Search Results (1,027)

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

In time series analysis, the ability to effectively handle data with varying sampling rates is crucial for accurate modeling and analysis. This paper presents the MR-BoF (Multi-Resolution Bag of Functions) framework, which leverages sampling-rate-independent techniques to decompose time series data while accommodating signals with differing resolutions. Unlike traditional methods that require uniform sampling frequencies, the BoF framework employs a flexible encoding approach, allowing for the integration of multi-resolution time series. Through a series of experiments, we demonstrate that the BoF framework ensures the precise reconstruction of the original data while enhancing resampling capabilities by utilizing decomposed components. The results show that this method offers significant advantages in scenarios involving irregular sampling rates and heterogeneous acquisition systems, making it a valuable tool for applications in fields such as finance, healthcare, industrial monitoring, IoT networks, and sensor networks. Full article

Recent theoretical and empirical studies highlight that information asymmetry and owner–manager conflict of interest can distort corporate investment decisions. Building on this premise, we hypothesize that superior environmental, social, and governance (ESG) performance mitigates these frictions by (H1) alleviating financing constraints and (H2) intensifying external analyst scrutiny. To test these hypotheses, we examine all Shanghai and Shenzhen A-share non-financial firms from 2009 to 2023. Using panel fixed-effects and two-stage least squares with an industry–province–year instrument, we find that higher ESG performance significantly reduces investment inefficiency; the effect operates through both lower financing constraints and greater analyst coverage. Heterogeneity analyses reveal that the improvement is pronounced in small non-state-owned, non-high-carbon firms but absent in large state-owned high-carbon emitters. These findings enrich the literature on ESG and corporate performance and offer actionable insights for regulators and investors seeking high-quality development. Full article

Artificial Intelligence (AI) and Generative AI are transformative yet double-edged technologies with evolving risks. While research emphasises trustworthy, fair, and responsible AI by focusing on its “what” and “why,” it overlooks practical “how.” To bridge this gap in financial services, an industry at the forefront of AI adoption, this study employs a qualitative approach grounded in existing Responsible AI and Corporate Digital Responsibility (CDR) frameworks. Through thematic analysis of 15 semi-structured interviews conducted with professionals working in finance, we illuminate nine non-technical barriers that practitioners face, such as sustainability challenges, trade-off balancing, stakeholder management, and human interaction, noting that GenAI concerns now eclipse general AI issues. CDR practitioners adopt a more human-centric stance, emphasising consensus-building and “no margin for error.” Our findings offer actionable guidance for more responsible AI strategies and enrich academic debates on Responsible AI and AI-CDR symbiosis. 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

From the perspective of credit ratings, this paper studies the impact of air pollution on corporate credit costs and the impact mechanism. Based on 2007–2022 data on A-share listed companies in the Chinese capital market, this paper uses a two-way fixed effects model to examine the impact of air pollution on corporate credit costs and the impact mechanism. The results show that air pollution increases the credit costs for enterprises because air pollution affects the sentiment of rating analysts, leading them to give more pessimistic credit ratings to enterprises located in areas with severe air pollution. The moderating effect analysis reveals that the effect of air pollution on the increase in corporate credit costs is more pronounced for high-polluting industries, manufacturing industries, and regions with weaker bank competition. Further analysis reveals that in the face of rising credit costs caused by air pollution, enterprises tend to adopt a combination strategy of increasing commercial credit financing and reducing the commercial credit supply to cope. Although this response behavior alleviates corporations’ own financial pressure, it may have a negative effect on supply chain stability. This paper provides new evidence that reveals that air pollution is an implicit cost in the capital market, enriching research in the fields of environmental governance and capital markets. Full article

As sustainability becomes a strategic priority across global financial services, its implementation in emerging insurance markets remains insufficiently understood. This study explores the integration of environmental, social, and governance (ESG) principles within Saudi Arabia’s insurance sector, combining content analysis of corporate disclosures with qualitative insights from industry stakeholders. The research investigates how insurers embed ESG principles into their operations, the development of sustainable insurance products, and their perceived financial and regulatory implications. The findings reveal gradual progress in ESG integration, primarily driven by governance reforms aligned with national development agendas, while social and environmental dimensions remain comparatively underdeveloped. Stakeholders identify regulatory ambiguity, data limitations, and technical capacity as persistent barriers, but also point to increasing investor and consumer interest in sustainability-aligned offerings. This study offers policy and managerial recommendations to advance ESG principle adoption, emphasizing standardized disclosures, capacity-building, and product innovation. It contributes to the limited empirical literature on ESG principles in Middle Eastern insurance markets and highlights the sector’s potential role in promoting inclusive and sustainable finance. Full article

This study investigates the impact and mechanisms of digital inclusive finance (DIF) on high-quality economic development in China. Drawing on panel data from 281 prefecture-level cities between 2011 and 2021, we employ a Spatial Durbin Model (SDM) to analyze both the direct effects and spatial spillovers of DIF. The results indicate that (1) DIF has a significantly positive effect on high-quality development, which remains robust after conducting various stability and endogeneity tests; (2) DIF strongly contributes to economic upgrading in eastern regions, while its impact is weaker or even negative in central and western regions, revealing notable regional disparities exist; (3) a key finding is the identification of a double-threshold effect, suggesting that the positive influence of DIF only emerges when financial and industrial development surpass certain thresholds; (4) results from the two-regime SDM further show that spillover effects are more prominent in non-central cities than in central ones; and (5) mechanism analysis reveals that DIF facilitates high-quality growth primarily by promoting industrial structure upgrading. These findings underscore the importance of region-specific policy strategies to enhance the role of DIF and reduce spatial disparities in development across China. Full article

Against the background of intensifying human–land contradictions, evaluation of land use benefits and identification of obstacles are crucial for sustainable land management and socioeconomic development. Taking Henan Province as an example, this research employed the entropy weight method and TOPSIS model to assess the land use benefits across its cities from 2011 to 2020, a period of rapid land use transformation, analyzed their spatiotemporal evolution, and identified key obstacles via an obstacle degree model. The results showed the following. (1) The social land use benefits consistently exceeded the ecological and economic benefits, with steady improvements observed in both the individual and comprehensive benefits. Spatially, the benefits showed a “one city dominant” pattern, decreasing gradually from the central region to the south, north, east, and west, with this spatial gradient further intensifying over time. (2) Economic factors were the primary obstacles, with significantly higher obstruction degrees than social or ecological factors. The main obstacles were the general budget revenue of government finance per unit land area, domestic garbage removal volume, and total retail sales of social consumer goods per unit land area. (3) The policy implications focus on strengthening regional differentiated development by leveraging Zhengzhou’s core role to boost the land-based economic benefits, integrating social–ecological strengths with agricultural modernization, and promoting “core–periphery linkage” to narrow gaps through targeted industrial and infrastructure strategies. This study could provide region-specific insights for sustainable land management in agricultural provinces undergoing rapid urbanization. Full article

This study investigates the effect of the Green Finance Reform Pilot Zone Policy (GFRPZP) on corporate sustainable development performance (SDP) using a multi-period difference-in-differences (DIDs) regression model. This model incorporates control variables, reflecting firm-level characteristics and regional economic conditions. The results show that GFRPZP significantly enhances corporate SDP, with stronger effects observed among non-state-owned enterprises (Non-SOEs), companies situated in eastern regions, those in non-heavily polluting industries, and high-tech companies. Mediation analysis indicates that the policy enhances sustainable development through four main channels: improving the quality and quantity of green innovation, easing financing constraints, and increasing analyst attention. Moderation analysis further demonstrates that digital transformation and internal control strengthen the policy’s effect. 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

To respond to China’s sustainable development goals, this study uses a dynamic panel data set (2009–2023) and the PSM-DID model to examine how blockchain adoption impacts corporate sustainable development performance (CSDP). The results show that blockchain significantly enhances CSDP by 9.8–12.3%, primarily through two channels (reducing financing constraints by improving transparency and decreasing chairman-CEO duality) to optimize governance. Regional environmental regulation strengthens this relationship. Heterogeneity analysis reveals stronger impacts in unregulated industries, private firms, and central–western regions, while state-owned firms show policy-driven governance improvements. The study enriches the understanding of blockchain’s dual role in balancing efficiency and sustainability, offering insights for integrating digital technology into green policy frameworks. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. To address these challenges, Artificial Intelligence and machine learning have emerged as powerful tools for enhancing the accuracy, adaptability, and automation of IDS solutions. This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. The framework is deployed using a Flask-based web interface in the Amazon Elastic Compute Cloud environment, capturing live network traffic and offering sub-second inference with visual alerts. Experimental evaluations using the NSL-KDD dataset demonstrate that the ensemble model outperforms individual classifiers, achieving a high accuracy of 99.40%, along with excellent precision, recall, and F1-score metrics. This research not only enhances detection capabilities but also bridges the trust gap in AI-powered security systems through transparency. The solution shows strong potential for application in critical domains such as finance, healthcare, industrial IoT, and government networks, where real-time and interpretable threat detection is vital. Full article

The coupled development of economic and ecological systems is crucial for achieving sustainable growth, with the financial system playing a pivotal adaptive role. Green financial innovation (GFI) is central to enhancing this adaptation. Urban land use eco-efficiency (ULUEE) serves as an effective measure of economic–ecological coupling. Using China’s Green Finance Reform and Innovation Pilot Zones (GFRPZs) as a quasi-natural experiment, this study assesses the impact of GFI on ULUEE, employing panel data from 283 prefecture-level cities (2013–2021). The results show that GFI significantly enhances ULUEE through technological spillovers, strengthened environmental regulation, industrial upgrading, and resource agglomeration. Heterogeneity analyses further reveal that GFI’s positive effects are more pronounced in economically developed regions, cities without legacy heavy-industry reliance, and those with deeper financial development. Additionally, GFI demonstrates cross-regional spillover effects, effectively interacting with other environmental policies. While GFI’s impact is more pronounced in economic growth, its ecological governance improvements are modest. This study provides critical insights for tailored green financial policies aimed at harmonizing economic and ecological objectives. Full article