Different types of environmental regulations and the heterogeneous influence on the environmental total factor productivity: Empirical analysis of China's industry

Faced with the dual constraints of economic growth and energy conservation and emission reduction, the unified environmental regulation policy has posed a dilemma for China. First, this paper adopts the method of Metafrontier Malmquist-Luenberger (MML) to analyze the influence of technology gaps and pollution emissions on the total factor productivity (TFP) of Chinese industry in the period of 2000-2016. Second, from the perspective of heterogeneity, the paper uses the threshold model to investigate the nonlinear dynamic influence of different types of environmental regulations on the environmental total factor productivity (ETFP) of industrial sectors, attempting to identify the optimal intensity of the environmental regulation and tools combination. The results indicate that due to the presence of industry heterogeneity, the different types of environmental regulations exerted heterogeneous influences on the ETFP in different industries. In heavy-pollution industries, an excessively high intensity of environmental regulation weakened the technological innovation of these enterprises. In moderate-pollution industries, there was found a medium intensity of environmental regulation, with the command-and-control and market-based types of environmental regulation being quite well coordinated. Among slightly polluting industries, there was a significant "N-shaped" characteristic between the market-based type of environmental regulation and the ETFP. The outcomes further imply that considering the presence of obvious industry heterogeneity, the establishment of environmental regulations should avoid both the unified adoption of static standards and the blind increase of regulation intensity. Based on the characteristics and realities of different industries, flexible and dynamic regulatory standards should be adopted, and multiple regulation instruments should be used. (C) 2018 Elsevier Ltd. All rights reserved.