A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty
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A methodology.pdf
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Author(s)
Gregory, Jeremy
Noshadravan, Arash
Olivetti, Elsa A.
Kirchain Jr, Randolph E
Date Issued
May 2016
Journal
Environmental Science & Technology
Publisher
American Chemical Society (ACS)
Citation
Gregory, Jeremy R. et al. “A Methodology for Robust Comparative Life Cycle Assessments Incorporating Uncertainty.” Environmental Science & Technology 50, 12 (June 2016): 6397–6405 © 2016 American Chemical Society
Version
Author's final manuscript
Abstract
We propose a methodology for conducting robust comparative life cycle assessments (LCA) by leveraging uncertainty. The method evaluates a broad range of the possible scenario space in a probabilistic fashion while simultaneously considering uncertainty in input data. The method is intended to ascertain which scenarios have a definitive environmentally preferable choice among the alternatives being compared and the significance of the differences given uncertainty in the parameters, which parameters have the most influence on this difference, and how we can identify the resolvable scenarios (where one alternative in the comparison has a clearly lower environmental impact). This is accomplished via an aggregated probabilistic scenario-aware analysis, followed by an assessment of which scenarios have resolvable alternatives. Decision-tree partitioning algorithms are used to isolate meaningful scenario groups. In instances where the alternatives cannot be resolved for scenarios of interest, influential parameters are identified using sensitivity analysis. If those parameters can be refined, the process can be iterated using the refined parameters. We also present definitions of uncertainty quantities that have not been applied in the field of LCA and approaches for characterizing uncertainty in those quantities. We then demonstrate the methodology through a case study of pavements.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1021/acs.est.5b04969
