Optimizing the vertical division of environmental responsibilities helps overcome free-riding behavior and regulatory fragmentation in border regions, thereby internalizing pollution externalities. This study employs panel data from 221 prefecture-level cities located along inter-provincial borders in China from 2011 to 2022, and applies a multi-period difference-in-differences model to examine the impact and underlying mechanisms of the vertical management reform of environmental protection institutions (VREP) on air pollution in these areas. The findings indicate that following departmental restructuring and the redistribution of administrative responsibilities, air pollution levels in border cities have decreased significantly, with the extent of improvement gradually increasing over time. This positive change is primarily driven by enhanced regulatory capacity and intensified environmental law enforcement. Furthermore, heterogeneity analysis further reveals that the pollution control efficacy of VREP is more pronounced in cities with stronger border attributes, higher coordination levels, or those classified as non-national border cities. The decomposition of spatial effects indicates that the current mitigation of pollution in border areas is primarily attributable to the improvement of local environmental governance capacity. Future efforts must prioritize deepening regional coordination to unlock its potential.

Corporate climate risk disclosure (CRD) is increasingly crucial for enterprises striving to navigate climate risks and foster sustainable development, particularly in the context of the dual-carbon goal. Against the backdrop of rapid financial technological advancements, the inquiry into whether enterprises can leverage these innovations to enhance their CRD performance has garnered significant attention within the framework of green development. This paper utilizes data from Chinese A-share listed enterprises in Shanghai and Shenzhen spanning from 2011 to 2022 to systematically investigate the direct influence and underlying mechanisms of Fintech on CRD. The findings indicate that Fintech notably facilitates CRD, a conclusion substantiated by various endogeneity and robustness tests. Two primary mechanisms are identified: firstly, external pressure manifested through analysts' attention effectively amplifies the impact of Fintech on CRD; secondly, institutional arrangements such as corporate internal control mechanisms bolster the effect of Fintech on CRD. Heterogeneity analysis further reveals that the influence of Fintech on CRD is particularly pronounced for state-owned enterprises, enterprises audited by the "Big Four" accounting enterprises, companies with highly educated management teams, and those operating in high energy-consuming industries. Lastly, the paper investigates the economic ramifications of Fintech on CRD, elucidating how the promotion of CRD by Fintech can enhance both the quantity and quality of corporate green innovation. These findings offer empirical insights into advancing Fintech adoption and reinforcing CRD efforts.

Digital infrastructure (DI) plays a catalytic role by facilitating the upgrading of heterogeneous industrial structures (ISU). This structural transformation optimizes resource allocation, spurs the growth of green emerging industries, and ultimately unlocks substantial potential for improvements in green total factor productivity (GTFP). Based on Chinese provincial panel data from 2012 to 2021 and using a fixed-effects model, this study systematically examines the direct impact of DI on GTFP and further explores the transmission mechanisms through multidimensional industrial structure upgrading. The results indicate a DI-heterogeneous industrial structure upgrading-GTFP mechanism. That is, DI significantly promotes industrial structure sophistication, rationalization, and heightening, thereby enhancing GTFP. Furthermore, spatial heterogeneity analysis shows that DI exerts a considerable influence on GTFP in both the eastern and central regions of China, with the impact being more pronounced in the eastern region compared to the central region, while its effect in the western region is relatively marginal. This study contributes to the literature by constructing a research framework for the DI-GTFP link and elucidating the critical mediating role of heterogeneous industrial structure upgrading, thereby offering a robust empirical foundation and actionable policy insights for leveraging digital infrastructure to advance industrial transformation and green development goals.

Can digitized supply chains be effective in mitigating the serious pollution problems in border areas caused by regional fragmentation and lack of regulatory cooperation? We attempt to deliver a response to this issue. This paper employs a difference-in-differences model, utilizing data from border cities between 2010 and 2022, to demonstrate that the “Supply Chain Innovation and Application Pilot” policy significantly enhances carbon emission efficiency in border regions, primarily by dismantling regional segregation and strengthening environmental regulatory capacity. Further analysis shows that boundary attributes reinforce to some extent the facilitating effect of supply chain digitization on carbon emission efficiency. In addition, the promotion effect of supply chain digitization on carbon emission efficiency is more prominent in border areas with lower levels of regional synergy and non-environmentally focused cities. This study provides a useful reference for improving the governance efficiency of administrative border areas in China.

As the threat of climate change intensifies, carbon emissions from agriculture, one of the primary sources of greenhouse gases, have garnered significant attention. Achieving a low-carbon agricultural transformation is imperative for meeting the “dual carbon” goals. This study constructs a provincial panel dataset covering 31 provinces throughout China between 2000 and 2022. Utilizing fixed-effect, mediation, and moderation analyses, it systematically explores the link between rural human capital (RHC) and agricultural carbon emissions (ACE), as well as the mechanisms underlying it. Empirical findings demonstrate a statistically noteworthy inverse U-shaped relationship between RHC and agricultural carbon dioxide emissions. Specifically, ACE increases with RHC until it reaches a threshold of 9.141, after which emissions begin to decrease. Furthermore, agricultural mechanisation and financial support for agriculture serve as important mediating and moderating channels through which RHC influences the mitigation of ACE. From a regional perspective, the nonlinear inverted U-shaped linkage between RHC and ACE is pronounced in the eastern provinces, whereas no such pattern is observed in the central and western regions. At the same time, the carbon reduction thresholds exhibit significant variation across northern and southern regions. Taking into consideration these findings, the paper presents several policy recommendations, including strengthening RHC development, advancing agricultural mechanisation, increasing financial support, and implementing regionally differentiated policies to accelerate China's low-carbon agricultural transition and achieve “dual carbon” goals.

The system of accountability audit of natural resource (AANR), which aims to transform the resource-dependent and crude mode of development, should be able to achieve significant results in pollution reduction. However, China's regional emission reduction puzzle is still prominent. Based on the provincial panel data from 2010 to 2021, this paper finds regional differences in the effectiveness of the governance of accountability audit of natural resource, and its pollution reduction effect is only visible in the eastern samples, while no significant effect is observed in the central-western samples. Further, through the moderating effect based on the perspective of spatial mismatch of land supply, it is found that the deviation of the construction land index from the direction of population flow weakens the effectiveness of AANR in pollution reduction in the central-western regions. The excess per capita construction land index gives the central-western regions the factor support and institutional space to maintain the resource-dependent rough development mode. At the same time, this mechanism does not exist in the eastern region where the per capita construction land index is scarce. The findings of this paper help to explore the boundaries of AANR's effectiveness, explore the factor allocation scheme to solve regional emission reduction problems, and provide new ideas and optimization suggestions for the quality and efficiency of AANR, which has already entered the period of national promotion.

In the ongoing pursuit of establishing a diversified environmental governance system, individuals, businesses, and various social groups are increasingly articulating their ecological demands in a conscious and organized manner. Recognizing the pivotal role of public attention (PA) in shaping business behavior is imperative for advancing a sustainable and environmentally friendly economy. Consequently, this study delves into the relationship between PA and corporate green innovation (CGI) by analyzing data from A-share industrial listed companies in Shanghai and Shenzhen spanning the years 2011-2020. The analysis employs fixed effects and a mediation model to provide a nuanced understanding of the dynamics involved. The study's findings underscore the following key points: (1) Elevated levels of public attention significantly influence CGI. (2) Government research and development (R&D) subsidies assume a regulatory role in the interplay between PA and CGI, particularly within state-owned enterprises, as well as the mining and manufacturing industries. However, it is noteworthy that an overly generous subsidy policy may inadvertently stifle corporate R&D innovation. (3) Government environmental regulations and corporate social responsibility indirectly shape the association between PA and corporate R&D innovation. Consequently, a fundamental strategy for fostering corporate green development lies in the effective management of government R&D subsidies and environmental regulations. Striking the right balance in these aspects while urging businesses to augment their sense of social responsibility is vital. This approach will pave the way for an efficient and enduring interaction system among the public, government, and companies, ultimately facilitating sustainable development and contributing to the realization of a beautiful China.

Due to the growing momentum of the digital economy and green growth, experts have begun to extensively study the relationship between digitization and the green transformation of industrial enterprises to promote sustainable development goals. Hence, this study empirically discusses the intrinsic mechanisms of digitalization on industrial enterprises’ green transformation by employing a two-way fixed effects model from the perspectives of financing constraints and corporate social responsibility using Chinese A-share listed industrial enterprises from 2011 to 2020. The results illustrate that digitalization is essential in facilitating industrial enterprises’ green transformation. This impact is significant in state-owned enterprises (SOEs) and manufacturing enterprises, whereas non-SOEs, mining, and energy-producing industries play a limited role. Manifestly, digitalization can leverage industrial enterprises’ green transformation by inhibiting financing constraints and increasing corporate social responsibility. These findings provide a theoretical and empirical foundation for comprehending the mechanism of digitalization and green transformation of industrial enterprises.Graphical abstract is mandatory for publication in this journal. Please provide the graphical abstract.File attached Please confirm if the author names are presented accurately and in the correct sequence (given name, family name). Author 3 Given name: [Md. Emran] Last name [Hossain]. Also, kindly confirm the details in the metadata are correct.Confirmed Please drop the affiliation 4. The author is no longer affiliated with this institution.

In the post-COVID-19 pandemic era, boosting the economy through infrastructure investment has emerged as an imperative tool. Apart from coping with the downward pressure on the economy caused by the pandemic, governments are concerned about green economic growth. Using data for 30 provincial-level administrative regions in China, we examine the impact of infrastructure investment on green economic growth. Our findings are as follows. Infrastructure investment significantly inhibits green economic growth; we discover this outcome to be robust. The impact of infrastructure investment on green economic growth differs for different regions. The negative effect of infrastructure investment on green economic growth is substantial in the central-western region, but it is found to be statistically insignificant in the eastern regions

In this study, a multi-dimensional digital economy evaluation index system is constructed to explore the impact of fiscal Science and Technology (S&T) expenditure on digital economy development in China. We reveal the following findings. First, we note that the China's digital economy level as a whole is rising each year, which is less affected by the COVID-19 pandemic. Second, we document that S&T expenditures positively stimulate digital economy development. Finally, we find that the promotion effect of S&T expenditure on the digital economy is statistically significant, and this promotion effect decreases from the southwest to the northeast due to the COVID-19 pandemic.

The advancement of eco-friendly mining is a crucial pathway for reforming and progressing China's mineral resource management system in the modern era. This approach serves as an essential response to the mining sector's distinctive resource landscape and developmental stage, aimed at promoting ecological civilization and implementing the revised orientation of land resource governance. FinTech emerges as a novel mechanism that facilitates the integration and efficient allocation of financial resources, thus catalyzing the eco-friendly transformation of mining enterprises. This study explores the effects and underlying mechanisms of FinTech on the eco-friendly transformation of mining enterprises by examining mineral enterprises listed on China's A-share market from 2013 to 2021. The analysis demonstrates that FinTech positively influences the eco-friendly transformation of these enterprises, a finding supported by rigorous robustness tests that include changes in estimation methods, substitution of explanatory variables, variations in sample size, and addressing endogeneity concerns. Moreover, a heterogeneity analysis, which examines enterprises' nature, size, and regional private finance levels, reveals that FinTech primarily enhances the eco-friendly transformation of private enterprises, with less substantial impacts on state-owned enterprises. Notably, the effectiveness of FinTech in promoting eco-friendly transformation is particularly significant in smaller mining enterprises and regions with lower levels of private finance. Mechanistic analysis further underscores FinTech's role in facilitating eco-friendly transformation through enterprise digitalization and technological upgrading.

The rapid advancements in the digital economy have created numerous opportunities and solutions for industrial green transformation. However, the complex relationship between these two has received relatively less attention. Therefore, this study analyses how the digital economy impacts industrial green transformation across 30 Chinese provinces. The empirical findings highlight the digital economy's significant role in driving industrial green transformation. Within this dynamic framework, two crucial operational mechanisms have been identified: heightened public awareness of environmental issues and the vigorous innovation of green technologies. It becomes evident that the digital economy can energize and sustain the momentum of industrial green transformation. Notably, this influence is most pronounced in eastern-central China. However, its impact in western China appears relatively weaker, especially at higher quantiles. We observe a strong correlation between the evolution of the digital economy and industrial green transformation in terms of space and time. Higher spatiotemporal regression coefficients are primarily found in areas south of the Hu line, while lower values are more common in the northern regions. These findings provide insights into how the digital economy can be strategically applied to drive industrial green transformation.

The regional carbon emission efficiency (RCEE) of 30 provinces in mainland China from 2011 to 2019 was calculated using a super-slack-based measure (Super-SBM) model. Then, using the system generalized method of moments (system GMM) model, spatial Durbin model (SDM), and mediating effect model, we examined the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on RCEE. It was found that DE significantly promoted regional RCEE, but had a negative effect on RCEE in provinces with a high economic correlation. The mechanism studies showed that DE improved RCEE by reducing the energy intensity and promoting industrial upgrading and green technology innovation. Regional heterogeneity analysis found that DE significantly improved RCEE in eastern provinces, but not in central and western provinces. While RCEE in economically developed areas was improved by DE, it was decreased in economically underdeveloped provinces. This paper provides some empirical and theoretical references for the development of DE to improve RCEE.

Under the system of political centralization and economic decentralization, the expanding scale of land finance and the increasingly severe environmental pressure have jointly become crucial features of China's urban development. Therefore, it is of great practical significance to study the intrinsic mechanism of land finance on haze pollution for China's economy to achieve kinetic energy transformation and green development. This paper empirically analyzes the impact of land finance on haze pollution using a dynamic spatial Durbin model based on panel data of 269 prefecture-level cities in China from 2004 to 2017. The statistical results show that haze pollution has a significant "snowball effect" and space spillover effect. Land finance has a significant positive effect on haze pollution. Land transfer both by agreement and by bid invitation, auction, and listing have significant positive effect on haze pollution. However, the promoting effect of land transfer by agreement on haze pollution is significantly higher than that of land sale by bid invitation, auction, and listing. Furthermore, regional heterogeneity implies that for cities in the eastern region, land finance is conducive to alleviating haze pollution. In contrast, for cities in the central and western regions, land finance significantly promotes haze pollution.

Natural resource consumption and the digital economy are increasingly critical in empowering industrial green transformation, which is also an ongoing and valuable topic worth exploring. Therefore, this study investigates the impact of natural resource use and the digital economy on industrial green transformation in 30 Chinese provincial-level administrative data from 2006 to 2020. The outcomes reveal that digital economy can significantly drive industrial green transformation by elevating natural resource consumption. Significant heterogeneity is observed in the effects of natural resource use and digital economy on industrial green transformation. Manifestly, the digital economy stimulates industrial green transformation by promoting natural resource use in the east-central regions of China, while the mechanism is insignificant in the western region of China. Finally, the positive impact of natural resource use on industrial green transformation is gradually strengthening with the continuous increase in the digital economy level. Our findings imply that policymakers should differentiate the execution of digital economy policies and build an empowered natural resource usage system led by the digital economy to accelerate industrial green transformation.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

As an ingredient of the marine economy, the marine fishery has the dual attributes of carbon source and carbon sink. Marine fisheries are vital to the sustainable development of the marine economy and pivotal to the dual carbon goals. To explore the new mode of low carbon development of marine fishery, this paper utilizes system dynamics to establish the simulation model of low carbon development complex system of Chinese marine fishery for 2010 to 2019. Then, the carbon emission process of Chinese marine fishery is characterized from the perspectives of mariculture, marine fishing, marine processing industry, and marine shellfish carbon sink. This paper also performs policy simulation applying the system dynamics model to forecast and analyze marine fishery carbon emission development trends in adjusting GDP growth rate, fishery industry structure, and marine employment ratio. The simulation results indicate that the socio-economic system can influence marine fishery system development, which is an essential factor driving marine fishery carbon emissions. The number of marine fishery employment is associated with the scale of marine fishery development, which is the primary positive driving factor of carbon emissions from the marine fishery. Under the premise of excluding the heterogeneity of aquatic products, the energy consumption of the marine fishing industry is significantly higher than that of the mariculture industry in producing a unit of marine products, i.e., adjusting the structure of the marine industry can cause an inhibitory effect on the carbon emission of the marine fishery. Our findings contribute to policy implications for policymakers to reasonably restructure the marine fishery industry and the ratio of marine capture to mariculture to facilitate the low-carbon development of marine fishery.

China's excessive reliance on natural resources has resulted in the dual challenges of sluggish economic growth and significant ecological damage. Addressing the resource curse and realizing sustainable economic development have emerged as paramount concerns in regional development. Exploring practical strategies to overcome these challenges and foster a more sustainable and resilient economic landscape is crucial. This study explores the impact of natural resource dependence (NRD) on green technology innovation (GTI) using Chinese provincial administrative regions data between 2011 and 2021. The finding demonstrates that natural resource dependence significantly impedes regional green technology innovation, especially in resource-based and central and western provinces. Furthermore, it investigates the effects of NRD on regional green innovation under different levels of government integrity and public environmental concern. The findings indicate that the inhibitory effect of NRD on regional green innovation is more pronounced with lower levels of government integrity. Conversely, public environmental concerns can partially alleviate the inhibitory effect of natural resource dependence on green technology innovation. Hence, promoting the development of new industries, actively constructing diversified economic structures, enhancing government integrity, and gradually improving the public participatory environmental regulatory system are essential measures to tackle the problem of resource dependency and promote the enhancement of regional green technology innovation.

The manufacture of products in the industrial sector is the principal source of carbon emissions. To slow the progression of global warming and advance low-carbon economic development, it is essential to develop methods for accurately predicting carbon emissions from industrial sources and imposing reasonable controls on those emissions. We select a support vector machine to predict industrial carbon emissions from 2021 to 2040 by comparing the predictive power of the BP (backpropagation) neural network and the support vector machine. To reduce noise in the input variables for BP neural network and support vector machine models, we use a random forest technique to filter the factors affecting industrial carbon emissions. The statistical results suggest that BP's neural network is insufficiently adaptable to small sample sizes, has a relatively high error rate, and produces inconsistent predictions of industrial carbon emissions. The support vector machine produces excellent fitting results for tiny sample data, with projected values of industrial carbon dioxide emissions that are astonishingly close to the actual values. In 2030, carbon emissions from the industrial sector will have reached their maximum level.