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Résumé

The aim of this paper is to propose guidelines for the selection of an accurate life cycle assessment (LCA) dataset of building products to be used as generic data for a national context. The guidelines are structured within a methodology, called NativeLCA. First, a review of available datasets for construction products is presented such as generic LCA and Environmental Product Declaration (EPD) databases for both national (e.g. France, Germany, Spain etc.) and European context. Secondly, a method is proposed to choose appropriate generic datasets by means of a hybrid methodology. A meta-analysis is conducted in the first step on the sample of collected datasets from the literature. When relevant, product-specific data (EPD of the different producers) are averaged to represent an average data or existing generic data are adapted to be more suitable for the context. Then, a data quality assessment enables to rank the different datasets according to the goal and scope of the study. This study provides consistent guidelines that can be used by building LCA practitioners to select relevant datasets depending on their goal and scope. A full case study for stone wool boards illustrates and demonstrates the applicability and usefulness of the proposed methodology, namely in the selection of a coherent dataset as generic data for a national context. This work highlights the issues in terms of choice and adaptation of existing data for a national context. Industry data cannot be adapted due to confidentiality issues unlike unit process generic data. The use of data quality indicators then helps to select the relevant generic data for each context according to user needs. While further efforts are needed to develop regional and sector-specific LCA databases adapted for each national context, the proposed guidelines showed that the current use or adaptation of existing data, if consistently done, can lead practitioners to increase the reliability of building LCA studies according to their goal and scope definition.

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