Search Results (527)

Access to Information and Communication Technologies (ICTs) is a critical component for social inclusion and population development. This study aimed to analyze ICT access in Argentine households, considering its distribution according to deprivation conditions and area of residence (urban–rural) at the regional level, and incorporating a spatial association perspective at the departmental level. The percentage of households with Internet access, computers (or tablets), and cell phones with connectivity was examined at the regional level, according to household deprivation type and area of residence. At the departmental level, the analysis was conducted through thematic maps and the estimation of spatial autocorrelation patterns (global and local Moran’s Index). Indicators were constructed using data from the 2022 Population, Household, and Housing Census. Results revealed significant disparities in ICT access, attributable to deprivation conditions and the geographic distribution of households. Spatial autocorrelation patterns with low ICT access were mainly identified in the Northwest (NOA) and Northeast (NEA) regions, while the highest coverage levels were concentrated in the Buenos Aires Metropolitan Area (AMBA), the Pampeana, and Patagonia regions. The evidence highlights the need to design public policies aimed at reducing digital divides. Full article

Climate volatility and market uncertainty pose significant challenges to agricultural stability. We assess whether and how agricultural industrial agglomeration shapes China’s agricultural economic resilience using province-level panel data for 2003–2023 and a transparent, entropy-weighted index spanning resistance, recovery, and adaptability. Four results stand out. First, in a two-way fixed-effects model, agglomeration is associated with higher resilience on average, and this finding remains robust across multiple robustness tests and after addressing endogeneity concerns. Second, regional subgroup analyses reveal pronounced heterogeneity, providing evidence for geographically targeted policy design. Third, mechanism analysis reveals that the agricultural research intensity serves as a partial mediator between agglomeration and resilience. Fourth, the agglomeration-resilience relationship is nonlinear—N-shaped in the aggregate, while panel quantile regressions reveal an inverted-U among low-resilience provinces and an N-shaped pattern at the median and upper end of the distribution. In an extension, global Moran’s I statistics for three alternative resilience indices reveal significant positive spatial autocorrelation, indicating that agricultural economic resilience tends to cluster geographically and that spatial spillovers are likely to be present. In conclusion, agglomeration is a net enhancer of agricultural economic resilience, but its payoffs are agglomeration- and distribution-dependent: gains taper or reverse around the mid-range for low-resilience provinces, while the median and upper segments benefit again as specialization deepens, in a setting where resilience itself is spatially clustered. Reinforcing the research channel and tailoring actions to local resilience levels are therefore pivotal. Full article

The Sichuan–Yunnan region, a primary seismic-prone zone on the Qinghai–Tibet Plateau, has experienced heightened seismic exposure due to rapid urbanisation. In order to address the issue of disaster risks and to promote sustainable urban development, this study establishes an integrated urban seismic resilience evaluation framework based on the DPSIR (Driving–Pressure–State–Impact–Response) model. The CRITIC–AHP combined weighting method was utilised to determine indicator weights, and data from 37 prefecture-level cities (2010, 2015, 2020) were analysed to reveal spatial–temporal evolution patterns and correlations. The results demonstrate a consistent improvement in regional seismic resilience, with the overall index increasing from 0.501 in 2010 to 0.526 in 2020. Sichuan exhibited a “decline-then-rise” trend (0.570 to 0.566 to 0.585), while Yunnan demonstrated continuous growth (0.517 to 0.557). The spatial pattern underwent an evolution from “west–low, central–eastern–high” to “south–high, north–low”, with over half of the cities attaining relatively high resilience by 2020. Chengdu and Kunming have been identified as dual high-resilience cores, diffusing resilience outward to neighbouring regions. In contrast, mountainous areas such as Garze and Aba have been found to exhibit low resilience levels, primarily due to high seismic stress and limited socioeconomic capacity. Subsystem analysis has revealed divergent resilience pathways across provinces, while spatial autocorrelation has demonstrated fluctuating global Moran’s I values and temporary local clustering. This research provides a scientific foundation for seismic disaster mitigation and offers a transferable analytical framework for enhancing urban resilience in earthquake-prone regions globally. Full article

This study aimed to develop a thinning tree selection system by applying genetic algorithms based on precisely estimated tree-level forest structural parameters derived from LiDAR data. Conventional thinning tree selection methods have limitations due to their dependence on subjective judgement and field experience of operators, resulting in inconsistency and variations according to skill levels. To address these issues, tree positions, diameters at breast height (DBH), and tree heights were extracted by integrating terrestrial laser scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (ULS) data, forming a Multi-Platform LiDAR dataset. The derived DBH and Hegyi competition index were utilized as indicators for thinning tree selection. Optimization of tree selection was performed using a genetic algorithm, with an objective function designed to maximize the average DBH and minimize the average competition index of the remaining trees, and the system’s performance was compared with results obtained by forestry experts. The results showed that tree detection accuracy exceeded 99%, DBH estimation exhibited an RMSE of 0.74 cm, and tree height estimation showed an RMSE of approximately 2 m, demonstrating the construction of precise forest structural parameters. Compared to expert driven selection, the Genetic Algorithm-based thinning system produced a higher average DBH (30.06 cm vs. 29.26 cm) and a lower Hegyi competition index (1.31 vs. 1.41) under Scenario 3. This indicates superior performance in competition alleviation and growing space allocation among individual trees. Spatial statistical analysis revealed that while expert selection maintained the existing spatial clustering pattern of stand structure (Global Moran’s I = 0.16), the machine learning system achieved an almost random distribution (Global Moran’s I = −0.04) under Scenario 3. This study demonstrates the potential of overcoming the limitations of conventional thinning practices dependent on subjective judgement by introducing an objective, consistent, data-driven quantitative decision support system for precision forest management. Full article

The agro-pastoral ecotone of northern China (APENC), a typical semi-arid and ecologically vulnerable zone, has experienced considerable shifts in eco-environmental quality (EEQ) over the past two decades under the combined pressures of climate change and human activities. However, systematic understanding of the spatiotemporal evolution and driving mechanisms of EEQ in this region remains limited. Based on multi-source remote sensing data from 2000 to 2020, this study constructed an ecological quality assessment index (EQAI) using principal component analysis (PCA) and quantitatively identified driving factors through geographical detector modeling. The results reveal a consistent improvement in EEQ over the study period, characterized by a marked expansion of higher-quality areas and a contraction of degraded zones, though spatial heterogeneity remained evident. Global and local spatial autocorrelation analyses (Moran’s I) confirmed a distinct clustering pattern, with persistent low-value clusters in the northwest and high-value clusters in the southeast and north. Notably, the most pronounced EEQ enhancement occurred between 2000 and 2005. Overall, 90.24% of the region exhibited an improving trend, while only 9.76% showed degradation. Hurst exponent analysis further indicated that this improving trend is likely to continue in the future across most areas. Factor detection identified meteorological drivers (precipitation) as the strongest influencer on EEQ, followed by land use type. Socioeconomic factors demonstrated relatively minor impact. These findings provide a scientific basis for ecological restoration policy-making and sustainable land management in the APENC and other ecologically fragile transitional regions. Full article

Land use intensification profoundly impacts ecological quality, with this dynamic relationship being particularly pronounced in China’s Central Yunnan Plateau Lake Basin (CYP-LBs), an ecologically fragile area of significant socioeconomic value. Despite the critical importance of their interaction, existing research has largely overlooked their dynamic interplay—especially within plateau lake basins. To address this gap, this study employs the Remote Sensing Ecological Index (RSEI) to assess the ecological quality dynamics of CYP-LBs from 2005 to 2025 and its association with land use intensity (LUI), revealing spatiotemporal patterns of ecological quality evolution and its linkage to land use. Results indicate that CYP-LBs maintained overall moderate ecological quality (average RSEI ~0.50), exhibiting an initial increase followed by decline, peaking at 0.5519 in 2015. The center of gravity for ecological quality shifted eastward in most watersheds, with Moran’s I index consistently above 0.50 indicating significant spatial autocorrelation. The LUI showed an overall upward trend, with high-intensity areas primarily concentrated in lakeshore zones (e.g., eastern Dianchi Lake, Xingyun Lake) and exhibiting regional expansion over time. RSEI and LUI generally showed a negative correlation, but positive correlations emerged in localized areas of eastern and northern Dianchi Lake due to concurrent urbanization and ecological restoration efforts. Among land types, grasslands and forests were identified as the primary drivers influencing ecological quality changes in CYP-LBs. These findings provide crucial scientific basis for integrated conservation, land use optimization, and sustainable development in ecologically fragile plateau lake basins. Full article

Understanding the spatial drivers of community satisfaction is crucial for achieving inclusive and sustainable urban development. However, traditional spatial regression models often assume linearity and fail to capture complex, spatially heterogeneous relationships. This study integrates a GeoShapley-based explainable GeoAI framework with the XGBoost algorithm to identify and quantify spatially varying factors influencing community satisfaction in Chengdu, China. By incorporating geographic coordinates as explicit spatial features, the GeoShapley method decomposes model outputs into intrinsic spatial effects and feature-specific interaction effects, enabling the interpretation of how and where each factor matters. Results show significant spatial clustering (Moran’s I = 0.60, p < 0.01) and a distinct south–north gradient in satisfaction. Built environment indicators—including building coverage ratio (BCR), walkability index (WI), and distance to green space (DGS)—exhibit nonlinear relationships and clear thresholds (e.g., BCR > 0.15, DGS > 590 m). Social vitality (Weibo check-ins) emerges as a key local differentiator, while education and healthcare accessibility remain spatially uniform. These findings reveal a dual structure of public service homogenization and spatial-quality heterogeneity, highlighting the need for place-specific, precision-oriented community renewal. The proposed GeoXAI framework provides a transferable pathway for integrating explainable AI into spatial sustainability research and urban governance. Full article

Water resources are fundamental to human survival, as well as critical to the sustainable progress of the economy and society. This study selects representative indicators and employs the TOPSIS model to evaluate the water resources carrying capacity (WRCC) in the Chang–Zhu–Tan region (2006–2022). Based on this, kernel density estimation and Moran’s I are applied to analyze the spatiotemporal distribution and evolution trends of WRCC. Additionally, the Lorenz curve, Gini coefficient, and imbalance index are utilized to examine the alignment between WRCC and socio-economic growth. Finally, a system dynamics model is used to simulate WRCC and matching dynamics under different scenarios. The findings reveal the following: (1) The overall WRCC is favorable but exhibits a declining temporal trend, with widening inter-district disparities and strong spatial agglomeration. (2) The match between WRCC and economic development is unbalanced, though alignment has gradually improved over time. (3) The WRCC varies across different scenarios. In current development scenario, WRCC declines significantly. In economic priority development and industrial restructuring scenarios, this reduction is slowed. Specifically, in water resource policy control scenario, WRCC can be enhanced. Aside from the industrial restructuring scenario, all other scenarios contribute to improving the coordination between WRCC and economic development. Full article

Rapid urbanization elevates land surface temperature (LST) through complex urban spatial relationships, intensifying the urban heat island (UHI) effect. This necessitates efficient methods to analyze surface urban heat island (SUHI) factors to help develop mitigation strategies. In this study, we propose an efficient global–local regression (EGLR) framework by integrating XGBoost-SHAP with global–local regression (GLR), enabling accelerated estimation of LST. In a case study of Wuhan, the EGLR reduces the computation time of GLR by 44.21%. The main contribution of computational efficiency improvement lies in the procedure of Moran eigenvector selecting executed by XGBoost-SHAP. Results of validation experiments also show significant time decrease of the EGLR for a larger sample size; in addition, transplanting the framework of the EGLR to two machine learning models not only reduces the executing time, but also increases model fitting. Furthermore, the inherent merits of XGBoost-SHAP and GLR also enables the EGLR to simultaneously capture nonlinear causal relationships and decompose spatial effects. Results identify population density as the most sensitive LST-increasing factor. Impervious surface percentage, building height, elevation, and distance to the nearest water body are positively correlated with LST, while water area, normalized difference vegetation index, and the number of bus stops have significant negative relationships with LST. In contrast, the impact of the number of points of interest, gross domestic product, and road length on LST is not significant overall. Full article

In recent years, China has faced the dual challenge of achieving high-quality economic development (HQED) alongside carbon emission reduction (CER), with new-quality productivity (NQP) emerging as a key driver integrating both agendas. Research on the coordinated development of these three dimensions remains limited but is critical for effective policy-making. Based on panel data from 30 Chinese provinces (2014–2023), this study constructs the NQP-CER-HQED evaluation indicator system; calculates the composite index using the entropy weight method and composite index calculation model; computes the coupling coordination degree (CCD) of the three components via the CCD model; analyzes the temporal evolution and future trends of CCD using kernel density and GM(1,1) models; examines the spatial evolution of CCD through Moran’s I index; employs traditional Markov chains and spatial Markov chains to investigate the spatial-temporal evolution patterns of CCD; and applies the geographic detector method to analyze the influencing factors of CCD among NQP, CER and HQED. The findings reveal that (1) the CCD of China’s NQP-CER-HQED has undergone six levels, showing an overall upward trend; (2) temporally, CCD levels improve annually, with all provinces expected to achieve coordinated development by 2026; (3) spatially, the CCD exhibits a “high-east, low-west” tiered distribution; (4) spatially/temporally, the transition of the CCD levels is primarily gradual rather than leapfrogging; and (5) the level of opening up and new-quality labor resources are identified as dominant influencing factors, with the interaction between new-quality labor resources and government support showing the strongest explanatory power. This study provides an analytical framework for understanding the NQP-CER-HQED synergy and offers a scientific basis for sustainable policy formulation. Full article

(1) Background: Climate change has intensified extreme heat and localized rainfall, exposing South Korea’s protected trees to new risks. Despite their ecological and cultural value, prior research has been largely local or qualitative, leaving little basis for nationwide prioritization. (2) Methods: We developed a composite risk index that integrates heat and rainfall exposure with species sensitivities, covering nearly the entire national inventory (≈10,000 individuals). Risks were calculated at the tree level, aggregated to district, provincial, and national scales, and tested for robustness across weighting and normalization choices. Spatial clustering was assessed with Moran’s I and LISA. (3) Results: High-risk clusters were consistently identified in southern and southwestern regions. Mean and tail indicators showed that average-based approaches obscure extreme vulnerabilities, while LISA confirmed significant High–High clusters. Rankings proved robust across scenarios, indicating that results reflect structural signals rather than parameter settings. Priority areas defined by the presence of extreme-risk individuals emerged as stable candidates for intervention. (4) Conclusions: The study establishes a transparent, operational rule for prioritization and offers tailored strategies—such as drainage infrastructure, shading, and root-zone management—while informing medium-term planning. It provides the first nationwide, empirically grounded framework for conserving protected trees under climate transition. Full article

Against the background of the rapid transformation of traditional economies and societies and continuous global climate change, how to ensure the long-term stability of the coastal ecological environment has become a key issue to be studied. In this paper, we take the 20 km buffer zone extending inland from the South African coastal zone as the study area. By constructing a vegetation vulnerability evaluation system, the current and future scenarios are compared in depth based on the base period (2010–2020), the near term (2030–2059), and the long term (2070–2099) with the help of GIS spatial analysis, the Moran index, and other methods. The results show that there are obvious spatial differences in vegetation vulnerability in the South African coastal zone. The extremely vulnerable areas of vegetation are mostly distributed on the west coast of South Africa, and some areas have obvious high–high aggregation patterns. The transfer of SSP1-2.6 scenarios in the near term is relatively stable, and the vegetation vulnerability level rebounds significantly in the long term; the vulnerability level of SSP2-4.5 scenarios has increased in both the near term and the long term, indicating that the risk of vegetation vulnerability has increased; while the SSP5-8.5 scenario has a significant deterioration trend in the long term, and the risk of vegetation vulnerability shifting to a high vulnerability level has increased significantly. Land use type has a significant impact on the response of vegetation vulnerability to SSP prediction. In the process of transformation from the base period to the long term, the proportion of vegetation vulnerability shifting to extremely vulnerable and severely vulnerable levels is notably high for both cultivated land and forest land—particularly under high-emission scenarios, driven by agricultural intensification for cultivated land and climate stress for forest land. This paper deeply explores the spatiotemporal evolution law and driving mechanism of vegetation vulnerability in the South African coastal zone under different shared socioeconomic pathway (SSP) scenarios, providing decision support for better development and protection of the South African coastal zone in the future. Full article

Amid rapid urbanization, Chinese cities face mounting ecological pressure, making it critical to balance environmental protection with public well-being. As visual perception accounts for over 80% of environmental information acquisition, it plays a key role in shaping experiences and evaluations of ecological space. However, current ecological planning often overlooks public perception, leading to increasing mismatches between ecological conditions and spatial experiences. While previous studies have attempted to introduce public perspectives, a systematic framework for analyzing the spatial relationship between ecological and visual sensitivity remains lacking. This study takes 56,210 street-level points in Tianjin’s central urban area to construct a coordinated analysis framework of ecological and perceptual sensitivity. Visual sensitivity is derived from social media sentiment analysis (via GPT-4o) and street-view image semantic features extracted using the ADE20K semantic segmentation model, and subsequently processed through a Multilayer Perceptron (MLP) model. Ecological sensitivity is calculated using the Analytic Hierarchy Process (AHP)—based model integrating elevation, slope, normalized difference vegetation index (NDVI), land use, and nighttime light data. A coupling coordination model and bivariate Moran’s I are employed to examine spatial synergy and mismatches between the two dimensions. Results indicate that while 72.82% of points show good coupling, spatial mismatches are widespread. The dominant types include “HL” (high visual–low ecological) areas (e.g., Wudadao) with high visual attention but low ecological resilience, and “LH” (low visual–high ecological) areas (e.g., Huaiyuanli) with strong ecological value but low public perception. This study provides a systematic path for analyzing the spatial divergence between ecological and perceptual sensitivity, offering insights into ecological landscape optimization and perception-driven street design. Full article

In-depth exploration of the spatial heterogeneity patterns of urban carbon emissions holds significant scientific importance for regional sustainable development. However, few scholars have examined the spatiotemporal characteristics of county-level carbon emissions in Inner Mongolia. This study focuses on the three major cities of Hohhot, Baotou, and Ordos in Inner Mongolia. By integrating NPP-VIIRS nighttime light data, the CLCD (China Land Cover Dataset) dataset, and statistical yearbooks, it quantifies county-level carbon emissions and establishes a spatiotemporal analysis framework of urban morphology–carbon emissions from 2013 to 2021. Six morphological indicators—Class Area (CA), Landscape Shape Index (LSI), Largest Patch Index (LPI), Patch Cohesion Index (COHESION), Patch Density (PD), and Interspersion Juxtaposition Index (IJI)—are employed to represent urban scale, complexity, centrality, compactness, fragmentation, and adjacency, respectively, and their impacts on regional carbon emissions are examined. Using a geographically and temporally weighted regression (GTWR) model, the results indicate the following: (1) from 2013 to 2021, The high-value areas of carbon emissions in the three cities show a clustered distribution centered on the urban districts. The total carbon emissions increased from 20,670 (10⁴ t/CO₂) to 37,788 (10⁴ t/CO₂). The overall spatial pattern exhibits a north-to-south increasing gradient, and most areas are projected to experience accelerated carbon emission growth in the future; (2) the global Moran’s I values were all greater than zero and passed the significance tests, indicating that carbon emissions exhibit clustering characteristics; (3) the GTWR analysis revealed significant spatiotemporal heterogeneity in influencing factors, with different cities exhibiting varying directions and strengths of influence at different development stages. The ranking of influencing factors by degree of impact is: CA > LSI > COHESION > LPI > IJI > PD. This study explores urban carbon emissions and their heterogeneity from both temporal and spatial dimensions, providing a novel, more detailed regional perspective for urban carbon emission analysis. The findings enrich research on carbon emissions in Inner Mongolia and offer theoretical support for regional carbon reduction strategies. Full article

Although Latin American countries have seen major advances in coverage and school attendance, there are still important geographical differences in educational quality, leading to inequalities. The objective of this study is to determine the influence of geographical context on academic achievement among primary school students in Chile. The methodology involves the estimation of spatial econometric models, specifically, an analysis of spatial dependence including the Moran index, New-GI tests and substantive and residual autocorrelation tests. The data used correspond to standardized test scores obtained from 4030 schools in Chile between 2014 and 2017. The results demonstrate the existence of spatially dependent effects on academic performance for both reading and math. The main indirect spatial effects arise from the concentration of indigenous and immigrant populations. There is also evidence of high spatial inequality in educational quality, as measured through Education Quality Measurement System (SIMCE) tests. Full article