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Abstract Modelling frameworks that aim to support policymakers in deriving effective measures to reduce environmental impacts should provide both: quantitative information on locally occurring consumption patterns and production systems as well as assessment of policy scenario outcomes. Regionalised models that can deliver on these aims are emerging, but are currently limited in resolution or have other restrictions. An advanced model can be achieved by exploiting the advantages and overcoming the limitations of top-down and bottom-up approaches. In this article, we describe a highly detailed, spatially-resolved modelling framework that quantifies local activities and simultaneously analyses system-wide environmental and economic effects of planned interventions. We combined an existing, highly detailed bottom-up model for Switzerland (focusing on individual households) with a macro-economic top-down approach by developing a new Swiss sub-national, multi-region input-output model. We conducted two case studies to demonstrate its abilities and to highlight its usefulness. First, production-based greenhouse gas emissions and consumption-based carbon footprints were computed for all Swiss cantons and regional differences, interdependencies as well as embodied carbon flows among regions were investigated. We find that rural cantons have higher production-based emissions per gross domestic product than more urban cantons because of different economic structures. In contrast, certain ‘city-cantons’ entail highest consumption carbon footprints per inhabitant due to high per-capita gross capital formation. Furthermore, this case study discusses the importance of providing regionalised information on effects of measures along the economic value chains. Second, a detailed scenario assuming a realistic lifestyle change for an actual household and a thorough physical retrofit of its home was set up. Regionalised environmental and economic consequences along the supply chains were evaluated. This case study exemplifies how the modelling framework can be used to inform policymakers about expected benefits and downsides of detailed scenarios and emphasises the importance of considering rebound effects.
Froemelt et al. (Wed,) studied this question.