Page 208 - Materials Chemistry, Second Edition

206 10. Advancing life cycle sustainability assessment using multiple criteria decision making

The concept of LCA evolved in the last four decades with the evolution in understanding
and importance of assessing alternatives’ environmental impacts (Ness et al., 2007). LCA con-
siders inputs and outputs throughout the life of an alternative and their combined environ-
mental impacts. Life cycle thinking is also embedded in life cycle costing (LCC) and social
impacts through social life cycle assessment (SLCA). Although the LCSA is an ambitious
approach to achieve holistic assessments, there are constraints in applying the framework
in reality. Fauzi et al. (2019) discuss numerous challenges in enabling LCSA, such as parity
in the assessment methods (e.g., different temporal scopes and scales applied between the
methods). One of the main challenges identified is integration of indicators across
the methods. To be able to achieve the LCSA, it is necessary to integrate the assessment results
obtained by methods such as LCA, LCC, and LCSA. One of the most common frameworks
used to integrate the results obtained from different tools/methods is multiple criteria
decision making (MCDM) (Costa et al., 2019; Hannouf and Assefa, 2018).
The current chapter is focused on discussing the MCDM applications in LCSA.
The MCDM methods overview is given in Section 10.2, applications of MCDM methods
in combination with LCSA are described in Section 10.3. Section 10.4 provides details on
challenges in the application of MCDM while carrying out LCSA. Finally, a framework
for MCDM based LCSA is proposed in Section 10.5, and conclusions are provided in
Section 10.7.

10.2 MCDM methods overview

Decision-making involves consideration of multiple criteria, which are usually conflicting
(for example, efficiency versus cost) with each other. LCSA also consists of evaluating
the alternatives based on conflicting criteria. LCSA also strives to include priorities of
all stakeholders into decision-making. Stakeholders have wide-ranging preferences, which
adds to the complexity of the decision-making process. MCDM methods are developed to
counter such complexities embedded in decision-making and provides a strategically suit-
able decision (Zopounidis and Pardalos, 2010; Hwang and Yoon, 1981). MADM can be diver-
sified into two groups, which are (a) multiattribute decision making (MADM)
and (b) multiobjective decision making (MODM) (Figueira et al., 2005; Hwang and Yoon,
1981). MADM is majorly associated with decision-making problems involving a finite num-
ber of alternatives (known as discrete variable problems), whereas MODM is concerned with
decision-making problems with an infinite number of alternatives (known as continuous
variable problems). In MODM, primary objective is to design/formulate an alternative that
shows maximum promise or performance corresponding to limited resources.
Literature suggests that many types of MADM methodologies to integrate information
processing of attributes with decision-making of humans involving logic and rational think-
ing have been developed. Asgharizadeh et al. (2017) classified MADM methods into input-
oriented and output-oriented. Input-oriented is largely subclassified in two categories:
“data available to DMs” and “type of data available.” There could be problems where
DMs’ may have no information on alternatives available to them, whereas, even if data is
available then the data could be purely qualitative, purely quantitative, or a mixture of