Search Results (157)

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

Artificial intelligence (AI), which is advancing rapidly, offers a novel and important tool for driving sustainable energy transition, although the spatiotemporal correlation between the two is complex. Taking China’s 30 provinces as the study subjects, this research constructs an evaluation index system from the perspective of energy transition outcomes to assess the level of China’s energy transition. It evaluates the level of AI development based on the foundation of AI development, AI technological innovation, and AI application, and analyzes its spatiotemporal evolution characteristics. Pearson correlation analysis and bivariate local spatial autocorrelation are employed to investigate the spatiotemporal associations between energy transition and AI. In addition, the dynamic mechanisms linking the two are further investigated using a geographically and temporally weighted regression (GTWR) model. The results indicate that, first, innovation and application in AI were on the rise, while regional disparities were widening and a polarization phenomenon was emerging; AI development was concentrated in the eastern regions, with a decreasing trend toward the northwestern inland areas. Second, the overall level of China’s energy transition continued to rise, with a box-shaped clustering pattern observed across regions; Beijing, Inner Mongolia, Jiangsu, and Shandong had achieved a relatively high level of energy transition. Third, the development of AI did not always correlate positively with the energy transition. There was a significant positive correlation between AI technological innovation and application and the energy transition. There were significant differences in the spatial patterns linking AI development and the energy transition. The positive correlation between the two was significant and widespread, concentrated in the central and eastern provinces. Full article

As a key component of ecosystems, the synergistic relationship between wetland carbon storage and habitat quality is vital for maintaining ecological functions, and its evolution is profoundly influence by changes in wetlands. This study focuses on wetlands in western Jilin Province. Based on four sets of land use data from 2010 to 2023 and utilizing the InVEST model, combined with methods such as spatial autocorrelation, the Coupled Coordination Degree Model, and the GeoDetector, the study analyzed the co-variation of carbon storage and habitat quality, as well as their response to landscape patterns. The study found that between 2010 and 2023, the wetland area increased by a net 858.13 km², and landscape fragmentation was generally alleviated, although local connectivity continued to degrade. Regional carbon storage increased by 68.1%, totaling 7.43 × 10⁶ Mg, while the habitat quality index exhibited high spatiotemporal stability, fluctuating marginally between 0.609 and 0.621. Spatially, high-value areas remained primarily concentrated within nature reserves. Results of bivariate spatial autocorrelation analysis revealed a strengthening of spatial positive autocorrelation between carbon storage and habitat quality, with Moran’s I increasing from 0.410 to 0.501. The coupled coordination degree model further confirmed that the level of synergy between the two services exhibited a pattern of higher values in the north and lower values in the south, and that areas of high coordination expanded significantly outward following restoration projects. GeoDetector analysis indicates that the largest patch index is the core factor driving the synergistic development of ecosystem services. The results also suggest that the integrity of core wetland patches and a heterogeneous landscape pattern can promote the synergistic improvement of carbon storage and habitat quality through boundary effects and habitat complementarity. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

Rapid urbanization and agricultural expansion in arid regions have profoundly altered carbon cycles and landscape stability. Focusing on the Hexi Corridor, China, this study integrates multi-source geospatial data (1990–2020) to analyze the spatiotemporal evolution and driving factors of land-use carbon emissions (LUCE) and landscape ecological risks (LER). By integrating carbon accounting, LER assessment, bivariate spatial autocorrelation, and the Optimal Parameter Geographic Detector (OPGD), we quantify the intricate relationship between carbon dynamics and landscape integrity. Results indicate a transformative pattern of anthropogenic expansion and natural contraction, with a 2315.49 km² net loss of unused land. Net carbon emissions surged 4.6-fold, while forest and grassland sinks exhibited a significant “lock-in effect” due to fragile ecological foundations. Simultaneously, LER followed an “inverted U-shaped” trajectory; the refined 5 × 5 km grid scale revealed a significant drop in high-risk areas from 44.65% to 10.96% following ecological restoration. Spatial analysis reveals a significant “spatial mismatch” between LUCE and LER, with oases manifesting “high carbon–low risk” clustering. Driver detection confirms a driving asymmetry. LUCE is dominated by anthropogenic factors (nighttime light, q > 0.90), whereas LER is profoundly constrained by natural backgrounds. Future governance must shift toward a collaborative system centered on source-based emission control and precise regional management to synergize low-carbon transition with landscape security. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Virtual vitality has become an important complementary dimension for describing urban vitality; however, the identification and formation mechanisms of its stable, high-vitality state during dynamic change remain insufficiently explored. Taking five urban districts of Harbin as the study area, this study uses TikTok short-video data from July to August 2024 (summer) and December 2024 to January 2025 (winter), together with Gaode Map POI data, as the core dataset. Kernel density differences between adjacent weeks are used to measure the dynamic changes in virtual vitality. Bivariate local spatial autocorrelation is applied to identify high-vitality stable zones, and a Random Forest model is employed to examine the nonlinear influence of physical vitality spatial structures. The results show the following: (1) Dynamic change patterns of virtual vitality differ significantly across seasons, and when online attention content points to specific physical spatial structures, a stable high-vitality state is more likely to be maintained. (2) Bivariate local spatial autocorrelation analysis indicates that high-vitality stable zones (HH zones) exhibit significant spatial clustering, with vitality-enhancing zones (LH zones) distributed around them and showing spillover effects, while vitality-declining zones (HL zones) are more scattered. (3) The Random Forest results show that the stable maintenance of high virtual vitality depends more on combinations of spatial structural characteristics with high recognizability, among which distance to activity center (tourism), functional composition dissimilarity (culture), and functional composition dissimilarity (shopping) have the strongest influence. These findings reveal a nonlinear relationship between the stable high-vitality state and the structure of physical vitality space, providing insights for guiding online attention to support physical spatial development. Full article

Since the start of the U.S. Conservation Reserve Program (CRP) in 1985, producers have enrolled environmentally sensitive land in exchange for annual rental payments, supporting multiple dimensions of sustainability through reduced soil loss, improved water quality, enhanced habitat provision, and strengthened climate resilience through land stewardship. Recent declines in enrollment raise concerns about whether participation remains spatially aligned with local environmental need and economic incentives. This study examines regional variation in CRP participation and its sustainability implications by identifying spatial patterns in participation and key drivers using exploratory spatial data analysis (ESDA). We analyze county-level CRP participation rates alongside three key drivers (CRP rental rates, soil erosion risk on cultivated cropland, and farm income) and assess spatial dependence using Global Moran’s I, univariate Local Indicators of Spatial Association (LISA), and bivariate LISA (BiLISA). Framed as an assessment of agri-environmental policy effectiveness for sustainable land management, the framework is applied to counties in the U.S. Midwest, a region with historically substantial CRP enrollment. Global Moran’s I statistics indicate significant positive spatial autocorrelation for CRP participation (I = 0.491), CRP rental rates (I = 0.892), and soil erosion (I = 0.503), confirming pronounced regional clustering across Midwestern counties. LISA results further show that more than 60% of counties fall into high–high (HH) or low–low (LL) clusters for CRP rental rates, while BiLISA results indicate that 22.9% of counties form HH clusters between CRP participation and soil erosion, suggesting only partial alignment between CRP participation and the environmental need. These findings indicate that the environmental benefits of CRP may vary across the region depending on where participation occurs. Overall, the findings support a shift toward a data-driven, spatially explicit CRP strategy that integrates environmental risk, economic incentives, and regional context to strengthen sustainability outcomes and enhance environmental effectiveness, economic efficiency, and the spatial equity of conservation benefits in the United States. 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

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

Against the backdrop of rapid development in megacities, urban park green spaces serve as essential public resources whose accessibility and equity directly affect residents’ quality of life and broader social justice. This study addresses the imbalance between the spatial distribution of green space resources and the socio-demographic characteristics of different population groups in megacities. It takes the six central districts of Beijing as the study area and integrates data from 457 urban parks. The research applies the Gaussian two-step floating catchment area (G2SFCA) method and bivariate spatial autocorrelation analysis (Moran’s I) to systematically evaluate the equity of urban park green space provision across multiple social dimensions, including economic status, educational attainment, and vulnerable groups. The results indicate that urban park green spaces in Beijing’s six central districts exhibit a pronounced central and northern advantage, with significant deficits in southern and peripheral areas. High accessibility and greater per capita green space are concentrated in core and high-housing-price districts, overlapping with high-income and highly educated populations. In contrast, vulnerable groups and migrant workers are more likely to reside in green-space-deficient areas, facing a structural “high population density–low green space provision” disadvantage, reflecting clear social inequities. In addition, inequity is more pronounced at the walking scale than at the cycling scale. The study reveals a dual mismatch in green space provision across both spatial and social dimensions within a megacity context. The findings suggest that future urban planning should shift from quantitative expansion to the optimization of existing green space resources. Planning strategies should prioritize vulnerable groups and adopt a people-oriented approach. Policymakers should allocate greater support to southern and peripheral areas, increase the provision of pocket parks, and improve slow-mobility systems. These measures can more precisely safeguard equitable access to green space for disadvantaged populations and promote the realization of spatial justice. Full article

Urban expansion and spatial restructuring significantly influence the urban thermal environment. This study investigates the central urban area of Jinan, developing a multi-dimensional spatial structure index system that integrates terrain, 2D/3D morphology, and layout based on multi-source data. Land surface temperature (LST) was derived from remote sensing imagery. Using road networks and triangulated irregular networks (TINs) generated from a digital elevation model (DEM), hybrid analysis units were created. Pearson correlation and bivariate global/local spatial autocorrelation analyses were applied to examine the mechanisms and spatial heterogeneity of how urban spatial structure affects LST. The results showed that (1) LST was strongly associated with urban spatial structure. Among the 12 significantly correlated indicators, building density showed the strongest positive correlation with LST (r = 0.5883), while DEM mean had the strongest negative correlation (r = −0.7444), indicating that compact built-up areas intensified heating, whereas terrain most strongly moderated surface temperature. (2) LST and indicator correlations varied with elevation. LST showed a negative correlation with the standard deviation of DEM, suggesting that greater terrain variability enhances cooling effects. This spatial variation in the dominant drivers of the thermal environment reflects a clear divergence of influencing factors across different elevational zones. The thermal environment exhibits a pronounced north–south split: cooling effects prevail in the south due to terrain, while warming effects dominate in the north due to building forms. (3) Bivariate spatial autocorrelation revealed clear spatial heterogeneity. High–high clustering of LST and spatial structure indicators in the northern plain denoted heat-aggregated zones. Low–low clustering in the topographically complex, sparsely built south formed cold-source zones, and transitional areas showed mixed high–low and low–high clustering. (4) Based on these findings, a zonal governance framework was advocated, prioritizing terrain assessment followed by spatial structure optimization. This promoted a shift from uniform to precise, zone-based thermal environment management, laying a scientific foundation for sustainable spatial planning. Full article

Land use transformation directly affects the stability and sustainability of regional ecosystems. Clarification of the trade-off/synergy dynamics among ecosystem services (ESs) provides a theoretical foundation to understand the transition of ES interactions from trade-offs to synergies, thereby facilitating the achievement in ecological sustainability in the ecoregion. This study, taking Jiangxi Province, China, as an example, utilized the InVEST model, Theil–Sen estimator, Mann–Kendall test, bivariate spatial autocorrelation, ecosystem service bundles (ESBs), and Random Forest (RF) models to conduct such an ecosystem-focused integrated analysis. According to land use changes from 1980 to 2020, the time-series spatiotemporal patterns of water yield (WY), soil conservation (SC), habitat quality (HQ), and carbon storage (CS) were analyzed. Differences in ES trade-off/synergy relationships and their underlying motivating factors were examined using a 3 km spatial grid framework. Compared with previous studies that mainly focused on typical subregions and of which driver analyses often remained at the individual ES level, this study introduced an explainable RF-SHAP framework based on the cooperative game theory at the grid scale, to quantitatively characterize the relative contributions of every motivating factor to ES trade-off/synergy relationships. The results indicate that from 1980 to 2020, forests and croplands constituted the predominant land use types, taking up 88% of the studied area. Throughout this period, forests, croplands, and grasslands decreased markedly, while built-up areas expanded notably, with a rise of 2876.65 km². Over the same time span, WY increased on average by 0.50% whereas SC, HQ, and CS declined by 0.50%, 0.98%, and 1.30%, respectively. Overall, these ESs demonstrated a geographical distribution characterized by low levels in SC, HQ and CS in the central area and high levels towards the provincial boundary. At the grid scale, the four ESs demonstrated predominantly a synergistic relationship while WY&HQ and WY&SC pairs were characterized by trade-offs. The constraint effect analysis revealed U-shaped relationships for SC&HQ, WY&HQ, and WY&SC, and inverted U-shaped relationships for SC&CS and HQ&CS, with clear threshold effects among these ES pairs. Based on self-organizing maps, the study area is partitioned into six ESBs, and the trade-off/synergy linkages of ESs are affected by the interplay of natural and societal forces. Elevation, slope, and rainfall emerge as the primary driving variables accompanied by population density and proximity to urban centers. These results are anticipated to offer reference to governments for their sustainable management in environmental resources to achieve United Nations Sustainable Development Goal (SDG) 15 (Life on Land: Protect, restore and promote sustainable use of terrestrial ecosystems). The methods used in this paper provide a replicable framework for exploring ES interactions and driving mechanisms in other ecologically sensitive regions in the world. Full article

Under the influence of China’s rapid urbanization, traditional villages and intangible cultural heritage (ICH), as an organically coupled cultural ecosystem, are faced with severe spatial imbalance. Quantitative analysis and mechanism interpretation of their spatial relationship are crucial for the integrated conservation and collaborative development of cultural heritage. This study takes Zhejiang Province as the research scope, integrates GIS spatial analysis, bivariate spatial autocorrelation, spatial mismatch index, and optimal parameter geographic detector (OPGD) to systematically reveal the spatial distribution characteristics, spatial associations, spatial mismatch patterns, and driving mechanisms of traditional villages and ICH. The results show that: (1) In terms of spatial distribution, traditional villages are highly concentrated in the hilly and mountainous southwest, with a hierarchical pattern featured by “two main cores and two secondary cores”, while ICH is abundant in the flat and coastal northeast and southeast, presenting a multi–center equilibrium pattern. (2) In terms of spatial relationships, there exists a weak but statistically significant negative correlation, which is embodied in typical clusters: “high–low” clusters mainly in the southwest and “low–high” clusters in the northeast and southeast, corresponding to negative spatial mismatch zones dominated by traditional villages and positive spatial mismatch zones dominated by ICH, respectively. (3) As for driving mechanisms, the spatial mismatch pattern is influenced by the tripartite interaction of “natural geographical constraints, socioeconomic drivers, and cultural policy adjustments,” with resident population, GDP, and public budget expenditure as the core driving factors. This study offers scientific recommendations for the conservation and governance of traditional villages and ICH in Zhejiang Province, while providing methodological guidance for cultural heritage preservation in comparable regions. Full article

The rapid urban expansion (UE) in the Yangtze River Economic Belt (YREB) in China has profoundly reshaped landscape patterns and ecosystem functions. Understanding the impact of UE on ecosystem health (EH) across different urban agglomerations is crucial for informing effective ecological governance and sustainability strategies. However, whether UE ultimately promotes or constrains EH across urban agglomerations under multi-scenario remains unclear. This study aims to address this gap by employing the Patch-generating Land Use Simulation model and the Vigor–Organization–Resilience–Service framework to simulate UE and EH in three major urban agglomerations of the YREB, while also examining the mechanisms through which UE influences EH. The results revealed substantial UE under all scenarios, with the Yangtze River Delta urban agglomerations exhibiting the most pronounced growth. The EH index showed a downward trend, from 0.621 in 2010 to 0.613 in 2020. Bivariate spatial autocorrelation and spatial regression analyses revealed a significant negative correlation between UE and EH. The study identified land fragmentation and occupation due to UE as the primary factors contributing to the deterioration of EH. The findings indicated the necessity of strategic urban planning to mitigate potential ecosystem risks while promoting sustainable urban development. Furthermore, regional cooperation is critical for addressing transboundary ecological challenges and ensuring the long-term sustainability and resilience of the YREB ecosystem. Full article