Eco-efficiency measurement of industrial sectors in China: A hybrid super-efficiency DEA analysis

Abstract

Eco-efficiency is of great significance in the attainment of a sustainable society, and is the most severe issue facing industrial sectors in China. In this work, a hybrid super-efficiency data envelopment analysis (DEA) was conducted to measure eco-efficiency and analyze the problem of inefficiency facing industrial sectors in China. DEA has been widely used in eco-efficiency analysis; however, existing DEA models focus on the evolution and comparison of eco-efficiency values, and do not consider the evolution characteristics of the input nor undesirable output inefficiency and neglect the radial and non-radial classifications of input and output indices simultaneously. Thus, this work proposes a hybrid super-efficiency DEA model which combines hybrid DEA with super-efficiency DEA, separating the input and undesirable output variables into radial and non-radial parts using variable correlations. For the panel data of the industrial sectors, the Malmquist index is introduced in this approach. Subsequently, this approach is illustrated using a real data set from 22 industrial sectors in China corresponding to the period of 2006–2015. Results show that eco-efficiencies of the 22 industrial sectors increased consistently from 2006 to 2015. Due to a lack of momentum in sustained growth, technological progress regarding the improvement of eco-efficiency requires acceleration. More and more sectors have achieved rapid enhancements in input and undesirable output inefficiencies, but 10 traditional sectors with high resource consumption and heavy emissions of pollutants remain inefficient.