Search Results (5,911)

Calcium carbide slag is a highly alkaline solid waste generated during acetylene production, but its long-term accumulation causes land occupation and persistent environmental risks such as soil alkalinization and water pollution. To support circular economy and carbon emission reduction goals, in this study, we develop an integrated physical decontamination–mineralization process combining calcination, magnetic separation, sedimentation, and CO₂ mineralization. After calcination, magnetic separation, and 8 h of gravity sedimentation, the removal efficiency of Si reaches about 67% (residual Si content reduces to 0.43%), while those of Fe and Al are 75.4% and 74.2%, respectively. The purified calcium-rich slurry is then used for CO₂ mineralization. Under a solid-to-liquid ratio of 10% and a CO₂ flow rate of 0.4 L/min, CO₂ is fixed as carbonate solids, yielding calcite-type CaCO₃ with 97.88% ± 0.35% purity. This process is centered on physical separation and uses no acids, alkalis, or ammonium salts, avoiding secondary pollution while achieving waste valorization and permanent CO₂ sequestration. In this study, we provide a scalable, low-impact pathway for alkaline solid waste valorization and carbon emission reduction, contributing to sustainable consumption and production (SDG 12) and climate action (SDG 13). Full article

This study investigates the volatile composition of twelve medicinal plant species belonging to the Lamiaceae family, which are widely recognized for their diverse biological activities, including antioxidant, antibacterial, and antifungal properties. Despite extensive studies on essential oils, comparative analyses using solvent-free techniques under different microclimatic conditions remain limited. This study investigates the volatile compounds in twelve medicinal plants from the Lamiaceae family using headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). Lamiaceae plants are recognized for their diverse medicinal properties, including antioxidative, antibacterial, and antifungal effects. A total of 74 volatile compounds were identified, encompassing terpenes, alcohols, esters, aldehydes, and ketones. Notably, Lavandula spica L. exhibited the highest number of unique volatiles (28), while Melissa officinalis L. had the fewest (16). Key compounds included Citral (65.48%) in Melissa officinalis L., Menthol (33.37%) and Menthyl acetate (30.53%) in Mentha piperita L., Carvone (45.86%) in Mentha spicata L., and Eucalyptol (54.71%) in Origanum syriacum L. Plants from Adana Botanic Park were rich in terpenes and ketones, whereas those from Osmaniye contained higher levels of alcohols, aldehydes, and esters. The findings emphasize the impact of geographic location on volatile profiles and suggest avenues for further research into medicinal efficacy and optimal dosage. This study supports the sustainable use of plant biodiversity (SDG 15) and highlights the importance of bioactive compounds for human health and well-being (SDG 3). Full article

This paper proposes a contingency-aware, sensitivity-based criterion for the optimal placement of shunt compensation in transmission power systems under N–1 security constraints. Conventional approaches typically rely on post-contingency voltage severity or heuristic optimization techniques, which may fail to capture the system-wide impact of reactive power support during the planning stage. The proposed method integrates contingency severity assessment with a system-wide sensitivity index to support structured and physically interpretable planning decisions. First, a global contingency index is used to identify the most critical operating condition under N–1 scenarios. Based on this condition, a reduced set of candidate buses is selected according to post-contingency voltage magnitudes. These candidates are then ranked using a sensitivity metric defined as the derivative of the contingency index with respect to reactive power injection ($𝜕J/𝜕Q_k$), which quantifies the global effect of local reactive support on system performance. The selected compensation locations are validated through AC optimal power flow simulations, enabling the evaluation of voltage profiles and active power losses under both normal and contingency conditions. The methodology is tested on the IEEE 14-, 30-, and 57-bus transmission systems to assess its scalability and consistency across networks of different sizes. Results show that the bus with the lowest post-contingency voltage is not necessarily the optimal compensation location. Instead, the proposed sensitivity-based criterion identifies buses that provide greater system-wide benefits, including reductions in active power losses and improved voltage recovery. The approach provides a transparent and reproducible planning-oriented decision criterion, supporting improved operational efficiency and aligning with sustainability-oriented objectives in modern power systems. The proposed method provides a reproducible and planning-oriented decision criterion that complements conventional optimization-based approaches. Full article

Sustainable development (SD) has become the most important global mission to provide peace and prosperity for people and the planet, for now and the future. Universities play a fundamental role in advancing SD through education, enabling students to become agents of change. In particular, engineers are essential contributors in this transition to achieve the SD goals through innovative and sustainable designs. This study examined the perspectives and positions of key academic stakeholders regarding the integration of SD in engineering in Lebanese universities, with the aim of understanding ongoing dynamics, strengths and challenges. Semi-structured interviews were conducted with senior engineering faculty members at four leading engineering faculties at three Lebanese universities. The results indicated notable discrepancies in SD integration. These variations are attributed to the impact of external factors, the lack of governmental initiatives, the role of politics, the need to train faculty members, the lack of collaboration between higher education institutions, and the ongoing financial crisis. Full article

The finance–environment nexus in Sub-Saharan Africa remains complex, particularly in nations where institutional quality and fiscal policies are in an early stage. To address this, the study evaluates the impact of financial development on environmental sustainability in Sub-Saharan Africa, emphasising the moderating roles of environmental taxes and regulatory quality. Using a balanced panel methodology across 11 SSA nations from 2006 to 2023, the study employs a multi-estimation model (fixed effects (FE), Fully Modified Ordinary Least Squares (FMOLS) and Autoregressive Distributed Lag (ARDL)) to capture both short- and long-run relationships. From the analysis, the FE and FMOLS estimates indicate that financial development significantly increases ecological footprints, while foreign direct investment and government expenditure are associated with lower environmental footprints. However, the ARDL estimates reveal that environmental taxes and regulatory quality significantly reduce the ecological footprint, motivating a policy shift. Most importantly, the moderation estimation reveals that environmental taxes condition the finance–environment nexus in SSA. This depicts that while financial development worsens environmental outcomes, its adverse effects are nullified and reversed under a stronger environmental tax framework. These findings are relevant to the Environmental Kuznets Curve theory and draw insights from the institutional and financial intermediation theory. The study provides evidence that financial development, when integrated with effective environmental taxation and institutional quality, promotes environmental sustainability in SSA. Policymakers are therefore urged to strengthen environmental tax frameworks, integrate green financial intermediation and intensify regulatory institutions to achieve a sustainable finance–environment model and support SDG 13 in SSA. Full article

In this research, the primary aim is to conduct a systematic review of the thematic evolution of green finance, which remains fragmented and unevenly represented in global academic debates. The objective of this analysis is to scientifically map out the scholarly output on green finance from 2015 to 2026, detailing its intellectual structure, trends, thematic clusters, and emerging lacunae in the field. Primary data extraction from Web of Science was employed to construct the bibliometric database, whereas the identification, screening, and selection of the final dataset were conducted in accordance with the PRISMA guidelines to ensure the study’s transparency and reliability. The main findings highlighted an increasing scholarly interest in the field’s publications from 2019 onward. Key occurrences and citation maps, using RStudio (version 4.1) and Biblioshiny (version 4.5.2), indicate dispersed clusters comprising sustainability transitions, digital finance, bibliometric methods, and a weak link to governance and behavioral perspectives. The co-authorship and country analyses confirm a pronounced geographic imbalance of green finance-related research in academia, with an overrepresentation in the Global North and an underrepresentation in Africa, Latin America, and the MENA region. The analysis further emphasizes the growing role of institutional and ESG regulatory frameworks in shaping research trajectories, while also identifying a limited integration of emerging technological dimensions such as digital finance and artificial intelligence. Thus, the study’s contribution to the literature relies on its critical understanding and structuring of the field’s evolution. The implications include synthesizing research gaps and the need for outcome-oriented impact assessments and mechanism-based models of green finance to ensure significant inclusivity and resilience in the subject’s future agenda. Full article

This study examines how digital transformation in higher education institutions (HEIs) contributes to public value creation, moving beyond efficiency-oriented narratives toward broader societal outcomes. Using a PRISMA-ScR approach, the study systematically reviews 47 peer-reviewed articles published between 2013 and 2025 across major academic databases. The review maps the evolution of scholarship and identifies the key mechanisms through which digital transformation influences public value. The findings reveal three interrelated dimensions shaping outcomes: digital competencies, institutional readiness, and governance alignment. Digital competencies enable the effective adoption and use of technologies, while institutional readiness—comprising digital infrastructure, leadership capacity, and organizational culture—acts as a mediating condition influencing implementation success. Governance alignment, including regulatory coherence, accountability mechanisms, and stakeholder engagement, plays a moderating role in determining whether digital transformation initiatives generate inclusive and socially beneficial outcomes. In addition to positive outcomes such as improved access, service quality, and transparency, the review identifies critical risks—including digital inequality, data governance challenges, and algorithmic bias—that may constrain public value creation, particularly in resource-constrained and Global South contexts. Building on these findings, the study develops the Global Digital Transformation—Public Value Creation (G-DTPVC) framework as an evidence-synthesized model derived from the reviewed literature. The framework specifies key constructs, causal relationships, and indicative measures to support future empirical research and policy application. By linking digital transformation processes in HEIs to broader public value outcomes and Sustainable Development Goals (SDGs 4, 9, and 16), this study advances theoretical understanding and provides actionable, context-sensitive guidance for policymakers and institutional leaders seeking to foster inclusive, accountable, and resilient higher education systems. Full article

Achieving sustainable development requires decoupling economic growth from environmental degradation. In this context, this study examines the effects of tourism arrivals on CO₂ intensity and energy intensity, two key indicators of environmental sustainability aligned with SDGs 7 and 13. Panel autoregressive distributed lag (ARDL) and nonlinear ARDL models are employed using a balanced panel of 54 countries over the period 1996–2023. In addition, Wald tests for long-run asymmetry, dynamic multiplier analysis, and Dumitrescu–Hurlin causality tests are applied. The results confirm the existence of stable long-run relationships between tourism arrivals and both CO₂ intensity and energy intensity. In the symmetric framework, tourism growth is associated with significant long-run reductions in CO₂ and energy intensity, while short-run effects are negative and significant only for CO₂ intensity. In the asymmetric framework, positive tourism shocks generate stronger and more persistent reductions in both intensity measures, whereas negative shocks lead to weaker environmental efficiency gains. Moreover, the Wald test shows the existence of long-run asymmetry between positive and negative tourism shocks. In addition, the dynamic multiplier analysis confirms that environmental intensity adjusts gradually over time following tourism shocks. Finally, Dumitrescu–Hurlin causality tests indicate bidirectional Granger causality relationships between tourism arrivals and environmental intensity indicators. The findings are robust to dynamic endogeneity, the COVID-19 shock, and country heterogeneity. Overall, the findings indicate that tourism arrivals contribute to lowering long-term environmental intensity, consistent with relative decoupling and the goals of sustainable tourism development. Full article

This paper examines how remittances-to-GDP are conditionally associated with GDP growth upswings and downturns in four lower-middle-income countries (LMICs) in mainland Southeast Asia—Cambodia, Laos, Myanmar, and Vietnam (CLMV)—over 2000–2021, conditional on other external inflows including foreign direct investment (FDI), official development assistance (ODA), and trade openness. Employing a nonlinear Autoregressive Distributed Lag (N-ARDL) model with a Dynamic Fixed Effects (DFE) estimator, this study estimates short- and long-run regime-differentiated associations between GDP growth regimes and remittances to GDP, controlling for foreign direct investment (FDI), official development assistance (ODA), and trade openness. GDP growth is decomposed into above- and below-median regimes, allowing the model to examine whether remittance dynamics differ across growth upswings and downturns. Panel estimates are complemented with dynamic multipliers that trace conditional adjustment paths over different horizons. The results reveal a high-growth-driven regime pattern rather than formal statistical evidence of unequal high- and low-growth coefficients. In the long run, above-median growth significantly reduces remittances to GDP (${\widehat{\theta }}_1=-0.130$, very strong evidence), consistent with the household insurance motive; below-median growth has no significant long-run association (${\widehat{\theta }}_2=-0.127$, no evidence). In the short run, above-median growth is positively associated with remittances (${\widehat{\tilde{\beta }}}_1^{+}=0.033$, very strong evidence), while below-median growth again shows no significant short-run response (${\widehat{\tilde{\beta }}}_1^{-}=0.051$, no evidence). Formal Wald tests do not reject equality between the high- and low-growth coefficients in either horizon; therefore, the findings should be interpreted as a regime-differentiated significance pattern within a nonlinear specification, not as formal proof of coefficient asymmetry. Taken together, these responses are consistent with a one-sided counter-cyclical interpretation of remittances: remittances to GDP decline when domestic growth is above the median, while no significant adjustment is observed during below-median growth episodes. The pattern documented here is therefore driven by the high-growth regime and should not be read as evidence of an active counter-cyclical surge during downturns. Trade openness and ODA exhibit significant positive short-run co-movement with remittances, whereas FDI shows a strong positive long-run association with remittances to GDP. The novelty of this study lies in providing new panel evidence on regime-differentiated remittance–growth associations for CLMV within a nonlinear N-ARDL and dynamic multiplier framework, while transparently reporting that formal Wald tests do not reject equality between high- and low-growth coefficients. Policy implications center on facilitating reliable remittance channels—reducing transfer costs and expanding financial inclusion—without assuming that remittance inflows automatically rise during downturns. Full article

Background/Objectives: Although urban greening interventions are increasingly implemented to improve livability, environmental quality, and adaptation capacity in cities, their evaluation still predominantly relies on physical outputs rather than validated, resident-centered outcomes. This study examined whether the five-item attachment dimension of the Urban Green Attachment Scale (UGAS) can reliably indicate the social integration of newly introduced greenery in an SDG 11-oriented evaluation context. The present adaptation of the UGAS captures the perceived importance of the planting, its contribution to well-being, anticipated loss, willingness to protect it, and aesthetic appreciation. Methods: Data were collected through two independent face-to-face surveys conducted among residents of the same housing estate shortly after a greening intervention in May 2025 (n = 150) and September 2025 (n = 191). The first sample was used for exploratory factor analysis (EFA) and the second for confirmatory factor analysis (CFA). Reliability was assessed using Cronbach’s α and McDonald’s ω; inter-item associations were estimated using Kendall’s tau-b; and construct validity was examined through known-groups comparisons with theoretically relevant appraisals and stewardship-related indicators. Results: The adapted UGAS demonstrated high internal consistency, low floor and ceiling effects, and moderate to strong inter-item associations. Exploratory factor analysis supported a unidimensional solution with high loadings and 65.7% explained variance, and confirmatory factor analysis corroborated this structure after minor, theory-guided localized refinements. Higher UGAS scores were consistently observed among residents who reported stronger calming and home-related effects, perceived healthier local conditions, expressed willingness to help care for the plants, and demonstrated a readiness to cooperate in improving the area. Conclusions: The results support the five-item UGAS attachment score as a compact, psychometrically adequate measure of residents’ attachment to newly planted urban greenery. Rather than replacing official SDG indicators, the UGAS can complement them at the project level by determining if urban greening becomes socially meaningful and accepted and if it supports stewardship. In this sense, UGAS offers municipalities a practical tool for linking physical greening outputs with resident-centered outcomes relevant to inclusive public spaces, participatory urban development, and the long-term social durability of urban greening interventions. Full article

Heritage-led urban redevelopment is increasingly adopted to advance cultural continuity and social vitality; however, its long-term sustainability is often compromised due to the absence of user-oriented assessment methods. Conventional Post-Occupancy Evaluation (POE) approaches are limited in their ability to capture experiential, social, and participatory dimensions of architectural and urban spaces. This study examines the potential of Virtual Reality (VR) as a participatory POE tool for sustainable heritage redevelopment through the case study of Souq Al Muharraq in Bahrain. A convergent mixed-method approach is employed, integrating immersive VR 360-degree walkthroughs, structured questionnaires, qualitative semi-structured interviews, and expert evaluation. The findings reveal significant discrepancies between design intentions and lived experience, specifically in thermal comfort, circulation, social usability, and informal spatial practices. The study demonstrates that VR supports a user-centered and experiential approach aligned with Sustainable Development Goals (SDGs) 9, 11, and 16. It further proposes a sustainable and cost-efficient framework for architecture and urban projects’ evaluation by enabling early and post-user-centered evaluation of projects to reduce costly revisions and the creation of inclusive, adaptive, and resilient architecture and urban spaces. Full article

With the 2030 deadline for the United Nations Sustainable Development Goals (SDGs) approaching, there is a growing global urgency to identify innovative, scalable, and inclusive AI-based or AI-enabled solutions capable of accelerating progress across sectors. Yet the benefits of AI remain unevenly distributed, particularly in low-resource settings where limited infrastructure, cost barriers, and unequal access to skills constrain adoption. This paper explores how Tiny Machine Learning (TinyML)—a low-power, low-cost edge AI paradigm—offers a concrete technological pathway aligned with the principles of Frugal AI, providing accessible, energy-efficient, and context-adapted tools for sustainable development. We evaluate how participatory citizen science, when combined with TinyML, enables communities to co-create AI applications that address locally defined challenges in environmental monitoring, agriculture, and public health. Drawing on early outcomes from workshops, collaborative projects, and innovation competitions, the paper examines how TinyML-enabled participatory approaches cultivate technical skills, stimulate grassroots entrepreneurship, and generate prototypes suited to low-resource environments. Using a qualitative multiple-case study of 50 participatory TinyML initiatives across 22 countries, we analyse how frugal edge-AI practices support skills formation, prototype development, and early entrepreneurial engagement. The analysis identifies the pedagogical, technical, and institutional frameworks that support successful participatory AI initiatives, emphasizing open educational resources, cross-sector partnerships, and community-driven problem formulation. We introduce the Frugal Edge AI Lean Canvas to help innovators identify novelty, ethical implications, and measurable impact. TinyML-based participatory innovation offers a promising route for accelerating SDG progress by expanding who can create, deploy, and benefit from AI. Full article

Ensuring the sustainability of ageing water-storage infrastructure is an increasingly urgent challenge under climate-driven hydrological extremes. In the Republic of Korea, approximately 18,000 small and medium-sized agricultural reservoirs—many several decades old—pose escalating risks to downstream communities and threaten progress toward SDGs 6, 11, and 13. This study presents a 0.5 m airborne LiDAR-based, GPU-accelerated two-dimensional shallow-water simulation of a hypothetical breach of the Geumosan Reservoir, South Korea, using a MUSCL + HLL solver verified against the Ritter (1892) and Stoker (1957) analytical dam-break solutions. Two scenarios are compared: Run A with a uniform Manning coefficient (n = 0.035) and Run B with spatially variable roughness derived from the Korean Ministry of Environment land-cover map (mean n = 0.0711). Mass conservation is preserved to within 0.01% during the closed-domain phase. Spatially variable roughness expands the total inundated area by 8.5% (3.05 → 3.31 km²) while reducing the Extreme-hazard zone, defined by the DEFRA hazard rating HR = h(v + 0.5), by 24% (1.49 → 1.14 km²); arrival times in the downstream urban corridor are delayed by up to 30 min. Uniform Manning assumptions therefore systematically overestimate extreme-hazard extents while underestimating the broader shallow-inundation footprint—biases comparable in magnitude to breach-parameter uncertainty. By delivering reproducible, georeferenced hazard, arrival-time, and damage-class maps for emergency action planning, the proposed framework supports risk-informed and sustainable management of ageing reservoir infrastructure and community-level disaster resilience aligned with the Sendai Framework and SDGs 6, 11, and 13. Full article

Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article

Quantitative sustainability assessments in the EU rarely differentiate between the roles of governments, businesses, and the population, making it difficult to empirically test theories of socio-technical transitions, stakeholder governance, and convergence/club convergence. To address this gap, the study constructs four stakeholder-specific indices: the Government Sustainability Index (GSI), Environmental Sustainability Index (ESI), Population Sustainability Index (PSI), and Business Sustainability Index (BSI) alongside a Composite Sustainability Index (CSI). The indices are built from harmonised Eurostat, European Environment Agency, and SDG Index data using min–max normalisation, covering all 27 EU Member States over the period of 2015–2024 (270 country–year observations). The empirical analysis applies K-means clustering, compound annual growth rates (CAGRs), and correlation analysis, complemented by a robustness module testing alternative weighting schemes, z-score normalisation, and ±10% indicator perturbations. The results identify four relatively stable sustainability tiers with limited inter-tier mobility, an S-curve-type relationship between initial performance levels and subsequent growth, a consistent hierarchy of stakeholder response speeds (ESI > GSI > PSI), and a structural slowdown after 2019. These patterns are robust across alternative specifications and imply that EU sustainability transitions follow multiple, tier-structured trajectories shaped by institutional lock-in rather than converging toward a single equilibrium. The framework offers a basis for tier-differentiated and stakeholder-sensitive policy strategies. Full article