Life cycle inventories of global metal and mineral supply chains: a comprehensive data review, analysis and processing

Author(s):
Lai, F.; Muller, S.; Philippe, A.; Istrate, R.; Miranda Xicotencatl, B.; Mansouri Aski, A.; Mas Fons, A.; Segura-Salazar, J.; Santillán Saldivar, J.; Cimprich, A.; Northey, S.; Silva Lima, L. da; Boone, L.; Yokoi, R.; Islam, K.; Paschalidou, I.; Cerdas, F.; Balboa-Espinoza, V.; Koyamparambath, A.; Hennioui, D.; Collignon, V.; Reys, A.; Ngadi Sakatadi, G.; Dewulf, J.; Steubing, B.; Helbig, C.; Lefebvre, G.; Blengini, G. A.; Superti, V.; Motoshita, M.; Sonnemann, G.; Awuah-Offei, K.; Young, S. B.; Murakami, S.; Beylot, A.
Published
2025
Publication type
Article / Letter to editor

Abstract

Reliable life cycle inventory (LCI) data are key to consistent life cycle assessment (LCA) results. This study provides a comprehensive and up-to-date overview of existing public LCI data related to metals and minerals production. It aims to deliver LCI models representing current supply chains and markets. For that purpose, this study conducts an in-depth analysis (including data quality) of 285 LCI datasets drawn from 130 different LCA studies related to metals and minerals. Following a selection process and a harmonised data compilation, processing and modelling approach, 220 individual LCI datasets were developed, covering 53 metal and mineral elements and distinguishing 163 production routes differentiated from geographical, geological, technological or material perspectives. Finally, these LCI datasets were gathered into market datasets, depicting global supply mixes. Elements such as germanium or manganese showed a limited market coverage, contrary to others such as lithium or aluminium.

Metals and mineralsSupply chainsData qualityLife cycle assessmentEnvironmental impactsFAIR dataneodymiumlithiumcobaltgraphiteSUSMAGPROMaDiTraCe
Date issued
2025-11-29
Language
English
Publisher
Elsevier BV
Is part of
Resources, Conservation and Recycling (2025), 108709
hdl
1887/4284358
doi
10.1016/j.resconrec.2025.108709
URL
https://doi.org/10.1016/j.resconrec.2025.108709
Dataset
https://doi.org/10.5281/zenodo.15075067

Funding

Sponsorship
Horizon 2020(H2020)
Grant number
821114