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Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study

Although the estimation of the elasticity of the household carbon footprint and income is a frequently analysed fact, unfortunately a fundamental aspect of this relationship has not been considered: it is not a constant factor for the whole population. To make an adequate estimate of this relationsh...

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Detalles Bibliográficos
Autores principales: de Arce, Rafael, Mahía, Ramón
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220352/
https://www.ncbi.nlm.nih.gov/pubmed/37251484
http://dx.doi.org/10.1016/j.heliyon.2023.e16394
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author de Arce, Rafael
Mahía, Ramón
author_facet de Arce, Rafael
Mahía, Ramón
author_sort de Arce, Rafael
collection PubMed
description Although the estimation of the elasticity of the household carbon footprint and income is a frequently analysed fact, unfortunately a fundamental aspect of this relationship has not been considered: it is not a constant factor for the whole population. To make an adequate estimate of this relationship, a Quantile Regression is proposed, obtaining significantly different results to those derived from the usual estimations using ordinary least squares (OLS), which have been carried out up to now. This fact is fundamental for the correct planning and evaluation of fiscal policies based on income taxation to reduce the carbon footprint. Our results confirm that the OLS estimation would overestimate the effects of income on CO2 reduction by 26%.
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spelling pubmed-102203522023-05-28 Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study de Arce, Rafael Mahía, Ramón Heliyon Research Article Although the estimation of the elasticity of the household carbon footprint and income is a frequently analysed fact, unfortunately a fundamental aspect of this relationship has not been considered: it is not a constant factor for the whole population. To make an adequate estimate of this relationship, a Quantile Regression is proposed, obtaining significantly different results to those derived from the usual estimations using ordinary least squares (OLS), which have been carried out up to now. This fact is fundamental for the correct planning and evaluation of fiscal policies based on income taxation to reduce the carbon footprint. Our results confirm that the OLS estimation would overestimate the effects of income on CO2 reduction by 26%. Elsevier 2023-05-19 /pmc/articles/PMC10220352/ /pubmed/37251484 http://dx.doi.org/10.1016/j.heliyon.2023.e16394 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
de Arce, Rafael
Mahía, Ramón
Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study
title Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study
title_full Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study
title_fullStr Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study
title_full_unstemmed Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study
title_short Unbiasing the estimate of the role of income in carbon footprint of households: Analysis of the Spanish case as a pilot study
title_sort unbiasing the estimate of the role of income in carbon footprint of households: analysis of the spanish case as a pilot study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10220352/
https://www.ncbi.nlm.nih.gov/pubmed/37251484
http://dx.doi.org/10.1016/j.heliyon.2023.e16394
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