Search Results (759)

Sustainable investments in construction are essential for the development of communities and for reducing environmental impacts. This study analyzes two scenarios: rehabilitation of an existing building and construction of a new NZEB-compliant building, based on a life cycle cost–benefit analysis. The results show that both scenarios generate negative Net Present Values (NPVs) due to the social nature of the project, but the new NZEB building presents superior performance (NPV: USD –2.61 million vs. USD –3.05 million for rehabilitation) and lower operational costs (USD 1.49 million vs. USD 1.92 million over 30 years). Key financial indicators (IRR, CBR), sensitivity analysis, and discount rate variation support the conclusion that the NZEB scenario ensures greater economic resilience. This study highlights the relevance of extended LCCBA in guiding sustainable investment decisions in social infrastructure. Full article

The purpose of this research is to examine employee perceptions regarding the implementation of ESG (environmental, social, and governance) practices in financial institutions, with a comparative focus on urban and rural banks in the Republic of Serbia. The study investigates how employees assess environmental, social, and governance aspects of ESG, as well as their own role in applying these principles in everyday work. The results reveal statistically significant differences between the two groups; employees in urban banks report greater engagement, more access to training, and stronger involvement in ESG decision-making. These findings suggest the existence of more developed institutional support, infrastructure, and organisational culture in urban banks. In contrast, employees in rural banks highlight the need for enhanced training, clearer ESG guidance, and improved oversight mechanisms. The study underlines the importance of investing in employee development and internal communication, particularly in rural contexts, to improve ESG outcomes. By focusing on employee-level perceptions, this research contributes to the understanding of how organisational and geographic factors influence the implementation of ESG-related practices in financial institutions. Full article

Agrarian women are at the forefront of rural livelihoods increasingly affected by the frequency and severity of climate change impacts. However, their household livelihood resilience (HLR) remains limited due to gender-blind policies, scarce sex-disaggregated data, and inadequate consideration of gender-specific needs in resilience-building efforts. Grounded in participatory feminist research, this study employed a multi-method qualitative approach, including semi-structured interviews and oral history narratives, with 60 women in two climate-vulnerable provinces. Data were analyzed through thematic coding, CATWOE (Customers, Actors, Transformation, Worldview, Owners, Environmental Constraints) analysis, and descriptive statistics. The findings identify nine major climate-related events disrupting livelihoods and reveal a limited understanding of HLR as a long-term, transformative concept. Adaptation strategies remain short-term and focused on immediate survival. Barriers to HLR include financial constraints, limited access to agricultural resources and technology, and entrenched gender norms restricting women’s leadership and decision-making. While local governments, women’s associations, and community networks provide some support, gaps in accessibility and adequacy persist. Participants expressed the need for financial assistance, vocational training, agricultural technologies, and stronger peer networks. Strengthening HLR among agrarian women requires gender-sensitive policies, investment in local support systems, and community-led initiatives. Empowering agrarian women as agents of change is critical for fostering resilient rural livelihoods and achieving inclusive, sustainable development. 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

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary competitiveness were selected to quantitatively analyze the competitiveness of 26 French-speaking African countries. Results show that their comprehensive competitiveness exhibits spatial patterns of “high in the north and south, low in the east and west” and “high in coastal areas, low in inland areas”. Algeria, Morocco, and six other countries demonstrate high competitiveness, while Central African countries generally show low competitiveness. The adaptive LASSO algorithm identifies three key influencing factors, including the proportion of R&D expenditure to GDP, high-tech exports, and total reserves, as well as five secondary key factors, including the number of patent applications and total number of domestic listed companies, revealing that scientific and technological investment, financial strength, and innovation transformation capabilities are core constraints. Based on these findings, sustainable development strategies are proposed, such as strengthening scientific and technological research and development and innovation transformation, optimizing financial reserves and capital markets, and promoting China–Africa collaborative cooperation, providing decision-making references for competitiveness improvement and regional cooperation of French-speaking African countries under the background of the “Belt and Road Initiative”. Full article

Recent years have seen a rise in combining deep learning and technical analysis for stock price prediction. However, technical indicators are often prioritized over technical charts due to quantification challenges. While some studies use closing price charts for predicting stock trends, they overlook charts from other indicators and their relationships, resulting in underutilized information for predicting stock. Therefore, we design a novel framework to address the underutilized information limitations within technical charts generated by different indicators. Specifically, different sequences of stock indicators are used to generate various technical charts, and an adaptive relationship graph learning layer is employed to learn the relationships among technical charts generated by different indicators. Finally, by applying a GNN model combined with the relationship graphs of diverse technical charts, temporal patterns of stock indicator sequences are captured, fully utilizing the information between various technical charts to achieve accurate stock price predictions. Additionally, we further tested our framework with real-world stock data, showing superior performance over advanced baselines in predicting stock prices, achieving the highest net value in trading simulations. Our research results not only complement the existing applications of non-singular technical charts in deep learning but also offer backing for investment applications in financial market decision-making. Full article

This study provides a comprehensive assessment and synthesis of the literature on short-selling. It performs a lexicometric analysis, providing a quantitative review of 1093 peer-reviewed journal articles to identify and illustrate the main themes in short-selling research. Almost half the published literature on short-selling is thematically clustered around portfolio management techniques. Other key themes involve short-selling as it relates to risk management, strategic management, and market irregularities. Descending hierarchical classification examines the overall structure of the textual corpus of the short-selling literature and the relationships between its key terms. Similarity analysis reveals that the short-selling literature is highly concentrated, with most conceptual groups closely aligned and fitting into overlapping or conceptually similar areas. Some notable groups highlight prior short-selling studies of market dynamics, behavioral factors, technological advancements, and regulatory frameworks, which can serve as a foundation for market regulators to make more informed decisions that enhance overall market stability. Additionally, this study proposes a conceptual framework in which short-selling can be either a driver or an outcome by integrating the literature on its antecedents, consequences, explanatory variables, and boundary conditions. Finally, it suggests directions for future research. Full article

Immigrant households frequently face liquidity constraints upon arrival, which potentially hinders their long-term economic integration. The Family Investment Hypothesis (FIH) suggests that couples may respond to these constraints by coordinating their labor supply: one spouse works to finance the other’s investment in local human capital. Previous studies have tested the FIH by comparing married immigrants to married natives, attributing differences in outcomes to financial constraints. However, this approach may conflate such constraints with other inherent differences between immigrants and natives. This paper introduces a novel identification strategy that compares the differences in labor market outcomes of married and single immigrants to those of their native-born counterparts, allowing for better isolation of the effects of liquidity. Applying this strategy to repeated cross-sectional data on immigrants from the Former Soviet Union who arrived in Israel during the 1990s, the analysis finds no supporting evidence for the FIH. One possible explanation for this finding is the substantial government support extended to these immigrants, which may have mitigated their financial constraints. Alternatively, the results may indicate that immigrant households do not systematically adjust their labor supply in accordance with the FIH framework. These findings highlight the importance of the institutional context in shaping household labor supply decisions. Full article

This paper examines the moderating role of corporate social responsibility (CSR) on the relationship between environmental, social, and governance (ESG) dimensions and investor decision-making in Jordan. Data were collected using a structured questionnaire designed for institutional investors and financial analysts, capturing perceptions of ESG, CSR, and investment behavior. A stratified random sample of 350 professionals across the financial, industrial, and service sectors was surveyed. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings show that environmental and social dimensions have positive effects on investor decisions, with governance dimensions having a negative effect. Notably, CSR has a negative moderating effect on the governance dimensions and investor decision, with no observed statistical moderating effect for environmental or social dimensions. This research unravels the multidimensional role of CSR in building the ESG-investor decision interface and identifies a counterintuitive negative moderating impact of CSR on governance, contributing to the existing literature on sustainability alignment in emerging markets. The results offer practical implications for companies aiming to attract sustainability-oriented investors by indicating the necessity for an integrated and genuine CSR and ESG approach. Full article

Understanding the price dynamics of oil futures is crucial for advancing green finance strategies and supporting sustainable energy transitions. This study investigates the macroeconomic and energy market determinants of oil futures prices through Granger causality, cointegration analysis, and the error correction model, using daily data. It focuses on the influence of economic development levels, exchange rate fluctuations, and inter-energy price linkages. The empirical findings indicate that (1) oil futures prices exhibit strong correlations with other energy prices, macroeconomic factors, and exchange rate variables; (2) economic development significantly affects oil futures prices, while exchange rate impacts are statistically insignificant based on the daily data analyzed; (3) there exists a stable long-term equilibrium relationship between oil futures prices and variables representing economic activity, exchange rates, and energy market trends; (4) oil futures prices exhibit significant short-term dynamics while adjusting steadily toward a long-run equilibrium driven by macroeconomic and energy market fundamentals. By enhancing the accuracy of oil futures price forecasting, this study offers practical insights for managing financial risks associated with fossil energy markets and contributes to the formulation of low-carbon investment strategies. The findings provide a valuable reference for integrating energy pricing models into sustainable finance and climate-aligned portfolio decisions. Full article

Hydrogen is increasingly recognized as a key element of the transition to a low-carbon energy system, leading to a growing interest in accurate and sustainable assessment of its economic viability. Levelized Cost of Hydrogen (LCOH) is one of the most widely used metrics for comparing hydrogen production technologies and informing investment decisions. However, traditional LCOH calculation methods apply a single discount rate to all cash flows without distinguishing between the risks associated with outflows and inflows. This approach may yield a systematic overestimation of costs, especially in capital-intensive projects. In this study, we adapt a binary cash flow discounting model, previously proposed in the finance literature, for hydrogen energy systems. The model employs two distinct discount rates, one for costs and one for revenues, with a rate structure based on the required return and the risk-free rate, thereby ensuring that arbitrage conditions are not present. Our approach allows the range of possible LCOH values to be determined, eliminating the methodological errors inherent in traditional formulas. A numerical analysis is performed to assess the impact of a change in the general rate of return on the final LCOH value. The method is tested on five typical hydrogen production technologies with fixed productivity and cost parameters. The results show that the traditional approach consistently overestimates costs, whereas the binary model provides a more balanced and risk-adjusted representation of costs, particularly for projects with high capital expenditures. These findings may be useful for investors, policymakers, and researchers developing tools to support and evaluate hydrogen energy projects. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

This study uses national data to contribute to ongoing discussions regarding social media’s role in influencing investors in the digital economy. Grounded in social network theory, social media engagement was examined for its influence on FinTech usage, specifically cryptocurrency investments, mobile trading applications, and financial podcasts. Results showed a significant relationship between social media use for investment decisions and the embrace of FinTech. Individuals who actively engage with social media for this purpose had higher odds of investing in cryptocurrency and a higher likelihood of using both mobile trading applications and financial podcasts. However, these results were not consistent across all platforms amongst social media users. Our findings show that social media platforms enable peer influence and recommendations through networks that shape financial decisions and behaviors. FinTech firms can strategically harness social ties and the inherent information flows within social networks to broaden their reach and impact in the financial services landscape. Full article