Search Results (250)

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

As smart city approaches increasingly extend to public open spaces, understanding what urban park users expect from digital park applications has become a critical issue for sustainable urban management. This study examines park users’ expectations of smart park applications through an exploratory sequential mixed-methods design. In the first phase (Study I), semi-structured interviews were conducted with 32 purposively selected participants representing four user groups—parents with children, sport-oriented users, older adults, and general adults—in urban parks in Eskişehir, Türkiye. Thematic analysis identified eight user expectation themes, which were subsequently operationalized into a seven-factor quantitative structure. In the second phase (Study II), a seven-factor scale derived from the qualitative findings was administered to 374 participants. Confirmatory factor analysis demonstrated a good overall model fit, and the scale exhibited strong reliability and convergent validity. One-way ANOVA revealed significant between-group differences in six of the seven dimensions, with sport-oriented users consistently reporting higher expectations than older adults. Safety and Activity Diversity was the only dimension showing no significant group differences, indicating a universal expectation across all user profiles. Multiple regression analysis showed that Independent Functionality was the strongest predictor of use intention, followed by Centrality and Communal Function and Safety. Integration of both phases through a joint display revealed that expectations are both universal and user profile-specific, underscoring the need for user-sensitive smart park design. By linking digital park services to user expectations, well-being-oriented park design, and the sustainable use of urban green spaces, these findings contribute to the literatures on smart cities, urban green spaces, and well-being, providing an empirically informed and user-centred framework for digital park applications that may inform efforts toward healthier, more inclusive, and more sustainable urban public spaces in line with SDGs 3 and 11. Full article

Urban sustainability management increasingly relies on large volumes of heterogeneous environmental data generated by smart city infrastructures. While these data streams offer significant potential for evidence-informed policymaking, environmental governance, and public engagement, their effective use is often constrained by technical barriers and persistent data-skills gaps among non-specialist stakeholders. Using urban air quality as a policy-relevant and data-rich sustainability domain, this paper presents a proof-of-concept dashboard that investigates how large language model (LLM)-enabled natural language interfaces can lower barriers to querying, analysing, and visualising urban environmental data. The system translates natural language questions into executable database queries and automatically generates visualisations over air-quality datasets. A controlled comparative benchmark of proprietary and open-source LLMs is conducted to assess their suitability for text-to-SQL generation in this application context. In this benchmark, proprietary GPT-based models achieved the highest observed query accuracy and robustness among the evaluated models, highlighting practical trade-offs between performance, transparency, reproducibility, and long-term governance. This paper makes a twofold contribution: First, it demonstrates the technical feasibility of an LLM-enabled natural language access layer for smart-city environmental data. Second, it uses the implemented system as a concrete case through which to analyse the trust, transparency, inclusivity, vendor-dependency, and data-quality challenges that arise when such systems are incorporated into sustainability-oriented decision-support workflows. The study provides a transferable design contribution for urban sustainability data access by showing how natural language interfaces, model benchmarking, automated visualisation, and governance-aware system design can be combined to support more inclusive interaction with complex environmental datasets. Full article

Automated power line inspection plays a crucial role in maintaining grid reliability within smart cities by identifying potential defects in towers, conductors, insulators, and fittings. While modern anomaly detection relies heavily on deep neural networks (DNNs), training these models requires massive amounts of high-quality image data. However, a significant scarcity of publicly available datasets persists because data acquisition not only demands highly specialized professional skills but also faces strict data protection regulations enforced by grid companies. To bridge this gap, this paper presents a comprehensive review of open-access image datasets dedicated to power line inspection. Based on strict inclusion criteria—specifically, unrestricted public availability and a direct focus on core power line components—19 datasets are systematically selected and analyzed. We provide a detailed taxonomy and comparative analysis of these datasets in terms of inspection targets, acquisition platforms, annotation toolkits, and labeling schemes. Furthermore, our investigation highlights current research trends and identifies critical gaps, such as the disproportionate focus on insulators and the notable scarcity of multimodal data. To address the limitations of small-scale datasets, we also discuss existing data augmentation strategies and synthetic data generation techniques. Ultimately, this review serves as a unified navigational guide, aiming to foster the development of more robust visual inspection algorithms and to inspire future high-quality dataset construction in the power domain. Full article

Rapid urbanization has intensified congestion, environmental pressures, and transport inequities, thereby increasing interest in Smart Urban Mobility (SUM) as an approach that combines digital technologies, sustainable transport strategies, and data-informed decision-making to respond to these challenges. However, the evaluation of SUM remains fragmented due to the absence of harmonized assessment frameworks and the diversity of methodologies applied across smart city contexts. This study presents a systematic literature review of evaluation approaches and indicator architectures for SUM in smart city contexts. Using a PRISMA-guided screening process, 33 eligible studies were selected from 412 retrieved records. Three main methodological groups were identified: quantitative approaches, multi-criteria decision-making methods, and qualitative or participatory frameworks. A total of 273 indicators were organized into eight factor categories, confirming the multidimensional nature of smart mobility assessment while also revealing limited consistency in indicator selection and application across studies. Across the selected studies, current evaluation practices are increasingly linked to project prioritization, planning, and decision support; however, their effectiveness remains constrained by data inconsistencies, governance fragmentation, and insufficient user inclusion. These findings highlight the need for assessment frameworks that are sufficiently comparable to enable cross-city learning, yet flexible enough to reflect local contexts and institutional realities. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

As climate risks intensify and urbanization accelerates, cities face growing challenges in safeguarding infrastructure, livelihoods, and public well-being. Sustainable insurance has emerged as a key tool for mitigating climate-related risks; however, existing models often lack integration with smart city frameworks and climate resilience strategies. This study conducts a bibliometric review to map the global research landscape of sustainable insurance in climate-resilient smart cities, providing insights into emerging trends, thematic clusters, and knowledge gaps. Using data from the Scopus database and VOSviewer-based keyword co-occurrence analysis, this study identifies four key research clusters: economic-policy integration, climate risk governance, digital urban innovation, and health within the SDG framework. The findings reveal that emerging models such as parametric insurance, microinsurance, and data-driven pricing can align financial protection with real-time climate risks, incentivizing resilience investments and expanding coverage to vulnerable communities. These clusters illustrate the field’s transition toward systems-based approaches, highlighting the need for integrated solutions that blend financial, technological, and social dimensions of resilience. Study recommendations emphasize the integration of insurance into urban planning, the expansion of public–private partnerships, regulatory modernization, and the use of smart city data for dynamic risk pricing. This research offers implications for insurers, governments, urban planners, and development agencies, and positions insurance as a cross-cutting enabler that bridges ESG principles, digital governance, and inclusive sustainability. Full article

This study presents a comprehensive and systematic integrative review of Neighborhood-Level Energy Hubs (NLEHs) as pivotal enablers of sustainable and resilient urban energy systems. In response to accelerating climate pressures, rapid urbanization, and the decentralization of energy production, NLEHs are conceptualized as multi-carrier platforms that enable coordinated energy generation, storage, conversion, and exchange at the neighborhood scale. Utilizing a PRISMA-informed methodology to synthesize 125 core studies, the review systematically evaluates recent advances across five interconnected dimensions: conceptual foundations, system typologies, energy flow architectures, urban integration, and optimization paradigms. Unlike conventional reviews, this study explicitly bridges the critical gap between techno-economic optimization and socio-environmental priorities. A key novelty is the proposed mathematical integration of energy justice and Social Life Cycle Assessment (S-LCA) directly into optimization algorithms (e.g., MILP and MPC) as dynamic constraints and penalty terms. Particular emphasis is placed on participatory governance models, lifecycle sustainability metrics, and digitalization tools such as AI-driven energy management systems and urban digital twins. The analysis further reveals critical research gaps, highlighting a stark geographic dichotomy between high-tech, market-driven NLEHs in the Global North and resilience-oriented hybrid microgrids in the Global South, alongside the lack of adaptive regulatory frameworks. By proposing a unified Cyber–Physical–Social perspective, this study provides actionable insights for planners, policymakers, and researchers to support the development of scalable, inclusive, and context-sensitive NLEH implementations. Ultimately, the paper contributes to redefining neighborhood-scale energy systems as not only efficient and low-carbon infrastructures, but also as socially equitable, globally scalable, and institutionally adaptive components of future smart cities. Full article

The accelerated development of Smart Cities globally, driven by rapid urbanization and urgent climate challenges, underscores the critical role of advanced energy infrastructures integrated with emerging digital technologies. This article explores the evolution of smart cities from an energy-centric viewpoint, emphasizing the interdependence among energy systems, digitalization and cutting-edge communication technologies. Adopting a system-of-systems perspective, we examine how different urban subsystems, including energy grids, transportation networks and data management systems, interact to improve overall urban functionality and long-term viability. Through a structured analysis of recent literature, we highlight the transformative potential of renewable energy integration, intelligent energy management systems and the crucial transition from 5G to 6G communication infrastructures, which collectively promise significant enhancements in urban sustainability, efficiency and resilience. Additionally, we address key challenges such as cybersecurity vulnerabilities, fragmented standardization frameworks and the need for comprehensive data governance. Viewing smart cities as a complex system of systems, this article argues for a holistic and interdisciplinary approach, emphasizing enhanced interoperability, robust cybersecurity protocols and inclusive participatory governance frameworks. Full article

The aim of this paper is to conceptualize smart culture as an important yet under-researched dimension of smart cities, and to empirically demonstrate the extent to which cultural events in Vilnius’ public spaces align with the key principles of smart culture. The theoretical section of the article provides a definition of smart culture in a smart city, based on which four categories of analysis are identified: accessibility, the integration of technology into the cultural experience, engagement of the population, and promotion of community building. The methodology consists of an instrumental case study, analysis of secondary sources, and directed content analysis. The research findings reveal that Culture Night festival events in Vilnius not only reduce social and geographical barriers to culture but also create spaces for active participation of the population, fostering community and the application of technological solutions in cultural activities. Culture Night represents a clear example of smart culture, highlighting the importance of this dimension in smart city policies. The study shows that the identified characteristics of smart culture may support inclusive and sustainable urban development trends associated with SDG 11 (Sustainable Cities and Communities) and SDG 10 (Reduced Inequalities). Full article

The concept of “human-centric, sustainable and resilient smart cities” in Industry 5.0 (I5.0) refers to urban socio-technical ecosystems in which digital infrastructures and services are explicitly oriented toward human well-being, ecological stewardship, and systemic resilience rather than purely technological optimization or automation. Grounded in the I5.0 framework, which promotes human-centricity, sustainability, and resilience as equally important pillars, this paradigm repositions smart cities as value-driven environments that integrate enabling technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), Extended Reality (XR), and related digital infrastructures within participatory, transparent, ethical, and accountable governance structures. From this perspective, technologies function as means through which cities develop higher-order capabilities for sensing, decision support, coordination, interaction, and adaptive service delivery. At the same time, they address digital divides and include measures that promote and protect inclusion, trust, and long-term socio-environmental viability. This entry synthesizes the conceptual foundations, technological enablers, capability-oriented architecture, governance implications, and emerging challenges that influence the transformation of smart cities into human-centric, sustainable, and resilient innovation systems in the I5.0 era. Full article

This paper investigates how social value is operationalised in urban regeneration and how digital reporting platforms shape the measurement and governance of social sustainability. Drawing on semi-structured interviews with UK social value professionals and a resident survey conducted within the Elephant and Castle regeneration programme in London, the study examines how platform-based systems translate procurement commitments into auditable performance categories. These systems embed predefined classification schemas, proxy valuation metrics and rule-based validation procedures that structure how outcomes become visible and comparable across projects. The findings indicate that digital reporting platforms enhance oversight and inter-project benchmarking but prioritise outcomes that align with measurable procurement indicators. Employment generation, apprenticeships and local procurement expenditure dominate reported performance, while relational and place-based outcomes, such as trust, belonging and neighbourhood continuity, remain marginal. Reporting requirements generate substantial evidencing burdens across supply chains, may introduce data distortions through proxy-based and threshold-led reporting, and can concentrate engagement at early project stages, limiting sustained community influence and creating technical barriers to participation. The analysis highlights how digital reporting platforms can operate as governance infrastructures within smart city environments, shaping what is prioritised, funded and recognised as credible impact. The findings provide practical insights for the design of more inclusive and proportionate digital accountability systems for sustainable local development. Full article

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article

This article evaluates how artificial intelligence (AI) and educational technology (EdTech) support inclusive learning in Russia and Kazakhstan, two Eurasian countries that share post-Soviet educational legacies but differ in their levels of digital infrastructure and teacher preparedness. Using an asymmetric mixed-methods design, the study draws on a primary survey of N = 2570 educators and staff in four Russian cities (Moscow, Saint Petersburg, Yekaterinburg, Novosibirsk; October–December 2024; response rate 59.8%) and secondary policy/indicator analysis for Kazakhstan. Russia exhibits higher broadband access, AI/EdTech platform adoption, and teacher digital skill levels compared with Kazakhstan. Structural equation modeling (SEM; SmartPLS 4.1) tested four latent constructs—learning environment (LE), general digital competencies (HCg), specialized AI skills (HCs), and inclusion (I)—with satisfactory validity (AVE > 0.5; HTMT ≤ 0.85). A three-stage Measurement Invariance of Composite Models (MICOM) procedure confirmed configural, compositional, and full mean/variance invariance across Russian city subgroups, enabling pooled path analysis. Kazakhstan indicators from secondary sources are discussed as a descriptive benchmark. Semi-structured interviews with 24 stakeholders (12 Russia, 12 Kazakhstan; March 2025; analyzed with NVivo 14) revealed four themes: policy coherence, teacher readiness, infrastructure access, and ethical AI governance. Key SEM paths were LE → HCg (β = 0.278), HCg → HCs (β = 0.652), and HCs → I (β = 0.188), all p < 0.001. A formal mediation analysis confirmed a significant indirect effect across the full LE → HCg → HCs → I chain. The findings indicate that infrastructure is necessary but insufficient: the key to inclusion lies in sustained development of both basic and specialized digital skills, supported by coherent policies and continuous professional development. China and India are discussed as secondary international benchmarks drawn from published reports, not as sites of primary data collection. Full article

This research is an analysis of the relationship between sustainable urbanization and economic inequality through smart city initiatives in developing countries such as India. Rapid urbanization in developing countries tends to have a detrimental impact on socioeconomic inequalities, and the effort to build smart cities may inadvertently increase exclusion when it is not planned with inclusiveness in mind. To reach this goal, an integrated Multi-Criteria Decision-Making (MCDM) approach using a combination of AHP, TOPSIS, and DEMATEL is adopted to systematically identify, assess, and identify the key criteria that affect the inclusive urban development. This study’s results show that infrastructure, governance, digital accessibility, and social inclusion play a key role in mitigating urban disparities and facilitating sustainable development. In particular, good governance and the availability of equitable digital infrastructure appear to be one of the critical factors in the reduction in inequalities and long-term urban resilience. This research provides policy-oriented insights for policymakers in designing inclusive smart city policies in accordance with the Sustainable Development Goals, as well as theoretical contributions to urban sustainability research. Full article