Abstract
Over decades, we have been racing to reach optimal solutions against vital environmental challenges—heat waves, extreme heat/cold thermal stress, excessive energy demand, inadequate daylight, and airflow—in the built environments due to the rapid growth and transformation of cities. This thesis, therefore, proposes a proactive, comprehensive urban design approach—Pyramid Urban Model (PUM)—to support urban designers in tackling such challenges through inclusive multi-criteria design de- cisions. PUM enables urban designers to improve the design quality of cities via evaluating various design parameters’ impact on multi-scalar environmental performances: microscale–urban heat island, building scale–energy use intensity and daylight autonomy, and human-scale–outdoor thermal comfort, urban ventilation potential. In the transition from conventional urban design to contemporary approaches, computational prediction tools provide the quantitative analysis of environmental performances when on-site measurement studies support the validation process in the present study. Meanwhile, the literature review establishes a qualitative base for determining urban design parameters and specifying current performance-driven hybrid cosimulation methods. Starting with comprehending the performance-parameter relations by applying inferential statistics, this thesis reveals potential improvements to obtain fast and comprehensive prediction methods by subjective assessment of microclimate prediction tools. As a novel improvement towards this vision, this thesis develops, validates and applies a simplified ground-surface temperature algorithm (g-STAr) to improve the accuracy of microclimate and outdoor thermal comfort predictions within a parametric design platform. Consequently, this thesis advances the current parametric co-simulation method via a novel synthesis in reducing the computational expense of predicting five critical environmental performance metrics for urban design analysis.