MEMORANDUM

TO: SHARYN LIE, ENVIRONM ENTAL PROTECTION AGE NCY

FROM: ENERGY AND ENVIRONME NTAL RESEARCH ASSOCI ATES, LLC
SUBJECT: HDV REBOUND EFFECT M ODEL

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 attent ion to a number of uncertainties regarding estimates of the re bound effect in HDVs , and the resulting discrepancie s in rebound effect estimates. This memo also highlights two potential issue s with the EPA’s current methodology to calculate the rebound effect

— the incorpo ration of vehicle capital costs and incorporation of the value of time

. I ssues described in th is memo were also mentioned in conference call s on April 7 th and April 21st between EPA and Energy and Environmenta l 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 f or 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 cons umption, which have been measured and reported in the EPA/NHTSA Draft Regulatory Impact Analysis (RIA) Proposed Rulemaking

. A s noted in the RIA, these policies may also reduce transportation costs for HDVs, thereby stimulating increased demand for HDV ser vices

— 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 reb ound 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 difficult y in

c omparing studies

, as they often examine very differ ent things. Different studies have examined the2 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 o r 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 vehic le fuel economy standards, freight demand elasticities were used to estimate the HDV rebound effect [1]

. In the following section s , we discuss the differences between estimates of the HDV rebound effect and freight elastici ty 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 A ND THE REBOUND EFFECT

T he 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 her e: 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

- tru ck 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 r ebound effect would seem to be ideal , as the EP A 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 uncer tain, for the reasons discussed below.

HIGHLY SPECIFIC

— HIGHLY VARIED Freight elasticity studies tend to be highly specific, a nd 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 substant ially depending upon commodity, timeframe, distan ce, 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, l inear, and logit) has important impacts on elasticity estimates, with estimates for all commodities reported to range from

- 0.05 for linear function a l 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, e ven when variables are controlled, there still exists a great deal of variation in estimates. A 2011 study explained 54% of the variati on in freight elasticity estimate s with the following variables: d emand 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

D istance traveled has also be en identified as resulting in significant difference s in freight elasticity estimates, with aggregate elasticities for short distances reported as being l ess elastic than long distance elasticities. For instance Christidis and Leduc report elasticities of

- 1.15 for distances over 1500km (932 miles)

, - 0.83 f or 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] . This3 distinction is of importance as the bulk of freight carried by truck in the U.S. tends to be shipped

o ver 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 ; t his is important because elasticity estimates will be high er where intermodal competition exists [11]

. Certain own

- price elastic ity estimates may incorpo rate impacts of intermodal competition and thus be overestimated if not handled properly [3]

. Further, Rich (2011) notes that i f distances between O

- D pairs are short, the likelihood of only one transport option (i.e. HDV) is mu ch greater, whereas for l onger 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 lar ge 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 shipme nts.

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 in stance, 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]

. T hese 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 LIMI TATIONS 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 stimu lated 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 ef fect for several reasons. First, the demand for freight is partially derived from the demand for the products that are shipped. Historically w hen fuel prices for HDVs have increased, they have also increased for all other forms of transport. F uel price changes have output impacts economy

wide [13, 14] : a s 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 truck ing . For instance, operational costs of trucking declined by ~11% per mile between 1990 and 2000 [15] . Real fuel costs declin ed by 25% between 1990 and 1999 [16] . As suming that fuel costs compris e 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

- wid e may be at least partially responsible for the4 changes in dema nd 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

. H owever the method of analysis in freight elasticity studies may not capture these interactions properly with respect to HDV fuel economy regulations. Moreover

, some studies m ay capture indirect impacts of fuel

- price changes economy

- wide that would not be expect ed 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 CALCU LATIONS

INCORPORATION OF CAP ITAL COS TS 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 t he 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 a ccording to statements by a truck ing 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 pr imary 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 tot al 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 undere stimated, and incremental costs for lat er owners will be overestimated

. In regards to the rebound effect, t he 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 literatur e review and research process.

INCORPORATION OF VAL UE 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 d elay 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 speed5 impacts fuel consumption more than any other operati onal 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 th e 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 speed ing , thereby i ncreasing fuel consumption

. Travel s peed is another aspect of the rebound effect that could be anticipated in addi tion to increased VMT. Indeed, a review of fuel and transportation elasticities confirmed that the price of fuel has a stronger impact on fu el consumption than on miles driven, and that the value of time is an important factor impacting travel speeds [4]

. Thoug h 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 considerati on. 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, a s higher s peeds increase fuel costs and maintenance costs ; further more, 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

T his memo highlight ed several uncertainties regarding estimates of freight elasticities and the rebound effect in HDVs. This memo also highlights two potential i ssues 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 an d 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 f reight elasticities is ongoing.6 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 e stimates. 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 reveal ed 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 Dut y 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 consumpt ion 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.go v/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, A n 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 Mountain

Pacific - 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 Natural8 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