Search Results (160)

Universal health coverage requires simultaneous progress in service coverage and financial protection, yet these dimensions are commonly assessed separately. We conducted a secondary ecological panel analysis using official World Health Organization/World Bank indicators. A document-oriented workflow preserved irregular subgroup structures before conversion to an unbalanced country–year panel comprising 981 observations from 159 countries/economies between 2000 and 2023. The primary inferential specification was a two-way fixed-effects model with country and year effects and country-clustered standard errors; a country fixed-effects model with a linear time trend, a random-intercept model, and generalized estimating equations were complementary robustness analyses. We also examined service domains, wealth- and rural-urban inequalities, beta convergence, and exploratory country typologies. Mean service coverage rose from 58.9 in 2000 to 74.0 in 2023, whereas mean financial hardship fell from 24.0% to 17.3%. In the primary model, each one-point increase in service coverage was associated with a 0.441-percentage-point reduction in hardship (95% confidence interval, −0.707 to −0.175; p = 0.001). Mean poorest–richest and rural–urban hardship gaps were 53.7 and 12.5 percentage points, respectively. The inverse association was largest in low-income settings, where socioeconomic gradients were also steepest. Service coverage gains should therefore not be interpreted as sufficient evidence of equitable universal health coverage; monitoring systems and financing reforms should jointly target access, financial protection, and distributional gaps. Full article

Urban expansion generates habitat fragmentation, light pollution, and vegetation loss, driving biodiversity decline globally. Insectivorous bats are particularly suited to track these changes: their taxonomic and functional diversity, combined with their differential sensitivity to landscape configuration and roost availability, makes them reliable bioindicators of urbanization impacts. This study characterized bat acoustic diversity and community composition along an urban–rural gradient in Tacna, Peru, using passive acoustic monitoring. Twenty-three stations equipped with autonomous ultrasonic recorders were established between December 2024 and April 2025. Sonotype identification was validated through linear discriminant analysis (DFA), achieving 94.47% accuracy by Leave-One-Out Cross-Validation (LOOCV). Nine sonotypes were identified, seven Molossidae and two Vespertilionidae; Myotis atacamensis showed the highest detection frequency across all districts. Diversity indices were highest in the peri-urban zone of Pocollay (S = 8; H′ = 1.664) and lowest in Gregorio Albarracín (S = 4; H′ = 0.964). The high dissimilarity between Pachía and the urban core of Tacna (BC = 0.817) points to marked compositional differences linked to urbanization intensity. Records of Nyctinomops laticaudatus and N. macrotis constitute the first documentation for the urban area of Tacna. These results suggest urban and peri-urban sectors may function as ecological oases for bat sonotypes within a hyperarid matrix, providing a baseline for evaluating the role of green spaces, landscape connectivity, and artificial lighting in arid cities. Full article

Metropolitan fringe areas are transitional spaces where urban expansion, industrial relocation, residential spillover, transport infrastructure, and ecological recreation jointly reshape land-use functions. However, urban vitality research has mainly focused on central urban areas, leaving limited evidence on how activity intensity is organized in metropolitan fringe spaces. Taking the Chengdu Metropolitan Area, China, as a case, this study delineates metropolitan fringe areas using POI kernel density analysis and density–distance relationships, calibrated with impervious-surface and remote-sensing evidence, measures village-level relative activity intensity using Baidu heatmap data, and examines nonlinear associations between built-environment variables and urban vitality using LightGBM and SHAP. The results show that vitality across the delineated fringe does not form a uniform core–periphery gradient. Instead, high-vitality units are clustered around industrial parks, residential spillover zones, transport corridors, public-service nodes, and ecological recreation spaces. Population density, nighttime light, gross domestic product, building density, and commercial POI density are the main predictors in both weekday and weekend models. Several variables show nonlinear and saturation-like associations rather than simple linear effects. These findings suggest that higher predicted fringe vitality is more likely to occur where population concentration, development intensity, service provision, transport linkage, and socioeconomic activity coexist, rather than where densification occurs alone. The study extends urban vitality research to metropolitan fringe areas and provides evidence for differentiated land-use planning in urban-rural transition zones. Full article

Identifying the spatio-temporal evolutionary patterns and driving mechanisms of rural tourism network attention is essential for predicting the development trends of the rural tourism industry and delivering refined industrial governance. Taking 356 prefecture-level cities in China from 2015 to 2024 as basic research units, this paper constructs a comprehensive evaluation system for rural tourism network attention based on multi-source data. Furthermore, its spatio-temporal evolution characteristics and internal influencing factors are systematically investigated by means of spatial autocorrelation analysis and geographically weighted regression. The results indicate that the overall level of rural tourism network attention in China shows an obvious fluctuating growth trend, which can be divided into three successive stages, namely steady growth (from 0.8530 in 2015 to 1.2028 in 2019), explosive growth (from 1.9563 in 2020 to 3.7471 in 2021) and high-level fluctuation (maintained in the high range of 2.4–3.4). In addition, with the continuous iteration of internet communication media, the guiding influence of traditional search platforms has gradually weakened, while emerging social media and short-video platforms have become the core carriers of online tourism traffic. Correspondingly, media innovation persistently reshapes the spatial distribution pattern of rural tourism network attention. In terms of spatial characteristics, rural tourism network attention has undergone a significant transformation from geographical gradient polarization to overall regional equilibrium. Specifically, from 2015 to 2024, the overall Moran’s I index remained positive, with values ranging from 0.0116 to 0.1358, indicating an overall trend of gradual decline. High-attention areas are predominantly concentrated in economically developed urban agglomerations, whereas remote and economically underdeveloped regions exhibit contiguous low-value aggregation characteristics, which reveals a remarkable trend of balanced development nationwide. In view of driving mechanisms, highway network density, tourism income, rural tourism resource and enrollment of university students are identified as the core driving factors dominating the spatio-temporal evolution of rural tourism network attention. Moreover, the intensity of the influence of each factor presents distinct spatial heterogeneity. This study further reveals that the spatial heterogeneity of rural tourism network attention calculated using multi-source fused data shows a remarkable convergent characteristic, which can reflect the actual distribution of the rural tourism market more objectively and accurately. Meanwhile, rural tourism network attention is typically characterized by scale-dependent with the spatial distribution at the macro-scale being more balanced than that at the meso- and micro-scales. Full article

The Brazilian Amazon is a contested frontier, shaped by destructive and conservationist forces. Most forest clearing is driven by agro-extractivism, an agrarian pathway based on raw commodity production. The socio-biodiversity economy (SBE) has emerged in response, widely regarded as a transformative agrarian pathway capable of reconciling environmental conservation and rural livelihoods. However, recent research suggests that, as socio-biodiversity products scale up, agro-extractivist dynamics can be reproduced within the SBE. We examine this tension in the cocoa frontier of the Transamazon, where cocoa is institutionally promoted as an SBE alternative. We conduct an exploratory, mixed-methods study combining a geospatial analysis of land use and land cover (LULC) change (2020–2025, random forest classification) with a qualitative analysis drawing on participatory mapping and 87 semi-structured interviews with farmers, cooperatives, buyers, and institutional actors. Using a gradient framework, we read LULC transitions and farmers’ land use practices along an agro-extractivism–SBE continuum. The cocoa frontier emerges as a hybrid geography. The landscape is predominantly stable but internally reorganizing: anthropogenic forest declines while full-sun monoculture expands over pasture, with intensification concentrated in peri-urban areas and restoration in remote ones. Land use practices form five recurring configurations, two firmly anchored at the socio-biodiversity or agro-extractivist poles and three whose alignment with the SBE depends on access to markets, knowledge, and institutions. We argue that the frontier contestation unfolds not only between distinct economies but within the cocoa economy itself, and we identify the policy areas relevant to sustaining SBE-oriented practices in the Transamazon. More broadly, this study suggests that the classification of Amazonian forest-based economies as inherent alternatives to agro-extractivism should be treated as an empirical question rather than an assumption. Full article

Sub-Saharan Africa is rich in agricultural biomass, yet its biogas utilization is far below its potential. Most earlier studies failed to identify the nonlinear, multi-factor relationships that shape real national biogas yields and fully clarify this imbalance. This study constructs a 2007–2023 panel dataset for ten sub-Saharan African countries, merging agricultural output, socioeconomic, and infrastructure metrics. Gradient Boosting model and SHapley Additive exPlanations (SHAP) analysis are applied for empirical evaluation. SHAP analysis confirms that charcoal consumption yields the largest contribution to biogas production, with a mean absolute SHAP value of 1.018. The correlation between the two variables is negative under the threshold and becomes positive beyond this critical level. Urbanization has an inverted U-shaped correlation with biogas output, and the marginal contributions of predictors vary substantially across sampled countries. Instead, fragile supply chains, rural labor loss, and fierce competition in clean energy markets curb local biogas production. Forecasts show that regional biogas output will continue to fall until 2030. Targeted national policies matching each country’s core influencing factors are therefore urgently required. Full article

Urbanization alters the local growth environment of vegetation, including local thermal regimes, moisture availability, and human activity intensity. Yet, the gradient-dependent changes in vegetation phenology and the factors responsible for these changes are still not fully clarified. Taking Beijing as the study area, this study used MOD13Q1 EVI records from 2005 to 2024 to reconstruct annual vegetation trajectories with Savitzky–Golay filtering. The start of the growing season (SOS) and the end of the growing season (EOS) were then identified based on a dynamic threshold criterion, and the length of the growing season (LOS) was derived from the interval between the two phenological dates. Urban–rural gradients were constructed by integrating Global Urban Boundary (GUB) and digital elevation model (DEM) data. Theil–Sen slope estimation and the Mann–Kendall test were then applied to analyze trends in vegetation phenology, while partial correlation analysis and the XGBoost-SHAP method were used to identify the relative effects of climatic and urbanization-related factors. The results showed that (1) vegetation phenology in Beijing varied markedly across space. Mountainous areas in the northwest and southwest had a later SOS, earlier EOS, and shorter LOS, whereas the central plain and southeastern regions were characterized by an earlier SOS, later EOS, and longer LOS; (2) from 2005 to 2024, the SOS advanced significantly by −0.65 days/year, EOS showed a weaker delay of 0.46 days/year, and LOS increased significantly by 0.90 days/year, indicating an overall extension of the vegetation growing season over the past two decades. (3) Urban areas had an earlier SOS, later EOS, and longer LOS, but the trends of SOS advancement, EOS delay, and LOS extension were more pronounced in suburban and rural areas; and (4) the SOS was mainly influenced by the combined effects of nighttime lights and heat-related factors, whereas the EOS was primarily affected by temperature and nighttime lights. Along the urban–rural gradient, the importance of nighttime lights gradually decreased from urban areas to suburban and rural areas, while the role of climatic factors became relatively stronger. These findings reveal the gradient differentiation of vegetation phenological status, temporal trends, and associated drivers in Beijing under rapid urbanization, and provide useful information for urban vegetation management and ecological planning. Full article

Composite environmental risks accumulate unevenly along urban–rural gradients, yet the conditional and nonlinear interaction between built environment quality (BEQ) and urban–rural functional connectivity (URFC) remains poorly quantified at fine resolution. This study aims to determine whether, and under what conditions, BEQ moderates the relationship between URFC and a population-weighted composite risk index (CRI), and to translate the result into spatia targeted green-infrastructure priorities. We use 744,714 grid cells at 100 m resolution over Guangzhou, China. The framework couples entropy-weighted BEQ from satellite and street-view imagery, gravity-model URFC computed on the real road network, and a two-stage population-weighted CRI of heat and air hazards. We apply nested ordinary least squares with incremental F-tests, spatial-lag and spatial-error models, generalised additive models with B-spline bases, gradient-boosted trees with SHAP interaction values, and Baron–Kenny mediation analysis. The main BEQ × URFC estimates are negative across the parametric and machine-learning specifications. The interaction is, however, small: a spatial-lag model on a 10,000-cell subsample returns β = −5.4 × 10⁻⁴, but a scalable generalised-method-of-moments spatial regression on the full grid—where the spatial autoregressive coefficient reaches ρ ≈ 0.99—shows the coefficient to be negative yet not statistically significant, and a five-seed re-estimation confirms that the subsample-based significance is draw-dependent. We therefore interpret the buffering as directionally supported but small and not robustly significant once spatial autocorrelation is fully modelled. The buffering response is nonlinear in the GAM main effects, and BEQ buffers across the entire observed connectivity range rather than switching sign at an interior threshold; URFC functions predominantly as a moderator rather than a mediator. Population-stratified estimation shows that the buffering is exposure-conditional: it is strongest where population exposure is high and weakens or reverses in sparsely populated cells, consistent with the risk = hazard × exposure structure of CRI. Sensitivity tests across values of the distance-decay parameter, 100 entropy perturbations and spatial scales corroborate the buffering direction. The framework provides an evidentiary basis for prioritising green infrastructure in functionally connected, populated but environmentally degraded transition zones. Full article

Microplastic pollution in tropical urban rivers has become an increasing environmental concern due to rapid urbanization, inadequate waste management, and hydrological transport processes. This study investigated the occurrence, characteristics, and spatiotemporal distribution of microplastics in the Can Tho River, Vietnam, along an urban–peri-urban–rural gradient during dry and wet seasons. Surface-water samples were collected at 15 sites and analyzed for microplastic abundance, density, shape, color, and size composition using stereomicroscopic identification and statistical analyses. Microplastics were detected at all sampling sites in both seasons, indicating widespread contamination throughout the river system. Although seasonal differences in overall abundance and density were not statistically significant at the basin scale, clear spatial variability was observed, particularly in urban and peri-urban regions. Fibers and fragments were the dominant shapes, while blue, purple, and green particles were the most common color categories. Particles larger than 1000 µm accounted for the largest proportion of detected microplastics, and continuous size-distribution analysis revealed broadly similar overall distributions, although a greater proportion of smaller particles was observed during the dry season. The results suggest that hydrological conditions, urbanization, and land-use characteristics may contribute to the observed spatial and seasonal patterns of microplastic distribution in the Can Tho River. Peri-urban zones exhibited the greatest seasonal variability, highlighting their role as transitional areas that may influence microplastic redistribution in tropical river systems. This study provides baseline information for understanding microplastic pollution in the Mekong Delta and supports future river management strategies. Full article

The urban fringe, a critical transitional zone between urban and rural areas, exhibits the most intense land-use conflicts and the most dynamic spatial restructuring. Its fragmentation not only undermines the efficiency of spatial land use but also threatens ecosystem stability, thereby posing a challenge to sustainable urban development. This study aims to examine the characteristics and future trends of spatial fragmentation within the urban fringe of Xi’an. Using multi-source datasets—including nighttime light imagery, land use data, and building vector data—we delineated the urban fringe boundary based on a light gradient threshold method. By combining landscape metrics with spatial overlay analysis, we assessed land use changes and spatial fragmentation patterns from 2000 to 2020 and projected land use conditions for 2030, 2040, and 2050. The results reveal that between 2000 and 2020, cultivated land became increasingly fragmented due to the expansion of construction land, while forest areas continued to expand under ecological restoration policies. Impervious surfaces exhibited infill aggregation. Rapid increases in nighttime light intensity were strongly correlated with heightened fragmentation (R² = 0.65, p < 0.01), confirming that urban expansion is the dominant driver of fragmentation. Projections indicate that the proportion of cultivated land will decline to 0.51, approaching a “critical warning threshold”. Full article

This study examines the retail food environment surrounding public schools in Michigan using a multiscale, multidimensional framework. A cross-sectional spatial analysis integrates relative healthfulness (modified Retail Food Environment Index, mRFEI), availability (outlet counts), and accessibility (network-based walking time) across school districts, census tracts, block groups, and school-centered buffers. The analysis includes 3530 public schools, 7680 fast food restaurants, and 2065 convenience stores. Results show pronounced spatial heterogeneity and clustering of unhealthful outlets (Nearest Neighbor Index = 0.284, p < 0.001), with many located near schools. Approximately 34% of schools are within a 10 min walk of a fast food restaurant, increasing to 65% within a 20 min walk. Urban schools face significantly greater exposure—2.27–2.80 times more fast food outlets and shorter walking times than rural schools (p ≤ 0.002)—with consistent gradients across city, suburban, town, and rural contexts. Overall, school neighborhood food environments are highly structured, obesogenic, and inequitable. By integrating multiple spatial scales and complementary measures of food environments, this study advances food environment research and provides policy-relevant evidence for targeted, place-based interventions to improve access to healthier food around schools. Full article

Rapid urbanization reshapes urban land systems and intensifies surface thermal heterogeneity, yet nonlinear day–night land surface temperature (LST) responses to grey–green spatial organization and building morphology remain insufficiently understood, particularly in thermally stressed areas across the urban–rural gradient. Using Xi’an, China, as a case study, this study develops a priority-area-based land–climate interaction framework. Priority areas were defined as grid cells where elevated LST coincided with relatively strong local explanatory relationships between LST and land-cover or morphological variables. Multiscale geographically weighted regression (MGWR), gradient boosting decision trees (GBDTs), SHAP-based interpretation, and threshold sensitivity analysis were combined to identify dominant drivers, nonlinear response patterns, and interaction structures of daytime and nighttime LST. The results show pronounced day–night differentiation: daytime hotspots were concentrated in the built-up core, whereas nighttime hotspots extended toward the urban–rural fringe. Daytime LST was mainly associated with building coverage and grey-space organization, while nighttime LST was more strongly related to mean building height and the cooling contribution of green-space coverage. The analysis further identified localized empirical response ranges for built-up intensity, grey-space connectivity, building height, and green-space coverage within the priority areas. These findings clarify how land-cover configuration and building morphology jointly shape day–night surface thermal responses and provide context-specific evidence for land-use planning and targeted urban heat mitigation. Full article

This study investigates the coupling relationship between urban–rural integration and the value realization efficiency of agricultural ecological products in China. Based on panel data from 30 provinces from 2012 to 2022, the SBM-GML model is used to measure agricultural ecological product value realization efficiency, and the entropy method is used to measure urban–rural integration. The coupling coordination model, Dagum Gini coefficient, kernel density estimation, and Markov chain analysis are then used to examine the spatiotemporal pattern, regional differences, dynamic evolution, and convergence characteristics of the coupling system. The results show that urban–rural integration and agricultural ecological product value realization improved overall, but regional disparities remain significant. The coupling coordination level presents a clear spatial gradient, with the eastern region performing better than the central, western, and northeastern regions. Regional disparities continue to widen, mainly due to inter-regional differences and trans-variation density. The Markov chain results show strong path dependence and spatial spillover effects, while the convergence analysis indicates limited long-term convergence. This study provides an integrated framework for understanding the interaction between urban–rural integration and ecological value realization. It also highlights China’s mixed pathway of government guidance, market participation, factor flow, and ecological value transformation, offering policy implications for coordinated regional development and ecological product value realization. Full article

In the context of dynamic environmental changes, accurate geospatial information is fundamental for evidence-based decision-making in land-use planning. As urban areas undergo rapid structural transformations, characterizing their spatial morphology becomes essential for assessing ecosystem conditions and identifying pressure points within the urban–rural gradient. Drawing on the indicators for ecosystem condition and pressure recommended by the Mapping and Assessment of Ecosystem Services (MAES) framework, reflecting their trends, this study presents a methodology for comprehensive geospatial mapping of urban and peri-urban morphology, using the Functional Urban Area (FUA) of Burgas, Bulgaria, as a case study. The approach enables multi-scale spatial analysis (regional and local), integrates the structure and functions of urban ecosystems, and reveals the spatial heterogeneity of complex socio-economic systems. At the regional level, ecosystems within the FUA were identified using the national land-use/land-cover database. At the local level, within the city of Burgas, urban morphology was classified by combining building and land-cover types into 14 distinct urban morphological zones (local climate zones—LCZs) using high-resolution unmanned aerial vehicle (UAV)-based orthophotos. This precise spatial data allowed for a detailed assessment of the balance between pervious and impervious surfaces within each LCZ. By integrating Google Earth Engine (GEE) data, the appropriate conditions and pressure indicators in the case study are assessed. Regional ecosystem pressure is effectively captured through the spatial distribution of the Final Pressure Index (IPr). Concurrently, the Urban Ecosystem Performance Index (UEPI) highlights sharp spatial polarization, with critical stress concentrated in the industrial and port zones of the urban core. The results provide policy-makers and stakeholders with critical insights into current pressures and environmental changes in urban and peri-urban ecosystems, offering a robust foundation for evidence-based management and climate change adaptation strategies. Full article

The escalating energy consumption in China’s rural residences necessitates the adoption of targeted energy-efficient design strategies. However, existing studies have mainly focused on urban buildings or cold-climate rural residences, and insufficient attention has been given to form-based energy optimization for rural housing in hot summer and cold winter regions. Hangzhou was selected because it is a representative city in this climate zone, where rural residences face both summer cooling and winter heating demands. This study systematically investigates passive design pathways for rural residential buildings by optimizing architectural forms. We conducted in-depth field surveys and data analysis on 76 diverse samples, including both self-built and unified construction types, to establish three representative typical residential models (rectangular, L-shaped, U-shaped) for the Hangzhou region. DesignBuilder was employed to simulate the impacts of eight morphological elements—Shape Coefficient, building area, aspect ratio, orientation, number of floors, floor height, floor height ratio, and roof slope—on building energy consumption. The Gradient Boosting Decision Tree (GBDT) method was then used to quantify the nonlinear effects and relative importance of these elements. The results indicate clear nonlinear relationships between elements and the energy-saving rate. Floor height is identified as the most critical factor affecting energy consumption, followed by roof slope, with building area and other elements also showing significant influence. Based on the quantitative analysis, this study proposes energy-efficient design optimization strategies for rural housing in Hangzhou, offering a validated methodological framework and practical design references for the sustainable development of rural residences in hot summer and cold winter regions. Full article