Transportation Research Part D 22 (2013) 49–53

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Transportation Research Part D

journal homepage: www.elsevier .com/ locate/ t rd

Estimating the CO2 intensity of intermodal freighttransportation

1361-9209/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.trd.2013.02.016

⇑ Corresponding author. Tel.: +1 617 253 1701.E-mail address: tcraig@mit.edu (A.J. Craig).

Anthony J. Craig a,⇑, Edgar E. Blanco a, Yossi Sheffi b

a Center for Transportation & Logistics, Massachusetts Institute of Technology, 77 Massachusetts Ave, E40-276, Cambridge, MA 02139, USAb Engineering Systems Division, Massachusetts Institute of Technology, 77 Massachusetts Ave, E40-261, Cambridge, MA 02139, USA

a r t i c l e i n f o

Keywords:Intermodal freight transportCarbon intensityGreenhouse gas emissions

a b s t r a c t

This paper looks at the environmental effects of shifting from road to rail freight transpor-tation. Little data is available to shippers to calculate the potential CO2 savings of an inter-modal shift. In this paper we analyze a data set of more than 400,000 intermodal shipmentsto calculate the CO2 intensity of intermodal transportation as a distinct mode. Our resultsindicate an average intensity of 67 g of CO2 per ton-mile, but can vary between 29 and220 g of CO2 per ton-mile depending on the specific origin–destination lane. We applythe market area concept to explain the variance between individual lane intensities anddemonstrate the complexity in predicting the potential carbon savings in a switch fromtruckload to intermodal.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Transportation as a whole accounts for 19% of global energy use, and emissions from transportation are expected to growby 50% by 2030 and by 100% by 2050 from 2007 levels. Within the transportation sector, freight, especially trucking, is ex-pected to experience the fastest growth. In the US, medium and heavy-duty freight trucks account for more than 60% of thefreight transportation emissions and are growing faster than any other mode (Greene and Plotkin, 2011). Given the projectedgrowth in demand for freight transportation, a number of strategies for reducing emissions have been considered, including:improved technological efficiency; improved operational efficiency; and shifting to more environmentally efficient modes,such as rail (Vanek and Morlok, 2000).

The International Energy Agency (2009) projects a possible reduction of 15% in greenhouse gas (GHG) emissionsfrom the baseline scenario by 2050 with appropriate road–rail intermodal shifts; a shift of 1% of current USintercity truck freight to intermodal could generate savings of 0.92–2.18 Tg of CO2 per year according to Bitzanand Keeler (2011). While a shift to intermodal freight may replace only a small amount of current truckload freighttraffic, it is increasingly popular with shippers. Despite this increased popularity, there is relatively little informationregarding the actual efficiency of intermodal in comparison to other modes (Bitzan and Keeler, 2011). In this paper,we fill this gap in the literature with an analysis of a large data set of intermodal shipments in North America. Wecompare the results of the analysis with estimates of serving those same lanes by truckload transportation, and ap-ply the market area concept to explain the difficulty in assessing an overall efficiency for intermodal as a distinctmode of transport.

Table 1CO2 Intensity.

Mode CO2 (tonnes) CO2 intensity (g/ton-mile) Savings

Intermodal 806,819 67 46%Truckload 1,490,986 125 NA

50 A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53

2. Methodology

We define an intermodal shipment to consist of an origin drayage movement performed by truck that takes the shipmentfrom the origin location to the origin ramp. At the ramp the shipment is transferred to rail and a linehaul between the originand destination ramps occurs. At the destination ramp the shipment is transferred back to a truck and a destination drayagemovement delivers the shipment to the consignee at the final destination. We calculate the carbon footprint of an intermodalshipment by disaggregating the shipment into separate drayage and rail movements using:

1 Theaverage

CIM ¼ dod � cd þ dr � cr þ ddd � cd ð1Þ

where dod is the distance of origin drayage, ddd is the distance of destination drayage, dr is the distance of rail haul, cd is thecarbon intensity of drayage, and cr is the carbon intensity of rail.

We apply this to a data set supplied by J.B. Hunt Transportation, the largest intermodal operator in North America. Thedata consists of records for more 400,000 individual intermodal shipments covering more than 35,000 origin–destinationlanes grouped by zip code in North America. Each record contains the zip code of the origin, origin ramp, destination ramp,and destination; the length of the origin and destination drayage; and the length of the rail haul. Additionally, the operatorsupplied a carbon intensity parameter for drayage based on its own fuel efficiency, empty miles, and out-of-route miles. Thecontracted rail companies provided the length of the rail haul to the intermodal operator. When this data was not availablethe rail distance was calculated using the Rail MILER commercial software program. The rail carbon intensity parameter wascalculated using intensity numbers supplied by the rail companies per ton-mile. This value was multiplied by the averageweight of the intermodal shipments, including equipment, to get an intensity parameter in terms of CO2 per mile.

For comparison, we estimate the CO2 for serving those same lanes by truckload service; truckload emissions being basedon data collected from the operator’s longhaul trucking business and calculated using:

CFTL ¼ ðdotr þ daeÞ � ctl ð2Þ

where dotr is the over the road distance, dae is the average empty distance per shipment, and ctl is the carbon intensity oftruckload transportation.

The distances between the origin and destination zip codes are calculated using over the road travel distance. The averageempty miles is a fixed quantity based on dividing the empty miles traveled by the fleet by the number of shipments. Finally,the carbon intensity factor is based on the fuel efficiency of the operator’s vehicles, an adjustment for out-of-route mileagevariance, and the carbon content of diesel fuel.

To allow comparison, we calculate the carbon intensity of both modes by dividing CO2 emissions by the net ton-milesworth of goods moved. Net ton-miles are calculated by multiplying shipment weights, excluding equipment, by the greatcircle distance between the origin and destination for individual shipments, then summing across shipments. Using the di-rect distance between origin and destination, rather than the actual distance traveled, provides a consistent basis for com-paring shipments across modes that vary in their circuity. The emissions from intermodal shipping, truckload shipping, andthe resulting intensities are shown in Table 1.1

3. Results

For the overall intermodal shipment to be more efficient than truckload, the length of the linehaul must be long enough tooffset the lower efficiency of drayage and the increased circuity of the rail network. For a specific shipment, the efficiency ofusing intermodal transportation can vary depending on the distances involved for each of the three segments. To reflect this,we calculate the intensity of the intermodal movements on a lane-by-lane basis. The intensity is found by dividing theCO2 forthe lane, calculated using Eq. (1), by the net ton-miles of cargo shipped. The average lane intensity is 70 g CO2/ton-mile, witha standard deviation of 13 g CO2/ton-mile. The distribution of the operator’s lane-by-lane intensities is shown in Fig. 1, with avertical line showing the average 125 g CO2/ton-mile intensity of truckload shipping.

While the majority of lanes offer savings similar to the average value, some lanes produce more emissions than if truckinghad been used. When making the decision on a specific shipment, a shipper balances the environmental considerations withother factors such as time, cost, and service quality. Unlike trucking, which provides more consistent carbon intensity acrosslanes, the intensity of intermodal varies considerably. Further, the analysis considers only lanes for which intermodal service

intermodal intensity is lower than the range estimated from Vanek and Morlok (2000), but the savings compared to trucking are consistent with therange found by the International Road Transport Union (2002).

0%

5%

10%

15%

20%

25%

Perc

ent o

f Lan

es

g CO2 per ton-mile

Fig. 1. Distribution of intermodal carbon intensity by lane.

O

A

Or

Origi

A

igin

n Drrayagge

Orrigin Ram

T

mp

Truckkload Ro

R

oute

Rail Lineehaul

Destin

Dest

B

natio

tinat

B

M

on Ra

D

tion

amp

Desti

p

natioon DDrayaage

Fig. 2. Intermodal versus truckload choice.

A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53 51

is currently being used. For a shipper considering a prospective change to intermodal transportation, the average may be apoor estimate of the actual savings on a specific lane.

Nierat (1997) describes the market area of intermodal transportation as the region of space around a rail terminal inwhich intermodal transportation is the most competitive mode. The space is defined around a rail terminal because an inter-modal shipment requires a fixed threshold cost to first be moved to the terminal via the origin drayage and rail line haul. Thecost to reach the destination is then this fixed cost plus the cost of the drayage move from the terminal to the final desti-nation. This cost increases as the destination moves away from the terminal due to the longer drayage move at the destina-tion. If the destination is too far from the terminal it may no longer be competitive to use intermodal transportation; insteada direct truckload shipment between origin and destination would be used.

The choice between intermodal and truckload is seen in Fig. 2. The shipment begins at point A and is destined M. If theshipment is sent intermodally, it goes to the origin terminal by drayage truck, and then to the destination terminal, B, by rail.This represents the fixed cost portion of the shipment: regardless of where M is, the shipment must first be taken to point B.From B to M the final movement is again made by drayage truck. If the shipment is sent by truck it travels directly from A toM. The intermodal market area for terminal B, the shaded region around the terminal in the figure, defines the range aroundB where M can be located and served more competitively by intermodal than direct truckload shipment.

Formally, Nierat (1997) defines this service area by calculating the costs for each shipment. The cost to reach point M byroad, defined as Cr(M), is a combination of a fixed cost, Cr(A), and a variable cost per unit of distance, xr. The intermodal cost,Ci(M), likewise consists of the fixed cost required to reach B, Ci(B), plus a variable cost per unit of distance from B to M, xi. Theboundary of the market area is found by setting the costs equal to one another.

CrðMÞ ¼ CiðMÞ () CrðAÞ þxrAM ¼ CiðBÞ þxiBM ð3Þ

Rearranging the terms and substituting x = xi/xr and k ¼ CiðBÞ�Cr ðAÞxr AB

gives:

AM �xBM ¼ kAB ð4Þ

52 A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53

The parameter x represents the relative cost of drayage operations to standard road trucking. When x > 1, the marketarea will have an oval shape oriented along the direction of travel from A to B.

The method used to calculate the cost of road and intermodal transportation by Nierat (1997)is similar to our previousmethod for calculating the carbon footprint of those modes. We extend the market area idea by defining the carbon marketarea of an intermodal terminal as the region of space around an intermodal terminal that can be served from a given originwith lower carbon emissions than by truckload transportation. Recall that the carbon footprint of a truckload shipment iscalculated using Eq. (2). In that equation, ctl and dae are fixed quantities based on the operator’s efficiency. If we defineCr(A) = dae � ctl, xr = ctl, and dotr ¼ AM, the equation becomes identical to the form used by Nierat to calculate the cost oftruckload shipping.

Similarly, the carbon footprint of an intermodal shipment is calculated by Eq. (1). By defining Ci(B) = dod � cd + dr � cr,BM ¼ ddd, and xi = cd, this expression also becomes identical to the one used by Nierat for the cost of intermodal shipping.With those substitutions in place the equation can be rewritten to describe the carbon market area for terminal B:

dotr �cd

ctlddd ¼

dod � cd þ dr � cr � dae � ctl

ctlð5Þ

Simplifying the calculation of the over the road distances as dotr = dgc � c, with dgc as the great circle distance between theorigin and destination and c the road circuity factor, produces an oval region oriented along the line from the origin to thedestination terminal.

The carbon market area provides an explanation for why the carbon intensity of intermodal shipments varies on a lane-by-lane basis. For any given origin location, only destinations that fall within the carbon market area of a terminal will pro-duce lower emissions than truckload transportation. The use of an overall average fails to capture the location dynamics andnetwork structure that affect the actual efficiency, instead assuming that intermodal is always more efficient. Other attemptsto gauge the competitiveness of intermodal, such as the break-even approach used by Morlok and Spasovic (1995) that spec-ifies a minimum distance needed for intermodal to be preferred, also fail to account for these complexities.

Consider a shipper needing to move goods from an origin facility to a number of distribution locations. Each destinationintermodal terminal will have a carbon market area of different size and orientation, or possibly no market area at all. With-out the detailed data regarding the intermodal network and operating efficiencies, the shipper lacks the capability to accu-rately determine whether a location falls within the carbon market area of a terminal or the potential savings of usingintermodal to serve specific destinations. The magnitude of the potential savings is necessary for shippers to properly bal-ance the carbon footprint of the shipment with the other criteria of the decision, such as the cost, transit time, and servicelevel. At any point within the market area, the reduction in the carbon footprint is given by:

CFTL � CFIM ¼ ððdotr þ daeÞctlÞ � ðdodcd þ drcr þ dddcdÞ ð6Þ

Evaluated across the entire intermodal network, the potential savings for any destination can be determined by selectingthe mode and terminal that produce the lowest emissions. Fig. 3 shows the potential savings compared to trucking for anintermodal shipment originating near San Diego. These are calculated by first determining the threshold level of emissionsrequired to reach 36 destination intermodal terminals across the country. Threshold emissions are calculated based on adrayage movement from San Diego to an intermodal ramp near Los Angeles, and then from a rail movement to the destina-tion ramp. The emissions required to reach a grid of destination points are estimated for drayage movements from each des-tination ramp, and the ramp that minimizes emissions to reach that point is selected. The emissions at each point are

LegendDestination RampOrigin RampShipment Origin

Estimated Savings (tonnes CO2)< 00 – 0.50.5 – 11 – 1.51.5 – 22 – 2.5> 2.5

Fig. 3. Potential intermodal carbon savings.

A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53 53

compared to a truckload shipment from the origin to calculate the projected savings. Finally, the map is created using ESRI’sArcMap GIS software and applying an inverse distance weighted interpolation to estimate the savings for all areas of themap.

The figure illustrates many of the results from the carbon market area concept. In general the savings tend to increase fordestinations farther from the origin, as the efficiency of the long rail haul increases the potential of intermodal. The savings,however, are also dependent on distance from the terminals and the direction of travel, giving rise to several distinct oval-shaped regions of higher potential savings surrounding a terminal and oriented along the direction of travel. As the destina-tion moves away from the terminal the savings are reduced, even as the length of the journey may increase. A significant areain the western portion of the US does not fall within any carbon market area, due to the relatively short distance of the railhaul and the lack of nearby terminals.

4. Conclusions

In this paper we calculated the overall CO2 intensity of intermodal transportation as a distinct mode using data suppliedby an intermodal operator. Our results confirm the assumption that, on average, intermodal improves on the carbon effi-ciency of truck transportation. We estimate the average carbon intensity of intermodal transport to be 67 g CO2/ton-mile,46% lower than truckload. This is lower than the range of 88–170 g CO2/ton-mile based on the work of Vanek and Morlok(2000), but consistent with results from the International Road Tansport Union (2002) that show potential energy reductionsof 20–50% compared to trucking.

Though average emissions intensity information can be useful in estimating the potential of intermodal shipping to re-duce emissions, the actual carbon intensity of intermodal shipment varied from 29 to 220 g CO2/ton-mile depending on thelane under consideration. We explain this variation through an application of market area theory to show that intermodalshipping is more efficient than truckload only in a specific area surrounding an intermodal terminal, called the carbon mar-ket area.

Acknowledgements

The authors wish to thank J.B. Hunt Transportation Co. for their help and cooperation in this research. This research waspartially supported by the Global Leaders for Environmental Assessment and Performance consortium http://leap.mit.edu/.

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