Internal insulation is a typical renovation solution in historic buildings with valuable facades. However, it entails moisture-related risks, which affect the durability and life-cycle environmental performance. In this context, the EU project RIBuild developed a risk assessment method for both hygrothermal and life-cycle performance of internal insulation, to support decision-making. This paper presents the stochastic Life Cycle Assessment method developed, which couples the LCA model to a Monte-Carlo simulation, providing results expressed by probability distributions. It is applied to five insulation solutions, considering different uncertain input parameters and building heating scenarios. In addition, the influence of data variability and quality on the result is analyzed, by using input data from two sources: distributions derived from a generic Life Cycle Inventory database and "deterministic" data from Environmental Product Declarations. The outcomes highlight remarkable differences between the two datasets that lead to substantial variations on the systems performance ranking at the production stage. Looking at the life-cycle impact, the general trend of the output distributions is quite similar among simulation groups and insulation systems. Hence, while a ranking of the solutions based on a "deterministic" approach provides misleading information, the stochastic approach provides more realistic results in the context of decision-making.
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