully internalizing fuel savings when 

deciding on vehicle purchases, then pre-buy and delayed purchase could occur and could result 

in a slight decrease in the GHG benefits of the regulation. 

Thus, whether pre-buy or delayed purchase is likely to play a significant role in the truck 

market depends on the specific behaviors of purchasers in that market. Without additional 

information about which scenario is more likely to be prevalent, the agencies are not projecting a 

change in fleet turnover characteristics due to this regulation. 

Whether vehicle sales appear to be affected by the HD Phase 1 standards could provide 

some insight into the impacts of the proposed standards. At the time of this NPRM, sales data 

are not yet available for 2014 model year, the first year of the Phase 1 standards. In addition, any

trends in sales are likely to be affected by macroeconomic conditions, which have been 

recovering since 2009-2010. As a result, it is unlikely to be possible, even when vehicle sales 

data are available, to separate the effects of the existing standards from other confounding 

factors. 

8.5 Monetized GHG Impacts

8.5.1 Monetized CO2 Impacts - Social Cost of Carbon

We estimate the global social benefits of CO2 emission reductions expected from the 

Proposed HD Phase 2 program using the social cost of carbon (SC-CO2) estimates presented in 

the 2013 Technical Support Document: Technical Update of the Social Cost of Carbon for 

Regulatory Impact Analysis Under Executive Order 12866 (2013 SCC TSD). We refer to these 

estimates, which were developed by the U.S., as “SC-CO2 estimates.” The SC-CO2 is a metric 

that estimates the monetary value of impacts associated with marginal changes in CO2 emissions 

in a given year. It includes a wide range of anticipated climate impacts, such as net changes in 

agricultural productivity and human health, property damage from increased flood risk, and 

changes in energy system costs, such as reduced costs for heating and increased costs for air 

conditioning. It is used in regulatory impact analyses to quantify the benefits of reducing CO2

emissions, or the disbenefit from increasing emissions. 

The SC-CO2 estimates used in this analysis were developed over many years, based on 

the best science available, and with input from the public. EPA and other federal agencies have 

considered the extensive public comments on ways to improve SC-CO2 estimation received via 

the notice and comment period that was part of numerous rulemakings since 2006. In addition, 

OMB’s Office of Information and Regulatory Affairs sought public comment on the approach 

used to develop the SC-CO2 estimates. The comment period ended on February 26, 2014, and 
OMB is reviewing the comments received. An interagency process that included EPA and other 

executive branch entities used three integrated assessment models (IAMs) to develop SC-CO2

estimates and selected four global values for use in regulatory analyses. The SC-CO2 estimates 

were first released in February 2010 and updated in 2013 using new versions of each IAM. 

The SC-CO2 estimates represent global measures because of the distinctive nature of the 

climate change problem. The climate change problem is highly unusual in at least two respects. 

First, emissions of most GHGs contribute to damages around the world even when they are 

emitted in the United States. The SC-CO2 must therefore incorporate the full (global) damages 

caused by GHG emissions in order to address the global nature of the problem. Second, climate 

change presents a problem that the United States alone cannot solve. The US now operates in a 

global, highly interconnected economy such that impacts on the other side of the world now 

affect our economy. Climate damages in other countries can affect the U.S. economy; climateexacerbated conflict can require military expenditures by the U.S. All of this means that the true 

cost of climate change to U.S. is much larger than impacts that simply occur in the U.S. A global 

number is the economically appropriate reference point for collective actions to reduce climate 

change.

A key objective in the development of the SC-CO2 estimates was to enable a consistent 

exploration of three IAMs (DICE, FUND, and PAGE) while respecting the different approaches 

to quantifying damages taken by the key modelers in the field. The selection of the three input 

parameters (equilibrium climate sensitivity, reference socioeconomic scenarios, discount rate) 

was based on an extensive review of the literature. Specifically, a probability distribution for 

climate sensitivity was specified as an input into all three models. In addition, the interagency

group used a range of scenarios for the socio-economic parameters and a range of values for the 

discount rate. All other model features were left unchanged, relying on the model developers’ 

best estimates and judgments. In DICE, these parameters are handled deterministically and 

represented by fixed constants; in PAGE, most parameters are represented by probability 

distributions. FUND was also run in a mode in which parameters were treated probabilistically. 

The use of three models and these input parameters allowed for exploration of important 

uncertainties in the way climate damages are estimated, including equilibrium climate 

sensitivity, reference socioeconomic and emission trajectories, and discount rate. As stated in 

the 2010 SCC TSD, however, key uncertainties remain as the existing models are imperfect and 

incomplete. See the 2010 SCC TSD for a complete discussion of the methods used to develop 

the estimates and the key uncertainties, and the 2013 SCC TSD for the updated estimates. 

Notably, the 2013 process did not revisit the 2010 interagency modeling decisions (e.g., 

with regard to the equilibrium climate sensitivity, reference case socioeconomic and emission 

scenarios, or discount rates). Rather, improvements in the way damages are modeled are 

confined to those that have been incorporated into the latest versions of the models by the 

developers themselves and used for analyses in peer-reviewed publications. The model updates 

that are relevant to the SC-CO2 estimates include: an explicit representation of sea level rise 

damages in the DICE and PAGE models; updated adaptation assumptions, revisions to ensure 

damages are constrained by GDP, updated regional scaling of damages, and a revised treatment 

of potentially abrupt shifts in climate damages in the PAGE model; an updated carbon cycle in 

the DICE model; and updated damage functions for sea level rise impacts, the agricultural sector, 

and reduced space heating requirements, as well as changes to the transient response of 
temperature to the buildup of GHG concentrations and the inclusion of indirect effects of 

methane emissions in the FUND model. The 2013 SCC TSD provides complete details.

When attempting to assess the incremental economic impacts of carbon dioxide 

emissions, the analyst faces a number of serious challenges. A 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. 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 2010 SCC TSD noted a number of limitations to the SC-CO2 analysis, including the 

incomplete way in which the IAMs 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. Current IAMs do not 

assign value to all of the important physical, ecological, and economic impacts of climate change 

recognized in the climate change literature due to a lack of precise information on the nature of 

damages and because the science incorporated into these models understandably lags behind the 

most recent research. The limited amount of research linking climate impacts to economic 

damages makes the modeling exercise even more difficult. These individual limitations do not 

all work in the same direction in terms of their influence on the SC-CO2 estimates, though taken 

together they suggest that the SC-CO2 estimates are likely conservative. In particular, the IPCC 

Fourth Assessment Report (2007) concluded that “It is very likely that [SC-CO2 estimates] 

underestimate the damage costs because they cannot include many non-quantifiable impacts.”

Nonetheless, these estimates and the discussion of their limitations represent the best 

available information about the social benefits of CO2 reductions to inform benefit-cost analysis. 

The new versions of the models used to estimate the values presented below offer some 

improvements in these areas, although further work is warranted. Accordingly, EPA and other 

agencies continue to engage in research on modeling and valuation of climate impacts with the 

goal to improve these estimates. The EPA and other federal agencies have considered the 

extensive public comments on ways to improve SC-CO2 estimation received via the notice and 

comment periods that were part of numerous rulemakings. In addition, OMB’s Office of 

Information and Regulatory Affairs sought public comment on the approach used to develop the 

SC-CO2 estimates (78 FR 70586; November 26, 2013). The comment period ended on February

26, 2014, and OMB is reviewing the comments received. OMB also responded in January 2014 

to concerns submitted in a Request for Correction on the SCC TSDs.U

 

U OMB’s 1/24/14 response to the petition is available at 

https://www.whitehouse.gov/sites/default/files/omb/inforeg/ssc-rfc-under-iqa-response.pdf
The four SC-CO2 estimates, updated in 2013, are as follows: $13, $46, $69, and $140 per 

metric ton of CO2 emissions in the year 2020 (2012$).VTable 8-7 presents the SC-CO2

estimates in selected years, rounded to two significant digits. The first three values are based on 

the average SC-CO2 from the three IAMs, at discount rates of 5, 3, and 2.5 percent, respectively. 

SC-CO2 estimates for several discount rates are included because the literature shows that the 

SC-CO2 is quite sensitive to assumptions about the discount rate, and because no consensus 

exists on the appropriate rate to use in an intergenerational context (where costs and benefits are 

incurred by different generations). The fourth value is the 95th percentile of the SC-CO2 from 

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

impacts from temperature change further out in the tails of the SC-CO2 distribution (representing 

less likely, but potentially catastrophic, outcomes). The SC-CO2 increases over time because 

future emissions are expected to produce larger incremental damages as economies grow and 

physical and economic systems become more stressed in response to greater climate change. 

Table 8-7 Social Cost of CO2, 2012 – 2050a(in 2012$ per Metric Ton)

CALENDAR

YEAR

DISCOUNT RATE AND STATISTIC

5% Average 3% Average 2.5% Average 3% 

95th percentile

2012 $12 $37 $58 $100

2015 $12 $40 $61 $120

2020 $13 $46 $69 $140

2025 $15 $51 $74 $150

2030 $17 $56 $81 $170

2035 $20 $60 $86 $190

2040 $23 $66 $93 $210

2045 $26 $71 $99 $220

2050 $28 $77 $100 $240

Note:

a The SC-CO2 values are dollar-year and emissions-year specific and have been rounded to two 

significant digits. Unrounded numbers from the 2013 SCC TSD were adjusted to 2012$ and used 

to calculate the CO2 benefits.

Applying the global SC-CO2 estimates, shown in Table 8-7, to the estimated reductions 

in domestic CO2 emissions for the proposed program, we estimate the dollar value of the climate 

related benefits for each analysis year. In order to calculate the dollar value for emission 

reductions, the SC-CO2 estimate for each emissions year would be applied to changes in CO2

emissions for that year, and then discounted back to the analysis year using the same discount 

rate used to estimate the SC-CO2. For internal consistency, the annual benefits are discounted 

back to net present value terms using the same discount rate as each SC-CO2 estimate (i.e. 5 

percent, 3 percent, and 2.5 percent) rather than the discount rates of 3 percent and 7 percent used 

 

V The SC-CO2 values have been rounded to two significant digits. Unrounded numbers from the 2013 SCC TSD 

were adjusted to 2012$ and used to calculate the CO2 benefits.
to derive the net present value of other streams of costs and benefits of the proposed rule.W The 

SC-CO2 estimates are presented in and the associated CO2 benefit estimates for each calendar 

year are shown in Table 8-8.

Table 8-8 Annual Upstream and Downstream CO2 Benefits and Net Present Values for the Given SC-CO2

Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B,

a,b (Millions 

of 2012$) 

CALEND

AR

YEAR

5%

(AVERAGE SC-CO2

= 

$12 IN 2012)

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012)

2.5% 

(AVERAGE SC-CO2

=

$58 IN 2012)

3%

(95TH PERCENTILE =

$105 IN 2012)

2018 $13 $43 $65 $130

2019 $26 $91 $130 $270

2020 $40 $140 $210 $420

2021 $92 $330 $500 $1,000

2022 $170 $590 $880 $1,800

2023 $250 $860 $1,300 $2,600

2024 $400 $1,300 $1,900 $4,000

2025 $540 $1,800 $2,600 $5,500

2026 $720 $2,300 $3,400 $7,000

2027 $890 $2,900 $4,200 $8,900

2028 $1,100 $3,500 $5,100 $11,000

2029 $1,300 $4,200 $5,900 $13,000

2030 $1,500 $4,800 $6,900 $15,000

2035 $2,500 $7,400 $11,000 $23,000

2040 $3,300 $9,700 $14,000 $30,000

2050 $5,000 $14,000 $19,000 $42,000

NPVb$22,000 $100,000 $160,000 $320,000

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

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 vehicles sold in the regulatory timeframe. In contrast to the calendar year analysis, the model 

year lifetime analysis shows the impacts of the program on each of these MY fleets over the 

course of their lifetimes. Full details of the inputs to this analysis can be found in RIA chapter 5. 

The CO2 benefits in the context of this MY lifetime analysis are shown in Table 8-9 for each of 

the four different social cost of carbon values. The CO2 benefits shown for each model year 

represent the net present value of the benefits in each year in the model year life discounted back 

 

W See more discussion on the appropriate discounting of climate benefits using SC-CO2 in the 2010 SCC TSD. 

Other benefits and costs of proposed regulations unrelated to CO2 emissions are discounted at the 3% and 7% rates 

specified in OMB guidance for regulatory analysis.
to the first year of the model year. The same discount rate used to discount the value of damages 

from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate the net present value 

of SCC for internal consistency.

Table 8-9 Discounted Model Year Lifetime Upstream & Downstream CO2 Benefits for the Given SC-CO2

Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 

2012$)a,b

MODEL

YEAR

5%

(AVERAGE SC-CO2 = 

$12 IN 2012)

3% 

(AVERAGE SC-CO2 = 

$37 IN 2012)

2.5% 

(AVERAGE SC-CO2 =

$58 IN 2012)

3%

(95TH PERCENTILE =

$105 IN 2012)

2018 $93 $380 $580 $1,100

2019 $90 $370 $570 $1,100

2020 $87 $360 $560 $1,100

2021 $520 $2,200 $3,400 $6,600

2022 $540 $2,300 $3,500 $6,900

2023 $550 $2,300 $3,600 $7,200

2024 $870 $3,700 $5,800 $11,000

2025 $900 $3,900 $6,100 $12,000

2026 $920 $4,000 $6,300 $12,000

2027 $1,100 $4,800 $7,600 $15,000

2028 $1,100 $4,800 $7,600 $15,000

2029 $1,100 $4,900 $7,700 $15,000

Sum $7,800 $34,000 $53,000 $100,000

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.

8.5.2 Sensitivity Analysis - Monetized Non-CO2 Impacts

One limitation of the primary benefits analysis is that it does not include the valuation of 

non-CO2 GHG impacts (CH4, N2O, HFC-134a). Specifically, the IWG did not estimate the 

social costs of non-CO2 GHG emissions using an approach analogous to the one used to estimate 

the SC-CO2. However, EPA recognizes that non-CO2 GHG impacts associated with this 

rulemaking (e.g., net reductions in CH4, N2O, HFC-134a) would provide benefits to society. To 

understand the potential implication of omitting these benefits, EPA has conducted sensitivity

analysis using two approaches: 1) an approximation approach based on global warming potential 

(GWP) gas comparison metrics that has been used in previous rulemakings, and 2) a set of 

recently published SC-CH4 and SC-N2O estimates that are consistent with the modeling 

assumptions underlying the SC-CO2 estimates (Marten et al. 2014). This section describes both 

approaches and presents estimates of the non-CO2 benefits of the proposed rulemaking. Other 

unquantified non-CO2 benefits are discussed in this section as well. 
8.5.2.1 Non-CO2 GHG Benefits Based on the GWP Approximation Approach

In the absence of directly modeled estimates, one potential method for approximating the 

value of marginal non-CO2 GHG emission reductions is to convert non-CO2 emissions 

reductions to CO2-equivalents that may then be valued using the SC-CO2. Conversion to CO2-

equivalents is typically based on the global warming potentials (GWPs) for the non-CO2 gases.

This approach, henceforth referred to as the “GWP approach,” has been used in sensitivity

analyses to estimate the non-CO2 benefits in previous EPA rulemakings (see US EPA 2012, 

2013).56 EPA has not presented these estimates in a main benefit-cost analysis due to the 

limitations associated with using the GWP approach to value changes in non-CO2 GHG 

emissions, and considered the GWP approach as an interim method of analysis until social cost 

estimates for non-CO2 GHGs, consistent with the SC-CO2 estimates, were developed.

The GWP is a simple, transparent, and well-established metric for assessing the relative 

impacts of non-CO2 emissions compared to CO2 on a purely physical basis. However, as 

discussed both in the 2010 SCC TSD and previous rulemakings (e.g., US EPA 2012, 2013), the 

GWP approximation approach to measuring non-CO2 GHG benefits has several welldocumented limitations (e.g., Reilly and Richards 199357; Schmalensee 199358; Fankhauser 

199459; Marten and Newbold 201260). Gas comparison metrics, such as the GWP, are designed 

to measure the impact of non-CO2 GHG emissions relative to CO2 at a specific point along the 

pathway from emissions to monetized damages (depicted in Figure 8-2), and this point may

differ across measures. The GWP measures the cumulative radiative forcing from a perturbation 

of a non-CO2 GHG relative to a perturbation of CO2 over a fixed time horizon. The GWP and 

other gas comparison metrics are not ideally suited for use in benefit-cost analyses to 

approximate the social cost of non-CO2 GHGs because they ignore important nonlinear 

relationships beyond radiative forcing in the chain between emissions and damages. These can 

become relevant because gases have different lifetimes and the SC-CO2 takes into account the 

fact that marginal damages from an increase in temperature are a function of existing 

temperature levels. Another limitation of gas comparison metrics for this purpose is that some 

environmental and socioeconomic impacts are not linked to all of the gases under consideration 

and will therefore be incorrectly allocated. For example, the economic impacts associated with 

increased agricultural productivity due to higher atmospheric CO2 concentrations included in the 

SC-CO2 would be incorrectly allocated to CH4 emissions with the GWP-based valuation 

approach. 

Figure 8-2 Path from GHG Emissions to Monetized Damages (Source: Marten et al., 2014)

Furthermore, the assumptions made in estimating the GWP are not consistent with the 

assumptions underlying SC-CO2 estimates in general, and the SC-CO2 estimates more 

specifically. For example the 100 year time horizon usually used in estimating the GWP is less 

than the 300 year horizon used in developing the SC-CO2 estimates. The GWP approach also 

treats all impacts within the time horizon equally, independent of the time at which they occur. 

This is inconsistent with the role of discounting in economic analysis, which accounts for a basic 
preference for earlier over later gains in utility, the small but positive probability of a large 

global catastrophe (e.g., large asteroid collision, super volcanic eruption, pandemic), and 

expectations regarding future levels of economic growth. In the case of CH4, which has a 

relatively short lifetime compared to CO2, the temporal independence of the GWP could lead the 

GWP approach to underestimate the SC-CH4 with a larger downward bias under higher discount 

rates (Marten and Newbold 2012).X

Similar to the approach used in the RIA of the Final Rulemaking for 2017-2025 LightDuty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy 

Standards (US EPA, 2013), EPA applies the GWP approach to estimate the benefits associated

with reductions of CH4, N2O and HFCs in each calendar year. Under the GWP Approach, EPA 

converted CH4, N2O, and HFC-134a to CO2 equivalents using the AR4 100-year GWP for each 

gas: CH4 (25), N2O (298), and HFC-134a (1,430).61 These CO2-equivalent emission reductions 

are multiplied by the SC-CO2 estimate corresponding to each year of emission reductions. As 

with the calculation of annual benefits of CO2 emission reductions, the annual benefits of nonCO2 emission reductions based on the GWP approach are discounted back to net present value 

terms using the same discount rate as each SC-CO2 estimate. The estimated non-CO2 GHG 

benefits using the GWP approach are presented in Table 8-10 through Table 8-11. The total net 

present value of the GHG benefits for this proposed rulemaking would increase by about $760 

million to $11 billion (2012$), depending on discount rate, or roughly 3 percent if these non-CO2

estimates were included.

 

X We note that the truncation of the time period in the GWP calculation could lead to an overestimate of SC-CH4 for 

near term perturbation years in cases where the SC-CO2 is based on a sufficiently low or steeply declining discount 

rate.
Table 8-10 Annual Upstream and Downstream CH4 GHG Benefits and Net Present Values for the Given SCCO2 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using the 

GWP Approach (Millions of 2012$) a,b

CALENDA

R

YEAR

5%

(AVERAGE SC-CO2

= 

$12 IN 2012)

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012)

2.5% 

(AVERAGE SC-CO2

=

$58 IN 2012)

3%

(95TH PERCENTILE 

=

$105 IN 2012)

2018 $0.3 $1.1 $1.6 $3.2

2019 $0.6 $2.2 $3.3 $6.6

2020 $1.0 $3.5 $5.2 $10

2021 $3.1 $11 $17 $33

2022 $6.0 $20 $30 $62

2023 $8.8 $30 $45 $93

2024 $14 $46 $68 $140

2025 $19 $62 $91 $190

2026 $25 $79 $120 $240

2027 $30 $99 $140 $300

2028 $36 $120 $170 $360

2029 $43 $140 $200 $420

2030 $49 $160 $230 $480

2035 $82 $240 $350 $760

2040 $110 $320 $440 $990

2050 $160 $440 $600 $1,400

NPVb$730 $3,400 $5,400 $11,000

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.
Table 8-11 Annual Upstream and Downstream N2O GHG Benefits and Net Present Values for the Given SCCO2 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using 

the GWP Approach (Millions of 2012$) a,b

CALENDA

R

YEAR

5%

(AVERAGE SC-CO2

= 

$12 IN 2012)

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012)

2.5% 

(AVERAGE SC-CO2

=

$58 IN 2012)

3%

(95TH PERCENTILE 

=

$105 IN 2012)

2018 $0.0 $0.0 $0.1 $0.2

2019 $0.0 $0.1 $0.2 $0.3

2020 $0.0 $0.2 $0.2 $0.5

2021 $0.1 $0.4 $0.5 $1.1

2022 $0.2 $0.6 $1.0 $1.9

2023 $0.3 $0.9 $1.4 $2.8

2024 $0.4 $1.4 $2.1 $4.4

2025 $0.6 $2.0 $2.9 $6.0

2026 $0.8 $2.6 $3.7 $7.8

2027 $1.0 $3.2 $4.7 $10

2028 $1.2 $3.9 $5.7 $12

2029 $1.5 $4.6 $6.6 $14

2030 $1.6 $5.3 $7.7 $16

2035 $2.8 $8.3 $12 $26

2040 $3.8 $11 $15 $34

2050 $5.6 $15 $21 $47

NPVb$25 $120 $180 $360

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.
Table 8-12 Annual Upstream and Downstream HFC-134a GHG Benefits and Net Present Values for the 

Given SC-CO2 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method 

B, using the GWP Approach (Millions of 2012$) a,b

CALENDA

R

YEAR

5%

(AVERAGE SC-CO2

= 

$12 IN 2012)

3% 

(AVERAGE SC-CO2

= 

$37 IN 2012)

2.5% 

(AVERAGE SC-CO2

=

$58 IN 2012)

3%

(95TH PERCENTILE 

=

$105 IN 2012)

2018 $0.0 $0.0 $0.0 $0.0

2019 $0.0 $0.0 $0.0 $0.0

2020 $0.0 $0.0 $0.0 $0.0

2021 $0.2 $0.8 $1.3 $2.6

2022 $0.5 $1.7 $2.6 $5.3

2023 $0.8 $2.7 $4.0 $8.1

2024 $1.1 $3.7 $5.4 $11

2025 $1.4 $4.7 $6.9 $14

2026 $1.8 $5.9 $8.6 $18

2027 $2.2 $7.1 $10 $22

2028 $2.5 $8.3 $12 $25

2029 $3.0 $10 $14 $29

2030 $3.4 $11 $16 $34

2035 $5.2 $15 $22 $48

2040 $6.1 $18 $25 $56

2050 $8.4 $23 $31 $71

NPVb$44 $200 $320 $630

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-CO2 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CO2 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CO2

for internal consistency. Refer to SCC TSD for more detail.

8.5.2.2 Non-CO2 GHG Benefits Based on Directly Modeled Estimates

Several researchers have directly estimated the social cost of non-CO2 emissions using 

integrated assessment models (IAMs), though the number of such estimates is small compared to 

the large number of SC-CO2 estimates available in the literature. As discussed in previous RIAs 

(e.g., EPA 2012), there is considerable variation among these published estimates in the models 

and input assumptions they employ. These studies differ in the emission perturbation year, 

employ a wide range of constant and variable discount rate specifications, and consider a range 

of baseline socioeconomic and emissions scenarios that have been developed over the last 20 

years. However, none of the other published estimates of the social cost of non-CO2 GHG are 

consistent with the SC-CO2 estimates, and most are likely underestimates due to changes in the 

underlying science since their publication. 

Recently, a paper by Marten et al. (2014) provided the first set of published SC-CH4 and 

SC-N2O estimates that are consistent with the modeling assumptions underlying the SC-CO2.

62

Specifically, the estimation approach of Marten et al. (2014) used the same set of three IAMs, 

five socioeconomic-emissions scenarios, equilibrium climate sensitivity distribution, and three 
constant discount rates used to develop the SC-CO2 estimates. Marten et al. also used the same 

aggregation method as the SC-CO2 to distill the 45 distribution of the SC-CH4 and SC-N2O 

produced for each emissions year into four estimates: the mean across all models and scenarios 

using a 2.5 percent, 3 percent, and 5 percent discount rate, and the 95th percentile of the pooled 

estimates from all models and scenarios using a 3 percent discount rate. Marten et al. used 

lifetimes and radiative efficiencies for CH4 and N2O based on the IPCC AR4 values. The 

authors also adjusted the CH4 radiative efficiency for CH4 to account for additional radiative 

effects due to increases in tropospheric ozone and stratospheric water vapor resulting from 

methane emissions, using the same adjustment used by in IPCC AR4 for calculating GWP 

values. Using this approach, Marten et al. (2014) finds that the GWP approach provides 

conservative estimates for the benefits of marginal reductions in CH4 and N2O emissions. 

The resulting SC-CH4 and SC-N2O estimates are presented in Table 8-13. More detailed 

results and a comparison to other published estimates can be found in Marten et al. (2014). The 

tables do not include HFC-134a because EPA is unaware of analogous estimates.

Table 8-13 Social Cost of CH4 and N2O, 2012 – 2050a[2012$ per metric ton] 

(Source: Marten et al. (2014))

YEAR SC-CH4 SC-N2O

5%

Average

3%

Average

2.5%

Average

3%

95th percentile

5%

Average

3% 

Average

2.5% 

Average

3%

95th

percentile

2012 $440 $1000 $1400 $2800 $4000 $14000 $20000 $37000

2015 500 1200 1500 3100 4400 15000 22000 39000

2020 590 1300 1700 3500 5200 16000 24000 44000

2025 710 1500 19000 4100 6000 18000 27000 50000

2030 840 1700 2300 4600 7000 20000 29000 55000

2035 990 2000 2500 5400 8100 23000 32000 61000

2040 1200 2300 2800 6000 9300 25000 35000 67000

2045 1300 2500 3100 6800 11000 27000 38000 73000

2050 1500 2700 3300 7400 12000 29000 41000 80000

Note:

a The values are emissions-year specific and have been rounded to two significant digits, as shown in Marten et al. 

(2014). These rounded numbers were used to calculate the GHG benefits.

The application of directly modeled estimates from Marten et al. (2014) to benefit-cost 

analysis of a regulatory action is analogous to the use of the SC-CO2 estimates. Specifically, the 

SC-CH4 and SC-N2O estimates in Table 8-13 are used to monetize the benefits of changes in 

CH4 and N2O emissions expected as a result of the proposed rulemaking. Forecast changes in 

CH4 and N2O emissions in a given year resulting from the regulatory action are multiplied by the 

SC-CH4 and SC-N2O estimate for that year, respectively. To obtain a present value estimate, the 

monetized stream of future non-CO2 benefits are discounted back to the analysis year using the 

same discount rate used to estimate the social cost of the non-CO2 GHG emission changes.

The CH4 and N2O benefits based on Marten et al. (2014) are presented for each calendar 

year in Table 8-14. Including these benefits would increase the total net present value of the

GHG benefits for this proposed rulemaking by about $1.5 billion to $12 billion (2012$), or 

roughly 4 percent to 7 percent, depending on discount rate. 
Table 8-14 Annual Upstream and Downstream CH4 GHG Benefits and Net Present Values for the Given SCCH4 Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using the 

Directly Modeled Approach, Calendar Year Analysis (Millions of 2012$) a,b

CALENDAR

YEAR

5%

(AVERAGE SC-CH4

= 

$440 IN 2012)

3% 

(AVERAGE SC-CH4

= 

$1000 IN 2012)

2.5% 

(AVERAGE SC-CH4

=

$1400 IN 2012)

3%

(95TH PERCENTILE 

=

$2800 IN 2012)

2018 $0.6 $1.3 $1.6 $3.3

2019 $1.1 $2.6 $3.4 $6.8

2020 $1.8 $3.9 $5.2 $10

2021 $5.8 $13 $17 $35

2022 $11 $24 $31 $65

2023 $17 $35 $49 $97

2024 $26 $56 $72 $150

2025 $35 $74 $95 $200

2026 $46 $99 $130 $260

2027 $57 $120 $150 $320

2028 $69 $140 $190 $390

2029 $82 $170 $220 $460

2030 $95 $190 $260 $520

2035 $160 $330 $400 $870

2040 $230 $430 $540 $1,200

2050 $350 $620 $770 $1,700

NPVb$1,500 $4,600 $6,400 $12,000

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-CH4 values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (SC-CH4 at 5, 3, and 2.5 percent) is used to calculate discounted values of SC-CH4

for internal consistency. 
Table 8-15 Annual Upstream and Downstream N2O GHG Benefits and Net Present Values for the Given SCN2O Value for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B, using 

the Directly Modeled Approach, Calendar Year Analysis (Millions of 2012$) a,b

CALENDAR

YEAR

5%

(AVERAGE SC-N2O 

= 

$4000 IN 2012)

3% 

(AVERAGE SC-N2O = 

$14000 IN 2012)

2.5% 

(AVERAGE SC-N2O =

$20000 IN 2012)

3%

(95TH PERCENTILE =

$37000 IN 2012)

2018 $0.0 $0.1 $0.1 $0.2

2019 $0.0 $0.1 $0.2 $0.3

2020 $0.1 $0.2 $0.3 $0.5

2021 $0.1 $0.4 $0.6 $1.2

2022 $0.3 $0.8 $1.1 $2.1

2023 $0.4 $1.1 $1.7 $3.1

2024 $0.6 $1.8 $2.5 $4.7

2025 $0.8 $2.4 $3.5 $6.5

2026 $1.0 $3.0 $4.5 $8.4

2027 $1.3 $4.0 $5.8 $11

2028 $1.6 $4.8 $6.9 $13

2029 $1.9 $5.8 $8.2 $15

2030 $2.2 $6.5 $9.3 $18

2035 $3.7 $10 $15 $28

2040 $5.2 $14 $19 $37

2050 $7.9 $20 $27 $53

NPVb$34 $150 $230 $400

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b The SC-N2O values are dollar-year and emissions-year specific. Note that discounted values of reduced GHG 

emissions are calculated differently than other benefits. The same discount rate used to discount the value of 

damages from future emissions (N2O at 5, 3, and 2.5 percent) is used to calculate discounted values of N2O for 

internal consistency. 

As illustrated above, compared to the use of directly modeled estimates the GWP-based 

approximation approach underestimates the climate benefits of the CH4 emission reductions by

12 percent to 52 percent, and the climate benefits of N2O reductions by 10 percent to 26 percent, 

depending on the discount rate assumption.

8.5.2.3 Additional non-CO2 GHGs Co-Benefits

In determining the relative social costs of the different gases, the Marten et al. (2014) 

analysis accounts for differences in lifetime and radiative efficiency between the non-CO2 GHGs 

and CO2. The analysis also accounts for radiative forcing resulting from methane’s effects on 

tropospheric ozone and stratospheric water vapor, and for at least some of the fertilization effects 

of elevated carbon dioxide concentrations. However, there exist several other differences 

between these gases that have not yet been captured in this analysis, namely the non-radiative 

effects of methane-driven elevated tropospheric ozone levels on human health, agriculture, and 

ecosystems, and the effects of carbon dioxide on ocean acidification. Inclusion of these 

additional non-radiative effects would potentially change both the absolute and relative value of 

the various gases.
Of these effects, the human health effect of elevated tropospheric ozone levels resulting 

from methane emissions is the closest to being monetized in a way that would be comparable to 

the SCC. Premature ozone-related cardiopulmonary deaths resulting from global increases in 

tropospheric ozone concentrations produced by the methane oxidation process have been the 

focus of a number of studies over the past decade (e.g., West et al. 200663). Recent studies have 

produced an estimate of a monetized benefit of methane emissions reductions, with results on the 

order of $1000 per metric ton of CH4 emissions reduced (Anenberg et al. 201264; Shindell et al. 

201265), an estimate similar in magnitude to the climate benefits of CH4 reductions estimated by

the Marten et al. or GWP methods. However, though EPA is continuing to monitor this area of 

research as it evolves, EPA is not applying them for benefit estimates at this time. 

8.6 Quantified and Monetized Non-GHG Health and Environmental Impacts

This section analyzes the economic benefits from reductions in health and environmental 

impacts resulting from non-GHG emission reductions that can be expected to occur as a result of 

the proposed Phase 2 standards. CO2 emissions are predominantly the byproduct of fossil fuel 

combustion processes that also produce criteria and hazardous air pollutant emissions. The 

vehicles that are subject to the proposed standards are also significant sources of mobile source 

air pollution such as direct PM, NOX, VOCs and air toxics. The proposed standards would affect 

exhaust emissions of these pollutants from vehicles and would also affect emissions from 

upstream sources that occur during the refining and distribution of fuel. Changes in ambient 

concentrations of ozone, PM2.5, and air toxics that would result from the proposed standards are 

expected to affect human health by reducing premature deaths and other serious human health 

effects, as well as other important improvements in public health and welfare. 

It is important to quantify the health and environmental impacts associated with the 

proposed standards because a failure to adequately consider these ancillary impacts could lead to 

an incorrect assessment of their costs and benefits. Moreover, the health and other impacts of 

exposure to criteria air pollutants and airborne toxics tend to occur in the near term, while most 

effects from reduced climate change are likely to occur only over a time frame of several decades 

or longer. 

EPA typically quantifies and monetizes the health and environmental impacts related to 

both PM and ozone in its regulatory impact analyses (RIAs) when possible. However, EPA was 

unable to do so in time for this proposal. EPA attempts to make emissions and air quality 

modeling decisions early in the analytical process so that we can complete the photochemical air 

quality modeling and use that data to inform the health and environmental impacts analysis. 

Time constraints precluded the agency from completing this work in time for the proposal. 

Instead, EPA has applied PM-related benefits per-ton values to its estimated emission reductions

as an interim approach to estimating the PM-related benefits of the proposal.66,YEPA also 

 

Y

See also: http://www.epa.gov/airquality/benmap/sabpt.html. The current values available on the webpage have 

been updated since the publication of the Fann et al., 2012 paper. For more information regarding the updated 

values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf

(accessed September 9, 2014).
characterizes the health and environmental impacts that will be quantified and monetized for the 

final rulemaking. 

This section is split into two sub-sections: the first presents the benefits-per-ton values 

used to monetize the benefits from reducing population exposure to PM associated with the 

proposed standards; the second explains what PM- and ozone-related health and environmental 

impacts EPA will quantify and monetize in the analysis for the final rule. EPA bases its analyses 

on peer-reviewed studies of air quality and health and welfare effects and peer-reviewed studies 

of the monetary values of public health and welfare improvements, and is generally consistent 

with benefits analyses performed for the analysis of the final Tier 3 Vehicle Rule,67 the final 

2012 PM NAAQS Revision,68 and the final 2017-2025 Light Duty Vehicle GHG Rule.69 

Though EPA is characterizing the changes in emissions associated with toxic pollutants, 

we are not able to quantify or monetize the human health effects associated with air toxic 

pollutants for either the proposal or the final rule analyses (see Chapter 8.6.2.3 for more 

information). Please refer to Chapter 5 for more information about the air toxics emissions 

impacts associated with the proposed standards.

8.6.1 Economic Value of Reductions in Criteria Pollutants

As described in Chapter 5, the proposed standards would reduce emissions of several 

criteria and toxic pollutants and their precursors. In this analysis, EPA estimates the economic 

value of the human health benefits associated with the resulting reductions in PM2.5 exposure. 

Due to analytical limitations with the benefit per-ton method, this analysis does not estimate 

benefits resulting from reductions in population exposure to other criteria pollutants such as 

ozone.ZFurthermore, the benefits per-ton method, like all air quality impact analyses, does not 

monetize all of the potential health and welfare effects associated with reduced concentrations of 

PM2.5.

This analysis uses estimates of the benefits from reducing the incidence of the specific 

PM2.5-related health impacts described below. These estimates, which are expressed per ton of 

PM2.5-related emissions eliminated by the proposed rule, represent the total monetized value of 

human health benefits (including reduction in both premature mortality and premature 

morbidity) from reducing each ton of directly emitted PM2.5, or its precursors (SO2 and NOx), 

from a specified source. Ideally, the human health benefits would be estimated based on changes 

in ambient PM2.5 as determined by full-scale air quality modeling. However, the length of time 

needed to prepare the necessary emissions inventories, in addition to the processing time 

associated with the modeling itself, has precluded us from performing air quality modeling for 

this proposal. We will conduct this modeling for the final rule. 

 

Z The air quality modeling that underlies the PM-related benefit per ton values also produced estimates of ozone 

levels attributable to each sector. However, the complex non-linear chemistry governing ozone formation prevented 

EPA from developing a complementary array of ozone benefit per ton values. This limitation notwithstanding, we 

anticipate that the ozone-related benefits associated with reducing emissions of NOX and VOC could be substantial.
The dollar-per-ton estimates used in this analysis are provided in Table 8-16. As the 

table indicates, these values differ among pollutants, and also depend on their original source, 

because emissions from different sources can result in different degrees of population exposure 

and resulting health impacts. In the summary of costs and benefits, Chapter 8.10, EPA presents 

the monetized value of PM-related improvements associated with the proposal.

Table 8-16 Benefits-per-ton Values (thousands, 2012$)

a

YEARC ON-ROAD MOBILE SOURCES UPSTREAM SOURCESD

Direct PM2.5 SO2 NOX Direct PM2.5 SO2 NOX

Estimated Using a 3 Percent Discount Rateb

2016 $380-$850 $20-$45 $7.7-$18 $330-$750 $69-$160 $6.8-$16

2020 $400-$910 $22-$49 $8.1-$18 $350-$790 $75-$170 $7.4-$17

2025 $440-$1,000 $24-$55 $8.8-$20 $390-$870 $83-$190 $8.1-$18

2030 $480-$1,100 $27-$61 $9.6-$22 $420-$950 $91-$200 $8.7-$20

Estimated Using a 7 Percent Discount Rateb

2016 $340-$770 $18-$41 $6.9-$16 $290-$670 $63-$140 $6.2-$14

2020 $370-$820 $20-$44 $7.4-$17 $320-$720 $67-$150 $6.6-$15

2025 $400-$910 $22-$49 $8.0-$18 $350-$790 $75-$170 $7.3-$17

2030 $430-$980 $24-$55 $8.6-$20 $380-$850 $81-$180 $7.9-$18

Notes:

a The benefit-per-ton estimates presented in this table are based on a range of premature mortality estimates derived 

from the ACS study (Krewski et al., 2009) and the Six-Cities study (Lepeule et al., 2012). 

b The benefit-per-ton estimates presented in this table assume either a 3 percent or 7 percent discount rate in the 

valuation of premature mortality to account for a twenty-year segmented cessation lag. 

c Benefit-per-ton values were estimated for the years 2016, 2020, 2025 and 2030. We hold values constant for 

intervening years (e.g., the 2016 values are assumed to apply to years 2017-2019; 2020 values for years 2021-2024; 

2030 values for years 2031 and beyond). 

d We assume for the purpose of this analysis that “upstream emissions” are most closely associated with refinery 

sector benefit per-ton values. The majority of upstream emission reductions associated with the NPRM are related 

to domestic onsite refinery emissions and domestic crude production. While upstream emissions also include 

storage and transport sources, as well as upstream refinery sources, we have chosen to simply apply the refinery 

values. Full-scale air quality modeling, and the associated benefits analysis, will include upstream emissions from 

all sources in the FRM.

The benefit per-ton technique has been used in previous analyses, including EPA’s 2017-

2025 Light-Duty Vehicle Greenhouse Gas Rule,70 the Reciprocating Internal Combustion Engine 

rules,71,72 and the Residential Wood Heaters NSPS.73 Table 8-17 shows the quantified PM2.5-

related co-benefits captured in those benefit per-ton estimates, as well as unquantified effects the 

benefits per-ton estimates are unable to capture. 

Table 8-17 Human Health and Welfare Effects of PM2.5

POLLUTANT QUANTIFIED AND MONETIZED 

IN PRIMARY ESTIMATES

UNQUANTIFIED EFFECTS

CHANGES IN:

PM2.5 Adult premature mortality 

Acute bronchitis

Hospital admissions: respiratory and 

cardiovascular

Emergency room visits for asthma

Nonfatal heart attacks (myocardial infarction)

Lower and upper respiratory illness

Minor restricted-activity days

Chronic and subchronic bronchitis cases

Strokes and cerebrovascular disease

Low birth weight

Pulmonary function

Chronic respiratory diseases other than chronic 

bronchitis

Non-asthma respiratory emergency room visits

Visibility
Work loss days

Asthma exacerbations (asthmatic population)

Infant mortality

Household soiling

Consistent with the cost-benefit analysis that accompanied the 2012 PM NAAQS 

revision, the benefits estimates utilize the concentration-response functions as reported in the 

epidemiology literature. AA,74 To calculate the total monetized impacts associated with quantified 

health impacts, EPA applies values derived from a number of sources. For premature mortality, 

EPA applies a value of a statistical life (VSL) derived from the mortality valuation literature. 

For certain health impacts, such as respiratory-related ailments, EPA applies willingness-to-pay

estimates derived from the valuation literature. For the remaining health impacts, EPA applies 

values derived from current cost-of-illness and/or wage estimates.

Readers interested in reviewing the complete methodology for creating the benefit-perton estimates used in this analysis can consult EPA’s “Technical Support Document: Estimating 

the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors.”BB Readers can also refer to 

Fann et al. (2012)75 for a detailed description of the benefit-per-ton methodology. 

As described in the documentation for the benefit per-ton estimates cited above, national 

per-ton estimates were developed for selected pollutant/source category combinations. The perton values calculated therefore apply only to tons reduced from those specific pollutant/source 

combinations (e.g., NO2 emitted from on-road mobile sources; direct PM emitted from electricity 

generating units). Our estimate of PM2.5 benefits is therefore based on the total direct PM2.5 and 

PM-related precursor emissions controlled by sector and multiplied by each per-ton value. 

As Table 8-17indicates, EPA projects that the per-ton values for reducing emissions of 

non-GHG pollutants from both vehicle use and upstream sources such as fuel refineries.

CC

These projected increases reflect rising income levels, which increase affected individuals’ 

willingness to pay for reduced exposure to health threats from air pollution.DD They also reflect 

future population growth and increased life expectancy, which expands the size of the population 

 

AA Although we summarize the main issues in this chapter, we encourage interested readers to see the benefits 

chapter of the RIA that accompanied the PM NAAQS for a more detailed description of recent changes to the 

quantification and monetization of PM benefits. Note that the cost-benefit analysis was prepared solely for purposes 

of fulfilling analysis requirements under Executive Order 12866 and was not considered, or otherwise played any 

part, in the decision to revise the PM NAAQS.

BB For more information regarding the updated values, see: 

http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf (accessed 

September 9, 2014).

CC As we discuss in the emissions chapter (Chapter 5), the rule would yield emission reductions from upstream 

refining and fuel distribution due to decreased petroleum consumption.

DD The issue is discussed in more detail in the 2012 PM NAAQS RIA, Section 5.6.8. See U.S. Environmental 

Protection Agency. (2012). Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality 

Standards for Particulate Matter, Health and Environmental Impacts Division, Office of Air Quality Planning and 

Standards, EPA-452-R-12-005, December 2012. Available on the internet: 

http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf
exposed to air pollution in both urban and rural areas, especially among older age groups with 

the highest mortality risk.EE

The benefit-per-ton estimates are subject to a number of assumptions and uncertainties: 

 The benefit-per-ton estimates used here reflect specific geographic patterns of 

emissions reductions and specific air quality and benefits modeling assumptions 

associated with the derivation of those estimates (see the TSD describing the 

calculation of the national benefit-per-ton estimates). 76,FF Consequently, these 

estimates may not reflect local variability in population density, meteorology, 

exposure, baseline health incidence rates, or other local factors associated with the 

current analysis. Therefore, use of these benefit-per-ton values to estimate nonGHG benefits may lead to higher or lower benefit estimates than if these benefits 

were calculated based on direct air quality modeling. EPA will conduct full-scale 

air quality modeling for the final rulemaking in an effort to capture this 

variability.

 This analysis assumes that all fine particles, regardless of their chemical 

composition, are equally potent in causing premature mortality. This is an 

important assumption, because PM2.5 produced via transported precursors emitted 

from stationary sources may differ significantly from direct PM2.5 released from 

diesel engines and other industrial sources. The PM ISA, which was twice 

reviewed by SAB-CASAC, concluded that “many constituents of PM2.5 can be 

linked with multiple health effects, and the evidence is not yet sufficient to allow 

differentiation of those constituents or sources that are more closely related to 

specific outcomes”.77 PM composition and the size distribution of those particles

vary within and between areas due to source characteristics. Any specific location 

could have higher or lower contributions of certain PM species and other 

pollutants than the national average, meaning potential regional differences in 

health impact of given control strategies. Depending on the toxicity of each PM 

species reduced by the proposed standards, assuming equal toxicity could over or 

underestimate benefits.

 This analysis assumes that the health impact function for fine particles is linear 

within the range of ambient concentrations under consideration. Thus, the 

estimates include health benefits from reducing fine particles in areas with varied 

concentrations of PM2.5, including regions that are in attainment with the fine 

particle standard. The direction of bias that assuming linear-no threshold model 

or alternative model introduces depends upon the “true” functional from of the 

relationship and the specific assumptions and data in a particular analysis. For 

 

EE For more information about EPA’s population projections, please refer to the following: 

http://www.epa.gov/air/benmap/models/BenMAPManualAppendicesAugust2010.pdf (See Appendix K)

FF See also: http://www.epa.gov/airquality/benmap/sabpt.html. The current values available on the webpage have 

been updated since the publication of the Fann et al., 2012 paper. For more information regarding the updated 

values, see: http://www.epa.gov/airquality/benmap/models/Source_Apportionment_BPT_TSD_1_31_13.pdf

(accessed September 9, 2014).
example, if the true function identifies a threshold below which health effects do 

not occur, benefits may be overestimated if a substantial portion of those benefits 

were estimated to occur below that threshold. Alternately, if a substantial portion 

of the benefits occurred above that threshold, the benefits may be underestimated 

because an assumed linear no-threshold function may not reflect the steeper slope 

above that threshold to account for all health effects occurring above that 

threshold. 

 There are several health benefit categories that EPA was unable to quantify due to 

limitations associated with using benefits-per-ton estimates, several of which 

could be substantial. Because the NOX and VOC emission reductions associated 

with this proposal are also precursors to ozone, reductions in NOX and VOC 

would also reduce ozone formation and the health effects associated with ozone 

exposure. Unfortunately, ozone-related benefits-per-ton estimates do not exist 

due to issues associated with the complexity of the atmospheric air chemistry and 

nonlinearities associated with ozone formation. The PM-related benefits-per-ton 

estimates also do not include any human welfare or ecological benefits. Please 

refer to Chapter 8.6.2for a description of the agency’s plan to quantify and 

monetize the PM- and ozone-related health impacts for the FRM and a description 

of the unquantified co-pollutant benefits associated with this rulemaking.

 There are many uncertainties associated with the health impact functions that 

underlie the benefits-per-ton estimates. These include: within-study variability

(the precision with which a given study estimates the relationship between air 

quality changes and health effects); across-study variation (different published 

studies of the same pollutant/health effect relationship typically do not report 

identical findings and in some instances the differences are substantial); the 

application of concentration-response functions nationwide (does not account for 

any relationship between region and health effect, to the extent that such a 

relationship exists); extrapolation of impact functions across population (we 

assumed that certain health impact functions applied to age ranges broader than 

that considered in the original epidemiological study); and various uncertainties in 

the concentration-response function, including causality and thresholds. These 

uncertainties may under- or over-estimate benefits.

 EPA has investigated methods to characterize uncertainty in the relationship 

between PM2.5 exposure and premature mortality. EPA’s final PM2.5 NAAQS 

analysis provides a more complete picture about the overall uncertainty in PM2.5

benefits estimates. For more information, please consult the PM2.5 NAAQS 

RIA.78

 The benefit-per-ton unit values used in this analysis incorporate projections of key

variables, including atmospheric conditions, source level emissions, population, 

health baselines, incomes, and technology. These projections introduce some 

uncertainties to the benefit per ton estimates.

As mentioned above, emissions changes and benefits-per-ton estimates alone are not a 

good indication of local or regional air quality and health impacts, as there may be localized 

impacts associated with the proposed rulemaking. Additionally, the atmospheric chemistry
related to ambient concentrations of PM2.5, ozone and air toxics is very complex. Full-scale 

photochemical modeling is therefore necessary to provide the needed spatial and temporal detail 

to more completely and accurately estimate the changes in ambient levels of these pollutants and 

their associated health and welfare impacts. As discussed above, timing constraints precluded 

EPA from conducting a full-scale photochemical air quality modeling analysis in time for the 

NPRM. For the final rule, however, a national-scale air quality modeling analysis will be 

performed to analyze the impacts of the standards on PM2.5, ozone, and selected air toxics. The 

benefits analysis plan for the final rulemaking is discussed in the next section.

8.6.2 Human Health and Environmental Benefits for the Final Rule

8.6.2.1 Human Health and Environmental Impacts

As noted above, to model the ozone and PM air quality benefits of the final standards, 

EPA plans to use the Community Multiscale Air Quality (CMAQ) model (see Chapter 6 for a 

description of the CMAQ model). The modeled ambient air quality data will serve as an input to 

the Environmental Benefits Mapping and Analysis Program (BenMAP).GG BenMAP is a 

computer program developed by EPA that integrates a number of the modeling elements used in 

previous RIAs (e.g., interpolation functions, population projections, health impact functions, 

valuation functions, analysis and pooling methods) to translate modeled air concentration 

estimates into health effects incidence estimates and monetized benefits estimates. 

Table 8-18 lists the PM- and ozone-related benefits categories we will use to quantify the 

non-GHG incidence impacts associated with the final standards. Table 8-18 also lists non-GHGrelated endpoints we are currently unable to quantify and/or monetize.

Table 8-18 Estimated Quantified and Unquantified Health Effects

BENEFITS 

CATEGORY

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED

EFFECT HAS 

BEEN 

MONETIZED

MORE 

INFORMATION

Improved Human Health

Reduced 

incidence of 

premature 

mortality and 

morbidity from 

exposure to PM2.5

Adult premature mortality based on 

cohort study estimates and expert 

elicitation estimates (age >25 or age 

>30)

  PM NAAQS RIA, 

Section 5.6

Infant mortality (age <1)   PM NAAQS RIA, 

Section 5.6

Non-fatal heart attacks (age > 18)   PM NAAQS RIA, 

Section 5.6

Hospital admissions—respiratory (all 

ages)

  PM NAAQS RIA, 

Section 5.6

Hospital admissions—cardiovascular 

(age >20)

  PM NAAQS RIA, 

Section 5.6

 

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

benmodels.html.
BENEFITS 

CATEGORY

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED

EFFECT HAS 

BEEN 

MONETIZED

MORE 

INFORMATION

Emergency department visits for 

asthma (all ages)

  PM NAAQS RIA, 

Section 5.6

Acute bronchitis (age 8–12)   PM NAAQS RIA, 

Section 5.6

Lower respiratory symptoms (age 7–

14)

  PM NAAQS RIA, 

Section 5.6

Upper respiratory symptoms 

(asthmatics age 9–11)

  PM NAAQS RIA, 

Section 5.6

Asthma exacerbation (asthmatics age 

6–18)

  PM NAAQS RIA, 

Section 5.6

Lost work days (age 18–65)   PM NAAQS RIA, 

Section 5.6

Minor restricted-activity days (age 

18–65)

  PM NAAQS RIA, 

Section 5.6

Chronic Bronchitis (age >26) — — PM NAAQS RIA, 

Section 5.6c

Emergency department visits for 

cardiovascular effects (all ages)

— — PM NAAQS RIA, 

Section 5.6c

Strokes and cerebrovascular disease 

(age 50–79)

— — PM NAAQS RIA, 

Section 5.6c

Other cardiovascular effects (e.g., 

other ages)

— — PM ISAa

Other respiratory effects (e.g., 

pulmonary function, non-asthma ER 

visits, non-bronchitis chronic 

diseases, other ages and populations)

— — PM ISAa

Reproductive and developmental 

effects (e.g., low birth weight, preterm births, etc.)

— — PM ISAa,b

Cancer, mutagenicity, and 

genotoxicity effects

— — PM ISAa,b

Reduced 

incidence of 

premature 

mortality and 

morbidity from 

exposure to 

ozone

Premature mortality based on shortterm study estimates (all ages)

  Ozone ISA

Premature mortality based on longterm study estimates (age 30–99)— — Ozone ISAc

Hospital admissions—respiratory 

causes (age > 65)

  Ozone ISA

Hospital admissions—respiratory 

causes (age <2)

  Ozone ISA

Emergency department visits for 

asthma (all ages)

  Ozone ISA

Minor restricted-activity days (age 

18–65)

  Ozone ISA

School absence days (age 5–17)   Ozone ISA

Decreased outdoor worker 

productivity (age 18–65)

  Ozone ISA

Other respiratory effects (e.g., 

premature aging of lungs)

— — Ozone ISAa

Cardiovascular and nervous system 

effects

— — Ozone ISAb
BENEFITS 

CATEGORY

SPECIFIC EFFECT EFFECT HAS 

BEEN 

QUANTIFIED

EFFECT HAS 

BEEN 

MONETIZED

MORE 

INFORMATION

Reproductive and developmental 

effects

— — Ozone ISAb

Reduced 

incidence of 

morbidity from 

exposure to air 

toxics

Cancer (benzene, 1,3-butadiene, 

formaldehyde, acetaldehyde)

Anemia (benzene)

Disruption of production of blood 

components (benzene)

Reduction in the number of blood 

platelets (benzene)

Excessive bone marrow formation 

(benzene)

Depression of lymphocyte counts 

(benzene)

Reproductive and developmental 

effects (1,3-butadiene)

Irritation of eyes and mucus 

membranes (formaldehyde)

Respiratory irritation (formaldehyde)

Asthma attacks in asthmatics 

(formaldehyde)

Asthma-like symptoms in nonasthmatics (formaldehyde)

Irritation of the eyes, skin, and 

respiratory tract (acetaldehyde)

Upper respiratory tract irritation and 

congestion (acrolein)

— — IRISa,b

Notes:

a We assess these benefits qualitatively because we do not have sufficient confidence in available data or methods.

b We assess these benefits qualitatively because current evidence is only suggestive of causality or there are other 

significant concerns over the strength of the association.

c We assess these benefits qualitatively due to time and resource limitations for this analysis.

Table 8-19 lists the specific PM- and ozone-related health effect exposure-response functions we 

will use to quantify the non-GHG incidence impacts associated with the final standards. 

Table 8-19 Health Impact Functions Used in BenMAP to Estimate Impacts of PM2.5 and Ozone Reductions

ENDPOINT POLLUTANT STUDY STUDY POPULATION

Premature Mortality

Premature mortality –

daily time series

O3 Multi-city

Bell et al (2004) (NMMAPS study)79– Nonaccidental

Huang et al (2005)80- Cardiopulmonary

Schwartz (2005)81– Non-accidental

Meta-analyses:

Bell et al (2005)82– All cause

Ito et al (2005)83– Non-accidental

All ages
ENDPOINT POLLUTANT STUDY STUDY POPULATION

Levy et al (2005)84– All cause

Premature mortality —

cohort study, all-cause

PM2.5 Krewski et al. (2009)

85

Lepeule et al. (2012)

86

>29 years

>25 years

Premature mortality, 

total exposures

PM2.5 Expert Elicitation (IEc, 2006)87 >24 years

Premature mortality —

all-cause

PM2.5 Woodruff et al. (1997)88 Infant (<1 year)

Chronic Illness

Nonfatal heart attacks PM2.5 Peters et al. (2001)89

Pooled estimate:

 Pope et al. (2006)90

 Sullivan et al. (2005)91

 Zanobetti et al. (2009)92

 Zanobetti and Schwartz (2006)93

Adults (>18 years)

Hospital Admissions 

Respiratory

O3

Pooled estimate:

Schwartz (1995) - ICD 460-519 (all resp)94

Schwartz (1994a; 1994b) - ICD 480-486 

(pneumonia)95,96

Moolgavkar et al. (1997) - ICD 480-487 

(pneumonia)97

Schwartz (1994b) - ICD 491-492, 494-496 

(COPD)

Moolgavkar et al. (1997) – ICD 490-496 

(COPD)

>64 years

Burnett et al. (2001)98 <2 years

PM2.5 Pooled estimate:

Zanobetti et al. (2009)—ICD 460-519 (All 

respiratory)

Kloog et al. (2012)—ICD 460-519 (All 

Respiratory)

99

>64 years

PM2.5 Moolgavkar (2000)—ICD 490–496 (Chronic 

lung disease)100

18–64 years

PM2.5 Pooled estimate:

Babin et al. (2007)—ICD 493 (asthma)101

Sheppard (2003)—ICD 493 (asthma)102

<18 years

Cardiovascular PM2.5 Pooled estimate: Zanobetti et al. (2009)—ICD 

390-459 (all cardiovascular)

Peng et al. (2009)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)103

Peng et al. (2008)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)104

Bell et al. (2008)—ICD 426-427; 428; 430-

438; 410-414; 429; 440-449 (Cardio-, cerebroand peripheral vascular disease)105

>64 years

PM2.5 Moolgavkar (2000)—ICD 390-429 (all

cardiovascular)

20–64 years

Asthma-related ER 

visits

O3 Pooled estimate:

Peel et al (2005)106

Wilson et al (2005)107

All ages

All ages
ENDPOINT POLLUTANT STUDY STUDY POPULATION

Asthma-related ER 

visits (cont’d)

PM2.5 Pooled estimate:

Mar et al. (2010)108

Slaughter et al. (2005)109

Glad et al. (2012)110

All ages

Other Health Endpoints

Acute bronchitis PM2.5 Dockery et al. (1996)111 8–12 years

Upper respiratory 

symptoms

PM2.5 Pope et al. (1991)112 Asthmatics, 9–11 

years

Lower respiratory 

symptoms

PM2.5 Schwartz and Neas (2000)113 7–14 years

Asthma exacerbations PM2.5 Pooled estimate:

Ostro et al. (2001)114 (cough, wheeze and 

shortness of breath)

Mar et al. (2004) (cough, shortness of breath)

6–18 yearsa

Work loss days PM2.5 Ostro (1987)115 18–65 years

School absence days

O3 

Pooled estimate:

Gilliland et al. (2001)116

Chen et al. (2000)117

5–17 yearsb

Minor Restricted 

Activity Days 

(MRADs)

O3 Ostro and Rothschild (1989)118 18–65 years

PM2.5 Ostro and Rothschild (1989) 18–65 years

Notes:

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 7 to 12 for the Mar et al. (2004) 

study. Based on advice from the SAB-HES, we extended the applied population to 6-18, reflecting the common 

biological basis for the effect in children in the broader age group. See: U.S. EPA-SAB (2004) and NRC (2002).

b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on advice 

from the National Research Council and EPA SAB-HES, we have calculated reductions in school absences for all 

school-aged children based on the biological similarity between children aged 5 to 17.

8.6.2.2 Monetized Estimates of Impacts of Reductions in Co-Pollutants

Table 8-20 presents the monetary values we will apply to changes in the incidence of 

health and welfare effects associated with reductions in non-GHG pollutants that will occur 

when these GHG control strategies are finalized. 
Table 8-20 Valuation Metrics Used in BenMAP to Estimate Monetary Co-Benefits

ENDPOINT VALUATION METHOD VALUATION (2011$)

Premature mortality Assumed Mean VSL $8,300,000

Myocardial Infarctions, Nonfatal Medical Costs Over 5 Years. Varies by age and 

discount rate. Russell (1998)119

---

Medical Costs Over 5 Years. Varies by age and 

discount rate. Wittels (1990)120

---

Hospital Admissions

 Respiratory, Age 65+ COI: Medical Costs + Wage Lost $37,000

 Respiratory, Ages 0-2 COI: Medical Costs $13,000

 Chronic Obstructive Pulmonary 

Disease (COPD)

COI: Medical Costs + Wage Lost $22,000

 Asthma COI: Medical Costs + Wage Lost $17,000

 Cardiovascular COI: Medical Costs + Wage Lost (18-64) $43,000

COI: Medical Costs + Wage Lost (65-99) $42,000

 ER Visits, Asthma COI: Average of Smith et al. (1997)121 $440

and Standford et al. (1999)122

Other Health Endpoints

 Acute Bronchitis WTP: 6 Day Illness, CV Studies $470

 Upper Respiratory Symptoms WTP: 1 Day, CV Studies $32

 Lower Respiratory Symptoms WTP: 1 Day, CV Studies $21

 Asthma Exacerbation WTP: Bad Asthma Day, Rowe and Chestnut 

(1986) 123

$57

 Work Loss Days Median Daily Wage, County-Specific (median = 

$150)

---

 Minor Restricted Activity Days WTP: 1 Day, CV Studies $66

 School Absence Days Median Daily Wage, Women 25+ $98

Source: Dollar amounts for each valuation method were extracted from BenMAP and adjusted to year 2011 dollars 

(from year 2000 dollars) using the Consumer Price Urban Index (CPI-U). For endpoints valued using measures of 

VSL, WTP, or are wage-based, we use the CPI-U for “all items”: 224.939 (2011) and 172.2 (2000). For endpoints

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

8.6.2.3 Other Unquantified Health and Environmental Impacts

In addition to the co-pollutant health and environmental impacts we plan to quantify for 

the analysis of the HD GHG standards, there are a number of other health and human welfare 

endpoints that we will not be able to quantify because of current limitations in the methods or 

available data. These impacts are associated with emissions of air toxics (including benzene, 

1,3-butadiene, formaldehyde, acetaldehyde, acrolein, and ethanol), ambient ozone, and ambient 

PM2.5 exposures. For example, we have not quantified a number of known or suspected health 

effects linked with ozone and PM for which appropriate health impact functions are not available 

or which do not provide easily interpretable outcomes (i.e., changes in heart rate variability). In 

addition, we are currently unable to quantify a number of known welfare effects, including 

reduced acid and particulate deposition damage to cultural monuments and other materials, and 

environmental benefits due to reductions of impacts of eutrophication in coastal areas. 

Although there will be impacts associated with air toxic pollutant emission changes that 

result from this action, we do not attempt to monetize those impacts. This is primarily because 
currently available tools and methods to assess air toxics risk from mobile sources at the national 

scale are not adequate for extrapolation to incidence estimations or benefits assessment. The 

best suite of tools and methods currently available for assessment at the national scale are those 

used in the National-Scale Air Toxics Assessment (NATA). EPA’s Science Advisory Board 

specifically commented in their review of the 1996 NATA that these tools were not yet ready for 

use in a national-scale benefits analysis, because they did not consider the full distribution of 

exposure and risk, or address sub-chronic health effects.124 While EPA has since improved these

tools, there remain critical limitations for estimating incidence and assessing benefits of reducing 

mobile source air toxics. 

As part of the second prospective analysis of the benefits and costs of the Clean Air 

Act,

125 EPA conducted a case study analysis of the health effects associated with reducing 

exposure to benzene in Houston from implementation of the Clean Air Act. While reviewing the 

draft report, EPA’s Advisory Council on Clean Air Compliance Analysis concluded that “the 

challenges for assessing progress in health improvement as a result of reductions in emissions of 

hazardous air pollutants (HAPs) are daunting...due to a lack of exposure-response functions, 

uncertainties in emissions inventories and background levels, the difficulty of extrapolating risk 

estimates to low doses and the challenges of tracking health progress for diseases, such as 

cancer, that have long latency periods.”126 EPA continues to work to address these limitations; 

however, we will not have the methods and tools available for national-scale application in time 

for the analysis of the final action.

HH

8.7 Additional Impacts

8.7.1 Cost of Noise, Congestion, and Accidents

Section 8.3 discusses the likely sign of the rebound effect. If net operating costs of the 

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

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

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

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

and 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.

 

HH In April, 2009, EPA hosted a workshop on estimating the benefits or reducing hazardous air pollutants. This 

workshop built upon the work accomplished in the June 2000 Science Advisory Board/EPA Workshop on the 

Benefits of Reductions in Exposure to Hazardous Air Pollutants, which generated thoughtful discussion on 

approaches to estimating human health benefits from reductions in air toxics exposure, but no consensus was 

reached on methods that could be implemented in the near term for a broad selection of air toxics. Please visit 

http://epa.gov/air/toxicair/2009workshop.html for more information about the workshop and its associated materials.
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.127 The FHWA estimates are intended to measure the increases 

in costs from added congestion, property damages and injuries in traffic accidents, and noise 

levels 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 2012-2016 vehicle rulemaking. 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 2012 dollars are included in Table 8-21.

Table 8-21 Low-Mid-High Cost Estimates (2012$/mile) 

NOISE

High Middle Low

Pickup Truck, Van $0.002 $0.001 $0.000

Vocational Vehicle $0.024 $0.009 $0.003

Combination Tractor $0.054 $0.021 $0.006

Accidents

High Middle Low

Pickup Truck, Van $0.086 $0.028 $0.015

Vocational Vehicle $0.050 $0.017 $0.008

Combination Tractor $0.073 $0.023 $0.011

Congestion

High Middle Low

Pickup Truck, Van $0.151 $0.051 $0.014

Vocational Vehicle $0.344 $0.117 $0.031

Combination Tractor $0.331 $0.113 $0.030

The agencies are using FHWA’s “Middle” estimates for marginal congestion, accident, 

and noise costs caused by increased travel from trucks.

128

 This approach is consistent with the 

methodology used in the HD GHG Phase 1 rule and both LD GHG rules. 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.

The results are shown in Table 8-22 through Table 8-24.

Table 8-22 Annual Costs & Net Present Values Associated with Increased Noise, Accidents and Congestion 

for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

CALENDAR

YEAR

HD PICKUP AND 

VANS

VOCATIONAL TRACTOR/TRAILER SUM

2018 $0 $0 $0 $0

2019 $0 $0 $0 $0

2020 $0 $0 $0 $0

2021 $9 $26 $82 $117

2022 $18 $51 $103 $172

2023 $27 $76 $123 $226

2024 $35 $102 $141 $279

2025 $44 $127 $159 $330

2026 $52 $151 $177 $379

2027 $59 $173 $192 $425

2028 $66 $194 $207 $467

2029 $72 $213 $221 $506

2030 $78 $230 $234 $542

2035 $100 $294 $282 $676

2040 $111 $330 $317 $758

2050 $126 $375 $370 $871

NPV, 3% $1,335 $3,934 $4,066 $9,334

NPV, 7% $593 $1,743 $1,867 $4,202

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
Table 8-23 Discounted Model Year Lifetime Costs Associated with Increased Noise, Accidents and 

Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (3% 

discount rate, Millions of 2012$) a

MODEL

YEAR

HD PICKUP AND 

VANS

VOCATIONAL TRACTOR/TRAILER SUM

2018 $0 $0 $132 $132

2019 $0 $0 $146 $146

2020 $0 $0 $162 $162

2021 $71 $203 $176 $450

2022 $69 $197 $173 $438

2023 $66 $191 $169 $427

2024 $65 $190 $168 $424

2025 $64 $189 $169 $422

2026 $64 $188 $168 $420

2027 $63 $185 $167 $415

2028 $61 $182 $166 $409

2029 $60 $178 $164 $402

Sum $583 $1,704 $1,959 $4,247

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-24 Discounted Model Year Lifetime Costs Associated with Increased Noise, Accidents and 

Congestion for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (7% 

discount rate, Millions of 2012$) a

MODEL

YEAR

HD PICKUP AND 

VANS

VOCATIONAL TRACTOR/TRAILER SUM

2018 $0 $0 $85 $85

2019 $0 $0 $94 $94

2020 $0 $0 $103 $103

2021 $45 $129 $110 $284

2022 $42 $120 $104 $266

2023 $39 $112 $98 $250

2024 $37 $108 $94 $239

2025 $35 $103 $91 $229

2026 $33 $98 $88 $219

2027 $32 $93 $84 $209

2028 $30 $88 $80 $198

2029 $28 $83 $76 $187

Sum $320 $935 $1,106 $2,362

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

8.7.2 Benefits of Reduced Refueling Time

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

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

buy, smaller fuel tanks. Keeping the fuel tank the same size will allow truck operators to 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 Section IX VIII.A and RIA Chapter 8.2: does the 

apparent existence of these savings reflect failures in the market for fuel economy, or does it 

reflect costs that are not addressed in this analysis? The response to these questions could vary

across truck segment. 

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

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

driver of each type of truck would 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 8.3 in this RIA, the average number of miles each type of 

vehicle is driven annually would likely increase under the regulation, as truck operators respond 

to lower fuel expenditures (the “rebound effect”). The estimates of refueling time with the 

proposal in effect allow for this increase in truck use. However, the estimate of the rebound 

effect does not account for any reduction in net operating costs from lower refueling time. 

Because the rebound effect should measure the change in VMT with respect to the net change in 

overall operating costs, refueling time costs would ideally factor into this calculation. The effect 

of this omission is expected to be minor because refueling time savings are generally 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 would save each year as a consequence of pumping less fuel into the 

vehicle’s tank. The calculation also includes a fixed time per refill event of 3.5 minutes which 

would not occur as frequently due to the fewer number of refills. 

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

that value by the tank volume and refill amount to get the number of refills, then multiplies that 

by the time per refill to determine the number of hours saved in a given year. The calculation 

then applies DOT-recommended values of travel time savings to convert the resulting time 

savings to their economic value. The input metrics used in the analysis are included in Table 

8-25. The equation for the calculation is shown below:

  = 

 − 

    × 

  

  

+    × 

$

h





The annual impacts associated with reduced refueling time are shown in Table 8-26 and the MY 

lifetime impacts are shown in Table 8-27 aFor an explanation of analytical Methods A and B, please see 

Preamble Section I.D; for an explanation of the less dynamic baseline, 1a, and more dynamic baseline, 1b, please 

see Preamble Section X.A.1
Table 8-27and Table 8-28. 

Table 8-25 Inputs to Calculate Refueling Time Savings

HD PICKUP AND VAN VOCATIONAL 

VEHICLE

TRACTOR

Fuel Dispensing Rate 

(gallon/minute)129

10 10 20

Refueling fixed time 

(minutes/refill)130

3.5 3.5 3.5

Tank volume (gallons)a30 40 200

Refill amount 

(%volume/refill) a

60% 75% 75%

Resultant time/refill

(minutes/refill)

5.3 6.5 11.0

Wage rate

(2012$/hr)131,b

$27.22 31.01 28.56

Notes:

a HD pickup and van values based on a NHTSA survey, other are estimated.

b A wage growth rate of 1.2% has been assumed for future years.

Table 8-26 Annual Refueling Benefits and Net Present Values for the Preferred Alternative Relative to the 

Less Dynamic Baseline and using Method B

(Dollar Values in Millions of 2012$) a

CALENDAR

YEAR

HD PICKUP AND VANS VOCATIONAL TRACTOR/TRAILER SUM OF 

Hours saved BENEFITS

(thousands)

Benefits Hours saved 

(thousands)

Benefits Hours saved 

(thousands)

Benefits

2018 0 $0 0 $0 90 $3 $3

2019 0 $0 0 $0 183 $6 $6

2020 0 $0 0 $0 278 $9 $9

2021 51 $2 122 $4 598 $19 $25

2022 192 $6 244 $9 999 $32 $47

2023 419 $13 365 $13 1,401 $46 $72

2024 732 $23 620 $22 2,046 $67 $113

2025 1,130 $36 874 $32 2,691 $90 $157

2026 1,611 $52 1,124 $41 3,327 $112 $205

2027 2,164 $70 1,558 $58 4,043 $138 $266

2028 2,699 $89 1,979 $74 4,731 $164 $327

2029 3,211 $107 2,387 $91 5,387 $188 $386

2030 3,694 $125 2,778 $107 6,011 $213 $444

2035 5,595 $200 4,343 $177 8,526 $320 $698

2040 6,695 $254 5,286 $229 10,205 $407 $890

2050 7,788 $334 6,260 $305 12,365 $556 $1,195

NPV, 3% $2,627 $2,347 $4,436 $9,410

NPV, 7% $1,067 $950 $1,851 $3,868

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
Table 8-27 Discounted Model Year Lifetime Refueling Benefits at 3% for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

MODEL

YEAR

HD PICKUP

AND VANS

VOCATIONAL TRACTOR/

TRAILER

SUM

2018 $0 $0 $23 $23

2019 $0 $0 $22 $22

2020 $0 $0 $21 $21

2021 $13 $36 $114 $163

2022 $35 $36 $113 $184

2023 $56 $35 $112 $203

2024 $77 $72 $176 $325

2025 $97 $73 $179 $349

2026 $118 $73 $181 $372

2027 $136 $123 $207 $466

2028 $135 $122 $207 $465

2029 $134 $121 $208 $463

Sum $801 $691 $1,563 $3,055

Note:

a

For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-28 Discounted Model Year Lifetime Refueling Benefits at 7% for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (Millions of 2012$) a

MODEL

YEAR

HD PICKUP

AND VANS

VOCATIONAL TRACTOR/

TRAILER

SUM

2018 $0 $0 $16 $16

2019 $0 $0 $15 $15

2020 $0 $0 $14 $14

2021 $8 $23 $71 $101

2022 $21 $21 $67 $110

2023 $33 $20 $64 $117

2024 $43 $40 $97 $181

2025 $52 $39 $95 $187

2026 $61 $38 $93 $191

2027 $68 $61 $102 $231

2028 $65 $59 $98 $222

2029 $62 $56 $95 $213

Sum $413 $357 $827 $1,597

Note:

a

For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

8.7.3 Benefits of Increased Travel Associated with Rebound Driving

The increase in travel associated with the rebound effect produces additional benefits to 

vehicle owners and operators, which reflect the value of the added (or more desirable) social and 

economic opportunities that become accessible with additional travel. The analysis estimates the 

economic benefits from increased rebound-effect driving as the sum of fuel expenditures 

incurred plus the vehicle owner/operator surplus from the additional accessibility it provides. As 
evidenced by the fact that vehicles make more frequent or longer trips when the cost of driving 

declines, the benefits from this added travel exceed added expenditures for the fuel consumed. 

Note that the amount by which the benefits from this increased driving exceed its increased fuel 

costs measures the net benefits from the additional travel, usually referred to as increased 

consumer surplus or, in this case, increased owner/operator surplus. The equation for the 

calculation of the total travel benefit is shown below:

  = () 

$





+ 

1

2

 () 

$





− 

$





The agencies’ analysis estimates the economic value of the increased owner/operator 

surplus provided by added driving using the conventional approximation, which is one half of 

the product of the decline in vehicle operating costs per vehicle-mile and the resulting increase in 

the annual number of miles driven. Because it depends on the extent of improvement in fuel 

economy, the value of benefits from increased vehicle use changes by model year and varies 

among alternative standards. Under even those alternatives that would impose the highest 

standards, however, the magnitude of the surplus from additional vehicle use represents a small 

fraction of this benefit. The benefits are shown in Table 8-29 through Table 8-31.

Table 8-29 Annual Value of Increased Travel and Net Present Values at 3% and 7% Discount Rates for the 

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

CALENDAR

YEAR

HD PICKUP AND 

VANS

VOCATIONAL TRACTOR/TRAILER SUM

2018 $0 $0 $0 $0

2019 $0 $0 $0 $0

2020 $0 $0 $0 $0

2021 $32 $68 $345 $445

2022 $64 $138 $434 $636

2023 $96 $208 $517 $821

2024 $127 $279 $595 $1,001

2025 $157 $349 $672 $1,179

2026 $186 $416 $744 $1,346

2027 $213 $481 $813 $1,506

2028 $237 $538 $872 $1,647

2029 $261 $593 $929 $1,783

2030 $282 $644 $983 $1,909

2035 $372 $855 $1,217 $2,445

2040 $437 $1,008 $1,428 $2,873

2050 $489 $1,141 $1,656 $3,286

NPV, 3% $5,021 $11,518 $17,700 $34,240

NPV, 7% $2,209 $5,045 $8,062 $15,316

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1
Table 8-30 Discounted Model Year Lifetime Value of Increased Travel for the Preferred Alternative Relative 

to the Less Dynamic Baseline and using Method B (3% discount rate, Millions of 2012$) a

MODEL

YEAR

HD PICKUP AND 

VANS

VOCATIONAL TRACTOR/TRAILER SUM

2018 $0 $0 $554 $554

2019 $0 $0 $618 $618

2020 $0 $0 $686 $686

2021 $252 $548 $711 $1,510

2022 $244 $539 $706 $1,488

2023 $236 $529 $698 $1,463

2024 $232 $522 $680 $1,434

2025 $229 $524 $689 $1,442

2026 $227 $525 $695 $1,447

2027 $223 $511 $688 $1,421

2028 $220 $507 $688 $1,415

2029 $218 $502 $686 $1,406

Sum $2,080 $4,706 $8,098 $14,884

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

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

to the Less Dynamic Baseline and using Method B (7% discount rate, Millions of 2012$) a

MODEL

YEAR

HD PICKUP AND 

VANS

VOCATIONAL TRACTOR/TRAILER SUM

2018 $0 $0 $353 $353

2019 $0 $0 $390 $390

2020 $0 $0 $429 $429

2021 $158 $343 $441 $942

2022 $148 $325 $422 $894

2023 $138 $307 $402 $847

2024 $130 $292 $377 $799

2025 $124 $282 $368 $774

2026 $118 $272 $358 $748

2027 $111 $255 $341 $708

2028 $106 $244 $328 $678

2029 $101 $232 $315 $649

Sum $1,134 $2,553 $4,524 $8,211

Note:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

8.8 The Effect of Safety Standards and Voluntary Safety Improvements on Vehicle 

Weight

Safety standards developed by NHTSA in previous rulemakings may make compliance 

with the fuel efficiency and 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. 

8.8.1 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 

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

8.8.1.1 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.132 To address these problems, the H and J load range tires could potentially use 

improved rubber compounds, which would add no weight to the tires, to reduce heat retention 

and improve the durability of the tires. The L load range tires, in contrast, appear to need to use

high tensile strength steel chords in the tire bead, carcass and belt areas, which would enable a 

weight reduction with no strength penalties. Thus, if the update to FMVSS No. 119 was 

finalized, we anticipate no change in weight for H and J load range tires and a small reduction in 

weight for L load range tires. This proposal could become a final rule with an effective date of 

MY 2016.
8.8.1.2 FMVSS No. 121, Airbrake Systems Stopping Distance

FMVSS No. 121 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, 

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. 

8.8.1.3 FMVSS No. 208, Motor coach Lap/Shoulder Belts

NHTSA is proposing lap/shoulder belts for all motorcoach seats. About 2,000 

motorcoaches are sold per year in the United States. Based on preliminary results from the 

agency’s cost/weight teardown studies of motor coach seats,133 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. 

8.8.2 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. 

8.8.3 Summary – Overview of Anticipated Weight Increases

Table 8-32 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 increase motor coach vehicle weight by 171-279 pounds and will increase other 

heavy-duty truck weights by 10 pounds. 

Table 8-32 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

a

0

a

208

Motor coaches only

161-269 73-122

MD/HD Vehicle Electronic Stability 

Control Systems

10 4.5

Total 

Motor coaches

171- 279 77.5-126.5

Total

All other MD/HD vehicles

10 4.5

Note: 

NHTSA’s final rule on Air Brakes, docket NHTSA-2009-0083, dated July 27, 2009, concluded that

a small amount of weight would be added to the brake systems but a weight value was not 

provided.

8.8.4 Effects of Vehicle Mass Reduction on Safety

NHTSA and EPA have been considering the effect of vehicle weight on vehicle safety for 

the past several years in the context of our joint rulemaking for light-duty vehicle CAFE and 

GHG standards, consistent with NHTSA’s long-standing consideration of safety effects in setting 

CAFE standards. Combining all modes of impact, the latest analysis by NHTSA for the MYs 

2012-2016 final rule134 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 

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.

8.9 Petroleum, Energy and National Security impact

8.9.1 Energy Security Impacts

The Phase 2 standards are designed to require improvements in the fuel efficiency of 

medium- and heavy-duty vehicles and, thereby, reduce fuel consumption and GHG emissions.

In turn, the Phase 2 standards help to reduce U.S. petroleum imports. A reduction of U.S. 

petroleum imports reduces both financial and strategic risks caused by potential sudden 

disruptions in the supply of imported petroleum to the U.S. This reduction in risk increases U.S. 

energy security. This section summarizes the agency’s estimates of U.S. oil import reductions 

and energy security benefits of the proposed Phase 2 standards. Additional discussion of this 

issue can be found in Section IX.H of the preamble.

The U.S., as a large oil importer and oil consumer, is economically vulnerable to 

outcomes in a volatile global oil market that relies on oil supplies from potentially unstable 

sources. Much of the world’s oil and gas supplies are located in countries facing social, 

economic, and demographic challenges, thus making them vulnerable to potential local 
instability. In 2010, just over 40 percent of world oil supply came from OPEC (e.g., 

Organization of Petroleum Exporting Countries) nations and the Annual Energy Outlook 2014 

(Early Release) projects that this share will rise steadily. In the AEO 2014 (Early Release) 

projections, OPEC nations supply over 44 percent by 2040.II

Approximately 31 percent of global supply is from Middle East and North African

countries alone, a share that is expected to grow.JJ Measured in terms of the share of world oil 

resources or the share of global oil export supply, rather than oil production, the concentration of 

global petroleum resources in OPEC nations is even larger. As another measure of 

concentration, of the 137 countries/principalities that export either crude or product, the top 12 

have recently accounted for over 55 percent of exports.135 Eight of these countries are members 

of OPEC, and a ninth is Russia.KK In a market where even a 1-2 percent supply loss raises prices 

noticeably, and where a 10 percent supply loss could lead to an unprecedented price shock, this 

regional concentration is of concern.LL Historically, the countries of the Middle East have been 

the source of eight of the ten major world oil disruptions, with the ninth originating in 

Venezuela, an OPEC country, and the tenth being Hurricanes Katrina and Rita.

MM

One impact of the Proposed Phase 2 HD National Program is that it promotes more 

efficient use of transportation fuels in the U.S. The result is that it reduces U.S. oil consumption 

and 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 

increases U.S. energy security. For this rule, an “oil premium” approach is utilized that

identifies those energy security related economic costs which are not reflected in the market 

price of oil, and which are expected to change in response to an incremental change in the level 

of U.S. oil imports.

 

II The agencies used the AEO 2014 (Early Release) since this version of AEO was available at the time that fuel 

savings from the rule were being estimated. The AEO 2014 (Early Release) and the AEO 2014 have very similar 

energy market and economic projections. For example, world oil prices are the same between the two forecasts.

JJ Middle East and North African oil supply share reaches 36 percent in 2040 in the AEO (Early Release) Reference 

Case.

KK The other three are Norway, Canada, and the EU, an exporter of product.

LL For example, the 2005 Hurricanes Katrina/Rita and the 2011 Libyan conflict both led to a 1.8 percent reduction in 

global crude supply. While the price impact of the latter is not easily distinguished given the rapidly rising postrecession prices, the former event was associated with a 10-15 percent world oil price increase. There are a range of 

smaller events with smaller but noticeable impacts. Somewhat larger events, such as the 2002/3 Venezuelan Strike 

and the War in Iraq, corresponded to about a 2.9 percent sustained loss of supply, and was associated with a 28 

percent world oil price increase. (Compiled from EIA oil price data, IEA2012 [IEA Response System for Oil Supply 

Emergencies] (http://www.iea.org/publications/freepublications/publication/EPPD_Brochure_English_2012_02.pdf) 

See table on P. 11. and Hamilton 2011 "Historical Oil Shocks,"(http://econweb.ucsd.edu/~jhamilto/oil_history.pdf) 

in *Routledge Handbook of Major Events in Economic History*, pp. 239-265, edited by Randall E. Parker and 

Robert Whaples, New York: Routledge Taylor and Francis Group, 2013).

MM The events IEA categorized as oil supply disruptions all had a gross peak oil supply loss of at least 1.5 million 

barrels a day as a result of wars, revolutions, embargoes or strikes involving major oil exporting nations or from 

major storm events or disasters (like the double Hurricane Katrina/Rita) affecting oil producing/processing regions. 

IEA 2011 “IEA Response System for Oil Supply Emergencies.”
8.9.2 Impact on U.S. Petroleum Imports

U.S. energy security is broadly defined as the continued availability of energy sources at 

an acceptable price. Most discussion of U.S. energy security revolves around the topic of the 

economic costs of U.S. dependence on oil imports. However, it is not imports alone, but both 

imports and consumption of petroleum from all sources, and their role in economic activity, that 

expose the U.S. to risk from price shocks in the world oil price. The relative significance of 

petroleum consumption and import levels for the macroeconomic disturbances that follow from 

oil price shocks is not fully understood. Recognizing that changing petroleum consumption will 

change U.S. imports, this assessment of oil costs focuses on those incremental social costs that 

follow from the resulting changes in imports, employing the usual oil import premium measure. 

The agencies will review any comments we receive on how to incorporate the impacts of 

changes in oil consumption, rather than imports exclusively, into our energy security analysis. 

The U.S.’s energy security problem is that the U.S. relies on imported oil from potentially

unstable sources. In addition, oil exporters have the ability to raise the price of oil by exerting 

monopoly power through the formation of a cartel, the Organization of Petroleum Exporting 

Countries (OPEC). These factors contribute to the vulnerability of the U.S. economy to episodic 

oil supply shocks and price spikes. In 2012, U.S. net expenditures for imports of crude oil and 

petroleum products were $290 billion, and total consumption expenditure was $634 billion (in 

2012$) (see Figure 8-3).136 Import costs have declined since 2011 but total oil expenditures 

(domestic and imported) remain near historical highs, at roughly triple the real oil costs 

experienced by the U.S. from 1986 to 2002. 
Figure 8-3 U.S. Expenditures on Crude Oil from 1970 through 2015137

The agencies used EPA’s MOVES model to estimate the reductions in U.S. fuel 

consumption due to this proposed rule for vocational vehicles and tractors. For HD pickups and 

vans, the agencies used both DOT’s CAFE model and EPA’s MOVES model to estimate the fuel 

consumption impacts. (Detailed explanations of the MOVES and CAFE models can be found in 

Chapters 5 and 10 of the draft RIA. See IX.C of the preamble for estimates of reduced fuel 

consumption from the proposed rule). Based on a detailed analysis of differences in U.S. fuel 

consumption, petroleum imports, and imports of petroleum products, the agencies estimate that 

approximately 90 percent of the reduction in fuel consumption resulting from adopting improved 

GHG emissions standards and fuel efficiency standards is likely to be reflected in reduced U.S. 

imports of crude oil and net imported petroleum products.NN Thus, on balance, each gallon of 

fuel saved as a consequence of the HD GHG and fuel efficiency standards is anticipated to 

 

NN We looked at changes in crude oil imports and net petroleum products in the Reference Case in comparison to 

two cases from the AEO 2014. The two cases are the Low Demand and Low VMT cases. See the spreadsheet 

“Impacts on Fuel Demands and ImportsJan9.xlsx” comparing the AEO 2014 Reference Case to the Low Demand 

Case. See the spreadsheet “Impact of Fuel Demand and Impacts January20VMT.xlsl” for a comparison of AEO 

2014 Reference Case and the Low VMT Case. We also considered a paper entitled “Effect of a U.S. Demand 

Reduction on Imports and Domestic Supply Levels” by Paul Leiby, 4/16/2013. This paper suggests that “Given a 

particular reduction in oil demand stemming from a policy or significant technology change, the fraction of oil use 

savings that shows up as reduced U.S. imports, rather than reduced U.S. supply, is actually quite close to 90 percent, 

and probably close to 95 percent”.

0

100

200

300

400

500

600

700

800

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

$Bill/yr, 2012$

Year

U.S. Expenditures on Crude Oil

Domestic

Imported
reduce total U.S. imports of petroleum by 0.90 gallons.OO Based upon the fuel savings estimated 

by the MOVES/CAFE models and the 90 percent oil import factor, the reduction in U.S. oil 

imports from this rule are estimated for the years 2020, 2025, 2030, 2040, and 2050 (in millions 

of barrels per day (MMBD)) in Table 8-27 below. For comparison purposes, Table 8-27 also 

shows U.S. imports of crude oil in 2020, 2025, 2030 and 2040 as projected by DOE in the 

Annual Energy Outlook 2014 (Early Release) Reference Case. U.S. Gross Domestic Product 

(GDP) is projected to grow by roughly 59 percent between 2020 – 2040 in the AEO 2014 (Early 

Release) projections.

Table 8-33 Projected U.S. Imports of Crude Oil and U.S. Oil Import Reductions in 2020, 2025, 2030, 2040 

and 2050 for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B

(Millions of barrels per day (MMBD)) a

YEAR U.S. OIL 

IMPORTS

REDUCTIONS FROM 

PROPOSED HD RULE

2020 4.94 0.01

2025 5.07 0.16

2030 5.38 0.37

2040 6.0 0.65

2050 X 0.78

Notes:

a

For an explanation of analytical Methods A and B, please see 

Preamble Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see Preamble 

Section X.A.1

X – The AEO 2014 (Early Release) only projects energy market 

and economic trends through 2040.

8.9.3 Methodology 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 ORNL study is an updated version of the approach used for estimating the energy

 

OO The NHTSA analysis uses a slightly different value that was estimated using unique runs of the National Energy 

Modeling System (NEMS) that forms the foundation of the Annual Energy Outlook. NHTSA ran a version of 

NEMS from 2012 (which would have been used in the 2013 AEO) and computed the change in imports of 

petroleum products with and without the Phase 1 MDHD program to estimate the relationship between changes in 

fuel consumption and oil imports. The analysis found that reducing gasoline consumption by 1 gallon reduces 

imports of refined gasoline by 0.06 gallons and domestic refining from imported crude by 0.94 gallons. Similarly, 

one gallon of diesel saved by the Phase 1 rule was estimated to reduce imports of refined diesel by 0.26 gallons and 

domestic refining of imported crude by 0.74 gallons. The agencies will update this analysis for the Final Rule using 

the model associated with AEO2014, modeling the Phase 2 Preferred Alternative explicitly.
security benefits of U.S. oil import reductions developed in a 1997 ORNL Report.

138 For EPA 

and NHTSA rulemakings, the ORNL methodology is updated periodically to account for 

forecasts of future energy market and economic trends reported in the U.S. Energy Information 

Administration’s Annual Energy Outlook. 

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

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

139

 In addition, EPA 

worked with ORNL to 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.

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. demand on the world oil price (i.e., the “demand” or “monopsony” costs); and 

(2) the risk of reductions in U.S. economic output and disruption to the U.S. economy caused by 

sudden disruptions in the supply of imported oil to the U.S. (i.e., macroeconomic 

disruption/adjustment costs). 

The literature on the energy security for the last two decades has 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 from a global perspective? Monopsony benefits represent avoided payments by U.S. 

consumers to oil producers that result from a decrease in the world oil price as the U.S. decreases 

its demand for oil. Although there is clearly an overall benefit to the U.S. when considered from 

a domestic perspective, the decrease in price due to decreased demand in the U.S. also represents

a loss to oil producing countries, one of which is the United States. Given the redistributive 

nature of this monopsony effect from a global perspective, and the fact that an increasing fraction 

of it represents a transfer from U.S. consumers and producers, it is excluded in the energy

security benefits calculations for this proposed rule. 

In contrast, the other portion of the energy security premium, the avoided U.S. 

macroeconomic disruption and adjustment cost that arises from reductions in U.S. petroleum 

imports, does not have offsetting impacts outside of the U.S., and, thus, is included in the energy

security benefits estimated for this proposed rule. To summarize, the agencies have included 

only the avoided macroeconomic disruption portion of the energy security benefits to estimate 

the monetary value of the total energy security benefits of this proposed rule. 
For this rulemaking, ORNL updated the energy security premiums by incorporating the 

most recent oil price forecast and energy market trends, particularly regional oil supplies and 

demands, from the AEO 2014 (Early Release) into its model.140 Table 8-28 provides estimates 

for energy security premiums for the years 2020, 2025, 2030 and 2040,

PP as well as a breakdown 

of the components of the energy security premiums for each year. The components of the energy

security premiums and their values are discussed below.

Table 8-34 Energy Security Premiums in 2020, 2025, 2030 and 2040 (20012$/Barrel)*

YEAR 

(RANGE)

MONOPSONY

(RANGE)

AVOIDED MACROECONOMIC 

DISRUPTION/ADJUSTMENT 

COSTS

(RANGE)

TOTAL MID-POINT

(RANGE)

2020 $4.91

(1.63 – 9.15)

$6.35

(3.07 – 10.15)

$11.25

(6.67 – 16.53)

2025 $5.46

(1.81 – 10.47)

$7.29

(3.57 – 11.67)

$12.75

(7.58 – 18.65)

2030 $6.04

(2.00 – 11.67)

$8.39

(4.12 – 13.41)

$14.43

(8.54 – 21.13)

2040 $7.17

(2.32 – 14.03)

$10.74

(5.36 – 17.22)

$17.91

(10.52 – 26.14)

Note:

 *Top values in each cell are the midpoints, the values in parentheses are the 90 percent confidence intervals.

8.9.3.1 Effect of Oil Use on the Long-Run Oil Price

The first component of the full economic costs of importing petroleum into the U.S. 

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

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

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

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

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

improvements in the fuel efficiency of medium- and heavy-duty vehicles is the potential

decrease in the crude oil price paid for all crude oil purchased.

The demand or monopsony effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $100 per barrel, its total daily bill for oil 

imports is one billion dollars. If a 10 percent decrease in U.S. imports to 9 million barrels per day

causes the world oil price to drop to $99 per barrel, the daily U.S. oil import bill drops to $891

million (9 million barrels times $99 per barrel). While the world oil price only declines $1, the 

resulting decrease in oil purchase payments of $109 million per day (one billion dollars minus 

$891 million) is equivalent to an incremental benefit of $109 per barrel of oil imports reduced, or 

$10 more than the newly-decreased world price of $99 per barrel. This additional $10 per barrel 

“import cost premium” represents the incremental external benefits to the U.S. for avoided 

 

PP AEO 2014 (Early Release) forecasts energy market trends and values only to 2040. The post-2040 energy security 

premium values are assumed to be equal to the 2040 estimate.
import costs beyond the price paid for oil purchases. This additional benefit from import 

reduction arises only to the extent that a reduction in U.S. oil imports affects the world oil price. 

ORNL estimates this component of the energy security benefit in 2020 to be $4.91/barrel, with a 

range of $1.63/barrel to $9.15/barrel of imported oil reduced.

A variety of oil market and economic factors have contributed to lowering the estimated 

monopsony premium compared to monopsony premiums cited in recent EPA/NHTSA 

rulemakings. Three principal factors contribute to lowering the monopsony premium: lower 

world oil prices, lower U.S. oil imports and less responsiveness of world oil prices to changes in 

U.S. oil demand. For example, between 2012 (using the AEO 2012 (Early Release)) and 2014 

(using the AEO 2014 (Early Release)), there has been a general downward revision in world oil 

price projections in the near term (e.g. 19 percent in 2020) and a sharp reduction in projected 

U.S. oil imports in the near term, due to increased U.S. supply (i.e., a 41 percent reduction in

U.S. oil imports by 2017 and a 36 percent reduction in 2020). Over the longer term, oil’s share 

of total U.S. imports is projected to gradually increase after 2020 but still remain 27 percent 

below the AEO 2012 (Early Release) projected level in 2035.

Another factor influencing the monopsony premium is that U.S. demand on the global oil 

market is projected to decline, suggesting diminished overall influence and some reduction in the 

influence of U.S. oil demand on the world price of oil. Outside of the U.S., projected OPEC 

supply remains roughly steady as a share of world oil supply compared to the AEO 2012 (Early 

Release). OPEC’s share of world oil supply outside of the U.S. actually increases slightly. Since 

OPEC supply is estimated to be more price sensitive than non-OPEC supply, this means that 

under AEO 2014 (Early Release) world oil supply is slightly more responsive to changes in U.S. 

oil demand. Together, these factors suggest that changes in U.S. oil import reductions have a 

somewhat smaller effect on the long-run world oil price than changes based the 2012 estimates.

These changes in oil price and import levels lower the monopsony portion of energy

security premium since this portion of the security premium is related to the change in total U.S. 

oil import costs that is achieved by a marginal reduction in U.S oil imports. Since both the price 

and the quantity of oil imports are lower, the monopsony premium component is 46 – 57 percent

lower over the years 2017–2025 than the estimates based upon the AEO 2012 (Early Release) 

projections. 

There is disagreement in the literature about the magnitude of the monopsony

component, and its relevance for policy analysis. Brown and Huntington (2013)141, for example, 

argue that the United States’ refusal to exercise its market power to reduce the world oil price 

does not represent a proper externality, and that the monopsony component should not be 

considered in calculations of the energy security externality. However, they also note in their 

earlier discussion paper (Brown and Huntington 2010)142 that this is a departure from the 

traditional energy security literature, which includes sustained wealth transfers associated with 

stable but higher-price oil markets. On the other hand, Greene (2010)143 and others in prior 

literature (e.g., Toman 1993)144 have emphasized that the monopsony cost component is policyrelevant because the world oil market is non-competitive and strongly influenced by cartelized 

and government-controlled supply decisions. Thus, while sometimes couched as an externality, 

Greene notes that the monopsony component is best viewed as stemming from a completely 
different market failure than an externality (Ledyard 2008)145

, yet still implying marginal social 

costs to importers.

There is also a question about the ability of gradual, long-term reductions, such as those 

resulting from this proposed rule, to reduce the world oil price in the presence of OPEC’s 

monopoly power. OPEC is currently the world’s marginal petroleum supplier, and could 

conceivably respond to gradual reductions in U.S. demand with gradual reductions in supply

over the course of several years as the fuel savings resulting from this rule grow. However, if 

OPEC opts for a long-term strategy to preserve its market share, rather than maintain a particular 

price level (as they have done recently in response to increasing U.S. petroleum production), 

reduced demand would create downward pressure on the global price. The Oak Ridge analysis 

assumes that OPEC does respond to demand reductions over the long run, but there is still a price 

effect in the model. Under the mid-case behavioral assumption used in the premium 

calculations, OPEC responds by gradually reducing supply to maintain market share (consistent

with the long-term self-interested strategy suggested by Gately (2004, 2007)).146 

It is important to note that the decrease in global petroleum prices resulting from this 

rulemaking could spur increased consumption of petroleum in other sectors and countries, 

leading to a modest uptick in GHG emissions outside of the United States. This increase in 

global fuel consumption could offset some portion of the GHG reduction benefits associated 

with this proposed rule. The agency have not quantified this increase in global GHG emissions. 

We will review any comments we receive, as well as data sources and methodologies for how 

global rebound effects may be quantified.

8.9.3.2 Macroeconomic Disruption Adjustment Costs 

The second component of the oil import premium, “avoided macroeconomic 

disruption/adjustment costs,” arises from the effect of oil imports on the expected cost of supply 

disruptions and accompanying price increases. 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. For example, ORNL estimates the combine value of these two factors to 

be $6.34/barrel when U.S. oil imports are reduced in 2020, with a range from $3.07/barrel to 

$10.15/barrel of imported oil reduced.

There are two main effects of macroeconomic disruption/adjustment costs. The first is 

the aggregate effect of the short-run price 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 high costs. 

Businesses’ and households’ emergency responses to supply disruptions and rapid price 

increases consume real economic resources. 

When households and businesses make decisions related to their oil consumption, such as 

whether to invest in fuel-saving technologies or use futures markets, they are unlikely to account 

for the effect of their petroleum consumption on the magnitude of costs that supply 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 avoided 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 and businesses. This aggregate output effect will last as long as the oil price is 

elevated. It depends on the extent and duration of any disruption in the world supply of oil, since 

these factors determine the magnitude of the resulting increases in prices for petroleum products, 

as well as how rapidly these prices return to their pre-disruption level.

The second main effect of macroeconomic disruption/adjustment costs is the 

macroeconomic losses due to “allocative” losses. These are the costs of temporary dislocation 

and underutilization of available resources due to the oil shock, such as labor unemployment and 

idle plant capacity. Because supply disruptions and resulting price increases occur suddenly, 

empirical evidence shows they impose additional costs on businesses and households that 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 

the likelihood or size of disruption, or from altering the short-run flexibility (e.g., elasticity) of 

petroleum use.

With updated oil market and economic factors, the avoided macroeconomic disruption 

component of the energy security premiums is slightly lower in comparison to avoided 

macroeconomic disruption premiums used in previous rulemakings. There are several reasons 

why the avoided macroeconomic disruption premiums change only moderately. One reason is 

that the macroeconomic sensitivity to oil price shocks is assumed unchanged in recent years 

since U.S. oil consumption levels and the value share of oil in the U.S. economy remain at high 

levels. For example, Figure 8-4 below shows that under AEO 2014 (Early Release), projected 

U.S. real annual oil expenditures continue to rise after 2015 to over $800 billion (2012$) by

2030. The value share of oil use in the U.S. economy remains between three and four percent, 
well above the levels observed from 1985 to 2005. A second factor is that oil disruption risks are 

little changed. The two factors influencing disruption risks are the probability of global supply 

interruptions and the world oil supply share from OPEC. Both factors are not significantly 

different from previous forecasts of oil market trends.

Factors that contribute to moderately lowering the avoided macroeconomic disruption 

component are lower projected GDP, moderately lower oil prices and slightly smaller price 

increases during prospective shocks. For example, oil price levels are 5 – 19 percent lower over 

the 2020 – 2035 period, and the likely increase in oil prices in the event of an oil shock are 

somewhat smaller, given small increases in the responsiveness of oil supply to changes in the 

world price of oil. Overall, the avoided macroeconomic disruption component estimates for the 

oil security premiums are 2 – 19 percent lower over the period from 2020-2035 based upon 

different projected oil market and economic trends in the AEO 2014 (Early Release) compared to

the AEO 2012 (Early Release). 

The energy security costs estimated here follow the oil security premium framework, 

which is well established in the energy economics literature. The oil import premium gained 

attention as a guiding concept for energy policy around the time of the second and third major 

post-war oil shocks (Bohi and Montgomery 1982, EMF 1982) .

147

 Plummer (1982)

148 provided 

valuable discussion of many of the key issues related to the oil import premium as well as the 

analogous oil stockpiling premium. Bohi and Montgomery (1982)149 detailed the theoretical 

foundations of the oil import premium established many of the critical analytic relationships 

through their thoughtful analysis. Hogan (1981)

150 and Broadman and Hogan (1986, 1988)151

revised and extended the established analytical framework to estimate optimal oil import premia 

with a more detailed accounting of macroeconomic effects. 

Since the original work on energy security was undertaken in the 1980’s, there have been 

several reviews on this topic. For example, Leiby, Jones, Curlee and Lee (1997)152 provided an 

extended review of the literature and issues regarding the estimation of the premium. Parry and 

Darmstadter (2004)153 also provided an overview of extant oil security premium estimates and 

estimated of some premium components. 

The recent economics literature on whether oil shocks are a threat to economic stability

that they once were is mixed. Some of the current literature asserts that the macroeconomic 

component of the energy security externality is small. For example, the National Research 

Council (2009) argued that the non-environmental externalities associated with dependence on 

foreign oil are small, and potentially trivial.154 Analyses by Nordhaus (2007) and Blanchard and 

Gali (2010) question the impact of more recent oil price shocks on the economy.

155

 They were 

motivated by attempts to explain why the economy actually expanded immediately after the last 

shocks, and why there was no evidence of higher energy prices being passed on through higher 

wage inflation. Using different methodologies, they conclude that the economy has largely 

gotten over its concern with dramatic swings in oil prices.

One reason, according to Nordhaus, is that monetary policy has become more 

accommodating to the price impacts of oil shocks. Another is that consumers have simply 

decided that such movements are temporary, and have noted that price impacts are not passed on 

as inflation in other parts of the economy. He also notes that real changes to productivity due to 
oil price increases are incredibly modest,

QQ and that the general direction of the economy matters 

a great deal regarding how the economy responds to a shock. Estimates of the impact of a price 

shock on aggregate demand are insignificantly different from zero.

Blanchard and Gali (2010) contend that improvements in monetary policy (as noted 

above), more flexible labor markets, and lessening of energy intensity in the economy, combined 

with an absence of concurrent shocks, all contributed to lessen the impact of oil shocks after 

1980. They find “... the effects of oil price shocks have changed over time, with steadily smaller 

effects on prices and wages, as well as on output and employment.”156 In a comment at the 

chapter’s end, this work is summarized as follows: “The message of this chapter is thus 

optimistic in that it suggests a transformation in U.S. institutions has inoculated the economy

against the responses that we saw in the past.”

At the same time, the implications of the “Shale Oil Revolution” are now being felt in the 

international markets, with current prices at four year lows. Analysts generally attribute this 

result in part to the significant increase in supply resulting from U.S. production, which has put 

liquid petroleum production on par with Saudi Arabia. The price decline is also attributed to the 

sustained reductions in U.S. consumption and global demand growth from fuel efficiency

policies and high oil prices. The resulting decrease in foreign imports, down to about one-third 

of domestic consumption (from 60 percent in 2005, for example157), effectively permits U.S.

supply to act as a buffer against artificial or other supply restrictions (the latter due to conflict or 

natural disaster, for example).

However, other papers suggest that oil shocks, particularly sudden supply shocks, remain 

a concern. Both Blanchard and Gali’s and Nordhaus work were based on data and analysis 

through 2006, ending with a period of strong global economic growth and growing global oil 

demand. The Nordhaus work particularly stressed the effects of the price increase from 2002-

2006 that were comparatively gradual (about half the growth rate of the 1973 event and one-third 

that of the 1990 event). The Nordhaus study emphasizes the robustness of the U.S. economy 

during a time period through 2006. This time period was just before rapid further increases in 

the price of oil and other commodities with oil prices more-than-doubling to over $130/barrel by

mid-2008, only to drop after the onset of the largest recession since the Great Depression. 

Hamilton (2012) reviewed the empirical literature on oil shocks and suggested that the 

results are mixed, noting that some work (e.g. Rasmussen and Roitman (2011) finds less 

evidence for economic effects of oil shocks, or declining effects of shocks (Blanchard and Gali 

2010), while other work continues to find evidence regarding the economic importance of oil 

 

QQ In fact, “... energy-price changes have no effect on multifactor productivity and very little effect on labor 

productivity.” Page 19. He calculates the productivity effect of a doubling of oil prices as a decrease of 0.11 percent 

for one year and 0.04 percent a year for ten years. Page 5. (The doubling reflects the historical experience of the 

post-war shocks, as described in Table 7.1 in Blanchard and Gali, p. 380.)
shocks. For example, Baumeister and Peersman (2011) found that an oil price increase of a 

given size seems to have a decreasing effect over time, but noted that the declining priceelasticity of demand meant that a given physical disruption had a bigger effect on price and 

turned out to have a similar effect on output as in the earlier data.”158 Hamilton observes that “a

negative effect of oil prices on real output has also been reported for a number of other countries, 

particularly when nonlinear functional forms have been employed” (citing as recent examples 

Kim 2012, Engemann, Kliesen, and Owyang 2011 and Daniel, et. al. 2011). Alternatively, rather 

than a declining effect, Ramey and Vine (2010) found “remarkable stability in the response of 

aggregate real variables to oil shocks once we account for the extra costs imposed on the 

economy in the 1970s by price controls and a complex system of entitlements that led to some 

rationing and shortages.”159

Some of the recent literature on oil price shocks has emphasized that economic impacts 

depend on the nature of the oil shock, with differences between price increases caused by sudden 

supply loss and those caused by rapidly growing demand. Most recent analyses of oil price 

shocks have confirmed that “demand-driven” oil price shocks have greater effects on oil prices 

and tend to have positive effects on the economy while “supply-driven” oil shocks still have 

negative economic impacts (Baumeister, Peersman and Robays, 2010). A recent paper by Kilian 

and Vigfusson (2014), for example, assigned a more prominent role to the effects of price 

increases that are unusual, in the sense of being beyond range of recent experience. Kilian and 

Vigfussen also conclude that the difference in response to oil shocks may well stem from the 

different effects of demand- and supply-based price increases: “One explanation is that oil price 

shocks are associated with a range of oil demand and oil supply shocks, some of which stimulate 

the U.S. economy in the short run and some of which slow down U.S. growth (see Kilian 2009a). 

How recessionary the response to an oil price shock is thus depends on the average composition 

of oil demand and oil supply shocks over the sample period.” 

The general conclusion that oil supply-driven shocks reduce economic output is also 

reached in a recently published paper by Cashin et al. (2014) for 38 countries from 1979-2011. 

“The results indicate that the economic consequences of a supply-driven oil-price shock are very

different from those of an oil-demand shock driven by global economic activity, and vary for oilimporting countries compared to energy exporters,” and “oil importers [including the U.S.] 

typically face a long-lived fall in economic activity in response to a supply-driven surge in oil 

prices” but almost all countries see an increase in real output for an oil-demand disturbance. 

Note that the energy security premium calculation in this analysis is based on price shocks from 

potential future supply events only.

Despite continuing uncertainty about oil market behavior and outcomes and the 

sensitivity of the U.S. economy to oil shocks, it is generally agreed that it is beneficial to reduce 

petroleum fuel consumption from an energy security standpoint. Reducing fuel consumption 

reduces the amount of domestic economic activity associated with a commodity whose price 

depends on volatile international markets. Also, reducing U.S. oil import levels reduces the 

likelihood and significance of supply disruptions. 
Figure 8-4 Projected and Historical U.S. Expenditures, and Expenditure Share, on Crude Oil160

8.9.3.3 Cost of Existing U.S. Energy Security Policies

The last often-identified component of the full economic costs of U.S. oil 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 the effect of the SPR in moderating 

price shocks 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, and because it is not clear that these 

outlays would decline in response to incremental reductions in U.S. oil imports. Most military

forces serve a broad range of security and foreign policy objectives. Attempts to attribute some 

share of U.S. military costs to oil imports are further challenged by the need to estimate how 

those costs might vary with incremental variations in U.S. oil imports.

0%

1%

2%

3%

4%

5%

6%

7%

0

100

200

300

400

500

600

700

800

900

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030

Expen

diture as Share of G

D

P

$Bill/yr, 2012$

Year

Projected and Historical U.S. Expenditures, 

and Expenditure Share, on Crude Oil

Domestic

Imported

US Oil Expenditures as Share of GDP
8.9.4 Energy Security Benefits of this Program 

Using the ORNL “oil premium” methodology, updating world oil price values and 

energy trends using AEO 2014 (Early Release) and using the estimated fuel savings from the 

proposed rule estimated from the MOVES/CAFE models, the agencies has calculated the energy

security benefits of this proposed rule for different classes for medium- and heavy-duty vehicles 

for the various years up to 2050.RR Since the agencies are taking a global perspective with 

respect to valuing greenhouse gas benefits from the rule, only the avoided macroeconomic 

adjustment/disruption portion of the energy security premium is used in the energy security 

benefits estimates present below. These results are shown below in Table 8-35. Table 8-36 and 

Table 8-37 show discounted model year lifetime energy security benefits for different classes of 

heavy-duty vehicles using a three and seven percent discount rate.

Table 8-35 Annual U.S. Energy Security Benefits and Net Present Values at 3% and 7% Discount Rates for 

the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (in Millions of 2012$) a

CALENDAR

YEAR

HD 

PICKUP 

& 

VANS

VOCATIONAL TRACTOR/

TRAILER

SUM

2018 $0 $0 $10 $10

2019 $0 $0 $20 $20

2020 $0 $0 $31 $31

2021 $3 $6 $69 $77

2022 $10 $12 $118 $140

2023 $23 $19 $170 $211

2024 $41 $33 $255 $328

2025 $65 $47 $344 $456

2026 $95 $63 $438 $596

2027 $131 $91 $548 $770

2028 $167 $120 $660 $947

2029 $204 $150 $772 $1,126

2030 $241 $180 $884 $1,306

2035 $413 $322 $1,421 $2,156

2040 $556 $443 $1,922 $2,920

2050 $647 $522 $2,328 $3,498

NPV, 3% $5,356 $4,209 $19,383 $28,947

NPV, 7% $2,163 $1,689 $8,005 $11,857

Note:

a

For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

 

RR In order to determine the energy security benefits beyond 2040, we use the 2040 energy security premium 

multiplied by the estimate fuel savings from the proposed rule. Since the AEO 2014 (Early Release) only goes to 

2040, we only calculate energy security premiums to 2040. 
Table 8-36 Discounted Model Year Lifetime Energy Security Benefits at a 3% Discount Rate for the 

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

MODEL

YEAR

HD PICKUP

AND VANS

VOCATIONAL TRACTOR/

TRAILER

SUM

2018 $0 $0 $86 $86

2019 $0 $0 $85 $85

2020 $0 $0 $84 $84

2021 $24 $55 $455 $534

2022 $66 $54 $458 $579

2023 $107 $54 $460 $621

2024 $148 $116 $732 $996

2025 $190 $118 $751 $1,060

2026 $233 $120 $768 $1,121

2027 $272 $216 $887 $1,375

2028 $273 $217 $898 $1,388

2029 $272 $217 $907 $1,397

Sum $1,587 $1,168 $6,571 $9,325

Note:

a

For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

Table 8-37 Discounted Model Year Lifetime Energy Security Benefits due to the Preferred Alternative at a 

7% Discount Rates for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B 

using the MOVES Analysis of HD Pickups and Vans and Relative to the Less Dynamic Baseline (Millions of 

2012$) a

MODEL

YEAR

HD PICKUP

AND VANS

VOCATIONAL TRACTOR/

TRAILER

SUM

2018 $0 $0 $60 $60

2019 $0 $0 $56 $56

2020 $0 $0 $53 $53

2021 $15 $34 $277 $326

2022 $40 $32 $269 $341

2023 $62 $31 $260 $353

2024 $82 $64 $400 $546

2025 $102 $63 $395 $560

2026 $120 $62 $390 $571

2027 $135 $107 $434 $676

2028 $130 $104 $423 $657

2029 $125 $100 $412 $637

Sum $810 $597 $3,430 $4,837

Note:

a

For an explanation of analytical Methods A and B, please see Preamble 

Section I.D; for an explanation of the less dynamic baseline, 1a, and more 

dynamic baseline, 1b, please see Preamble Section X.A.1

8.10 Summary of Benefits and Costs

This section presents the costs, benefits, and other economic impacts of the proposed 

Phase 2 standards. It is important to note that NHTSA’s proposed fuel consumption standards 
and EPA’s proposed GHG standards would both be in effect, and would jointly lead to increased 

fuel efficiency and reductions in GHG and non-GHG emissions. The individual categories of 

benefits and costs presented in the tables below include: 

• the vehicle program costs (costs of complying with the vehicle CO2 and fuel 

consumption standards),

• changes in fuel expenditures associated with reduced fuel use by more efficient vehicles 

and increased fuel use associated with the “rebound” effect, both of which result from the 

program,

• the global economic value of reductions in GHGs,

• the economic value of reductions in non-GHG pollutants,

• costs associated with increases in noise, congestion, and accidents resulting from 

increased vehicle use, 

• savings in drivers’ time from less frequent refueling,

• benefits of increased vehicle use associated with the “rebound” effect, and 

• the economic value of improvements in U.S. energy security impacts.

For a discussion of the cost of ownership and the agencies’ payback analysis of vehicles 

covered by this proposal, please see Chapter 7 of this draft RIA.

The agencies conducted coordinated and complementary analyses using two analytical 

methods referred to as Method A and Method B. For an explanation of these methods, please 

see Section I.D for the preamble. And as discussed in preamble Section X.A.1, the agencies 

present estimates of benefits and costs that are measured against two different assumptions about 

improvements in fuel efficiency that might occur in the absence of the Phase 2 standards. The

first case (Alternative 1a) uses a baseline that projects very little improvement in new vehicles in 

the absence of new Phase 2 standards, and the second (Alternative 1b) uses a more dynamic 

baseline that projects more significant improvements in vehicle fuel efficiency.

Table 8-38 shows benefits and costs for the proposed standards from the perspective of a 

program designed to improve the nation’s energy security and conserve energy by improving 

fuel efficiency. From this viewpoint, technology costs occur when the vehicle is purchased. 

Fuel savings are counted as benefits that occur over the lifetimes of the vehicles produced during 

the model years subject to the Phase 2 standards as they consume less fuel. The table shows that 

benefits far outweigh the costs, and the preferred alternative is anticipated to result in large net 

benefits to the U.S economy.
Table 8-38 Lifetime Benefits & Costs of the Preferred Alternative for Model Years 2018 - 2029 Vehicles 

Using Analysis Method A (Billions of 2012$ discounted at 3% and 7%)

CATEGORY BASELINE 1A BASELINE 1B

3% 7% 3% 7%

Vehicle Program: Technology and 

Indirect Costs, Normal Profit on 

Additional Investments

25.4 17.1 25.0 16.8

Additional Routine Maintenance 1.1 0.6 1.0 0.6

Congestion, Accidents, and Noise 

from Increased Vehicle Use

4.7 2.8 4.5 2.6

Total Costs 31.1 20.5 30.5 20.0

Fuel Savings (valued at pre-tax prices) 175.1 94.2 165.1 89.2

Savings from Less Frequent Refueling 3.1 1.6 2.9 1.5

Economic Benefits from Additional 

Vehicle Use

15.1 8.4 14.7 8.2

Reduced Climate Damages from GHG 

Emissions a

34.9 34.9 32.9 32.9

Reduced Health Damages from NonGHG Emissions38.8 20.7 37.2 20.0

Increased U.S. Energy Security 8.9 4.7 8.1 4.3

Total Benefits 276 165 261 156

Net Benefits 245 144 231 136

Note:

a Benefits and net benefits use the 3 percent average global SCC value applied only to CO2 

emissions; GHG reductions include CO2, CH4, N2O and HFC reductions, and include benefits to 

other nations as well as the U.S. See Draft RIA Chapter 8.5 and Preamble Section IX.G for further 

discussion.

Table 8-39, Table 8-40 and Table 8-41 report benefits and cost from the perspective of 

reducing GHG. Table 8-39 shows the annual impacts and net benefits of the preferred 

alternative for selected future years, together with the net present values of cumulative annual 

impacts from 2018 through 2050, discounted at 3 percent and 7 percent rates. Table 8-40 and 

Table 8-41 show the discounted lifetime costs and benefits for each model year affected by the 

Phase 2 standards at 3 percent and 7 percent discount rates, respectively.
Table 8-39 Annual Benefits & Costs and Net Present Values for the Preferred Alternative Relative to the 

Less Dynamic Baseline and using Method B

(Billions of 2012$)

a,b,c

2018 2021 2024 2030 2035 2040 2050 NPV,

3%

NPV,

7%

Vehicle

program

-$0.1 -$2.4 -$3.7 -$5.4 -$5.9 -$6.3 -$7.0 -$86.8 -$41.1

Maintenance $0.0 $0.0 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$1.8 -$0.9

Pre-tax Fuel $0.2 $1.7 $6.9 $24.0 $37.2 $47.8 $57.5 $495.6 $206.7

Energy 

security

$0.0 $0.1 $0.3 $1.3 $2.2 $2.9 $3.5 $28.9 $11.9

Accidents/

Congestion/

Noise

$0.0 -$0.1 -$0.3 -$0.5 -$0.7 -$0.8 -$0.9 -$9.3 -$4.2

Refueling $0.0 $0.0 $0.1 $0.4 $0.7 $0.9 $1.2 $9.4 $3.9

Travel value $0.0 $0.4 $1.0 $1.9 $2.4 $2.9 $3.3 $34.2 $15.3

Non-GHG $0.0 to 

$0.1

$0.4 to 

$0.9

$1.0 to 

$2.4

$3.3 to 

$8.3

$4.8 to 

$12.1

$5.7 to 

$14.3

$7.0 to 

$17.5

$69.7 to 

$157.0

$26.6 to 

$60.4

SCC

SC-CO2; 5% 

avg

$0.0 $0.1 $0.4 $1.5 $2.5 $3.3 $5.0 $22.1 $22.1

SC-CO2; 3% 

avg

$0.0 $0.3 $1.3 $4.8 $7.4 $9.7 $13.6 $103.1 $103.1

SC-CO2; 

2.5% avg

$0.1 $0.5 $1.9 $6.9 $10.6 $13.7 $18.5 $164.1 $164.1

SC-CO2; 3% 

95th

$0.1 $1.0 $4.0 $14.6 $23.2 $30.3 $42.0 $320.5 $320.5

Net benefits d

SC-CO2; 5% 

avg

$0.2 $0.4 $6.4 $28.8 $46.8 $60.6 $74.6 $605.8 $257.1

SC-CO2; 3% 

avg

$0.2 $0.7 $7.3 $32.1 $51.7 $66.9 $83.2 $686.8 $338.1

SC-CO2; 

2.5% avg

$0.2 $0.8 $7.9 $34.2 $54.9 $70.9 $88.2 $747.8 $399.1

SC-CO2; 3% 

95th

$0.3 $1.3 $10.0 $41.9 $67.5 $87.6 $111.7 $904.1 $555.5

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

b Note that net present value of reduced CO2 emissions is calculated differently than other benefits. The same 

discount rate used to discount the value of damages from future emissions (SC-CO2 at 5, 3, 2.5 percent) is used to 

calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail. 

c

Section 8.5 of the draft RIA notes that SC-CO2 increases over time. Corresponding to the years in this table (2020-

2050), the SC-CO2 estimates range as follows: for Average SC-CO2 at 5%: $7-$16; for Average SC-CO2 at 3%: 

$27-$46; for Average SC-CO2 at 2.5%: $43-$67; and for 95th percentile SC-CO2 at 3%: $83-$140. Section VIII.F 

also presents these SC-CO2 estimates.

d Net impacts are the summation of results within columns of the table with the exception that the net impacts at 

each SC-CO2 value include only the SC-CO2 impacts at that value.

The table shows the benefits of reduced CO2 emissions—and consequently the annual 

quantified benefits (i.e., total benefits)—for each of four SC-CO2 values estimated by the 

interagency working group. As discussed in Section 8.5, there are some limitations to the SC-
CO2 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 in this Section 8.10, the value of these 

reductions should not be interpreted as zero. The reader is referred to Section 8.5.2 of this draft 

RIA to see the value of those monetized benefits. Also, note that the net reductions in non-CO2

GHGs will contribute to this rule’s climate benefits, as explained in Section III.F of the 

preamble. 

The agencies have also conducted a separate analysis of the total benefits over the model 

year lifetimes of 2018 through 2029 model year vehicles. In contrast to the calendar year 

analysis presented in Table 8-39, the model year lifetime analysis shows the impacts of the 

program on vehicles produced during each of the affected model years over the course of their

expected lifetimes. The net societal benefits over the full lifetimes of vehicles produced during 

each of the model years are shown in Table 8-40 and Table 8-41 at both 3 percent and 7 percent 

discount rates, respectively. 
Table 8-40 Discounted Model Year Lifetime Impacts for the Preferred Alternative Relative to the Less 

Dynamic Baseline and using Method B

(Billions of 2012$; 3% Discount Rate) a,b,c

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 SUM

Vehicle 

Program

-$0.1 -$0.1 -$0.1 -$2.0 -$1.9 -$1.9 -$2.8 -$2.7 -$2.7 -$3.7 -$3.6 -$3.5 -$25.1

Maintenance -$0.1 $0.0 $0.0 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$1.1

Pre-tax Fuel $1.9 $1.9 $1.8 $11.1 $11.5 $11.9 $18.9 $19.6 $20.2 $24.1 $24.1 $24.1 $171.1

Energy 

Security

$0.1 $0.1 $0.1 $0.5 $0.6 $0.6 $1.0 $1.1 $1.1 $1.4 $1.4 $1.4 $9.3

Accidents, 

Noise, 

Congestion 

-$0.1 -$0.1 -$0.2 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$0.4 -$4.2

Refueling $0.0 $0.0 $0.0 $0.2 $0.2 $0.2 $0.3 $0.3 $0.4 $0.5 $0.5 $0.5 $3.1

Travel value $0.6 $0.6 $0.7 $1.5 $1.5 $1.5 $1.4 $1.4 $1.4 $1.4 $1.4 $1.4 $14.9

Non-GHG $0.2 

to 

$0.5

$0.2 

to 

$0.4

$0.2 

to 

$0.4

$2.0 

to 

$4.5

$2.0 

to 

$4.5

$2.0 

to 

$4.5

$2.9 

to 

$6.6

$3.0 

to 

$6.8

$2.6 

to 

$5.9

$3.1 

to 

$6.9

$3.1 

to 

$6.9

$3.1 

to 

$7.0

$24.4 

to 

$55.0

SCC

SC-CO2; 5% 

avg

$0.1 $0.1 $0.1 $0.5 $0.5 $0.5 $0.9 $0.9 $0.9 $1.1 $1.1 $1.1 $7.8

SC-CO2; 3% 

avg

$0.4 $0.4 $0.4 $2.2 $2.3 $2.3 $3.7 $3.9 $4.0 $4.8 $4.8 $4.9 $34.0

SC-CO2; 

2.5% avg

$0.6 $0.6 $0.6 $3.4 $3.5 $3.6 $5.8 $6.1 $6.3 $7.6 $7.6 $7.7 $53.4

SC-CO2; 3% 

95th

$1.1 $1.1 $1.1 $6.6 $6.9 $7.2 $11.5 $12.0 $12.4 $14.9 $15.0 $15.1 $105.0

Net benefits d

SC-CO2; 5% 

avg

$2.8 $2.7 $2.7 $14.6 $15.1 $15.5 $23.9 $25.0 $25.1 $29.2 $29.4 $29.4 $215.5

SC-CO2; 3% 

avg

$3.0 $3.0 $3.0 $16.2 $16.8 $17.3 $26.8 $28.0 $28.2 $33.0 $33.1 $33.2 $241.7

SC-CO2; 

2.5% avg

$3.2 $3.2 $3.2 $17.4 $18.1 $18.6 $28.9 $30.2 $30.5 $35.7 $35.9 $36.0 $261.1

SC-CO2; 3% 

95th

$3.8 $3.8 $3.7 $20.7 $21.5 $22.1 $34.5 $36.0 $36.6 $43.1 $43.3 $43.5 $312.7

Notes: 

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

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

expected under this program (see draft RIA Chapter 8.5). Although EPA has not monetized changes in non-CO2

GHGs, the value of any increases or reductions should not be interpreted as zero.

c 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 (SC-CO2 at 5, 3, 2.5 percent) is used to 

calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail.

d Net impacts are the summation of results within columns of the table with the exception that the net impacts at 

each SC-CO2 value include only the SC-CO2 impacts at that value.
Table 8-41 Discounted Model Year Lifetime Impacts for the Preferred Alternative Relative to the Less 

Dynamic Baseline and using Method B (Billions of 2012$; 7% Discount Rate) a,b,c

2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 SUM

Vehicle 

Program

-$0.1 -$0.1 -$0.1 -$1.6 -$1.4 -$1.4 -$1.9 -$1.8 -$1.7 -$2.3 -$2.1 -$2.0 -$16.6

Maintenance $0.0 $0.0 $0.0 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 -$0.1 $0.0 -$0.6

Pre-tax Fuel $1.4 $1.3 $1.2 $6.9 $6.9 $6.8 $10.5 $10.4 $10.4 $11.9 $11.5 $11.0 $90.1

Energy 

Security

$0.1 $0.1 $0.1 $0.3 $0.3 $0.4 $0.5 $0.6 $0.6 $0.7 $0.7 $0.6 $4.8

Accidents, 

Noise, 

Congestion 

-$0.1 -$0.1 -$0.1 -$0.3 -$0.3 -$0.2 -$0.2 -$0.2 -$0.2 -$0.2 -$0.2 -$0.2 -$2.4

Refueling $0.0 $0.0 $0.0 $0.1 $0.1 $0.1 $0.2 $0.2 $0.2 $0.2 $0.2 $0.2 $1.6

Travel value $0.4 $0.4 $0.4 $0.9 $0.9 $0.8 $0.8 $0.8 $0.7 $0.7 $0.7 $0.6 $8.2

Non-GHG $0.1 

to 

$0.3

$0.1 

to 

$0.3

$0.1 

to 

$0.3

$1.1 

to 

$2.5

$1.1 

to 

$2.4

$1.0 

to 

$2.3

$1.4 

to 

$3.3

$1.4 

to 

$3.2

$1.2 

to 

$2.7

$1.3 

to 

$3.0

$1.3 

to 

$2.9

$1.3 

to 

$2.8

$11.5 

to 

$26.0

SCC

SC-CO2; 5% 

avg

$0.1 $0.1 $0.1 $0.5 $0.5 $0.5 $0.9 $0.9 $0.9 $1.1 $1.1 $1.1 $7.8

SC-CO2; 3% 

avg

$0.4 $0.4 $0.4 $2.2 $2.3 $2.3 $3.7 $3.9 $4.0 $4.8 $4.8 $4.9 $34.0

SC-CO2; 

2.5% avg

$0.6 $0.6 $0.6 $3.4 $3.5 $3.6 $5.8 $6.1 $6.3 $7.6 $7.6 $7.7 $53.4

SC-CO2; 3% 

95th

$1.1 $1.1 $1.1 $6.6 $6.9 $7.2 $11.5 $12.0 $12.4 $14.9 $15.0 $15.1 $105.0

Net benefits d

SC-CO2; 5% 

avg

$1.9 $1.8 $1.7 $8.7 $8.7 $8.7 $13.0 $13.1 $12.7 $14.3 $13.8 $13.4 $111.8

SC-CO2; 3% 

avg

$2.2 $2.1 $2.0 $10.3 $10.4 $10.5 $15.8 $16.1 $15.8 $18.0 $17.6 $17.2 $138.0

SC-CO2; 

2.5% avg

$2.4 $2.3 $2.2 $11.5 $11.7 $11.8 $17.9 $18.3 $18.1 $20.7 $20.4 $20.0 $157.4

SC-CO2; 3% 

95th

$2.9 $2.8 $2.8 $14.8 $15.1 $15.3 $23.6 $24.2 $24.2 $28.1 $27.8 $27.4 $209.0

Notes:

a

For an explanation of analytical Methods A and B, please see Preamble Section I.D; for an explanation of the less 

dynamic baseline, 1a, and more dynamic baseline, 1b, please see Preamble Section X.A.1

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

expected under this program (see draft RIA Chapter 8.5). Although EPA has not monetized changes in non-CO2

GHGs, the value of any increases or reductions should not be interpreted as zero.

c 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 (SC-CO2 at 5, 3, 2.5 percent) is used to 

calculate net present value of SC-CO2 for internal consistency. Refer to the SCC TSD for more detail.

d Net impacts are the summation of results within columns of the table with the exception that the net impacts at 

each SC-CO2 value include only the SC-CO2 impacts at that value. 
8.11Employment Impacts

8.11.1 Introduction

Executive Order 13563 (January 18, 2011) directs federal agencies to consider regulatory 

impacts on, among other criteria, job creation.161 According to the Executive Order “Our 

regulatory system must protect public health, welfare, safety, and our environment while 

promoting economic growth, innovation, competitiveness, and job creation. It must be based on 

the best available science.” Analysis of employment impacts of a regulation is not part of a 

standard benefit-cost analysis (except to the extent that labor costs contribute to costs).

Employment impacts of federal rules are of general interest, however, and have been particularly

so, historically, in the auto sector during periods of challenging labor market conditions. For this 

reason, we are describing the connections of these proposed standards to employment in the 

regulated sector, the motor vehicle manufacturing sector, as well as the motor vehicle body and 

trailer and motor vehicle parts manufacturing sectors.

The overall effect of the proposed rules on motor vehicle sector employment depends on 

the relative magnitude of output and substitution effects, described below. Because we do not 

have quantitative estimates of the output effect, and only a partial estimate of the substitution 

effect, we cannot reach a quantitative estimate of the overall employment effects of the proposed 

rules on motor vehicle sector employment or even whether the total effect will be positive or 

negative.

According to the U.S. Bureau of Labor Statistics, in 2014, about 850,000 people in the 

U.S. were employed in the Motor Vehicle and Parts Manufacturing Sector (NAICS 3361, 3362, 

and 3363),

162 the directly regulated sector. The employment effects of these proposed rules are 

expected to expand beyond the regulated sector. Though some of the parts used to achieve the 

proposed standards are likely to be built by motor vehicle manufacturers (including trailer 

manufacturers) themselves, the motor vehicle parts manufacturing sector also plays a significant 

role in providing those parts, and will also be affected by changes in vehicle sales. Changes in 

truck sales, discussed in Chapter8.4.2, could also affect employment for truck and trailer 

vendors. As discussed in Chapter 7.2, this proposed rule is expected to reduce the amount of fuel 

these vehicles use, and thus affect the petroleum refinery and supply industries as well. Finally, 

since the net reduction in cost associated with these proposed 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). The proposed rules are not expected to have any notable inflationary or 

recessionary effect. 

The employment effects of environmental regulation are difficult to disentangle from 

other economic changes and business decisions that affect employment, over time and across 

regions and industries. In light of these difficulties, we lean on economic theory to provide a 

constructive framework for approaching these assessments and for better understanding the 

inherent complexities in such assessments. Neoclassical microeconomic theory describes how 

profit-maximizing firms adjust their use of productive inputs in response to changes in their 

economic conditions.163 Berman and Bui (2001, pp. 274-75) model two components that drive 
changes in firm-level labor demand: output effects and substitution effects. 164,TT Regulation can 

affect the profit-maximizing quantity of output by changing the marginal cost of production. If 

regulation causes marginal cost to increase, it will place upward pressure on output prices, leading to 

a decrease in the quantity demanded, and resulting in a decrease in production. The output effect 

describes how, holding labor intensity constant, a decrease in production causes a decrease in labor 

demand. As noted by Berman and Bui, although many assume that regulation increases marginal 

cost, it need not be the case. A regulation could induce a firm to upgrade to less polluting and more 

efficient equipment that lowers marginal production costs, or it may induce use of technologies that 

may prove popular with buyers or provide positive network externalities (see Chapter 8.2 for 

discussion of this effect). In such a case, output could increase.

The substitution effect describes how, holding output constant, regulation affects labor

intensity of production. Although increased environmental regulation may increase use of pollution 

control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. 

For example, equipment inspection requirements, specialized waste handling, or pollution 

technologies that alter the production process may affect the number of workers necessary to produce 

a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on 

pollution control equipment and expenditures required by the regulation and the corresponding 

change in labor intensity of production. 

In summary, as output and substitution effects may be positive or negative, theory alone 

cannot predict the direction of the net effect of regulation on labor demand at the level of the 

regulated firm. Operating within the bounds of standard economic theory, empirical estimation of 

net employment effects on regulated firms is possible when data and methods of sufficient detail and 

quality are available. The literature, however, illustrates difficulties with empirical estimation. For 

example, studies sometimes rely on confidential plant-level employment data from the U.S. Census 

Bureau, possibly combined with pollution abatement expenditure data that are too dated to be 

reliably informative. In addition, the most commonly used empirical methods do not permit 

estimation of net effects.

The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry. Employment impacts at an individual plant do not necessarily 

represent impacts for the sector as a whole. The approach must be modified when applied at the 

industry level. 

At the industry level, labor demand is more responsive if: (1) the price elasticity of demand 

for the product is high, (2) other factors of production can be easily substituted for labor, (3) the 

supply of other factors is highly elastic, or (4) labor costs are a large share of total production 

costs.165 For example, if all firms in an industry are faced with the same regulatory compliance costs 

and product demand is inelastic, then industry output may not change much, and output of individual 

firms may change slightly.

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

 

TT Berman and Bui also discuss a third component, the impact of regulation on factor prices, but conclude that this 

effect is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer 

and Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 

2) a cost effect; and 3) a factor-shift effect. 
effect depends on input substitutability. Suppose, for example, that new equipment for fuel efficiency 

improvements requires labor to install and operate. In this case, the substitution effect may be 

positive, and with a small output effect, the total effect may be positive. As with potential effects for 

an individual firm, theory cannot determine the sign or magnitude of industry-level regulatory effects 

on labor demand. Determining these signs and magnitudes requires additional sector-specific 

empirical study. For environmental rules, much of the data needed for these empirical studies is not 

publicly available, would require significant time and resources in order to access confidential U.S. 

Census data for research, and also would not be necessary for other components of a typical RIA.

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors. For example, the proposed standards are expected to 

increase demand for fuel-saving technologies. This increased demand may increase revenue and 

employment in the firms providing these technologies. At the same time, the regulated industry is 

purchasing the equipment, and these costs may impact labor demand at regulated firms. Therefore, it 

is important to consider the net effect of compliance actions on employment across multiple sectors 

or industries. 

If the U.S. economy is at full employment, even a large-scale environmental regulation is 

unlikely to have a noticeable impact on aggregate net employment.UU Instead, labor would primarily 

be reallocated from one productive use to another, and net national employment effects from 

environmental regulation would be small and transitory (e.g., as workers move from one job to 

another).

167

Affected sectors may experience transitory effects as workers change jobs. Some workers 

may retrain or relocate in anticipation of new requirements or require time to search for new jobs, 

while shortages in some sectors or regions could bid up wages to attract workers. These adjustment 

costs can lead to local labor disruptions. Although the net change in the national workforce is 

expected to be small, localized reductions in employment may adversely impact individuals and 

communities just as localized increases may have positive impacts. 

If the economy is operating at less than full employment, economic theory does not clearly 

indicate the direction or magnitude of the net impact of environmental regulation on employment; it 

could cause either a short-run net increase or short-run net decrease.

168 An important research 

question is how to accommodate unemployment as a structural feature in economic models. This 

feature may be important in assessing large-scale regulatory impacts on employment.169

Environmental regulation may also affect labor supply. In particular, pollution and other 

environmental risks may impact labor productivity or employees’ ability to work.170 While the

theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is 

more difficult to study empirically. There is a small emerging literature described in the next section 

that uses detailed labor and environmental data to assess these impacts. 

 

UU Full employment is a conceptual target for the economy where everyone who wants to work and is available to 

do so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 
To summarize, economic theory provides a framework for analyzing the impacts of 

environmental regulation on employment. The net employment effect incorporates expected 

employment changes (both positive and negative) in the regulated sector and elsewhere. Labor 

demand impacts for regulated firms, and also for the regulated industry, can be decomposed into 

output and substitution effects which may be either negative or positive. Estimation of net 

employment effects for regulated sectors is possible when data of sufficient detail and quality are 

available. Finally, economic theory suggests that labor supply effects are also possible. In the next 

section, we discuss the empirical literature.

8.11.1.1 Current State of Knowledge Based on the Peer-Reviewed Literature

In the labor economics literature there is an extensive body of peer-reviewed empirical 

work analyzing various aspects of labor demand, relying on the above theoretical framework.171

This work focuses primarily on the effects of employment policies, e.g. labor taxes, minimum 

wage, etc.172 In contrast, the peer-reviewed empirical literature specifically estimating 

employment effects of environmental regulations is very limited. Several empirical studies,

including Berman and Bui (2001),

173 Morgenstern, Pizer and Shih (2002),174 Gray et al 

(2014),175 and Ferris, Shadbegian and Wolverton (2014)176 suggest that net employment impacts 

may be zero or slightly positive but small even in the regulated sector. Other research suggests 

that more highly regulated counties may generate fewer jobs than less regulated ones.177

However, since these latter studies compare more regulated to less regulated counties, they

overstate the net national impact of regulation to the extent that regulation causes plants to locate 

in one area of the country rather than another. List et al. (2003)178 find some evidence that this 

type of geographic relocation may be occurring. Overall, the peer-reviewed literature does not 

contain evidence that environmental regulation has a large impact on net employment (either 

negative or positive) in the long run across the whole economy.

Analytic challenges make it very difficult to accurately produce net employment 

estimates for the whole economy that would appropriately capture the way in which costs, 

compliance spending, and environmental benefits propagate through the macro-economy. 

Quantitative estimates are further complicated by the fact that macroeconomic models often have 

very little sectoral detail and usually assume that the economy is at full employment. EPA is 

currently in the process of seeking input from an independent expert panel on modeling 

economy-wide impacts, including employment effects. For more information, see: 

https://federalregister.gov/a/2014-02471.

8.11.2 Employment Impacts in the Motor Vehicle and Parts Manufacturing Sector

This chapter describes changes in employment in the motor vehicle, trailer, and parts 

(hence, motor vehicle) manufacturing sectors due to these proposed rules. We focus on the 

motor vehicle manufacturing sector because it is directly regulated, and because it is likely to 

bear a substantial share of changes in employment due to these proposed rules. We include 

discussion of effects on the parts manufacturing sector, because the motor vehicle manufacturing 

sector can either produce parts internally or buy them from an external supplier, and we do not 

have estimates of the likely breakdown of effort between the two sectors.
We follow the theoretical structure of Berman and Bui 179 of the impacts of regulation in 

employment in the regulated sectors. In Berman and Bui’s (2001, p. 274-75) theoretical model, 

as described above, the change in a firm’s labor demand arising from a change in regulation is 

decomposed into two main components: output and substitution effects.VV As the output and 

substitution effects may be both positive, both negative, or some combination, standard 

neoclassical theory alone does not point to a definitive net effect of regulation on labor demand 

at regulated firms. 

Following the Berman and Bui framework for the impacts of regulation on employment 

in the regulated sector, we consider two effects for the motor vehicle sector: the output effect 

and the substitution effect. 

8.11.2.1 The Output Effect

If truck or trailer sales increase, then more people will be required to assemble trucks, 

trailers, and their components. If truck or trailer sales decrease, employment associated with 

these activities will decrease. The effects of this proposed rulemaking on HD vehicle sales thus 

depend on the perceived desirability of the new vehicles. On one hand, this proposed 

rulemaking will increase truck and trailer costs; by itself, this effect would reduce truck and 

trailer sales. In addition, while decreases in truck performance would also decrease sales, this 

program is not expected to have any negative effect on truck performance. On the other hand, 

this proposed rulemaking will reduce the fuel costs of operating the trucks; by itself, this effect 

would increase truck sales, especially if potential buyers have an expectation of higher fuel 

prices. The agencies have not made an estimate of the potential change in truck or trailer sales. 

However, as discussed in Chapter 8.3, the agencies have estimated an increase in vehicle miles 

traveled (i.e., VMT rebound) due to the reduced operating costs of trucks meeting these proposed 

standards. Since increased VMT is most likely to be met with more drivers and more trucks, our 

projection of VMT rebound is suggestive of an increase in vehicle sales and truck driver 

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). 

8.11.2.2 The Substitution Effect

The output effect, above, measures the effect due to new truck and trailer sales only. The 

substitution effect includes the impacts due to the changes in technologies needed for vehicles to 

meet the proposed standards, separate from the effect on output (that is, as though holding output 

constant). This effect includes both changes in employment due to incorporation of abatement 

 

VV The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) the demand effect; 2) 

the cost effect; and 3) the factor-shift effect. See Morgenstern, Richard D., William A. Pizer, and Jhih-Shyang Shih. 

“Jobs Versus the Environment: An Industry-Level Perspective.” Journal of Environmental Economics and 

Management 43 (2002): 412-436 (Docket EPA-HQ-OAR).
technologies and overall changes in the labor intensity of manufacturing. We present estimates 

for this effect to provide a sense of the order of magnitude of expected impacts on employment, 

which we expect to be small in the automotive sector, and to repeat that regulations may have 

positive as well as negative effects on employment.

One way to estimate this effect, given the cost estimates for complying with the proposed 

rule, is to use the ratio of workers to each $1 million of expenditures in that sector. The use of 

these ratios has both advantages and limitations. It is often possible to estimate these ratios for 

quite specific sectors of the economy: for instance, it is possible to estimate the average number 

of workers in the motor vehicle body and trailer manufacturing sector per $1 million spent in the 

sector, rather than use the ratio from another, more aggregated sector, such as motor vehicle 

manufacturing. As a result, it is not necessary to extrapolate employment ratios from possibly

unrelated sectors. On the other hand, these estimates are averages for the sectors, covering all 

the activities in those sectors; they may not be representative of the labor required when 

expenditures are required on specific activities, or when manufacturing processes change 

sufficiently that labor intensity changes. For instance, the ratio for the motor vehicle 

manufacturing sector represents the ratio for all vehicle manufacturing, not just for emissions 

reductions associated with compliance activities. In addition, these estimates do not include 

changes in sectors that supply these sectors, such as steel or electronics producers. They thus 

may best be viewed as the effects on employment in the motor vehicle sector due to the changes 

in expenditures in that sector, rather than as an assessment of all employment changes due to 

these changes in expenditures. In addition, this approach estimates the effects of increased 

expenditures while holding constant the labor intensity of manufacturing; it does not take into 

account changes in labor intensity due to changes in the nature of production. This latter effect 

could either increase or decrease the employment impacts estimated here.WW

Some of the costs of these proposed rules will be spent directly in the motor vehicle

manufacturing sector, but it is also likely that some of the costs will be spent in the motor vehicle 

body and trailer and motor vehicle parts manufacturing sectors. The analysis here draws on 

estimates of workers per $1 million of expenditures for each of these sectors.

There are several public sources for estimates of employment per $1 million 

expenditures. The U.S. Bureau of Labor Statistics (BLS) provides its Employment 

Requirements Matrix (ERM),180 which provides direct estimates of the employment per $1 

million in sales of goods in 202 sectors. The values considered here are for Motor Vehicle 

Manufacturing (NAICS 3361), Motor Vehicle Body and Trailer Manufacturing (NAICS 3362),

and Motor Vehicle Parts Manufacturing (NAICS 3363) for 2012. 

The Census Bureau provides the Annual Survey of Manufacturers

181 (ASM), a subset of 

the Economic Census, based on a sample of establishments; though the Census itself is more 

 

WW As noted above, Morgenstern et al. (2002) separate the effect of holding output constant into two effects: the cost 

effect, which holds labor intensity constant, and the factor shift effect, which estimates those changes in labor 

intensity.
complete, it is conducted only every 5 years, while the ASM is annual. Both include more 

sectoral detail than the BLS ERM: for instance, while the ERM includes the Motor Vehicle 

Manufacturing sector, the ASM and Economic Census have detail at the 6-digit NAICS code 

level (e.g., light truck and utility vehicle manufacturing). While the ERM provides direct 

estimates of employees/$1 million in expenditures, the ASM and Economic Census separately

provide number of employees and value of shipments; the direct employment estimates here are 

the ratio of those values. At this time, the Economic Census values for 2012 (the most recent 

year) are not fully available; we therefore do not report them, and instead provide the 2011 ASM 

results (the most recent available). The values reported are for Motor Vehicle Manufacturing 

(NAICS 3361), Light Truck and Utility Vehicle Manufacturing (NAICS 336112), Heavy Duty

Truck Manufacturing (33612), Motor Vehicle Body and Trailer Manufacturing (3362), and 

Motor Vehicle Parts Manufacturing (NAICS 3363). The values used here are adjusted to remove 

the employment effects of imports through use of a ratio of domestic production to domestic 

sales of 0.78.

XX

Table 8-42 provides the values, either given (BLS) or calculated (ASM) for employment 

per $1 million of expenditures, all adjusted to 2012 dollars using the Bureau of Economic 

Analysis’s Implicit GDP Price Deflators. Although the ASM appears to provide slightly higher 

values than the ERM, the different data sources provide similar patterns for the estimates for the 

sectors. Body and trailer manufacturing and parts manufacturing appear to be more laborintensive than vehicle manufacturing; light truck and utility vehicle manufacturing appears to be 

less, and heavy duty truck manufacturing appears to be more, labor-intensive than motor vehicle 

manufacturing as a whole. 

Table 8-42 Employment per $1 Million Expenditures (2012$) in the Motor Vehicle Manufacturing Sectora

SOURCE SECTOR RATIO OF 

WORKERS PER 

$1 MILLION 

EXPENDITURE

S

RATIO OF WORKERS 

PER $1 MILLION 

EXPENDITURES, 

ADJUSTED FOR 

DOMESTIC VS. 

FOREIGN 

PRODUCTION

BLS ERM Motor vehicle mfg (3361) 0.460 0.355

BLS ERM Motor vehicle body & trailer mfg (3362) 1.450 1.153

BLS ERM Motor vehicle parts mfg (3363) 1.950 1.590

ASM Motor vehicle mfg (3361) 0.538 0.414

ASM Light truck & utility vehicle mfg 

(336112)

0.443 0.341

ASM Heavy duty truck mfg (33612) 0.832 0.641

ASM Motor vehicle body & trailer mfg (3362) 2.797 2.156

ASM Motor Vehicle Parts Mfg (3363) 1.635 1.260

 

XX To estimate the proportion of domestic production affected by the change in sales, we use data from Ward’s 

Automotive Group for total truck production in the U.S. compared to total truck sales in the U.S. For the period 

2004-2013, the proportion is 78 percent (Docket EPA-HQ-OAR-), ranging from 68 percent (2009) to 83 percent 

(2012) over that time. 
Note:

a BLS ERM refers to the U.S. Bureau of Labor Statistics’ Employment Requirement Matrix, 2012 values. 

ASM refers to the U.S. Census Bureau’s Annual Survey of Manufactures, 2011 values. 

Over time, the amount of labor needed in the motor vehicle industry has changed: 

automation and improved methods have led to significant productivity increases. The BLS 

ERM, for instance, provided estimates that, in 1993, 1.52 workers in the Motor Vehicle 

Manufacturing sector were needed per $1 million, but only 0.53 workers by 2012 (in 2005$).

182

 

Because the ERM is available annually for 1993-2012, we used these data to estimate 

productivity improvements over time. We regressed logged ERM values on a year trend for the 

Motor Vehicle Manufacturing, Motor Vehicle Body and Trailer Manufacturing, and Motor 

Vehicle Parts Manufacturing sectors. We used this approach because the coefficient describing 

the relationship between time and productivity is a direct measure of the average percent change 

in productivity per year. The results suggest a 5.1 percent per year productivity improvement in 

the Motor Vehicle Manufacturing Sector, and a 4.7 percent per year improvement in the Motor 

Vehicle Parts Manufacturing Sector. The Motor Vehicle Body and Trailer Manufacturing Sector 

results were more complex: the workers/$1 million values before 2009 are substantially higher 

(averaging 5.90 in 2005$) than those in 2009 and after (averaging 2.45 in 2005$); we used 

dummy variables to account for this shift, and estimate productivity gains of 0.4 percent per year 

before 2009, and 22 percent after. This dramatic difference may suggest taking care when 

relying on the data for this sector. As discussed further below, we only report maximum and 

minimum employment impacts, and the Motor Vehicle Body and Trailer Manufacturing 

estimates provide the minimum values; they may therefore create greater uncertainty about the 

lower bound of the substitution-effect employment.

We then used the regression results to project the number of workers per $1 million 

through 2027. We calculated separate sets of projections (adjusted to 2012$) for both the BLS 

ERM data as well as the ASM for all three sectors discussed above. The BLS ERM projections

were calculated directly from the fitted regression equations since the regressions themselves 

used ERM data. For the ASM projections, we used the ERM’s ratio of the projected value in 

each future year to the projected value in 2011 (the base year for the ASM) to determine how 

many workers will be needed per $1 million of 2012$. In other words, we apply the projected 

productivity growth estimated using the ERM data to the ASM numbers. 

Finally, to simplify the presentation and give a range of estimates, we compared the 

projected employment among the 3 sectors for the ERM and ASM, and we provide only the 

maximum and minimum employment effects estimated for the ERM and the ASM. We provide 

the range rather than a point estimate because of the inherent difficulties in estimating 

employment impacts; the range gives an estimate of the expected magnitude. The details of the 

calculations may be found in the docket. The ERM estimate in the Motor Vehicle Parts 

Manufacturing Sector are consistently the maximum values. The ERM estimate in the Motor 

Vehicle Body and Trailer Manufacturing Sector are the minimum values for all years but 2018-

2019, where the ASM value for the Light Truck and Utility Vehicle Manufacturing Sector 

(336112) provide the minimum values. 

Chapter 7 of the draft RIA discusses the vehicle cost estimates developed for these

proposed rules. The final step in estimating employment impacts is to multiply costs (in $
millions) by workers per $1 million in costs, to estimate employment impacts in the regulated 

and parts manufacturing sectors. Increased costs of vehicles and parts would, by itself, and 

holding labor intensity constant, be expected to increase employment between 2018 and 2027

from none to a few thousand jobs each year.

While we estimate employment impacts, measured in job-years, beginning with program 

implementation, some of these employment gains may occur earlier as motor vehicle

manufacturers and parts suppliers hire staff in anticipation of compliance with the standards. A 

job-year is a way to calculate the amount of work needed to complete a specific task. For

example, a job-year is one year of work for one person. 

Table 8-43 Employment Effects due to Increased Costs of Vehicles and Parts (Substitution Effect), in Jobyears

YEAR COSTS 

(MILLIONS OF 

2012$)

MINIMUM EMPLOYMENT 

DUE TO SUBSTITUTION 

EFFECT (ERM ESTIMATES, 

EXPENDITURES IN THE 

PARTS SECTORa)

MAXIMUM EMPLOYMENT DUE 

TO SUBSTITUTION EFFECT (ERM 

ESTIMATES, EXPENDITURES IN 

THE BODY AND TRAILER MFG 

SECTOR)

2018 $ 116 0 100 

2019 $ 113 0 100 

2020 $ 112 0 100 

2021 $ 2,173 300 2,300 

2022 $ 2,161 300 2,200 

2023 $ 2,224 200 2,100 

2024 $ 3,455 300 3,200 

2025 $ 3,647 200 3,200 

2026 $ 3,736 200 3,100 

2027 $ 5,309 200 4,200 

Note:

a For 2018 and 2019, the minimum employment effects are associated with the ASM’s Light Truck and Utility 

Vehicle Manufacturing sector.

8.11.2.3 Summary of Employment Effects in the Motor Vehicle Sector

The overall effect of these proposed rules on motor vehicle sector employment depends 

on the relative magnitude of the output effect and the substitution effect. Because we do not 

have quantitative estimates of the output effect, and only a partial estimate of the substitution 

effect, we cannot reach a quantitative estimate of the overall employment effects of these

proposed rules on motor vehicle sector employment or even whether the total effect will be 

positive or negative. 

The proposed standards are not expected to provide incentives for manufacturers to shift 

employment between domestic and foreign production. This is because the proposed standards 

will apply to vehicles sold in the U.S. regardless of where they are produced. If foreign 

manufacturers already have increased expertise in satisfying the requirements of the standards, 

there may be some initial incentive for foreign production, but the opportunity for domestic 

manufacturers to sell in other markets might increase. To the extent that the requirements of 

these proposed rules might lead to installation and use of technologies that other countries may
seek now or in the future, developing this capacity for domestic production now may provide 

some additional ability to serve those markets. 

Some vehicle parts are made in-house and would be included directly in the regulated 

sector. Others are made by independent suppliers and are 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. If demand for 

these parts increases due to the increased use of these parts, employment effects in this sector are 

expected to be positive. If the demand effect in the regulated sectors is significantly negative 

enough, it is possible that demand for other parts may decrease. As noted, the agencies do not 

predict a direction for the demand effect.

8.11.3 Employment Impacts in Other Affected Sectors

8.11.3.1 Transport and Shipping Sectors

Although not directly regulated by these proposed 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, 

depending on the relative elasticities of supply and demand, will be passed along to customers. 

With lower prices, demand for shipping would lead to an increase in demand for truck shipping 

services (consistent with the VMT rebound effect analysis) and therefore an increase in 

employment in the truck shipping sector. 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. If 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.183,184 Such a shift would, however, be at the expense of 

employment in the sectors that are losing business to trucking. The first effect – a gain due to 

lower shipping costs – is likely to lead to a net increase in employment. The second effect, due 

to mode-shifting, may increase employment in trucking, but decrease employment in other 

shipping sectors (e.g., rail or barge), with the net effects dependent on the labor-intensity of the 

sectors and the volumes.

8.11.3.2 Fuel Suppliers

In addition to the effects on the trucking industry and related truck parts sector, these 

proposed 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. 

Chapter 7.2 of this draft RIA provides estimates of the effects of these proposed 

standards on expected fuel consumption. While reduced fuel consumption represents savings for 
purchasers of fuel, it also represents a loss in value of output for the petroleum refinery industry, 

which will result in reduced sectoral employment. Because this sector is material-intensive, the 

employment effect is not expected to be large.YY

8.11.3.3 Fuel Savings 

As a result of this proposed rulemaking, it is anticipated that trucking firms will 

experience fuel savings. Fuel savings lower the costs of transportation goods and services. In a 

competitive market, some of the fuel savings 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. Some of the savings might also be retained by firms for investments or for 

distributions to firm owners. Again, how much accrues to customers versus firm owners will 

depend on the relative elasticities of supply and demand. Regardless, the savings will accrue to 

some segment of consumers: either owners of trucking firms or the general public, and 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 described in Chapter 7.2, the value of fuel savings from this proposed rulemaking is 

projected to be $15.1 billion (2012$) in 2027, according to Table 7-19. If all those savings are 

spent, 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 could 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. Thus, while fuel savings are expected to 

decrease employment in the refinery sector, they are expected to increase employment through 

increased consumer expenditures.

8.11.4 Summary of Employment Impacts

The primary employment effects of these rules are expected to be found throughout 

several key sectors: truck and engine manufacturers, the trucking industry, truck parts 

manufacturing, fuel production, and consumers. These rules initially take effect in model year 

2018, a time period sufficiently far in the future that the unemployment rate at that time is 

unknowable. In an economy with full employment, the primary employment effect of a 

rulemaking is likely to be to move employment from one sector to another, rather than to 

increase or decrease employment. For that reason, we focus our partial quantitative analysis on 

employment in the regulated sector, to examine the impacts on that sector directly. We discuss 

the likely direction of other impacts in the regulated sector as well as in other directly related 

sectors, but we do not quantify those impacts, because they are more difficult to quantify with 

reasonable accuracy, particularly so far into the future.

 

YY In the 2012 BLS ERM cited above, the Petroleum and Coal Products Manufacturing sector has a ratio of workers 

per $1 million of 0.242, lower than all but two of the 181 sectors with non-zero employment per $1 million.
For the regulated sector, we have not quantified the output effect. The substitution effect 

is associated with potential increased employment from none to a few thousand jobs per year 

between 2018 and 2027, depending on the share of employment impacts in the affected sectors 

(Motor Vehicle Manufacturing, Motor Vehicle Body and Trailer Manufacturing, and Motor 

Vehicle Parts Manufacturing). These estimates do not include potential changes, either greater 

or less, in labor intensity of production. As mentioned above, some of these job gains may occur 

earlier as auto manufacturers and parts suppliers hire staff to prepare to comply with the 

standard. 

Lower prices for shipping are expected to lead to an increase in demand for truck 

shipping services and, therefore, an increase in employment in that sector, 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. Under conditions of full employment, any changes in employment levels in the 

regulated sector due to this program are mostly expected to be offset by changes in employment 

in other sectors.

8.12 Oil Price Sensitivity Analysis using Method B

In this section, EPA presents a sensitivity analysis examining the impact on net benefits 

using AEO’s “low oil price” and “high oil price” cases. The sensitivity analysis is based on the 

preferred alternative relative to the less dynamic baseline as the “primary” case using Method B.

Fuel price changes were not used as an input to technology application rates (i.e., a constant 

$/vehicle has been used throughout this sensitivity analysis). The primary analysis (presented 

earlier in this chapter) uses the AEO reference case oil prices. The primary case and both high 

and low oil price case $/gallon values are shown in Table 8-44.

Table 8-44 AEO Fuel Prices in the Low Oil Price Case, our Primary Analysis Case, and the AEO High Oil 

Price Case (2012$)

YEAR RETAIL UNTAXED

Diesel Gasoline Diesel Gasoline

Low Primary High Low Primary High Low Primary High Low Primary High

2018 $2.94 $3.53 $4.89 $2.54 $3.02 $4.10 $2.48 $3.07 $4.43 $2.13 $2.61 $3.68

2019 $2.95 $3.61 $4.94 $2.54 $3.03 $4.13 $2.49 $3.15 $4.49 $2.13 $2.62 $3.71

2020 $2.96 $3.67 $4.99 $2.55 $3.08 $4.16 $2.51 $3.22 $4.54 $2.15 $2.67 $3.74

2021 $3.00 $3.74 $5.03 $2.59 $3.12 $4.17 $2.55 $3.29 $4.59 $2.19 $2.71 $3.76

2022 $3.01 $3.82 $5.08 $2.59 $3.17 $4.18 $2.56 $3.37 $4.64 $2.19 $2.77 $3.77

2023 $3.01 $3.87 $5.10 $2.57 $3.22 $4.19 $2.57 $3.43 $4.67 $2.18 $2.82 $3.78

2024 $3.02 $3.92 $5.14 $2.56 $3.26 $4.21 $2.58 $3.48 $4.70 $2.16 $2.86 $3.80

2025 $3.03 $3.98 $5.17 $2.55 $3.29 $4.23 $2.59 $3.54 $4.74 $2.16 $2.89 $3.82

2026 $3.04 $4.02 $5.24 $2.55 $3.32 $4.26 $2.60 $3.59 $4.81 $2.16 $2.92 $3.86

2027 $3.03 $4.08 $5.30 $2.54 $3.36 $4.31 $2.60 $3.65 $4.88 $2.15 $2.96 $3.91

2028 $3.03 $4.12 $5.39 $2.54 $3.37 $4.38 $2.61 $3.69 $4.97 $2.16 $2.98 $3.98

2029 $3.04 $4.16 $5.47 $2.54 $3.40 $4.43 $2.61 $3.74 $5.06 $2.16 $3.01 $4.03

2030 $3.06 $4.20 $5.51 $2.55 $3.43 $4.45 $2.64 $3.79 $5.09 $2.17 $3.04 $4.06

2031 $3.06 $4.25 $5.58 $2.55 $3.46 $4.51 $2.64 $3.84 $5.17 $2.17 $3.08 $4.12
2032 $3.06 $4.30 $5.66 $2.56 $3.50 $4.57 $2.64 $3.89 $5.25 $2.18 $3.12 $4.18

2033 $3.07 $4.36 $5.72 $2.57 $3.54 $4.62 $2.66 $3.95 $5.32 $2.20 $3.16 $4.24

2034 $3.08 $4.43 $5.77 $2.58 $3.61 $4.66 $2.67 $4.02 $5.37 $2.21 $3.24 $4.28

2035 $3.08 $4.47 $5.83 $2.58 $3.65 $4.71 $2.67 $4.06 $5.44 $2.22 $3.27 $4.33

2036 $3.09 $4.51 $5.89 $2.59 $3.69 $4.77 $2.68 $4.11 $5.50 $2.23 $3.32 $4.39

2037 $3.09 $4.54 $5.96 $2.59 $3.73 $4.81 $2.69 $4.15 $5.57 $2.23 $3.36 $4.44

2038 $3.10 $4.58 $6.03 $2.60 $3.77 $4.87 $2.70 $4.19 $5.65 $2.24 $3.40 $4.49

2039 $3.11 $4.65 $6.12 $2.61 $3.83 $4.95 $2.71 $4.26 $5.74 $2.25 $3.47 $4.57

2040 $3.11 $4.73 $6.23 $2.61 $3.90 $5.04 $2.72 $4.34 $5.85 $2.26 $3.54 $4.67

Note: 

Our Primary case values are the AEO reference case values and are taken from AEO2014 Early Release; other 

values from AEO2014.

The impacts of using the low and high oil price cases on our estimated fuel savings and 

net benefits are shown in Table 8-45.

Table 8-45 MY2018-2029 Lifetime Sensitivity on Net Benefits using AEO2014 Low and High Oil Price Cases

for the Preferred Alternative Relative to the Less Dynamic Baseline and using Method B (Billions of 2012$) a

LOW OIL 

PRICE 

CASE

PRIMARY 

CASE

HIGH OIL 

PRICE 

CASE

Vehicle program -$25 -$25 -$25

Maintenance -$1.1 -$1.1 -$1.1

Fuel $117 $171 $230

Benefits $93 $97 $101

Net benefits $184 $242 $305

Note:

a

For an explanation of analytical Methods A and B, please see 

Preamble Section I.D; for an explanation of the less dynamic 

baseline, 1a, and more dynamic baseline, 1b, please see 

Preamble Section X.A.1
References

1 Environmental Protection Agency and Department of Transportation, “Light-Duty Vehicle Greenhouse Gas 

Emissions Standards and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 75(88) (May 7, 

2010), especially Sections III.H.1 (pp. 25510-25513) and IV.G.6 (pp. 25651-25657); Environmental Protection 

Agency and Department of Transportation, ”2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas 

Emissions and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 77(199) (October 15, 

2012), especially Sections III.H.1 (pp. 62913-62919) and IV.G.5.a (pp. 63102-63104).

2

State of Massachusetts v. EPA, 549 U.S. at 533.

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

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

of Medium- and Heavy-Duty Vehicles,” (hereafter, “NAS 2010”). Washington, D.C. The National Academies 

Press. Available electronically from the National Academies Press Website at 

http://www.nap.edu/catalog.php?record_id=12845 (accessed September 10, 2010).

4 Klemick, Heather, Elizabeth Kopits, Keith Sargent, and Ann Wolverton (2014). “Heavy-Duty Trucking and the 

Energy Efficiency Paradox.” US EPA NCEE Working Paper Series. Working Paper 14-02; Roeth, Mike, Dave 

Kircher, Joel Smith, and Rob Swim (2013). “Barriers to the Increased Adoption of Fuel Efficiency Technologies in 

the North American On-Road Freight Sector.” NACFE report for the International Council on Clean Transportation; 

Aarnink, Sanne, Jasper Faber, and Eelco den Boer (2012). “Market Barriers to Increased Efficiency in the European 

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5 Vernon, David and Alan Meier (2012). “Identification and quantification of principal-agent problems affecting 

energy efficiency investments and use decisions in the trucking industry.” Energy Policy, 49(C), pp. 266-273

6 Blumstein, Carl and Margaret Taylor (2013). “Rethinking the Energy-Efficiency Gap: Producers, Intermediaries, 

and Innovation,” Energy Institute at Haas Working Paper 243, University of California at Berkeley; Tirole, Jean 

(1998). The Theory of Industrial Organization. Cambridge, MA: MIT Press, pp.400, 402. This first-mover 

disadvantage must large enough to overcome the incentive normally offered by the potential to for first movers to 

earn unusually high (but temporary) profit levels. 

7 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, September 2013). 

8 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report-acgoperatingcost2005-2005-e-2-1727.htm, accessed on July 16, 2010. 

9 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for 

on-road freight transportation. Energy Policy 48, 252-259.

10 Greene, D.L., Kahn, J.R., Gibson, R.C., 1999, “Fuel economy rebound effect for U.S. household vehicles”, The 

Energy Journal, 20.

11 For a discussion of the wide range of definitions found in the literature, see Appendix D: Discrepancy in Rebound 

Effect Definitions, in EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, 

Excerpts of Draft Final Report of Phase 1 under EPA contract EP-C-13-025. (Docket ID: EPA-HQ-OAR-2014-

0827). See also Greening, L.A., Greene, D.L., Difiglio, C., 2000, “Energy efficiency and consumption — the 

rebound effect — a survey”, Energy Policy, 28, 389-401.

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

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

of Medium- and Heavy-Duty Vehicles,” Washington, D.C. The National Academies Press. Available electronically 

from the National Academies Press Website at http://www.nap.edu/catalog.php?record_id=12845 (last accessed 

September 10, 2010).

13 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, September 2013. 

14 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. 

15 These factors are discussed more fully in a report to EPA from EERA, which illustrates in a series of diagrams the 

complex system of decisions and decision-makers that could influence the magnitude and timing of the rebound 

effect. See Sections 2.2.2, 2.2.3, 2.2.4, and 2.3 in EERA (2014), “Research to Inform Analysis of the Heavy-Duty 

Vehicle Rebound Effect”, Excerpts of Draft Final Report of Phase 1 under EPA contract EP-C-13-025. 

16 A useful framework for understanding how various responses interact to determine the rebound effect is presented

in Section 2 and Appendix B of De Borger, B. and Mulalic, I. (2012), “The determinants of fuel use in the trucking 

industry – volume, fleet characteristics and the rebound effect”, Transportation Policy, Volume 24, pp. 284–295. 
See also Section 3.4 of EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, 

Excerpts of Draft Final Report of Phase 1 under EPA contract EP-C-13-025. 

17 Gately, D., The U.S. Demand for Highway Travel and Motor Fuels, The Energy Journal, Volume 11, No. 3, July 

1990, pp.59-73.

18 Matos, F. J. F., and Silva, F. J. F., “The Rebound Effect on Road Freight Transport: Empirical Evidence from 

Portugal,” Energy Policy, 39, 2011, pp. 2833–2841.

19 De Borger, B. and Mulalic, I., “The determinates of fuel use in the trucking industry – volume, fleet 

characteristics and the rebound effect”, Transportation Policy, Volume 24, November 2012, pp. 284–295.

20 FHWA Travel Analysis Framework Development of VMT Forecasting Models for Use by the Federal Highway 

Administration May 12, 2014 http://www.fhwa.dot.gov/policyinformation/tables/vmt/vmt_model_dev.pdf. Volpe’s 

work was advised by a panel of approximately 20 experts in the measurement, analysis, and forecasting of travel, 

including academic researchers, transportation consultants, and members of local, state, and federal government 

transportation agencies. It was also summarized in the paper “Developing a Multi-Level Vehicle Miles of Travel 

Forecasting Model,” November, 2011, which was presented to the Transportation Research Board’s 91st Annual 

Meeting in January, 2012. 

21 EPA/NHTSA, August 2011. Chapter 9.3.3, Final Rulemaking to Establish Greenhouse gas Emission Standards & 

Fuel Efficiency Standards for Medium-and Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis. EPA420-R-11-901. (http://www.epa.gov/otaq/climate/documents/420r11901.pdf)

23 EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, Excerpts of Draft Final 

Report of Phase 1 under EPA contract EP-C-13-025. 

24 EERA (2015), “Working Paper on Fuel Price Elasticities for Heavy Duty Vehicles”, Draft Final Report of Phase 2

under EPA contract EP-C-11-046. 

25 Gately, D. 1993. The Imperfect Price-Reversibility of World Oil Demand. The Energy Journal, International 

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vol. 41, pp. 59-65.

26 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for 

on-road freight transportation. Energy Policy 48, 252-259.

27 See, for example, Appendix E in EERA (2014), “Research to Inform Analysis of the Heavy-Duty Vehicle 

Rebound Effect”, Draft Final Report of Phase 1 under EPA contract EP-C-13-025. 

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

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29 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for 

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

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38 Cambridge Systematics, Inc., Assessment of Fuel Economy Technologies for Medium and Heavy Duty Vehicles: 

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39 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and International 

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Phase II Report, National Cooperative Highway Research Program Project 8-30, June 1995. 

42 The own (i.e., self) price elasticity provides a measure for describing how the volume of truck shipping (demand) 

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131 U.S. Department of Transportation, Valuation of Travel Guidance, July 9, 2014, at page 14.

132 “Preliminary Regulatory Impact Analysis, FMVSS No. 119, New Pneumatic Tires for Motor Vehicles with a 

GVWR of More Than 4,536 kg (10,000 pounds), June 2010. 

133 Cost and Weight Analysis of Two Motorcoach Seating Systems: One With and One Without Three-Point 

Lap/Shoulder Belt Restraints, Ludtke and Associates, July 2010.

134 “Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2012 - MY 2016 Passenger Cars 

and Light Trucks”, NHTSA, March 2010, (Docket No. NHTSA-2009-0059-0344.1). 

135Based on data from the CIA, combining various recent years, https://www.cia.gov/library/publications/the-worldfactbook/rankorder/2242rank.html

136

 See EIA Annual Energy Review, various editions. For data 2011-2013, and projected data: EIA Annual Energy 

Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_11.xls"

137 For historical data: EIA Annual Energy Review, various editions. For data 2011-2013, and projected data: EIA 

Annual Energy Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_11.xls"

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

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

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

September 2007. 

140 Leiby, P., Factors Influencing Estimate of Energy Security Premium for Heavy-Duty Phase II Proposed Rule, 

11/18/2014, Oak Ridge National Laboratory.
141 Brown, Stephen P.A. and Hillard G. Huntington. 2013. Assessing the U.S. Oil Security Premium, 

Energy Economics, vol. 38, pp 118-127.

142 Reassessing the Oil Security Premium. RFF Discussion Paper Series, (RFF DP 10-05). doi: RFF DP 10-05

143 Greene, D. L. 2010. Measuring energy security: Can the United States achieve oil independence? Energy Policy, 

38(4), 1614–1621. doi:10.1016/j.enpol.2009.01.041

144 Reassessing the Oil Security Premium. RFF Discussion Paper Series, (RFF DP 10-05). doi:RFF DP 10-05

145 Ledyard, John O. "Market Failure." The New Palgrave Dictionary of Economics. Second Edition. Eds. Steven N. 

Durlauf and Lawrence E. Blume. Palgrave Macmillan, 2008

146 Gately, Dermot 2004. “OPEC's Incentives for Faster Output Growth”, The Energy Journal,25(2):75-96; Gately, 

Dermot 2007. "What Oil Export Levels Should We Expect From OPEC?", The Energy Journal, 28(2):151-173.

147 Bohi, Douglas R. And W. David Montgomery 1982. “Social Cost of Imported and U.S. Import Policy”, Annual 

Review of Energy, 7:37-60. Energy Modeling Forum, 1981. World Oil, EMF Report 6 (Stanford University Press: 

Stanford 39 CA. https//emf.stanford.edu/publications/emf-6-world-oil.

148 Plummer, James L. (Ed.) 1982. Energy Vulnerability, “Basic Concepts, Assumptions and Numercial Results”, 

pp. 13-36, (Cambridge MA: Ballinger Publishing Co.)

149 Bohi, Douglas R. And W. David Montgomery 1982 “Social Cost of Imported and U.S. Import Policy”, Annual 

Review of Energy, 7:37-60.

150 Hogan, William W., 1981. “Import Management and Oil Emergencies”, Chapter 9 in Deese, 5 David and Joseph 

Nye, eds. Energy and Security. Cambridge, MA: Ballinger Publishing Co.

151 Broadman, H. G. 1986. “The Social Cost of Imported Oil,” Energy Policy 14(3):242-252. Broadman H.G. and 

W.W. Hogan, 1988. “Is an Oil Import Tariff Justified? An American Debate: The Numbers Say ‘Yes’ ”, The Energy 

Journal 9: 7-29.

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

and Costs, ORNL-6851, Oak Ridge National Laboratory, November 1, 1997.

153 Parry, Ian W.H. and Joel Darmstadter 2004. “The Costs of U.S. Oil Dependency,” Resources for the Future, 

November 17, 2004 (also published as NCEP Technical Appendix Chapter 1: Enhancing Oil Security, the National 

Commission on Energy Policy 2004 Ending the Energy Stalemate - A Bipartisan Strategy to Meet America’s 

Energy Challenges.)

154 National Research Council, 2009. Hidden Costs of Energy: Unpriced Consequences of Energy Production and 

Use. National Academy of Science, Washington, DC.

155 See, William Nordhaus, “Who’s Afraid of a Big Bad Oil Shock?,” available at 

http://aida.econ.yale.edu/~nordhaus/homepage/Big_Bad_Oil_Shock_Meeting.pdf , and Olivier Blanchard and Jordi 

Gali, “The macroeconomic Effects of Oil price Shocks: Why are the 2000s so different from the 1970s?”, pp. 373-

421, in The International Dimensions of Monetary Policy, Jordi Gali and Mark Gertler, editors, University of 

Chicago Press, February 2010, available at http://www.nber.org/chapters/c0517.pdf

156 Blanchard and Gali, p. 414.

157 See, Oil price Drops on Oversupply, http://www.oil-price.net/en/articles/oil-price-drops-on-oversupply.php, 

10/6/2014.

158 Hamilton, J. D. (2012). Oil Prices , Exhaustible Resources , and Economic Growth. In Handbook of Energy and 

Climate Change. Retrieved from http://econweb.ucsd.edu/~jhamilto/handbook_climate.pdf

159 Ramey, V. A., & Vine, D. J. (2010). “Oil, Automobiles, and the U.S. Economy: How Much have Things Really 

Changed?”, National Bureau of Economic Research Working Papers, WP 16067(June). Retrieved from 

http://www.nber.org/papers/w16067.pdf

160 Historical data are from EIA Annual Energy Review, various editions. For data since 2011 and projected data: 

source is EIA Annual Energy Outlook (AEO) 2014 (Reference Case). See Table 11, file "aeotab_11.xlsx" and Table 

20 (Macroeconomic Indicators,” (file “aeotab_20.xlsx”).

161 http://www.whitehouse.gov/sites/default/files/omb/inforeg/eo12866/eo13563_01182011.pdf.

162 U.S. Department of Labor, Bureau of Labor Statistics. “Automotive Industry; Employment, Earnings, and 

Hours”, http://www.bls.gov/iag/tgs/iagauto.htm , accessed 8/18/14.

163 Layard, P.R.G., and A. A. Walters (1978), Microeconomic Theory (McGraw-Hill, Inc.), Chapter 9 (Docket EPAHQ-OAR-2011-0135). 

164Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South 

Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR-2011-0135). 
165 Ehrenberg, Ronald G., and Robert S. Smith (2000), Modern Labor Economics: Theory and Public Policy 

(Addison Wesley Longman, Inc.), , p. 108. 

166 This discussion draws from Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR), p. 

293. 

167 Arrow et al. (1996). “Benefit-Cost Analysis in Environmental, Health, and Safety Regulation: A Statement of 

Principles.” American Enterprise Institute, The Annapolis Center, and Resources for the Future. See discussion on 

bottom of p. 6. In practice, distributional impacts on individual workers can be important, as discussed later in this 

section. 

168 Schmalensee, Richard, and Robert N. Stavins. “A Guide to Economic and Policy Analysis of EPA’s Transport 

Rule.” White paper commissioned by Excelon Corporation, March 2011.

169 Klaiber, H. Allen, and V. Kerry Smith (2012). “Developing General Equilibrium Benefit Analyses for Social 

Programs: An Introduction and Example.” Journal of Benefit-Cost Analysis 3(2). 

170 Graff Zivin, J., and M. Neidell (2012). “The Impact of Pollution on Worker Productivity.” American Economic 

Review 102: 3652-3673). 

171 Hamermesh (1993), Labor Demand (Princeton, NJ: Princeton University Press), Chapter 2 (Docket EPA-HQOAR-2011-0135). 

172 Ehrenberg, Ronald G., and Robert S. Smith (2000), Modern Labor Economics: Theory and Public Policy 

(Addison Wesley Longman, Inc.), Chapter 4 (Docket EPA-HQ-OAR-),. 

173Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South 

Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR-2011-0135). 

174Morgenstern, 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 (Docket EPA-HQOAR-2011-0135-0057).

175 Gray, Wayne B., Ronald J. Shadbegian, Chunbei Wang, and Merve Meral. “Do EPA Regulations Affect Labor 

Demand? Evidence from the Pulp and Paper Industry.” Journal of Environmental Economics and Management: 68, 

2014, 188-202.

176 Ferris, Ann, Ronald J. Shadbegian and Ann Wolverton. “The Effect of Environmental Regulation on Power 

Sector Employment: Phase I of the Title IV SO2 Trading Program.” Journal of the Association of Environmental 

and Resource Economists. (Forthcoming 2014).

177 Greenstone, M. (2002). “The Impacts of Environmental Regulations on Industrial Activity: Evidence from the 

1970 and 1977 Clean Air Act Amendments and the Census of Manufactures,” Journal of Political Economy 110(6): 

1175-1219 (Docket EPA-HQ-OAR-2011-0135); Walker, Reed. (2011).“Environmental Regulation and Labor 

Reallocation." American Economic Review: Papers and Proceedings 101(3): 442-447 (Docket EPA-HQ-OAR2011-0135).

178List, J. A., D. L. Millimet, P. G. Fredriksson, and W. W. McHone (2003). “Effects of Environmental Regulations 

on Manufacturing Plant Births: Evidence from a Propensity Score Matching Estimator.” The Review of Economics 

and Statistics 85(4): 944-952 (Docket EPA-HQ-OAR-2011-0135).

179Berman, E. and L. T. M. Bui (2001). “Environmental Regulation and Labor Demand: Evidence from the South 

Coast Air Basin.” Journal of Public Economics 79(2): 265-295 (Docket EPA-HQ-OAR-2011-0135). 

180 http://www.bls.gov/emp/ep_data_emp_requirements.htm.

181 http://www.census.gov/manufacturing/asm/index.html.

182 http://www.bls.gov/emp/ep_data_emp_requirements.htm; this analysis used data for sectors 81 (Motor Vehicle 

Manufacturing), 82 (Motor Vehicle Body and Trailer Manufacturing), and 83 (Motor Vehicle Parts Manufacturing) 

from “Chain-weighted (2005 dollars) real domestic employment requirements tables.”

183 American Transportation Research Institute, “An Analysis of the Operational Costs of Trucking: 2011 Update.” 

See http://www.atri-online.org/research/results/Op_Costs_2011_Update_one_page_summary.pdf. 

184 Association of American Railroads, “All Inclusive Index and Rail Adjustment Factor.” June 3, 2011. See

http://www.aar.org/~/media/aar/RailCostIndexes/AAR-RCAF-2011-Q3.ashx