Evaluating Sustainable Materials Management Strategies with the United States Environmentally-Extended Input-Output Model (USEEIO)

Environmentally-extended input-output (EEIO) models add value to Industrial Ecology, particularly because they are able to link economic models of industrial activity by industry with environmental models to provide... [ view full abstract ]

Environmentally-extended input-output (EEIO) models add value to Industrial Ecology, particularly because they are able to link economic models of industrial activity by industry with environmental models to provide streamlined life cycle assessment of production and consumption alternatives. They can be used to support decision making related to sustainable materials management, which can be defined as the effective use of materials to minimize environmental impacts over their life cycle. Strategies to manage materials more sustainably may involve, among others: material substitution, cleaner technology implementation, material mass reduction, localizing production and consumption networks, material recycling, and waste reduction.

The United States Environmentally-Extended Input Output model is a new EEIO model developed in support of the US EPA’s Sustainable Materials Management program (USEEIO) (Ingwersen et al. 2016). This type of model has been demonstrated previously to provide material/product and service hotspot identification at a national scale (USEPA 2009). Using this type of model to evaluate scenarios related to sustainable materials management strategies poses new challenges in the input-output approach and material tracking. We first provide a brief overview of USEEIO, describing the underlying economic and environmental data and assumptions embedded in the model. We then define three simplified scenarios embodying three of these sustainable materials management strategies and evaluate these in the context of the US food system using USEEIO. For material substitution, we evaluate the effect of a dietary change that changes the composition of food eaten by consumers. For localizing production and consumption networks, we evaluate the effect of reducing farm-to-plate transportation distances. For waste reduction, we evaluate reductions in food loss across the life cycle. In each of the scenarios, we evaluate the outcomes relative to the baseline scenario using indicators of environmental and socio-economic impact and we formally evaluate the quality of the underlying data influencing the results. We discuss the limitations of the model specifically related to modeling these scenarios and future research needs to increase the robustness of model results.