Lights and Shadows in Consequential LCA

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.

Rationales for and limitations of preferred solutions for multi-functionality problems in LCA: is increased consistency possible?

“…The current status of subdivision and system expansion, if understood as reporting at the level of all co-products, in the first tier of the ISO 14044 multi-functionality hierarchy is defensible from a physical modeling perspective (see below), since these options are most likely to maintain the integrity of the physical relationships modeled. The notion that system expansion and substitution are commensurate, along with the preferred status it confers to substitution in attributional data modeling contexts is, however, contentious among some practitioners Heijungs and Guinée 2007;Mathiesen et al 2009;Wardenaar et al 2012;Zamagni et al 2012).…”

“…In consequential data modeling, the degree of representativeness and robustness of LCA models using system expansion + substitution to solve multi-functionality problems will depend on how accurately the assumed substitution scenarios are defined. In practice, the assumptions regarding the one-toone substitutions of marginal market equivalents that can be central to consequential modeling are questionable and may cast doubt as to the realism of the resultant model outcomes (Ekvall and Finnveden 2001;Heijungs and Guinée 2007;Pelletier 2010;Pelletier and Tyedmers 2011;Brander and Wylie 2011;Zamagni et al 2012). In reality, markets will seldom (if ever) be as predictable as such models/assumptions imply.…”

“…As highlighted by Heijungs and Guinée (2007), substitution imposes a philosophical problem: "how can one decide which process is avoided given the fact that it is avoided, and hence not there?" Although system expansion + substitution is, per se, the only possible approach to resolving multi-functionality problems in consequential modeling, it might therefore be argued that consequential models should be explicitly presented as scenarios, with all such assumptions and limitations clearly described (Heijungs and Guinée 2007;Zamagni et al 2012). The use of system expansion + substitution as a basis for allocation in attributional data modeling similarly faces several challenges.…”

A typology for world electricity mix: Application for inventories in Consequential LCA (CLCA)

“…CLCA has been discussed since the nineties (Weidema, 1993;Weidema et al, 1999) but its development is more recent. Indeed, Zamagni et al (2012) emphasized the evolution of this method with an increasing number of publications devoted to "Consequential" and "LCA" as keywords, highlighting the growing interest of LCA practitioners for assessing the consequences of change in addition to product Attributional assessments.…”

“…Only short-term country-level data have recently become available in the literature (Amor et al, 2014). A consistent approach concerning electricity production for CLCA has not been established till now to our knowledge and the development of more generalized electricity production CLCI data represents a major challenge for CLCA application (Zamagni et al, 2012;Ekvall and Andrae, 2006). If short-term country-level data start to be available in literature, reliable data are still lacking in a long-term perspective.…”