Green or gray: government-business relations and urban greening construction

Is low-carbon energy technology a catalyst for driving green total factor productivity development? The case of China

This paper uses the SBM-GML model to measure and evaluate green total factor productivity based on the panel data of 30 provinces and cities in China from 2012 to 2018. It examines the impact of different dimensions of financial decentralisation on green total factor productivity. The research results show that: 1) green total factor productivity in China is improved year by year and better in central and western regions; 2) the decentralisation of fiscal revenue and expenditure significantly weakens the increase of green total factor productivity in provincial level; 3) fiscal decentralisation inhibits green total factor productivity in central and western regions with regional heterogeneity; 4) local government competition affects the relationship between fiscal decentralisation and green total factor productivity, weakens the negative effect of fiscal decentralisation on green total factor productivity. Finally, the study aims to promote green total factor productivity and sustainable development from the perspective of financial decentralisation. This paper expands the literature and evidence of financial decentralisation on green total factor productivity and offers suggestions for governments and policymakers working toward sustainable development.

Facing the new form and situation of the Huaihe Economic Zone, it is of great significance to analyze the sources of growth and the intrinsic mechanism of the green total factor productivity of its economic-ecological system, to grasp the spatial and temporal characteristics of green total factor productivity, and to study the influence of each factor on green total factor productivity to achieve sustainable economic development in the Huaihe Economic Zone. Based on the clarification of economic growth theory, green economy theory, carbon cycle theory, and green total factor productivity theory, this paper identifies and discusses the limitation that the existing research literature often ignores the endogenous role of carbon sinks when measuring green total factor productivity. Then, the green total factor productivity of Huaihe Economic Zone based on carbon cycle from 2004 to 2017 is measured using the superefficient nonradial SBM model. Combined with the GML productivity index, it is decomposed into technical progress and technical efficiency and analyzed in comparison with the green total factor productivity without considering ecological purification capacity (carbon sink) from the perspective of time and space. Finally, the spatial Durbin model is used to analyze the effects of seven variables, including the level of economic development, environmental regulation, R&D level, and openness to the outside world, on green total factor productivity in the Huaihe Economic Zone, and to analyze the direct and indirect effects of each variable on green total factor productivity. TFP based on expected output carbon sink and GDP overall outperforms TFP based on expected output GDP only, mainly because the growth of technical efficiency is underestimated when carbon sink is not considered. Technical efficiency and technological progress are equally important for the growth of TFP in an eco-economic perspective. It is of great practical significance for both the comprehensive understanding of the green total factor productivity level and the improvement path of the ecosystem and the coordinated and sustainable development of the Huaihe Economic Zone.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

The development of information technology has created conducive conditions for the digital economy. The digital economy is regarded as a critical pathway for transforming traditional economic models. Green total factor productivity serves as an indicator for assessing the quality of economic development. During pivotal periods of economic transition, the digital economy and green total factor productivity have emerged as two prominent themes for achieving sustainable economic development. But the impact of digital economy on green total factor productivity is less discussed. Innovation environment refers to a confluence of conditions shaped by factors such as talent, funding, cultural atmosphere and government policies, all of which collectively support innovative activities within a region. The institutional environment encompasses the aggregate of economic, political, social, and legal rules. Currently, there is little discussion on bringing innovation environment and institutional environment into the impact of digital economy on green total factor productivity. To fill the research gap, this paper adopts the Slack based measure-Directional distance function model and Malmquist-Luenberger productivity index to measure green total factor productivity in each region based on the panel data collected from 30 provinces in China from 2004 to 2019. Generalized Method of Moments method is constructed to carry out an empirical study on the impact of digital economy on green total factor productivity. This paper constructs a panel threshold model with innovation environment and institutional environment as threshold variables. In further analysis, this paper employs panel quantile regression for the empirical analysis of the impact of the digital economy on green total factor productivity. Further analysis elucidates the evident disparities in the influence of the digital economy on green total factor productivity at various levels. The research results can provide a guide for discussing the green value of the digital economy and its role in fostering the development of a green economy.

Green total factor productivity is not merely an inevitable choice to continuously increase the quality of China’s economy, but also a booming demand to promote global development. With the fast development of the new generation information technology represented by world comprehensive web technology, Internet growth may well play a more crucial role in enhancing green total factor productivity in China. Based on 2009-2017 China’s inter-provincial panel data, this article uses the threshold regression model and fixed-effect model to empirically investigate the influence intensity and internal mechanism of green total factor productivity in areas affected by the Internet development. We ultimately come to the following conclusions. First, there is a digital divide between the regions of China. Second, many factors such as Internet development, human capital, urbanization, energy efficiency, and external dependence all exert a positive influence on China’s green total factor productivity. At the same time, government intervention is not conducive to green total factor productivity. Third, the influence of Internet growth on China’s green total factor productivity is non-linear, based on the significant double threshold effect of human capital. As the level of human capital continues to exceed the threshold value, the effect of Internet expansion on the green total factor productivity of China has undergone a structural change. The result has changed from a weak negative influence to a positive one, and the significance is increasing. To advance the smart, green, and coordinated development among regions, it is necessary to bring “Internet +” into full play in promoting China’s green total factor productivity, strengthen the deep integration of Internet development and industrial development, and improve the level of clean production utilizing network information.

With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises. Green total factor productivity is a key indicator of green sustainable development. While traditional total factor productivity does not consider the constraints of natural resources and the environment, green total factor productivity remedies this deficiency by incorporating environmental protection indicators, such as pollutant emissions, into the accounting system. To further clarify the relationship between AI technology and corporate green total factor productivity, this study uses a two-way fixed effects model to examine the impact of AI technology on the corporate green total factor productivity of A-share listed companies in China from 2013 to 2020 while examining how corporate slack resources affect the relationship between the two. The results show that the AI application positively contributes to the green total factor productivity of enterprises. Meanwhile, firms’ absorbed, unabsorbed, and potential slack resources all positively moderate the positive impact of AI technology on firms’ green total factor productivity. This study offers a theoretical basis for a comprehensive understanding of digital technology and enterprises’ green development. It also contributes practical insights for the government to formulate relevant policies and for enterprises to use digital technology to attain green and sustainable development.

Investing in green total factor productivity is the key to realizing high-quality economic development in China. Can green finance effectively enhance green total factor productivity and promote high-quality economic development? Based on the panel data of 30 provinces in China from 2010 to 2020, this study empirically examines the direct, indirect, nonlinear, and spatial effects of green finance on green total factor productivity by using the two-way fixed effects model, the mechanism test model, the threshold effect model, and the dynamic spatial Durbin model. The results show that (1) green finance can directly promote green total factor productivity, specifically a 1% increase in the standard deviation of green finance increases green total factor productivity by 2.98% relative to the mean; (2) substantial green technological innovation, industrial structure supererogation, and industrial structure rationalization are important mechanisms for green finance to enhance green total factor productivity; (3) when the economic development level and urbanization level are used as threshold variables, the impact of green finance on green total factor productivity has a nonlinear characteristic of increasing “marginal effect”; (4) there is a significant positive spatial spillover effect of green finance on green total factor productivity, which is dominated by the short-run spatial effect; (5) the effects of green finance on the enhancement of green total factor productivity are more obvious in the eastern regions (2.0580), the high marketization level regions (0.7866), and the weak environmental protection enforcement strength regions (0.7699). This study reveals the internal logic of the role of green finance in green total factor productivity growth and provides empirical support for green finance to promote high-quality economic development. Therefore, local governments should actively promote the development of green finance, and at the same time take into account the differences in the economic development stage and urbanization process of each region and formulate differentiated green finance development policies.

Environmental regulation effect on green total factor productivity in the Yangtze River Economic Belt

How does smart transportation technology promote green total factor productivity? The case of China

As a convenient means of transportation, high-speed rail (HSR) plays an important role in green development. In the context of the rapid development of China’s HSR, this study selects the SBM-DDF-SML model to construct the green total factor productivity (GTFP) index to measure urban green development; moreover, it empirically tests the impact of the opening of the HSR on GTFP using the spatial difference-in-differences (SDID) model. The results show that the opening of the HSR could significantly promote GTFP for HSR-served cities. In addition, the opening of HSR has a positive effect on the GTFP for neighboring HSR-served cities but a negative impact on that for neighboring non-HSR-served cities. The mechanism test shows that HSR can influence GTFP by promoting urban green innovation and entrepreneurial vitality. This study is a supplement to the research on the impact of HSR on the GTFP, in order to provide corresponding policy advice. The government should optimize the layout of HSR and help cities achieve green and sustainable development.

This study utilizes the super-efficiency SBM model to assess green total factor productivity, employs textual analysis to assess formal environmental regulation, and applies the entropy weighting method to assess informal environmental regulation using a dataset of 284 cities between 2003 and 2020. This study also employs the two-way fixed effects model and SDM to empirically examine the impact of dual environmental regulation on urban green total factor productivity. Based on the research results, the overall trend indicates that dual environmental regulation has a positive “U”-shaped impact on the green total factor productivity of both local and neighboring areas, and the improvement of green total factor productivity in the local area will lead to a corresponding increase in the green total factor productivity of neighboring cities. Heterogeneity analysis shows that formal environmental regulation has a significant effect in the Yangtze River Delta, the Pearl River Basin, and non-resource-based cities, but not in the Bohai Rim Economic Circle or resource-based cities; in all regions outside the Pearl River Basin, informal environmental regulation has a non-linear “marginal increasing effect” on green total factor productivity. These findings remain robust to a number of robustness and endogeneity issues. The study findings indicate that to optimize the influence of dual environmental regulation on green total factor production, governments should meticulously devise new environmental regulations and build novel channels for regional collaboration to enhance their supportive effects.

Effects of financial agglomeration on green total factor productivity in Chinese cities: Insights from an empirical spatial Durbin model

Green total factor productivity (GTFP) improvement is an important way to achieve sustainable development, and how to improve GTFP has become the focus of attention of governments and scholars. This paper constructs a GTFP evaluation index system to characterize social, economic, ecological, cultural, and politically sustainable development, and analyzes the impact of environmental regulations on GTFP in the context of increasing innovative labor force. The results of the study are as follows: Firstly, China's GTFP continues to improve, with a decrease in low-value provinces and an increase in high-value provinces; there is an agglomeration effect of GTFP in the eastern and western regions. Secondly, under the role of innovative human capital, the threshold effect of China and the eastern and western regions is significantly positive in the first stage and insignificant in the second stage. The threshold effect of the central region is not significant in the first stage, but significantly negative in the second stage (- 11.650); the effect of environmental regulation in the eastern region is the strongest. Thirdly, the control variables in the upper period GTFP, national and eastern R&D investment, level of foreign openness, local fiscal expenditure, central and western information construction, western tertiary industry development, urbanization, foreign direct investment, level of foreign openness, and local fiscal expenditure can increase GTFP. In this regard, the government should adhere to innovative talent cultivation and investment in science and technology to build a talent ecological environment for regional sustainable development, adjust environmental regulations in time to meet the demand for sustainable development to realize the GTFP regional linkage enhancement.

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.