Why using different Life Cycle Assessment software tools can generate different results for the same product system? A cause–effect analysis of the problem
There are different software tools to perform Life Cycle Assessment (LCA) and results may be different according to which software the user chooses. This paper aims to present how different LCA results can be achieved due to using different LCA software tools for the same product system. The present...
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| Principais autores: | , , , , , |
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| Formato: | article |
| Idioma: | English |
| Publicado em: |
Elsevier
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/32443 |
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| Resumo: | There are different software tools to perform Life Cycle Assessment (LCA) and results may be different according to which software the user chooses. This paper aims to present how different LCA results can be achieved due to using different LCA software tools for the same product system. The present study focuses on analyzing four LCA software tools: SimaPro, Gabi, Umberto® and openLCA, and a standard case study was designed for the LCA comparisons for the particleboard production in Brazil. The product system was modeled in terms of gate-to-gate (G2G) and cradle-to-gate (C2G) approaches, and the ILCD midpoint was the Life Cycle Impact Assessment (LCIA) method. Characterized and normalized impacts were calculated and compared in terms of maximum/minimum relative deviation for five different impact categories. An analysis of the current software tools indicates that photochemical ozone formation and ecotoxicity freshwater categories were highlighted because of their high relative impacts. However, the G2G impacts for all the software tools were less affected than the C2G impacts, which indicate there are differences in the causes of the impacts for the background datasets. Furthermore, an analysis of the Characterization Factors (CFs) was designed and the results were revealed: i) missing CFs in some software, ii) additional CFs in some software, and iii) different CFs for the same flows. Based on that, a cause–effect analysis was performed, and two root causes were identified: import process for background datasets, and lack of rules for implementing LCIA methods in the software tools. To deal with such root causes, a roadmap was proposed and we recommended to include LCIA methods into a node at the Global LCA Data network, and consequently all software tools should update their databases from there. This would help to at least reducing the discrepancies of LCA results |
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