Classifying design-related uncertainties in LCA-based building optimization : A systematic review

Handling design-related uncertainties (DRUs) is critical for improving the reliability of life cycle assessment (LCA)-based building optimization, especially in the preliminary stages of the design process. Existing studies either focus on single aspects of DRUs’ influence or oversimplify their handling in optimization. Building on a broader research effort aimed at addressing uncertainties in LCA, this paper proposes a dual-dimension classification approach for DRUs, based on their impact on both building environmental performance and subsequent design decisions, and provides a practical guidance for their handling in LCA-based building optimization. Through a systematic literature review (SLR), 16 DRUs were identified from 58 papers, and their decision-making stages in optimization processes were documented. Two semi-quantitative evaluation metrics, optimization interest (OI) and decision-making priority (DP), were introduced to evaluate the characteristics of DRUs in optimization. Based on evaluation results, a classification into four categories for the DRUs is proposed with targeted uncertainty-handling strategies: Monte Carlo simulation for High OI-High DP DRUs; scenario analysis for Low OI-High DP DRUs; simplified assumptions for High OI-Low DP DRUs; and default values for Low OI-Low DP DRUs. This study aims to simplify LCA methods and tools in building design practice, while establishing a foundation for developing robust optimization frameworks in the future.