To address the fragmented multi-scale spatial data and the challenge of integrating energy assessment into multi-objective optimization for urban renewal, this paper proposes a low-carbon decision-making method integrating NSGA-II and energy consumption simulation within a BIM–CIM framework.
First, a multi-objective low-carbon decision framework is established. Then, through a unified parametric approach, deep coupling between energy simulation and evolutionary optimization is achieved. Multi-level building-city parameters are bidirectionally mapped to optimization variables. A physics-based energy simulation model constructs objectives for operational energy and carbon emissions, allowing simulation results to directly guide non-dominated sorting and elitist evolution. The process ultimately outputs constraint-satisfying solutions with spatial consistency, forming a constrained Pareto optimal set.
Experiments show the population’s convergence distance decreased from 165.40 initially to 105.20 by the 50th generation, with continued convergence thereafter. Annual energy use and carbon emissions show clear synergy, peaking in low-carbon evaluation at 90.85 kWh/m2 and 27.80 kgCO2/m2, with a score of 0.672.
The presented framework establishes a verified computational pipeline linking multi-scale parametric generation with evolutionary search mechanisms for guiding data-driven urban environment configurations.
