Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass‐based biofuel production

• Published in: Global change biology. Bioenergy 2024-08, Vol.16 (8), p.n/a
• Main Authors: Kim, Seungdo, Dale, Bruce E., Basso, Bruno
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Biodiesel fuels; Biofuels; Biomass; cellulosic biofuel; Climate change; Data processing; Electricity; Emission analysis; Emissions; Ethanol; Global warming; global warming intensity; Greenhouse effect; greenhouse gas emission factor; Greenhouse gases; Laboratories; LCA database; Life cycle analysis; Life cycle assessment; Mathematical analysis; Monte Carlo simulation; Organic carbon; Organic soils; Panicum virgatum; Parameter identification; Parameter sensitivity; Parameter uncertainty; Renewable resources; Research centers; Sensitivity analysis; Simulation; Soil analysis; Sorghum; Sugarcane; switchgrass; Uncertainty analysis
• ISSN: 1757-1693; 1757-1707
• DOI: 10.1111/gcbb.13179

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production. Our research aimed to understand the uncertainties in greenhouse gas emission factors in biofuel production, specifically ethanol from switchgrass. We used multiple life cycle assessment databases to compare static and stochastic estimates, employing Monte Carlo simulations to identify key sensitive parameters. This study highlights the importance of comprehensive data sources and advanced methodologies to reduce uncertainties, providing valuable insights for policymakers and industry leaders to make informed decisions. Our findings emphasize the need for precise data and robust analysis techniques in developing sustainable biofuel systems that can effectively contribute to climate change mitigation.