Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data
Author(s)
Type
Journal Article
Abstract
Material flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as available data are often limited and uncertain, leading to an under-determined system with an infinite number of possible stocks and flows values. Bayesian statistics is an effective way to address these challenges by principally incorporating domain knowledge, quantifying uncertainty in the data, and providing probabilities associated with model solutions. This paper presents a novel MFA methodology under the Bayesian framework. By relaxing the mass balance constraints, we improve the computational scalability and reliability of the posterior samples compared to existing Bayesian MFA methods. We propose a mass-based, child and parent process framework to model systems with disaggregated processes and flows. We show posterior predictive checks can be used to identify inconsistencies in the data and aid noise and hyperparameter selection. The proposed approach is demonstrated in case studies, including a global aluminum cycle with significant disaggregation, under weakly informative priors and significant data gaps to investigate the feasibility of Bayesian MFA. We illustrate that just a weakly informative prior can greatly improve the performance of Bayesian methods, for both estimation accuracy and uncertainty quantification.
Date Issued
2024-12
Date Acceptance
2024-09-01
Citation
Journal of Industrial Ecology, 2024, 28 (6), pp.1409-1421
ISSN
1088-1980
Publisher
Wiley
Start Page
1409
End Page
1421
Journal / Book Title
Journal of Industrial Ecology
Volume
28
Issue
6
Copyright Statement
© 2024 The Author(s). Journal of Industrial Ecology published by Wiley Periodicals LLC on behalf of International Society for Industrial Ecology.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
License URL
Identifier
http://dx.doi.org/10.1111/jiec.13550
Publication Status
Published
Date Publish Online
2024-09-30