Diagnosing Green-Space Provision in Evolving Urban Forms: A Supply–Form Framework for Sustainable Urban Renewal
Jing Wang, Xiaojin Huang, Chang Yang, Yang LiuUrban green-space provision depends not only on green quantity but also on how greenery is positioned relative to buildings as urban form evolves. However, most existing assessments aggregate greenery within predefined spatial units and rely on static snapshots, thereby obscuring both its distance-sensitive distribution around buildings and its variation across urban-development trajectories. This study develops a Supply–Form framework to examine this relationship across heterogeneous development trajectories. Using multi-temporal very-high-resolution imagery from Beijing and Tianjin, we introduce Quantified Green-Space Supply (QGS), a distance-weighted indicator that accumulates greenery around buildings over nested ranges of 5–2000 m. Building-expansion trajectories are then linked to evolution-stratified XGBoost models interpreted with consensus-grouped Partition SHAP, allowing recurrent morphological groups and their within-group contributions to be compared across contexts. QGS was more strongly associated with the selected urban-form variables than conventional green coverage (R2=0.717 versus 0.633). Both cities showed higher mean QGS in 2022 than in 2012, but their internal patterns diverged: Beijing developed more continuous central low-QGS clusters, whereas Tianjin’s central cold spots contracted and fragmented. Across 15 trajectory-specific models, building density was the leading morphological group in 12, while the importance of patch size, edge structure, aggregation–adjacency, and spatial configuration varied by trajectory. Nonlinear responses showed a stable positive association for building density, diminishing returns for edge density and dispersion, adverse effects at very high adjacency, and intermediate-range benefits for patch dominance and compactness. The framework advances green-space assessment from aggregate greenness mapping to a relational, trajectory-conditioned diagnosis of potential surrounding green-space supply, providing a basis for context-sensitive urban renewal.