Abstract
In a circular economy, recovering materials from end of life (EoL) products without an appropriate estimation of the cost and revenue may result in unnecessary economic loss. Although research has been carried out on the economic analysis of value recovery from EoL products, few quantitative approaches have examined generalized revenue-cost models. To fill this gap, this study proposes an empirical model to identify economic feasibility of product recycling by integrating machine learning and the Sherwood principle. A boundary that differentiates profitable and non-profitable EoL products for material recycling is quantified and visualized. It is envisioned that the proposed method can be adopted in other circular economy domains for better decision-making.
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