Robust multi-criteria weighting in comparative LCA and S-LCA: A case study of sugarcane production in Brazil

Abstract

This article presents a novel multi-criteria decision analysis (MCDA) model for performing robust indicator weighting in Life Cycle Assessment (LCA) and Social Life Cycle Assessment (S-LCA). This model integrates stochastic weights analysis with preference information that utilizes the value judgements of decision makers, benefitting from the diversity of interests and familiarities of decision makers regarding each indicator. The model considers all decision makers on an equal basis but does not assume they have the same importance. The MCDA model was applied to support the evaluation of the overall environmental and social impacts of manual and mechanical sugarcane harvesting in Brazil based on LCA and S-LCA. Brazilian experts were surveyed on the weights of relevant environmental and social indicators. The novel MCDA approach explores all the possible convex combinations of the weights provided by the surveyed group. The results of the MCDA model show that mechanical harvesting compared to manual harvesting had lower environmental life cycle impacts at the end-point level and better social impacts for all these convex combinations. Decision-making based on environmental impacts at the mid-point level is less clear: manual harvesting is more likely (67% of the convex combinations of the weights) to be considered better than mechanical harvesting; but the advantage of mechanical harvesting over manual harvesting can be greater than the reverse (almost twice as large). This article recommends presenting both mid-point and end-point LCIA results for a thoroughly informed decision-making. The MCDA model developed in this article can also be used to support weighting in future comparative LCA or S-LCA studies.