Characterizing, Propagating, and Analyzing Uncertainty in Life‐Cycle Assessment: A Survey of Quantitative Approaches

Abstract

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.

Interpretation of comparative LCAs: external normalization and a method of mutual differences

“…Probability distributions at characterization used in the overlap area calculations, derive from an uncertainty analysis using the Pedigree Matrix coefficients available in Ecoinvent 3.01 (Lewandowska et al 2004;Lloyd and Ries 2007;Muller et al 2016). Issues particular to the estimation of uncertainty parameters and issues in implementation in LCA software packages are out of the scope of the paper.…”

How to manage uncertainty in future Life Cycle Assessment (LCA) scenarios addressing the effect of climate change in crop production

“…(Basset-Mens et al, 2009;Bojacá et al, 2014;Fedele et al, 2014). However, we are aware that other methods for parameter uncertainty analysis can be applied (Lloyd and Ries, 2007), but the one recommended can be easily implemented without further data collection.…”

Design of Sustainable Biofuel Processes and Supply Chains: Challenges and Opportunities

“…These sources of uncertainty introduce variability at each stage of the analysis, which are compounded and propagated with subsequent modeling scales and the use of higher complexity models. Several commons approaches are often utilized to quantify uncertainty in environmental sustainability analysis including stochastic modeling and one-at-a-time (OAT) sensitivity analysis [177][178][179]. It is important to note that the primary utility of environmental sustainability analysis is to identify potential environmental impacts or damages of emerging technologies at early stages of R&D. However, recommendations at the design/conceptual phase typically have high uncertainty, which is often only reduced after large investments and progress in R&D have been made.…”