Shaowei Zhang

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.

This study examines the evolutionary characteristics and potential influencing factors of production–living–ecological spaces (PLES) in resource-exhausted cities, using Zaozhuang City in Shandong Province—the first coal-based resource-exhausted city in eastern China—as a case study. A territorial spatial transition matrix, dynamic degree model, Geo-information Tupu, chord diagram visualization, and Pearson correlation analysis were integrated to examine PLES evolution from 2000 to 2020. The results show the following: (1) Spatial conversions were relatively frequent during 2000–2010 but slowed markedly after 2010, indicating clear stage-specific characteristics. (2) Urban living space, rural living space, and industrial and mining production space continued to expand, while water ecological space increased with fluctuations. In contrast, agricultural production space and terrestrial ecological space continued to decline. Urban living space expanded mainly around the built-up areas of Shizhong District, Xuecheng District, and Tengzhou City and around major industrial parks, while conversions between rural living space and agricultural production space were particularly frequent. (3) PLES changes showed varying degrees of association with resource endowment, economic and industrial development, population and transport, and planning and policy, with differentiated responses among spatial types. Zaozhuang’s designation as a resource-exhausted city in 2009 occurred shortly before spatial evolution became more stable after 2010, providing important contextual evidence for understanding this stage transition. Overall, PLES evolution shifted from outward expansion under resource development toward spatial stabilization and stock-based restructuring during the resource-exhausted stage. These findings provide empirical insights for resource-exhausted cities with similar coal-dependent development trajectories and transformation contexts.

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.

The canyon hilly terrain of northwestern China significantly influences wind field characteristics within the grotto zone, consequently affecting the degree of wind erosion on grotto heritage. In the present study, computational fluid dynamics (CFD) numerical simulations were employed to investigate the effects of mountain length, slope, and spacing on the wind field characteristics of a typically canyon hilly terrain, with the Xumishan Grottoes as a case study. The results show a significant wind speed acceleration at canyon entrances and summits. Variations in mountain length and slope non-linearly affect wind field distribution, with wind speeds at the side and summit stabilizing when the mountain length exceeds three times the mountain height (L ≥ 3H). Based on the simulation results, an improved acceleration ratio formula incorporating mountain length, slope, and spacing was proposed, which demonstrated a discrepancy of only 9.05% compared with the field-validated CFD results for Cave 5 at Xumishan. This study elucidates the wind field formation mechanisms in canyon hilly terrain and provides a scientific basis for addressing the stone carving erosion of grotto heritage, contributing to the advancement of preventive conservation strategies for grottoes in complex terrains.

Systematically assessing the supply–demand disparities of urban–rural ecosystem services (ES) is a key pathway to optimizing resource allocation, promoting urban–rural integration and advancing regional sustainable development. Taking Zhengzhou City as a case study, this research evaluates and compares urban–rural differences across four dimensions: potential supply, actual supply, real human needs (RHN), and effective supply. Furthermore, focusing on actual supply, the study integrates a geographical detector and Bayesian belief network to identify key driving factors, delineate optimal optimization zones, and propose differentiated management strategies. The results show that: (1) Urban RHN accounts for 69.70% of the total in Zhengzhou, with a spatial pattern of “higher in the east and core, lower in the west and periphery”, and the internal heterogeneity is significantly greater than that of rural areas. (2) Potential supply is “higher in rural areas and in the west”, whereas actual supply is concentrated in central urban districts, reflecting a net service flow from rural to urban areas. (3) High-level effective supply areas cover 37.28% of urban regions, about 18 percentage points higher than rural regions. Rural deficits are primarily caused by low conversion efficiency of supply rather than insufficient potential. (4) Optimal urban optimization zones are mainly distributed in peripheral urban streets, while rural zones are concentrated in eastern townships. Through multidimensional supply–demand comparison and spatial optimization, this study provides a scientific basis for the coordinated enhancement of urban–rural ES, differentiated governance and regional sustainable development.