Search Results (73)

This paper presents a dataset developed to characterize the citizens’ perceptions of urban resilience applied to the Veracruz—Boca del Río Metropolitan Area (VBMA) in Mexico. The data were obtained by conducting online surveys, which were administered to a total of 147 subjects, including 89 from the municipality of Veracruz, 35 from Boca del Río, 15 from Medellín de Bravo, and 8 from Alvarado, with ages ranging from 16 years to over 61 years. The survey was designed to estimate the population’s perception of the Urban Resilience Index (URI) and the Urban Resilience Profile (URP). It was developed derived from a methodology based on IMPLAN and enriched with questionnaires from Villada and SEDATU, resulting in a final questionnaire comprising 10 axes, 33 indicators, and 156 variables. A novel contribution was implemented as a significant study case, which uses the dataset to estimate the URI and URP to the VBMA applying the Entropy Method, considering three criteria: age, gender, and municipality. Here, citizens’ perceptions about urban resilience have been estimated in an URI equal to 0.4571, resulting in a moderate level of resilience. Moreover, this perception could be improved by conducting a full-scale survey with substantial financial investment. Full article

Urban stormwater management presents significant challenges for municipalities seeking to balance environmental resilience with financial considerations and social equity. This study investigates the factors shaping residents’ willingness to pay (WTP) for a proposed stormwater management fee in Norwalk, Connecticut, within the context of local sustainability plans. A survey of 457 residents assessed demographics, personal beliefs, perceptions of benefits, risks, and WTP. Since participation was voluntary and open, an exact response rate could not be calculated, and the resulting respondent profile differed from city benchmarks. The results were analyzed using descriptive and inferential statistics alongside a Random Forest machine learning model assessing two payment scenarios, achieving classification accuracies above the majority-class baseline (approximately 60–68%). Across both scenarios, expectations of tangible and locally visible outcomes, including infrastructure upgrades and climate resilience improvements, were the strongest determinants of WTP. When respondents evaluated a specific fee amount rather than a general modest fee, concerns about affordability and program effectiveness became more influential and revealed the conditional nature of financial support. The findings illustrate the value of machine learning for analyzing public attitudes toward environmental finance and highlight how policy framing, transparency, and communication shape acceptance of sustainability measures. These insights provide a data-driven foundation for future research on public engagement and equity in local environmental policy and stormwater plan development. Full article

(1) Background: The present study aimed to develop and validate the Scale for the Identification of Emotional Resilience in Children (SIER-C), a psychometric instrument designed to assess key dimensions of emotional intelligence and resilience among children aged 6 to 12 years. (2) Methods: The sample comprised 367 participants (52.3% male, 47.7% female) drawn from both urban and rural educational settings across Romania, selected through stratified random sampling to ensure demographic representativeness. The SIER-C consists of 30 items distributed across six subscales: Recognition and Understanding of Emotions (RUE), Emotion Regulation (ER), Empathy (E), Attitude Toward Failure (ATF), Coping Strategies (CS), and Perseverance and Self-Motivation (PSM), with items rated on a 5-point Likert scale. An Exploratory Factor Analysis (EFA) was initially conducted to examine the underlying factor structure, followed by Confirmatory Factor Analysis (CFA) to validate the model. (3) Results: The EFA suggested a six-factor structure consistent with the intended subscales, which was subsequently confirmed by CFA, demonstrating satisfactory model fit indices and confirming the scale’s construct validity. Internal consistency indices and composite reliability coefficients further indicated robust psychometric properties across subscales. (4) Conclusions: The findings underscore the relevance of SIER-C as a reliable and valid tool for identifying nuanced profiles of emotional intelligence and resilience in children. These profiles provide valuable insights for early detection of emotional and adaptive vulnerabilities and for the design of targeted interventions within educational and clinical frameworks. Future research should explore the longitudinal stability of these constructs and examine the integration of SIER-C within social–emotional learning programs to support the development of emotional competencies from a preventive and developmental perspective. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

Nanotechnology is reshaping the built environment by enabling the development of materials that improve structural performance, energy efficiency, durability, and environmental quality. This paper reviews nano-enabled construction materials through a micro–meso–macro lens, linking material mechanisms to building behavior and urban impacts. It highlights both their potential contributions to decarbonization, public health, and urban resilience, and the parallel challenges of energy-intensive production, uncertain toxicological profiles, and regulatory gaps. Finally, it argues for responsible integration based on life-cycle thinking, precautionary risk governance, and updated architectural and engineering education so that nano-enabled innovation supports truly sustainable, equitable cities rather than new forms of hidden risk. Full article

Over recent decades, planners in the U.S. have increasingly adopted mixed-use projects to reduce automobile dependency and strengthen local community identity, although results remain inconsistent across cities. Urban health and fitness outcomes are shaped by complex interactions between the built environment, socioeconomic factors, and demographic characteristics. This study introduces a Health and Fitness Index (HFI) for 28,758 U.S. ZIP codes, derived from normalized measures of walkability, healthcare facility density, and carbon emissions, to assess spatial disparities in health-supportive environments. Using four modeling approaches—lasso regression, multiple linear regression, decision trees, and k-nearest neighbor classifiers—we evaluated the predictive importance of 15 urban and socioeconomic variables. Multiple linear regression produced the strongest generalization performance (R² = 0.60, RMSE = 0.04). Key positive predictors included occupied housing units, business density, land-use mix, household income, and racial diversity, while income inequality and population density were negatively associated with health outcomes. This study evaluates five statistical formulations (Metropolis Hybrid Models) that incorporate different combinations of walkability, land-use mix, environmental variables, and socioeconomic indicators to test whether relationships between urban form and socioeconomic conditions remain consistent under different variable combinations. In cross-sectional multivariate regression, although mixed-use development in high-density areas is strongly associated with healthcare facilities, these areas tend to serve younger and more racially diverse populations. Decision tree feature importance rankings and clustering profiles highlight structural inequalities across regions, suggesting that enhancing business diversity, land-use integration, and income equity could significantly improve health-supportive urban design. This research provides a data-driven framework for urban planners to identify underserved neighborhoods and develop targeted interventions that promote walkability, accessibility to health infrastructure, and sustainability. It contributes to the growing literature on urban health analytics, integrating machine learning, spatial clustering, and multidimensional urban indicators to advance equitable and resilient city planning. Full article

Energy consumption is a key driver of economic and social development, particularly in rapidly expanding sectors such as Information and Communication Technology (ICT). This study explores the energy demand of Quito’s ICT sector across technical, organizational, economic, social, and environmental dimensions, aiming to inform sustainable urban strategies. A mixed-methods approach was applied, combining quantitative and qualitative analyses. Data was collected via questionnaires from 398 ICT companies and analyzed using descriptive statistics and multivariate techniques, including the Gower similarity coefficient, Principal Coordinate Analysis (PCoA), and biplots. The VENSIM PLE x64 version 9.1.1 was used to model energy consumption dynamics. Results indicate that most ICT firms are micro and small enterprises focused on software development and e-commerce, employing highly skilled personnel. Energy use is concentrated in computing and printing equipment, with limited reliance on climate control systems. While 93% of firms express environmental awareness, fewer than 10% have formal energy efficiency policies. Financial constraints and limited access to efficient equipment are the main barriers to improved energy management. The study concludes that, despite a moderate energy profile, there is an urgent need to strengthen internal energy practices. The findings offer a contextualized framework to guide energy policy and organizational strategies, contributing to more resilient and sustainable urban ICT ecosystems. Full article

Urban CO₂ mitigation strategies typically aim at particular zones or sectors but do not account for spatial interdependencies among different components within the city. Understanding how land uses emit within and across districts can reveal systemic leverage points for climate-resilient urban planning. This study applies a bipartite spatial network approach using high-resolution Urban Atlas land-use data and a hierarchical spatial framework for emissions and sequestration estimation. The approach links urban land uses to their emissions profiles, offering a structural view of how different areas interconnect within urban carbon dynamics, moving beyond fragmented emission accounting. Using the Reggio Calabria Functional Urban Area in Italy as a case study, the analysis identifies influential areas and emission-intensive land uses. Subsequently, using centrality metrics highlights the spatial units with strong connections to emission-dense land uses, marking them as points of intervention. Results show that although 53% of districts act as net carbon sinks, their sequestration capacity is outweighed by the intensity of a smaller group of emitter districts. Among these, five central districts (IDs 94, 82, 107, 108, and 72) emit over 500 million kg CO₂ per year, making them leverage points for systemic mitigation. The integration of bipartite spatial network and multiscale territorial analysis provides a replicable, data-driven framework for urban CO₂ mitigation. Ultimately, the study demonstrates that mapping emissions through spatial interdependencies enables planners to target interventions where localized action yields the greatest network-wide climate impact. Full article

Coastal regions in Mexico face significant exposure to hydrometeorological hazards, often resulting in severe flooding and socioeconomic disruption. This study assesses the hydrometeorological resilience of the Veracruz–Boca del Río Conurbation (VBC), a region comprising two coastal municipalities with shared hazard exposure despite distinct governance structures. The hydrometeorological resilience evaluation employs the City Resilience Index (CRI), developed by Bahena which integrates the Technical Resilience Index (TRI) and the Technical Profile of Resilience (TPR) across nine hierarchical indicators. Results reveal moderate resilience levels—59.83% for Veracruz and 58.32% for Boca del Río—with Disaster Risk Reduction Plans and Vital Services indicators as the strongest contributors, while Risk Assessments and Budget Allocation for Emergency Response indicators scored lowest due to limited municipal data. These findings highlight the need for enhanced data transparency, institutional coordination, and resource allocation in disaster management. Beyond its local significance, this study advances the global understanding of resilience assessment frameworks in data-scarce contexts, offering insights applicable to similar regions worldwide. As the first hydrometeorological resilience assessment for the VBC, this research provides a methodological and empirical foundation for future studies and informs targeted resilience strategies for Mexico’s coastal urban areas. Full article

University students face rising mental health pressures, making restorative environmental perception (REP) in campus forests critical for psychological recovery. While environmental factors are recognized contributors, Socio-Ecological Systems (SES) theory emphasizes that environmental and social processes are interdependent. Within this context, informal social interaction (ISI)—low-effort encounters such as greetings or small talk—represent a key social dimension that may complement environmental restoration by fostering comfort and embedded affordances. However, most studies examine these factors separately, often using coarse measures that overlook heterogeneity in restorative mechanisms. This study investigates how environmental-exposure and social–environmental context dimensions jointly shape REP in campus forests, focusing on distributional patterns beyond average effects. Using a Public Participation Geographic Information Systems (PPGIS) approach, 30 students photographed 1294 tree-dominant scenes on a forest-rich campus. Environmental features were quantified via semantic segmentation, and ISI was rated alongside REP. Quantile regression estimated effects across the REP distribution. Three distributional patterns emerged. First, blue exposure and ISI acted as reliable resources, consistently enhancing REP with distinct profiles. Second, green exposure functioned as a threshold-dependent resource, with mid-quantile attenuation but amplified contributions in highly restorative scenes. Third, anthropogenic and demographic factors created conditional barriers with distribution-specific effects. Findings demonstrate that campus forest restoration operates through differentiated socio-ecological mechanisms rather than uniform pathways, informing strategies for equitable, restoration-optimized management. More broadly, the distributional framework offers transferable insights for urban forests as socio-ecological infrastructures supporting both human well-being and ecological resilience. Full article

Fires remain a critical threat to the resilience and safety of the built environment, yet current research is often fragmented across building types, technologies, and functions. This study investigates typology-specific gaps in fire safety by conducting a scientometric review of peer-reviewed articles published between 2010 and 2025. Following a PRISMA-guided protocol, a total of 83 studies indexed in the Web of Science were systematically screened and analyzed using VOSviewer (v1.6.19) and the R-based Bibliometrix package (version 4.2.1). The dataset was classified according to building typologies, fire safety functions—detection, suppression, and evacuation—and applied technologies such as BIM, simulation platforms, and AI-based models. The results show a strong research bias toward evacuation modeling in high-rise and general-purpose buildings, while critical typologies including healthcare facilities, heritage structures, and informal settlements remain underexplored. Suppression systems and real-time detection technologies are rarely integrated, and technological applications are often fragmented rather than interoperable. A conceptual matrix is proposed to align tools with typology-specific risk profiles, highlighting mismatches between research priorities and building functions. These findings emphasize the need for integrated, data-driven, and context-sensitive fire safety strategies that bridge methodological innovation with practical application, offering a roadmap for advancing resilient and adaptive fire safety in diverse urban settings. Full article

Urban green spaces (UGS) are crucial for mitigating rising urban land surface temperatures (LST). Rapid urbanization presents unresolved questions regarding (a) seasonal variations in the spatial co-distribution of UGS and LST, (b) the temporal and spatial changes in UGS cooling, and (c) the dominant factors driving cooling effects during different periods. This study focuses on Beijing’s Fifth Ring Road area, utilizing nearly 40 years of Landsat remote sensing imagery and land cover data. We propose a novel nine-square grid spatial analysis approach that integrates LST retrieval, profile line analysis, and the XGBoost algorithm to investigate the long-term spatiotemporal evolution of UGS cooling capacity and its driving mechanisms. The results demonstrate three key findings: (1) Strong seasonal divergence in UGS-LST correlation: A significant negative correlation dominates during summer months (June–August), whereas winter (December–February) exhibits marked weakening of this relationship, with localized positive correlations indicating thermal inversion effects. (2) Dynamic evolution of cooling capacity under urbanization: Urban expansion has reconfigured UGS spatial patterns, with a cooling capacity of UGS showing an “enhancement–decline–enhancement” trend over time. Analysis through machine learning on the significance of landscape metrics revealed that scale-related metrics play a dominant role in the early stage of urbanization, while the focus shifts to quality-related metrics in the later phase. (3) Optimal cooling efficiency threshold: Maximum per-unit-area cooling intensity occurs at 10–20% UGS coverage, yielding an average LST reduction of approximately 1 °C relative to non-vegetated surfaces. This study elucidates the spatiotemporal evolution of UGS cooling effects during urbanization, establishing a robust scientific foundation for optimizing green space configuration and enhancing urban climate resilience. Full article

Wheat is an important crop in Rwanda; however, rapid population growth, urbanization, and shifting dietary preferences have driven demand far beyond domestic production capacity, resulting in a steady increase in imports. Closing this gap requires a variety of management strategies that jointly optimise yield, processing quality, and sustainability. This study evaluated ten widely cultivated wheat (Triticum aestivum L.) varieties in Rwanda through an integrated assessment of grain yield, quality traits, and rheological properties. Yields ranged from 4.3 to 6.3 t ha⁻¹, with Nyaruka and Gihundo achieving the highest productivity. Quality attributes, including protein content (PC), wet gluten (WG), gluten index (GI), falling number (FN), and Zeleny sedimentation value (ZSV), varied significantly, with Cyumba and Reberaho showing superior protein levels. Mixolab-based rheological analyses revealed marked diversity in dough development time, torque, and water absorption, with Keza and Nyangufi exhibiting favorable baking profiles. Statistical analyses highlighted trade-offs between yield and quality, as high-yielding varieties such as Nyaruka showed weaker baking characteristics. These findings demonstrate that linking agronomic performance with grain and dough quality traits provides a pathway towards targeted breeding, sustainable intensification, and enhanced food security. Integrating genetic selection with tailored management and processing strategies can improve both productivity and product value, strengthening the resilience and economic viability of Rwanda’s wheat sector. Full article

Urban drone operations are exposed to unpredictable risks, including engine failure and deliberate signal interference. A recent and ongoing disruption in Jeddah, Saudi Arabia, has seen widespread GPS spoofing that misleads devices by hundreds of kilometers, illustrating how fragile unmanned aerial vehicle (UAV) operations can become when over-reliant on GNSS-based navigation. Such disruptions highlight the urgent need for contingency planning in drone traffic management systems. This study introduces a safety-aware pre-flight path planning framework that proactively integrates emergency landing and GPS fallback options into UAV trajectory pre-flight planning. The planner considers proximity to predesignated emergency landing zones, communication coverage, and airspace restrictions, enabling UAVs to safely complete their operations. The approach is evaluated across realistic mission profiles such as delivery, inspection, and surveillance. Results show that the planner successfully maintains mission feasibility while embedding emergency readiness throughout each flight. This work contributes toward safer, failure-resilient drone integration in urban airspace, ensuring that contingency plans are proactively incorporated into path planning before the failure even occurs. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article