Search Results (186)

This paper contributes to the field of urban waste collection systems, which are crucial for advancing sustainability, urban cleanliness, and the aesthetic quality of cities. Specifically, it introduces a novel framework designed to support planners and decision makers in the design of efficient and responsive textile waste collection systems, aligned with both environmental objectives and citizen engagement. To this end, the framework exploits a hybrid simulation platform that realistically models the logistics infrastructure in a spatially explicit environment. Also, within the framework, citizens are represented as adaptive agents whose environmental attitudes evolve through personal experience, social influence, and perceived service quality. The behavioural layer is the core element of the framework. It enables dynamic analysis of the two-way feedback between citizen participation and service effectiveness to underscore the often-overlooked role of citizen behaviour in shaping overall system performance. The model was tested in a representative urban scenario under varying operational conditions. The results highlight how policy incentives and smart collection infrastructure can significantly boost participation, while social segregation may hinder the adoption of sustainable practices. The framework ultimately offers a generalisable decision-support tool to explore the behavioural dimension of circular economy initiatives and develop robust, scenario-based strategies. Full article

Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models. Full article

The vulnerability of older adults in rural mountainous regions presents a critical challenge for sustainable development, particularly in the context of smart city and digital town construction. In this study, we develop a comprehensive analytical framework and evaluation index to assess Vulnerability to Elderly Poverty (VEP) and adaptive capacity, with a focus on its integration with smart infrastructure and age-friendly rural built environment strategies. Using Shizhu County in Chongqing, China, as a case study, we explore spatial disparities in VEP and apply quantile regression to identify the driving factors of adaptability. Our findings indicate that subsidy-dependent, middle-aged, and empty-nest older adults are the most vulnerable groups, with limited capacity to adapt to changing environments. A geographically alternating “high–low–high–low” VEP pattern reflects uneven development in infrastructure, accessibility, and public service construction. These disparities highlight the need for targeted planning and building interventions in rural settings. The key factors influencing adaptability include individual attributes, intergenerational support, and macro-level conditions such as policy design and digital infrastructure deployment. The integration of aging-friendly building strategies, smart infrastructure, and digital tools significantly enhances older adults’ resilience and social inclusion. Based on our results, we propose four adaptation models for aging populations in rural areas, emphasizing the construction of inclusive digital infrastructure, aging-sensitive building design, and community-based support systems. Strategic recommendations include promoting digital literacy through built environment interventions, enhancing intergenerational living arrangements, and embedding elderly-responsive features into smart construction planning. This research offers new insights into construction management practices that support aging in place and poverty alleviation through inclusive and resilient built environments. Full article

Artificial intelligence (AI) soft skills have become increasingly vital in today’s technology-driven world, as they support decision-making systems, strengthen collaboration among stakeholders, and enable individuals to adapt to rapidly changing environments—factors that are fundamental for achieving the sustainability goals of smart cities. Even though AI soft skills are becoming more important, no scale specifically designed to identify and evaluate individuals’ AI soft skills has been found in the existing literature. Therefore, this paper aimed to develop a reliable and valid scale to identify the AI soft skills of individuals. A sample of 685 individuals who were employed in AI-active sectors, with a minimum of a bachelor’s degree, and at least one year of AI-related work experience, participated in the study. A sequential exploratory mixed-methods research design was utilized. Exploratory factor analysis (EFA) identified a five-factor structure that accounted for 67.37% of the total variation, including persuasion, collaboration, adaptability, emotional intelligence, and creativity. Factor loadings ranged from 0.621 to 0.893, and communalities ranged from 0.587 to 0.875. Confirmatory factor analysis (CFA) supported this structure, with strong model fit indices (GFI = 0.940, AGFI = 0.947, NFI = 0.949, PNFI = 0.833, PGFI = 0.823, TLI = 0.972, IFI = 0.975, CFI = 0.975, RMSEA = 0.052, SRMR = 0.035). Internal consistency for each factor was high, with Cronbach’s alpha values of dimensions ranging from 0.804 to 0.875, with a value of 0.921 for the overall scale. Convergent and discriminant validity analyses further confirmed the construct’s robustness. The finalized AI soft skills (AISS) scale, consisting of 24 items, offers a psychometrically valid and reliable tool for assessing essential AI soft skills in professional contexts. Ultimately, this developed scale enables the determination of the social and cognitive skills needed in the human-centered and participatory governance structures of smart cities, supporting the achievement of specific Sustainable Development Goals such as SDG 4, SDG 8, and SDG 11, and contributes to the design of policies and training programs to eliminate the deficiencies of individuals in these areas. Thus, it becomes possible to create qualified human resources that support sustainable development in smart cities, and for these individuals to take an active part in the labor market. Full article

This paper presents an integrated Smart City platform that combines digital twin technology, advanced machine learning, and a private blockchain network to enhance data-driven decision making and operational efficiency in both public enterprises and small and medium-sized enterprises (SMEs). The proposed cloud-based business intelligence model automates Extract, Transform, Load (ETL) processes, enables real-time analytics, and secures data integrity and transparency through blockchain-enabled audit trails. By implementing the proposed solution, Smart City and public service providers can significantly improve operational efficiency, including a 15% reduction in costs and a 12% decrease in fuel consumption for waste management, as well as increased citizen engagement and transparency in Smart City governance. The digital twin component facilitated scenario simulations and proactive resource management, while the participatory governance module empowered citizens through transparent, immutable records of proposals and voting. This study also discusses technical, organizational, and regulatory challenges, such as data integration, scalability, and privacy compliance. The results indicate that the proposed approach offers a scalable and sustainable model for Smart City transformation, fostering citizen trust, regulatory compliance, and measurable environmental and social benefits. Full article

Smart governance—the integration of digital technologies into urban governance—is increasingly recognized as a transformative approach to addressing complex urban challenges such as rapid urbanization, climate change, social inequality, and resource constraints. As a foundational pillar of the smart city paradigm, it enhances decision-making, service delivery, transparency, and civic participation through data-driven tools, digital platforms, and emerging technologies such as AI, IoT, and blockchain. While often positioned as a pathway toward sustainability and inclusivity, existing research on smart governance remains fragmented, particularly regarding its relationship to urban sustainability. This study addresses that gap through a systematic literature review using the PRISMA methodology, synthesizing theoretical models, empirical findings, and diverse case studies. It identifies key enablers—such as digital infrastructure, data governance, citizen engagement, and institutional capacity—and highlights enduring challenges including digital inequity, data security concerns, and institutional inertia. In response to this, the study proposes a multidimensional framework that integrates governance, technology, and sustainability, offering a holistic lens through which to understand and guide urban transformation. This framework underscores the importance of balancing technological innovation with equity, resilience, and inclusivity, providing actionable insights for policymakers and planners navigating the complexities of smart cities and urban development. By aligning smart governance practices with the United Nations’ sustainable development goals (SDG)—particularly SDG 11 on sustainable cities and communities—the study offers a strategic roadmap for fostering resilient, equitable, and digitally empowered urban futures. Full article

The increasing disparity in global food distribution has amplified the urgency of addressing food waste and food insecurity, both of which exacerbate economic, environmental, and social inequalities. Traditional food bank models often struggle with logistical inefficiencies, limited accessibility, and a lack of transparency in food distribution, hindering their effectiveness in mitigating these challenges. This study proposes a novel Food Bank Network Redesign (FBNR) that leverages the Quito Metro system to create a decentralized food bank network, enhancing efficiency and equity in food redistribution by introducing strategically positioned donation lockers at metro stations for convenient drop-offs, with donations transported using spare metro capacity to designated stations for collection by charities, reducing reliance on dedicated transportation. To ensure transparency and operational efficiency, we integrate a blockchain-based traceability system with smart contracts, enabling secure, real-time tracking of donations to enhance stakeholder trust, prevent food loss, and ensure regulatory compliance. We develop a multi-objective optimization framework that balances food waste reduction, transportation cost minimization, and social impact maximization, supported by a mixed-integer linear programming (MIP) model to optimize donation allocation based on urban demand patterns. By combining decentralized logistics, blockchain-enhanced traceability, and advanced optimization techniques, this study offers a scalable and adaptable framework for urban food redistribution, improving food security in Quito while providing a replicable blueprint for cities worldwide seeking to implement circular and climate-resilient food supply chains. Full article

As urban populations continue to rise, cities face mounting challenges related to infrastructure strain, resource management, and environmental degradation. Sustainable urban development has emerged as a crucial strategy to balance economic growth, social equity, and environmental preservation. In this context, artificial intelligence offers transformative potential, particularly through predictive modeling, which enables data-driven decision making for more efficient and resilient urban planning. This paper explores the role of AI-powered predictive models in supporting sustainable urban development, focusing on key applications such as infrastructure optimization, energy management, environmental monitoring, and climate adaptation. The study reviews current practices and real-world examples, highlighting the benefits of predictive analytics in anticipating urban needs and mitigating future risks. It also discusses significant challenges, including data limitations, algorithmic bias, ethical concerns, and governance issues. The discussion emphasizes the importance of transparent, inclusive, and accountable AI frameworks to ensure equitable outcomes. In addition, the paper presents comparative insights from global smart city initiatives, illustrating how AI and IoT-based strategies are being applied in diverse urban contexts. By examining both the opportunities and limitations of AI in this domain, the paper offers insights into how cities can responsibly harness AI to advance sustainability goals. The findings underscore the need for interdisciplinary collaboration, ethical safeguards, and policy support to unlock AI’s full potential in shaping sustainable, smart cities. Full article

Urban rail transit system planning is significant for alleviating traffic congestion and optimizing spatial resource allocation in cities with scarce land resources. However, the long period of rail transit construction, large-scale investment, and its planning involve a variety of factors, which require scientific and reasonable evaluation methods to ensure that its construction can realize the expected economic and social benefits. To solve this problem, this study first establishes an appropriate evaluation system by selecting suitable evaluation indicators. Then, the comprehensive assignment method combining the ordinal relationship method (G1 method) and the improved entropy weight method is applied to assign weights to the indicators in the evaluation system, and the correlation degree is calculated by combining with the matter-element model for evaluating the planning scheme of the urban rail transit system. Finally, the urban rail transit system planning scheme of Zhengzhou City is verified by example. The results show that the proposed method can balance the practical significance and dynamics of the evaluation indices, evaluate the importance of each index more objectively, and provide methodological support for dynamic decision-making in rail transportation planning in the context of a smart city, which is of guiding significance for the sustainable development of the city. Full article

Urban agriculture is key to sustainable city development, particularly through public engagement with the Water–Energy–Land–Food–People (WELFP) Nexus. This study examines the effectiveness of serious games in enhancing WELFP understanding and promoting pro-environmental and pro-social behaviors. A game-based learning model was developed using the Stimulus–Organism–Response (SOR) and Easy–Attractive–Social–Timely (EAST) frameworks, along with the Revised New Ecological Paradigm (NEP) Scale. The model simulates real-world urban agriculture challenges to foster participatory decision-making. A survey of 200 urban agriculture practitioners, analyzed via structural equation modeling (SmartPLS 4.0), found that perceived timeliness (PT) and perceived usefulness (PU) significantly influenced both the perceived sustainable livelihood value (PT: p = 0.000; PU: p = 0.006) and users’ attitudes toward the game (PT: p = 0.000; PU: p = 0.038). While enjoyment positively affected attitude (p = 0.002), it negatively impacted perceived value (p = 0.002), revealing a trade-off between fun and practical relevance. Perceived ease of use improved perceived value (p = 0.000) but did not affect attitude, suggesting emotional engagement matters more. Both attitude and perceived value strongly predicted users’ intention to engage with the game. Post-game reflections highlighted the need for cross-sector collaboration, strategic resource use, access to real-time data, and responsive crisis management. Participants also stressed the importance of public awareness, civic responsibility, and volunteerism in advancing community-driven sustainable agriculture. These findings highlight the need to balance engagement and educational depth in game-based learning for sustainability. Full article

Urban green infrastructure (UGI) plays a key role in fostering sustainability, resilience, and ecological balance in cities. However, the task of identifying priority areas for UGI implementation remains complex due to the multifactorial nature of urban systems and prevailing uncertainties. This study proposes a fuzzy inference system (FIS)-based framework composed of seven interconnected modules designed to assess diverse criteria, including flood vulnerability, water quality, habitat connectivity, vegetation condition, and social vulnerability. The model was applied in the urban watersheds of São José dos Campos, Brazil, a municipality recognized for its smart city initiatives and urban environmental complexity. Through the integration of multi-criteria spatial data, the framework effectively prioritized urban areas, highlighting critical zones for extreme event mitigation, water quality preservation, habitat conservation, and recreational space provision. The case study demonstrated that São José dos Campos, with an 11.73% urbanized area and 737,310 inhabitants, benefits from targeted UGI typologies, including sustainable drainage systems and green public spaces, aligning infrastructure interventions with specific spatial demands. Notably, the expert validation process involving 18 multidisciplinary specialists confirmed the model’s relevance and coherence, with the majority classifying the outcomes as “highly coherent”. The system’s modular structure, use of triangular membership functions, and incorporation of the gamma operator allow for adaptable prioritization across different planning horizons. By offering a transparent, expert-validated, and data-driven approach, the proposed method advances evidence-based decision-making and equips planners with a practical tool for UGI implementation in dynamic urban contexts. Full article

Municipal solid waste (MSW) accumulation is a critical global challenge for society and governments, impacting environmental and social sustainability. Efficient separation of MSW is essential for resource recovery and advancing sustainable urban management practices. However, manual classification remains a slow and inefficient practice. In response, advances in artificial intelligence, particularly in machine learning, offer more precise and efficient alternative solutions to optimize this process. This research presents the development of a light deep neural network called R3sNet (three “Rs” for Reduce, Reuse, and Recycle) with residual modules trained end-to-end for the binary classification of MSW, with the capability for faster inference. The results indicate that the combination of processing techniques, optimized architecture, and training strategies contributes to an accuracy of 87% for organic waste and 94% for inorganic waste. R3sNet outperforms the pre-trained ResNet50 model by up to 6% in the classification of both organic and inorganic MSW, while also reducing the number of hyperparameters by 98.60% and GFLOPS by 65.17% compared to ResNet50. R3sNet contributes to sustainability by improving the waste separation processes, facilitating higher recycling rates, reducing landfill dependency, and promoting a circular economy. The model’s optimized computational requirements also translate into lower energy consumption during inference, making it well-suited for deployment in resource-constrained devices in smart urban environments. These advancements support the following Sustainable Development Goals (SDGs): SDG 11: Sustainable Cities and Communities, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Action. Full article

Social media is crucial for gathering public feedback on government services, particularly in the traffic sector. While Aspect-Based Sentiment Analysis (ABSA) offers a means to extract actionable insights from user posts, analyzing Arabic content poses unique challenges. Existing Arabic ABSA approaches heavily rely on supervised learning and manual annotation, limiting scalability. To tackle these challenges, we suggest an integrated framework combining unsupervised BERTopic-based Aspect Category Detection with distance supervision using a fine-tuned CAMeLBERT model for sentiment classification. This is further complemented by transformer-based summarization through a fine-tuned AraBART model. Key contributions of this paper include: (1) the first comprehensive Arabic traffic services dataset containing 461,844 tweets, enabling future research in this previously unexplored domain; (2) a novel unsupervised approach for Arabic ABSA that eliminates the need for large-scale manual annotation, using FastText custom embeddings and BERTopic to achieve superior topic clustering; (3) a pioneering integration of aspect detection, sentiment analysis, and abstractive summarization that provides a complete pipeline for analyzing Arabic traffic service feedback; (4) state-of-the-art performance metrics across all tasks, achieving 92% accuracy in ABSA and a ROUGE-L score of 0.79 for summarization, establishing new benchmarks for Arabic NLP in the traffic domain. The framework significantly enhances smart city traffic management by enabling automated processing of citizen feedback, supporting data-driven decision-making, and allowing authorities to monitor public sentiment, identify emerging issues, and allocate resources based on citizen needs, ultimately improving urban mobility and service responsiveness. Full article

Food delivery apps (FDAs) have emerged as transformative tools in the digital age, reshaping consumer behavior and urban mobility through their convenience and accessibility. This study explores the factors influencing the adoption of FDAs among university students in a secondary city in Thailand, framed within the broader context of smart mobility. This study employs an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, incorporating key constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and environmental concerns. Data were collected from 396 students at Mahasarakham University through a structured questionnaire and analyzed using structural equation modeling. The results reveal that effort expectancy, social influence, and environmental concerns significantly impact behavioral intention, while behavioral intention and facilitating conditions drive actual usage behavior. Environmental concerns emerged as a critical determinant, reflecting the growing alignment between consumer preferences and sustainability goals. The findings underscore the role of FDAs as key enablers of smart mobility, optimizing urban logistics, reducing transportation inefficiencies, and supporting sustainable city systems. By integrating environmental concerns into the UTAUT model, this study contributes to understanding technology adoption dynamics in secondary cities. Practical implications include promoting eco-friendly practices, enhancing digital infrastructure, and leveraging FDAs to foster sustainable and inclusive mobility ecosystems. Full article

The convergence of the Internet of Physical–Virtual Things (IoPVT) and the Metaverse presents a transformative opportunity for safety and health monitoring in outdoor environments. This concept paper explores how integrating human activity recognition (HAR) with the IoPVT within the Metaverse can revolutionize public health and safety, particularly in urban settings with challenging climates and architectures. By seamlessly blending physical sensor networks with immersive virtual environments, the paper highlights a future where real-time data collection, digital twin modeling, advanced analytics, and predictive planning proactively enhance safety and well-being. Specifically, three dimensions of humans, technology, and the environment interact toward measuring safety, health, and climate. Three outdoor cultural scenarios showcase the opportunity to utilize HAR–IoPVT sensors for urban external staircases, rural health, climate, and coastal infrastructure. Advanced HAR–IoPVT algorithms and predictive analytics would identify potential hazards, enabling timely interventions and reducing accidents. The paper also explores the societal benefits, such as proactive health monitoring, enhanced emergency response, and contributions to smart city initiatives. Additionally, we address the challenges and research directions necessary to realize this future, emphasizing AI technical scalability, ethical considerations, and the importance of interdisciplinary collaboration for designs and policies. By articulating an AI-driven HAR vision along with required advancements in edge-based sensor data fusion, city responsiveness with fog computing, and social planning through cloud analytics, we aim to inspire the academic community, industry stakeholders, and policymakers to collaborate in shaping a future where technology profoundly improves outdoor health monitoring, enhances public safety, and enriches the quality of urban life. Full article