Quantifying the excess carbon footprint and its main determinants of Spanish households
New evidence is provided on the determinants of the carbon footprint (CF) at the household level, using the Spanish case as an example and data from the Household Budget Survey (HBS) and the E-MRIO database. The research presents two new contributions. On the one hand, the basis of analysis on what...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/709729 |
| Acceso en línea: | http://hdl.handle.net/10486/709729 https://dx.doi.org/10.1177/0958305X221140582 |
| Access Level: | acceso abierto |
| Palabra clave: | carbon footprint GHG emissions Household consumption quantile regression unbiased elasticity estimates Ciencias Sociales |
| Sumario: | New evidence is provided on the determinants of the carbon footprint (CF) at the household level, using the Spanish case as an example and data from the Household Budget Survey (HBS) and the E-MRIO database. The research presents two new contributions. On the one hand, the basis of analysis on what we call ‘Excess per capita CF’, that is, the part of CF that exceeds a threshold associated with a minimum per capita consumption of each product in a household, below which level it is difficult to expect reductions in consumption. Second, the use of a quantile regression (QR) approach for the estimation of the drivers of CF. Both issues imply important changes in the consideration of the influence of some drivers considered so far in the literature, related to which CF quantile the household is in. These differences between an ordinary least squares (OLS) and the QR are especially significant for variables such as income, household size, occupation, age, household composition, housing area and area of residence |
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