Income inequality and environmental quality in China: a semi-parametric analysis applied to provincial panel data

Céline Bonnefond (), Matthieu Clement and Huijie Yan
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Céline Bonnefond: CREG - Centre de recherche en économie de Grenoble - UGA - Université Grenoble Alpes
Matthieu Clement: GREThA - Groupe de Recherche en Economie Théorique et Appliquée - UB - Université de Bordeaux - CNRS - Centre National de la Recherche Scientifique
Huijie Yan: CEARC - Cultures, Environnements, Arctique, Représentations, Climat - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - CNRS - Centre National de la Recherche Scientifique

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Abstract: This article contributes to the literature on the inequality-environment nexus in China, at the provincial level, by filling three major gaps. First, we enlarge the scope of environmental variables so as to include several air and water pollutants. Second, we combine different data sources to construct several measures of income inequality. Third, we propose to use flexible semi-parametric methods in order to analyse the potential nonlinearities in the inequality-environment relationship and control for endogeneity issues. Four conclusions can be drawn from our empirical investigations. (i) Provincial inequality has a negative effect on air and water pollution. (ii) This negative association is primarily explained by inequality between urban and rural areas, which also has a negative impact on environment quality. (iii) Urban inequality contributes to increasing soot emissions and water pollution, which confirms the deleterious impact of inequality for localised pollutions. (iv) Rural inequality has no clear effect on environmental quality.

Keywords: inequality; air pollution; water pollution; environmental Kuznets curve; semi-parametric analysis; China (search for similar items in EconPapers)
Date: 2021
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Published in Post-Communist Economies, 2021, 33 (5), pp.541-565. ⟨10.1080/14631377.2020.1793604⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-02936605

DOI: 10.1080/14631377.2020.1793604

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