Search Results (711)

Grounded in coupling theory, this study investigates the interplay among three key elements of economic growth, namely the digital economy, carbon emissions efficiency, and high-quality economic development. Drawing on data from 30 Chinese provinces from 2000 to 2023, we employ exploratory spatiotemporal data analysis and the GeoDetector model to examine the spatial–temporal evolution and underlying driving forces of coupling coordination. This research enriches the theoretical framework of multi-system synergistic development in a green transition context and offers empirical insights and policy recommendations for fostering regional coordination and sustainable development. The results reveal that (1) both the digital economy and high-quality economic development show a steady upward trend, while carbon emissions efficiency has a “U-shaped” curve pattern; (2) at the national level, the degree of coupling coordination has evolved over time from “mild disorder” to “on the verge of disorder” to “barely coordinated,” while at the regional level, this pattern of coupling coordination shifts over time from “Eastern–Northeastern–Central–Western” to “Eastern–Central–Northeastern–Western”; (3) although spatial polarization in coupling coordination has improved, disparities fluctuate in a “decline–rise” pattern, with interregional differences being the main source of that variation; (4) the degree of coupling coordination has a positive spatial correlation, but with a declining trend with fluctuations; and (5) improvements in the level of economic development, human capital, industrial structure, green technological innovation, and market development capacity all contribute positively to coupling coordination. Among them, green technological innovation and market development capacity are the most influential drivers, and the interactions among all driving factors further enhance their collective impact. Full article

This study investigates rural settlement patterns and land suitability in Rwanda’s Western Highlands, a mountainous region highly vulnerable to geohazards like landslides and flooding. Its primary aim is to inform sustainable, climate-resilient development planning in this fragile landscape. We employed high-resolution satellite imagery, a Digital Elevation Model (DEM), and comprehensive geospatial datasets to analyze settlement distribution, using Thiessen polygons for influence zones and Kernel Density Estimation (KDE) for spatial clustering. The Analytic Hierarchy Process (AHP) was integrated with the GeoDetector model to objectively weight criteria and analyze settlement pattern drivers, using population density as a proxy for human pressure. The analysis revealed significant spatial heterogeneity in settlement distribution, with both clustered and dispersed forms exhibiting distinct exposure levels to environmental hazards. Natural factors, particularly slope gradient and proximity to rivers, emerged as dominant determinants. Furthermore, significant synergistic interactions were observed between environmental attributes and infrastructure accessibility (roads and urban centers), collectively shaping settlement resilience. This integrative geospatial approach enhances understanding of complex rural settlement dynamics in ecologically sensitive mountainous regions. The empirically grounded insights offer a robust decision-support framework for climate adaptation and disaster risk reduction, contributing to more resilient rural planning strategies in Rwanda and similar Central African highland regions. Full article

Ancient temple complexes in China’s mountainous landscapes exemplify a profound synthesis of environmental adaptation and cultural expression. This research investigates the spatial logic underlying the Wudang Mountain temple complex—a UNESCO World Heritage site—through integrated geospatial analysis of environmental factors. Using GIS-based modeling, GeoDetector, and regression analysis, we systematically assess how terrain, hydrology, climate, vegetation, and soil conditions collectively influenced site selection. The results reveal a clear hierarchical clustering pattern, with dense temple cores in the southwestern highlands, ridge-aligned belts, and a dominant southwest–northeast orientation that reflects intentional alignment with mountain ridgelines. Temples consistently occupy zones with moderate thermal, hydrological, and vegetative stability while avoiding geotechnical extremes such as lowland humidity or unstable slopes. Regression analysis confirms that site preferences vary across temple types, with soil pH, porosity, and bulk density emerging as significant influencing factors, particularly for cliffside temples. These findings suggest that ancient temple planning was not merely a passive response to sacred geography but a deliberate process that actively considered terrain, climate, soil, and other environmental factors. While environmental constraints strongly shaped spatial decisions, cultural and symbolic considerations also played an important role. This research deepens our understanding of how environmental factors influenced the formation of historical landscapes and offers theoretical insights and ecologically informed guidance for the conservation of mountain cultural heritage sites. Full article

Wind erosion poses a major threat to ecosystem stability and land productivity in arid and semi-arid regions. Accurate identification of its spatiotemporal dynamics and underlying driving mechanisms is a critical prerequisite for effective risk forecasting and targeted erosion control. This study applied the Revised Wind Erosion Equation (RWEQ) model to assess the spatial distribution, interannual variation, and seasonal dynamics of the Soil Wind Erosion Modulus (SWEM) across Inner Mongolia from 1990 to 2022. The GeoDetector model was further employed to quantify dominant drivers, key interactions, and high-risk zones via factor, interaction, and risk detection. The results showed that the average SWEM across the study period was 35.65 t·ha⁻¹·yr⁻¹ and showed a decreasing trend over time. However, localised increases were observed in the Horqin and Hulun Buir sandy lands and central grasslands. Wind erosion was most intense in spring (17.64 t·ha⁻¹·yr⁻¹) and weakest in summer (5.57 t·ha⁻¹·yr⁻¹). Gale days, NDVI, precipitation, and wind speed were identified as dominant drivers. Interaction detection revealed non-linear synergies between gale days and temperature (q = 0.40) and wind speed and temperature (q = 0.36), alongside a two-factor interaction between NDVI and precipitation (q = 0.19). Risk detection indicated that areas with gale days > 58, wind speed > 3.01 m/s, NDVI < 0.2, precipitation of 30.17–135.59 mm, and temperatures of 3.01–4.23 °C are highly erosion-prone. Management should prioritise these sensitive and intensifying areas by implementing site-specific strategies to enhance ecosystem resilience. Full article

Ecological security underpins sustainable regional development and human well-being. Tianjin is in the eastern coastal area of China and features coastal wetlands and river systems. Over the past decade, Tianjin has undergone rapid urbanization. Tianjin faces the dual challenges of maintaining ecological security with economic growth, making it crucial to assess Tianjin’s ecological security status. This study constructed a comprehensive framework incorporating ecosystem health, services, and risk data to evaluate the ecological security status of Tianjin in 2012, 2017, and 2022. The results show the following: (1) Land use transfer mainly shows other land use types transferred to construction land. (2) The ecological security index of Tianjin ranges from 0.003 to 0.865, and the annual average values from 2012 to 2022 are 0.496, 0.493, and 0.499, with security levels dominated by medium, medium-high, and high security levels, respectively. The change in ecological security was relatively stable and was dominated by areas with unchanged levels, accounting for 63.72% of the total area. (3) The natural environment, human activities, and ecosystem status jointly influence Tianjin’s ecological security level. Shannon diversity, Shannon evenness, vegetation type, elevation, and mean annual temperature were the main factors affecting changes in ecological security in Tianjin, among which the interaction of Shannon diversity and vegetation type had the most significant influence. This study combines positive and negative aspects to assess ecological security, providing a reference for other regions to conduct ecological security assessments and a scientific basis for ecological management and urban planning decisions in similar regions. Full article

This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP (q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor (q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems. Full article

Amid global efforts to balance sustainable development and food security, ecosystem service value (ESV), a critical bridge between natural systems and human well-being, has gained increasing importance. This study explores the spatiotemporal dynamics and driving factors of land use changes and ESV from a food security perspective, aiming to inform synergies between ecological protection and food production for regional sustainability. Using Guangdong Province as a case study, we analyze ESV patterns and spatial correlations from 2005 to 2023 based on three-phase land use and socioeconomic datasets. Key findings: I. Forestland and cropland dominate Guangdong’s land use, which is marked by the expansion of construction land and the shrinking of agricultural and forest areas. II. Overall ESV declined slightly: northern ecological zones remained stable, while eastern/western regions saw mild decreases, with cropland loss threatening grain self-sufficiency. III. Irrigation scale, forestry output, and fertilizer use exhibited strong interactive effects on ESV, whereas urban hierarchy influenced ESV independently. IV. ESV showed significant positive spatial autocorrelation, with stable agglomeration patterns across the province. The research provides policy insights for optimizing cropland protection and enhancing coordination between food production spaces and ecosystem services, while offering theoretical support for land use regulation and agricultural resilience in addressing regional food security challenges. Full article

Traditional villages are irreplaceable cultural heritages, embodying complex human–environment interactions. This study uses historical geography analysis, kernel density estimation, centroid migration modeling, and Geodetector techniques to analyze the 2000-year spatiotemporal evolution and formation mechanisms of 224 nationally designated traditional villages in China’s Qinling-Daba Mountains. The findings are as follows: (1) These villages significantly cluster on sunny slopes of hills and low mountains with moderate gradients. They are also closely located near waterways, ancient roads, and historic cities. (2) From the embryonic stage during the Qin and Han dynasties, through the diffusion and transformation phases in the Wei, Jin, Song, and Yuan dynasties, to the mature stage in the Ming and Qing dynasties, the spatial center of these villages shifted distinctly southwestward. This migration was accompanied by expansion along waterway transport corridors, an enlarged spatial scope, and a decrease in directional concentration. (3) The driving forces evolved from a strong coupling between natural conditions and infrastructure in the early stage to human-dominated adaptation in the later stage. Agricultural innovations, such as terraced fields, and sociopolitical factors, like migration policies, overcame environmental constraints through the synergistic effects of cultural and economic networks. Full article

Guangxi is among China’s regions most severely affected by karst rocky desertification (KRD). Over the past two decades, global climate change and human activities have jointly led to significant changes in the extent and intensity of KRD in Guangxi. Given this context, it is crucial to comprehensively analyze the spatiotemporal evolution of KRD in Guangxi and its driving forces. This study proposed a novel three-dimensional feature space model for monitoring KRD in Guangxi. We then applied transition matrices, dynamic degree indices, and landscape metrics to analyze the spatiotemporal evolution of KRD. We also proposed a Spatiotemporal Interaction Intensity Index (STII) to quantify mutual influences among KRD patches. Finally, we used GeoDetector to analyze the driving factors of KRD. The results indicate the following: (1) The three-dimensional model showed high applicability for large-scale KRD monitoring, with an overall accuracy of 92.86%. (2) KRD in Guangxi exhibited an overall recovery–deterioration–recovery trend from 2000 to 2023. The main recovery phases were 2005–2015 and 2020–2023. During these phases, both severe and moderate KRD showed strong signals of recovery, including significant declines in area, number of patches, and Landscape Shape Index, along with persistently low STII values. In contrast, from 2015 to 2020, KRD predominantly deteriorated, primarily characterized by transitions from no KRD to potential KRD and from potential KRD to light KRD. (3) For severe KRD patches, the intensity of interaction required from neighboring patches to promote recovery exceeded that which led to deterioration, indicating the difficulty of reversing severe KRD. (4) Slope, land use, and elevation were the main drivers of KRD in Guangxi from 2000 to 2023. Erosive rainfall exhibited a higher explanatory power for KRD than average precipitation. Two-factor interactions significantly enhanced the driving forces of KRD. These findings provide a scientific basis for KRD management. Full article

The evolutionary patterns and influencing factors of the coupling coordination among multiple functions of cultivated land serve as an important basis for emphasizing the value of cultivated land utilization and promoting coordinated regional development. The entropy weight TOPSIS model, coupling coordination degree (CCD) model, spatial autocorrelation analysis, and Geodetector were employed in this study along with panel data from 125 cities in the Yangtze River Economic Belt (YREB) for 2010, 2015, 2020, and 2022. Three key aspects in the region were investigated: the spatiotemporal evolution of cultivated land functions, characteristics of coupling coordination, and their underlying influencing factors. The results show the following: (1) The functions of cultivated land for food production, social support, and ecological maintenance are within the ranges of [0.023, 0.460], [0.071, 0.451], and [0.134, 0.836], respectively. The grain production function (GPF) shows a continuous increase, the social carrying function (SCF) first decreases and then increases, and the ecological maintenance function (EMF) first increases and then decreases. Spatially, these functions exhibit non-equilibrium characteristics: the grain production function is higher in the central and eastern regions and lower in the western region; the social support function is higher in the eastern and western regions and lower in the central region; and the ecological maintenance function is higher in the central and eastern regions and lower in the western region. (2) The coupling coordination degree of multiple functions of cultivated land is within the range of [0.158, 0.907], forming a spatial pattern where the eastern region takes the lead, the central region is rising, and the western region is catching up. (3) Moran’s I index increased from 0.376 in 2010 to 0.437 in 2022, indicating that the spatial agglomeration of the cultivated land multifunctionality coupling coordination degree has been continuously strengthening over time. (4) The spatial evolution of the coupling coordination of cultivated land multifunctionality is mainly influenced by the average elevation and average slope. However, the explanatory power of socioeconomic factors is continuously increasing. Interaction detection reveals characteristics of nonlinear enhancement or double-factor enhancement. The research results enrich the study of cultivated land multifunctionality and provide a decision-making basis for implementing the differentiated management of cultivated land resources and promoting mutual enhancement among different functions of cultivated land. Full article

As human activities increasingly encroach on ecologically sensitive lake zones, China’s lake-ring urban agglomerations struggle to balance the intensifying human footprint (HF) and declining habitat quality (EQ). Addressing the spatiotemporal interactions between HF and EQ is essential for achieving human–environment coordination. This study examined five major freshwater lake-ring urban agglomerations in China during the period from 2000 to 2020 and developed an HF–EQ assessment framework. First, the coupling coordination degree (CCD) model quantified the spatiotemporal coupling between HF and EQ. Second, GeoDetector identified how HF and EQ interact to influence CCD. Finally, the four-quadrant static model and CCD change rate index formed a dual-dimensional management framework. The results indicate that the spatiotemporal evolution patterns of HF and EQ are highly complementary, exhibiting a significant coupling interaction. High-CCD zones expanded from lakeside urban areas and transport corridors, while low-CCD zones remained in remote, forested areas. HF factors such as GDP, land use intensity, and nighttime lights dominated CCD dynamics, while EQ-related factors showed increasing interaction effects. Five human–environment coordination zones were identified based on the static and dynamic characteristics of HF and EQ. Synergy efficiency zones had the highest coordination with diverse land use. Ecological conservation potential zones were found in low-disturbance hilly regions. Synergy restoration zones were concentrated in croplands and urban–rural fringe areas. Imbalance regulation zones were in forest areas under development pressure. Conflict alert zones were concentrated in urban cores, transport corridors, and lakeshore belts. These findings offer insights for global human–environment coordination in lake regions. Full article

[Objective] To quantify key ecosystem services within the Dongting Lake region, clarify the trade-off/synergy relationships, and detect the driving factors in order to support the ecological sustainable development of the Dongting Lake region. [Methods] Using the InVEST model, taking the area around Dongting Lake as the study area, four ecosystem services including water yield, carbon storage, soil conservation, and habitat quality were quantitatively assessed. Interdependencies between ecosystem services were assessed using correlation analysis to quantify trade-offs/synergies, and the geodetector model was used to detect their driving factors. [Results] (1) From 2000 to 2020, the soil retention service and water yield service in the Dongting Lake area showed an increasing trend over time. The total water yield increased from 4.93 × 10¹⁰ m³ to 6.71 × 10¹⁰ m³, while the total soil retention increased from 4.46 × 10⁹ t to 5.77 × 10⁹ t; habitat quality and total carbon storage continued to decline, with habitat quality decreasing from 0.6906 to 0.6785 and carbon storage decreasing from 1.480 × 10⁹ t to 1.476 × 10⁹ t. (2) In the study area, significant synergistic effects existed between carbon storage and habitat quality, carbon storage and soil retention, carbon storage and water yield, habitat quality and soil retention, and soil retention and water yield. However, there was a significant trade-off relationship between habitat quality and water yield. (3) During the study period, ecosystem service trade-offs and synergy relationships in the Dongting Lake area were jointly influenced by natural factors and human activities. Ranked by the magnitude of driving factor influence, they were land use type, land use intensity, vegetation coverage, temperature, and nighttime light. [Conclusions] Synergies dominated the ecosystem services in the research region, and the influence of natural factors behind them was greater than that of human activities. These research conclusions offer a scientific foundation for the institutional construction of the ecological compensation mechanism in the Dongting Lake basin. Full article

The spatiotemporal evolution of coastal rural settlements varies significantly across different geomorphic environments, yet this variation is underexplored in current research. Guided by Coupled Human and Natural Systems, this study examines the adaptation mechanisms between coastal rural settlements and landforms using an integrated framework that combines various bay types, spatiotemporal characteristics, and dynamic drivers. Four representative bay types along Guangdong’s coast were analyzed: Hilly Ria Coast, Platform Ria Coast, Barrier-Lagoon Coast, and Estuarine Delta Coast. Using multi-source remote sensing data and optimized Geodetector modeling (1972 vs. 2022), we identified the patterns of spatiotemporal evolution and their driving forces. The results reveal distinct adaptation pathways: Hilly Ria Coast settlements expanded in a constrained manner, supported by tunnel–bridge infrastructure; Platform Ria Coasts developed multi-nucleated, port-oriented clusters through harbor-linked road networks; Barrier-Lagoon Coasts achieved balanced growth through integrated land–river–sea governance; and Estuarine Delta Coasts experienced urban–rural restructuring accompanied by water network degradation. This study proposes governance strategies tailored to specific landforms to support sustainable coastal planning. Full article

Field-based landslide mapping is a crucial task for geo-hydrological risk assessment but is often limited by the lack of integrated tools to capture accurate spatial and temporal data. This research investigates a Direct Numerical Cartography (DNC) system’s ability to capture both spatial and temporal landslide features during fieldwork. DNC enables fully digital surveys, minimizing errors and delivering real-time, spatially accurate data to experts on site. We tested an integrated approach combining the Ground Operative System for GIS Input Remote-data Acquisition (GOGIRA) with the Expert-based Multitemporal AI Detector (ExMAD). GOGIRA is a low-cost system for efficient georeferenced data collection, while ExMAD uses AI and multitemporal Sentinel-2 imagery to detect landslide triggering times. Upgrades to GOGIRA’s hardware and algorithms were carried out to improve its mapping accuracy. Field tests in Western Greece compared data to 64 expert-confirmed landslides, with the Range-R device showing a mean spatial error of 50 m, outperforming the tripod-based UGO device at 82 m. Operational factors like line-of-sight obstructions and terrain complexity affected accuracy. ExMAD applied a pre-trained U-Net convolutional neural network for automated temporal trend detection of landslide events. The combined DNC and AI-assisted remote sensing approach enhances landslide inventory precision and consistency while maintaining expert oversight, offering a scalable solution for landslide monitoring. Full article

Rural post-poverty alleviation areas are not on a solid developmental footing and therefore remain at risk of returning to poverty in the midst of rapid urbanization. Vulnerability assessment of socio-ecological systems is critical for identifying risks and enhancing resilience in rural areas transitioning out of poverty. Based on research data from 2012, 2017, and 2022 in the post-poverty alleviation areas of Hunan Province, this research establishes a Vulnerability-Scoping-Diagram (VSD) assessment framework for rural development vulnerability and Spatially-Explicit-Resilience-Vulnerability (SERV) analysis model from a socio-ecological system perspective. It comprehensively analyzes the spatial and temporal variations of the Rural Development Vulnerability Index (RDVI) in the study area. Geodetector is used to explore the main factors influencing the spatial and temporal variability of RDVI, and vulnerability type zones are classified by combining the dominant elements method. The findings indicate that: (1) The rural development vulnerability index of post-poverty alleviation areas in Hunan Province has obvious characteristics of spatial and temporal differentiation. The RDVI in western Hunan and southern Hunan is always high, while the RDVI in ChangZhuTan and Dongting Lake regions decreases year by year. (2) The RDVI of post-poverty alleviation areas in Hunan Province is determined by the three dimensions of exposure, sensitivity, and adaptability, exhibiting significant spatial and temporal variations. (3) Spatial autocorrelation analysis showed that areas with similar rural socio-ecological vulnerability in post-poverty alleviation areas of Hunan Province were significantly clustered spatially. (4) The core influencing factors of RDVI in Hunan’s post-poverty alleviation areas have shifted from natural disaster risk to multiple risk dimensions encompassing social resource load and ecological environment risk superimposition, resulting in more complex and diversified influencing factors. (5) By combining results from the RDVI assessment with the dominant elements method, the regions can be classified into multiple vulnerability type districts dominated by multiple elements or single-element dominance, leading to corresponding development suggestions. The study aims to examine the process of changes in vulnerability within rural development in post-poverty alleviation areas and its causal factors from a socio-ecological system perspective. This will provide a foundation for policy formulation to consolidate the results of post-poverty alleviation and promote the sustainable development of rural areas. Full article