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

Characterizing, Propagating, and Analyzing Uncertainty in Life-Cycle Assessment: A Survey of Quantitative Approaches,10.1162/jiec.2007.1136,Journal of

Characterizing, Propagating, and Analyzing Uncertainty in Life-Cycle Assessment: A Survey of Quantitative Approaches   (Citations: 24)
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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 determin- istic models to approximate environmental outcomes. This approach fails to capture the variability and uncertainty inher- ent 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 re- liable outcomes and enabling better decisions is discussed. Ap- proaches that may lead to overconfident or unreliable results are discussed and guidance for improving uncertainty analysis in LCA is provided.
Journal: Journal of Industrial Ecology - J IND ECOL , vol. 11, no. 1, pp. 161-179, 2008
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    • ...2007), therefore numerous and varied typologies can be found (Heijungs and Huijbregts 2004, Lloyd and Ries 2007)...

    Y. Xuet al. Cost Engineering for manufacturing: Current and future research

    • ... 2007), therefore numerous and varied typologies can be found (Heijungs and Huijbregts 2004, Lloyd and Ries 2007)...

    Y. Xuet al. Cost Engineering for manufacturing: Current and future research

    • ...Lloyd and Ries (2007) report that 67% of LCA studies that undertake quantitative uncertainty analysis use stochastic modeling (Monte Carlo and Latin Hypercube), 29% scenario probabilities, 17% fuzzy data sets, and 8% uncertainty propagation...

    Kwame Awuah-Offeiet al. Application of life cycle assessment in the mining industry

    • ...Depending on the specific LCA model of interest, any of the three types of uncertainty can have the largest contribution to the final result of the LCA model (Lloyd and Ries 2007)...
    • ...Of all LCA studies examined by (Lloyd and Ries 2007), only 29% considered these sources of uncertainty and often only in the life cycle impact assessment...
    • ...Introducing and analyzing parameter uncertainty in LCA is an established practice (Lloyd and Ries 2007)...
    • ...Sources of information about uncertainty were the ecoinvent v1.3 database (Frischknecht et al. 2007), literature (Lloyd and Ries 2007; Huijbregts et al. 2003), and information available within P&G...
    • ...The influence of correlation coefficients in LCA models is largely unexplored (Lloyd and Ries 2007)...

    Arjan de Koninget al. Uncertainties in a carbon footprint model for detergents; quantifying ...

    • ...Lloyd and Ries [41] adopted the same categorization and found from a survey that the parameter uncertainty was the type of uncertainty most frequently addressed in LCA...
    • ...methods are often useful in estimation where data are limited, vague, ambiguous, or imprecise [41], [90]...

    Yee Mey Gohet al. Uncertainty in Through-Life Costing–Review and Perspectives

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