Improving the PSILCA Database Approach for Social Life Cycle Assessment: A Case of End-of-Life Electric Vehicle Policies

Heng Yi Teah; Suyi Yang; Jiaqi Yang; Eri Amasawa; Yasunori Kikuchi

doi:10.55845/jos-2025-1237

Authors

Heng Yi Teah Presidential Endowed Chair for "Platinum Society", Organization for Interdisciplinary Research Project, The University of Tokyo
Suyi Yang Department of Chemical System Engineering, The University of Tokyo
Jiaqi Yang Sustainable Lab Inc., FINOLAB
Eri Amasawa Waseda Center for a Carbon Neutral Society, Waseda University
Yasunori Kikuchi Presidential Endowed Chair for "Platinum Society", Organization for Interdisciplinary Research Project, The University of Tokyo; Department of Chemical System Engineering, The University of Tokyo; Institute for Future Initiatives, The University of Tokyo; UTokyo LCA Center for Future Strategy, The University of Tokyo

DOI:

https://doi.org/10.55845/jos-2025-1237

Keywords:

Social Risk Assessment, Reference Scale Assessment, Worker Well-being, Worker Hours, PSILCA Database, Critical Battery Materials, Battery Recycling

Abstract

Social life cycle assessment (S-LCA) is rapidly evolving to complement sustainability assessment beyond environmental impact. This study focuses on improving the Product Social Impact Life Cycle Assessment (PSILCA) database approach through a case study on end-of-life electric vehicle policies in Japan. We show that the social risks associated with acquiring recycled battery materials are lower compared to importing virgin materials, thereby supporting the regulation on electric vehicle battery recycling. The social risks increase when recycling occurs in less developed countries such as Pakistan. Additionally, we point out critical methodological issues when using the PSILCA database. The built-in method weights non-worker stakeholders based on worker hours, and underestimates unavailable inventory data, and inconsistently groups social indicators. We address the issues by proposing a screening method for key supply chain activities and removing inapplicable indicators. The results become more accurate and easier to interpret. Overall, the method guides future S-LCA studies with an improved PSILCA database approach.

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