Life Cycle Assessment (LCA) tools are time-consuming, require burdensome data collection, and are poorly integrated with the design process. Additionally, use phase impacts are often left out of LCAs because predicting the energy consumption of a building is itself a complex problem. We present a streamlined building LCA methodology called the Building Attribute to Impact Algorithm (BAIA) that allows the simultaneous calculation of embodied (attributable to materials) and use-phase impacts based on uncertain or low-fidelity definitions of materials and building attributes. BAIA captures the variability in predicted impacts introduced by the low-fidelity building definition. This is accomplished through the underspecification of building attributes, where unknown or uncertain attribute values may be represented by a group of related attribute values. LCAs are then conducted in a Monte Carlo framework where one value is randomly selected for each underspecified attribute. The resulting probabilistic distribution of life cycle impacts quantifies the variability introduced by underspecification, and statistical analysis of these results provides feedback on which attributes contribute the most to the variability in predicted impacts.
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