Optimizing the Copper Recycling Network in Europe under the Critical Raw Materials Act (CRMA)

Assessing the Impact of the CRMA on Copper Supply Chains

Master Thesis (2025)
Author(s)

V.C. van Citters (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

P.S.A. Stokkink – Mentor (TU Delft - Technology, Policy and Management)

A.J. van Binsbergen – Mentor (TU Delft - Civil Engineering & Geosciences)

L.A. Tavasszy – Graduation committee member (TU Delft - Civil Engineering & Geosciences)

Faculty
Civil Engineering & Geosciences
More Info
expand_more
Publication Year
2025
Language
English
Graduation Date
15-04-2025
Awarding Institution
Delft University of Technology
Programme
Transport, Infrastructure and Logistics
Faculty
Civil Engineering & Geosciences
Downloads counter
215
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

The European Union’s (EU’s) Critical Raw Materials Act (CRMA) aims to strengthen the EU’s resource resilience by increasing the autonomy of Critical Raw Material (CRM) supply through EU-based extraction, processing, and recycling, thereby reducing external dependencies and promoting a circular economy. The CRMA mandates, among others, that at least 25% of CRMs come from EU-based recycling. Copper, a key CRM, faces growing demand, and with mining alone, future supply will not fulfill demand. This research analyzed the copper supply chain before and after the CRMA. It was found that recycling is the most promising solution for adherence to the CRMA and a sustainable and resilient future. The study develops a Mixed Integer Linear Programming (MILP) model to optimize the European copper recycling network under the CRMA recycling requirement. The model minimizes total costs while determining the optimal number, locations, and capacities of recycling facilities. The model is tested through a case study of a natural resource company and is usable to similar firms. Results identify four optimal facility locations: Stuttgart, Geithain, Bologna, and Barcelona, which were selected based on, among others, geographic centrality and supply and demand quantities. Sensitivity and scenario analyses reveal the network is vulnerable to facility outages, supply shortages, and significant demand increases. Decentralizing the network by adding a fifth facility improves resilience with limited additional cost.

Files

License info not available