Research on the Impact of Environmental Regulations on Industrial Green Total Factor Productivity: Perspectives on the Changes in the Allocation Ratio of Factors among Different Industries

Abstract

This paper constructs a two-sector manufacturer model of endogenous technological progress. We analyze the impact of environmental regulations on the factor input and output of different industries. Then, we reveal the intermediary role of inter-industry factor allocation in the impact of environmental regulations on industrial green total factor productivity (GTFP). Finally, the paper uses panel data from 30 provinces in China’s industry from 2000 to 2017 to conduct empirical tests. We can draw the following conclusions: (1) The relative magnitude of the output compensation of the production department and the innovation compensation of the R&D department could change the impact of environmental regulations on the input and output of inter-industry factors, and the comprehensive effects of both input and output will affect the level of GTFP. (2) The curve of the direct impact of environmental regulations on GTFP is in an inverted “U” shape. However, the production factor allocation ratio can “reverse” the inhibitory effect of high-intensity regulations on GTFP. (3) The capital factor has a greater impact on the regulatory effect, but the labor factor has a more lasting impact on the regulatory effect. High-strength environmental regulations can enhance manufacturers’ preference for human capital. Therefore, formulating environmental regulatory policies oriented to improve the ratio of factor allocation, mixing different types of regulatory policies, and increasing investment in human capital are all conducive to accelerating the transformation and upgrading of China’s industrial structure and achieving high-quality development of the industrial economy.

Effects of Environmental Regulation on Green Total Factor Productivity: An Evidence from the Yellow River Basin, China

“…The main reason is that, compared with other regions, the Yellow River Basin has special innate natural conditions and acquired social function positioning, and is subject to strong environmental regulations, so the green total factors fluctuate greatly. The conclusion that environmental regulation significantly promotes the growth of GTFP is consistent with most scholars' research, which verifies the establishment of Porter's hypothesis from the perspective of productivity, indicating that pollution control and the improvement of productivity may become a win-win situation [34,35]. However, a considerable number of studies have shown that the high-intensity environmental regulation has an inhibitory effect on industrial GTFP [36,37].…”

Scale of Operation, Financial Support, and Agricultural Green Total Factor Productivity: Evidence from China

“…They found that the green technological innovation of the province can significantly improve its AGTFP but restrains the neighboring provinces’ AGTFP. Yuan and Zhang [ 13 ] analyzed the impact of environmental regulations on the input and output of production factors and found that environmental regulation has an inverted “U” curve on AGTFP. However, the allocation rate of production factors can reverse the inhibitory effect of strict environmental regulations on agricultural total factor productivity.…”

Tax Policies of Low Carbon in China: Effectiveness Evaluation, System Design and Prospects

“…Second, in terms of the validation of classical low carbon taxation theory, the first is the "double dividend" effect, which Yuan and Zhang (2021) argue can be achieved by environmental regulation policies that can lead to economic growth and pollution reduction. From an economic perspective, Cao et al (2021) conduct a multimodel comparison of a carbon tax policy in China and find substantial differences in the change in energy use and economic activity in response to a steadily rising carbon tax.…”

“…To avoid affecting the accuracy of the model regression results due to the omission of important variables, based on the practices of Tu et al (2019), Yuan and Zhang (2021) and Yang et al (2022) and combined with the characteristics of the sample data in this paper, local competition (Compete), industrial structure (Ind), the degree of government intervention (Gov), fiscal decentralization (Fd), environmental regulation (Er) and population density (Den) are used as control variables, and the specific definitions of the variables are shown in Table 4.…”