Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

Interagency Working Comments on U.S. Environmental Protection Agency (EPA) and 

National Highway Traffic Safety Administration (NHTSA) draft proposed rule titled, 

“Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles - Phase 2.” RIN 2060-AS16; RIN 2127-AL52

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.1,2,3

 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.4 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 heavy-duty vehicle (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)5

, 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 

1 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. 2 Greene, D.L., Kahn, J.R., Gibson, R.C., 1999, “Fuel economy rebound effect for U.S. household vehicles”, The 

Energy Journal, 20.

3 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”, Draft 

Final Report of Phase I 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. 4 We discuss other potential rebound effects in section IX.D.3, such as the indirect and economy-wide rebound 

effects.

5 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).

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

effect in our analysis of this proposed rule, 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 emissions 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 

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. We request comment on how the agencies should consider the 

relationship between changes in vehicle miles traveled, changes in vehicle ton-miles achieved, 

and overall fuel consumption when considering how best to measure the rebound effect.

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 would likely view 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 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

HDVs’ fuel efficiency. To the extent the response differs, such differences may be smaller for 

The potential exception is HD pick-ups and vans, which share some similarities with LDV. 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).6,7

 Equipment 

depreciation costs associated with the purchase or lease of an HDV are another significant 

component of total operating costs. Even when HDV purchases involve upfront, one-time 

payments, HDV operators must recover the depreciation in the value 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. 

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. We seek comment and data on how our proposed standards could impact these and 

other types of HDV operating costs, as well as on our procedure for adapting the VMT rebound 

effect to estimate the response of HDV use to changes in total operating costs. 

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. 

6 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking, September 2013 

(Docket ID: EPA-HQ-OAR-2014-0827). 7 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report-acgoperatingcost2005-2005-e-2-1727.htm, accessed on July 16, 2010 (Docket ID: EPA-HQ-OAR-2010-0162). 

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

Factors Affecting the Magnitude of HDV VMT Rebound 

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

different factors.8

 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. Alternatively, HDV operators could simply use fuel savings to spend 

more on other costs such as driver labor or vehicle capital (e.g., holding freight output constant), 

which may not 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 more than offsets the fuel saved due to efficiency improvements (i.e., no rebound 

effect greater than 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 demand moreship more frequently shipments, 

ship products or materials over longer distances, or shift traffic to HDVs 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. 

Also, as discussed in Chapter 8.3.3 of the 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. 

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 

8 These factors are discussed more fully in a report to EPA from Energy and Environmental Research Associates, 

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”, Draft Final Report of Phase I under EPA contract 

EP-C-13-025. (Docket ID: EPA-HQ-OAR-2014-0827).

Commented [A1]: Commenters understand that this behavior, as 

described here, would not be consistent with profit maximization in 

a competitive market. No profit maximizer takes savings from one 

input and gives it to another input for no apparent reason. 

Commenter requests that you either please either delete or provide a 

testable explanation of how it is in fact consistent with profit 

maximization. 

Commented [A2]: Commenters note that for this to be the case 

wouldn’t technology costs have to be greater than after-tax fuel 

process? Is that consistent with the analysis supporting the rule? 

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

directly estimate this elasticity9 for 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. We seek comment on the applicability of the findings from these analyses, as well as 

additional data and research on the topic 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 

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.10 

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). 

9 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. 10 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”, 

Draft Final Report of Phase I under EPA contract EP-C-13-025. (Docket ID: EPA-HQ-OAR-2014-0827).

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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 

1979.11 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.12

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

economic activity (GDP) and the energy cost of HDV use (measured by 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 

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 

11 Gately, D., The U.S. Demand for Highway Travel and Motor Fuels, The Energy Journal, Volume 11, No. 3, July 

1990, pp.59-73. 12 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.

13 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.

Commented [A3]: Commenters note that all of the points made 

in this paragraph taken together appear to indicate UNCERTAINTY, 

but not necessarily unequivocal BIAS in a particular direction as it 

might be applied to the U.S. Commenters note that it is not 

sufficient to establish a degree of uncertainty with regard to their 

application to the U.S. the agencies must establish unambiguous and 

substantial bias in these estimates for purposes of applying to this 

rule. Commenters do not see sufficient justification for giving it 

zero weight in the analysis of this rule. 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

characteristics, and HDV fuel consumption.14 As De Borger and Mulalic state, “we precisely 

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

behavioural 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).15 Work conducted 

by the Volpe Center during 2009-11 to develop the original version of FHWA’s forecasting 

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

9-2 in that document, which is reproduced below as Table XX).16 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 cost per mile as an explanatory variable. This 

14 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. 15 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. 

16 EPA/NHTSA, August 2011. Chapter 9.3.3, Final Rulemaking to Establish Greenhouse gas Emissions 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)

Commented [A4]: Previous comment applies here as well.

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

analysis used data from 1970 – 2008 for its national aggregate model, and data for the 50 

individual states from 1994 – 2008 for its state-level model.17, 18

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 XX summarizes Volpe’s Phase 1

estimates of the elasticity of truck VMT with respect to fuel cost per mile.19 As it indicates, 

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

substantially. 

Table XX. 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 XX, 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 

17 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).

18 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. 19 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.

Commented [A5]: Commenters request clarity on relevance of 

this point. Once again this indicates uncertainty but there is no 

substantiated claim they are biased in one direction of another. 

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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.

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.

20 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, and we seek comment on this work.21 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 fuel is more responsive 

to fuel price increases than to decreases.22 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 

20 EERA (2014), “Research to Inform Analysis of the Heavy-Duty vehicle Rebound Effect”, Excerpts of Draft Final 

Report of Phase I under EPA contract EP-C-13-025. (Docket ID: EPA-HQ-OAR-[insert]). 21 EERA (2015), “Working Paper on Fuel Price Elasticities for Heavy Duty Vehicles”, Draft Final Report of Phase 

II under EPA contract EP-C-11-046. (Docket ID: EPA-HQ-OAR- 2014-0827) 22 Gately, D. 1993. The Imperfect Price-Reversibility of World Oil Demand. The Energy Journal, International

Association for Energy Economics, vol. 14 (4), pp. 163-182; Dargay, J.M., Gately, D. 1997. The demand for 

transportation fuels: imperfect price-reversibility? Transportation Research Part B 31(1); and Sentenac-Chemin, E., 

2012. Is the price effect on fuel consumption symmetric? Some evidence from an empirical study. Energy Policy, 

vol. 41, pp. 59-65.

Commented [A6]: Presumably it will be available for the final 

rule, no? Commenters request clarity onWhy not lay out here and 

seek comment?

Commented [A7]: Is this evidence on the responsiveness of 

truck use, vehicle use, or aggregate energy use? Commenters

suggest clarifying whether this literature is directly applicable to 

heavy trucks.

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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. The agencies request 

comment on all of the studies presented in this section. 

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 

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.23 

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

trucks for a number of reasons.24 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.25 

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 

23 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).

24 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. 25 See, for example, Appendix E in EERA (2014), “Research to Inform Analysis of the Heavy-Duty Vehicle 

Rebound Effect”, Draft Final Report of Phase I under EPA contract EP-C-13-025. (Docket ID: EPA-HQ-OAR2014-0827).

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

Section IX.E.b.iii 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. 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.26 

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.27 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 widely28, 

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 

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

revealed preference studies of inter-city freight demand. Transport Policy, 2011. 27 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. 

28 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.

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

Freight Transportation (GIFT) model developed by Winebrake and Corbett, which includes 

information on infrastructure and other route characteristics in the U.S.29,30 

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 I 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.31,32 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.

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.33 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 

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

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

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

http://www.its.ucdavis.edu/events/2009book/Chapter13.pdf (Docket ID: EPA-HQ-2014-0827) 30 Winebrake, J. J.; Corbett, J. J.; Falzarano, A.; Hawker, J. S.; Korfmacher, K.; Ketha, S.; Zilora, S., Assessing 

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

Management Association, 58(8), 2008 (Docket ID: EPA-HQ-OAR-2010-0162-0008). 31 See GIFT Solutions, LLC, “Potential for Mode Shift due to Heavy Duty Vehicle Fuel Efficiency Improvements”. 

February, 2012.

32 Winebrake, James, J. Corbett, J. Silberman, E. Erin, & B. Comer, 2012. Potential for Mode Shift due to Heavy 

Duty Vehicle Fuel Efficiency Improvements: A Case Study Approach. GIFT Solutions, LLC. (Docket ID: EPAHQ-OAR-2014-0827) 33 Cambridge Systematics, Inc., Assessment of Fuel Economy Technologies for Medium and Heavy Duty Vehicles: 

Commissioned Paper on Indirect Costs and Alternative Approaches, 2009. 

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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

Council on Clean Transportation (ICCT).34

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

0.5 to -1.5)35 and cross-price freight elasticities (0.35 to 0.59) 36 obtained from the literature.37 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.38 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 

34 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and International 

Council on Clean Transportation, Reducing Heavy-Duty Long Haul Truck Fuel Consumption and CO2 Emissions, 

September 2009. See http://www.nescaum.org/documents/heavy-duty-truck-ghg_report_final-200910.pdf 35 Graham and Glaister, “Road Traffic Demand Elasticity Estimates: A Review,” Transport Reviews Volume 24, 3, 

pp. 261-274, 2004. 36 Based upon a study for the National Cooperative Highway Research Program by Cambridge Systematics, Inc., 

Characteristics and Changes in Freight Transportation Demand: A Guidebook for Planners and Policy Analysts 

Phase II Report, National Cooperative Highway Research Program Project 8-30, June 1995. 37 The own (i.e., self) price elasticity provides a measure for describing how the volume of truck shipping (demand) 

changes with its price while the cross-price elasticity provides a measure for describing how the volume of rail 

shipping changes with truck price. In general, an elasticity describes the percent change in one variable (e.g. demand 

for trucking) in response to a percent-change in another (e.g. price of truck operations). 38 American Transportation Research Institute, “An Analysis of the Operational Costs of Trucking”, October 2008.

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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. 

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.39

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.

How the Agencies Estimated the HDV Rebound Effect for this Proposal

(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 by CSI and the Volpe Center.40 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% rebound effect we had 

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

vehicles. 

39 Guerrero, Sebastian. Modeling fuel saving investments and fleet management in the trucking industry: The impact 

of shipment performance on GHG emissions. Transportation Research Part E, May 2014.

40 The Gately study was also available, however, the agencies were not aware of the work at the time.

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

(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 discern 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. 

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 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 the 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 

Commented [A8]: Commenters want to know if you mean 

“differentiate” here? 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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.

Section 9.3.3 in the draft RIA provides more details on our assessment of HDV VMT 

rebound. We invite comment on our approach, the rebound estimates, and the related 

assumptions we made. In particular, we invite comment on the most appropriate methodology 

for factoring new vehicle purchase or leasing costs into the per-mile operating costs. Also, for 

the purposes of this proposal, we have not taken into account any potential fuel savings or GHG 

emission reductions from the rail sector due to mode shift because estimates of this effect seem 

too speculative at this time. We invite comment on this assumption, as well as suggestions on 

alternative modeling frameworks that could be used to assess mode shifting implications of these 

proposed regulations.

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.41 

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.42 Therefore, 

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 welcome comments and 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 

41 Note that there is more than one way to measure HDV energy services. The agencies’ analyses use VMT as a 

measure of HDV energy services; other potential measures include ton-miles, cube-miles, and fuel consumption. 42 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).

 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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. 

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. 

Table Error! No text of specified style in document.-1 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 XX summarizes the impact of these alternative assumptions on fuel and GHG 

emissions savings, total costs, total benefits, and net benefits. As it indicates, a 5 percent value 

for the rebound effect reduces benefits and costs of the proposed standards by identical amounts, 

leaving net benefits unaffected. Higher values of the rebound effect increase costs and reduce 

benefits from their values in the main analysis, thus reducing net benefits of the proposed 
Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft 

NPRM. Subject to further policy review.

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 

to address energy security and the environment.