Accurate regional carbon sequestration estimates are essential for China’s emission reduction and carbon sink enhancement efforts to address climate change. Enhancing the spatial precision of vegetation carbon sink estimates is crucial for a deeper understanding of the underlying response mechanisms, yet this remains a significant challenge. In this study, the Beijing–Tianjin–Hebei (BTH) region was selected as the study area. We employed the GF-SG (Gap filling and Savitzky–Golay filtering) model to fuse Landsat and MODIS data, generating high-resolution imagery to enhance the accuracy of NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) estimates for this region. Subsequently, the Sen+MK model was used to analyze the spatiotemporal variations in carbon sinks. Finally, the land use intensity index, which reflects human activity disturbances, was applied, and the bivariate Moran’s spatial autocorrelation method was used to analyze the response mechanisms of carbon sinks. The results indicate that the fused GF-SG NDVI (Normalized Difference Vegetation Index) data provided highly accurate 30 m resolution imagery for estimating NPP and NEP. The spatial distribution of carbon sinks in the study area showed higher values in the northeastern forest regions, relatively high values in the southeastern plains, and lower values in the northwestern plateau and central urban areas. Additionally, 58.71% of the area exhibited an increasing trend, with 11.73% showing significant or strongly significant growth. A generally negative spatial correlation was observed between land use intensity and carbon sinks, with the impact of land use intensity on carbon sinks exceeding 0.3 in 2010. This study provides methodological insights for obtaining vegetation monitoring data and estimating carbon sinks in large urban agglomerations and offers scientific support for developing ecological and carbon reduction strategies in the BTH region.

With rapid advancements in industrialization and urbanization, Beijing is increasingly facing severe urban heat island effects and air pollution, particularly from haze. Urban parks play a vital role in improving the local microclimate and facilitating the dispersion of fine particulate matter (PM_2.5). However, most existing studies have focused primarily on the cooling and humidifying functions of urban parks, with limited attention given to the combined assessment of their regulatory effects on both the microclimate and air pollutants. Moreover, the influence of seasonal variation on these ecological services has rarely been systematically examined. To address these research gaps, this study selected three representative urban parks in Beijing and conducted a quantitative analysis of four key environmental parameters—air temperature, relative humidity, wind speed, and PM_2.5 concentration—during spring, summer, and winter. Using Landsat remote sensing imagery and the ENVI-met v3.1 computational fluid dynamics (CFD) model, this study simulated dynamic changes in the microclimate and pollutant dispersion within parks. Model feasibility was evaluated through validation metrics and comparisons with field observations. The results show the following: (1) Urban parks significantly improve the local microclimate and reduce PM_2.5 concentrations, with the most notable effects observed in summer when the ecological functions of vegetation are at their peak. (2) The ENVI-met model can be used to simulate the microclimate and PM_2.5 dispersion in the three parks, with the highest simulation accuracy occurring during the summer season. This study provides valuable insights for urban park planning in Beijing, particularly for developing strategies to enhance microclimatic conditions and mitigate air pollution.

Sustainable urban development increasingly depends on the efficient and equitable provision of municipal infrastructure, and waste bins, as critical end-point facilities in urban solid waste management systems, directly influence waste disposal accessibility, collection efficiency, and service equity. However, existing studies remain limited in achieving effective demand–supply matching, controlling spatial redundancy, and evaluating equity in waste facility allocation. This study developed a multi-source spatial evaluation framework integrating waste generation demand, road accessibility, and building functional characteristics, and proposed a two-stage waste bin allocation approach by coupling Adaptive Non-Maximum Suppression (ANMS) with an improved Coverage Location Problem with Overlap Control (CLPOC) model. The proposed framework first generated a theoretical candidate site pool and applied ANMS to achieve spatially balanced candidate refinement, followed by CLPOC-based optimization to improve service coverage and reduce redundant configurations. The results demonstrated that the optimized scheme substantially reduced redundant facility locations while maintaining high service coverage, thereby improving spatial allocation efficiency. Although urban areas exhibited notable improvements in service equity, the enhancement in mountainous and scenic areas was constrained by terrain conditions and spatial clustering effects. This study provides an efficiency–equity balanced framework for fine-scale waste bin planning in hilly cities, offering methodological support for sustainable urban solid waste management and contributing to the broader goal of sustainable urban development.