Search Results (16)

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

Terrestrial net primary productivity (NPP) integrates vegetation responses to climate, terrain, and human activities, yet their combined effects in mountainous–basin regions remain unclear. Focusing on the Chengdu–Chongqing Economic Circle (CCEC) in southwest China, we build a framework that couples spatial diagnosis, interaction-aware attribution, and scenario-based projection. Using 500 m MODIS NPP (2000–2020) with climatic, topographic, land-use, and socio-economic data, we quantify NPP trends, use optimal-parameter GeoDetector and partial correlations to separate driver contributions and interactions, and train a random forest (RF)–SHAP model driven by CMIP6–SSP climate projections to 2050. The CCEC shows strong greening: 85.17% of the area exhibits increasing NPP and 68.56% shows extremely significant increases, with productivity peaking at mid-elevations (~1950 m) and intermediate slopes. Elevation, NDVI, and temperature dominate, while precipitation, slope, and soil moisture are secondary, and enhancement-type interactions, especially between elevation and precipitation, prevail. Land-use statistics and NPP transfer matrices highlight cropland-to-forest/grassland conversion as the main greening source. CMIP6-based simulations indicate stable or modestly higher NPP through 2050, with western mountain forests remaining key carbon sinks and basin lowlands constrained by warming and land-use pressure. Full article

The unique geo-ecological conditions of karst river basins (KRBs) heighten rural vulnerability to compound disturbances; therefore, enhanced rural resilience (RR) is critical for regional ecological security and sustainable development. In this study, the Wujiang River Basin was chosen as the study area. A comprehensive evaluation index system was first established to assess RR. Key driving factors were identified using the Optimal Parameters-based Geographical Detector (OPGD) mode. The Geographically and Temporally Weighted Regression (GTWR) model was then applied to analyze the spatiotemporal heterogeneity in the driving mechanisms of RR. Our results show that from 2010 to 2022: (1) RR in the study area increased significantly, and disparities among counties decreased notably, indicating a trend toward more balanced regional development. (2) RR displayed strong positive spatial autocorrelation, with spatial clusters evolving dynamically under the influence of policy interventions and environmental constraints. (3) The main drivers of spatial heterogeneity in RR included urban–rural income disparity, road network density, agricultural machinery power, etc. Their driving mechanisms exhibited significant spatiotemporal non-stationarity. The findings inform the development of targeted strategies to enhance regional resilience. Additionally, the methodology and empirical insights can serve as valuable references for RR research and practice in other similar KRBs worldwide. Full article

The impact of the built environment on urban vitality is multifaceted, yet a holistic understanding that simultaneously considers its scale dependence, spatial heterogeneity, and interactive mechanisms remains limited. To unravel these multi-scalar mechanisms, this study develops an integrated analytical framework. Taking Xi’an, China, as a case study, we first construct a multidimensional built environment indicator system grounded in Jane Jacobs’ theory of vitality. Empirically, we employ the Optimal Parameters-based GeoDetector (OPGD) to objectively identify the optimal spatial scale and detect non-linear and interaction effects. Meanwhile, the Multiscale Geographically Weighted Regression (MGWR) model is used to delineate spatial heterogeneity. Our findings systematically unravel the complex mechanisms: (1) The optimal analysis scale is identified as a 2 km grid; (2) All elements significantly influence vitality, but through distinct linear or non-linear pathways; (3) The effects of attraction density, road network structure, and bus stop density exhibit significant spatial heterogeneity; and (4) Third place density and population density act as key catalysts, non-linearly enhancing the effects of other elements. This research presents a synthesized perspective and nuanced evidence for precision urban regeneration, demonstrating the necessity of integrating scale, heterogeneity, and interaction to understand the drivers of urban vitality. Full article

Coastal zones face mounting pressures from rapid urban expansion and ecological degradation, posing significant challenges to achieving synergistic carbon storage and emissions reduction under China’s “dual carbon” goals. Yet, the identification of spatially explicit zones of carbon synergy (high storage–low emissions) and conflict (high emissions–low storage) in these regions remains limited. This study integrates the PLUS (Patch-generating Land Use Simulation), InVEST (Integrated Valuation of Ecosystem Services and Trade-offs), and OPGD (optimal parameter-based GeoDetector) models to evaluate the impacts of land-use/cover change (LUCC) on coastal carbon dynamics in China from 2000 to 2030. Four contrasting land-use scenarios (natural development, economic development, ecological protection, and farmland protection) were simulated to project carbon trajectories by 2030. From 2000 to 2020, rapid urbanization resulted in a 29,929 km² loss of farmland and a 43,711 km² increase in construction land, leading to a net carbon storage loss of 278.39 Tg. Scenario analysis showed that by 2030, ecological and farmland protection strategies could increase carbon storage by 110.77 Tg and 110.02 Tg, respectively, while economic development may further exacerbate carbon loss. Spatial analysis reveals that carbon conflict zones were concentrated in major urban agglomerations, whereas spatial synergy zones were primarily located in forest-rich regions such as the Zhejiang–Fujian and Guangdong–Guangxi corridors. The OPGD results demonstrate that carbon synergy was driven largely by interactions between socioeconomic factors (e.g., population density and nighttime light index) and natural variables (e.g., mean annual temperature, precipitation, and elevation). These findings emphasize the need to harmonize urban development with ecological conservation through farmland protection, reforestation, and low-emission planning. This study, for the first time, based on the PLUS-Invest-OPGD framework, proposes the concepts of “carbon synergy” and “carbon conflict” regions and their operational procedures. Compared with the single analysis of the spatial distribution and driving mechanisms of carbon stocks or carbon emissions, this method integrates both aspects, providing a transferable approach for assessing the carbon dynamic processes in coastal areas and guiding global sustainable planning. Full article

The Upper Yellow River Region plays an irreplaceable role in water conservation and ecological protection in China. Due to both natural and human-induced factors, this area has experienced significant grassland deterioration, land desertification, and salinization. Consequently, evaluating the region’s environmental status plays a vital role in promoting ecological conservation and sustainable growth in the Upper Yellow River Basin. This study constructed an ecological index based on remote-sensing data and examined its spatiotemporal changes from 1990 to 2020. Future ecological dynamics were predicted using the Hurst index, while key influencing factors were examined through an optimal-parameter-based GeoDetector and geographically weighted regression. The findings revealed the following: (1) RSEI values were generally lower in the north and increased progressively toward the south, indicating a notable spatial disparity. (2) Ecological conditions remained largely stable, with notable improvements observed in 65.47% of the study area. (3) It was anticipated that 52.76% of the region would continue to improve, whereas 24% is expected to experience further degradation. (4) Precipitation, temperature, elevation, and land cover were major factors contributing to ecological variation. Their impact on ecological quality varies across different geographic locations. These research findings provided references for the sustainable development and ecological civilization construction of the Upper Yellow River Region. Full article

As a national ecological civilization pilot, Zhejiang’s growing vegetation carbon sink capacity is important for both regional ecological security and China’s carbon neutrality goals, but current studies lack a comprehensive assessment of multi-factor interactions. This study employed an improved Carnegie–Ames–Stanford Approach (CASA) and soil respiration empirical equation to estimate Net Ecosystem Productivity (NEP) in Zhejiang Province, and trend analysis, partial correlation analysis, and the GeoDetector model based on optimal parameters (OPGD) were utilized to investigate the spatiotemporal variations and driving factors of vegetation NEP. The results showed that the multi-year average NEP and carbon sink capacity in Zhejiang Province were 387.67 g C m⁻² a⁻¹ and 38.84 Tg C a⁻¹, exhibiting an increasing trend at an average rate of 2.15 g C m⁻² a⁻¹ and 0.23 Tg C a⁻¹, respectively. Spatially, NEP was higher in the western and southern mountainous regions and lower in the eastern coastal and northern plains. NEP in Zhejiang Province was driven by both natural and anthropogenic factors, with NDVI (q = 0.502) and elevation (q = 0.373) being the primary natural drivers, and nighttime light intensity (q = 0.327) and impervious surface dynamics (q = 0.295) being the main anthropogenic drivers. Moreover, the interactions among these factors all exhibited synergistic enhancement effects. Overall, Zhejiang Province functioned predominantly as a carbon sink, with its sequestration capacity gradually strengthening over time. The combined effects of natural and anthropogenic factors drove the spatiotemporal heterogeneity of vegetation NEP. These findings highlight the importance of coordinated ecosystem management strategies that consider both natural and anthropogenic-induced impacts to enhance the achievement of regional carbon sink goals. Full article

Factors related to catering distribution are typically characterized by local changes, but few studies have quantitatively investigated the inherent spatial nonstationarity correlations. In this study, a multiscale geographically weighted regression (MGWR) model was adopted to locally examine the impact of various factors on catering distribution, which were obtained through a novel method incorporating GeoDetector analysis and exploratory factor analysis (EFA) using point of interest (POI) data. GeoDetector analysis was used to identify the effective variables that truly contribute to catering distribution, and EFA was adopted to extract interpretable latent factors based on the underlying structure of the effective variables and thus eliminate multicollinearity. In our case study in Nanjing, China, four primary factors, namely commuting activities, shopping activities, tourism activities, and gathering activities, were retained from eight categories of POIs with respect to catering distribution. The results suggested that GeoDetector working in tandem with EFA could improve the representativeness of factors and infer POI configuration patterns. The MGWR model explained the most variations (adj. R²: 0.903) with the lowest AICc compared to the OLS regression model and the geographically weighted regression (GWR) model. Mapping MGWR parameter estimates revealed the spatial variability of relationships between various factors and catering distribution. The findings provide useful insights for guiding catering development and optimizing urban functional spaces. Full article

Developing industries and promoting employment are essential for rural revitalization. This study establishes a theoretical framework to support the coordinated development of rural industry and employment (RIE) with Chongqing, China as the study area. Methods include GIS spatial analysis, the entropy-weighted TOPSIS method, a coupled coordination degree model, and an optimal-parameter-based GeoDetector. The analysis examines the spatio-temporal evolution and driving mechanisms of the coordinated development of RIE. The main findings are as follows. (1) During the study period, Chongqing’s RIE improved significantly overall, although rural industry is relatively lagging. (2) The evolution characteristics of the coordinated development of RIE exhibit “spatio-temporal ripple” and “spindle-shaped” patterns, and the spatial agglomeration has been enhanced. The growth of RIE is accompanied by the spatial diffusion of rural industry and the spatial echo of rural employment. (3) The primary driving mechanism for the coordinated development of RIE is “human-centered, natural resource-based socio-economic development.” Finally, this study discusses employment-centered strategies for rural industrial development, providing a theoretical foundation for policy-making in rural industrial development. Full article

Studying the temporal and spatial variation characteristics and driving factors of carbon reserves in the middle reaches of the Yangtze River urban agglomeration is crucial for achieving sustainable development and regional ecological conservation against the backdrop of the “double carbon” plan. Based on three periods of land use data from 2000 to 2020, combined with the InVEST model(Version 3.14.2), the spatiotemporal changes in carbon storage in the urban agglomeration in the middle reaches of the Yangtze River were analyzed. The PLUS model (Version 1.3.5) was used to predict three scenarios of natural development, urban development, and eco-development in the urban agglomeration in the middle reaches of the Yangtze River in 2035 and estimate the carbon storage of the ecosystems under different scenarios, and it used optimal parameter GeoDetectors (Version 4.4.2) to reveal the driving factors affecting the spatiotemporal differentiation of carbon storage. The results show that farmland and construction land area increased and forestland area continued to decrease from 2000 to 2020. Carbon storage decreased by 1 × 10⁶ t, with forestland conversion to farmland and construction land being the main decreasing drivers. The carbon storage of natural and urban developments decreased by 0.26 × 10⁶ t and 0.32 × 10⁶ t, while it increased by 0.16 × 10⁶ under ecological development. The results of the factor detector showed that the NDVI (Normalized Difference Vegetation Index) had the highest explanatory power on the spatiotemporal variation in carbon storage (q = 0.588), followed by the slope (q = 0.454) and elevation (q = 0.391), and the explanatory power of natural environmental factors on the spatiotemporal variation in of carbon storage was dominant. The interaction detector results showed that the spatiotemporal variation in carbon storage was affected by multiple factors, the interaction intensity between each driving factor was stronger than that of a single factor, and the synergy between the NDVI and slope was the strongest, at q = 0.646. Full article

In the context of sustainable urban development, elucidating urban heat island (UHI) dynamics in arid regions is crucial. By thoroughly examining the characteristics of UHI variations and potential driving factors, cities can implement effective strategies to reduce their impacts on the environment and public health. However, the driving factors of a UHI in arid regions remain unclear. This study analyzed seasonal and diurnal variations in a surface UHI (SUHI) and the potential driving factors using Pearson’s correlation analysis and an Optimal Parameters-Based Geographic Detector (OPGD) model in 22 cities in Xinjiang, northwest China. The findings reveal that the average annual surface urban heat island intensity (SUHII) values in Xinjiang’s cities were 1.37 ± 0.86 °C, with the SUHII being most pronounced in summer (2.44 °C), followed by winter (2.15 °C), spring (0.47 °C), and autumn (0.40 °C). Moreover, the annual mean SUHII was stronger at nighttime (1.90 °C) compared to during the daytime (0.84 °C), with variations observed across seasons. The seasonal disparity of SUHII in Xinjiang was more significant during the daytime (3.91 °C) compared to nighttime (0.39 °C), with daytime and nighttime SUHIIs decreasing from summer to winter. The study also highlights that the city size, elevation, vegetation cover, urban form, and socio-economic factors (GDP and population density) emerged as key drivers, with the GDP exerting the strongest influence on SUHIIs in cities across Xinjiang. To mitigate the UHI effects, measures like urban environment enhancement by improving surface conditions, blue–green space development, landscape optimization, and economic strategy adjustments are recommended. Full article

The Yellow River Basin (YB) acts as a key barrier to ecological security and is an important experimental region for high-quality development in China. There is a growing demand to assess the ecological status in order to promote the sustainable development of the YB. The eco-environmental quality (EEQ) of the YB was assessed at both the regional and provincial scales utilizing the remote sensing-based ecological index (RSEI) with Landsat images from 2000 to 2020. Then, the Theil–Sen (T-S) estimator and Mann–Kendall (M-K) test were utilized to evaluate its variation trend. Next, the optimal parameter-based geodetector (OPGD) model was used to examine the drivers influencing the EEQ in the YB. Finally, the geographically weighted regression (GWR) model was utilized to further explore the responses of the drivers to RSEI changes. The results suggest that (1) a lower RSEI value was found in the north, while a higher RSEI value was found in the south of the YB. Sichuan (SC) and Inner Mongolia (IM) had the highest and the lowest EEQ, respectively, among the YB provinces. (2) Throughout the research period, the EEQ of the YB improved, whereas it deteriorated in both Henan (HA) and Shandong (SD) provinces. (3) The soil-available water content (AWC), annual precipitation (PRE), and distance from impervious surfaces (IMD) were the main factors affecting the spatial differentiation of RSEI in the YB. (4) The influence of meteorological factors (PRE and TMP) on RSEI changes was greater than that of IMD, and the influence of IMD on RSEI changes showed a significant increasing trend. The research results provide valuable information for application in local ecological construction and regional development planning. Full article

In the context of global urbanization, traditional villages have garnered increasing scholarly interest due to their role in preserving rich ethnic cultures and their potential contributions to cultural heritage. Existing literature has predominantly attributed the spatial heterogeneity of traditional villages to natural, environmental, and economic factors. However, cultural elements, which are equally crucial to the inheritance and continuation of traditional villages, are rather deficient in current research. By establishing a tripartite framework encompassing “natural environment—space economy—social culture” elements, this article first employs relevant geographic spatial analysis to examine the overall distribution patterns of Chinese traditional villages. Subsequently, it utilizes the Optimal Parameter-based GeoDetector model to assess the maximum impact of single factors and interactions among factors on the spatial heterogeneity of Chinese traditional villages. The paper then integrates spatial production theory to reveal the mechanisms underlying the interactions among these tripartite elements. The research findings indicate that cultural factors exert the most substantial influence on the spatial distribution of traditional Chinese villages, in contrast to previous research records that suggested natural elements had the greatest impact. Additionally, population and genealogy emerge as the two most critical factors, with their interaction having the most significant effect on the spatial pattern of Chinese traditional villages (q = 0.82663). Finally, we put forward regional-level recommendations for the preservation of traditional villages. Overall, our work can not only provide valuable insights for global research on traditional villages in developing countries based on traditional agriculture but also offer recommendations for the preservation of traditional villages in China. Full article

Out-of-hospital cardiac arrest (OHCA) causes a high mortality rate each year, which is a threat to human well-being and health. An automated external defibrillator (AED) is an effective device for heart attack-related diseases and is a panacea to save OHCA. Most relevant literature focuses on the spatial distribution, accessibility, and configuration optimization of AED devices, which all belong to the characteristics of the spatial distribution of AED devices. Still, there is a lack of discussion on related potential influencing factors. In addition, analysis of AED facilities involving multiple city comparisons is less considered. In this study, data on AED facilities in two major cities in China were obtained through the WeChat applet. Then, the AED equity at the city and block scales and its socioeconomic factors were analyzed using the Gini coefficient, Lorenz curve, and optimal parameters-based geo-graphical detector (OPGD) model. Results show that the number of AEDs in Shenzhen was about eight-times that of in Guangzhou. The distribution of AEDs in Shenzhen was more equitable with a global Gini of 0.347, higher than that in Guangzhou with a global Gini of 0.504. As for the determinants of AED equity, residential density was the most significant determinant in both Guangzhou and Shenzhen from the perspective of individual effects on AED equity. Differently, due to the aging population in Guangzhou, the proportion of the elderly in blocks was influential to local AED equity. The local economic development level was crucial to local AED equity in Shenzhen. The results of the interaction detector model illustrate that relatively equitable AED distributions were found in the high-density residential areas with a balance of employment and housing, high-aging residential areas, and high-mobility residential areas in Guangzhou. The area with a high level of local economic development, dense population, and large mobility was the area with a relatively equitable distribution of AEDs in Shenzhen. The results of this paper are conducive to understanding the equity of AEDs and its socio-economic determinants, providing scientific reference for the optimization and management of AEDs. Full article

Land use/cover change (LULCC) is an integral part of global environmental change and is influenced by both natural and socioeconomic factors. This study aims to comprehensively analyze land use and land cover (LULC) in Kwazulu-Natal and Mpumalanga provinces in eastern South Africa from 1995 to 2020 and to identify the driving force behind LULCC. Utilizing Landsat series satellite imagery as a data source and based on the Google Earth Engine (GEE) platform and eCognition software 9.0, two different classification methods, pixel-based classification and object-oriented classification, were adopted to gather LULC data every five years. The spatiotemporal characteristics of the data were then analyzed. Using an optimal parameter-based geodetector (OPGD), this study explored the driving factors of LULCC in this region. The results show the following: (1) Of the two classification methods examined, the object-oriented classification had higher accuracy, with an overall accuracy of 80–90%. The pixel-based classification had lower accuracy, with an overall accuracy of 62.33–72.14%. (2) From 1995 to 2020, the area of farmland in the study area showed a fluctuating increase, while the areas of forest and grassland declined annually. The area of constructed land increased annually, whereas the areas of water and unused land fluctuated over time. (3) Socioeconomic factors generally had greater explanatory power than natural factors, with population growth and economic development being the main drivers of LULCC in the region. This study provides a reliable scientific basis for the formulation of sustainable land resource development strategies in the area, as well as for the management and implementation of urban and rural planning, ecological protection, and environmental governance by relevant departments. Full article