Page 57 - Materials Chemistry, Second Edition
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52          3. Life cycle thinking tools: Life cycle assessment, life cycle costing and social life cycle assessment

                 S-LCA is used as a business-oriented methodology, where the social assessment is based on
                 the behavior of the organizations that are involved in the processes under study
                 (Arzoumanidis et al., 2018).
                   Kolotzek et al. (2018) developed a model combining LCA and S-LCA for the assessment of
                 raw material supply risks and used the analytic hierarchy process to weight the indicators.
                 Santos et al. (2017) performed an S-LCA of school buildings for higher education, focusing
                 on the criteria of health and comfort. They used analytic hierarchic process to obtain the
                 weighting scheme to rate social performance. Chandrakumar et al. (2017) elaborated a
                 multicriteria decision support system based on an S-LCA framework for evaluating three san-
                 itation system designs. They applied the analytic hierarchy process to solve their proposed
                 model. Halog and Manik (2011) proposed a framework adopting LCA, LCC, S-LCA, and
                 stakeholders analysis supported by multicriteria decision techniques for the assessment of
                 the development of biofuel supply chain networks.
                   Currently, new guidelines are under development for the application of S-LCA, they will
                 consider and incorporate methodological advancements and recent practical experiences.
                 They will also deal with harmonization of S-LCA methods, specification of application of
                 S-LCA for organizations, and scale up of scientific debate (Benoit Norris et al., 2018). The fol-
                 lowing phases are usually conducted to develop an S-LCA according to the current guidelines
                 (UNEP/SETAC, 2009).


                 3.3.1 Definition of goal and scope
                   The first phase of an S-LCA study is the definition of the goal and scope of the study.
                 A clear statement of purpose, namely the goal of the study and the intended use, is needed.
                 Based on the goal, a critical review may be planned. It is important to take into consideration
                 that the ultimate objective is improving of social conditions and of the socio-economic per-
                 formance of a product throughout its life cycle for all of its stakeholders (UNEP/SETAC,
                 2009). Successively, the scope has to be defined; the function of the product under study,
                 its utility, and the functional unit, defined in time and space need to be determined. To define
                 the functional unity, the following properties need to be considered: functionality, technical
                 quality, additional services, aesthetics, image, costs related to purchase, use, and disposal
                 (UNEP/SETAC, 2009). The definition of the functional unit is a key issue; indeed, in some
                 cases, it is difficult to conceptualize it (Sala et al., 2015), and even if it is required, it does
                 not seem to be a common practice to define it (Arzoumanidis et al., 2018). In addition, the
                 following actions need to be conducted (UNEP/SETAC, 2009):

                 – Determine the unit processes to be included in the assessment, namely the system
                   boundaries.
                 – Organize data collection; identify which data will be collected, for instance generic or
                   specific data.
                 – Specify impact categories and subcategories.
                 – Define the stakeholders involved and the type of critical review, if needed.
                 – Define the types of impact to be evaluated and the related indicators and methods.
                 – Define allocation procedures.
                 – Plan the interpretation and identify assumptions, limitations, analyze data quality.
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