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This content will become publicly available on July 3, 2026

Title: Using life cycle assessment to design emerging electricity generation technologies
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.  more » « less
Award ID(s):
2316124
PAR ID:
10616919
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Elsevier
Date Published:
Journal Name:
Energy Conversion and Management: X
Volume:
27
ISSN:
2590-1745
Page Range / eLocation ID:
101122
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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