Life cycle thinking (LCT) is a concept that systematically considers the impacts caused by the life cycle of products, services, technologies, and policies on the environment, society, and the economy (Life Cycle Initiative 2013; Igos et al. 2019). LCT studies are based on two different perspectives: attributional and consequential. The attributional perspective (aLCT), or “LCT of the accountancy type”, aims to describe the status quo of a product life cycle. In contrast, the consequential perspective (cLCT), or “change-oriented LCT” describes a modeling approach that seeks to investigate the consequences of a making-decision, considering not only the changes inside the boundary but also internalizing the interferences from impacts outside of the boundary initially defined (Weidema et al. 2009). The choice of the LCT approach should not be arbitrary, given that each one answers a different question (Tillman 2000).
Consequential Life Cycle Assessment (cLCA) has emerged as a promising tool for answering specific future questions about environmental sustainability (Jørgensen et al. 2010). This life cycle approach incorporates casual market relationships from a future change in the “post-product”, providing the decision-makers with broader and more assertive information about the cause and effect of the given decision (Weidema and Heijungs 2009; Zamagni et al. 2012; Ekvall et al. 2016), as shown in Fig. 1.
Figure 1 - Line chart with the number of documents published from 2003 to 2020, which contain the words “consequential” and “LCA” in their abstract, title, or keywords (Scopus® database: TITLE-ABS-KEY (consequential AND lca)).
The “prospective approach” estimates how the life cycle inventory (LCI) input and output flows will change because of different decisions (e.g., increased or decreased product demand). In the short term, the life cycle system will respond with changes in the existing production capacity (need for power plants, factories, roads, etc.). Concerning the long-term, the system’s response would be in the timing or the nature of investments related to the new production capacity (Curran et al. 2005).
The criteria for choosing between a retrospective or a prospective LCA must be based on the study’s central question; if it is “how are things flowing within the chosen temporal window?”, the approach must be attributional. But, if the question is “how will flows change in response to decisions?”, the system must be consequential (Curran et al. 2005), or, as Frischknecht and Stucki (2010) named it, “decisional LCA (dLCA)”.
However, it is essential to ensure objectivity and confidence for decision-making, which is interpreting results. A cLCA study can result not only in a considerable number of indicators (categories), trade-offs, and complex interpretations but also numerous uncertainty embedded in the calculations of future scenarios that consider technologies not yet in use (Clavreul et al. 2013). When the goal of a study is to assess the sustainability, performance, or even the viability of a product’s life cycle (be in the current or future scenario), the subjectivity in the results arises as a limiting factor, generating unclear conclusions (McManus and Taylor 2015; Maranduba et al. 2017; Igos et al. 2019).
In this context, alternative criteria that better address subjectivity has been proposed. Fuzzy Logic has been identified as the one with solid potential to assess technical, environmental, economic, or social variables in an integrated and objective manner (Chan et al. 2014; Agarski et al. 2015; Wang et al. 2015; Sabaghi et al. 2016; Martinkus et al. 2019; Zupko 2021).
Developed by Lofti A. Zadeh in 1960 (Zadeh 1965), Fuzzy Logic is a superset of conventional Logic extended to handle the concept of partial truth. It is considered one of the best tools for developing control systems for decision-making on uncertain processes, such as medicine, engineering, and environmental sciences (Bécaert et al. 2006; Benetto et al. 2008; Sabaghi et al. 2016).
Many studies have discussed and tested models to incorporate Fuzzy Logic as a synergetic tool for LCT, mainly interpreting the results. Fuzzy sets provide greater decision-making reliability than the usual LCT procedures. A more detailed state-of-the-art of the relation among LCT, MCDA, and Fuzzy Logic as a decision-maker support approach can be seen in the Supplementary Material (Table S1).
Despite the variabilities of the objectives in the literature, it is possible to identify some convergence points:
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The approach “LCT + MCDA + Fuzzy Logic” has excellent potential to improve LCT studies’ interpretation and could be a reliable tool to support the decision-makers.
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Fuzzy Logic is an attractive alternative to treat the LCI and LCIA uncertainties.
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The approach “LCT + MCDA + Fuzzy Logic” can be used for any comparative assessment of products, services, technologies, or scenarios.
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Many results possibilities are presented, sophisticated or more straightforward.
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The aLCT is the one approach to the life cycle associated with MCDA and Fuzzy Logic to decision-making models. The researchers ignored the consequential LCT, even with cLCT being an approach purely decisional (Curran et al. 2005; Garraín et al. 2016).
As cLCA is based on future assumptions with a certain degree of subjectivity, classical Logic becomes inadequate for interpreting results from complex systems in a no-linear reality since it is only possible to say “yes”, “no”, "sustainable" or" not sustainable" (Zadeh 1997). Thus, it is necessary to use an alternative logic, scientifically reliable, that allows associating, in an integrated way, the inaccuracies of the levels of "pertinence" of the data resulting from a cLCA to a set, in this case, referring to a single score of the sustainability.
So, the synergy between cLCA and Fuzzy Logic comes up with the great potential to build a scientifically robust and reliable tool for decision-makers from the public or private sector to decide about a product, technology, or scenario considering the degree of relevance on a sustainability scale predetermined to each study case (Maranduba et al. 2017).
This study proposes developing a multicriteria fuzzy controller (MFC) to assess products' life cycle sustainability through single scores weighted by different perspectives (environmental, exergetic, and economical). It will consider the consequential approach and define variables as indicators from three selected perspectives.