Monitoring domestic material consumption at lower territorial levels: A novel data downscaling method
The availability of harmonized and granular information is critical for the design of place-sensitive policies toward more sustainable economies. However, accessibility to disaggregated data at sub- national levels remains an exception in many geographies and policy domains. In this article, we deve...
| Autores: | , , |
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| Formato: | artículo |
| Fecha de publicación: | 2020 |
| País: | España |
| Recursos: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/76796 |
| Acesso em linha: | http://hdl.handle.net/10810/76796 |
| Access Level: | acceso abierto |
| Palavra-chave: | circular economy domestic material consumption (DMC) economy-wide material flow analysis (EW-MFA) industrial ecology regional MFA social metabolism |
| Resumo: | The availability of harmonized and granular information is critical for the design of place-sensitive policies toward more sustainable economies. However, accessibility to disaggregated data at sub- national levels remains an exception in many geographies and policy domains. In this article, we develop a novel three-stage—specification, optimization, extrapolation (SOE)—econometric approach to infer harmonized regional level estimates from broadly available socioeconomic data. The approach is tested by estimating domestic material consumption (DMC) in more than 280 European regions (at NUTS 2 level). Unlike previous methods based on similar econometric tech- niques, our method makes explicit the socio-metabolic profiles of subnational territories by esti- mating and applying country-specific elasticities. Our DMC estimates are consistent with those obtained by ad hoc material flow studies that could be accessed for a sample of regions. The SOE method presented in this paper provides decision-makers with a powerful tool to explore socio-metabolic profiles at subnational level and therefore to understand the potential effects of policies aimed at supporting circular economy transitions at such levels. The method can also be adapted with relative ease to support policy designs in other policy areas challenged by severe data scarcity. |
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