Climate and Transportation Solutions: 

Findings from the 2009 Asilomar Conference on 

Transportation and Energy Policy

Daniel Sperling

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Institute of Transportation Studies

University of California, Davis

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For more information contact its@ucdavis.edu 
145

Chapter 13:

Improving the Energy Effi ciency and Environmental 

Performance of Goods Movement

by James J. Winebrake and James J. Corbett

In the United States (U.S.), light duty vehicles (LDVs) have traditionally been the focus of regulatory action 

aimed at improving the fuel economy and environmental performance of transportation. The fi rst LDV 

tailpipe emissions standards were promulgated in the mid-1960s, and corporate average fuel economy 

(CAFE) standards for LDVs were instituted a decade later and recently revised in May 2009. By contrast, 

heavy duty vehicles (HDVs), trains, and ships have only recently been affected by emissions standards, 

and currently do not face any restrictions affecting their fuel effi ciency. Yet, it is clear that all opportunities 

for improving effi ciency in all sectors of our economy must be exploited in order to avoid the calamitous 

consequences posed by climate change (Liu, J. et al. 2007a and 2007b). For the transportation sector, this 

includes improving the effi ciency of our freight transportation systems through a variety of options.

This chapter discusses the role of freight transportation as an important contributor to greenhouse gas 

(GHG) emissions. It presents a new context for discussing interrelated technology and policy options for 

reducing these emissions through an “IF-TOLD” analytical framework. In particular, the IF-TOLD framework 

is applied to evaluate opportunities for mode shifting, a credible method for reducing energy consumption 

and emissions in the freight sector. Based on this framework, a potential appears to exist for mode-shifting 

to help achieve energy and environmental goals, but expected benefi ts are likely overstated. Mode-shifting 

can have only limited impacts given the existing goods movement infrastructure in the United States. This 

argues for a more holistic approach to effi ciency improvements in the freight sector. The chapter concludes 

with a discussion of the importance of technology policy mechanisms that encourage freight performance 

improvements across a range of long and near-term goals.

Overview of Goods Movement

The relationship between goods movement and economic activity in the United States is displayed in 

Figure 13-1, which depicts ton-miles of freight movement as a function of gross domestic product (GDP) 

over a 20-year period. In the consumer-based economy, the relationship is intuitive: the more economic 

activity there is, the greater the use of freight transportation services. 

____________________

J. Winebrake is a professor in the Department of STS/Public Policy at the Rochester Institute of Technology. J. Corbett is a professor in the College of Earth Ocean and Environment at the University of Delaware. 
146 Winebrake and Corbett

Chapter 13 Climate and Transportation Solutions

About 80 percent of this freight service is provided by truck and rail service, with other modes, including 

intermodal service, constituting the difference. The full breakdown of ton-miles by mode is shown in Figure 

13-2.

Energy is required to move these goods, and most of this energy is derived from fossil fuels that power the 

fl eets of trucks, trains, and ships employed in freight operations. This fuel consumption has had a large 

impact on GHG emissions in the United States. Driven almost entirely by petroleum, the freight sector 

represents about 25 percent of the GHG emissions from the transportation sector, as shown in Figure 

13-3. Since the transportation sector represents about 30 percent of the total U.S. GHG inventory, freight 

3000

3200

3400

3600

3800

4000

4200

4400

4600

4800

6000 7000 8000 9000 10000 11000 12000

Ton-Miles (Billions)

GDP (Billions, 2000$)

Ton-Miles v. GDP for the U.S. (1987-2005)

Figure 13-1: Relationship between freight transportation services 

and GDP for the U.S., 1987-2005

Source: Derived from BTS 2007a and BEA 2009

Truck

41.4%

Rail

38.6%

Water

5.2%

Air (w/ truck/air)

0.1%

Parcel/USPS

0.9%

Truck/rail

5.6%

Truck/water

1.5%

Rail/water

0.9%

Other multiple 

modes

5.7%

Ton-Miles by Mode (Single and Intermodal) for the U.S. (2007)

Figure 13-2: Ton-miles by mode (single and intermodal) 

for the U.S. in 2007 

Source: Derived from BTS 2007b
Chapter 13 Climate and Transportation Solutions

147 Winebrake and Corbett

transportation accounted for about 9 percent of total GHG emissions. This is signifi cant, especially given its 

rate of growth has generally exceeded the overall growth in economic activity. The increasing importance 

of this sector makes it important to identify ways that allow this sector to serve economic needs most 

effi ciently.

Modal Comparisons

Different modes of transportation are used for different types of freight services, and the energy-to-work 

relationships are asymmetric across modes. Domestic ships and trains are often used for long-distance 

travel greater than 300 miles and for bulk cargo transport services, often when cargo is not time sensitive. 

These modes are capable of moving the greatest volumes with the least energy per unit work. Trucks are 

used for shorter trips, time sensitive cargo, and delivery to locations where ship and rail infrastructure is 

not available. Independently routed trucks, higher speeds, and smaller package densities increase energy 

required by trucks per unit work relative to bulk transport. Rail can perform similarly to either water modes 

or trucking, depending on payload densities, distances, and backhaul usage. Airplanes are used typically 

for time-sensitive shipments where transportation costs are a small percentage of overall cargo value. The 

additional energy for fl ight compared to Archimedes principles of fl oatation or rolling friction make work 

done by this mode the most energy intensive. 

These freight modes have different characteristics dictated by a number of factors, including: 

• Technology, for example, types of engines used and emissions control systems

• Fuel quality, including the sulfur, carbon, and energy content of fuel

• Transportation routes, such as gradients and distance

• Transport speed

• Operator behavior, including idling patterns and driving behavior

• Logistics, for example, dwell times and intermodal cargo transfer emissions

National statistics implicitly combine these characteristics, contributing to freight performance comparisons, 

often ranking trucks highest in surface transportation energy intensity. Using top-down calculations, which 

Figure 13-3: Transportation-related carbon dioxide emissions by mode 

for the U.S. in 2008

Source: Derived from EIA 2009
148 Winebrake and Corbett

Chapter 13 Climate and Transportation Solutions

is the total annual energy use in a sector divided by total freight services performed, one can derive energy 

intensities on the order of 3,500 to 4,000 BTU/ton-mile for trucks, and only 350 to 500 BTU/ton-mile for 

trains and ships (EIA 2009). Air freight energy intensities are an order of magnitude greater than trucking 

statistics.

These composite comparisons allow for useful generalizations about different transport modes, but there 

are at least two dangers in using them for conducting modal comparisons for specifi c shipping events. 

First, top-down calculations include energy used in moving empty containers, repositioning equipment, 

and providing services that do not necessarily involve cargo movement. When comparing modes for a 

specifi c shipping service, an activity-based approach is recommended that accounts for cargo, logistics 

and technology characteristics. Figure 13-4 presents carbon dioxide intensities for ships, trains, and trucks 

derived using a range of activity-based parameters. The ranges show the variability that exists depending 

on modal, route, and operating characteristics. Figure 13-4 also implies an opportunity for reducing energy 

consumption and emissions by shifting from high energy intensity modes to lower energy intensity modes.

Second, modal comparisons ignore the inherent complementarities among the modes, mistakenly posing 

them as simple substitutes. Where the origin/destination pairs do not offer multiple unimodal connectivity, 

the modes must be combined in an intermodal network. In other words, route-specifi c barriers and 

asymmetries may apply to any of the modal effi ciencies in Figure 13-4 that result in re-ranking the best-toleast effi ciencies by mode. 

Route-specifi c modal comparisons have been explored and illustrated by a team of researchers at the 

University of Delaware (UD) and the Rochester Institute of Technology (RIT). The Geospatial Intermodal 

Freight Transportation (GIFT) model is a model jointly developed by the UD/RIT team with support from the 

U.S. Maritime Administration, the Great Lakes Maritime Research Institute, and the California Air Resources 

Board, among others. The model connects multiple road, rail, and waterway transportation networks at 

intermodal transfer facilities using a “hub-and-spoke” concept. Activity-based calculations are embedded 

in the model, adjustable by the user. Within this intermodal network, the model assigns various economic, 

time-of-delivery, energy, and environmental attributes to real-world goods movement routes. In this way, 

network optimization algorithms can be run to determine optimal routing for achieving different objectives, 

such as least cost, least carbon, and least time (Winebrake et al. 2008; Comer et al. 2009). 

With such a model, the measures of performance improvement to one characteristic, for example, fuel 

economies, increased transit times, or payload densities, are presented along with coupled changes in 

Figure 13-4: Range of carbon dioxide intensities for various cargo carriers

Source: Buhaug et al. 2008
Chapter 13 Climate and Transportation Solutions

149 Winebrake and Corbett

emissions, delay, or energy intensity. The GIFT tool is currently being applied to study several dimensions 

of goods movement performance and is enabling a new context in which to explore freight technology/

policy pathways to meet energy, environmental, and economic goals. 

Opportunities for Mode-Shifting

There are three-dimensional interactions among fuels, technology, and vehicle miles traveled (VMT) and 

demand within the LDV sector. This has been a convenient framework for LDVs and has allowed for a 

simple, yet effective, way of communicating effi ciency options to policy makers and the public. 

Two key limitations are implicit in this three-dimensional context, however. First, individual driving patterns 

of citizens are treated as conditionally independent of the infrastructure, and second, routing choices are 

considered to be somewhat inelastic, for example, mostly ad hoc, discretionary, or commute-related. This 

has allowed economists and policy makers to identify technology providers as exercising greater infl uence 

on LDV fl eet performance than drivers, except in regions where modal substitutes exist and where strong 

price signals are possible. This framework suggests that emissions can be addressed by a combination 

of fuel improvements, such as low carbon fuels; technology advancements, including more effi cient 

technologies; or VTM reductions through demand management policies.

The freight sector is much different than the LDV sector, and a three-dimensional foundation fails to provide 

insights into the greater range of options that strong economic coupling makes available for freight sector 

emissions reductions. Moreover, informing good policy decisions requires that the long- and near-term 

planning activities by the public and private sectors be reconciled by considering dynamic response among 

decision drivers. 

The IF-TOLD model is a better framework for considering freight options. The letters of this acronym 

represent the following:

• Intermodalism/infrastructure: employing different transportation modes and infrastructure to improve 

freight services

• Fuels: burning low carbon or clean fuels

• Technology: applying effi cient technologies within each mode

• Operations: using of best practices in operator behavior

• Logistics: improving supply chain management

• Demand: reducing the amount of goods that are shipped, measured in ton-miles

Using the IF-TOLD model, a greater range of opportunities can be identifi ed for improving the effi ciency 

of goods movement. Intermodalism and mode-shifting are often looked upon as providing signifi cant 

opportunities for emissions reduction in the freight transportation sector (Winebrake et al. 2008; NPWI 

2004; Janic 2007; Komor, P. 1995; Kreutzberger et al. 2003; Patterson, Z. et al. 2008; Racunica and Wynter 

2005).

Given the lower energy intensity of modes such as rail or ship, conventional wisdom holds that movement 

of goods from truck to these other modes will greatly enhance the environmental performance of the freight 

sector. However, although ships and trains tend to be less energy intensive than trucks when measured 

per unit payload basis, the actual opportunities for system-wide mode-shifting with today’s intermodal 

infrastructure must be evaluated.

The following equation describes the overall energy impacts associated with shifting a set of commodities 

(k) from one mode (i) to another mode (j):
150 Winebrake and Corbett

Chapter 13 Climate and Transportation Solutions

Δ = ∑[ ] ⋅ ⋅ ⋅ ( ) − k

ij ik ijk ijk ijk Ei Ej E W c f p

 (1)

Where, 

ΔEij = energy savings due to modal shift from mode i to mode j

Wik = work done by mode i for commodity k (ton-miles)

cijk = shipment compatibility fraction of i to j for k (cargo)

f

ijk = shipment feasibility fraction of i to j for k (infrastructure)

pijk = shipment practicality fraction of i to j for k (economic)

Ei = energy intensity factor for mode i (Btu/ton-mile)

Ej = energy intensity factor for mode j (Btu/ton-mile)

The Compatibility, Feasibility, and Practicality Fractions

This equation introduces three new parameters that are important: compatibility fraction, feasibility fraction, 

and practicality fraction. Each represents the fraction of the total goods to be shipped that are affected by 

cargo compatibility, infrastructure feasibility, and economic practicality. 

The cargo compatibility fraction is a refl ection of how compatible a given cargo is with a different transportation 

mode, and whether there are options for consolidation, containerization, and facility location. For example, 

some commodities are shipped by truck for logistical considerations, such as time-of-delivery requirements 

associated with livestock transportation, while others must be shipped by train or ship due to the nature of 

the cargo, for example the shipment of chemicals. Figure 13-5 depicts a sample of commodities and the 

percentage of ton-miles of those commodities moved by different modes. 

In addition to whether a commodity is compatible with a given mode, the infrastructure must be available 

to support transport and delivery by one or more modes. Intermodal or multimodal routes are not always 

available, as some parts of the country do not have adequate rail infrastructure, waterways, or port or 

railyard transfer facilities that would allow effective mode-shifting. While mostly considered to be a long 

Figure 13-5: Percentage of goods movement in the U.S. by 

commodity and mode in 2002 

Source: Derived from BTS 2007b
Chapter 13 Climate and Transportation Solutions

151 Winebrake and Corbett

term and semi-public investment choice, reconfi guration and improvement of transportation nodes and 

segments can greatly affect the feasibility of intermodal transportation effi ciencies. 

Lastly, the economics of freight transportation play a signifi cant role in determining whether mode-shifting 

opportunities exist. To understand the potential role for mode-shifting, consider Figure 13-6, which presents 

the average miles per shipment for U.S. freight transportation in 2007 by mode. This chart demonstrates 

that on average, truck transportation is used for shipments of less than 200 miles, while rail is used for 

shipments greater than 800 miles. These distances are a function of compatibility, infrastructure, and the 

economics of freight transportation. Of course, operational choices can involve more cost-minimization 

measures than mode choice. Freight transportation consists of both marginal costs, such as fuel costs 

associated with moving goods over network segments, and fi xed costs, including transfer costs associated 

with moving cargo from one mode to another mode. Typically, ships and trains have much lower marginal 

costs than trucks, but vehicle fi xed costs for ships and locomotives are higher because of fewer unit sales 

and larger size. Moreover, the freight-only network embedded fi xed costs are much higher because rails 

and waterways rarely share infrastructure with passenger use. For long-distance transport, the marginal 

cost advantages of trains and ships offset higher fi xed costs and lead to lower average costs compared to 

trucks. For shorter distances, however, the opposite is true. 

The Potential of Mode-Shifting

Application of Equation 1 demonstrates conceptually the overall opportunity for mode-shifting in the United 

States. Figure 13-7 depicts a box of 100 cells, each representing 1 percent of ton-miles service in the U.S. 

freight sector and divided by mode from 2007 data. The lightest shade at the bottom in the fi gure represents 

the trucking mode and is the target of the analysis, which is to move freight from truck to some other mode, 

such as rail or ship. Assuming values for cijk, f

ijk, and pijk of 0.50, 0.70, and 0.35, respectively, the fi gure 

shows how the potential opportunity for truck beginning at 41 percent of the total ton-miles is reduced to a 

situation where only about 5 percent of the total ton-miles representing 12 percent of truck ton-miles can 

be moved from truck to rail or ship. If energy intensity of truck is fi ve times greater than rail or ship, then 

this implies an 8 percent reduction in total energy consumption, certainly not negligible, but much less than 

would be expected without the compatibility, infrastructure and practicality constraints on the system.

Source: Derived from BTS 2007b

Figure 13-6: Average miles per shipment by mode for the U.S. 

in 2007 
152 Winebrake and Corbett

Chapter 13 Climate and Transportation Solutions

Policies for Promoting Effi ciency

The opportunities for mode shifting identifi ed above cannot be achieved without creating different signals to 

industry. These can be “invisible hand” signals as fuel prices rise, the cost of inventory falls, and consumption 

changes result from recession or boom years. More relevant may be the policy choices to commit to energy 

and environmental goals not currently priced in the market, through policy instruments. 

A wide variety of policy mechanisms are available to provide incentives or disincentives within the IFTOLD context, as shown in Table 13-1. This table provides each element of IF-TOLD in columns, and a 

set of common policy instruments in rows. Cells are identifi ed where a particular policy instrument may 

directly infl uence the behavior within an IF-TOLD element. For example, emissions or effi ciency standards 

may improve modal performance of the most polluting or energy-intensive modes to achieve targets 

without mode-shift through the application of control devices; economic instruments, including taxes, may 

Figure 13-7: Estimation of mode-shifting potential for the U.S.

U O O O O O OWWW

I I I I I I I I WW

RRRRRRRRRR

RRRRRRRRRR

RRRRRRRRRR

RRRRRRRRRT

TTTTTTTTTT

TTTTTTTTTT

T T T T T T T T MM

T T T T T T T MMM

cijk = 0.5 

fijk = 0.70 

pijk = 0.35 

U O O O O O OWWW

I I I I I I I I WW

RRRRRRRRRR

RRRRRRRRRR

RRRRRRRRRR

RRRRRRRRRT

TTTTTTTTTT

TTTTTTTTTT

TTTTTTTTTT

TTTTTTTTTT

T Truck (T)

R Rail (R)

W Water (W)

I Intermodal (I)

U USPS/Parcel (U)

O Other (O)

M Modal Shift (M)

Table 13.1: Cross matrix of policy instruments and IF-TOLD framework elements

Transportation PoliciesIntermodalismFuelTechnologyOperationsLogisticsDemand

Policy Instruments I FTOLD

Performance standards •••

Taxes ••••••

Subsidies •••

Technology mandates •

Infrastructure Investment • •

R&D investment • •

Alternative/low-carbon fuels • •

Size/weight restrictions • ••

Operator training/education •

Demand management • •
Chapter 13 Climate and Transportation Solutions

153 Winebrake and Corbett

have greater potential to infl uence all decisions within the IF-TOLD context, offering choice in meeting 

performance goals through multi-attribute optimization. More research to evaluate the effectiveness of 

different policy instruments on the freight transport system could access the IF-TOLD rubric for how to 

structure those analyses.

Conclusion

The goods movement sector in the United States is highly correlated with economic activity. As such, it 

is expected that freight transportation will continue to grow with the economy. With this growth comes the 

responsibility for an increasing energy and environmental burden, mostly due to the high energy intensity 

of certain modes, especially trucks, and system characteristics. Options exist to reduce this energy 

consumption, and this chapter presented some ideas on the potential of mode-shifting as one possible 

approach. However, the freight system benefi ts of mode-shifting are limited by factors not considered 

previously and will vary depending on vessel, vehicle, locomotive, and route characteristics.

In addition to mode-shifting, there are other ways to improve the environmental performance of freight 

transport, captured in the IF-TOLD framework discussed above. Importantly, achieving energy and 

environmental goals will require policy makers to look at the freight sector as a system of different modes 

operating under asymmetric constraints even where there may be common objectives. The IF-TOLD 

framework is one useful way to explore policy options, enabling better application of research designs, 

such as wedge analyses, to describe the role new policy decisions might play.

Acknowledgements

The authors acknowledge the contributions made by the faculty and students working at the RIT Laboratory 

for Environmental Computing and Decision Making, including Bryan Comer, Chris Prokop, Dr. Scott Hawker, 

and Dr. Karl Korfmacher.

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