Search Results (3,057)

International organizations have highlighted the importance of consistent and reliable environment, social and governance (ESG) disclosure and metrics to inform business strategy and investment decisions. Greater corporate disclosure is a positive signal to investors who prioritize sustainable investment. In this study, economic and climate sentiment are extracted from the integrated and sustainability reports of the top 40 corporates listed on the Johannesburg Stock Exchange, employing domain-specific natural language processing. The intention is to clarify the complex interactions between climate risk, corporate disclosures, financial performance and investor sentiment. The study provides valuable insights to regulators, accounting professionals and investors on the current state of disclosures and future actions required in South Africa. A time series analysis of the sentiment scores indicates a noticeable change in the corporates’ disclosures from climate-related risks in the earlier years to climate-related opportunities in recent years, specifically in the banking and mining sectors. The trends are less pronounced in sectors with good ESG ratings. An exploratory regression study reveals that climate and economic sentiments contain information that explain stock price movements over the longer term. The results have important implications for asset allocation and offer an interesting direction for future research. Monitoring the sentiment may provide early-warning signals of systemic risk, which is important to regulators given the impact on financial stability. Full article

The construction of prefabricated concrete houses in Ecuador poses significant challenges in terms of environmental and social sustainability, amid growing housing demand and the urgent need to mitigate adverse impacts associated with the construction processes and materials. In particular, the lack of a comprehensive assessment of these impacts limits the development of effective strategies to improve the sustainability of the sector. In addition, in rural areas, the design of flexible and adapted solutions is required, as evidenced by recent studies in the Andean area. This study conducts a comprehensive assessment of the impacts and sustainability indicators for prefabricated concrete houses, employing international certification systems such as LEED, BREEAM, and VERDE, to validate various relevant environmental and social indicators. The methodology used is the Hierarchical Analytical Process (AHP), which facilitates the prioritization of impacts through paired comparisons, establishing priorities for decision-making. Hydrological, soil, faunal, floral, and socioeconomic aspects are evaluated in a regional context. The results reveal that the most critical environmental impacts in Ecuador are climate change (28.77%), water depletion (13.73%) and loss of human health (19.17%), generation of non-hazardous waste 8.40%, changes in biodiversity 5%, extraction of mineral resources 12.07%, financial risks 5.33%, loss of aquatic life 4.67%, and loss of fertility 3%, as derived from hierarchical and standardization matrices. Despite being grounded in a literature review and being constrained due to the scarcity of previous projects in the country, this research provides a useful framework for the environmental evaluation and planning of prefabricated housing. To conclude, this study enhances existing methodologies of environmental assessment techniques and practices in the construction of precast concrete and promotes the development of sustainable and socially responsible housing in Ecuador. Full article

The US has undertaken both unconventional and conventional monetary policy stances in response to the COVID-19 pandemic and the Ukraine–Russia conflict, and there has been much debate on the effects of these various monetary policies on global financial markets. Our study considers the debate in the context of South Africa and uses the quantile–frequency connectedness approach to examine static and dynamic systemic spillover between the US shadow short rate (SSR) and South African equity, bond and currency markets between 1 December 2019 and 2 March 2023. The findings from the static analysis reveal that systemic connectedness is concentrated at their tail-end quantile distributions and US monetary policy plays a dominant role in transmitting these systemic shocks, albeit these shocks are mainly high frequency with very short cycles. However, the dynamic estimates further reveal that US monetary policy exerts longer-lasting spillover shocks to South African financial markets during periods corresponding to FOMC announcements of quantitative ‘easing’ or ‘tapering’ policies. Overall, these findings are useful for evaluating the effectiveness of the Reserve Bank’s macroprudential policies in ensuring market efficiency, as well as for enhancing investor decisions, portfolio allocation and risk management. Full article

Accurate exchange rate forecasting is crucial for investment decisions, multinational corporations, and national policies. The nonlinear nature and volatility of the foreign exchange market hinder traditional forecasting methods in capturing exchange rate fluctuations. Despite advancements in machine learning and signal decomposition, challenges remain in high-dimensional data handling and parameter optimization. This study mitigates these constraints by introducing an innovative enhanced prediction framework that integrates the optimal complete ensemble empirical mode decomposition with adaptive noise (OCEEMDAN) method and a strategically optimized combination weight prediction model. The grey wolf optimizer (GWO) is employed to autonomously modify the noise parameters of OCEEMDAN, while the zebra optimization algorithm (ZOA) dynamically fine-tunes the weights of predictive models—Bi-LSTM, GRU, and FNN. The proposed methodology exhibits enhanced prediction accuracy and robustness through simulation experiments on exchange rate data (EUR/USD, GBP/USD, and USD/JPY). This research improves the precision of exchange rate forecasts and introduces an innovative approach to enhancing model efficacy in volatile financial markets. Full article

Food loss and waste are critical global issues, particularly in developing economies where they exacerbate food insecurity and environmental degradation. This study focuses on fruit and vegetable loss (FVL) in retail and wholesale markets in North Lebanon, a region marked by socio-economic challenges and infrastructural deficiencies. The research aims to identify the underlying drivers of FVL, assess current management practices, and identify aspects impacting it. Data was collected through surveys of seventy wholesalers and retailers employing descriptive statistics and multinomial logistic regression for analysis. The findings reveal that 85.7% of the sample generate little or no FVL. Being a retailer or wholesaler, operating on a small or large scale, or being open 24/7 or part-time does not affect FVL. Conversely, inadequate display and storage, hot weather, and pricing practices significantly impact FVL. The market faces challenges such as low consumer purchasing capacity, financial difficulties, legal constraints, and lack of knowledge. Various practices are used to prevent FVL, including strategic supply chain decisions, price reductions, and donations to charities. The study underscores the need for improved infrastructure, financial support, and regulatory frameworks to mitigate FVL, thereby enhancing food security and environmental sustainability in North Lebanon. Full article

The shortage of math and science teachers is a pressing issue in the United States (US) and globally. This review closely follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and synthesizes findings from 43 peer-reviewed empirical studies published between 2005 and 2024 on the recruitment and retention of math and science teachers, offering a new perspective for understanding and mitigating math and science teacher shortages. This review revealed (a) the qualifications and characteristics of math and science teachers who enter and remain in the teaching profession; (b) that financial incentives, experiential learning, mentorship, and professional development are commonly used strategies and mechanisms to recruit and retain math and science teachers; and (c) that psychological, sociocultural, and working conditions are factors that influence math and science teachers’ decisions to enter and remain in the teaching field. While this review primarily focuses on the US context, it offers valuable insights for researchers, practitioners, policymakers, and other key stakeholders worldwide by identifying strategies, mechanisms, and factors that shape teacher recruitment and retention in math and science. This review also discusses gaps in the literature, directions for future research, and implications for research, policy, and practice that emerge from the empirical evidence. Full article

Farmers frequently face the decision to retain or replace dairy cows, with 20% to 40% of cows replaced annually. In Switzerland, this translates to over 100,000 cows replaced each year, representing a significant financial investment for farms and the dairy industry. The average productive lifespan of a dairy cow is currently three to four parities worldwide as in Switzerland, shorter than the optimal five to six parities, leading to financial losses from premature culling. Factors influencing suboptimal replacement decisions include inaccurate valuation of production parameters, replacement costs, and health issues. This study bridges the gap between theoretical models and real-world practices by analyzing replacement decisions from 29 Swiss dairy farmers over five years, comparing them to theoretical models and evaluating economic impacts. On average, suboptimal decisions resulted in an economic loss of 161 ± 164 CHF per farm per month (1.55 ± 1.58 CHF per cow per month), with losses from retaining unprofitable cows being approximately three times greater than those from premature culling. The results indicate that farmers typically make economically sound decisions regarding cow replacement; this contrasts with findings from previous studies on the topic. Nonetheless, replacing cows prematurely, particularly during their first parity, is not ideal from ecological, animal welfare, and sustainability standpoints. Consequently, enhancing animal health and fertility becomes essential for reducing culling rates and improving the longevity of dairy cows. Full article

This paper examines a prospect scenario of adopting Child Development Accounts (CDAs) as a social welfare innovation in Jordan. CDAs are considered as an asset-building policy aimed at enhancing financial inclusion and socio-economic well-being. This paper discovers the feasibility of CDAs that have proven successful in several countries, as their potential in Middle Eastern countries, particularly in Jordan, remains unexplored. The application of CDAs in the social welfare system aims to support sustainable asset accumulation and improve the living standards of diverse segments in Jordan by integrating CDAs within the efforts made by Jordan to achieve financial inclusion, alleviate poverty, and supplement household income through asset development. There are opportunities to implement the program in Jordan, including expanding the scope of microfinance, public–private partnerships, and targeted programs for women, youth, and refugees. However, several challenges may hinder its application, including limited financial literacy, high unemployment rates, income inequality, regulatory obstacles, and difficulties in implementing social reforms. The paper contributes to the debate on social welfare policies adopted in developing countries by providing solutions based on global practices in CDA execution and has implications and recommendations for decision makers to achieve economic development. Future research in Middle East and North Africa (MENA) countries should target pilot projects and comparative studies to refine CDA strategies. Full article

Treatment-resistant mental health concerns significantly contribute to society in terms of financial costs and individually by creating emotional and functional costs. An important yet little-recognized cause of treatment-resistant mental health conditions is tetrahydrobiopterin (BH4) deficiency. BH4 is an essential cofactor for producing serotonin, dopamine, norepinephrine, and nitric oxide—molecules critical to mood and focus. The enzyme GTP Cyclohydrolase 1 (GCH1), produced by a gene of the same name, catalyzes the first step in synthesizing BH4. Variants in this gene have been associated with low BH4 levels, as well as depression and ADHD. The case reports presented in this article illustrate that a partial BH4 deficiency, as conveyed by the GCH1 rs841 variant, may contribute to wider issues in mental and neurological health including depression and ADHD but also severe treatment-resistant anxiety, Premenstrual Dysphoric Disorder, insomnia, complex behavioral issues, and autism. The effects of GCH1-mediated BH4 deficiency may be able to be rescued with a low-dose BH4 replacement, as illustrated by these cases, where substantial observational improvements in mental health concerns were reported in all five cases. This paper also demonstrates how a genomics clinical decision support tool can non-invasively flag “low producers” by identifying individuals with the AA genotype for GCH1 rs841, as well as other modifiable genomic contributing factors to mental health concerns. These cases broaden the understanding of BH4′s psychiatric relevance and also serve to further the medical literature by documenting positive responses to low-dose BH4 (ranging from 0.09 to 0.3 mg/kg/day) and other genotype-guided interventions across diverse mental and neurological health presentations, highlighting the potential benefits and importance of a genomically targeted, precision approach to psychiatry. Full article

This study investigates the optimal sizing of photovoltaic (PV) and battery storage (BAT) systems for Estonian households operating under grid constraints that prevent selling surplus energy. We develop and compare three sizing models of increasing complexity, ranging from a simple heuristic to a full simulation based optimization. Their performance is evaluated using a multi-criteria decision analysis (MCDA) framework that integrates Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index Ratio (PIR), and payback period. Sensitivity analyses are used to test the robustness of each configuration against electricity price shifts and market volatility. Our findings reveal that standalone PV-only systems are the most economically robust investment. They consistently outperform combined PV + BAT and BAT-only configurations in terms of investment efficiency and overall financial attractiveness. Key results demonstrate that the simplest heuristic-based model (Model 1) identifies configurations with a better balance of financial returns and capital efficiency than the more complex simulation-based approach (Model 3). While the optimization model achieves the highest absolute NPV, it requires significantly higher investment and results in lower overall efficiency. The economic case for batteries remains weak, with viability depending heavily on price volatility and arbitrage potential. These results provide practical guidance, suggesting that for grid constrained households, a well-sized PV-only system identified with a simple model offers the most effective path to cost savings and energy self-sufficiency. Full article

This study examines how different instructional media—face-to-face classes, live streaming, pre-recorded videos, and educational games—affect student learning outcomes in finance education. A sample of first-year economics students was assessed on their knowledge of basic financial principles before being randomly assigned to five groups. Four groups attended the same finance course delivered through different media formats, while a fifth group served as a control and received no instruction. After the course, all students completed a second (post-course) assessment. By comparing individual pre- and post-test results, as well as learning gains across the groups, we evaluated the effectiveness of each delivery method. The results show that all four instructional formats significantly improved financial knowledge compared to the control group. Among the media types, educational games proved to be an effective and reliable tool for delivering finance content. However, the differences in learning gains between face-to-face instruction, live streaming, and pre-recorded videos were not statistically significant. These findings indicate that a range of delivery models can be used effectively in finance education. The study contributes to current debates on cost-effective teaching strategies and supports evidence-based decisions on curriculum design in digitally transformed higher education environments after COVID-19. Full article

In the present era where digitalization, FinTech, and blockchain technologies are reshaping the global financial landscape, traditional measures of bank performance need to evolve. This paper introduces a hybrid approach that combines multi-criteria efficiency assessment and econometric modeling to evaluate bank performance within the context of digital transformation in emerging economies. Focusing on a panel of banks across selected emerging markets, this study first applies a multi-criteria decision-making technique (Data Envelopment Analysis) to assess operational efficiency using both conventional indicators and digitalization-driven metrics, such as mobile banking penetration and blockchain adoption. We then employ a panel econometric model to investigate the factors that shape efficiency outcomes, with special attention to FinTech and blockchain innovations as potential drivers. The results reveal a nuanced picture of how digital technologies can influence bank performance, highlighting both opportunities and constraints for financial institutions in less developed markets. The findings offer actionable insights for bank managers, regulators, and policymakers striving to balance traditional operational priorities with the demands of digital transformation. By linking efficiency measurement with an examination of the digitalization process, this paper provides a timely contribution to the literature on banking and financial innovation, serving as a foundation for future research and strategic decision-making in the FinTech and blockchain era. Full article

Accurately predicting stock prices is crucial for investment and risk management, but the non-stationarity of the financial market and the complex correlations among stocks pose challenges to traditional models (ARIMA, LSTM, XGBoost), resulting in difficulties in effectively capturing dynamic patterns and limited prediction accuracy. To this end, this paper proposes the Financial Spatio-Temporal Graph Attention Network (FSTGAT), with the following core innovations: temporal modelling through gated causal convolution to avoid future information leakage and capture long- and short-term fluctuations; enhanced spatial correlation learning by adopting the Dynamic Graph Attention Mechanism (GATv2) that incorporates industry information; designing the Multiple-Input-Multiple-Output (MIMO) architecture of industry grouping for the simultaneous learning of intra-group synergistic and inter-group influence; symmetrically fusing spatio-temporal modules to construct a hierarchical feature extraction framework. Experiments in the commercial banking and metals sectors of the New York Stock Exchange (NYSE) show that FSTGAT significantly outperforms the benchmark model, especially in high-volatility scenarios, where the prediction error is reduced by 45–69%, and can accurately capture price turning points. This study confirms the potential of graph neural networks to model the structure of financial interconnections, providing an effective tool for stock forecasting in non-stationary markets, and its forecasting accuracy and industry correlation capturing ability can support portfolio optimization, risk management improvement and supply chain decision guidance. Full article

Pre-disaster and post-disaster allocation strategies are widely investigated as the single optimization problem in humanitarian supply chain management, while integrated decisions including the above two problems are seldom discussed in the existing literature. Here, this paper proposes a mixed-integer programming model to determine these decisions, including the location of central warehouses and emergency storage points and the quantities of relief items pre-deployed and distributed. Specially, two preferences regarding costs and cost-resilience are considered, and a comparison of two models concerning the above preferences is performed. The results are as follows: (i) When the impact of disasters is at a relatively low or moderate level, the cost-oriented model can reduce the government’s financial burden and increase the coverage of relief items. However, when the severity of the disaster is high, the cost resilience-oriented model can respond to the needs of victims within the shortest time, although these needs cannot be completely met. (ii) Increasing the initial inventory level of emergency storage points and enhancing the victims’ tolerance time through social support can effectively reduce the total costs, while increasing the transportation speed can effectively reduce the response delay time. (iii) Adjusting the unit penalty cost can make the total penalty costs and transportation costs decline within a certain range, but such an adjustment has no influence on the response delay time. This paper not only proposes an integrated framework for pre- and post-disaster allocation decisions but also highlights the importance of incorporating resilience into relief item allocation in disaster contexts. Full article

Sustainable energy entrepreneurs promote sustainable development by focusing more on energy efficiency. This study examines the interdependence and driving–dependent relationships among the socioeconomic factors (SEFs) influencing sustainable energy enterprises (SEEs). A mixed-methods approach is used, beginning with a literature review and expert consensus, followed by total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. Seven key SEFs are finalized through interviews with 12 experts. Data are then collected from 11 SEEs. The study reveals that the regulatory and institutional framework emerges as the primary driving factor influencing other SEFs, including financial accessibility, market demand, technological innovation, and infrastructure readiness. Social and cultural acceptance is identified as the most dependent factor. The study proposes future research directions by identifying the United Nations sustainable development goals (SDGs) related to the antecedents, decisions, and outcomes with theoretical linkages through the Antecedents–Decisions–Outcomes (ADO) framework. The major SDGs identified are SDG 4 (education), SDG 7 (energy), SDG 9 (industry), SDG 11 (communities), and SDG 13 (climate). The study highlights that regulatory support, funding access, skill development, and technology transfer are required areas for strategic focus. Understanding the hierarchy of SEs supports business model innovation, investment planning, and risk management. Full article