Global Trend Analysis and Forecasting Model Construction of Mineral Resource Markets

Abstract

Global economic expansion and technological innovation have driven sustained growth in demand for mineral resources, making accurate analysis and forecasting of their market trends a core need for policymakers, investors, and industry practitioners. This study focuses exclusively on strategic minerals (lithium, cobalt, rare earths)—core raw materials for green technologies such as electric vehicle batteries and wind power equipment—and proposes a specialized forecasting model integrating econometrics and machine learning to provide targeted decision support for stakeholders.

First, based on 1990–2023 specialized data from authoritative institutions (World Bank, IMF, USGS, IEA), including production, consumption, trade, prices of strategic minerals, and green technology indicators (e.g., electric vehicle sales, wind power installed capacity), this study uses econometric methods to systematically analyze consumption patterns and trade characteristics of the three minerals. Second, key empirical findings are embedded into a machine learning framework, integrating three core factors—green technology penetration, resource-country geopolitical policies, and macroeconomic indicators (U.S. dollar index, global GDP)—to optimize short-term (1–3 years) and long-term (5–10 years) forecasting accuracy.

The model clarifies quantitative impacts of green technologies on demand (e.g., a 10% increase in electric vehicle penetration drives a 15%±2% growth in lithium demand). Two scenarios—"EU carbon tariff adjustment" and "Congo (Kinshasa) cobalt supply disruption"—are designed, combined with historical cases (2022 cobalt mine ban in Congo, 2021 China rare earth export quota adjustment) to quantify market resilience. Finally, a risk assessment tool for strategic minerals is developed, providing scientific and practical references for global mineral resource management and investment decisions.