A Tier-Wise Method for Evaluating Uncertainty in Life Cycle Assessment

Awais Mahmood, Viganda Varabuntoonvit, Jitti Mungkalasiri, Thapat Silalertruksa and Shabbir H. Gheewala ()
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Awais Mahmood: The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Road, Bangmod, Tungkru, Bangkok 10140, Thailand
Viganda Varabuntoonvit: Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand
Jitti Mungkalasiri: Technology and Informatics Institute for Sustainability, National Science and Technology Development Agency, Pathum Thani 12120, Thailand
Thapat Silalertruksa: Department of Environmental Engineering, Faculty of Engineering, King Mongkut’s University of Technology Thonburi, Bangkok 10140, Thailand
Shabbir H. Gheewala: The Joint Graduate School of Energy and Environment (JGSEE), King Mongkut’s University of Technology Thonburi, 126 Pracha Uthit Road, Bangmod, Tungkru, Bangkok 10140, Thailand

Sustainability, 2022, vol. 14, issue 20, 1-19

Abstract: As a decision support tool, life cycle assessment (LCA) is prone to multiple uncertainties associated with the data, model structures, and options offered to practitioners. Therefore, to make the results reliable, consideration of these uncertainties is imperative. Among the various classifications, parameter, scenario, and model uncertainty are widely reported and well-acknowledged uncertainty types in LCA. There are several techniques available to deal with these uncertainties; however, each strategy has its own pros and cons. Furthermore, just a few of the methods have been included in LCA software, which restricts their potential for wider application in LCA research. This paper offers a comprehensive framework that concurrently considers parameter, scenario, and model uncertainty. Moreover, practitioners may select multiple alternatives depending on their needs and available resources. Based on the availability of time, resources, and technical expertise three levels—basic, intermediate, and advanced—are suggested for uncertainty treatment. A qualitative method, including local sensitivity analysis, is part of the basic approach. Monte Carlo sampling and local sensitivity analysis, both of which are accessible in LCA software, are suggested at the intermediate level. Advanced sampling methods (such as Latin hypercube or Quasi-Monte Carlo sampling) with global sensitivity analysis are proposed for the advanced level.

Keywords: life cycle assessment; framework development; uncertainty analysis; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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