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Lithium Supply Chain Optimization: A Global Analysis of Critical Minerals for Batteries

Erick C. Jones ()
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Erick C. Jones: Department of Industrial, Manufacturing, and Systems Engineering, College of Engineering, University of Texas at Arlington, 701 S Nedderman Dr, Arlington, TX 76019, USA

Energies, 2024, vol. 17, issue 11, 1-31

Abstract: Energy storage is a foundational clean energy technology that can enable transformative technologies and lower carbon emissions, especially when paired with renewable energy. However, clean energy transition technologies need completely different supply chains than our current fuel-based supply chains. These technologies will instead require a material-based supply chain that extracts and processes massive amounts of minerals, especially critical minerals, which are classified by how essential they are for the modern economy. In order to develop, operate, and optimize the new material-based supply chain, new decision-making frameworks and tools are needed to design and navigate this new supply chain and ensure we have the materials we need to build the energy system of tomorrow. This work creates a flexible mathematical optimization framework for critical mineral supply chain analysis that, once provided with exogenously supplied projections for parameters such as demand, cost, and carbon intensity, can provide an efficient analysis of a mineral or critical mineral supply chain. To illustrate the capability of the framework, this work also conducts a case study investigating the global lithium supply chain needed for energy storage technologies like electric vehicles (EVs). The case study model explores the investment and operational decisions that a global central planner would consider in order to meet projected lithium demand in one scenario where the objective is to minimize cost and another scenario where the objective is to minimize CO 2 emissions. The case study shows there is a 6% cost premium to reduce CO 2 emissions by 2%. Furthermore, the CO 2 Objective scenario invested in recycling capacity to reduce emissions, while the Cost Objective scenario did not. Lastly, this case study shows that even with a deterministic model and a global central planner, asset utilization is not perfect, and there is a substantial tradeoff between cost and emissions. Therefore, this framework—when expanded to less-idealized scenarios, like those focused on individual countries or regions or scenarios that optimize other important evaluation metrics—would yield even more impactful insights. However, even in its simplest form, as presented in this work, the framework illustrates its power to model, optimize, and illustrate the material-based supply chains needed for the clean energy technologies of tomorrow.

Keywords: lithium; critical minerals; supply chain optimization; electric vehicles; circular economy; energy storage; batteries; macroenergy systems; clean energy transition; mathematical optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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