Search Results (5,148)

The accelerating global transition to electric mobility demands data-driven infrastructure planning that balances technical, economic, and spatial considerations. This study develops a scenario-based demand and economic modeling framework to estimate electric vehicle (EV) charging infrastructure needs across Abu Dhabi’s urban and rural regions through 2050. Two adoption pathways, Progressive and Thriving, were constructed to capture contrasting policy and technological trajectories consistent with the UAE’s Net Zero 2050 targets. The model integrates regional travel behavior, energy consumption (0.23–0.26 kWh/km), and differentiated charging patterns to project EV penetration, charging demand, and economic feasibility. Results indicate that EV stocks may reach 750,000 (Progressive) and 1.1 million (Thriving) by 2050. The Thriving scenario, while demanding greater capital investment (≈108 million AED), yields higher utilization, improved spatial equity (Gini = 0.27), and stronger long-term returns compared to the Progressive case. Only 17.6% of communities currently meet infrastructure readiness thresholds, emphasizing the need for coordinated grid expansion and equitable deployment strategies. Findings provide a quantitative basis for balancing economic efficiency, spatial equity, and policy ambition in the design of sustainable EV charging networks for emerging low-carbon cities. Full article

Background: Sustainable operations and management are imperative in many sectors, including aquaculture, to adapt to the increasing complexity and unprecedented challenges across the supply chain. Although research in sustainable supply chain management (SSCM) has grown significantly, it remains inadequate for fully addressing the distinct challenges of the aquaculture supply chain (ASC). Therefore, this paper aims to introduce the concept of the sustainable management of the aquaculture supply chain (SMASC) and identify research gaps for future research directions. Methods: This study conducts a systematic literature review using the Web of Science and Scopus databases to retrieve peer-reviewed articles published between 2000 and 2025. A total of 116 articles were subjected to an in-depth content analysis, leading to the conceptualization of SMASC. Results: The findings indicate that ASC exhibits considerable heterogeneity in structure and performance measures, reflecting the inherent diversity of species and culture systems. The proposed conceptual framework provides a coherent understanding of SMASC by extending generic SSCM to incorporate distinctive characteristics of aquaculture, while systematically identifying the core pillars and their interrelationships. Conclusions: The SMASC framework establishes a unified theoretical foundation for the comprehensive management of ASCs, offering conceptual and practical insights for both researchers and practitioners. Full article

This study examines the social determinants of the digital divide in pre-service teacher education through the design and validation of the Digital Hospitality Scale (DSBD-HD-FID). The instrument was developed to diagnose social inequalities across six key dimensions: socioeconomic status, geographic location, gender, age, disability status, and interculturality. These dimensions are understood as structural factors shaping access to, use of, and participation in digital environments within teacher education. The research followed a non-experimental, quantitative, and cross-sectional design, including content validation through expert judgment and statistical analysis based on a pilot sample of education students from Chile and Portugal. An exploratory factor analysis was conducted, and internal consistency was assessed using Cronbach’s alpha coefficient. The results confirm strong content and construct validity, as well as high reliability (α = 0.93). Empirical findings indicate that socioeconomic status and geographic location significantly condition access to connectivity and digital literacy, while gender differences emerge mainly in recreational uses and frequency of digital training. Beyond these results, the study highlights the relevance of addressing digital inequalities in teacher education through inclusive and equity-oriented training policies. The findings support the integration of digital hospitality, human rights education, and the Sustainable Development Goals into initial teacher training curricula as measurable and evaluable dimensions, providing an evidence-based framework to inform future teacher education policies aimed at reducing digital divides and promoting social cohesion. Full article

Accurate quantification of aircraft emissions and their uncertainties is essential for well-informed policy-making, air quality management, and the development of sustainable airport strategies. This study addresses uncertainties in aircraft emission estimates implemented for local air pollutants with hourly resolution at six European airports. Publicly available flight-tracking data were used to determine aircraft movements and types, but they typically lack detailed information on aircraft engine models, thus contributing to uncertainties in emission factors. Times-in-mode for take-off, climb-out, and approach modes followed International Civil Aviation Organization (ICAO) recommendations, while taxi times, known to vary between airports, were modeled using statistical distributions derived from Eurocontrol, and the contribution of taxi time to overall uncertainty in emission estimates was investigated. Monte Carlo simulation combined with Sobol sensitivity analysis identified the relative contribution of each uncertainty source. On average, the results indicate an uncertainty of 23% for CO, 34% for HC, 7% for NO_x, and 21% for PM across the airports analyzed. Overall, the proposed methodology introduces a novel framework utilizing publicly available, hourly resolved flight-tracking data with robust uncertainty analysis to estimate airport-level emissions with enhanced reliability, providing crucial information for local air quality assessment and policy development. Full article

The northern coast of Mauritania presents a strategic opportunity for clean energy investment due to its remarkable potential for green hydrogen production through wind energy. To determine the best location for wind-based green hydrogen production, this paper established a Multi-Criteria Decision-Making framework (MCDM) that combines the Analytic Hierarchy Process (AHP) and Evidential Reasoning (ER) to assess five coastal sites: Nouakchott, Nouamghar, Tasiast, Boulanoir, and Nouadhibou. Four main criteria (i.e., economic, technical, environmental, and social) and twelve sub-criteria were taken into account in the assessment. To ensure reliability and contextual accuracy, the data used in this study were obtained from geographic databases, peer-reviewed literature, and structured expert questionnaires. The results indicate that site 5 (Nouadhibou) is the most suitable location for green hydrogen generation using wind energy. Sensitivity analysis confirms the robustness of the ranking results, validating the reliability of the proposed hybrid framework. The findings of this study provide critical, data-driven decision-support insights for investors and policymakers, guiding the strategic development of sustainable wind-based green hydrogen projects along Mauritania’s coastline. Full article

Digital technologies have become increasingly crucial during and, after the COVID-19 pandemic, have sparked significant scientific interest around their impact on sustainable well-being. Despite extensive research, conclusive evidence on whether digital technologies enhance or undermine sustainable well-being remains elusive. Saudi Arabia has made significant progress in its technological infrastructure, but comprehending the implications of this progress still poses a challenge. Drawing on the prior literature and grounded in the theoretical perspective of the Capability Approach, this study proposes five dimensions of digital inclusion (accessibility, usability, digital skills, affordability, and connectivity) and examines their collective influence on students’ sustainable well-being, specifically happiness and life satisfaction. This study employs a cross-sectional design, with data collected from 238 university students in Saudi Arabia using convenience sampling. Ten hypotheses were tested using partial least squares structural equation modeling in SmartPLS-4. This study supports the conceptualization of digital inclusion as a multidimensional construct comprising five key dimensions. The results indicate that affordability, usability, connectivity, and digital skills have a substantial impact on happiness, whereas accessibility, usability, connectivity, and digital skills have a considerable effect on life satisfaction. Nonetheless, the correlations between accessibility and happiness, as well as between affordability and life satisfaction, were not found to be supported. This implies that these dimensions might have different effects on the affective and cognitive aspects of sustainable well-being. These results suggest that digital inclusion may play a role in shaping individuals’ interactions with technology and their perceived sustainable well-being. This study proposes and evaluates a strategic framework that may guide efforts to promote digital inclusion and support sustainable well-being among university students. It provides valuable insights for policymakers, educational institutions, and industry stakeholders seeking to enhance digital access and capabilities. The findings highlight the potential value of developing strategies that address students’ digital needs as part of a holistic approach to sustainable well-being. The findings also highlight the importance of viewing digital inclusion as an interconnected framework, rather than as a set of discrete, unrelated factors. By demonstrating how digital inclusion promotes sustainable well-being, this study contributes to the broader sustainability agenda by highlighting digital equity as an essential component of socially sustainable development in the Saudi context. Full article

In an era where environmental consciousness increasingly shapes consumer behaviour, the tourism industry faces the dual challenge of promoting destinations while ensuring ecological sustainability. This study explores how web analytics and big data can be leveraged to enhance the visibility and attractiveness of eco-friendly destinations. Building upon digital marketing and sustainability frameworks, the authors develop a data-driven methodology that integrates website performance metrics, search behaviour patterns, and social media engagement indicators. After data collection, statistical and content analyses were implemented, followed by a Fuzzy Cognitive Map (FCM) to visualise the interrelationships between online user behaviour, environmental awareness, and destination appeal. Full article

This study aims to improve the prediction accuracy of rockburst intensity grades in gently dipping rock strata, and provide reliable technical support for risk prevention, long-term stable production and sustainable development in underground engineering construction. Therefore, a rockburst intensity grade prediction model combining multi-strategy improved particle swarm optimization (MIPSO) with random forest (RF) is proposed, and the stress coefficient (SCF), brittleness coefficient (B) and elastic energy index (W_et) are selected as input indicators. After the algorithm and model are validated using benchmark test functions and the five-fold cross-validation method, their performance is compared with that of the other four models based on evaluation metrics, and the Shapley interpretability analysis (SHAP) is conducted. The results show that the performance of the model is superior to that of other models, and the importance ranking of the prediction indicators is SCF, W_et, and B. Finally, the application software developed based on the model is used for rockburst intensity grade prediction; rockburst prediction indicators are obtained through experiments and numerical simulations, and the prediction results obtained after importing them into the software are consistent with the actual situation, which proves that the rockburst prediction framework constructed in this paper has practicality. Full article

The ball mill is a critical size reduction equipment in industries such as mining and metallurgy. However, the sustainable reliability modeling of the entire system is challenging due to its complex service conditions. This paper presents a systematic framework for the reliability analysis of ball mills based on Stress–Strength Interference Theory (SSIT). Based on a reliability block diagram (RBD), this study establishes a system-level reliability model for the ball mill. Within this framework, the cylinder model is developed using the energy conservation principle between impact energy and strain energy; the gear model comprehensively considers both contact and bending fatigue failure modes; and the bolt model is constructed through mechanical analysis in conjunction with Hooke’s law. In the case study, a laboratory-scale mill (Φ5.5 × 2.6 m shell, effective grinding chamber: 5.3 m inner diameter × 2.376 m length) operating at 14 RPM under dry grinding conditions is analyzed. The reliability of individual components and the entire system is computed using Monte Carlo simulation. The results indicate that the overall system reliability increases when one of the following three conditions is met: the surface hardness of the gear is higher and the tangential force is lower; the impact velocity on the cylinder is lower and the impacted area is larger; or the tensile force on the bolt is reduced. Full article

This study develops a field-based techno-economic model and decision framework for a CO₂-enhanced oil recovery and storage project under joint market uncertainty. Historical drilling and completion expenditures calibrate investment cost functions, and three years of production data are fitted with segmented hyperbolic Arps curves to forecast 20-year oil output. Markov-chain models jointly generate internally consistent pathways for crude oil, ETA, and purchased CO₂ prices, which are embedded in a Monte Carlo valuation. The framework outputs probability distributions of NPV and deferral option value; under the mid scenario, their mean values are USD 18.1M and USD 2.0M, respectively. PRCC-based global sensitivity analysis identifies the dominant value drivers as oil price, CO₂ price, utilization factor, oil density, pipeline length, and injection volume. Techno-economic boundary maps in the joint oil and CO₂ price space then delineate feasible regions and break-even thresholds for key design parameters. Results indicate that CCUS-EOR viability cannot be inferred from oil price or any single cost factor alone, but requires coordinated consideration of subsurface constraints, engineering configuration, and multi-market dynamics, including the value of waiting in unfavorable regimes, contributing to low-carbon development and sustainable energy transition objectives. Full article

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article

The goal of this scientific study is to clarify whether and how digital–intelligent integration contributes to urban carbon efficiency and to identify the conditions under which this contribution becomes nonlinear and policy-relevant. Focusing on 39 prefecture-level cities in the middle reaches of the Yellow River Basin during 2011–2022, we adopt an integrated measurement–modelling approach that combines efficiency evaluation, machine-learning interpretation, and dynamic–spatial validation. Specifically, we construct two super-efficiency DEA indicators: an undesirable-output SBM incorporating CO₂ emissions and a conventional super-efficiency CCR index. We then estimate nonlinear city-level relationships using XGBoost and interpret the marginal effects with SHAP, while panel vector autoregression (PVAR) and spatial diagnostics are employed to validate the dynamic responses and spatial dependence. The results show that digital–intelligent integration is positively associated with both carbon-related and conventional efficiency, but its marginal contribution is strongly conditioned by human capital, urbanisation, and environmental regulation, exhibiting threshold-type behaviour and diminishing returns at higher digitalisation levels. Green efficiency reacts more strongly to short-run shocks, whereas conventional efficiency follows a steadier improvement trajectory. Heterogeneity across urban agglomerations and evidence of spatial clustering further suggest that uniform policy packages are unlikely to perform well. These findings highlight the importance of sequencing and policy complementarity: investments in digital infrastructure should be coordinated with institutional and structural measures such as green finance, environmental standards, and industrial upgrading and place-based pilots can help scale effective digital applications toward China’s dual-carbon objectives. The proposed framework is transferable to other regions where the digital–climate nexus is central to smart and sustainable urban development. Full article

Brown algae of the Cystoseira genus are recognized as valuable sources of bioactive compounds, including polysaccharides. Within the framework of current restoration efforts regarding damaged Ericaria amentacea populations in the Mediterranean Sea, the valorization of apices derived from ex situ cultivation waste represents a sustainable opportunity for industrial and biomedical applications. In this study, sodium alginate (SA) was extracted from E. amentacea apex by-products using a hydrothermal–alkaline method and subsequently chemically characterized. FTIR analysis showed O-H, C-H, and COO- stretching compatible with commercial alginates, while 1H-NMR spectroscopy indicated high β-D-mannuronic acid content, with an M/G ratio of 2.33. The extracted SA displayed a molecular weight of 1 × 10⁴ g/mol and a polydispersity index of 3.5. The bioactive properties of the SA extract were investigated in chemico and in vitro. SA exhibited remarkable antioxidant activity, showing significant DPPH and nitric oxide-radical-scavenging capacity. Furthermore, SA demonstrated a strong anti-inflammatory effect in LPS-stimulated macrophages through modulation of several inflammatory mediators (i.e., IL-6, IL-8/CXCL5, MCP-1, and TNF-α). In particular, SA promoted a striking iNOS gene expression inhibition, which, paired with its direct NO-scavenging ability, paves the way for future pharmacological use of E. amentacea derivatives, particularly if sustainably obtained from restoration activity waste. Full article

Hot-fluid injection in thermal-enhanced oil recovery (thermal-EOR, TEOR) imposes temperature-driven volumetric strains that can substantially alter in situ stresses, fracture geometry, and wellbore/reservoir integrity, yet existing TEOR modeling has not fully captured coupled thermo-poroelastic (thermo-hydro-mechanical) effects on fracture aperture, fracture-tip behavior, and stress rotation within a displacement discontinuity method (DDM) framework. This study aims to examine the influence of sustained hot-fluid injection on stress redistribution, hydraulic-fracture deformation, and fracture stability in thermal-EOR by accounting for coupled thermal, hydraulic, and mechanical interactions. This study develops a fully coupled thermo-poroelastic DDM formulation in which fracture-surface normal and shear displacement discontinuities, together with fluid and heat influx, act as boundary sources to compute time-dependent stresses, pore pressure, and temperature, while internal fracture fluid flow (Poiseuille-based volume balance), heat transport (conduction–advection with rock exchange), and mixed-mode propagation criteria are included. A representative scenario considers an initially isothermal hydraulic fracture grown to 32 m, followed by 12 months of hot-fluid injection, with temperature contrasts of ΔT = 0–100 °C and reduced pumping rate. Results show that the hydraulic-fracture aperture increases under isothermal and modest heating (ΔT = 25 °C) and remains nearly stable near ΔT = 50 °C, but progressively narrows for ΔT = 75–100 °C despite continued injection, indicating potential injectivity decline driven by thermally induced compressive stresses. Hot injection also tightens fracture tips, restricting unintended propagation, and produces pronounced near-fracture stress amplification and re-orientation: minimum principal stress increases by 6 MPa for ΔT = 50 °C and 10 MPa for ΔT = 100 °C, with principal-stress rotation reaching 70–90° in regions adjacent to the fracture plane and with markedly elevated shear stresses that may promote natural-fracture activation. These findings show that temperature effects can directly influence injectivity, fracture containment, and the risk of unintended fracture or natural-fracture activation, underscoring the importance of temperature-aware geomechanical planning and injection-strategy design in field operations. Incorporating these effects into project design can help operators anticipate injectivity decline, improve fracture containment, and reduce geomechanical uncertainty during long-term hot-fluid injection. Full article