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Life Cycle Assessment (LCA) has been extensively used in the building sector for assessing the environmental performance of construction materials, products or a whole building.
Although numerous building LCAs have been performed, most of them took the deterministic way which assumed deterministic point values and simplified linear quantification model to compile life cycle inventory, which may lead to inaccuracy of LCA results and further influence the decision making.
This research aimed to improve the reliability of traditional deterministic building LCA, by incorporating both data uncertainties and model uncertainties through a stochastic approach, which combined Monte Carlo simulation and Markov Chain modeling with the assistance of data quality indicators (DQI) and classical statistics.
The case study showed that deterministic LCA may generate outcomes with low probabilities, which could lead to biased conclusions. The proposed stochastic LCA will provide distributions of output rather than deterministic point value. Thus, it will provide more information with additional confidence for decision makers and allow more objective decision making.