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Copyright of Tourism Tribune / Lvyou Xuekan is the property of Tourism Institute of Beijing Union University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Studies have shown that energy consumption from economic development leads to an increase in carbon emissions. Emerging economies, as important sources of carbon emissions with high growth potential, play a crucial role in global decarbonisation efforts. However, the spatial pattern and evolution trend of carbon emissions in emerging economies have not been studied in depth. Therefore, this paper uses the improved gravitational model and carbon emission data from 2000 to 2018 to construct a spatial correlation network of carbon emissions in 30 emerging economies around the world, aiming to reveal the spatial characteristics and influencing factors of carbon emissions at the national level. The results show that the spatial network structure of carbon emissions in emerging economies is closely linked, forming a "big network" of interconnection. Amongst them, Argentina, Brazil, Russia, Estonia, etc. are at the centre of the network and play a leading role. Geographical distance, economic development level, population density, and scientific and technological level have a significant impact on the formation of spatial correlation between carbon emissions. Further use of GeoDetector shows that the explanatory power of two-factor interaction on centrality is greater than that of a single factor, indicating that a single economic development cannot well enhance the influence of countries in the carbon emission network, and needs to be combined with factors such as industrial structure and scientific and technological level. These results are helpful to understand the correlation between carbon emissions between countries from the perspective of the whole and part and provide a reference for optimizing the carbon emission network structure in the future. [ABSTRACT FROM AUTHOR]

Heavy metals (HMs) entering soils are redistributed by natural processes such as surface runoff and soil erosion. These natural processes are characterized by topographic factors (TFs, e.g., Topographic Wetness Index (TWI), Total Catchment Area (TCA), Slope, and Aspect), which are commonly quantified by a digital elevation model (DEM) of a certain spatial resolution. Nevertheless, few studies have examined how DEM resolutions affect the detection of influential TFs of soil HMs. In this study, we first applied the GeoDetector method to explore the coupling between the eight TFs and the concentrations of eight soil HMs under a gradient of DEM resolutions. We found that the important sorting of eight TFs on one HM at different resolutions is inconsistent. For example, for Hg, TWI emerged to be the dominant factor among the eight TFs at 90 m resolution, whereas TCA took the lead at 3000 m resolution. Moreover, the results strongly deny the existence of an optimal resolution (OR) among the HMs for any specific topographical factor. We further applied a source apportionment model (Positive Matrix Factorization—PMF) to explore the effects of five identified pollution sources and the underlying environmental processes on the inconsistent ORs. The main reason for such OR inconsistency is that each HM may be released from various sources and subsequently undergo environmental processes that are topographically modulated at different spatial scales. The main reason for such OR inconsistency is that each HM may have various sources and subsequent environmental processes that happen at different spatial scales. Moreover, each TF could simultaneously reflect different transport and transformation processes. Therefore, the apparent OR for one metal is jointly composed of the preferences of all the sources it contains; thus, it cannot be determined by the OR preferences of a single source alone. Based on the composition and intensity of pollution sources, we propose three possible strategies for a more robust GeoDetector analysis. The findings reported here provide new insights into the proper use of GeoDetector for selecting the appropriate DEM resolutions when identifying influential environmental factors. [ABSTRACT FROM AUTHOR]

Establishing the National Comprehensive Disaster-Reduction Demonstration Community (NCDDC) is crucial for enhancing comprehensive disaster risk reduction at the grassroots level in China. Studying the distribution characteristics and influencing factors of NCDDCs can guide future NCDDC layout optimization and related policy adjustments. Using the standard deviation ellipse, nearest neighbor index, kernel density, spatial autocorrelation, and Geodetector, we analyzed the spatiotemporal distribution characteristics of NCDDCs in China from 2008 to 2021 and detected their influencing factors. The findings are as follows: (1) NCDDCs exhibit an uneven distribution at different scales, including spatial, urban–rural, and county scales. (2) The spatial distribution of NCDDCs mainly follows a northwest–southeast pattern during 2008–2014 and shows a northeast–southwest trend after 2014. (3) The positive spatial correlation and spatial agglomeration of NCDDCs increase annually. (4) NCDDCs show a concentrated and contiguous distribution pattern in 2021, based on "core density zone–ring-core decreasing area–ring-core expansion group–Ɔ-shaped area–belt-shaped area". (5) The main factors affecting the NCDDC distribution are hospital density, road density, GDP density, and population density, with factors' interactions exhibiting bilinear and nonlinear enhancement effects. This study reveals the NCDDC spatiotemporal distribution characteristics and its influence mechanism, providing a scientific basis for future NCDDC layout optimization and related policy adjustments. [ABSTRACT FROM AUTHOR]

The ceramic industry has been vital to the city's development and prosperity in Jingdezhen, but the development of the ceramics industry in Jingdezhen has been unclear since China's economic reforms, which will become a bottleneck limiting the sustainable development of the city. This study explored the spatial agglomeration and spatiotemporal evolution of the ceramics industry in Jingdezhen from 1980 to 2020 using enterprise directory data. The study opted for a microscopic perspective and employed kernel density estimation and exploratory spatial data analysis to obtain the necessary results. It also analyzed the influencing factors using a Geodetector. The results show that the temporal evolution of the ceramics industry in Jingdezhen went through two stages from 1980 to 2020. The number of enterprises experienced exponential growth, with fluctuations. The spatial evolution of the ceramics industry transitioned from a "single-center" to a "double-center" model and further evolved into a "multi-center" model. Moreover, the spatial agglomeration of the ceramics industry underwent the process of "agglomeration-diffusion-polarization", ultimately developing into four ceramic industrial agglomeration patterns in six hotspots. Agglomeration, historical, technological, policy, and transportation factors had positive effects on the evolution of the ceramics industry in Jingdezhen, with agglomeration being the top contributor. Likewise, there were obvious interactions between the factors. This study can provide a basis for formulating policies to support urban spatial planning for urban revitalization, and provide foundation for the development of the national ceramic culture inheritance and innovation pilot zone in Jingdezhen. [ABSTRACT FROM AUTHOR]

The linking of ecosystem service flows (ESFs) with urban land management is still in its nascent stage. The spatial process modeling of ESFs plays a crucial role in establishing connections between urban land sustainability and human benefits. However, the spatial processes and driving mechanisms associated with urban cooling services (UCS) remain ambiguous. In this study, we selected the area within the 6th Ring Road of Beijing as the study area, where the population is highly concentrated and the urban greenery is relatively developed. We modeled the spatial processes of cooling service flow (UCSF) in this area and elucidated the contribution of landscape patterns to UCSF. Firstly, the cooling capacity, referred to as UCS, of the urban blue–green landscape, was estimated using the InVEST tool. Subsequently, the UCSF spatial process was simulated by employing a two-dimensional Gaussian function at the pixel level. In order to characterize the landscape features in the study area, eight landscape indices were selected, and Fragstats v4.2 was employed for their calculation. Finally, GeoDetector was utilized to explore the driving mechanisms of landscape patterns on UCSF. The predominant area for both UCS and UCSF lies between the 5th and 6th Ring Road in Beijing, exhibiting a declining trend from the 6th Ring Road toward the city center. The UCSF coverage area, which represents the beneficiary area, accounted for approximately 87.78% of the study area, with the largest increase occurring within the 2nd Ring Road. The Landscape Shape Index demonstrated the strongest individual contribution to UCSF, while its combined bivariate contribution was significant. Geometry exerted a greater influence on UCSF compared to landscape scale and spatial configuration. This study presents novel insights for assessing the omnidirectional flow of ESFs through the modeling of flow functions. The findings of this study can serve as a valuable reference for sustainable urban landscape management and planning. [ABSTRACT FROM AUTHOR]

In China, the rural human settlement system (RHSS) reflects the relationship between rural people and land but is affected by land use and land cover change (LUCC). Maintaining a harmonious development between RHSS resilience (RHSSR) and LUCC is an important rural development issue. However, the spatial association between LUCC and RHSSR remains unclear, constraining effective land use and rural policymaking. The association between RHSSR and LUCC was assessed from a spatial perspective. Using county-level spatial panel data for Hunan Province, China (2000–2020), an evaluation indicator system was established to measure RHSS and analyze the spatiotemporal evolution of RHSSR using a geospatial analysis and geodetector model. The average RHSSR level increased from 0.158 to 1.406. The RHSSR generally presented a three-level stepped spatial distribution feature of high in the east and low in the west, with a belt-like distribution. Additionally, the RHSSR was consistent with land use intensity (LUI), but there was significant spatial heterogeneity in the spatial relationship between LUI and RHSSR. The evolution of RHSSR occurred through industrial development, medical service improvement, increased income, and environmental protection. This has important implications for future rural development strategies, the sustainable development of rural land, and the integration of RHSS into regional planning. [ABSTRACT FROM AUTHOR]

Water and soil resources are the key elements required to maintain the stability and sustainable development of oases in inland drylands. This study takes oases in Xinjiang as an example, and reveals the spatial and temporal distribution characteristics of oases across Xinjiang, as well as the dynamic migration and transformation process of oases and the mutual influence of human activities on oasis resources, through a combination of land movement and attitude indicators, use of a gravity model, and Geodetector. The results show that: (1) the area containing the oases in the study has expanded, increasing by 24.0% between 1990 and 2020; (2) both artificial oases (AO) and natural oases (NO) were tilted in a northeast–southwest direction, with the center point of AOs moving slightly to the northeast, and the centripetal force and dispersion degree did not change significantly; the center point of NO moved significantly to the northeast, and the dispersion degree weakened, showing spatial convergence; (3) cultivated land and grassland were the main land types of oasis evolution, and their contribution to evolution was over 70%; and (4) the Geodetector results show that the three main factors driving the evolution of oases were vegetation cover, population density, and GDP. The research results have practical implications for land-use management planning and ecological environmental protection in arid zones. [ABSTRACT FROM AUTHOR]

Digital rural development has become an emerging dynamic force for high-quality rural development in China. This paper constructs the "environmental-economic-social" analysis framework for digital rural development, analyzes the spatial variation of the digital rural development level (DRDL) in Chinese counties in 2020, and conducts the factor detection and interaction detection of its influencing factors. It is found that: (1) digital rural development has its own unique spatial differentiation mechanism, which can be analyzed from three dimensions: environmental system, economic system, and social system, which play a fundamental role, decisive role, and a magnifying effect on digital rural development, respectively. (2) The DRDL in China's counties has significant spatial distribution, spatial correlation, and spatial clustering characteristics. The DRDL in general shows a decreasing distribution trend from coastal to inland regions, and the overall differences in DRDL mainly come from intra-regional differences rather than inter-regional differences. The rural infrastructure digitalization dimension has stronger spatial correlation while the spatial correlation of the rural governance digitalization dimension is weaker. There are obvious hotspot and coldspot areas in the DRDL, with large differences between the coldspot and hotspot areas of different sub-dimensions. (3) The spatial divergence of the DRDL is closely related to geographical elements and is the result of the combined effect of several geographical factors. The factor detection results show that the dominant factors within the four regions are significant different. The interaction detection results show that the driving force of the two-factor interaction is stronger than that of the single-factor interaction and that the interaction among the factors further deepens the spatial differentiation of the DRDL. [ABSTRACT FROM AUTHOR]

A land resource asset (LRA) table is highly valued by the Chinese government for resource management. However, most of China's researches on LRA table focused on theoretical construction. The difficulty in obtaining data hinders the practice and promotion of existing methods, and it limits the selection of data for many practical studies. Thus, this study proposes a land value quantification (LVQ) model that is suitable for a large spatio–temporal scale background to solve the above problems. Based on this model, the LRA table in the middle reaches of the Yellow River was successfully compiled. The results indicated that: (1) the assets of regional land resources were in an unbalanced development state; (2) the LRAs in the middle Yellow River had changed frequently; (3) there were significant spatial differences in the distributions of various types of LRAs in the middle reaches of the Yellow River; and (4) topography and soil erosion were the main factors restricting optimal regional land resource spatial allocation. Based on the research findings, this study provides scientific policy recommendations for the optimal allocation and sustainable development of land resources in the middle reaches of the Yellow River. Moreover, the LVQ model proposed in this research provides a model reference for LRA accounting with large spatial and temporal scales in China and similar regions. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the context of global warming, timely and accurate drought monitoring is of great importance to ensure regional ecological security and guide agricultural production. This study established the Drought Severity Index (DSI), based on the potential evapotranspiration (PET), evapotranspiration (ET) and normalized difference vegetation index (NDVI) data from 2001 to 2020, to compensate for the low accuracy of drought spatial and temporal evolution due to the uneven distribution of stations. The DSI index was established to reveal the spatial and temporal variation of droughts in Inner Mongolia in the past 20 years, using trend analysis, gravity shift and geographic probes, and to explore the influence of different factors on the DSI. The results were as follows. (1) The results showed that the spatial distribution of DSI in Inner Mongolia during 2001–2020 had strong spatial heterogeneity, and generally showed distribution characteristics of drought in the west and wet in the east. In addition, the changes in DSI all exhibited a rising tendency, with the highest tendency in deciduous broadleaf forests (DBF) and the lowest tendency in grassland (GRA). (2) The center of gravity of wet, normal and arid areas showed a migration trend from northeast to southwest, with migration distances of 209 km, 462 km and 826 km, respectively. (3) The four combinations of temperature and elevation, temperature and slope, temperature and land use, and temperature and rainfall contributed the most. The results obtained in this study are important for the scheduling of ecological early warnings and drought prevention and control. [ABSTRACT FROM AUTHOR]

The quality of the ecological environment determines human well-being, and the degree of ecological environment quality has a significant impact on regional sustainable development. Currently, the assessment content of ecological environment quality in Luoyang is relatively single-indicator-based and is insufficient to comprehensively reflect the changes in the ecological environment quality of Luoyang city. Therefore, the study aims to use the Remote Sensing Ecological Index (RSEI), a comprehensive evaluation model, with Landsat remote sensing images and statistical yearbooks as the data sources, to evaluate the spatiotemporal dynamic changes in the ecological environment quality of Luoyang city from 2002 to 2022 through trend analysis and mutation testing; the study employs geographical detectors to analyze the driving factors about the changes in ecological environment quality. The study found that: (1) the average RSEI value in Luoyang city has increased by 0.102 in the past 20 years, indicating an overall improvement in the ecological environment quality of Luoyang city. (2) The northern region of the study area has lower RSEI values, while the southern region has better ecological environment quality, which corresponds to the fact that the northern part of Luoyang city has intensive human activities, while the southern part is characterized by higher vegetation coverage in mountainous areas. (3) The proportion of areas with medium and above ecological environment quality grades have increased from 47.2% to 67.5%, indicating a positive trend in future ecological environment quality changes. (4) The population change was the strongest single factor influencing the ecological environment quality change in Luoyang city. The interaction between temperature and GDP was relatively the strongest. The current ecological environment status in the study area is the result of the combined effects of natural and anthropogenic factors. The research conclusions contribute to improving regional ecological environment quality and are of great significance for the regional ecological environment planning and the achievement of sustainable development goals. [ABSTRACT FROM AUTHOR]

The Songnen Plain of Northeast China is one of the three largest soda saline–alkaline regions in the world. To better understand soil alkalinization in this important agricultural region of China, it is vital to explore the driving factors of soil alkalinity. Combined with prior research on the Wuyu'er–Shuangyang River Basin, this study examined the driving factors of soil alkalinity using the Geodetector method. First, the analysis results of the risk detector, the factor detector, and the ecological detector revealed the primary driving factors of soil alkalinity in the study area. Next, the analysis results of the interaction detector determined how combinations of driving factors impacted soil alkalinity in the study area. In general, the natural driving factors of altitude and spring temperature, especially altitude, played a key role in soil alkalinization. These results indicated that the closed terrain and warming trends were the main causes of soil alkalinization in the study area. In addition, there were significant enhance-nonlinear and enhance-bivariate relationships among the driving factors, which indicated that combined driving factors contributed more to soil alkalinization than individual driving factors. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ecology & Rural Environment is the property of Journal of Ecology & Rural Environment Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Under the tremendous challenges of frequent disasters, disaster resilience is imperative for risk reduction and sustainable management in poverty and disaster-prone areas. Ganzi Prefecture has a complicated topography and vulnerable ecosystems. Geological disasters have historically been the most serious risks in the region. To fully understand the potential risks and strengthen resilience, the study investigates the resilience level of 18 counties in Ganzi. Firstly, the paper develops a multidimensional index system based on the Baseline Resilience Indicators for Communities (BRIC) framework. And the entropy weighting method is used to calculate Ganzi's disaster resilience level from the aspects of "society-economy-infrastructure-environment." Then, the study uses exploratory spatial data (ESDA) to analyze the spatial–temporal evolution of disaster resilience. Finally, Geodetector is used to investigate the main driving factors of disaster resilience and their interactions. The results indicated that Ganzi's disaster resilience had maintained an upward trend from 2011 to 2019, with significant spatial divergence, which shows high resilience in the southeast and low resilience in the northwest. The economic indicator is the driving factor in the spatial difference of disaster resilience, and the interaction factor has a significantly stronger explanatory power for resilience. Therefore, the government should strengthen ecotourism development to help alleviate poverty in special industries and promote synergistic regional development. [ABSTRACT FROM AUTHOR]

Small lakes (areas ranging from 0.01 km2 to 1 km2) are highly sensitive to climate change and human activities. However, few studies have investigated the long-term intra-annual trends in the number and area of small lakes and their driving mechanisms in the Qinghai–Tibet Plateau (QTP). As a significant water tower in northwest China, the Qilian Mountains region (QMR) in the QTP is essential for sustaining regional industrial and agricultural production, biodiversity, and human well-being. We conducted an analysis of the dynamics of small lakes in the QMR region. In this study, we employed Geodetector and examined nine factors to investigate the driving mechanisms behind the long-term variations in the small lake water bodies (SLWBs). We specifically focused on understanding the effects of single-factor and two-factor interactions. The results indicate that the number and area of small lakes had a fluctuating trend from 1987 to 2020. Initially, there was a decrease followed by an increase, which was generally consistent with trends in the large lakes on the QTP. All basins had far more expanding than shrinking lakes. The area of seasonal SLWBs in each basin was increasing more rapidly than permanent SLWBs. The distribution and trends in the area and number of small lakes varied widely across elevation zones. Runoff, snow depth, and temperature contributed the most to SLWB changes. Human activities and wind speed contributed the least. However, the main drivers varied across basins. The impact of two-factor interactions on SLWB changes in basins was greater than that of single factors. Our results provide useful information for planning and managing water resources and studies of small lakes. [ABSTRACT FROM AUTHOR]

Exploring the spatial distribution characteristics of regional cultural and tourism resources (CTRs) is crucial to the sustainable development of cultural and tourism industries. Based on 651 CTRs obtained from the latest round of field surveys in the Xihu District of Hangzhou, this article analyzed the spatial distribution of these CTRs from an overall, categorical, and hierarchical multiple perspective using the average nearest neighbor index and kernel density analysis and explored the reasons for the differences in the spatial distribution of the above different types of CTRs using multiple linear regression and Geodetector. The results indicate that the CTRs present a spatially clustered state, and the distribution of CTRs in different categories and grades has diverse characteristics, as natural resources have three high-density areas, humanistic resources have one high-density area, and both general and superior resources have only one high-density area, but the low-density areas are different. In addition, the spatial distribution of the overall, different categories, and different grades of CTRs in this region is influenced by several factors, with the West Lake generating the greatest impact. Additionally, interactive factors can have a greater impact than single factors. These results enrich the research content of Hangzhou's cultural and tourism industries and provide theoretical support for the high-quality development of Xihu District's cultural and tourism industries. [ABSTRACT FROM AUTHOR]

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

To explore the spatial distribution characteristics of soil manganese(Mn) in Guizhou Province, the spatial interpolation of soil Mn content at 376 sampling points in Guizhou Province was performed. The data were spatially superimposed with vector data of different types of environmental factors, and the main effects on the spatial heterogeneity of soil Mn were analyzed using Geodetector. The results indicated clear differences in the Mn content of different types of soils in Guizhou Province. The order of the average content was lime soil>brown soil>red soil>yellow-brown soil>yellow soil>purple soil. The Mn content of the topsoil was 2.57-5 203.98 mg·kg-1, with an average value of 876.92 mg·kg-1. The variable coefficient was 0.89 with substantial variation. Soil Mn content can be divided into four zones in Guizhou Province:the low Mn zone(0-500 mg·kg-1), middle Mn zone(>500-700 mg·kg-1), rich Mn zone(>700-1 200 mg·kg-1), and high Mn zone(>1 200 mg·kg-1); 54.05% of the soil in Guizhou Province could be classified as Mn-rich soil. The nugget coefficient of soil Mn in Guizhou Province was 0.51, indicating that the distribution of soil Zn had moderate spatial autocorrelation. The quantitative evaluation by Geodetector found that the explanatory power of the influencing factors significantly correlated with Mn content(P<0.05), in the order of chronostratigraphy(0.20), lithology(0.17), elevation(0.09), and land use type(0.03). The combination of chronostratigraphy and lithology is the most important factor affecting the spatial heterogeneity of soil Mn, and based on their combination, elevation and land use type further affect the spatial heterogeneity of soil Mn. The spatial distribution of soil Mn is the result of the combined effect of many factors. The zoning of regional soil Mn content can help guide regional ecological management and safe utilization of land resources. [ABSTRACT FROM AUTHOR]

According to the 1 : 50000 scale soil geochemical survey from a several of key selenium (Se) rich areas in the traditional considered Se deficiency area of northern Hebei Province, China, the content, distribution, and the influencing factors of surface soil Se distribution in this area were determined. The Se concentration of surface soil in this area was calculated to be 0.06–3.74 mg/kg, with a mean of 0.4 mg/kg. The Se-rich (Se ≥ 0.4 mg/kg) domains are about 116.8 km2, accounting for 29.05% of the total areas. The concentration of Se in surface soils tends to decrease in the north and increase in the south. The soil Se content and distribution were affected by various factors. Soil parent rocks were the initial sources of Se, but their effects on soil Se content and distribution were limited. The Se content in surface soil had a strong positive correlation with organic carbon (Corg), S, and Fe2O3, but a weak correlation with topographic parameters. The Se content of various soil types, including meadow chestnut soil, calcareous meadow soil, gley meadow soil, and saline meadow soil, and land use types, including natural grassland and other grassland, were relatively high, mainly because of the indirect effect from abundant organic matter and high water content in soil. Additionally, the analysis result of GeoDetector showed that S and Corg were the main driving factors of soil Se distribution and the influence of other factors was limited. [ABSTRACT FROM AUTHOR]

Nowadays, the challenges of energy depletion, environmental pollution and food security caused by extensive agriculture development are attracting global attention. In China, the construction of ecological farms is a key initiative to effectuate the goal of peaking carbon dioxide emissions and achieving carbon neutrality, contributing to high-quality agricultural development. Based on this, this study selects the national-level ecological farms directories issued by the Ministry of Agriculture and Rural Affairs (MARA) of China in 2021 and 2022, and collects the corresponding economic, social and physical geographic data for GIS spatial analysis and Geodetector. The results are as follows: (1) The distribution of ecological farms in various provinces of China is uneven and spatially clustered. It generally presents a 'high in the east and low in the west with concentrated cores' pattern. The construction scope significantly expanded over time, and the high-value areas of nuclear density are concentrated in East China, with the development core transitioned from East China to Central China. (2) Environmental conditions, industrial foundation, economic and social development level, science and technology level and financial support all significantly affect the spatial distribution of ecological farms in China, among which the science and technology level has the most significant enhancement effect on other factors. (3) Environmental conditions provide the construction basis for ecological farms, while economic and social development level and financial support determine the number of ecological farms. The industrial foundation affects the scale of ecological farms in China, while the level of science and technology eliminates the restrictions of other factors to a certain extent. This study provides a reference for optimizing the spatial distribution pattern of ecological farms in China and promoting ecological agriculture. In addition, it presents a viable approach to safeguarding food security. [ABSTRACT FROM AUTHOR]

In China's new era with a territorial spatial governance framework, the delineation of the "Three Zones and Three Lines" is a crucial step in establishing a comprehensive and vertically integrated spatial control system. The changes in the "Three Zone Space," including ecological space, production space and living space, derived from land use abstractions, reflect the extent and manner of the impact of human activities. It serves as an important link between the macroscale (main functional zones) and the microscale (land use planning). The Three-River-Source Region is an important ecological security barrier and a demonstration area for ecological civilization in China. It is also considered one of the least suitable areas for human habitation in China. This region encompasses extensive protected natural areas, and human settlement space is scarce and valuable. The urban space, as an important spatial component of human habitation, often gives rise to significant conflicts between human activities and the environment during the implementation of development and conservation policies for remote areas in China. However, there is currently insufficient attention given to the human settlement space in this area. Therefore, it is necessary to study the evolution and driving mechanisms of urban spatial development from the perspective of the holistic and systematic nature of the "Three Zone Space". To achieve this, the article first analyzes the characteristics of urban spatial changes from 1992 to 2020. Then, it utilizes the optimized parameter Geodetector to analyze the driving mechanisms behind these changes. The results show that: (1) urban spatial expansion has continued to grow over the past 30 years, with an increase of 774.56%; (2) agricultural space is the main source of conversion for urban spatial expansion; (3) natural factors have limited influence on urban spatial expansion, while human factors play a significant role with evident spatiotemporal heterogeneity. This study is significant for the governance and protection of river sources, the conservation of ecosystems in ecologically fragile areas, and the sustainable development of cities. It can also provide scientific references for decision-making in ecological environmental protection and the formulation of land use and spatial planning at various levels in pastoral areas. [ABSTRACT FROM AUTHOR]

Examining and assessing the characteristics of innovation networks among science and technology firms at the city level is essential for comprehending the innovation patterns of cities and improving their competitiveness. Nevertheless, the majority of studies in this field solely rely on patent and paper data, neglecting the analysis of networks across diverse scales and dimensions. Websites offer a novel platform for companies to exhibit their products and services, and the utilization of hyperlink data better captures the dynamics of innovative cooperation. Thus, to attain a more realistic and precise comprehension of China's technology enterprise cooperation networks, enhance the understanding of intra-city and cross-border cooperation within innovation networks, and offer more scientific guidance to cities in enhancing their innovation capabilities by investigating the factors influencing innovation scenarios and the mechanisms of their interactions, this study constructs an innovation network based on the hyperlink data extracted from Chinese science and technology enterprises' websites in 2022. It explores the network's inherent characteristics and spatial patterns across multiple dimensions and scales. Additionally, it employs GeoDetector to analyze the driving factors behind the heterogeneity of city quadrants across each dimension. The findings suggest the following: (1) Evident polarization of innovation capability exists, with a more pronounced differentiation of cities between high capability zones. (2) Contrary to the conventional notion of geographical proximity, cross-region website cooperation prevails, with cross-provincial cooperation being more prevalent than intra-provincial cross-city cooperation. (3) Enterprise cooperation tends to align with partners of similar scale, and small and medium-sized enterprises primarily engage in internal cooperation, primarily concentrated in second and third-tier cities. (4) Cities with high degree centrality and structure holes are primarily located in the construction areas of Chinese urban agglomerations, while those with low degree centrality and structure holes are situated near double-high cities. (5) The spatial heterogeneity of innovation networks across the four dimensions is primarily influenced by STI, while cooperation intensity and innovation capacity dimensions are strongly influenced by traffic capacity. The intra- and inter-city cooperation intensity dimensions are significantly impacted by administrative grade, and the enterprise scale and network location dimensions are most affected by the level of digital infrastructure. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Normal University (Natural Science Edition) / Huanan Shifan Daxue Xuebao (Ziran Kexue Ban) is the property of Journal of South China Normal University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987–2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11–0.19), sunshine duration (0.03–0.17), wind speed (0.03–0.11), and precipitation (0.03–0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration. [ABSTRACT FROM AUTHOR]

The industrial transfer of heavy industries such as non-metallic mineral manufacturing, metal smelting and manufacturing from the eastern coast of China to the central region is beneficial to the economic development of the central region on the one hand, but increases carbon emissions in the central region on the other hand. In February 2022, the National Development and Reform Commission approved the "14th Five-Year Plan for the Development of the Urban Agglomeration in the Middle Reaches of the Yangtze River". This indicates that the urban agglomeration of the middle reaches of the Yangtze River is an important region for implementing green development in the central area. The spatial and temporal evolution of carbon emissions and influencing factors in this region are the foundation for achieving carbon peaking and the carbon neutrality goal. This paper calculates the total carbon emissions of the cities in the urban agglomeration of the middle reaches of the Yangtze River and uses models such as spatial autocorrelation, geographically weighted regression, and Geodetector to explore the spatial–temporal pattern of carbon emissions. The results show the following: (1) The total carbon emissions of the middle reaches of the Yangtze River urban agglomeration showed fluctuations during 2010–2020, and the carbon emission reduction effect is unstable. Additionally, the carbon emissions of the middle reaches of the Yangtze River city cluster show obvious spatial variability, but the high carbon emission area is always concentrated in Wuhan, and this remains unchanged. (2) In 2010, 2014 and 2017, population size was the most important factor affecting carbon emission divergence, and in terms of interaction, the interaction between energy intensity and GDP and urbanization is the reason for the increasing carbon emissions. (3) The influence of population size on carbon emissions decreases from north to south, the influence of energy intensity on carbon emissions shows a spread from the most influential region in the northwest to the centre and then to the northeast, and the GDP per capita has little influence on the difference of carbon emissions spatial distribution. [ABSTRACT FROM AUTHOR]

Face the issues of rural youth outflow, increase of aging population, and rural decline, this research explored the spatiotemporal evolution characteristics and influencing factors of the rural human-land relationship using the quantification index of rural land dependence. The research defines the concept of rural land dependence and constructs an evaluation index system for rural land dependence. By using the entropy weight method, the research quantitatively measures the high and low levels of rural land dependence in 30 provincial administrative units in China from 2006 to 2020. Besides, the GIS spatial analysis and geodetector are used to explore and analyze the spatial-temporal pattern and influencing factors of China's land dependence. The results show that: (1) Temporal evolution: Since 2006, the level of rural land dependence in China has shown a decreasing trend, with economic dependence > population dependence > spatial dependence in terms of partial dependence differences. (2) Spatial pattern: During the study period, there are regional differences in the spatial distribution of rural land dependence in China, with a "high in the southwest and low in the eastern coastal areas" characteristic. The distribution of high-dependence areas has changed from being concentrated to being dispersed, while the distribution of low-dependence areas has changed from being dispersed to being concentrated. (3) Influencing factors analysis: From 2006 to 2018, the number of large agricultural employees is the main influencing factor of land dependence. With the decrease of population dependence and the increase of spatial dependence indexes, as well as the influence of the social environment, the area of rural areas and the total area of the region became the main influencing factor of land dependence from 2018 to 2020. The results can provide scientific basis for deepening the research on rural land use efficiency, and also provide decision support for promoting rural sustainable development and achieving common prosperity. [ABSTRACT FROM AUTHOR]

[Objective] The variation rule of water transparency and the driving factors of its spatial differentiation were analyzed in order to provide a scientific basis for the management of lake reservoirs and the restoration of lake reservoir ecosystems. [Methods] A remote sensing inversion model of water transparency in Pingzhai reservoir was constructed based on sentinel-2 MSI satellite images and quasi-synchronous measured transparency data collected on May 18, August 26, and November 14, 2020. We quantitatively analyzed the drivers affecting spatial differentiation of water transparency using the GeoDetector package in R. [Results] ① The water transparency of Pingzhai reservoir was most sensitive to the B3 band of Sentinel-2 MSI, and the transparency inversion model constructed by using the band combination B3×B4 as the most sensitive factor had high accuracy (R2=0.81, RMSE=0.11 m, MRE=16.91%). ② The water transparency of Pingzhai reservoir showed a spatial distribution trend of high in the central reservoir area, low in the upstream region of each tributary, and low on both sides of the near water body. Water transparency was greatest in November followed by August and then May. [Conclusion] The contents of total suspended solids, chlorophyll a, and total organic carbon were the main factors affecting the spatial differentiation of water transparency in Pingzhai reservoir. Total phosphorus, total nitrogen, water temperature, and wind speed affected the spatial distribution of water transparency by affecting the content of total suspended solids, chlorophyll a, and total organic carbon in the water.

[Objective] The spatial-temporal evolution characteristics and driving mechanism of agricultural carbon emissions in Henan Province were determined to predict the change trend of agricultural carbon emissions during the next ten years in order to formulate an agricultural carbon sequestration and emission reduction scheme, and to promote the transformation to low-carbon and green agricultural production. [Methods] We used a carbon emission equity evaluation model, GeoDetector, and the GM (1,1) model to accomplish the study objectives. [Results] ① Agricultural net carbon emissions in Henan Province declined over time during the study period, with a distribution pattern of "high in the southwest and low in the northeast" . Carbon emissions from animal husbandry accounted for a large proportion of total emissions, and carbon emissions were mainly from cattle, sheep, and pigs. Wheat, corn and vegetables contributed more to carbon absorption than other sinks. ② The ecological carrying coefficient of agricultural carbon emissions was higher in the north and south, and lower in the west. The coefficient of economic contribution was high in the southeast and low in the southwest. ③ The agricultural employee population, per capita disposable income of rural residents, agricultural machinery gross power, and fiscal expenditures on education were the key factors affecting spatial differences in agricultural carbon emissions, and interactions among these factors were strong. ④ Agricultural net carbon emissions in Henan Province will continue to decrease from 2021 to 2030. It is estimated that Henan Province will achieve the goal of agricultural carbon neutrality by 2029. [Conclusion] In the future, Henan Province should strengthen science popularization, actively promote low-carbon agricultural production technology, and increase the efficiency of comprehensive utilization of agricultural resources. Additionally, all localities should give increased attention to system integration, strengthening regional cooperation, and achieving regional integration of agricultural carbon emission reduction.

【Objective】The problem of non-grain cultivated land brings risks to food security. It is urgent to implement cultivated land protection measures. As a first-tier city in China, Guangzhou has a widespread phenomenon of non-grain cultivated land. The research aims to analyze the spatial-temporal evolution characteristics of non-grain cultivated land in Guangzhou, explore the driving force of non-grain process, and put forward relevant suggestions to prevent non-grain cultivated land, which can provide references for the control of non-grain cultivated land in Guangzhou.【Method】GIS spatial analysis method was used to study the spatial-temporal evolution process of non-grain cultivated land in Guangzhou, and Geodetector Model was used to study the influencing factors of non-grain cultivated land.【Result】(1) From 2005 to 2016, the non-grain rate of cultivated land in Guangzhou increased slightly from 60.46% to 67.17%. From 2016to 2018, the non-grain rate of cultivated land increased significantly from 67.17% to 87.53%. From 2018 to 2020, the non-grain rate of cultivated land decreased from 87.53% to 86.91%, indicating that the non-grain problem of cultivated land in Guangzhou is serious.(2) From a regional perspective, the non-grain level of the central urban area was generally higher than that of the peripheral urban area. In 2020, the non-grain levels of the northern Conghua District was low with the non-grain rates of cultivated land 58%. The non-grain level of Liwan District, Haizhu District and Tianhe District were the highest with the nongrain rate of cultivated land 100%.(3) The non-grain cultivated land conversion in Guangzhou is affected by many factors, and most of the influencing factors show a two-factor enhancement relationship, and a few show nonlinear enhancement relationship.Among the single factors, urbanization rate, consumption level and GDP per land are the most important influencing factors, with an average influence of 0.9176, 0.7059 and 0.6674, respectively, while the factor influence of natural resource endowment dimension is relatively soft.【Conclusion】The overall non-grain level of Guangzhou is high, and the imbalance of internal economic development has a profound impact on the spatial distribution pattern of non-grain level. More attention should be paid to the protection of cultivated land, strengthen the construction of cultivated land protection system and implement the responsibilities of all parties. Guangzhou should further improve the policies for benefiting farmers, establish high-standard permanent basic farmland, provide financial and technical support, facilitate to improve the quality and efficiency of grain production, and enhance the enthusiasm of farmers to grow grain. And it should cultivate professional talents, promote industrial integration and upgrading, and increase the additional income of grain production. Countermeasures should be taken to prevent the illegal occupation of cultivated land from many aspects, stabilize the sown area of crops, prevent the non-grain use of cultivated land, and ensure the increase of grain production and income.

Vegetation plays an important role in global or regional environmental change. In this study, the spatial–temporal variations of NDVI and its response to climate in China and its seven sub-regions were investigated based on MODIS NDVI data, ERA5-land precipitation (PRE) and temperature (TEM) data from 2001 to 2020. The inter-annual growth rate of NDVI in China was 0.0021/yr in the past 20 years. The inter-annual growth rates of NDVI in seven sub-regions had significant differences at regional or seasonal scales. The ratio of improved vegetation area to the total studied area reached about 70%. In summer, vegetation degradation was concentrated in East China and Southwest China. The vegetation in Central China and South China improved more obviously in autumn than in the other seasons. The vegetation of Northeast China had a remarkable degradation in autumn and winter, especially in winter. The influence degree of PRE (q = 0.54, P

Exploring urban land use change is a classical problem in urban geography. Taking Zhengzhou as an example, this paper analyzes the spatial and temporal characteristics and driving factors of urban land use change, and simulates the spatial pattern of urban land use in the future. The results of the study show that the land use types in Zhengzhou city were mainly farmland and construction land, the area of forestland, grassland, water area, and unused land was smaller, and the main land use change was the transformation of farmland into construction land. The accuracy check of the simulated land use type data in 2020 showed that the kappa coefficient reached 0.9445, which met the accuracy requirement. Then, according to the predicted land use data in 2025, it was found that the area of grassland, construction land, and water area may have decreased, and the area of farmland, forestland and unused land may have increased. Based on the driving force analysis of land use changes, its prediction results can provide an important reference basis for the formulation and planning of policies related to urban construction.

The soil organic matter (SOM) and soil total nitrogen (STN) is a significant concern in Qinghai–Tibet Plateau, China. This study analysed the spatial distribution of SOM and STN and determined their influencing factors to support the conservation of cultivated soil and development of sustainable agricultural strategies in the Plateau. In total, 120 soil samples were collected from the 0–20-cm soil layer in Huzhu County, Qinghai Province. Traditional statistical and geostatistical methods were used to analyse the spatial distribution of SOM and STN; a geographical detector (GeoDetector)was used to explore the factors influencing the spatial variation. The SOM and STN concentrations were 6.92–44.57 and 0.52–2.54 g/kg, respectively. The Cokriging interpolation map showed a similar spatial distribution pattern for SOM and STN concentrations, which decreased from the northeast to southwest directions in the study area. GeoDetector results revealed that the three primary factors influencing the spatial variability of SOM and STN were soil type, annual accumulated precipitation and elevation, with their explanatory power ranging between 38.4% and 59.5%. Two-factor interactions enhanced the explanatory power of the spatial variability of SOM and STN. The research results provide a reference for conservation tillage and precision agriculture.

Abstract Background Dyslipidemia is a key factor causing cardio cerebrovascular diseases, and the total cholesterol (TC) is an important lipid indicator among them. Studies have shown that environmental factors have a strong association with TC levels. Previous studies only focused on the seasonal variation of TC level and the short-term effects of some environmental factors on TC level over time, and few studies explored the geographical distribution of TC level and quantified the impact of environmental factors in space. Methods Based on blood test data which was from China Health and Retirement Longitudinal Study (Charls) database, this study selected the TC level test data of middle-aged and elderly people in China in 2011 and 2015, and collected data from 665 meteorological stations and 1496 air pollutant monitoring stations in China. After pretreatment, the spatial distribution map of TC level was prepared and the regional statistics were made. GeoDetector and geographically weighted regression (GWR) were used to measure the relationship between environmental factors and TC level. Results The TC level of middle-aged and elderly in China was higher in females than in males, and higher in urban areas than in rural areas, showing a clustered distribution. The high values were mainly in South China, Southwest China and North China. Temperature, humidity, PM10 and PM2.5 were significant environmental factors affecting TC level of middle-aged and elderly people. The impact of pollutants was more severe in northern China, and TC level in southern China was mainly affected by meteorological factors. Conclusions There were gender and urban-rural differences in TC levels among the middle-aged and elderly population in China, showing aggregation in geographical distribution. Meteorological factors and air pollutants may be very important control factors, and their influencing mechanism needs further study.

Abstract Background Utilizing population-based survey data in epidemiological research with a spatial perspective can integrate valuable context into the dynamics of HIV prevalence in West Africa. However, the situation in the Mano River Union (MRU) countries is largely unknown. This research aims to perform an ecological study to determine the HIV prevalence patterns in MRU. Methods We analyzed Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) data on HIV prevalence in MRU from 2005 to 2020. We examined the country-specific, regional-specific and sex-specific ratios of respondents to profile the spatial–temporal heterogeneity of HIV prevalence and determine HIV hot spots. We employed Geodetector to measure the spatial stratified heterogeneity (SSH) of HIV prevalence for adult women and men. We assessed the comprehensive correct knowledge (CCK) about HIV/AIDS and HIV testing uptake by employing the Least Absolute Shrinkage and Selection Operator (LASSO) regression to predict which combinations of CCKs can scale up the ratio of HIV testing uptake with sex-specific needs. Results In our analysis, we leveraged data for 158,408 respondents from 11 surveys in the MRU. From 2005–2015, Cote d'Ivoire was the hot spot for HIV prevalence with a Gi_Bin score of 3, Z-Score 8.0–10.1 and P

As the carrier of human economic activities, the change of territorial space affects the level of regional carbon balance. Therefore, with regional carbon balance as the goal, this paper proposed a framework from the perspective of production-living-ecological space and took Henan Province of China as a study area for empirical research. First, the study area established an accounting inventory that considers nature, society, and economic activities to calculate carbon sequestration/emission. Then, the spatiotemporal pattern of carbon balance was analyzed by ArcGIS from 1995 to 2015. Later, the CA-MCE-Markov model was used to simulate the production-living-ecological space pattern in 2035, and carbon balance in three future scenarios was predicted. The study showed that from 1995 to 2015, the living space gradually expanded, and the aggregation rose while the production space decreased. Carbon sequestration (CS) was less than carbon emission (CE) and presented an unbalanced state of negative income in 1995, while CS exceeded CE and showed a positive income imbalance in 2015. In 2035, living space has the highest carbon emission capacity under natural change scenario (NC), while ecological space has the highest carbon sequestration capacity under ecological protection scenario (EP), and production space has the highest carbon sequestration capacity under food security scenario (FS). The results are crucial for understanding the carbon balance changes in territorial space and supporting regional carbon balance goals in the future. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

This paper explores the spatiotemporal evolution characteristics and spatial correlation patterns of green building development differences in 41 cities in the Yangtze River Delta region from 2012 to 2020 by means of the gravity center analysis model and spatial autocorrelation analysis. In addition, it further clarifies the impact factors of the spatial differentiation pattern of green building development in combination with GeoDetector based on four dimensional factors of population and economy, market environment, policy, and other factors. The results showed that: (1) According to the analysis of the number of green buildings in each city from 2012 to 2020 and the natural discontinuity method, the development pattern of green buildings in the Yangtze River Delta region city clusters shows an imbalance, being highly concentrated in the eastern coastal areas with Suzhou (1) and Shanghai as the core. The overall trajectory of the center of gravity shows the development from southeast to northwest. (2) The global Moran's I of green buildings in the Yangtze River Delta region city clusters is greater than 0, and all passed the significance test (Z > 1.96, p < 0.01), indicating that the green buildings in the Yangtze River Delta region city clusters show typical spatial aggregation characteristics. By using the local LISA index, it is found that in the H-H spatial autocorrelation distribution pattern with Suzhou (1) and Shanghai as the core, the core city has a strong attraction ability and relatively low radiation ability. (3) Based on the explanatory power mean, the main driving factors of the spatial differentiation pattern of green building development in the Yangtze River Delta region city clusters are education level (0.6656), technical level (0.6269), and the gross domestic product (0.6091). The factor interaction shows a two-factor enhancement and nonlinear enhancement effect, and there is neither a weakening nor an independent relationship. [ABSTRACT FROM AUTHOR]

Most of the previous studies exploring the changing patterns of wetland in the Yellow River Delta (YRD) were conducted based on sparse time-series images, which ignored its severe environmental gradient and rapid evolution process of the wetland. The changes in the dominant factors in the evolution of the wetland in the YRD are not clear. This study used the dense time-series Sentinel-2 images to establish a wetland database of the YRD, and then analyzed the spatial distribution characteristics of, and temporal changes in, the wetland during 2015–2022. Finally, the dominant factors of the spatio-temporal evolutions of the wetland were explored and revealed. The results showed the following. (1) During 2015–2022, the wetland in the YRD was dominated by artificial wetland, accounting for 54.02% of the total wetland area in the study area. In 2015–2022, the total wetland area increased by 309.90 km2, including an increase of 222.63 km2 in natural wetlands and 87.27 km2 in artificial wetlands. In the conversion between wetland types, 218.73 km2 of artificial wetlands were converted into natural wetlands, and 75.18 km2 of natural wetlands were converted into artificial wetlands. The patch density of rivers, swamps, and salt pans increased, showing a trend of fragmentation. However, the overall degree of landscape fragmentation in wetlands weakened. The trend of changes in the number of patches and landscape shape index was the same, while the trend of changes in Shannon's diversity index and Contagion index was completely opposite. (2) Natural factors, such as precipitation (0.51, 2015; 0.65, 2016), DEM (0.57, 2017; 0.47, 2018; 0.49, 2020; 0.46, 2021), vegetation coverage (0.59, 2019), and temperature (0.48, 2022), were the dominant influencing factors of wetland changes in the YRD. The dominant single factor causing the changes in artificial wetlands was vegetation coverage, while socio-economic factors had lower explanatory power, with the average q value of 0.18. (3) During 2015–2022, the interactions between the natural and artificial factors of the wetland changes were mostly nonlinear and showed double-factor enhancement. The interactions between temperature and sunshine hours had the largest explanatory power for natural wetland change, while interactions between precipitation and vegetation coverage, and between temperature and vegetation coverage, had large contribution rates for artificial wetland change. The interactions among natural factors had the greatest impacts on wetland change, followed by interactions between natural factors and socio-economic factors, while interactions among socio-economic factors had more slight impacts on wetland change. The results can provide a scientific basis for regional wetland protection and management. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) has a critical impact on the energy balance of land surface processes and ecosystem stability. Meanwhile, LST is controlled by multiple factors at the surface, resulting in heterogeneity of its spatial distribution. To understand the drivers of LST spatial heterogeneity and their contributions, the effects of air temperature, normalized difference vegetation index (NDVI), soil moisture, net surface radiation, precipitation, aerosol optical depth (AOD), evapotranspiration, water vapor, digital elevation model (DEM), climate type, and land cover type on LST spatial heterogeneity was analyzed in this study with GeoDetector. The results showed that the explanatory ability of air temperature to impact the spatial heterogeneity of LST was the largest in each year with a mean value of 0.74, followed by water vapor with a mean value of 0.7, and the driving effect of the factors on LST showed an increasing trend year by year. However, the land cover type did not have an effect on the spatial heterogeneity of LST for the univariate analysis in this study. In addition, the interaction analysis indicated that the spatial distribution of LST was jointly driven by all the driving factors. Among them, air temperature had the strongest interaction with other factors, with the strength of the effect in the range of 0.73–0.8. In terms of the highly sensitive area of LST for each driver, AOD has the largest driving area, accounting for 15.8% of the total area, followed by WV, TA, and ET at about 11%, and the remaining variables are less than 10%. During the study period, the area of the highly sensitive region of LST for each factor showed an overall decreasing trend, indicating that the influence of the driving factors on LST will be stronger and more concentrated. Generally, this study provides meaningful understanding of the spatial heterogeneity of LST since 2003 and provides a scientific reference for coping with climate change, analyzing surface environmental patterns, and protecting ecological environment. [ABSTRACT FROM AUTHOR]

Many developed cities in the world put forward a spatial development strategy in their construction planning. Analyzing the development level of the urban spatial structure and the influence of driving factors has become a hot topic. Based on 709,232 points of interest data in Beijing, China, this paper integrates nighttime light data and population density data to select eight key driving factors from three perspectives: urban function configuration, economic activity intensity and population spatial distribution. Geodetector is used to optimize data discreteness and highlight the spatial heterogeneity of the development level. We use the technique for order of preference by similarity to ideal solution (TOPSIS) model improved with the entropy weight method to grade the spatial differentiation characteristics of the comprehensive development level. The driving factors and their effects on space are further discussed using Geodetector. The results are as follows: (1) The quartile method can achieve the optimal dispersion of all urban functions. The standard deviation can achieve the optimal dispersion of economic activity intensity and population spatial distribution; (2) A comparison with the "Beijing Urban Master Plan (2016–2035)", finds that the optimized evaluation system can effectively reflect the spatial heterogeneity of urban spatial structure development. It verifies the rationality of the evaluation methods and factors; (3) The driving force of the population under single-factor driving is 0.8428. The dual-factor driving force with population participation ranges from 0.8992 to 0.9550. The results of the study are significant and reflect the prominent role of population on the development level of spatial structure in Beijing. This paper aims to provide a new idea for the study of the interior space planning of large inland cities. [ABSTRACT FROM AUTHOR]

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Revealing the temporal and spatial evolution pattern of reservoirs and their driving mechanisms is of great significance for promoting the coordinated development of the national economy and natural ecology. In this study, the northern slope of the Tianshan Mountains (NSTM) in Xinjiang was selected as the study area. Based on remote sensing data from 1990 to 2020, the temporal and spatial evolution characteristics of reservoirs on the NSTM in the past 30 years were analyzed using the grid cells method and the nuclear density analysis method, and the mechanisms by which natural geographical conditions and socio-economic development influence the temporal and spatial evolution of reservoirs in the NSTM were analyzed using GeoDetector. The results indicate the following: (1) In terms of temporal change, the area of reservoirs in the NSTM showed an overall expansion trend from 1990 to 2020, with a growth rate of 42.77%, and the number of reservoirs increased from 123 to 238 during the same period, with a growth rate of 93.50%. (2) In terms of spatial change, the distribution of reservoir accumulation areas on the NSTM gradually developed horizontally from the middle section with rapid economic development to the western and eastern sections. (3) The GeoDetector results showed that the main driving factors affecting the spatial differentiation of reservoir area in the NSTM from 1990 to 2020 were the average elevation (0.201), cultivated land area (0.112), and GDP (0.070), while the main driving factors combinations were the average elevation and cultivated land area (0.374), the average elevation and GDP (0.325), and the average elevation and average population (0.319). The research results can provide a theoretical basis and a decision-making reference for water conservancy project planning and water resources management in the NSTM. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of soil organic matter (SOM) is important for land use management, but conventional sampling methods require significant human and financial resources. How to map SOM and monitor its changes using a limited number of sample points combined with remote sensing techniques that provide long-time series data is crucial. This study aimed to generate a regional-scale near-surface SOM map using 70 soil samples and covariate environmental factors extracted mainly from Landsat 8 OLI. Firstly, the sensitivity of each environmental factor to SOM was tested using a geographic detector model (GDM). Secondly, the tested factors were selected for modeling and mapping by ordinary least squares (OLS) and geographically weighted regression kriging (GWRK). The performance of these two models was compared. Finally, the mapping results of the better model (GWRK) were compared and analyzed with the traditional interpolation results based solely on sampling points to verify the rationality of the proposed method. The results show that three environmental factors, ratio vegetation index (RVI), differential vegetation index (DVI), and terrain roughness (TR), have a strong influence on the spatial variability of SOM. Using these three factors in combination with the GWRK method, a more accurate and refined spatial distribution map of SOM can be obtained. Comparing the SOM maps of GWRK and the traditional interpolation method, the results show that the accuracy of GWRK (R2 = 0.405; mean absolute error = 0.637, and root mean square error = 0.813) is higher than that of traditional interpolation methods (R2 = 0.291, MAE = 0.609, and RMSE = 0.863). The spatial recognition rate (fineness) of SOM patches at all levels using the GWRK method increased by more than 73 times compared to the traditional kriging. We conclude that the combination of limited SOM samples, environmental variables, GDM, and GWRK is a pragmatic approach for estimating regional-scale SOM. [ABSTRACT FROM AUTHOR]

Road construction will have a large impact on the ecosystem in the road area, and carbon stock, as an important indicator to measure the scale and quantity of primary productivity of the ecosystem, will also change, but the specific pattern is not clear. It is important to study the impact of road construction on carbon stock for regional ecosystem protection and sustainable economic and social development. Based on the InVEST model, this paper quantifies the spatial and temporal changes of carbon stocks in Jinhua, Zhejiang Province, from 2002 to 2017, using remote sensing image classification data land cover types as model driving data, geodetector, trend analysis, and buffer zone analysis methods, explores the driving effect of road construction on carbon stocks, and analyzes the spatial and temporal impacts of road construction on carbon stocks within the buffer zone. Results indicate that the total carbon stock in the Jinhua area showed a decreasing trend during the 16 years, decreasing by about 8.58 × 106 t. The spatial changes in the areas with higher carbon stocks were not significant. The explanatory power of road network density on carbon stock reaches 37%, and the anisotropic effect of road construction on carbon stock is strong and had a significant driving effect on carbon storage reduction. The new highway construction will accelerate the rate of carbon stock decline in the buffer zone, and the spatial situation is generally "the farther away from the highway, the higher the carbon stock." [ABSTRACT FROM AUTHOR]

Land degradation destroys human habitats, and vegetation is a marker reflecting land degradation. In this article, the Balochistan Province of Pakistan, which has a fragile ecological environment, was selected as a typical case to analyze its land degradation over 21 years. Relevant studies that used the NDVI and remote sensing data to monitor land degradation already existed. Based on the data product of MODIS, this study obtained the spatio-temporal trends of the normalized difference vegetation index (NDVI) changes from 2000 to 2020 using the sen+ Mann–Kendall (MK) test and Hurst index and analyzed the driving factors of land degradation and restoration by employing the multiple stepwise regression method. The residual analysis method was an effective tool for distinguishing between anthropogenic and climatic impacts, given that not all regions have a significant correlation between the NDVI and rainfall. The main climatic drivers of the NDVI were derived based on the Geodetector analysis and stripped of the main climatic factors by residual analysis to explore the influence of anthropogenic factors on the NDVI. The results show the following: (1) Balochistan is dominated by land restoration. Land restoration is mainly dominated by climate as well as both climate and human factors, and land degradation is mainly dominated by climate and human factors. (2) The Geodetector-based study found high correlations between the NDVI and TMP, MAP, AET and PET, complementing most previous residual analyses that considered only precipitation and temperature. In Balochistan, TMP, AET, PET and MAP were the dominant climatic factors affecting the spatial distribution of the NDVI; TMP with MAP and TMP with AET were the main interactive factors in the spatial distribution of the NDVI. (3) The article quantifies the impact of the anthropogenic drivers on land degradation. Human activities positively influenced the NDVI in 91.02% of the area and negatively influenced it in 8.98% of the area. (4) The overall trend of the NDVI was mainly stable, with stronger improvement than degradation, and showed strong persistence. The above findings enrich our understanding of the climatic impacts of land degradation and human impacts in arid or semi-arid regions and provide a scientific basis for ecological engineering to achieve ecological conservation and quality development in Balochistan, Pakistan. [ABSTRACT FROM AUTHOR]