Search Results (7,232)

Elderly individuals in rural China are highly vulnerable to extreme weather events and temperature fluctuations due to inadequate infrastructure in the built environment and constrained economic conditions, thereby increasing their health risks. Outdoor spaces represent one of the primary daily activity settings for rural older adults. However, existing research rarely links spatiotemporal patterns of outdoor activities to evidence-based thermal environment optimization, leaving a critical knowledge gap for age-friendly and sustainable rural design. This study focuses on the spatiotemporal differentiation patterns of daily outdoor activities among elderly people aged 60 years and above in rural Xi’an, as well as the optimization of spatial variations in thermal environments. Using on-site interviews, thermal environment measurements, thermal comfort questionnaires, continuous thermal environment monitoring, and machine learning based on random forest, this study drew the following conclusions: (1) outdoor activities in winter were concentrated between 9:00–11:00 and 13:00–17:00, while in summer, they shifted to the morning and evening periods, namely 6:00–9:00 and 17:00–21:00. (2) Models for outdoor clothing adjustment, thermal sensation, and thermal acceptability among elderly residents were established. The calculated neutral temperature was 10.19 °C, with a 90% outdoor thermal acceptability range of 9.6–27.2 °C and an 80% outdoor thermal acceptability range of 6.2–30.6 °C. These findings differ from those documented in regions with distinct climate zones and geographical settings. This discrepancy stems from regional climatic features, lifestyle variations between urban and rural older adults, and differences in the thermal environment quality of elderly-oriented outdoor activity spaces. (3) In winter, the acceptable period of the Universal Thermal Climate Index (UTCI) at south-facing entrances (10:30–16:30) was significantly longer than that in the courtyard (13:30–14:00). In summer, the comfortable period in the courtyard (before 10:00 and after 20:00) was longer than that at north-facing entrances (before 09:00). A random forest model for thermal sensation was established, and the relative importance of each parameter influencing thermal sensation was analyzed. On this basis, priority improvement pathways and strategies for the thermal environment, as well as suggestions for the subjective adaptive behaviors of elderly residents, were proposed. The research results of this study can provide technical solutions for age-friendly thermal environment design in rural areas, thereby safeguarding the comfort, health, and social well-being of the elderly population in rural areas. Full article

Improving the comprehensiveness of old community renewal is a key approach to enhancing residents’ quality of life and the community environment. Currently, research on improving comprehensiveness from both supply and demand perspectives remains limited. This study constructs an evaluation system comprising 27 indicators that cover three dimensions: physical infrastructure, community services, and community governance. Adopting the approach of “single indicator, two-way assessment, and comprehensive evaluation,” this study conducts evaluations from both supply and demand perspectives. On the supply side, facility coverage is calculated through field surveys, POI data, and ArcGIS spatial analysis; on the demand side, resident satisfaction is measured via questionnaires, and an evaluation framework for supply–demand matching is constructed using the IPA model. An empirical analysis using Community X in Beijing as a case study reveals that the completeness of community renewal exhibits significant hierarchical differentiation: supply–demand matching and conditions are favorable for basic services, elderly care and services for special groups, and cultural services; supply and demand for buildings, infrastructure, and public safety are balanced and moderately complete; environmental facilities exhibit oversupply and excessive completeness; and long-term management and resident participation suffer from insufficient supply and lack of completeness, emerging as core constraints. Based on these findings, targeted optimization strategies are proposed, which can provide scientific guidance for the development of comprehensive communities and the renewal of existing urban stock. Full article

Tourism development can both support and strain regional sustainability. Sustainable tourism matters especially in highly urbanized metropolitan areas, where resident mobility and tourist demand jointly use transport systems. This study evaluates transport infrastructure adequacy and quality under tourism pressure in regions containing Russia’s largest urban agglomerations. Because official tourist-flow statistics are available at the regional rather than agglomeration level, the analysis uses an exploratory regional proxy approach. The methods combine comparative analysis, correlation and regression analysis, index analysis, and sensitivity checks. Tourist flows show the strongest statistical associations with absolute indicators of bus infrastructure. Rail transport, especially commuter rail, also shows a stable positive association, which matters for large metropolitan areas and regions with intensive intermunicipal mobility. Overall, tourist flows in the studied regions correlate primarily with the scale of the existing passenger transport system. Therefore, the results represent diagnostic associations rather than causal estimates of tourist transport behavior. The study proposes a comparative index of tourism transport infrastructure adequacy that characterizes how well the selected territories’ transport systems can absorb tourist traffic under data limitations. The index reveals pronounced differentiation among the Moscow, Saint Petersburg, and Kaliningrad cases. Full article

Building change detection from bi-temporal remote-sensing imagery underpins urban planning, infrastructure monitoring, and disaster assessment. Existing deep-learning methods achieve high accuracy but rely on large parameter counts, while pixel-level supervision provides limited boundary guidance. We propose POCA-lite, a lightweight encoder–decoder with an inference-coupled geometry branch: three geometric prediction heads—distance transform, boundary, and center heatmap—whose outputs are fused back into the decoder via a feedback pathway active at both training and inference. On the LEVIR-CD benchmark under a unified retraining protocol, multi-seed evaluation shows that POCA-lite matches SNUNet in mean F1 while using 47% fewer parameters and 53% fewer FLOPs. Boundary F1 improves by 9.22 pp over the no-geometry baseline. Decomposition ablations reveal two complementary improvement sources: geometric supervision alone recovers 85% of the total gain, while the feedback fusion pathway recovers 92%; their combination achieves the full result. Geometry-aware targets outperform a generic multitask control. Cross-architecture transfer to SNUNet yields +1.06 pp F1. However, cross-dataset evaluation on WHU-CD shows that the method underperforms SNUNet on dense urban morphology, and zero-shot cross-dataset transfer is not established. These results indicate that inference-coupled geometric supervision is effective for lightweight, boundary-sensitive change detection on domains with well-separated building morphology, but its applicability is scope-bounded. Full article

With global warming and the increasing frequency of extreme heat events, heat health risk assessment (HHRA) has become a critical topic in climate change studies. However, the study themes, methods, and governance orientation of HHRA vary significantly across spatial scales, limiting the comparability and practical integration of assessment outcomes. This study conducts a review of the HHRA literature from 2007 to 2025, analyzing publication trends and evolving research paradigms. The results indicate the following: (1) rapid growth in the field with a notable shift from identifying static vulnerabilities to adopting “Hazard–Exposure–Vulnerability–Adaptability” (HEVA) frameworks, particularly at the micro-scale; (2) a clear scale-dependent hierarchy in assessment focus, where macro-scale studies identify regional trends, meso-scale research targets urban spatial heterogeneity and green–blue infrastructure, and micro-scale assessments emphasize housing conditions and individual perceptions; and (3) machine learning has been widely applied to capture complex nonlinear mechanisms and threshold effects. Finally, this study further emphasizes the importance of establishing a full-process feedback mechanism from macro-level early warning to meso-scale planning and micro-scale intervention, bridging the gap between regional policy and community-level action and providing a theoretical foundation for building climate-resilient cities. Full article

This study presents a multivocal literature review (MLR) on the implementation of Shared Autonomous Vehicles (SAVs), a relatively new concept in urban mobility that merges autonomous driving with shared transportation. The purpose of this review is to analyse the feasibility, challenges and potential impacts of SAV deployment by aggregating and synthesising insights from the academic literature and grey sources. The review addresses factors influencing deployment, including social acceptance, environmental impact, business models, policy frameworks, needs and barriers, and lessons from existing pilot programmes. The findings reveal that successful SAV implementation depends on combining technology, regulation and infrastructure. Public trust and perception of safety, cost and convenience can also significantly influence the adoption of this technology, as well as potential sustainability benefits (like reduced emissions and fewer private vehicles). Case studies from cities like Phoenix, San Francisco and Singapore show promising results but also context-specific challenges. This study concludes that future research should apply these insights to specific cities, where urban layouts and public transport reliance demand customised approaches to successfully deploy SAVs. Full article

With the rapid electrification of urban transportation and increasing penetration of renewable energy, maintaining frequency stability in smart-city multi-microgrids (MMG) systems increasingly depends on coordinated vehicle-to-grid (V2G) flexibility. However, existing load frequency control strategies typically treat electric vehicles (EVs) as homogeneous resources and overlook the impacts of charging-infrastructure diversity, user mobility constraints, and extreme weather conditions on regulation availability. To address these challenges, this study proposes a weather-adaptive intelligent load frequency control strategy for smart-city MMG considering heterogeneous charging stations and energy requirements of EV users. Fast and slow charging infrastructures are modeled separately to reflect their distinct regulation characteristics, while time-varying charging and discharging margins are derived from travel demand, parking duration, and state-of-charge preferences and further adjusted under extreme weather scenarios. Based on these dynamic constraints, an enhanced multi-agent soft actor–critic (MA-SAC) controller coordinates micro gas turbines and charging stations for distributed frequency regulation. Simulations demonstrate MA-SAC outperforms PID, Fuzzy, and MA-DDPG methods, achieving a 98.51% frequency excellent rate normally and 91.47% during extreme weather. It reduces maximum deviations by up to 80% versus PID, while preserving user travel requirements. The proposed framework provides a practical pathway for integrating electrified mobility into resilient smart-city MMG frequency regulation. Full article

The rapid growth of electric vehicle (EV) adoption requires the scalable and cost-effective deployment of publicly accessible charging infrastructure, where cost-effectiveness is understood in terms of infrastructure reuse rather than explicit economic optimisation. Integrating slow AC charging units into existing public lighting networks represents a promising infrastructure reuse strategy, though spatial feasibility, electrical constraints, and regulatory requirements must be addressed. This study proposes an integrated GIS–MCDA–MILP framework for the optimal allocation of EV charging stations within public lighting systems. GIS-based spatial analysis identifies feasible poles based on parking accessibility and demand indicators, while MCDA ranks candidate locations and a MILP model determines optimal deployment under capacity constraints and phased rollout scenarios. The framework also incorporates AFIR-based policy benchmarking to assess compliance under current and future EV adoption levels. A real-world case study identifies 1223 feasible poles with a structural hosting capacity of 368 chargers. The results demonstrate that such integration is viable at the spatial and cabinet-capacity planning level but structurally limited, with a critical fleet growth multiplier of approximately 3.4 identified as the threshold beyond which lighting-integrated deployment alone becomes insufficient for AFIR compliance. The proposed framework advances the state of practice by coupling spatial, electrical, and regulatory analysis within a single reproducible methodology, offering a transferable decision-support tool for sustainable urban EV charging planning. Full article

Does public transportation infrastructure expansion necessarily improve urban air quality? Using panel data from 168 Chinese cities, this study examines the impact of public transportation infrastructure development on air quality by applying GTWR (Geographically and Temporally Weighted Regression) models to capture spatial–temporal heterogeneity. Partial Dependence Plots (PDPs) are further employed to identify nonlinear relationships, alongside mechanism analysis. The results indicate that the effects of public transportation infrastructure on air quality are significant but highly heterogeneous across cities and over time. Transport development is associated with air quality through channels related to industrial transformation and agglomeration dynamics, with the latter showing a stronger relationship. Moreover, several key variables exhibit nonlinear relationships with identifiable threshold effects. These findings suggest that the environmental benefits of public transportation infrastructure are context-dependent rather than universal. This study provides a more comprehensive understanding of transport–environment linkages and offers policy insights for optimizing urban transport systems and promoting sustainable development. Full article

As urban areas expand, the sustainable management of municipal water becomes a critical challenge, especially in arid and semi-arid regions facing severe water scarcity. Accurate assessment of urban plant water requirements (PWR) is essential for developing sustainable landscape architecture and resilient green infrastructure. In this study, a new quantitative equation (PWR_q) was developed as a regional proof of concept to adjust reference evapotranspiration estimates for hyper-arid conditions. A Tree Morphology Coefficient (K_tm) is introduced to combine canopy features (form, height) and leaf traits (size, density) with an updated drought-resistance coefficient (K_dr). Field measurements of 277 mature trees, representing 27 native and introduced species in Riyadh and Jeddah, Saudi Arabia, were analyzed. The framework explicitly includes an empirical multiplier to account for extreme urban heat island (UHI) effects and aerodynamic canopy scaling. Instead of direct empirical validation, the PWR_q model was benchmarked against established reference indices: Water Use Classification of Landscape Species (WUCOLS) and Simplified Landscape Irrigation Demand Estimation (SLIDE), showing strong alignment with established categorical indices and structural traits. The results confirm that the morphology-based method effectively makes previously subjective classifications objective. Notably, the quantitative assessment found that the dominant introduced species require about 3.5 times more water than native species. As a proof of concept, future research should empirically validate these findings against direct physical measurements, such as sap flow sensors or lysimeters. The proposed framework presents a practical, objective decision-support tool for municipal policymakers and landscape architects to optimize species selection, implement nature-based solutions (NBS), and achieve long-term sustainability in urban greening. Full article

Robust tsunami casualty estimation is vital for Peru’s central coast. While static maps ignore evacuation dynamics, precise agent-based models (ABMs) are often too computationally demanding for rapid screening. To bridge this gap, we propose an efficient geospatial workflow coupling TUNAMI-N2 simulations with shortest-path routing. Evaluating four subduction scenarios across Chorrillos and Villa El Salvador, the model tracks census-block evacuation progress. By intersecting evacuation trajectories with tsunami arrival times, casualties are calculated using empirical depth-dependent fragility functions. Results highlight that delayed reaction times significantly increase mortality. Furthermore, a counterintuitive dynamic emerges in spatially constrained corridors lacking vertical evacuation: higher walking speeds can paradoxically increase fatalities by advancing evacuees into deeper inundation zones before being overtaken. This highlights that behavioral preparedness must be coupled with structural urban interventions. Ultimately, our scalable approach enables DRR (Disaster Risk Reduction) managers to rapidly map mortality hotspots and prioritize critical infrastructure improvements in highly exposed coastal zones. Full article

International evidence has been positioning the built environment as an active component of the development of students with Autism Spectrum Disorder (ASD); nevertheless, a gap persists in the empirical quantification of that relationship and, above all, in its dimensional hierarchization, a gap that becomes more pronounced in urban educational contexts with limited infrastructure such as those in Latin America. Within this framework, and with the aim of contributing empirical evidence to guide design decisions in comparable contexts, the present study analyzed the association between multisensory architecture and the cognitive development of students with ASD at a Special Basic Education Center (CEBE) in San Miguel, Metropolitan Lima, organizing the findings into a dimensional hierarchy that makes it possible to compare the relative strength of each spatial criterion. To address this objective, a non-experimental, cross-sectional, and correlational design was adopted, in which cognitive development was assessed through proxy informants (specifically, immediate family members with daily and sustained contact with the students), given that students with ASD present limitations for standardized verbal self-reporting. On this basis, a sample of 101 proxy informants completed, through the QuestionPro platform, a structured questionnaire of 24 Likert-scale items previously validated by expert judgment, exploratory factor analysis, and internal consistency analysis; inferential analysis was then conducted using Spearman’s rho, in keeping with the non-normal nature of the data. The results revealed a positive and statistically significant association between multisensory architecture and cognitive development, and they further allowed that relationship to be dimensionally ordered: on the built-environment side, physical-spatial conditions reached the greatest magnitude of association, followed by environmental conditions and, lastly, functional conditions; on the cognitive side, concentration emerged as the dimension most sensitive to the environment, followed by self-regulation and accessibility. Taken together, this empirical hierarchization offers architects, educational administrators, and therapeutic teams a practical reference for prioritizing design decisions in contexts with limited infrastructure and, to that extent, contributes to the fulfillment of Sustainable Development Goals 3 and 11, which connect health with inclusive urban environments. Full article

This study examines how tech-enabled municipal service delivery can generate a digital city dividend, measured as residents’ expected financial gains in urban context. The purpose is to identify the beliefs and enabling conditions that most strongly shape these expectations. We collected resident survey data and analysed the proposed model using PLS-SEM in SmartPLS. The reflective measurement model was evaluated for reliability and convergent validity (composite reliability; AVE) and for discriminant validity using both the Fornell–Larcker criterion and HTMT. We then tested the structural model through bootstrapping to assess the hypothesized paths. The results show that expected financial gains are driven primarily by behavioral intention, and are also supported directly by perceived value and trust. Behavioral intention rises mainly with trust and performance expectancy, while the effects of other adoption drivers are comparatively weaker. Service delivery quality contributes indirectly by strengthening perceived usefulness and trust, which subsequently improves intention and the expected dividend. The findings indicate that perceived financial benefits depend on a clear value pathway, credible institutional trust, and consistent service performance. The study therefore highlights practical priorities for cities: improve reliability and responsiveness, strengthen confidence through transparency and resolution mechanisms, and make the value-for-money case more legible to residents. Full article

Existing flood risk models have improved predictive accuracy, but they prioritize natural and hydrological factors while giving limited attention to fine-grained urban morphology. This study develops an interpretable deep learning framework to examine how high-resolution, three-dimensional urban form shapes two dimensions of flood risk: inundation risk, measured by grid-level inundated area, and infrastructure risk, measured by flood-related disruptions, including water supply interruption, power outage, road blockage, and collapse-related damage. Using Zhengzhou, China, as a case study, we combine multi-source spatial data, convolutional neural networks, ablation analysis, SHAP interpretation, and Gaussian Mixture Model classification to examine how fine-grained urban morphology affects these two risk dimensions. Incorporating urban morphology improved inundation risk prediction, reducing MSE from 0.0431 to 0.0371. The improvement was greater for infrastructure risk, with accuracy increasing from 0.7327 to 0.8218, and ROC-AUC from 0.83 to 0.95. SHAP results show that inundation risk is associated with vegetation, elevation, hydrological proximity, and localized spatial disorder, whereas infrastructure risk is amplified by vertical intensity, imperviousness, building concentration, porosity, and shape. Spatially, very high infrastructure-risk areas accounted for only 2.30% of the city but 12.88% of the central districts, while 74.62% of very high infrastructure-risk zones were concentrated in dense mid- to high-rise morphology. These findings suggest that flood-resilient planning should move beyond hydrology-sensitive flood management toward morphology-sensitive planning. Full article

Makerspaces can serve as distributed urban infrastructures for repair, reuse, tool sharing, and peer learning, yet their contributions to circular economy (CE) goals often occur without being explicitly recognized or framed as CE practices. Inspired by practice theory and the literature on quiet sustainability, this study introduces implicit circularity as circular practices enacted without an explicit sustainability/CE framing by participants, and examines how such practices shape bottom-up circular transitions. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we examined three linked questions: which circular practices occur in makerspaces and how they cluster into domains, how these practices vary across makerspace types, and which barriers and governance arrangements shape makerspaces’ consolidation as circular urban infrastructure. A qualitative multi-method design was employed in Czechia, combining field mapping with in-depth qualitative inquiry. Data included 40 semi-structured interviews with makerspace founders and operators, documentary analysis based on websites, social media, event listings, rules, and other documents, and 21 observations. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we analyzed how circular practices cluster into domains, how implicit versus explicit circularity varies across makerspace types, which barriers constrain makerspaces’ consolidation as circular urban infrastructure, and what governance arrangements could mitigate them. Circularity was dominated by implicit, routine practices rather than formal, CE-branded programs. Three practice domains were identified: repair and maintenance, material flows, and learning/education. Explicit programming was comparatively less common and context-dependent. Barriers formed a reinforcing system spanning institutional fragmentation and coordination deficits, capability gaps, infrastructural constraints, and tensions around autonomy and legitimacy, which together kept many circular contributions low-visibility. Makerspaces constitute an under-recognized form of circular micro-infrastructure that couples technical capacity with social learning and can translate CE ambitions into everyday practice. To mobilize these latent capacities, cities need hybrid governance, especially light-touch coordination platforms, long-horizon operational support, and integration of makerspaces into municipal material-flow systems and repair/reuse strategies. The study offers a practice-based framework and a cross-case typology to support comparative research and grounded urban CE policy design. Full article