A tool to guide the selection of impact categories for LCA studies by using the representativeness index. Esnouf, A., Heijungs, R., Coste, G., Latrille, É., Steyer, J., Hélias, A., & Barcelo, D. Science of The Total Environment, 658:768-776, Elsevier, 3, 2019.
A tool to guide the selection of impact categories for LCA studies by using the representativeness index [link]Website  abstract   bibtex   
• Representativeness index (RI) contex-tualizes the LCA results of each LCI result. • An operational tool to compute RI is proposed (python package). Understanding the environmental profile of a product computed from the Life Cycle Assessment (LCA) framework is sometimes challenging due to the high number of environmental indicators involved. The objective here, in guiding interpretation of LCA results, is to highlight the importance of each impact category for each product alternative studied. For a given product, the proposed methodology identifies the impact categories that are worth focusing on, relatively to a whole set of products from the same cumulated database. The approach extends the analysis of Representativeness Indices (RI) developed by Esnouf et al. (2018). It proposes a new operational tool for calculating RIs at the level of impact categories for a Life Cycle Inventory (LCI) result. Impact categories and LCI results are defined as vectors within a standardized vector space and a procedure is proposed to treat issues coming from the correlation of impact category vectors belonging to the same Life Cycle Impact Assessment (LCIA) method. From the cumulated ecoinvent database, LCI results of the Chinese and the German electricity mixes illustrate the method. Relevant impact categories of the EU-standardized ILCD method are then identified. RI results from all products of a cumulated LCI database were therefore analysed to assess the main tendencies of the impact categories of the ILCD method. This operational approach can then significantly contribute to the interpretation of the LCA results by pointing to the specificities of the inventories analysed and for identifying the main representative impact categories.
@article{
 title = {A tool to guide the selection of impact categories for LCA studies by using the representativeness index},
 type = {article},
 year = {2019},
 identifiers = {[object Object]},
 keywords = {Dimension reduction,Interpretation tools,LCA,Life Cycle Impact Assessment,Life Cycle Inventory,Representativeness},
 pages = {768-776},
 volume = {658},
 websites = {https://www.sciencedirect.com/science/article/pii/S0048969718350575?via%3Dihub},
 month = {3},
 publisher = {Elsevier},
 day = {25},
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 created = {2019-01-29T10:16:05.250Z},
 accessed = {2019-01-29},
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 abstract = {• Representativeness index (RI) contex-tualizes the LCA results of each LCI result. • An operational tool to compute RI is proposed (python package). Understanding the environmental profile of a product computed from the Life Cycle Assessment (LCA) framework is sometimes challenging due to the high number of environmental indicators involved. The objective here, in guiding interpretation of LCA results, is to highlight the importance of each impact category for each product alternative studied. For a given product, the proposed methodology identifies the impact categories that are worth focusing on, relatively to a whole set of products from the same cumulated database. The approach extends the analysis of Representativeness Indices (RI) developed by Esnouf et al. (2018). It proposes a new operational tool for calculating RIs at the level of impact categories for a Life Cycle Inventory (LCI) result. Impact categories and LCI results are defined as vectors within a standardized vector space and a procedure is proposed to treat issues coming from the correlation of impact category vectors belonging to the same Life Cycle Impact Assessment (LCIA) method. From the cumulated ecoinvent database, LCI results of the Chinese and the German electricity mixes illustrate the method. Relevant impact categories of the EU-standardized ILCD method are then identified. RI results from all products of a cumulated LCI database were therefore analysed to assess the main tendencies of the impact categories of the ILCD method. This operational approach can then significantly contribute to the interpretation of the LCA results by pointing to the specificities of the inventories analysed and for identifying the main representative impact categories.},
 bibtype = {article},
 author = {Esnouf, Antoine and Heijungs, Reinout and Coste, Gustave and Latrille, Éric and Steyer, Jean-Philippe and Hélias, Arnaud and Barcelo, D},
 journal = {Science of The Total Environment}
}

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