Land economic density (LED) is vital for optimizing industrial structure and promoting intensive resource utilization. However, most existing studies have focused on city or county scales, with limited attention to township-level patterns. To address this research gap, we take 86 townships in Anyang City as research units and develop a four-dimensional evaluation system for LED. The study aims to reveal the spatial patterns and driving mechanisms of township-level LED evolution. This study is based on township-level land use, statistical, and socioeconomic data from 2005 to 2023. Using ArcGIS 10.5 for spatial analysis, spatial autocorrelation, standard deviation ellipse, and geographically weighted regression methods were applied to explore the spatiotemporal evolution and driving mechanisms of LED in Anyang City. The results indicate that (1) high-LED areas form a ring around the central city with dual cores in western Linzhou county and southeastern Huaxian county, while low-LED areas are concentrated at the northwestern and northeastern margins; (2) global spatial autocorrelation is weak, with low–low clusters shrinking from contiguous patches to only three townships by 2023, while high–high clusters expand from isolated points to multi-centered diffusion; (3) the ellipse consistently shows a northwest–southeast orientation, with the rotation angle increasing from 128.24° to 130.35°, the flatness ratio rising from 0.432 to 0.445, and the centroid shifting northwest then southeast; (4) The geographically weighted regression (GWR) results highlight economic foundation, industrial upgrading, and government support as the dominant drivers. Based on these findings, we propose a “One Core–Four Poles, Three Axes–Five Zones” spatial optimization framework to promote coordinated urban–rural development. This study provides a practical and multidimensional evaluation approach at the township level, offering methodological support for regional territorial spatial planning and sustainable development.

Land economic density (LED) is vital for optimizing industrial structure and promoting intensive resource utilization. However, most existing studies have focused on city or county scales, with limited attention to the township level patterns. To address this research gap, we take 86 townships in Anyang City as research units and develop a four-dimensional evaluation system for LED. Using spatial autocorrelation, standard deviation ellipse, and geographically weighted regression (GWR) methods, we analyze the spatiotemporal evolution and driving mechanisms of LED from 2005 to 2023. Results indicate that: (1) high LED areas form a ring around the central city with dual cores in western Linzhou county and southeastern Huaxian county, while low LED areas are concentrated at the northwestern and northeastern margins; (2) global spatial autocorrelation is weak, with low–low clusters shrinking from contiguous patches to only three townships by 2023, while high–high clusters expand from isolated points to multi-centered diffusion; (3) the ellipse consistently shows a northwest–southeast orientation, with the rotation angle increasing from 128.24° to 130.35°, the flatness ratio rising from 0.432 to 0.445, and the centroid shifting northwest then southeast; (4) GWR results highlight economic foundation, industrial upgrading, and government support as the dominant drivers. Based on these findings, we propose a “One Core–Four Poles, Three Axes–Five Zones” spatial optimization framework to promote coordinated urban–rural development. This study provides a practical and multidimensional evaluation approach at the township level, offering methodological support for regional territorial spatial planning and sustainable development.

Ecological civilization education has become an important component of environmental education and sustainable development strategies in China. Field-based learning in real ecological environments is essential for helping students understand ecological processes and human–environment relationships. However, limited attention has been paid to whether schools can spatially access ecological learning sites within feasible travel-time ranges, especially in plateau and mountainous regions where transportation constraints are strong. From the perspective of ecosystem service flow, forest parks can be regarded as supply sites of ecological learning services, secondary schools as demand sites, and road networks as the pathways through which such services are transformed into actual educational opportunities. To address this gap, this study evaluates the accessibility of forest-park-based ecological learning resources for secondary schools in the Tibet Autonomous Region, China. Taking 11 national forest parks and 137 secondary schools as research objects, a network-based travel-time model was constructed using road network data, and 1 h, 3 h, and 6 h service areas were generated to assess forest park service capacity and school accessibility. The results show that forest park educational service capacity is highly uneven. Nimu, Jiedexiu, and Caina Forest Parks have the highest service capacity, each covering more than 60 secondary schools within 6 h, while nearly half of the parks show relatively limited coverage. School accessibility also presents a clear core–periphery pattern, with high-accessibility schools concentrated around Lhasa and nearby transport corridors, whereas many schools in western and northern Tibet remain outside the 6 h service range. Based on the accessibility levels, differentiated field education strategies are proposed, including short field observation, one-day field trips, cross-regional fieldwork, and virtual or school-based alternatives. This study provides a spatial analytical framework for understanding the flow of ecological learning services from forest parks to schools and offers evidence for optimizing geography field education and improving equitable access to ecological civilization education in mountainous regions.

Understanding how urban ecological quality changes across space and why cities follow different trajectories is important for differentiated land and environmental governance. We assessed 288 Chinese prefecture-level and above cities from 2015 to 2024. The Urban Ecological Quality Index (UEQI) was constructed solely from four ecological components: built-up green coverage, forest coverage, PM_2.5 concentration, and carbon-dioxide emission intensity. A separate set of 15 variables describing demographic, economic, urban-form, resource-use, governance, innovation, and digital-connectivity conditions was linked to the completed UEQI as an explanatory predictor matrix. Pearson correlation characterized bivariate associations, whereas XGBoost with SHAP quantified the multivariable nonlinear contributions of these variables to UEQI variation; none of them entered the index calculation. The analytical framework followed ecological change over the decade, diagnosed spatial clustering, examined nonlinear driver relationships, and tested whether those relationships transferred to later years and previously unseen cities. Mean UEQI increased from 55.40 in 2015 to 68.78 in 2024, while its cross-city standard deviation declined from 15.10 to 12.54. All 288 cities had positive long-term slopes, with a median increase of 1.52 UEQI points per year, and 268 retained statistically significant improving trends after false-discovery-rate correction. Strong spatial clustering persisted throughout the decade (Moran’s I = 0.692–0.728), and improvement rates were also spatially clustered (I = 0.372). Predictive accuracy was high under mixed city–year sampling (R² = 0.868) but declined for later years (R² = 0.775) and previously unseen cities (R² = 0.456). Population density and electricity intensity emerged as the two most influential external predictors, and both showed nonlinear relationships with UEQI. Because neither variable was used to construct the index, these results describe conditional associations with the four-component UEQI; they do not mean that either variable is part of the index or that it causally changes ecological quality. Overall, urban ecological quality improved widely but unevenly, and national diagnostic relationships require local validation before being translated into city-specific policy.