Search Results (964)

India exhibits substantial variation in neonatal mortality across regions and socioeconomic groups. This study used nationally representative survey data (2019–2021) to examine wealth-based inequalities in neonatal mortality. Socioeconomic disparities were assessed using Erreygers’ Normalized Concentration Index (ECI) and concentration curves, with subgroup analyses by residence, state development status (Empowered Action Group (EAG) vs. non-EAG), district typology, and region. Inequality was further decomposed using the Wagstaff method. Analysis of 176,843 most recent live births revealed marked rural–urban disparities, with neonatal mortality in rural areas (18.3 per 1000 live births) 1.6 times higher than in urban areas (11.5). Neonatal mortality was significantly concentrated among poorer households (ECI: −0.0123; p < 0.001), with greater inequality in urban areas, EAG states, and non-aspirational districts. Regional variation was evident, with the highest inequality in the Western and Central regions. Decomposition analysis showed that inequality was primarily driven by adverse household conditions and maternal risk factors concentrated among poorer populations. Key contributors included unclean cooking fuel, higher parity, large family size, normal delivery and inadequate antenatal care. These findings highlight the need for equality-focused strategies addressing both social determinants and gaps in access to quality maternal and newborn care. Full article

Urban freight distribution is essential for supplying commercial activities, but it also increases pressure on curb space, vehicular circulation, pedestrian movement, and public space management, especially in medium-sized cities where dedicated loading and unloading infrastructure is often limited. Although recent literature emphasizes the need for data-driven urban logistics planning, empirical evidence from intermediate Latin American cities remains scarce. This study develops and applies a spatial-operational framework to characterize urban freight distribution, identify patterns of conflict and informality, estimate loading and unloading bay requirements, and prioritize intervention areas in a medium-sized city. A quantitative, observational, exploratory–descriptive, and correlational design was applied, based on 642 georeferenced loading and unloading operations recorded through a digital field survey. The analysis integrated data cleaning, descriptive and inferential statistics, logistic models, an operational sustainability risk/pressure index, DBSCAN spatial clustering, logistics pressure and sustainable transport priority indices, and a capacity model based on average daily operations. The results revealed spatial concentration of logistics activity, a predominance of light trucks, frequent use of paid parking areas and roadways, and a high presence of operational conflicts. The study provides a replicable and planning-oriented framework for prioritizing curbside management interventions for sustainable urban freight distribution in medium-sized Latin American cities. Full article

Transportation emissions raise critical environmental justice concerns, yet most studies overlook the distinct inequity patterns between passenger and freight systems. This study aims to compare the spatial disparities and driving mechanisms of exposure injustice from passenger and freight emissions at the U.S. county level. Using 2020 county-level cross-sectional data, we construct an environmental injustice index (EII) and apply spatial autocorrelation analysis, a two-stage multi-task TabNet model, and SHAP interpretation to identify spatial divergence, key determinants, and heterogeneous effects of urban compactness. Results show that passenger EII features continuous regional clustering, while freight EII concentrates along corridors and nodes with limited spatial overlap. Passenger injustice is driven by population density, auto dependence, and public transit, whereas freight injustice is dominated by truck intensity, freight network location, and logistics employment. Urban compactness has dual impacts on passenger injustice but consistently exacerbates freight injustice. These findings highlight the necessity of differentiated governance and provide empirical support for equitable low-carbon transport policies. Full article

Public transport reliability largely determines the performance of smart urban mobility systems, as it directly affects passenger satisfaction and network efficiency. However, the strategic planning of public transport infrastructure is often carried out without dynamic, data-driven insights into operational performance, instead relying solely on static historical records of network operations. This study develops a data-driven framework based on the XGBoost machine learning algorithm to support the prioritization of infrastructure interventions by predicting delay severity and identifying reliability hotspots along an urban bus route. Delay severity is categorized into three classes (minor, moderate, and severe), using a model that incorporates spatial, temporal, operational, and meteorological variables. The XGBoost framework achieves a high predictive performance, with classification accuracies of 91.5% and 89.7% for the outbound and inbound bus route directions, respectively. Feature importance analysis indicates that seasonal and meteorological variables are critical factors influencing delay severity, highlighting the role of broader external environmental conditions on corridor performance. Furthermore, spatial analysis identifies specific bus stops with high delay probabilities, indicating hotspots where infrastructure upgrades should be prioritized at the stop and corridor levels. This study proposes a decision-support tool that enables targeted infrastructure investments at locations where they are most needed, contributing to more efficient and resilient public transport systems in smart cities. Full article

This paper develops a dynamic multidimensional ecosystem service value index for 280 prefecture-level cities in China from 2000 to 2023. The index is constructed by integrating remote sensing, GIS, and ecological–economic indicators, with machine learning used as a data-driven tool to aggregate multidimensional ecological information. Building on this measurement framework, the paper applies a staggered Difference-in-Differences (DID) model to evaluate the impact of the ecological conservation redline policy on regional ecosystem service value. The results show that the policy significantly increases urban ecosystem service value and that the effect is cumulative over time. Mechanism analysis suggests that the policy mainly works through three channels: ecological benefit improvement, ecological spatial reconstruction, and community public participation. Heterogeneity analysis further shows that the effect is stronger in early pilot cities and in high-ecological-function zones. In addition, policy coordination and local governance capacity significantly strengthen policy effectiveness. By combining multidimensional ecosystem service measurement with causal policy evaluation, this study extends existing research on ecological conservation redline and provides empirical evidence for improving land spatial governance and ecological protection policy design in China. Full article

This study examines pedestrian perceptions of streetscapes in Isfahan’s cultural heritage site by integrating deep learning–based image segmentation with urban morphological analysis. It addresses the opportunity to develop a scalable and context-sensitive method for assessing pedestrian-oriented heritage streetscapes, particularly where conventional street-view datasets are unavailable. Using a U-Net model applied to First-Person Pedestrian View (FPPV) images, five perceptual indices, imageability, enclosure, human scale, greenness, and walking index, were quantified to examine their associations with pedestrian experience. Street width was incorporated as a morphological variable to explore its relationship with perceptual qualities using Spearman correlation and visual trend analysis. The results indicate exploratory associations between visual composition and perceptual outcomes within the analysed heritage streetscape context, particularly between imageability, enclosure, and vegetation structure. In contrast, variables such as human scale and walking index showed weak or negligible associations with street width, indicating that pedestrian activity patterns within the analysed heritage streetscape may be influenced by additional spatial, landscape, and socio-functional factors beyond dimensional characteristics alone. Segmentation-based analysis achieved an accuracy of 83% in classifying dominant streetscape elements, offering a reproducible alternative to traditional survey-based methods. This study contributes a data-driven framework for assessing pedestrian streetscapes, emphasising morphological continuity, human-scale design, and green infrastructure as important dimensions of walkability assessment. It also identifies key challenges, including fragmented spatial morphology and inconsistent urban furniture placement, which may affect pedestrian comfort and use of space. These findings offer evidence-informed design considerations for historic streetscape assessment, with implications for balancing heritage conservation and contemporary pedestrian needs. Future research may refine perceptual metrics, incorporate behavioural or longitudinal validation, and extend the approach across diverse urban contexts. Full article

Rapid urbanization exerts profound pressure on urban biodiversity, yet long-term assessments integrating multi-source remote sensing data remain scarce. Objective: Focusing on the Hangzhou Bay Urban Agglomeration, a rapidly developing region in China’s Yangtze River Delta, this study aims to construct a remote sensing-based Biodiversity Index (BI) and analyze its spatiotemporal evolution and underlying drivers. Six Essential Biodiversity Variables derived from satellite observations (2000–2024) were integrated using Principal Component Analysis. Spatial autocorrelation and Geodetector models were then applied to examine BI dynamics and driving factors. The regional BI declined gradually from 0.80 in 2000 to 0.72 in 2024, with the rate of decline slowing after 2020 and a partial recovery observed in Zhoushan. Marked inter-city heterogeneity exists: Huzhou retains the highest and most stable BI due to extensive forest cover, whereas Jiaxing exhibits the lowest BI and the most pronounced decline, driven by rapid expansion of construction land. Land use/cover (LULC) and fractional vegetation cover (FVC) emerge as the dominant drivers (average q-values of 0.196 and 0.208, respectively), and their interaction explains over 46% of the spatial variance in BI. Road density shows a consistently increasing influence over time. This study demonstrates the utility of remote sensing-based frameworks for monitoring urban biodiversity dynamics and provides actionable insights for evidence-based land use planning and ecological restoration. Full article

The three-dimensional (3D) built environment encodes critical information about urban form intensity, environmental exposure, and resource consumption. However, previous studies have often overlooked the integration of long-term analyses of both horizontal expansion and vertical growth. This study aims to identify the spatial differentiation, morphology types, and vertical growth trajectories of major Chinese megacities over the past three decades. Using high-resolution GABLE building data and time-series GAIA impervious surface data, we examine the evolution of urban 3D morphology across six major Chinese megacities from 1991 to 2023 through a retrospective analysis of building construction years combined with spatial gradient analysis. The results reveal that although the megacities exhibit distinct differences in vertical structure, shape complexity, and spatial compactness, they share a consistent center-to-periphery gradient across most 3D indicators. The most active volumetric growth was concentrated in a zone 8–14 km from city centers, which accounted for 23.6% of total new development, whereas the inner core within 6 km contributed less than 2.68%. In terms of temporal dynamics, Beijing, Shanghai and Guangzhou follow an inverted-V-shaped 3D expansion trajectory driven by mid-rise construction; Tianjin and Hangzhou show accelerated growth with a higher proportion of high-rise clusters; while Shenzhen demonstrates an early peak and a decelerated growth rate, accompanied by a pronounced polycentric pattern. While recent global-scale studies have suggested a shift from outward urban sprawl to vertical development, our findings indicate that horizontal expansion still dominates in the selected Chinese megacities, with outward sprawl exceeding vertical densification during the study period. The integrated approach provides a robust framework for mapping 3D urbanization and offers practical insights for policymakers seeking to manage horizontal expansion, guide vertical intensification, and optimize land-use efficiency in rapidly urbanizing megacities. Full article

The rapid growth of organic waste volumes in urban areas and increasing environmental pressures necessitate the transition toward sustainable and risk-informed municipal waste management systems. This study aims to develop a data-driven decision support framework for the risk-informed management of municipal organic waste within the context of sustainable urban development. The proposed approach integrates multi-source municipal data, advanced preprocessing techniques, entropy-based feature weighting, and an ensemble of machine learning models, including Random Forest, Gradient Boosting, and XGBoost. An integrated environmental risk index is formulated to quantify the state of the waste management system and to support predictive analytics. The results demonstrate high predictive performance and reveal that key risk drivers include demographic pressure, transport accessibility, infrastructure characteristics, and seasonal variability of waste generation. The developed framework enables the integration of predictive risk analytics into municipal decision support systems, facilitating optimized waste collection logistics, infrastructure planning, and early identification of critical conditions. The findings confirm that data-driven approaches can significantly enhance the efficiency and adaptability of urban waste management systems. The proposed framework contributes to sustainable urban development by supporting circular economy principles and enabling proactive, risk-aware governance of municipal organic waste systems. Full article

Using panel data from Chinese cities spanning 2010–2023 and leveraging the natural experiment provided by the establishment of the National Big Data Comprehensive Pilot Zone (NBDPZ), we employed the difference-in-differences (DID) method alongside double machine learning (DML) to systematically examine how these policies influence urban economic resilience efficiency. The empirical results demonstrate that the NBDPZ significantly enhances urban economic resilience efficiency. This finding is robust under parallel trend and placebo tests, confirming that the improvement is a policy-driven causal effect. Mechanism analysis reveals that the policy enhances urban economic resilience efficiency primarily by promoting the upgrading and rationalization of industrial structure to consolidate the micro-foundation of sustainable economic transformation; increasing innovation output to facilitate the sustainable accumulation of knowledge capital; and enhancing urban entrepreneurial activity to inject sustainable endogenous vitality into the economic system. Heterogeneity analysis indicates that the positive effects are more pronounced in eastern and western regions, second-tier cities, and cities with lower industrial agglomeration, better digital infrastructure, and stronger legal and regulatory environments. The study’s findings offer both theoretical support and practical guidance for refining the policy framework of the NBDPZ policy and promoting sustainable urban economic development. Full article

Against the backdrop of intensifying global uncertainties, enhancing the livelihood resilience of urban and rural residents is of paramount importance for promoting balanced regional development. This research establishes a 29-indicator evaluation system based on a three-dimensional analytical framework encompassing “buffering, adaptive, and transformative capacities”. resilience capacities. Utilizing county-level panel data from five pivotal former revolutionary base areas, specifically the Jiangxi–Fujian–Guangdong Former Central Soviet Area, Sichuan–Shaanxi Revolutionary Base Area, Shaanxi–Gansu–Ningxia Revolutionary Base Area, Dabie Mountains Revolutionary Base Area, and Zuojiang–Youjiang Revolutionary Base Area regions spanning from 2011 to 2023, through the integrated application of methodologies, including entropy weighting, kernel density estimation, the Theil index, and convergence analysis, we systematically examine the spatio-temporal variations and evolutionary mechanisms of livelihood resilience. Research findings indicate a general enhancement of livelihood resilience in old revolutionary base areas, albeit with notable regional disparities, presenting a tiered pattern characterized by Jiangxi–Fujian–Guangdong leading, Dabie Mountains and Sichuan–Shaanxi regions being intermediate, while Shaanxi–Gansu–Ningxia and Zuojiang–Youjiang areas lag behind. Buffering capacity predominates, while regenerative capacity constitutes the critical driver of regional disparities. The overall regional disparities are primarily driven by internal differences, with significant conditional β-convergence observed in livelihood resilience. This study proposes sustained advancement in infrastructure development to consolidate buffering capacity, a reinforcement of public services and technological innovation to enhance adaptive and regenerative capabilities, and the implementation of differentiated governance strategies, thereby fostering an overall improvement in livelihood resilience and coordinated regional development in old revolutionary base areas. Full article

Urban soil contamination by potentially toxic elements (PTEs) is a recognized health concern in densely populated urban environments. Through a systematic meta-analysis of 91 peer-reviewed studies (2000–2025) reporting 12,174 sampling sites in capital and core cities, we characterized regional patterns in the spatiotemporal dynamics and health risks of eight PTEs across two well-represented continental subsets (Asia, k = 18–36 per element; Europe, k = 11–23 per element) with comparative reference to the Americas, Africa, and Oceania. Given the uneven geographic distribution of qualifying primary studies, continental comparisons should be interpreted as hypothesis-generating: Asia (k = 18–36 per element) and Europe (k = 11–23 per element) provide the statistically robust core of the synthesis, while results for the Americas (k = 3–7 for several elements), Africa (k = 4–15), and Oceania (k = 2) are presented as illustrative rather than statistically representative. Pooled concentrations followed Zn (138.59) > Pb (56.97) > Cr (54.26) > Cu (47.00) > Ni (31.94) > As (8.56) > Hg (3.13) > Cd (1.23) mg·kg⁻¹. Within the well-represented Asian and European subsets, Asian cities showed the most severe enrichment of As, Cd, Cr, and Hg (I_geo > 4 in hotspots such as Kathmandu I_geo (Cd) = 7.06 and Jinan I_geo (Hg) = 5.27), whereas European centres exhibited substantial legacy Pb accumulation (pooled mean 87.69 mg·kg⁻¹). A reproducible pollution gradient was identified across functional zones: industrial > transportation ≥ residential > commercial > agricultural > urban green areas. The deterministic non-carcinogenic Hazard Index (HI = 1.49) for children in Asia exceeded the safe threshold (HI > 1), driven primarily by As and Cr exposure via incidental soil-and-dust ingestion. Monte Carlo probabilistic assessment (N = 10,000) confirmed elevated cumulative non-carcinogenic risk at the median of the exposure distribution for children in the data-rich Asian (P50 = 1.55; P(HI > 1) = 81.9%) and European (P50 = 1.28; P(HI > 1) = 69.8%) subsets, with adults in both subsets remaining well below the safety threshold (P(HI > 1) = 0.0%). Temporal analysis revealed a decoupling between economic growth and PTE accumulation in long-established cities, together with an inverse Ni–population correlation indicative of strategic resource allocation. For Asian capital and core cities, where the evidence base is strongest (k = 18–36 per element), the present synthesis supports further investigation of risk-based, child-centric soil management as a public-health priority. For European cities (k = 11–23 per element), the same direction of risk is indicated but should be confirmed in regionally focused syntheses. Policy considerations for under-represented regions should await expansion of the primary monitoring base. Full article

Efficient and equitable allocation of educational resources is fundamental to building sustainable education systems and achieving inclusive, equitable, and quality education under Sustainable Development Goal 4. This study employs the slack-based measure (SBM) model to evaluate the resource allocation efficiency of 882 regular senior high schools in China and applies configurational analysis to explore multiple pathways toward high efficiency. The results show that, first, the overall resource allocation efficiency of regular senior high schools, measured through educational outputs related to talent cultivation, remains at a moderately low level. Both overall technical efficiency and pure technical efficiency have substantial room for improvement. The primary challenge in current resource allocation lies not in scale imbalance but in insufficient resource utilization, low internal governance efficiency, and weak capacity to transform existing resources into educational outcomes under current operational scales. Second, significant disparities in resource allocation efficiency are observed across urban–rural locations, school ownership types, and school tiers, revealing a notable “resource-abundance paradox”: schools with relatively limited resources may achieve higher resource utilization efficiency. Third, high resource allocation efficiency is not driven by isolated factors, but by the synergistic interaction of multiple conditions. Four distinct pathways to high efficiency are identified, in which principal instructional leadership recurrently appears as a core condition across the identified sufficient configurations. Accordingly, this study proposes targeted policy implications for improving resource allocation efficiency in regular senior high education, including establishing a performance-oriented resource allocation system, promoting categorized governance and differentiated policy design, strengthening school-based empowerment and internal governance mechanisms, and developing a data-driven monitoring and decision-making framework for educational resources. Full article

Open, non-governmental building datasets have become increasingly important for urban analysis, exposure modelling, and policy support. Despite their growing use, little is known about the consistency, completeness, and comparability of the semantic information they provide at a continental scale. This study presents the first systematic comparison of the semantic attributes of six major pan-European open building datasets—OpenStreetMap, EUBUCCO, Microsoft Global ML Building Footprints, Overture Maps, GHS-OBAT, and the Digital Building Stock Model (DBSM)—using the 27 EU Member States as a common reference area. Five key semantic attributes (height, typology, building age, number of floors, and building material) were harmonised and analysed in terms of completeness and value distributions across countries and degrees of urbanisation. The workflow combines API-based data ingestion, distributed geospatial processing, and high-performance computing to handle around 1.250 billion building footprints. Results reveal pronounced heterogeneity in semantic content across datasets. Remote-sensing-derived products (GHS-OBAT and DBSM) exhibit the highest levels of attribute completeness for height, typology, and building age, but rely on aggregated or coarse semantic representations. In contrast, community-driven and conflated datasets (OpenStreetMap and Overture Maps) provide richer and more detailed semantic schemas, albeit with low and spatially uneven completeness. Completeness patterns vary substantially across countries and urbanisation classes, and high completeness values often mask limited semantic informativeness due to the prevalence of unknown or aggregated attribute values. Overall, the findings demonstrate that no single dataset is universally optimal regarding consistency and completeness of building footprints’ semantic attributes. Nonetheless, the paper provides practical guidance for selecting suitable data sources depending on spatial scale, attribute requirements, and analytical objectives. Full article

Traditional urban building energy modeling often overlooks the complexity of spatial configurations and mutual shading effects, thereby limiting its accuracy. This study proposes a novel, interpretable, data-driven framework based on heterogeneous graph neural networks (GNNs) to uncover and characterize the complex interrelationships between building morphology and urban topology. Using a parametric platform, this study generated a graph dataset of 285 residential blocks in Wuhan, structured as a dual-level graph: Building Zone Graphs (BZGs) and Building Layout Graphs (BLGs). Four GNN models were trained based on the dataset, and the evaluated results demonstrate that GraphTransformer outperforms GCN, GAT, and GraphSAGE in capturing long-range spatial relationships―particularly those arising from shading and solar access interactions. On a validation set, GraphTransformer achieved superior predictive accuracy, with R² scores exceeding 0.85 and 0.90 for cooling and heating energy predictions, respectively. After that, post hoc interpretability analysis by GNNExplainer identified three important morphology features influencing building energy consumption. Critically, the model found that shading relationships encoded as graph edges―especially those between southern and western façades―had statistically significant influence on building energy consumption. Finally, this work establishes an efficient, interpretable surrogate modeling framework for urban-scale energy analysis, delivering quantifiable, design-actionable insights to support sustainable urban development. Full article