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Given the increasing threat of economic downturns and global warming, the governments of developing countries urgently need effective measures that can both stimulate economic development and promote low-carbon transitions. As a widely accepted method for stimulating economic growth, the improvement of the business environment and its effect on economic activity have been fully discussed. However, little is known about whether and how the business environment affects the low-carbon transition. By leveraging city-level data from China, this paper exploits whether and through which channels a favorable business environment promotes a low-carbon transition. We find that improving the business environment has a positive impact on carbon efficiency. This relationship is verified to be robust through various alternative tests. Additionally, it is demonstrated that enhancing the business environment can increase carbon efficiency by promoting green technology progress and stimulating new firm entry. Heterogeneity analysis indicates that the positive effect of the business environment is pronounced for cities with intensive environmental regulation, sufficient environmentally targeted fiscal expenditure, and loose economic growth requirements. This paper reveals the unintentional environmental gains of business environment optimization and provides important insights into the low-carbon transition for developing economies. • We measure the indexes of the business environment and the carbon efficiency. • We examine the impact of the business environment on carbon efficiency. • The improvement of the business environment has a positive impact on carbon efficiency. • The business environment can improve carbon efficiency by promoting green technology progress and stimulating new firm entry. • The government plays a crucial role in business environment's effect on carbon efficiency. [ABSTRACT FROM AUTHOR]

Implementing a Carbon Peak Action Plan at the regional level requires comprehensive consideration of the developmental heterogeneity among different provinces, which is an effective pathway for China to realize the goal of carbon peak by 2030. However, there is currently no clear provincial roadmap for carbon peak, and existing studies on carbon peak pathways inadequately address provincial heterogeneity. Therefore, this paper employs the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model to decompose assess 8 factors influencing carbon emissions of 30 provinces. According to scenario analysis, the paper explores the differentiated pathways for provincial carbon peaks based on policy expectation indicators (including population, economy, and urbanization rate) and comprises policy control indicators (including the energy structure, energy efficiency, industrial structure, transportation structure, and innovation input). The results indicate that population, per capita GDP, urbanization rate, and innovation input are the primary factors for influencing (negatively) the growth of carbon emissions. In contrast, the optimization and upgrading of the industrial structure, energy intensity, energy structure, and transportation structure have mitigating effects on carbon emissions, especially for the first two factors. The forecasting results reveal that robust regulations of the energy and industry can effectively accelerate carbon peak at a reduced magnitude. If developed at BAU, China cannot achieve carbon peak by 2030, continuing an upward trend. However, by maximizing the adjustment strength of energy and industrial transformation within the scope of provincial capabilities, China could achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. In this scenario, 24 provinces could achieve carbon peak before 2030. Overall, this study suggests the feasibility of differentiated pathway to achieve carbon peaks in China, exploring the carbon peak potential and paths of 30 provinces, and identifying provinces where carbon peak is more challenging. It also provides a reference for the design of carbon peak roadmaps at both provincial and national levels and offers targeted recommendations for the implementation of differentiated policy strategies for the government. [Display omitted] • Exploring the national carbon peaking path from a provincial perspective. • Expanding the STIRPAT model combined with scenario analysis to study provinces. • Adjusting industry and reducing energy intensity are key to carbon control. • China can achieve carbon peak as early as 2025, with a peak of 12.069 billion tons. • Research on carbon peaking pathway based on provincial heterogeneity. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between digital transformation(DT) and environmental, social, and governance(ESG) greenwashing using Chinese listed companies as a sample from 2012 to 2022. Furthermore, it analyzes the enterprise and regional heterogeneity as well as the influencing mechanisms on this relationship. The research results indicate that corporate digital transformation significantly inhibits ESG greenwashing, with a more pronounced effect on companies in non-high-pollution industries, high-tech industries, and the eastern region. In addition, mechanism tests reveal that internal control and financing constraints play a partial mediating role. Digital transformation suppresses ESG greenwashing by enhancing the quality of internal control and alleviating financing constraints. The primary contribution of this paper lies in demonstrating that digital transformation can serve as a strategic tool to mitigate ESG greenwashing. This enriches the research on the outcomes of digital transformation as well as the factors influencing ESG greenwashing. The conclusions of this paper provide theoretical foundations and policy recommendations for better ESG development by enterprises and governments in emerging markets. At the same time, this paper has a certain guiding role for the introduction and implementation of policies to encourage digital transformation. • This study examines the relationship between digital transformation (DT) and corporate greenwashing behavior in the context of ESG. • The study reveals that corporate digital transformation significantly inhibits greenwashing behavior in the ESG domain. • The inhibitory effect of digital transformation is more pronounced in non-polluting enterprises, high-tech enterprises, and companies located in the central-western region. • The internal control level and financing constraints of enterprises play a partial mediating role, indicating that digital transformation suppresses greenwashing by enhancing internal control and alleviating financing constraints. [ABSTRACT FROM AUTHOR]

In the context of China's efforts to combat climate change and promote sustainable development, the solid waste treatment industry's environmental, social, and corporate governance (ESG) performance is receiving significant attention. To comprehensively assess the ESG performance of the solid waste treatment industry and identify company types, this study constructs a targeted ESG evaluation index system based on existing literature, SASB industry standards, and company reports and utilizes a random forest approach combined with K-means clustering to determine indicator weights. Based on this index system, the paper evaluates the ESG performance of 71 solid waste disposal companies (SWDCs) from 2013 to 2021 and identifies their ESG types from static and dynamic perspectives. In the static view, company types are determined based on annual ESG performance, while the dynamic view considers time-series changes to observe the evolution of company ESG types. The results show that the overall ESG performance of SWDCs falls within the 2-8-point range, indicating a noticeable high-low imbalance. Key initiatives to improve ESG performance in this industry include enhancing waste management measures, developing emergency plans, and reinforcing ESG disclosure. From a static perspective, this paper can identify companies into three categories: delayed development, single-wheel-driven, and coordinated development. Finally, from a dynamic perspective considering the time factor, companies are further subdivided into five types: continual leading, growth catch-up, slow progress, fluctuating change, and retrogressive inertia. This study not only provides targeted recommendations for different types of ESG companies but also helps various sectors of society better understand the ESG conditions of this high environmental risk industry, thereby enhancing the regulation and support for its sustainable development. [Display omitted] • ESG evaluation index system for solid waste disposal companies is proposed. • A random forest method based on k-means clustering is used to construct the index. • Comparing E, S, and G scores with industry averages to identify static ESG types. • Analyzing trends in annual static ESG types to identify dynamic ESG types. • Identify companies as three static ESG types and five dynamic ESG types. [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality target, renewable energy sources have been transforming from "supplementary energy" to "main energy", which have promoted the green and low-carbon transition of global energy supply system. In-depth analyzing the spatial patterns and driving mechanisms of renewable energy expansion are of significance for optimizing the spatial layout of clean power, and avoiding the phenomenon of wind and solar power curtailment. In this paper, we proposed an ensemble learning model to examine the nonlinear effects of physical geography, resource endowment, and socio-economic factors on solar photovoltaic (PV) capacity at the prefecture-level city scale in China. Using the city-level multi-sources geospatial big data, we extensively collected a total of 175 related explanatory variables and cumulative installed capacity of solar PV power for 295 prefecture-level cities of China. The recursive feature elimination algorithm (SVM-REF) is firstly used to extract the optimal feature subset of urban PV capacity from multi-dimensional features variables. Furthermore, three advanced machine learning models (random forest, decision tree, extreme gradient boosting) are developed to identify the key influencing factors and nonlinear driving effect of urban solar PV power expansion in China. The results show that China's PV installation capacity is highly concentrated in Northern and Northwest parts of China, with the occupancy over 70% in 2019. Moreover, the XGBoost model has the best prediction accuracy (R2 = 0.97) among three methods. We also found that total amount of urban water resources, average solar radiation, and population density are the most important controlling factors for urban solar PV capacity expansion in China, with contribution of 35.6%, 17.7%, and 13.3%, respectively. We suggested that urban solar PV layout mode in China is recommended to gradually shift from resource orientation to the "resource-environment-demand" comprehensive orientation. The paper provides a replicable, scalable machine learning models for simulating solar PV power capacity at the prefecture-level city scale, and serves as a motivation for decision-making reference of the macro siting optimization and sustainable development of China's green power industry. • This study examines the impact of high-dimensional features variables on urban solar energy development. • Over 70% of solar PV installation capacity is concentrated in Northern and Northwest parts of China. • Water resources, solar radiation, population density have significant impact on solar PV distribution. • Solar PV distribution in China should shift from resource orientation to the comprehensive orientation. [ABSTRACT FROM AUTHOR]

Employment creation and climate change mitigation are core tasks for achieving sustainable development goals. Whether or not carbon mitigation policy facilitate employment deserves deep exploration. Through the construction of multi-regional dynamic computable general equilibrium model (CGE) with more scientific energy & environment block, this paper first evaluates regional employment effects of the national emission trading scheme (ETS) in China. Furthermore, we explore the Okun's law of the national ETS based on the mediating effect model. The results show that whether in carbon-intensive industries (CIIs) or non-carbon-intensive industries (NCIIs), employment effects of the national ETS are differentiated across regions. Specifically, the national ETS generally promotes CIIs' employment in Southern, Eastern, Middle Yangtze River and Southwest regions, and has negative effects on CIIs' employment in other regions. Meanwhile, the national ETS brings employment creation to NCIIs of Southern region, while there are opposite results in NCIIs of Northeast region and mixed results in NCIIs of other regions. Moreover, the Okun's law of the national ETS holds in CIIs of each region, but it not fits the data for NCIIs. Therefore, it is important for the Chinese government to consider the differentiated employment effects in different regions carefully rather than adopt one-size-fit-all solution when constructing the national carbon market. [Display omitted] • Multi-regional dynamic CGE model is constructed for investigate. • The carbon emission accounting method in this paper is more scientific. • This paper focuses on employment effects of the national ETS at regional level. • Almost all regions would unavoidably witness more or less employment losses. • The Okun's law of the national ETS does not always hold. [ABSTRACT FROM AUTHOR]

To cope with global climate risks, promoting energy transformation has become a global consensus. China issued a reform policy in 2010 to promote the convergence of the three major information industries, namely broadcasting and television networks, telecommunications networks and the Internet (TPR policy). However, is the convergence of information industries able to promote energy transition? This study constructs a quasi-natural experimental framework using China's TPR policy as a representative case of industrial convergence. Using city-level panel data between 2003 and 2016 in China and a multi-period difference-in-differences model, this study examines the impact of TPR policy on energy transition. The results show that TPR policy positively contributes to energy transition at a 1% significance level. Extensive robustness tests support this finding. This positive effect is particularly evident in regions with less developed communications infrastructure and stricter environmental regulations. Mechanism analysis suggests that TPR policy promotes energy transition by improving the level of information networks, promoting technological innovation, and optimizing industrial structure. The findings of this paper emphasize the importance of information industry convergence in facilitating energy transformation. • This paper examines the impact of TPR on energy transition by using a DID model. • TPR promotes energy transition at a 1% significance level. • TPR's effect shows heterogeneity in the level of communication and regulations. • Information level, innovation and industrial upgrading play a key role in this process. [ABSTRACT FROM AUTHOR]

In the context of global low-carbon emission reduction, how to achieve green and high-quality development has become a major issue for the Communist Party of China (CPC) and the Chinese government recently. Based on the data of China's listed companies from 2013 to 2020, this paper uses Python to implement text analysis of annual reports, and explores the relationship between political acuity and carbon-efficiency synergies (CES) from the perspective of enterprise initiative. We found that (1) political acuity positively affects carbon-efficiency synergies. (2) Increased political acuity can reduce carbon emissions, but the effect on economic efficiency is not obvious. That is, low carbon takes the lead in raising the level of carbon-efficiency synergies. (3) Environmental regulations can positively regulate the relationship between political acuity and carbon-efficiency synergies. (4) Political acuity in southern China, carbon neutral and non-state-owned enterprises (NSOEs) will have a more pronounced effect on carbon-efficiency synergies. From the perspective of the root causes of political linkages, we find the synergies between formal and informal institutions, and the key factors for policy implementation. This paper is helpful for enterprises to improve the synergies of emission reduction and efficiency promotion, and has practical implications for the government to promote green and high-quality development. • Assess the value of the low-carbon economy from its political roots. • We build a carbon-efficiency synergy evaluation system. • Political acuity and formal institutions are synergistic. • The influence of political acuity on CES can be replicated. • This paper expands the technical approach to low-carbon research. [ABSTRACT FROM AUTHOR]

Green innovation is important and beneficial for the green development of enterprises. The development of digital technology could improve the asymmetry of information and affect the enterprise's financing modes. Currently, a definitive conclusion about the relationship between the digital transformation and green technology innovation has not reached. Additionally, studies have not yet fully explored the impact pathways of corporate digital transformation on green innovation through corporate financing modes. To fill this void, this study collected data of Chinese A-share listed companies from 2010 to 2020. The empirical analysis results showed that digital transformation significantly enhanced enterprises' green innovation abilities. Furthermore, mediating models were utilized to explore how enterprise financing modes mediate the impact of digital transformation on the enterprise green innovation. Results indicated that through internal financing and bank loans as "bridges", digital transformation promoted enterprise green innovation. However, digital transformation did not have a significant impact on enterprise green innovation through venture capital. Moreover, heterogeneity analyses revealed significant differences among different groups of enterprises. This study expands the research field related to digital transformation and enterprise green innovation by providing empirical evidence to uncover the impact of digital transformation on enterprise green innovation. Findings of this study contribute not only to enterprise managerial decision-makers to better leverage digital transformation through corporate financing to accelerate green innovation, but also to government policymakers to design policies to stimulate enterprise green innovation. • Firstly, our paper empirically examines and validates the impact of digital transformation on enterprise green innovation again by using empirical data of biggest development counties, i.e., China. • Secondly, our paper combines enterprise digital transformation, financing modes, and green innovation within the same framework to study the "bridge" role of financing modes between digital transformation and enterprise green innovation. • Thirdly, Thirdly, heterogeneity analyses were carried out, the results of the different enterprises groups are significant different. [ABSTRACT FROM AUTHOR]

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities. [Display omitted] •The synergistic trends of carbon emissions and population aging are not apparent. •Population aging significantly reduces carbon emissions at the city scale. •Linkage effect of industrial upgrading and energy conservation on carbon emission reduction. •Energy conservation effect of population aging amplified by industrial upgrading. [ABSTRACT FROM AUTHOR]

The impact investing literature largely focuses on private equity investing and overlooks the investments made through debt financing that actually dominate the market. To address this research gap, this paper investigates whether impact financing is associated with financial benefits. By using COVID-19 as an exogenous shock to China's stock market, this paper applies fixed effects panel data analysis with a difference-in-differences research design to provide robust empirical outcomes. The results reveal those financial institutions that better integrated environmental impacts into their financing process experienced positive stock return changes in response to the shock. This study answers the question of how well an impact scales. The findings suggest that impact financing is an effective model, as the impacts incorporated in the debt can be scaled up compared to impact investing funds with low volumes. Impact financing has enormous potential for financial institutions to engage in the green transition since they can derive pecuniary utility while delivering environmental impacts. The revelation of financial benefits also contributes to overcoming the lack of knowledge about impact financing and helps to remove the barriers that advance industry growth. • This paper studies whether impact financing is associated with financial benefits. • Financial firms that better integrate environmental impact in financing have positive stock return changes during COVID-19. • Financial firms can derive pecuniary utility from pursuing impact financing. • The findings suggest that impact financing can be an effective model to bring impact to a substantial scale. [ABSTRACT FROM AUTHOR]

In the global context, countries must reduce carbon dioxide emissions to "net zero" by 2050 to limit global warming to 1.5 °C above pre-industrial levels. China's Nationally Determined Contributions (NDCs) are to peak carbon dioxide emissions by 2030 and achieve the carbon neutrality target by 2060. To monitor the achievements of the NDCs, establishing an annual-based accounting mechanism is necessary to record the carbon stocks in China, especially for Blue Carbon. The paper aims to present China's first national-scale systematic Blue Carbon accounting for mangroves, salt marshes, and seagrasses, covering the accounting subjects of physical and monetary assets, as well as relevant indicators for investors. It focuses on the design of the accounting method and system, the selection of data categories applied to this system, and the application of China's Blue Carbon accounting based on this accounting system. Taking Blue Carbon accounting as a starting point, this paper analyses the restoration prospects of these ecosystems and their application potential for NDCs in China compared with the terrestrial ecosystems. The results indicate that mangroves are the most cost-effective type of ecosystem, even compared to terrestrial forests. Thus, the paper provides policymakers with a new perspective on the decision-making of carbon sequestration relevant decisions, aiming to promote the monitoring, restoration, and expansion of China's coastal blue carbon ecosystem through the establishment of a sound Blue Carbon accounting system, and to help achieve the carbon neutrality goal in China's NDCs through regular and systematic monitoring of its national Blue Carbon inventories. • China's coastal blue carbon ecosystems are carbon sources rather than carbon sinks. • Mangroves are the most cost-effective coastal blue carbon ecosystem compared to salt marshes and seagrass beds. • The Blue Carbon of mangroves is more cost-effective than the Green Carbon of terrestrial forests when meeting China's Nationally Determined Contributions target of increasing forest stocks. • Establishing guidelines for Blue Carbon data requirements will lay a scientific foundation for formulating strategies and policies to cope with global climate change. [ABSTRACT FROM AUTHOR]

Enhancing the green economy efficiency (GEE) is crucial for building a sustainable economy. How can the rapidly advancing digital transformation contribute to this process? The paper empirically examines the direct and spatial spillover effects of digital transformation on cities' GEE in China. This study utilizes the National E-commerce Pilot City (NEPC) policy as a quasi-natural experiment of regional digital transformation and employs the staggered difference-in-differences (DID) method with heterogeneous effects. The findings reveal that (i) implementing the NEPC policy significantly increases urban GEE by 2.6%, corresponding to a 16% increase in the mean of GEE. This effect is particularly pronounced in non-resource-based cities and cities with high Internet penetration. (ii) The mechanism test shows that the pilot policy positively affects GEE by promoting green structural transformation, enhancing green innovation, and strengthening public environmental concerns. (iii) The study highlights a positive spatial spillover effect of the NEPC policy on the GEE of nonpilot cities. (iv) The adoption of the NEPC plays a pivotal role in advancing energy use and carbon emission efficiency. This paper expands the existing knowledge on the green development effects of the digital economy while offering valuable policy insights for building an "Inclusive Green Economy". [Display omitted] • The staggered differences-in-differences method is employed to examine the direct and spatial spillover effects of NEPC on cities' GEE in China. • This paper uses NEPC as a quasi-experiment for digital transformation. • NEPC significantly improves green economic efficiency by 2.6%. • Green structural transformation, innovation, and environmental concerns are main channels. • Positive spillover effect of NEPC on green economy efficiency in non-pilot cities. [ABSTRACT FROM AUTHOR]

Building a marine ecological security shelter (MESS) has become the main strategy to adapt marine ecological threats in China. As China's marine policy lacks a robust framework document, it is necessary to consider whether the policy system can effectively support the construction of MESS. However, the linkage between the construction measures of MESS and related policies is not clear. Therefore, the purpose of this paper is to clarify the concept of MESS and its connection with policy, by adopting the policy content analysis method to analyze the evolution process of MESS-related policy system. The legislative shortcomings and implementation obstacles of the MESS-related policy system are then summarized and discussed. The results show that from 1981 to 2021 the MESS-related policy system has been continuously improved. However, the policy system's support and guarantee capacity for building MESS still needs to be improved. (1) Due to the lack of basic laws and special laws, the coordination among governance subjects and among policies lacks legislative guarantee. (2) The construction of MESS continues the inter-regional and inter-department administrative barriers in collaborative governance of marine environment. To establish an effective collaborative governance model, it is essential to improve the governance structure and mechanism. (3) The government-led governance pattern faces the problem of mechanism failure. The command and control instrument accounts for more than 82%, and the public and enterprises lack strong policy guarantees to participate in marine governance. (4) The policy system's adaptability to emerging threats must be improved. Marine policies rarely involve emerging threats such as climate change and new pollutants. Meanwhile, the real-time supervision and monitoring mechanism is weak. The real-time supervision is only accounting for about 10%. Generally speaking, as a complex and long-term system engineering, the construction of MESS will inevitably encounter contradictions in politics, culture, and economy. China should deepen the construction of marine ecological civilization and form a governance concept based on ecosystems. Overall, this paper helps to understand the internal connection between MESS and policy comprehensively and provides a new perspective for improving China's marine governance capacity. [Display omitted] • We conduct a content analysis of 403 Chinese marine ecological security policies. • The policy instruments are mainly regulatory but lack flexibility and precision. • The policy implementation lacks assurance for poor governance mechanisms. • The policy system lacks adaptiveness to climate change and new pollutants. • The study provides ideas to enhance climate change adaptation of marine governance. [ABSTRACT FROM AUTHOR]

The proposal of the dual carbon goal and the blue economy in China has sparked a keen interest in carbon emissions reduction from sewage treatment. Carbon accounting in urban sewage plants serves as the foundation for carbon emission reduction in sewage treatment. This paper re-evaluated carbon accounting in the operational processes for urban sewage treatment plants to develop a novel carbon emission evaluation model for anaerobic-anoxic-oxic treatment plants. The results show that the carbon emissions generated by non-carbon dioxide gases far exceed the carbon emissions from carbon dioxide alone. Moreover, the recycling of sewage leads to carbon emissions reduction that offsets the carbon emissions generated during the operation of the sewage plant. Also, the carbon emissions generated by sewage treatment plants are lower than those generated by untreated sewage. The findings and insights provided in this paper provide valuable references for carbon accounting and the implementation of low-carbon practices in urban sewage treatment plants. [Display omitted] • A novel carbon emission evaluation model for AAO treatment plants was proposed. • Mechanical processes generated higher carbon emissions than energy and chemicals. • The determination of direct and indirect emissions of CO 2 from sewage plants. • Sewage recycling can offset carbon emissions from the operation of sewage plants. [ABSTRACT FROM AUTHOR]

Corporate water performance and sustainable development are currently vital focus areas for scholars. Therefore, this paper investigates the experience of Chief Executive Officers (CEOs) with water shortage influences corporate water performance by focusing on listed companies in water-intensive and high-water-risk industries in China between 2014 and 2019. This paper manually collected information relative to the cities and provinces where CEOs grew up to evaluate their early exposure to water shortages. Furthermore, this paper develops an evaluation scale, based on the Enterprise Water Conservation Evaluation Guide (GB/T 7119-2006), to compute the enterprise water management practices scores. These results will constitute the enterprise water performance evaluation score. Moreover, this paper focuses on CEOs who have experienced water resource shortages through a positive impact on the water resource performance of their companies. As for the findings, they demonstrate that CEOs who grew up in regions with higher water scarcity have a more pronounced positive effect on their water shortage experience through corporate water performance. Furthermore, CEO power intensity positively moderates the relationship between the CEO's water shortage experience and corporate water resource performance. Further investigation reveals generational differences in the impact of CEO water shortage experience on company water performance. CEOs, who grew up during periods with a higher incidence of droughts, demonstrate a more significant promoting effect on corporate water performance. To sum up, this study expands the understanding of factors influencing corporate water resource performance and deepens the knowledge of the early life experiences of CEOs. • We construct a set of scales to measure corporate water performance. • We explore the relationship between CEO water shortage experience and corporate water resource performance. • CEO water shortage experience has a positive effect on corporate water performance. • CEO power intensity plays a positive moderating role. [ABSTRACT FROM AUTHOR]

Carbon sequestration is vital to maintaining carbon neutrality and mitigating climate change's impact on humans and the environment. As the government's role in eco-advocacy is primarily designed to safeguard the environment, this paper discusses the impact of government environmental regulations on carbon sequestration. By analyzing 286 cities from 2000 to 2019 in China, we propose that government environmental regulation can lead to a 0.77% increase in carbon sequestration. Additionally, industrial agglomeration and industrial structure advancement are regarded as viable channels. As a result of the differences in city types, government intervention, and public concern across different cities, the impact is likely to vary as well. • Environmental regulation can effectively enhance carbon sequestration. • The heterogeneity considers city type, government intervention and public attention. • The mechanisms include industry agglomeration and industrial structure optimization. [ABSTRACT FROM AUTHOR]

The use of agrochemical inputs has significantly enhanced agricultural yields in China; however, their excessive utilization has also caused a range of environmental issues. This paper examines the costs associated with reducing agrochemicals by employing shadow prices, which represent the value of the marginal product of agrochemicals, to further develop cost-effective environmental policy measures for reducing their usage. To this end, the shadow prices of agrochemicals have been assessed by adopting a newly developed convex expectile regression approach and using statistical data from 31 provinces in China spanning from 2005 to 2020. Furthermore, the present study investigates the disparities between shadow prices and market prices for different agrochemicals across various regions in China. The findings suggest that the costs of reducing chemical fertilizers are higher than those of reducing pesticides and plastic films. Moreover, the results indicate that central China exhibits relatively high potential for decreasing agrochemical usage. Finally, these findings can inform the Chinese government's restructuring of producer support and environmental policy in a cost-effective way to mitigate agrochemicals use in the future. Additionally, the research method employed in this study holds potential for extension to other agrochemicals-dependent countries. • Point No.1: Shadow prices of different agrochemicals are estimated in a holistic way. • Point No.2: Convex expectile regression is used to address the impacts of inefficiency. • Point No.3: Different types of market distortion are identified. • Point No.4: Suggest suitable cost-effective environmental management measures. [ABSTRACT FROM AUTHOR]

Urban areas contribute 85% of China's CO 2 emissions. Green finance is an important means to support green energy development and achieve the low-carbon transformation of high-energy-consuming industries. The motivation of this article is to investigate the impact and mechanism of green finance on urban carbon intensity. Most existing literature uses linear models to investigate urban carbon intensity, ignoring the nonlinear relationships between economic variables. The nonparametric models can fill the inherent shortcomings of linear models and effectively simulate the nonlinear nexus between economic variables. Based on the 2011–2021 panel data of 237 cities in China, this paper applies the nonparametric additive model to survey the influence of green finance on urban carbon intensity. Empirical findings exhibit that green finance exerts an inverted U-shaped effect on urban carbon intensity, indicating that the carbon reduction effect of green finance has gradually shifted from inconspicuous in the early stages to prominent in the later stages. Then, from the perspectives of region, city size, and carbon intensity, this article conducts heterogeneity analysis. The results show that the impact of green finance on various carbon intensities all exhibits obvious nonlinear feature. Furthermore, this article employs a mediation effect model to conduct mechanism analysis. The results display that technological progress and industrial structure are two important mediating variables, both of which produce an inverted U-shaped nonlinear impact on urban carbon intensity. • The nexus between green finance and urban carbon intensity is a ∩-shaped pattern. • The high-carbon intensity cities receive a U-shaped impact from green finance. • Technological progress exerts a ∩-shaped effect on carbon intensity. • Green finance has a ∩-shaped impact on carbon intensity in the eastern and western regions. [ABSTRACT FROM AUTHOR]

Transboundary lakes are shared by multiple administrative regions. The key to balance the development and protection of transboundary lakes is to properly measure the value of water resources. Most of previous studies on the measurement of lake water resources value have not fully considered the ecosystem service function. This paper proposes a new concept "composite water value" to measure the value of transboundary lakes by integrating the external runoff value and the internal runoff value of water resources. The study constructs a composite water value measurement system for transboundary lakes, further analyzes its influencing factors,and applies the system to the case of Nansi Lake, a representative transboundary lake in eastern China. The results show that: (1) The composite water value of lakes is influenced by various factors, including industrial structure, water withdrawal, and water use methods, which impact the external runoff water value; meanwhile, the composite water quality and fluctuations in lake level are closely associated with the internal runoff water value. From 2008 to 2021, the average annual composite water value of Nansi Lake was 39.628 billion yuan, exhibiting a "rising-falling-fluctuating rising" trend due to pollution control policies, reduced precipitation, and enhanced water-saving technologies successively. (2) From a long-term perspective, it is necessary to focus on the internal runoff water use value of lakes. The internal runoff water value of Nansi Lake has been over 75% of the composite water value, and flood storage and water conservation are important manifestations of its ecosystem service value. (3) The external runoff water value of lake is closely related to the internal runoff water value, and relevant departments need to consider the balance between the water withdrawal of multiple cities along the lake and the retained water volume of the lake to achieve the maximum benefit of composite water value. [Display omitted] • Lake water value includes multiple values derived from external runoff water out of lakes and internal runoff water in lakes. • The ecosystem service value of internal runoff water is much higher than the economic value of external runoff water. • There is a tradeoff relationship between the internal runoff water and external runoff water. • Composite water value measurement can help balance transboundary lakes development and protection. [ABSTRACT FROM AUTHOR]

With the growing emphasis on sustainable development, green policies have become a crucial factor influencing both environmental pollution and the career progression of officials in China and other countries. However, the mechanisms behind this relationship remain unclear. This paper aims to enhance the understanding of how environmental pollution impacts official promotion by analyzing the performance of provincial leaders in China and their incentives to address pollution. Using provincial panel data from 1998 to 2020 and a probit model, our study uncovers significant findings. We demonstrate that the intensified green attention by China's central government has notably reduced the promotion prospects for provincial officials with poor environmental protection records, particularly since 2013. Furthermore, our research extends the analysis of micro-level mechanisms, illustrating how the central government's political incentives effectively influence local environmental governance. This study underscores the central government's capability to leverage its personnel system to achieve desired outcomes in sustainable development. • Analysis unveils incentives for provincial governors to tackle pollution issues. • Green attention from top leadership lowers promotion chances for officials with poor environmental records. • Impact is stronger during periods of heightened national environmental awareness. • Central government's green attention influences local leaders' priorities towards quality or quantity. [ABSTRACT FROM AUTHOR]

Digitalization, as a crucial engine for promoting sustainable development, has created new prospects for enterprise green transformation. Utilizing panel data from enterprises registered in China's resource-based cities from 2010 to 2021, this study innovatively examines the role of digitalization in promoting green transformation across two key performance dimensions. The findings indicate that enterprise digitalization significantly enhances the green innovation performance, while its impact on environmental performance, although positive, is not statistically significant. Overall, enterprise digitalization greatly facilitates green transformation. Moreover, mechanism analysis reveals that enterprise digitalization promotes green transformation through the alleviation of financial mismatch and the increase of external market attention. In addition, heterogeneity analysis shows that the effectiveness of enterprise digitalization in promoting green transformation is significantly different in different opportunist risk scenarios, proving more effective in areas with low-marketization, high-competition industries, and high environmental regulation intensity. In terms of the policy level, the National Big Data Comprehensive Pilot Zone (NBDCPZ) and the Carbon Emission Trading Policy (CETP) played a positive stimulating role in the process of enterprise digitalization affecting green transformation. This paper broadens the scope of research on green transformation, offers new development philosophy for enterprises in resource-based cities, and provides new directions for the synergistic development of digitalization and greenization. • We explore the nexus between digitalization and green transformation. • We verify this positive effect by applying multiple fixed-effects models. • We identify the mechanism paths from the perspective of external market attention. • We implement heterogeneity analysis from three aspects. • We reinforce the reliability of this positive effect under considering policy interference. [ABSTRACT FROM AUTHOR]

Situated in the north of the Qinghai-Tibet Plateau, the Qaidam Basin experiences limited precipitation and significant evaporation. Despite these conditions, it stands out as one of the most densely distributed lakes in China. The formation of these lakes is controversial: whether the lake water primarily originates from local precipitation or external water sources. To address this issue, this paper explores the recharge sources of lakes in the Qaidam Basin and the circulation patterns of groundwater from a remote sensing perspective. Based on deep learning networks, we optimized the soft object regions of the Object-Contextual Representations Network (OCRNet) and proposed the Remote·Sensing Adaptive-Improved OCRNet (RSA-IOCRNet). Compared with seven other networks, RSA-IOCRNet obtained better experimental results and was used to construct an area sequence of 16 major lakes in the Qaidam Basin. Combined with multi-source data, the comprehensive analysis indicates no significant correlation between climatic factors and lake changes, while an obvious correlation between lakes and groundwater changes in the eastern Qaidam, consisting with the results of the field survey. Deep-circulating groundwater recharges numerous Qaidam lakes through upwelling from fault zones, such as Gasikule Lake and Xiaochaidan Lake. Groundwater in the Qaidam Basin is more depleted in hydrogen-oxygen isotope characteristics than surface water in the basin, but similar to some river water in the endorheic Tibetan Plateau. This indicates that Tibetan seepage water, estimated at approximately 540 billion m3/a, is transported through the Qaidam Basin via deep circulation. Moreover, it rises to recharge the groundwater and lakes within this basin through fracture zones, extending to various arid and semi-arid regions such as Taitema Lake. This work provides a new perspective on the impact of deep groundwater on lakes and water circulation in these areas. [Display omitted] • RSA-OCRNet constructs area changes of 16 major lakes in the Qaidam Basin. • Changes in the Qaidam lakes are strongly linked to groundwater recharge. • Groundwater in Qaidam may come from seepage in Qiangtang by deep circulation. [ABSTRACT FROM AUTHOR]

Mineral extraction plays a pivotal role in economic growth and development of the Global South, serving as a cornerstone for numerous industries and contributing significantly to national economies. However, its exploitation often comes with significant environmental and social challenges. This research paper examines the impacts of mineral extraction on environmental sustainability and social equity in the Global South, focusing particularly on China over the past two decades (2002–2022). Utilizing the Dynamic Autoregressive Distributed Lag (DARDL) methodology, we offer a comprehensive empirical analysis of the impact of mineral extraction on environmental and social implications. Our findings provide an in-depth perspective, highlighting the multifaceted nature of this relationship. The study underscores the temporal dynamics and evolving nature of the environmental and social repercussions associated with mineral extraction. It offers critical insights for policymakers, industry stakeholders, and civil society organizations engaged in the Global South. • Mineral extraction drives economic growth in the Global South but poses environmental and social challenges. • This study focuses on China's mineral extraction from 2002 to 2022. • Utilizing DARDL methodology, it analyzes environmental and social implications. • Findings offer critical insights for policymakers and stakeholders in the Global South. [ABSTRACT FROM AUTHOR]

Promoting the formation of the green lifestyle (GL) is a crucial step in achieving comprehensive green transformation of urban economic and social development. The widespread adoption of GL is influenced by various environmental regulations. Previous research mainly focused on the impact of individual policies on GL from the single policy perspective. The mechanisms of the combined effects of policies have not been thoroughly explored, particularly the contributions of each policy during periods of overlap. This paper takes the dual-policy of the New-type Urbanization Policy (NUP) and Smart City Policy (SCP) in China as an example. It employs panel data collected from 271 cities in China during 2007–2019 and establishes a multi-period difference-in-difference model to identify the combined effects of the dual-policy on residents' GL. Additionally, the Shapley value decomposition method is utilized to identify the contribution magnitude of each policy when they act simultaneously. The following conclusions are yielded. Firstly, the combined effects of dual-policy are more effective than a single policy in influencing residents' GL. Secondly, the Shapley value decomposition method reveals that when both policies are simultaneously implemented, SCP contributes a greater weight compared to NUP. Thirdly, the dual-policy can promote residents' adoption of GL through mechanisms such as green technological innovation, public participation in environmental protection, and the agglomeration of tertiary industries. Fourthly, the impact of dual-policy on residents' GL varies across different types and sizes of cities. This study attempts to unseal the "black box" of how the dual-policy influences residents' GL during the green transformation of cities in China, providing theoretical references for relevant urban policies in other countries and contributing to Chinese solutions and experience to global urban green development. • The dual-policy of NUP and SCP can jointly promote residents' green lifestyle. • The weight of SCP's contribution is higher than NUP's by the Shapely value method. • It explored three potential channels between the dual-policy and GL. • There are heterogeneous effects of dual-policy on GL in different types of cities. [ABSTRACT FROM AUTHOR]

Climate change is considered one of the major systemic risks facing the world in the 21st century. To address climate change, China has adopted a series of climate policies, but the uncertainty brought about by frequent climate policy issuance has increased pressure on enterprises, which may not be conducive to enterprises reducing emissions. This paper uses data on 1211 listed companies on the A-share market in China from 2012 to 2022 to study the impact of climate policy uncertainty on enterprise pollutant emissions. The research findings show that climate policy uncertainty increases corporate pollution emissions; climate policy uncertainty mainly generates negative impacts on enterprise environmental regulation, social responsibility, and R&D investment, thereby negatively affecting enterprise emissions reduction. Further heterogeneity analysis shows that climate policy uncertainty in China has a more significant impact on non-state-owned enterprises, technology-intensive enterprises, lightly polluting enterprises, and enterprises in western regions. These findings emphasize the importance of enterprise social responsibility, environmental regulation, and R&D investment in enterprise emissions reduction and provide policy implications for Chinese enterprises to optimize their energy-saving and emission reduction strategies in the face of climate policy uncertainty. • We investigate the correlation between uncertainty in climate policy and the corporate pollution emissions. • We utilized an extensive dataset comprising publicly listed Chinese firms spanning the period from 2012 to 2022. • Climate policy uncertainty negatively impacts corporate environmental regulation, social responsibility, and R&D investment. [ABSTRACT FROM AUTHOR]

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance. • Environmental performance index is constructed based on endogenous directions. • A Euclidean distance function is used to identify endogenous directions. • Optimization pathways and environmental performance show heterogeneity among cities. • Mitigating NO x emissions can improve environmental performance significantly. [ABSTRACT FROM AUTHOR]

The fiscal system plays an important role in the government's environmental governance efforts. There is currently no consensus on how fiscal structure adjustments impact pollution and carbon reduction. This paper uses China's fiscal "province-managing-county" reform (FPMCR) implemented in 2004 as a quasi-natural experiment, utilizing panel data from 1670 counties in China from 2000 to 2020 to investigate the impact of fiscal decentralization on reduction pollution and carbon emissions (RPCE), as well as its underlying mechanisms. The results show that (1) from 2000 to 2020, China's RPCE shows an overall trend of fluctuating increase, with its value turning positive after 2013. China's RPCE exhibits a spatial pattern characterized by "lower in the north, higher in the south; higher in the east, lower in the west". (2) After implementing FPMCR , the RPCE levels in reformed counties decreased by −1.44%, showing that reformed county-level governments prioritize economic development over environmental protection. (3) The mechanism analysis found that after implementing FPMCR , reformed counties experienced a 9.16% increase in nighttime light intensity (NLI), and a 3.99% and 4.34% increase in the number of large-scale industrial enterprises (NLIE) and industrial agglomeration (IA), respectively. This suggests that FPMCR leads to radical urbanization and rapid industrialization in counties, which is detrimental to the improvement of RPCE levels. (4) The spatial heterogeneity analysis found that FPMCR's impact coefficient on RPCE levels in the eastern regions is −1.96%, while in the western regions it is −1.16%. This indicates that reformed counties in the eastern regions are more likely to invest expanded fiscal resources in economic development projects, leading to a decrease in RPCE levels. (5) The temporal heterogeneity analysis found that after the promulgation of the " Three-Year Action Plan to Win the Blue Sky Defense Battle " in 2018, the adverse impact of FPMCR on RPCE is completely reversed, leading to a 1.76% increase in RPCE levels. (6) Further analysis reveals that localizing leaders can slow down the promotion of county-level urbanization and industrialization by the FPMCR , benefiting the improvement of RPCE levels. In other words, "the outsider monk will not recite scriptures as well as a local one". This study has clarified the causal relationship and underlying mechanisms between fiscal decentralization and environmental governance, providing reliable theoretical support for optimizing grassroots fiscal systems and reducing environmental pollution in other transitional economies. It enriches the field of environmental economics related to fiscal decentralization. • Leader localization can shape government behavior following an increase in financial resources. • Enriched environmental economics research related to fiscal decentralization. • Increase more refined reduction of pollution and carbon emissions indicators to improve evaluability. • Clarify the causal relationship and internal mechanism between fiscal decentralization and environmental governance. [ABSTRACT FROM AUTHOR]

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms' optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings. Fig. 1. Introduction of the research questions Fig. 2. Framework of this paper. [Display omitted] • Carbon emission trading and green certificate trading policies were introduced into a dynamic CGE model. • The comprehensive performance of different scenarios were measured using total factor productivity index. • The policy effects of different scenarios were explored from 2020 to 2060. • The even more effective reform measures were adopt, within a single policy. • A sensible mix were proposed, within different policies. [ABSTRACT FROM AUTHOR]

With digital technological change and the increasing frequency of interregional innovation links, the spatial correlation and diversity of strategic emerging industries' green innovation efficiency (SEI-GIE) need to be explored in depth. This paper innovatively constructs the SEI-GIE input-output index system under digital economy. The proposed grey model FINGBM(1,1) with ω -order accumulation and weighted initial value optimization realizes effective prediction of 7 input-output indicators of 30 provinces in China from 2021 to 2025. Super-SBM-DEA, gravity model, and social network analysis are applied to explore spatial network structure's dynamic process of SEI-GIE from 12th to 14th Five-Year-Plan period (2011–2025). Empirical results show that (1) Under the effect of digital economy, the SEI-GIE in China generally shows a U-shaped fluctuation trend, in which the growth trend in the central region is obvious, and the western region shows significant fluctuations. (2) The spatial correlation network of SEI-GIE presents a complex and stable center-periphery circle. Particularly, the overall increase in network efficiency highlights the strong small-world characteristics. (3) Beijing, Shanghai, Zhejiang and Jiangsu have always been in the leading core position, with strong influence and control; And Tianjin's core position in the network will decline. Additionally, Guangxi and Chongqing have great potential, but Guangdong needs to strengthen its radiation effect. (4) Block model shows that plate-I (Beijing, Tianjin) receive spatial spillovers from others, while plates-III,IV have significant spillover effects. This study provides theoretical reference for policymakers from a network perspective to promote development of China's SEI-GIE. • SEI-GIE input-output index under digital economy is innovatively constructed. • Indicators of China's 30 provinces are effective predicted by novel FINGBM(1,1). • Spatial correlation network dynamic process of SEI-GIE is explored from 2011 to 2025. • Complex center-periphery circles are presented in spatial network of SEI-GIE. • Theoretical reference for policymakers from a network perspective is provided. [ABSTRACT FROM AUTHOR]

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006–2014 and 2015–2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed. Figure Geographical map of ETWP from 2014 to 2022. [Display omitted] • The concept of energy transition welfare performance was defined and accounted for. • The driving factors of energy transition welfare performance were assessed. • The spatial differences of energy transition welfare performance was studied. [ABSTRACT FROM AUTHOR]

In order to deal with the environmental problems such as pollution emissions and climate change, sustainable development in the field of transportation has gradually become a hot topic to all sectors of society. In addition, promoting the green and low-carbon transformation of China's transportation is also an important issue in the new era. Thus, it is particularly important to correctly identify the green effect of high-speed rail. However, the traditional causal reasoning model faces several challenges such as 'dimensional curse' and multicollinearity. Based on the panel data of 283 prefecture-level cities in China from 2003 to 2019, this study uses the double machine learning model to explore the impact of transportation infrastructure upgrading on the efficiency of urban green development in China. The research shows that the upgrading of transportation infrastructure can effectively improve the efficiency of urban green development by 4%. Service industry agglomeration and green innovation are verified as two mediating channels. Moreover, the synthetic difference in difference model is employed to evaluate the regional impact of high-speed rail, and finds that the regional impact of transportation policies often exceeds the impact of individual cities. We further apply the conclusions of this paper to the research at the micro enterprise level. Goodman-Bacon decomposition and a variety of robustness tests confirm the validity of our conclusions. The study's comprehensive empirical analysis not only validates the positive effects of transportation upgrades on green development, but also offers novel insights into the underlying mechanisms and policy implications of transportation upgrading. • We investigate the nexus between transportation facilities and green development. • We employ the double machine learning model to perform quasi-merge regression. • We affirm this positive effect from the micro enterprise level. • We verify the mediation channels of service industry agglomeration and green innovation level. [ABSTRACT FROM AUTHOR]

Livestock is one of major sources of greenhouse gas (GHG) emissions in China. Clarifying spatiotemporal characteristics of GHG emissions from livestock and exploring influencing factors can provide reference for grasping regional changes of GHG emission and formulate strategies of carbon reduction for livestock industry. However, existing literatures considered both spatial and temporal impacts and dynamic evolution trend of these factors seldomly. This paper used the life cycle assessment (LCA) method to estimate GHG emissions of livestock in 114 cities of the YRB from 2000 to 2021. On this basis, spatiotemporal heterogeneity of influencing factors was analyzed by using geographically and temporally weighted regression (GTWR) model. Finally, future evolution trend of GHG emissions from livestock was predicted by combining traditional and spatial Markov chain. Four main results were listed as follows. Firstly, GHG emission in the life cycle of livestock industry increased from 57.202 million tons (Mt) carbon dioxide equivalent (CO 2 e) in 2000 to 77.568 Mt CO 2 e in 2021. Secondly, structure of livestock industry, labor flow and mechanization were vital factors that led to increase of GHG emissions from livestock. Positive effects of labor flow and mechanization were increasing year by year, while negative effect of urbanization and positive effect of economic development were decreasing year by year. Markov chain analysis shown that probability of keeping high level of GHG emissions of livestock in the YRB unchanged were 96% (T = 1) and 90% (T = 5), and there also existed a Matthew effect. In addition, probability of level transfer of GHG emission in urban livestock was spatially dependent. Government should formulate strategies for livestock development and optimize low-carbon transformation of energy structure for livestock and poultry husbandry based on local conditions and key driving factors in the future. Meanwhile, boundaries of administrative divisions should be broken to promote reduction of GHG emissions in livestock comprehensively. • GHG emissions from livestock in cities of the YRB continued to rise from 2000 to 2021. • GHG emissions per unit of GDP of livestock shown a downward trend. • Analyzing parameter of factors from spatiotemporal aspect by using GTWR model. • Probability of GHG emission transfer in urban livestock was spatially dependent. [ABSTRACT FROM AUTHOR]

Based on the panel data of 22 inland provinces in China from 2010 to 2020, this study constructs and measures the level of rural ecological environment in China. The impact of the financial performance of green-listed companies on the rural ecological environment and its moderating and threshold effects are analyzed. The following conclusions are drawn: (1) During 2010–2020, China's rural ecological environment shows a trend of "fluctuating-decreasing-rising" with significant regional non-equilibrium characteristics. (2) The financial performance of green-listed companies has a significantly negative impact on rural ecology. This negative impact has a crucial heterogeneous feature, with a more significant negative impact in areas with a higher rural ecological environment index and less substantial performance in regions with a lower rural ecological environment index. (3) There is a significant positive moderating effect of education level and digitalization on the relationship between the financial performance of green-listed companies on the level of rural ecological development. As moderating variables, the digitalization and education level weakens the negative impact of green-listed companies' performance on the ecological environment. The positive impact of the financial performance of green-listed companies on the development level of the rural ecological environment is more vital in areas with higher per capita education levels and digitalization in rural areas. (4) There is a significant threshold effect on the financial performance of green-listed companies on the level of rural ecological development. When the financial performance of green-listed companies exceeds a particular threshold value, the impact of the financial performance of green-listed companies on the development level of the rural ecological environment is significantly positive. Based on the above findings, this paper puts forward corresponding countermeasure suggestions. • From 2010 to 2020, the development level of China's rural ecological environment showed a trend of "fluctuating-decreasing-rising," with noticeable regional differences. • The income level of green-listed companies has a negative impact on the rural ecological environment, and there is regional heterogeneity in this impact. • The rural education and digitalization levels have a positive moderating effect on the ecological environment. • It is found that the impact of green-listed companies on the ecological environment has a threshold effect. [ABSTRACT FROM AUTHOR]

The increasing volume of electronic waste (e-waste) poses significant challenges for efficient collection in China. Despite many measures were taken over the past two decades, the e-waste collection rate was still not high. To this end, the Chinese government issued a new policy, the collection target responsibility (CTR) policy. Under the CTR policy, however, it is essential for participants to know how to share the responsibility of collection and how much reasonable targets are set to ensure the efficiency of the collection models. Therefore, the purpose of this paper is to explore the determination of optimal collection targets and the corresponding performance from the perspective of responsibility sharing to support the successful implementation of the CTR. Firstly, the study focuses on participants including the government, manufacturers, and recyclers, and develops three CTR models, independent collection model, government cost-sharing model, and enterprise collaboration model. Secondly, collection target equations for each model are established by employing dynamic differential game analysis, and corresponding collection performances are derived. Thirdly, through practical case simulations, the evolution of collection performance is dynamically analyzed to determine reasonable collection targets for the three models, as 23.8%, 32.3%, and 34.4%, respectively. The findings highlight the effectiveness of CTR in improving e-waste collection targets and performance, with the highest levels attained when the collection responsibilities are shared by government cost-sharing and enterprise collaboration. This study provides theoretical support for setting reasonable collection targets under CTR, and assists decision-makers in developing targeted CTR implementation measures. • The CTR model frameworks involving the main participants are developed. • Three CTR models are constructed from the perspective of shared responsibility. • The collection target for each CTR model is analyzed and determined. • The performances of CTR models are analyzed and compared. • Differences in the CTR models from cost-sharing and enterprises collaboration are observed. [ABSTRACT FROM AUTHOR]

As digital economy develops, its impact on green innovation and energy efficiency has become the focus of current research. To explore the impact of the current development of the digital economy on the energy industry, this paper selects the parameter of green innovation resilience, analyzes the impact mechanism of green innovation resilience on energy efficiency under the shock of digital economic development, and uses relevant data from 284 cities in China from 2011 to 2019 for empirical testing. It is found that: green innovation resilience promotes energy efficiency; low level of green innovation resilience inhibits the improvement of energy efficiency, while high level of resilience promotes energy efficiency; the initial stage of digital economic development generates resource grabbing and the effect of technological constraints, which weakens the role of green innovation resilience in promoting energy efficiency. The results indicate that the resilience of the green innovation system should be strengthened in order to fully tap the potential for promoting energy efficiency; the policy orientation of "digital greening—energy efficient" should be pursued in the development of digital economy; the rational allocation of resources and the implementation of green standards should be strengthened in the process of digital economic development; and the constraints on energy efficiency improvement in the early stage of digital economic development should be broken through by accelerating the digitalization process. • Green innovation resilience can promote energy efficiency. • Early digital economy development can lead to resource grabbing and technology constraints effects. • Pursuing the policy orientation of "digital greening—energy efficient". [ABSTRACT FROM AUTHOR]

Spatial misallocation of resource elements impedes the coordination in regional co-benefit in carbon emission and pollution reduction. Guided by scope economy principles, this study aims to quantifies co-benefits and explores spatial variation patterns to facilitate mutual synergy. The findings offer valuable insights for identifying optimal focus points and shaping targeted collaborative policies. Based on the multi-source provincial panel data spanning 2000 to 2021, this paper quantitatively assesses the co-benefit through the lens of marginal abatement cost. Then, the Dagum Gini coefficient was employed to investigate the disparities at the spatial level. Additionally, geographical detector is introduced to analyze the source of disparities at the factor level. Results indicate: (1) Under joint reduction, the marginal abatement cost reduces by 57.86% and 79.97% respectively, with an overall 68.77% increase in co-benefit fluctuation. (2) Provinces with low co-benefit significantly decreased after 2007, while provinces with high co-benefit concentrated in the northwest, southwest, and east. (3) Overall disparities decreased, east-central regions hold the lowest spatial disparities in co-benefit and east-west hold the most; intragroup differences and supervariable density primarily contributes to the disparity. (4) FDI significantly influence the co-benefit over the past two decades, with spatial disparities influenced by both endogenous and exogenous factors across development stages. • Joint reduction significantly lowers the marginal abatement cost and enhanced co-benefit. • East-central regions holds the lowest spatial disparity in co-benefit and east-west holds the most. • Intragroup disparities and supervariable density primarily contributed to spatial disparity. • The interplay between internal and external factors increasingly influences spatial disparities. [ABSTRACT FROM AUTHOR]

Numerous studies have discussed the economic impacts of the COVID-19 pandemic in recent years. However, the effectiveness and trade-offs of diverse countermeasures still need to be investigated, particularly under the long-term goal of low-carbon transition, which is crucial for understanding the potential impacts of the future public health emergency (PHE) related economic crisis. Given that China still faces big pressures from the potential PHE and carbon neutrality, this paper assesses the effectiveness of policy instruments in restoring the economy and advancing green development after the PHE using the Dynamic Stochastic General Equilibrium framework. Our findings reveal that the PHE imposes more constraints on the economy because of the decrease in productivity on the supply side and in consumption on the demand side. Compared to the other counterparts, the mixed stimulus can overcome the adverse impacts of the PHE while contributing to carbon reduction. Furthermore, all types of low-carbon policies investigated in this study can contribute to carbon reduction at the expense of economic growth. Meanwhile, the carbon tax realizes the target of reducing emissions with the smallest negative impact on economic growth. Thus, we suggest adopting the carbon tax policy as the most effective low-carbon measure to address uncertainties associated with the PHE. • This study explores pathways for advancing economic recovery and green development after the public health emergency. • We develop a scenario-based dynamic stochastic general equilibrium (DSGE) model. • We characterize public health emergency shocks from a supply and demand perspective. • The model introduces different carbon abatement mechanisms. [ABSTRACT FROM AUTHOR]

Global warming has accentuated the effects of extreme heat on health. Health insurance, functioning as a risk management tool, has the potential to alleviate these impacts. Consequently, this paper investigates the correlation between extreme heat events and the demand for health insurance in China. Using data from the China Health and Nutrition Survey, we have observed a substantial increase in the likelihood of residents purchasing health insurance during extreme heat events. To be specific, for every extra day of extreme heat events annually, there is a 0.3% increase in the probability of purchasing health insurance. This effect is not uniform across different demographic groups. It is particularly pronounced among middle-aged and elderly individuals, rural residents, those with lower educational levels, higher income brackets, and individuals residing in underprivileged areas with limited access to green spaces and healthcare facilities. Furthermore, our study indicates that the increased frequency of extreme heat events not only impacts individuals' physical health but also triggers negative emotions, which in turn drive risk-averse behavior related to health insurance purchases. These findings carry substantial policy implications for mitigating the economic consequences of climate change. • Extreme heat increases the likelihood of residents purchasing health insurance. • Each additional day of extreme heat boosts health insurance purchases by 0.3%. • This effect varies among different populations. • Mechanistic analyses indicate extreme heat impacting physical and mental health. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon trading prices using deep learning models can help enterprises understand the operational mechanisms and regulations of the carbon market. This is crucial for expanding the industries covered by the carbon market and ensuring its stable and healthy development. To ensure the accuracy and reliability of the predictions in practical applications, it is important to evaluate the model's robustness. In this paper, we built models with different parameters to predict carbon trading prices, and proposed models with high accuracy and robustness. The accuracy of the models was assessed using traditional survey indicators. The robustness of the CNN-LSTM model was compared to that of the LSTM model using Z-scores. The CNN-LSTM model with the best prediction performance was compared to a single LSTM model, resulting in a 9% reduction in MSE and a 0.0133 shortening of the Z-score range. Furthermore, the CNN-LSTM model achieved a level of accuracy comparable to other popular models such as CEEMDAN, Boosting, and GRU. It also demonstrated a training speed improvement of at least 40% compared to the aforementioned methods. These results suggest that the CNN-LSTM enhances model resilience. Moreover, the practicality of using Z-score to evaluate model robustness is confirmed. • Deep learning by combined CNN-LSTM networks for carbon price forecasting. • Hyperparameter tuning of deep learning models for improving forecast accuracy. • CNN-LSTM models outperform single CNN models and single LSTM models. • Performance metric of deep learning for abrupt changes in carbon price variation. [ABSTRACT FROM AUTHOR]

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals. • The current study analyses the role of green factor productivity in zero carbon transition. • We use 30 chinese provincial datasets from 2005 to 2020. • Energy transition is the most effective channel to improve green total factor productivity. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been proved to be an important engine of green economic development, yet how it will affect the internal structure of green economy is unknown. The aim of this study is to examine the impact and its mechanism of AI on green total factor productivity (GTFP) from the internal-structure perspective, by using provincial panel data of China from 2009 to 2021 and global Malmquist index. The main research results show that: (1) the development of AI contributes to China's GTFP growth. And this effect is more significant in undeveloped areas; (2) AI promotes China's GTFP growth mainly by improving resource allocation efficiency, while it exerts little impact through the paths of technological progress and scale efficiency; (3) the transmission mechanism of AI on GTFP varies greatly among China's three main regions. In the eastern region, AI improves GTFP mainly by both advancing technological progress and improving resource allocation efficiency, while in central region AI contributes to GTFP growth mainly through technological progress. Compared with the eastern and central regions, AI in the western region plays a stronger impact on GTFP through the channel of improving scale efficiency. This study helps to understand the pathways of artificial intelligence affecting the transformation of green economic growth and formulate differentiated regional policies in light of local conditions. • This paper studies the effects of AI on China's GTFP from internal-structural perspective. • GTFP is decomposed into three internal factors by the global Malmquist index. • AI has positive effect on GTFP through different impacts on internal factors. • Improving resource allocation efficiency is the most critical channel of AI on GTFP. • AI plays a more significant role in promoting GTFP in less developed areas. [ABSTRACT FROM AUTHOR]

A significant milestone in China's carbon market was reached with the official launch and operation of the National Carbon Emission Trading Market. The accurate prediction of the carbon price in this market is crucial for the government to formulate scientific policies regarding the carbon market and for companies to participate effectively. Nevertheless, it remains challenging to accurately predict price fluctuations in the carbon market because of the volatility and instability caused by several complex factors. This paper proposes a new carbon price forecasting framework that considers the potential factors influencing national carbon prices, including data decomposition and reconstruction techniques, feature selection techniques, machine learning forecasting techniques for intelligent optimisation, and research on model interpretability. This comprehensive framework aims to improve the accuracy and understandability of carbon price projections to respond better to the complexity and uncertainty of carbon markets. The results indicate that (1) the hybrid forecasting framework is highly accurate in forecasting national carbon market prices and far superior to other comparative models; (2) the factors driving national carbon prices vary according to the time scale. High-frequency series are sensitive to short-term economic and energy market indicators. Medium- and low-frequency series are more susceptible to financial markets and long-term economic conditions than high-frequency series. This study provides insights into the factors affecting China's national carbon market price and serves as a reference for companies and governments to develop carbon price forecasting tools. • A hybrid carbon price prediction model named FS-CEEMDAN-VMD-GWO-LightGBM is proposed. • Accurately predicted the national carbon price in China using multi-influence factor time series data. • Using a data decomposition and reconstruction strategy, the model's accuracy was improved. • The SHAP algorithm is used to assess the impact of various influencing factors on the fluctuations of carbon prices. [ABSTRACT FROM AUTHOR]

To address global climate change, achieving carbon peak and carbon neutrality has become a global consensus. However, the means to simultaneously achieve carbon reduction and promote green economic development, particularly in developing countries, require further investigation. This study evaluates the impact of e-commerce on CO 2 emissions. Through an examination of the effects of the National E-Commerce Demonstration City (NEDC) policy from 2006 to 2017, this paper reveals that e-commerce growth facilitated by the NEDC policy resulted in a 7.89% reduction in total CO 2 emissions and a per capita reduction of 1.1146 tons in the pilot cities. Mechanism analysis demonstrates that the upgrading of industrial structure, development of digital finance, and the growth of innovation and entrepreneurship serve as primary pathways for this impact. The robustness of the findings is supported by parallel trend tests, placebo tests, and additional sensitivity analyses. Furthermore, the research reveals that the NEDC policy exhibits a more significant reduction in CO 2 emissions in cities with higher levels of economic development and non-resource-based cities. Welfare analyses show that the NEDC policy has significant socio-economic effects. These findings provide new evidence on the environmental effects of the digital economy and offer insights into achieving carbon neutrality. • The National E-commerce Demonstration Cities (NEDC) policy reduced CO 2 emissions. • Optimizing industrial structure, enhancing digital finance, and promoting innovation and entrepreneurship are mechanisms. • The effect is more pronounced in cities with high economic levels and high resource dependence. • The NEDC policy substantially enhanced social welfare by reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Renewable energy (RE) plays a crucial role in global energy transformation, and a thorough study of the potential impact of RE on regional carbon emissions is of great significance. This is particularly relevant to China, which needs to clarify its path to carbon reduction. Using the sample data of 30 provinces in China from 2000 to 2021, this paper uses the Granger causality test to verify the causal relationship between carbon emission intensity (CEI) and other factors. It builds a mediation effect model on this basis to explore the direct impact effect and indirect transmission path of renewable energy utilization (REU) on CEI. The results show that REU has a one-way causal relationship with CEI. REU can directly and indirectly reduce CEI by improving social wealth and changing the direction of energy investment. In addition, REU indirectly increases CEI through the transmission paths of investment in the energy industry - social affluence and industrial level-social affluence. The CEI is indirectly reduced through the conduction paths of (social affluence-Urbanization rate), (Investment in the energy industry-Urbanization rate), (Industrial level-Urbanization rate), and (Industrial level-Investment in the energy industry). These conclusions will assist policymakers in exploring targeted pathways for low-carbon power development, providing a reference for strategic and sustainable carbon reduction policies. [Display omitted] • Enrich the view of renewable energy's indirect impact on carbon emissions. • Renewable energy's carbon emission reductions go beyond replacing fossil fuels. • Constructed a mediating effect model between renewable energy and carbon emissions. • Renewable energy can promote or inhibit carbon emissions through various pathways. • Research helps develop more effective renewable energy sustainability strategies. [ABSTRACT FROM AUTHOR]

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide. • An integrated framework combining DBN, SD, PLUS and InVEST models was proposed. • Impact of natural and socioeconomic factors on ESs was determined under climate scenarios. • The spatial pattern of ESs was optimized and partitioned using DBNs. • ESs in arid oasis urban agglomerations are mainly affected by natural factors. [ABSTRACT FROM AUTHOR]

Resource-based cities generally face the dual pressures of ecological damage and high carbon emissions. Taking the National Sustainable Development Plan for Resource-Based Cities (NSDP) issued in China as a quasi-natural experiment, this paper for the first time, uses the difference-in-difference method to test the coordinated effect of green expansion and carbon reduction brought by NSDP at the county level using rich remote sensing data. The results show that NSDP has significantly promoted the ecological quality improvement and CO 2 emission mitigation in resource-based cities, and the above coordinated benefits mainly occur in energy, mature, declining, northeastern, and municipal resource-based cities. Through the mechanism analysis, we find that NSDP mainly works by promoting the transformation of industry sector to agriculture, animal husbandry, accommodation and catering, finance, and environment management sectors, which are reflected in output, employment, product yield, land type and land transaction area. Further discussion on the tertiary industry suggests that developing tourism, easing financing constraint and strengthening environment regulation, are the key channels to borne fruit in green expansion and carbon reduction. • Analysis of placed-based policy on resource at the county-level. • Coordinated effects of green expansion and carbon reduction are tested. • Key areas of policy effects are identified. • Industrial structure transformation and its related mechanisms are discussed. [ABSTRACT FROM AUTHOR]