Multidimensional Urban Morphology and Carbon Intensity: Nonlinear Drivers in Dense Cities

The multidimensional morphological attributes, encompassing three-dimensional metrics (e.g., FAR, SVF, and BHI) and two-dimensional indicators (e.g., BCD and BAD), were systematically quantified at the block scale. GIS-based zonal statistics were employed to integrate these morphological datasets with environmental constraints (such as Slope and DEM) and socioeconomic proxies (ANL and POI density). The functional typologies of urban blocks were rigorously categorized based on a "Dominant Function Identification" strategy, systematically integrating land-use data with POI ratios. Carbon emission intensity (CEI) was derived from the 2023 ODIAC high-resolution inventory. Additionally, Land Surface Temperature (LST) data were acquired from Landsat 8 imagery in August 2023 to physically validate the thermodynamic interaction effects. All statistical modeling, including XGBoost-SHAP analysis and spatial autocorrelation diagnostics, was executed using custom Python scripts.