Forecasting Rare Earth Element Demands for Clean Energy Technologies with the Bass Diffusion Model

Published

May 7, 2024

Author(s)

Nehika Mathur, Thomas Maani, Chuanbing Rong, John Sutherland

Abstract

The push to decarbonize has spurred the demand for clean energy technologies such as electric vehicles (EVs) and wind turbines (WTs). These technologies rely on rare earth permanent magnets (REPMs), namely Neodymium-Iron-Boron (NdFeB) magnets that in turn rely on rare earth elements (REEs), including Neodymium (Nd), and Dysprosium (Dy). As the demand for clean energy technologies increases, so will the demand for Nd and Dy-containing REPMs. Both Nd and Dy are critical elements that are prone to supply chain risks. As our reliance on them increases it becomes essential to anticipate future market dynamics for these elements. This paper aims to address the current gap in the literature in the context of forecasting future Nd and Dy demand quantities. The Bass Diffusion Model is a widely used approach to forecast the adoption of new products or technologies and, to predict demand trends. Over the years, the model has found applicability in a wide variety of sectors including consumer durables, medical services, and agricultural innovations. This paper demonstrates the use of the Bass Diffusion Model to estimate future global Nd and Dy demands, enabling a better understanding of swiftly evolving clean energy technology and REPM-related market dynamics.

Proceedings Title

Procedia CIRP 2024

Conference Dates

June 19-21, 2024

Conference Location

Turin, IT

Conference Title

CIRP LCE 2024

Pub Type

Conferences

Download Paper

https://doi.org/10.1016/j.procir.2024.01.009

Local Download

Keywords

Rare Earth Elements, Neodymium, Dysprosium, Bass Diffusion Model, Clean energy technologies

Citation

Mathur, N. , Maani, T. , Rong, C. and Sutherland, J. (2024), Forecasting Rare Earth Element Demands for Clean Energy Technologies with the Bass Diffusion Model, Procedia CIRP 2024, Turin, IT, [online], https://doi.org/10.1016/j.procir.2024.01.009, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956730 (Accessed June 29, 2024)