Synergistic Optimization of Daylighting and Carbon Emissions in University Teaching Buildings from the Perspective of Spatial Heterogeneity

In the context of global carbon reduction, university teaching buildings, as typical densely populated public spaces, face the dual challenges of high operational carbon emissions and strict daylighting demands. This study therefore focuses on jointly improving daylighting performance and reducing operational carbon emissions. However, current optimization practices are largely constrained by a “homogeneous” design paradigm applied to spatial layouts and building envelopes, which fails to fully exploit the passive energy-saving potential of buildings. To address this limitation, this study proposes a daylighting-carbon emission synergistic optimization framework integrating parametric modeling, Non-dominated Sorting Genetic Algorithm II (NSGA-II) multi-objective optimization, Stacking ensemble prediction models, and Shapley Additive Explanations (SHAP) analysis. The results indicate that: (1) compared to the homogeneous baseline model, the comprehensive Pareto optimal solution reduces the operational carbon emission intensity (CEI) by 12.5%, while simultaneously increasing spatial daylight autonomy (sDA) and useful daylight illuminance (UDI) by 14.1% and 18.9%, respectively; (2) the Pareto optimal solutions exhibit distinct heterogeneous distribution characteristics in spatial morphology and facade configuration. Specifically, the floor plan adopts an asymmetrical layout characterized by “deep south (9.0–9.5 m) and shallow north (6.5–7.0 m)” zones, while the facade window-to-wall ratio (WWR) displays a vertical gradient of “larger upper and smaller lower” openings (with a high-to-low zone WWR ratio of 1.07–1.25); (3) the two developed Stacking prediction models achieve R2 values greater than 0.90, substantially reducing the computational cost of dynamic simulations while ensuring high predictive accuracy; (4) SHAP analysis quantifies the contributions of 24 design parameters and 12 spatial heterogeneity indicators (HIs) to model predictions of daylighting and carbon performance. In conclusion, this study proposes heterogeneous design strategies, providing a scientific reference for the synergistic optimization of daylighting and carbon emissions in teaching buildings located in cold climates.