Structural, Innovation and Efficiency Effects of Environmental Regulation: Evidence from China’s Carbon Emissions Trading Pilot

With increasingly severe constraints on resources and the environment, it is the mainstream trend of economic development to reduce industrial pollution emissions and promote green industrial development. In this paper, a super-efficiency slacks-based measure (SBM) model is adopted to measure the industrial green development efficiency (IGDE) of 289 cities in China from 2008 to 2018. Moreover, we analyze their spatiotemporal differentiation pattern. On this basis, the multiscale geographical weighted regression (MGWR) model is used to analyze the scale differences and spatial differences of the driving factors. The results show that the IGDE is still at a low level in China. From 2008 to 2018, the overall polarization of IGDE was relatively serious. The number of high- and low-efficiency cities increased, while that of medium-efficiency cities greatly decreased. Secondly, the IGDE presented an obvious spatial positive correlation. MGWR regression results show that the technological innovation, government regulation, and consumption level belonged to the global scale, and there was almost no spatial heterogeneity. Other driving factors were urbanization, industrial structure, economic development, and population density according to their spatial scale. Lastly, the influence of economic development and technological innovation had a certain circular structure in space; the influence of population size mainly occurred in the cities of the southeast coast and northeast provinces; the influence of urbanization was more obvious in the most northern provinces of the Yangtze River, while that of industrial structure was mainly concentrated in the most southern cities of the Yangtze River Economic Belt (YREB). Spatially, the influence of consumption was manifested as a distribution trend of decreasing from north to south, and the government regulation was manifested as increasing from west to east and then to northeast.

Section: Methodsmentioning

confidence: 99%

Analysis of China’s Industrial Green Development Efficiency and Driving Factors: Research Based on MGWR

Qiao

Zhou

2021

“…Regulatory power rent seeking is an important cause of environmental economic corruption that leads to weak environmental enforcement [53]. In addition, the region's energy consumption endowment (mainly the proportion of coal consumption) is also an important source of the spatial heterogeneity of environmental policy effects [54].…”

Section: Heterogeneity Analysismentioning

confidence: 99%

An Analysis of the Impact of the Emissions Trading System on the Green Total Factor Productivity Based on the Spatial Difference-in-Differences Approach: The Case of China

Wang

Chen

Han

2021

How to effectively identify the spatial effect of the emissions trading system(ETS) on urban green total factor productivity(GTFP) generated through the linkage of economic factors between cities is a necessary part of scientifically evaluating the effect of ETS policy in emerging- market countries. This study aims to examine the spatial effect, mechanism, and heterogeneity of the ETS on urban GTFP based on the panel data of 281 cities from 2004 to 2017 in China, applying spatial difference-in-differences(DID) Durbin model (SDID-SDM) with multidimensional fixed effect (FE). The results show that ETS significantly improves the GTFP of the pilot cities, produces a spatial spillover effect and the results are robust to the placebo test, propensity score matching SDID (PSM-SDID) test, and Carbon-ETS interference test. Further analysis shows that the policy effect is mainly driven by improving energy efficiency, promoting green innovation, and optimizing the industrial structure. In addition, we found that ETS performs better in regions with a high degree of marketization, strong environmental law enforcement, and a low proportion of coal consumption. In general, the identification method of this study can be used as a scientific reference for conducting similar research in other emerging countries.

“…Another pathway from environmental regulations to opportunity cost through technical change is the indirect effect, where technical change is the mediator. In addition, in this paper, we assume the separability condition holds, i.e., the explanatory variables in the second stage affect only the distance from the frontier (distribution of efficiency) but not the shape of the frontier (production possibilities), as done in Liu et al [28]. Simar and Wilson [29] pointed out this may not be realistic.…”

Section: Mediation Modelmentioning

confidence: 99%

Opportunity Cost of Environmental Regulation in China’s Industrial Sector

Wang

Lin

2021

In this paper, we employ a directional distance function to estimate the opportunity cost arising from environmental regulations in China’s industrial sector. The change of opportunity cost is decomposed mathematically into two components including technical change and input change. Our results show that the opportunity cost attributed to environmental regulation is nil in some regions. The change of opportunity cost is marginal at the national level, as the positive effect of technical change is canceled out by the negative impact of input change on opportunity cost. Built on our mathematical decomposition, we further estimate the effects of environmental regulations on opportunity cost using a mediation model. It shows that environmental regulation has a significantly positive direct effect and a significantly negative indirect effect through foreign direct investment on opportunity cost. Our findings suggest, firstly, that inward FDI in China’s industrial sector represents relatively dirty production technology; and, secondly, industrial production has transited towards a less carbon-intensive input mix. This paper, therefore, provides new insights for the recent dynamics of carbon abatement performance of China’s industrial sector with policy implications.