Historical Penetration Patterns of Automobile Electronic Control Systems and Implications for Critical Raw Materials Recycling

Eliette Restrepo, Amund N. Løvik, Rolf Widmer, Patrick Wäger and Daniel B. Müller
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Eliette Restrepo: Empa, Swiss Federal Laboratories for Material Science and Technology, CH-9014 St. Gallen, Switzerland
Amund N. Løvik: Empa, Swiss Federal Laboratories for Material Science and Technology, CH-9014 St. Gallen, Switzerland
Rolf Widmer: Empa, Swiss Federal Laboratories for Material Science and Technology, CH-9014 St. Gallen, Switzerland
Patrick Wäger: Empa, Swiss Federal Laboratories for Material Science and Technology, CH-9014 St. Gallen, Switzerland
Daniel B. Müller: Industrial Ecology Programme and Department of Energy and Process Engineering, Norwegian University of Science and Technology–NTNU, NO-7491 Trondheim, Norway

Resources, 2019, vol. 8, issue 2, 1-20

Abstract: Car electronics form a large but poorly utilized source for secondary critical raw materials (CRMs). To capitalize on this potential, it is necessary to understand the mechanism in which car electronics enter and exit the vehicle fleet over time. We analyze the historical penetration of selected car electronic control systems (ECS) in 65,475 car models sold in the past 14 years by means of statistical learning. We find that the historical penetration of ECS tends to follow S-shaped curves, however with substantial variations in penetration speed and saturation level. Although electronic functions are increasing rapidly, comfort-related ECS tend to remain below 40% penetration even after 14 years on the market. In contrast, safety regulations lead to rapid ECS penetration approaching 100%, while environmental emission regulations seem to indirectly push related ECS to a medium penetration level (e.g., growing to 60% after six years). The trend towards integration of individual ECS poses long-term challenges for car electronics dismantling and recycling. Monitoring the ECS embedded in new cars, such as carried out in this study, can inform timely updates for such strategies. The results also provide a framework for developing scenarios to identify related future CRM stocks and flows.

Keywords: car electronics; technological diffusion; critical raw materials; urban mine; machine learning; statistical learning (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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