Evaluation of the Policy Effect of China's Environmental Interview System for Effective Air Quality Governance

Int J Environ Res Public Health. 2021 Aug 26;18(17):9006. doi: 10.3390/ijerph18179006.

Abstract

The Ministry of Ecology and Environment of the People's Republic of China formally proposed an environmental interview system in May 2014, which applies pressure on local governments to fulfill their responsibility toward environmental protection by conducting face-to-face public interviews with their officials. In this paper, 48 cities that were publicly interviewed from 2014-2020 were considered the experimental group and 48 cities surrounding them were the control group. First, the dynamic panel model is applied to initially determine the effect of the policy. Then, a regression discontinuity method (Sharp RD) is used to analyze the short-term and long-term effects and compare the reasons for the differences observed among the estimates of various types of samples. Finally, a series of robustness tests were also conducted. The results show that the environmental interview system can improve air quality. However, because an emergency short-term local governance system exists at present, the governance effect is not long-term and, therefore, not sustainable. Therefore, it suggests that the government should continue to improve the environmental interview system, establish an optimal environmental protection incentive mechanism, and encourage local governments to implement environmental protection policies effectively in the long term. The results of the research are of great significance to the environmental impact assessment system of the world, especially in countries with similar economic systems, which are facing a trade-off between economic growth and environmental sustainability.

Keywords: air governance; dynamic panel model; environmental interview; sharp RD.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollution* / prevention & control
  • China
  • Cities
  • Environmental Policy
  • Humans
  • Local Government