Search Results (2,172)

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Spatiotemporal heterogeneity of land-use multifunction (LUMF) is crucial for the preservation and management of large-scale national cultural parks in alleviating potential human-land conflicts. Using 28 multidimensional indicators across economic, social, and environmental dimensions, this study established an LUMF index system for the Long March National Cultural Park of China (CLMNCP). LUMF values for 77 prefecture-level cities were quantified from 2008 to 2023, and their spatiotemporal heterogeneity was examined using a spatial autocorrelation model. Subsequently, the Optimal Parameters-based GeoDetector (OPGD) model was applied to identify key driving factors. The main findings are as follows: (1) From 2008 to 2023, the total, economic (EF), social (SF), and environmental (EnF) functions in the CLMNCP exhibited a consistent upward trend. (2) Significant spatial heterogeneity characterized the trade-offs and synergies among these functions. The EF-EnF interaction displayed a concave synergistic relationship, while the EF-SF and SF-EnF interactions showed convex, fluctuating patterns during their transitions between trade-off and synergy. (3) The primary drivers varied across function pairs. The EF-SF synergy was predominantly influenced by agricultural production, resource supply, and cultural service factors. The EF-EnF interaction was mainly shaped by natural conditions and environmental improvement factors. In contrast, the SF-EnF interaction was primarily driven by economic development, cultural services, and resource supply. These findings support functional zoning and targeted management of large-scale national cultural park to balance development and conservation while reducing human-land conflicts. Full article

Monitoring forest density is essential for understanding ecosystem health, wildfire risk, and post-disturbance recovery. This study proposes a robust methodology to extract forest density classes exclusively using Sentinel-2 multispectral imagery combined with vegetation indices (VIs), textural parameters, and spatial clustering metrics. The approach was applied to the northern part of Euboea Island, Greece, as a pilot area severely affected by a wildfire in August 2021. Four cloud-free Sentinel-2 images (2017–2024) were selected to capture pre- and post-fire conditions. A set of nine VIs—representing vegetation vigor, chlorophyll content, soil exposure, and canopy moisture—were calculated and statistically assessed for independence. To enhance classification accuracy, texture measures (homogeneity, correlation, and entropy) and spatial autocorrelation metrics (Moran’s I, Getis-Ord Gi) were derived for selected VIs. Supervised classification was performed using the Maximum Likelihood algorithm, yielding overall accuracies up to 89.4% and kappa coefficients above 0.85 when combining VIs with texture and spatial metrics. Results revealed a dramatic 49.3% reduction in forest cover immediately after the wildfire, with partial recovery (to 77.9% of pre-fire levels) three years later, mainly as a low-density forest. Approximately 12.1% of forest cover failed to regenerate, indicating potential long-term ecosystem degradation. The proposed approach provides a computationally efficient, high-accuracy alternative to data-fusion methods involving (Light Detection and Ranging) LiDAR or (Synthetic Aperture Radar) SAR datasets, making it suitable for operational forest monitoring and fire-risk management. Full article

This study is based on time-series land use data of Xinjiang from 2000 to 2022. Using grid tools, bivariate autocorrelation models and other methods, we systematically analyzed the spatiotemporal variation characteristics of land use and ecosystem service value. The results show the following: Firstly, from 2000 to 2022, Xinjiang’s LUCC exhibits differentiated evolution characteristics: cropland, forestland, and built-up land expanded continuously, while the areas of grassland and unused land showed a steady reduction trend, and the area of water bodies showed a fluctuating growth pattern. Secondly, according to the calculation of carbon emissions from LUCC in Xinjiang from 2000 to 2022, the carbon emissions from LUCC have increased significantly, from 27.79 million tons in 2000 to 226.43 million tons in 2022, with built-up land being the main source of carbon emissions, but the continuous reduction in grassland area has led to the weakening of carbon sequestration capacity. Thirdly, from 2000 to 2022, Xinjiang’s ESV shows a fluctuating upward trend, increasing from 1880.528 billion yuan in 2000 to 1894.198 billion yuan in 2022, with grassland and water area being the core contributors to ESV, accounting for over 80% of the total contribution. Fourthly, in terms of spatial distribution, there is an overall negative correlation between the intensity of carbon emissions from LUCC and the intensity of ESV, mainly aggregated as “low–low” and “low–high”, with “high–low” aggregation primarily distributed in the desert areas of the Tarim Basin and Junggar Basin and “low–high” aggregation concentrated in the marginal mountainous areas and oasis regions of Xinjiang. The findings provide a solid scientific basis for the optimization of land use structure, the achievement of carbon emission reduction targets, and the protection of ecosystems in Xinjiang and similar arid regions worldwide. Full article

The coupling coordination between Urbanization Quality (UQ) and Water Ecological Carrying Capacity (WECC) represents a critical nexus for sustainable regional development within the Yangtze River Economic Belt (YREB). Focusing on 16 cities in Hubei Province over the period 2020–2024, this study constructed comprehensive indicator systems for UQ and WECC, Spatial Autocorrelation Analysis and Key Factor Analysis are then applied to analyze spatiotemporal evolution, identify key influencing factors. The results reveal that: (1) Both UQ and WECC demonstrated upward trajectories, with UQ increasing from 0.369 to 0.409, although WECC exhibited fluctuating patterns; (2) Spatial analysis identified pronounced “core–periphery” clustering effects with Wuhan as the dominant center, confirmed by the positive Global Moran’s I; (3) Hubei’s CCD advanced from 0.626 to 0.661, progressing toward initially coordinated stages, with Wuhan pioneering this transition, while 81.25% of cities remained at the moderately coordinated stage; (4) Grey relational analysis identified aquatic biological resources as the principal constraint, with piscivore biomass ratios and pension insurance participation rates (γ = 0.752) emerging as key biophysical and socioeconomic drivers, respectively. These findings provide empirical evidence for targeted interventions promoting balanced urban–water ecological development in the YREB, while contributing a novel analytical framework for examining UQ-WECC interactions in rapidly urbanizing regions globally. Full article

Childhood stunting remains a major public health challenge in India and is influenced by multiple socioeconomic and environmental factors. This ecological study examined district-level correlates of childhood stunting, including Crimes Against Women (CAW), the Multidimensional Poverty Index (MPI), and drought severity, using data from NFHS-5, the National Crime Records Bureau, NITI Aayog’s MPI reports, and the Drought Atlas of India. Spatial autocorrelation and Spatial regression models were applied alongside machine learning approaches and SHAP-based Explainable AI (XAI) interpretation. Childhood stunting exhibited significant spatial clustering (Moran’s I = 0.520, p < 0.001), with hotspots in northern, central, and eastern India. Higher stunting was associated with higher birth order, low maternal BMI, child anaemia, and MPI, and negative associations with iodised salt usage, electricity access, and timely postnatal care. A significant spatial lag parameter (ρ = 0.348) indicated substantial spillover effects. Machine learning models consistently identified MPI, drought severity, and CAW as key predictors. The integrated spatial and machine learning framework identifies key correlates and spatial dependencies of childhood stunting, highlighting the need for region-specific, multisectoral interventions. Full article

Urban expansion is a key driver of land-use change and environmental pressure in rapidly urbanizing regions. Existing assessments of urban expansion often rely on predefined indicator systems and fixed weighting schemes, which limits their adaptability to evolving research priorities and regional contexts. This study develops an adaptive framework for urban expansion assessment by integrating a transformer-based language model with multi-source spatial data. A BERT-based semantic extraction process is used to identify relevant indicators and derive their relative weights from the scientific literature, enabling the construction of a literature-driven Urban Expansion Index (UEI). The framework is applied to the Central Plains Mega-city Region (CPMR), China, to examine spatial patterns and temporal dynamics of urban expansion between 2010 and 2020. Results show that UEI is primarily driven by land-use expansion indicators, while socioeconomic, infrastructure, and environmental indicators jointly reflect the multidimensional nature of expansion processes. Spatial patterns reveal a persistent concentration of high expansion intensity in core cities, alongside heterogeneous environmental responses and gradual outward growth. Changes in UEI display weaker spatial coherence than static levels, indicating differentiated local expansion dynamics. Local spatial autocorrelation analysis further identifies shifting clusters of urban expansion intensity, suggesting a reorganization of expansion centers within the agglomeration over time. By linking transformer-based indicator extraction with spatial analysis, this study advances urban expansion assessment beyond outcome-oriented mapping toward a more adaptive and knowledge-informed approach. The proposed framework is transferable to other mega-city regions and provides a useful tool for supporting territorial spatial planning and sustainable urban development. Full article

As urban populations age, the built environment becomes a vital determinant of health equity. This research evaluates the sidewalk infrastructure, surrounding the Health Center in Egaleo, Greece, in order to quantify its impact on healthcare accessibility for older adults. Using a GIS-based approach to simulate realistic navigation, a routing algorithm prioritized the “easiest” path over the shortest distance by transforming accessibility scores into traversal costs. The results revealed a significant disadvantage in healthcare access, with routes to the Health Center scoring lower than the average accessibility of the greater study area. In addition, the negative correlation (r = −0.20, p < 0.001) confirms the pattern of accessibility disparity, where neighborhoods with the highest older adult density consistently face the poorest infrastructure. Eventually, Global Moran’s I of 0.912 confirms strong spatial autocorrelation, Local Indicators of Spatial Association (LISA) identifies “Accessibility Deserts” which comprise a 92.5% absence of crosswalks and an 81.7% rate of obstructions. This study outlines that those who depend most on the sidewalk network are disproportionately affected by inadequate urban planning conditions. By underscoring the necessity to remediate these low-accessibility clusters, public health is improved, ensuring equitable healthcare access and supporting healthy aging. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Clarifying the spatiotemporal evolution and driving mechanisms of bike-sharing-to-metro feeder trips (BSMF) is key to optimizing urban public transport’s first-and-last-mile connectivity and advancing low-carbon development. Existing studies on BSMF mostly ignore spatiotemporal heterogeneity, lack in-depth exploration of multi-factor interaction effects, and have subjective stratification or model specification bias, which hinder the accurate depiction of BSMF’s complex evolutionary patterns. Taking Xi’an as a case with 126 metro stations as analysis units, this study integrates multi-source data including shared bike trip records, metro network and built environment attributes to address the above issues. A framework combining kernel density estimation, spatial autocorrelation analysis, Optimal Parameter Geographic Detector (OPGD) and Geographically and Temporally Weighted Regression (GTWR) models (OPGD-GTWR) is constructed to identify BSMF’s spatiotemporal patterns, screen key influencing factors and reveal their spatiotemporal heterogeneity and interactive mechanisms. Results show Xi’an’s BSMF trips feature a “double-peak and double-valley” temporal tidal pattern and core-periphery spatial agglomeration. The OPGD-GTWR model (R² = 0.853) outperforms traditional models in capturing spatiotemporal heterogeneity. These findings provide empirical evidence and refined references for shared mobility resource allocation, bike-metro integration improvement and transit-oriented urban planning. Full article

In the context of the “dual carbon” goals, this study examines the spatiotemporal patterns and evolution of urban low-carbon coordinated development (LCCD). Based on the integrated Economy–Energy–Environment–Society (3E1S) framework, this study constructs a multidimensional evaluation index system for urban LCCD and applies a composite system coordination degree model to quantitatively assess and analyze the spatiotemporal evolution of LCCD across 271 prefecture-level and above cities in China from 2005 to 2020. The results indicate that (1) from a temporal perspective, the level of urban LCCD in China exhibits an overall upward trend during the study period, with relatively rapid growth from 2005 to 2015, a subsequent slowdown after 2015, and a stage-wise decline observed in 2020, reflecting a transition from rapid improvement to gradual adjustment; (2) from a spatial perspective, urban LCCD demonstrates a certain degree of spatial autocorrelation and an overall spatial structure characterized by a southwest–northeast-oriented axis, with spatial agglomeration features gradually strengthening over time; (3) from a system structure perspective, the coordinated evolution of the 3E1S subsystems shows clear differentiation, with the energy and economic subsystems following an inverted U-shaped trajectory, the environmental subsystem exhibiting a fluctuating upward trend, and the social subsystem maintaining continuous improvement, highlighting the inherent imbalance in the multidimensional process of subsystem coordination. From a multisystem coordination perspective, this study systematically identifies the spatiotemporal evolutionary characteristics and subsystem coupling relationships of urban low-carbon coordinated development, providing empirical evidence for a deeper understanding of multidimensional low-carbon coordination processes in cities. Full article

Agricultural biomass recycling efficiency is central to advancing the green and sustainable transition of agriculture. Drawing on panel data for 30 Chinese provinces from 2019 to 2023, this study measures recycling efficiency using a three-stage super-efficiency SBM model with undesirable output and examines its determinants with a panel Tobit model. The second-stage SFA indicates that the effects of external conditions on input slacks are input-specific. In particular, GDP is statistically significant only in the biomass-generation slack equation, whereas topographic relief and rural road network density do not show robust associations with any slack measure once controls are included. After removing the influence of environmental factors and random shocks, the overall national level of agricultural biomass recycling efficiency remains moderate. The national mean Stage 3 efficiency decreased from 0.586 in 2019 to 0.427 in 2022 and recovered to 0.543 in 2023. The five-year average was 0.510, which is close to the Stage 1 average of 0.503. Spatial analysis indicates weak global spatial autocorrelation, with only occasional local clustering. The efficiency centroid oscillated during the study period rather than following a one-way migration path, with a total displacement of 70.05 km. The determinant analysis indicates that the number of specialised agricultural machinery has the most stable positive association with recycling efficiency, while other policy, market, and human capital variables do not show robust significance in the short panel. These findings underline the need to align equipment deployment and collection systems with local terrain and transport conditions, expand machinery leasing and service provision, and strengthen capacity building in low-efficiency regions. Establishing a national information sharing and dispatch platform would facilitate cross-regional resource flows and more efficient allocation, while improving local service outlets would make participation more convenient for farmers and reduce transaction costs. Full article

Fine particulate matter (PM2.5) and ozone (O₃) are the key factors restricting the continuous improvement of air quality in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZT). Due to the potential correlation between variations in urban PM2.5–O₃ concentration, the analysis of its composite pollution characteristics is helpful in formulating accurate and thorough air control policies. Based on the long-term concentration change in PM2.5 and O₃, this study analyzed the features and synergistic factors of PM2.5–O₃ pollution in the CZT by using spatial autocorrelation and a linear driving model of PM2.5–O₃. The results showed that from 2017 to 2023, under the current Chinese atmospheric environment standard, the CZT saw four combined pollution days. However, if the daily limit values were viewed in line with Grade II of the WHO transition period (O₃: 120 μg/m³, PM2.5: 50 μg/m³), the combined pollution days would reach 111. The concentration of O₃ in Zhuzhou and Xiangtan was about 10 μg/m³ lower than that in Changsha. Lower SO₂ levels in Changsha might influence the partitioning of OH radicals and reactive nitrogen species, potentially affecting local O₃ formation efficiency. NO₂ and meteorological conditions jointly influence the co-variation in PM2.5 and O₃, with NO₂ playing a more significant role in PM2.5 formation. The long-term time series and daily concentrations of PM2.5 and O₃ in the CZT showed opposing values, but there were short-term synergistic events on the scale of daily concentrations, and the time period was typically 3–10 days. Low humidity and strong sunlight may cause antagonistic events in which the concentration of O₃ rises rapidly. Under static and stable weather conditions with low wind speed, no rainfall and moderate humidity, the concentration of PM2.5 and O₃ rose alternately on sunny and cloudy days, demonstrating synergistic growth. Full article

Addressing the governance dilemmas of urban vacant land (UVL) has become a major challenge in the process of global urban sustainable development. Taking Chongqing as a case study area, this paper employs Kernel Density Analysis, Bivariate Spatial Autocorrelation, and the PLUS model to explore the quantitative characteristics and spatial distribution patterns of UVL. Three scenarios—the Baseline Development Scenario, Incremental Development Scenario, and Stock Development Scenario—are constructed to simulate the evolutionary trends of UVL and investigate the regulatory effects of different urban development models. The results are as follows: (1) From 2021 to 2025, the scale of UVL shows an expanding trend. The number of UVL plots increased from 1393 to 2308, and the total area rose from 5127.73 hectares to 11,842.43 hectares, with its proportion in the built-up area increasing from 7.37% to 16.98%. (2) The spatial scope of UVL continued to expand, and the agglomeration correlation between different land types was enhanced. The spatial distribution pattern of UVL was significantly influenced by policy factors. (3) Scenario simulations show that the growth rate of UVL in 2030 is ranked as follows: Incremental Development Scenario (95.93%) > Baseline Development Scenario (69.52%) > Stock Development Scenario (43.12%). The stock development model can effectively resolve the urban contradiction between “development and protection” and represents the optimal path for future urban development. This study has clarified the evolutionary patterns of urban vacant land and their compatibility with urban development models, providing a reference for optimising vacant land management and sustainable development in similar cities. However, certain limitations exist in data acquisition and the scope of the research. Full article

Cassiopea andromeda is an invasive alien jellyfish that is increasingly reported across the Mediterranean Sea, yet its invasion dynamics and ecological implications remain poorly understood. This study provides an updated assessment of its spatial and temporal distribution, evaluates its potential impacts on ecosystem services and biodiversity, and explores management options through the 8Rs framework. An aggregated dataset of georeferenced records (1886–2025) was compiled from scientific literature and citizen-science platforms. Spatio–temporal analyses—including kernel density, key spatial distribution characteristics, spatial autocorrelation, and local hotspot detection—were applied to identify invasion phases, aggregation patterns, and directional dispersion. Results reveal two distinct invasion stages: a century-long arrival phase confined to the Levantine Basin, followed by an accelerated expansion since 2008, with a persistent hotspot in the eastern Mediterranean Sea and a westward dispersal trajectory. Evidence of ecological impacts within the Mediterranean Sea remains limited, however studies from other regions indicate both potential benefits and localized negative interactions with marine organisms. Application of the 8Rs model highlights implemented, feasible and challenging coordinated basin-wide strategies to support adaptive management of this alien resource. Full article