How informal environmental regulations constrain carbon dioxide emissions under pollution control and carbon reduction: Evidence from China

Environ Res. 2024 Mar 20;252(Pt 1):118732. doi: 10.1016/j.envres.2024.118732. Online ahead of print.

Abstract

Exploring whether informal environmental regulations (INER) can achieve carbon reduction in the context of pollution reduction and carbon reduction, as well as how to achieve carbon reduction, can help solve the dual failures of the market and government in environmental protection. Based on the polycentric governance theory and considering the characteristics of social subject environmental participation, the Stackelberg game is used to demonstrate the impact mechanism of INER on CO2. In addition, using the panel data of China's 30 provinces from 2003 to 2018, this paper validates the effectiveness of INER by Pooled Ordinary Least Square (POLS) and threshold panel model. Then, the mediating effect model is used to test the mechanism of INER's effect on carbon reduction. The results show that corruption is not conducive to CO2 reduction. The reduction effect of INER on CO2 exhibits heterogeneity with changes in other non-greenhouse gas pollutants. While INER effectively reduces local corruption, its more substantial indirect impact on CO2 reduction is prominent when levels of other pollutants are lower. Comparative analysis reveals that there are still biased governance behaviors to cope with INER's pressure in some regions nowadays. The findings show that for countries facing the dual task of pollution control and carbon reduction, the key to leveraging the supervisory role of INER should be focused on mitigating information asymmetry caused by the characteristics of CO2. Therefore, in the process of environmental protection, the public environmental participation system should be improved, and the process of disclosing polluters' carbon information should be accelerated.

Keywords: Carbon dioxide emissions; Corruption; Informal environmental regulations; Threshold regression methods.