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Incremental Material Flow Analysis with Bayesian Inference

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  • Richard C. Lupton
  • Julian M. Allwood

Abstract

Material flow analysis (MFA) is widely used to study the life cycles of materials from production, through use, to reuse, recycling, or disposal, in order to identify environmental impacts and opportunities to address them. However, development of this type of analysis is often constrained by limited data, which may be uncertain, contradictory, missing, or over‐aggregated. This article proposes a Bayesian approach, in which uncertain knowledge about material flows is described by probability distributions. If little data is initially available, the model predictions will be rather vague. As new data is acquired, it is systematically incorporated to reduce the level of uncertainty. After reviewing previous approaches to uncertainty in MFA, the Bayesian approach is introduced, and a general recipe for its application to material flow analysis is developed. This is applied to map the global production of steel using Markov Chain Monte Carlo simulations. As well as aiding the analyst, who can get started in the face of incomplete data, this incremental approach to MFA also supports efforts to improve communication of results by transparently accounting for uncertainty throughout.

Suggested Citation

  • Richard C. Lupton & Julian M. Allwood, 2018. "Incremental Material Flow Analysis with Bayesian Inference," Journal of Industrial Ecology, Yale University, vol. 22(6), pages 1352-1364, December.
    • Handle: RePEc:bla:inecol:v:22:y:2018:i:6:p:1352-1364
    DOI: 10.1111/jiec.12698
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    References listed on IDEAS

    as
    1. David Laner & Helmut Rechberger & Thomas Astrup, 2014. "Systematic Evaluation of Uncertainty in Material Flow Analysis," Journal of Industrial Ecology, Yale University, vol. 18(6), pages 859-870, December.
    2. Oliver Schwab & Ottavia Zoboli & Helmut Rechberger, 2017. "A Data Characterization Framework for Material Flow Analysis," Journal of Industrial Ecology, Yale University, vol. 21(1), pages 16-25, February.
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    Cited by:

    1. Jiayuan Dong & Jiankan Liao & Xun Huan & Daniel Cooper, 2023. "Expert elicitation and data noise learning for material flow analysis using Bayesian inference," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1105-1122, August.
    2. John D. Morley & Rupert J. Myers & Yves Plancherel & Pablo R. Brito-Parada, 2022. "RETRACTED: A Database for the Extraction, Trade, and Use of Sand and Gravel," Resources, MDPI, vol. 11(4), pages 1-16, April.
    3. Stefan Pauliuk & Niko Heeren, 2020. "ODYM—An open software framework for studying dynamic material systems: Principles, implementation, and data structures," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 446-458, June.
    4. Karan Bhuwalka & Eunseo Choi & Elizabeth A. Moore & Richard Roth & Randolph E. Kirchain & Elsa A. Olivetti, 2023. "A hierarchical Bayesian regression model that reduces uncertainty in material demand predictions," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 43-55, February.
    5. John D. Morley & Rupert J. Myers & Yves Plancherel & Pablo R. Brito-Parada, 2022. "A Database for the Stocks and Flows of Sand and Gravel," Resources, MDPI, vol. 11(8), pages 1-17, August.

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