| Autor: |
Yang Y; Key Lab of Urban Environment and Health, Institute of Urban Environment , Chinese Academy of Sciences , Xiamen , Fujian 361021 , China.; Department of Bioproducts and Biosystems Engineering , University of Minnesota , St. Paul , Minnesota 55108 , United States.; Institute on the Environment , University of Minnesota , St. Paul , Minnesota 55108 , United States., Pelton REO; Institute on the Environment , University of Minnesota , St. Paul , Minnesota 55108 , United States., Kim T; Department of Bioproducts and Biosystems Engineering , University of Minnesota , St. Paul , Minnesota 55108 , United States.; Institute on the Environment , University of Minnesota , St. Paul , Minnesota 55108 , United States., Smith TM; Department of Bioproducts and Biosystems Engineering , University of Minnesota , St. Paul , Minnesota 55108 , United States.; Institute on the Environment , University of Minnesota , St. Paul , Minnesota 55108 , United States. |
| Abstrakt: |
Efforts to compile life cycle inventory (LCI) data at more geographically refined scales or resolutions are growing. However, it remains poorly understood as to how the choice of spatial scale may affect LCI results. Here, we examine this question using U.S. corn as a case study. We compile corn production data at two spatial scales, state and county, and compare how their LCI results may differ for state and national level analyses. For greenhouse gas (GHG) emissions, estimates at the two scales are similar (<20% of difference) for most state-level analyses and are basically the same (<5%) for national level analysis. For blue water consumption, estimates at the two scales differ more. Our results suggest that state-level analyses may be an adequate spatial scale for national level GHG analysis and for most state-level GHG analyses of U.S. corn, but may fall short for water consumption, because of its large spatial variability. On the other hand, although county-based LCIs may be considered more accurate, they require substantially more effort to compile. Overall, our study suggests that the goal of a study, data requirements, and spatial variability are important factors to consider when deciding the appropriate spatial scale or pursuing more refined scales. |
