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With the rapid development of China’s economy and the acceleration of urbanization, the rapid expansion of urban space has led to a growing demand for land that has resulted in the destruction and expropriation of a large amount of ecological land in China. In the face of the current situation of China’s resource constraints, environmental degradation, and ecosystem destruction, it is necessary to thoroughly study the territorial ecological space of China and its evolution rules. Based on previous studies on the classification of ecological land use, this study proposes the concept of territorial ecological space and distinguishes it from urban space and agricultural space. A matching ecological space classification system has been established, which may help in understanding the ecosystem model and related ecological processes. According to the constructed ecological space classification system, ecological spatial data processing was conducted. The standard deviation ellipse model was used to analyze the pattern of various ecological spaces in China and the migration of their barycenter. The results of the study show the following: 1) China’s territorial ecological space area showed a downward trend from 2000 to 2015. From the aspect of flow, the area of ecological space mainly flows into urban space, followed by agricultural space. According to the division of the first-level ecological space, the main ecological space structure of China is grassland ecological space and woodland ecological space. 2) The spatial distribution characteristics of China’s territorial ecological space are more prominent, and the distribution is very uneven. During the study period, the variation of the standard deviation ellipse angle θ of the ecological space is small, and the migration of the barycenter is not obvious, indicating that China’s territorial ecological space is in a relatively stable state. 3) It is necessary to implement a differentiated policy on the optimization and control of territorial ecological space according to the non-equilibrium of territorial space, and build a unified and coordinated territorial space management and control system. Such a differentiation policy would provide a basis for decision making and a reference for formulating strategies for the sustainable development of the regional ecological environment and optimizing the spatial layout of the territory.

Given the restrictions on special geographic locations in development processes, the measurement and analysis of the ecological quality of the Hami Oasis are of great significance for the protection of this fragile oasis. In this study, the ecological quality of the Hami Oasis was monitored by constructing a remote sensing ecological index (RSEI) for arid areas. Using the standard deviation ellipse and moving window method, the ecological status and space–time changes were explored for both their external and internal factors in the Hami Oasis. Finally, a geo-detector was employed to determine the driving factors of the ecological quality of the Hami Oasis. The results revealed that: (1) In the remote sensing ecological index constructed in the Hami Oasis, the main influencing factors were dryness and wetness. The average value of the ecological quality of the oasis was less than 0.5, and the ecological quality level was relatively poor. Among the five grades of ecological quality in the Hami Oasis, the poor grade and the good grade showed the largest changes, decreasing by 200 and increasing by 300, respectively, which were mainly concentrated in the periphery of the oasis. (2) The improved ecological quality of the Hami Oasis was mainly manifested in the expansion of the artificial oasis, while the deteriorated area was manifested as an increase in the built-up area. Moreover, the ecological quality of the Hami Oasis presented a ringlike nesting distribution pattern from the internal built-up area to the artificial oasis periphery. (3) The external expansion direction of the ecological quality of the Hami Oasis featured southeast–northwest expansion, which was consistent with the direction of the rivers and traffic roads. The transformation between different ecological qualities in the oasis and the expansion of the built-up area were the reasons for the fragmentation of the Hami Oasis’ landscape. (4) Compared to a single factor, the dual-factor for the ecological quality of the Hami Oasis had stronger explanatory power. Moreover, changes in land use types caused changes in the ecological quality of the Hami Oasis. During the study period, we found that human activities had a more significant impact than natural factors on the development of the Hami Oasis. (5) The Moran’s I Index increased from 0.835268 in 2000 to 0.923976 in 2018, and the p values in the study area all reached a 0.05 significant level. At the same time, the areas with p values above the 0.01 and 0.001 significant levels have also increased significantly in the past 18 years.

Based on Panel data collected from 2011 to 2020 targeted to 50 prefecture-level cities in the Yellow River Basin, this paper adopted standard deviation ellipse and spatial Dubin model to explore the nonlinear effects and spatial spillover effects of urbanization on air pollution and ecological resilience in the Yellow River Basin. The results show that the degree of air pollution in the southeast of the Yellow River Basin is higher than that in the northwest of the Yellow River Basin, the distribution range of air pollution is shrinking, the concentration of ecological resilience is enhanced, and the ecological environment is developing for the better. There is a significant U -shaped relationship between urbanization and air pollution in the Yellow River Basin, and an inverted U -shaped relationship between urbanization and ecological resilience. For every 1% increase in urbanization, air pollution decreases by 0.0873%, ecological resilience increases by 0.4046%. For every 1% increase in the square term of urbanization, air pollution increases by 0.2271%, ecological resilience decreases by 0.1789%. The urbanization of the Yellow River Basin has a spatial spillover effect on air pollution and ecological resilience, and urbanization has a significant negative impact on the ecological environment of neighboring cities. The robustness of the above conclusions is verified by introduce an inverse distance weight matrix replacing the spatial weight matrix.

IntroductionThe agricultural sector is the second largest emitter of greenhouse gases, accounting for 23% of global anthropogenic carbon emissions. Analysis of the basic state of carbon emissions from China's agriculture is helpful to achieve carbon reduction targets.MethodsAgricultural carbon emissions were calculated using the emission factor method, based on data from the China Rural Statistical Yearbook and various provincial statistical yearbooks. To analyze spatial patterns, the standard deviation ellipse method and the center of gravity migration model were employed, uncovering the migration path of agricultural carbon emissions. Regional disparities and the driving factors of agricultural carbon emissions were further examined using the Theil index and the Logarithmic Mean Divisia Index (LMDI) model.ResultsThe analysis indicated that the emissions center has gradually shifted towards the central and western regions, reflecting changes in agricultural production activity areas. Intraregional differences are the primary contributors to the imbalance in agricultural carbon emissions, with pronounced disparities in grain production and consumption balance regions. Key influencing factors include agricultural production efficiency, adjustments in agricultural industrial structure, economic structure and output, and urbanization levels. The economic output effect and urbanization effect are identified as the main drivers of increased carbon emissions, while declining production efficiency has hindered emission reduction efforts.ConclusionThe findings provide valuable insights for regional management and policymaking in China's agricultural sector, highlighting the need to enhance production efficiency and optimize agricultural structure to reduce emissions.

Rapid urban development and human activities have led to drastic changes in land use, resulting to heightened ecological pressures and risks to ecosystems, especially in cities along the lower reaches of the Yellow River (CLRYR), China. However, the landscape pattern and associated ecological risks in the CLRYR in the past twenty years remain unclear. In this study, we employed the land use data to identify the primary landscape types and their transformations, providing an in-depth analysis of the prevailing landscape pattern and the landscape ecological risk (LER) in the CLRYR. Additionally, we explored the spatial distribution of LER and investigated the underlying driving forces behind these changes. The results reveal that: (1) Cropland is the main landscape type in the CLRYR region; however, the area of cropland decreases with the transition to impervious. (2) Due to human activities, landscape fragmentation and diversity have gradually increased in CLRYR, while aggregation has gradually decreased, until there was some improvement between 2015 and 2020. (3) The LER in the CLRYR region exhibits instability, with values of 0.1761, 0.1751, 0.1760, 0.1773, and 0.1751 displaying a fluctuating downward trend. Directional distribution analysis indicates a movement of the LER center of gravity towards the mouth of the Yellow River, accompanied by an increasing dispersion pattern. Analysis of driving forces suggests that natural factors hold greater explanatory power compared to social factors. Moreover, interaction detection results reveal that the combined effect of any two factors surpasses that of a single factor. The findings offer a theoretical foundation for enhancing planning policies aimed at striking a balance between environmental preservation and social advancement within the CLRYR region.

Since the implementation of the great western development strategy in 2000, the ecological environment in the western region of China has been significantly improved. In order to explore the temporal and spatial characteristics of vegetation coverage in the western region, this paper adopted the method of Maximum Value Composite (MVC) to obtain the mean Normalized Difference Vegetation Index (NDVI) of vegetation on the basis of the Moderate-resolution Imaging Spector audiometer (MODIS) data of 2000/2005/2010/2015/2018. Thereafter, the spatio-temporal differentiation characteristics of vegetation in western China were analyzed. The results show that: (1) According to the time characteristics of vegetation coverage in the western region, the average annual NDVI value of vegetation coverage in the growing season in the western region fluctuated between 0.12 and 0.15, among which that of 2000 to 2010 fluctuated more greatly but did not show obvious change trend. (2) Based on Sen trend and Mann-Kendall test analysis, the area of vegetation coverage improvement in the western region from 2000 to 2018 was larger than that of significant vegetation degradation. (3) From the perspective of global autocorrelation coefficient, Moran’s I values were all positive from 2000 to 2018, which indicates that the vegetation coverage in the west showed strong positive autocorrelation in each period. According to the average value and coefficient of variation of vegetation coverage, the vegetation coverage was lower in 2000, its internal variation was smaller, and the vegetation coverage increased with time. According to the local spatial autocorrelation analysis, the vegetation coverage levels in different regions varied greatly. (4) The standard deviation ellipse method was used to study the spatial distribution and directional transformation of vegetation. It makes the result more intuitive, and the three levels of gravity center shift, direction shift, and angle shift were considered: the vegetation growth condition in the spatial aggregation area improved in 2015; the standard deviation ellipses in 2000 and 2018 overlapped and shifted eastward, which indicates that the vegetation coverage conditions in the two years were similar and got ameliorated.

Analyzing the evolution characteristics of China’s agricultural spatial patterns and identifying the influencing factors are of great significance for clarifying the sources of agricultural carbon emissions across regions and enhancing the efficiency of emission reduction. Using agricultural carbon emission data from 31 Chinese provinces (municipalities and autonomous regions) from 2000 to 2020, this study examines the spatial patterns of agricultural carbon emissions through methods, including the standard deviational ellipse, Gini coefficient, kernel density estimation, and spatial autocorrelation analysis. Furthermore, a dynamic Spatial Durbin model was employed to analyze the influencing factors. The results reveal that China’s agricultural carbon emissions increased initially, followed by a decrease, reflecting an overall declining trend. The spatial distribution pattern tends to align along a northeast–southwest orientation. Significant disparities were observed, with the eastern region showing the greatest variation and pronounced differences existing between eastern and western China. Interregional differences were identified as the primary source of overall variation in agricultural carbon emissions. The key influencing factors included the value-added of the primary industry, total agricultural machinery power, chemical fertilizer application, rural electricity consumption, and crop sowing area. Among these, an increase in the value-added of the primary industry suppresses agricultural carbon emissions, whereas the other factors contribute to higher emissions. This study provides a scientific basis for optimizing China’s agricultural carbon emission reduction strategies and formulating cross-regional collaborative mitigation plans, while also offering valuable insights for other developing countries at similar stages of development.

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

The sustainable development of water resources, social economy and ecological environment (WSE) system is crucial for achieving regional prosperity, and it is necessary and urgent to improve the sustainability of WSE system. Therefore, a sustainability assessment and multi-angle diagnosis analysis of WSE system are conducted in this study. Firstly, a novel assessment indicator system is constructed based on the Driving force-Pressure-State-Impact-Ecological basis-Response-Management (DPSIERM) model. Subsequently, the TODIMSort is innovatively improved by introducing a machine learning clustering algorithm (i.e., mean shift clustering algorithm) to obtain the sustainability grade. Finally, the sustainable development of the WSE system is diagnosed by employing the standard deviation ellipse model, coupling coordination degree (CCD) model, and the obstacle degree model. To validate the practicability of the proposed comprehensive assessment method, this paper selects Shanxi Province of China as a case study area. The results indicate that from 2012 to 2021, the WSE system sustainability in all cities of Shanxi Province has significantly improved, and the spatial distribution pattern gradually shifts from a north-south direction to a northeast-southwest direction. In addition, from 2012 to 2021, the CCD of each city in Shanxi Province is generally on the rise, and it is found that there is a certain positive correlation between the sustainability of WSE system development and CCD. Furthermore, the primary obstacle factors to WSE system sustainability in Shanxi Province in 2021 are population density, per capita water resources, modulus of water resources production, and investment in environmental conservation as a percentage of GDP. Finally, some management suggestions for promoting the sustainable development of WSE system are put forward based on result analysis and discussion.

Objective The outbreak of the COVID-19 pandemic has drawn attention from all sectors of society to the level of public health services. This study aims to investigate the level of public health service supply in the four major regions of Guangdong Province, providing a basis for optimizing health resource allocation. Methods This article uses the entropy method and panel data of 21 prefecture-level cities in Guangdong Province from 2005 to 2021 to construct the evaluation index system of public health service supply and calculate its supply index. On this basis, the standard deviation ellipse method, kernel density estimation, and Markov chain are used to analyze the spatiotemporal evolution trend of the public health service supply level in Guangdong Province. The Dagum Gini coefficient and panel regression model are further used to analyze the relative differences and the key influencing factors of difference formation. Finally, the threshold effect model is used to explore the action mechanism of the key factors. Results Overall, the level of public health service supply in Guangdong Province is on an upward trend. Among them, polarization and gradient effects are observed in the Pearl River Delta and Eastern Guangdong regions; the balance of public health service supply in Western Guangdong and Northern Mountainous areas has improved. During the observation period, the level of public health services in Guangdong Province shifted towards a higher level with a smaller probability of leapfrogging transition, and regions with a high level of supply demonstrated a positive spillover effect. The overall difference, intra-regional difference and inter-regional difference in the level of public health service supply in Guangdong Province during the observation period showed different evolutionary trends, and spatial differences still exist. These differences are more significantly positively affected by factors such as the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate. Under different economic development threshold values, the degree of fiscal decentralization and urbanization rate both have a double threshold effect on the role of public health service supply level. Conclusion The overall level of public health service supply in Guangdong Province has improved, but spatial differences still exist. Key factors influencing these differences include the level of regional economic development, the degree of fiscal decentralization, and the urbanization rate, all of which exhibit threshold effects. It is suggested that, in view of the actual situation of each region, efforts should be made to build and maintain their own advantages, enhance the spatial linkage of public health service supply, and consider the threshold effects of key factors in order to optimize the allocation of health resources.