Building Retrofit Measures and Design: A Probabilistic Approach for LCA

Claudio Favi, Elisa Di Giuseppe, Marco D’Orazio, Marta Rossi and Michele Germani
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Claudio Favi: Department of Engineering and Architecture, Parco Area delle Scienze 181/A, Università di Parma, 43124 Parma, Italy
Elisa Di Giuseppe: Department of Building, Civil Engineering and Architecture, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Marco D’Orazio: Department of Building, Civil Engineering and Architecture, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Marta Rossi: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy
Michele Germani: Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche, via Brecce Bianche 12, 60131 Ancona, Italy

Sustainability, 2018, vol. 10, issue 10, 1-15

Abstract: Green building design and architecture have become widespread tenets in the development of sustainable buildings. In this context, the use of sustainable materials and the awareness of resource/energy consumption are strategic aspects to consider for the improvement of building performances. This paper presents a new and structured approach to address uncertainty and sensitivity analysis in Life Cycle Assessment (LCA) to support the decision-making process in building renovation. This “probabilistic” approach to LCA allows for the obtaining of results expressed as ranges of environmental impacts and for alternative solutions, offering an idea of the meaning of input parameters’ uncertainties and their influence on the result. The approach includes (i) the assessment of inputs’ uncertainties (represented by Probability Density Functions—PDF); (ii) the data sampling; and (iii) the uncertainty propagation (Monte Carlo method). Variance decomposition techniques have been used to sample inputs’ PDFs and assess their impact on the LCA result distribution (sensitivity analysis). The methodology application is illustrated through a case study where three building retrofit measures were assessed. Results provide an insight about the uncertainties of LCA indicators in terms of climate change and nonrenewable energy. The input parameters related to the use phase are confirmed as the most influential in building LCA.

Keywords: probabilistic LCA; building retrofit; inputs’ uncertainties; Monte Carlo; sensitivity analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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