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Novel energy technologies, especially decentralized electricity generation systems, are increasingly being designed and implemented. However, potential environmental impacts are frequently recognized after installing new energy systems at full scale, at which point modification comes at a high cost. Life cycle assessment (LCA) can be used throughout the design-to-commercialization process to prevent this outcome, despite the challenges of emerging energy technology LCAs, like comparability, lack of data, scale-up difficulties, and uncertainties that are not typically faced while evaluating existing and established systems. The complexity and urgency of evaluating climate change impacts of novel energy technologies during the research and development stage reveal the need for guidance, presented in this study, with an emphasis on data collection, data processing, and uncertainty analysis. We outline best practices in choosing among several methods that have been employed in LCA studies to fill gaps in input data, including machine learning. Additionally, we discuss how design can be guided by LCA through assessment setting and delineation of scenarios or case studies, in order to prevent unnecessary effort and maximize the amount of useful, interpretable results. We also discuss the utility of complementary analyses, including global sensitivity analysis, neural network, Monte Carlo analysis that differentiates between uncertainty and variability parameters, and optimization. This guidance has the potential to make emerging electricity generation system implementation ultimately effective in reducing greenhouse gas emissions, through the methodological use of LCA in the design process.
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Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass‐based biofuel production
Abstract 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.
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- Award ID(s):
- 2224712
- PAR ID:
- 10536358
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- GCB Bioenergy
- Volume:
- 16
- Issue:
- 8
- ISSN:
- 1757-1693
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/gcbb.13179
