Building envelope materials and configurations significantly impact both embodied and operational carbon emissions. Improving thermal performance typically increases initial costs, while poor performance raises operational energy use and costs. This presents a paradox among the seven variables. This study presents a multi-objective optimization model using Rhino-Grasshopper and the BP-NSGA-II algorithm to minimize embodied and operational carbon emissions and global costs for building envelopes. A high-rise residential building in a cold region of Northern China is used as a case study. The optimization, identifies the optimal trade-offs between carbon emissions and cost. Using the entropy-weighted TOPSIS method for decision-making, the optimal solution would reduce would embodied carbon by 50.1%, operational carbon by 2.1%, and global cost by 10.8% compared to the baseline. The proposed model offers an efficient decision- support tool for reducing carbon emissions and optimizing costs in building envelope design.
Zou et al. (Mon,) studied this question.