Bayesian model selection for network discrimination and risk-informed decision-making in material flow analysis

Abstract

Material flow analyses (MFAs) provide insight into supply chain-level opportunities for resource efficiency. MFAs can be represented as networks with nodes that represent materials, processes, sectors, or locations. MFA network structure uncertainty (i.e., the existence or absence of flows between nodes) is pervasive and can undermine the reliability of the flow predictions. This article investigates MFA network structure uncertainty by proposing candidate node-and-flow structures and using Bayesian model selection to identify the most suitable structures and Bayesian model averaging to quantify the parametric mass flow uncertainty. The results of this holistic approach to MFA uncertainty are used in conjunction with the input-output (I/O) method to make risk-informed resource efficiency recommendations. These techniques are demonstrated using a case study on the US steel sector where 16 candidate structures are considered. The model selection highlights two networks as most probable based on data collected from the United States Geological Survey and the World Steel Association. Using the I/O method, we then show that the construction sector accounts for the greatest mean share of domestic US steel industry emissions while the automotive and steel products sectors have the highest mean emissions per unit of steel used in the end-use sectors. The uncertainty in the results is used to analyze which end-use sector should be the focus of demand reduction efforts under different appetites for risk. This article's methods generate holistic and transparent MFA uncertainty that accounts for structural uncertainty, enabling decisions whose outcomes are more robust to the uncertainty.