Dynamic Assessment of Environmental Efficiency in Chinese Industry: A Multiple DEA Model with a Gini Criterion Approach

Abstract

While China’s rapid industrialization has brought great pressure on environmental pollution, great variations appear in terms of environmental pollution levels among industries. The effective assessment of the environmental performance of different industries is not only conducive to identifying the major sources of pollution in China but also of great significance to the Chinese government in formulating differentiated industry environmental control policies in a targeted manner. Using data of 36 Chinese industries from 2006 to 2015 and a multiple data envelopment analysis (DEA) with a Gini criterion as well as a systematic clustering approach, this study first calculates the environmental efficiency score of Chinese industries and then identifies those pollution sources based on a ranking and clustering analysis. The main result indicates that the ranking of environmental efficiency of various industries overall varies greatly by time. In addition, using a clustering analysis, this study finds that 13 labor-intensive light industries and heavy chemical industries with high energy use and high emissions are medium- and high-pollution industries. Important policy implications are drawn to achieve green industrial development.