Against the backdrop of stock-space urban renewal, green-space governance in megacity cores increasingly requires a shift from expansion-oriented supply to the optimisation of existing spatial resources. This study examines the spatial equity of green-space provision in the central districts of seven Chinese megacities using high-resolution remote-sensing data from 2015, 2020, and 2025. We combined semantic segmentation, nested-buffer measurement, the Gini coefficient, Extreme Gradient Boosting (XGBoost), and Shapley Additive Explanations (SHAP) to evaluate the relationships between urban morphology and green-space provision. We first derived a pixel-level cumulative green-space provision index from nested-buffer measurements and calculated city-year Gini coefficients to quantify distributional inequality. XGBoost models were then used to predict the cumulative green-space provision index from urban morphology, and SHAP was applied to interpret the resulting nonlinear, model-derived associations. The results show that changes in the green-space provision index and its distributional inequality were not synchronous across cities. Beijing showed the largest increase in inequality, whereas Chengdu showed the strongest improvement; Tianjin recorded the fastest increase in the green-space provision index, while Beijing experienced a marked decline. Urban form showed nonlinear associations with the unit-level green-space provision index, helping to explain the spatial differentiation underlying the city-year Gini coefficients. Building density was the primary contributor in most city-year models, whereas compactness showed threshold-like associations, with positive effects at lower levels and inhibitory effects at higher levels. Statistical testing and clustering further indicated substantial heterogeneity among cities, which were categorised into three spatial patterns: centripetal agglomeration, peripheral expansion, and clustering/ring-shaped fluctuation. These findings provide morphology-based evidence for differentiated green-space governance in high-density megacity cores, though they should not be interpreted as a complete socioeconomic assessment of green-space equity.

Image-based streetscape assessments commonly report separate perception scores or an aggregate index. Joint profiles additionally identify which unfavorable scores occur together. We examine joint lower-tail profiles of transferred safety, beauty, and liveliness estimates in 2304 grid cells represented by 48,011 street-view records from central urban Shanghai. A Perceptual Streetscape Assessment Chain separates visible content, estimated impressions, joint shortfalls, and contextual distribution. The Spatial Multidimensional Tail-Equity (SMTE) model represents eight profiles jointly and derives compound and dimension-specific probabilities with multilevel and spatial structure. At the primary lower-quartile threshold, 24.44% of cells had at least two low scores. Strong empirical overlap between safety and beauty made domain construction central to the interpretation of this classification. In this specification, the clearest conditional price-rank difference concerned low liveliness; its interval-based support varied across alternative thresholds. Mathematical exchanges toward greenery were associated with lower fitted compound risk under the specified composition model. The contribution is a profile-based screening approach that makes dependence and identification choices explicit. Its findings concern relative model-estimated visual conditions and housing-price contexts within the sampled central urban area, with resident ratings and household circumstances defining further targets for locally grounded evaluation.