Lifecycle Assessment and Policy: Implications of the Renewable Fuel Standard for Upper-Midwest Energy Supply
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Lifecycle Assessment and Policy: Implications of the Renewable Fuel Standard for
Upper-Midwest Energy Supply
Michael Brodeur-Campbell1, Jordan Klinger1, Kathleen Halvorsen2,3, and David Shonnard1,4
 Department of Chemical Engineering, Michigan Technological University
 Department of Social Sciences, Michigan Technological University
 School of Forest Resources and Environmental Science, Michigan Technological University
 Director Sustainable Futures Institute, Michigan Technological University
The use of biofuels derived from lignocellulosic biomass has received significant public attention with Congressional policies such as the Energy Policy Act of 2005 (EPAct), updated in the Energy Independence and Security Act of 2007 (EISA) which included the Renewable Fuel Standard (RFS). The RFS mandates the blending of ethanol and advanced / cellulosic biofuels into the U.S. transportation fuel supply for each year until 2022. The Renewable Fuel Standard has a lifecycle perspective and contains lifecycle greenhouse gas emissions reduction requirements (relative to petroleum gasoline) for the production of biofuels. Conventional ethanol derived from corn starch must meet a 20% reduction level and is capped at 15 billion gallons/yr. ?Cellulosic? ethanol must meet a 60% reduction standard and is expected to make up 16 billion gallons/yr. by 2022. ?Advanced? biofuel that meet a 50% reduction must make up the remaining 5 billion gallons/yr mandated by 2022 (36 billion gallons/yr total ethanol). In addition to demanding an accounting for both direct and indirect land use change CO2 emissions, the policy also specifies several other environmental and social metrics to consider in evaluating biofuel production. Environmental metrics specified include air quality, water quantity and quality, wetlands and ecosystem health, and wildlife habitat. Other indirect effects on society include energy security, commercial fuel production and infrastructure, consumer fuel prices, job creation, agriculture impacts, rural economic development, and future food prices.
While many studies have been performed on agricultural residues such as corn stover (Sheehan, Aden et al. 2004, Hsu 2010) and wheat straw (Kabel, Bos et al. 2007), much less attention has been given to woody feedstocks. For this LCA, several regionally important potential woody feedstocks are analyzed (hybrid poplar, hybrid willow, and mixed hardwood logging residues) as well as two herbaceous feedstocks (switchgrass monoculture, and a diverse prairie grass ecosystem). This LCA is directly driven by the requirements of the RFS and includes metrics for water and air quality, as well as global warming potential. Eutrophication potential was chosen as the most important measure for water quality, as the effects of fertilizer use in biomass cultivation generally dominate all categories of water quality. Particulate matter emissions were chosen as the most important measure for air quality, as this category has the greatest implication for human health.
SimaPro 7.2 and the EcoInvent database were used to model the process and assign environmental burdens. A co-product credit is assigned to the renewable electricity generated from the non-fermentable portion of the biomass. This electricity is modeled to displace grid electricity at the regional mix for the states of Michigan, Wisconsin, Minnesota, Iowa, and Illinois, which is derived approximately 70% from coal-fired power plants. Inputs and outputs for the conversion were based on the National Renewable Energy Laboratory Technical Report NREL/TP-510-32438 (Aden, Ruth, et al. 2002). The ratio of fuel to electricity produced from each feedstock was modified based on typical values for cellulose, hemicellulose, lignin, ash, and extractives for each feedstock. Table 1 below summarizes the results for Global Warming Potential, Eutrophication Potential, and Particulate Matter Emissions for the feedstocks analyzed.
Table 1: Summary of LCA Results for Cellulosic Ethanol
Global Warming Potential (g CO2 eq. / MJ)
Eutrophication Potential (g N eq. / MJ)
Particulate Matter <2.5 µm (mg / MJ)
All fuel pathways analyzed meet 60% greenhouse gas reduction requirements for a cellulosic biofuel, even before any co-product credit is applied. With co-product credit some pathways result in net negative carbon emissions due to coal displacement. Eutrophication potential is increased in all cases due to feedstock production activities; the highest is for switchgrass which is the most fertilizer-intensive feedstock, while the lowest is for multispecies prairie. Particulate emissions are reduced for all cases except logging residues, largely due to avoided coal power. Particulate matter emissions for logging residues are high because of the low productivity per acre and long transportation distances required to supply enough material for conversion.
Future work will focus on refining our feedstock conversion emission estimates using the ASPEN Plus process simulation software, and on including land use change estimates for carbon flows.
Aden, A., M. Ruth, K. Ibsen, J. Jechura, K. Neeves, J. Sheehan, B. Wallace, L. Montague, A. Slayton, and J. Lukas (2002). Lignocellulosic Biomass to Ethanol Process Design and Economics Utilizing Co-Current Dilute Acid Prehydrolysis and Enzymatic Hydrolysis for Corn Stover. National Renewable Energy Laboratory, Golden, Colorado.
Hsu, D., D. Inman, G.A. Heath, E.J. Wolfrum, M.K. Mann, and A. Aden (2010). Life Cycle Environmental Impacts of Selected U.S. Ethanol Production and Use Pathways in 2022. Environmental Science & Technology; 44, 5289-5297.
Kabel, M.A., Bos, G., Zeevalking, J., Voragen, A.G.J., Schols, H.A., (2007). Effect of pretreatment severity on xylan solubility and enzymatic breakdown of the remaining cellulose from wheat straw. Bioresource Technology; 98, 2034?2042.
Sheehan J, Aden A, Paustian K, Killian K, Brenner J, Walsh M, et al. (2004) Energy and environmental aspects of using corn stover for fuel ethanol. Journal of Industrial Ecology;7(4):117e46.