Using geographically temporally weighted regression to assess the contribution of corruption governance to global PM2.5.

Abstract

In the face of the global haze crisis, exploring the driving force of political factors for controlling minute atmospheric particles has become essential for managing PM2.5 pollution. In this study, the political drivers of haze pollution were examined by combining kernel density estimation, exploratory spatial data analysis, and a geographically temporally weighted regression model. The results showed that global haze pollution was increasing annually, and that differences and similarities in PM2.5 pollution between different countries coexisted. Furthermore, the multi-dimensional driving elements of haze pollution showed obvious spatial and temporal non-stationarity, and different driving factors present multiple distribution trends. In general, the strengthening of anti-corruption measures addressed PM2.5 concentration, but the direction and intensity of political drivers differed due to factors such as economic development, national culture, and natural conditions. Therefore, African countries should endeavor to control corruption, so as to achieve economic development and control haze pollution. However, from the perspective of political factors, it is likely North American countries will not be able to effectively control haze pollution.