Environmental Performance Evaluation of Key Polluting Industries in China-Taking the Power Industry as an Example

This paper analyzes the environmental performance, spatial and temporal characteristics, and optimization paths of key polluting industries, represented here by the power industry, using the super-efficient MinDS model. The study shows that the environmental performance as a whole presents the characteristics of an inverted U-shaped and then a U-shaped trend; each region presents an asymmetric state of convergent development followed by differentiated development, with 2014 as the structural change point; the development trend of environmental performance in each region is divided into three categories (rising, falling, and stable) and four types of spatial clustering (ultra-high, high, medium, and low levels); and input-output indicators of environmental performance in China and across regions have varying degrees of redundancy, with labor input redundancy being the greatest, followed by capital input, technology input, and pollution emissions. On this basis, we propose to improve the monitoring and inspection mechanism of the implementation process of pollution control in key polluting industries and to improve the level of environmental performance of key polluting industries by optimizing the combination of labor, capital, and technology input factors in each region according to local conditions and adopting differentiated strategies. The main contributions of this paper are threefold: first, we incorporate technological inputs into the environmental performance evaluation index system of the electric power industry, which can better reflect the real inputs of the electric power industry and measure the results more accurately; second, we adopt the MinDS model for measuring the environmental performance level, which can quantitatively analyze the gap between each indicator and the optimal level; and third, we propose a redundancy index, which can be used to compare the redundancy of each indicator and then judge the main efficiency levels of the different factors.