Search Results (540)

In the era of growing energy demand and the need to reduce greenhouse gas emissions, the development of renewable energy sources, including photovoltaic farms, is becoming a key element of a sustainable energy transition. In this context, the careful selection of cadastral plots on which farms can be built is crucial, as appropriate location influences the investment’s energy efficiency and minimizes environmental and planning risks. This article presents a proprietary methodology for identifying cadastral plots that are suitable for locating a photovoltaic farm. The presented methodology integrates the Fuzzy-AHP multi-criteria analysis method and the Fuzzy Membership fuzzy logic method, thereby reducing the subjectivity of expert assessments and improving the accuracy of estimating the values of factors considered in the research. A key element of the methodology is a detailed analysis of land and building register data, which results in the identification of specific plots with high investment potential. The multi-criteria analysis considered eight key factors related to climate, terrain, land cover, and cadastral data. Based on this, eight plots and 32 plot complexes were selected as the most suitable for the construction of PV farms. The most favorable locations were identified primarily in the eastern part of Częstochowa Poviat, as well as in the northern municipalities. The proposed methodology provides a ready-to-use, practical solution to the investment challenge of selecting specific cadastral plots for new solar investments. According to the reviewed literature, each of the 40 designated sites could support a photovoltaic farm of an estimated capacity of at least 1 MW. The obtained results provide significant input into the renewable energy investment planning process and emphasize that careful selection of plot locations is crucial for the investment’s success and the region’s sustainable energy transformation. Full article

Forests are crucial for achieving carbon neutrality and the Sustainable Development Goals (SDGs). This study contributes to SDG 13 (Climate Action) and SDG 15 (Life on Land) by constructing a comprehensive evaluation system for high-quality forestry development (HQDF), integrating economic efficiency, ecological functions, and social benefits. Using provincial panel data for China from 2010 to 2023 and applying two-way fixed effects, panel quantile regression, and instrumental-variable methods, we examine the catalytic role of green finance. The results show that green finance significantly promotes HQDF and displays an inverted U-shaped effect over the development cycle. Regional heterogeneity is marked: the strongest effects appear in western and southern China, moderate effects in central regions, and negative effects in some eastern and northern provinces. Among specific instruments, green investment and green bonds exert the largest positive impacts, followed by green insurance and fiscal funds, while green credit plays an important role at particular stages. These findings provide evidence from a major emerging economy and offer practical guidance for optimizing forestry-related green finance strategies worldwide. Full article

Accurately predicting asset returns remains a central challenge in finance, with significant implications for portfolio optimization and risk management. In response to the challenge, this study evaluates the predictive performance of machine learning algorithms in estimating excess returns of U.S. industry portfolios, within the out-of-sample prediction framework of the Fama–French three-, four-, five- and six-factor asset pricing models. In the analysis, Support Vector Regression, Multilayer Perceptron, Linear Regression, and k-Nearest Neighbor were employed using monthly return data from 1992 to 2022, covering 5-, 10-, 12-, 17-, 30-, 38-, 48-, and 49-portfolio configurations composed of NYSE, AMEX, and NASDAQ-listed firms. The findings reveal that support vector regression achieved the highest number of top-ranked results, producing the most successful outcomes in 305 out of 836 model–portfolio combinations. However, multilayer perceptron achieved the best fit in the largest number of portfolios, ranking first in all groups except the 5-industry configuration. Furthermore, the Fama–French five-factor model outperformed other specifications across all groupings, confirming the value of incorporating profitability and investment information. Predictive performance also varied by industry, as wholesale and manufacturing sectors exhibited strong alignment, whereas utilities and energy-related sectors, likely constrained by structural or regulatory features, remained less responsive and exposed to long-term risks. Full article

Big players in information and communication technologies are investing in the metaverse for their businesses. Meta, as the main player in social media worldwide, is massively developing its “social” metaverse as a new paradigm by depicting it with nice and endless features and by expecting current social media to become engrained within it. What is the attitude of users towards this future scenario? Very few studies specifically focusing on this question were found. In this work, a scale for assessing the attitude of people towards the social metaverse was developed. A questionnaire composed of 38 Likert items, inspired by features of the social metaverse, was generated and administered to 184 Italian subjects. The results were analyzed with exploratory factor analysis, and the final scale is composed of 15 items covering four factors that were interpreted. Aspects consistent with both the preliminary work of the authors and with some previous works were found. Considerations are also made in relation to the analysis of the contents of Meta. Full article

Wind power has become a strategic cornerstone of China’s renewable-energy transition and industrial upgrading, making it essential to understand how policy interventions shape the behaviour of its industry chain. This study examines how major wind power policies issued between 2015 and 2024 transmit shocks across nine upstream, midstream, and downstream sectors. Using four contagion tests based on higher-order co-moments, combined with a policy sensitivity index, the analysis identifies distinct transmission patterns across policy types. The results show that market-mechanism reforms induce the strongest and most systemic contagion effects, reflecting their ability to align financial incentives with renewable-integration objectives. Upstream sectors—particularly equipment and key material industries—exhibit the highest responsiveness, while midstream construction and downstream operation and maintenance display more moderate and delayed adjustments. Development and construction policies generate broader but less intensive contagion, whereas industry-support measures trigger selective, sector-specific responses. These findings offer practical guidance for improving policy coordination, investment planning, and industrial upgrading within China’s wind power value chain. Future research could extend the analysis by incorporating firm-level data, longer policy cycles, and interactions with other structural shocks such as electricity-market reforms and climate-related risks. Full article

Technological innovation and the efficiency of resource allocation in Chinese new energy vehicle enterprises represent critical factors influencing the sustainable development of the industry. By applying a two-stage dynamic network DEA model to analyze the comprehensive and stage-specific technological innovation efficiency of 13 A-share-listed new energy vehicle enterprises between 2017 and 2024, this study reveals that both overall and phase-specific innovation efficiencies remain below optimal levels. Moreover, the average technological R&D efficiency across these firms is found to be lower than their average achievement transformation efficiency, highlighting the urgent need to improve innovation performance in this sector. Grey relational analysis of influencing factors identifies six key determinants of technological innovation efficiency: the shareholding ratio of the largest shareholder, R&D investment intensity, the proportion of employees holding bachelor’s degrees or higher, management capability, return on equity, and total asset turnover. In comparison, government subsidies and total assets exhibit relatively limited influence on technological innovation efficiency. Full article

Rice production in Sub-Saharan Africa (SSA) faces significant challenges due to insect pest infestations, which threaten food security and farmer livelihoods. This review examines the major insect pests affecting rice in SSA and highlights sustainable management strategies, drawing on successful case studies. It explores successful methods, including the use of biological control agents in Nigeria; neem-based pesticides in Tanzania; push-pull technology in Kenya; agroecological practices in Mali; resistant rice varieties in Ghana and Nigeria; integrated farming systems in Liberia, Guinea Conakry, Nigeria, Kenya and Madagascar; and farmer field schools in Zambia. Emerging technologies such as biotechnology and precision agriculture offer further additional opportunities to enhance pest control when effectively integrated within existing IPM frameworks. However, financial constraints, limited awareness, policy-related challenges, and inadequate infrastructure continue to limit widespread adoption. In this context, the review identifies critical research gaps, including the need for region-specific solutions, improved biopesticides, and long-term assessment of sustainable practices. Policy recommendations call for greater government investments, capacity-building programs, supportive regulatory environments, and stronger collaboration among researchers, development partners, and local stakeholders. Addressing these challenges can foster resilient and sustainable rice production systems across SSA. Full article

Stock trend prediction is a complex and crucial task due to the dynamic and nonlinear nature of stock price movements. Traditional models struggle to capture the non-stationary and volatile characteristics of financial time series. To address this challenge, we propose the Hybrid Relational Transformer (HRformer), which specifically decomposes time series into multiple components, enabling more accurate modeling of both short-term and long-term dependencies in stock data. The HRformer mainly comprises three key modules: the Multi-Component Decomposition Layer, the Component-wise Temporal Encoder (CTE), and the Inter-Stock Correlation Attention (ISCA). Our approach first employs the Multi-Component Decomposition Layer to decompose the stock sequence into trend, cyclic, and volatility components, each of which is independently modeled by the CTE to capture distinct temporal dynamics. These component representations are then adaptively integrated through the Adaptive Multi-Component Integration (AMCI) mechanism, which dynamically fuses their information. The fused output is subsequently refined by the ISCA module to incorporate inter-stock correlations, leading to more accurate and robust predictions. Extensive experiments on the NASDAQ100 and CSI300 datasets demonstrate that HRformer consistently outperforms state-of-the-art methods, e.g., achieving about 0.83% higher Accuracy and 1.78% higher F1-score than TDformer on NASDAQ100, with Sharpe Ratios of 1.5354 on NASDAQ100 and 0.5398 on CSI300, especially in volatile market conditions. Backtesting results validate its practical utility in real-world trading scenarios, showing its potential to enhance investment decisions and portfolio performance. Full article

Transport is among the fastest-growing contributors to carbon dioxide (CO₂) emissions in the Gulf Cooperation Council (GCC) region, where rapid urbanization, population growth, and high mobility demand continue to shape energy use. This study aims to quantify the extent to which economic growth and urbanization drive transport-related CO₂ emissions across Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates between 2012 and 2022. Using sector-specific data from the International Energy Agency and World Bank, we apply panel and country-level log–log regression models to estimate long-run and short-run elasticities of transport CO₂ emissions with respect to GDP and urban population. The analysis also includes robustness checks excluding the COVID-19 pandemic year to isolate structural effects from temporary shocks. Results show that transport emissions remain strongly correlated with GDP in most countries, indicating emissions-intensive growth, while the influence of urbanization varies: positive in Kuwait and Saudi Arabia, where expansion is car-dependent, and negative in Oman and Qatar, where compact urban forms and transit investments mitigate emissions. The findings highlight the importance of differentiated policy responses—fuel-pricing reform, vehicle efficiency standards, electrification, and transit-oriented planning—to advance low-carbon mobility. By integrating elasticity-based diagnostics with decoupling analysis, this study provides the first harmonized empirical framework for the GCC to assess progress toward transport-sector decarbonization. Full article

In recent years, video games have become an increasingly popular form of entertainment and enjoyment for consumers of all ages. Given their rapid rise in production, projects such as PlayMyData aim to organize the immense amounts of data that accompany these games into sets of data for public use in research, primarily games bound specifically to modern platforms that are still being actively developed or further improved. This study aims to examine the particular differences in video game review scores using this set of data across the four listed platforms—Nintendo, Xbox, PlayStation, and PC—for different gaming titles relating to each platform. Through analysis of variance (ANOVA) testing and several other statistical analyses, significant differences between the platforms were observed, with PC games receiving the highest amount of positive scores and consistently outperforming the other three platforms, Xbox and PlayStation trailing behind PC, and Nintendo receiving the lowest review scores overall. These results illustrate the influence of platforms and their differences on player ratings and provide insight for developers and market analysts seeking to develop and invest in console platform video games. Full article

Financial literacy, defined as the set of knowledge, skills, and attitudes that enable individuals to make informed economic decisions and manage resources efficiently, is fundamental for social inclusion and the reduction of inequalities. This study, through a systematic review of the scientific literature using the PRISMA methodology, selected 120 primary studies that met the inclusion and exclusion criteria and presented a low risk of bias. These studies examined aspects related to financial literacy programs, the populations benefited, their effects, the challenges encountered, and the lessons that can guide the replication of these initiatives. The results show that the most frequent programs include training in basic financial concepts—savings, budgeting, access to banking services and microfinance—as well as workshops, seminars, and group training sessions. The populations most benefited were rural communities and women, although informal workers, migrants, and refugees could also significantly improve their financial inclusion and economic resilience. Among the positive effects, improvements were observed in income and expense management, increased savings, investment planning, preparation for emergencies and retirement, and the strengthening of economic empowerment and the sustainability of microenterprises and small enterprises. These findings highlight the importance of implementing financial literacy programs adapted to specific contexts to promote inclusion and economic well-being. Full article

The assessment of energy savings is not a trivial matter, as we have direct meters for consumption, but not for the absence of consumption. Calculating a simple difference between consumption before and after the implementation of an energy saving measure is also an incomplete assessment. The only way to determine energy savings is to compare the consumption that would have occurred in the absence of the saving measure with the actual consumption, with reference to the same external conditions and the same period. This is what the international IPMVP^® protocol establishes. This study, based on two case studies of industrial energy saving measures, explores the aspects of the calculation related to decarbonization and economic evaluation. In particular, sensitivity analyses of energy and economic indicators are carried out based on factors that evolve over time, such as the rate of inflation and discounting of investments and the variation in the carbon dioxide emission factor for electricity production. The main results highlight that the assumption of a constant electricity emission factor leads to an overestimation of the total CO₂ savings from energy efficiency interventions that can be more than 40%. The uniqueness of this paper is the application of a standardized savings evaluation procedure (IPMVP^®) in order to analyze the sensitivity of economic savings towards some key financial parameters, and the specific fitting of an electricity emission model to the Italian power sector in order to correct the carbon savings evaluation to the projected emission factor evolution. Full article

The COVID-19 pandemic has profoundly affected global economies, including the electricity sector. Governments implemented strict containment measures to mitigate the health crisis, including lockdowns, social distancing, and event cancelations. These interventions, while essential for public health, also disrupted energy demand and supply patterns. This study supports regulators by quantifying the short- and long-term impacts of the pandemic on local electricity prices (LEPs) in the Nord Pool market (Norway, Sweden, Denmark, Finland, Estonia, Latvia, and Lithuania) during 2020. The findings highlight a crucial link between crisis response strategies and the transition to sustainable energy systems. In times of uncertainty, governments tend to prioritize renewable energy investments, particularly wind power, which offers a clean and resilient alternative to fossil-fuel-based electricity generation. Using the PMG-ARDL estimator, our analysis reveals a significant long-term negative association between government interventions and LEP, as well as between wind energy production (WEP) and LEP. Specifically, an additional gigawatt of wind energy generation reduces local electricity prices by up to EUR 0.09, confirming the merit-order effect. These findings emphasize the environmental and economic benefits of expanding wind energy capacity as a stabilizing force in electricity markets. Moreover, while health-related news influenced LEP fluctuations in the long run, government restrictions had a limited short-term impact, likely due to the inelastic nature of electricity demand and supply. This study reinforces the argument that integrating more renewable energy sources can enhance market resilience, reduce price volatility, and contribute to long-term sustainable development, making the energy transition an essential pillar of post-pandemic recovery strategies. Full article

The association between emotions and decision-making is evident. Our article aims to demonstrate, for individual investors, the development of negative emotional charges on stock markets in a perceived negative trend. The research question concerns how negative emotions may be associated with specific behavioral responses. Our results indicate a four-phase process involving, first, decisional “nonchalance”; second, decisional hesitation; third, partial disengagement; and, finally, decisional paralysis. The first phase appears related to the lack of experience of the individual investor, the second phase corresponds to the uncertainty related to stock market operations, while the last two phases seem to coincide with a deteriorating decision-making environment and the accumulation of negative experiences, resulting from financial expectations not being met. Emotional paralysis raises questions about the possibility of individual investors renewing their investment strategies. These results come from a qualitative approach based on experimental finance and supported by the analysis of data from semi-structured interviews. Our study proposes a new four-phase model (nonchalance, hesitation, partial disengagement, and paralysis) that delineates the emotional and behavioral trajectories of individual investors during a perceived bear market. Our qualitative perspective also contributes to existing literature by highlighting the underexplored phase of “nonchalance”. Full article

The rise of large language models (LLMs) offers new forms of academic support for STEM students engaged in self-directed study. This study evaluates the impacts of multiple assistance approaches on laboratory course performance, focusing on engineering students in electronics-related disciplines. A cohort of 218 students underwent a redesigned lab course, and their outcomes were compared to those of 177 students from earlier years who did not receive such support. Specifically, we implemented five types of support approaches for students completing laboratory coursework: (1) Teaching Assistant (TA) only, (2) Generic-LLM-only model, (3) Expert-tuned-LLM-only model, (4) TA + Generic LLM model, and (5) TA + Expert-tuned LLM model. Our key findings are as follows: I. Compared to the historical baseline with no support, students assisted by the generic-LLM-only model did not show a significant improvement in performance. II. Teaching assistant involvement was associated with marked improvements in student outcomes, and performance across all TA-involved approaches showed little variation. III. The expert-tuned LLM was more effective than the generic LLM in improving student outcomes. IV. The combined TA + LLM configurations enhanced learning efficiency overall, although they required greater time investment in the early stages of the course. These results highlight the promising role of LLM technologies in the future of engineering education, while also underscoring the continued importance of domain-specific expertise in delivering effective learning support. Full article