IDEAS home Printed from https://ideas.repec.org/a/gam/jresou/v8y2019i2p58-d218718.html
   My bibliography  Save this article

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

Author

Listed:
  • Eliette Restrepo

    (Empa, Swiss Federal Laboratories for Material Science and Technology, CH-9014 St. Gallen, Switzerland
    Industrial Ecology Programme and Department of Energy and Process Engineering, Norwegian University of Science and Technology–NTNU, NO-7491 Trondheim, Norway)

  • 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)

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.

Suggested Citation

  • Eliette Restrepo & Amund N. Løvik & Rolf Widmer & Patrick Wäger & Daniel B. Müller, 2019. "Historical Penetration Patterns of Automobile Electronic Control Systems and Implications for Critical Raw Materials Recycling," Resources, MDPI, vol. 8(2), pages 1-20, March.
  • Handle: RePEc:gam:jresou:v:8:y:2019:i:2:p:58-:d:218718
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2079-9276/8/2/58/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2079-9276/8/2/58/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wei, Pengfei & Lu, Zhenzhou & Song, Jingwen, 2015. "Variable importance analysis: A comprehensive review," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 399-432.
    2. Abel Ortego & Alicia Valero & Antonio Valero & Eliette Restrepo, 2018. "Vehicles and Critical Raw Materials: A Sustainability Assessment Using Thermodynamic Rarity," Journal of Industrial Ecology, Yale University, vol. 22(5), pages 1005-1015, October.
    3. Yuna Seo & Shinichirou Morimoto, 2017. "Analyzing Platinum and Palladium Consumption and Demand Forecast in Japan," Resources, MDPI, vol. 6(4), pages 1-13, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marta Iglesias-Émbil & Alejandro Abadías & Alicia Valero & Guiomar Calvo & Markus Andreas Reuter & Abel Ortego, 2022. "Criticality and Recyclability Assessment of Car Parts—A Thermodynamic Simulation-Based Approach," Sustainability, MDPI, vol. 15(1), pages 1-22, December.
    2. Felipe Bitencourt de Oliveira & Anders Nordelöf & Maria Bernander & Björn A. Sandén, 2024. "Assessing Metal Use and Scarcity Impacts of Vehicle Gliders," Circular Economy and Sustainability, Springer, vol. 4(3), pages 1851-1875, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Asma Shaheen & Javed Iqbal, 2018. "Spatial Distribution and Mobility Assessment of Carcinogenic Heavy Metals in Soil Profiles Using Geostatistics and Random Forest, Boruta Algorithm," Sustainability, MDPI, vol. 10(3), pages 1-20, March.
    2. Shang, Xiaobing & Su, Li & Fang, Hai & Zeng, Bowen & Zhang, Zhi, 2023. "An efficient multi-fidelity Kriging surrogate model-based method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Matteo Rucco & Giovanna Viticchi & Lorenzo Falsetti, 2020. "Towards Personalized Diagnosis of Glioblastoma in Fluid-Attenuated Inversion Recovery (FLAIR) by Topological Interpretable Machine Learning," Mathematics, MDPI, vol. 8(5), pages 1-27, May.
    4. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    5. Mohamed Zine & Fouzi Harrou & Mohammed Terbeche & Mohammed Bellahcene & Abdelkader Dairi & Ying Sun, 2023. "E-Learning Readiness Assessment Using Machine Learning Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
    6. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2021. "Cohort Shapley value for algorithmic fairness," Papers 2105.07168, arXiv.org.
    7. Cheng, Kai & Lu, Zhenzhou, 2018. "Sparse polynomial chaos expansion based on D-MORPH regression," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 17-30.
    8. Wenxuan Wang & Hangshan Gao & Pengfei Wei & Changcong Zhou, 2017. "Extending first-passage method to reliability sensitivity analysis of motion mechanisms," Journal of Risk and Reliability, , vol. 231(5), pages 573-586, October.
    9. Masayoshi Mase & Art B. Owen & Benjamin B. Seiler, 2022. "Variable importance without impossible data," Papers 2205.15750, arXiv.org, revised Apr 2023.
    10. Lambert, Romain S.C. & Lemke, Frank & Kucherenko, Sergei S. & Song, Shufang & Shah, Nilay, 2016. "Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 128(C), pages 42-54.
    11. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    12. Yun, Wanying & Lu, Zhenzhou & Jiang, Xian, 2019. "An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 174-182.
    13. Kleijnen, Jack P.C., 2017. "Regression and Kriging metamodels with their experimental designs in simulation: A review," European Journal of Operational Research, Elsevier, vol. 256(1), pages 1-16.
    14. McFarland, John & DeCarlo, Erin, 2020. "A Monte Carlo framework for probabilistic analysis and variance decomposition with distribution parameter uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 197(C).
    15. F. P. Brito & Jorge Martins & Francisco Lopes & Carlos Castro & Luís Martins & A. L. N. Moreira, 2020. "Development and Assessment of an Over-Expanded Engine to be Used as an Efficiency-Oriented Range Extender for Electric Vehicles," Energies, MDPI, vol. 13(2), pages 1-18, January.
    16. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    17. Ehre, Max & Papaioannou, Iason & Straub, Daniel, 2020. "Global sensitivity analysis in high dimensions with PLS-PCE," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
    18. Matthias Bogaert & Michel Ballings & Martijn Hosten & Dirk Van den Poel, 2017. "Identifying Soccer Players on Facebook Through Predictive Analytics," Decision Analysis, INFORMS, vol. 14(4), pages 274-297, December.
    19. Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
    20. Zhou, Xiaoyi & Lu, Pan & Zheng, Zijian & Tolliver, Denver & Keramati, Amin, 2020. "Accident Prediction Accuracy Assessment for Highway-Rail Grade Crossings Using Random Forest Algorithm Compared with Decision Tree," Reliability Engineering and System Safety, Elsevier, vol. 200(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jresou:v:8:y:2019:i:2:p:58-:d:218718. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.