## Assessment of Fuel Economy Technologies for Medium and Heavy

Duty Vehicles: Commissioned Paper on Indirect Costs and

Alternative Approaches

Draft final paper

, Cambridge Systematics, Inc., revised September 21 , 2009



### Introduction

This paper was commissione d by the National Academy of Sciences in support of its committee on medium

- duty and heavy

- duty truck fuel economy standards as directed by the Energy Independence and Security Act of 2007 . The Academy working group is charged with identifying the potenti al costs and other impacts of fuel economy technologies on the operation of medium

- duty and heavy

- duty trucks

. The paper discusses two sets of issues related to potential Federal regulations to improve fuel economy of commercial motor vehicles. The first set of issues is the potential indirect costs and benefits of commercial vehicle fuel economy regulations

– i.e., effects on vehicle costs, congestion, safety transportation service, and other factors in addition to the direct effects on fuel usage. The s econd set of issues is the potential for alternatives to fuel economy regulations for improving fuel efficiency.
This paper , authored by Cambridge Systematics, Inc. (CS), examines a subset of these issues and complements a companion paper authored by Eas tern Research Group, Inc. (ERG). Some of the issues are addressed by both authors. For example, for congestion effects, ERG‘s paper discusses the relationship between technology and performance, while CS‘ paper discusses the resulting impacts of performa nce on highway congestion. The issues were divided between the two firms based on their respective areas of expertise as follows:

C S Paper First subset of issues (indirect costs) Vehicle

- miles traveled and the rebound effect ; Vehicle c lass shifting ; Impl ications of t ransportation service and performance effects (with ERG input on performance) ; Congestion impacts (with ERG i n put on performance) ; and Safety impacts (of changes in traffic volume or performance)

. Second subset of issues (alternatives) Fuel ta x ; Congestion pricing ; and Intermodal transport

. ERG Paper First subset of issues (indirect costs ) Fleet turnover effects ; Environmental costs and co

- benefits ; Transportation s ervice and performance effects ; and Safety impacts ( of changes in fuel type )

. Second subset of issues (alternatives) Driver training

.Th is paper relies on findings from the literature on these issues but also includes new, sketch

level analysis to translate literature findings (which may only indirectly address the issue) into results that directly address the issues being investigated.
The key findings of this paper regarding indirect costs are as follows:

Vehicle

- miles traveled (VMT) and the rebound effect

– The ―rebound effect‖ refers to an increase in VMT that may occur as a result of lower shipping costs caused by lower fuel costs. The additional fuel consumption would partially offset the fuel savings of more efficient trucks. Fuel economy regulations that achieve consumption reductions on the order of 4,500 million gal lons (17.6 percent of current fuel use ) would reduce national Class 8 fuel consumption from 25,500 million gallons annually to 21,000 million gallons. This level of fuel efficiency gain would result in a n average Class 8 per

- mile operating cost reduction of $0.08 (considering increased vehicle costs amortized over the life of the vehicle as well as decreased fuel costs) , which could lead to a n increase in truck demand and a decrease in rail demand. Using a range of assumptions regarding the elasticity of truck travel with respect to price, the increase in truck demand due to the decrease in truck operating costs w ould increase fuel consumption by 500 to 1,400 million gallons , diminishing the fuel consumption reduction to 3,100 to 4,000 million gallons, or a rebound of 11 to 31 percent of the initial 4,500 million gallon reduction. T he decrease in rail demand
, due to the diversion of rail demand to truck, produces an additional fuel consumption reduction of 70 to 110 million gallons , counteracting the truc k rebound effect by 1 to 2 percent of initial 4,500 million

- gallon reduction

. The net rebound effect of the truck fuel consumption increase and the rail fuel consumption decrease is therefore about 9 to 30 percent

— meaning that the actual reduction in fuel use would be about 3,200 to 4,100 million gallons ( a 13 to 16 percent reduction in total truck fuel consumption)

. For larger fuel economy improvements ( on the order of 39 percent ) , the rebound effect would be smaller on a percentage basis

– in the range of 5 to 16 percent

– for a net reduction in fuel use in the range of 32 to 37 percent . This is because the technologies used to achieve the higher fuel efficiency standard would be somewhat less cost

- effective, raising the initial capital cost of the veh icle and leading to a lower per

- mile operating cost savings compared to the most cost

- effective technologies used to achieve the 17.6 percent reduction.

Vehicle c lass shifting

– The potential for vehicle purchasers to shift to different c lass es of vehicl es will depend upon the specific nature of the fuel economy regulations. However, hypothetical regulatory scenarios suggest that significant c lass

- shifting is unlikely to occur in most conceivable situations. This is particularly true for long

- haul truck s, which account for nearly three

- quarters of total truck fuel consumption

. Transportation service and performance effects

– Analysis in the concurrent paper developed by ERG suggests that fuel economy regulations would have no appreciable effect on vehicl e performance. Therefore, impacts on transportation service (e.g., delivery times, reliability) will be minimal or non

- existent. Congestion impacts

– Analysis in the concurrent paper developed by ERG suggests that fuel economy regulations would have no ap preciable effect on vehicle performance and therefore- 3

-

no effect on congestion as a result of degraded performance. However, increased truck traffic as a result of the rebound effect could increase congestion. The effect is likely to be minimal
on most ro ads, but could be significant at times and locations where the roadway is near capacity and where truck traffic represents a significant fraction of total traffic

. An estimate using marginal per

- mile congestion cost s from the literature suggests that the increase in truck traffic volumes described above for the rebound effect could result in an increased congestion cost (to all road users) in the range of $0.3 to $1.4 billion nationwide. Safety impacts

– Analysis in the concurrent paper developed by ERG su ggests that fuel economy regulations would have no appreciable effect on vehicle performance and therefore no effect on safety as a result of degraded performance. I ncreased truck traffic volumes from the rebound effect could be expected to cause an incre ase in the range of 80 to 360 fatalities per year and about 1,600 to 7,700 injuries per year nationally

. An alternative estimate using marginal safety costs from the literature suggests that the increased truck VMT could result in an increased safety cost (to all road users) of in the range of $ 0.09 to $0.4 b illion nationwide

. Key findings regarding alternative approaches to reduce truck fuel consumption are as follows:

Fuel tax

– To achieve reductions in fuel consumption of the same order of magnitude as assumed in the analysis of the rebound effect (20 to 40 percent) is estimated to require an increase in the fuel tax on the order of $1 to $2 per gallon, which would lead to a combination of both reductions in truck VMT and increases in vehicle fuel effici ency. However, elasticities of fuel consumption with respect to fuel price are not well

- documented for freight trucks so this estimate should be considered particularly uncertainty. Congestion pricing

– No analysis has specifically examined the impacts of a comprehensive congestion pricing system on truck fuel consumption. Estimates for all vehicles have found modest impacts, on the order of a 0.5 to 1. 1 percent reduction in total fuel consumption if congestion pricing were widely applied in the U.S., alt hough this is based on data extrapolated from only two simulation studies. This is the result of a variety of effects, including a reduction in overall demand, shifting of demand to less congested periods, and more free

- flowing traffic during peak travel periods. Analysis of cordon pricing in London and Stockholm has found very small impacts on truck fuel consumption, with minimal impacts on truck VMT but some benefits through reduced truck idling

. An upper bound can be placed on the potential fuel savin gs from congestion pricing ( or other highway congestion mitigation strategies ) by estimating the total nationwide fuel ―wasted‖ by trucks in congestion. This figure is estimated to be no more than 640 million gallons, or about 2 percent of total truck fue l consumption

. Intermodal transport

– The potential for truck

- to

- rail mode shifting through investment in rail and intermodal infrastructure and other incentives has not been extensively studied. One study of the Mid

- Atlantic region estimated that an in vestment of $12 billion could reduce fuel consumption b y 42 to 114 million gallons annually, at a cost of $ 110 to $ 290 per gallon of fuel saved

. The findings of o ne study of the I

- 81 corridor suggest that an investment of $7.9 billion could reduce fuel co nsumption by 145 million gallons annually, at a cost of $54 per gallon of fuel saved , while another suggests that an investment of $2.5 billion for the Norfolk Southern ―Crescent Corridor,‖ also paralleling I

- 81 between New York and the Southeast, could sa ve 170 million gallons annually.- 4

-



### 1. Indirect Costs and Benefits

L ittle , if any, previous research has directly investigated the effects of commercial vehicle fuel economy standards on the factors addressed in these papers . There is a considerable amount o f literature that can inform a general assessment of potential effects. The specific effects, however, depend greatly on how the fuel economy regulation is structured, as well as on external issues such as fuel prices, economic trends, and technology deve lopment. Therefore, the approach taken in this paper is to develop hypothetical scenarios that allow for examine what would happen given various fuel economy regulation structures and other key factors . The objective is to provide a ―what

- if‖ assessment of the impact on fuel economy regulations on performance, congestion, costs, etc. , rather than a definitive assessment of impacts

. At a minimum
, this will help determine whether or not each issue is significant enough to warrant consideration in designing a commercial vehicle fuel economy standard.
Each issue is addressed in the following format:

Key question

– What is the key question of interest that is being investigated? Background

– What does the literature have to say relevant to this issue? Approach

– What quantification method was used to estimate potential impacts related to the key question? What key assumptions are made in the analysis? Findings

– What are the results of the analysis under alternative hypothetical scenarios generated for this re port

– i.e., what impact could commercial vehicle fuel economy standards potentially have on VMT and the rebound effect, vehicle class shifting, etc. ?



### (ii) Vehicle

- Miles Traveled (VMT) and the Rebound Effect

Key Question : If fuel economy improvements redu ce vehicle operating costs, will truck traffic volume increase?
Background: A ―rebound effect‖ in heavy

- duty vehicle travel can be characterized as the extent to which cost savings from heavy

- duty vehicle fuel efficiency improvements result in increased d emand for truck shipping and decreased demand for rail shipping because truck travel is made less expensive per

- mile due to reduced fuel costs and therefore reduced truck operating cost s

. This increased truck travel partially offsets the total fuel saving s benefits. The rebound effect has been extensively studied for light

- duty (personal) vehicle travel, but somewhat less so for freight truck travel. 1 However, a number of studies have documented relationships between price and demand for truck travel. T his literature is focused on long

- haul freight truck movements because 1) the effect is likely to be strongest for long

- haul movements (due to available modal competition) ; 2) these movements represent the carriage of a significant volume of the nation‘s f reight ; and 3) these movements account for the vast majority of the medium

- and heavy

- duty

1 The National Highway and Traffic Safety Administration (NHTSA), in its preliminary regulatory impact assessment for the CAFE standards rulemaking, selected a value of 15 percent as their best estimate, based on a review of t he literature. The assessment also concluded that this effect could plausibly range from 10 to 20 or even 25 percent (NHTSA, 2008).- 5

-

vehicle fuel consumption ( 78 percent). 2 Little or no research exists to provide information on a rebound effect for medium

- duty trucks. The sketch analysis present ed herein will focus on long haul trucks, which are generally Class 8 trucks with 53

- foot trailers and a gross vehicle weight (GVW) of up to 80,000 pounds.
Caution should be exercised when considering the price

- demand relationships found in the literature for freight trucks. T he results appear to be heavily reliant on factors including the type of demand measures analyzed (vehicle

- miles of travel, ton

- miles, or tons), analysis geography, trip lengths, markets served, and commodities transported

. 3 The lite rature does not treat differences between short

- and long

- run effects consistently, and, in general, presents inconsistent results across studies. To provide an envelope of potential effects, the analysis presented herein provides reasonable upper and low er bounds of potential effects as well as a mid

- point or ―likely‖ estimate

. An additional issue is the difference between the average and marginal per

- mile cost of truck shipping . The marginal cost considers only the fuel savings. The average cost also considers any increase in the capital cost of the vehicle, amortized over the lifetime of the vehicle. For the purposes of this assessment i t is assumed that vehicle owners will pass these capital costs along to their customers, and therefore it is the a verage (net) cost or cost savings that is important. The rebound effect in heavy

- duty vehicles works in three ways. The first two have been measured and characterized with price elasticities while the third is speculative:

An increase in overall demand f or truck shipping, if total shipping costs are reduced (the self

price elasticity effect); A shift of some commodities from other modes (especially rail) to truck due to lower truck shipping costs (the cross

- price elasticity effect); and

Less efficient uti lization of trucks (e.g., lighter loads), because lower costs reduce cost pressures on industry to maximize efficiency.

The self

- price elasticity provides a measure for describing how the volume of truck shipping (demand) changes with its price while the c ross

- 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). For example, a price elasticity of truck demand with respect to truck prices (self

- price elasticity) of

- 0.5 implies that the demand for trucking will grow by 5 percent if truck operations costs decrease by 10 percent (+5 percent change in truck demand/

- 10 percent change in truck cost =

- 0.5). Similarly , a cross

- price elasticity of rail demand with respect to truck prices (cross

- price elasticity) of 0.5 implies that the demand for rail will decrease by 5 percent if truck operations costs decrease by 10 percent (

- 5 percent change in rail demand/

- 10 percent change in truck price = 0.5 ).

2 Oak Ridge National Laboratory, Transportation Energy Data Book, Edition 28 , prepared for the US Department of Energy, 20 09. See table 5.4 for relevant data.

3 Graham and Glaister, ―Road Traffic Demand Elasticity Estimates: A Review,‖ Transport Reviews Volume 24, 3, pp. 261

- 274, 2004 and Oum, Waters, and Jong Say Yong, ―A Survey of Recent Estimates of Price Elasticities of Demand for Transport,‖ prepared for the World Bank, Infrastructure and Urban Development Department, January 1990.- 6

-

Oum et al 4 collected 17 estimates of freight transportation demand price elasticities. The authors focused on self

- price elasticities and presented elasticity figures as both a range and a most likely estimate. The authors found that transportation is relatively inelastic since it is a derived demand

– transportation demand only exists because there is demand for other products or services

– with the exception of freight shipments that are subject to intermodal rail competition. The paper found truck self

- price elasticity to fall in the range of

- 0.05 to

- 1.34 with the most likely range of

- 0.7 to

- 1.10 ( the measures of demand were not ide ntified and varied by study)

. 5 Furthermore, while the general literature on road transportation elasticities has distinguished short

- run vs. long

- run effects, this has not typically been done in the literature on truck elasticities. A more recent study by Christides and Leduc reviewed recent price elasticity literature for use in a truck size and weight study for the European Commission. 6 The literature review collected self

- price elasticities ranging from

- 0.3 and

- 1.75 and cross

- price elasticities rangi ng from 0.11 to 1.9. For use in their study, they selected values of

- 0.416 for truck self

- price elasticity, measured in tons, and 0.38 for rail cross

- price elasticity, measured in tons.

Graham and Glaister
, 7 in a comprehensive review of recent price e lasticity results
, found that truck self

- price elasticity is likely to fall between

- 0.5 and

- 1.5, but did not identify a demand measure. The Federal Highway Administration (FHWA) used a self

- price elasticity of

- 0.97, measured in vehicle

- miles
, for a fre ight benefit and cost study

. 8 A study for the National Cooperative Highway Research Program 9 cited a rail cross

- price elasticity of 0.52 based on the Intermodal Competition Model 10 and identified a range of cross

- price elasticities between 0.35 and 0.59 fr om an analysis of Class 1 railroads

. 11 Approach: To estimate the potential rebound effects and resulting effects on fuel consumption it is necessary to describe a base case and to develop potential fuel economy improvement alternatives. It is necessary for the base case to describe current truck and rail volumes; truck and rail fuel economy; truck and rail fuel consumption; average truck operating cost per

- mile; and truck purchase price . For alternatives, it is necessary to describe the potential fuel c onsumption

4 Oum, Waters, and Jong Say Yong, ―A Survey of Recent Estimates of Price Elasticities of Demand for Transport,‖ prepared for the World Bank , Infrastructure and Urban Development Department, January 1990.

5 While the authors find that demand is ―relatively inelastic‖ this is not always the case, as an elasticity of 1.0 or

- 1.0 can be considered a dividing line between ―elastic‖ and ―inelastic ,‖ with higher (absolute) values considered elastic.

6 Christidis and Leduc, ―Longer and Heavier Vehicles for freight transport,‖ European Commission Joint Research Center‘s Institute for Prospective Technology Studies, 2009.

7 Graham and Glaister, ―Road Traffic Demand Elasticity Estimates: A Review,‖ Transport Reviews Volume 24, 3, pp. 261

- 274, 2004.

8 HDR

- HLB Decision Economics, Inc. and ICF International, Freight Benefit/Cost Study: Phase III Analysis of Regional Benefits of Highway

- Freight Improvemen ts , prepared for the Federal Highway Administration Office of Freight Management and Operations, February 2008.

9 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.

10 Scott M. Dennis, The Intermodal Competition Model , Association of American Railroads, September 1988

. 11 J. Jones, F. Nix, and C. Schwier, The Impact of Changes in Road U ser Charges on Canadian Railways, prepared for Transport Canada by the Canadian Institute of Guided Ground Transport, Kingston, Ontario, September 1990

.- 7

-

reduction; logical fuel economy technologies to achieve the reduction; improved fuel economy; increased truck purchase price; resulting operating cost (including savings from fuel economy improvements and increased capital cost from increased pu rchase price); and self

- price and

cross

- price elasticities

. Scenarios : Three scenarios are outlined below:

Base c ase : Total affected truck volume is 142,706 million VMT and total affected rail volume is 1,852 billion ton

- miles; 12 truck fuel economy is 5 .59 mpg 13 and average rail fuel economy is 436 gallons per ton

- mile; 14 truck fuel consumption is 25,500 million gallons and rail fuel consumption is 4,250 million gallons (based on estimates of travel and fuel economy); total truck operating costs are $1.73 per

- mile ($247 billion nationally = $1.73/mile * 142,706 million miles ), including $0.70 fuel

- oil related costs (fuel costs and fuel tax costs) and $0.206 per

- mile truck and/or trailer lease or purchase payments ; 15 and the current purchase price of a Class 8
tractor

- trailer is $100,000

. 16

Alternative 1
, 17.8 percent reduction in fuel use : In alternative 1, a set of technologies are implemented to achieve a 17.8 percent reduction in fuel consumption. The selection of technologies and this target reduction we re based on the forthcoming Northeast States Center for a Clean Air Future (NESCCAF) and International Council on Clean Transportation (ICCT) report 17 that outlines technologies, their impacts and their costs. The technologies include best

- available aero

- k it (fully aerodynamic mirrors, cab side extenders, integrated sleeper cab roof fairings, aerodynamic bumper, full fuel tank fairings, side skirt fairings, and boat tails), wide single base tires, aluminum wheels, idle reduction, and improved lubricants. Th e technology improves fuel economy from 5.59 to 6.80 mpg at a cost of $22,930 per vehicle. Accounting for decreases in operating cost and increased capital cost, the net per

- mile operating cost will be reduced to $1.65, $0.57 of which is fuel

- oil related c osts, down from $0.70 in the base case, and $0.25 of which is truck/trailer payments, up from $0.21 in the base case. The total national operating cost is $236 billion (142,706 vehicle

- miles * $1.65/mile). The chosen range of self

- price elasticity of truc k demand with respect to truck price is between

- 0.5 and

- 1.5 and the cross

- price elasticity of rail demand with respect to truck price is between 0.35 and 0.59

. These range s were chosen to include the majority of the estimates while excluding the outlier s

.

Alternative 2
, 38.6 percent reduction in fuel use : In alternative 2, a set of technologies , again based on the ICCT report, are implemented to achieve a 38. 6 percent reduction in fuel

12 Bureau of Transportation Statistics, Pocket Guide to Transportation , January 2009.

13 Energy Informa tion Administration, ―Annual Energy Outlook, 2009,‖ March 2009.

14 Association of American Railroads, ―Freight Railroads and Greenhouse Gas Emissions,‖ June 2008.

15 American Transportation Research Institute, An Analysis of the Operational Costs of Trucki ng , December 2008.

16 Anthony Grezler, ―US Heavy Duty Vehicle Fleets Technologies for Reducing CO 2 : An Industry Perspective,‖ Volvo Powertrain Corporation, August 23, 2007.

17 Northeast States Center for a Clean Air Future, Southeast Research Institute, TI AX, LLC., and International Council on Clean Transportation, Reducing Heavy

- Duty Long Haul Truck Fuel Consumption and CO 2 Emissions, September 2009.- 8

-

consumption . This set of technologies includes all alternative 1 tec hnologies plus continued streamlining of the cab, a reshaped trailer, boat tail, full skirting of the cab and trailer, tractor

trailer gap fairing, very low resistance tires, bottoming cycle, parallel hybrid system, and road speed governors set to 60 mph. The technology improves fuel economy from 5.59 to 9.10
mp g at a cost of $71,630 per vehicle. Accounting for the reduced fuel cost and increased capital cost, the net per

- mile operating cost will be reduced to $1.61, $0.43 of which are fuel

- oil related cos ts, down from $0.70 in the base case, and $0.35 of which are truck and trailer payments, up from $0.21 in the base case . The t otal national operat ing cost is $230 billion (142,706 vehicle

- miles * $1.61/mile)

. The elasticity ranges are the same as those

e mployed in the alternative 1 analysis.
Findings : The impact of the three potential rebound effects on the alternatives are described below:
The first rebound effect

– truck VMT i ncrease
Before accounting for the rebound effect, the fuel economy improvement s of alternatives 1 and 2 will reduce fuel consumption from 25,500 million gallons to 21,000 and 15,700 million gallons, respectively. This represents a reduction of 4,500

- 9,800 million gallons of fuel

. Class 8 long

- haul demand will increase (rebound) b y 3.2

- 9.5 billion VMT for alternative 1 and 5.0

- 15.0 billion VMT for alternative 2, based on the projected net decrease in operating costs associated with fuel efficiency improvements of $0.08 and $0.12 cents per mile, respectively

. The VMT increase wi ll cause a rebound in fuel use. The rebound for alternative 1 will reduce the total fuel savings from 4,500 million gallons to 3,100 to 4,000 million gallons. Put another way, the rebound effect for Class 8 vehicle travel will range from 11 to 31 percent if the technologies in alternative 1 are implemented. The rebound for alternative 2 will reduce the total fuel savings from 9,800 million gallons to 8,20 0 to 9,300 million gallons , depending on the elasticity. This will cause a rebound ranging from 5 to 16 percent

. The rebound effect varies depending on the elasticity and the cost

- effectiveness of the technology. If the technology is more cost

- effective, then there will be a larger rebound effect because the cost of trucking will come down to a larger d egree, increasing the total growth in demand for trucking. Alternative 2 shows a lower rebound effect (on a percentage basis) than alternative 1 because alternative 1 deploys the most cost

- effective technologies, with diminishing returns gained from the t echnologies deployed under alternative 2. The second rebound effect: diversion from rail Some of the increased truck travel will be diverted from rail. With truck per

- mile operating costs dropping from $1.73 in the base case to $1.65 and $1.61 in alternat ives 1 and 2, shippers will shift 28.7 to 48.4 billion ton

- miles from rail carriers to truck carriers in alternative 1 and 45.4 to 76.5 billion ton

- miles from rail shippers in alternative 2

. The shift from rail to truck will reduce rail fuel consumption by 66 to 111 million gallons for alternative 1 and 104 to 176 million gallons for alternative 2

. Considering truck fuel savings, the truck rebound effect, and the rail fuel savings for alternative 1 together, the fuel reductions of 4,500 million gallons wil l drop to 3, 200 to 4, 100- 9

-

million gallons, for a combined rebound effect of about 9 to 30 percent (compared to 11

- 31 percent without rail fuel savings). Considering the same for alternative 2, the fuel reductions of 9,800 million gallons will drop to 8, 300 to 9, 500 million gallons , for a combined rebound effect of 3 to 15 percent (compared to 5

- 16 percent without rail fuel savings). The third rebound effect: utilization reduction Trucking is a cost

- competitive business and profits are historically razor

- thi n. While trucking companies work diligently to reduce operating costs out of competitive necessity, continued cost increases force fleet owners to continue to look for ways to reduce operating costs. The relief that comes from decreased operating cost (4 percent in alternative 1 and 7 percent in alternative 2) is not likely to create a significant change in utilization optimization. However, there is no evidence in the literature to quantify the degree to which trucking companies maximize the utilization of their equipment or to describe how this might change with total truck operating costs.



### (iii) Vehicle Class Shifting

Key Question : If regulations or new technologies change the relative costs and performance characteristics of various classes of mediu m and heavy vehicles, will some buyers choose larger or smaller vehicles than they would have before the change?
Background: Past regulations have had an impact on the choice of vehicle characteristics. Prior to deregulation of the trucking industry, 4

- ax le trucks were common. After deregulation, most carriers shifted to 5

- axle trucks since these trucks could carry more freight and therefore operate more economically, given weight per axle restrictions.

To induce class

- shifting, truck fuel economy regul ations would need to significantly increase the cost (or decrease the performance) of one class of truck relative to another. The specific nature of a vehicle c lass

- shifting effect will therefore depend upon how the regulations are structured. A

― class

- n eutral‖ regulation could be conceived that does not lead to any shifts.

There is little or no literature that describes the cross

- class mode shift between truck types based
on cost. The potential for class

- shifting appears limited , however, because most equipment is chosen to fit the physical requirements of a particular shipment, not because of changes in fuel price. A shipper will hire carriers to minimize total logistics cost and the carrier will deliver a service at the minimum possible cost (to max imize their profits). The shipment could be long

haul (more than 500 miles, the one

- day round

- trip distance), short

- haul (less than 500 miles), a drayage shipment between an intermodal facility and the final destination, or a local shipment between a regi onal warehouse and a grocery store. Shippers and carriers will give additional consideration to whether the commodity is high

- value and time

- sensitive or low

- value and less time sensitive, whether the commodity will weigh

- out or cube

- out, and the labor co st. Other vocational medium

- and heavy

- duty truck owners have very specific carrying and sizing needs (e.g. dump trucks, garbage trucks, buses, and utility trucks). A shipper and carrier will examine the set of requirements, negotiate a rate, and choose the optimal truck for the shipment. Shifting between trucks and truck class es is complicated, but it is possible to conceive of some examples where certain policies would create cases where a truck from one class might become fungible with a truck from an other class

.- 10

-

Approach: Analysis first requires the identification of hypothetical regulations and, second, the identification of specific truck class es and shipment type that have potential for shifting given the regulations.
Findings: The following is a s ample of potential regulatory approaches and their possible impacts. The impacts are intended only to show the likely direction of shifting. A quantitative analysis would require a more specific understanding of alternative regulations, the distribution of vehicle class es, the development of complete weight and operating cost tables for each vehicle type and class , and potentially
, research with vehicle owners to determine how these impacts might affect their purchase decisions.
Regulations that would inc rease or decrease the weight of a vehicle . If fuel economy regulations were to require all trucks to implement weight

- increasing or

– decreasing technologies, there are several potential outcomes, depending on the current use of the truck. Two potential ou tcomes are described below:

Trucks used for certain types of specialized shipments that have similar trucks in different class es are the most likely to be interchangeable. For example, a FedEx delivery truck can be one of three class es, including Class 3, 4, or 5

. 18 If regulations were to increase the weight of a Class 3 FedEx delivery truck and make it Class 4, FedEx might decide to simply purchase

more Class 4 trucks going forward. Other truck types that are fungible between Class es include ci ty delivery ( Class es 3, 4, and 5), conventional van ( Class es 3 and 4), and conventional tractors ( Class es 7 and 8). In the long

- run, these changes might result in larger (or smaller) trucks being used for deliveries of all sorts, which would lead to an in crease in travel

. Trucks used for certain types of specialized shipments that do not have similar trucks in different c lass es and are not likely to be interchangeable may still be impacted by a policy that would increase or decrease the gross vehicle weigh t. License requirements for Class 7 vehicles make marginal Class 6 vehicles most likely to experience c lass

- shift pressure from a regulation that would increase general gross vehicle weight. For example, if regulations were to require school buses to add a significant amount of weight, it is possible that they would become Class 7 vehicles. If that were to happen, school bus drivers would need to gain additional licensure or buses would need to counteract the weight gain and lose capacity. Other Class 6 vehicles that are marginal now (school bus, beverage trucks, rack trucks, and single axle vans) might bump over to Class 7, which would require special driver licensure.
Regulations that would be implemented based on geography of truck travel ( e.g., requi ring aero fairings on trucks traveling outside of the city):

Some fuel

- saving technologies are only cost

- effective for certain types of uses. For example, aerodynamic improvements are most beneficial for high

- speed, long

- haul travel, while hybridized driv etrains are most beneficial for stop

- and

- go duty cycles. Fuel economy could be directly regulated depending upon the intended use of the vehicle, or technology requirements imposed based on intended use. Under the assumption that a regulation wo uld

18 Cummins, ―Heavy Duty Fuel Efficiency Regulations ,‖ July 2009.- 11

-

be defined as something similar to  ̳percent of miles of travel on roads within Census Urban Area boundaries,‘ trucks that travel half in the city and half outside of the city represent a likely marginal case. Trucks meeting the marginal description ar e likely to be trucks that, for example, carry goods between warehouses on the urban fringe and urban retailers. These carriers would likely make the argument that their travel was most often inside of the urban

area boundary and, therefore, they should n ot be required to install fairings. They are not likely to shift class es to avoid regulation, as their shipments are not likely to change. Monitoring and enforcement of such a regulation would likely be a challenge, especially without the deployment of technologies such as global positioning systems (GPS) monitors as a basis for charging VMT fees. Additionally, urban area boundaries change after each decennial census , further reducing the practicality of this type of regulation

. Regulations that impact a certain engine size :
Regulations that would impact only a certain engine size would implicitly impact a certain class truck more than another. For example, if engine regulations were implemented on any engine with peak horsepower greater than 400, this would select for Class 8 trucks

— either line

- haul or other vocational loads. However, any regulations that would increase the operating cost for an engine size d for Class 8 use would be doing so for the class of trucks that are least likely to switch. Voc ational trucks would still be used for job

- specific purposes and other users of Class 8 trucks are extremely unlikely to shift to smaller trucks because labor costs would still far outweigh the increased operating cost that would accompany the required inc rease in shipments.
For example, a steering column manufacturer in Vermont fills four 53

- foot trailer

- loads of steering columns bound for an automobile manufacturing plant in Mississippi. If he were to shift this into a smaller Class 7 carrying a 28

- foo t pup trailer, it would take nearly twice as many truckloads, doubling the total number of shipments. The impact of switching to Class 7 trucks will significantly increase total cost because fuel costs of Class 7 trucks are roughly 75 percent of Class 8 f uel costs ($0.48 compared to $0.634 per mile ) and labor is likely to remain similar since the shipment requirements are comparable. 19 Total labor costs will double (eight trucks compared to four) and fuel expense will increase by about 50 percent (eight tr ucks * $0.48

- four trucks * $0.634), resulting in a much more expensive shipment.



### (v) Transportation Service and Performance Effects

Key Question : Are r egulations likely to reduce the quality of the services provided by vehicles, for example by affecti ng speed, reliability, or cargo capacity? If so, what are the implications for costs and benefits?

Discussion : Initial research conducted by ERG for the concurrent white paper suggests that the impacts of fuel economy regulations on truck eng ine power output will be minimal, if any. Therefore, the impacts of fuel economy regulations on quality of service are likely to be minimal

19 Antich, ―Medium

- Duty Operating Costs In crease in 2008

- CY,‖ reprinted from Work Truck Magazine, May/June 2009 and American Transportation Research Institute, An Analysis of the Operational Costs of Trucking , December 2008.- 12

-

or non

- existent. One exception is that some regulations may lead to the use of technologies that add modestly to the weight of the vehicle, which will modestly reduce cargo capacity on a weight basis. This will only be an issue for shipments that ―weigh out‖ (i.e., are limited by the maximum load allowed), and not those that ―cube out‖ (i.e., are limited by volum e). It is also conceivable that some truck purchasers might purchase smaller engines due to the higher cost of meeting more stringent fuel economy standards. If this is the case, the purchaser has still determined that the reduced performance will not be a significant detriment to their ability to conduct business, and therefore, by definition, there is no significant impact to shippers or to the overall economy.



### (vi) Congestion Impacts

Key Question : If r egulations reduce truck performance and/or reduce or increase truck VMT, what are the potential implications for congestion?
Background: Section (ii) describes how the demand for long

- haul trucking could increase if fuel economy regulations effectively reduce the net operating cost of long

- haul trucking. Truck demand is estimated to increase by 3.2 to 15 billion VMT (an increase of 2.2 to 10.5 percent), depending on the technology alternative selected, the prevailing national truck price elasticity and rail cross price elasticity, regulation, and techno logy response. While degraded truck performance could also impact congestion, t he concurrent white paper produced by ERG suggests that the impacts of fuel economy regulations on truck performance will be minimal, if any

.
Th e remainder of this analysis focuses on basic freeway sections because fuel economy regulations are most likely to affect long

- haul truck traffic
, which spend the vast majority of their time on freeways. (T ruc ks have a significant impact on congestion at other traffic control locations
, such as signalized and unsignalized intersections, merge sections, and freeway

- to

- freeway off

- ramps , but the data to analyze these impacts is not readily available.) In the hig hway capacity manual (HCM), in basic freeway analysis, trucks are represented as ―passenger car equivalents‖ (PCE). 20 The PCE concept is meant to capture the effect that heavy vehicles have on traffic flow on a freeway because heavy vehicles occupy more sp ace, travel more slowly up steep grades and more quickly down them, accelerate more slowly, brake more slowly, and change lanes more slowly than passenger cars. Furthermore, different passenger car operators react differently to the presence of trucks; fo r example, following further behind trucks than cars. An increase in the total truck traffic PCEs on the road could be attributable to a number of effects, most significantly an increase in total traffic, although there could also be modest impacts if tru cks have lower power output , higher weight, or regulations that encourage class

- shifting.
The HCM increases the PCE for trucks based on roadway conditions such as grade, number of lanes, distance to roadside obstructions, and width of lanes. It does not, however, distinguish between truck PCEs under congested and uncongested conditions . FHWA simulated the effect of combination trucks as part of its Truck Size and Weight Study to estimate how effective truck PCEs change based on the weight

- to

- horsepower ra tio of the truck itself, the grade of the roadway, the road type, geography, and congestion levels. They did not estimate the impacts of

20 Transportation Research Board, Highway Capacity Manual, Washington, D. C., 2000.- 13

-

other roadway characteristics such as lane width or distance to obstructions

. 21 The tables from the FHWA report are re produced in this paper and to show how different scenarios change how trucks impact roadway volumes. Generally, steeper grades, longer hills, fewer lanes, and a higher weight

- to

- horsepower ratio increase the number of PCEs for a single truck. Truck PCE

co nversions are necessary to calculate a volume

- to

- capacity (V/C) ratio, which can be used to estimate congestion and delay measurement. Figure A

- 1 at the end of this document shows PCEs for trucks on rural highways.

While different formulas have been devel oped to estimate traffic speeds based on V/C ratios, an illustrative example can be provided through the use of t he Bureau of Public Roads (BPR) formula:

Congested Speed = (Free

- Flow Speed) / (1 + 0.15 * [volume/capacity] ^ 4)

The formula implies that a s a basic freeway segment approaches capacity (as the V/C ratio approaches 1 .0 ), traffic speed will be reduced. To calculate delay, one must calculate both congested and free

- flow travel time from the segment length and congested and free

- flow speeds, the n measure the difference between the free

- flow travel time and the congested travel time.
An alternative perspective on congestion measurement uses estimates from literature of the marginal cost of one combination truck on overall congestion. In o ne study , Parry estimates the marginal congestion cost of combination trucks to be $0.168 per mile in urban areas and $0.037 per mile in rural areas. 22 The marginal congestion cost describes the cost, measured in lost travel time, that a single additional combinat ion truck imposes on the rest of the traffic already on the roadway. Generally, as congestion increases, the marginal cost increases. In his analysis, Parry uses an estimate of PCEs that is consistent with the HCM but does not take into account the full impacts of trucks on congestion. To wit, the marginal cost estimates are likely to be larger.
Approach :

F or estimation of change in delay : The approach taken is to c onceptualize an average high

- truck traffic basic freeway scenario, create a comparison s cenario with additional 2.2

- 10.5 percent truck traffic (from Section (1)(ii)), estimate the V/C ratio for each scenario, estimate congested segment speeds based on the BPR formula, and calculate delay for each scenario. This approach assumes that the av erage nationwide increase in truck traffic occurs on this hypothetical congested segment.

F or estimation of change in marginal cost : An alternative approach uses Parry‘s marginal congestion
costs. To estimate total increased marginal congestion costs, the current urban and rural Class 8 truck VMT is identified
, and the percent change in VMT calculated in Section (1)(ii ) applied to estimate the total marginal costs

.

21 Battelle, ―Traffic Operations and Truck Size and Weight Regulations, Working Paper 6,‖ prepared for the Federal Highway Administration, February 2005.

22 Parry, Ian, ―How Should Heavy

- Duty Trucks Be Taxed?‖ Resources for the Future, April 200 6. See Table 1 for his benchmark parameter values for mileage

- related marginal external costs.- 14

-

Scenarios for estimation of change in delay : A basic four

- lane freeway segment (two lanes i n either direction) is at capacity when there are at least 2,200 passenger cars per lane per hour, or 4,400 passenger cars traveling in either direction. The roadway has a free

- flow travel speed of 70 mph, no horizontal obstructions, two 12

- foot travel lan es, no grade, and is 10 miles in length. The two scenarios below illustrate the impact of trucks on highway capacity under a condition of moderately heavy demand :

Base scenario : A roadway that carries a significant number of trucks and passenger vehicles but currently operates at an acceptable level of service, and does not experience many hours of delay. The volume (demand) consists of 3,400 total vehicles on the basic freeway segment per hour . Ten percent of them (340 vehicles) are trucks and they have , on average, a 500 horsepower engine and a fully

- loaded gross

- vehicle weight of 80,000 lbs, which produces a weight to horsepower ratio of 160 lb/hp (80,000/500) ;
Truck VMT increase scenario : This scenario is identical to the base scenario described above except that trucks are increased between 2.2 and 10.5 percent to grow with the increase in traffic described in Section (1)(ii)

. Findings:

Base scenario for estimation of change in delay : To estimate the volume to capacity ratio, take the 3,400 vehicles a nd add the additional PCEs created by the trucks. Each truck on this segment occupies the same capacity as 2.6 passenger vehicles (From Figure A

- 1 , each truck is worth 2.6 PCEs). The road operates as if there are 3,060 autos plus 884 PCEs (340*2.6), iden tical to a scenario where there are no trucks and 3,994 passenger cars. The estimated V/C ratio is 3,944/4,400, or 0.90. Using the BPR formula, the congested speed for this segment is 63.8 mph ( (70) / (1 + 0.15 * [3,944/4,400] ^ 4) =63.8 mph ). It takes 8.6 minutes to travel the 10

- mile roadway in free

- flow conditions and 9.4 minutes in congested conditions, which implies a delay of 0.8 minutes per vehicle under the operating conditions described in the scenario. For all 3,400 vehicles, this implies a to tal delay of 2,720 minutes of delay , approximately 45 hours

.
Truck VMT increase scenario for estimation of change in delay : In this scenario, truck volumes increase by 2.2

- 10.5 percent over the base scenario conditions, an increase of 8

- 36 trucks or an increase in total vehicles of 0.2

– 1.1 percent . The road now operates as though there are 3,060 autos plus 904

- 978 PCEs (348*2.6; 376*2.6), identical to a scenario where there are no trucks and 3,964

- 4,038 passenger cars. The V/C ratio is 3,964/4,4 00

– 4,038/4,400, or 0.90

- 0.92. Using the BPR formula, the congested speed for this segment is 63.7

- 63.3 mph . This marginally increases the travel time for the 10

- mile segment to 9.4

- 9.5 minutes in congested conditions, which implies a delay of 0.8

- 0.9 minutes per vehicle. For all 3,408

- 3,436 vehicles, this implies a total delay of 2,726

- 3,092, approximately 45

- 52 hours

– an increase in delay of 0

– 16 percent compared to the base scenario

. This increase in delay is small compared to the o verall travel time on this segment ( although it is valid only at this particular point on the BPR curve. ) Furthermore, the majority of VMT on U.S. freeways and arterial roadways occurs at acceptable levels of service (71 percent of urban VMT and 92 percen t of rural VMT currently operate s at level of service D or better) and impacts on these roadways are likely to be minimal. However, the marginal impact on delay will become larger as the capacity of the road is approached. For some roadway s operating at or near- 15

-

capacity and with high truck volumes
, it is possible that increased truck travel as a result of the rebound effect from higher fuel efficiency could have a n impact on delay that is not insignificant

. For estimation of change in marginal cost : The increase in r ural and urban Class 8 truck VMT is 1.9 to 8.7 billion VMT ( 58 percent of the previously estimated 3.2 to 15 b illion VMT increase ) and 1.3 to 6.3 billion VMT ( 42 percent of the 3.2 to 15 billion VMT increase ) , respectively

. 23 Using Parry‘s

ma rginal congestion costs of $0.037 per

- mile in rural areas and $0.168 per

- mile in urban areas , the total increase in cost ranges from $ 0. 3 billion to $1

. 4 b illion (e.g. $0. 3 billion = 1.9 billion rural VMT * $0.037 per

- mile + 1.3 billion urban VMT * $0.168 per

- mile)

.


### (vii) Safety Impacts

Key Question : If r egulations reduce truck performance and/or reduce or increase truck VMT, what are the potential implications for safety?
Background: Section (1)(ii) describes how the demand for long

- haul trucking will in crease if fuel economy regulations effectively reduce long

- haul truck operating costs. Truck traffic is estimated
to increase between 3.2 and 15 billion VMT (an increase of 2.2

- 10.5 percent), depending on the technology alternative s and assumed demand e lasticit ies . Literature shows that truck traffic has a direct correlation with injuries and fatalities. Estimates of increased truck traffic can be multiplied by the injury and fatality crash rates to estimate the range of potential deaths and injuries c aused by the increase in travel.
The concurrent white paper produced by ERG suggests that the impacts of fuel economy regulations on truck performance will be minimal, if any, and therefore performance impacts on safety are not considered further.

The saf ety impacts of changes in truck traffic can also be valued using marginal safety cost estimates from the literature . T he marginal cost that one combination truck has on the safety of the population describes the cost, measured in property damage, medical c osts, value of a statistical life, etc. that a single additional combination truck imposes on the rest of the traffic already on the roadway. The marginal safety cost of combination trucks has been estimated by Parry to be $ 0.018 per mile in urban areas a nd $ 0.034 per mile in rural areas

. 24 Generally, the marginal costs are higher in rural areas than in urban areas because the speeds are higher and there is a higher likelihood of a larger speed differential, a key determining factor in highway accident seve rity and likelihood

. Findings :
For estimation of increase in injuries and fatalities: Recent data for highway crashes indicate crash rates for truck tractors with trailers of 2.4 per 100 million VMT for fatal crashes and 51.1 per 100

23 Federal Highway Administration, Highway Statistics , 2009 and Bureau of Transportation Statistics, Pocket Guide to Transportation , January 2009.

24 Parry, Ian , ―How Should Heavy

- Duty Trucks Be Taxed?‖, Resources for the Future, April 2006. See Table 1 for his benchmark parameter values for mileage

- related marginal external costs.- 16

-

million VMT for injur y crashes

. 25 Based on these average rates, the rebound effect analysis (3. 2

- 15 billion additional VMT annually) implies a national increase of 80

- 360 fatalities per year and 1,6 00

– 7, 700 i njuries per year.
For estimation of change in marginal cost : T he increase in rural and urban Class 8 truck VMT is 1.9 to 8.7 billion VMT (58 percent of the previously estimated 3.2 to 15 billion VMT increase) and 1.3 to 6.3 billion VMT (42 percent of the 3.2 to 15 billion VMT increase), respectively. 26 Using Parry‘s marginal congestion costs of $0.0 34 per

- mile in rural areas and $0. 018 per

- mile in urban areas , the total increase in cost ranges from $0.09 billion to $0.4 billion (e.g. $0.09 billion = 1.9 billion rural VMT * $0.034 per

- mile + 1.3 billion urban VMT * $0. 018 per

- mile)



### 2. Alternative Approaches to Improving Fuel Efficiency

Three alternative approaches to improving fuel efficiency are discussed in this section: a fuel tax, congestion pricing, and improvements in intermodal transport to divert freight from truck to rail.



### (i) Fuel Tax

Key Question : What impact would an increa se in the fuel tax (assessed on diesel fuel, all motor vehicle fuel s , or an all fuels based on carbon content ) have on truck fuel consumption ?
Background : A fuel tax would affect truck fuel consumption in two ways: first, it would reduce truck traffic vol umes , and second, it would encourage the purchase of more fuel

- efficient vehicles and the retrofitting of existing vehicles with fuel

- saving technology . It is possible to estimate the first effect, truck VMT reduction due to operating cost increase, using the elasticities of VMT with respect to price of truck travel as described in section (1)(ii). The second effect can similarly be evaluated using evidence on the elasticity of truck fuel efficiency with respect to fuel price. While there is considerable literature on this subject as applied to overall on

- road vehicle traffic, this literature generally does not distinguish between personal and commercial vehicle elasticities and therefore the primary effects represented are for personal vehicles and travel . For example, Small and Van Dender (2007) provide a review of previous studies as well as recent estimates using data through 2004. The authors estimate elasticities of VMT, fuel intensity (gallons/mile), and total fuel consumption (VMT multiplied by fu el intensity) with respect to fuel price changes. Elasticities are estimated both over a historical time period (1966 to 2004) and within the past few years (2000 to 2004) to examine how elastic ities might be changing. These findings are shown in Table 1.

25 Federal Motor Carrier Safety Administration, ―Commercial Vehicle Facts,‖ Novem ber 2007 http://www.fmcsa.dot.gov/facts

- research/facts

- figures/analysis

- statistics/cmvfacts.htm (Accessed September 16, 2009).

26 Federal Highway Admin istration, Highway Statistics , 2009 and Bureau of Transportation Statistics, Pocket Guide to Transportation , January 2009.- 17

-



### Table 1 Historical and Recent Long

- Run Elasticities With Respect to Fuel Price

Calculated Long

- Run Price Elasticities With Respect to Fuel Price of: Elasticity 1966 to 2004 Elasticity 2000 to 2004
Vehicle Miles Traveled

- 0.210

- 0.057 Fuel Intensity

- 0.193

- 0.191 Fuel Consumption

- 0.363

- 0.237 Rebound Effect (Percentage) 21.0% 5.7%

Source: Small and Van Dender (2007). The values shown in this table are “ long

- run ” elasticities

– i.e., response

over a multi

- year period after the price change. Long

- run elasticities will be greater than the imme

diate or short

- term response since travelers are able to make more fundamental adjustments to their activity patterns, such as changing residence or worksite locations or changing the number and types of vehi cles owned.
The findings imply, for example, that based on data from the 1966 to 2004 period, a 100 percent increase in fuel price should lead to a 21 percent reduction in VMT and a 19 percent increase in the fuel efficiency of vehicles, for an overall n et decrease in fuel consumption of 36 percent. For the more recent period of 2000 to 2004, the elasticity of VMT with regard to fuel price declined substantially, such that a 100 percent increase in fuel price would lead to only a 6 percent long term decl ine in VMT. Table 1 shows that historically, about half of the impact of a fuel price increase would be on VMT reduction and the other half on fuel efficiency; but that in recent years, the VMT response to price increases has become much lower

– less than one

- third the magnitude of the fuel efficiency effect. Another recent review of elasticities by Sperling 27 suggests that elasticity values are higher, but also reache s a similar conclusion that elasticities with respect to VMT have declined in recent year s. Sperling found that long run elasticities of fuel con sumption with regard to fuel price “ may be as low as

- 0.2. ”

C onverting Small and van Dender‘s fuel intensity elasticity, which is measured in response to fuel prices, to an elastic ity based on ov erall vehicle operating expenses , would imply about a two to three times higher elasticity (since fuel cost represents only about one

- half to one

- third of total operating costs, as discuss in Section (1) (ii)) , or up to around two to three times

- 0. 19 (abou t

- 0.4
to

- 0.6)

. Doing the same with the 1966

- 2004 VMT elasticity produces a similar result, which falls at the lower end of the range identified previously through studies specific to truck traffic , and about half the value selected above based on FHWA‘s report (

- 0.97)

. Approach : The question investigated here is what level of fuel tax would be required to reduce truck fuel consumption by roughly 20 to 40 percent, the same range of benefits assumed in the scenarios evaluated in Section ( 1 ) (ii). It is not known how well the elasticities reported by Small and Van Dender represent responses for the commercial vehicle sector. However, if it is assumed that the fuel intensity response is about the same magnitude as the VMT response, the previous analysis of VMT response can be used as a basis for estimating the combined response , by answering the question of what fuel price increase would be required to reduce VMT by 10 to 20 percent (and

27 Sperling, D an, ― Consumer Response to Fuel Price Changes: Implications for Policy ,‖ January 15, 2008

.- 18

-

assuming that a similar decrease in fuel intensity of 10 to 20 perce nt is also achieved, for a net effect in the range of 20 to 40 percent). T he analysis is simplified by selecting an elasticity for both rail and truck demand used in relevant U.S. studies . The truck price elasticity of

- 0.97 used by FHWA and rail cross

- e lasticity of 0.52 from AAR were selected. Findings :
T he fuel tax necessary to achieve 20

- 40 percent lower fuel consumption in the average long

- haul truck is $0. 18

- $0. 36 per mile. Because the cost of fuel per mile is currently $0.634 28 at an average fu el economy of 5.59 mpg, the data imply a fuel price of $3.54 gallon ($0. 634 per mile * 5.59 miles per gallon). Using the same conversion, the increase in the per

- mile fuel tax translates to a n increase in the per - gallon fuel tax of $ 1 .01

- $ 1

. 98 , for the 20 and 40 percent scenarios respectively. This will reduce truck fuel consumption by 5,100

- 10,100 million gallons (20

- 40 percent from current fuel consumption levels of 25,500 million gallons).

This corresponds to a decrease in truck traffic from curr ent annual VMT of 71 , 400 to 67
, 000 or 43
, 000 for the 20 and 40 percent reduction cases, respectively. Based on the cross

- price elasticity of 0.52, rail ton

- miles will increase from 1,852 billion ton

- miles to 1 , 95 3

- 2, 05 3 billion ton miles in the 20

- 40 percent cases. This increase in rail traffic will increase rail fuel consumption by 230

- 460 million gallons. This will reduce the impact of the truck fuel tax increase from 5,100

- 10,100 to 4,870

- 9,640 million gallons, reducing the effectiveness of the reduction by approximately 5 percent in both cases.

Potential for Government Promotion: An increase in the Federal diesel motor fuel tax would be easy to administer as the mechanism already exists for levying and collecting this tax. However, the inc rease required to achieve significant fuel efficiency benefits

– estimate d to be in the range of $1 to $2 per gallon

– is much higher than would be acceptable in the current policy environment (where, in fact, even small increases of five to 10 cents per g allon may be considered intolerable). It also significantly exceeds the likely fuel price increase under a national
cap

- and

- trade program. Analysis by the Energy Information Administration (EIA) of cap and trade legislation under the previous Congress sho wed that a $50 per ton ne allowance price

– at the upper range of what would be expected in 2030

– corresponds to a gasoline price increase of about 45 cents per gallon in 2030. 29 A lower allowance price of $30 per ton ne ( 27 cents per gallon) is consistent with the range in the draft American Clean Energy and Security Act of 2009 . Using the same assumptions as the above analysis, this price would result in a decrease in commercial vehicle fuel consumption of only about 5 percent.


### (ii) Congestion Pricing

K ey Question : What impact would congestion pricing have on fuel consumption ?
Background: Congestion pricing could take different forms, such as area

- wide network pricing on freeways and possibly arterials, ―cordon‖ or area pricing in central business dist ricts, or

28 American Transportation R esearch Institute, An Analysis of the Operational Costs of Trucking , December 2008.

29 U.S. Department of Energy, Energy Information Administration, ― Energy Market and Economic Impact s of S. 2191, the Lieberman

- Warner Climate Security Act of 2007
, ‖ 2008. http://www.eia.doe.gov/ oiaf/service_rpts.htm- 19

-

truck

- specific congestion pricing such as the varying time

- of

- day gate fees implemented at the Ports of Los Angeles and Long Beach.
Congestion pricing could affect truck fuel consumption by:

Shifting trips to less

- congested off

- peak hours;

Redu cing congestion for trucks continuing to operate during peak periods; Reducing overall goods movement and related truck traffic due to higher costs; and Shifting logistics patterns

– e.g., leading industries to establish consolidation centers on the edges of urban areas to reduce truck activity within the congested area.

Area

- wide congestion pricing is applicable to freeways and major arterials where there is significant congestion. C ordon pricing strategies are only applicable in major urban areas with

s ignificant congestion. The limited geographic applicability of these two scenarios limits the fuel reduction potential. Area

- wide congestion pricing has greater potential since it is estimated that nearly 30 percent of urban VMT occurs at level of servic e E or F. Cordon pricing of metropolitan area central business districts, however, is estimated to affect only 3 percent of total VMT nationwide. Furthermore, evidence suggests that there will be little, if any, overall impact on total truck traffic (as the added costs are likely to be marginal, or the option of moving to the off

- peak period acceptable), but rather that the benefits will occur from trucks operating under improved flow conditions, and therefore using less fuel due to idling or stop

- and

- go operations. This will have a larger impact on smaller urban trucks since larger long distance trucks operate mostly on uncongested highways.
It should be noted that while reducing congestion should improve fuel economy up to a point (e.g., increasing ave rage speeds from 10 to 20 or 20 to 30 mph), truck fuel consumption rates also tend to increase at higher speeds (over 45

- 55 mph), and therefore, increasing speeds from mid

- range congestion (30

- 40 mph) to free

- flow highway speeds may have a negative fuel

ec onomy impact. Congestion is likely to affect urban service and delivery movements more than long

- haul freight, and therefore it is the fuel consumption characteristics of smaller trucks that are most important.
If congestion pricing is implemented only on a limited basis (e.g., only freeways), diversion of traffic to other nontolled facilities is likely to be a significant concern because of the impacts on neighborhood and local traffic . Increases in VMT on alternate routes could offset the fuel savings achieved from reductions in VMT and congestion on the facility itself. Therefore, congestion pricing will be most effective at reducing fuel consumption if it is implemented universally (on all major roads in an area).

W hile reducing congestion can save fuel, there is an implicit limit on these savings, bounded by the total fuel wasted in existing congestion. In this analysis, a sketch

- level estimate is made of the total fuel ―wasted‖ by trucks, and therefore the potential upper bound benefits of congest ion relief strategies

. This analysis only considers the fuel savings from improved traffic operations, and not any reduction in fuel use due to an overall decrease in demand.
Finding s : Most s tudies of the impact of congestion pricing have focused on all traffic, rather than distinguishing impacts on personal vs. commercial vehicle traffic. A study for the U.S.- 20

-

Department of Energy used travel demand models in Minneapolis

- St. Paul and Seattle, in conjunction with speed

- fuel efficiency relationships, to ev aluate the combined benefits of travel reductions and operating efficiencies from areawide systems of managed lanes. 30 The results from different scenarios ranged from an 0.1 to 2.5 percent impact on fuel consumption and GHG emissions depending upon the sc enario. Extrapolating these results to a national level based on projected 2030 congestion levels in different urbanized areas led to an overall estimated reduction in national fuel consumption ranging from 0.5 to 1.1 percent. 31 Another national study of greenhouse gas (GHG) emission reduction strategies estimated that c ordon pricing could potentially reduce VMT on the order of 3 percent if applied to all metropolitan areas in the United States

. 32 These are rough estimates for all vehicles, however, that m ay not be transferable to truck traffic.

Evaluations of c ordon pricing schemes implemented in both London and Stockholm examined effects specifically on truck traffic. Experience in London suggests that the reduction in overall vehicle

- kilometers of trave l (VKT) has come almost exclusively from passenger vehicles rather than trucks. While t he cordon pricing scheme reduced total VKT by 16 percent within the pricing zone, the data show that truck demand did not change once the scheme was introduced, remaini ng at a constant level of 0.07 million VKT in 2006. Truck travel speeds also did not change significantly. On the other hand
, truck trips crossing the cordon declined, suggesting t hat each truck makes more deliveries, generating an equivalent truck VKT

. Furthermore, these trucks have benefited from reduced queuing and subsequently, truck idling and associated fuel consumption was reduced.
To measure the impact of London‘s cordon pricing scheme on truck fuel consumption, it is necessary to compare truck idling before and after the scheme was introduced. Once the scheme was introduced, excess delays were reduced by 26 percent, from 2.3 to 1.7 minutes per kilometer. 33 Given 70,000 truck

- kilometers traveled and a reduction in excess idling delay of 0.6 min utes per kilometer (2.3

- 1.7 minutes per kilometer), the scheme reduced truck idling by a total of 42,000 minutes (700 hours). With each truck hour of idling consuming 0.8 gallons per hour, 34 the truck fuel consumption reduction from congestion pricing woul d have been 560 gallons annually

– a very small amount

.

30 These systems included high

- occupancy/toll (HOT) lanes on freeways, in which drivers of single

occupancy vehicles can use the lane if they pay a fee which depends upon the congestion on the untolled travel lanes. Depending upon the scenario, either existing/planned high

- occupancy vehicle (HOV) lanes were converted to HOT lanes, or a new HOT lane was constructed alongside an existing/pl anned HOV lane to form two HOT lanes.

31 Energy an d Environmental Analysis, Inc., ― Market

- Based Approaches to Fuel Economy: Summary of Policy Options ,‖ p repared for National Energy Technology Laboratory, U.S. Department of Energy
, 2008.

32 Cambridge Syste matics, Inc. , Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions
, Urban Land Institute: Washington, D.C
, July 2009.

33 Transport for London, Central London Congestion Charging: Impacts Monitoring, Fourth Annual Report , June 2006.

34 U.S. Environmental Protection Agency, ―A Glance at Clean Freight Strategies: Idle Reduction,‖ February, 2004.- 21

-

In Stockholm, the cordon pricing scheme reduced total GHG emissions from vehicles by 10 to 14 percent in the pricing zone

. According to the manual counts of travel within the zone, there has been a 1 3 percent reduction of truck trips , but only a 7.8 percent reduction in truck VMT once the scheme was implemented. The total VMT is 2,185,000 and heavy truck VMT is 55,994, 2.8 percent of total VMT. If the GHG were reduced by 14 percent, then trucks were responsible for 0.4 percent of the total reductions
, a modest amount

.

To estimate the maximum potential fuel savings from congestion reduction, it is necessary to estimate total freight truck delay as well as total fuel consumption by hour of delay. Wi nston and Langer 35 estimate the cost of congestion to freight trucks to be approximately $10 billion in the year 2000
, based on the Texas Transportation Institute‘s measures of urban delay in 2000 and the FHWA Freight Analysis Framework estimates of total o rigin

- destination delay. Using the authors‘ value of time of $30 per hour of delay , the results imply a total of 333 million hours of delay ($10 billion/$30 per hour). According to a 1993 FHWA study, which produced fuel consumption estimates for the High way Performance Monitoring System, a combination truck consumes 1.934 gallons of fuel per hour in congestion. 36 These values imply that, in 2000, the total excess national fuel consumption resulting from highway congestion was at most 500 million gallons ( 333 million hours * 1. 51 gallons/hour) , or 2.0 percent of (current) truck fuel consumption. 37 This value provides the upper bound for the potential fuel consumption savings from congestion reduction s trategies ( including congestion pricing ) , assuming that only operational (and not demand) effects are considered.

Potential for Government Promotion : Congestion pricing has been experimented with in a number of areas, primarily on existing tolled facilities, but has not yet gained widespread popularity. From a technical standpoint, congestion pricing is relatively easy to implement on facilities that already are tolled. The broader

- scale application of this strategy beyond existing or proposed toll highway facilities , however, is likely to require the univers al deployment of electronic toll collection technologies. This will require coordination by a state or regional transportation agency. The U.S. DOT is encouraging greater experimentation in this area. In 2007, the Department awarded $853 million in fund ing to five metro areas for Urban Partnership Agreements to reduce congestion, which include a significant focus on toll ing/ pricing strategies.



### (iii) Intermodal Transport

Key Question : What impact would public

- sector initiatives to improve intermodal transport have on fuel consumption ?

35 Winston and Langer, ―The Effect of Government Highway Spending on Road Users‘ Congestion Costs,‖ AEI

- Brookings, May 2006.

36 Science Applications International Corporation, Speed Determination Models for the Highway Performance Monitoring System, prepared for the Federal Highway Administration, 1993

. 37 Two factors may cause this estimate to be overstated

– first, the use o f the combination truck fuel consumption rate slightly overestimates the total wasted fuel since this rate will be less for single

- unit trucks (1.607 gallons/hr); and second, trucks have become more fuel

- efficient since that time (average truck fuel effici ency increased by 22 percent between 1993 and 2007, according to the Transportation Energy Data Book Edition 28, Table 5.1). On the other hand, total congestion has increased since the Winston and Langer study was conducted in 2000.- 22

-

Background : Improvements to intermodal transport, such as rail capacity improvements and bottleneck relief, intermodal (truck

- rail) terminals, and financial/pricing incentives, could potentially encourage shippers to make greater use of rail in place of truck, increasing the efficiency of freight movement on a ton

- mile basis.
Work in pro gress for U.S. DOT on transportation GHG reduction strategies includes a literature review of the potential GHG benefits of shifting freight from truck to rail through intermodal improvements. 38 Reductions in fuel consumption on the order of 60 percent per ton

- mile are typical for shifts from trucking (trailers or containers) to long

- haul intermodal rail, with reductions decreasing with shorter distances. Savings can vary significantly, however, depending upon the distance of the movement and type of cargo

. Estimates of total potential freight mode

- shifting have been aspirational in nature, rather than based on empirical data, due in large part to the complex nature of competition between trucks and rail. The potential for mode

- shifting is limited to cer tain types of commodities

— those that are heavy, low

- value, and do not have an acute need for reliable and timely delivery

— e.g., building stone and waste , as well as certain movements

— in particular, long

- haul movements where the efficiency benefits of rail outweigh the additional handling/logistics costs and time at either end
, generally shipments longer than 1,000 miles . Furthermore, m arket demand both affects and is dependent upon the quality of service . R ail service improves significantly as demand betw een market pairs increases

– increased traffic (trains per day) increases the level of service that railroads provide to customers , and means that improved access is possible since (shippers need access to rail facilities to ship via rail). In short, ship pers choose a mode that minimizes their total logistics cost.
There are numerous ways to estimate diversion, but each has its flaws. In general, simple techniques (e.g., the  ̳Delphi Method,‘ comparative market analysis, and elasticity methods) rely

on s implifying assumptions and sketch planning techniques while complicated techniques ( such

as FHWA and the Federal Railroad Administration ‘s Intermodal Transportation and Inventory Cost Model 39 and econometric models) require significant data resources, time resources, and computation power. Furthermore, complicated techniques are very sensitive to inputs and the inputs are often modeled. For example, public truck flow data, by commodity, do not exist while rail data is sampled, proprietary, and requires tra ffic modeling for model estimation, all of which decrease the reliability of results.
Despite the difficulties of estimating the size of diversion impacts, it is generally accepted that this phenomenon exists and that there are a consistent set of variable s that impact the outcome. Actions that can affect a truck

- rail mode shift include investment in rail and intermodal terminal infrastructure, direct operating subsidies for railroads, land use regulations (for example, to preserve rail sidings for rail

- or iented businesses) , and taxes to increase the cost of truck travel, as previously discussed

.

38 Cambridge Systemati cs, Inc., Transportation’s Role in Reducing Greenhouse Gas Emissions , Forthcoming
, prepared for Federal Highway Administration

. 39 Federal Highway Administration and Federal Railroad Administration, Intermodal Transportation and Inventory Cost Model, Highw ay

- to

- Rail Intermodal User’s Manual , March 2005.- 23

-

Findings : The following studies either estimated diversion potential from rail investment in specific corridors

.

Mid

- Atlantic Rail Operations (MAROps) study : 40 Th e MAROps study used the comparative market approach to estimate the diversion potential in the Mid

- Atlantic region. The comparative market approach looked at every market pair in the country and compared rail travel distance to truck travel distance (a me asure of the circuity of rail), commodity, traffic density (tons of good shipped), and, finally, mode

- share of truck and rail. Mid

- Atlantic markets were identified that, given similar operating conditions to other markets nationally, could carry more rail traffic, which would reduce truck traffic on the parallel highway corridor. The study estimated the potential diversion from projects representing a $12 billion investment in the Mid

- Atlantic rail network to be somewhere between 0.67 and 1.8 million
truc k trips annually, or 237 to 638 million VMT (4 to 11 percent of total regional truck VMT) in 2035 (unpublished result). This diversion would save 42 to 114 m illion gallons of diesel fuel in trucks (based on today‘s 5.59 mpg fuel economy) , not accounting for increased rail traffic . This implies a range of $7,000 to $18,000 per diverted truck trip in the Mid

- Atlantic and between $ 110 and $ 290 in investment per gallon of fuel saved based on 2008 dollars and 2035 traffic.

Norfolk Southern Crescent Corridor : 41 The Crescent Corridor will provide premium service intermodal trains to compete with I

- 81 long

- haul truck markets between the New York area and the Southeast. This market is underserved by rail today and has significant potential for growth. Improvement s include new track, upgrading signals, and removing choke points. Norfolk Southern estimates that a $2.5 billion investment will result in the diversion of 1,000,000 truck trips annually . Over an assumed 30 year life of the project, this implies a cost of $ 83 per truck trip ($2.5 billion / (1 million trips * 30 years)
, and a saving s of more than 170 million gallons annually.

The Northeast

- Southeast

- Midwest Corridor Marketing Study: 42 Th is study used the Reebie Associates ‘ Diversion Model , a detailed statistical model
, to estimate diversion from truck to rail based on reductions in rail operating costs stimulated by public investment in rail infrastructure . The study found that a long

- term ( 13 to 17 years), corridor

- wide (the entire I

81 corridor), public investment in rail intermodal infrastructure of $7.9 billion could divert approximately 812 million truck VMT. The study did not estimate the probable reduction in fuel consumption but the findings imply that truck fuel consumption would be reduced by 145 million gallons annually (812 million VMT/5.59 mpg), implying $54 in investment costs per gallon of fuel saved in 2003 dollars

.

40 Cambridge Systematics, Inc., Mid

- Atlantic Rail Operations Study, prepared for the I

- 95 Corridor Coalition, forthcoming.

41 http://www.nscorp.com/nscportal/nscorp/Media/News%20Releases/2009/greencastle.html (Accessed September 16, 2009)

42 Reebie Associates, The Northeast

- Southeast

- Midwest Corridor Marketing Study , prepared for Virginia Department of Rail and Public Transportation, December 2003.- 24

-

Potential for Government Promotion : The government could promote rail diversion through the promotion of fre ight villages ; aiding in the siting of intermodal terminals , transload facilities, and bul k storage facilities; expand market reach for regional railroads; and the continued improvement in rail infrastructure, including signal, track, bridge, terminal, an d clearance upgrades.
While freight rail infrastructure investment has traditionally been left to the private sector, the Federal government as well as a number of states have increasingly become involved in this issue for purposes of economic development and road traffic reduction. There are several state and Federal programs that will fund rail improvement to help bridge the gap between investment needs and the availability of private capital. The Federal

- aid highway funding program also allows some fl exibility in using funds for non

- highway freight transportation projects.
To date most of the easier rail capacity improvement projects have been built, leaving primarily the more difficult and expensive projects. In addition to being expensive, many of the remaining critical needs are set in urban environments where there are substantial constraints on right

- of

- way as well as added costs for mitigation of impacts. These barriers will pose challenges to large

- scale improvements in freight infrastructure sufficient to leverage significant truck

- rail mode shift.- 25

-



### Figure A

- 1 Vehicle Passenger Car Equivalents on Rural Highways

Source: Battelle, ―Traffic Operations and Truck Size and Weight Regulations, Working Paper 6,‖

prepared for the Federal Highway Ad ministration, February 2005.