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The identification and quantification of soil heavy metal (HM) pollution sources and the identification of driving factors is a prerequisite of soil pollution control. In this paper, the Sabaochaqu Basin of the Tuotuo River, located in the Tibetan Plateau and the headwater of the Yangtze River, was selected as the study area. The soil pollution was evaluated using geochemical baseline, and the source apportionment of soil HMs was performed using absolute principal component score-multiple linear regression (APCS-MLR), edge analysis (UNMIX) and positive matrix decomposition (PMF). The driver of the source factor was identified with the geodetector method (GDM). The results of pollution evaluation showed that the HM pollution of soil in the study area was relatively light. By comparison, UNMIX model was considered to be the preferred model for soil HMs quantitative distribution in this study area, followed by PMF model. The UNMIX model results show that source 1 (U-S1) was dominated by As, with a contribution rate of 53.31%; source 2 (U-S2) was dominated by Cd and Zn, whose contribution rates are 50.35% and 46.60% respectively; source 3 (U-S3) was dominated by Pb, with a contribution rate of 45.58%; source 4 (U-S4) was dominated by Cr, Cu, Hg and Ni, with contribution rates of 60.58%, 60.07%, 51.58% and 56.45%, respectively. The GDM results showed that the main driving factors of U-S1 were distance from lake (explanatory power q = 0.328) and distance from wind channel (q = 0.168), which were defined as long-distance migration sources. The main driving factors of U-S2 were parent material type (q = 0.269) and distance from Tuotuo river (q = 0.213), which were defined as freeze-thaw sources. The main driving factors of U-S3 were distance from town (q = 0.255) and distance from county road (Yanya Line) (q = 0.221), which were defined as human activity sources. The main drivers of U-S4 were V (q = 0.346) and Sc (q = 0.323), which were defined as natural sources. The GDM results of the 3 models were generally consistent with the analytical results of similar types of sources, especially the results of PMF model and Unmix model can basically verify each other. The research results can provide important theoretical reference for the analysis of HM sources in the soil of high-cold and high-altitude regions. [ABSTRACT FROM AUTHOR]

Introduction: Investigation of the evolutionary trend of habitat quality in karst and rocky desertification zones is crucial for enhancing ecological security and conservation. Methods: Analysis of land use statistics from the years 2000, 2010, and 2020, changes in habitat quality (HQ) and land use (LULC) between 2000 and 2020 were analyzed using Huize County in Yunnan Province as an example. The InVEST and FLUS models were applied to simulate LULC under different scenarios in 2030 and 2040 and assess changes in spatial gradients of habitat quality at each timepoint and factors influencing them. Results: The findings indicated that (1) The predominant land use types are grassland and woodland, experiencing the most significant growth in urbanized areas, the main sources of which are paddy fields and high-cover grassland. (2) The habitat quality between 2000 and 2020 was average and displayed a consistent decline. The spatial distribution pattern indicates low HQ in urban areas, high HQ in the outskirts, low HQ in the south-west, and high HQ in the north-east. In all four scenarios, habitat quality predominantly decreases in urban areas and regions with a dense concentration of built-up land. (3) Habitat quality spatial distribution is primarily affected by the type of land use, with NDVI being the secondary determinant. Discussion: The ecological environment of Huize County must be restored and safeguarded with a focus on ecological priorities and harmonious development scenarios. This study provides methodological lessons for ecorestoration and policymakers in areas of karstic rocky desertification. [ABSTRACT FROM AUTHOR]

The identification of spatiotemporal heterogeneity, its key determinants, and the interaction effects between the driving factors of urban resilience (UR) within and between subregions is fundamental for understanding its underlying mechanisms. A resilience evaluation model was applied to analyze the temporal and spatial differences in UR in Hunan Province, China. A hot spot analysis identified clusters of cold and hot spots. The contributions of spatiotemporal differences in UR within and between subregions were assessed using the Theil index. A geodetector analysis determined the factors influencing UR and their interactions. There was an increasing trend in UR from 0.2692 in 2014 to 0.3422 in 2022. The number of cities with a high resilience gradually increased from 2014 to 2022, while there was a decreasing gradient in UR from northeast to southwest across the province. High-resilience cities had positive spillover effects on the surrounding area. Hot spots were predominantly located in the northeast, while cold spots were concentrated in the southwest. The barycenter of UR shifted from northeast to southwest by 2018, before moving southeast by 2022. The Theil index values declined over time both within and between subregions. Per capita GDP, the average wages of on-the-job employees, per capita social consumption, and doctor density were found to enhance UR. Two-factor interactions had a greater influence on the spatiotemporal heterogeneity of UR than single factors. Two-factor and nonlinear enhancements were identified as the primary mode of influence. [ABSTRACT FROM AUTHOR]

Agricultural greenhouses (AGs) are an effective solution to address the growing demand for vegetables despite limited cropland, yet significant soil quality problems often accompany them, particularly in high-altitude regions. However, the effects of natural factors and production management on soil quality are not well understood in such fragile environments. This study analyzed soil quality differences between AGs and adjacent open cropland (OCs) in the Lhasa River Valley, Tibetan Plateau, based on 592 soil samples and 12 key soil physicochemical indicators. GeoDetector was used to identify the dominant factors and their interactions with these differences. The results showed that AG soils had significantly lower pH, with an average decrease of 20%, indicating acidification, while nutrient levels and total salinity were significantly higher compared to OC soils. Specifically, available phosphorus, available potassium, the soil fertility quality index, and total soluble salt increased by 281%, 102%, 38%, and 184%, respectively. Planting, topographic, and fertilizer factors were identified as the dominant factors contributing to these differences. Interaction analysis showed that the interaction of these factors increased the explanatory power by 20.2% to 41.32% compared to individual factors. The interaction between planting year and fertilizer type had the highest explanatory power for nutrient increases and pH decline, while fertilizer amount and slope aspect contributed to salinity accumulation. These findings provide valuable insights and practical guidance for optimizing AG management and ensuring sustainable agricultural development in high-altitude regions. [ABSTRACT FROM AUTHOR]

Fractional vegetation cover (FVC) plays a significant role in assessing ecological quality and protection, as well as soil and water conservation. As a typical rare-earth resource county in China, Dingnan County has experienced rapid development due to rare-earth mining, resulting in significant alterations to vegetation cover. To elucidate the spatio-temporal changes in vegetation within Dingnan County over the past 35 years and the effects of natural and human factors on these changes, the spatial and temporal variations in FVC were analyzed using Landsat-TM/OLI multispectral images taken in 1988, 1995, 1997, 2002, 2006, 2013, 2017, and 2023. The findings indicate that (1) vegetation coverage in Dingnan County decreased from 1988 to 2002, followed by a gradual increase; (2) high vegetation cover is predominantly found in forested areas that maintain their natural state, while the central town and mining areas exhibit generally low coverage; (3) there are regional differences in the relationship between vegetation cover and environmental factors in Dingnan County. This research facilitates the alignment of ion-type rare-earth mining with ecological protection, thereby promoting the sustainable development of the mining area and providing scientific guidance for local governments to formulate more effective management and protection strategies for the mining ecosystem. Additionally, this research offers a scientific foundation for mining areas globally to develop sustainable policies and informed decision-making regarding environmental protection and sustainable development. [ABSTRACT FROM AUTHOR]

The Northeast China Tiger and Leopard National Park (NCTLNP) is a critical habitat for the endangered Amur tiger and Amur leopard, making it a global biodiversity hotspot. This study explores how changes in landscape patterns have influenced habitat quality in the park, aiming to develop strategies for enhancing biodiversity conservation and ensuring the park's long-term sustainability. From 2012 to 2017, habitat quality in the NCTLNP experienced a significant decline; however, the launch of the national park pilot program in 2017 resulted in improvements, particularly in core protected areas, where habitat quality increased and landscape fragmentation decreased. These findings indicate that the national park initiative reduced the degradation of habitat quality. Key landscape metrics, especially the Shannon Diversity Index (SHDI), were found to significantly affect habitat quality. Additionally, the interaction between SHDI and landscape contagion (CONTAG) played a pivotal role in shaping habitat quality over time. Areas with high SHDI and low CONTAG showed declines in habitat quality, pointing to the need for focused conservation efforts. This study offers valuable insights for policymakers seeking to improve habitat quality through targeted landscape management practices. [ABSTRACT FROM AUTHOR]

Isolated marsh wetlands are crucial for maintaining regional hydrological connectivity and biological contiguity. The Sanjiang Plain is the most typical area of marsh wetland change in China. A large number of isolated marshy wetlands have been formed here due to natural and anthropogenic influences. However, there have been few quantitative studies of the dynamics of isolated marsh wetlands and their drivers at the regional scale. This study used Landsat series image data provided by the Google Earth Engine. Through field surveys, combined with visual interpretation and the Random Forest Algorithm, the distributional changes in isolated marsh wetlands, non-isolated marsh wetlands, and natural marsh wetlands in the Sanjiang Plain from 1975 to 2020 were identified and extracted. The dynamic change characteristics as well as the patch importance values (dIIC) of isolated and non-isolated marsh wetlands were analyzed using the dynamic degree, standard deviation ellipse model, and the integral index of connectivity (IIC). Finally, the driving factors and interactions affecting the distribution of isolated marsh wetlands were analyzed by the Geodetector model. The results show that (1) the temporal dynamics of the three types of marsh wetlands are less than 0 from 1975 to 2020, and the temporal dynamics of isolated marsh wetlands are the largest. The lost wetlands were concentrated in the northeastern and east–central regions of the Sanjiang Plain. The center of mass of the standard deviation ellipse moved from northeast to southwest, and the isolated marsh wetlands moved the most. (2) The IIC of non-isolated marsh wetlands and natural marsh wetlands decreased and then increased, and the non-isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern and east–central regions. On the other hand, the IIC of isolated marsh wetlands increased and then decreased, and the isolated marsh wetlands with high-grade connectivity were mainly distributed in the northeastern region. (3) The elevation is the most important driving factor affecting the distribution of isolated marsh wetlands in the Sanjiang Plain. The interaction between the driving factors had a significantly higher effect on the distribution of isolated marsh wetlands than that of a single driving factor, with the strongest interaction between aspect and elevation in 1975, 1986, 2000, and 2010, and between aspect and slope in 2020. [ABSTRACT FROM AUTHOR]

Food security is the primary condition for the development of human society. The Great River Basin is very important to ensure the accessibility and availability of agricultural irrigation, which is vital for food security. The Yellow River Basin plays a significant role in China's food security, with counties serving as key administrative units for guaranteeing this security. This study uses the Yellow River Basin in China as a case study to construct an evaluation index system for county-level food security. It assesses the food security of 22 counties (districts) in Ningxia from 2013 to 2022, applying spatial correlation theories and driving factor analysis methods to explore the factors influencing county-level food security. The results reveal the following: (1) Overall, the food security index in Ningxia has been on the rise, but there is significant internal variation among counties. (2) Spatially, the food security index is relatively low in administrative centers, while the irrigation areas along the Yellow River play a crucial role in maintaining food security, and the overall food security index in the central arid areas is improving. (3) Food security is driven by multiple factors including economic, social, and climatic influences. To enhance food security in the Yellow River Basin, it is necessary to manage land resources systematically, improve grain production technology, and balance ecological protection with food security. [ABSTRACT FROM AUTHOR]

This study examined the location preferences and changes in pollution-intensive firms by analyzing the spatiotemporal distribution and drivers in the Yangtze River Economic Belt, a transitional manufacturing region in China. To analyze the distribution of firms under natural growth conditions prior to the implementation of the national "Great Protection of the Yangtze River" policy in 2016, this study utilized data on newly expanded industrial land use from 2007 to 2016. The results indicated that new pollution-intensive firms predominantly focused on water pollution, occupying over 40% of the total area annually. The new pollution-intensive firms preferred the geographic agglomeration siting strategy, mostly along the Yangtze River or in urban agglomerations, while gradually moving westward. The total area and number of new pollution-intensive firms in the Yangtze River Economic Belt showed an overall trend of "inverted U-shaped" variation during the study period, and the average size of the pollution-intensive firms gradually decreased. GeoDetector analysis revealed that geographical factors have always been significant. Local economic factors attracted new pollution-intensive firms, but later in the study period, these factors showed some inhibitory effect on the increase in pollution-intensive firms in the lower reaches. Government intervention worked less effectively but was significantly enhanced after interaction with other factors. Finally, the results suggested that local governments should build a stronger synergy between industrial land policies and environmental regulations to ensure sustainable growth and rational allocation of pollution-intensive firms. [ABSTRACT FROM AUTHOR]

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods. [ABSTRACT FROM AUTHOR]

Habitat quality is a key indicator for evaluating the biodiversity of a region. This study aims to assess the current status and characteristic changes of habitat quality in Donggang District, Rizhao while analyzing the potential factors influencing the quality of the habitat. The study employed the Integrated Valuation of Ecosystem Services and Tradeoffs Habitat Quality (InVEST-HQ) mode to assess the spatiotemporal variations in habitat quality in the Donggang District from 2008 to 2022. GeoDetector was utilized to investigate the impact of various factors on the spatial differentiation characteristics of habitat quality. The potential correlation between economic development and habitat quality was further explored using the Spearman correlation coefficient and Grayscale Association Analysis. The results reveal that habitat quality in Donggang District is highest in the east and lowest in the west, with significant correlations to land use and vegetation coverage. The study highlights a decline in habitat quality over the period, linked to rapid economic growth and industrial expansion. These insights are crucial for balancing urban biodiversity with economic development. [ABSTRACT FROM AUTHOR]

In low mountain and hilly regions, vegetation cover is higher and plant growth has an accumulative effect, sequestering carbon more strongly. The traditional remote sensing based ecological index (RSEI) lacks the consideration of vegetation productivity, and using it to evaluate ecological environment in low mountain and hilly regions will be biased. In this study, the vegetation productivity was introduced to construct a natural remote sensing based ecological index (NRSEI) that responds to the low mountain and hilly regions, as an example of Gaizhou City, China. Additionally, this study explored the spatiotemporal evolution of ecological environment quality from 2014 to 2020 and quantified the influences of factors. The results show that the first principal component (PC1) increased from 56 to 67% to 65–87% and considered the accumulation process in the ecosystem. NRSEI was more valid. From 2014 to 2020, the quality of the ecological environment generally declined and then increased. The area with "Excellent" increased from 23 to 38%. The quality of ecosystems in the west, northwest, and south deteriorated significantly, a distribution pattern of "high in the center, low in the north and south". Landuse and topographic conditions dominate the impacts on the ecosystem in the context of social, economic and policy influences. The interactions of the factors were two-factor enhancement that together affect the ecological environment. The results contribute to the development of urban conservation policies in low mountain and hilly regions. [ABSTRACT FROM AUTHOR]

Ertan Hydroelectric Power Station was the first large-scale water conservancy facility in western China, and with its completion, the ecological pattern of the reservoir area has changed dramatically; however, the changes in habitat quality before and after the completion of the reservoir have not yet been systematically monitored and evaluated. In this study, we analyzed the spatial and temporal characteristics of the remote sensing ecological index (RSEI), used CA-Markov model and GeoDetector, evaluated the ecological environment of the Ertan Reservoir area in each five-year period from 1995 to 2020, simulated the ecological quality of the Ertan Reservoir area in 2025, and revealed the nine drivers and their interactions affecting ecological quality of the watershed from a geologic point of view, and finally put forward reasonable planning and targeted protection suggestions. Finally, rational planning and targeted protection recommendations were proposed. The main results were as follows: (1) After the completion of the Ertan Hydropower Station, the spatial distribution of the RSEI in the reservoir area varied significantly, with a trend of "rising-declining-rising" in time, and the area share of "good" area increased the most, by 19.83%. (2) The degree of grading of each driving factor had different degrees of influence on the size of the RSEI value, and its interaction enhanced the spatial differentiation of the RSEI. (3) The average value of the RSEI is 0.66 in 2025, and the ecological environment quality will show a steady improvement in five years. The results of the study can provide a reference for constructing RSEI indicators for large hydropower facilities. [ABSTRACT FROM AUTHOR]

One of the key conditions for achieving superior regional growth is ensuring the harmonious development of both the layout and functions of territorial space. Territorial space, which includes production space, living space, and ecological space, serves as a critical system and venue for economic, cultural, and social activities in a region. The harmonized growth of production–living–ecological space (PLES) is essential for attaining sustainable development goals. Research on PLES offers a fresh perspective on promoting sustainable development of the spatial domain and the sustainable use of resources. However, studies on PLE functions in ecologically fragile areas are lacking. Therefore, in this study, which adopts a PLES perspective, land-use data are used to classify land according to the dominant functions of production, ecology, and living. Integration of point-of-interest (POI) data with socio-economic data was established to spatially describe indicators at the grid level and create a scoring system for PLES indicators in ecologically fragile areas. Finally, the entropy weight method, holistic assessment methods, coupling coordination degree model (CCDM), and geodetector were employed to explore the coupling coordination relationships and factors influencing PLESs in the Turpan–Hami Basin from 2010 to 2020. The results indicate that the Turpan–Hami Basin consists predominantly of potential ecological space, mainly in the central and northern regions, which are characterized by the Gobi Desert and bare rock landforms. Over the past decade, the PLES framework has seen a notable rise in the allocation of residential and ecological areas, whereas the portion dedicated to production spaces has diminished. The overall coupling coordination degree (CCD) of PLES in the Turpan–Hami Basin is at a coordinated level and gradually increasing. The most significant impact on the degree of PLES coupling coordination is exerted by population and natural factors. The research findings provide theoretical support for the sustainable utilization of resources in the Turpan–Hami Basin and other ecologically fragile areas while also offering scientific evidence to promote the coupling coordination of PLES, thereby contributing to high-quality regional development. [ABSTRACT FROM AUTHOR]

Exploring land use/cover (LULC) change is essential for the sustainable development of ecologically fragile areas. The main objective of this study was to clarify the characteristics and differences in the spatiotemporal changes of LULC on the Loess Plateau (LP) based on the transfer matrix and land use dynamics and to quantitatively describe the impact of natural factors on LULC using a geodetector. The results indicated that the overall LULC change in the LP was characterized by a decrease in the area of cropland, grassland, and bare land, and an increase in the area of woodland and built-up land. This trend shows a clear phase-change characteristic around 2000. LULC changes were primarily affected by human activities in the southeastern agricultural region. The project of returning farmland to forest and grassland had a great impact on LULC change in the central region. Vegetation was sensitive to temperature and precipitation, and the impact of LULC change was significantly higher than that in the humid region in the northwest arid region. NDVI, PRE, and TEM were determined to be the main contributors to LULC changes in the LP. These results provide a scientific basis for the sustainable development of LP. [ABSTRACT FROM AUTHOR]

Green innovation has now become an important component of high-quality development, but China's provinces still face imbalances in green innovation development. Clarifying the current state of development and the causes of the imbalance in each province is urgently necessary. But only a single or overall indicator cannot well reflect the structural differences within each province. We used the multi-way efficiency analysis (MEA) method to analyze the overall and structural efficiency of green innovation in each province of China, thus overcoming the shortcomings of comprehensive indicators. In addition, based on the decomposition of regional differences, policy factors resulting in heterogeneity among provinces are analyzed using Geodetector. The research results reveal the diversity of green innovation systems, the severe symmetry in resource utilization, and the internal and external sources of regional differences. We categorize the provinces into four development models by combining the internal structural characteristics of green innovation efficiency, as a way to propose suitable green innovation policies for each province, to take into account, the different development environments of the provinces assessed. Our research has significant implications for effectively improving green innovation efficiency and guides the formulation of more precise policies. [ABSTRACT FROM AUTHOR]

Changes in the ecological environment quality (EEQ) in the main inland Tarim River Basin in China substantially impact the regional development. Indeed, comprehensive ecological environment measures have been implemented in the Tarim River Basin since 2000. In this context, the main objective of the present study was to investigate the spatiotemporal evolution of the EEQ and monitor the effectiveness of ecological restoration measures in the Tarim River Basin over the 2000–2020 period using remote sensing data. First, a Remote Sensing Ecological Index (RSEI) was constructed based on the Moderate Resolution Imaging Spectroradiometer remote sensing data. Second, the spatial distributions and factors of the RSEI were analyzed by using Moran's Index and Geodetector. The results indicated that the overall RSEI values for the Tarim River Basin increased from 0.22 in 2000 to 0.25 in 2020. Moreover, the values for areas with poor EEQ decreased from 50.7% to 44.73%, while those with moderate EEQ increased from 11.45% to 16.91%. Therefore, the results demonstrated a slight overall improvement in the EEQ of the study area over the 2000–2020 period. On the other hand, the EEQ in the Tarim River Basin exhibited a significant spatial autocorrelation in the 2000–2020 period, with a relatively stable overall spatial distribution. Areas with high–high aggregation were distributed in the high-elevation mountainous areas in the western, northern, and southern parts of the study area. In contrast, areas with low–low aggregation were observed in the central and eastern low-elevation desert areas. The EEQ in the Tarim River Basin was driven by the interactions of several factors, including the normalized difference vegetation index, land surface moisture, land surface temperature, normalized differential build-up and bare soil index, and elevation. In particular, heat was the main driving factor that severely impacted the EEQ in the study area. Indeed, increase in the heat values could directly enhance meltwater runoff from glaciers in the basin, thereby resulting in short-term improvement in the basin EEQ. Furthermore, rapid urbanization from 2015 to 2020 resulted in a decrease in the average RSEI value of the Tarim River Basin by 0.1 over this period, consequently, the EEQ level decreased slightly. Briefly, the EEQ in the Tarim River Basin showed an overall increasing trend from 2000 to 2020, further demonstrating the effectiveness of a series of implemented ecological restoration measures in the Tarim River Basin over this period. [ABSTRACT FROM AUTHOR]

As a novel industrial space to cope with global competition, industrial parks have gradually become important growth poles to promote regional development and provide a large number of employment opportunities. This study utilizes mobile phone signaling data to identify the commuting origins and destinations (OD) of different industrial parks in Nanjing while comparing the distribution of the working population, residential population, and commuting patterns across varying types and levels of industrial parks. The level of coordination of the employment–residential system in each park is quantified by calculating the resident commuting index (HSCi), employee commuting index (WSCi), and their coupling coordination degree. Additionally, geographic detectors are employed to identify the influencing factors and interaction effects that impact the employment–residential balance in industrial parks. Results show that industrial parks located in the central urban area attract more residential and working populations. The commuting volume of national and municipal as well as high-tech industrial parks is higher than other types of industrial parks. Most industrial parks experience more inward than outward commuting, and there is an uneven distribution of commuting flows, resulting in a network-like pattern of "central dense, peripheral sparse". Various industrial parks exhibit a highly coupled job–housing system, and those with high HSCi tend to have high WSCi as well. The coupling coordination of industrial parks ranged from 0.16 to 0.93, with 13 being primary coordination or above and 3 being disordered. Industrial parks are classified into three types: employment-oriented, residential-oriented, and employment–residential balanced, with the residential-oriented type being predominant. The density of public transportation stops, park area, and land use mix are the primary factors affecting the employment–residential balance. Industrial parks with larger scale, better land allocation, and higher service facility levels are more likely to achieve coordination in the employment–residential system. Our work utilizes mobile signaling data to characterize the commuting patterns of industrial parks, providing insights for industrial park planning and promoting the integration of industry and city. [ABSTRACT FROM AUTHOR]

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Given the background of ecological fragility in western China, the northward migration of the livestock industry, and the "carbon peak" in China, it is practically significant to discuss the evolution of carbon dioxide equivalent emission intensity (CEI) in major livestock (pigs, cattle and sheep) rearing in the Shaanxi–Gansu–Ningxia (SGN) region. This discussion aims to protect the ecology of western China, achieve sustainable and healthy development of the livestock industry, and realize the national goal of "double carbon". In this study, we utilized statistical data from 2010 to 2021 for pigs, cattle, and sheep at the municipal level in the SGN region. We applied the methodology provided by the IPCC to comprehensively measure the carbon dioxide equivalent emissions (CEs), explore spatial and temporal trends, and analyze the driving forces behind spatial variations in the intensity with the assistance of GeoDetector. The following conclusions were drawn: Firstly, the total CEs generally exhibit fluctuating and increasing patterns. Moreover, the total CEs in different cities (states) within the region show obvious variations, with a tendency to shift toward the north. Secondly, the CEI demonstrates a clear downward trend. However, the CEI in different cities (states) exhibits increasing spatial heterogeneity. Furthermore, the western part of the region is evolving toward high-value areas, while the eastern part is evolving toward low-value areas. Lastly, the results of the GeoDetector indicate that the core driving factors are the pig, cattle, and sheep rearing structure; the urban population proportion; and the per capita gross national product. In summary, the total amount of CEs demonstrates a fluctuating increase, while the intensity shows a clear downward trend. Therefore, it is recommended to reduce CEs from livestock rearing in this region by optimizing the rearing structure of pigs, cattle, and sheep, promoting low-carbon consumption, and moderately importing livestock products. [ABSTRACT FROM AUTHOR]

Land Use Land Cover (LULC) change is a complex phenomenon driven by various natural and anthropogenic factors, significantly impacting carbon storage potential. By applying integrated models of ANN-CA Markov, GeoDetector, and InVEST model, this study aimed to analyze LULC change, their driving factors, and implications on carbon storage in the Forest Management Unit (FMU) of Ampang Plampang in West Nusa Tenggara, Indonesia. Several data sources were utilized in the modelling approach, including DEM (Digital Elevation Model), topographical map, Landsat imageries (2011, 2016, 2021), measured carbon density (above ground, below ground, soil, dead organic), and socio-economic data (number of populations, farmer, and agricultural land). The dryland forest in the study area constitutes the most extensive LULC that has experienced significant declines due to deforestation, predominantly transforming into agricultural land, and these are predicted to continue until 2031 with different magnitudes. The significant driving factors of LULC change were elevation, population pressure on land, and distance from settlement. The LULC change also greatly influenced the decline of carbon storage historically (2011–2016) and in projected LULC (2026–2031). The conversion of forested areas to non-forest LULCs has released carbon emissions of about 1.89 Mt CO2-eq. The study findings implied that the integration of ANN-CA Markov, GeoDetector, and InVEST models has been helpful for comprehending complicated interactions among LULC change, driving factors, and carbon dynamics. The results also contribute to the scientific knowledge base for land management decision-making and policy formulation. Effective management of LULC changes through low carbon development is suggested to mitigate the loss of carbon storage capacities, foster sustainable development goals (SDGs), support Nationally Determined Contribution (NDC), and improve ecosystem resilience.

Against the backdrop of cyclical fluctuations in the global economy, significant attention has been devoted to the resilience of urban economic systems in response to external shocks. Current academic understanding of the characteristics and primary types of urban economic resilience remains limited, with research in this area still at a preliminary stage. An in-depth analysis of the characteristics and patterns of economic resilience within urban systems could not only improve the understanding of urban economic resilience but also provide differentiated case studies for cities aiming to withstand future global financial crises. Based on this premise, Complex Adaptive Systems (CAS) theory is employed to analyze the potential factors influencing urban economic resilience. This study uses 54 county-level units in Jiangsu Province as empirical subjects, examining their economic resilience characteristics in response to the 2008 financial crisis across two dimensions: resistance and resilience. The study further analyzes five highly explanatory influencing factors to perform a grouping analysis of economic resilience among Jiangsu counties. The findings reveal that: 1) during the financial crisis, the level of impact across Jiangsu's county areas displayed a spatial pattern of "high in the south and low in the north;" 2) the agglomeration of enterprise entities and industrial diversity during the recovery period had a positive effect on county-level economic resilience; 3) the impact of foreign trade intensity on economic resilience varied across cities and stages, reflecting a dual role; 4) there exist two and five pathways to achieve high economic resilience during resistance and recovery periods, respectively, with close spatial proximity, industrial diversification, core industry agglomeration, and balanced trade flows emerging as critical elements for enhancing economic resilience. This study identifies a potential adaptive path for urban economic resilience: to counteract recessionary forces and stabilize fundamentals by developing sectors less impacted by shocks during the resistance period. During the recovery phase, diversifying industrial sectors can foster new industry strengths, reposition the urban economy, and enable dynamic structural adjustments to promote recovery and create new economic development.

Abstract In low mountain and hilly regions, vegetation cover is higher and plant growth has an accumulative effect, sequestering carbon more strongly. The traditional remote sensing based ecological index (RSEI) lacks the consideration of vegetation productivity, and using it to evaluate ecological environment in low mountain and hilly regions will be biased. In this study, the vegetation productivity was introduced to construct a natural remote sensing based ecological index (NRSEI) that responds to the low mountain and hilly regions, as an example of Gaizhou City, China. Additionally, this study explored the spatiotemporal evolution of ecological environment quality from 2014 to 2020 and quantified the influences of factors. The results show that the first principal component (PC1) increased from 56 to 67% to 65–87% and considered the accumulation process in the ecosystem. NRSEI was more valid. From 2014 to 2020, the quality of the ecological environment generally declined and then increased. The area with “Excellent” increased from 23 to 38%. The quality of ecosystems in the west, northwest, and south deteriorated significantly, a distribution pattern of “high in the center, low in the north and south”. Landuse and topographic conditions dominate the impacts on the ecosystem in the context of social, economic and policy influences. The interactions of the factors were two-factor enhancement that together affect the ecological environment. The results contribute to the development of urban conservation policies in low mountain and hilly regions.

Abstract The stability of ecosystems in high mountain canyon areas is poor, and the interaction between humans and the land is complex, making these ecosystems more vulnerable to destruction. Quantitatively assessing the ecosystem service value (ESV) in high mountain canyon areas and revealing its spatiotemporal evolution patterns and driving factors play a crucial role in the construction of regional ecological barriers and the assurance of ecological security. This study focuses on the Upper Minjiang River as the research area, using the InVEST model and the Equivalent Factor Method to estimate ESV. This combination aims to address the inadequacy of the Equivalent Factor Method in reflecting the variability of ESV across different regions, and the sensitivity of the InVEST model to data changes that results in insufficient accuracy of ESV assessments. By harnessing spatial au-tocorrelation and the geodetector method, we unravel the spatiotemporal evolution characteristics and driving factors of ESV. The results show that: (1) From 2000 to 2020, the ESVs estimated by the two estimations increased by 31.28% and 22.47%, respectively, both indicating that the eco-environment quality of the upper Minjiang River has been continuously improved. (2) When Moran’s I was greater than 0.5 (p

[Objective] Grassland is an essential component of terrestrial ecosystems, and the aboveground biomass (AGB) of grassland can directly reflect the current status of grassland resources. Accurately assessing the aboveground biomass of grassland and revealing its long-term change trends are fundamental to maintaining and enhancing grassland productivity, determining reasonable livestock carrying capacities, and ensuring sustainable grassland utilization. [Methods] Based on the grassland zoning in China, this study used 11703 records of accumulated field survey quadrat data to establish random forest regression models and multiple stepwise linear regression models for aboveground biomass in seven grassland zones of the Xinjiang Uygur Autonomous Region. The optimal model was determined through accuracy assessment. Subsequently, Landsat imageries were employed to invert the results of 30 m resolution grassland aboveground biomass in Xinjiang from 1990 to 2020. After revealing the spatiotemporal change of grassland aboveground biomass over 31 years, 14 potential driving factors were selected from four aspects: meteorology, terrain, soil, and human activities. Geographical detector was then used to analyze the primary driving factors, aiming to provide a scientific evidence for the future management, protection, and sustainable utilization of grassland resources. [Results] The main findings are as follows: (1) The average R² of the random forest regression models for the seven grassland zones was 0.74, with an average RMSE of 786.89 kg/hm², outperforming the multiple stepwise linear regression models. (2) From 1990 to 2020, Xinjiang’s grassland AGB showed an overall increasing trend, with an average AGB of 2137.31 kg/hm², and an annual average change of 15.05 kg/hm²/a. (3) The spatial distribution pattern of AGB in Xinjiang indicated higher values in mountainous areas compared to plains and higher values in northern Xinjiang compared to southern Xinjiang. The Ili River Valley and Altay region had higher AGB, whereas the Junggar Basin and the southeastern Tarim Basin had lower AGB. (4) Geodetector analysis results for three time periods showed that precipitation and soil organic carbon content significantly influenced Xinjiang’s grassland AGB. Notably, the influence of the human footprint factor has intensified during the period from 2010 to 2020. [Conclusion] Xinjiang’s grassland AGB has shown a continuous growth trend from 1990 to 2020, driven by both meteorological and soil factors. In the future, greater attention should be given to the impact of human activities on AGB to ensure the sustainable development of Xinjiang’s grassland ecosystems.

Environmental factors are essential input variables for susceptibility assessment models of mountain geohazards. However, the existing literature provides a limited understanding of the relative contribution of these factors to the occurrence of geohazards with a warming climate, posing tremendous challenges for risk management in mountainous areas. Ya'an city is susceptible to hazards because of its steep terrain, abundant precipitation and active seismic activity. In this regard, we utilise the GeoDetector model to extract critical environmental factors affecting the spatial patterns of mountain geohazards (i.e., landslide, debris flow and rockfall) in Southwest China. The analysis indicates that the factors with the highest explanatory power for the spatial distribution of landslides, debris flows, and rockfalls are soil property, extreme precipitation and extreme temperature, respectively. Notably, we revealed the synergistic effects among factors given their larger q-value than individual ones. We further explored the responses of mountain geohazards to climate change, including the rising temperature and precipitation, because the frequent occurrence of mountain geohazards is closely related to a warming climate. The variation in snow water equivalent caused by antecedent snowfall and snowdrifts acts as a crucial indicator for geohazards, highlighting the significance of snow and wind observations in meteorological nowcasting and disaster prewarning. We disclose the phenomenon of the geohazard hysteresis to the precipitation peak resulting from the top–down (i.e., precipitation-runoff and surface-deep soil moisture) peak shifts. Our work is expected to enhance the precision of susceptibility assessment models and the reliability of short-term forecasts for mountain geohazards.

Abstract The spatiotemporal distribution of intangible cultural heritage in Fujian Province, China, and the factors that influence it were explored using multiple spatial scales. The samples include five batches of Chinese national-level and six batches of Chinese provincial-level intangible cultural heritage items, totaling 554. The samples involve city-scale, county-scale, and traditional dwellings, and the analysis uses various methods, such as gravity migration theory and the GeoDetector model. The results show that, in terms of historical timescales, the moving trajectory of the center of gravity of the intangible cultural heritage distribution in Fujian Province involves reciprocation from north to south, and the center moves south in time from the pre-Qin period to the modern period. Spatially, intangible cultural heritage in Fujian Province forms an overall aggregate pattern, among which traditional skills fall into an aggregate pattern; five categories, including traditional drama, fall into an aggregate-random pattern; and four categories, including traditional medicine, fall into a random pattern. The traditional art category and other intensive distribution areas are located in coastal areas, and the traditional dance category is scattered in northwestern Fujian Province. Experiments on the factors influencing intangible cultural heritage distribution were conducted at the city and county levels. At the city level, human geography factors, such as the resident population, urbanization rate, and gross domestic product, have a greater influence on distribution. At the county level, the influence of physical geographical factors clearly increases. The dominant factors for coastal cities are the normalized difference vegetation index (NDVI), average precipitation, and coastline, and that for inland cities is elevation. From the perspective of categories, the dominant factors of traditional music and Quyi are human geography factors, and those of other types of intangible cultural heritage are the synergistic effects of physical geography and human geography. The conclusions of this study are helpful for systematically protecting and developing the intangible cultural heritage industry and improving research theories and methodological systems of the spatiotemporal distribution and mechanism influencing intangible cultural heritage.

The continuous rise in atmospheric CO2 levels has led to persistent ocean acidification, which negatively impacts marine environments crucial for marine life and alters the chemical composition of seawater. This phenomenon carries significant implications for human society. Utilizing surface seawater pH data from the North Pacific spanning 1995 to 2019, this study investigates the overall and localized spatiotemporal variations in pH within the region, as well as the factors influencing these variations. Additionally, it conducts a quantitative analysis of the different influencing factors. The findings reveal a consistent downward trend in surface seawater pH in the North Pacific, decreasing from 8.073 to 8.029, with notable seasonal variations. The highest pH values are recorded in winter, followed by spring, with lower values in autumn and summer. Spatially, the pH values are higher in the northwest and lower in the southeast, with the most pronounced acidification occurring in the central and western regions, while other areas exhibit more uniform acidification levels. Spatial correlation analysis indicates that surface seawater pH in the North Pacific generally shows a negative correlation with sea surface temperature (SST), salinity (SSS), and chlorophyll-a concentration (chl a) and a positive correlation with dissolved oxygen (DO). Among these factors, SST exerts the greatest influence on seawater pH, followed by DO and SSS. The degree of acidification varies across different regions, and the dominant influencing factors differ accordingly. In the equatorial central region (A), the primary factors are chl a and SST; in the eastern regions of China and Japan (B) and the western region of Canada (C), DO and SSS are the main controlling factors. An interaction analysis of each pair of dominant factors using the geodetector shows that their respective contributions to regions A, B, and C are 70%, 90%, and 50%, respectively. Understanding the primary factors driving acidification in different regions can aid in comprehending the biological and environmental impacts of acidification in those areas and provide valuable insights for mitigating marine acidification. [ABSTRACT FROM AUTHOR]

The capacity of carbon sinks varies among the different types of ecosystems, and whether national parks, as an important type of nature reserve, have a high carbon sink capacity (CSC) and whether eco-tourism in national parks affects their CSC are the main scientific issues discussed. Using MODIS Net Primary Production (NPP) product data, this study analysed the spatiotemporal variation in carbon sources and sinks (CSSs) in the ecosystem of Huangshan National Park from 2000 to 2020, as well as the impact of tourism on these carbon sources and sinks. The findings indicate that, while the ecosystems of national parks generally have a strong CSC, they may not always function as carbon sinks, and during the study period, Huangshan National Park served as a carbon source for four years. Temporally, the CSSs in the ecosystem of the national park exhibit a cyclical pattern of change with a four-year cycle and strong seasonality, with spring and autumn functioning as carbon sinks, and summer and winter as carbon sources. Spatially, the CSSs of the national park ecosystem exhibited a vertical band spectrum of spatial distribution, and the CSC showed a trend of gradual enhancement from low altitude to high altitude. Tourism is a major factor that has an impact on the CSC of national park ecosystems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 108 m3; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 108 m3 higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies. [ABSTRACT FROM AUTHOR]

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

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

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

The Henan Yellow River Basin is an ecological support belt for the entire basin. It holds a significant position in high-quality development and ecological conservation within the Yellow River Basin. However, due to improper development activities, such as urban expansion and deforestation of farmland, certain areas of the region have encountered a series of ecological issues, posing significant challenges to ecosystem services. The scientific foundation for the sustainable development of the ecological environment in the Henan Yellow River Basin is established by research on the evolution characteristics and driving factors of ecosystem service functions. This study focuses on the Henan Yellow River Basin, by introducing remote sensing data and biomass data, assessing the spatiotemporal variations in ecosystem service by the InVEST model—including carbon stock, water yield, and soil conservation—from 2000 to 2020. It analyzes the ecosystem service functions of different land use types. It employs the Geodetector to identify the dominant driving factors behind the changes in these functions based on the improved InVEST model evaluated results. The findings reveal that from 2000 to 2020, total carbon stock increased by 1.86%, carbon stock per unit area rose by 1.81%, and the spatial distribution remained largely stable. The high-value regions were clustered in the west and part of the north, primarily consisting of forest land. Carbon stock capacity in other regions, mainly farmland and construction land, was poor, with forest land having the strongest carbon sequestration capacity, followed by grassland. Total water yield decreased by 20.08%, and water yield per unit area decreased by 20.03%, with a spatial distribution closely following the trend of precipitation distribution. The high-value regions were clustered in the south, primarily in forest land and farmland. The total amount of soil conservation decreased by 19.96%, and soil conservation per unit area decreased by 19.93%, with spatial distribution patterns similar to those of carbon stock and water yield. The high-value regions were concentrated in the southwestern and northern forested regions, while soil conservation capacity in areas primarily consisting of farmland and construction land was weaker. The divergence of carbon stock was most influenced by population density, water yield by precipitation, and soil conservation by slope. In conclusion, during the study period, while carbon storage increased, the significant decline in water yield and soil conservation highlighted critical issues in the ecosystem service functions of the region. These findings indicate the need for targeted conservation measures and sustainable development strategies to address the decline in ecosystem services and mitigate adverse environmental impacts, ensuring the long-term health of the region's ecosystems. This study offers an in-depth understanding of the differentiation of ecosystem service functions and their driving factors, enabling precise assessment of regional ecosystem services, and providing a theoretical foundation for formulating effective regional ecological conservation policies. [ABSTRACT FROM AUTHOR]

Ecological environment quality reflects the overall condition and health of the environment. Analyzing the spatiotemporal dynamics and driving factors of ecological environment quality across large regions is crucial for environmental protection and policy-making. This study utilized the Google Earth Engine (GEE) platform to efficiently process large-scale remote sensing data and construct a multi-scale Remote Sensing Ecological Index (RSEI) based on Landsat and Sentinel data. This approach overcomes the limitations of traditional single-scale analyses, enabling a comprehensive assessment of ecological environment quality changes across provincial, municipal, and county levels in Fujian Province. Through the Mann–Kendall mutation test and Sen + Mann–Kendall trend analysis, the study identified significant change points in the RSEI for Fujian Province and revealed the temporal dynamics of ecological quality from 1987 to 2023. Additionally, Moran's I statistic and Geodetector were employed to explore the spatial correlation and driving factors of ecological quality, with a particular focus on the complex interactions between natural factors. The results indicated that: (1) the integration of Landsat and Sentinel data significantly improved the accuracy of RSEI construction; (2) the RSEI showed a consistent upward trend across different scales, validating the effectiveness of the multi-scale analysis approach; (3) the ecological environment quality in Fujian Province experienced significant changes over the past 37 years, showing a trend of initial decline followed by recovery; (4) Moran's I analysis demonstrated strong spatial clustering of ecological environment quality in Fujian Province, closely linked to human activities; and (5) the interaction between topography and natural factors had a significant impact on the spatial patterns of RSEI, especially in areas with complex terrain. This study not only provides new insights into the dynamic changes in ecological environment quality in Fujian Province over the past 37 years, but also offers a scientific basis for future environmental restoration and management strategies in coastal areas. By leveraging the efficient data processing capabilities of the GEE platform and constructing multi-scale RSEIs, this study significantly enhances the precision and depth of ecological quality assessment, providing robust technical support for long-term monitoring and policy-making in complex ecosystems. [ABSTRACT FROM AUTHOR]

Net ecosystem productivity (NEP) plays an important role in the quantitative evaluation of carbon source/sinks in terrestrial ecosystems. This study used Theil–Sen median trend analysis, the Mann–Kendall method, and the Geodetector model to analyze the variation patterns and its dominant factors of NEP from 2010 to 2020. The results showed the following: (1) During 2010–2020, the spatial distribution of carbon sinks in China's terrestrial ecosystems showed a pattern of high in the southeast and low in the northwest. The area with NEP < 0 accounted for 44.74% of the total area, while the area with NEP > 0 accounted for 55.26%. (2) The northwest region belonged to the significant carbon source, while the other regions belonged to significant carbon sinks. (3) The annual average NEP in different sub-regions showed an increasing trend. During 2010–2020, the overall NEP in China showed a trend in shifting from low-level to high-level, indicating that the NEP of terrestrial ecosystems in China increased during the past 11 years. (4) The NEP gravity center in Northeast China showed a trend in moving southward and then northward, while that of the NEP gravity center in East China showed a circular migration trend of 'northwest-southwest–northeast–southeast'. The gravity center of NEP in Northwest China was moving northeastward. The migration trajectory of the NEP gravity center in Southwest China presented a "Z" shape. The change in the gravity center of NEP in the central and southern regions had a strong circuitous nature, and the overall trend was to migrate southward. (5) The combined actions of climate change and human activities were the main reason for the change in NEP in China's terrestrial ecosystem from 2010 to 2020. (6) There were obvious differences in the dominant driving factors of NEP evolution in different regions and different periods in the past 40 years. [ABSTRACT FROM AUTHOR]

This research was conducted based on 701 soil sampling points in cultivated land (0–20 cm) in Shiyan, a mountainous area in northwest Hubei Province, China. The methods of classical statistics, geostatistics, and geodetector were used to explore the spatial differentiation characteristics and influencing factors of soil available potassium (AK) in cultivated land in Shiyan. The results showed that the soil AK content in the study area ranged from 17.00 to 350.00 mg/kg, with an average value of 118.95 mg/kg and a coefficient of variation of 54.06%, exhibiting moderate variability. The spatial structure was well fitted by a spherical model; the block gold effect was 0.027, indicating strong spatial autocorrelation; and spatial variation was mainly caused by structural factors. The spatial differentiation characteristics of the soil AK content are obvious; overall, there was a spatial distribution pattern of high in the northeast and low in the southwest. The factor detection results show that soil pH plays a dominant role in the spatial variation in soil AK in the study area, followed by parent material and annual average temperature. The interaction detection results show that each environmental factor exhibits non-linear or dual factor enhancement between factors, with soil pH slope ranking first in explanatory power. The explanatory power of the interaction between soil pH, parent materials, annual average temperature, and other factors dominates. In the process of the fine management of soil AK in cultivated land in the study area, when considering the influence of dominant factors, the impact of the interaction of various factors on the spatial variation in soil AK should also be taken into account. This study could provide a theoretical reference for improving the soil and farmland improvement, improving farmland quality in this area. [ABSTRACT FROM AUTHOR]

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

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

Studying the spatial pattern distribution of respiratory health risks and the role of spatial environmental factors can improve our understanding of pathogenic mechanisms in the spatial environment. However, previous air quality index (AQI) sensor models lacked comprehensive air quality reflection, limiting risk prediction accuracy. In this study, we improved the AQI sensor model and combined it with population density data to establish a respiratory health risk exposure assessment model. Twenty-five spatial environmental variables were selected as potential factors. Pearson correlation analysis and a geodetector were used to assess the spatial risk patterns of respiratory diseases and determine the characteristics of the selected factors. The results indicated that (1) the health risk index showed autumn < summer < spring < winter ranking, with the health risk gradually decreasing from the center outwards. (2) The positive effect of the volume ratio, a spatial morphology factor, was the strongest, and the negative effect of the sky openness was the greatest. (3) The geodetector results showed significant spatial heterogeneity in the degree of effect of the spatial environmental factors on respiratory health risks. Moreover, the explanatory power of the interaction between any two factors (except the volume ratio) far exceeded that of a single factor. [ABSTRACT FROM AUTHOR]

Carbon storage (C-storage) is a critical indicator of ecosystem services, and it plays a vital role in maintaining ecological balance and driving sustainability. Its assessment provides essential insights for enhancing environmental protection, optimizing land use, and formulating policies that support long-term ecological and economic sustainability. Previous research on C-storage in the Yellow River Basin has mainly concentrated on the spatiotemporal fluctuations of C-storage and the investigation of natural influencing factors. However, research combining human activity factors to explore the influences on C-storage is limited. In this paper, based on the assessment of the spatiotemporal evolution of C-storage in the region along the Middle and Lower Yellow River (MLYR), the influences of anthropogenic and natural factors on C-storage were explored from the perspective of sustainable development. The findings reflected the relationship between socio-economic activities and the ecological environment from a sustainable development perspective, providing important scientific evidence for the formulation of sustainability policies in the region. We noticed the proportion of arable land was the highest, reaching 40%. The increase of construction land because of the fast urbanization mainly came from arable land and grassland. During the past 15 years, the cumulative loss of C-storage was 71.17 × 106 t. The high-value of C-storage was primarily situated in hilly areas, and the area of C-storage hotspots was shrinking. The aggregation effect of low-value C-storage was strengthening, while that of high-value C-storage was weakening. The dominant factors (q > 0.5) influencing the spatiotemporal variation of C-storage in the region along the Middle Yellow River (MYR) were temperature and precipitation, while the primary factor in the region along the Lower Yellow River (LYR) was temperature. Overall, meteorological factors were the main determinants across the entire study area. Additionally, compared to the MYR, anthropogenic factors had a smaller impact on the spatiotemporal evolution of C-storage in the LYR, but their influence has been increasing over time. [ABSTRACT FROM AUTHOR]

Understanding and managing land use/cover changes (LUCC) is crucial for ensuring the sustainability of the region. With the support of remote sensing technology, intensity analysis, the geodetic detector model, and the Mixed-Cell Cellular Automata (MCCA) model, this paper constructs an integrated framework linking historical evolutionary pattern-driving mechanisms for future simulation for LUCC in the Lower Liaohe Plain. From 1980 to 2018, the increasing trends were in built-up land and water bodies, and the decreasing trends were in grassland, cropland, forest land, unused land, and swamps. Overall, the changes in cropland, forest land, and built-up land are more active, while the changes in water bodies are more stable; the sources and directions of land use conversion are more fixed. Land use changes in the Lower Liaohe Plain are mainly influenced by socio-economic factors, of which population density, primary industry output value, and Gross Domestic Product (GDP) have a higher explanatory power. The interactive influence of each factor is greater than any single factor. The results of the MCCA model showed high accuracy, with an overall accuracy of 0.8242, relative entropy (RE) of 0.1846, and mixed-cell figure of merit (mcFoM) of 0.1204. By 2035, the built-up land and water bodies will increase, while the rest of the land use categories will decrease. The decrease is more pronounced in the central part of the plains. The findings of the study provide a scientific basis for strategically allocating regional land resources, which has significant implications for land use research in similar regions. [ABSTRACT FROM AUTHOR]

Rapid urbanization inevitably exerts pressure on the surrounding ecological environment, and balancing the relationship between the ecological environment and urbanization is crucial for sustainable urban development. Taking the Yangtze River Delta urban agglomeration (YRDUA) as a case study, this paper utilizes MODIS data and nighttime light data to construct the MODIS Remote Sensing Ecological Index (MRSEI) and Comprehensive Nighttime Light Index (CNLI) distributions to depict ecological environment quality and urbanization levels. Based on this, the Coupled Coordination Degree (CCD) model is employed to calculate the coupling coordination level between the two, and the Geodetector is used to analyze the underlying causes affecting the CCD. The results indicate the following: (1) the overall ecological environment of the YRDUA tends to be stable, but there are significant differences between regions. Areas with deteriorating ecological conditions are concentrated in cities with higher rates of urbanization changes. (2) All cities are developing towards coordination, but there are imbalances in development among different regions. (3) The key factors affecting the CCD are derived from socioeconomic elements rather than natural elements, with the interaction between GDP and DEM having the strongest explanatory power for the CCD. (4) The CNLI is positively correlated with the CCD, the MRSEI is negatively correlated with the CCD, and the level of urbanization is the decisive factor for CCD changes. The research findings can provide theoretical guidance for promoting sustainable urban development. [ABSTRACT FROM AUTHOR]

The spatiotemporal distribution of intangible cultural heritage in Fujian Province, China, and the factors that influence it were explored using multiple spatial scales. The samples include five batches of Chinese national-level and six batches of Chinese provincial-level intangible cultural heritage items, totaling 554. The samples involve city-scale, county-scale, and traditional dwellings, and the analysis uses various methods, such as gravity migration theory and the GeoDetector model. The results show that, in terms of historical timescales, the moving trajectory of the center of gravity of the intangible cultural heritage distribution in Fujian Province involves reciprocation from north to south, and the center moves south in time from the pre-Qin period to the modern period. Spatially, intangible cultural heritage in Fujian Province forms an overall aggregate pattern, among which traditional skills fall into an aggregate pattern; five categories, including traditional drama, fall into an aggregate-random pattern; and four categories, including traditional medicine, fall into a random pattern. The traditional art category and other intensive distribution areas are located in coastal areas, and the traditional dance category is scattered in northwestern Fujian Province. Experiments on the factors influencing intangible cultural heritage distribution were conducted at the city and county levels. At the city level, human geography factors, such as the resident population, urbanization rate, and gross domestic product, have a greater influence on distribution. At the county level, the influence of physical geographical factors clearly increases. The dominant factors for coastal cities are the normalized difference vegetation index (NDVI), average precipitation, and coastline, and that for inland cities is elevation. From the perspective of categories, the dominant factors of traditional music and Quyi are human geography factors, and those of other types of intangible cultural heritage are the synergistic effects of physical geography and human geography. The conclusions of this study are helpful for systematically protecting and developing the intangible cultural heritage industry and improving research theories and methodological systems of the spatiotemporal distribution and mechanism influencing intangible cultural heritage. [ABSTRACT FROM AUTHOR]

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

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

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

As global climate change intensifies and human activities escalate, changes in vegetation cover, an important ecological indicator, hold significant implications for ecosystem protection and management. Shandong Province, a critical agricultural and economic zone in China, experiences vegetation changes that crucially affect regional climate regulation and biodiversity conservation. This study employed normalized difference vegetation index (NDVI) data, combined with climatic, topographic, and anthropogenic activity data, utilizing trend analysis methods, partial correlation analysis, and Geodetector to comprehensively analyze the spatiotemporal variations and primary driving factors of vegetation cover in Shandong Province from 2001 to 2020. The findings indicate an overall upward trend in vegetation cover, particularly in areas with concentrated human activities. Climatic factors, such as precipitation and temperature, exhibit a positive correlation with vegetation growth, while land use changes emerge as one of the key drivers influencing vegetation dynamics. Additionally, topography also impacts the spatial distribution of vegetation to a certain extent. This research provides a scientific basis for ecological protection and land management in Shandong Province and similar regions, supporting the formulation of effective vegetation restoration and ecological conservation strategies. [ABSTRACT FROM AUTHOR]