Page 90 - Materials Chemistry, Second Edition

4.5 Outlook: Perspective and opportunities 85
structure as ELCA to SLCA, suggesting, at the same time, new classification and character-
ization models, following United Nations Environment Program/Society for Environmental
Toxicology and Chemistry (UNEP/SETAC) guidelines on S-LCA. Nevertheless, this
remains an open issue, which should be perceived as a starting point for every future
development of LCSA.

4.5.2 Implementation of a multimethod approach
Following the call for integration of different aspects of LCSA and in order to fill the gap
between modeling and reality, i.e., reducing the uncertainties inherent to the modeling activ-
ity, a multimethod approach is widely regarded as the main opportunity available (Halog and
Manik, 2011; Gloria et al., 2017; Ren, 2018a,b). From the beginning of LCSA application, the
intrinsic trans-disciplinary nature of the subject was recognized as requiring an integration of
methods and models (Guin ee et al., 2011), to address specific sustainability issues. This raised
the issue of selection, sharing, and availability of these models and of the proper matching
between models and sustainability questions (Gloria et al., 2017).
Within the framework of a growing circular economy, LCSA should be also able to adapt
and include tools able to model industrial symbiosis, circular material flow analyses, and re-
source scarcity. Many analytical approaches have been recently explored as opportunities to
improve the traditional LCSA. An overview is provided in Appendix B and some significant
examples are reported here in the following.
Plevin (2016), for instance, as presented by Gloria et al. (2017), explored the opportunity
offered by Global Change Assessment Model (an integrated assessment model) to improve
the comprehensiveness and robustness of Climate-LCA of biofuels. In particular, following
the approach set by consequential LCA, market interactions, cascade consequences at global
scale, and evolution in socio-economic indicators, such as population and GDP, and technical
knowledge are addressed. Wu et al. (2017), on the other hand, integrated an agent-based
modeling approach to the life cycle inventory analysis in order to address temporal and spa-
tial variation of indicators into the case study of green building development. Moreover, an
agent-based modeling has been applied to address micro-level interactions and heterogeneity
displayed at individual level (Gloria et al., 2017).
The same Kua (2017), abovementioned, proposes an integrated and unified analysis, con-
sidering “soft” indicators, such as vulnerability, resilience, and stakeholders’ risk aversion
through approaching “cross-links indicators, inter- and intradimensional consequences, re-
bound effects, and potential ‘transitioning’ of these indicators into a single framework”
(Gloria et al., 2017: 1451). He et al. (2019), addressing the subject of sustainability assessment
of products, set accuracy as the first space for improvement of LCSA and they proposed an
indicators’ analysis approach to mitigate the impact of uncertainties over the final results; the
set of indicators belonging to five conceptual areas, namely energy, environment, resource,
technology, and economy (Fig. 4.16).
Ren (2018a,b), still focused on uncertainties management, proposed a comprehensive life
cycle sustainability prioritization framework for ranking the energy systems. As presented in
Fig. 4.17, a multistage approach was applied. In particular, a first step involved a fuzzy two-
stage logarithmic goal programming method, used to determine the weights of the criteria for