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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Elisa Di Giuseppe & Marco D’Orazio & Guangli Du & Claudio Favi & Sébastien Lasvaux & Gianluca Maracchini & Pierryves Padey, 2020. "A Stochastic Approach to LCA of Internal Insulation Solutions for Historic Buildings," Sustainability, MDPI, vol. 12(4), pages 1-35, February.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1535-:d:322255
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    References listed on IDEAS

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    Cited by:

    1. Edoardo Baldoni & Silvia Coderoni & Elisa Di Giuseppe & Marco D’Orazio & Roberto Esposti & Gianluca Maracchini, 2021. "A Software Tool for a Stochastic Life Cycle Assessment and Costing of Buildings’ Energy Efficiency Measures," Sustainability, MDPI, vol. 13(14), pages 1-24, July.
    2. Yohei Endo & Hideki Takamura, 2021. "Evaluation of Life-Cycle Assessment Analysis: Application to Restoration Projects and New Construction in Alpine Climate, Japan," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    3. Marco D’Orazio & Elisa Di Giuseppe & Marta Carosi, 2023. "Life Cycle Assessment of Mortars with Fine Recycled Aggregates from Industrial Waste: Evaluation of Transports Impact in the Italian Context," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    4. Valentina Marincioni & Virginia Gori & Ernst Jan de Place Hansen & Daniel Herrera-Avellanosa & Sara Mauri & Emanuela Giancola & Aitziber Egusquiza & Alessia Buda & Eleonora Leonardi & Alexander Rieser, 2021. "How Can Scientific Literature Support Decision-Making in the Renovation of Historic Buildings? An Evidence-Based Approach for Improving the Performance of Walls," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    5. Rosa Agliata & Alfonso Marino & Luigi Mollo & Paolo Pariso, 2020. "Historic Building Energy Audit and Retrofit Simulation with Hemp-Lime Plaster—A Case Study," Sustainability, MDPI, vol. 12(11), pages 1-15, June.
    6. Gianluca Maracchini & Rocco Di Filippo & Rossano Albatici & Oreste S. Bursi & Rosa Di Maggio, 2023. "Sustainable Retrofit of Existing Buildings: Impact Assessment of Residual Fluorocarbons through Uncertainty and Sensitivity Analyses," Energies, MDPI, vol. 16(7), pages 1-22, April.

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