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With the growing influences of anthropogenic activities and climatic change, the problem concerning the vulnerability of the Water-Energy-Food-Carbon (WEFC) system in the Yellow River Basin is prominent. Using the VSD framework, the WEFC system vulnerability evaluation index system was constructed with 60 cities in the Yellow River Basin as the samples, and the WEFC system vulnerability of each city was measured from 2010 to 2019. Kernel density estimation, Theil index, and spatial correlation analysis were employed to investigate spatio-temporal evolution characteristics. Geodetector was utilized to analyze the driving factors of WEFC system vulnerability. The results demonstrate that: (1) The vulnerability of the WEFC system in the Yellow River Basin tends to decrease, with a spatial pattern of "low in the middle and high on both sides"; the vulnerability is largest in the upper and lower reaches, while smallest in the middle reaches. (2) The spatial difference in vulnerability narrows in the middle and lower reaches, while expanding in the upper reaches. The differences among the three major regions mainly originate from within the region, with the upper reaches having the largest difference and contribution; the vulnerability is featured with a significant spatial correlation, with the upper and lower reaches cities mostly displaying a "high-high" agglomeration and the middle reaches mainly showing a "low-low" one. (3) Factors, including the carbon and ecological carrying capacity coefficient, water resource development and utilization rate, and urbanization rate, mainly influence the WEFC system vulnerability; the spatial heterogeneity of core drivers at the regional scale is obvious, with the upper reaches regions being more strongly influenced by factors of the water resources system, while the middle and lower reaches regions are more sensitive to factors concerning industrial pollution of the energy subsystem. The explanatory power of carbon ecological carrying capacity reaches its peak in the middle reaches. The interaction of factors increases the strength of the impact on vulnerability. This study provides decision support and policy suggestions for achieving a balanced and coordinated development of water resource utilization, energy development, food production, and carbon cycle system in the Yellow River Basin. Investigating WEFC system vulnerability to support SDG 11 provided valuable insights and policy strategies for building cities that are inclusive, secure, resource-efficient, and resilient in the face of climate change and disaster risks. [ABSTRACT FROM AUTHOR]

The variation of land surface temperature (LST) has a vital impact on the energy balance of the land surface process and the ecosystem stability. Based on MDO11C3, we mainly used regression analysis, GIS spatial analysis, correlation analysis, and center-of -gravity model, to analyze the LST variation and its spatiotemporal differentiation in China from 2001 to 2020. Furthermore, we employed the Geodetector to identify the dominant factors contributing to LST variation in 38 eco-geographic zones of China and investigate the underlying causes of its pattern. The results indicate the following: (1) From 2001 to 2020, the LST climate average in China is 9.6°C, with a general pattern of higher temperatures in the southeast and northwest regions, lower temperatures in the northeast and Qinghai-Tibet Plateau, and higher temperatures in plains compared to lower temperatures in mountainous areas. Generally, LST has a significant negative correlation with elevation, with a correlation coefficient of −0.66. China's First Ladder has the most pronounced negative correlation, with a correlation coefficient of −0.76 and the lapse rate of LST is 0.57°C/100 m. (2) The change rate of LST in China during the study is 0.21°C/10 a, and the warming area accounts for 78%, demonstrating the overall spatial pattern a "multi-core warming and axial cooling". (3) LST's variation exhibits prominent seasonal characteristics in the whole country. The spatial distribution of average value in winter and summer differs significantly from other seasons and shows more noticeable fluctuations. The centroid trajectory of the seasonal warming/cooling area is close to a loop shape and displays corresponding seasonal reverse movement. Cooling areas exhibit more substantial centroid movement, indicating greater regional variation and seasonal variability. (4) China's LST variation is driven by both natural influences and human activities, of which natural factors contribute more, with sunshine duration and altitude being key factors. The boundary trend between the two dominant type areas is highly consistent with the "Heihe-Tengchong Line". The eastern region is mostly dominated by human activity in conjunction with terrain factors, while the western region is predominantly influenced by natural factors, which enhance/weaken the change range of LST through mutual coupling with climate, terrain, vegetation, and other factors. This study offers valuable scientific references for addressing climate change, analyzing surface environmental patterns, and protecting the ecological environment. [ABSTRACT FROM AUTHOR]

Significant changes to the world's climate over the past few decades have had an impact on the development of plants. Vegetation in high latitude regions, where the ecosystems are fragile, is susceptible to climate change. It is possible to better understand vegetation's phenological response to climate change by examining these areas. Traditional studies have mainly investigated how a single meteorological factor affects changes in vegetation phenology through linear correlation analysis, which is insufficient for quantitatively revealing the effects of various climate factor interactions on changes in vegetation phenology. We used the asymmetric Gaussian method to fit the normalized difference vegetation index (NDVI) curve and then used the dynamic threshold method to extract the phenological parameters, including the start of the season (SOS), end of the season (EOS), and length of the season (LOS), of the vegetation in this study area in the Tundra-Tagar transitional zone in eastern and western Siberia from 2000 to 2017. The monthly temperature and precipitation data used in this study were obtained from the climate research unit (CRU) meteorological dataset. The degrees to which the changes in temperature and precipitation in the various months and their interactions affected the changes in the three phenological parameters were determined using the GeoDetector, and the results were explicable. The findings demonstrate that the EOS was more susceptible to climate change than the SOS. The vegetation phenology shift was best explained by the climate in March, April, and September, and the combined effect of the temperature and precipitation had a greater impact on the change in the vegetation phenology compared with the effects of the individual climate conditions. The results quantitatively show the degree of interaction between the variations in temperature and precipitation and their effects on the changes in the different phenological parameters in the various months. Understanding how various climatic variations effect phenology changes in plants at different times may be more intuitive. This research provides as a foundation for research on how global climate change affects ecosystems and the global carbon cycle. [ABSTRACT FROM AUTHOR]

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Slope‐cultivated land (SCL) is essential for food security and ecological protection in Southwest China's karst mountainous areas (KMA). Constructing a unified conceptual framework to study SCL's spatiotemporal evolution characteristics thoroughly and driving mechanisms can comprehensively reveal the development of regional human–land relations and sustainable land use. Therefore, this article first constructs a conceptual framework for the spatiotemporal evolution of SCL in the KMA of Southwest China and carries out a case study in Puding County, a typical karst mountainous area, based on high‐resolution remote sensing images from 2004 to 2020 and the digital elevation model (DEM), to analyze the spatiotemporal evolution characteristics of SCL in different topographic gradients and introduce the Geodetector further to explore the driving factors of SCL evolution. The results show that the SCL is evolving toward fragmentation, driven by socio‐economic factors. The active policy of the state encourages to abandon steep SCL with poor productivity in the KMA, and the abandonment of SCL in high altitude steep slope areas into woodland and grassland brings remarkable ecological benefits, while the evolution of SCL in low altitude gentle slope areas toward economic fruit forest generates significant obvious ecological‐economic win‐win benefits. The evolution of SCL in southwestern karst mountains has promoted regional ecological restoration and made an important contribution to the greening of the world by reducing land degradation (karstic rocky desertification, KRD). The present research results can provide ideas for the utilization, functional transformation, and ecologically sustainable development of similar mountainous SCL. [ABSTRACT FROM AUTHOR]

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley's K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region. [ABSTRACT FROM AUTHOR]

The main challenge in protecting ecosystems and improving the supply of ecosystem services is to quantify the ecological services value (ESV). However, the detailed spatiotemporal changes, sensitivity, spatial autocorrelation, and driving mechanisms of ESV are not clear in rapidly developing regions, particularly subsidence, floods, landslides, and the rapid urban development of Anhui province, China. In this paper, the ecological service value of Anhui Province in the past 30 years was calculated using the improved equivalent factor assessment method from satellite remote sensing such as Landsat. The spatiotemporal evolution characteristics of ESV were analyzed and the driving mechanism of ESV changes was studied using Geodetector. Finally, The GeoSOS-FLUS model was selected to predict the ecosystem service value until 2030 with three scenarios: business as usual (BAU), ecological protection (EP), and cultivated land protection (CLP). The main results were obtained: (1) the ESV in Anhui Province continued to decrease by 2.045 billion yuan (−6.03%) from 1990 to 2020. The top two contributors were the forest land, followed by water area. (2) The global Moran's I of ESV at the landform subdivision, county, town, and grid scales in Anhui Province were −0.157, 0.321, 0.357 and 0.759, respectively. (3) The order of influence degree of driving factors was: precipitation (F4), distance to intercity road (F9), net primary productivity, NPP (F6), distance to urban road (F8), population (F13), temperature (F5), aspect (F3), distance to settlement (F11), slope (F2), elevation (F1), GDP (F14), distance to water (F12), distance to railway (F10), and soil erosion (F7). (4) In 2030, the simulated ESV under the three scenarios will decrease to varying degrees. Compared with 2020, the ESV of the three scenarios will decrease successively as follows: BAU (−1.358 billion yuan), EP (−0.248 billion yuan), and CLP (−1.139 billion yuan). [ABSTRACT FROM AUTHOR]

Analyzing vegetation cover provides a basis for detecting ecological and environmental health in urban areas. We analyzed the temporal and spatial changes in vegetation cover using NDVI data from the central Yunnan urban agglomeration (CYUA). The dimidiate pixel model (DPM) and intensity analysis were used to study changes at three levels: time intervals, category, and transition. Analysis of time series data from 1990–2020 using the Theil–Sen Median with Mann–Kendal test identified the overall trends. Geodetector explored the relationship between natural and human factors in vegetation cover change. The CYUA's vegetation cover gradually decreases from west to east and south to north, with middle–high and high vegetation occupying over 55%. During 1990–2020, significant improvement was observed in the east and north regions, with an increase of 22.49%. The anthropogenic core area showed severe degradation with nearly 1.56% coverage. The transformation intensity of middle vegetation coverage was dominant from 1990–2010 but was replaced by middle–high vegetation coverage from 2010–2020. Meanwhile, high vegetation coverage became the most prominent gains target, and the conversion of middle–high to high vegetation showed a system tendency to exceed the average in absolute number and relative intensity. Spatial and temporal differences in vegetation cover were mostly affected by land cover (q = 0.4726, p < 0.001), and the most influential topographic factor was the slope (q = 0.1491, p < 0.001). The impact of human activities has increased to 16%, double that of 2000. The CYUA's vegetation cover improved more than it degraded, but required site-specific forest management due to human activities. [ABSTRACT FROM AUTHOR]

Particulate organic carbon (POC) is an essential component of the carbon pump within marine organisms. Exploring estimation methods for POC holds substantial significance for understanding the marine carbon cycle. In this study, we investigated the spatial heterogeneity of 30 factors and POC concentrations using geodetector to account for nonlinearity, diversity, and complexity. Ultimately, 20 factors including sea surface temperature, sea surface salinity, and chlorophyll-a were selected as modeling variables. Six machine learning algorithms, RF exhibits the strongest performance, with a root mean square error of 0.11 [log(mg/m3)] and an average percentage deviation of 2.73%. Global annual average sea surface POC concentrations were estimated for 2007 and compared to NASA's POC product. The outcomes indicate that the RF model-based estimation method displays enhanced accuracy in estimating POC concentrations within intricate coastal environments, while the backpropagation neural network performed better in estimating POC concentrations in open ocean areas. Leveraging the RF model, global sea surface POC concentrations were estimated for the years 2007 through 2016, enabling a spatiotemporal analysis. The analysis unveils heightened POC concentrations in coastal regions and lower levels in open ocean areas. Furthermore, POC concentrations were greater in high-latitude regions compared to mid and low latitude counterparts. In conclusion, the global sea surface POC product in this study exhibits heightened spatial resolution and improved data completeness in contrast to other products. It enhances the accuracy of conventional POC estimation methods, particularly within coastal regions. [ABSTRACT FROM AUTHOR]

Understanding the net ecosystem productivity (NEP) is essential for understanding ecosystem functioning and the global carbon cycle. Utilizing meteorological and The Advanced Very High Resolution Radiometer (AVHRR) remote sensing data, this study employed the Carnegie–Ames–Stanford Approach (CASA) and the Geostatistical Model of Soil Respiration (GSMSR) to map a monthly vegetation NEP in China from 1982 to 2020. Then, we examined the spatiotemporal trends of NEP and identified the drivers of NEP changes using the Geodetector model. The mean NEP over the 39-year period amounted to 265.38 gC·m−2. Additionally, the average annual carbon sequestration amounted to 1.89 PgC, indicating a large carbon sink effect. From 1982 to 2020, there was a general fluctuating increasing trend observed in the annual mean NEP, exhibiting an overall average growth rate of 4.69 gC·m−2·a−1. The analysis revealed that the majority of the vegetation region in China, accounting for 93.45% of the entirety, exhibited increasing trends in NEP. According to the Geodetector analysis, precipitation change rate, solar radiation change rate, and altitude were the key driving factors in NEP change rate. Furthermore, the interaction between the precipitation change rate and altitude demonstrated the most significant effect. [ABSTRACT FROM AUTHOR]

Identifying the current pollution status, future trends, and driving factors is a crucial prerequisite for protecting and managing soil environmental quality in light of the challenging soil heavy metal pollution situation. In this study, a typical industrial city in southeast China was selected as the study area. The environmental capacity of 12 heavy metals was first measured for the evaluation of soil heavy metal pollution, and then spatiotemporal change was simulated using indicator Kriging to warn the pollution risk. Finally, the driving factors of heavy metal pollution were analyzed using random forest and geographical detectors. The results revealed that the average environmental capacity index was 0.768, at a medium capacity level. The environmental capacity of each heavy metal showed a decreasing trend from 2016 to 2035. In 2035, the dynamic environmental capacity of each heavy metal element will have decreased by more than 75% if no preventative and remedial measures are taken. The distance from rivers, fine particulate matter, organic matter content, population density, and distance from industrial enterprises were the main factors contributing to heavy metal pollution in soil. The interaction effect of two environmental factors will mostly lead to the intensification of pollution. Based on the results, this study discussed strategies for controlling and remediating the polluted land. These findings could support the source investigation and early warning of soil heavy metal pollution, which are crucial to manag soil pollution and alleviating environmental quality issues. [ABSTRACT FROM AUTHOR]

This paper takes the Chaohu Lake Basin as the study area, constructs a remote sensing ecological index (RSEI) based on Landsat TM/OLI series remote sensing data from 1999 to 2020 through the Google Earth Engine cloud computing platform, and uses spatial autocorrelation and geodetectors and other methods to conduct large-scale, long time-series dynamic monitoring and analysis of the ecological environmental quality of the Chaohu Lake Basin. The results show the following: (1) the mean value of the RSEI in the study area increased from 0.68 in 1999 to 0.72 in 2020, showing an overall improvement trend, and the ecological environment of the study area was mainly excellent and good. (2) The global Moran's I index in the study area was greater than 0. The ecological environmental quality of the Chaohu Lake Basin showed a clustering trend in terms of global autocorrelation with significant positive spatial correlation, and the "high-high" clustering and "low-low" clustering characteristics were most obvious from 1999 to 2020. (3) The ecological environment in the study area has been influenced by multiple factors, among which human factors had a greater impact on the ecological environment of Chaohu Lake Basin in 2008, which led to a decline in ecological environment quality. [ABSTRACT FROM AUTHOR]

Benggang is one of the most severe types of soil erosion in the granite areas of southern China, due to the large erosion, strong explosiveness, and fast development speed. Accurate assessment of susceptibility is of great significance for the prevention and control of Benggang damages. In this study, different combinations of environmental factors and negative sample selection strategies were explored the impact on the assessment of Benggang susceptibility. A case study was taken from the granite area of Xingguo County, Ganzhou City, Jiangxi Province, China. A systematic detection was implemented to determine the explanatory power of 17 environmental factors on the development of Benggang using a GeoDetector (GD). According to the cumulative explanatory power percentage, 56.89%, 78.55%, 92.88%, and 100.00% were selected as the environmental factor combinations, corresponding to 4, 7, 10, and 17 environmental factors, respectively. Single random undersampling (SRU) was used to construct a negative sample dataset equal to positive samples using frequency ratio (FR). The susceptibility was calculated in the study area using automatic landslide susceptibility analysis (ALSA). Negative sample data was selected equal to positive samples in the low and extremely low susceptibility areas. The sample dataset was divided into the training and testing datasets in a 7:3 ratio. The training dataset was used to train the random forest (RF) model, and then the trained RF model was to calculate the testing dataset. The prediction accuracy of the model was evaluated to calculate the Benggang susceptibility using the receiver operating characteristic (ROC). The results show that: 1) The model accuracy under the three negative sample selection strategies decreased first and then increased with the increase of the number of factors. The area under curve (AUC) values of the model considering four environmental factors were 0.729, 0.909, and 0.909, respectively. The model accuracy was the lowest at 7 environmental factors, with the AUC values of 0.711, 0.869, and 0.893, respectively. The AUC values of the 10 environmental factors were 0.745, 0.942, and 0.919, respectively. The model accuracy was highest at 17 environmental factors, while the AUC values were 0.755, 0.947, and 0.929, respectively. There was the nonlinear correlation between model accuracy and cumulative explanatory power percentage. The difference was only 0.020-0.038, although the accuracy of the model for 4 environmental factors was lower than that of 17 environmental factors. Therefore, the relatively ideal accuracy was achieved when considering the main controlling environmental factors; 2) The improved frequency ratio method was significantly improved the accuracy of the model. When the number of environmental factors was 4, the AUC values of FR and ALSA were both 0.909, and the negative samples selected by FR and ALSA were the most reasonable; When the number of environmental factors was 7, the AUC value of ALSA was 0.893, and the negative sample selected by ALSA was the most reasonable; When the environmental factors were 10 and 17, the AUC values of the FR were 0.942 and 0.947, respectively. In summary, the FR can be expected to select the most reasonable negative samples; 3) The acerage rainfall erosivity was closely related to the development of Benggang. Particularly, the acerage rainfall erosivity was ranged from 9 133.24 to 9 981.49 MJ∙mm/(hm²∙h∙a) within the scope of the study area. The majority of high and extremely high susceptibility areas were distributed in the southwest of the study area, whereas, a small number of extremely high susceptibility areas were distributed in the central and eastern parts of Xingguo County, and the majority of extremely low susceptibility areas were distributed in the northern and eastern. This study has explored the impact of different combinations of environmental factors and negative sample selection strategies on the susceptibility assessment of Benggang. The finding can provide the scientific basis for the disaster prevention and reduction in granite areas. [ABSTRACT FROM AUTHOR]

Climate change, population growth, and socio-economic transformations present multifaceted challenges to the water resource systems in the four major river basins of Henan Province. Consequently, to gain a comprehensive understanding of water security within the study area, a quantitative assessment of water resource vulnerability (WRV) is imperative. In this investigation, the vulnerability scoping diagram (VSD) model serves as the analytical framework, subdividing vulnerability into three critical subsystems: exposure, sensitivity, and adaptive capacity. Utilizing a comprehensive evaluation index system, this study assesses WRV in Henan Province's four primary watersheds. Temporal variations and spatial patterns of WRV from 2000 to 2020 are systematically examined through the standard deviational ellipse (SDE) and GeoDetector methods. The findings indicate that vulnerability within these river basins is shaped by the interactions among exposure, sensitivity, and adaptive capacity. Moreover, exposure and sensitivity are found to be more influential than adaptive capacity. Although there are indications of future improvements in the system's resilience against water resource vulnerability, the overarching scenario remains precarious, particularly within the Hai and Yellow River basins. Considering the diverse characteristics of the watershed, it is recommended that adaptive management strategies be implemented. This study enhances the understanding of WRV dynamics in Henan Province, thereby aiding more effective decision making in sustainable water resource management. [ABSTRACT FROM AUTHOR]

Given China's status as one of the most water-scarce countries globally, its rapid development of urbanization and sustained economic growth have led to increasing pressure on the urban water supply. Water pricing is also receiving increasing attention as an important tool for water resource management. This study analyzes the spatial and temporal characteristics of domestic water prices in China and their drivers. To this end, domestic water price data from 285 cities in China were collected. Spatial statistical analysis and geodetector were used to examine the spatial distribution and temporal patterns of domestic water prices from 2007 to 2020, as well as to identify the primary factors influencing these prices. The following results are noted: (1) The national average domestic water price has increased from 2 RMB/m3 to 3.12 RMB/m3, where the northeast and eastern regions have higher prices than the national average, while the central and western regions have lower prices. (2) The spatial distribution of urban domestic water prices presents clear differences characteristic of north–south and spatial agglomeration effects; the high-value area of domestic water prices is mainly concentrated in Beijing–Tianjin–Hebei. (3) On a national and regional scale, the price of domestic water is closely related to economic development, water resources, and resident's income level. Furthermore, this study revealed that the interaction between pairwise factors played a more significant role in influencing domestic water prices compared with the individual impact of each factor. This finding contributes to a deeper understanding of the spatiotemporal heterogeneity in domestic water pricing and offers valuable insights and guidance for water pricing reform in China. [ABSTRACT FROM AUTHOR]

The carbon balance of the global ecosystems is significantly influenced by the soil organic carbon (SOC) pool in China's terrestrial ecosystems. Nevertheless, the current understanding of the main controlling factors of SOC in different ecosystems and their discrepancies is limited. The goal of this research was to better understand the human and environmental variables affecting SOC in China's terrestrial ecosystems. We designed a conceptual framework using 2674 samples collected from four ecosystems (grasslands, shrublands, wetlands, and croplands) in China during the 2000–2014 period, combining geodetector and multiple regression (MR) approaches to investigate the effects of environmental conditions, human activity, and their interplay on surface SOC (0–20 cm). Results showed that there were large discrepancies in the strength of influencing factors among different ecosystems. Total nitrogen (TN), mean annual temperature (MAT), and bulk density (BD) were the major factors influencing SOC in grasslands. BD, TN, and pH dominated in shrublands. For wetlands, SOC stocks were primarily attributed to maximum temperature (TMMX), MAT, and potential evapotranspiration (PET). Croplands are predominantly controlled by minimum temperature (TMMN), MAT, and TN. These results highlight that natural factors, particularly climatic and soil characteristics, were the dominant factors controlling SOC stocks in China's terrestrial ecosystems. This work also highlights that the interaction of two influencing factors, especially, pairs of soil characteristics factors, pairs of climate and soil characteristics factors, can well explain the drivers of SOC on the surface soil in China. Our study emphasizes the spatial heterogeneity of the factors that influence SOC in terrestrial ecosystems, enhancing knowledge of SOC at the national level, and providing the guideline for devising better policy to improve C sequestration and mitigate climate changes. [ABSTRACT FROM AUTHOR]

Elucidating material sources and investigating the impact of various environmental factors on material source accumulation are important for the environmental restoration of the Qinghai–Tibet Plateau. This study was conducted within the Borhan Buda Mountain Range of Dulan County, Qinghai Province, China, where 6274 surface soil samples were collected. The geoaccumulation index was employed to assess the levels of heavy metals, including As, Cr, Cu, Hg, Ni, Pb, Sb, Sn, and Zn, in the soil. A comprehensive approach combining principal component analysis (PCA) and geodetector analysis was employed to assess the spatial variation in soil heavy metal origins and the factors that influence them. The findings indicate that the mean concentrations of Pb exceed the background values for the soil in Qinghai Province, with Hg exhibiting low pollution, whereas the other elements showed no contamination. PCA indicated that the soil elements in the Borhan Buda Mountain Range were influenced by four sources, all attributed to the geological background. Geodetector analysis of the factor contributions suggested that geological factors had the strongest explanatory power for the four material sources. Furthermore, the risk detection results demonstrated significant variations in the material source contributions among most subregions when considering three environmental factors in pairs. Interaction detection revealed that the combined influence of two environmental factors on material source contributions was greater than that of the individual factors. Additionally, ecological detection demonstrated significant differences in material source contributions one, two, and three between land cover types and geological backgrounds, whereas no significant differences were observed in material source four between land cover types and geological backgrounds. This study offers valuable insights into the sources of soil elements in Dulan County and the influence of environmental factors on these sources, thereby contributing an essential knowledge base for the protection and management of soil heavy metals in the region. [ABSTRACT FROM AUTHOR]

Urban green spaces provide very important environmental and social services. Their dynamic changes are driven by a combination of natural and socioeconomic factors. However, the coupling mechanism of these factors has not been systematically studied. In this study, we examined dynamic characteristics of green space in Guangzhou from different zoning perspectives and explored the regional heterogeneity of the individual and interactive effects of driving factors using the geographic detector. The results showed that (1) from 2000 to 2020, the annual change rate of green space area in the central area was more apparent than that in the suburban areas. The fragmentation of green space in the near suburbs had increased. (2) Changes in green space were influenced by the combination of topography, climate, and socioeconomic development. From 2010 to 2020, the expansion of built-up land and GDP growth gradually had a substantial effect on the change in green space in the central area and the near suburbs. (3) The q-values of the interaction detections of the geodetector showed that the explanatory power of most factor interactions exceeded that of individual factors. Green space in the central area was primarily influenced by the interaction of economic and built-up land expansion. In contrast, green space in the near suburbs was mainly influenced by the interaction of urban expansion and topography and climate. Green space in the far suburbs was mainly affected by climate factors and human activity intensity. The results and methods of this study can provide decision support for the zoning planning of urban green space system in other cities or regions. [ABSTRACT FROM AUTHOR]

The protection and development of traditional villages are crucial for improving the human settlement suitability (HSS). The paper takes 703 traditional villages in Hunan Province as the research object and establishes the HSS evaluation system by using the pressure-state-response model. Then this paper introduces the vector autoregressive model to explore the interactions and contributions within the three major subsystems. Finally, this paper adopts Geodetector model and GTWR model to study the external driving effects and temporal-spatial influence mechanisms. The main findings of this paper are as follows. First, the overall trend of the composite index of traditional villages is upward. Its spatial pattern transitions from a low index in the northwest to a medium index in the central region and a high index in the southeast. Second, the state system becomes the main driver of the response system change and it is highly influenced by the pressure system. Distance from medical facilities, Distance from educational institutions, Distance from the intangible cultural heritage sites, and Degree of relief are the four most important driving factors affecting the HSS in Hunan Province. At the same time, Distance to medical facilities and Distance to intangible cultural heritage sites have a positive impact, while Distance to educational institutions and Degree of relief have a negative impact. Fourly, these four factors have a significant spatiotemporal impact on the HSS in the Xiangxi region. This paper provides a scientific basis for the sustainable development and conservation of traditional villages in Hunan from multiple perspectives.

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of “overall dispersion, partial concentration”, with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

The balance between ecosystem service supply–demand and landscape ecological risk are two crucial aspects influencing the sustainable development of regional ecosystems. Current evaluations ignore these aspects and lead to partial landscape planning in urban areas. As a result, a framework was built for regional planning in urban areas based on multidimensional evaluations of ecosystems for high-efficiency land use planning. First, to determine the ecosystem service balance in regional scale, the supply and demand of five ecosystem services were quantified—biodiversity conservation, carbon sequestration, water conservation, food production, and landscape recreation. Second, landscape ecological risks were identified using the “natural–social–landscape” research framework combined with spatial principal component analysis. Third, spatial clustering analysis of ecosystem service supply–demand ratios and landscape ecological risks was then performed through spatial autocorrelation, which aimed to identify areas for protection and restoration priority. Finally, GeoDetector was used to determine the main factors influencing ecosystem service balance and landscape ecological risks and develop targeted planning measures. The results revealed an imbalance in total ecosystem service supply–demand in Beijing's plain area, coupled with a high level of landscape ecological risk. The combined ecosystem service supply–demand ratios exhibited a significant negative correlation and aggregation with landscape ecological risk, and the significant area accounted for approximately 31.9% of the total area. Priority areas for protection and restoration accounted for 10.39% and 19.94%, respectively, of the total regional area. Land use, distance to settlements, and vegetation cover were the primary environmental variables influencing the distributions of the combined ecosystem service supply–demand ratios and landscape ecological risk. So, this study emphasizes the need to strengthen intensive land use and prevent the uncontrolled expansion of urban land and settlements. It is crucial to enhance urban green infrastructure construction, promote the development of biodiversity-friendly agriculture, and implement measures such as afforestation restoration to increase regional vegetation cover. Overall, by integrating a framework of ecosystem services and landscape ecological risk, a comprehensive scientific reference can be provided for the efficient land use and sustainable development planning in urban areas.

[Objective] The distribution pattern of ecosystem service supply and demand and their main driving factors were analyzed in order to provide a scientific reference for regional ecological protection practices and ecological environmental improvement. [Methods] The supply and demand of five ecosystem services in Hu’nan Province from 2000 to 2020 were assessed, including food production, carbon sequestration, water conservation, soil conservation, and habitat quality by use of the InVEST model, ArcGIS, and other tools to analyze their spatial and temporal evolution patterns and spatial matching relationships, and to determine the driving factors affecting the supply-demand ratios of ecosystem services at the grid scale and county scale using an optimal parameter geodetector model. [Results] ① The total supply of ecosystem services in Hu’nan Province was greater than the total demand. The supply of carbon sequestration services and habitat quality services as well as the demand for water conservation services decreased over time, while the supply and demand of other ecosystem services increased. ② The distribution of supply and demand of various ecosystem services in Hu’nan Province, the clustering characteristics of supply-demand ratios, and the spatial heterogeneity of the spatial matching of supply and demand were obvious. ③ There were some differences in the dominant factors of ecosystem service supply-demand ratios between the two scales, and the influence of socio-economic factors was more significant at the county scale than at the grid scale. The interaction between land use factors and other factors explained the spatial variation of ecosystem service supply-demand ratios more strongly. [Conclusion] The contradiction between supply and demand of ecosystem services at different scales was exceptional, and government agencies at all levels should develop more precise ecological management practices in the future, and use them as a basis to promote the rational use of regional land and to ultimately achieve the goal of regional green development.

[Objective] The change characteristics and influencing factors of vegetation coverage and its spatial distribution in the Binchang mining area were analyzed, and the ecological status of the mining area was evaluated in order to provide a scientific reference and theoretical basis for reclamation and ecological restoration of the mining area. [Methods] Landsat Surface Reflectance Tier 1 data (30 m resolution) from 1986 to 2021 were obtained from the Google Earth Engine cloud platform. Based on a pixel binary model, the trend analysis method and F-test were used to quantitatively analyze the temporal and spatial variation of vegetation coverage in the Binchang mining area over many years. Geodetector was used to analyze the spatial heterogeneity of vegetation coverage. [Results] ① Vegetation coverage in the Binchang mining area increased from 1986 to 2021, and the overall growth trend was good (average growth rate of 0.64%/a). The average multi-year vegetation coverage in the study area was relatively high, with the area of medium coverage and greater accounting for 87.14% of the total area. The spatial distribution was characterized as “higher in the southeast and lower in the northwest”. ② In terms of the change trend of vegetation coverage, the study area was dominated by significantly improved areas, accounting for 56.65% of the total area. However, there were still some areas with significant vegetation degradation, mainly concentrated in the area near urban river roads. ③ The factors influencing vegetation coverage followed the order of slope > elevation > annual precipitation > GDP> population density > annual average temperature > vegetation type > soil type. The interaction between slope and annual precipitation had the strongest influence on the spatial heterogeneity of vegetation coverage. [Conclusion] From 1986 to 2021, the vegetation coverage in the Binchang mining area was good, showing a significant growth trend as a whole, and the vegetation improvement was obvious. The dominant factor affecting the spatial heterogeneity of vegetation coverage in the study area was slope.

[Objective] The spatial-temporal variation characteristics of water yield services in Harbin City, Heilongjiang Province from 2000 to 2020 were determined, and the driving mechanism of the spatial-temporal differentiation characteristics of Harbin City’s water yield was analyzed in order to provide a scientific basis for the management and sustainable development of water resources in Harbin City. [Methods] The study was conducted in Harbin City, a typical cold land city. The spatial-temporal variation characteristics of water yield in 2000, 2010, and 2020 were analyzed based on the InVEST model’s water yield module, and the spatial and temporal evolution of water yield services in Harbin City was determined by using a parameter optimal geographic detector. [Results] ① The water output of Harbin City from 2000 to 2020 increased from 9.68×109 m3 in 2000 to 2.23×1010 m3 in 2020. The spatial distribution pattern of water yield in different years was basically similar. The overall spatial distribution of water yield was characterized as “higher in the east and lower in the west”. ② There was a strong positive spatial correlation in the distribution of water yield in the study area, with the main types being low-low aggregation and high-high aggregation. The proportions of the two areas relative to the city’s total area showed a downward trend from 2000 to 2020. ③ The influence of each driving factor on water yield exhibited obvious spatial heterogeneity. Actual evapotranspiration and land use type were the main driving factors of the economic quality development zones. In the nature-dominated ecological barrier area, the comprehensive driving forces of actual evapotranspiration and land use type were far less than in the economic quality development area. [Conclusion] Water yield assessment was closely related to ecosystem services and ecological product value, and serves as the basis of water conservation research. The temporal and spatial changes of water production in Harbin City from 2000 to 2020 were significant, and meteorological and land use factors were the main driving factors.

The redistribution of cropland to areas of higher elevation in China has long affected agricultural development and could seriously threaten national food security. However, there is currently little research reported on this phenomenon, which may limit the improvement of cropland protection policies. To fill this gap, we analyzed the spatiotemporal characteristics and driving mechanisms of increased cropland elevation in China during the period 1980–2020. The average cropland elevation in China increased by 17.38 m from 1980 to 2020. The gravity center of the cropland area and average cropland elevation in China moved to the northwest by 81.00 km and 51.47 km, respectively. The amount of newly added cropland in eastern China was less than that in occupied regions; however, the average elevation of newly added cropland was greater than that of occupied cropland, though the opposite phenomenon was observed in western China. Slope, temperature, land-use intensity, population, economic density, and distance to main roads were the main factors affecting the redistribution of cropland to areas of higher elevation. The effects of these major driving factors exhibited significant spatial and temporal variations in China. This study has important implications for improving existing cropland protection policies and developing more effective cropland management systems in China.

The dried roots of Scutellaria baicalensis (S. baicalensis) have been widely used as a traditional medicine. Recently, climate change and human activities have caused the degeneration of its wildlife habitat. However, there is rare knowledge on the effect and interactive effect of different variables on the spatial heterogeneity of S. baicalensis and how the pattern of suitable cultivation area in China would shift in response to climate change. Based on the Geodetector and Habitat Suitability Index (HSI) method, we proposed an assessment model to identify the critical environmental variable(s) affecting the distribution of suitable cultivation area for S. baicalensis in China and to project its shift under climate change. The results showed that soil and mean annual temperature are two determining variables in its spatial heterogeneity in China. Compared to 1981–2010, future climate change may result in a decrease in its suitable area, and yet may result in an increase in the extremely suitable area (about 1.00–1.35 million km2). S. baicalensis in the southern and northwestern part of its current distribution and the southwestern part and small area of northern China may experience expansion during the 21st century, while S. baicalensis in southern China, the Huang-Huai-Hai plain, and the midwest of northwestern China may experience contractions. Meanwhile, climate warming is expected to shift its distribution northwest through an expansion at the northern (at least 84 km) and western (at least 62 km) boundary and contraction at the southern (at least 529 km) boundary, respectively. These results could provide valuable information to policy-makers for the conservation and scientific introduction of S. baicalensis.

Soil organic matter (SOM) is essential for assessing land quality and enhancing soil fertility. Understanding SOM spatial and temporal changes is crucial for sustainable soil management. This study investigates the spatial and temporal variations and influencing factors of SOM content in the Songnen Plain (SNP) and Sanjiang Plain (SJP) of Heilongjiang Province, China, based on high-precision SOC content data (RMSE = 4.84 g/kg−1, R2 = 0.75, RPIQ = 2.43) from 1984 to 2021, with geostatistical analyses and geodetector models. This study aims to quantitatively reveal and compare the long-term spatial and temporal characteristics of SOM changes and their influencing factors across these two plains. The results show that SOM content in both plains has decreased over the past 37 years. In the SNP, the average SOM decreased from 48.61 g/kg to 45.6 g/kg, representing a reduction of 3.01 g/kg, or a 6.10% decrease; SOM decreased spatially from northeast to southwest, covering 63.1% of the area. In the SJP, the average SOM declined from 48.41 g/kg to 44.31 g/kg, a decrease of 4.1 g/kg, or an 8.50% decrease; no pronounced spatial pattern was observed, but the declining area comprises 67.49%. Changing SOM hotspots are concentrated in southern SNP and central and northwestern SJP, showing clear heterogeneity across counties. Geodetector model analysis indicates annual mean temperature as the primary driver of SOM variations in SNP; while elevation is the main driver in SJP, the combined explanatory power of multiple factors surpasses individual ones. There is a positive correlation between SOM and temperature in SNP, and policy protection positively influences SOM in both plains. These findings provide insights into the differential protection of SOM in SNP and SJP.

Guizhou is a typical mountainous province and is also one of the lowland regions in China that has attracted a population influx. Here, using population density data from 2000 to 2020 as the basic dataset and the coefficient of variation method and standard deviation ellipse analysis, we investigated the spatial characteristics across different years. The results show: Firstly, Guizhou’s population has a distinct spatial distribution, characterized by a lower population density in the southeast and a higher density in the northwest as well as an increasing polarization of population concentration toward the centers of prefecture-level cities and provincial capitals. Fluctuations in population density resemble a central siphon effect, which is particularly pronounced in the provincial capital and show a significant gravitational pull. Secondly, the coefficient of variation in population density across Guizhou’s counties is spatially divided by Guiyang, showing higher values in the east and lower values in the west. Furthermore, the ellipse of the standard deviation of population density is gradually shrinking, indicating an increasingly concentrated population distribution. Thirdly, the explanatory power of the population and socio-economic systems on the population distribution in Guizhou is significantly greater than that of the natural systems. Population distribution and migration patterns have shifted from purely “economic driven” to coexisting with “economic and comfort-oriented” trends, and there is an urgent need to improve the comfort level of public services as a typical supply, in order to boost Guizhou’s population attraction.

In the face of persistent global environmental challenges, evaluating ecological environment quality and understanding its driving forces are crucial for maintaining the ecological balance and achieving sustainable development. Based on a case study of Changle District in Fuzhou, China, this research employed the Remote Sensing Ecological Index (RSEI) method to comprehensively assess ecological environment quality and analyze the impact of various driving factors from 2000 to 2020. Based on the GeoSOS-FLUS model, this study simulated and predicted land use classifications if maintaining the RSEI factors. The results reveal an overall improvement in the southern and southwestern regions, while the northwest and eastern areas face localized degradation. The RSEI index increased from 0.6333 in 2000 to 0.6625 in 2022, indicating significant ecological shifts over the years. The key driving factors identified include vegetation coverage, leaf area index, and aerosol levels. Industrial emissions and transportation activities notably affect air quality, while land use changes, particularly the expansion of construction land, play a critical role in altering ecological conditions. If maintaining the current RESI factors without any improvement, Changle District will experience continued urbanization and development, leading to an increase in built-up areas to 32.93% by 2030 at the expense of grasslands. This study offers valuable insights for policymakers and environmental managers to formulate targeted strategies aimed at reducing industrial and traffic emissions, optimizing land use planning, and enhancing ecological sustainability. The methodology and findings provide a robust framework for similar assessments in other rapidly urbanizing regions, contributing to the broader discourse on sustainable land use and ecological conservation. By advancing the understanding of ecological environment quality and its driving forces, this research supports the development of informed environmental protection and sustainable development strategies for coastal regions in developing countries globally.

Amid unprecedented challenges to protect arable land, China’s permanent capital farmland (PCF) has played a crucial role in grain production. However, a clear legal and physical boundary between PCF and arable land seems to be unable to stop the spread of non-grain production. To address it, an analysis framework for PCF non-grain production was developed to examine the state and drivers of village-scale PCF non-grain production based on the logical relationship between PCF and arable land in the rapid urbanization of Liyang. The results suggested that PCF comprised approximately 70% arable land and 30% adjustable land. Meanwhile, forest land and aquaculture ponds occupied over 25% of PCF, while nearly 20% of PCF is unsuitable for the resumption of crop cultivation. The transition state (scenario SR) offered a realistic representation of PCF non-grain production, with an average non-grain production of 48.88%. This is 14.00% lower than the current state (scenario SD) and 9.65% higher than the future state (scenario ST). Furthermore, PCF area and agricultural income per capita significantly encouraged PCF non-grain production, with explanatory powers of 51.60% and 42.40%, respectively. In contrast, urbanization rate (with an explanatory power of 35.30%) significantly discouraged it. Therefore, this paper proposed PCF redefinition, flexible PCF, and diversified economic incentives to mitigate PCF non-grain production at the village scale.

Analyzing the impacts of urban landscape patterns on the thermal environment has become one of the key research areas in addressing urban heat islands (UHIs) and improving the living environment. A case study was carried out in Fuzhou, Fujian Province of China, and bi-temporal Landsat imagery was selected to calculate land surface temperature (LST), percent impervious surface area (ISA), and fractional vegetation cover (FVC). The urban area was further divided into three concentric urban zones, ranging from the city center to the urban periphery, based on urban development densities. The spatial pattern of LST and its variance were analyzed and compared between different zones and different dates. The thermal environment contribution of different zones was also quantified to indicate the change in urban landscape patterns resulting from urban expansion in different zones. Furthermore, Geodetector was used to explore the single factors and interaction factors controlling the spatial patterns of LST in each zone. The results showed that (i) urban expansion primarily increased in Zone 2 and Zone 3, and the areal proportion of high and sub-high LST areas increased from 56.11% and 21.08% to 62.03% and 32.49% in Zone 2 and Zone 3, respectively, from 2004 to 2021; (ii) the heat effect contribution of Zones 2 and 3 reached from 75.16% in 2004 to 89.40% in 2021, indicating that the increase in ISA with >LSTmean was more pronounced in Zone 3 and Zone 2 during the period; (iii) the driving factors of LST spatial distribution were regionally different because of the different landscape patterns, and the explanatory power for the heterogeneity of LST in Zone 1 was weaker than in Zone 2 and Zone 3 in the study area; (iv) the interaction of different factors had a higher explanatory power in the spatial distribution of LST than a single factor in each zone because the distributions of land cover types are heterogeneous in urban areas. The results of this study can be used to improve urban planning for urban ecology and UHI mitigation.

The water quality of sources in the Huaihe River Basin significantly affects the lives and health of approximately 16.7% of China’s population. Identifying and quantifying pollution sources and risks is essential for effective water resource management. This study utilized Monte Carlo simulations and Geodetector to assess water quality and eutrophication, as well as to evaluate the sources of heavy metals and the associated health risks for both adults and children. The results showed that eutrophication of water sources in Huaihe River was severe, with an overall EI value of 37.92; 67.8% of the water sources were classified as mesotrophic and 32.2% classified as eutrophic. Water quality and eutrophication levels in the southern mountainous regions were better than those in the densely populated northern areas. Adults were found to have a higher carcinogenic risk than children, whereas children faced a higher noncarcinogenic risk than adults. Cr presented the highest carcinogenic risk, affecting more than 99.8% of both adults and children at levels above 1 × 10−6 but not exceeding 1 × 10−4. The noncarcinogenic risk from metals did not surpass a level of 1, except for Pb. As was primarily influenced by agricultural activities and transportation, whereas Cd, Cr, and Pb were mainly affected by industrial activities, particularly in local textile industries such as knitting and clothing manufacturing. The analysis demonstrated that the influence of anthropogenic factors on heavy metal distribution was significantly enhanced by indirect natural factors. For example, the explanatory power of Precipitation and Road Network Density on As was 0.362 and 0.189, respectively, whereas their interaction had an explanatory power as high as 0.673. This study indicates that the geodetector method is effective in elucidating the factors influencing heavy metal distribution in water, thereby providing valuable insights into pollution sources in global drinking water.

Soil organic matter (SOM) in cultivated land is vital for land quality and food security. This study examines SOM distribution and influencing factors in northeastern China, providing insights for sustainable agriculture. Utilizing 10 m resolution SOM data, the analysis covers regions including the Greater and Lesser Khingan Mountains, Liaohe Plain, Sanjiang Plain, Songnen Plain, the northwest semi-arid region, and the low hilly areas of Paektu Mountain. The Geodetector method is employed to assess various influencing factors. The key findings are as follows: (1) The average SOM content in Northeast China (37.70 g/kg) surpasses the national average, is highest in the Greater and Lesser Khingan Mountains (49.32 g/kg), and lowest in the northwest semi-arid region (26.15 g/kg). (2) SOM content is maximized in regions with high altitudes, steep slopes, low temperatures, and moderate precipitation. (3) The annual average temperature is the primary factor influencing SOM distribution, with a combination of temperature and administrative divisions providing better explanatory power. (4) SOM trends vary across protected areas, with slope being critical in semi-humid plains, elevation in arid regions, and no dominant factors identified in the Sanjiang Plain. These findings underscore the need for tailored black soil protection policies to effectively leverage local resources and preserve ecosystem integrity.

The interactions between economic development, environmental sustainability, population growth, and urbanization are vital in assessing the ecological dynamics of urban agglomerations. This study explores the relationship between economic development, environmental sustainability, population growth, and urbanization within the Xia-Zhang-Quan urban agglomeration in Fujian Province from 1989 to 2022. Utilizing Landsat remote sensing images, we calculated the Remote Sensing Ecological Index (RSEI) to evaluate changes in ecological quality. The results show that the average RSEI values for 1989, 2000, 2010, and 2022 were 0.5829, 0.5607, 0.5827, and 0.6195, respectively, indicating an initial decline followed by a significant increase, culminating in an overall upward trend. The spatial distribution of RSEI classification shows that the study area has the largest proportion of mainly “good” ecological quality. The proportion of areas with “excellent” ecological environmental quality has increased (13.41% in 1989 and 25.12% in 2022), while those with “general” quality has decreased (28.03% in 1989 and 21.21% in 2022). Over the past three decades, Xiamen experienced substantial ecological degradation (RSEI change of −0.0897), Zhangzhou showed marked improvement (RSEI change of 0.0519), and Quanzhou exhibited slight deterioration (RSEI change of −0.0396). Central urban areas typically had poorer ecological conditions but showed signs of improvement, whereas non-central urban regions demonstrated significant environmental enhancement. The factor detector analysis identified land use as the dominant factor influencing ecological environmental quality, with precipitation having a relatively minor impact. Interaction analysis revealed that all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone. Land use consistently showed solid explanatory power. Temperature also exhibited significant influence in 2022 when interacting with other factors. Due to urban planning that can plan for land use, these findings suggest that effective urban planning can harmonize economic development with ecological protection within the Xia-Zhang-Quan urban agglomeration.

Green buildings are characterized by energy saving and emissions reduction. To achieve the goals of peak emissions and carbon neutrality, large-scale development of green buildings contributes to relieving the pressure of resource shortage and environmental degradation. The extant literature on China's green buildings has mainly focused on standards and rating systems. With regard to the rapid growth of green buildings, research has increasingly focused on the development patterns and mainly concentrated on the nationwide or regional scales. It is essential to study the spatial and temporal patterns of green buildings and explore their dynamics in urban China within a particular city. Taking Guangzhou as a case study, this study comprehensively explored the spatial and temporal patterns of green buildings which were awarded the Chinese Green Building Evaluation Standard or LEED certification from 2008 to 2021, and further identified main factors in terms of market, economy, environment, and resources driving such patterns with the aid of ArcGIS spatial analysis and GeoDector. It was found that: (1) Temporally, the growth of green buildings demonstrates a policy-driven trend of rising first and then descending. (2) Spatially, there are significant differences in green buildings project, construction area, and types in terms of green office, residential, and other public buildings. The green buildings in the city center of Guangzhou have considerable differentiation and complex levels. The high-level green buildings are primarily concentrated in Tianhe, Zengcheng, and Huangpu Districts, while the low-level green buildings are mostly located in Conghua, Panyu, and Nansha Districts. (3) The spatiotemporal patterns of green buildings exhibited the following features: The gravity center of green buildings moved northward and then southward;. The range of distribution has continuously expanded. The developing trend in Guangzhou present northwest-southeast direction with gradually enhanced directionality. (4) With regard to the driving forces, resource factors are the primary driving force in the development of green buildings, followed by economic, market, and environmental factors. Specifically, among the three types of green buildings commercial, residential, and office, resource factors are the most important driving force in developing green commercial and residential buildings, while market factors deeply affect the spatial variation of green office buildings. Notably, environmental factors also play a key role in green residential buildings. From the perspective of geography in the spatiotemporal dimension, this study systematically reveals the evolution and determinants of green buildings in China. The findings provide scientific evidence for governmental decision-makers and urban planners in green buildings, new insights into green buildings development and enrich the theoretical framework of green buildings.

Abstract Traditional villages are repositories for preserving human artifacts and cultural heritage. An investigation of the spatial distribution characteristics and factors influencing traditional villages in provincial administrative regions can provide new insights regarding the protection of traditional villages and rural development. This study focused on 275 traditional villages in Henan Province. Using ArcGIS and GeoDa software, we analysed the spatial autocorrelation and heterogeneity of the nearest neighbour index, Gini coefficient, Moran’s I, and kernel density of the villages. Additionally, in conjunction with the Python sklearn library and GeoDetector, 15 indicators were selected to construct a decision tree model, spatial lag regression model, and geographic detector. Then the influence and interaction mechanisms of each indicator were analysed. The results revealed that (1) the spatial distribution of traditional villages in Henan Province was clustered and uneven, with a spatial layout comprising “3 high-density areas + 1 medium-density belt”; (2) overall, the number of traditional villages was negatively correlated with altitude, slope, rainfall, population density, proportion of the minority population, and historical-cultural intensity; and (3) the decision tree model results demonstrated that the selected 15 indicators had good predictive ability and that population density was particularly important. The spatial lag regression model results showed that the spatial distribution of traditional villages was positively correlated with distance from rivers, urbanization rate, and tourism resources, and negatively correlated with population density, per capita GRP, historical-cultural intensity, and NDVI. (4) The GeoDetector results indicated that historical-cultural intensity and population density were the two factors with the most significant explanatory power for the spatial differentiation of traditional villages in Henan Province. In terms of interactive factors, population density $$\cap$$ ∩ population was the strongest interactive driving force, followed by population $$\cap$$ ∩ historical-cultural intensity.

[Objective] The changes in ecological environmental quality in the Taiyuan urban agglomeration from 2002 to 2021 were studied in order to provide scientific recommendations for sustainable urban development and green transformation. [Methods] We used the GEE platform, MODIS images, and principal component analysis to construct the ecological remote sensing index (RSEI) by coupling greenness, humidity, heat, and dryness. The spatial properties of ecological quality changes in the Taiyuan urban agglomeration were studied by using combining stability analysis and spatial autocorrelation analysis. The influence of various factors on RSEI was quantified by geographic detectors. [Results] The contribution rate of the first principal component was greater than 75% in all years, indicating that RSEI values extracted based on the first principal component could comprehensively characterize the ecological environmental quality in the study area. The ecological environmental quality of the Taiyuan urban agglomeration showed an overall upward trend from 2002 to 2021, increasing from 0.433 to 0.488, with a growth rate of about 0.0029/yr. The ecological improvement area accounted for 17.1%, mainly located in Lanxian County and Jingle County in the northwest. There was obvious spatial autocorrelation in the change of ecological environmental quality in the study area, and the Moran’s I index was 0.729. The high-high and low-low aggregation areas basically coincided with the ecological improvement and degradation areas, respectively. The ecological environmental quality of the Taiyuan urban agglomeration was significantly correlated with the climatic factors of relative humidity, air temperature, and potential evapotranspiration. [Conclusion] The ecological environmental quality of the Taiyuan urban agglomeration has improved from 2002 to 2021, and the vegetation coverage and urban expansion factors have had great impacts. RSEI can effectively monitor changes in the ecological environmental quality in the study area.

[Objective] The habitat quality of the urban agglomeration in the lower reaches of the Yellow River, and the relevant influencing factors were studied in order to provide a reference for ecological protection and quality development in the region. [Methods] The InVest model was used to analyze the evolution characteristics of regional habitat quality based on land use data from 2005 to 2020. The agglomeration effect of habitat quality was analyzed through spatial analysis. Influencing factors of habitat quality were determined using GeoDetector. [Results] ① From 2005 to 2020, the overall habitat quality of urban agglomeration in the Lower Yellow River basin was low, with an average value index of 0.402 2. During this period, habitat quality initially decreased and then increased. ② Spatial autocorrelation analysis showed that habitat quality was highly correlated, showing a polarized clustering pattern. Habitat quality showed a cluster of high values (H-H) in the western and northern parts of the Central Plains urban agglomeration and the central part of the Shandong Peninsula urban agglomeration, and a cluster of low values (L-L) in cities with more prominent economic development in the urban agglomeration, such as Zhengzhou and Qingdao City. ③ The results of the GeoDetector analysis showed that land use was the main factor influencing habitat quality of urban agglomeration in the Lower Yellow River basin, and that the interaction between different factors was stronger than the influence of individual factors on habitat quality. [Conclusion] Regions of urban agglomeration in the Lower Yellow River basin with high habitat quality should continue to protect the ecological environment, while regions with low habitat quality should strictly control land exploitation and the expansion of construction land, and improve land use efficiency.

Abstract Background With the acceleration of aging and urbanization, how to make cities more age-friendly has become a hot topic. During the long-term demographic transition, the health of the elderly has become an important consideration in urban planning and management. The health of the elderly is a complex issue. However, previous studies have mainly focused on the health defects related to disease incidence, loss of function, mortality, etc., yet a comprehensive evaluation of health status is lacking. The Cumulative Health Deficit Index (CHDI) is a composite index that combines psychological and physiological indicators. Health deficits can reduce the quality of life of the elderly and increase the burden on families, cities and even society, so it is indispensable to understand the individual factors and regional factors that affect CHDI. The research on the spatial differentiation of CHDI and its driving factors can provide scientific geographic information basis for the construction of age-friendly cities and healthy city planning. It also has great significance in narrowing the differences in the health status among regions and reducing the overall burden of the country. Methods This research analyzed a nationwide dataset, the China Longitudinal Aging Social Survey in 2018 conducted by the Renmin University of China, which contained 11,418 elderly aged 60 years and older from 28 provinces/municipalities/autonomous regions that represent 95% of the population in mainland China. The Cumulative Health Deficit Index (CHDI) was the first time constructed using the entropy-TOPSIS method to evaluate the health status of the elderly. Entropy-TOPSIS is to quantify the importance of each indicator by calculating the entropy value to improve the reliability and accuracy of the results and avoid the influence of previous researchers’ subjective assignments and model assumptions on the results. The selected variables include physical health 27 indicators (self-rated health, basic mobility assessment, daily activity ability, disease and treatment) and mental health 36 indicators (cognitive ability, depression and loneliness, social adjustment, and filial piety concept). The research used the Geodetector methods (factor detection and interaction detection) that combined individual and regional indicators to analyze the spatial variation characters and reveal the driving factors of CHDI. Results The weight of mental health indicators (75.73) is three times that of physical health indicators (24.27), and its composition formula is: CHDI value = (14.77% disease and treatment + 5.54% daily activity ability + 2.14% health self-assessment + 1.81% basic mobility assessment) + (33.37% depression and loneliness + 25.21% cognitive ability + 12.46% social adjustment + 4.7% filial piety). Individual CHDI was more associated with age and was more evident in females than males. CHDI average values show the distribution trend of Hu Line (HL) in the geographic information graph that the CHDI in West HL regions are lower than in the East HL regions. The highest CHDI cities are in Shanxi, Jiangsu, and Hubei, whereas the lowest CHDI cities are Inner Mongolia, Hunan and Anhui. The geographical distribution maps of the 5-levels of CHDI levels show very different CHDI classification levels among the elderly in the same region. Further, the top three influential factors are personal income, empty nest, aged 80+, and regional factors also obviously affect CHDI values, such as the proportion participating in insurance, population density, and GDP. The two different factors in individual and regional factors all show a two-factor interaction effect, and enhancement or nonlinear enhancement. The top three ranks are personal income ∩ quality of air (0.94), personal income ∩ GDP (0.94), and personal income ∩ urbanization rate (0.87). Conclusions CHDI is a subjective and objective comprehensive index, and mental indicators are primary factors. Thus attaching importance to the psychological care of the elderly is the key to building a healthy aging society. The large individual heterogeneity and spatial differentiation of CHDI in the elderly were demonstrated by map visualization. The analysis of the influencing factors of CHDI by the Geodetector method proves that spatial differentiation is mainly affected by individual economic and social security factors, but also by the interaction with regional factors such as quality of air, GDP, and urbanization rate. This research fills a gap in the elderly health status in the field of spatial geography. The results can provide empirical evidence for policymakers to take measures according to local conditions to improve the health status of the elderly according to regional differences in physical and mental conditions. It also plays a guiding role for the country in balancing regional economic development, promoting healthy and sustainable urban development, and creating age-friendly cities.

The escalating impacts of human activities on the ecological environment underscore the significance of valuing ecological resources within the paradigm of sustainable development. In this context, the ecological service value (ESV) of the Danjiangkou watershed (Hubei section) was assessed using a revised equivalent factor evaluation method. Additionally, the study determined the human activity intensity (HAI) and employed GeoDetector to delineate the spatial heterogeneity of ESV, thereby investigating the underlying causes. Results show that vegetation coverage attains a high level of 94%, with a minor 1.37% land use change between 2016 and 2020. Secondly, ESV exhibited a substantial variation, rising from 75.55 billion to 79.81 billion yuan, a growth rate of 5.64%. Lastly, the explanatory degree for the synergistic interplay of HAI and expenditure on the spatial variation of ESV surpassed 30%, and the average sensitivity coefficient of up to 0.82 further indicated the apparent impacts of land use and human interference activities on ESV. The study makes an excellent effort to clarify the complex coupling relationships between different land use types and ESV, which could serve as an example and model for other studies in the field.

Amid global industrialization and urbanization, mountainous rural settlements, especially those in metropolitan fringe area, are experiencing significant spatial changes in location and scale. This study takes Pingnan County, Fujian Province, China, as an example. Utilizing land use data and employing methods including standard deviation ellipse, average nearest neighbor index, kernel density estimation, spatial hotspot detection, binary logistic regression model, and Geodetector, this study aims to scientifically identify the spatial pattern characteristics and influencing factors of its settlements. The results show that: (1) The spatial distribution of settlements in Pingnan County is biased toward the southern part of the county; the center of settlement's spatial distribution is located south of the junction of Gufeng Town and Pingcheng Town; the spatial distribution trend of settlements is north-east-southwest. Settlements are generally aggregated, and the aggregation degree of Gufeng Town is obviously lower than that of other towns. (2) The density distribution of settlements presents a “core-periphery” structure and a “north-south linear” structure in space; the spatial pattern characteristics show high-density, large patches in Gufeng Town, high-density, small patches in Changqiao Town, Tangkou Town and Gantang Town, and medium-density or low-density, small patches in other towns. (3) Settlement location is mainly affected by the elevation, distance to cultivated land, and distance to main roads, while settlement scale is mainly affected by slope, relief degree of land surface, and distance to urban centers. The interaction between these factors exhibits enhancement effects, with natural terrain and location conditions exerting the most prominent influence. These findings underscore the strong constraints posed by natural topography on mountainous rural settlements in metropolitan fringe areas, coupled with a more pronounced influence from socio-economic factors. The study's results hold significant implications for optimizing the layout of such settlements, guiding land spatial planning, and promoting rural revitalization.

As a key component of plant nitrogen-fixing enzymes and a variety of human coenzyme factors, molybdenum (Mo) plays an essential role in supporting both plant growth and human health. Soil is a key medium for the cycling of Mo in the biosphere. However, the driving anthropogenic and natural factors governing the spatial distribution of Mo in soil and their interactions are not well understood. To determine the factors that affect the spatial patterns of Mo in topsoil, 6980 samples were collected from the Longitudinal Range–Gorge Region (Linshui County, Sichuan Province, China). In this area, tall mountains are adjacent to deep valleys. Topsoil with enriched Mo is mostly distributed in mountainous areas. The most important factors influencing Mo in topsoil are soil parent materials (q = 0.482), altitude (q = 0.256), and soil type (q = 0.259). There are synergistic effects among the various driving factors [q(X1 ∩ X2) > Max[q(X1), q(X2)]]. The Geodetector model was used to validate the magnitude of the interaction effects. The contribution to interacting factors is nonlinearly enhanced when the contribution of a single factor was low (any two factors of aspect, road distance, land use type, and S). The contribution to interacting factors is enhanced bidirectionally when the contribution of a single factor was high (any two factors of altitude, soil type, soil parent material, OM, and TFe2O3). When the contribution of one factor is high and the other is low, the contributing to interacting factors is mostly enhanced bidirectionally and a few are nonlinearly enhanced.

Coastal areas are characterized by high levels of urbanization and also the most active and highly sensitive to geological activities. Due to the impact of global climate change, landslides occur frequently in coastal areas, which have significantly impacted the sustainable socio-economic development of the region. Therefore, studying the spatial distribution of landslides and the factors that drive them holds immense practical significance for the geohazards prevention and mitigation work and optimization of ecological security patterns in coastal areas. This study takes the southern Liaodong Peninsula as the study area and analyzes the spatial distribution characteristics and influence mechanism of landslides. We first applied the Getis-Ord Gi*, kernel density analysis techniques, and Ripley’s K function to explore the spatial distribution characteristics of landslides in the study area at different scales. Then, we employed the information value method to test for the linkage relationships between the driving factors and landslides. Finally, we utilized the geographical detector to reveal the potential impacts and interaction of the driving factors on landslides. The results showed that: (1) landslides in the Southern Liaodong Peninsula exhibit strong spatial clustering characteristics, and have a spatial scale effect; (2) the high susceptibility areas are mainly concentrated in the southern hilly regions and the mountainous regions in the northern parts of the study area; (3) The information value method reveals that there is an optimal combination of factors driving landslides. (4) The factor detector analysis reveals that the primary driver of landslide spatial distribution is the geomorphological types (q-value of 39.10%). (5) The interaction detector indicates that the interplay of all driving factor pairs exhibited an enhanced effect, which is not a simple superposition effect but bivariate and non-linear. The collective influence of natural factors and human activities holds a more substantial influence over landslide development and distribution. The research offers guidance for the execution of landslide prevention initiatives and the establishment of preservation policies for the ecological environment in the southern Liaodong Peninsula region.

With the accelerated development of urbanization, the exploration and usage of land resources is becoming more and more frequent, which leads to the decline of soil quality, resulting in a series of soil ecological issues, such as soil nutrient loss, soil quality degradation and destruction. At present, the contradiction between soil erosion and sustainable development of human society has become one of the hot issues studied by scholars. The Yellow River Basin is an important experimental area for high-quality development in China, constructing the Yellow River Ecological Economic Belt play an important role in China’s regional coordinated development. Although most of the affected area of the Lower Yellow River (AALYR) is in the plain, they have a large population density and are in the historical farming area. In latest years, because of the development and transformation of modern society, their ecological environment has become more fragile and soil erosion problems has become increasingly serious. Studying and analyzing soil erosion is of vital meaning for ecological protection and can provide scientific support for soil conservation work. Depending on the data of precipitation, soil properties, land use, population, etc., this paper studies and analyzes the soil erosion in AALYR from 2000 to 2020 through the RUSLE. We found that during the 20 years the proportion of very slight and slight grade area increased, and the distribution of moderate and above erosion grade was less, mainly in Zibo, Jinan, Anyang, Zhengzhou, and Tai 'an. Nearly three quarters of the regional soil erosion grade didn’t change, apart from the increase of slight grade area, the other erosion grades area showed a downward trend. We take the city, county and town zoning analysis find that as the scale decreases, the area of serious erosion grades increases, and the distribution is gradually detailed. Land use is the main influencing factor of erosion except DEM. Forestland and grassland are larger of the soil erosion in various types of land use. Through these conclusions in this paper, it is promising to provide theoretical references for the ecological environment governance and high-quality and sustainable development of great river basins of the world and similar regions.

This paper first determines the research method of rural tourism and the selection of research objects and carries out the corresponding pre-processing operation for the data. Secondly, the spatial distribution characteristics of key villages of rural tourism are analyzed by using the nearest neighbor index, standard deviation ellipse, geographic concentration index and geographic detector so as to understand the development of rural tourism in the five regions of east, west, south, north and central. The spatial clustering structure of key villages in rural tourism is calculated again using the fractal model to explore grid aggregation and correlation of high-quality development in rural tourism. From the spatial distribution characteristics, the nearest neighbor index of the southern region is 0.838, showing an aggregation-random distribution pattern, and the standard deviation ellipse of the southern region is 51.67°. From the spatial clustering structure, the aggregation dimension of the southern region is 0.485, and the correlation dimension of the four regions of east, west, south, north and south is relatively close to that of highway accessibility, for example. Thus, rural tourism in the context of the Internet needs to further strengthen infrastructure construction, enhance the means of training high-quality personnel, build diversified marketing channels, and realize the rural revitalization strategy to boost rural tourism.

The mountainous region of southwest China serves as a significant treasure for resources and ecological security. Due to its topographic heterogeneity and cultural diversity, the wildfire regime in this area exhibits unique features. To uncover the distinct mechanisms that drive wildfire incidents and assess their occurrence probability, an enhanced weight-of-evidence (WofE) method was proposed by improving the stratification of evidence layers. This approach seeks to heighten effectiveness while maintaining procedural transparency and robustness. In our case study focusing on Yunnan Province, we extracted wildfire data from medium-resolution remote sensing imagery from 2006 to 2020. Various wildfire drivers were considered including wildfire environment, fuel conditions, and ignition sources. We utilized the WofE method grounded on Bayesian principles to clearly calculate the spatial association strength between these drivers and wildfires, thereby developing a reliable wildfire occurrence probability map. To enhance the explanatory power of the drivers within the WofE method, a discretization method based on the theory of spatial stratified heterogeneity was incorporated. Our results suggested that the WofE method can be optimized using the spatial stratified heterogeneity measured via GeoDetector, leading to an improved solution. Implementing optimized discrete drivers amplified the spatial explanatory power of wildfires by an average of 7.55%, supporting their inclusion as evidence layers within the WofE method. The optimized discrete WofE method yielded a wildfire occurrence probability map with an Area Under the Curve (AUC) value of 0.91, indicating high predictive accuracy. This map revealed notable spatial clustering and regional variations in wildfire occurrences across Yunnan Province. Additionally, we observed variable spatial correlation between each driver and wildfire occurrence, and the wildfire drivers indicative of ignition sources characteristics were relatively more dominant locally. This research contributes valuable insights towards enhancing the WofE method and provides a helpful reference for local wildfire management practices and resource allocation strategies.

Ecosystem services (ES) are defined as the benefits of the natural environment that sustain human life. The analyses of the balance of ecosystem services (ES balance) and its driving factors are critical for achieving the region’s sustainable development goals. This study explored the ES balance in the Beijing-Tianjin-Hebei-Inner Mongolia (JJJM) region, considering the region’s rapid urbanization, implementation of ecological restoration projects, and intense agricultural and pastoral activities. It quantified the ES balance on different scales and selected nine natural and anthropogenic driving factors to reveal its spatio-temporal heterogeneity and change characteristics in 2000–2020. The main conclusions were as follows: (1) The ES balance in the JJJM region exhibited a significant spatial differentiation: generally higher in its eastern and northern areas and lower in its western and southern parts; (2) The ES balance in the Beijing-Tianjin-Hebei region showed a general trend of initial increase and subsequent decrease, while Inner Mongolia exhibited a continuous upward trend. High-value areas (>46) and low-value areas (

The study of the spatiotemporal evolution of landscape ecological hazard and human and natural influences is essential for conservative management and regional sustainable development. This study applied a landscape pattern analysis method and geodetector to multi-source data for 2000, 2010, and 2020 to analyze changes in and drivers of landscape ecological hazard in Laos. The results indicated that: (1) There were more prominent changes in landscape types in Laos. Forest area decreased, whereas the areas of other landscape types increased. There was an overall steady change in the landscape patterns of Laos. Besides for significant changes in the artificial surface landscape index, landscape indices remained stable; (2) The cumulative high and extreme ecological hazard areas increased by 1,947.81 km2, whereas the cumulative areas of low and minimal ecological hazard decreased by 8,461.8 km2. Areas of low and moderate ecological hazard accounted for > 85% of the total area. Areas of low ecological hazard were mainly in the northwest and southeast. The area of high ecological hazard was concentrated in the central and northeastern regions. The distributions of different landscape ecological hazards in Laos during the study period were similar, with general patterns of decreasing hazard from north to south; (3) A positive Moran's I of landscape ecological hazard in Laos was obtained. While the agglomeration effect was pronounced, it decreased over time, resulting in a weakening in spatial autocorrelation. A significant positive autocorrelation was observed in the spatial distribution of landscape ecological hazard in the study area. Agglomerated areas of high and low ecological hazard were mainly concentrated in the northeast and southeast, respectively; (4) The spatiotemporal evolution of landscape ecological hazard in Laos over the last 20 years could be attributed to interactions between natural and anthropogenic influences. Natural influences were a significant driver of changes to landscape ecological hazard in Laos, with annual precipitation and average temperature being the most significant. Anthropogenic influences, including socioeconomic factors and regional accessibility, significantly impacted local ecological deterioration in Laos. [ABSTRACT FROM AUTHOR]

The critical prerequisite for the prevention and control of soil heavy metal (HM) pollution is the identification of factors that influence soil HM accumulation. The dominant factors have been individually identified and apportioned in existing studies. However, the accumulation of soil HMs results from a combination of multiple factors, and the influence of a single factor is less than the interaction of multiple parameters on soil HM pollution. In this study, we employed Geodetector to delve into the interaction effect of the influencing factors on the variations of soil HMs. We performed partial dependence plot to depict how these factors interact with each other to affect the HM content. We found that both individually and interactively, pH and agricultural activities significantly impact soil HM content. Except for Hg and Cu, the pairs with the most significant interaction effects all involve pH. For Pb, As and Zn, interaction with pH has the most significant driving force compared to the other factors. For Cu, Hg, and Ni, all environmental factor interactions increased their explanatory power, while for Cr, the single most significant driver decreased its driving power when interacting with other factors. Additionally, the study area exhibited a widespread prevalence of changes in HM concentration being governed by the synergistic effect of two factors. For the response of HMs to the interaction of pH and fertilizer, soil HM concentration was sensitive to pH, while fertilizer had less effect. These results provide a dependable method of investigating the interaction of environmental factors on soil HM content and put forth efficacious and potent tactical measures for soil HM pollution prevention and control based on the interaction type. [ABSTRACT FROM AUTHOR]