Abstract
Purpose
Consequential LCA (CLCA) is becoming widely used in the scientific community as a modelling technique which describes the consequences of a decision. However, despite the increasing number of case studies published, a proper systematization of the approach has not yet been achieved. This paper investigates the methodological implications of CLCA and the extent to which the applications are in line with the theoretical dictates. Moreover, the predictive and explorative nature of CLCA is discussed, highlighting the role of scenario modelling in further structuring the methodology.
Methods
An extensive literature review was performed, involving around 60 articles published over a period of approximately 18 years, and addressing both methodological issues and applications. The information was elaborated according to two main aspects: what for (questions and modes of LCA) and what (methodological implications of CLCA), with focus on the nature of modelling and on the identification of the affected processes.
Results and discussion
The analysis points out that since the modelling principles of attributional LCA (ALCA) and CLCA are the same, what distinguishes the two modes of LCA is the choice of the processes to be included in the system (i.e. in CLCA, those that are affected by the market dynamics). However, the identification of those processes is often done inconsistently, using different arguments, which leads to different results. We suggest the use of scenario modelling as a way to support CLCA in providing a scientifically sound basis to model specific product-related futures with respect to technology development, market shift, and other variables.
Conclusions
The CLCA is a sophisticated modelling technique that provides a way to assess the environmental consequences of an action/decision by including market mechanisms into the analysis. There is still room for improvements of the method and for further research, especially in relation to the following aspects: clarifying when and which market information is important and necessary; understanding the role of scenario modelling within CLCA; and developing a procedure to support the framing of questions to better link questions to models. Moreover, we suggest that the logic of mechanisms could be the reading guide for overcoming the dispute between ALCA and CLCA. Going further, this logic could also be extended, considering CLCA as an approach—rather than as a modelling principle with defined rules—to deepen LCA, providing the conceptual basis for including more mechanisms than just the market ones.
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Notes
The decade of elaboration makes reference to a period characterised by the development of multiple different approaches, ranging from dynamic LCA to spatially differentiated LCA, and environmentally extended input–output based LCA, to mention just a few.
A consequential social life cycle assessment would take into account not only social impacts on the stakeholders in the life cycle, but also those related to the non-implemented product life cycle (i.e. when not carrying out a process or using a product).
It is worthy to note that stating the purpose of the assessment is perfectly in line with what required by the ISO standard in the goal and scope phase (ISO 2006). However, this proved not to be sufficient to properly address and model the problem.
Presently LCA includes only technological and environmental relations in the inventory and impact assessment phase, respectively (Heijungs et al. 2010).
Constraints can be technical, political, natural, and marked-related. A common simplification adopted in CLCA is to consider them as fixed entities due to the difficulties in modelling them. Thus, it is assumed that they do not change, even when the long-term horizon is analysed.
The CPA, assuming away any interference from the system, works under the hypothesis of isolation. The temporal isolation assumes that those factors we consider frozen under the CPA move so slowly relative to the others that can be considered constant at any point in time. The causal isolation considers that the same factors are not significantly affected by the processes under study. Thus, the more the mechanisms are added, the more the CPA and its related constraints are relaxed, the more the steady-state and static models become tight because causes and effects are always intertwined in time.
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Acknowledgements
This research was partially funded by the Italian Ministry for Education, University and Research-MIUR, with the support of the LECOP LABORATORY of Emilia-Romagna High Technology Network and the EU-FP7 Sustaining Ethical Aquaculture Trade (SEAT) project.
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Zamagni, A., Guinée, J., Heijungs, R. et al. Lights and shadows in consequential LCA. Int J Life Cycle Assess 17, 904–918 (2012). https://doi.org/10.1007/s11367-012-0423-x
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DOI: https://doi.org/10.1007/s11367-012-0423-x