Measuring the bias of technical change of industrial energy and environment productivity in China: a global DEA-Malmquist productivity approach.

Thanks to the booming industry, China has made a huge economic achievement during the past several decades. However, it is suffering severe environmental and sustainable problems now. To find a sustainable development path, it is necessary to assess Chinese industrial energy and environment productivity and explore the contributing reasons. It is known that the technical change is the one power that drives the growth of the industrial productivity. Nevertheless, the technical change bias of Chinese industrial energy and environment productivity has rarely been analyzed, such that the secrets of Chinese industrial energy and environment productivity cannot be further uncovered. Thus, in this paper, we first propose a global DEA-Malmquist productivity index to evaluate the industrial energy and environment productivity of China and then figure out the Chinese industrial technical change biases by relaxing the Hicks' neutral assumption and decomposing the industrial technical change. We find out that both the global DEA-Malmquist productivity and the technical change are increased. Furthermore, the technical change drives the improvement of the global Malmquist productivity, but the technical progress is mainly driven by labor, energy consumption and CO2 emission biases. A multinomial logistic model is employed to find out the reasons for these biases. It finds that (1) the economic foundation has a significant positive impact on labor bias, while the infrastructures have negative impacts on labor bias. (2) CO2 emission bias is influence by energy prices positively. (3) The energy prices and the energy consumption structure have a negative influence on labor and energy bias, but the cost of curbing air pollutants and the size of the firm influence labor and energy bias positively. (4) The infrastructures and energy prices affect energy and CO2 emission bias positively, and the economic foundation and the size of the firm have negative impacts on energy and CO2 emission bias.