scholarly journals Research on the Nonlinear Impact of Environmental Regulation on the Efficiency of China’s Regional Green Economy: Insights from the PSTR Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shuangliang Yao ◽  
Xiang Su

This paper uses the super-efficiency SBM model to measure the green economic efficiency considering undesired output and analyzes the spatial distribution difference of green economic efficiency; secondly, the nonlinear panel threshold model is used to empirically study the nonlinear relationship between environmental regulations and green economic efficiency, and further analyzed the threshold effect of environmental regulations on the efficiency of green economy and concluded as follows. (1) The green economy efficiency index in the eastern region is mostly more significant than 1, and the green economy efficiency in most provinces in the eastern region has improved. These provinces have higher regional production levels and less environmental pollution. The green economy efficiency of the central region is second only to the eastern region. The green economy efficiency of provinces in the western region except Chongqing is less than 1, indicating that these provinces have insufficient regional production, severe environmental pollution, or extensive resource depletion. (2) The impact of environmental regulations on the efficiency of the green economy presents an inverted “U” shape, with a threshold of 0.5128 for environmental regulations. The impact of the industrial structure on the efficiency of the green economy changes from inhibition to promotion after crossing the threshold of the intensity of environmental regulation, and the degree of opening to the outside world has a complementary effect on the efficiency of the green economy. The impact of urbanization on the efficiency of the green economy changes from promotion to suppression after surpassing the threshold of the intensity of environmental regulations.

Author(s):  
Qingyang Wu

Abstract:This paper uses the balanced panel data from 29 provinces (autonomous regions and municipalities) in China for a total of 17 years from 2000 to 2016 as a research sample, and establishes an empirical model to examine the impact of environmental regulations and technological innovation on the quality of economic growth. Then this paper test technological innovation as a threshold variable, in which play a regulatory role. Taking the provincial balanced panel data as a research sample, a fixed effect model, a system GMM model, and a panel threshold model were established for empirical testing and the robustness test. Based on the empirical results, this article draws the following conclusions: from a national perspective, environmental regulations and technological innovation can significantly promote the quality of economic growth; from a regional perspective, there are regional differences in impact effects. Under the constraints of environmental regulations, the promotion effect of technological innovation on the quality of economic growth will be reduced; the impact of environmental regulation on the quality of economic growth will have a "threshold effect", and environmental regulation can significantly promote the quality of economic growth only after crossing the threshold and the threshold of technological innovation.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
You-Qun Wu ◽  
Huai-Xin Lu ◽  
Xin-Lin Liao ◽  
Jia-Bao Liu ◽  
Jia-Ming Zhu

Based on the theoretical mechanism analysis of FDI, regional innovation, and green economic efficiency, this article uses China’s provincial panel data to calculate the provincial green economic efficiency level based on the three-stage DEA method and uses the system GMM model, intermediary effect model, and threshold model to empirically test the specific effects and transmission paths of FDI on the efficiency of the green economy. Research shows that FDI is one of the important factors that promote the improvement of green economic efficiency. Subregional tests have found that FDI has a significant regional heterogeneity in promoting the efficiency of the green economy. The mediation effect test found that the mediation effect of regional innovation is significant, and FDI can significantly promote the growth of green economic efficiency through regional innovation. The threshold effect analysis found that there are significant and effective double thresholds for regional economic levels, and the impact of FDI on green economic efficiency is heterogeneous within different threshold intervals. The research conclusions provide new inspiration for China to allocate FDI more rationally and efficiently under the new development pattern.


2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Xubin Lei ◽  
Shusheng Wu

Based on the distinction of different types of environmental regulations, this paper attempts to test the threshold effect of environmental regulation on the total factor productivity (TFP) by employing a panel threshold model and a province-level panel data set during 2006–2016. Research results show that the influence of command-and-control and market incentive environmental regulation on the total factor productivity has a single threshold conversion characteristic of foreign direct investment (FDI) and financial scale, but the impact behavior and influence degree around the threshold are inconsistent. The effect of voluntary conscious environmental regulation on the total factor productivity has a single threshold conversion feature of human capital, and moderately enhanced intensity of environmental regulation is conducive to promoting the total factor productivity after crossing the threshold. Finally, in order to enhance the regional total factor productivity, relevant policy recommendations are proposed.


Author(s):  
Gongli Luo ◽  
Xiaotong Wang ◽  
Lu Wang ◽  
Yanlu Guo

This study examined the relationship between environmental regulations (ER) and green economic efficiency (GEE) based on the panel data of 30 provinces in China from 2008 to 2017. Firstly, GEE was calculated and evaluated using the super-efficiency SBM model with undesirable outputs. Secondly, the impact of ER on GEE was studied with the Tobit model. Finally, this article draws conclusions based on the above analysis and offers some suggestions for government and enterprise. The results show that the GEE of China is generally low. The GEE of the eastern region is much higher than that of the middle and western regions, with the western region performing slightly better than the middle. From west to east, there is a V shape, with high efficiency in the west and east and low efficiency in the middle. The impact of ER on GEE has the characteristics of nonlinearity and spatial heterogeneity. At the national level, as well as in the middle and western regions, the impact of ER on GEE shows an inverted U shape that first rises and then falls. ER are currently within the range conducive to the development of GEE. If the intensity of ER exceeds the critical value, they will have a negative impact on GEE. In the eastern region, the impact of ER on GEE is shown as a U shape that first falls and then rises. At present, the ER are not of sufficient intensity to contribute to the improvement of GEE. Only when the intensity of the ER exceeds the critical value will they have a positive influence on the GEE.


2020 ◽  
Vol 12 (16) ◽  
pp. 6526
Author(s):  
Shi-Zheng Huang ◽  
Ka Yin Chau ◽  
Fengsheng Chien ◽  
Huawen Shen

Under the environment of a green economy, green innovation serves as the only way for enterprises to grow, upgrade their competitiveness and seek continued business. Based on a questionnaire survey of 212 enterprises established within 4 years in the Pearl River Delta of China, this research utilizes structural methods to analyze the impacts of exploratory and applied learning (dual learning) on green innovation capability and verifies the environmental protection awareness of senior executives and the adjustment effects of environmental regulation. The results suggest that (1) exploratory and applied learning have a positively significant impact on green innovation capability; (2) under the regulation of environmental protection awareness of internal executives, there are differences in green innovation capabilities under the dual influences of exploratory and applied learning; and (3) under the adjustment of external environmental regulation, there are differences in green innovation capabilities under the dual influences of exploratory and applied learning. The findings indicate that new start-up ventures should raise awareness of environmental protection among senior executives under dual learning and perceive the changes of the government’s environmental regulations to enhance their green innovation capabilities.


2021 ◽  
Vol 13 (10) ◽  
pp. 5439
Author(s):  
Chenggang Li ◽  
Tao Lin ◽  
Zhenci Xu ◽  
Yuzhu Chen

With the development of economic globalization, some local environmental pollution has become a global environmental problem through international trade and transnational investment. This paper selects the annual data of 30 provinces in China from 2000 to 2017 and adopts exploratory spatial data analysis methods to explore the spatial agglomeration characteristics of haze pollution in China’s provinces. Furthermore, this paper constructs a spatial econometric model to test the impact of foreign direct investment (FDI) and industrial structure transformation on haze pollution. The research results show that the high-high concentration area of haze pollution in China has shifted from the central and western regions to the eastern region and from inland regions to coastal regions. When FDI increases by 1%, haze pollution in local and neighboring areas will be reduced by 0.066% and 0.3538%, respectively. However, the impact of FDI on haze pollution is heterogeneous in different stages of economic development. FDI can improve the rationalization level of industrial structure, and then inhibit the haze pollution. However, FDI inhibits the upgrading level of industrial structure to a certain extent, and then aggravates the haze pollution. The research in this paper provides an important decision-making basis for coordinating the relationship between FDI and environmental pollution and realizing green development.


2021 ◽  
Vol 13 (5) ◽  
pp. 2907
Author(s):  
Shiwen Liu ◽  
Zhong Zhang ◽  
Guangyao Xu ◽  
Zhen Zhang ◽  
Hongyuan Li

As for the academics and policymakers, more attention has been given to the issue on how to reduce environmental pollution through the cooperation of environmental regulation and local officials’ promotion incentives. With the use of a city-level panel data of 266 Chinese cities from 2005 to 2016, this study preliminary explores the impacts of environmental regulations, local officials’ promotion incentives, and their interaction terms on urban environmental pollution at national and regional levels by using the spatial Durbin model. The results indicate that the impacts of environmental regulations and local officials’ promotion incentives on urban environmental pollution have achieved the desired goal with the other’s cooperation, and their interaction term’s coefficients on urban environmental pollution are significantly negative. Moreover, spatial heterogeneity is established, and the uneven development of urban environmental pollution among different regions deserves more attention. In order to effectively reduce the level of urban environmental pollution in China, the government should focus on such solutions as enhancing the implementation and supervision efficiency of environmental regulation, optimizing the performance appraisal system of local officials, improving the synergistic effects of environmental regulations and local officials’ promotion incentives, and establishing a multi-scale spatial cooperation mechanism based on both geographical and economic correlations.


2019 ◽  
Vol 12 (4) ◽  
pp. 175
Author(s):  
Pham ◽  
Nguyen ◽  
Ramiah ◽  
Mudalige ◽  
Moosa

This study examines the impact of environmental regulation on the Singapore stock market using the event study methodology. Several asset pricing models are used to estimate sectoral abnormal returns. Additionally, we estimate the change in systematic risk after the introduction of the carbon tax and related regulation. We conduct various robustness tests, including the Corrado non-parametric ranking test, the Chesney non-parametric conditional distribution approach, a representation of market integration, and Fama–French five-factor model. We find evidence showing that the environmental regulations tend to achieve their desired effects in Singapore in which several big polluters (including industrial metals and mining, forestry and papers, and electrical equipment and services) were negatively affected by the announcements of environmental regulations and carbon tax. In addition, our results indicate that the electricity sector, one of the biggest polluters, was negatively affected by the announcement of environmental regulations and carbon tax. We also find that environmental regulations seem to boost the performance of environmentally-friendly sectors whereby we find the alternative energy industry (focusing on new renewable energy technologies) experienced a sizeable positive reaction following the announcements of these regulations.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244083
Author(s):  
Bing Zhou ◽  
Jing Wu ◽  
Sidai Guo ◽  
Mingxia Hu ◽  
Jing Wang

Objective The answer to this article lies in: Does the financial activities of physical enterprises have an adverse impact on their main business? Is it conducive to the sustainable development of the national economy? However, when most scholars study the impact of environmental regulations on companies performance, they have not classified companies performance. This article will study the relationship between environmental regulations and performance levels based on the classification of companies performance, and then divide the nature of industry pollution, companies location and nature of property for in-depth research. Methods First, this article uses a random effect variable-intercept model to measure companies financial performance and non-financial performance. Then, the variables are divided into two variable groups: light pollution and heavy pollution according to the nature of industry pollution. Next, the companies are divided into three variable groups: the eastern region, the central region, and the western region. Finally, the company is divided into two variable groups: state-owned and non-state-owned according to the nature of property. Conclusions The study found that: (1) Environmental regulations have inhibited companies financial activities. And the inhibitory effect of environmental regulations on the financial performance of enterprises is more obvious in the heavily polluting industries and enterprises in central and eastern regions. (2) Environmental regulations and companies non-financial performance are also negatively related, environmental regulations have also inhibited the non-financial performance of companies, this effect is more pronounced in heavily polluting industries and enterprises in western regions. (3) Income crowding effect brought by China's environmental regulations is greater than the income compensation effect brought by stimulating technological innovation.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Hui Li ◽  
Chuandang Zhao ◽  
Xiaoying Tang ◽  
Jiawei Cheng ◽  
Guanyang Lu ◽  
...  

Environmental regulation policies are being continuously enriched today. To effectively improve green innovation efficiency through environmental regulations, it is urgent to better understand the impact of different environmental regulations on green innovation efficiency (GIE). However, due to the defects of previous methods for measuring GIE, existing studies may have deviations when analysing the effect of environmental regulations on GIE. To fill this gap, using Shaanxi, China, as a case study, the present study proposes a network data envelopment analysis (DEA) model based on neutral cross-efficiency evaluation to accurately measure the GIE of Shaanxi during the period of 2001–2017. On this basis, this study further analysed the impact of different types of environmental regulations on GIE from three aspects: causality, evolutionary relationships, and effect paths. The results indicate that (1) the GIE of Shaanxi Province showed a “fluctuation-slow growth-steady growth” trend during 2001–2017, and after 2014, the problem of an uncoordinated relationship between technology research and design (R&D) and technology transformation began to appear; (2) there was a linear evolutionary relationship between command-and-control environmental regulation and GIE and a “U”-shaped evolutionary relationship between market-based/voluntary environmental regulation and GIE; and (3) command-and-control environmental regulation and voluntary environmental regulation affected GIE mainly at the technology R&D stage, while market-based environmental regulation ran through the entire process of green innovation activities. This study improves the evaluation methods and theoretical systems of GIE and provides the scientific basis for government decision-makers to formulate environmental regulation policies.


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