MEMORANDUM

TO: SHARYN LIE, ENVIRONMENTAL PROTECTION AGENCY

FROM: ENERGY AND ENVIRONMENTAL RESEARCH ASSOCIATES, LLC

SUBJECT: HDV REBOUND EFFECT MODEL

DATE: 6/8/2011

PURPOSE

The purpose of this memo is to help the Environmental Protection Agency (EPA) in their 

task of calculating the expected rebound effect due to EPA’s Greenhouse Gas (GHG) Emissions 

Standards for HDVs. This memo calls attention to a number of uncertainties regarding estimates of 

the rebound effect in HDVs, and the resulting discrepancies in rebound effect estimates. This memo 

also highlights two potential issues with the EPA’s current methodology to calculate the rebound 

effect—the incorporation of vehicle capital costs and incorporation of the value of time. Issues 

described in this memo were also mentioned in conference calls on April 7th and April 21st between

EPA and Energy and Environmental Research Associates (EERA). In addition, some of the content 

of this memo is expected to be delivered to EPA as part of a final report/paper currently being 

developed.

INTRODUCTION

The EPA/NHTSA rule, Proposed Greenhouse Gas (GHG) Emissions Standards and Fuel Efficiency 

Standards for HDVs, is intended to decrease the emissions and energy intensity of HDVs on the basis 

of freight traffic (g/ton-mile—or ton-km). These policies are expected to have direct benefits of 

reduced GHG and criteria pollutant emissions and petroleum fuel consumption, which have been 

measured and reported in the EPA/NHTSA Draft Regulatory Impact Analysis (RIA) Proposed Rulemaking. 

As noted in the RIA, these policies may also reduce transportation costs for HDVs, thereby 

stimulating increased demand for HDV services—leading to the “rebound effect”, where reductions 

in emissions and fuel consumption are offset to an extent by increased use of HDVs. 

EPA has attempted to quantify the rebound effect in the RIA, using estimates of the HDV 

vehicle miles traveled (VMT) rebound effect (i.e., increase in VMT expected due to a decrease in 

vehicle operational costs per mile) from a 2008 NHTSA study. EPA has requested input regarding

this methodology to identify whether there is the potential to improve estimates of the rebound 

effect for the proposed rule.

A definitive estimate of the rebound effect is challenging for a number of reasons. In the 

HDV sector in particular, there exist misunderstanding and disagreement regarding the rebound 

effect’s influence and magnitude. In this memo, we present to EPA a discussion of uncertainties 

involved in determining the rebound effect in the HDV sector, specifically in the context of the 

proposed rule. We call out discrepancies between estimates of the rebound effect, and discuss how 

different estimates may or may not apply to the rebound effect expected under this regulation. 

There is a great deal of literature related to the rebound effect in general, but the application 

and study of the rebound effect with respect to HDVs is a bit limited. Strictly speaking, the 

“rebound effect” refers to an increase in fuel demand in response to fuel economy improvements. 

Part of the abovementioned misunderstanding in the literature emerges from the difficulty in 

comparing studies, as they often examine very different things. Different studies have examined the 
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changes in demand for fuel or for VMT (measures which are more closely linked to the rebound 

effect), or have examined changes in demand for HDV ton-miles or even tons (measures which are 

more accurately described as freight demand elasticities). Though they measure different things, the 

rebound effect and demand elasticities have been used interchangeably in some contexts. For 

example, in a Cambridge Systematics report commissioned by the National Academies of Sciences in 

support of its committee on medium-duty and heavy-duty vehicle fuel economy standards, freight 

demand elasticities were used to estimate the HDV rebound effect [1].

In the following sections, we discuss the differences between estimates of the HDV rebound 

effect and freight elasticity estimates, and explain where these estimates may or may not be applicable 

to the HDV rebound effect in the United States. An example set of freight elasticity results from the 

literature is included in Appendix A of this memo.

HDV FREIGHT ELASTICITY AND THE REBOUND EFFECT

The literature on freight elasticities tends to measure a change in volume demanded (tons or 

ton-miles) for HDV services, based on a change in freight rates. There are two main freight 

elasticities of interest here: truck own-price elasticity (which measures an increase in demand for 

trucking services in response to a decrease in price for trucking services), and modal cross-price 

elasticity (which measures a decrease in demand for transport services from a non-truck mode (e.g., 

rail, ship, air) in response to a decrease in price for trucking services). The change in freight volume 

for each of these elasticities is typically measured in tons or ton-miles. The use of ton-mile elasticities 

as a measure for the HDV rebound effect would seem to be ideal, as the EPA standards will regulate 

GHG emissions and fuel consumption on a per ton-mile basis. However, in many ways using freight 

elasticities to estimate the HDV rebound effect may be inappropriate, or may be highly uncertain, for 

the reasons discussed below.

HIGHLY SPECIFIC—HIGHLY VARIED

Freight elasticity studies tend to be highly specific, and are focused on a specific 

commodity(ies) in a certain region being shipped to an industry during a fixed period of time (often 

short-run). The highly specific nature of these studies is important, as elasticity measures have been 

shown to vary substantially depending upon commodity, timeframe, distance, region, and the method 

of analysis used etc. [2-4]. Reported elasticity estimates range from a positive 1.72 to negative 7.92, 

with typical reported ranges of -0.5 to -1.5 [2, 3, 5]. The functional form of the model (i.e. translog, 

log-linear, linear, and logit) has important impacts on elasticity estimates, with estimates for all 

commodities reported to range from -0.05 for linear functional form to -1.34 for log-linear; for a 

single commodity estimates range from -0.38 (linear) to -1.54 (log-linear) [2-4]. Because of the small 

number of studies, it is difficult to separate the effects of specific variables and methods on the 

elasticity estimate[4]. However, even when variables are controlled, there still exists a great deal of 

variation in estimates. A 2011 study explained 54% of the variation in freight elasticity estimates with 

the following variables: demand elasticity measure, mode, commodity class, model estimation form, 

country, and temporal nature of data; however, nearly half of the variation remained unexplained 

quantitatively [6].

DISTANCE TRAVELED

Distance traveled has also been identified as resulting in significant differences in freight 

elasticity estimates, with aggregate elasticities for short distances reported as being less elastic than 

long distance elasticities. For instance Christidis and Leduc report elasticities of -1.15 for distances 

over 1500km (932 miles), -0.83 for 800-1500km (497 to 932 miles), and -0.21 for distances less than 

800km (demand measured in tons); Beuthe (2001) reports elasticities of -1.06 at distances less than 

300 km (186 miles) vs. -1.31 for longer distances (demand measured in ton-km) [4, 5, 7, 8]. This 
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distinction is of importance as the bulk of freight carried by truck in the U.S. tends to be shipped 

over relatively short distances, approximately 200 miles on average [9, 10]. 

POTENTIAL FOR MODAL SHIFTS

Another variable of importance in freight elasticity estimates is the extent to which the 

potential for modal shifts (or lack thereof) is taken into account [3]. Many freight elasticity studies 

examine intercity freight transport between relatively distant origin-destination (O-D) pairs where 

other modal alternatives (i.e. rail and barge) are available; this is important because elasticity estimates

will be higher where intermodal competition exists[11]. Certain own-price elasticity estimates may 

incorporate impacts of intermodal competition and thus be overestimated if not handled properly [3].

Further, Rich (2011) notes that if distances between O-D pairs are short, the likelihood of 

only one transport option (i.e. HDV) is much greater, whereas for longer distance O-D pairs, there is 

a greater likelihood of multiple modal options [11]. HDV freight shipments are carried only ~200 

miles on average, where rail freight shipments are typically carried over 700 miles—and rail is not 

usually seen as being cost-effective for distances less than 500-800 miles. Therefore, a large portion 

of U.S. HDV freight shipments are unlikely to have modal substitutes in rail, so elasticity estimates 

from studies that examined longer-distance O-D pairs with modal alternatives would likely

overestimate elasticity in the majority of HDV shipments. 

COMMODITY TYPES

Though the HDV share of freight shipments tends to decline as distance increases, this 

trend has been shown to vary by commodity—being moderate for higher-value commodities, and 

stronger for lower-value commodities (e.g. bulk) [11]. As noted above, elasticity estimates also vary 

by commodity and commodity class. For instance, bulk commodities have been shown to be less 

elastic than non-bulk commodities, and elasticities for lumber, wood, textiles and refined petroleum 

products have been shown to be substantially lower than elasticities for chemicals, paper, plastic and

rubber products [2, 6]. These findings indicate that the application of freight elasticity estimates from 

highly specific studies may be inappropriate or may under- or overestimate the HDV rebound effect, 

if applied to all HDV freight movements in the U.S. 

ADDITIONAL LIMITATIONS IN APPLYING FREIGHT 

ELASTICITES TO FUEL REBOUND EFFECT

There are additional limitations in applying aggregate freight elasticities of demand to the 

HDV rebound effect. These limitations are due to inadequate accounting of output that may be 

stimulated by changes in freight costs, or in accounting for variables that are causing changes in the 

freight system (e.g., fuel prices). This has been noted in Bonilla and Salling [12] and Oum [2, 3], 

among others.

The consideration of output decisions is of importance in the context of the HDV fuel 

rebound effect for several reasons. First, the demand for freight is partially derived from the demand 

for the products that are shipped. Historically when fuel prices for HDVs have increased, they have 

also increased for all other forms of transport. Fuel price changes have output impacts economywide [13, 14]: as fuel prices increase, consumers are left with less disposable income, and less buying 

power—and therefore demand fewer products.

Further, the price of fuel is an important component in the total operational costs of 

trucking. For instance, operational costs of trucking declined by ~11% per mile between 1990 and 

2000 [15] . Real fuel costs declined by 25% between 1990 and 1999[16]. Assuming that fuel costs 

comprise roughly 40% of total operational costs, nearly all reductions in operational costs could be 

attributed to fuel savings. Therefore, historical changes in freight rates may be largely attributable to 

changes in fuel prices. Fuel price changes economy-wide may be at least partially responsible for the 
4

changes in demand for products, which in turn affects output decisions by shippers. All of these 

interactions are important in the context of the potential rebound effect due to HDV fuel economy 

regulations. However the method of analysis in freight elasticity studies may not capture these 

interactions properly with respect to HDV fuel economy regulations. Moreover, some studies may 

capture indirect impacts of fuel-price changes economy-wide that would not be expected in the case 

of HDV fuel economy standard implementation. Further research is required to determine the 

applicability of specific elasticity estimates to the HDV rebound effect. 

EPA HDV REBOUND EFFECT CALCULATIONS

INCORPORATION OF CAPITAL COSTS

Resale Value of Fuel Efficiency in HDVs

Although issues of properly incorporating capital costs in rebound effect calculations were 

presented in a previous memo to EPA, we have subsequently identified additional information 

regarding the resale value of fuel efficiency in HDVs. The current approach used by the EPA to 

calculate the rebound effect assumes a constant incremental vehicle cost per mile over time, which is 

calculated by dividing the total incremental vehicle cost by the HDV’s lifetime VMT. This calculation 

levelizes the HDV capital cost over an entire lifetime of 30 years. However, in reality many HDVs 

are retained by the original owner for a short time (~five years) before being sold to the next owner. 

The incremental vehicle costs and behavior of initial owners and later owners may differ significantly, 

depending on the incremental resale value of HDVs with increased fuel economy (here, MPG). 

Quantitatively, the extent to which improved MPG influences resale value of HDVs is 

unknown; however, we would assume some positive value is derived by consumers, ceteris paribus, 

even in the used vehicle market. When fuel costs are high (as they are currently -- comprising

roughly 40% of total per-mile costs for HDVs), we would assume increased fuel economy is an even 

more desirable vehicle attribute [15]. Yet according to statements by a trucking industry expert, 

improved MPG has zero impact on resale value—that is, MPG is not taken into consideration in a 

consumer’s willingness-to-pay in the HDV resale market. Rather, factors such as quality of the 

exterior and functional operation are the primary decision making attributes [17]. If this is true, the 

exclusion of MPG into consideration in the HDV secondary market implies that the original owner 

must bear the total incremental costs for the vehicle (and achieve a payback with the original owner’s 

use of the vehicle). Therefore the true incremental vehicle costs to the initial owner would be 

underestimated, and incremental costs for later owners will be overestimated. In regards to the 

rebound effect, the expected impacts of the disparity in incremental costs to the original and 

subsequent owners are uncertain, and more research is needed to better understand these impacts. 

Consideration of Average vs. Marginal Costs 

The EPA has opted to use the average cost per mile as a metric in estimating the HDV 

rebound effect. However, depending on how decisions are made by trucking companies, marginal 

costs may more accurately reflect the costs that are taken into consideration when making 

operational and other decisions related to fuel consumption (and therefore the rebound effect). The 

role of marginal costs v. average costs in the decision making process will be examined further in the 

literature review and research process.

INCORPORATION OF VALUE OF TIME AND SPEED

Finally, the EPA does not consider the value of time (and thus speed) for HDVs in their 

rebound effect calculations. Time is an important consideration in the HDV freight market, where 

an hour of travel time (or delay avoided) is valued anywhere between $32 to over $370 per hour [15, 

17]. Time is important in relationship to HDV fuel consumption because increased speed equates to 

decreased travel time—and higher speeds increase fuel consumption. In fact, as noted by EPA speed
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impacts fuel consumption more than any other operational factor, with a 1% increase in fuel 

consumption associated with every mph increase above 55 mph [18]. 

Speed is a relevant factor to consider related to HDV fuel economy regulations in that 

improved fuel economy reduces the fuel costs per mile, and effectively reduces the costs of 

increasing speed. As economic rationale to increase speed (and thus arrive at a destination earlier) 

already exists due to the high value of time in the HDV market (see above), improved MPG could 

actually further incentivize speeding, thereby increasing fuel consumption. Travel speed is another 

aspect of the rebound effect that could be anticipated in addition to increased VMT. Indeed, a 

review of fuel and transportation elasticities confirmed that the price of fuel has a stronger impact on 

fuel consumption than on miles driven, and that the value of time is an important factor impacting

travel speeds [4].

Though improved MPG will create an incentive for increased speeds, we are unaware of any 

quantifications of the extent to which increased speeds would be expected. In fact, according to a 

trucking industry expert, many trucking companies already make use of electronic speed limiter 

technology on HDVs, so that drivers can go no faster than 65 mph [17]; these would technically 

prevent increased speeds (unless speed limiters were removed or disabled). Further, the time saved 

by speeding is actually significantly lower than would be expected, once stops and starts, slowing and 

speed up time are taken into consideration. For instance in an SAE test of two HDV trucks traveling 

204 miles at different speeds (60 mph vs. 65 mph), the trucks arrived at the destination only 2 

minutes apart—when one would expect a difference of almost 16 minutes [17]. Finally, according to 

the same trucking industry expert, the economic incentive to speed is not as substantial as it would 

seem, as higher speeds increase fuel costs and maintenance costs; furthermore, once speeds reach 75 

mph, higher horsepower engines may be required [17]. 

Nevertheless, improved fuel economy may indirectly incentivize increased speeds by HDVs, 

which could increase fuel consumption and contribute to the rebound effect. If these increased 

speeds are not accounted for in rebound calculations, the fuel consumption rebound effect could be 

underestimated.

CONCLUSION

This memo highlighted several uncertainties regarding estimates of freight elasticities and the 

rebound effect in HDVs. This memo also highlights two potential issues with the EPA’s current 

methodology to calculate the rebound effect, including incorporation of vehicle incremental capital 

costs over time, and the value of time and speed in the HDV market. Future work may delve 

quantitatively into the extent of and importance of these factors in estimating the rebound effect. 

We note that this memo does not present an exhaustive review of the literature, and that research 

related to the rebound effect and freight elasticities is ongoing.
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REFERENCES

1. Cambridge Systematics, Assessment of Fuel Economy Technologies for Medium and Heavy Duty 

Vehicles: Commissioned Paper on Indirect Costs and Alternative Approaches (Draft Final Paper). 2009, 

Cambridge Systematics.

2. Oum, T.H., W.G. Waters II, and J.S. Yong, A survey of recent estimates of price elasticities of demand 

for transport. 1990, The World Bank.

3. Oum, T.H., W.G. Waters, and J.-S. Yong, Concepts of price elasticities of transport demand and recent 

empirical estimates. Journal of Transport Economics and Policy, 1992. May 1992: p. 139-154.

4. Graham, D.J. and S. Glaister, Road traffic demand elasticity estimates: A review. Transport Reviews, 

2004. 24(3): p. 261-274.

5. Beuthe, M., et al., Freight transportation demand elasticities: A geographic multimodal transportation 

network analysis. Transportation Research Part E, 2001. 37: p. 253-266.

6. 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.

7. Rich, J., P.M. Holmblad, and C.O. Hansen, A weighted logit freight mode-choice model.

Transportation Research Part E, 2009. 45: p. 1006-1019.

8. Christdis, P. and G. Leduc, Longer and heavier vehicles for freight transport. 2009.

9. Winebrake, J.J. The potential of intermodalism for meeting energy and environmental goals. in 4th Meeting 

of the National Academies Assessment of Fuel Economy Technologies for Medium and Heavy Duty 

Vehicles. 2009. Washington, DC.

10. U.S. DOT, United States 2007: 2007 Economic Census Transportation 2007 Commodity Flow Survey, 

Research and Innovative Technology Administration and Bureau of Transportation 

Statistics, Editor. 2010, U.S. Department of Transportation and U.S. Census Bureau.

11. Rich, J., O. Kveiborg, and C.O. Hansen, On structural inelasticity of modal substitution in freight 

transport. Journal of Transport Geography, 2011. 19: p. 134-146.

12. Bonilla, D. and K. Salling, Energy consumption and truck freight transport demand: Is Denmark a 

special case? 2008.

13. U.S. DOE. Energy Price Impacts on the U.S. Economy. 2001 [cited 2011; Available from: 

http://www.eia.doe.gov/oiaf/economy/energy_price.html

14. Jiménez-Rodríguez, R. and M. Sánchez, Oil Price Shocks and Real GDP Growth: Empirical 

Evidence for Some OECD Countries. 2004, Eurpoean Central Bank: Frankfurt, Germany.

15. American Transportation Research Institute, An analysis of the operational costs of trucking. 2008: 

Atlanta, GA.

16. U.S. DOE, EIA Short-Term Energy Outlook, February 2011: Annual Average Diesel Price. 2011, 

United States Department of Energy Energy Information Administration.

17. Drinkard, D., Phone call on truck operations and the rebound effect, E. Green and J.J. Winebrake, 

Editors. 2011.

18. U.S. Environmental Protection Agency, Regulating Greenhouse Gas Emissions Under the Clean Air 

Act; Proposed Rule. 2008, Federal Register. p. 44456.
APPENDIX A. Table of elasticity of demand estimates for truck freight showing low/high elasticity values based on various study elements. Compiled by E. Green, B. Comer, 

and J. Winebrake (2011).

Author/Date Evaluation based 

on ...

Least 

Elastic

Description of Least 

Elastic Value

Most 

Elastic

Description of Most Elastic 

Value Region Demand Units of Measurement Commodity

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

Official

Probability of 

mode choice Varies

Spady & Friedlaender 

(1980) Commodity -0.15 Wood and wood 

products -5.06 Electrical Machinery U.S. ICC 

Southern ton-miles Varies

Spady & Friedlaender 

(1980) Commodity -1.00 Food Products -3.55 Electrical Machinery U.S. ICC Average ton-miles Varies

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

Winston (1981) Commodity -0.14 Lumber, wood and 

Furniture -2.96 Transport Equipment USA tons Varies

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

Category) -1.30 Natural Resources USA, Italy & 

India tonne-km Varies

Campisi and Gastaldi 

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

Friedlaender and Spady 

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

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

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

modes available Scandinavia tonne-km Agricultural products

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

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

India tonne, tonne-km Natural Resources

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

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

Christidis and Leduc 

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

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

Graham and Glaister 

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

Li et al. (2011) Panel vs. CS -0.93 Panel Translog -1.30 CS Translog USA, Italy & 

India tonne-km Natural Resources

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

mode choice Metal products

Spady & Friedlaender 

(1980) Region -1.66 US ICC MountainPacific -5.06 U.S. ICC Southern Various U.S. ton-miles Electrical Machinery

Li et al. (2011) Region -0.86 Canada -1.96 Australia Various tonne-km Natural 
8

Author/Date Evaluation based 

on ...

Least 

Elastic

Description of Least 

Elastic Value

Most 

Elastic

Description of Most Elastic 

Value Region Demand Units of 

Measurement Commodity

Resources

Graham and Glaister 

(2004) All elasticity 1.72 Least elastic end of range -7.92 Most elastic end of range Various Various Various