Spatial variability in yields and greenhouse gas emissions from soils has been identified as a key source of variability in life cycle assessments (LCAs) of agricultural products such as cellulosic ethanol. This study aims to conduct an LCA of cellulosic ethanol production from switchgrass in a way that captures this spatial variability and tests results for sensitivity to using spatially averaged results. The Environment Policy Integrated Climate (EPIC) model was used to calculate switchgrass yields, greenhouse gas (GHG) emissions, and nitrogen and phosphorus emissions from crop production in southern Wisconsin and Michigan at the watershed scale. These data were combined with cellulosic ethanol production data via ammonia fiber expansion and dilute acid pretreatment methods and region-specific electricity production data into an LCA model of eight ethanol production scenarios. Standard deviations from the spatial mean yields and soil emissions were used to test the sensitivity of net energy ratio, global warming potential intensity, and eutrophication and acidification potential metrics to spatial variability. Substantial variation in the eutrophication potential was also observed when nitrogen and phosphorus emissions from soils were varied. This work illustrates the need for spatially explicit agricultural production data in the LCA of biofuels and other agricultural products.
Modeling the life cycle of fuel pathways for cellulosic ethanol (CE) can help identify logistical barriers and anticipated impacts for the emerging commercial CE industry. Such models contain high amounts of variability, primarily due to the varying nature of agricultural production but also because of limitations in the availability of data at the local scale, resulting in the typical practice of using average values. In this study, 12 spatially explicit, cradle-to-refinery gate CE pathways were developed that vary by feedstock (corn stover, switchgrass, and Miscanthus), nitrogen application rate (higher, lower), pretreatment method (ammonia fiber expansion [AFEX], dilute acid), and co-product treatment method (mass allocation, sub-division), in which feedstock production was modeled at the watershed scale over a nine-county area in Southwestern Michigan. When comparing feedstocks, the model showed that corn stover yielded higher global warming potential (GWP), acidification potential (AP), and eutrophication potential (EP) than the perennial feedstocks of switchgrass and Miscanthus, on an average per area basis. Full life cycle results per MJ of produced ethanol demonstrated more mixed results, with corn stover-derived CE scenarios that use sub-division as a co-product treatment method yielding similarly favorable outcomes as switchgrass-and Miscanthus-derived CE scenarios. Variability was found to be greater between feedstocks than watersheds. Additionally, scenarios using dilute acid pretreatment had more favorable results than those using AFEX pretreatment.
Increasing demand for renewable alternative fuels, such as ethanol, is driven by decreasing availability of fossil resources and increasing attention to climate change. Life cycle assessment (LCA) is the tool used to evaluate environmental impacts, such as energy intensity (EI) and global warming potential (GWP), from ethanol production, but the application of this tool varies greatly. The goals of this study were to enumerate the life cycle EI, net energy value (NEV), and GWP of corn grain ethanol production in Wisconsin, to explore ethanol production scenarios which differ at the treatment of the whole stillage (WS) coproduct, and to evaluate the various solutions to the multifunctionality problem which arises in LCA. In Scenario 1, all suggested solutions to the multifunctionality problem are considered by transforming WS into the animal feed dried distillers grains with solubles (DDGS). Scenario 2 avoids allocation using an integrated system which recycles the WS with an anaerobic biodigester and a combined heat and power (CHP) plant to provide electricity and steam to the ethanol refinery and returns the residue to the land as fertilizer. Based on the Scenario 1 analysis, we recommend the use of the subdivision (SD) solution to the multifunctionality problem because it enables clear comparisons between different ethanol production systems, it distinguishes between the environmental impacts from ethanol production and coproduct processing and it reduces the number of assumptions in the LCA calculations. From the comparison of both scenarios, we find that recycling the WS into electricity, heat, and fertilizer is the most environmentally beneficial coproduct use because it results in a 54% lower EI and a 67% lower GWP than the processing of WS into DDGS.
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