## 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., September 17, 2009



### Introduction

This paper was commissioned by the National Ac ademy of Sciences in support of its committee on medium-duty and heavy-duty truck fuel ec onomy standards as directed by the Energy Independence and Security Act of 2007. Th e Academy working group is charged with identifying the potential costs and other impacts of fuel economy technologies on the operation of medium-duty and heavy-duty trucks. The pape r discusses two sets of issues related to potential Federal regulations to improve fuel ec onomy 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, cong estion, safety transportation service, and other factors in addition to the direct effects on fuel us age. The second set of issues is the potential for alternatives to fuel economy regulati ons for improving fuel efficiency.

This paper, authored by Cambridge Systematics, In c. (CS), examines a subset of these issues and complements a companion paper authored by Eastern Research Group, Inc. (ERG). Some of the issues are addressed by both authors. For exampl e, for congestion effects, ERG’s paper discusses the relationship between technology and perform ance, while CS’ paper discusses the resulting impacts of performance on highway congestion. The issues were divided between the two firms based on their respective areas of expertise as follows:

CS Paper First subset of issues (indirect costs)

• Vehicle-miles traveled and the rebound effect;

• Vehicle class shifting;

• Implications of transportation service and performance effects (with ERG input on performance);

• Congestion impacts (with ERG input on performance); and

• Safety impacts (of changes in traffic volume or performance). Second subset of issues (alternatives)

• Fuel tax;

• Congestion pricing; and

• Intermodal transport. ERG Paper First subset of issues (indirect costs)

• Fleet turnover effects;

• Environmental costs and co-benefits;

• Transportation service and performance effects; and

• Safety impacts (of changes in fuel type). Second subset of issues (alternatives)

• Driver training.This paper relies on findings from the literature on these issues but also includes new, sketchlevel analysis to translate literature findings (whi ch may only indirectly address the issue) into results that directly address the issues being investigated.

The key findings of this paper rega rding 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 partiall y offset the fuel savings of more efficient trucks. Fuel economy regulati ons that achieve consumption reductions on the order of 4,500 million gallons (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 an average Clas s 8 per-mile operating cost reduction of $0.08 (considering increased vehicle costs amortized ov er the life of the vehicle as well as decreased fuel costs), which could lead to an increase in truck demand and a decrease in rail demand. Using a range of assumptions regarding the elastici ty of truck travel with respect to price, the increase in truck demand due to the decrease in truck operating costs would increase fuel consumption by 500 to 1,400 million gallons, dimi nishing 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. The 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 truck rebound effect by around 2 percent of initial 4,500 million-gallon reduction. The net rebound effect of the truck fuel consumption in crease 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 some what less cost-effectiv e, raising the initial capital cost of the vehicle 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 class shifting – The potential for vehicle purchasers to shift to different classes of vehicles will depend upon the specific nature of the fuel economy regulations. However, hypothetical regulatory scenarios suggest that si gnificant class-shifting is unlikely to occur in most conceivable situations. This is particular ly true for long-haul trucks, 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 vehicle 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 appreciable effect on vehicle performance and thereforeno 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 roads, but could be significant at times and locations where the roadway is near capacity and where truck traffic represents a signif icant fraction of total traffic. An estimate using marginal per-mile congestion costs 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) of approximately $13 billion nationwide.

• Safety impacts – Analysis in the concurrent paper developed by ERG suggests that fuel economy regulations would have no appreciable effect on vehicle performance and therefore no effect on safety as a result of degraded performance. Increased tr uck traffic volumes from the rebound effect could be expected to cause a very small increase of less than one fatality per year and about two to eight injuries per ye ar nationally. An alternative estimate using marginal safety costs from the literature suggest s that the increased truck VMT could result in an increased safety cost (to all road user s) of approximately $4 billion 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 efficiency. However, elasticities of fuel consumption with respect to fuel pric e are not well-documented for freight trucks so this estimate should be consid ered particularly uncertainty.

• Congestion pricing – No analysis has specifically examined the impacts of a comprehensive congestion pricing system on truck fuel consum ption. 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., although 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, shifti ng of demand to less congested periods, and more free-flowing traffic during peak travel peri ods. 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.

• Intermodal transport – The potential for truck-to-rail mode shifting through investment in rail and intermodal infrastructure and other ince ntives has not been extensively studied. One study of the Mid-Atlantic region estimated that an investment of $12 billion could reduce fuel consumption by 10 to 26 billion gallons annually, at a cost of $0.47 to $1.26 per gallon of fuel saved.



### 1. Indirect Costs and Benefits

Little, if any, previous research has directly in vestigated the effects of commercial vehicle fuel economy standards on the factors addressed in thes e papers. There is a considerable amount of literature that can inform a general assessment of po tential 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 development. Therefore, the approach taken in this paper is to develop hypothetical scenarios that allow for examine what would

- 3 -happen given various fuel economy regulation struct ures 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 a ssessment of impacts. At a minimum, this will help determine whether or not each issue is si gnificant 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 assumpti ons are made in the analysis?

• Findings – What are the results of the analys is under alternative hypothetical scenarios generated for this report – 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 reduce vehicle operating costs, will truck traffic volume increase?

Background: A “rebound effect” in heavy-duty vehicle travel can be characte rized as the extent to which cost savings from heavy-duty vehicle fu el efficiency improvements result in increased demand for truck shipping and decreased demand fo r rail shipping because truck travel is made less expensive per-mile due to reduced fuel cost s and therefore reduced truck operating costs. This increased truck travel partially offsets the to tal fuel savings benefits. The rebound effect has been extensively studied for light-duty (personal) ve hicle travel, but somewhat less so for freight truck travel. 1 However, a number of studies have do cumented relationships between price and demand for truck travel. This literature is focu sed 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 freight; and 3) these movements account for the vast majority of the medium- and heavy-duty vehicle fuel consumption (78 percent). 2 Little or no research exists to provide information on a rebound effect for medium-duty trucks. The sket ch analysis presented herein will focus on long haul trucks, which are generally Class 8 trucks wi th 53-foot trailers and a gross vehicle weight (GVW) of up to 80,000 pounds.

Caution should be exercised when considering the price-demand relati onships found in the literature for freight trucks. The results appear to be heavily reliant on factors including the type of demand measures analyzed (vehicle-miles of tr avel, ton-miles, or tons), analysis geography,

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 the literature. The assessment also concluded that this effect could plausibly range from 10 to 20 or even 25 percent (NHTSA, 2008).

2 Oak Ridge National Laboratory, Transportation Energy Data Book, Edition 28 , prepared for the US Department of Energy, 2009. Se e table 5.4 for relevant data.

- 4 -trip lengths, markets served, and commodities transported. 3 The literature does not treat differences between short- and long-run effect s consistently, and, in general, presents inconsistent results across studies. To provide an envelope of potential effects, the analysis presented herein provides reason able upper and lower bounds of potential effects as well as a mid-point or “likely” estimate. In nearly all cases, the price elasticities from the literature reflect the first effect of the price increase – the decrease of demand. However, they do not describe how firms will react to the cost savings resulting from lower fuel costs. A firm with higher income will purchase more inputs, produce more goods, and demand more freight transport. The figures presented here should be considered on the high side since the secondary effect would counter the primary effect in all cases.

An additional issue is the difference between th e 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 lif etime of the vehicle. For the purposes of this assessment it is assumed that vehicle owners will pass these capital costs along to their customers, and therefore it is the average (net) cost or cost savings that is important.

The reboun d effect in heavy-duty vehicles wo rks in three ways. The first two have been measured and characterized wi th price elasticities while the third is speculative:

• An increase in overall demand for truck shipping , if total shipping costs are reduced (the selfprice 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 utilization of trucks (e.g., lighter loads), because lower costs reduce cost pressures on industry to maximize efficiency. The self-price elasticity provides a measure fo r describing how the volume of truck shipping (demand) changes with its price while the cross-pric e 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 ex ample, 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 decrea se 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 (cro ss-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).

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,” pr epared for the World Bank, Infrastructure and Urban Development Department, January 1990.

- 5 -Oum et al 4 collected 17 estimates of freight transportation demand price elasticities. The authors focused on self-price elasticities and presented el asticity 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 ther e is demand for other products or services – with the exception of freight ship ments that are subject to intermodal rail competition. The paper found truck self-price elasticity to fall in the rang e of -0.05 to -1.34 with the most likely range of -0.7 to -1.10 (the measures of demand were not identified and varied by study). 5 Furthermore, while the general literature on road transportatio n elasticities has dist inguished 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 review ed 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 fr om -0.3 and -1.75 and cross-price elasticities ranging 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 an d Glaister, 7 in a comprehensive review of recent price elasticity results, found that truck self- price elasticity is likely to fall betw een -0.5 and -1.5, but did not identify a demand measure. The Federal Highway Administration (F HWA) used a self-price elasticity of -0.97, measured in vehicle-miles, for a freight 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 from 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 consumptio n; average truck operating cost per-mile; and truck purchase price. For alternatives, it is ne cessary to describe the potential fuel consumption

4 Oum, Waters, and Jong Say Yong, “A Survey of Re cent 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 inel astic” 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 Improvements , 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 Hi ghway 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 User Charges on Canadian Railways, prepared for Transport Canada by the Canadian In stitute of Guided Ground Transport, Kingston, Ontario, September 1990.

- 6 -reduction; logical fuel economy technologies to achieve the reduction; im proved fuel economy; increased truck purchase price; resulting operatin g cost (including savi ngs from fuel economy improvements and increased capital cost from increased purchase price); and self-price and cross-price elasticities.

Scenarios : Three scenarios are outlined below:

• Base case : Total affected truck volume is 142,706 m illion 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 es timates 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 leas e or purchase payments; 15 and the current purchas e 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 reductio n in fuel consumption. The selection of technologies and this target reduction were ba sed 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-kit (fully aerodynamic mirrors, cab side exte nders, integrated sleeper cab roof fairings, aerodynamic bumper, full fuel tank fairings, side skirt fairings, and boat tails), wide single base tires, aluminum wheels, id le reduction, and improved lubricants. The 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 costs, down from $0.70 in the base case, and $0.25 of which is tr uck/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 truck 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 ranges were chosen to include the majority of the estimates while excluding the outliers.

• 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 Transp ortation Statistics, Pocket Guide to Transportation , January 2009.

13 Energy Information Administration, “A nnual 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 Trucking , 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, TIAX, LLC., and International Council on Clean Transportation, Reducing Heavy-Duty Long Haul Truck Fuel Consumption and CO 2 Emissions, September 2009.

- 7 -consumption. This set of technologies includes all alternative 1 technologies plus continued streamlining of the cab, a reshaped trailer, boat tail, full skirting of the cab and trailer, tractortrailer gap fairing, very low resistance tires, bo ttoming cycle, parallel hybrid system, and road speed governors set to 60 mph. The technolo gy improves fuel economy from 5.59 to 9.10 mpg at a cost of $71,630 per vehicle. Accoun ting for the reduced fuel cost and increased capital cost, the net per-mile operating cost w ill be reduced to $1.61, $0.43 of which are fueloil related costs, 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. Th e total national operating cost is $230 billion (142,706 vehicle-miles * $1.61/mile). The elasticity ranges are the same as those employed 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 increase

• Before accounting for the rebound effect, the fuel economy improvements 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 redu ction of 4,500 - 9,800 million gallons of fuel.

• Class 8 long-haul demand will increase (rebou nd) by 3.2 - 9.5 million VMT for alternative 1 and 5.0 - 15.0 million VMT for alternative 2, base d on the projected net decrease in operating costs associated with fuel efficiency impr ovements of $0.08 and $0.12 cents per mile, respectively.

• The VMT increase will 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 ar e implemented. The rebound fo r alternative 2 will reduce the total fuel savings from 9,800 million gallons to 8,200 to 9,300 million gallons, depending on the elasticity. This will cause a rebound ranging from 5 to 16 percent

.

• The rebound effect varies depe nding on the elasticity and the cost-effectiveness of the technology. If the technology is more cost-effectiv e, then there will be a larger rebound effect because the cost of trucking will come down to a larger degree, 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 tec hnologies deployed under alternative 2. The second rebound effect: diversion from rail

• Some of the increased truck travel will be divert ed from rail. With truck per-mile operating costs dropping from $1.73 in the base case to $1 .65 and $1.61 in alternatives 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 w ill 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 re bound effect, and the rail fuel savings for alternative 1 together, th e fuel reductions of 4,500 million gallons will drop to 3,200 to 4,100

- 8 -million gallons, for a combined rebound effect of about 9 to 30 percent (compared to 11-31 percent without rail fuel savings). Considering th e 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 profit s are historically razor-thin. 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 signific ant change in utilization optimization. However, there is no evidence in the literature to qu antify 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 medium 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 ch oice of vehicle charac teristics. Prior to deregulation of the trucking industry, 4-axle trucks were common. After deregulation, most carriers shifted to 5-axle trucks since these truc ks could carry more freight and therefore operate more economically, given weight per axle restrictions.

To induce class-shifting, truck fuel economy regu lations 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 class-shifting effect will therefore de pend upon how the regulations are structured. A “class-neutral” regulation could be conceived that does not lead to any shifts.

There is little or no literature that describes th e cross-class mode shift between truck types based on cost. The potential for class-shifting appe ars limited, however, because most equipment is chosen to fit the physical requirements of a part icular 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 co st (to maximize their profits). The shipment could be longhaul (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 regional warehouse and a grocery store. Shippers and carriers will give additional consideration to whether the commodity is high-v alue and time-sensitive or low-value and less time sensitive, whether the commodity will weig h-out or cube-out, and the labor cost. Other vocational medium- and heavy-duty truck owners have very specific carrying and sizing needs (e.g. dump trucks, garbage trucks, buses, and ut ility trucks). A shipper and carrier will examine the set of requirements, negotiate a rate, and choo se the optimal truck for the shipment. Shifting between trucks and truck classes 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 another class.

- 9 -Approach: Analysis first requires the identification of hypothetical regulations and, second, the identification of specific truck classes and shipment type that have potential for shifting given the regulations.

Findings: The following is a sample 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 classes, the development of complete we ight 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 increase or decrease the weight of a vehicle . If fuel economy regulations were to require all trucks to implement weight-increasin g or –decreasing technologies, there are several potential outcomes, depending on the current us e of the truck. Two potential outcomes are described below:

• Trucks used for certain types of specialized sh ipments that have similar trucks in different classes are the most likely to be interchangeable. For example, a FedEx delivery truck can be one of three classes, including Class 3, 4, or 5 (Cummins). If regulation s 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 Classes include city delivery (Classes 3, 4, an d 5), conventional van (Classes 3 and 4), and conventional tractors (Classes 7 and 8). In the long-run, these changes might result in larger (or smaller) trucks being used for deliveries of a ll sorts, which would lead to an increase in travel.

• Trucks used for certain types of specialized shipments that do not have similar trucks in different classes and are not likely to be interchangeable may still be impacted by a policy that would increase or decrease the gross vehicl e weight. License requirements for Class 7 vehicles make marginal Class 6 vehicles most lik ely to experience class-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 amou nt of weight, it is possible that they would become Class 7 vehicles. If that were to ha ppen, school bus drivers would need to gain additional licensure or buses would need to co unteract the weight gain and lose capacity. Other Class 6 vehicles that are marginal now (s chool 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 geogra phy of truck travel (e.g., requiring 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 benefici al for high-speed, long-haul travel, while hybridized drivetrains are most benefits for st op-and-go duty cycles. Fuel economy could be directly regulated depending upon the inte nded use of the vehicle, or technology requirements imposed based on intended use. Under the assumption that a regulation would 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 are likely to be trucks that, for

- 10 -example, carry goods between warehouses on the urban fringe and urban retailers. These carriers would likely make the argument that thei r travel was most often inside of the urbanarea boundary and, therefore, they should not be required to install fairings. They are not likely to shift classes to avoid regulation, as their shipments are not likely to change. Monitoring and enforcement of such a regulati on 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 prac ticality of this type of regulation.

Regulations that impact a certain engine size :

• Regulations that would impact only a certain en gine 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. Howeve r, any regulations that would increase the operating cost for an engine sized for Class 8 use would be doing so for the class of trucks that are least likely to switch. Vocational truc ks would still be used for job-specific purposes and other users of Class 8 truc ks are extremely unlikely to sh ift to smaller trucks because labor costs would still far outweigh the increa sed operating cost that would accompany the required increase 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-foot pup trailer, it would take nearly twice as many truckloads, doubling the total number of sh ipments. The impact of switching to Class 7 trucks will significantly increase total cost be cause fuel costs of Cla ss 7 trucks are roughly 75 percent of Class 8 fuel costs ($0.48 compared to $0.634 per mile) and labor is likely to remain similar since the shipment requirements are comparable. 18 Total labor costs will double (eight trucks compared to four) and fuel ex pense will increase by about 50 percent (eight trucks * $0.48 - four trucks * $0.634), result ing in a much more expensive ship ment.



### (v) Transportation Service and Performance Effects

Key Question : Are regulations likely to reduce the qua lity of the services provided by vehicles, for example by affecting speed, reliability, or cargo capacity? If so, what are the implications for costs and benefits?

Discussion: Initial research conducted by ERG for th e concurrent white paper suggests that the impacts of fuel economy regulations on truck perf ormance will be minimal, if any. Therefore, the impacts of fuel economy regulations on quality of service are likely to be mi nimal or non-existent. The 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),

18 Antich, “Medium-Duty Operating Costs Increase 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.

- 11 -and not those that “cube out” (i.e., are limited by volume). [Details to be adde d pending completion of ERG paper – what potential weight increase? Wh at impact on %cargo capacity? Could this require more trucks on the road to meet the same demand?]

- 12 -

### (vi) Congestion Impacts

Key Question : If regulations reduce truck performanc e 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 op erating cost of long-haul trucking. Truck demand is estimated to increase by 3.2 to 15.1 million 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 te chnology response. The concurrent white paper produced by ERG suggests that the impacts of fuel economy regulations on truck performance will be minimal, if any, and th erefore performance impacts on congestion are not considered further.

The remainder of this analysis focuses on ba sic freeway sections because fuel economy regulations are most likely to affect long-haul truc k traffic, which spend the vast majority of their time on freeways. (Trucks have a significant im pact on congestion at other traffic control locations, such as signalized and unsignalized intersections, merge sections, and freeway-tofreeway off-ramps, but the data to analyze these im pacts is not readily available.) In the highway capacity manual (HCM), in basic freeway analysi s, trucks are represented as “passenger car equivalents” (PCE). 19 The PCE concept is meant to capture the effect that heavy vehicles have on traffic flow on a freeway because heavy vehicles o ccupy more space, 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. Furthe rmore, different passenger car operators react differently to the presence of trucks; for exampl e, following further behind trucks than cars.

The HCM increases the PCE for trucks based on ro adway conditions such as grade, number of lanes, distance to roadside obstructions, and widt h of lanes. It does not, however, distinguish between truck PCEs under congested and unconges ted 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 ratio of the truck itself, the grade of the roadway, the road type, geography, and congestion levels. They did not estimate the impacts of other roadway characteristics such as l ane width or distance to obstructions. 20 The tables from the FHWA report are reproduced in this paper and to show how different scenarios change how trucks impact roadway volumes. Generally, st eeper grades, longer hills, fewer lanes, and a higher weight-to-horsepower ratio increase the number of PCEs for a single truck. Truck PCE conversions are necessary to calculate a volume-to-capacity (V/C) ratio, which can be used to estimate congestion and delay measurement. Figu re A-1 at the end of this document shows PCEs for trucks on urban highways.

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

19 Transportation Research Board, Highway Capacity Manual, Washington, D.C., 2000.

20 Battelle, “Traffic Operations and Truck Size and Weight Regulations, Working Paper 6,” prepared for the Federal Highway Administration, February 2005.

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

The formula implies that as a basic freeway se gment 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 se gment length and congested and free-flow speeds, then 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 one 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. 21 The marginal congestion cost descri bes the cost, measured in lost travel time, that a single additional combination 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 :
For estimation of change in delay: The approach taken is to conceptualize an average high-truck traffic basic freeway scenario, create a comparis on scenario 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 calc ulate delay for each scenario. This approach assumes that the average nationwide increase in truck traffic occurs on this hypothetical congested segment.

For estimation of change in marginal cost : An alternative approach uses Parry’s marginal congestion costs. To estimate total increased marginal cong estion 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.

Scenarios for estimation of change in delay : A basic four-lane freeway se gment (two lanes in either direction) is at capacity when there are at leas t 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 lanes, 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 nu mber 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-vehi cle weight of 80,000 lbs, which produces a weight to horsepower ratio of 160 lb/hp (80,000/500);

21 Parry, Ian, “How Should Heavy-Duty Trucks Be Taxed?” Resources for the Future, April 2006. See Table 1 for his benchmark parameter values fo r mileage-related marginal external costs.

- 14 -• Truck VMT increase scenario : This scenario is identical to the base scenario described above except that trucks are increased between 2.2 an d 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 and add the additional PCEs create d by the trucks. Each truck on this segment occupies the same capacity as 2.6 passenger vehi cles (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), identical 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 total delay of 2,72 0 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 condit ions, 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 3,964/4,400 – 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 increa se in delay of 0 – 16 percent compared to the base scenario.

This increase in delay is small compared to the over all travel time on this segment (although it is valid only at this particular po int on the BPR curve.) Furthermore, the majority of VMT on U.S. freeways and arterial roadways occurs at acceptab le levels of service (71 percent of urban VMT and 92 percent of rural VMT currently operates at level of service D or better) and impacts on these roadways are likely to be minimal. Howe ver, the marginal impact on delay will become larger as the capacity of the road is approached. For some roadways operating at or near capacity and with high truck volumes, it is possibl e that increased truck travel as a result of the rebound effect from higher fuel efficiency could hav e an impact on delay that is not insignificant.

For estimation of change in marginal cost : Rural and urban Class 8 truck VMT is 82,768 (58 percent of 142,703 million VMT) and 59,935 (42 percent of 142,703 million VMT), respectively. 22 Using Parry’s marginal costs of $0.037 per-mile in rura l areas and $0.168 per-mile in urban areas, the total increase in cost is $13,131 m illion ($3,062 million rural + $10,069 urban).

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

- 15 -

### (vii) Safety Impacts

Key Question : If regulations reduce truck performanc e and/or reduce or increase truck VMT, what are the potential implications for safety?

Background: Section (1)(ii) describes how the demand fo r long-haul trucking will increase if fuel economy regulations effectively reduce long-haul tr uck operating costs. Truck traffic is estimated to increase between 3.2 and 15.1 million VMT (an incr ease of 2.2 - 10.5 percent), depending on the technology alternatives and assumed demand elasticities. 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 caused 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 safety impacts of changes in truck traffic can also be valued using marginal safety cost estimates from the literature. The marginal cost that one combination truck has on the safety of the population describes the cost, measured in property damage, medical costs, 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 and $0.034 per mile in rural areas. 23 Generally, the marginal costs are higher in ru ral areas than in urban areas be cause the speeds are higher and there is a higher likelihood of a larger speed differential, a key determining factor in highway accident severity 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 pe r 100 million VMT for fatal crashes and 51.1 per 100 million VMT for injury crashes. 24 Based on these average rates, the rebound effect analysis (3.3 - 15.1 million additional VMT annually) implies a national increase of 0.08 - 0.36 fatalities per year and 1.7 – 7.8 injuries per year.

For estimation of change in marginal cost : Rural and urban Class 8 truck VMT is 82,768 (58 percent of 142,703 million VMT) and 59,935 (42 percent of 142,703 million VMT), respectively. 25 Using Parry’s marginal costs of safety of $0.034 per-m ile in rural areas and $0.018 per-mile in urban areas, the total increase in cost is $3,893 million ($2,814 million rural + $1,079 urban). This is a much larger cost than would be expected based on the estimated change in injury and fatality

23 Parry, Ian, “How Should Heavy-Duty Trucks Be Taxed?”, Resources for the Future, April 2006. See Table 1 for his benchmark parameter values fo r mileage-related marginal external costs.

24 Federal Motor Carrier Safety Administration, “Commercial Vehicle Facts,” November 2007 http://www.fmcsa.dot.gov/facts- research/facts-figures/analysis-statistics/cmvfacts.htm (Accessed September 16, 2009).

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

- 16 -rates shown above (the latest value of life estima tes are in the range of $5 million and injury costs would be considerably less; while there would be more property damage only crashes, costs for these crashes are also much less than average injury or fatality costs). Potential reasons for these differences have not been investigated.



### 2. Alternative Approaches to Improving Fuel Efficiency

Three alternative approaches to impr oving 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 increase in the fuel tax (assessed on diesel fuel, all motor vehicle fuels, or an all fuels based on carbon content) have on truck fuel consumption?

Background : A fuel tax would affect truck fuel consum ption in two ways: first, it would reduce truck traffic volumes, and second, it would encour age 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 co st 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 us ing evidence on the elasticity of truck fuel efficiency with respect to fuel price. While th ere 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, Sm all and Van Dender (2007) provide a review of previous studies as well as recent estimates us ing data through 2004. The authors estimate elasticities of VMT, fuel intensity (gallons/mile ), and total fuel consumption (VMT multiplied by fuel 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 elasticities might be changing. These findings are shown in Table 1.



### Table 1 Historical an d Recent Long-Run Elastic ities With Respect to Fuel Price

Calculated Long-Run Pr ice 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. Lo ng-run elasticities will be greater than the immediate or short-term response since travelers are ab le to make more fundamental adjustments to their

- 17 -activity patterns, such as changing residence or worksite locations or changi ng the number and types of vehicles 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 perc ent reduction in VMT and a 19 percent increase in the fuel efficiency of vehicles, for an overall net decrease in fuel consumption of 36 percent. For the more recent period of 2000 to 2004, the elastici ty 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 decline in VMT. Table 1 shows that histor ically, about half of the impact of a fuel price increase would be on VMT reduction and the othe r 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. Anot her recent review of elasticities by Sperling 26 suggests that elasticity values ar e higher, but also reaches a simila r conclusion that elasticities with respect to VMT have declined in recent year s. Sperling found that long run elasticities of fuel consumption with regard to fuel price “ may be as low as -0.2. ”
Converting Small and van Dender’s fuel intensity elasticity, which is measured in response to fuel prices, to an elasticity based on overall ve hicle 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 (about -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 ba sed 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 ho w 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 esti mating the combined response, by answering the question of what fuel price increase would be required to reduce VMT by 10 to 20 percent (and assuming that a similar decrease in fuel intensit y of 10 to 20 percent is also achieved, for a net effect in the range of 20 to 40 percent). The an alysis is simplified by selecting an elasticity for both rail and truck demand used in relevant U.S. st udies. The truck price elasticity of -0.97 used by FHWA and rail cross-elasticity of 0.52 from AAR were selected. Findings : The 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 27 at an average fuel 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 in crease in the per-mile fuel tax translates to an increase in the per-gallon fuel tax of $1.01 - $1.98 , for the 20 and 40 percent scenarios

26 Sperling, Dan, “Consumer Response to Fuel Price Changes: Implications for Policy,” January 15, 2008.

27 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking , December 2008.

- 18 -respectively. This will reduce truck fuel cons umption by 5,100 - 10,100 million gallons (20 - 40 percent from current fuel consumptio n levels of 25,500 million gallons). This corresponds to a decrease in truck traffi c from current annual VMT of 71,400 to 67,000 or 43,000 for the 20 and 40 percent reduction cases, re spectively. Based on th e cross-price elasticity of 0.52, rail ton-miles will increase from 1,852 bi llion ton-miles to 1,953 - 2,053 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 effe ctiveness 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 increase required to achieve si gnificant fuel efficiency benefits – estimated 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 gallon may be considered intolerable). It also significantly exceeds the likely fuel price increase under a national cap-andtrade program. Analysis by the Energy Information Administration (EIA) of cap and trade legislation under the previous Congress showed that a $50 per tonne 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. 28 A lower allowance price of $30 per tonne (27 cents per gallon) is consistent with the range in the draft Amer ican 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

Key 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 cent ral business districts, or truckspecific congestion pricing such as the varying ti me-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;

• Reducing congestion for trucks continui ng to operate during peak periods;

• Reducing overall goods movement and relate d truck traffic due to higher costs; and

• Shifting logistics patterns – e.g., leading indust ries 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. Cordon pricing strategies are only applicable in major urban areas with

28 U.S. Department of Energy, En ergy Information Ad ministration, “ Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007 ,” 2008. http://www.eia.doe.gov/oiaf/service_rpts.htm

- 19 -significant congestion. The limited geographic a pplicability 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 service 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 li ttle, if any, overall impact ontotal truck traffic (as the added costs are likely to be marginal, or the option of moving to the offpeak 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 tr ucks since larger long distance trucks operate mostly on uncongested highways.

It should be noted that while reducing congesti on should improve fuel economy up to a point (e.g., increasing average speeds from 10 to 20 or 20 to 30 mph), truck fuel consumption rates alsotend to increase at higher speeds (over 45-55 mph), and therefore, increasing speeds from midrange congestion (30-40 mph) to free-flow hi ghway speeds may have a negative fuel economy impact. Congestion is likely to affect urban service and delivery movements more than long-haul freight, and therefore it is the fuel consumptio n 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).

Findings : Most studies of the impact of congestion pricing have focused on all traffic, rather than distinguishing impacts on pers onal vs. commercial vehicle traffic. A study for the U.S. Department of Energy used travel demand models in Minneapolis-St. Paul and Seattle, in conjunction with speed-fuel efficiency relationships, to evaluate the combined benefits of travel reductions and operating efficiencies fr om areawide systems of managed lanes. 29 The results from different scenarios ranged from an 0.1 to 2.5 percent impact on fuel consumption and GHG emissions depending upon the scenario. Extrapol ating these results to a national level based on projected 2030 congestion levels in different urba nized areas led to an overall estimated reduction in national fuel consumption ranging from 0.5 to 1.1 percent. 30 Another national study of greenhouse gas (GHG) emission reduction strate gies estimated that cordon pricing could potentially reduce VMT on the order of 3 percen t if applied to all metropolitan areas in the

29 These systems included high-occupancy/toll (HOT) lanes on freeways, in which drivers of singleoccupancy vehicles can us e the lane if they pay a fee which depends upon the congestion on the untolled travel lanes. Depending upon the scenar io, either existing/planned high-occupancy vehicle (HOV) lanes were converted to HOT lanes, or a new HOT lane was constructed alongside an existing/planned HOV lane to form two HOT lanes.

30 Energy and Environmental Analysis, Inc., “Market-Ba sed Approaches to Fuel Economy: Summary of Policy Options,” prepared for National Energy Te chnology Laboratory, U.S. Department of Energy, 2008.

- 20 -United States. 31 These are rough estimates for all vehicl es, however, that may not be transferable to truck traffic.

Evaluations of cordon pricing schemes implemen ted in both London and Stockholm examined effects specifically on truck traffic. Experience in London suggests that the reduction in overall vehicle-kilometers of travel (VKT) has come almo st exclusively from passe nger vehicles rather than trucks. While the cordon pricing scheme re duced total VKT by 16 percent within the pricing zone, the data show that truck demand did not change once the scheme was introduced, remaining at a constant level of 0.07 million VKT in 2006. Truck travel speeds also did not change significantly. On the other hand, truck trip s crossing the cordon declined, suggesting that 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 pric ing 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. 32 Given 70,000 truck-kilometers traveled and a reduction in excess idling delay of 0.6 minutes per kilometer (2.3-1.7 minutes per kilome ter), 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, 33 the truck fuel consumption reduction from co ngestion pricing would have been 560 gallons annually – a very small amount.

In Stockholm, the cordon pricing scheme reduced total GHG emissions from vehicles by 10 to 14 percent in the pricing zone. According to the manu al counts of travel within the zone, there has been a 13 percent reduction of truck trips, but on ly 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.

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, congesti on pricing is relatively easy to implement on facilities that already are tolled. The broader-scal e application of this strategy beyond existing or proposed toll highway facilities, however, is likely to require the universal deployment of electronic toll collection technologies. This will require coordination by a state or regional transportation agency. The U.S. DOT is encourag ing greater experimentation in this area. In 2007, the Department awarded $853 million in funding to five metro areas for Urban Partnership Agreements to reduce congestion, which include a significant focus on tolling/pricing strategies.

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

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

33 U.S. Environmental Protection Agency, “A Glan ce at Clean Freight Strategies: Idle Reduction,” February, 2004.

- 21 -

### (iii) Intermodal Transport

Key Question : What impact would public-sector init iatives to improve intermodal transport have on fuel consumption?

Background : Improvements to intermodal transport, such as rail capacity improvements and bottleneck relief, intermodal (truck-rail) term inals, 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 progress 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. 34 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 limit ed to certain 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 th e additional handling/logistics costs and time at either end, generally shipments longer than 1,000 miles. Furthermore, market demand both affects and is dependent upon the quality of se rvice. Rail service improves significantly as demand between market pairs increases – increased traffic (trains per day) increases the level of service that railroads provide to customers, an d means that improved access is possible since (shippers need access to rail facilities to ship via rail). In short, shippers 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 simplifying assumptions and sketch planning techniques while complicated techniques (such as FHWA and the Federal Railroad Administrati on’s Intermodal Transportation and Inventory Cost Model 35 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 tr affic 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 consis tent set of variables that impact the outcome. Actions that can affect a truck-rail mode shift in clude investment in rail and intermodal terminal

34 Cambridge Systematics, Inc., Transportation’s Role in Reducing Greenhouse Gas Emissions , Forthcoming, prepared for Federal Highway Administration.

35 Federal Highway Administration an d Federal Railroad Administration, Intermodal Transportation and Inventory Cost Model, Highway-to-Rail Intermodal User’s Manual , March 2005.

- 22 -infrastructure, direct operating subsidies for ra ilroads, land use regulations (for example, to preserve rail sidings for rail-oriented businesses), and taxes to increase the cost of truck travel, as previously discussed.

Findings : The following studies either estimated di version potential from rail investment in specific corridors (Crescent Corridor and Mid-Atlantic Rail Operations Study).

• Norfolk Southern Crescent Corridor : 36 The Crescent Corridor will provide premium service intermodal trains to compete with I-81 long-h aul truck markets between the New York area and the Southeast. This market is underserved by rail today and has significant potential for growth. Improvements 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, implying a cost of $2, 500 per truck trip, and saving more than 170 million gallons annually.

• Mid-Atlantic Rail Operations (MAROps) study : 37 The MAROps study used the comparative market approach to estimate the diversion potential in the Mid-Atlantic region. The comparative market approach look ed at every market pair in the country and compared rail travel distance to truck travel distance (a measure 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 re duce truck traffic on the parallel highway corridor. The study estimated the potential di version from projects representing a $12 billion investment in the Mid-Atlantic rail network to be somewhere between 0.67 and 1.8 million truck 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 9.5 to 25.6 billion gallons of diesel fuel in trucks (based on today’s 5.59 mpg fu el economy), not accounting for increased rail traffic. This implies a range of $7,000 to $18,00 0 per diverted truck trip in the Mid-Atlantic and between $0.47 and $1.26 in investment per gallon of fuel saved based on 2008 dollars and 2035 traffic.

Potential for Government Promotion : The government could prom ote rail diversion through the promotion of freight villages; aiding in the siting of intermodal terminals, transload facilities, and bulk storage facilities; expand market reac h for regional railroads; and the continued improvement in rail infrastructure, including si gnal, track, bridge, te rminal, and clearance upgrades.

While freight rail infrastructure in vestment 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

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

37 Cambridge Systematics, Inc., Mid-Atlantic Rail Operations Study, prepared for the I-95 Corridor Coalition, forthcoming.

- 23 -needs and the availability of private capital. The Federal-aid highway funding program also allows some flexibility in using funds for no n-highway freight transportation projects.

To date most of the easier rail capacity improv ement projects have been built, leaving primarily the more difficult and expensive pr ojects. In addition to being ex pensive, many of the remaining critical needs are set in urban environments where there are subs tantial constraints on right-ofway as well as added costs for mitigation of impact s. These barriers will pose challenges to largescale improvements in freight in frastructure sufficient to levera ge significant truck-rail mode shift.

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### Figure A-1 Vehicle Passenger Ca r Equivalents on Urban Highways

Source: Battelle, “Traffic Operations and Tr uck Size and Weight Regulations, Working Paper 6,” prepared for the Federal Highway Administration, February 2005.

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