Responses of carbon emissions to corruption across Chinese provinces

Yi-Shuai Ren, Chao-Qun Ma, Nicholas Apergis () and Basil Sharp

Energy Economics, 2021, vol. 98, issue C

Abstract: In response to the recent growth of multitudes of theoretical literature analysing the corruption impact on the economy and environment, this paper subjects the corruption–carbon emission relationship in China to a detailed empirical examination through the autoregressive distributed lag modelling approach and panel quantile regressions. Based on panel data from Chinese provinces, spanning the period 1998–2016, this study explores the impact of long- and short-term corruption on per capita carbon emissions by considering the heterogeneous distribution of those emissions. The results document that corruption increases per capita carbon emissions in Chinese provinces in the short run, reducing per capita carbon emissions in the long run. Moreover, an increase in corruption leads to an increase in carbon emissions per capita in all quantiles, indicating that these emissions increase with corruption severity. The coefficients in low quantiles are slightly larger than those in high quantiles, indicating that corruption leads to more carbon emissions in provinces with lower per capita carbon emissions.

Keywords: Carbon emissions; Corruption; Panel ARDL model; Panel quantile regression model; Chinese provinces (search for similar items in EconPapers)
JEL-codes: C33 D73 Q50 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:98:y:2021:i:c:s0140988321001468

DOI: 10.1016/j.eneco.2021.105241

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