A comprehensive framework covering Life Cycle Sustainability Assessment, resource circularity and criticality

Life Cycle Sustainability Assessment (LCSA) is a recognised methodology for analyzing and communicating the sustainability of product systems. However, the fragmented analysis of the environmental, social and economic dimensions of sustainability remains an obstacle to its wider application. In addition, the increasing demand for raw materials leads to resource availability issues that are not necessarily addressed by LCSA, but are addressed by Criticality and Circularity (here called C2s) assessment of materials. Based on these methodologies, we propose an integrated comprehensive LCSA framework that includes criticality and circularity assessments (here called LC3SA) and addresses the issue of inconsistencies, such as overlaps. To do this, we review the most relevant literature on the topics and analyze the scope of the LCSA and C2 methodologies in detail. We analyze how the definition of the product system is dealt with, how the methodologies view the product system, including their similarities and differences, and what the ultimate goal of an integrated assessment is. The analysis shows that all the assessment methods look at the product system from different perspectives. That is, they use different approaches, different system boundaries and different data and information. We argue that the consistency of an integrated assessment does not necessarily depend on the alignment of these perspectives. It mainly concerns the consistent definition of the product system, including the collection of available data and information to characterizes the system. Subsequently, the definition of indicators based on the areas of protection (AoP) is key to identify, understand and track the cause-effect mechanisms to improve the chances of making conscious choices towards a more sustainable system. Based on the discussion and conclusion of the analysis of each of these features, we propose a stepwise LC3SA framework. We argue that each of the steps improves the interconnectedness, consistency, credibility and transparency of the assessment by considering common modelling assumptions. However, further research is needed to address data gaps or issues in the interpretation of results.