Global Warming Potential of Biomass-to-Ethanol: Review and Sensitivity Analysis through a Case Study

Rui Pacheco and Carla Silva
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Rui Pacheco: Instituto de Bioengenharia e Biociências, Instituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1, 1049-001 Lisboa, Portugal
Carla Silva: Instituto Dom Luiz, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal

Energies, 2019, vol. 12, issue 13, 1-18

Abstract: In Europe, ethanol is blended with gasoline fuel in 5 or 10% volume (E5 or E10). In USA the blend is 15% in volume (E15) and there are also pumps that provide E85. In Brazil, the conventional gasoline is E27 and there are pumps that offer E100, due to the growing market of flex fuel vehicles. Bioethanol production is usually by means of biological conversion of several biomass feedstocks (first generation sugar cane in Brazil, corn in the USA, sugar beet in Europe, or second-generation bagasse of sugarcane or lignocellulosic materials from crop wastes). The environmental sustainability of the bioethanol is usually measured by the global warming potential metric (GWP in CO 2 eq), 100 years time horizon. Reviewed values could range from 0.31 to 5.55 gCO 2 eq/L ETOH . A biomass-to-ethanol industrial scenario was used to evaluate the impact of methodological choices on CO 2 eq: conventional versus dynamic Life Cycle Assessment; different impact assessment methods (TRACI, IPCC, ILCD, IMPACT, EDIP, and CML); electricity mix of the geographical region/country for different factory locations; differences in CO 2 eq factor for CH 4 and N 2 O due to updates in Intergovernmental Panel on Climate Change (IPCC) reports (5 reports so far), different factory operational lifetimes and future improved productivities. Results showed that the electricity mix (factory location) and land use are the factors that have the greatest effect (up to 800% deviation). The use of the CO 2 equivalency factors stated in different IPCC reports has the least influence (less than 3%). The consideration of the biogenic emissions (uptake at agricultural stage and release at the fermentation stage) and different allocation methods is also influential, and each can make values vary by 250%.

Keywords: life cycle assessment; time horizon; impact category method; electricity mix; factory lifetime; dynamic LCA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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