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Investigating the experience and improvement measures for China’s distinctive land supply participation in macro-control processes holds significance for full utilization of land policy. However, the spatial heterogeneity and its theoretical and comprehensive analysis of drivers are still poorly revealed. This paper uses spatial analysis methods and micro-scale big data on land transactions to depict the spatiotemporal heterogeneity of land supply, and analyses its driving mechanisms via an endogenous–exogenous factor framework and regression models. Land supply experienced fluctuating “growth–decline–growth” trends in 2001–2021, spatially showed a large cluster in the east, a small cluster in the center and scattering in the west, with the gravity center relocating southwest, and formed a multi-core, hierarchical, circular structure of high density in core cities, density in peripheral cities and sparseness in districts. Endogenously, total land resources and road accessibility facilitated land supply, while topographic relief and urban proximity showed inhibitory effects; land supply positively correlated with land finance dependence, officials’ appraisal pressure, local government competition and officials’ corruption but negatively related with fiscal tax revenues and fiscal transparency; construction land indicators directly determined land supply, while the intensity of use control restricted the conversion of arable land and weakened land supply. Exogenously, urbanization, industrialization, capital investment, technological innovation and marketization level promoted land supply, while the substitution of human capital reduced the demand for land; economic fluctuations showed non-significant relationships with land supply. Differentiated impacts of multiple factors on land supply pattern are emphasized and should be integrated into formulating land policy and optimizing land allocation.

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China’s PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a ‘club convergence’ phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the “dual carbon” goal.

Since China’s reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a “slow decrease–rapid increase” trend, forming a “V-shaped” pattern, while abandonment shows a “rapid decrease–slow decrease–slow increase” trend, forming a “U-shaped” pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation’s center of gravity is “slightly southeast–slightly northeast–southwest”, whereas the abandonment’s center of gravity shifts “northeast–southwest–northeast”. Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen.

Accurately identifying the expansion characteristics and driving mechanisms at different development stages of urban agglomerations is crucial for their coordinated development. Using the Central Yunnan Urban Agglomeration as a case study, we employ a data fusion approach to fuse nighttime light data with LandScan data and utilize the U-net neural network to systematically analyze the expansion characteristics and driving mechanisms of the urban agglomeration. The results indicate that, from 2008 to 2013, the Central Yunnan Urban Agglomeration was in an initial expansion stage, primarily driven by economic development levels and population size. From 2013 to 2018, the agglomeration entered an accelerated expansion stage, driven mainly by industrial structure transformation and the population agglomeration effect. From 2018 to 2023, the agglomeration experienced a steady expansion stage, with industrial structure upgrading and government support as the primary driving forces. Furthermore, we found that, over time, the influence of economic development levels and population size as driving forces gradually weakened, while the impact of industrial structure and government support significantly increased. Through the fusion of multi-source data and analysis of driving mechanisms at different developmental stages, we comprehensively revealed the development trajectory of the Central Yunnan Urban Agglomeration and provided valuable insights for future urban agglomeration development planning and policymaking.

The Ili River Basin (IRB) is located in the northwest of China. With its large-scale zone and abundant resources, it is believed to be a “wet island” and a biotic resource storehouse in the dry regions of Eurasia. The IRB has stable ecological conditions and abundant water resources, providing natural conditions for agricultural production and human settlements. With the population increasing and economic development advancing, the competition for land resources is becoming fierce, leading to some ecological problems in this region. Therefore, understanding the spatiotemporal changes and driving mechanisms of the “three-zone space” (TZS) in the IRB is of significant practical importance for promoting sustainable development and optimizing the territorial spatial pattern. This study first analyzes the characteristics and intensity of the TZS changes from 2000 to 2020. Then, it utilizes the optimized parameter Geodetector (OPGD) to analyze the driving mechanisms behind these changes. The results show the following. Firstly, the agricultural space (AS) increased by a total of 837.5 km2, the urban space (US) increased by 519.64 km2, and other ecological space (OES) increased by 1518.83 km2. Green ecological space (GES) decreased by 2875.97 km2. Secondly, intensity analysis indicated that the total TZS change in IRB was 11.07%. At the spatial-type level, the increased intensities of OES, US, and AS were active. In spatial transformation intensity, US and OES tended to transform into AS; AS tended to transform into US; and OES and GES had a mutual transformation tendency. Thirdly, AS converted into US around emerging cities like Khorgas and Cocodala. The conversion towards GES was scattered. The mutual conversion between OES and GES showed spatial distribution consistency, mainly occurring in the Borohoro ranges and the Halik ranges. Lastly, regarding the driving mechanisms, the evolution of US in the IRB was driven by social and economic factors. Location and climate factors accelerated agricultural development, facilitating the transformation of GES and OES into AS. Climate and economic factors played a crucial role in the scale of conversions between OES and GES. The findings can provide a basis for the governance and protection of the IRB, help to form a rational territorial spatial pattern, and offer scientific guidance for sustainable land management.

Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in Xinjiang and projects future trends using Theil-Sen trend analysis, the Mann–-Kendall test, and the Hurst Index. By integrating meteorological data, this study employs partial correlation analysis, the Miami model, and residual analysis to explore the driving mechanisms behind NPP changes influenced by climatic factors and human activities. The results indicate that: (1) The average NPP in Xinjiang has increased over the years, displaying a spatial pattern with higher values in the north and west. Regions with increasing NPP outnumber those with declining trends, while 75.18% of the area shows un-certain future trends. (2) Precipitation exhibits a stronger positive correlation with NPP compared to temperature. (3) Climate change accounts for 28.34% of the variation in NPP, while human activities account for 71.66%, making the latter the dominant driving factor. This study aids in monitoring ecological degradation risks in arid regions of China and provides a scientific basis for developing rational coping strategies and ecological restoration initiatives.

Long-term stability is crucial in cropland for maintaining stable agricultural production and ensuring national food security. However, relatively few studies have been conducted on the long-term stability of cropland at the national level. This study assessed the long-term stability of cropland in China from 1990 to 2018 using a fine-resolution land use dataset. The experimental results indicated that the average area of unstable cropland in China from 1990 to 2018 amounted to 2.08 × 106 km2, 47.31% of the total. The Qinghai–Tibet Plateau exhibited the highest average proportion of unstable cropland at 65.9%, followed by the northern arid and semiarid region, Southern China, and the Yunnan–Guizhou Plateau. The quantity of unstable cropland in China initially declined before increasing, reaching a final growth rate of 5.09%. Furthermore, this study explored the relevant driving factors of cropland’s long-term stability from both natural factors and human activities based on artificial neural networks. The relative importance of distance to vegetation reached a value of 0.30, indicating that it had the most significant influence on the long-term stability of cropland, followed by relief amplitude and soil type. This phenomenon may be attributed to the inadequate execution of the Grain for Green Policy and the requisition–compensation balance of cropland policy, along with the depletion of young and middle-aged laborers due to urban migration from rural areas. Local governments should focus on addressing the unsustainable exploitation of sloped land in rural mountainous or hilly regions while preventing urban developers from appropriating fertile cropland to compensate for less fertile areas.

Accurately identifying the supply and demand of ecosystem services at multiple scales and determining the factors that influence the supply–demand relationship are crucial for guiding the sustainable management and restoration of regional ecosystem services. In view of this, we quantified the supply and demand of five ecosystem services at multiple scales in the Qinling–Daba Mountain area based on spatial and statistical data, exploring the relationships between the supply and demand for ecosystem services at multiple scales and examining the mechanisms by which factors like natural and human activities affect the evolution of the supply and demand patterns of these services. The results show that (1) there was no risk associated with supply and demand of ESs in the Qinling–Daba Mountain area, and numerous ESs were in excess. The impact of ES supply and demand became increasingly clear as the spatial scale was increased. (2) Under multiple spatial scales, the relationship between the supply and demand of ESs will change. At the mesoscale, the relationship between ES supply and demand was the most significant, whereas at the macroscale, the relationship between ES demands was the most significant. (3) Cultivated land, grass land, and forest land are the key land use categories in regional ecosystem service hotspots, providing richer ecosystem service functions for the region. (4) Precipitation and NDVI are the main elements determining the supply of ecosystem services. While GDP and population density have a significant impact on the demand for ecosystem services, natural causes are primarily responsible for trade-offs in ecosystem services. This study aims to evaluate the supply–demand relationship and driving factors of multiple scale in the Qinling–Daba Mountains, providing a scientific basis for the sustainable management of ecosystems in the region.

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.

In the face of the combined effects of economic development and climate change, ecosystems are experiencing unprecedented pressures. It is imperative to diagnose changes in the quantity and quality of ecosystems, as well as identify the integrated natural and social driving mechanisms that underlie these changes, in order to facilitate ecosystem restoration and maintenance. In this study, we analyzed the changes in forest, grassland, farmland, and water ecosystems in Hubei Province, China from 2000 to 2020. We examined the changes in ecosystem quantity by assessing their area, and for evaluating ecosystem quality, we utilized an ecosystem quality index (EQI). To further understand the integrated natural–social driving mechanisms behind the degradation of different types of ecosystems, we selected four natural factors and fifteen socio-economic factors, based on the influences of climate change and human activities. We employed stepwise regression models for analysis. Our study reveals significant degradation of farmland and grassland ecosystems in Hubei Province from 2000 to 2020, reducing by 5.16% and 82.46%, respectively. The water ecosystems have slightly decreased by 1.08%, while and the forest ecosystems has increased by 2.64%. The analysis further highlights that the total area of ecosystem quality degradation in Hubei Province reached 5.34%. Additionally, our findings indicate that human activities have a greater impact on the quantitative degradation of ecosystems, while climate change has a greater impact on the quality degradation of ecosystems. Specifically, the forestry output value has a significant negative impact on the area of farmland and grassland ecosystems, while rural per capita net income and fishery output value have a significant negative impact on water area. Annual precipitation and annual average temperature have a significant positive effect on the quality of ecosystems in the good-quality level, while ecosystems in the low-quality level are mainly influenced by annual evaporation. Our results provide valuable insights for policymakers seeking to restore and manage ecosystems effectively in order to promote regional sustainable development.

Rural land consolidation projects (RLCPs) have become one of the largest organized human activities to change land use patterns and impact terrestrial ecosystems, and it may also be an important precondition to improving ecosystem service value (ESV). Evaluating the change in ecosystem service value (ESV) is an important basis for measuring the effectiveness of RLCPs. Therefore, this paper, taking RLCPs implemented at County Level in Hubei Province, China, as an example, uses the improved ESV evaluation model to analyze the spatial differentiation of ESV change in RLCPs and then adopts geographic detectors and a geographically weighted regression model to identify the dominant factors affecting the ESV change in RLCPs. The results showed that (1) although RLCPs make the unevenness of land use obvious, they reduce the complexity of land use evidently and improve the dominance of land use significantly; (2) The ESV of RLCPs in 71 counties of Hubei Province increased, with an average increase of USD 2.37 × 107 a−1. The ESV increase is large in central Hubei, while small in eastern and western Hubei. However, the increase rate of ESV is high in eastern and central-north Hubei, while low in western and central-south Hubei. This indicates that RLCPs can effectively promote ESV, but there are significant regional differences, and (3) the ESV increase is positively correlated with GDP and construction scale, but negatively linked with investment and per capita income of rural residents. The ESV increase rate is negatively associated with cultivated land proportion and land use diversification index change, but it is positively related to the change in the land use evenness index. However, their driving effects have significant spatial heterogeneity.

Cropland abandonment is driven by various mechanisms and is best viewed from multiple perspectives to suggest targeted policy changes which may change the status quo of abandonment. Here, we systematically analyze the characteristics of abandonment and its driving mechanisms by different farming households (pure, part-time, and non-farm) in three topographic regions of the Poyang Lake region using a binary logistic regression model. Results show that: (1) The overall abandonment probability in the Poyang Lake region is largest for non-farm households, followed by part-time households and pure households. In the mountainous region, abandonment is largest for non-farm households, followed by pure households and part-time households. Both the hilly and plain regions show the largest abandonment probability for pure households, followed by part-time households, and non-farm households. (2) The low agricultural economic benefits and the uneconomical investments of time in plots are the main abandonment determinants for pure households. Economic efficiency, both the time invested in plots and economic efficiency, are key abandonment determinants for pure households in the mountainous and plain regions, respectively. (3) Labor shortage and plots which are time-consuming and unfavorable to cultivation are the main abandonment determinants for part-time households, with different factors in different topographic regions. (4) For non-farm households, many factors can influence the occurrence of abandonment. Non-farm households in the mountainous and hilly regions are more influenced by non-farm work and the number of farming workers, respectively; in addition, the inconvenience of using agricultural machinery has a significant influence.