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Dealing with the relationship between environment and economic development is the core issue of China’s sustainable development. At present, China’s economic transformation is urgent, and green finance is being widely concerned. This paper measured the development level of China’s green finance from the perspective of green credit, green securities, green investment, and green insurance. Then, it used a spatial dynamic panel model to empirically test the mechanism of the impact of green finance on carbon emissions with panel data of 30 Chinese provinces from 2005 to 2018. The following can be seen from the results: (1) The development of green finance contributes to carbon emission reduction. (2) The spatial spillover effect of green finance is significant. Specifically, the development of green finance can not only reduce the carbon emissions of the local region but also inhibit that of adjacent areas. (3) The development of green finance indirectly leads to a decrease in carbon emissions by reducing financing constraints and boosting green technology innovation. In order to stimulate the carbon emission reduction effect of green finance to a greater extent, we should further support the development of green finance, reduce the financing constraints of energy-saving and environmental-protection enterprises, and encourage the research and development of green innovative technologies.

The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

As an essential way to promote ecological civilization, green finance is attracting wide attention. However, whether green finance can successfully regulate the green technology innovation effect of heterogeneous environmental regulations and boost green technology innovation in coordination with heterogeneous environmental regulations remains unclear. Based on the re-measurement of the green finance development index of various provinces and cities in China, this study uses the spatial Durbin model to test the above problems empirically. The results show that green finance and “market incentive” environmental regulations can promote regional green technology innovation, while “command and control” environmental regulations inhibit regional green technology innovation. Green finance plays a negative regulatory role in the mechanism of heterogeneous environmental regulations affecting green technology innovation. Green finance alleviates the negative impact of “command and control” environmental regulations on green technology innovation and weakens the positive impact of “market-incentive” environmental regulations on green technology innovation. In terms of spillover effects, green finance can effectively promote green technology innovation in neighboring regions, while heterogeneous environmental regulations have a crowding-out effect on green technology innovation in neighboring regions.

Air pollution is an important factor affecting the quality and sustainability of the development of China’s economy, and urban sprawl is also a typical by-product of the non-intensive development of urban land. At the same time, Chinese-style fiscal decentralization promotes urban sprawl through top-down yardstick competition, which has a serious impact on air pollution. Therefore, exploring the effect of fiscal decentralization and urban sprawl on air pollution is of great significance for regulating local government behavior, curbing urban sprawl, and accurately identifying the causes of air pollution. The dynamic spatial Durbin model with economic geography weight matrix is employed to analyze the direct and moderating effects of fiscal decentralization and urban sprawl on air pollution on the basis of 269 prefecture-level cities in China from 2004 to 2018. The results show that air pollution has a significant retarded time effect and space spillover effect. Both fiscal decentralization and urban sprawl have contributed significantly to air pollution. The moderating effect of urban sprawl and fiscal decentralization on air pollution is significantly positive. From the short-term effects, the coefficients of the total spillover effect, direct spillover effect, and indirect spillover effect of urban sprawl and fiscal decentralization on air pollution are significantly positive, respectively. In terms of long-term effects, the total spatial spillover effect of urban sprawl and fiscal decentralization on air pollution is significantly negative, while the direct and indirect effects of those are negative but not significant. Further research finds that there is significant regional heterogeneity in the influence of urban sprawl and fiscal decentralization on air pollution.

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.

The Paris agreement is a unified arrangement for the global response to climate change and entered into force on 4 November 2016. Its long-term goal is to hold the global average temperature rise well below 2 °C. China is committed to achieving carbon neutrality by 2060 through various measures, one of which is green technology innovation (GTI). This paper aims to analyze the levels of GTI in 30 provinces in mainland China between 2001 and 2019. It uses the spatial econometric models and panel threshold models along with the slack based measure (SBM) and Global Malmquist-Luenberger (GML) index to analyze the spatial spillover and nonlinear effects of GTI on regional carbon emissions. The results show that GTI achieves growth every year, but the innovation efficiency was low. China’s total carbon dioxide emissions were increasing at a marginal rate, but the carbon emission intensity was declining year by year. Carbon emissions were spatially correlated and show significant positive agglomeration characteristics. The spatial spillover of GTI plays an important role in reducing carbon dioxide emissions. In the underdeveloped regions in China, this emission reduction effect was even more significant.

In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.

China is entering a new period characterized by reaching peak and carbon neutralization, and environmental taxes are increasingly crucial for breaking the “carbon curse” of resource-based cities. Accordingly, using the implementation of China’s Environmental Protection Tax Law (EPT Law) as a quasi-natural experiment, this study utilizes the DID model to assess this environmental tax reform’s effect in terms of reducing carbon emissions. The research results are as follows: (1) The environmental tax reform (ETR) reduced the intensity of carbon emissions; it additionally promoted reducing total carbon emissions from resource-based cities. (2) The carbon abatement effect can also be achieved by upgrading industrial structures and improving innovation in the area of green technology. (3) The ETR has impacted carbon abatement in resource-based cities more significantly in China’s eastern region than in the central or western regions. In contrast, it had less effect on resource-based cities in the regenerative stage than on cities in other stages. (4) The spatial spillover effect of the ETR was significantly positive, aggravating the level of carbon emissions in neighboring cities. Thus, the “pollution haven hypothesis” was tested. Overall, this study deepens the knowledge of ETR and carbon emissions and provides theoretical support and policy suggestions for supporting resource-based cities in a green transformation.

Correct understanding of the positive role and mechanism of green finance in promoting ecological environment is an important premise and guarantee for promoting green finance to better serve the improvement of ecological environment. Based on the panel data of 31 provinces (municipalities and autonomous regions) in China from 2009 to 2017, this paper constructs a spatial Dubin model based on the distance weight matrix and empirically analyzes the impact of green finance on the ecological environment and its spatial spillover effects. The empirical results show that (1) the development of green finance promotes the improvement of the ecological environment in this region and (2) the influence of green finance on the ecological environment has a significant positive spatial spillover effect, that is, the development of green finance in this region will promote the improvement of the ecological environment in the surrounding areas.

Based on the perspective of government-dominated and market-driven industrial co-agglomeration mode, the effect of producer services and manufacturing industrial co-agglomeration on the ecological environment pollution control is explored by using spatial Durbin model, and the mediating effect of technological innovation is further tested. The results show that: (1) At the national level, the government-dominated industrial co-agglomeration only significantly promotes the local ecological environment pollution control, while the market-driven industrial co-agglomeration also can promote the ecological environment pollution control in the surrounding region through its spatial spillover effect. Moreover, there is a significant inverted “U-shaped” curve relationship between the economic development level and ecological environment pollution. Additionally, the environment regulation is also conducive to promoting the ecological environment pollution control, while the industrial structure and foreign direct investment will lead to more serious ecological environment pollution; (2) In the east region, the government-dominated and market-driven industrial co-agglomeration can promote the ecological environment pollution control in the local and surrounding regions, and the promotion effect and spatial spillover effect of market-driven industrial co-agglomeration are greater. However, in the central and west regions, the government-dominated industrial co-agglomeration and market-driven industrial co-agglomeration only promote the local ecological environment pollution control. (3) Technological innovation has partial mediating effect in the impact of government-dominated and market-driven industrial co-agglomeration on the ecological environment pollution control, namely that the government-dominated and market-driven industrial co-agglomeration not only can directly promote the ecological environment pollution control, but also can indirectly promote the ecological environment pollution control through the mediating effect of technological innovation.