Weighting and Scoring in Social Life Cycle Assessment

Abstract

Social impact evaluation is one of the cornerstones of products and services sustainability. Social Life Cycle Assessment (S-LCA hereafter) focuses on studying potential social impacts of products’ life cycle. As it is a relatively new analytical approach, no globally shared application tools have been developed for it yet. Communicating S-LCA results to decision-makers in order to promote social sustainable decisions is a challenge because it involves the aggregation of companies’ performances across impact categories through numerical variables based on value-choices. Currently, the weighting process (used for performance aggregation) considered for type I analysis in the literature presents some limits: lack of transparency, implicit choices, no standard weighting method and the failure to take into account the uncertainty of these value choices. This paper aims to address these limits by proposing a standard approach to conduct the weighting process for type I S-LCA. It starts after characterization phase and comprises four stages: (i) impact level scoring, (ii) functional unit aggregation, (iii) weighting factors definition and (iv) performances aggregation across impact categories. This approach is able to consider determinist or stochastic numerical variables, depending on the inclusion or not of the uncertainty associated to people’ value judgments. In terms of results, this paper presents an illustrative case study in order to exemplify how to conduct the weighting process in S-LCA. Considering the results, we identified some limits related to our approach: (i) depending on the subjects involved in S-LCA and the subcategory indicators considered for the assessment, it might not be possible to define standard weighting factors for all case studies; (ii) the type of uncertainty tackled on this approach is only associated with value choices – no other source of uncertainty is addressed and; (iii) the method used to assess qualitative social performances (scoring, check list or social hotspot database) can influence the aggregated social performance of product systems.