Responses of carbon emissions to corruption across Chinese provinces

• Published in: Energy economics Vol. 98; p. 105241
• Main Authors: Ren, Yi-Shuai, Ma, Chao-Qun, Apergis, Nicholas, Sharp, Basil
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 01.06.2021; Elsevier Science Ltd
• Subjects: Autoregressive models; Carbon; Carbon emissions; Chinese provinces; Corruption; Economic impact; Emissions; Empirical analysis; Energy economics; Impact analysis; Panel ARDL model; Panel data; Panel quantile regression model; Per capita; Provinces; Quantiles; Time series; D73; C33; Corruption; Panel ARDL model; Panel quantile regression model; Chinese provinces; Q50; Carbon emissions
• ISSN: 0140-9883; 1873-6181
• DOI: 10.1016/j.eneco.2021.105241

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. •The paper explores the impact of corruption on CO2 emissions in China provinces.•It uses quantile methods, spanning the period 1998–2016.•The results show that corruption increases CO2 emissions in the short run.•In the long run it reduces CO2 emissions.•The findings imply that emissions increase with the severity of corruption.