Uncertainty and Sensitivity Analyses for Evaluating the Building Element‘s Replacement in Building LCA

Goulouti, Kyriaki; Padey, Pierryves; Galimshina, Alina; Habert, Guillaume; Lasvaux, Sébastien

doi:10.23967/dbmc.2020.143

Cited by 1 publication

(1 citation statement)

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“…[46] Service life calculation, design, technological change, repair cost and availability of parts, household affluence, residual and resale values, aesthetic and functional quality, fashion, advertising, and social pressure. [82] Estimating the range of expected values of the environmental impacts for the specified variability in the uncertainty in transportation distances involved in the remedial option implementation. [73] Unreliability, incompleteness, technological difference, and spatial and temporal variation in life cycle impact assessment (LCIA) data, as well as the natural variability in hydrologic data.…”

Section: Uncertainty Sources Refmentioning

confidence: 99%

Life Cycle Assessment under Uncertainty: A Scoping Review

Barahmand

Eikeland

2022

World

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Today, life cycle assessment (LCA) is the most widely used approach to model and calculate the environmental impacts of products and processes. The results of LCAs are often said to be deterministic, even though the real-life applications are uncertain and vague. The uncertainty, which may be simply ignored, is one of the key factors influencing the reliability of LCA outcomes. Numerous sources of uncertainty in LCA are classified in various ways, such as parameter and model uncertainty, choices, spatial variability, temporal variability, variability between sources and objects, etc. Through a scoping review, the present study aims to identify and assess the frequency with which LCA studies reflect the uncertainty and what are the tools to cope with the uncertainty to map the knowledge gaps in the field to reveal the challenges and opportunities to have a robust LCA model. It is also investigated which database, methodology, software, etc., have been used in the life cycle assessment process. The results indicate that the most significant sources of uncertainty were in the model and process parameters, data variability, and the use of different methodologies and databases. The probabilistic approach or stochastic modeling, using numerical methods such as Monte Carlo simulation, was the dominating tool to cope with the uncertainty. There were four dominant LCA methodologies: CML, ReCiPe, IMPACT 2002+, and TRACI. The most commonly used LCA software and databases were SimaPro® and Ecoinvent®, respectively.

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Section: Uncertainty Sources Refmentioning

confidence: 99%