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- Creators: Chester, Mikhail V
Description
Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights.
ContributorsPrado, Valentina (Author) / Seager, Thomas P (Thesis advisor) / Chester, Mikhail V (Committee member) / Kullapa Soratana (Committee member) / Tervonen, Tommi (Committee member) / Arizona State University (Publisher)
Created2015
Description
'Attributional' Life Cycle Assessment (LCA) quantitatively tracks the potential environmental impacts of international value chains, in retrospective, while ensuring that burden shifting is avoided. Despite the growing popularity of LCA as a decision-support tool, there are numerous concerns relating to uncertainty and variability in LCA that affects its reliability and credibility. It is pertinent that some part of future research in LCA be guided towards increasing reliability and credibility for decision-making, while utilizing the LCA framework established by ISO 14040.
In this dissertation, I have synthesized the present state of knowledge and application of uncertainty and variability in ‘attributional’ LCA, and contribute to its quantitative assessment.
Firstly, the present state of addressment of uncertainty and variability in LCA is consolidated and reviewed. It is evident that sources of uncertainty and variability exist in the following areas: ISO standards, supplementary guides, software tools, life cycle inventory (LCI) databases, all four methodological phases of LCA, and use of LCA information. One source of uncertainty and variability, each, is identified, selected, quantified, and its implications discussed.
The use of surrogate LCI data in lieu of missing dataset(s) or data-gaps is a source of uncertainty. Despite the widespread use of surrogate data, there has been no effort to (1) establish any form of guidance for the appropriate selection of surrogate data and, (2) estimate the uncertainty associated with the choice and use of surrogate data. A formal expert elicitation-based methodology to select the most appropriate surrogates and to quantify the associated uncertainty was proposed and implemented.
Product-evolution in a non-uniform manner is a source of temporal variability that is presently not considered in LCA modeling. The resulting use of outdated LCA information will lead to misguided decisions affecting the issue at concern and eventually the environment. In order to demonstrate product-evolution within the scope of ISO 14044, and given that variability cannot be reduced, the sources of product-evolution were identified, generalized, analyzed and their implications (individual and coupled) on LCA results are quantified.
Finally, recommendations were provided for the advancement of robustness of 'attributional' LCA, with respect to uncertainty and variability.
In this dissertation, I have synthesized the present state of knowledge and application of uncertainty and variability in ‘attributional’ LCA, and contribute to its quantitative assessment.
Firstly, the present state of addressment of uncertainty and variability in LCA is consolidated and reviewed. It is evident that sources of uncertainty and variability exist in the following areas: ISO standards, supplementary guides, software tools, life cycle inventory (LCI) databases, all four methodological phases of LCA, and use of LCA information. One source of uncertainty and variability, each, is identified, selected, quantified, and its implications discussed.
The use of surrogate LCI data in lieu of missing dataset(s) or data-gaps is a source of uncertainty. Despite the widespread use of surrogate data, there has been no effort to (1) establish any form of guidance for the appropriate selection of surrogate data and, (2) estimate the uncertainty associated with the choice and use of surrogate data. A formal expert elicitation-based methodology to select the most appropriate surrogates and to quantify the associated uncertainty was proposed and implemented.
Product-evolution in a non-uniform manner is a source of temporal variability that is presently not considered in LCA modeling. The resulting use of outdated LCA information will lead to misguided decisions affecting the issue at concern and eventually the environment. In order to demonstrate product-evolution within the scope of ISO 14044, and given that variability cannot be reduced, the sources of product-evolution were identified, generalized, analyzed and their implications (individual and coupled) on LCA results are quantified.
Finally, recommendations were provided for the advancement of robustness of 'attributional' LCA, with respect to uncertainty and variability.
ContributorsSubramanian, Vairavan (Author) / Golden, Jay S (Thesis advisor) / Chester, Mikhail V (Thesis advisor) / Allenby, Braden R. (Committee member) / Dooley, Kevin J (Committee member) / Arizona State University (Publisher)
Created2016