Search Results (16,052)

This study presents a systematic bibliometric analysis and critical review of assessment indicators and multi-criteria decision-making methods for rural energy systems from 2010 to 2025. It examines the evolving definitions and regional variations in these indicators and methods. The research hotspots of rural energy systems have shifted from basic rural electrification to multi-dimensional assessment indicators and hybrid multi-criteria decision-making methods. The assessment indicators for rural energy systems demonstrate a marked imbalance, dominated by economic and technical dimensions. Specifically, economic evaluations for rural energy systems frequently utilize net present cost and levelized energy cost, shifting from static capital comparisons to comprehensive lifecycle assessments. Meanwhile, loss of power supply probability is identified as the primary inherent constraint among technical assessment indicators for rural energy systems. Geographically, assessment indicators for rural energy systems priorities exhibit significant divergence. Developing regions prioritize basic power supply and affordability, whereas developed regions focus on grid stability and market risk resilience. In addition, environmental evaluations for rural energy systems remain fixated on carbon emissions. Developed nations emphasize global climate benefits, while developing nations focus on localized dividends like indoor air quality improvement. Critically, despite an increasing focus on rural livelihoods, social indicators remain systematically marginalized in rural energy systems, leading to the neglect of local requirements and increasing technical risks. The field of rural energy system assessment is advancing toward multi-criteria decision-making indicators. Future methodologies must integrate robust, dynamic adaptive mechanisms that respond to evolving developmental priorities in order to effectively address inherent data scarcity and complex socio-economic uncertainties of rural energy systems. Full article

Historically, technological progress has driven shifts in the labour market, leading to the disappearance of certain jobs while simultaneously creating new roles fueled by the need to work with emerging technologies. The technological advancements of the early 2020s are inherently linked to Artificial Intelligence (AI) and the rise in chatbots, whose accessibility and ease of use have become paramount for business development. Given this context, the aim of this study is to analyse frameworks describing the AI competencies of students who will constitute the future workforce. Based on an analysis of existing frameworks, a new framework is formulated through synthesis and operationalized into survey items representing AI-related competencies. These survey items are measured by primary research focused on a sample of undergraduate students at a selected faculty and university. The research provides valuable insights for curriculum development policy by highlighting competencies that students perceive as significant versus those they find less important. Building on these findings, the study offers policy recommendations for curriculum designers. The proposed recommendations enable the creation of educational programmes with relevance to the practical needs of the business sector, increasingly impacted by the emergence of AI. Full article

Against the backdrop of deepening coordinated policy governance, the systemic synergy between digitalization and green transformation policies and their impact on corporate ESG performance has become a key issue requiring urgent exploration. Unlike existing studies that focus on the effects of individual policies, this paper adopts a policy system synergy framework to systematically investigate the impact of the coordinated implementation of big data administrative reform and low-carbon city pilot policies on corporate ESG performance. Using a sample of Chinese A-share listed companies from 2010 to 2022, this study applies a multi-period difference-in-differences (DID) method for empirical analysis. The findings show that the systemic synergy between digital and green policies significantly enhances corporate ESG performance, with this promoting effect substantially stronger than that of single pilot policies. Further causal re-identification using a double machine learning (DML) approach verifies the robustness of the baseline conclusion. Heterogeneity analysis indicates that the synergistic effect of digital and green policies is more pronounced in firms with higher levels of digital transformation, greater patient capital, and heavier tax burdens. Mechanism tests reveal that digital–green policy synergy improves ESG performance by enhancing external supervision from government, society, and the market, increasing green government subsidies, and incentivizing firms to engage in green innovation. At the same time, policy system synergy also reduces firms’ perceived uncertainty regarding economic policies and stabilizes their expectations, further enhancing ESG performance. This paper extends the research on the determinants of corporate ESG performance from the perspective of system synergy governance, providing new empirical evidence for understanding the interaction mechanisms between digital governance and green transformation policies. Full article

Artificial intelligence has become one of the major driving forces for business change in the modern business world. This study focuses on the link between marketing strategies, such as social media marketing and content marketing, and business sustainability, and on the role of artificial intelligence as a mediator for SMEs in Nablus. This research used a survey design based on 373 employees working for SMEs mainly based in Nablus. This research used exploratory and confirmatory factor analyses to validate the measurement model, and structural equation modeling and SPSS v.25 PROCESS macro analysis to verify the proposed relationships. This research found that marketing strategies positively link to business sustainability; the strongest direct link was found for content marketing. Artificial intelligence also significantly mediated the relationships between social media marketing and content marketing and business sustainability. This research highlights the importance of incorporating artificial intelligence into marketing strategies to improve the effectiveness of marketing strategies and support decisions for enhancing business sustainability for SMEs in emerging economies. Full article

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

Housing markets display geographical linkages that contravene conventional regression assumptions; yet, Central and Eastern European towns are markedly underrepresented in spatial econometric research. This study provides a systematic spatial econometric analysis of Zagreb’s housing market. It looks at both asking sale and rental prices throughout the city’s 17 administrative districts. There are five model specifications used in the analysis: Ordinary Least Squares (OLS), Spatial Lag of X (SLX), Spatial Autoregressive Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). The findings demonstrate significant positive spatial autocorrelation in both markets: Global Moran’s I = 0.29 (p = 0.007) for sales and 0.42 (p < 0.001) for rents. LISA analysis finds important groups of high-priced homes in the center districts and lower-priced homes on the edges. Spatial models significantly surpass OLS: SLX exhibits AIC enhancements of 9.90 (sales) and 20.20 (rentals), but SAR and SEM yield no enhancements, suggesting that local spillover effects from adjacent characteristics prevail over global spatial diffusion or correlated shocks. The higher Moran’s I and AIC gains in rental markets show that there are different spatial processes for different types of tenure. These results address a significant empirical deficiency in post-socialist housing research, illustrate that neglecting spatial dependencies may lead to biased estimates and reduced model performance, and furnish methodologically sound evidence that spatial econometric techniques are essential for accurate modeling for precise urban housing analysis in intermediate-sample scenarios. Policy implications stress the need to use spatial approaches in choices about property value, forecasting, and urban planning. Full article

Over the past three decades, artificial intelligence (AI) has substantially reshaped marketing research and practice, yet the discipline has not established a systematic understanding of its evolutionary trajectory and intellectual structure. A bibliometric analysis of 1923 Scopus publications (1992–2025) was conducted using CiteSpace to explore collaboration patterns, conceptual development, and thematic organization. It identified six evolutionary stages with accelerating innovation cycles, starting with neural networks (1992–2000) and ending with generative AI (2024–2025), with research attention per stage compressing from approximately 9 years to just 2 years. The analysis of the collaboration network shows that the key contributors are India, China, the USA, and the UK. Co-citation analysis indicates that there are three thematic dimensions with seven clusters, namely: (i) AI technological foundations and capabilities, (ii) AI marketing applications and transformation, and (iii) responsible AI governance and ethics. It suggests a Three-Force Evolutionary Framework, which combines technology-push, market-pull, and governance-moderator forces to describe the dynamics of the field. This framework shows that the Regulatory Awakening of 2018 (e.g., GDPR and the Cambridge Analytica incident) guided, not limited, innovation, and highlighted the critical personalization–privacy paradox on which modern developments are based. It identifies three priority research directions: generative AI in creative marketing, consumer trust in the personalization–privacy paradox, and organizational adaptation to fast innovation cycles. This study provides scholars with a comprehensive knowledge map, practitioners with strategic imperatives for responsible AI adoption, and policymakers with evidence that well-designed regulation accelerates innovation by balancing commercial value with societal concerns. Full article

The Mediterranean Basin faces increasing risks from extreme weather events, particularly heat stress, which severely threatens the productivity of sensitive crops, like processing tomato (Solanum lycopersicum L.). This study evaluated the agronomic, physiological, quality, and economic performance of using Mater-Bi^®-based biodegradable mulch films—varying in color (black and White/Black) and thickness (12 µm and 15 µm)—in two distinct Southern Italian pedoclimatic sites: Sicily and Campania. The aim was to define site-specific optimization strategies by comparing three biodegradable mulch film treatments, 12 µm (BDM12), 15 µm (BDM15), and Black/White (BDBW), against bare soil (BS). The results confirmed that biodegradable mulching enhances plant physiological status, such as chlorophyll and nitrogen balance index (NBI), and marketable yield compared to BS. The effectiveness of the films depended significantly on the environment. In Sicily, the BDBW (White/Black) film provided the maximum marketable yield (804.7 q ha⁻¹), confirming its crucial role in mitigating high soil temperatures through radiation reflection. Conversely, in the more favorable Campanian environment, the thicker black film (BDN15) achieved the highest yield (867.3 q ha⁻¹), indicating that microclimate stability is prioritized over heat mitigation under optimal conditions. Quality analysis showed high variability; while the Sicilian site generally favored color and antioxidant capacity, total soluble solids (°Brix) exhibited a trade-off. BDBW achieved the highest °Brix (6.1) in Sicily, while BS yielded the highest (6.03) in Campania, suggesting that slight water stress can concentrate sugars at the expense of total yield. The economic analysis demonstrated that the °Brix increase achieved with biodegradable films provided a net additional economic return superior to BS in both sites (up to +52.92% with BDBW). These findings suggest that the adoption of biodegradable mulching represents a key strategy for the sustainable intensification of processing tomato. Future cultivation strategies must mandatorily integrate the personalized selection of film color and thickness as a key element to synergistically maximize yield, quality, and economic return, tailored to the specific pedoclimatic conditions of each production site. Full article

In the pursuit of global industrial sustainable development and carbon neutrality goals, the aviation manufacturing sector serves as a strategic pillar for advancing global economic growth, driving technological innovation and enhancing national competitiveness. Its green innovation has thus become a critical pathway to achieving carbon neutrality targets and spearheading the sustainable transformation of the industrial sector. This study investigates the enabling effect of artificial intelligence (AI) on green innovation within aviation manufacturing enterprises. The findings indicate that AI exerts a promotional impact on green innovation via three primary channels: technological empowerment, labor structure optimization and resource access improvement. Specifically, AI drives the digital transformation of operational processes in aviation manufacturing, rationalizes the human resource framework of the sector, and eases the financing pressures confronted by aviation manufacturing enterprises. A heterogeneity analysis reveals that regional resource endowments, enterprise production attribute characteristics and external market attention can form synergistic interactions with AI technology. What is more prominent is that the positive influence of AI on green innovation is especially distinct in three scenarios: in economically developed urban areas, among enterprises with traditional production attributes, and for enterprises that garner high levels of analyst attention. Full article

Generative artificial intelligence (AI) is increasingly used to support decision-making in manufacturing quality assurance (QA), but its adoption raises concerns regarding governance, traceability, and auditability. This paper proposes a proactive framework that integrates generative AI into quality management and auditing while preserving standards alignment and human oversight. The framework structures quality activities across supplier, in-process, and post-market domains and across three hierarchical levels—product, process, and operation—to link quality outcomes with documentary evidence requirements. A proof-of-concept (PoC) study in electronics manufacturing focused on New Product Introduction (NPI) planning and compared two parallel workflows: an expert QA team and a generative AI-assisted chatbot workflow. Within a fixed time window, both workflows produced an aligned Process Failure Mode and Effects Analysis (PFMEA), Control Plan, supplier Production Part Approval Process (PPAP) request package, and internal audit evidence pack. Three independent experts evaluated the integrated deliverable package using five indices covering documentation quality and audit readiness, detection and containment logic, process capability and stability, governance and provenance safeguards, and execution (time) efficiency. Compared with the expert package, the generative AI–assisted workflow produced more traceable, governance-rich documentation (ownership, versioning, clause-to-evidence links) and reduced manual audit-evidence consolidation, supporting quality planning and change-control readiness. Full article

This study examines how market leadership in Indian equities has structurally shifted away from foreign institutional investors (FIIs) toward domestic institutional investors (DIIs) and mutual funds (MFs), and it evaluates the systemic risks created by this rebalancing. Using monthly transaction data from April 2007 to January 2026, we analyze evolving investment patterns among FIIs, DIIs, and MFs by employing trend analysis, Pearson’s and Spearman’s correlation analyses, phase decomposition, stationarity tests, Granger causality analysis, ARIMA modelling, and GARCH volatility estimation. Since 2021, FIIs have recorded cumulative net outflows exceeding ₹8.68 lakh crore (US$95.36 billion), while DIIs mainly led by mutual funds financed largely through Systematic Investment Plans (SIPs) have made net purchases of over ₹19.37 lakh crore (US$212.67 billion), effectively absorbing FII selling and helping to maintain elevated index levels. The trend continues with SENSEX having remained above 80,000 points through 2025 despite persistent FII disengagement. The DII share of total market purchases rose from approximately 39% in 2017 to over 54% by January 2026, documenting a structural shift in market composition. The results show that DII flows have stayed positively and significantly correlated with SENSEX, with FII flows being significantly negatively correlated. Granger causality tests suggest market-responsive rather than market-driving behavior by domestic institutions. Drawing upon Minsky’s financial instability hypothesis and behavioral finance frameworks, we interpret that prolonged domestic absorption of FII exists where direct fundamental evidence is unavailable. The Minsky-type fragility interpretation is offered as a structured hypothesis for future empirical investigation. The findings carry important implications for retail investors, fund managers, and regulators. Full article

Accurate wind speed forecasting is essential for the efficient integration of wind energy into power systems, as it directly influences generation scheduling, grid stability, and energy market operations. Forecast errors can lead to significant economic losses, including increased balancing costs, inefficient dispatch of backup generation, and penalties in electricity markets. However, wind behaviour is highly complex due to the influence of synoptic weather systems, terrain variability, and turbulence, which makes accurate prediction particularly challenging. This paper proposes a hybrid modelling framework that combines a linear regression mean model with Gaussian process (GP) residual modelling to improve forecast accuracy. Monitoring stations were grouped based on geographic coordinates and elevation, with cluster validation using the Hopkins statistic and silhouette analysis. The results show that for high-elevation inland stations (cluster 2), GP residual modelling improves forecast accuracy by up to 16.3%. In contrast, for low-elevation coastal stations (cluster 1), the GP approach does not yield improvements, indicating that its effectiveness depends strongly on the underlying wind regime. Full article

The article provides a comprehensive overview of the role of hydrogen as a key vector in the decarbonization of the global transport sector. The study situates hydrogen within the broader context of energy transition and climate neutrality targets, emphasizing its potential to replace fossil fuels in road, rail, maritime, and aviation applications. The analysis integrates a review of current technological, infrastructural, and policy developments, covering both combustion-based and fuel-cell hydrogen propulsion systems. Quantitative and qualitative data were assessed from international reports, scientific publications, and ongoing industrial projects to evaluate performance, efficiency, safety, and cost parameters such as Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO). The results indicate that while hydrogen remains economically challenging, technological progress in electrolysis, fuel cells, and refueling infrastructure significantly improves its competitiveness, particularly in heavy-duty and long-range transport. The paper highlights the critical role of international strategies, including the European Hydrogen Strategy and Fit for 55 package, in driving market adoption and regulatory alignment. The conclusions suggest that by 2050, hydrogen could contribute up to one-quarter of total transport energy demand, positioning it as a cornerstone of sustainable mobility and a bridge toward a fully decarbonized transport ecosystem. Full article

Rapid urbanization and population growth in West Africa are intensifying pressure on natural resources and reconfiguring telecoupled supply chains, especially for essential household fuels like charcoal and firewood, here collectively referred to as woodfuel, that link urban consumers to distant production landscapes. However, these cross-regional linkages remain poorly understood. This study, therefore, investigates how urban dynamics structure telecoupled woodfuel flows in Benin, based on quantitative and qualitative surveys of roadside charcoal and firewood traders along the country’s major long-distance roads RNIE#2 and RNIE#3. Collected data included sources, destinations, quantities, pricing, and organizational aspects, combined into a system analysis of fuelwood trading across sending, receiving, and corridor (spillover) areas. Results show consumers growing reliance on charcoal, which in our study amounted to 35,770 t year⁻¹ (97% of the total surveyed flow) to urban areas. Roadside trading depends heavily on connectivity, traffic, and regional trade links, with RNIE#2 emerging as the main corridor, channeling 30,960 t year⁻¹ (84% of the total surveyed flow). Contrary to assumptions that woodfuel sources reflect vegetation density, distances to reported sources were short, with supply shadows averaging 11.3 km (SD = 14.5). Urban demand shapes woodfuel flows by concentrating most trade in major cities—especially the Cotonou–Porto-Novo area, which received 83% (28,770 t year⁻¹) of charcoal and 84% (850 t year⁻¹) of firewood traded along the surveyed flow axes of Benin, with market reach distances varying between 1 and 390 km. Full article

Electric vehicles represent a promising path toward reducing transportation-related greenhouse gas emissions, but partisan polarization presents a significant barrier to their widespread adoption in the United States. This study provides a detailed look at the auto industry’s strategies for reframing electric vehicles (EVs) to resonate with mainstream American consumers, and it contributes to scholarly understanding of how sustainable products are framed to politically diverse audiences. Through a comprehensive content analysis, we analyze the persuasive strategies in all available EV video advertisements run in the U.S. from 2018 to 2023. Spanning 263 unique advertisements and 62 vehicle models, our analyses reveal the ways that nature and the environment are used in EV ads. Our data show that 90% of EV ads do not make any reference to sustainability, and 71% do not employ nature in any way. Instead, EV ads tend to emphasize vehicle features and performance, and they portray EVs as a futuristic transportation revolution. We situate these findings within the broader literature on partisan polarization of environmental issues, identity signaling in green consumer behavior, and green marketing strategy. We argue that the near-total absence of sustainability messaging in EV advertising reflects an industry-wide strategy to decouple electric vehicles from environmental identity and reframe them as mainstream consumer products. Full article