Search Results (252)

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

As the labor market has tightened and businesses have increased their part-time and hourly wages, recruiting and retaining high-quality college students to work on campus in part-time and hourly roles has remained a stubbornly problematic issue. As a result, this study provides a unique perspective into the minds of student workers by leveraging NASPA/ACPA’s professional competency areas and Tull’s synergistic supervision as conceptual and theoretical frameworks to understand how a subset of college student workers view supervisors in recruiting and retaining them, as well as providing pre-professional development opportunities. Through semi-structured qualitative focus groups with 54 college students working as peer financial mentors within student affairs units, qualitative data suggest that student workers view supervisors as critical to their recruitment if the work is positioned as flexible and career-oriented. Moreover, student workers appreciated supervisors who promised and then delivered professional development during employment, preparing them for the workforce. Additionally, student workers want and need a supervisor who builds a professional relationship with them and who empowers them to develop a sense of confidence through their work. Implications for student affairs research, policy, and practice are addressed. Full article

This study investigates whether internationally diversified firms substitute between accrual-based and real earnings management and examines how institutional supervision moderates this relationship. Drawing on a sample of Taiwanese firms listed on the Taiwan Stock Exchange from 2003 to 2016, we conduct regression analyses to test our hypothesis. We find that internationally diversified firms actively shift between accrual and real earnings management strategies depending on the constraints they face. Specifically, firms tend to rely more on accrual-based manipulation when information asymmetry is high and switch to real earnings management when accruals are more easily detected. We also show that stronger institutional supervision—measured by information transparency and investor protection—significantly curbs accrual-based earnings management. These findings reflect the higher volatility and agency problems associated with international operations, such as exposure to foreign risks and the distance between parent and subsidiary firms. By highlighting the conditions under which firms manage earnings and the supervisory mechanisms that constrain such behavior, this study offers practical insights for managers seeking to smooth earnings, investors aiming to evaluate firm transparency, and policymakers designing regulations to deter opportunistic financial reporting. Full article

To promote the deepening of reform and the effective implementation of policies, the State Council launched the special supervision of the liquidation of local governments’ arrears in project funds in 2016, which supports the optimization of the government debt structure. Based on the quasi-natural experiment of the special supervision action, in this study, we use the difference-in-difference (DID) method to investigate the effect and mechanism of the optimization of the government debt structure on the financing constraints of private enterprises. This research is particularly relevant for private enterprises, which face acute financing challenges and are critical for promoting inclusive economic growth, employment, and innovation—key pillars of sustainable development. The results are as follows. Firstly, the special supervision significantly reduces the financing constraints of private enterprises. Secondly, it has heterogeneous effects on the financing constraints of different types of enterprises, and the alleviating effect is particularly significant for enterprises that rely on the funding support of local governments. This highlights the importance of institutional reforms in fostering equitable access to financial resources for vulnerable enterprise groups such as private enterprises. Thirdly, the optimization of the government debt structure eases enterprises’ financing constraints by improving their capital turnover and trade credit. By enhancing liquidity and creditworthiness, these changes create a more resilient financial environment for private enterprises, supporting their long-term development and contribution to sustainable economic systems. Full article

This study examines the role of supervisory frameworks in shaping the risk management behavior of Greece’s four systemic banks during the period of 2015–2024. It explores how regulatory reforms under Capital Requirements Regulation II, Basel III, and European Central Bank oversight influenced capital adequacy, asset quality, and liquidity metrics. Employing a quantitative methodology, this study analyzes secondary data from Pillar III disclosures, annual financial reports, and supervisory statements. Key risk indicators (capital adequacy ratio, non-performing exposure ratio, liquidity coverage ratio, and risk-weighted assets) are evaluated in conjunction with regulatory interventions, such as International Financial Reporting Standards 9 transitional relief, the Hercules Asset Protection Scheme, and European Central Bank liquidity measures. The findings reveal that enhanced supervision contributed to improved resilience and regulatory compliance. International Financial Reporting Standards 9 transitional arrangements were pivotal in maintaining capital thresholds during stress periods. Supervisory flexibility and extraordinary European Central Bank support measures helped banks absorb shocks and improve risk governance. Differences across banks highlight the impact of institutional strategy on regulatory performance. This study offers a rare longitudinal assessment of supervisory influence on bank risk behavior in a high-volatility Eurozone context. Covering an entire decade (2015–2024), it uniquely links institutional strategies with evolving regulatory frameworks, including crisis-specific interventions such as International Financial Reporting Standards 9 relief and asset protection schemes. The results provide insights for policymakers and regulators on how targeted supervisory interventions and transitional mechanisms can enhance banking sector resilience during protracted crises. Full article

This study examines the degree of alignment between the actual environmental performance and the ESG disclosures of 365 listed financial institutions in Europe for the fiscal year 2024. Although ESG reporting has become a standard practice in the financial sector, there are still concerns that the quality of the disclosure may not accurately reflect substantive environmental action, which increases the risk of greenwashing. This study addresses this issue by incorporating both ESG disclosure indicators and green accounting metrics into a multi-criteria decision-making framework. This framework is supported by entropy-based weighting to assure objectivity in criterion importance, as outlined in the PROMETHEE II method. The Greenwashing Risk Index (GWI) is a groundbreaking innovation that quantifies the discrepancy between an institution’s classification based on ESG transparency and its performance in green accounting indicators, including environmental penalties, provisions, and resource usage. The results indicate that there is a substantial degree of variation in the performance of ESGs among institutions, with a significant portion of them exhibiting high disclosure scores but insufficient environmental substance. These discrepancies indicate that reputational sustainability may not be operationally sustained. The results have significant implications for regulatory supervision, sustainable finance policy, and ESG rating methodologies. The framework that has been proposed provides a replicable, evidence-based tool for identifying institutions that are at risk of greenwashing and facilitates the implementation of more accountable ESG evaluation practices in the financial sector. Full article

This paper investigates the spillover effects and transmission networks of systemic risk within China’s national economic sectors under extreme conditions from both time and frequency domain perspectives, building upon the spillover index methodology and calculating the ∆CoVaR index for Chinese industries. The findings indicate the following: (1) Extreme-risk spillovers synchronize across industries but exhibit pronounced time-varying peaks during the 2008 Global Financial Crisis, the 2015 crash, and the COVID-19 pandemic. (2) Long-term spillovers dominate overall connectedness, highlighting the lasting impact of fundamentals and structural linkages. (3) In terms of risk volatility, Energy, Materials, Consumer Discretionary, and Financials are most sensitive to systemic market shocks. (4) On the risk spillover effect, Consumer Discretionary, Industrials, Healthcare, and Information Technology consistently act as net transmitters of extreme risk, while Energy, Materials, Consumer Staples, Financials, Telecom Services, Utilities, and Real Estate primarily serve as net receivers. Based on these findings, the paper suggests deepening the regulatory mechanisms for systemic risk, strengthening the synergistic effect of systemic risk measurement and early warning indicators, and coordinating risk monitoring, early warning, and risk prevention and mitigation. It further emphasizes the importance of avoiding fragmented regulation by establishing a joint risk prevention mechanism across sectors and departments, strengthening the supervision of inter-industry capital flows. Finally, it highlights the need to closely monitor the formation mechanisms and transmission paths of new financial risks under the influence of the pandemic to prevent the accumulation and eruption of risks in the post-pandemic era. Authorities must conduct annual “Industry Transmission Reviews” to map emerging risk nodes and supply-chain vulnerabilities, refine policy tools, and stabilize market expectations so as to forestall the build-up and sudden release of new systemic shocks. Full article

The current study examines the application of advanced machine learning (ML) techniques for forecasting credit risk in Islamic (participation) and traditional banks in the United Kingdom in 2010–2023. Leveraging an equally weighted panel dataset and guided by robust empirical literature, we integrate structural econometric modeling—i.e., the stochastic frontier approach (SFA) to measuring the Lerner index of market power—with current best-practice tree-based ML algorithms (CatBoost, XGBoost, LightGBM, and Random Forest) to predict non-performing loans (NPLs). The results show that bank-level financial performance measures, particularly loan ratio, profitability, and market power, outperform macroeconomic factors in forecasting credit risk. Among the models tested, CatBoost was more accurate and explainable, as confirmed by SHAP-based explainability analysis. The implications of the research have practical applications for risk managers, regulators, and policymakers in terms of valuing the explanatory power of explainable AI tools to enhance financial oversight and decision-making in post-crisis UK banking. Full article

Under the “Dual Carbon” strategy, collaborative governance of port atmospheric pollutants and carbon emissions is critical for low-carbon transformation. Focusing on Ningbo-Zhoushan Port (48% regional ship emissions), this study examines government, port enterprises, and public interactions. A tripartite evolutionary game model with numerical simulation reveals dynamic patterns and key factors. The results show the following: (1) A substitution effect exists between government incentive costs and penalty intensity—increased environmental governance budgets reduce the probability of government incentives, whereas higher public reporting rewards accelerate corporate emission reduction convergence. (2) Public supervision exhibits cyclical fluctuations due to conflicts between individual rationality and collective interests, with excessive reporting rewards potentially triggering free-rider behavior. (3) The system exhibits two stable equilibria: a low-efficiency equilibrium (0,0,0) and a high-efficiency equilibrium (1,1,1). The latter requires policy cost compensation, corporate emission reduction gains exceeding investments, and a supervision benefit–cost ratio greater than 1. Accordingly, the study proposes a three-dimensional “Incentive–Constraint–Collaboration” governance strategy, recommending floating penalty mechanisms, green financial instrument innovation, and community supervision network optimization to balance environmental benefits with fiscal sustainability. This research provides a dynamic decision-making framework for multi-agent collaborative emission reduction in ports, offering both methodological innovation and practical guidance value. Full article

The research and application of green fertilizers have long been constrained by financial and technical barriers. Farmers’ adoption of green fertilizers is also highly dependent on government policy support. As an intermediary organization bridging the government and farmers, the STB plays a crucial role in encouraging the use of green fertilizers. In order to explore the impact of the STB’s research and development investment, as well as government intervention on farmers’ green production behavior, this paper constructs a tripartite dynamic game model involving farmers, the STB, and the government. The study systematically analyzes the decision-making mechanisms of the different stakeholders and their evolutionary paths. The results show the following: (1) Under certain conditions, the system converges to two stable states: government withdrawal (1,1,0) and continued government participation (1,1,1). (2) Government intervention shows a phased decrease. As the green fertilizer production system matures, farmers and the STB gradually form a stable collaborative mechanism. At this stage, the government shifts from direct participation to a supervisory role, with its implementation coefficient increasing to between 0.75 and 1, indicating that government supervision becomes the primary mode of action. (3) The research and development efforts of the STB are influenced by both the intensity of government support and technological breakthroughs. During periods of high-intensity government support (with a research and development investment coefficient between 0.05 and 0.15), and when technological accumulation achieves a critical breakthrough, the growth rate of investment increases significantly (the coefficient jumps to 0.15–0.3). (4) Farmers’ demand for green fertilizers is stable and consistent, and the market-oriented collaboration between the STB and farmers tends to favor green production technology, which verifies the feasibility of the government’s withdrawal of functions in the later stage of the green agricultural transformation. This study provides a scientific basis for decision-making regarding the STB’s research and development of green fertilizers, while also laying a theoretical foundation for promoting the green transformation of farmers through green fertilizer innovation. Full article

Regulatory sandboxes have emerged as policy instruments designed to support FinTech innovation while maintaining supervisory oversight. By allowing firms to test financial products in controlled environments, sandboxes aim to reduce regulatory uncertainty and facilitate market entry. Despite their growing adoption, empirical evidence of their effectiveness remains limited, particularly in emerging markets. This study explores the impact of regulatory sandboxes on innovation and market access through a qualitative comparative case study of the United Kingdom, Singapore, and Hungary. Drawing on document analysis and thematic coding, the research evaluates sandbox design, regulatory support, and innovation outcomes across the three jurisdictions. Findings show that sandboxes enhance access to funding, accelerate product development, and foster regulator–firm collaboration. While the UK and Singapore benefit from mature ecosystems and structured frameworks, Hungary illustrates sandbox potential in developing markets. The paper contributes to FinTech regulation literature and provides policy recommendations for optimizing sandbox design across varied institutional contexts. Full article

Accurate gold price forecasting is essential for informed financial decision-making, as gold is sensitive to economic, political, and social factors. This study presents a hybrid framework for multivariate gold price prediction that integrates classical econometric modelling, traditional machine learning, modern deep learning methods, and their combinations. The framework incorporates financial, macroeconomic, and sentiment indicators, allowing it to capture complex temporal patterns and cross-variable relationships over time. Empirical validation on an eleven-year dataset (2014–2024) demonstrates the framework effectiveness across diverse market conditions. Results show that advanced supervised techniques outperform traditional econometric models under dynamic market environment. Key advantages of the framework include its ability to handle multiple data types, apply a structured variable selection process, employ diverse model families, and support model hybridisation and meta-modelling, providing practical guidance for investors, institutions, and policymakers. Full article

There is an ongoing debate about the role of income diversification in enhancing bank stability within the financial services industry in Europe. Some advocate for diversification, while others argue that its importance should not be overstated. Some financial institutions are encouraged to focus on their traditional investments instead of income diversification, while others suggest that income diversification can stabilize or destabilize, depending on the regulatory environment. These conflicting results indicate a lack of clear evidence regarding the effectiveness of income diversification. Therefore, this paper aims to study the impact of income diversification on bank stability and enhance the predictive performance of bank stability by analyzing the period from 2000 to 2021 using a sample from 26 European countries, based on aggregate bank data. It employs a hybrid method that combines econometric techniques, specifically the generalized method of moments and a fixed-effects model, with machine-learning algorithms such as Random Forest and Support Vector Machine. These methods are applied to enhance the reliability and predictive power of the analysis by addressing the problem of endogeneity (via generalized method of moments) and capturing non-linearities, interactions, and high-dimensional patterns (via machine learning). The econometric findings reveal that income diversification can reduce non-performing loans, improve bank solvency, and enhance the Z-score, indicating the significant role of income diversification in improving bank stability. Conversely, the results also show that the machine-learning algorithms used play a crucial role in enhancing the predictive performance of bank stability. Full article

To address the challenges of label sparsity and feature incompleteness in structured data, a self-supervised representation learning method based on multi-view consistency constraints is proposed in this paper. Robust modeling of high-dimensional sparse tabular data is achieved through integration of a view-disentangled encoder, intra- and cross-view contrastive mechanisms, and a joint loss optimization module. The proposed method incorporates feature clustering-based view partitioning, multi-view consistency alignment, and masked reconstruction mechanisms, thereby enhancing the model’s representational capacity and generalization performance under weak supervision. Across multiple experiments conducted on four types of datasets, including user rating data, platform activity logs, and financial transactions, the proposed approach maintains superior performance even under extreme conditions of up to 40% feature missingness and only 10% label availability. The model achieves an accuracy of 0.87, F1-score of 0.83, and AUC of 0.90 while reducing the normalized mean squared error to 0.066. These results significantly outperform mainstream baseline models such as XGBoost, TabTransformer, and VIME, demonstrating the proposed method’s robustness and broad applicability across diverse real-world tasks. The findings suggest that the proposed method offers an efficient and reliable paradigm for modeling sparse structured data. Full article

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative performance in the context of student loans has not been thoroughly investigated. This study aims to evaluate and compare the predictive effectiveness of four supervised learning models—such as Random Forest, Gradient Boosting, Support Vector Machine, and Deep Neural Network (implemented with PyTorch version 2.6.0)—in forecasting student credit eligibility. Primary data were collected from 1024 university students through structured surveys covering academic, financial, and personal variables. The models were trained and tested on the same dataset and evaluated using a comprehensive set of classification and regression metrics. The findings reveal that each model exhibits distinct strengths. Deep Learning achieved the highest classification accuracy (85.55%), while random forest demonstrated robust performance, particularly in providing balanced results across classification metrics. Gradient Boosting was effective in recall-oriented tasks, and support vector machine demonstrated strong precision for the positive class, although its recall was lower compared to other models. The study highlights the importance of aligning model selection with specific application goals, such as prioritizing accuracy, recall, or interpretability. It offers practical implications for financial institutions and universities in developing machine learning and deep learning tools for student loan eligibility prediction. Future research should consider longitudinal data, behavioral factors, and hybrid modeling approaches to further optimize predictive performance in educational finance. Full article