Distance and backhaul in commodity transport modeling
- Joyce Smith CooperAffiliated withDepartment of Mechanical Engineering, University of Washington Email author
- , Liila WoodsAffiliated withFive Winds International
- , Seung Jin LeeAffiliated withDepartment of Mechanical Engineering, University of Washington
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Background, aims and scope
The goal of this work is to provide methodological information for modeling distance and backhaul for commodity transport in a life cycle inventory (LCI). The scope includes a review of modeling parameters accounted for in transport unit process models and accounted for in unit process models using transported materials. Assumptions related to backhaul (or return trip) and transport distance are characterized and evaluated. A case study explores the contribution of transport and the bearing of assumptions on the life cycle of select US-produced metals.
Backhaul and distance estimation assumptions and methods are described and applied. Commodity transport energy consumption and emissions (including life cycle fuel production) are estimated as a function of assumptions for distances traveled (based on data from a commodities transportation survey) and backhaul (based on a review of related literature) for transport unit processes and example US metal LCIs. The results estimate the contribution of transportation to life cycle total, fossil, and petroleum energy consumption and eight air emissions: CH4, CO, CO2, N2O, NO x , PM, SO x , and NMVOC for aluminum, brass, copper, iron, and carbon, and stainless steels.
When evaluating transportation processes unincorporated into the metals LCIs, we find a 21–62% and a 61–91% increase in life cycle flows for the inclusion of distance data confidence intervals and the inclusion of backhaul, respectively. We presume that these results also apply to transport-dominated LCIs, such as those evaluating alternative uses for wastes. Next, when commodity transport is incorporated into the metal LCIs, we find the contribution to the metals life cycle to exceed 10% of the life cycle values of NMVOC emissions (aluminum, iron, and carbon steels), CO emissions (secondary aluminum), NO x emissions (aluminum, secondary copper, iron, and steels), N2O emissions (aluminum, brass, secondary copper, iron, and carbon steels), and SO x , CH4, CO2, and greenhouse gas emissions (basic oxygen furnaces (BOF) carbon steel) and to exceed 25% for NMVOC emissions (BOF steel), NO x emissions (secondary aluminum, iron, and carbon steels), and N2O emissions (BOF steel). Also, when commodity transport is incorporated into the metal LCIs, we find little variation in the results related distance confidence intervals and the inclusion of backhaul, seeing on average a 2% change in the contribution of commodity transport to the metal LCIs for variation in assumptions. Finally, our normalization revealed a very small, consistent commodity transport contribution of NO x for all metals and a small contribution to all emissions by carbon steel on a national scale. Thus, we find the importance of distance and backhaul assumptions on a sliding scale: we find travel distances, distance confidence intervals, and the inclusion of backhaul to be important to transportation processes and presumably, transport-dominated LCIs; we find travel distances to be important when commodity transport is incorporated into metal LCIs; and we find commodity transport not important to our normalized results for the metals studied.
We presume all LCIs will fall somewhere on this sliding scale, and that the estimation methods presented here will be useful in future studies. To support transparency of results, LCI construction should clearly state transport assumptions and provide well-developed meta data noting regional vehicle/vessel energy and emission intensities; load factor; vehicle/vessel manufacturing, maintenance, and disposal; and infrastructure construction, operation/maintenance, and disposal for transport unit processes as well as load and distance for unit process models using transported materials. Finally, the diminishing returns of transportation data collection should be evaluated on the basis of whether the commodity transport distance can actually be changed.
Commodity transport can be important to un-normalized LCI results and can be sufficiently captured by representative (as opposed to a range of) distance assumptions for the metals studied. Because commodity transportation was found to be important in our un-normalized results, we note that, depending upon the goal of the study, intentional omission of commodity transport in metal LCIs, and until shown otherwise, other LCIs, without further investigation, may not be defensible.
Recommendations and perspectives
More attention is being paid to the contribution of transportation within system life cycles, particularly for food and bioproducts systems. Here, we note that well-documented assessments culminating in transparent results can be used to identify local sourcing or facility location priorities for metals and thus for materials beyond notorious transportation-oriented systems. Further, whereas it may be possible to develop reliable regional and commodity-specific rules-of-thumb for backhaul and distance/co-location that can be automated within the construction of a LCI, comprehensive commodity transport modeling remains dependent upon data for regional production mixes and the estimation of representative transport distances. Recommendations for future research include assessments beyond metals; assessment of a broader range of emissions, land use, and noise; and the inclusion of transportation from and to overseas destinations.
KeywordsBackhaul Co-location Commodities Distance estimation Metals Return trip Transportation
- Distance and backhaul in commodity transport modeling
The International Journal of Life Cycle Assessment
Volume 13, Issue 5 , pp 389-400
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