Search Results (548)

With the increasing global emphasis on sustainable development goals, and in the context of pursuing high-quality sustainable development of the economy and enterprises, this study empirically examines the effect of digital finance on corporate financing constraints and the impact on corporate green innovation with a sample of China’s A-share-listed companies in the period of 2011–2020 and explores the issue from the perspectives of management power and corporate social responsibility (CSR) at the micro level of enterprises. The empirical results show that digital finance can indeed alleviate corporate financing constraints. Still, the synergistic effect of the two on corporate green innovation produces a “quantitative and qualitative separation” effect, which only promotes the enhancement of iconic green innovation, and the effect on substantive green innovation is not obvious. The power of management and CSR performanceshave different moderating roles in the alleviation of financing constraints by the empowerment of digital finance. Management power and corporate social responsibility have different moderating effects on digital financial empowerment to alleviate financing constraints. The findings of this study enrich the research in related fields and provide more basis for the promotion of digital financial policies and more solutions for the high-quality development of enterprises. Full article

This paper examines the changing role of emissions trading systems (ETSs) within the macro-financial framework of energy markets, emphasizing price dynamics and systemic spillovers. Utilizing monthly data from seven ETS jurisdictions spanning January 2021 to December 2024 (N = 287 observations after log transformation and first differencing), which includes four auction-based markets (United States, Canada, United Kingdom, South Korea), two secondary markets (China, New Zealand), and a government-set fixed-price scheme (Germany), this research estimates a panel vector autoregression (PVAR) employing a Common Correlated Effects (CCE) model and augments it with machine learning analysis utilizing XGBoost and explainable AI methodologies. The PVAR-CEE reveals numerous unexpected findings related to carbon markets: ETS returns exhibit persistence with an autoregressive coefficient of −0.137 after a four-month lag, while increasing inflation results in rising ETS after the same period. Furthermore, ETSs generate spillover effects in the real economy, as elevated ETSs today forecast a 0.125-point reduction in unemployment one month later and a 0.0173 increase in inflation after two months. Impulse response analysis indicates that exogenous shocks, including Brent oil prices, policy uncertainty, and financial volatility, are swiftly assimilated by ETS pricing, with effects dissipating completely within three to eight months. XGBoost models ascertain that policy uncertainty and Brent oil prices are the most significant predictors of one-month-ahead ETSs, whereas ESG factors are relevant only beyond certain thresholds and in conditions of low policy uncertainty. These findings establish ETS markets as dynamic transmitters of macroeconomic signals, influencing energy management, labor changes, and sustainable finance under carbon pricing frameworks. Full article

A multilayer linear response model (MLRM) is proposed to generate time-series data based on linear response theory. The proposed MLRM is designed to generate data for anomalous dynamics by extending the conventional single-layer linear response model (SLRM) into multiple layers. While the SLRM is a linear equation with respect to external forces, the MLRM introduces nonlinear interactions, enabling the generation of a wider range of dynamics. The MLRM is applicable to various fields, such as finance, as it does not rely on machine learning techniques and maintains interpretability. We investigated whether the MLRM could generate anomalous dynamics, such as those observed during the coronavirus disease 2019 (COVID-19) pandemic, using pre-pandemic data. Furthermore, an analysis of the log returns and realized volatility derived from the MLRM-generated data demonstrated that both exhibited heavy-tailed characteristics, consistent with empirical observations. These results indicate that the MLRM can effectively reproduce the extreme fluctuations and tail behavior seen during high-volatility periods. Full article

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

Data annotation serves as a critical foundation for artificial intelligence (AI) and machine learning (ML). Recently, AI agents powered by large language models (LLMs) have emerged as effective solutions to longstanding challenges in data annotation, such as scalability, consistency, cost, and limitations in domain expertise. These agents facilitate intelligent automation and adaptive decision-making, thereby enhancing the efficiency and reliability of annotation workflows across various fields. Despite the growing interest in this area, a systematic understanding of the role and capabilities of AI agents in annotation is still underexplored. This paper seeks to fill that gap by providing a comprehensive review of how LLM-driven agents support advanced reasoning strategies, adaptive learning, and collaborative annotation efforts. We analyze agent architectures, integration patterns within workflows, and evaluation methods, along with real-world applications in sectors such as healthcare, finance, technology, and media. Furthermore, we evaluate current tools and platforms that support agent-based annotation, addressing key challenges such as quality assurance, bias mitigation, transparency, and scalability. Lastly, we outline future research directions, highlighting the importance of federated learning, cross-modal reasoning, and responsible system design to advance the development of next-generation annotation ecosystems. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

The exponential increase in textual data generated across sectors such as healthcare, finance, and smart manufacturing has intensified the need for effective Big Data analytics. Large language models (LLMs) have become critical tools because of their advanced language processing capabilities. However, their static nature limits their ability to incorporate real-time and domain-specific knowledge. Retrieval-augmented generation (RAG) addresses these limitations by enriching LLM outputs through external content retrieval. Nevertheless, traditional RAG systems remain inefficient, often exhibiting high retrieval latency, redundancy, and diminished response quality when scaled to large datasets. This paper proposes an innovative structured RAG framework specifically designed for large-scale Big Data analytics. The framework transforms unstructured partial prompts into structured semantically coherent partial prompts, leveraging element-specific embedding models and dimensionality reduction techniques, such as principal component analysis. To further improve the retrieval accuracy and computational efficiency, we introduce a multi-level filtering approach integrating semantic constraints and redundancy elimination. In the experiments, the proposed method was compared with structured-format RAG. After generating prompts utilizing two methods, silhouette scores were computed to assess the quality of embedding clusters. The proposed method outperformed the baseline by improving the clustering quality by 32.3%. These results demonstrate the effectiveness of the framework in enhancing LLMs for accurate, diverse, and efficient decision-making in complex Big Data environments. 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

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

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

Prior research often portrays Corporate Social Responsibility (CSR) as a coercive institutional force compelling firms to passively conform for legitimacy. More recent studies, however, suggest firms actively pursue CSR to gain sustainable competitive advantages. Yet, how and when CSR buffers firms against adverse shocks of crises remains insufficiently understood. This study addresses this gap by using multiple regression analysis to examine the buffering effects of CSR investments during the COVID-19 crisis, which severely disrupted capital markets and firm valuation. Drawing on signaling theory and CSR literature, we analyze the stock market performance of China’s A-share listed firms using a sample of 2577 observations as of the end of 2019. Results indicate that firms with higher CSR investments experienced significantly greater cumulative abnormal returns during the pandemic. Moreover, the buffering effect is amplified among firms with higher debt burdens, greater financing constraints, and those operating in regions with stronger social trust and more severe COVID-19 impact. These findings are robust across multiple robustness checks. This study highlights the strategic value of CSR as a resilience mechanism during crises and supports a more proactive view of CSR engagement for sustainable development, complementing the traditional legitimacy-focused perspective in existing literature. Full article

This paper highlights the importance of accounting for both the maturity structure of government debt and the composition of fiscal instruments when studying the macroeconomic effects of fiscal policy. Using a dynamic stochastic general equilibrium (DSGE) model featuring a debt maturity structure and six exogenous fiscal shocks spanning both the expenditure and revenue sides, we show that long-maturity debt systematically weakens the expansionary effects of fiscal policy under dovish monetary policy, particularly in response to increases in government purchases, government investment, and capital income tax cuts, where long-term financing leads to the significant crowding-out of private activity. In contrast, short-term debt financing yields output multipliers that often exceed unity. The maturity structure also alters the relative efficacy of fiscal instruments: while labor income tax cuts produce the largest multipliers under short-term debt, government purchases become more potent under long-term debt financing. We also show that the stark difference between short- and long-term debt becomes muted under a hawkish monetary regime. Our results have important policy implications, suggesting that the maturity composition of public debt should be carefully considered in the design of fiscal policy, particularly in high-debt economies. Full article

Achieving sustainable economic growth requires a careful balance between public debt accumulation and the macroeconomic stability necessary for long-term development. While public debt can support growth through productive public investment, excessive debt may crowd out private investment, raise borrowing costs, and undermine financial stability, ultimately threatening economic sustainability. In this context, the quality of institutions plays a pivotal moderating role by fostering responsible debt management and ensuring that debt-financed investments contribute to sustainable development. In this context, this study investigates the relationship between public debt and economic growth, with a focus on the moderating role of institutional quality (IQ). Utilizing an unbalanced panel of 115 countries over the period from 1996 to 2021, this study tests the hypothesis that robust institutional frameworks mitigate the negative impact of public debt on economic growth. To address potential endogeneity, this study employs the dynamic system Generalized Method of Moments (GMM) estimation technique. The results reveal that, although the direct effect of public debt on economic growth is negative, the interaction between public debt and IQ yields a positive influence. Furthermore, the results indicate the presence of a threshold beyond which public debt begins to exert a beneficial effect on economic growth, whereas its impact remains adverse below this threshold. These findings underscore the critical importance of sound debt management strategies and institutional development for policymakers, suggesting that effective government governance is essential to harnessing the potential positive effects of public debt on economic growth. Full article

The effective design of green financial policies is crucial for balancing the operational pressures of heavily polluting enterprises with the goal of sustained carbon emission reduction. This study investigates the impact of the Green Finance Reform and Innovation Pilot Zone (GFRIPZ) policy by employing a multi-period difference-in-differences (DID) model based on firm-level panel data from 2012 to 2021, covering A-share listed enterprises in Shanghai and Shenzhen. The results show that GFRIPZs significantly reduced carbon emissions in pilot regions, with heterogeneous effects observed across enterprise types—particularly among large enterprises, state-owned enterprises, and those located in financially developed areas. To uncover the underlying mechanisms, we compare two behavioral responses: green innovation, marked by long-term investment in green technologies, and expedient compliance, involving short-term, strategic compliance behaviors. Our findings indicate that GFRIPZs did not effectively promote green innovation. Instead, it has encouraged a shift from productive capital investment toward un-productive, symbolic actions aimed at fulfilling policy requirements. These responses risk undermining the long-term objective of green transformation and may contribute to a broader shift from real economic activity toward speculative or less productive investments, raising concerns about the quality and sustainability of the low-carbon transition. Full article

As digital connectivity transforms financial decision-making, this study offers one of the first empirical investigations into the relationship between social media use and socially responsible investing (SRI). Using data from the 2021 National Financial Capability Study, multinomial regression analysis was used to explore whether people who rely on social media for investment decisions were more likely to invest in ways that reflect their values. The results show that investors who use social media for investment information are more likely to value being socially responsible as an important reason for investing. Younger, less experienced, and more risk-tolerant investors were especially likely to follow SRI strategies, and certain platforms like Twitter were more associated with SRI interest than others. These findings suggest that social media is not just a platform for sharing information; it may also shape how people think about investing and the role their money can play in making a societal difference. As online platforms continue to influence financial behavior, understanding their impact on values-based investing becomes increasingly important. This research contributes novel insights to the emerging intersection of social media, behavioral finance, and values-driven investing. Full article