Impact of Environmental Regulatory Types and Green Technological Innovation on Green Total Factor Productivity in Polluted Areas of China

Green total factor productivity (GTFP) is an essential indicator to measure economic and environmental efficiency. Moreover, formulating a reasonable environmental regulation system and promoting green technological innovation is a systematic way to improve GTFP. However, previous related studies lack to investigate the impact of environmental regulation on GTFP from the perspective of green technological innovation. For this purpose, this paper aims to examine the specific impact of environmental regulation on GTFP based on the perspective of green technology innovation, so as to provide some policy insights for the formulation of more effective implementation of environmental regulation, improve green technology innovation level, and achieve a win-win situation for both economic growth and environmental protection. Furthermore, epsilon-based measure (EBM), which includes both radial and non-radial distance functions, is used to measure the GTFP. The spatial autoregressive method is also employed to quantify the impact of environmental regulation on GTFP from the perspective of green technological innovation using panel data of 269 prefecture-level cities in China from 2004 to 2018. The main findings indicate that there is a significant spatial autocorrelation between environmental regulation and GTFP. Environmental regulation has a significant positive effect on GTFP. Environmental regulation in the local regions also significantly contributes to GTFP in neighboring regions. Besides, environmental regulation indirectly promotes GTFP by enhancing green technological innovation level. Regional heterogeneity results show that environmental regulation can not only directly promote GTFP but also indirectly significantly promote GTFP through green technological innovation in the eastern and central regions, but insignificant in the western region. Based on the above findings, we conclude that policymakers should not only develop differentiated environmental regulation standards and steadily improving the intensity and rationality of environmental regulation but also add green innovation funds supply, enhance green innovation factor allocation efficiency, and strengthen R&D talents, funds, and policies to green technology innovation, so as to drive GTFP improvement.

Green total factor productivity (GTFP), as an important indicator considering both economic development and environmental protection, has prompted countries around the world to actively explore ways to improve it in the context of the global transition to a green economy. The Low-Carbon City Policy (LCCP) implemented by the Chinese government, along with the National Big Data Comprehensive Pilot Zone Policy (NBDCPZ), which serve as key carriers of green regulation and digital innovation, respectively, play an important role in improving green total factor productivity (GTFP) and achieving high-quality economic development. This study aims to deeply explore whether there is a collaborative enabling effect of the Low-Carbon City Policy (LCCP) and the National Big Data Comprehensive Pilot Zone Policy (NBDCPZ) on green total factor productivity (GTFP) and to reveal the internal mechanism by which they improve GTFP through green technological innovation and industrial agglomeration. Specifically, based on the panel data of 269 prefecture-level cities in China from 2006 to 2022, a “dual-pilot” policy is constructed through LCCP and NBDCPZ, and a multi-period difference-in-differences model (DID) is used to evaluate the collaborative effect of the “dual-pilot” policy on GTFP. The results show that the “dual-pilot” policy has a significant collaborative effect on green total factor productivity (GTFP), and its enabling effect is more obvious than that of the “single-pilot” policy. These conclusions still hold after a series of endogeneity and robustness tests. Mechanism analysis shows that the “dual-pilot” policy can also improve green total factor productivity (GTFP) through green technological innovation and industrial agglomeration. Heterogeneity analysis reveals that the collaborative enabling effect of the “dual-pilot” policy is influenced by geographical location and population density. Specifically, the “dual-pilot” policy significantly promotes green total factor productivity (GTFP) in coastal cities and those with high population density. These research results provide a scientific basis for formulating green development policies in China and other countries, as well as a direction for subsequent research on the collaborative enabling effect of multiple policies.

Green technological innovation, green finance, and financial development and their role in green total factor productivity: Empirical insights from China

This paper employs the SBM-DDF method to measure the index of green total-factor productivity (GTFP), based on the panel data of 279 prefecture-level cities in China from 2007 to 2019, and constructs a spatial Durbin model (SDM) and a threshold effect to empirically test the effects of dual environmental-regulations and green technological innovation on GTFP. The results are as follows: (1) the SDM supports a nonlinear contribution of dual environmental-regulations spillover to GTFP. The relationship between formal environmental-regulation and GTFP is an inverted U-shape, while a U-shaped nonlinear relationship is found between informal environmental regulation and GTFP. (2) Green technology innovation has a significant negative moderating effect on the process of dual environmental-regulations affecting GTFP in local regions, but a positive moderating effect on informal environmental regulation in neighboring regions. (3) There is a significant green technology innovation threshold effect of dual environmental-regulations affecting GTFP. Specifically, the promotion effect of dual environmental-regulations on GFFP gradually increases as the level of green technology innovation increases.

Environmental regulation is a crucial tool for government intervention in the field of green technology innovation. It can boost an enterprise’s competitiveness and encourage green technology innovation, both of which have a major effect on luring foreign investment. This paper first systematically elaborates on the relationship between environmental regulation, foreign direct investment (FDI), and green total factor productivity (GTFP) and then combines panel data from Chinese cities to empirically test these relationships using various methods, such as the mediation effect model, two-stage least squares, and difference-in-differences method. The study found that environmental regulation significantly boosts FDI and GTFP. FDI helps to improve GTFP, and environmental regulation can impact GTFP indirectly through FDI. The way that FDI and environmental regulations affect GTFP demonstrates regional variation. Large cities with high economic growth gain more from environmental regulation. FDI has a stronger promotion effect on GTFP in medium- and small-sized cities than in large-sized cities, and it does not significantly impact GTFP in cities with high levels of economic development or in the eastern region.

Foreign Direct Investment (FDI) is a double-edged sword, promoting the economic growth of host countries while also bringing severe environmental issues. The implementation of different types of environmental regulations and the inflow of FDI both impact the growth of green total factor productivity (GTFP) in host countries. How to cope with the relationship between these three factors is vital for developing countries. This paper utilizes panel data from 30 provinces in China for the period of 2005- 2020 to analyze the effects of command-and-control, market-incentive-based, and public voluntary environmental regulations on GTFP at the national level and the coastal and inland levels. Furthermore, a stepwise regression approach is employed to explore the moderating effect of FDI on the relationship between environmental regulation and GTFP. The findings provide insights into the coordinated development of the environment, FDI, and the economy. The results indicate that: Firstly, at the national level, the three types of environmental regulations have different effects on GTFP in China. Commandand- control regulation inhibits GTFP growth, market-incentive-based regulation promotes GTFP growth, and public voluntary environmental regulation shows no significant impact on GTFP. Additionally, FDI has a significant moderating effect on both command-and-control and market-incentive-based regulations with GTFP, strengthening the negative relationship between command-and-control regulation and GTFP and weakening the positive relationship between market-incentive-based regulation and GTFP. Secondly, at the coastal and inland levels, command-and-control regulation inhibits the growth of GTFP in inland areas, while market-incentive-based regulation promotes the growth of GTFP in coastal and inland regions. Public voluntary regulation has no significant impact on GTFP in coastal areas but has a negative effect on GTFP in inland areas. Besides, there is heterogeneity in the moderating effect of FDI between the three types of environmental regulations and GTFP.

Effect of green technology innovation on green total factor productivity in China: Evidence from spatial durbin model analysis

Environmental pollution and resource waste in Chinese cities have become important obstacles to sustainable economic development, and it is urgent to change the mode of economic development and improve the quality of economic development. In response to this challenge, this study proposes environmental regulation as a solution and empirically tests the impact of environmental regulation on green total factor productivity. The empirical results show that environmental regulation can significantly improve urban green total factor productivity, the public environmental concern and green finance strengthen the positive effect of environmental regulation on urban green total factor productivity. The mechanism test shows that environmental regulation can improve urban green total factor productivity through green technological innovation and industrial structure upgrading. Heterogeneity analysis reveals that, compared to the resource-based cities, the positive effect of environmental regulation on urban green total factor productivity is more significant in the non-resource-based cities with relatively developed traditional finance and high levels of industrial modernisation. Compared to the central and western as well as the northeast regions of China, the positive effect of environmental regulation on urban green total factor productivity is more significant in the eastern region due to capital accumulation and technological constraints. The results of the study remain reliable after a series of endogeneity and robustness tests. These studies provide an important research basis for providing more targeted environmental regulation programmes and better improving green total factor productivity.

The enhancement of green total factor productivity of resource-based enterprises has a crucial impact on building an environmentally friendly society, promoting a circular economy, and guaranteeing the security of national resource supplies. This study takes the green transformation of resource-based enterprises as the main line, and then constructs the research framework. A random forest algorithm is used to discover the effects of different characteristics of green transformation on green total factor productivity. Further, using panel data-based qualitative comparative analysis and necessary condition analysis, it clarifies what kind of green transformation group path promotes green total factor productivity from the group perspective. This study shows that (1) environmental regulations, green cognition, and green investment are important factors in condition configuration and there exists a complex nonlinear relationship among them, indicating the need for further research from a configurational perspective; (2) green technological innovation, green investment, green development strategies, green governance level, green cognition, environmental subsidies, and environmental regulation are not necessary conditions for a high level of green total factor productivity; (3) there are three grouping paths for green total factor productivity improvement in resource-based enterprises; and (4) there are time and case differences in the explanatory strength of the three grouping paths. Finally, based on the results of this study, and in conjunction with China’s green transformation practice, countermeasures are proposed from both the enterprise and government perspectives.

Improving the green total factor productivity of the electric power industry is crucial to realize China's clean and low-carbon transition and sustainable economic growth. The super-efficient SBM model and ML index method were used to measure the green total factor productivity and its growth rate in the power industry in 30 provinces of China from 2005 to 2021, and the dynamic green total factor productivity is split to analyze the impact of technological progress and changes in technological efficiency. The spatial transfer paths and spatiotemporal differences of dynamic green total factor productivity in the electric power industry are further investigated through a spatial Markov chain model and panel Tobit model, and their influencing factors are empirically examined. The results show that: ① The static green total factor productivity of the electric power industry in each province during the study period was low and showed large differences, with considerable room for improvement. ② The green total factor productivity of China's electric power industry increased at an average annual rate of 3.9%, of which the average annual growth rate of technological progress was 3%. Among the six power grid regions, the ML index of green total factor productivity of the power industry of East China, Central China, and South China Power Grid was among the top three in China, and technological progress was the main driving force. ③ The transfer probability of the green total factor productivity level in the power industry between regions was affected by the spatial lag effect. The transfer trend of regions with different levels varied, showing a low state maintenance probability and high transfer probability. ④ Economic level, power industry capital intensity, industrial structure, and green technology innovation were the key factors for the growth of green total factor productivity in the power industry, and there was heterogeneity in the mechanisms of influencing factors in the six power grid regions.

Improving green total factor productivity (GTFP) in the power industry is crucial for achieving China's clean and low-carbon transition and sustainable economic growth. Green technological innovation (GTI) is a key factor driving carbon emissions reduction and improving economic efficiency in the power industry. Therefore, this study utilizes data from 30 provinces in China covering the period 2005–2021 and employs a multiple period difference-in-difference (DID) model to investigate the impact of the carbon trading pilot policy (CTPP) on GTFP in the power industry from the perspective of GTI. The results indicate the following: First, CTPP significantly enhances GTFP in the pilot regions of the power industry. Second, GTI plays a positive mediating role in the relationship between CTPP and GTFP in the power industry, validating the existence of Porter effect in China's power industry carbon emission trading system. Third, there are heterogeneities in the policy effects across the pilot regions, with Beijing and Guangdong achieving outstanding results in improving GTFP compared to other carbon trading pilots. Finally, relevant policy recommendations are provided for carbon market development and improving GTFP in the power industry.

The impact of local government attention on green total factor productivity: An empirical study based on System GMM dynamic panel model

Amidst resource loss and environmental protection constraints, achieving green development necessitates enhancing green total factor productivity (GTFP) as a means of promoting rational and efficient resource allocation, thereby balancing economic growth and environmental preservation. Meanwhile, literature on the subject matter of GTFP from a sustainability viewpoint is minimal. As a result, this study employs the panel dataset from 30 provinces of China spanning the period 2005 to 2020 and utilizes the method of moments quantile regression (MMQR) developed by Machado and Santos Silva (2019) to analyze the heterogeneous role of green innovation, environmental regulations, and fiscal expenditure on GTFP. Moreover, the controlling variable for this study includes renewable energy and economic growth. Furthermore, this study investigates the heterogeneous combined impact of green innovation and fiscal expenditure (GTE*FSE) on GTFP. The findings of the MMQR reveal that green innovation has a positive impact on GTFP, while fiscal expenditure, environmental regulations, and renewable energy consumption have a negative impact. GTE*FSE has a positive and significant effect on GTFP, indicating that FSE can reinforce and increase the positive impact of GTE on GTFP in the long run. The study also reveals that economic growth has a mixed effect on GTFP, depending on the quantiles. Furthermore, environmental regulation has a significant and negative impact on GTFP, contradicting the Porter hypothesis. Likewise, the robustness of the findings is confirmed by the results of the fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimations, which indicate a similar impact of the determinants on GTFP as observed in the MMQR analysis. This reinforces the validity of the findings and suggests that the observed relationships are robust to different estimation techniques. Furthermore, the findings of the Dumitrescu and Hurlin (D-H) panel causality test reveal significant bidirectional causality between renewable energy consumption and GTFP and fiscal expenditure and GTFP. Policy-makers need to channel a large chuck of their fiscal spending into green innovation so as to boost sustainability.

With the increasingly obvious restriction of the ecological environment on economic development, environmental regulations are widely used to achieve “green production,” that is, to improve green total factor productivity (GTFP). First, through the econometric model, it can be concluded that command-based environmental regulations could improve GTFP, while market-based environmental regulations have no significant impact on GTFP. Unlike traditional econometric models, machine learning has no specific data requirements and research assumptions. We use Lasso regression to verify the above results by obtaining the optimal tuning parameter. Furthermore, considering that the leap of China’s economy is inseparable from foreign direct investment (FDI), we use FDI as a threshold variable. The threshold model results showe that when the intensity of FDI in China ranges between 1.2492 and 1.588, both types of environmental regulations can significantly promote GTFP. These conclusions passed the robustness test. Given the differences in economy and resource endowment among different regions in China, a regional heterogeneity test is conducted. The results show that the current environmental regulations in eastern and central China have no significant impact on GTFP. However, when the intensity of FDI in central China is greater than 3.6868, environmental regulations have a significant promoting effect on GTFP. In western China, when FDI intensity ranges between 1.3950 and 1.5880, market-based environmental regulations can significantly promote GTFP. Further, the path test of the mediation effect model reveals that command-based environmental regulations reduce GTFP by reducing FDI. The above conclusions provide empirical data for the intensity of FDI in different regions of China to improve GTFP.

Does the transformation of resource-dependent cities promote the realization of the carbon-peaking goal? An analysis based on typical resource-dependent city clusters in China