Date of Award
2024
Document Type
Open Access Master's Report
Degree Name
Master of Science in Mining Engineering (MS)
Administrative Home Department
Department of Geological and Mining Engineering and Sciences
Advisor 1
Snehamoy Chatterjee
Committee Member 1
Luke Bowman
Committee Member 2
Nathan Manser
Abstract
The transition to electric vehicles (EVs) is pivotal in achieving global climate goals, with nations aiming to integrate a significant portion of new passenger vehicles as electric by 2030. This shift towards electric mobility includes a notable rise in battery-electric, plug-in hybrid, and fuel-cell electric vehicles to curb carbon emissions and foster sustainable transportation. Central to this transition is the critical role of lithium, often referred to as the "white gold of the 21st century," in electric vehicle batteries, especially rechargeable lithium-ion batteries renowned for their high energy efficiency, density, and durability. While lithium has historically found extensive use across industries and medical applications, recent market trends have seen a substantial shift toward its dominance in rechargeable batteries. Despite its limited world reserves, the projected escalations in demand, driven primarily by EVs and renewable energy storage systems, highlight the imperativeness of understanding the global lithium demand dynamics and ensuring supply sustainability. Projections from the United Nations suggest that the world population is expected to increase by nearly 2 billion persons in the next 30 years, from the current 8 billion to 9.7 billion in 2050. Hence, accurate forecasting of global lithium demand and pricing over the next three (3) decades is crucial for informed decision-making across economic, governmental, corporate, and societal domains.
Conventional forecasting methods must often be revised to capture long-term projections or adequately model inter-variable relationships. However, the autoregressive distributed lag (ARDL) multivariate method offers a promising alternative, particularly when combined with an error correction model (ECM). ARDL methodologies have found widespread application, demonstrating effectiveness in analyzing both short- and long-run relationships, even amidst structural breaks in data. This study employs the ARDL methodologies to explore the short- and long-run co-integration among key potential variables influencing global lithium production and pricing. The outcome was compared with the autoregressive integrated moving average (ARIMA) univariate model and the multiple linear regression (MLR) models for the most robust model for the analysis. Results indicate that the ARDL model outperformed the other models regarding robust cointegration, enabling forecasts of lithium production demand and pricing dynamics.
The forecasted results indicate a steady increase in global lithium demand over the years, with projections reaching approximately 300,000 tons by 2034, 350,000 tons by 2044, and nearly 380,000 tons by 2053. Conversely, the forecast suggests a declining trend in global lithium pricing, starting from it’s all-time highest value of $68,100 per ton in 2022 and dropping to $30,000 per ton by 2026. However, there is a subsequent gradual increase to slightly above $40,000 per ton by 2031, fluctuating until 2035. Beyond 2036, the pricing will exhibit a smooth upward trend, reaching approximately $50,000 per ton by 2053. Evaluation of the ARDL model's performance under stochastic shocks confirms its robustness in forecasting lithium production and pricing, even under varying levels of volatility. The findings suggest a sustained increase in global lithium demand over the next three decades, necessitating proactive measures to strengthen lithium supply chains and capitalize on potential revenue streams. Moreover, governmental initiatives to expedite mine development could stimulate economic growth and job creation while mitigating potential supply disruptions.
Recommended Citation
Nii- Okai, Enoch, "FORECASTING OF THE NEXT THREE (3) DECADES GLOBAL LITHIUM DEMAND AND PRICING USING MULTIVARIATE STOCHASTIC MODEL.", Open Access Master's Report, Michigan Technological University, 2024.