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

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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
    Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation (MHESI), Bangkok 10400, 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
    Center of Excellence on Energy Technology and Environment (CEE), Ministry of Higher Education, Science, Research and Innovation (MHESI), Bangkok 10400, Thailand
    School of Postgraduate Studies, Diponegoro University, Semarang 50241, Indonesia)

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.

Suggested Citation

  • Awais Mahmood & Viganda Varabuntoonvit & Jitti Mungkalasiri & Thapat Silalertruksa & Shabbir H. Gheewala, 2022. "A Tier-Wise Method for Evaluating Uncertainty in Life Cycle Assessment," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13400-:d:945344
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    References listed on IDEAS

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    1. Joyce Smith Cooper & James A. Fava, 2006. "Life‐Cycle Assessment Practitioner Survey: Summary of Results," Journal of Industrial Ecology, Yale University, vol. 10(4), pages 12-14, October.
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    3. Peter Ylmén & Johanna Berlin & Kristina Mjörnell & Jesper Arfvidsson, 2020. "Managing Choice Uncertainties in Life-Cycle Assessment as a Decision-Support Tool for Building Design: A Case Study on Building Framework," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    4. Stéphanie Muller & Christopher Mutel & Pascal Lesage & Réjean Samson, 2018. "Effects of Distribution Choice on the Modeling of Life Cycle Inventory Uncertainty: An Assessment on the Ecoinvent v2.2 Database," Journal of Industrial Ecology, Yale University, vol. 22(2), pages 300-313, April.
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    Cited by:

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