Fine particulate matter (PM_2.5) is harmful to human health. Although the relationship between urban land use and PM_2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM_2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM_2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM_2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM_2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM_2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM_2.5. The higher the PM_2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM_2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM_2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM_2.5.

In this paper, we study the characteristics of the spatial–temporal pattern of land used for transportation at the county level since the implementation of the reform and opening-up policy in China and discuss the factors that influence the spatial differences between lands used for transportation in order to provide a reference for the formulation of traffic policies. The authors used ArcGIS spatial analysis, an ordinary least squares (OLS) regression model, and a geographic detector model based on the data of the transportation network at the county level in China from 1978 to 2018. We obtained the following results: (1) The land used for transportation at the county level in China is divided by the Hu Huanyong Line, which is characterized by spatial variation, where the southeastern region is higher than the northwestern region. (2) Counties with a high proportion of land used for transportation show obvious changes, characterized by the transformation from the “corridor” zonal distribution of arteries to the “diamond” group distribution of major city clusters, reducing the gap in land used for transportation at the county level in China. (3) The level of industrialization, per capita gross regional product (PGRP), and ratio of the non-agricultural working population all have an incentivizing impact on the increase in land used for transportation at the county level in China. We conclude that the land used for transportation at the county level in China is jointly decided by the economy, industry, and population. Therefore, we believe that it is necessary to promote fast economic growth, the upgrading of industrial structures, and population density to achieve the balanced development of land used for transportation at the county level in China.

Uneven regional development has long been a focal issue for both academia and policymakers, with numerous studies over the past decades actively engaging in discussions on measuring regional development disparities. Generally, most existing studies measure the Human Development Index (HDI) using relatively simple indicators, with a focus on national and provincial scales. As a crucial component of regional development, counties can directly reflect the regional characteristics of socio-economic progress. This study employs a multi-dimensional approach to develop an improved Human Development Index (improved HDI) system, using machine learning techniques to establish the relationship between nighttime light (NTL) data and the improved HDI. Subsequently, NTL data are utilized to infer the spatial distribution characteristics of the improved HDI across China’s county-level regions. The improved HDI for county-level areas in the Ningxia Hui Autonomous Region was validated using a machine learning model, resulting in a Pearson correlation coefficient of 0.93. The adjusted R-squared value for the linear fit was 0.86, and the residuals were relatively balanced, ensuring the accuracy of the simulations. This study reveals that 1439 county-level units, representing 50% of all county-level units in China, have development levels at or above the medium level. At the provincial and national levels, the improved HDI shows significant clustering, characterized by a multi-center pattern with declining diffusion. The spatial distribution of the improved Human Development Index remains closely associated with the natural geographic background and socio-economic development levels of the county regions. Lower HDI values are predominantly found in the inland areas of central and western China, often in ecologically sensitive areas, inter-provincial border zones, and mountainous regions of mainland China, sometimes forming contiguous distribution patterns. This underscores the need for the government and society to focus more on these specific geographic development areas, promoting continuous improvements in health, education, and living standards to achieve coordinated regional development.

Rapid urbanization in China has reshaped the coupling coordination between pollution and carbon emissions. However, existing studies largely rely on linear approaches and lack multidimensional and nonlinear assessments of urban growth patterns. Using panel data for 289 prefecture-level cities from 2010 to 2023, including built-up land, nighttime lights, CO₂ emissions, and PM_2.5 concentrations, this study develops three indicators: Urban Expansion Intensity (UEI), Urban Sprawl Index (USI), and Urban Compactness (UC). By integrating a coupling coordination model, K-means clustering, Geographically and Temporally Weighted Regression (GTWR), and interpretable XGBoost-SHAP analysis, four urban growth patterns are identified: High-Speed Low-Efficiency Expansion (HLE), Low-Speed Low-Efficiency Expansion (LLE), High-Speed High-Efficiency Compact (HHC), and Low-Speed High-Efficiency Compact (LHC). Results indicate that: (1) USI and UC exhibit significant nonlinear threshold effects on CCD; moderate expansion and higher compactness enhance synergy, whereas excessive dispersion or over-compactness weakens coordination. (2) UEI plays a relatively indirect and spatially heterogeneous role. (3) HHC and LHC cities achieve the highest CCD levels, while HLE cities perform the lowest. (4) Urban expansion shows an overall contraction trend, yet substantial regional disparities persist. These findings highlight nonlinear and spatially heterogeneous mechanisms linking urban growth patterns and pollution–carbon coupling coordination, providing implications for differentiated spatial governance.

Poverty remains a critical challenge to global sustainable development. While China has eradicated absolute poverty, persistent spatial “islanding effects” in disadvantaged regions underscore unresolved vulnerabilities. Current research predominantly emphasizes spatial governance and poverty targeting, overlooking individual-level determinants of sustainable poverty reduction. Through a mixed-methods approach integrating geographic weighted regression (GWR), quantile regression, and multilevel modeling, this study analyzes village-scale environmental factors and household-level attributes in Huangping County—a mountainous karst region in Southwest China. Key results reveal: (1) A 22% decline in poverty incidence (88,000 individuals lifted from poverty), yet concentrated residual poverty in remote mountainous areas exhibiting islanding effects; (2) Geospatial analysis identifies terrain variability, soil erosion severity, and ecological land coverage as significant poverty drivers, while accessibility reduces poverty risk; (3) Multilevel models demonstrate village characteristics explain 52.3% of income variance, with individual factors—particularly gender-based educational gaps and occupational mismatch—disproportionately constraining low-income groups. The findings establish that geographic isolation and human capital deprivation interact to sustain poverty traps. Policy interventions must concurrently address ecological constraints through infrastructure investment and dismantle gender barriers via targeted skill development to achieve resilient poverty reduction.