Analysis on the Environmental Governance Efficiency of Local Governments in China and Its Influencing Factors

摘要

In this paper, the Three-stage DEA model and Malmquist index method are used to measure the environmental governance efficiency and total factor productivity of 113 cities in China from 2014 to 2017. The empirical results of the Three-stage DEA model show that foreign investment, industrial structure, economic development level, population density, financial pressure and other external factors do have an impact on the efficiency of environmental governance of local governments in China, and seriously underestimate the comprehensive technical efficiency, pure technical efficiency and scale efficiency of environmental governance of local governments. After eliminating the influence of environmental factors and random interference, most cities still have efficiency loss caused by ineffective management. The results of DEA Malmquist index show that the total factor productivity of environmental governance of local governments in China is less than 1 during the sample observation period, which is mainly due to the fact that the comprehensive technical efficiency has not improved and the rate of technological progress has regressed during the observation period. Provincial capital cities suffer more losses than non provincial capital cities due to inefficient resource allocation and management. Therefore, in order to improve the efficiency of environmental governance of local governments in China, we should first improve the efficiency of resource allocation, attach importance to the rational allocation of the scale of resources invested in environmental governance, improve the efficiency of resource allocation and use, and avoid resource waste; secondly, improve the awareness of environmental protection of local governments, and reduce Finally, increase investment in technological innovation, encourage institutional innovation, improve the rate of technological progress, and promote the improvement of total factor productivity.