- Email: [email protected]
Estimating the CO2 intensity of intermodal freight transportation
Transportation Research Part D 22 (2013) 49–53
Contents lists available at SciVerse ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Estimating the CO2 intensity of intermodal freight transportation Anthony J. Craig a,⇑, Edgar E. Blanco a, Yossi Sheffi b a b
Center for Transportation & Logistics, Massachusetts Institute of Technology, 77 Massachusetts Ave, E40-276, Cambridge, MA 02139, USA 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 transport Carbon intensity Greenhouse gas emissions
a b s t r a c t This paper looks at the environmental effects of shifting from road to rail freight transportation. Little data is available to shippers to calculate the potential CO2 savings of an intermodal shift. In this paper we analyze a data set of more than 400,000 intermodal shipments to calculate the CO2 intensity of intermodal transportation as a distinct mode. Our results indicate an average intensity of 67 g of CO2 per ton-mile, but can vary between 29 and 220 g of CO2 per ton-mile depending on the specific origin–destination lane. We apply the market area concept to explain the variance between individual lane intensities and demonstrate the complexity in predicting the potential carbon savings in a switch from truckload to intermodal. Ó 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 grow by 50% by 2030 and by 100% by 2050 from 2007 levels. Within the transportation sector, freight, especially trucking, is expected to experience the fastest growth. In the US, medium and heavy-duty freight trucks account for more than 60% of the freight transportation emissions and are growing faster than any other mode (Greene and Plotkin, 2011). Given the projected growth 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) emissions from the baseline scenario by 2050 with appropriate road–rail intermodal shifts; a shift of 1% of current US intercity truck freight to intermodal could generate savings of 0.92–2.18 Tg of CO2 per year according to Bitzan and Keeler (2011). While a shift to intermodal freight may replace only a small amount of current truckload freight traffic, it is increasingly popular with shippers. Despite this increased popularity, there is relatively little information regarding 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. We compare the results of the analysis with estimates of serving those same lanes by truckload transportation, and apply the market area concept to explain the difficulty in assessing an overall efficiency for intermodal as a distinct mode of transport.
⇑ Corresponding author. Tel.: +1 617 253 1701. E-mail address: [email protected] (A.J. Craig). 1361-9209/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2013.02.016
50
A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53 Table 1 CO2 Intensity. Mode
CO2 (tonnes)
CO2 intensity (g/ton-mile)
Savings
Intermodal Truckload
806,819 1,490,986
67 125
46% NA
2. Methodology We define an intermodal shipment to consist of an origin drayage movement performed by truck that takes the shipment from the origin location to the origin ramp. At the ramp the shipment is transferred to rail and a linehaul between the origin and destination ramps occurs. At the destination ramp the shipment is transferred back to a truck and a destination drayage movement delivers the shipment to the consignee at the final destination. We calculate the carbon footprint of an intermodal shipment by disaggregating the shipment into separate drayage and rail movements using:
C IM 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 the carbon 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. The data consists of records for more 400,000 individual intermodal shipments covering more than 35,000 origin–destination lanes 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 operator supplied a carbon intensity parameter for drayage based on its own fuel efficiency, empty miles, and out-of-route miles. The contracted rail companies provided the length of the rail haul to the intermodal operator. When this data was not available the rail distance was calculated using the Rail MILER commercial software program. The rail carbon intensity parameter was calculated using intensity numbers supplied by the rail companies per ton-mile. This value was multiplied by the average weight 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 based on data collected from the operator’s longhaul trucking business and calculated using:
CF TL ¼ ðdotr þ dae Þ ctl
ð2Þ
where dotr is the over the road distance, dae is the average empty distance per shipment, and ctl is the carbon intensity of truckload transportation. The distances between the origin and destination zip codes are calculated using over the road travel distance. The average empty 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 mileage variance, 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-miles worth of goods moved. Net ton-miles are calculated by multiplying shipment weights, excluding equipment, by the great circle distance between the origin and destination for individual shipments, then summing across shipments. Using the direct distance between origin and destination, rather than the actual distance traveled, provides a consistent basis for comparing shipments across modes that vary in their circuity. The emissions from intermodal shipping, truckload shipping, and the 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 to offset the lower efficiency of drayage and the increased circuity of the rail network. For a specific shipment, the efficiency of using 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 for the lane, calculated using Eq. (1), by the net ton-miles of cargo shipped. The average lane intensity is 70 g CO2/ton-mile, with a standard deviation of 13 g CO2/ton-mile. The distribution of the operator’s lane-by-lane intensities is shown in Fig. 1, with a vertical 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 trucking had been used. When making the decision on a specific shipment, a shipper balances the environmental considerations with other factors such as time, cost, and service quality. Unlike trucking, which provides more consistent carbon intensity across lanes, the intensity of intermodal varies considerably. Further, the analysis considers only lanes for which intermodal service 1 The intermodal intensity is lower than the range estimated from Vanek and Morlok (2000), but the savings compared to trucking are consistent with the average range found by the International Road Transport Union (2002).
A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53
51
25%
Percent of Lanes
20%
15%
10%
5%
0%
g CO2 per ton-mile Fig. 1. Distribution of intermodal carbon intensity by lane.
Desttinattion
M T kload Ro Truck oute Desti D natio on Draya age
Origin
A
B natio on Ra amp p Destin
O n Drrayag Origi ge R Line Rail ehaul Orrigin Ram mp Fig. 2. Intermodal versus truckload choice.
is currently being used. For a shipper considering a prospective change to intermodal transportation, the average may be a poor 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 in which intermodal transportation is the most competitive mode. The space is defined around a rail terminal because an intermodal shipment requires a fixed threshold cost to first be moved to the terminal via the origin drayage and rail line haul. The cost to reach the destination is then this fixed cost plus the cost of the drayage move from the terminal to the final destination. This cost increases as the destination moves away from the terminal due to the longer drayage move at the destination. If the destination is too far from the terminal it may no longer be competitive to use intermodal transportation; instead a 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 the shipment 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 to M. The intermodal market area for terminal B, the shaded region around the terminal in the figure, defines the range around B 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 by road, 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. The boundary of the market area is found by setting the costs equal to one another.
C r ðMÞ ¼ C i ðMÞ () C r ðAÞ þ xr AM ¼ C i ðBÞ þ xi BM Rearranging the terms and substituting x = xi/xr and k ¼
AM xBM ¼ kAB
ð3Þ C i ðBÞC r ðAÞ xr AB
gives:
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 market area 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 previous method for calculating the carbon footprint of those modes. We extend the market area idea by defining the carbon market area of an intermodal terminal as the region of space around an intermodal terminal that can be served from a given origin with lower carbon emissions than by truckload transportation. Recall that the carbon footprint of a truckload shipment is calculated using Eq. (2). In that equation, ctl and dae are fixed quantities based on the operator’s efficiency. If we define Cr(A) = dae ctl, xr = ctl, and dotr ¼ AM, the equation becomes identical to the form used by Nierat to calculate the cost of truckload 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 dod cd þ dr cr dae ctl ddd ¼ ctl ctl
ð5Þ
Simplifying the calculation of the over the road distances as dotr = dgc c, with dgc as the great circle distance between the origin and destination and c the road circuity factor, produces an oval region oriented along the line from the origin to the destination terminal. The carbon market area provides an explanation for why the carbon intensity of intermodal shipments varies on a laneby-lane basis. For any given origin location, only destinations that fall within the carbon market area of a terminal will produce lower emissions than truckload transportation. The use of an overall average fails to capture the location dynamics and network structure that affect the actual efficiency, instead assuming that intermodal is always more efficient. Other attempts to gauge the competitiveness of intermodal, such as the break-even approach used by Morlok and Spasovic (1995) that specifies 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 destination intermodal terminal will have a carbon market area of different size and orientation, or possibly no market area at all. Without the detailed data regarding the intermodal network and operating efficiencies, the shipper lacks the capability to accurately determine whether a location falls within the carbon market area of a terminal or the potential savings of using intermodal to serve specific destinations. The magnitude of the potential savings is necessary for shippers to properly balance the carbon footprint of the shipment with the other criteria of the decision, such as the cost, transit time, and service level. At any point within the market area, the reduction in the carbon footprint is given by:
CF TL CF IM ¼ ððdotr þ dae Þctl Þ ðdod cd þ dr cr þ ddd cd Þ
ð6Þ
Evaluated across the entire intermodal network, the potential savings for any destination can be determined by selecting the mode and terminal that produce the lowest emissions. Fig. 3 shows the potential savings compared to trucking for an intermodal shipment originating near San Diego. These are calculated by first determining the threshold level of emissions required to reach 36 destination intermodal terminals across the country. Threshold emissions are calculated based on a drayage movement from San Diego to an intermodal ramp near Los Angeles, and then from a rail movement to the destination ramp. The emissions required to reach a grid of destination points are estimated for drayage movements from each destination ramp, and the ramp that minimizes emissions to reach that point is selected. The emissions at each point are
Legend Destination Ramp Origin Ramp Shipment Origin Estimated Savings (tonnes CO2) <0 0 – 0.5 0.5 – 1 1 – 1.5 1.5 – 2 2 – 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’s ArcMap GIS software and applying an inverse distance weighted interpolation to estimate the savings for all areas of the map. The figure illustrates many of the results from the carbon market area concept. In general the savings tend to increase for destinations 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 ovalshaped regions of higher potential savings surrounding a terminal and oriented along the direction of travel. As the destination moves away from the terminal the savings are reduced, even as the length of the journey may increase. A significant area in the western portion of the US does not fall within any carbon market area, due to the relatively short distance of the rail haul 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 supplied by an intermodal operator. Our results confirm the assumption that, on average, intermodal improves on the carbon efficiency 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 reductions of 20–50% compared to trucking. Though average emissions intensity information can be useful in estimating the potential of intermodal shipping to reduce emissions, the actual carbon intensity of intermodal shipment varied from 29 to 220 g CO2/ton-mile depending on the lane under consideration. We explain this variation through an application of market area theory to show that intermodal shipping is more efficient than truckload only in a specific area surrounding an intermodal terminal, called the carbon market area. Acknowledgements The authors wish to thank J.B. Hunt Transportation Co. for their help and cooperation in this research. This research was partially supported by the Global Leaders for Environmental Assessment and Performance consortium http://leap.mit.edu/. References Bitzan, J., Keeler, T., 2011. Intermodal traffic, regulatory change and carbon energy conservation in us freight transport. Applied Economics 43, 3945–3963. Greene, D.L., Plotkin, S.E., 2011. Reducing Greenhouse Gas Emissions from US Transportation. Pew Center on Global Climate Change, Arlington. International Energy Agency, 2009. Transport, Energy and CO2: Moving Towards Sustainability. IEA, Paris. International Road Transport Union, 2002. Comparative Analysis of Energy Consumption and CO2 Emissions of Road Transport and Combined Transport Road/Rail, Geneva. Morlok, E., Spasovic, L., 1995. Approaches to improving drayage in rail-truck intermodal service. In: Proceedings of The 1995 Pacific Rim Transportation Conference. Nierat, P., 1997. Market area of rail-truck terminals: pertinence of the spatial theory. Transportation Research Part A 31, 109–127. Vanek, F., Morlok, E., 2000. Improving the energy efficiency of freight in the United States through commodity-based analysis: justification and implementation. Transportation Research Part D 5, 11–29.
Contents lists available at SciVerse ScienceDirect
Transportation Research Part D journal homepage: www.elsevier.com/locate/trd
Estimating the CO2 intensity of intermodal freight transportation Anthony J. Craig a,⇑, Edgar E. Blanco a, Yossi Sheffi b a b
Center for Transportation & Logistics, Massachusetts Institute of Technology, 77 Massachusetts Ave, E40-276, Cambridge, MA 02139, USA 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 transport Carbon intensity Greenhouse gas emissions
a b s t r a c t This paper looks at the environmental effects of shifting from road to rail freight transportation. Little data is available to shippers to calculate the potential CO2 savings of an intermodal shift. In this paper we analyze a data set of more than 400,000 intermodal shipments to calculate the CO2 intensity of intermodal transportation as a distinct mode. Our results indicate an average intensity of 67 g of CO2 per ton-mile, but can vary between 29 and 220 g of CO2 per ton-mile depending on the specific origin–destination lane. We apply the market area concept to explain the variance between individual lane intensities and demonstrate the complexity in predicting the potential carbon savings in a switch from truckload to intermodal. Ó 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 grow by 50% by 2030 and by 100% by 2050 from 2007 levels. Within the transportation sector, freight, especially trucking, is expected to experience the fastest growth. In the US, medium and heavy-duty freight trucks account for more than 60% of the freight transportation emissions and are growing faster than any other mode (Greene and Plotkin, 2011). Given the projected growth 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) emissions from the baseline scenario by 2050 with appropriate road–rail intermodal shifts; a shift of 1% of current US intercity truck freight to intermodal could generate savings of 0.92–2.18 Tg of CO2 per year according to Bitzan and Keeler (2011). While a shift to intermodal freight may replace only a small amount of current truckload freight traffic, it is increasingly popular with shippers. Despite this increased popularity, there is relatively little information regarding 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. We compare the results of the analysis with estimates of serving those same lanes by truckload transportation, and apply the market area concept to explain the difficulty in assessing an overall efficiency for intermodal as a distinct mode of transport.
⇑ Corresponding author. Tel.: +1 617 253 1701. E-mail address: [email protected] (A.J. Craig). 1361-9209/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.trd.2013.02.016
50
A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53 Table 1 CO2 Intensity. Mode
CO2 (tonnes)
CO2 intensity (g/ton-mile)
Savings
Intermodal Truckload
806,819 1,490,986
67 125
46% NA
2. Methodology We define an intermodal shipment to consist of an origin drayage movement performed by truck that takes the shipment from the origin location to the origin ramp. At the ramp the shipment is transferred to rail and a linehaul between the origin and destination ramps occurs. At the destination ramp the shipment is transferred back to a truck and a destination drayage movement delivers the shipment to the consignee at the final destination. We calculate the carbon footprint of an intermodal shipment by disaggregating the shipment into separate drayage and rail movements using:
C IM 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 the carbon 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. The data consists of records for more 400,000 individual intermodal shipments covering more than 35,000 origin–destination lanes 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 operator supplied a carbon intensity parameter for drayage based on its own fuel efficiency, empty miles, and out-of-route miles. The contracted rail companies provided the length of the rail haul to the intermodal operator. When this data was not available the rail distance was calculated using the Rail MILER commercial software program. The rail carbon intensity parameter was calculated using intensity numbers supplied by the rail companies per ton-mile. This value was multiplied by the average weight 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 based on data collected from the operator’s longhaul trucking business and calculated using:
CF TL ¼ ðdotr þ dae Þ ctl
ð2Þ
where dotr is the over the road distance, dae is the average empty distance per shipment, and ctl is the carbon intensity of truckload transportation. The distances between the origin and destination zip codes are calculated using over the road travel distance. The average empty 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 mileage variance, 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-miles worth of goods moved. Net ton-miles are calculated by multiplying shipment weights, excluding equipment, by the great circle distance between the origin and destination for individual shipments, then summing across shipments. Using the direct distance between origin and destination, rather than the actual distance traveled, provides a consistent basis for comparing shipments across modes that vary in their circuity. The emissions from intermodal shipping, truckload shipping, and the 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 to offset the lower efficiency of drayage and the increased circuity of the rail network. For a specific shipment, the efficiency of using 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 for the lane, calculated using Eq. (1), by the net ton-miles of cargo shipped. The average lane intensity is 70 g CO2/ton-mile, with a standard deviation of 13 g CO2/ton-mile. The distribution of the operator’s lane-by-lane intensities is shown in Fig. 1, with a vertical 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 trucking had been used. When making the decision on a specific shipment, a shipper balances the environmental considerations with other factors such as time, cost, and service quality. Unlike trucking, which provides more consistent carbon intensity across lanes, the intensity of intermodal varies considerably. Further, the analysis considers only lanes for which intermodal service 1 The intermodal intensity is lower than the range estimated from Vanek and Morlok (2000), but the savings compared to trucking are consistent with the average range found by the International Road Transport Union (2002).
A.J. Craig et al. / Transportation Research Part D 22 (2013) 49–53
51
25%
Percent of Lanes
20%
15%
10%
5%
0%
g CO2 per ton-mile Fig. 1. Distribution of intermodal carbon intensity by lane.
Desttinattion
M T kload Ro Truck oute Desti D natio on Draya age
Origin
A
B natio on Ra amp p Destin
O n Drrayag Origi ge R Line Rail ehaul Orrigin Ram mp Fig. 2. Intermodal versus truckload choice.
is currently being used. For a shipper considering a prospective change to intermodal transportation, the average may be a poor 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 in which intermodal transportation is the most competitive mode. The space is defined around a rail terminal because an intermodal shipment requires a fixed threshold cost to first be moved to the terminal via the origin drayage and rail line haul. The cost to reach the destination is then this fixed cost plus the cost of the drayage move from the terminal to the final destination. This cost increases as the destination moves away from the terminal due to the longer drayage move at the destination. If the destination is too far from the terminal it may no longer be competitive to use intermodal transportation; instead a 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 the shipment 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 to M. The intermodal market area for terminal B, the shaded region around the terminal in the figure, defines the range around B 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 by road, 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. The boundary of the market area is found by setting the costs equal to one another.
C r ðMÞ ¼ C i ðMÞ () C r ðAÞ þ xr AM ¼ C i ðBÞ þ xi BM Rearranging the terms and substituting x = xi/xr and k ¼
AM xBM ¼ kAB
ð3Þ C i ðBÞC r ðAÞ xr AB
gives:
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 market area 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 previous method for calculating the carbon footprint of those modes. We extend the market area idea by defining the carbon market area of an intermodal terminal as the region of space around an intermodal terminal that can be served from a given origin with lower carbon emissions than by truckload transportation. Recall that the carbon footprint of a truckload shipment is calculated using Eq. (2). In that equation, ctl and dae are fixed quantities based on the operator’s efficiency. If we define Cr(A) = dae ctl, xr = ctl, and dotr ¼ AM, the equation becomes identical to the form used by Nierat to calculate the cost of truckload 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 dod cd þ dr cr dae ctl ddd ¼ ctl ctl
ð5Þ
Simplifying the calculation of the over the road distances as dotr = dgc c, with dgc as the great circle distance between the origin and destination and c the road circuity factor, produces an oval region oriented along the line from the origin to the destination terminal. The carbon market area provides an explanation for why the carbon intensity of intermodal shipments varies on a laneby-lane basis. For any given origin location, only destinations that fall within the carbon market area of a terminal will produce lower emissions than truckload transportation. The use of an overall average fails to capture the location dynamics and network structure that affect the actual efficiency, instead assuming that intermodal is always more efficient. Other attempts to gauge the competitiveness of intermodal, such as the break-even approach used by Morlok and Spasovic (1995) that specifies 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 destination intermodal terminal will have a carbon market area of different size and orientation, or possibly no market area at all. Without the detailed data regarding the intermodal network and operating efficiencies, the shipper lacks the capability to accurately determine whether a location falls within the carbon market area of a terminal or the potential savings of using intermodal to serve specific destinations. The magnitude of the potential savings is necessary for shippers to properly balance the carbon footprint of the shipment with the other criteria of the decision, such as the cost, transit time, and service level. At any point within the market area, the reduction in the carbon footprint is given by:
CF TL CF IM ¼ ððdotr þ dae Þctl Þ ðdod cd þ dr cr þ ddd cd Þ
ð6Þ
Evaluated across the entire intermodal network, the potential savings for any destination can be determined by selecting the mode and terminal that produce the lowest emissions. Fig. 3 shows the potential savings compared to trucking for an intermodal shipment originating near San Diego. These are calculated by first determining the threshold level of emissions required to reach 36 destination intermodal terminals across the country. Threshold emissions are calculated based on a drayage movement from San Diego to an intermodal ramp near Los Angeles, and then from a rail movement to the destination ramp. The emissions required to reach a grid of destination points are estimated for drayage movements from each destination ramp, and the ramp that minimizes emissions to reach that point is selected. The emissions at each point are
Legend Destination Ramp Origin Ramp Shipment Origin Estimated Savings (tonnes CO2) <0 0 – 0.5 0.5 – 1 1 – 1.5 1.5 – 2 2 – 2.5 > 2.5
Fig. 3. Potential intermodal carbon savings.
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compared to a truckload shipment from the origin to calculate the projected savings. Finally, the map is created using ESRI’s ArcMap GIS software and applying an inverse distance weighted interpolation to estimate the savings for all areas of the map. The figure illustrates many of the results from the carbon market area concept. In general the savings tend to increase for destinations 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 ovalshaped regions of higher potential savings surrounding a terminal and oriented along the direction of travel. As the destination moves away from the terminal the savings are reduced, even as the length of the journey may increase. A significant area in the western portion of the US does not fall within any carbon market area, due to the relatively short distance of the rail haul 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 supplied by an intermodal operator. Our results confirm the assumption that, on average, intermodal improves on the carbon efficiency 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 reductions of 20–50% compared to trucking. Though average emissions intensity information can be useful in estimating the potential of intermodal shipping to reduce emissions, the actual carbon intensity of intermodal shipment varied from 29 to 220 g CO2/ton-mile depending on the lane under consideration. We explain this variation through an application of market area theory to show that intermodal shipping is more efficient than truckload only in a specific area surrounding an intermodal terminal, called the carbon market area. Acknowledgements The authors wish to thank J.B. Hunt Transportation Co. for their help and cooperation in this research. This research was partially supported by the Global Leaders for Environmental Assessment and Performance consortium http://leap.mit.edu/. References Bitzan, J., Keeler, T., 2011. Intermodal traffic, regulatory change and carbon energy conservation in us freight transport. Applied Economics 43, 3945–3963. Greene, D.L., Plotkin, S.E., 2011. Reducing Greenhouse Gas Emissions from US Transportation. Pew Center on Global Climate Change, Arlington. International Energy Agency, 2009. Transport, Energy and CO2: Moving Towards Sustainability. IEA, Paris. International Road Transport Union, 2002. Comparative Analysis of Energy Consumption and CO2 Emissions of Road Transport and Combined Transport Road/Rail, Geneva. Morlok, E., Spasovic, L., 1995. Approaches to improving drayage in rail-truck intermodal service. In: Proceedings of The 1995 Pacific Rim Transportation Conference. Nierat, P., 1997. Market area of rail-truck terminals: pertinence of the spatial theory. Transportation Research Part A 31, 109–127. Vanek, F., Morlok, E., 2000. Improving the energy efficiency of freight in the United States through commodity-based analysis: justification and implementation. Transportation Research Part D 5, 11–29.