Copper ore is a non-renewable resource with lower ore grades, which means that the extraction of more rock material is required to produce the same amount of copper, implying a greater consumption of materials, reagents, water, and energy. Since there is a greater amount of copper sulfide present in nature, concentration using the bubble flotation method will generate a greater number of tailings. This article discusses the environmental issues resulting from tailings dams and how multi-criteria decision analysis can help prioritize those sites in order to promote circular economy measures to compensate for and reduce the impacts of this type of waste generated by the copper mining industry. This work aims to contribute to this purpose by taking information from abandoned and non-active tailings, which are currently present as a result of the lack of regulations in times prior to environmental obligations and because they are metallurgical waste from old operations that had metal recovery rates that were much lower than the current rates. We propose a model based on the multi-criteria Promethee method to prioritize the tailings dams according to the commercial value of the existing materials in the deposits. A case study with an application of the model to 103 dams in the mining region of Coquimbo in northern Chile is shown.

The mining industry generates substantial volumes of operational waste, including lubricant oils, lead-acid batteries, steel scrap, and end-of-life tires (ELTs). Their valorization is increasingly required under Extended Producer Responsibility (EPR) frameworks, but Centre for Innovation in Technology and Design of Materials for the Built Environment—CIMAC_ANID CTI250005 investment decisions are constrained by limited capital and competing sustainability objectives. This study proposes a dynamic Mixed-Integer Linear Programming (MILP) model to optimize a multi-waste valorization investment portfolio for the copper industry. The model maximizes Net Present Value (NPV) over a multi-period horizon while satisfying EPR targets, capital constraints, and sustainability requirements. Using the ε-constraint method, it quantifies trade-offs between economic performance and social value by incorporating permanent local employment as a binding constraint. Applied to a single case study of a Chilean SX–EW plant, the model selects an Oil Filtration Plant, a Scrap Recycling Yard, and an ELT Pyrolysis Plant, achieving a maximum NPV of USD 5.363 million. Sensitivity analysis of the employment target identifies three decision zones: up to 18 jobs, the target is not binding and the NVP-optimal portfolio is unchanged; between 19 and 26 jobs, the Battery Processing Plant must also be built, reducing NPV by approximately USD 0.49 million (9%); above 26 jobs, the problem becomes infeasible. The framework integrates economic, environmental, and social dimensions, providing a decision-support tool for EPR compliance and sustainable waste valorization planning.