Incremental Material Flow Analysis with Bayesian Inference

Richard C. Lupton, Julian M. Allwood

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1352-1364
Number of pages13
JournalJournal of Industrial Ecology
Volume22
Issue number6
Early online date27 Nov 2017
DOIs
Publication statusPublished - 18 Oct 2018

Keywords

  • Bayesian inference
  • industrial ecology
  • Markov Chain Monte Carlo
  • material flow analysis
  • steel
  • uncertainty

ASJC Scopus subject areas

  • Environmental Science(all)
  • Social Sciences(all)

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