Low-Carbon Urban Morphology as a Configuration Problem: Empirically Constrained Bayesian Optimization at the Intra-Urban Scale

Urban compactness and functional mixing are widely recognized as important carbon mitigation strategies. However, determining appropriate levels of compactness and functional mixing, and their potential carbon reductions, remains challenging due to nonlinear morphology–emission relationships and strong spatial dependence. This study develops a spatially explicit Bayesian optimization (SEBO) framework integrating spatial relationship analysis, nonlinear response-interval identification, and constrained configuration optimization. Using 4,347 grid cells in the Nanjing metropolitan region, spatial econometric models identify sector-specific morphology–emission associations and green-blue moderation effects. Tree-based machine learning then identifies responsive ranges for morphological adjustment, which constrain Bayesian optimization to explore lower-emission configurations and assess mitigation potential. Results reveal substantial sectoral differences in morphology–emission relationships and response intervals. Vertical intensity and functional mix are associated with transportation, residential, and power-related emissions, while horizontal density primarily relates to residential emissions. Optimizing vertical intensity yields average simulated reductions of 14.09%, 19.25%, and 5.75% in transportation, residential, and power-related emissions, respectively. However, mitigation potential varies with initial morphological and ecological conditions, with some scenarios yielding higher predicted emissions. These findings support spatially differentiated, context-specific urban morphology optimization rather than universal compactness targets.