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Effects of Officials' Cross-Regional Redeployment on Regional Environmental Quality in China.

Bing ZhouYumeng LiXiaoli LuShengzhong HuangBing Xue
Published in: Environmental management (2019)
Cross-regional redeployment (or relocation) of government officials has a significant effect on the local economic development and environmental protection. Based on the panel data of 31 provinces (municipalities) in China from 2001 to 2016 and the environmental pollution index obtained by Entropy method, the dynamic panel regression model was applied to verify the relationship between the officials' cross-regional redeployment and environmental pollution. The results show that environmental pollution was positively correlated with officials' relocation and their tenure after the redeployment. As the officials' tenure increases to the critical value, the positive correlation between the official's tenure and environmental pollution would change. By measuring this threshold, we find that the average critical value for China was 5.14 years, which were the same as the average tenure of Chinese officials. Moreover, the result also illustrates the difference between central eastern China and western China, with the average threshold being 4.01 years and 5.89 years, respectively. In addition, the impact of officials' cross-regional redeployment on the environment would also be affected by the initial condition of the region. According to the result, the environmental governance within the central eastern regions was better than that in the western region. In the last part of this paper, we proposed measures and suggestions, such as changing the incentive policies of officials, perfecting the local policies and the cultivate and exchange system of cadres, as well as strengthen the power of social supervision, for the sake of facilitating the healthy and green development of the regional economy.
Keyphrases
  • human health
  • risk assessment
  • heavy metals
  • life cycle
  • south africa
  • particulate matter
  • climate change
  • healthcare
  • mental health
  • artificial intelligence
  • drinking water
  • deep learning