Search Results (598)

In recent years, growing concerns over environmental degradation and deepening awareness of the necessity of sustainable development have propelled green and low-carbon energy transition into a focal issue for both academia and policymakers. By decomposing energy transition into the transformation of energy structure and the upgrading of energy efficiency, this study investigates the impact and mechanisms of green finance on energy transition across 30 provinces (municipalities and autonomous regions) in China, with the exception of Tibet. In addition, the impact of climate change is incorporated into the analytical framework. Empirical results demonstrate that green finance development significantly accelerates energy transition, a conclusion robust to rigorous validation. Analysis of the mechanism shows that green finance promotes energy transition through the facilitation of technological innovation and the upgrade of industrial structures. Moreover, empirical evidence reveals that climate change undermines the promotional influence of sustainable finance on energy system transformation. The magnitude of this suppression varies nonlinearly across provincial jurisdictions with differing energy transition progress. Regional heterogeneity analyses further uncover marked discrepancies in climate–finance interactions, demonstrating amplified effects in coastal economic hubs, underdeveloped western provinces, and regions with mature eco-financial markets. According to these findings, actionable policy suggestions are put forward to strengthen green finance and accelerate energy transition. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

China’s economy is shifting from an era of rapid expansion to one focused on high-quality development, making it imperative to tackle environmental degradation linked to energy use. Understanding how New Quality Productive Forces (NQPF) interact with energy efficiency, along with the mechanisms driving this relationship, is essential for economic transformation and long-term sustainability. This study establishes an evaluation framework for NQPF, integrating technological, green, and digital dimensions. We apply fixed-effects models, the spatial Durbin model (SDM), a moderation model, and a threshold model to analyze the influence of NQPF on Green Total Factor Energy Efficiency (GTFEE) and its spatial implications. This underscores the necessity of distinguishing it from traditional productivity frameworks and adopting a new analytical perspective. Furthermore, by considering dimensions such as input, application, innovation capability, and market efficiency, we reveal the moderating role and heterogeneous effects of artificial intelligence (AI). The findings are as follows: The development of NQPF significantly enhances GTFEE, and the conclusion remains robust after tail reduction and endogeneity tests. NQPF has a positive spatial spillover effect on GTFEE; that is, while improving the local GTFEE, it also improves neighboring regions GTFEE. The advancement of AI significantly strengthens the positive impact of NQPF on GTFEE. AI exhibits a significant U-shaped threshold effect: as AI levels increase, its moderating effect transitions from suppression to facilitation, with marginal benefits gradually increasing over time. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article

As the fundamental physical carrier for human production and socio-economic endeavors, enhancing urban land green use efficiency (ULGUE) is crucial for realizing sustainable development. To effectively enhance urban land green use efficiency, this study systematically examines the intrinsic relationship between industrial policies and ULGUE based on panel data from 286 Chinese cities (2010–2022), employing an integrated methodology that combines the Difference-in-Differences (DID) model, Super-Efficiency Slacks-Based Measure Data Envelopment Analysis model, and ArcGIS spatial analysis techniques. The findings clearly demonstrate that the establishment of the “Made in China 2025” pilot policy significantly improves urban land green use efficiency in pilot cities, a conclusion that endures following a succession of stringent evaluations. Moreover, studying its mechanisms suggests that the pilot policy primarily enhances urban land green use efficiency by promoting industrial upgrading, accelerating technological innovation, and strengthening environmental regulations. Heterogeneity analysis further indicates that the policy effects are more significant in urban areas characterized by high manufacturing agglomeration, non-provincial capital/non-municipal status, high industrial intelligence levels, and less sophisticated industrial structure. This research not only provides valuable policy insights for China to enhance urban land green use efficiency and promote high-quality regional sustainable development but also offers meaningful references for global efforts toward advancing urban sustainability. Full article

The age of entrepreneurship calls for the evolving of learning and development (L&D) models to meet the dynamic demands of innovation, sustainability, and technology innovation. This study examines the trends and issues of L&D models for entrepreneurs, more so focusing on how these models influence business success in a rapidly changing global landscape. The research employs bibliometric analysis, VOSviewer cluster analysis, and co-citation analysis to explore the literature from 1994 to 2024. Data collected from the Web of Science Core Collection database reflect significant trends in entrepreneurial L&D, with particular emphasis on the use of digital tools, sustainability processes, and governance systems. Findings emphasize the imperative role of L&D in fostering entrepreneurship, more so in areas such as digital transformation and the adoption of new technologies. The study also identifies central regions propelling this field, such as UK and USA. Future studies will be centered on the role of digital technologies, innovation, and green business models within entrepreneurial L&D frameworks. This study provides useful insight into the future of L&D within the entrepreneurial domain, guiding academia and companies alike in the planning of effective learning strategies to foster innovation and sustainable business growth. Full article

In order to fulfill the commitment to the “dual carbon goal” at an early date, China has implemented a series of carbon control policies. However, the actual impact of these policy combinations on green innovation in Chinese cities remains unknown. Taking the implementation of the low-carbon pilot policy (LCP) and the carbon emission trading pilot policy (CET) as the research opportunity, this paper uses panel data from 276 prefecture-level cities and a multiple-period difference-in-differences (DID) model to explore the impact of carbon control policy combination on green innovation in China and their mechanisms. The results indicate the following: A single LCP or CET can significantly boost green innovation. However, the impact of cross-tool carbon control policy combination on green innovation is notably greater than that of a single policy, with a trend of increasing effectiveness over time. Even after a series of robustness tests, this conclusion remains valid. Heterogeneity analysis shows that the promotion effect is more significant in the eastern region and high-level administrative cities. The policy combination incentivizes green innovation through fiscal technology expenditure and public environmental awareness, focusing more on fostering strategic green innovation. Consequently, the Chinese government should tailor policy combinations to specific contexts, expand their implementation judiciously, and consistently drive forward green innovation. Full article

Government support has long been viewed as a key driver of sustainable transformation and green technological progress. However, the underlying mechanisms (“how”) through which preferential policies influence green innovation, as well as the contextual conditions (“when”) that shape their effectiveness, remain insufficiently understood. Drawing on resource dependence theory, this study develops a dual-mediation framework to investigate how preferential tax policies promote both the quantity and quality of green innovation—by enhancing R&D investment as an internal mechanism and alleviating financing constraints as an external mechanism. These effects are especially salient among non-state-owned enterprises, firms in resource-constrained industries, and those situated in environmentally challenged regions—contexts that entail higher dependence on external support for sustainable development. Leveraging China’s 2017 R&D tax reduction policy as a quasi-natural experiment, this study uses a sample of high-tech small- and medium-sized enterprises (SMEs) to test the hypotheses. The findings provide robust evidence on how preferential policies contribute to corporate sustainability through green innovation and identify the conditions under which policy tools are most effective. This research offers important implications for designing targeted, sustainability-oriented innovation policies that support SMEs in transitioning toward more sustainable practices. Full article

This study focuses on the impact of China’s digital economy on sustainable modernization from 2011 to 2021, using provincial panel data for empirical analysis. By applying threshold and mediation models, we find that the digital economy promotes modernization through industrial upgrading (with a mediating effect of 38%) and trade openness (coefficient = 0.234). The research reveals “U-shaped” nonlinear threshold effects at specific levels of digital development (2.218), market efficiency (9.212), and technological progress (12.224). Eastern provinces benefit significantly (coefficient ranging from 0.12 to 0.15 ***), while western regions initially experience some inhibition (coefficient = −0.08 *). Industrial digitalization (coefficient = 0.13 ***) and innovation ecosystems (coefficient = 0.09 ***) play crucial roles in driving eco-efficiency and equity, in line with Sustainable Development Goals 9 and 13. Meanwhile, the impacts of infrastructure (coefficient = 0.07) and industrialization (coefficient = 0.085) are delayed. Economic modernization improves (coefficient = 0.37 ***), yet social modernization declines (coefficient = −0.12 *). This study not only enriches economic theory but also extends the environmental Kuznets curve to the digital economy domain. We propose tiered policy recommendations, including the construction of green digital infrastructure, carbon pricing, and rural digital transformation, which are applicable to China and offer valuable references for emerging economies aiming to achieve inclusive low-carbon growth in the digital era. Future research could further explore the differentiated mechanisms of various digital technologies in the modernization process across different regions and how to optimize policy combinations to better balance digital innovation with sustainable development goals. Full article

This study investigates the mechanisms by which green finance mitigates non-point source pollution. Based on provincial panel data from China spanning 2005 to 2023, this study conducts an empirical analysis that yields several key findings: (1) The development of green finance significantly reduces the intensity of agricultural non-point source pollution. (2) Green finance indirectly contributes to pollution reduction by incentivizing farmers to adopt environmentally sustainable production practices. (3) The pollution control effects of green finance are amplified in regions with advanced digital infrastructure. (4) The impact of green finance on agricultural pollution demonstrates a threshold effect associated with regional innovation capacity—only when innovation capability exceeds a certain threshold does the emission reduction effect of green finance become evident. Theoretically, this study broadens the research dimensions of green finance by integrating farmer behavioral factors and revealing boundary conditions related to technology and innovation. Policy implications include the need to tailor green financial products for agriculture, accelerate the development of rural digital infrastructure, and implement innovation-driven differentiated policies to enhance precision. Full article

In the digital economy era, digitalization and intelligent technologies have profoundly influenced regional green development. This study uses data from 277 prefecture-level and above cities in China spanning the years 2011 to 2022 and employs a two-way fixed effects model along with machine learning techniques to explore the effect of digital intelligence on regional green development. We find that digital intelligence primarily drives regional green development. Positive impacts show a steady upward trend from 2011 to 2022 and predominate in eastern regions, large cities, and non-resource-dependent cities, while adverse effects are more prevalent in small and resource-dependent cities. Effect magnitude scales with green development levels, exhibiting monotonic amplification. Mechanism tests indicate that digital intelligence improves regional green development by promoting green technological innovation, advancing the industrial structure, and strengthening environmental protection. Full article

China’s national strategies emphasize both achieving carbon peaking and neutrality (“dual carbon” objectives) and fostering high-quality economic development. This dual focus highlights the critical importance of the Green and Low-Carbon Transition (GLCT) of the economy and the development of New Quality Productive Forces (NQPF). Firms are central actors in this transformation, prompting the core research question: How does corporate engagement in GLCT contribute to the formation of NQPF? We investigate this relationship using panel data comprising 33,768 firm-year observations for A-share listed companies across diverse industries in China from 2012 to 2022. Corporate GLCT is measured via textual analysis of annual reports, while an NQPF index, incorporating both tangible and intangible dimensions, is constructed using the entropy method. Our empirical analysis relies primarily on fixed-effects regressions, supplemented by various robustness checks and alternative econometric specifications. The results demonstrate a significantly positive relationship: corporate GLCT robustly promotes the development of NQPF, with dynamic lag structures suggesting delayed productivity realization. Mechanism analysis reveals that this effect operates through three primary channels: improved access to financing, stimulated collaborative innovation and enhanced resource-allocation efficiency. Heterogeneity analysis indicates that the positive impact of GLCT on NQPF is more pronounced for state-owned enterprises (SOEs), firms operating in high-emission sectors, those in energy-efficient or environmentally friendly industries, technology-intensive sectors, non-heavily polluting industries and companies situated in China’s eastern regions. Overall, our findings suggest that corporate GLCT enhances NQPF by improving resource-utilization efficiency and fostering innovation, with these effects amplified by specific regional advantages and firm characteristics. This study offers implications for corporate strategy, highlighting how aligning GLCT initiatives with core business objectives can drive NQPF, and provides evidence relevant for policymakers aiming to optimize environmental governance and foster sustainable economic pathways. Full article

Amid mounting global climate change, resource scarcity, and environmental pressures, regional economies are accelerating their transition towards green and inclusive growth models. This research examines how China’s New Energy Demonstration City (NEDC) policy influences inclusive green growth (IGG), including its underlying mechanisms. Harnessing policy interventions as quasi-natural experiments, we use 2006–2022 panel datasets of 284 Chinese cities to develop a spatial difference-in-differences (SDID) model for causal inference. The findings are as follows: (1) The NEDC policy significantly enhances IGG in pilot cities while generating positive spatial spillover effects on neighboring cities, exhibiting an inverted U-shaped pattern; (2) The policy effects demonstrate pronounced regional heterogeneity, with the strongest impact observed in western China; (3) Mechanism analysis confirms that green technology innovation serves as a critical pathway through which the NEDC policy drives IGG. These findings provide robust empirical evidence for designing scalable policy promotion mechanisms and refining innovation-driven governance frameworks. Full article