Chen Li

Understanding how urbanization, ecosystem-service conditions, and climate are associated with environmental responses is important for regional land management. Using three discrete county-level snapshots from the Yellow River Basin in 2013, 2018, and 2023, this study examined two environmental response variables: land surface temperature (LST) and the concentration of fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5), in relation to urbanization intensity (UII), carbon storage (CS), water yield (WY), soil retention (SR), air temperature (T), and wind speed (WS). Pearson correlation analysis, multiple linear regression, and extreme gradient boosting (XGBoost) combined with SHapley Additive exPlanations (SHAP) were used to characterize linear and nonlinear associations. To reduce spatial leakage, model performance was evaluated using nested five-fold buffered spatial block validation, in which contiguous spatial blocks were held out, and counties within 50 km of each test block were excluded from model training. The pooled out-of-block R² values ranged from 0.765 to 0.777 for LST and from 0.728 to 0.854 for PM2.5. T was the dominant predictor of both responses, accounting for 79.26–86.62% of the total SHAP importance for LST and 53.12–65.99% for PM2.5. UII showed positive model-based associations with both responses that generally appeared to level off at higher observed values, whereas the associations of ecosystem-service variables differed among the three independently analyzed years. Because the three years were modelled separately, these cross-year contrasts are interpreted descriptively rather than as continuous temporal trends. These findings characterize contrasting explanatory structures for surface thermal conditions and particulate pollution and may inform the formulation of hypotheses for spatially differentiated environmental management in the Yellow River Basin.

Rapid urbanization and climate change have intensified the interconnected challenges of surface heating, air pollution, and declining ecosystem functions, with important implications for regional sustainability. Taking Henan Province, China, as the study area, this study selected 2013, 2018, and 2023 as representative years and used land surface temperature (LST), fine particulate matter (PM2.5), ozone (O3), and net primary productivity (NPP) to characterize the thermal environment, air pollution, and carbon sequestration capacity. Pearson correlation analysis, multiple linear regression, and XGBoost-SHAP were integrated to examine bivariate associations, independent linear associations, factor importance, nonlinear responses, and potential threshold characteristics associated with natural, ecological, and anthropogenic factors. The results showed marked spatial differences in the four environmental variables. The multiple linear regression models explained 57.4–69.0% of the variation in LST, 23.8–72.0% in O3, 81.0–84.8% in PM2.5, and 57.4–62.5% in NPP. Natural factors generally showed relatively large and temporally stable standardized coefficients. Precipitation and potential evapotranspiration were positively associated with LST, whereas elevation and precipitation were negatively associated with PM2.5 and O3. NDVI showed an environmentally favorable pattern, being negatively associated with LST, PM2.5, and O3 but positively associated with NPP. Anthropogenic variables generally exhibited smaller and less temporally stable coefficients. The XGBoost models demonstrated good predictive performance, particularly for PM2.5, with R² values of 0.945, 0.920, and 0.905 in 2013, 2018, and 2023, respectively. SHAP analysis identified DEM, PRE, PET, and NDVI as the main contributors to model predictions and revealed nonlinear responses and potential threshold characteristics. These findings indicate that coordinated management of vegetation cover, hydrothermal conditions, and urban development can support heat mitigation, air pollution control, ecosystem productivity, and more sustainable, climate-resilient, and low-carbon development in rapidly urbanizing regions.

Against the backdrop of global climate change and increasing pressure to mitigate carbon emissions, counties serve as critical units for urban–rural spatial development and carbon governance. However, their carbon emission performance (CEP) and underlying spatial mechanisms remain insufficiently understood. This study focuses on 157 counties in Henan Province, selecting three time points: 2013, 2018, and 2023. The study measures the CEP and analyzes its spatiotemporal differentiation characteristics. First, considering that carbon emissions are undesirable outputs generated during the economic production process, this study employs the undesirable output slack-based measure (UN_SBM) model and the super-efficiency slack-based measure model with undesirable outputs (Un_Super_SBM) to evaluate county-level carbon emission performance. Second, landscape pattern indicators, including expansion, complexity, and compactness, are selected, and regression models are constructed to explore the influence of different factors on carbon emission performance. The results show the following: (1) The overall CEP of counties in Henan Province improved from 2013 to 2023, but there were significant spatial differences. (2) Both “Total landscape area” (TA) and “Area-weighted mean shape index” (AWMSI) had significant positive impacts on CEP, whereas the “Splitting index” (SPLIT) inhibited CEP. (3) The effects of vegetation cover and transportation conditions varied, reflecting the heterogeneity of development stages and spatial functional positioning across different counties. This study reveals the relationship between urban–rural spatial form and carbon emission performance at the county level, providing empirical evidence for optimizing construction land spatial structure, enhancing CEP, and promoting regional low-carbon development.