# The R ebound Ef fe ct fr om Hea vy Duty Ve hicle Fuel E fficiency Impr ovements: A R eview of the Literatur e and a Case Study Analy sis of Rail - to -Tr uck Mode Shift Po te nt ial Wo rk completed fo r the U .S. EP A under P .O. EP - 11 - C - 000065 Pr oject Lead: James J. Winebrake James J. Corbett Bry an Comer Erin H. Gr een Jor dan Silberman 1

# Ov er view

• In tr oduction • Pa rt I: Lit er atur e r eview • Pa rt II: Case s tudy analysis e xploring the pot en tial for modal shift • Conclusions

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# INTR **ODUCTION**

3

# Po licy Driv ers
**Energy Security**  **Concerns**

**En vironmental**  **Concerns** 

# Fr eigh t

Tr ansport Ef ficiency Po licies

4

# One P olicy: Impr ove HDV Fuel Eff iciency
Impr ove HDV Fuel E fficiency
Re duce Fuel Consumption
Re duce Fuel Cost and Emissions

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# Wh y focus on Impr oving HD V Fuel

Efficiency? • Tr ucking sect or GHG emissions ha ve risen ov er 70% between 1990 - 2008 (USEP A, 2010) • Medium and HD Vsmake up ~20% of US tr ansport ation GHG emissions (USEP A, 2010) • HD V fuel ef

ficiency has r emained flat for decades ( Siv ak and Ts imhoni , 2009)

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# Dir ect R ebound E ffect Manifes tat ions

7



### Decr ease in costs of HDV services Demand Effects Operational Effe cts
Mode Choice Effe cts Lower costs of HDV services create greater demand
Lower costs of HDV operations increase less - efficient operations (e.g., ro uting, speed, load) Lower costs of HDV services create modal shifts from non - HDV modes to HDVs 

### Stimulus: HD V Fuel Efficiency Impr ovements

# PA **RT I: LITERA** 

**TURE REVIEW**

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# Thr ee Componen ts of the R ebound

Effe ct Dir ect • Cos t of energy ser vices is r educed thr ough e fficiency impr ovements • Demand for energy ser vices increases Indirect • Cos t of energy ser vices is r educed thr ough e fficiency impr ovements • Cos t sa vings ar e used to pur chase other goods or ser vices, the manufactur e and use of which also consume energy Econom y - Wide • Re duced demand fo r energy reduces its market price • Demand for energy in other applications increases

We focus on the direct rebound effect in the HDV sector 9

# Understa nding the Dir ect R ebound Effe ct and Making P olicy Decisions

• Po licies that r educe fuel consumption and emissions may also r educe HD V fr eight transport costs and may incr ease demand for HDV fr eight, thus tempering the benefits of the re gulation. 

## The rebound effect limits energy and en vir onmental benefits

• Few studies have been conducted e xploring the r ebound effect specific to the trucking sector . 

## We ha ve an incomplete understa nding of re bound effect impacts

• A framework has not been established that would allow policymakers to effectively sift thr ough r ebound effect studies and consider them in a **HDV** policymaking conte xt. 

## Need a framework to help policymakers make decisions
10

# Es tima ting the Dir ect R ebound E ffect:

Using Fr eigh t Elas ticities

• Change in HDV energy consumption (and ther efore emissions) when fuel efficiency impr oves.



# • Change in ser vices (ton - miles) fr om HD Vswhen the price of fr eigh t tr ansport changes ($/ton - mile).

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# Fr eigh t Elas ticities ar e an Imperfect Measur e of the R ebound Ef fe ct

• Fr eight Elas ticity Ca ve at s:

– Assume that cost savings fr om gr eat er fuel efficiency ar e passed on to consumers (shippers) via lower fr eight rates.

– Assume consumer r esponse to a price incr ease is pr oportional to their re sponse to a price decr ease (wher eas r esponse ma y be asymmetric).

– Assume fuel efficiency is not affect ed by fuel prices (not alw ays the case).

– Ma y not pr operly account for interactions re lated to fuel prices and economic output.

– Re quir es analysts to make assumptions about the r elationship between HD V fuel consumption, VMT

, and pa yload to appr ox imate fuel demand.

12

# Fr eigh t Elas ticity Es timates ar e Highly Specific and Varied,

Making Them Difficult to Compar e

13

# Fa ct ors Af

fe cting Variability of Fr eigh t Elas ticity Es timat es

• D istance shipped

– short distances less elastic than long distances 

### • I ntermodal competition

– higher elasticity estimates where modal competition ex ists 

### • C ommodity type

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

### • Re gion

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

### • Timeframe

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

### • M odel functional fo rm

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

### • M easur e of demand

– To ns vs. ton - miles 

### • M acr oeconomic 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

# HD V Dir ect R ebound E ffect Es tima te s
• Range fr om 4 - 24% (similar to the LD V r ebound effect) • However these r esults:

– Do not measur e the re bound effect due to efficiency impr ovements (but rat her chang es in fuel costs), so ha ve some of the same ca ve at s as fr eigh t elasticities

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

, some use ton - mi or tonne - km)

– Ex amine dif

fe re nt re gions in diffe re nt periods of time

– Ar e not easily compar able
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# Pa rt 1: Conclusions 1. Curr ent understa nding of the HD V re bound effect is limit ed due to the lack of fo cused studies and the het er ogeneity of the trucking sector 2. Existing studies oft en cr eat e biased or err oneous r ebound effect estimates by re lying on elasticities and applying metrics that omit import ant factors that influence fuel consumption 3. Large range of estimat es in lit erature, lack of comparability , and pot entially biased/err oneous estimat es makes it difficult to apply these estimat es with confidence in a policymaking context 4. Further quantit ative resear ch is needed to bette r assess the scale of HD V re bound effects. R esear ch should focus on obt aining a bette r understa nding of how decisions ar e made by HD V owner - operators and fr eight customers, and applying those decision - making rubrics to simulat e and v alidate macr o - level effects.

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# PA **RT 2: CASE S** **TUDIE S ON RAIL** **- TO**  **** **TRUCK MODE SHIFT PO TENTIAL** 

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# When Migh t Modal Shift Occur?

• If pos t - re gulation truck fr eigh t rates become less than ra il fr eigh t rates, then...

...modal shift fr om rail to truck ma y occur . Mode Shift Tr uck < Rail 

# Re gulation

18

# Fo ur Case Studies t o Ex amine the

Po te nt ial for Modal Shift 1. Rubber Tir es fr om LA t o F ort Worth 

# 2. Plas tic P ellets fr om LA t o F ort Worth 

# 3. Newsprint fr om New Orleans t o Chicago 

# 4. Grave l fr om Chatta noog a t o 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 t o Model

• Goods transport ed >500 miles ar e mor e likely to be sensitive to modal shift.

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

### • Commodity must be transport ed by rail in or der for mode shift to occur .

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

– Cases 1 - 3 ha ve significant proportion of tons and ton - miles tr ansported by both ra il and truck. 

### • One case should model the tr ansport of a commodity that r epr esents a significant portion of fr eight truck activity.

– Case 4 models the tr ansport 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**  **To ns**  **transported >500 mi (%)**  **To ns** 

**Tr ansported by**  **Rail (R) and Tr**  **uck (T) (%)**  **To n**  **- mi** 

**Tr ansported by**  **Rail (R) and Tr**  **uck (T) (%)**  **Va lue** 

**Tr ansported by**  **Rail (R) and Tr**  **uck (T) (%)** 1 & 2 Plastics and rubber 33% 20%R 70%T 25%R

58%T 9%R

77%T 3 Pulp, newsprint,

paper , and paperboar d 34% 19%R 71%T 29%R

53%T 13%R

77%T 4 Gravel and Crushed

Stone 1% 4%R 89%T 16%R

55%T 4%R

90%T 20

# Choosing the O/D P airs t o Model

• Chose origins that e xported a high pr oportion of each commodity gr oup. • Chose des tinations that imported a high pr oportion of each commodity group. • Va lidated that O/D pairs r eflected r ealis tic fr eight tr ansport activity. • Ve rified tha t facilities atorigins and des tinations could receiv e both truck and r ail using the GIFT model. • Ex ample (highlighted):

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

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

– TX is the #1 trading partner of CA
**Case Commodity**  **Group Origin**  **(state exporter ra**  **nk) Destination**  **(state importer ra**  **nk) Highway**  **Distance (miles)**  1 Plastics and rubber Los Angeles, CA (2w 1 , 2v 2 ) Fort Worth, TX (1w , 1v) 1,409 2 Plastics and rubber 3 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

# Calcula ting the P os t - Re gula tion

Tr uck Fr eigh t Ra te

= ∗ 100% − + ∗ ∗ ( 100% − % ∆ ) + Non - fuel operating cost component Fuel operating cost component

Change in efficiency New tech. costs

**Assumptions Input**  **Value** C fuel 66.4 cents/mi C op 158.2 cents/mi C fuel /C op 42% ∆η 23 % C tech 0.03 cents/ton - mi 22

# Case 1: Rubber Tir es fr om LA t o F ort Worth Tr uck nev er becomes less ex pensiv e than r ail pos t - re gula tion **Freight Rate (**  **cents /ton**  **- mi)**  **Mode** 18.4 Rail B (RR O 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: Plas tic P ellets fr om LA t o F ort Worth Tr uck nev er becomes less ex pensiv e than r ail pos t - re gula tion **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) **Tr uck 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. Wo rth 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) fo r each truck compan y. 24

# Case 3: Newsprint fr om New Orleans t o Chicago Tr uck nev er becomes less ex pensiv e than r ail pos t - re gula tion **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)

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# Case 4: Gr ave l fr om Chatta noog a to Indianapolis Tr uck becomes less ex pensive than ra il post regulation when considering total price to ship 300 short tons of gr avel (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

# Pa rt 2:Conclusions 1. New regulations do not seem to create obvious economic conditions that would lead to a modal shift from rail to truck ex cept in one case (Case 4 evaluation of total shipment price). 2. Caveats: a. S hipping 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., pack aging /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. D etermining 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

# CO **NCL USIONS** 

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# Ov er all Conclusions 1. There ma y be a direct rebound effect fr om new r egulations t o impr ove HDV fuel efficiency; yet a good under sta nding of this pot en tial phenomenon is not est ablished. 2. New regulations might lead t o a shift fr om rail t o truck transport fo r some pr oportion of cert ain c ommodities; however , the r esults of the analy sis sugg est that modal shift is unlikely due t o ec onomic c onditions alone. 3. More resear ch is needed t o bette r under sta nd how shipper s and ca rriers make decisions. R esults of that resear ch c ould be used t o bette r inform models that estimat e rebound effects.

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# Acknowledg emen ts

• EP A’ s Tra nsport at ion and Climat e Division

– Sar ah Fr oman

– Sharyn Lie

– Jefferson Cole

– Michael Shelby


# • Ro ches te r Ins titut e of T echnology

• University of Delaw ar e
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# Re fe re nces Siv ak, M., Ts imhoni , O. , 2009. Fuel ef ficiency of ve hicles on US r oads: 1923 - 2006. En ergy Po licy 37, 3168 - 3170. USEP A , 2010. In ven to ry of U.S. Greenhouse Gas Em issions and Sink

s: 1990- 2008, W ashingto n , DC .

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## Appendix: R esults of HDV Own - Price Fr eight Elas ticity 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 Mountain Pacific - 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