469925 Optimizing Land Use Change and Life Cycle Greenhouse Gas Emissions of Biofuels

Sunday, November 13, 2016: 4:45 PM
Union Square 19 & 20 (Hilton San Francisco Union Square)
Daniel Garcia, Northwestern University, Evanston, IL and Fengqi You, Department of Chemical and Biological Engineering, Northwestern University, Evanston, IL

Biofuels are a potential source of renewable liquid fuel, in contrast to the nonrenewable fossil fuels we rely on today. Another driver for the interest in biofuels is the possibility that life cycle greenhouse gas (GHG) emissions for biofuels might be smaller than fossil fuels, providing an environmental benefit in addition to the benefits of a secure and renewable fuel supply. However, there still exist GHG emissions sources throughout the biofuel life cycle, and many of them do not exist at a reasonable level for fossil fuels. For example, there are cultivation GHG emissions associated with cultivating the biomass feedstocks, land use change (LUC) GHG emissions incurred by transforming land into cropland to grow the biomass feedstocks, etc. GHG emissions from LUC due to biofuels are perhaps the most difficult to characterize and quantify; they are not released from a smokestack or a tailpipe. A host of researchers propose different characterization and quantification methods to calculate GHG emissions from LUC due to biofuels, but there is still significant disagreement on just how many GHG emissions from LUC biofuels are responsible for.1,2 In other words, no one has been able to show which mix of biofuel production pathways and biomass sourcing strategies are best to minimize life cycle GHG emissions including LUC emissions. It is imperative to reduce uncertainty on this issue as current biofuels legislation and policies are based on these estimates. Furthermore, previous studies explore fixed biofuel production scenarios, e.g. 14 Bgal/year of bioethanol from corn, and are not able to identify better pathways with diminished GHG emissions from LUC. Thus, not only are current policies and biofuel production and biomass sourcing strategies based on uncertain LUC GHG emissions estimates, but they are also not founded on optimized stratagems.

In this work, we develop a first-ever LUC life cycle optimization (LCO) model that incorporates cutting-edge characterization and calculation methods for bioethanol LUC GHG emissions within an optimization framework. This modeling framework identifies an optimal bioethanol production and biomass sourcing strategy that minimizes LUC GHG emissions by selecting any combination and size of 14 bioethanol production pathways and 5 biomass feedstocks. We choose bioethanol production pathways that are either already commercialized or are commercialization-ready, using data from technoeconomic analyses/reports from the U.S. DOE or the peer-reviewed scientific community.3-6 LUC and direct processing GHG emissions are considered as well as excess electricity production credits from processing. We calculate LUC GHG emissions from exhaustive data bases for soil organic carbon (SOC), carbonaceous biomass cover, foregone CO2 sequestration by forested land, and the net primary productivity (NPP) of each land type in each region around the world.7-10 The CGE is integrated into the LCO modeling framework with the land carbon data to determine LUC GHG emissions around the world due to economic forces arising from increased bioethanol production in the US. Thus, for the first time, cutting-edge LUC characterization and calculation methods are integrated into an LCO modeling framework. Furthermore, this study presents the first time an integrated CGE-LCO model has been developed and utilized.

We consider a case study of US bioethanol production levels ranging from 10 – 100 Bgal/year. We find that minimized LUC GHG emissions from our results are lower than previous estimates, indicating that these previous estimates are at least possible. Corn stover is always preferred, as LUC due to its utilization is negligible. Switchgrass is then selected when the sustainably harvestable corn stover resource base is exhausted. Two-stage dilute acid pretreatment followed by distillation or dilute acid pretreatment followed by pervaporation are preferred technologies for corn stover conversion. Dilute acid pretreatment followed by distillation is always the selected bioethanol production pathway for switchgrass. Forested land is largely preserved around the world whenever possible, and pastureland is by far preferred for conversion to cropland. However, some of the recent LUC GHG emissions estimates for biofuels are close to our minimized value, indicating that some might be optimistic. Nonetheless, if the optimal bioethanol production and biomass sourcing strategies produced by our work are followed, then life cycle GHG emissions including LUC GHG emissions of bioethanol can be managed.


1. Qin Z, Dunn JB, Kwon H, Mueller S, Wander MM. Influence of spatially‐dependent, modeled soil carbon emission factors on life‐cycle greenhouse gas emissions of corn and cellulosic ethanol. GCB Bioenergy. 2016.

2. Plevin RJ, Beckman J, Golub AA, Witcover J, O’Hare M. Carbon accounting and economic model uncertainty of emissions from biofuels-induced land use change. Environmental science & technology. 2015;49(5):2656-2664.

3. Pfromm PH, Amanor-Boadu V, Nelson R, Vadlani P, Madl R. Bio-butanol vs. bio-ethanol: a technical and economic assessment for corn and switchgrass fermented by yeast or Clostridium acetobutylicum. Biomass and Bioenergy. 2010;34(4):515-524.

4. Zhu Y, Gerber MA, Jones SB, Stevens DJ. Analysis of the Effects of Compositional and Configurational Assumptions on Product Costs for the Thermochemical Conversion of Lignocellulosic Biomass to Mixed Alcohols – FY 2007 Progress Report,. Richland, Washington: PNNL;2009.

5. Zhu Y, Jones SB. Techno-economic Analysis for the Thermochemical Conversion of Lignocellulosic Biomass to Ethanol via Acetic Acid Synthesis. Richland, Washington: PNNL;2009.

6. Dal-Mas M, Giarola S, Zamboni A, Bezzo F. Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty. Biomass and Bioenergy. 2011;35(5):2059-2071.

7. Change IPoC. 2006 IPCC guidelines for national greenhouse gas inventories: Intergovernmental Panel on Climate Change; 2006.

8. Monfreda C, Ramankutty N, Hertel TW. Global agricultural land use data for climate change analysis. Economic analysis of land use in global climate change policy. 2009;14:33.

9. West TO, Brandt CC, Baskaran LM, et al. Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting. Ecological Applications. 2010;20(4):1074-1086.

10. Gibbs H, Yui S, Plevin R. New estimates of soil and biomass carbon stocks for global economic models: Center for Global Trade Analysis, Department of Agricultural Economics, Purdue University;2014.

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See more of this Session: Life Cycle Analysis of Bio-Based Fuels, Energy, and Chemicals
See more of this Group/Topical: Sustainable Engineering Forum