Assessing the pollution convergence across Chinese cities by considering ecological indicators: A continuous distribution dynamics approach

Abstract

We employ a nonparametric distribution dynamics approach to examine the long-run trend of particulate matter (PM2.5) air pollution in China. Analysis of the data from 288 Chinese prefectural and above (PAA) level cities over the 2001–2019 period shows evidence of convergence in the estimated long-run distribution of air pollution, which is consistent with Environmental Kuznets Curve hypothesis (EKC). However, presence of strong persistence suggests that convergence process takes considerable time. Conditioning analysis shows that China’s environment policy since 2013 has been successful in driving the air pollution in several cities to converge to a lower level. Furthermore, we uncover a poverty-environment trap in the estimated long-run distribution. Our analysis highlights the need for targeted environmental policies in heterogeneous city clusters in China. Lessons learned from China’s experience can also be useful to policy makers in other developing countries.

Keywords

• PM2.5 concentration
• Distribution dynamics
• Poverty-environment trap
• Conditioning analysis