Distance and backhaul in commodity transport modeling
- 288 Downloads
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, NOx, PM, SOx, 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), NOx emissions (aluminum, secondary copper, iron, and steels), N2O emissions (aluminum, brass, secondary copper, iron, and carbon steels), and SOx, CH4, CO2, and greenhouse gas emissions (basic oxygen furnaces (BOF) carbon steel) and to exceed 25% for NMVOC emissions (BOF steel), NOx 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 NOx 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
- American Council for an Energy-Efficient Economy (1997) Asilomar Conference on Sustainable Transportation Energy Strategies, Decicco JM, Delucchi MA (eds) Transportation, energy, and environment: how far can technology take us? American Council for an Energy Efficient Econom, Washington, DC 278pGoogle Scholar
- American Iron and Steel Institute (2006) Steel glossary. American Iron and Steel Institute, Washington, DCGoogle Scholar
- AusLCANet (1998) Australian LCA Network: tinplate report and SimaPro 5.0—Australian LCI data on steel, auslcanet.rmit.edu.au/Google Scholar
- Baumel C, Hurburgh C, Lee T (1985) Estimates of total fuel consumption in transporting grain from Iowa to major grain-importing countries by alternative modes and routes. Iowa State University, AmesGoogle Scholar
- Braam J, Tanner T, Askham C, Hendriks N, Maurice B, Malkki H, Vold M, Wessman H, de Beaufort A (2001) SETAC-Europe LCA working group ‘Data availability and data quality’ energy, transport and waste models. Int J Life Cycle Assess 6(3):135–139Google Scholar
- Bureau of Transportation Statistics (2004) Transportation–commodity flow survey United States, EC02TCF-US. U.S. Census Bureau, Washington, DCGoogle Scholar
- Ekvall T, Frees N, Nielsen P, Person L, Ryberg A, Weidema B, Wesnaes M, Widheden J (1998) Life cycle assessment on packaging systems for beer and soft drinks. Main report. Danish Environmental Protection Agency (Environmental Project 399), CopenhagenGoogle Scholar
- Emissions Factors and Policy Applications Center (1986) AP42 5th Edition compilation of air pollutant emission factors, vol. 1: stationary point and area sources: iron and steel production. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
- Energy Information Administration (2006) Annual energy review, www.eia.doe.gov/emeu/aer/contents.html
- Federal Highway Administration (2002) Freight analysis framework highway capacity—Version 1: methodology report. Department of Transportation, Washington, DCGoogle Scholar
- Fowler J (2001) The commodity flow survey and hazardous materials safety data: perspectives on use, content, and needs for the future. Transportation Research Board 80th Annual Meeting, Washington, DCGoogle Scholar
- Frees N, Weidema B (1998) Life cycle assessment of packaging systems for beer and soft drinks: energy and transport scenarios. Danish Environmental Protection Agency (Environmental Project 406), CopenhagenGoogle Scholar
- He D, Wang M (2000) Contribution of feedstock and fuel transportation to total fuel-cycle energy use and emissions. Society of Automotive Engineers. SAE 2000-01-2976Google Scholar
- International Energy Agency (2006) Statistics and balances, www.iea.org/Textbase/stats/
- International Iron and Steel Institute (2004a) Steel statistical yearbook. Brussels, BelgiumGoogle Scholar
- International Iron and Steel Institute (2004b) Worldwide LCI database for steel industry products methodology report, www.worldsteel.org
- Jørgensen A, Ywema P, Frees N, Exner S, Bracke R (1996) Transportation in LCA: a comparative evaluation of the importance of transport in four LCAs. Int J Life Cycle Assess 1(4):218–220Google Scholar
- Jungmeier G, Werner F, Jarnehammar A, Hohenthal C, Richter K (2002) Allocation in LCA of wood-based products: experiences of cost action E9—part II. Int J Life Cycle Assess 7(6):369–375Google Scholar
- Office of Transportation and Air Quality (2000) Analysis of commercial marine vessels emissions and fuel consumption data, EPA420-R-00-002. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
- Office of Air and Radiation (2006) Airtrends. U.S. Environmental Protection Agency, Washington, DCGoogle Scholar
- Silvenius F, Grönroos J (2003) Fish farming and the environment results of inventory analysis. Finnish Environment Institute, HelsinkiGoogle Scholar
- Swiss Centre for Life Cycle Inventories (2006) ECOINVENT—the Swiss national life cycle inventory database, www.ecoinvent.ch/
- United States Geological Society (2003) Minerals yearbook. U.S. Department of the Interior, Washington, DCGoogle Scholar
- United States Geological Society (2005) Mineral commodity summaries. U.S. Department of the Interior, Washington, DCGoogle Scholar
- Upper Great Plains Transportation Institute, North Dakota State University: Fargo, www.ugpti.org/pubs/pdf/SP143.pdf
- Vachal K, Reichert H (2000) Identity preserved grain—logistical overview. Upper Great Plains Transportation Institute Identity Preserved GrainGoogle Scholar
- Wang M (2005) The greenhouse gases, regulated emissions, and energy use in transportation (GREET) Model: Version 1.6. Argonne National Laboratory: Argonne (unit process data extracted prior to final software results), www.transportation.anl.gov/software/GREET/
- Woods L (2006) Modeling the transport of raw materials in life cycle assessment. Thesis in partial fulfillment of the requirements for the degree of Master of Science. University of Washington, SeattleGoogle Scholar