Xianchao Zhao

This study examines the spatiotemporal patterns of cropland change in Hunan Province and identifies the factors associated with net cropland decrease from 2000 to 2020. Using land-use transition analysis, spatial autocorrelation, Lorenz curves, Gini coefficients, and an interpretable XGBoost-SHAP model, this study analyzed cropland outflow, cropland inflow, net cropland change, and their associated explanatory patterns. The results show that: (1) cropland outflow was mainly concentrated in central and western Hunan, whereas cropland inflow was relatively more evident in central Hunan, but also occurred in parts of western and eastern Hunan. Total cropland area increased by 2961.73 km² from 2000 to 2020, but decreased by 1467.91 km² after peaking in 2009, indicating an inverted U-shaped trajectory; (2) the Gini coefficient of cropland outflow decreased from 0.4024 to 0.2891, while that of cropland inflow decreased from 0.3780 to 0.2538, indicating stronger spatial concentration of cropland outflow, although its spatial imbalance weakened over time; and (3) XGBoost-SHAP results showed that mechanical efficiency, gross domestic product (GDP), and fiscal conditions made the highest contributions to net cropland decrease, with mean absolute SHAP values of 0.21, 0.17, and 0.16, respectively. Overall, cropland change exhibited clear spatial heterogeneity, and socioeconomic and human-activity factors were dominant factors associated with net cropland decrease. These findings provide support for differentiated cropland protection and sustainable land-use management in major grain-producing regions.

Understanding the evolution of ecosystem services (ESs) is crucial for sustainable development in rapidly urbanizing regions. This study quantifies the spatiotemporal evolution of habitat quality (HQ), water yield (WY), and carbon storage (CS) in the Chang-Zhu-Tan City Cluster (CZTCC) of China from 2000 to 2020. Results showed that rapid urbanization—where construction land expanded from 2.78% to 7.40% of the area—was the primary driver of ES degradation. Specifically, low-quality habitat (HQ) areas expanded by 162%, and total carbon storage (CS) declined by 1.83% (11.78 Tg C), while regional WY fluctuated with precipitation. Inter-service relationships also became more conflictual: the trade-off between HQ and WY intensified (Pearson’s R strengthened from −0.696 to −0.735), a persistent trade-off between WY and CS was observed (e.g., R = −0.607 in 2020), while the HQ-CS synergy remained stable and strong (R > 0.88). These findings highlight escalating ecological conflicts and offer a robust scientific basis for optimizing land use to manage ES trade-offs and promote sustainable development.

This study takes Sichuan’s major economic zones as a case to explore the evolution of ecological environment development. By improving the traditional ecological footprint model based on the concept of “provincial hectare,” we analyzed the characteristics of ecological utilization and ecological security status in Sichuan’s economic zones from 2013 to 2022. A comprehensive evaluation was conducted through factor analysis driven by geographical detectors. The results indicate that (1) from 2013 to 2022, Sichuan Province generally exhibited a “supply falling short of demand” situation, with a slight decrease in per capita ecological carrying capacity and a significant decreasing trend in GDP ecological footprint. The industrial structure of the Panxi and Chengdu Plain economic zones was relatively reasonable, with a notable improvement in resource utilization efficiency, while the Northeastern Sichuan Economic Zone lagged behind. (2) The ecological security focus was distributed in the southeastern region, showing an overall “northeast–southwest” distribution pattern. The ecological restoration in the Southern and Northwestern Sichuan Economic Zones needs to be strengthened. (3) The output value of the secondary industry, R&D expenditure, and urban construction land were the main factors influencing ecological footprint changes. The average urban temperature interacted with other factors, showing a significant effect.

Urban agglomerations are key to balancing carbon emissions (CEs) and ecosystem services (ESs), yet structural imbalances exist between LUCE and ESs due to the lack of standardized frameworks and clear governance strategies. This study investigates the relationship between LUCE and ESs in the Chang-Zhu-Tan urban agglomeration using multi-source data from 2010 to 2023. The study aims to address three main research questions: (1) How do LUCE and ES networks evolve over time? (2) What factors drive their heterogeneity? (3) How do urbanization and ecological restoration impact LUCE and ES network dynamics? To answer these, we apply centrality metrics and develop heterogeneity indices to evaluate connectivity, accessibility, and driving factors. The findings show that both LUCE and ES networks exhibit corridor-like structures, with asymmetric node distributions. The LUCE-Network’s degree centrality increased from 0.16 to 0.29, while the ES-Network’s rose from 0.16 to 0.23. Heterogeneity was initially positive but turned negative by 2023, indicating a shift from LUCE dominance to an increased emphasis on ES. This transition was influenced by urbanization, land use changes, and ecological restoration efforts. Notably, the proportion of built-up land (X11) grew from 0.0187 in 2010 to 0.1500 in 2023, intensifying the disparity between LUCE and ESs. Similarly, urbanization (X7) surged to 0.1558 in 2023, increasing CEs and the demand for ESs. A collaborative pathway is proposed to address these challenges, involving controlled urban development, restoration of green spaces, and prioritizing multimodal transport and energy efficiency. This framework offers actionable diagnostics for improving low-carbon and ecological governance in urban agglomerations.

Urban agglomerations are crucial for promoting the balanced development of carbon emissions (CE) and ecosystem services (ES). However, a structural imbalance exists between land use carbon emissions (LUCE) and ES, compounded by the absence of standardized measurement frameworks and clear governance strategies. This research uses the Chang-Zhu-Tan urban agglomeration as a case study to establish a spatial association network linking LUCE and ES, based on multi-source data from 2010 to 2023. Centrality metrics are utilized to assess connectivity, accessibility, and channel functions. To identify key factors, three heterogeneity indices are developed for evaluation and driving factor analysis. The findings are as follows: (1) Both LUCE and ES networks display corridor-like and cross-domain connections, but with asymmetric node structures. The degree centrality of the LUCE-Network increased from 0.16 to 0.29, while that of the ES-Network rose from 0.16 to 0.23. (2) Heterogeneity was generally positive in the initial stages but turned negative by 2023, signifying a shift from a dominance of carbon networks to one dominated by ecological networks. The positive bias is concentrated along primary urban corridors and boundaries, whereas negative bias is observed in green zones and mountainous water networks. (3) The heterogeneity is influenced by spatial layout, natural environments, and human activities. Among the driving factors, the proportion of built-up land (X11) grew from 0.0187 in 2010 to 0.1500 in 2023, intensifying the disparity between LUCE and ES networks. The urbanization rate (X7) surged to 0.1558 in 2023, up from 0.0761 in 2010, which contributed to increased CE and heightened demand for ES. Forest cover (X10) progressively strengthened the ES-Network, reaching 0.1378 by 2023. In general, urbanization and land use shifts have significantly altered the spatial organization of LUCE and ES. (4) Based on structural correction and process coupling, a collaborative pathway is proposed: stringent control of new developments and rehabilitation of existing structures in high positive-bias corridors and hubs, with a focus on ecological and river-lake connectivity projects; extensive restoration and expansion of green zones and mountainous water networks; reduction of single-channel dependency through public transport prioritization, energy efficiency upgrades, clean energy transitions, and multimodal transport strategies; and the establishment of a threshold-monitoring-assessment system through heterogeneity indices and key variables. This research suggests transforming average coupling assessments into actionable diagnostics for network structural differences and enables the identification of driving factors and the implementation of collaborative pathways, offering a replicable framework for low-carbon and ecological governance in urban agglomerations.

Amid the increasing demands for ecological civilization and food security, addressing conflicts between agricultural and ecological functions has become a critical priority in spatial governance. Focusing on the Chang-Zhu-Tan Urban Cluster, this study establishes a multi-indicator evaluation framework and employs a weighted model to measure agricultural and ecological functions. The ESDA model characterizes the spatial distribution and clustering patterns of conflicts, while the RF model identifies the key drivers and underlying mechanisms. The results indicate the following: (1) Agricultural functions exhibit a “center-weak, periphery-strong” spatial pattern, with high-function zones covering over 60% of the area, whereas ecological functions are primarily concentrated in low-function zones, with high-function areas localized in the northeast. Overall, agro-ecological functionality declined from 2000 to 2020, accompanied by increased gradient differentiation. (2) High-conflict zones decreased by 7.73% during the study period, while medium-conflict and conflict-free zones expanded. Spatially, a trend of “peripheral mitigation of high conflicts and central expansion of low conflicts” emerged. (3) Natural environmental factors were the primary drivers of conflict dynamics, while land use factors gained significance over time. Elevation and slope dominated in 2000 and 2020, whereas land use economic density and crop planting area were more influential in 2010. Synergistic effects were observed, with slope–precipitation interactions providing the strongest explanatory power. This study offers empirical insights into managing agricultural–ecological conflicts, thereby contributing to enhanced spatial governance and sustainable development practices.

With rapid urbanization, the spatial layout and functional characteristics of urban facilities have a strong correlation with the spatial distribution of criminal activities. Using Changsha City as a case study, this research analyzes 2023 urban crime data, Point of Interest (POI) data, and socioeconomic data. The Multi-scale Geographically Weighted Regression (MGWR) model and clustering analysis are applied to examine how different types of urban facilities influence the spatial heterogeneity of crimes and propose tailored urban planning recommendations and crime prevention strategies. The findings reveal the following: (1) The spatial distribution of crimes in Changsha’s central urban area demonstrates significant spatial heterogeneity. Property crimes dominate in frequency and spatial distribution, primarily clustering around commercial hubs and transport nodes, while violent crimes are more common in scenic areas and open spaces with high pedestrian flow. (2) The impact of built facilities on crime exhibits spatial variability. Facilities such as Financial Services Facilities (FSF) and Shopping facilities (SHF) significantly contribute to property crime in core urban areas, while Scientific, educational, and cultural facilities (SEC) suppress crime in university towns. Scenic spots and facilities (SPF) are associated with violent crimes near scenic site entrances and transport hubs. (3) Facility resource allocation and preventive strategies should be optimized based on dominant factors in different areas to enhance security management efficiency through precise and differentiated planning, fostering sustainable urban safety systems. This study provides insights into the spatial patterns of crime distribution and its dominant factors from the perspective of urban facilities, offering a scientific basis for improving urban crime management and facility planning.

The accelerated aging of China’s population has made the optimization of elderly care facility locations a critical priority. The field of big data presents innovative approaches for determining the optimal site selection for such facilities. This study uses Xi’an City, in Shaanxi Province, China, as a case study to explore how big data and the ID3 decision tree model can enhance the optimization of elderly care service facility locations. The study begins with a comprehensive analysis of Xi’an’s aging demographics, focusing on the current aging trends and the distribution characteristics of existing elderly care facilities. Utilizing the Baidu Map API, the study collected Point of Interest (POI) data for Xi’an, which were spatially analyzed using ArcGIS 10.8 software to identify the distribution patterns of elderly care facilities and their relationships with other public amenities. The ID3 algorithm was then employed to construct a decision tree model to simulate and predict optimal sites for elderly care facilities in Xi’an. By classifying and filtering POI data and dividing Xi’an into 500 m × 500 m grid units, the model was trained and validated, achieving an accuracy of 85.8%. The findings suggest that suitable sites for elderly care facilities in Xi’an should prioritize proximity to government offices and medical institutions, which would better address the healthcare needs of the elderly population. The application of the ID3 algorithm in planning the locations of elderly care facilities helps mitigate human biases and provides valuable insights for the planning of other public amenities.

In the context of China’s New Urbanization Strategy, a growing number of cities are beginning to experience significant changes in their intra-city structure and size distribution, but systematic research is still lacking. This paper investigated the intra city-size distribution of 214 cities in China’s Yangtze River Delta Region (YRDR) from 1990 to 2020. The study used the Pareto index to portray the equalization differences in intra city-size distribution, and investigated the heterogeneity of its influencing factors using multiscale geographically weighted regression (MGWR). The results showed that most of the cities in the YRDR had a city-size distribution consistent with a Pareto distribution but still tended to be polarized. Among them, the better-developed large cities and regions were more consistent with the Zipf distribution, as were the county-level cities and prefecture-level cities. In terms of spatio-temporal distribution, the intra city-size distribution showed a spatial agglomeration pattern of “polarization in the west and equalization in the east” and the scope and intensity of cold and hot spots reduced. Regarding driving factors, land, industry, and population were essential influences on the intra city-size distribution. Of these, the natural environment and location were important global influencing factors, and economic and social factors only had a more significant influence in 1990 and 2020. The impact of industry restructuring gradually increased, and the population was a significant influence only in the early years. By exploring the intra city-size distribution, this paper provides a scientific basis for the optimization of town structure and policy formulation in small and medium-sized cities, and some new concerns have been proposed.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional “double carbon” goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial–Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15′57″~112°25′43″ E and 27°43′13″~27°49′21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the “northwest–southeast” pattern to the “north–south” pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.