The relationship between corruption and pollution is complex and ambiguous. Corruption can influence greenhouse gas emissions (GHG) through multiple interconnected factors, making its overall impact on emissions theoretically inconclusive despite the intuitive assumption that more corruption leads to more pollution. In this paper, we untangle this ambiguity empirically by estimating a standard Environmental Kuznets Curve (EKC) using a recent econometric methodology, namely the sequential model selection approach within the generalized method of moments (GMM) estimation framework. This approach allows us to draw robust inference from a panel dataset in a dynamic setting. The panel dataset covers 161 countries over the period 1990–2020. The results of a two-step system GMM show that an increase in political and regime corruption leads to a rise in CO2 emissions and that the corruption elasticity in the long run is more than three times larger than in the short run. Our findings also indicate that there is no evidence of a turning point in the proposed EKC. These results have significant but largely untapped potential for policy action. If we acknowledge the positive relationship between corruption and pollution, it is logical to recognize the environmental effectiveness of any anti-corruption policy. Moreover, compared with standard environmental policies, a policy focused on reducing corruption provides additional benefits.
