Customized scoring and weighting approaches for quantifying and aggregating results in social life cycle impact assessment

In social life cycle assessment (SLCA), to measure the social performance, it is necessary to consider the subcategory indicators related to each stakeholder dimension, such as workers, local community, society, consumers and value chain participants. Current methods in SLCA scientific literature consider a standard arbitrary linear score set to translate qualitative performances into a quantitative assessment for all subcategory indicators, i.e., it translate a A, B, C, D scoring into a 4, 3, 2, 1 ordinal scale. This assumption does not cover the complexity of the subcategory indicators in the social life cycle assessment phase. The aim of this paper is to set out a customized scoring and weighting approach for impact assessment in SLCA beyond the assumption of arbitrary linearity and equal weighting.

This method overcomes the linearity assumption and develops specific value functions for each subcategory indicator and an approach to establish the weighting factors between the indicators for each social dimension (workers, local community, and society). The value function and weighting factors are based on the considered opinions of SLCA experts in Québec.

The results show that value functions with different shapes used to score the performance of the product within each subcategory indicator influence SLCA results and have the potential to reverse the conclusions. The customized score is more realistic than the linear score because it can better capture the complexity of the subcategory indicators based on SLCA expert judgment.

Our approach addresses a methodological weakness of the impact assessment phase of SLCA through a more representative performance of the potential social impacts based on the judgment of the SLCA expert rather than a simplified assumption of linearity and equal weighting among indicators. This approach may be applied to all types of product systems.

The value functions and weighting factors cannot be generalized for all cases and the proposed approach must be adapted for each study. We stopped at the aggregation of the subcategory indicators based on expert judgment at the stakeholder level. If a complete aggregation in a single score is required, we recommend developing a framework that accounts for the value judgment of the decision-maker rather than the SLCA expert.