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Panzhihua City, a typical eco-fragile region for agro-sylvo-pastoral industry in China, is located in the dry-hot valley of the Jinsha River, characterized by its big landform undulation, great elevation difference, uneven hydrothermal conditions, and complex geological structure. As a crucial ecological barrier in upper reaches of the Yangtze River, this area is abundant in water resources and mineral resources, such as vanadium and titanium. However, due to its over-development for nonnatural urban economy in the mining industry, agriculture, and animal husbandry, ecological problems are getting worse. Such problems as soil erosion and groundwater pollution have led obvious ecological degeneration in Panzhihua city. Therefore, for protecting the eco-environment and planning construction, it is significant to scientifically recognize that how eco-environment changes based on spatial-temporal, and how the driving mechanism affects Panzhihua city. Nowadays, there are some theories and methods that study eco-environmental protection and city construction in Panzhihua, but they are not comprehensive enough to study its spatial-temporal evolution and driving-force system. This study takes Panzhihua City as the research area of which evaluation factors, for example, topography, soil, vegetation, and meteorological factors, are chosen to construct an evaluation system suitable for the ecological environment vulnerability of Panzhihua City. These factors are selected in three aspects, which are ecological sensitivity, ecological recovery, and ecological pressure from 2005 to 2015 in this area. Then, spatial principal component analysis method, CA-Markov model, and dynamic degree model are applied to analyze the spatial-temporal evolution for ecological vulnerability based on three periods from 2005 to 2015 in Panzhihua City. Besides, GeoDetector is used to quantitatively analyze how spatial-temporal disparities change and what drives them to change. The results show that (1) during these 10 years, the overall ecological fragility of Panzhihua City is steadily increasing from northwest to southeast. The overall ecological quality is moderate, and regional differences are obvious. Places of moderate vulnerability or above are distributed in central and eastern regions of frequent human activities; places of mild vulnerability or below are distributed in the regions of Yanbian County and Miyi County. (2) The comparison of the changing rates based on vulnerability levels is severe > potential > moderate > mild > slight. The overall vulnerability changes within a small trend, showing a balanced two-way transition state between adjacent vulnerability levels. The comprehensive index for overall ecological vulnerability decreases period by period. (3) The interactions between each two factors toward spatial differentiation and explanatory power by ecological vulnerability show a two-factor-enhanced relation, indicating that multiple factors form the ecological vulnerability of Panzhihua City.

Green development is an approach that harmonizes human beings with nature and leads to sustainable social and economic development. However, the factors influencing the level of green development are uncertain due to regional differences, especially in the alpine meadow regions. To explore the factors affecting green development in the alpine meadow region, we took the Gannan Tibetan Autonomous Prefecture as an example. Through a green development level evaluation index system, the DPSIR (Driver, Pressure, State, Impact, and Response) was constructed to comprehensively evaluate and analyze the green development level of the study area from 1990 to 2020. On this basis, the key influencing factors of the green development level was analyzed based on the GeoDetector. The findings indicate that the total level of green development in Gannan has an upward trend within the range of 0.044–0.535, moving towards high quality and sustainable development. Except for 2005, the spatial clustering characteristics of the green development level did not display obvious high values of spatial autocorrelation. The green development space is concentrated and stable, and the centre of gravity is in Zhuoni. The impact and response dimensions make the greatest contribution to green development. Different evaluation indicators have different impacts on green development during the study period. Specifically, people’s productive lives, the impact of social production and positive policy responses are the main factors contributing to green development. The findings will provide theoretical basis and case support for ecological security and green sustainable development in the alpine meadow regions.

Urban eco-efficiency (EE) refers to the ability of a city to balance economic growth with environmental protection through the optimal use of resources and environmental management, reflecting the city’s performance in sustainable development. Quantitative EE analysis is a crucial evaluation criterion for measuring the process of sustainable development (SD). To assess the current state of sustainable development in China, this study employs the slacks-based super efficiency model (Super-SBM) to measure the the urban eco-efficiency in China (CUEE) of 284 cities. The study utilizes Kernel Density Estimation (KDE) and Spatial Autocorrelation Analysis (SAA) to investigate the fundamental patterns of CUEE in terms of inter-city differences and relationships. Complex Network Analysis (CNA) and Link Prediction methods are applied to examine the characteristics and future trends of the CUEE network. Additionally, Geodetector and Geographically Weighted Regression (GWR) are used to analyze the influencing factors of CUEE. The findings are as follows: Overall CUEE is relatively low but shows a steady increase. The spatial association network of CUEE is becoming more complex, with increasing association strengths. The Chengdu-Chongqing Economic Circle (CCEC) is expected to further enhance its spatial association strength and may emerge as a new core area. The effectiveness of the interactions among selected factors is greater than that of individual factors. Degree centrality (X1) is identified as a core explanatory factor influencing CUEE, showing a positive correlation with CUEE. Scientific expenditure (X5) is also positively correlated with CUEE. Local fiscal budget expenditure (X3), industrial output value of foreign-invested enterprises (X7), and total cargo transportation volume (X9) are negatively correlated with CUEE. The analytical framework and research conclusions of this study offer valuable insights into the development of urban ecological efficiency in China and provide constructive recommendations for promoting sustainable urban development.

Ecological vulnerability (EV) assessment is an important means of reflecting the evolutionary process of the local ecological environment, which is crucial for ecological security. The current EV assessment still faces challenges in comprehensively revealing its spatio-temporal change characteristics due to diversity, multi-scale complexity and non-linearity of ecosystems. In this study, we comprehensively revealed the spatio-temporal change features and driver mechanisms of EV in Yunnan Province (YP) using methods such as breaks for additive seasonal and trend (BFAST) and Geodetector. According to the findings, (1) the ecological vulnerability index (EVI) in YP decreased by 0.0265 on the raster scale between 2000 and 2018, and the ecological vulnerability level (EVL) was mainly dominated by level III. At the city scale, EVL in YP was dominated by level IV. At the basin scale, the average EV of the Jinsha River basin was much greater other basins. (2) The linear trend of EV in the YP mainly showed an insignificant decrease, mostly concentrated in the YP’s eastern region. The non-linear trend mainly showed a monotonous decrease, with the mutation time concentrated in 2009. The future persistence trend of EV under different coupling modes was dominated by anti-persistence decrease (Sen-MK + Hurst) and anti-persistence monotonous decrease (BFAST + Hurst), with an area percentage of 40.19 % and 25.13 %, respectively. (3) Gross primary productivity was the priority factor influencing YP’s EV (q = 0.4137). This study not only enriches the cases of EV assessment studies in the high mountain valley area but also bridges the gap of analysing the multi-spatial scale characteristics and change trends of regional EV.

Human activity has transformed natural land use patterns and exerted a significant impact on the mechanisms underlying ecosystem service (ES) provisioning. This has disrupted the equilibrium between human development and ecological protection and hasled to further degradation of desert ecosystems endemic to arid regions. In this study, we employed the three SSP-RCP scenarios provided by CMIP6 to represent varying rates of development within the Tarim River Basin (TRB) and modeled land use in 2050 based on the coupled system dynamics (SD) + patch-generating land use simulation (PLUS) + InVEST model. A dynamic simulation of the trade-off/synergy between key land use types and ESs in the ecologically vulnerable areas of the TRB was performed to identify the impacts of the individual effects of single factors and the scale effects of multiple factors on ESs. The results showed that (1) in the SSP126, SSP245, andSSP585 scenarios, the ratio of outflows to inflows of converted unused land gradually increased in the following periods:79.28 %, 79.42 %, 79.54 %in 2020–2030; 96.19 %, 91.62 %, 66.07 %in 2030–2040; and 93.76 %, 91.65 %, 60.96 %in 2040–2050, respectively.(2) A high water yield (WY) was observed in the highland mountains; high habitat quality (HQ) was observed in the highland mountains, along the Tarim River, and around cities; high carbon storage (CS) was observed in the highland mountains, along the Tarim River, and in urban areas. (3) Under the three scenarios, WY, HQ, and CS were mainly affected by environmental factors, with the highest single-factor tests represented by temperature (0.594; 0.595; 0.596), precipitation(0.582; 0.589; 0.590); NDVI(0.312; 0.295; 0.299), soil erosion(0.227; 0.221; 0.219); and NDVI(0.298; 0.282; 0.310), soil erosion(0.122; 0.125; 0.132), respectively. Additionally, the interaction factors with the highest explanatory power for WY, HQ, and CS were precipitation ∪ NDVI (0.73); elevation ∪ NDVI (0.37); and soil ∪ NDVI (0.31), respectively. Moreover, the human aggregation level exerted positive (0.117), negative (−0.173), and negative (−0.286) impacts on WY, HQ, and CS, respectively. The results show that the combination of GeoDetector and the partial least squares structural equation modeling (PLS-SEM) provides a complementary and creative framework for investigating the driving mechanisms of ecosystems.

Understanding the value of ecosystem services (ESV) and the mechanisms that influence it is pivotal for upholding ecological equilibrium and fostering sustainable development in the densely populated regions of the Tibetan Plateau. In this study, we used several improved ESV calculation methods to describe the dynamic changes of ESV, and investigated the individual effects, synergistic effects, local spatial clustering patterns and spatial correlations of each factor using Geodetector and Geoda. The results reveal: (1) Over the past 35 years, ESV in the study area exhibited an overall increasing trend, with a total increase of 20.36 billion Chinese yuan. Grassland accounted for the largest proportion, approximately 60 %, playing a vital role in maintaining ecological balance. Climate regulation services had the highest share at around 24 %, with hydrological regulation and soil conservation services dominating. (2) Human activity, elevation, and temperature had the most significant impact on ESV in the Tibetan Plateau human activity intensive area, followed by Fractional Vegetation Cover (FVC) and precipitation. The synergistic effects of two factors indicated that the interaction between elevation and FVC exhibited a decreasing influence on ESV, while the interaction between human activity and temperature showed an increasing impact. (3) In medium to high-altitude areas and urban agglomeration zones, human activity and temperature had the most pronounced negative impact on ESV, highlighting the need for more effective land management and sustainable development plans. Elevation and annual precipitation showed a positive correlation with ESV, but beyond a certain elevation threshold, elevation and precipitation had an adverse effect on ESV. Similarly, in extremely high-altitude regions and water source areas like the Yellow River, ESV exhibited a positive correlation with temperature.

Ecosystem degradation and decline are central issues that urgently require resolution within global environmental protection efforts. Accordingly, accurately analyzing the spatiotemporal evolution of regional ecological environmental quality and exploring its natural and anthropogenic driving factors of ecological environmental quality are crucial for protecting regional ecological environments and advancing sustainable development strategies. Therefore, this study created a new remote sensing ecological index to investigate the patterns of ecological quality change in vegetation-covered areas over a long time series and identified the intensity and local response relationships of various driving factors, including climate, topography, soil, and urbanization. The results revealed an upward trend in the ecological quality of the Heilongjiang region, with a high degree of spatial autocorrelation in ecological quality. The ecological grades in forest-covered areas significantly surpassed those in other vegetated, urban, and desert regions. Through a collaborative analysis of various geographical statistical methods, the intensities of the driving factors and their local response relationships were determined. This study provides a method for accurately and rapidly assessing regional ecological environmental quality and exploring the complex interactions of driving factors, thus offering a theoretical basis for monitoring regional-scale ecological conditions, balancing ecological and economic development, and informing environmental protection policies.

Urban open spaces offer both environmental and social benefits. However, comprehensive studies that integrate both quantitative and qualitative evaluations of the factors driving change in these spaces and their long-term predictions are lacking. Most existing studies concentrate on land-use development rather than conducting empirical research specific to urban open spaces in Shanghai. This study addresses this gap by employing a geographic detector (geodetector) to analyze the influence of various driving factors on open-space changes. These factors were then used as weight values in a multicriteria CA-Markov model to simulate and predict change in Shanghai's urban open spaces by 2050. The advantage of analyzing driving forces lies in their ability to capture the multifactor synergy influencing change in urban open spaces, aligning with the aim of this study to quantitatively evaluate the interaction between natural, climatic, and socioeconomic factors. Additionally, semi-structured interviews were conducted with 10 policymakers and planners to assess the reliability of the quantitative predictions. The results indicate that socioeconomic factors are the primary drivers of change in urban open spaces. Specifically, the interaction between the normalized difference vegetation index (NDVI) and population density (PD) emerged as the most influential variables. For prediction outcomes, the unconstrained scenario predicts a decrease in open-space area from 5610.94 km2 in 2020 to 5124.36 km2 in 2050. The planning intervention scenario anticipates minimal changes in Shanghai's total urban open-space area with almost no floating changes. However, the economic development scenario predicts a rapid decline in open spaces. Experts and planners evaluated these three scenarios and confirmed the reliability and accuracy of the prediction models. The methods and findings of this study can support zoning planning for urban open-space systems in other cities and regions.

The excessive presence of heavy metals (HMs) in soil poses a significant threat to both ecosystems and human health. Consequently, there is a compelling need for quantitative analysis of HMs concentration in soil and the prediction of potential contamination. In this study, 58 surface soil samples were systematically collected from 11 different townships in Luolong County. Using ArcGIS 10.7, the fishing net interpolation resampling was performed to obtain model data. The GeoDetector model was employed to determine the key driving factors and their interrelationships affecting soil composition. Subsequently, influential driving factors with higher explanatory power and employed a Random Forest (RF) model to generate a contamination prediction map. The results revealed that arsenic (As), cadmium (Cd) and lead (Pb) exceeded the risk screening values by 8.62%, 10.34%, and 10.34%, respectively. The GeoDetector model identified factors such as elevation, annual average precipitation, distance to the nearest river, geomorphic type in natural sources, geological type in geological sources, distance from roads, proximity to mining sites, per capita income of inhabitants, total potassium content and organic matter content in anthropogenic sources significantly influencing the spatial distribution of HMs concentration in the soil. The interactions among the primary driving factors increased their explanatory capacity. By using RF model to predict the spatial distribution of the main influencing factors of HMs, it was found that areas with a high probability of As contamination were mainly concentrated in the northern, central and southeast regions of Luolong County. Regions with Cd concentration exceeding the risk screening value were primarily concentrated in the east, northeast and a few northern areas of Luolong County, while the likelihood of Pb contamination was higher in the northern and southwestern regions of Luolong County. This study integrates spatial stratified heterogeneity with the random forest model to mitigate overfitting in the prediction of soil HM contamination, a common issue in traditional machine learning methods. This approach is essential for elucidating the environmental drivers of soil HM pollution, predicting high-risk areas in regions with complex environmental conditions and limited data, and ensuring the safety and stability of agricultural production as well as the well-being of local residents.

The advancement of industrialization and urbanization has exacerbated global water pollution. Chemical Oxygen Demand (COD) serves as a crucial metric for assessing organic contamination in aquatic environments. Consequently, the spatiotemporal characteristics and driving factors of COD emissions have emerged as a significant area of current research interest. This study employs spatial autocorrelation and geodetector methods to rigorously analyze the spatial–temporal patterns and principal drivers of COD emissions in China’s wastewater, drawing on eleven geographic and socio-economic datasets spanning from 2011 to 2019. This study indicates that COD emissions in China’s wastewater have consistently decreased annually between 2011 and 2019, with a marked reduction observed post-2016. Regarding spatial distribution, COD emissions in the eastern region significantly surpass those in the western region, highlighting pronounced regional disparities. The analysis of the global Moran’s Index indicates that the COD emissions across China’s provincial regions demonstrate spatial clustering of similar values, with this clustering intensifying annually. Furthermore, the local Moran’s Index identifies the precise locations of these spatial clusters. Geodetector analysis identified that the consumption of nitrogen fertilizers and the consumption of chemical fertilizers had substantial impacts on COD emissions, whereas the proportion of urban population at year-end and the number of granted inventions exerted lesser effects. Furthermore, the interaction between two factors notably enhances the explanatory power regarding COD emissions. This research underpins the formulation of efficacious pollution control and environmental protection strategies, contributing vital insights for the stewardship of water quality.

Identifying ecosystem service functions, clarifying the spatiotemporal trade-offs and synergies of terrestrial ecosystem services and their driving mechanisms, and exploring differentiated ecological functional zoning are crucial steps in achieving healthy regional ecosystem management, and are conducive to developing diversified ecological restoration strategies, establishing a robust cross-regional collaborative management mechanism, implementing differentiated ecological management strategies, and contributing to the construction of a Beautiful China. This paper, based on the InVEST model, Pearson correlation, GeoDetector, and Spatiotemporal Geographically Weighted Regression models, constructs a spatial quantification model of the trade-offs and synergies among five key ecosystem service functions—habitat quality, soil retention, water conservation, food supply, and carbon sequestration—of China’s terrestrial ecosystems from 2000 to 2020. It explores the influencing factors of terrestrial ecosystem services in China and their spatiotemporal heterogeneity, thereby investigating the future strategies for ecological functional zoning and management of China’s national land space. The results indicate that: (1) during 2000–2020, China’s food supply and soil conservation have increased. However, the habitat quality, water conservation, and carbon sequestration have decreased. (2) Significant spatial and temporal heterogeneities exist in the key ecosystem services of China’s terrestrial ecosystems. (3) Natural, economic, and social factors all impact China’s terrestrial key ecosystem services. Among them, slope, annual average precipitation, land development intensity, and vegetation coverage are the main influencing factors, and different factors exhibit significant spatial heterogeneity. (4) Significant trade-offs/synergy effects among critical terrestrial ecosystem services exist in China. (5) China’s national territory is divided into four ecological protection functional zones: ecological restoration areas, ecological control areas, resilient development areas, and ecological conservation areas, and explores differentiated zoning optimization control paths.

Global biodiversity is still being lost at an alarming rate. How to slow the destruction of habitats to protect biodiversity is an urgent issue. As one of the most rapidly developing and active economic regions in China, the habitat quality assessment and impact factor analysis in the Yangtze River Delta region are important for promoting socio-economic development and improving the local ecological environment. The InVEST mode and Geodetector method was used to explore the spatial and temporal variations of the habitat quality and its driving factors in the Yangtze River Delta region. The results show that the averaged habitat quality index showed a decreasing trend during 1980–2020, which accounted about 71.86 % of the Yangtze River Delta region. The habitat quality showed an overall spatial pattern of high in the south and low in the north, and the regional distribution of low habitat quality was dominated. The Moran's I values were larger than 0.8 during 1980–2020, showing a significant positive spatial correlation and a high degree of spatial clustering. The high-high clusters of habitat quality were mainly located in the southwest of the study area, while the low-low clusters were mainly located in the north. During 2025–2060, the habitat quality under the three scenarios exhibited SSP1-RCP19 > SSP2-RCP45 > SSP5-RCP85 and all showed a trend of decreasing in the north of the study area and increasing in the south. Under the two scenarios SSP1-RCP19 and SSP2-RCP45, the habitat quality generally showed an increasing trend, while under the SSP5-RCP85 scenario, it showed a decreasing trend. The slope, DEM, precipitation, construction land index and their interactions played a dominant role in the spatio-temporal variations of the habitat quality. Therefore, the slope, DEM, precipitation and construction land index should be considered when formulating habitat quality protection countermeasures.

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.

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.

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.

As earth surface human activities become more frequent, global ecosystem service functions and especially biodiversity maintenance functions are challenged. This study aimed to analyze spatiotemporal changes in Xi‘an section of the northern foothills of the Qinling Mountains from 1990 to 2020. Temporal and spatial changes in habitat quality in the study area were visualized using InVEST model and land use data, and factors affecting habitat quality were analyzed using Geodetector. The results showed that during the study period, the cultivated land, grassland, and water decreased by 16.40%, 74.37%, and 35.39%, respectively, while the area of forest land and construction land increased, among which the construction land increased by 117.70%, the largest increase, and the forest land increased by 8.47%. The main changes in land use are the conversion of cultivated land into forest land and construction land, and the conversion of grassland into forest land and cultivated land. During the period 1990–2020, the average habitat quality index in the study area changed from 0.8617 to 0.8585, showing a slow decreasing trend. The spatial distribution of habitat quality showed a trend of “high in the south, moderate in the north, and low in the northwest”. The high habitat quality was mainly concentrated in the southern forest land, the middle habitat quality was mainly distributed in the northern cultivated land, and the low habitat quality was mainly distributed in the northwest construction land. The land use type has a great influence on habitat quality, and the interaction between any two factors is stronger than that of a single factor. The temporal and spatial variation of habitat quality is influenced by both natural and human factors. This study provides a theoretical basis for ecological protection and nature reserve planning in the Qinling Mountains region.

An analysis of the spatiotemporal evolution of ecological quality and its driving factors in the Lanxi urban agglomeration is important for ensuring environmental protection and high-quality, sustainable development in the region. We used moderate resolution imaging spectroradiometer (MODIS) remote sensing images to construct a remote sensing ecological index (RSEI) using principal component analysis (PCA) to reflect the ecological quality of the Lanxi urban agglomeration. The spatial and temporal characteristics and future changes in the RSEI of the study area from 2000 to 2020 were explored using the Sen and Mann–Kendall test and Hurst index, and the effects of natural and human factors on spatial variation in the RSEI of the study area were analyzed using Geodetector and geographically weighted regression (GWR) models. There were three main results. (1) In the past 20 years, the annual average RSEI value in the Lanxi urban agglomeration has been increasing at a rate of 0.0057/a, and areas of unsatisfactory ecological environmental quality have been reduced. (2) The Hurst index indicates that the majority of the area (47.54 % of the study area) will probably experience a degradation trend in the future, with 19.98 % of the area improving and random changes occurring in 9.45 % of the study area. (3) Vegetation type, soil type, and precipitation were the main reasons for the spatial differentiation in RSEI, land use type was the main human influence, and the influence of socioeconomic factors such as population density and gross domestic product (GDP) increased significantly. Vegetation, soil, and land use types were positively correlated with RSEI. The research results are of great significance for promoting the construction of an ecological civilization and coordinating a balance between social development and ecological environmental protection.

Vegetation in China has experienced significant improvements under the combined effects of human activities and climate change. However, when exploring the effects of climate change on vegetation dynamics, existing studies mainly focus on two meteorological factors (temperature and precipitation) and rarely consider aerosol optical depth (AOD) and photosynthetically active radiation (PAR). Therefore, in this study, we further explored the potential dependence of leaf area index (LAI) on temperature (Tem), precipitation (Pre), AOD, and PAR, based on the analysis of the spatiotemporal variations of LAI in different vegetation types in China. Results showed that the LAI increased significantly from 2001 to 2020, with an overall growth rate of 0.0202/yr; the response of LAI for different vegetation types to each influencing factor varied greatly, and the deciduous broadleaf forests were more sensitive to climate change. Quantitative analysis based on the GeoDetector method showed that the most important driving force of LAI change is Pre (0.596); while Tem (0.394) and AOD (0.325) showed similar effects; by contrast, PAR (0.279) had the lowest driving force on LAI. Among them, the change of Pre is the dominant factor affecting LAI for most vegetation types. From the perspective of regional research, we found that the increase of LAI in the South was greater than that in North China, while human activities (urbanization) were unfavorable to the increase of LAI, such as the Yangtze River Delta where the LAI decreased significantly. This study contributes to a better understanding of the response of different vegetation types to climate and atmospheric changes in China over the past 20 years and provides a scientific reference for developing and implementing ecological restoration measures and promoting sustainable development. Overall, China is 'turning green', and air quality is improving.

The distribution of terrestrial soil organic carbon changes with climate and substrate conditions. This study investigated the spatial and temporal distribution of soil organic carbon and its influencing elements in the Yarlung Tsangpo River Basin using meteorological, subsurface, and remotely sensed data over the past 39 years, combined with random forests and geodetector. The results of the study showed that soil organic carbon has been increasing in the study area over the past 39 years, and this change has been concentrated in the central part of the watershed. Among the environmental factors, precipitation, temperature, and leaf area index were the main contributors to the soil organic carbon changes, and the effects of the three factors were more significant at higher altitudes, with a 33 % increase in the significance level. Notably, the interactions among the factors exceeded their individual effects, and the combination of climate and leaf area index showed a significant two-factor enhancement effect, with q-value enhancements generally ranging from 0.15 to 0.3. The two-factor enhancement effect was more pronounced below 3000 m above sea level, where the topography of the study area concentrates soil organic carbon in the gently sloping areas of the catchment.

Urbanization, driven by rapid economic growth, has resulted in dramatic land use/cover change (LUCC), posing substantial risks to urban ecological security. While constructing ecological networks is a productive strategy for achieving a balance between ecological protection and urbanization, the limited research conducted on the impacts of LUCC on the spatial patterns of ecological networks during urbanization poses a hindrance to the scientific construction of ecological networks. Therefore, this study focuses on Jinan City, an important city situated in the downstream region of the Yellow River Basin, China. The study utilized simulation approaches, including MSPA, MCR, InVEST model and gravity model, to investigate and analyze the ecological networks of Jinan City from 2000 to 2020. A set of patterns capturing the changes in ecological network elements was proposed to explore the evolutionary characteristics of the spatial pattern of ecological networks. Finally, Geodetector was utilized to investigate the impact mechanism of LUCC on the spatial pattern of ecological networks. The study findings indicate that: (1) During the last two decades, the urban development in Jinan City has witnessed an expansion of approximately 588.83 km2, and the proportion of ecological land has also increased, but the overall structure and spatial distribution of construction and ecological land are not balanced. (2) Despite the expansion of ecological areas, the escalation of ecological resistance has resulted in significant degradation of the functional capacity of ecological corridors, with a decrease of 8.37 % in the number of primary corridors. The reduced spatial connectivity of the ecological networks has impeded the efficient cycling of ecological processes between ecosystems. (3) The complex interactions and cumulative effects of LUCC during urbanization led to the evolution of the spatial pattern of ecological networks. The transformation of cropland and construction into ecological land has improved the composition elements of the ecological network, while the encroachment on ecological land and disorderly changes in internal types are the principal factors causing the degradation of ecological network composition elements. (4) The spatial pattern of ecological networks in Jinan City is influenced by the coupling of urban expansion and ecological protection policies. However, there is a need for the government to strengthen policy coordination and optimize spatial layout to better manage and conserve ecological networks. This study holds the potential to offer valuable scientific insights and references for guiding spatial planning, ecological protection, and the formulation of sustainable development policies throughout the urbanization process.

Both net primary productivity (NPP) and vegetation phenology play essential roles in influencing the carbon sequestration of terrestrial ecosystems. However, the relationship between vegetation phenology and NPP remains unclear under the effects of global warming. This study used Geodetector to analyze the interaction mechanisms between climate, phenology, elevation, and NPP in Yunnan, China. The results are as follows. (1) NPP is positively correlated with NDVI, LOS, EOS, TEMP, PREC and SRAD, and negatively correlated with ELEV, NDBI and SOS. However, the main influencing factors leading to the variation of NPP differ with the region. (2) The spatial distribution of NPP and LOS in Yunnan Province is mainly influenced by the monsoon, showing a pattern of high in the southwest and low in the northeast, and the interaction between PREC and LOS is the main reason for the variation of NPP. (3) In the high-altitude region of northwest Yunnan, the interaction between altitude and PREC is the primary factor affecting NPP variation. (4) In the tropical monsoon forest region of southern Yunnan, the interaction between SRAD and PREC is the main cause of NPP changes. (5) Despite having a small effect on NPP, SOS was the only phenological factor that showed a significant linear relationship with NPP. This study reveals the complex and diverse interactions between climate, phenology, elevation, and NPP in different regions of Yunnan, and provides a new perspective for understanding the carbon cycle and ecological processes of terrestrial ecosystems under global warming.

As homestays have developed rapidly, it is necessary to understand the factors affecting their density and type distribution at the micro scale. Based on the Internet data of home accommodation, the spatial distribution characteristics of homestays in Shenzhen are studied by the nearest neighbour analysis, and type distribution characteristics are analysed by NVivo. The impacts of factors are discussed through geographical detectors. We conclude that Shenzhen's homestays are multi-centre agglomeration and development in space with the flat floor as the main house structure and the modern style as the main decoration style. The spatial distribution is mainly affected by social and economic factors such as the density of bus stations and subways. The density and diversity of the homestays is high with the nearest and farthest distance to the railway station and the popular tourist destination beach while low with the mediate distance, featured with dumbbell shape.

The urban area of Yunfu City is a typical mountain environment with the thin surface soil layer, broken terrain, and the fragile geological environment, which is a landslide hazard prone region, seriously threatening the safety of residents' lives and property. It is crucial to accurately reveal the landslide risk and its causative factors to develop regional geological disaster prediction and mitigation strategies. However, the risk assessment and cause analysis of landslides in Yunfu City are rarely studied at present. Therefore, this study selects 11 causative factors (elevation, slope, slope aspect, bedding structure, distance from fault, lithology, precipitation, distance from water, normalized vegetation index (NDVI), distance from road, and land use), and integrates the modified frequency ratio model and geographic detector method to analyze the spatial distribution pattern of landslide risk and its causative factors. The results indicate that the maximum predicted area under the receiver operating characteristic curve (AUC) and the average predicted AUC of the modified frequency ratio model perform best at the precision of 5, with the values of 0.83921 and 0.81872, respectively, which can accurately reveal the landslide risk evaluation in the study area. According to the experimental results, the landslide risk in the study area generally shows a trend of high in the north and low in the south. Besides, the overall risk of landslide hazards is low, with the area of very low grade and low grade accounting for 75.20% of the total area. Among the selected 11 potential factors, precipitation, NDVI, slope aspect, distance from water and slope are the main causative factors to induce landslide hazards. Furthermore, this study also describes in detail the combination of factors that are conducive to landslide hazards, which can provide a decision basis for relevant departments to carry out regional landslide hazards prevention and mitigation strategies, and can also provide a reference for geological hazard risk evaluation study in other mountain environment.

The southern red soil region (SRSR) of China, a region that is environmentally sensitive, suffers severe soil erosion (SE) as a result of climate change and human activity. The factors driving SE changes in the SRSR of China under changing settings have, however, only been the subject of a small number of research. Changting County is a typical SE area in the SRSR of China. The spatiotemporal variation characteristics of SE in Changting County from 2000 to 2020 were investigated using the revised universal soil loss equation (RUSLE), while the dominant factors influencing the spatiotemporal dynamic change of SE were analyzed using the optimal parametric geographical detector (OPGD) and logarithmic mean Divisia Index (LMDI). The results showed that the average annual soil erosion modulus in Changting County shows a decreasing trend, with the SE modulus decreased from 10.00 t·hm−2·a−1 in 2000 to 3.38 t·hm−2·a−1 in 2020. Slight SE intensity was the dominant SE class, accounting for more than 75% of the total area, and the proportion is trending upwards. Natural and anthropogenic factors showed significant effects on the spatial variation of SE, of which land use and vegetation cover were the main factors influencing the spatial distribution of SE. Furthermore, the interaction between these two dominant factors explained greatly the spatial variation of SE. The mitigation of SE was mainly attributed to the increase in vegetation cover following the implementation of various ecological projects, contributing to the reduction of SE by 68.7%. In addition, the changes in land use types and rainfall amounts also played positive roles in reducing SE, showing contributions of 20.2% and 11.1%, respectively.

Yellow River Basin of China is an important ecological reserve. However, the spatiotemporal patterns of Gross Primary Production (GPP) in the Yellow River Basin of China are unclear from interannual, seasonal and vegetation-type perspectives. The interactive effects of temperature, precipitation, leaf area index and evapotranspiration on GPP and the climate-driven and human-activity contributions to GPP remain unclear. This study uses solar-induced chlorophyll fluorescence GPP data as indicator. Spatiotemporal characteristics of GPP are explored in the Yellow River Basin of China during 2001–2020 from interannual, seasonal and vegetation-type perspectives. Geodetector and Terrestrial ecosystem model are introduced to dissect the driving mechanisms of GPP under the interaction of multiple factors and to clarify the contributions of climate-driven and human-activity to GPP. The results show a general upward trend of GPP in the Yellow River Basin of China during 2001–2020. However, GPP in local areas where climate change and human activity are intense show a rapid decline, especially in large and medium-sized cities with human settlements. The ranking of single factors driving GPP is leaf area index> precipitation> evapotranspiration> temperature, and the explanatory power for GPP spatial heterogeneity after multi-factor interaction shows nonlinear enhancement. The positive climate-driven effect on GPP is concentrated in the grassland of the Qinghai Tibet Plateau and Qilian Mountains in the west of the Yellow River Basin. The positive human-activity effect on GPP is mainly manifested through crops, concentrated in the southeastern farming region, showing significant seasonal variations. This study provides a theoretical basis for the ecological protection and sustainable development of the Yellow River Basin in China.

Vegetation is an important factor linking the atmosphere, water, soil, and biological functions, and it plays a specific role in the climate change response and sustainable development of regional economies. However, little information is available on vegetation vulnerability and its driving mechanism. Therefore, studying temporal and spatial change characteristics of vegetation and their corresponding mechanisms is important for assessing ecosystem stability and formulating ecological policies in the Kherlen River Basin. We used Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing images from 2000 to 2020 to analyse temporal changes in NDVI with the autoregressive moving average model (ARMA) and the breaks for additive season trend (BFAST) in the basin and to assess natural, anthropogenic and topographic factors with the Geodetector model. The results show that: 1) the long NDVI time series remained stable in the Kherlen River Basin from 2000 to 2020, with a certain significant mutation period from 2013 to 2017; 2) the coefficient of variation (CV) in the analysis of the spatial NDVI was generally constant, mainly at the level of 0.01–0.07, and the spatial NDVI change was minimally impacted by external interference; and 3) temperature and precipitation are the key factors affecting the NDVI in the basin, and changes in local hydrothermal conditions directly affect the local NDVI. The results of this study could provide a scientific basis for the effective protection of the ecological environment and will aid in understanding the influence of vegetation change mechanisms and the corresponding factors. [ABSTRACT FROM AUTHOR]

Tourism ecological security (TES) is an important index reflecting the sustainable development of the regional economy. The construction of the China and ASEAN Free Trade Area has increased the total tourist consumption of Guangxi province by 36.48%. Unfortunately, overconsumption of resources, air pollution, disturbance of indigenous life, and other environmental degradation problems emerged due to the significant increase in tourists. Measuring the resilience of the tourism ecosystem is an urgent need to promote the high-quality development of tourism in Guangxi. To explore the dynamic changes in TES and its driving mechanism, the DPSIR (driver–pressure–state–impact–response) model for the tourism ecosystem was developed. The dynamic changes in TES and its driving mechanism from 2010 to 2019 were analyzed using fuzzy matter-element analysis, Markov chains, Geodetector, and other methods. The results show that: (1) the TES value increased steadily by 72.73%; the improvement speed was Northeast > Southwest > Southeast > Northwest; (2) TES was negatively correlated with location, 14 cities developed independently; (3) the TES has a smaller probability to shift the lower level; (4) urbanization, water consumption, green area, tourism revenue, and the number of students in colleges had significant effects on TES. Four policies were proposed to improve TES: (1) developing forest tourism; (2) implementing greening projects in abandoned mining areas; (3) increasing tourism technical personnel; and (4) reducing clearance time for inbound tourists.

Urbanization induced changes have attracted widespread attention. Key challenges arise from the inherent uncertainties in attribution models diagnosing the driving mechanisms and the interrelationships of the attributes given by the complexity of interactions within a city. Here, we investigate urbanization dynamics from nighttime light signals before analyzing their driving mechanisms from 2014 to 2020 on both provincial and regional scale and a flat versus mountainous urbanization comparison. Model uncertainties are discussed comparing the contribution results from Geodetector and the Gini importance from Random Forest analyses. The method is applied to Shaanxi Province, where flat urban land is located mainly in its center and mountainous urban land is situated in the North and South. The following results are noted: i) Employing the Geodetector based maximum contribution method for urban region extraction of night time light reveals a notable accuracy improvement in flat urban land compared with the closest area method. ii) Geographical factors attain high contribution for mountainous urban land of Shannan, while for flat urbanization land dynamics, economic factors and community factors prevail. iii) The most obvious driving mechanisms are economic factors which, associated with local urban development strategies, show highest contribution values in 2014 (2018) over the flat (mountainous) urban land of Guanzhong Plain (Northern Shaanxi Plateau or Shanbei region) linked with an early (late) development. iv) Population factors achieve high contribution values in the initially low populated urban land of the northern mountainous land initiating huge migration. v) The contributions resulting from Geodetector are in agreement with the Gini importance from Random Forest in agriculture, geographical and population factors (R2 > 0.5) but not in economy, community and climatic factors (R2

As the core area in the transformation of Kunming into an international center city, studying the changes in ecological environment quality and causes of the Dianchi Lake Basin is of great significance for its future optimization of the landscape pattern. This study is based on the Google Earth Engine (GEE) platform to calculate the Remote Sensing Ecological Index (RSEI) of the Dianchi Lake Basin from 1990 to 2020. Then we used Mann-Kendall mutation detection to obtain the time points when significant changes in RSEI occurred. Finally, the Geodetector and MGWR models were combined to analyse the driving factors of ecological quality changes in the Dianchi Lake Basin. The results show that: (1) The ecological environment quality of the Dianchi Lake Basin showed an increasing trend from 1990 to 2020, with the mean value of RSEI increasing from 0.49 to 0.52. (2) According to the results of the mutation test, the years 1990, 1993, 2006, 2015, and 2020 were used as the time points for monitoring the ecological environment quality of the Dianchi Lake Basin over a long time series. The ecological environment quality of the study area in the past 30 years was mainly in an improved state, accounting for 49.43%. The ecological deterioration areas are mainly located in the northeastern part of Xishan District (north of Caohai Lake), the southwestern part of Guandu District, Kunyang Town in Jining District, and the northern areas of Jincheng and Shangsuan Town. (3) The single factor detection results show that elevation and slope have the strongest influence on RSEI. The q-value of average annual temperature has changed the most, from the 6th to the 3rd place. This indicates that the urban heat island effect and the expansion of construction land have had a greater impact on the quality of the local ecological environment in recent years. The multi-factor interaction test shows that the influence of each factor on RSEI is enhanced after the interaction. (4) The MGWR regression results show that the actual scales of action of the factors are inconsistent, with the most significant spatial heterogeneity in the Percentage of cropland area. Based on the above findings, it can provide data to support the future urban planning of the Dianchi Lake Basin. It also provides a new means of integrating Geodetector and MGWR into the study of ecological quality analysis.

Sustainable urban development is one of the most urgent problems worldwide, especially in China, which is in rapid urbanization. Reducing the negative impacts of urbanization and improving the relationship between the two have become research priorities. Using 260 cities in China as the research object, this study evaluated the level and temporal and spatial variation of urbanization and natural ecosystem health, explored the relationships between them, and influence mechanism of urbanization on natural ecosystem health. The results showed that the level of population urbanization, social urbanization, economic urbanization and land urbanization was significantly improved in 2000 compared to 2015, and the spatial distribution was higher in the east of China than in the west. The overall level of ecosystem health improved slightly throughout the same period, and its spatial distribution was higher in the north of China than in the south. In terms of the four dimensions of ecosystem health, the ecosystem services and organization were improved, and the ecological environment and elasticity were decreased. Urbanization mainly affected ecosystem health by changing the demand for the quantity and type of ecosystem services through the growth and aggregation of population. However, under the comprehensive effect of the four dimensions of urbanization on natural ecosystem health, the overall constraint intensity of urbanization on natural ecosystem health was relatively stable. The coupling and coordination level between urbanization and natural ecosystem health showed improvement from 2000 to 2015, and the social and economic factors were highly influential. According to the level of urbanization and natural ecosystem health, all cities were classified, which showed the urban development was mainly the medium-level balanced. On this basis, management suggestions were put forward for each type of city. By analyzing the action process and mechanism between urban development and natural ecosystem health, it provides a theoretical basis for in-depth understanding and coordinating the relationship of the urban ecological economy.

Regional function identification and transformation mechanism exploration provide an entry point for a comprehensive understanding of the pattern and direction of rural development. This paper proposes a new analytical framework by establishing first, the rural production-living-ecological evaluation index; second, the optimal functional clustering of 186 counties in the Central Plains Urban Agglomeration area of China by combining the Self Organizing Feature Map network, Geodetector model, and Mann-Kendall test method; and third, the transformation mechanism by comparing the 2-phase zoning results with an interval of 10 years. An overall positive trend was found in the transformation of the rural function. The proportion of lagging development functional areas decreased by 20.5 %. The proportions of dominant function areas and composite coordination function areas increased by 5.4 % and 15.1 %, respectively. There was a synergistic relationship between production and living functions and an opposing relationship between ecological functions and the other two. The functional area with a synergistic relationship had the highest conversion rate, and most of the functional areas with trade-off relationships remained unchanged. The transfer-out rate of functional areas with synergistic and trade-off relations is negatively correlated with its lagging functional inferiority. The degree of difficulty in transitioning to functional areas with synergistic or trade-off relations is similar. The research reveals the mechanism and law of the evolution of rural regional functions and provides a methodology reference and policy planning basis for further research on rural areas.

Background: Mangrove forests are a significant contributor to the global carbon cycle, and the accurate estimation of their gross primary productivity (GPP) is essential for understanding the carbon budget within blue carbon ecosystems. Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems, as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP. Methods: This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve, China. A leaf area index (LAI)-based light-use efficiency (LUE) model was combined with Landsat data on Google Earth Engine (GEE) to reveal the variations in mangrove GPP using the Mann-Kendall (MK) test and Theil-Sen median trend. Moreover, the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators. Furthermore, the effects of six geo-environmental factors (species distribution, offshore distance, elevation, slope, planar curvature and profile curvature) on GPP were investigated using Geodetector and multi-scale geo-weighted regression (MGWR). Results: The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ​ha in 2000 to 718.29 ​ha in 2020, mainly due to the conversion to farming, terrestrial forest and aquaculture zones. Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones, and low levels of aggregation but high species diversity and distribution density near building zone. The mean value of mangrove GPP continuously increased from 6.35 ​g ​C·m−2·d−1 in 2000 to 8.33 ​g ​C·m−2·d−1 in 2020, with 23.21% of areas showing a highly and significantly increasing trend (trend value ​> ​0.50). The Geodetector and MGWR analyses showed that species distribution, offshore distance and elevation contributed most to the GPP variations. Conclusions: These results provide guidelines for selecting GPP products, and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode, direction, pathway and intensity of the influencing factors on mangrove GPP variation. The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.

Liaoning Province, one of China’s top 13 grain-producing areas, has recently implemented various ecological construction and protection plans. To ensure regional environmental protection, national food security, and the development of scientific ecological protection strategies, it is crucial to understand the spatial and temporal evolutionary patterns and driving forces of the landscape’s ecological risk. This study used spatial statistics and spatial autocorrelation to examine the scale response features of landscape risk in Liaoning Province. The best study scale was then selected and the GeoDa software platform was used to examine the pattern of landscape risk evolution in Liaoning Province from 2000 to 2020. Finally, the main spatial drivers of ecological risk in Liaoning Province were analyzed hierarchically according to the magnitude of the explanatory power of the q-statistic by combining the driving force-pressure-state-impact-response model with Geodetector. Several experiments were conducted to determine the optimal taxonomy and classification number for the discretization of each driver. The results showed that (1) the Liaoning River had a high-risk value and thus required careful consideration to maintain sustainable and healthy growth of the area and conserve ecological environment of the southeastern coastline of Liaoning Province. (2) The township scale was regarded as the ideal scale because all nine scales of landscape risk indices in Liaoning Province showed positive spatial connections and the greatest significant difference with it. (3) Clustering and significance analysis determined that townships in Liaoning Province with higher landscape risk indices were more likely to cluster spatially and had less spatial variance. (4) The two factors with the greatest impact on landscape risk in Liaoning Province were temperature and gross domestic product per capita. Landscape risk was the result of the combined influence of these multiple factors. Hence, the carrying capacity of the environment itself cannot be ignored while pursuing economic growth. Our findings offer theoretical support for government to formulate ecological protection policies.

Understanding the spatial distribution characteristics and drivers of ecological service value (ESV) in the six northwestern provinces, which are the main components of the Chinese section of the “Silk Road Economic Belt”, is crucial to the sustainable social and economic development of the six northwestern provinces and the countries along the economic belt and informs regional environmental protection policies. Based on the land use data of the six northwestern provinces from 2000 to 2018, the spatial distribution of ESV was determined using the modified equivalent factor method to calculate their ecological service values, and the Geodetector was used to determine the main factors affecting the distribution of ESV. The results show that (1) the total number of ESV in the six northwestern provinces is increasing year by year. (2) The spatial distribution of ESV in the six northwestern provinces varied significantly. (3) The single-factor drivers affecting the spatial distribution of ESV were normalized vegetation index (NDVI) and precipitation, and the multi-factor interactions were NDVI ∩ temperature and NDVI ∩ precipitation, with the above factors contributing most to the spatial distribution of ESV in the six northwestern provinces. Based on the results of the study, it is possible to provide a scientific basis for ecological protection and optimal land management, as well as to propose an applicable methodology for assessing regional ESV for similar regions.

Assessing the risk and sources of heavy metals in cultivated soil is important for the agricultural development of the Qinghai–Tibet Plateau. A survey was conducted in this study to determine the spatial distribution and possible sources of heavy metals in the agricultural soils in Huzhu county, a representative agricultural suburb in Qinghai-Tibet Plateau. A total of 120 surface soil samples were collected, soil pH and concentrations of chromium(Cr), cadmium(Cd), plumbum(Pb), arsenic(As), and hydrargyrum(Hg) were analyzed. The pollution risk degree of heavy metal pollution was also evaluated. The mean concentrations of the heavy metals were 70.8, 0.24, 23.7, 12.2, and 0.08 mg/kg for Cr, Cd, Pb, As, and Hg, respectively, higher than the soil background values of Qinghai Province with the exception of As, but lower than the guideline values of Chinese standard for soil contamination of agricultural land. The sampling point contamination risk of the heavy metals showed a site feature, and most sampling points of Cd and Hg was exhibit polluted. The potential ecological risk index indicated that most of the cultivated soils have low and moderate ecological risks. Multivariate and geostatistical analysis suggested that Cr, As, and Pb were due mainly to soil parent materials, Cd contamination was caused by agricultural and transportation activities, and Hg contamination was caused by coal combustion and industrial activities. The results of this study is a basis for undertaking more effective and accurate heavy metal control strategies to protect the safety of agricultural ecosystems.

To understand the spatiotemporal evolution and driving mechanisms of vegetation in the Yellow River Basin, as well as to promote regional ecological construction, we used MODIS NDVI data as the vegetation growth indicator, and the methods of Theil-Sen slope statistics, Mann-Kendall, GeoDetector, correlation analysis, and residual trend analysis. The results show that the spatial distribution of NDVI grows stepwise from northwest to southeast, leached soil with high organic matter content and weak water erosion can make vegetation grow better. The growing season average NDVI is a fluctuating upward trend of 0.005/a during 2000–2020, areas with obviously improved vegetation mainly distributed in the Qinling Mountains, the Northern Shaanxi Plateau, and the Lvliang Mountains in the basin. The partial correlation coefficients of NDVI with precipitation and temperature are 0.57 and 0.53, respectively, and the impact of precipitation on vegetation is higher than temperature. The areas where human activities increase vegetation rapidly are mainly distributed in the northern Shaanxi Plateau, the Lvliang Mountains, and southern Ningxia. The areas where human activities decrease vegetation growth are mainly distributed in cities with strong human activities such as Yinchuan, Baotou, Xi'an, Luoyang, Zhengzhou, and Taiyuan. Human activities and climate change contribute 66% and 34% of the vegetation change respectively in the YRB.

As a result of rapid economic development, ecological environmental problems have become increasingly serious. Ecological vulnerability (EV) assessment can scientifically analyse the causal mechanism and change laws of ecosystem vulnerability, providing a basis for decision-making for ecological environmental protection. Based on the pressure-state-response (PSR) model, this paper selects 13 indicators, including the geological hazard index, soil erosion sensitivity, population density, proportion of developed land, remote sensing ecological index, and landscape adaptability index, to construct an EV evaluation index system for Badong County, China. Furthermore, the analytic hierarchy process (AHP)-coefficient of variation (CV) method and the self-organizing map (SOM) model are used to comprehensively and quantitatively evaluate the EV of Badong County in 2020. The main factors affecting the EV are explored through GeoDetector. The results show that the EV in Badong County is at a moderate level and above; it is high in the north and south and low in between. According to the analysis of the EV results, the 12 townships in Badong County can be divided into 3 classes. Class I includes Xinling and Dongdukou towns, where the EV is high. Ecological governance measures should be strengthened in these two towns. Based on the GeoDetector results, the spatial distribution of EV in Badong County is mainly related to five factors: the geological hazard index, habitat quality index, proportion of developed land, remote sensing ecological environment index, and soil erosion sensitivity.

The Changshagongma wetlands is the Chinese National Nature Reserve were listed as a Ramsar Wetland of International Importance in 2018. Here, we examined four periods (1992, 2002, 2013, and 2020) of remote sensing image data to analyze the changes in wetland landscape patterns and the ecological risk in Changshagongma Wetland Nature Reserve over the past 30 years. The results showed that wetlands account for approximately 30% of the study area, and swamp meadows were the main type of wetland, accounting for approximately 95% of the total wetland area. In terms of landscape patterns, wetland fragmentation declined, wetland patch shapes became less complicated, and spatial connectivity increased. The landscape fragmentation of non-wetland alpine meadows was reduced. The patches of sandy grasslands tended to be regular, and their spatial connectivity was reduced. The wetland regions of high ecological risk are concentrated in the central and southern parts of the Changshagongma Wetland Nature Reserve. Low-risk regions are mainly concentrated in the contiguous swamp meadows in the northwest and wetlands in the southwest. From 1992 to 2020, the level of ecological risk of the Changshagongma Wetland Nature Reserve showed a “∧”-shaped trend, with the highest risk in 2002 and the lowest risk in 2020. Among the selected indicators, climate conditions constituted the main factor affecting the ecological risk of the Changshagongma Wetland Nature Reserve, followed by topographical conditions, and human activities were the least influential. Over the past 30 years, the temperature and precipitation in the study area increased significantly. The climate in the study area can be roughly divided into two periods bounding 2002, and the climate has been changing from cold and dry to warm and wet. The ecological environment of the study area is affected by natural and human activities. Cold and dry climatic conditions and uncontrolled grazing accelerate the destruction of the wetland ecological environment, and warm and wet climatic conditions and ecological conservation policies are conducive to the ecological restoration of wetlands. In general, the wetland landscape structure in the study area has become less complex, landscape heterogeneity has decreased, and ecological quality has improved.

Measuring the value of natural capital provides decision support in regard to the trade-off between natural capital use and protection. Evaluating the natural capital value of land from the perspective of the rural land ecosystem is helpful for better understanding its value. Moreover, research on the spatiotemporal variation characteristics of the natural capital value of land facilitates determination of the rural land natural capital value (RLNCV) evolution trend. This study constructed an accounting system of the RLNCV in Chongqing from the donor side based on emergy theory combined with a spatial analysis method. The spatiotemporal characteristics, spatial correlation, and spatial differentiation in the RLNCV were studied. The main conclusions are described as follows: (1) The RLNCV in Chongqing increased year by year, from 31.035 billion yuan in 2005 to 204.087 billion yuan in 2018. The high-value areas were mainly distributed in the northeast and southeast regions, and the low-value areas were largely located in the Metropolitan Economic Circle (MEC) and the region around the main city. The change rates of most districts and counties in the southeastern region were high, while those of certain districts and counties in the northeastern region were low. However, due to the large base, the value was relatively high. (2) The global Moran's I of the RLNCV in Chongqing increased from 0.602 in 2005 to 0.624 in 2018, indicating that the geographical spatial autocorrelation was increasing, there was a spatial agglomeration effect, and the coefficient of variation increased from 0.096 in 2005 to 0.808 in 2018; these findings indicate that the regional difference was increasing, and the spatial imbalance was notable. (3) The RLNCV spatial association types in Chongqing indicated a significant positive correlation-related agglomeration distribution. The high-value agglomeration association types were mainly distributed in the northeastern and southeastern regions, and the low-value agglomeration association types largely occurred in the western part of the region near the main city. (4) The rainfall factor attained the highest explanatory power regarding the spatial differentiation in the RLNCV in Chongqing, and the human activity factor had limited influence. However, the synergistic effect of the human and socioeconomic factors continuously increased, and the influence of the policy factors increased. Chongqing was as selected the case study in this work, and it yields important reference significance for rural land natural capital evaluation in the unique area of human-land relationship change and facilitates the implementation of appropriate government policies for rural land natural resource management. Based on this study, policymakers should optimize the spatial differentiation control mechanism, accounting evaluation mechanism, and green development. This study provides a reference for understanding the RLNCV from the donor side and can guide the maintenance and appreciation of natural capital in areas with complex and diverse human land relationships and unbalanced development.

Under conditions of rapid urbanization, quantitative measurement of the expansion characteristics of urban landscapes and evaluation of their spatial and temporal differentiation properties is an important scientific problem. The Multi-order Adjacency Index (MAI) is an effective indicator of the quantitative characteristics of urban landscape expansion; however, it cannot directly reflect the spatial heterogeneity of this expansion. Therefore, it is necessary to combine the MAI with a spatial heterogeneity detection method to measure and evaluate the spatiotemporal divergence features of urban landscape expansion. In this study, taking Chenggong District, Kunming City, Yunnan Province, China as the study area, we apply MAI to analyze the changing process of urban landscape pattern expansion, and use the Geodetector to analyze the drivers affecting the spatial divergence of this expansion. We compare the results with the Landscape Extension Index and geographically-weighted regression models. The results show that MAI can reveal urban landscape expansion well at the micro-level, and analysis of the urban landscape expansion process reveals that it is mainly socio-economic factors that lead to the spatial heterogeneity of urban landscape expansion. Combining MAI and Geodetector can solve the problem that existing methods cannot directly reflect the heterogeneity of urban expansion landscape patterns and their causes.

Selenium (Se) is essential for human health. Its concentration in surface water is determined by diverse environmental factors. However, few studies have discussed the distribution of the surface water Se concentration in Tibet; quantitative analysis of how multiple environmental impact factors affect the distribution of surface water Se concentration is also lacking. In this study, the relationship between surface water Se concentration and environmental indicators is investigated in a broad scope by chemical examination, and the spatial distribution of 702 surface water samples across 66 counties in Tibet is comprehensively analyze. The principle of spatial stratified heterogeneity is used not only to measure the individual or interactive influences of 10 potential environmental impact factors, but also to identify the highest potential impact types or ranges of each factor. Results show that the surface water Se concentrations in Tibet are very low, with a mean value of 0.519 ± 0.551 μg/L (range of 0–3.944 μg/L). Spatially, surface water Se concentrations exhibited a reducing trend toward the central and southeast parts of Tibet. In general, surface water in desert areas with low precipitation, which feature alkaline soil environments and magmatic rock outcrops, had higher Se concentration, while overall, the temperature and geographical environment showed little impact on surface water Se concentration. This study contributes to a better understanding of the spatial distribution and environmental impact mechanism of surface water Se concentration in Tibet, and provides baseline indicators for the establishment of a prediction model for the distribution of Se in surface water based on spatial characteristics.

Urban green space (UGS) is essential to the improvement of human well-being and health, and the inequity of UGS supply has received widespread concern. The urban green space availability (UGSA) can effectively evaluate UGS supply. However, the UGSA under different daily transportation patterns has not received enough attention, as well as the potential of UGS that is not available by transportation patterns. Meanwhile, the inequity of UGS supply between cities with different economic development levels at the regional or national scale is still unclear. So, we constructed seven UGS indicators to represent the UGS supply: UGSA considering four kinds of daily transportation patterns (UGSA0.5 km, UGSA2.5 km, UGSA10km, and UGSA30km), the potential of UGS (PUGS), the proportion of PUGS (PPUGS), and stock of UGS indicator (SUGS). The spatial patterns of availability, potential, and stock of UGS in 366 cities on the Chinese mainland in 2015 were captured. Then, the concentration curve and concentration index (CI) were applied to describe the inequity of UGS supply between the cities. Finally, we used correlation analysis, regression analysis, and GeoDetector techniques to explore the driving factors of the spatial pattern of availability and potential of UGS. The results showed that: (1) The SUGS, PUGS, and PPUGS were lower in the southeastern region indicated by the Hu Huanyong Line than that in the northwestern region. Only 44.11% of China's UGS were available through the daily transportation patterns. The spatial patterns of the four UGSA were similar, while the UGSA30km’s hot spots appeared in Southwest China. (2) Less developed cities occupied more SUGS, PUGS, and PPUGS. With the increase of buffer radius, the equity of UGSA among cities was gradually improved, while developed cities always possessed more UGSA. (3) With the increase of buffer radius, the dominant driving factors of UGSA indicators gradually changed from social economy and infrastructure variables to physical geography variables. Our study provides new evidence for a comprehensive understanding of UGSA, and the results of UGS supply and its inequity will contribute to sustainable UGS development in targeting cities or regions in China.

The Yangtze River basin contains one-third of the important ecological function areas in China. In recent years, pine wilt disease (PWD) has broken out in many provinces in this region, causing serious damage to the forest ecological environment. There have been many studies on the risk attribution of PWD at the stand scale but few macroscale studies. In this study, 1104 county-level administrative regions in the Yangtze River basin constituted the study area. We surveyed the epidemic of PWD and environmental factor data for the region. Taking into account the spatial heterogeneity of the risk of PWD occurrence, we conducted a macroscale attribution study on the risk of PWD outbreaks using GeoDetector. In 2000, 2005, 2010 and 2015, there were 51, 100, 101 and 126 affected counties in the Yangtze River basin, and the global Moran's I index of the incidence of PWD was 0.445, 0.220, 0.151 and 0.143, respectively. The spatial autocorrelation of the incidence of PWD has been decreasing annually, showing a trend with multi-point bursts. The number of affected counties has continued to increase in the central and western parts of the Yangtze River basin, and the mean centre of the affected areas has continued to move to the southwest. The risk detector shows that areas with high wind speed and many sunshine hours are the potential high-risk areas for PWD. The factor detector shows that the normalized difference vegetation index (NDVI), average wind speed and sunshine hours have the strongest effect on the incidence of PWD, with explanatory power of 20.5%, 20.3% and 10.4%, respectively. The interaction between average wind speed and NDVI and the interaction between average relative humidity and sunshine hours show a significant nonlinear enhancement effect, with their explanatory power reaching 64.7% and 60.8%, respectively. Analysis of the spatiotemporal data on PWD in the Yangtze River basin revealed a westward transmission pattern of PWD in the study area, and the interaction of environmental factors could increase the risk of this disease.

Exploring vegetation dynamics and their responses to different natural and anthropogenic factors is crucial for understanding ecosystem processes in the context of global change. As an important ecological security barrier in Southwest China, the northwestern Yunnan Plateau (NYP) provides a variety of ecosystem services. In this study, we investigated the spatiotemporal variation in vegetation cover and quantitatively analysed the relative contributions of potential influencing factors and their interactions to vegetation change on the NYP from 2005 to 2015 using a novel spatial analysis method, the Geodetector model (GDM). Additionally, the most suitable types or ranges of the main influencing factors that were conducive to vegetation growth were identified. Our results showed that the trend of vegetation cover on the NYP was generally negative, with a rate of − 0.0031 yr−1 during the 11-year study period, and was spatially heterogeneous. Areas with obviously decreasing trends were almost twice as large as those with increasing trends (27.49% and 14.37%, respectively) and were mainly concentrated in southeastern and northern Dali as well as the central part of Diqing. Vegetation dynamics were primarily driven by soil type (24.8%), elevation (18.6%), geomorphic type (16.1%), and vegetation type (13.2%), and anthropogenic factors played a weak role in vegetation change, with a contribution of less than 10%, demonstrating that the influences of natural factors on vegetation change were greater than those of anthropogenic factors. Moreover, the interaction of pairwise factors played a more important role in affecting vegetation dynamics than did each factor individually. These findings are helpful for better understanding the complex mechanisms of vegetation change and providing scientific suggestions for the prevention of vegetation degradation in fragile ecosystems.

There were dramatic changes in the ecological vulnerability (EV) of the China-Pakistan Economic Corridor (CPEC) region due to the effects of climate change and human activity. Obtaining field observation and statistical data for the evaluation of the EV of the CPEC region is difficult due to its transnational status. This study proposes a novel remote sensing ecological vulnerability index (RSEVI) based on the Moderate Resolution Imaging Spectroradiometer (MODIS), STRM3, DMSP-OLS, and NPP-VIIRS products. The RSEVI index was applied to the CPEC region to investigate the spatiotemporal changes in EV and the influencing factors during 2000–2019. The results showed that: (1) the large-scale application of the RSEVI index showed good applicability to the CPEC region, with a precision of 89.75%; (2) the average RSEVI value for the CPEC region was 0.83, thereby falling into a category of “intensive vulnerability”; (3) a stable trend in RSEVI was observed for the entire CPEC region during the study period, except for the Indus Basin where there was a significant change; (4) the increasing rate of the RSEVI in the northeastern parts exceeded that of other parts during 2000–2019; (5) precipitation, temperature, and vegetation coverage showed negative relationships with RSEVI, whereas RSEVI showed a positive relationship with slope. The RSEVI results indicated that ice and bare land were the most vulnerable, whereas cropland was the least vulnerable; (6) there were differences in the dominant factor and dominant interactive factor among different sub-regions. The results of this study could provide important decision support for the protection of the ecological environment and for economic development.

Understanding the interactions of climate-vegetation and human-vegetation has been a critical issue and increasingly attracting attention from scientific community in the field of global change research. This study investigated the heterogeneous impacts of climate change and anthropogenic activities on vegetation change by applying the trend analysis and Geodetector approach. The spatial and temporal patterns of MODIS NDVI and LAI during 2003–2017 were firstly examined in China. We then quantified the contribution and interactions effects of climatic factors (temperature and precipitation) and anthropogenic factors (population, gross domestic product and other four categories of forestry investment) on vegetation change in five typical areas of China. Both NDVI and LAI across China demonstrated a significant increasing trend over the past two decades. However, the eastern developmental regions such as Beijing-Tianjin-Hebei Region and Yangtze River Delta exhibited a decreasing trend due to fast urbanization. Socio-economical inputs (the explanatory power of forestry investment > 40%, the range of explanatory power is 0 to 100%) were identified as the dominant driving forces of vegetation change for the most of study areas. Precipitation was the most important natural influencing factor of vegetation change. We also found that the interactions between forestry investment and other factors presented much greater explanatory power on vegetation change than a single factor. Our research highlights that the afforestation program in China during the past several decades plays an important role in contributing to vegetation greening across the country. Greening and degradation, however, are largely related to landscape context, which could be due to natural change and anthropogenic impact. To maintain high levels of forests, conserving the vegetation is more important than increasing the economic development.

Reliable detection and observation of vegetation changes are increasingly essential for investigating the dynamic balance of regional ecosystems. The Yellow River Basin (YRB) serves as an ecological barrier in northern China, where vegetation is extremely sensitive to climate change and human activities. However, whether there has been a significant change in the intensity of vegetation dynamics in the YRB during the 21st century and how much anthropogenic disturbances contribute to the change are still poorly documented so far; thus, this work may hold a far-reaching significance for maintaining the eco-balance of northern China and even all of China. In this study, based on the annually and seasonally averaged normalized deviation vegetation index (NDVI), a comprehensive analysis was conducted to detect the trends of vegetation changes derived by NDVI at six different time scales during the period 2000–2018 using Theil-Sen statistics, and the Mann-Kendall (M-K) method was employed to test the significance levels of vegetation greening and browning. Subsequently, based on Moderate Resolution Imaging Spectroradiometer land cover data, in situ climatic and meteorological data and socioeconomic and anthropogenic factors were included to explore the driving forces for vegetation change through the GeoDetector method. Our results show that the YRB experienced both significant vegetation greening and browning with great spatial heterogeneity. More than 94% of the regions in the YRB showed vegetation greening trends at average rates of 0.0040 yr−1 in the annually averaged NDVI (NDVIAN) and 0.0053 yr−1 in the growing season averaged NDVI (NDVIGS) during the 21st century; the trend was significant in more than 71% (p

Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K-means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.