Fully integrated modelling for sustainability assessment of resource recovery from waste

This paper presents an integrated modelling approach for value assessments, focusing on resource recovery from waste. The method tracks and forecasts a range of values across environmental, social, economic and technical domains by attaching these to material-flows, thus building upon and integrating unidimensional models such as material flow analysis (MFA) and lifecycle assessment (LCA). We argue that the usual classification of metrics into these separate domains is useful for interpreting the outputs of multidimensional assessments, but unnecessary for modelling. We thus suggest that multidimensional assessments can be better performed by integrating the calculation methods of unidimensional models rather than their outputs. To achieve this, we propose a new metric typology that forms the foundation of a multidimensional model. This enables dynamic simulations to be performed with material-flows (or values in any domain) driven by changes in value in other domains. We then apply the model in an illustrative case highlighting links between the UK coal-based electricity-production and concrete/cement industries, investigating potential impacts that may follow the increased use of low-carbon fuels (biomass and solid recovered fuels; SRF) in the former. We explore synergies and trade-offs in value across domains and regions, e.g. how changes in carbon emissions in one part of the system may affect mortality elsewhere. This highlights the advantages of recognising complex system dynamics and making high-level inferences of their effects, even when rigorous analysis is not possible. We also indicate how changes in social, environmental and economic 'values' can be understood as being driven by changes in the technical value of resources. Our work thus emphasises the advantages of building fully integrated models to inform conventional sustainability assessments, rather than applying hybrid approaches that integrate outputs from parallel models. The approach we present demonstrates that this is feasible and lays the foundations for such an integrated model.