Carbon Storage Patterns and Sequestration Enhancement Strategies of Urban Green Spaces in High-Density Urban Areas: Insights from Beijing Within the 5th Ring Road

Urban green spaces critically govern urban carbon cycling, though fragmentation and vegetation differences hinder precise carbon stock calculation. Targeting Beijing’s Fifth Ring Road area (666.5 km2), this study combined GF-7 sub-meter remote sensing imagery and field data from 750 sampling plots to build four machine learning models (RF, XGBoost, GBDT, BP neural network), adopting SHAP analysis to interpret feature importance and formulate carbon improvement schemes. XGBoost with the Boruta-selected feature set achieved robust performance (R2 = 0.83, RMSE = 1.79 kg/m2, MAE = 1.13 kg/m2), with its R2 elevated by 6.4%--38.7% compared to other algorithms. Canopy height dominated carbon storage variation (SHAP contribution: 0.321, 31.7% explanatory rate), and removing it cut model R2 by 47.98%. The region’s total above-ground vegetation carbon stock reached 9.04 × 105 Mg at a mean above-ground carbon density of 4.80 kg/m2; deciduous trees contributed the largest share (80.61%) of total above-ground carbon stock) and presented a low-inner, high-outer ring spatial distribution. Raising canopy height by 1 m could increase above-ground carbon storage by 21.38% within tree-covered areas, equivalent to a 4.10% increase in the total study-area carbon stock. The combined implementation of canopy height enhancement, a 10% replacement of shrub/grassland areas, and low-carbon sink area renovation could increase the total carbon storage by 8.82 × 104 Mg (with the low-carbon zone renovation alone contributing 1.76 × 104 Mg), representing a 9.76% enhancement potential over the current above-ground baseline.