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• W3036685783 endingPage "104925" @default.
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• W3036685783 abstract "Abstract In a circular economy (CE), materials, components and products should be kept at the highest level of functionality, while phenomena like dilution, mixing and contamination, often referred to as the loss of resources, should be avoided. One method that can assess the performance of systems to concentrate or avoid dilution of resources is Statistical Entropy Analysis (SEA). Up till now, the method has been applied on the substance level (elements and compounds) only, but showed its applicability to various scales and a variety of systems. Further development of the method allowed to consider information on the product, component and material levels, which makes the method applicable to different combinations of CE strategies, both destructive (e.g. recycling) and non-destructive (e.g. reuse). The method is demonstrated on a simplified vehicle life-cycle, which is modeled through four component groups and six materials. It shows that the method allows to evaluate different CE strategies and identify critical stages which lead to the most severe resource and functionality losses. Based on the method's results, it is possible to determine a perfect circularity reference level, representing a system state that preserves functionality and avoids resource losses. The introduction of a circularity reference level enables the establishment of a framework for resource effectiveness in which diluting and concentrating effects of activities (e.g. sorting) are quantified. The distance of a system to an ideal circular state determines the deviation from a resource-effective system that maintains the original product functionality over a maximum period of time, with minimal efforts." @default.
• W3036685783 created "2020-06-25" @default.
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• W3036685783 date "2020-10-01" @default.
• W3036685783 modified "2023-10-03" @default.
• W3036685783 title "Evaluation of the resource effectiveness of circular economy strategies through multilevel Statistical Entropy Analysis" @default.
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• W3036685783 doi "https://doi.org/10.1016/j.resconrec.2020.104925" @default.
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