Why using different Life Cycle Assessment software tools can generate different results for the same product system? A cause–effect analysis of the problem

生命周期评估 软件 产品(数学) 可靠性工程 过程(计算) 计算机科学 工程类 生产(经济) 数学 几何学 操作系统 宏观经济学 经济 程序设计语言
作者
Diogo Aparecido Lopes Silva,Andréa Oliveira Nunes,Cassiano Moro Piekarski,Virgínia Aparecida da Silva Moris,Luri Shirosaki Marçal de Souza,Thiago Oliveira Rodrigues
出处
期刊:Sustainable Production and Consumption [Elsevier BV]
卷期号:20: 304-315 被引量:127
标识
DOI:10.1016/j.spc.2019.07.005
摘要

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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