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Characterizing, Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches
Author(s) -
Lloyd Shan M.,
Ries Robert
Publication year - 2007
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1162/jiec.2007.1136
Subject(s) - life cycle assessment , industrial ecology , uncertainty analysis , computer science , product (mathematics) , expert elicitation , risk analysis (engineering) , sensitivity analysis , resource (disambiguation) , fuzzy logic , uncertainty quantification , resource efficiency , operations research , production (economics) , management science , sustainability , engineering , economics , simulation , mathematics , business , machine learning , ecology , computer network , statistics , geometry , artificial intelligence , biology , macroeconomics
Summary Life‐cycle assessment (LCA) practitioners build models to quantify resource consumption, environmental releases, and potential environmental and human health impacts of product systems. Most often, practitioners define a model structure, assign a single value to each parameter, and build deterministic models to approximate environmental outcomes. This approach fails to capture the variability and uncertainty inherent in LCA. To make good decisions, decision makers need to understand the uncertainty in and divergence between LCA outcomes for different product systems. Several approaches for conducting LCA under uncertainty have been proposed and implemented. For example, Monte Carlo simulation and fuzzy set theory have been applied in a limited number of LCA studies. These approaches are well understood and are generally accepted in quantitative decision analysis. But they do not guarantee reliable outcomes. A survey of approaches used to incorporate quantitative uncertainty analysis into LCA is presented. The suitability of each approach for providing reliable outcomes and enabling better decisions is discussed. Approaches that may lead to overconfident or unreliable results are discussed and guidance for improving uncertainty analysis in LCA is provided.