The rapid diffusion of artificial intelligence (AI) is adding a fast-growing workload to the data centres that host it, with consequences for electricity systems, household energy costs, and the environment. This article examines the sustainability implications of AI-driven data-centre expansion in Europe along three connected dimensions—environmental sustainability, energy and social equity, and the sustainability of the digital transition—and asks who gains and who pays. Doctrinal analysis of European Union (EU) law and policy was combined with a comparative, descriptive assessment of published data. The study synthesised evidence from international agencies, EU institutions, national statistical offices, regulators, and peer-reviewed research, and traced household electricity costs and data-centre development from 2023 onward in three purposively selected cases: Ireland, the United Kingdom, and Spain. Data-centre electricity demand, of which AI workloads are an increasingly important component, is growing far faster than overall demand and is highly concentrated geographically. In exposed regions this concentration is associated with grid-capacity constraints and may contribute to upward pressure on network and system costs. Household prices in the period were, however, shaped predominantly by gas prices, taxation, subsidies, renewable output, and network charges, and the study does not estimate causal effects. The expansion also generates environmental externalities, notably carbon emissions and a growing stream of electronic waste. Because energy costs weigh most heavily on low-income households, these pressures carry a distributional risk: a potential “double burden” in which households already in energy poverty benefit least from AI while bearing part of its infrastructure costs. The article maps the EU response by legal status—measures in force, formally proposed, and under discussion—and argues that assessments of AI’s social value should internalise its energy, climate, resource, and distributional costs. It closes with actor-specific recommendations on clean-supply additionality, cost-reflective network-cost allocation, safeguards for vulnerable consumers, hardware circularity, and sustainability metrics.
This study explores students’ perceptions, awareness, and engagement with sustainability and energy-efficiency initiatives in higher education institutions, with particular attention to the visibility of campus sustainability practices. An exploratory cross-sectional survey was conducted among 149 students from five universities. Data were collected through a structured questionnaire and analyzed using descriptive statistical techniques, including frequencies, percentages and mean scores. Students demonstrated strong environmental concern and positive attitudes towards sustainability. However, awareness of institutional sustainability initiatives, environmental management systems (EMS) and energy-efficiency measures was generally low. Limited communication and visibility of sustainability actions were identified as potential barriers to student engagement. The findings should be interpreted in light of the convenience sampling approach and reliance on self-reported perceptions. Future studies could employ broader samples and more advanced analytical methods. Higher education institutions can strengthen sustainability engagement by improving communication, increasing the visibility of sustainability initiatives and creating more opportunities for student participation. Greater student involvement in sustainability initiatives may contribute to the development of environmentally responsible attitudes, behaviors and sustainability competencies. The study provides exploratory evidence on sustainability visibility and student engagement in higher education and highlights the importance of communication and institutional transparency in fostering sustainability awareness and participation.
This study examines factors associated with external auditors’ intention to engage in sustainability assurance in Vietnam, where sustainability reporting is expanding while assurance practice remains uneven. The research model is informed by institutional theory, self-efficacy theory, and the attitude-intention relationship in the Theory of Planned Behavior (TPB). Survey data from 317 external auditors working in independent audit firms were analyzed using partial least squares structural equation modeling (PLS-SEM). The results show that perceived regulatory and institutional pressure, perceived sustainability literacy, and sustainability assurance self-efficacy are positively associated with attitude toward sustainability assurance, whereas professional skepticism is not significantly associated with attitude. Perceived regulatory and institutional pressure, sustainability assurance self-efficacy, and attitude are positively associated with intention to engage. Mediation analysis shows significant indirect relationships through attitude for perceived regulatory and institutional pressure, perceived sustainability literacy, and sustainability assurance self-efficacy, but not for professional skepticism. The study extends sustainability assurance research by examining auditors’ individual-level readiness and distinguishing how institutional signals, perceived sustainability literacy, task-specific self-efficacy, and attitude relate to readiness to engage in sustainability assurance. The findings highlight the professional capacity needed to support the development of credible sustainability reporting and assurance in an emerging economy.
This study investigates the determinants of household intention to adopt solar photovoltaic (PV) systems in an island community using a stimulus organism response (SOR)-based framework informed by selected concepts from the Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM). Data were collected from 100 households in Pulau Tidung, Seribu Islands, Indonesia, using purposive sampling and analyzed through partial least squares structural equation modeling (PLS-SEM) to examine the effects of economic, environmental, social, regulatory, technical, and market stimuli on attitude and adoption intention. The results indicate that attitude toward solar PV exhibited the largest positive association with adoption intention (β = 0.827, p < 0.001), while economic stimulus was positively associated with attitude (β = 0.411, p = 0.018). Regulatory stimulus directly increases adoption intention (β = 0.349, p = 0.042), whereas technical barriers significantly reduce it (β = −0.306, p = 0.036). Environmental, social, and market stimuli do not exhibit statistically significant effects. The model explains 95.5% of the variance in adoption intention (R² = 0.955), indicating substantial explanatory power within the study context. These findings demonstrate that favorable attitudes, supportive regulatory conditions, and reduced technical barriers are central to strengthening household solar PV adoption intentions in island communities. Accordingly, policies should combine targeted economic incentives and regulatory support with accessible technical assistance and community-based implementation mechanisms to facilitate renewable energy uptake in geographically constrained areas.
This study aims to systematically review the literature on CO₂ emissions, carbon intensity, and sustainable economic growth in the Western Balkans, with particular attention to macroeconomic, institutional, financial, industrial, sustainability, and energy-related factors. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, the review was conducted using the Scopus database and structured search strings covering CO₂ emissions, carbon intensity, macroeconomic drivers, energy transition, green finance, urbanization, industrial structure, and sustainable development. After identification, screening, eligibility assessment, and quality assessment, 30 studies were included in the final qualitative synthesis. The findings indicate that CO₂ emissions, carbon intensity, and sustainable economic growth in the Western Balkans are influenced by the interaction among economic growth, energy consumption, fossil-fuel dependence, renewable energy deployment, trade openness, financial development, institutional quality, urbanization, construction activity, carbon pricing, and European Union (EU) climate policy instruments such as the carbon border adjustment mechanism (CBAM). The energy structure emerges as a key determinant of both carbon intensity and CO₂ emissions in the Western Balkans. Continued reliance on coal and inefficient energy systems is associated with higher emissions, whereas renewable-energy deployment and green investment contribute to lower CO₂ emissions and improved carbon efficiency. At the same time, this systematic review identifies important regional differences in fossil-fuel dependence, exposure to carbon-related trade risks, urbanization pressures, construction activity, renewable-energy potential, green investment, and financial-sector transition risks across the Western Balkans. These differences indicate that the region does not follow a uniform decarbonization pathway. The study concludes that effective decarbonization requires an integrated policy framework combining renewable-energy development, stronger institutions, green finance, carbon-pricing mechanisms, sustainable urbanization, and industrial upgrading.
Tourism villages surrounding Mount Merapi have demonstrated remarkable resilience, recovering from various disruptions and periods of inactivity. However, these communities continue to face significant sustainability challenges that threaten their long-term viability. These challenges stem from both external factors—particularly their location in a disaster-prone volcanic region—and internal factors related to local community capacity and tourism management capabilities. Building and maintaining resilience in these tourism villages is therefore crucial for ensuring sustainable tourism development in the region. This research develops probabilistic scenarios to assess tourism village resilience in the Mount Merapi area of Yogyakarta Special Region, Indonesia, employing a participatory Bayesian Network approach. The study classified administrative villages into two infrastructure-based clusters and selected ten representative tourism villages across both clusters. While the same Directed Acyclic Graph (DAG) framework was applied to both clusters, probability assessments were conducted independently through cluster-specific focus group discussions (FGDs). Key findings reveal that Cluster B demonstrates a higher baseline probability of achieving “Stable” resilience compared to Cluster A. Through strength analysis, government involvement and internal policy frameworks emerged as critical factors with significant interconnections to other variables. Sensitivity analysis identified economic activities, agrotourism development, and cultural preservation as particularly influential variables in both clusters, though their probability conditions differ between clusters. Forward propagation analysis demonstrated how specific states of sensitive variables affect the likelihood of achieving “Stable” resilience in both clusters. Conversely, backward propagation analysis identified the probability conditions associated with the desired “Stable” resilience state. The study concludes that enhancing tourism village resilience requires an integrated approach that simultaneously addresses economic activities, agrotourism development, cultural preservation, social capital, internal policy frameworks, government roles, and natural disaster response. This holistic strategy is essential for these communities to successfully navigate future sustainability challenges and maintain their resilience in an inherently volatile environment.
Traffic-related air pollution constitutes one of the primary threats to public health and environmental sustainability in urban centers worldwide. Historic centers face particular challenges because their narrow streets, irregular urban configuration, and high vehicle concentration during peak hours hinder the implementation of conventional environmental monitoring systems. This research assessed the pollutant emissions generated by vehicular traffic in the historic center of Ayacucho, Peru, an Andean city located at 2,761 meters above sea level with approximately 29,476 inhabitants in the study area. A mesoscopic simulation model was developed in PTV Vissim using the Mahut and Wiedemann car-following algorithm. Data were collected with unmanned aerial vehicles at 14 access intersections and complemented with traffic signal cycle measurements and speed recordings under free-flow and congested conditions. Emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOC), and fuel consumption were quantified across an area of 0.48 km². Model calibration achieved Geoffrey E. Havers (GEH) values below 5 at 81% of the calibration points, and every point fell below a GEH of 10. The Wilcoxon signed-rank test showed significant differences between emissions under free-flow and congested conditions. A heat map identified Jr. 2 de Mayo, Jr. Pizarro, and Jr. Libertad as the segments with the highest emission rates. The findings provide scientific evidence for authorities and urban planners to implement targeted mitigation strategies, and the approach is replicable in historic centers of intermediate Latin American cities with comparable topographic and urban characteristics.
Fast-fashion supply chains are under pressure to deliver greater traceability, provenance, accountability, and measurable sustainability outcomes, yet the evidence base for blockchain-enabled transparency remains uneven. This scoping review mapped primary research on blockchain applications for supply-chain transparency in fast fashion and directly transferable textile-apparel contexts. In this review, fast fashion is defined by compressed product cycles, high stock-keeping-unit turnover, frequent sourcing changes, intense buyer-driven price pressure, and extensive subcontracting. The search concept from the retained review record was translated into six standard bibliographic databases, and the numerically verified screening set comprised 122 de-duplicated records. After title/abstract screening and full-text assessment, 31 primary studies published between 2020 and 2025 were included. Data were charted on study design, setting, blockchain architecture, transparency function, implementation stage, reported outcome, barrier, enabler, and sustainability implication. The synthesis distinguishes findings specific to fast-fashion or apparel supply chains from transferable evidence derived from adjacent textile-fiber settings. Blockchain most consistently enabled tamper-resistant provenance records, audit-ready documentation, product authentication, and more granular environmental accounting when combined with the Internet of Things (IoT), radio frequency identification (RFID), and quick response (QR) identifiers, enterprise systems, or digital product passports. However, direct evidence of social-sustainability effects, such as improved labor rights, wages, working conditions, or grievance resolution, remained limited. The review therefore supports a qualified conclusion: blockchain is a conditional transparency infrastructure rather than an isolated technological solution, and its value depends on credible source-data capture, supplier incentives, governance arrangements, and policy standards that connect material traceability with environmental, economic, and social accountability.
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