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A Stochastic Approach to LCA of Internal Insulation Solutions for Historic Buildings

Elisa Di Giuseppe (), Marco D’Orazio (), Guangli Du (), Claudio Favi (), Sébastien Lasvaux (), Gianluca Maracchini () and Pierryves Padey ()
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Elisa Di Giuseppe: Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Marco D’Orazio: Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Guangli Du: Department of the Built Environment, Aalborg University Copenhagen, A.C. Meyers Vænge 15, 2450 Copenhagen, Denmark
Claudio Favi: Department of Engineering and Architecture, Università degli Studi di Parma, Parco Area delle Scienze 181/A, 43124 Parma, Italy
Sébastien Lasvaux: Institute of Thermal Engineering, School of Management and Engineering Vaud, HES-SO, University of Applied Sciences and Arts Western Switzerland, Avenue des Sports 20, 1401 Yverdon-les-Bains, Switzerland
Gianluca Maracchini: Department of Civil and Building Engineering and Architecture, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Pierryves Padey: Institute of Thermal Engineering, School of Management and Engineering Vaud, HES-SO, University of Applied Sciences and Arts Western Switzerland, Avenue des Sports 20, 1401 Yverdon-les-Bains, Switzerland

Sustainability, 2020, vol. 12, issue 4, 1-35

Abstract: Internal insulation is a typical renovation solution in historic buildings with valuable façades. 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.

Keywords: historic building; energy efficiency; internal insulation; LCA; Monte-Carlo simulation; uncertainty analysis; EPD (search for similar items in EconPapers)
JEL-codes: Q Q0 Q2 Q3 Q5 Q56 O13 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:4:p:1535-:d:322255

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