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Title
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Resource effectiveness of the European automotive sector : a statistical entropy analysis over time
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Author
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Abstract
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| The European automotive sector is faced with potentially disruptive challenges. In particular, the projected increase in the share of electric vehicles (EVs) and calls to prepare for the implementation of more circular economy (CE) strategies are increasingly demanding systemic adaptations. Given the goals of the CE, the adaptations should enable a maximal preservation of the function and value of products (e.g. extension of lifetime), components (e.g. reuse of parts) and materials (e.g., material recycling), thus saving on the energy, materials and effort that would be required to restore the lost functionalities. In this context, statistical entropy analysis (SEA) is proposed as a methodology to assess the effort needed for preserving and restoring functionality at different product, component and material life cycle stages. Effort is measured as changes in statistical entropy that are caused by concentration and dilution activities in the production - consumption - End-of-Life (EoL) system. SEA was applied to a generic model of the European automotive system, in combination with a stock-driven model and a material flow analysis (MFA), allowing statistical entropy changes to be projected over time. The paper demonstrates how SEA can facilitate decision making on the transition towards a more circular economy by quantifying the effects of particular CE strategies and their combinations. The results show that without any additional system adaptations, an increasing share of EVs towards the year 2050 will lead to substantially increased effort in production as well as end-of-life vehicle treatment. |
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Language
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English
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Source (journal)
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Resources, conservation and recycling. - Amsterdam
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Publication
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Amsterdam
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2021
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ISSN
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0921-3449
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DOI
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10.1016/J.RESCONREC.2021.105558
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Volume/pages
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169
(2021)
, 17 p.
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Article Reference
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105558
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ISI
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000657320800074
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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