The Rebound Effect from Heavy Duty Vehicle

Fuel Efficiency Improvements:

A Review of the Literature and a Case Study Analysis 

of Rail-to-Truck Mode Shift Potential

Work completed for the U.S. EPA under P.O. EP-11-C-000065

Project Lead: James J. Winebrake

James J. Corbett

Bryan Comer

Erin H. Green

Jordan Silberman

1
Overview

• Introduction

• Part I: Literature review

• Part II: Case study analysis exploring the 

potential for modal shift

• Conclusions

2
INTRODUCTION

3
Policy Drivers

Energy 

Security 

Concerns

Environmental 

Concerns

Freight 

Transport 

Efficiency 

Policies

4
One Policy: Improve HDV Fuel Efficiency

Improve HDV 

Fuel Efficiency

Reduce Fuel 

Consumption

Reduce Fuel Cost 

and Emissions

5
Why focus on Improving HDV Fuel 

Efficiency?

• Trucking sector GHG emissions have risen over 

70% between 1990-2008 (USEPA, 2010)

• Medium and HDVs make up ~20% of US 

transportation GHG emissions (USEPA, 2010)

• HDV fuel efficiency has remained flat for 

decades (Sivak and Tsimhoni, 2009)

6
Direct Rebound Effect Manifestations

7

Decrease in 

costs of HDV 

services

Demand Effects

Operational 

Effects

Mode Choice 

Effects

Lower costs of 

HDV services 

create greater 

demand

Lower costs of HDV 

operations increase 

less-efficient 

operations (e.g., 

routing, speed, load)

Lower costs of HDV 

services create 

modal shifts from 

non-HDV modes to 

HDVs

Stimulus: HDV 

Fuel Efficiency 

Improvements
PART I: LITERATURE REVIEW

8
Three Components of the Rebound 

Effect

Direct

• Cost of energy 

services is reduced 

through efficiency 

improvements

• Demand for 

energy services 

increases

Indirect

• Cost of energy 

services is reduced 

through efficiency 

improvements

• Cost savings are 

used to purchase 

other goods or 

services, the 

manufacture and 

use of which also 

consume energy

Economy-Wide

• Reduced demand 

for energy reduces 

its market price

• Demand for 

energy in other 

applications 

increases

We focus on the direct rebound 

effect in the HDV sector

9
Understanding the Direct Rebound 

Effect and Making Policy Decisions

• Policies that reduce fuel consumption and emissions may 

also reduce HDV freight transport costs and may increase 

demand for HDV freight, thus tempering the benefits of the 

regulation.

The rebound effect limits 

energy and 

environmental benefits

• Few studies have been conducted exploring the rebound 

effect specific to the trucking sector.

We have an incomplete 

understanding of 

rebound effect impacts

• A framework has not been established that would allow 

policymakers to effectively sift through rebound effect 

studies and consider them in a HDV policymaking context.

Need a framework to 

help policymakers make 

decisions

10
Estimating the Direct Rebound Effect: 

Using Freight Elasticities

• Change in HDV energy consumption (and therefore emissions) when fuel 

efficiency improves.

• Change in services (ton-miles) from HDVs when the price 

of freight transport changes ($/ton-mile).

11
Freight Elasticities are an Imperfect 

Measure of the Rebound Effect

• Freight Elasticity Caveats:

– Assume that cost savings from greater fuel efficiency are passed on to 

consumers (shippers) via lower freight rates.

– Assume consumer response to a price increase is proportional to their 

response to a price decrease (whereas response may be asymmetric).

– Assume fuel efficiency is not affected by fuel prices (not always the case).

– May not properly account for interactions related to fuel prices and economic 

output.

– Requires analysts to make assumptions about the relationship between HDV 

fuel consumption, VMT, and payload to approximate fuel demand.

12
Freight Elasticity Estimates are Highly Specific and Varied, 

Making Them Difficult to Compare

13
Factors Affecting Variability of Freight Elasticity 

Estimates

• Distance shipped

– short distances less elastic than long distances

• Intermodal competition 

– higher elasticity estimates where modal competition exists

• Commodity type

– bulk commodities have been shown to be less elastic than non-bulk commodities

• Region

– The economies of different regions and countries will impact freight elasticities

• Timeframe

– Elasticities reflect a “snapshot” in time and can change seasonally or annually

• Model functional form

– Translog v. log-linear v. other model forms

• Measure of demand

– Tons vs. ton-miles

• Macroeconomic variables

– HDV freight transport is a function of GDP

– Fuel prices affect output decisions by shippers; when the price of fuel increases, the price of 

many other products also increases, leading to decreased economy-wide demand.

14
HDV Direct Rebound Effect Estimates

• Range from 4-24% (similar to the LDV rebound 

effect)

• However these results:

– Do not measure the rebound effect due to efficiency 

improvements (but rather changes in fuel costs), so 

have some of the same caveats as freight elasticities

– Do not use the same definition of “useful work” (e.g., 

some use VMT, some use ton-mi or tonne-km)

– Examine different regions in different periods of time

– Are not easily comparable

15
Part 1: Conclusions

1. Current understanding of the HDV rebound effect is limited due to the lack of 

focused studies and the heterogeneity of the trucking sector

2. Existing studies often create biased or erroneous rebound effect estimates by 

relying on elasticities and applying metrics that omit important factors that 

influence fuel consumption

3. Large range of estimates in literature, lack of comparability, and potentially 

biased/erroneous estimates makes it difficult to apply these estimates with 

confidence in a policymaking context

4. Further quantitative research is needed to better assess the scale of HDV 

rebound effects. Research should focus on obtaining a better understanding of 

how decisions are made by HDV owner-operators and freight customers, and 

applying those decision-making rubrics to simulate and validate macro-level 

effects.

16
PART 2: CASE STUDIES ON RAIL-TOTRUCK MODE SHIFT POTENTIAL

17
When Might Modal Shift Occur?

• If post-regulation truck freight rates become less 

than rail freight rates, then...

...modal shift from rail to truck may occur.

Mode 

Shift Regulation Truck < Rail

18
Four Case Studies to Examine the 

Potential for Modal Shift

1. Rubber Tires from LA to Fort Worth

2. Plastic Pellets from LA to Fort Worth

3. Newsprint from New Orleans to Chicago

4. Gravel from Chattanooga to Indianapolis

http://myallterraintires.com/wp-content/uploads/2010/02/Tires-3.jpg

http://www.9to5seating.com/images/9to5_Opedia_Images/Virgin_Plastic_Pellets_2.jpg

http://dynamic.exportersindia.com/product_images/bc-small/239000/kraft-paper-765573.jpg

http://photo-dict.faqs.org/photofiles/list/3486/14192gravel.jpg

19
Choosing the Commodities to Model

• Goods transported >500 miles are more likely to be sensitive to modal shift.

– Commodity groups in Cases 1-3 have at least 1/3 of their tons transported >500 mi

• Commodity must be transported by rail in order for mode shift to occur. 

– All cases have some tons and ton-miles transported by rail .

– Cases 1-3 have significant proportion of tons and ton-miles transported by both rail and truck.

• One case should model the transport of a commodity that represents a significant 

portion of freight truck activity.

– Case 4 models the transport of gravel

– 89% of tons and 55% of ton-miles are transported by truck

– Gravel and crushed stone represent the largest portion of the freight transport sector by 

weight.

Case Commodity Group Tons 

transported 

>500 mi (%)

Tons 

Transported by 

Rail (R) and 

Truck (T) (%)

Ton-mi 

Transported by 

Rail (R) and 

Truck (T) (%)

Value 

Transported by 

Rail (R) and 

Truck (T) (%)

1 & 2 Plastics and rubber 33% 20%R

70%T

25%R

58%T

9%R

77%T

3

Pulp, newsprint, 

paper, and 

paperboard

34% 19%R

71%T

29%R

53%T

13%R

77%T

4

Gravel and Crushed 

Stone 1% 4%R89%T

16%R

55%T

4%R

90%T

20
Choosing the O/D Pairs to Model

• Chose origins that exported a high proportion of each commodity group.

• Chose destinations that imported a high proportion of each commodity 

group.

• Validated that O/D pairs reflected realistic freight transport activity.

• Verified that facilities at origins and destinations could receive both truck 

and rail using the GIFT model.

• Example (highlighted): 

– CA is the #2 exporter of plastics and rubber by weight (w) and value (v)

– TX is the #1 importer of plastics and rubber by weight (w) and value (v)

– TX is the #1 trading partner of CA

Case Commodity Group Origin

(state exporter rank)

Destination

(state importer rank)

Highway 

Distance (miles)

1 Plastics and rubber Los Angeles, CA (2w1, 2v2) Fort Worth, TX (1w, 1v) 1,409

2 Plastics and rubber3 Los Angeles, CA (2w, 2v) Fort Worth, TX (1w, 1v) 1,409

3 Pulp, newsprint, etc. New Orleans, LA (2w, 4v) Chicago, IL (2w, 1v) 926

4 Gravel and Crushed Stone Chattanooga, TN (1w, 2v) Indianapolis, IN (1w, 2v) 419

21
Calculating the Post-Regulation 

Truck Freight Rate

 =  ∗ 100% −





+  ∗





∗ (100% − %∆) + 

Non-fuel operating cost component Fuel operating cost component

Change in 

efficiency

New tech. costs

Assumptions

Input Value

Cfuel 66.4 cents/mi

Cop 158.2 cents/mi

Cfuel/Cop 42%

∆η 23%

Ctech 0.03 cents/ton-mi

22
Case 1:

Rubber Tires from LA to Fort Worth

Truck never becomes less expensive 

than rail post-regulation

Freight Rate

(cents/ton-mi) Mode

18.4 Rail B (RRO or SO)*

18.0 Rail A (RRO)

17.8 Truck A (pre-regulation)

16.8 Truck B (pre-regulation)

16.1 Truck A (post-regulation)

15.2 Truck B (post-regulation)

11.8 Rail A (SO)

Freight Rate to Ship 300 

Short Tons ($)

Mode

$92,250 Rail B (RRO or SO)

$89,960 Rail A (RRO)

$77,000 Truck A (pre-regulation)

$72,590 Truck B (pre-regulation)

$69,720 Truck A (post-regulation)

$65,740 Truck B (post-regulation)

$59,190 Rail A (SO)

23

*RRO = Railroad owned equipment; SO = shipper owned equipment.
Case 2:

Plastic Pellets from LA to Fort Worth

Truck never becomes less expensive 

than rail post-regulation

Freight Rate

(cents/ton-mi) Mode

10.8 Truck A (pre-regulation)

10.4 Truck B (pre-regulation)

9.8 Truck A (post-regulation)

9.4 Truck B (post-regulation)

8.3 Rail A (SO)

6.5 Rail B (SO)

Freight Rate to Ship 

300 Short Tons ($)

Mode

$47,960 Truck A (pre-regulation)

$46,190 Truck B (pre-regulation)

$43,490 Truck A (post-regulation)

$41,890 Truck B (post-regulation)

$41,790 Rail A (SO)

$32,850 Rail B (SO)

Truck companies do not appear to adjust truckload rates based on actual changes in fuel consumption.

Despite hauling 81⁄2 more tons of cargo per truckload, the price to ship 22.5 tons of plastic pellets from LA to Ft. 

Worth in Case 2 was essentially the same as the price to ship 14 tons of tires from LA to Ft. Worth in Case 1 

(~$3,400/truckload) for each truck company.

24
Case 3:

Newsprint from New Orleans to Chicago

Truck never becomes less expensive 

than rail post-regulation

Freight Rate

(cents/ton-mi) Mode

8.8 Rail A (RRO or SO)

8.5 Truck A (pre regulation)

8.5 Truck B (pre regulation)

7.8 Truck A (post-regulation)

7.7 Truck B (post-regulation)

6.8 Rail C (RRO or SO)

5.3 Rail B (RRO or SO)

Freight Rate to Ship 

300 Short Tons ($)

Mode

$24,770 Rail A (RRO or SO)

$24,500 Truck A (pre regulation)

$24,390 Truck B (pre regulation)

$22,240 Truck A (post-regulation)

$22,140 Truck B (post-regulation)

$19,040 Rail C (RRO or SO)

$14,900 Rail B (RRO or SO)

25
Case 4:

Gravel from Chattanooga to Indianapolis

Truck becomes less expensive than rail post 

regulation when considering total price to 

ship 300 short tons of gravel (highlighted)

Freight Rate

(cents/ton-mi) Mode

9.5 Truck A (pre regulation)

8.7 Truck A (post-regulation)

7.7 Rail A (RRO)

6.9 Rail A (SO)

6.0 Truck B (pre regulation)

5.4 Truck B (post-regulation)

5.4 Rail B (RRO)

4.6 Rail B (SO)

Freight Rate to Ship 300 

Short Tons ($)

Mode

$12,600 Truck A (pre-regulation)

$11,980 Rail A (RRO)

$11,430 Truck A (post-regulation

$10,780 Rail A (SO)

$8,420 Rail B (RRO)

$7,910 Truck B (pre-regulation)

$7,220 Rail B (SO)

$7,190 Truck B (post-regulation)

26
Part 2:Conclusions

1. New regulations do not seem to create obvious economic conditions that would lead to a modal shift from 

rail to truck except in one case (Case 4 evaluation of total shipment price).

2. Caveats:

a. Shipping price is only one factor that a shipper must consider when making freight transport 

decisions (other factors could include time-of-delivery, volume/frequency of shipments, 

infrastructure/capacity constraints)

b. We assume that full fuel cost savings as well as the increased vehicle costs for new HDVs will be 

passed on to shippers via changes in freight rates; however we have found evidence to the contrary 

(i.e., the Case 1 and Case 2 $/truckload prices were the same despite changes in payload)

c. We assume that truck and rail routes and payload will not change post-regulation 

d. Additional costs that a shipper would bear to prepare the commodity for shipment via another 

mode are not explicitly accounted for (e.g., packaging/palletizing, loading/unloading)

e. Drayage costs to or from an origin or destination are not factored into the analysis

3. More research should be conducted focusing on: 

a. Determining the generalizability of the results at the national level

b. Investigating how varying the price of diesel fuel might affect truck and rail freight rates differently

c. Exploring the role of other factors affecting logistics decision-making in a multi-modal context

d. Understanding how decisions are made by HDV owner-operators and freight customers

e. Estimating the quantity of freight that is actually prone to shift from other modes to HDV

27
CONCLUSIONS

28
Overall Conclusions

1. There may be a direct rebound effect from new regulations to 

improve HDV fuel efficiency; yet a good understanding of this 

potential phenomenon is not established.

2. New regulations might lead to a shift from rail to truck transport 

for some proportion of certain commodities; however, the results 

of the analysis suggest that modal shift is unlikely due to 

economic conditions alone.

3. More research is needed to better understand how shippers and 

carriers make decisions. Results of that research could be used to 

better inform models that estimate rebound effects.

29
Acknowledgements

• EPA’s Transportation and Climate Division

– Sarah Froman

– Sharyn Lie

– Jefferson Cole

– Michael Shelby

• Rochester Institute of Technology

• University of Delaware

30
References

Sivak, M., Tsimhoni, O., 2009. Fuel efficiency of 

vehicles on US roads: 1923-2006. Energy Policy 

37, 3168-3170.

USEPA, 2010. Inventory of U.S. Greenhouse Gas 

Emissions and Sinks: 1990-2008, Washington, 

DC.

31
Appendix: Results of HDV Own-Price Freight Elasticity of Demand Studies

Variable of Comparison Author/Date Least Elastic Description of Least 

Elastic Value Most Elastic Description of Most Elastic Value Region

Demand 

Measure

Commodity

Commodity

Abdelwahab (1998) -0.75

Construction 

materials -1.40 Textile products U.S. ICC Official Mode Choice Varies

Friedlaender & Spady

(1980) -1.00 Food Products -3.55 Electrical Machinery U.S. ICC Average

ton-miles Varies

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

Winston (1981) -0.14

Lumber, wood and 

Furniture

-2.96 Transport Equipment USA tons Varies

Friedlaender and Spady 

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

Friedlaender & Spady 

(1980) -0.15

Wood/wood 

products -5.06 Electrical machinery Southern USA ton-miles Varies

Campisi and Gastaldi 

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

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

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

O-D Pairs where 

competing modes 

available

Scandinavia tonne-km Agricultural 

products

Demand Measure

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

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

India --

Natural 

Resources

Distance

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

Winston (1981) -0.34

<900 miles 

(average) -1.56 >900 miles (average) USA tons Varies

Christidis and Leduc 

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

Region

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

Friedlaender & Spady 

(1980) -1.66

US ICC MountainPacific -5.06 U.S. ICC Southern Various U.S. ton-miles Electrical 

Machinery

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

Resources

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

India tonne-km Natural Resources

Model Functional Form Oum (1992) -0.69 Translog 1.34 Log-linear Various All Aggregate

Total Range†

Graham and Glaister 

(2004) 1.72

Least elastic end of 

range

-7.92

Most elastic end of 

range

All All All

Likely Range†

Graham and Glaister

(2004) -0.50 Least elastic -1.50 Most elastic All All All

32