Search Results (13,289)

The sustained growth of urban areas has increased the complexity of managing services, infrastructure, and mobility, creating a need for advanced technological solutions capable of responding dynamically to rapidly changing environments. In this context, adaptive smart urban systems have emerged as an innovative alternative that integrates artificial intelligence (AI) to optimize real-time decision making. This study presents a systematic literature review and bibliometric analysis of 64 scientific articles focused on the frameworks underpinning these systems. The methodology applied is based on the selection and critical analysis of indexed scientific publications, enabling the identification of predominant approaches such as machine learning, deep learning, multi-agent systems, and reinforcement learning. The findings reveal a strong convergence between AI, the Internet of Things (IoT), and Big Data, as well as significant limitations in terms of interoperability, data governance, and scalability. It is concluded that, while the advances are promising, the consolidation of these systems requires a comprehensive approach that combines technological innovation, appropriate regulation, and social sustainability. Full article

Despite the growing adoption of AI-enabled green hospitality services, limited research has examined how these services influence customer loyalty through the sequential mediating roles of service quality and green trust. This research investigates the influence of AI-enabled green hospitality services on customer loyalty in Egyptian four- and five-star hotels, mediated sequentially by service quality and green trust. A cross-sectional survey was conducted using convenience sampling, and data were collected from 432 guests staying in four- and five-star hotels in Egypt. The proposed model was analyzed using PLS-SEM. Results indicated that AI-enabled green hospitality services enhance service quality (β = 0.796, p < 0.001), which positively impacts green trust (β = 0.244, p < 0.001). Green trust also showed a positive influence on customer loyalty (β = 0.208, p < 0.001). Both service quality and green trust mediated the relationship between AI-enabled green hospitality services and customer loyalty, both individually and sequentially. The findings also confirmed a significant sequential mediation effect of service quality and green trust in the relationship between AI-enabled green hospitality services and customer loyalty. The study concludes that technological innovation coupled with enhanced service experiences and credible environmental practices increases customer loyalty. Hotels should integrate AI with sustainability initiatives, improve service quality, and communicate environmental commitments transparently to encourage trust and long-term loyalty. Full article

The objective of this paper is to examine the United Arab Emirates (UAE) as a test case of Gulf energy transition finance by analyzing how a hydrocarbon-dependent economy is constructing the financial, regulatory, and institutional architecture required to move from net-zero pledges to climate finance flows. Rather than treating climate finance as a set of isolated instruments, the paper conceptualizes the UAE’s approach as a state-led transition-finance model shaped by Gulf state capitalism, sovereign wealth accumulation, national oil company strategy, financial regulation, and post-COP28 climate diplomacy. Using a qualitative policy and institutional review, the paper maps the UAE’s transition-finance architecture across three interrelated dimensions: institutions and governance, financial instruments, and policy alignment. It examines the role of federal strategies such as Net Zero 2050 and the UAE Energy Strategy 2050, regulatory actors including the Central Bank of the UAE, the Securities and Commodities Authority (SCA), Abu Dhabi Global Market (ADGM), and Dubai Financial Services Authority (DFSA), and key financial mechanisms including green bonds and sukuk, sustainability-linked finance, sovereign wealth fund investments, national oil company decarbonization strategies, blended-finance platforms, and carbon-market mechanisms. The analysis finds that the UAE has developed a distinctive state-led, finance-centric model for financing the energy transition. This model enables rapid capital mobilization, de-risking of private investment, and strong international positioning, particularly following COP28 and the launch of ALTÉRRA. However, its effectiveness is constrained by unresolved tensions between net-zero ambition and hydrocarbon expansion, fragmented sustainable-finance regulation, limited carbon-pricing signals, uneven disclosure practices, underdeveloped domestic green capital markets, and restricted access to green finance for SMEs. The paper argues that the UAE’s climate-finance architecture is best understood neither as simple green diversification nor as symbolic climate positioning, but as an emerging Gulf model of transition finance: well-capitalized, and institutionally coordinated, yet structurally shaped by the same hydrocarbon rents and state-led governance logics it seeks to transform. By positioning the UAE as a benchmark, the paper contributes to debates on climate finance, state capitalism, and transition governance in hydrocarbon-dependent economies, while identifying the coherence gaps to be addressed for climate finance to support economy-wide decarbonization. Full article

The assimilation capacity of the biosphere and the sustainability of the living resources are enhanced by the efficient and continued contribution of the vegetation from all ecoregions of the Earth. The urban greenery fulfills many regulatory ecosystem services (RES) as well. In this regard, the importance of urban green spaces (UGS) in helping to reduce the adverse impacts of overcrowding and changing climate is of pertinence. Lack of quantitative information from urban settings in different climatic regions seriously constrains the recognition of the important role UGS play in carbon storage and sequestration. To assess how the UGS is aiding the retention of carbon, which is photosynthetically assimilated into biomass and/or sequestered, relevant field parameters were collected from 4010 trees belonging to 34 different species, different hedge plants, and groundcover grasses spread in 24,991 m² area in three parks of Panaji city, India. Standard methods were followed to derive carbon stock and sequestration rates by trees, hedge plants, and groundcover. Notwithstanding wide differences between tree species, the weighted mean of CO₂ sequestered per tree averaged 55 kg y⁻¹ (ca. 78.82 tons ha⁻¹) in Panaji city. Accordingly, the CO₂ sequestration potential of trees, in the UGS of Panaji (by 76,751 trees) and Tumkur (with an estimated 38,152 trees) cities, respectively, was 4221.31 tons y⁻¹ ha⁻¹ and 2098 tons ha⁻¹ y⁻¹ @ 55 kg tree⁻¹ y⁻¹. It is apparent from this first-time study that calculated tree carbon biomass and species-wise yearly carbon sequestration rates (CSRs) of 78.82 tons ha⁻¹ y⁻¹ and that of carbon production rates of 31.77 tons ha⁻¹ y⁻¹ are far higher than the previously reported CSR estimates variously from 1 to 8 tons ha⁻¹ y⁻¹ and carbon production rates 3.23 to 6.55 tons ha⁻¹ y⁻¹. The hedge row carbon biomass averaged 13.18 tons ha⁻¹ and sequestration of 48.38 tons ha⁻¹ y⁻¹ CO₂. Similarly, occupying over 42% of the UGS, the groundcover carbon biomass averaged 14.69 tons ha⁻¹ with sequestration of 53.92 tons CO₂ ha⁻¹ y⁻¹. Combined CSP of existing trees, groundcover, and hedge plants in Panaji and Tumkur city UGS apparently neutralize carbon footprint of over 4550 and 2200 Indians at an annual per capita emission of 1.94-ton. It is thus undeniable that in our global fight against climate change, the addition of inputs and data from studies like these can aid in planning mitigation measure as well as in fulfilling local/regional sustainability plans and needs. Full article

The rapid growth of e-commerce has intensified last-mile delivery activities in urban areas, creating challenges for city logistics systems related to operational efficiency, congestion, and environmental impacts. In this context, understanding the factors that influence customer satisfaction with logistics operators is increasingly important, as mismatches between customer expectations and delivery service characteristics may lead to operational inefficiencies such as failed delivery attempts and repeated delivery rounds. This study proposes a machine learning framework for predicting customer satisfaction with postal and logistics operators in urban delivery systems using survey data on customer characteristics, preferences, and service perceptions. Several machine learning algorithms were developed and evaluated to identify the key determinants of customer satisfaction and assess their predictive performance. Beyond predictive accuracy, the study interprets customer satisfaction as an indicator of the alignment between customer expectations and delivery service configurations. Improved alignment may support service configurations that reduce delivery mismatches and repeated delivery attempts, which are recognized as a significant source of additional transport activity in urban freight systems. By identifying customer segments whose expectations are not adequately addressed by existing delivery services, the proposed framework can support more informed service design and operational decision-making. From a city logistics perspective, the potential reduction in failed deliveries and repeated delivery rounds may contribute to lower vehicle kilometers travelled, congestion, energy consumption, and emissions associated with urban freight transport, although these operational and environmental indicators were not directly measured in this study. The proposed approach therefore provides a data-driven decision-support tool that can help operators improve service quality and serve as a basis for future integration with operational and environmental indicators in sustainable last-mile delivery planning. Full article

The transition towards Industry 4.0 has significantly heightened the need for manufacturing firms to assess their organisational readiness for the digital transformation of production systems while aligning with sustainable manufacturing goals. Despite increasing scholarly attention to Industry 4.0, there is limited evidence of how organisational and technological capabilities, through readiness, translate into sustainable manufacturing outcomes in emerging economies such as Pakistan. This study addresses this gap by examining the relationships among the six IMPULS dimensions, namely strategy and organisation, smart factory, smart operations, smart product, data-driven services, and employees, together with additional organisational and technological readiness factors, Industry 4.0 readiness, and sustainable manufacturing in the context of Pakistani manufacturing firms. A quantitative cross-sectional design was used, while data were collected from 470 respondents across various manufacturing sectors using a structured questionnaire constructed on validated measurement scales. This study develops and empirically tests an extended IMPULS-informed readiness-to-sustainability framework that integrates established Industry 4.0 readiness dimensions with additional organisational and technological capability factors relevant to sustainable manufacturing. The study makes a methodological distinction by operationalising an IMPULS-informed framework that connects its six dimensions with Industry 4.0 readiness and sustainable manufacturing within a single empirical model. The proposed model was examined using partial least squares structural equation modelling (PLS-SEM). The findings reveal that strategy and organisation, employees, smart products, and smart operations substantially contribute to Industry 4.0 readiness. Conversely, smart factories and data-driven services do not have a significant direct impact on readiness. Sustainable manufacturing is strongly influenced by Industry 4.0 readiness, smart operations, and the smart factory. Mediation analysis further indicates that Industry 4.0 readiness serves as a significant transmission mechanism linking strategy and organisation, employees, smart products, and smart operations to sustainable manufacturing. The study extends the IMPULS framework by validating its relevance in an emerging economy and by demonstrating that sustainability gains from digital transformation rely more on coordinated organisational and operational readiness than on isolated technology adoption. The results provide evidence-based prioritisation guidance for managers and policymakers seeking to prioritise strategic alignment, workforce skills, and operational integration for successful, sustainable industrial transformation. Full article

The increasing demand for urban last-mile delivery services has intensified the need for routing approaches that simultaneously address operational efficiency and environmental sustainability. This study introduces the Green Multi-Courier Delivery Routing Problem (GMCDRP), a heterogeneous routing and assignment problem involving pedestrian couriers, electric bicycles, and motorized vehicles under capacity, distance, and service-time constraints. To solve the problem, a state-aware constructive heuristic named Emission-Minimizing Green Routing (EMGRO) is proposed. Unlike conventional metaheuristics that evaluate emissions after route generation, EMGRO integrates emission awareness directly into the assignment process by considering the real-time operational state of each courier and prioritizing the lowest-emission feasible alternative. The proposed method is evaluated using 20 large-scale scenarios derived from the real road network of Adana, Türkiye, each containing up to 1000 delivery requests. Its performance is compared with Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) approaches. Experimental results demonstrate that EMGRO achieves the lowest average emission per delivered package (0.512 g CO₂/package), outperforming ACO, PSO, and GA by 47.9%, 44.1%, and 41.9%, respectively, while maintaining identical delivery coverage. Furthermore, EMGRO generates solutions within seconds, providing substantial computational advantages over population-based metaheuristics. The findings indicate that embedding environmental considerations directly into the decision-making process can significantly improve both sustainability and computational efficiency in heterogeneous urban delivery systems. Full article

This study explores the behavioral intersection of nutritional health and environmental literacy among pre-service teachers within the national higher education context. Utilizing the Sustainable Healthy Diet Index (SHED Index) for the first time in Croatia, this research systematically examines the dietary habits, lifestyle choices, and socio-cultural patterns of future educators (N = 164) at the University of Split. The survey instrument evaluated the core SHED domains, Healthy Eating (HE) and Sustainable Eating (SE), alongside supplementary indicators monitoring food logistics, hydration, and waste management. Descriptive analysis revealed moderately high standardized overall SHED scores (M = 62.86), aligning with the original normative distribution. Notably, students achieved significantly higher descriptive sub-scores in the HE domain (M = 24.68) than in the SE domain (M = 18.06). Although domestic food consumption and circular recycling practices were well integrated, critical biospheric behaviors—such as reducing animal protein, consuming legumes, purchasing organic food, and composting—remain limited by cultural resistance and municipal infrastructure deficits. Furthermore, an independent t-test indicated no significant differentiation across study levels, highlighting a potential institutional stagnation throughout the five-year teacher education program. Regression analysis demonstrated that sustainable dietary choices appear to be strongly anchored in personal health concerns rather than biospheric altruism, with healthy eating emerging as the single strongest explanatory factor for of sustainable behavior. These findings indicate that to cultivate authentic ecological literacy in the future teaching workforce, higher education curricula require a systemic redesign that explicitly links sustainability to personal well-being through localized, experiential learning. Full article

Livelihood space serves as a fundamental medium for the livelihood practices of rural households. Understanding its optimization and reconstruction is essential for promoting the sustainable development of resettled households. This study adopts a spatial perspective to develop a multidimensional measurement framework for livelihood space. Focusing on resettled households in the karst regions of Guizhou, China, we integrate the Optimal Parameter-based Geographical Detector (OPGD) with fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically analyze the driving mechanisms and equifinal configuration pathways involved in the reconstruction of livelihood space. The key results are as follows: (1) The overall livelihood space is characterized as moderate. Among the various dimensions, institutional and cultural spaces receive the highest scores, followed by social and production spaces, while residential space is rated the lowest. (2) Excluding residential space, there are significant dimensional disparities across different resettlement and livelihood typologies. Households resettled in county towns outperform those in non-county areas. In terms of livelihood types, integrated and off-farm households rank the highest, whereas farming-dependent and subsidy-dependent households rank the lowest. (3) The OPGD analysis indicates that household income and accessibility to public services are primary drivers, exhibiting a bivariate nonlinear synergistic enhancement. (4) The fsQCA findings confirm that the reconstruction of livelihood space is not driven by a single factor; rather, it is influenced by four equifinal pathways formed through nonlinear interactions of multiple conditions, collectively contributing to the enhancement of high-livelihood space. The integration of OPGD and fsQCA moves beyond conventional linear approaches, yielding fresh empirical evidence and new perspectives on livelihood space reconstruction and the sustainability of fragile regions. Full article

Sizing charging infrastructure for battery electric bus (BEB) fleets requires an accurate estimate of per-trip energy demand before capital is committed. Accurate sizing is itself a sustainability decision in resource-constrained settings: undersized infrastructure delays the shift away from diesel fleets, while oversized infrastructure diverts capital that could otherwise fund a wider electrification programme. Yet most pre-investment planning still relies on a single fleet-average consumption rate that cannot capture how route, operating conditions, and climate interact. This paper presents a rolling stock digital twin for the Golden Arrow Bus Services (GABS) electric fleet at the Arrowgate depot in Cape Town, South Africa, one of the first large BEB fleets in sub-Saharan Africa. A LightGBM gradient-boosted model is trained on 596 trips drawn from two five-day measurement periods in different seasons (winter and early spring) to predict the per-trip consumption rate (kWh/km) from route-geometry and operating-condition features derived from GPS traces, duty schedules, and public weather data, requiring no vehicle specification beyond a single usable-battery-capacity figure. Under two grouped cross-validation schemes representing known and genuinely unseen routes, the model predicts the per-trip state-of-charge drop, and hence arrival state of charge given a known departure SoC, to within 2.95 and 3.46 percentage points, respectively. SHAP analysis shows that departure time window and ambient temperature, rather than route geometry, dominate the prediction, and the model removes a structured time-of-day bias that the flat rate cannot represent: a roughly 4% underestimate of the morning peak and a 6–7% overestimate of midday and afternoon demand. Independent OCPP charging records corroborate the reconstructed depot demand profile. By replacing an assumed consumption figure with a validated, empirically grounded one, this work supports capital-efficient, lower-risk electrification pathways for transit operators in the Global South, where infrastructure budgets are especially constrained. The twin supplies an empirically grounded, transferable demand layer for depot-level pre-investment planning in resource-constrained operating contexts. Full article

Improving municipal efficiency and governance resilience has become increasingly critical in the face of fiscal pressure, demographic growth, service delivery complexity, and the growing demand for evidence-based operational sustainability and municipal efficiency. However, prior studies have largely examined municipal efficiency evaluation, predictive analytics, and optimization techniques separately, limiting their practical value for integrated decision support in public sector governance. To address this gap, this study proposes an integrated analytical framework combining data envelopment analysis (DEA), machine learning, and metaheuristic optimization to support operational sustainability and governance resilience in Jordan. The empirical analysis is based on panel data from 20 Jordanian municipalities covering the period 2011–2020. First, input-oriented CCR, BCC, and slack-based measure DEA models were employed to estimate technical, pure technical, scale, and slack-adjusted efficiency. Second, random forest, XGBoost, support vector machine, and artificial neural network models were employed to predict the scores for efficiency derived from DEA using validation procedures based on time-based criteria. Third, a particle swarm optimization (PSO) technique was employed to determine operationally and financially viable solutions for allocating municipal resources. The findings reveal significant variation in terms of efficiency among the municipalities. Furthermore, numerous possibilities exist to improve the sustainability and performance of municipal governance. In terms of prediction models, random forest exhibited higher predictive accuracy compared to other models. Full article

Training educators to integrate sustainability into their practice is crucial for the 2030 Agenda. This article presents a mixed-method design to explore perceived changes in sustainability-oriented professional identity among pre-service primary teachers following a Service-Learning intervention. The design combines an instrumental case study with convergent mixed methods and a qualitative emphasis. Core instruments include reflective diaries structured around Kolb’s experiential learning cycle and a 10-item questionnaire on perceived competence development. Participants were 37 pre-service primary teachers in their fourth year of study. Inductive thematic analysis of the diaries revealed a shift from a teacher identity centred on content transmission towards one focused on facilitating experiential learning and acting as a community agent. Descriptive analysis showed mean scores between 3.59 and 4.51, with significant differences from the scale’s neutral point (p < 0.001). Methodological triangulation evidenced a gap between high endorsement of the community role (M = 4.30) and lower self-efficacy in collaboration strategies (M = 3.62) and in addressing complexity (M = 3.59). The article suggests that this design offers a promising framework that may be transferable to similar teacher education contexts, potentially providing a useful tool for operationalising Sustainable Development Goal 4.7 assessment in higher education. Its strengths, limitations, and transferability potential are discussed. Full article

Prolonged displacement can transform temporary settlements into enduring socio-spatial environments. This article examines selected dimensions of social sustainability in Antakya following the 2023 Kahramanmaraş earthquakes. It combines a visual content analysis of 169 publicly available Instagram images with a Q-methodology study involving 50 residents across 30 temporary settlement areas. Public representations foreground the built environment (97.4%), everyday experience (94.7%), and the reconstruction of place (86.8%), whereas historical continuity is less visible (28.9%). Four rotated factors explain 35.8% of the variance: cultural-symbolic continuity and collective place-making; functional domestic stabilisation and organised settlement life; integrated socio-spatial anchoring and service-supported belonging; and pragmatic shelter stabilisation under uncertainty. Place attachment is treated as a qualitative, interpretive diagnostic rather than as a direct measure of institutional performance. The findings indicate that residents preserve cultural identity, establish domestic routines, depend on integrated social and service infrastructure, or prioritise shelter adequacy while remaining selective about emotional investment. The study derives factor-specific implications for neighbourhood continuity, dwelling adaptation, social infrastructure, and credible transitions to permanent housing. Full article

Agentic artificial intelligence (AI) is a consequential technological frontier in banking because it shifts AI from passive assistance and generative interaction toward goal-directed workflow execution. Responsible and sustainable banking transformation therefore depends not simply on whether banks experiment with agentic AI, but on the readiness conditions under which selected agentic capabilities can move from pilots to governed production and responsible value realization. This study develops a configurational forecasting framework for agentic AI deployment readiness in banking. Because comparable initiative-level evidence remains scarce and commercially sensitive, the paper adopts a transparent, case-informed synthetic configurational simulation. The analysis should therefore be read as a theory-development and foresight exercise, not as empirical evidence of actual bank projects or banking-sector prevalence. Drawing on public banking AI cases, technology-diffusion and foresight literature, AI governance research, and role-based stakeholder archetypes, we construct a synthetic dataset of 90 banking-related agentic AI initiatives and apply fuzzy-set Qualitative Comparative Analysis (fsQCA). Within this bounded simulation, production maturity is internally consistent with the conjunction of data readiness, leadership commitment, governance maturity, workflow redesign capability, human–agent collaboration maturity, and low legacy-system complexity. Supplementary analyses indicate that deployment alone is insufficient for value realization in the simulated design: value requires deployment to be combined with redesigned workflows, governed data use, and human–agent collaboration. The results are not causal estimates; rather, they specify falsifiable readiness expectations that future empirical research can test with real initiative-level data. The study offers a reproducible readiness logic for responsible value realization, customer protection, workforce capability, and financial-system resilience. Full article

Mixed crop–livestock systems remain central to sustainable intensification because they reconnect feed, biomass, soil cover, livestock productivity, and household livelihoods. However, forage integration benefits are neither automatic nor uniformly transferable across regions. This structured critical integrative review asks when forage integration generates net system-level gains. It also asks when integration merely shifts costs among production, labor, water, nutrients, emissions, household risk, or territorial nutrient balances. The review synthesizes evidence on forage legumes, tropical and temperate grasses, dual-purpose crops, grazed cover crops, pasture rotations, silvopastoral arrangements, and beyond-farm feed–manure exchanges. Examples from the Brazilian Cerrado, western São Paulo, and Minas Gerais illustrate tropical pathways involving Urochloa/Brachiaria integration, crop–pasture rotations, crop–livestock–forest systems, and habitat-mediated pest regulation. These examples are interpreted alongside evidence from African, Asian, European, and temperate systems to maintain regional balance. The review contributes a diagnostic framework for assessing system design, evidence strength, and scaling feasibility. The framework links forage portfolios to integration niches, management quality, resource constraints, gendered labor implications, external-input dependency, and adaptive scaling pathways. Forage integration can improve feed quality, animal performance, soil cover, nutrient cycling, biodiversity functions, and emission intensity. These gains require careful management of establishment, grazing, manure distribution, phosphorus and potassium balances, water demand, labor allocation, markets, and governance. Future research should move beyond short-term demonstrations toward causal inference, longitudinal whole-system accounting, transparent evidence-quality grading, and documentation of failed, partial, or discontinued interventions. Full article