Supply-Demand Models for Critical Minerals

Supply-demand modeling for critical minerals is both more important and more challenging than for traditional commodities. The energy transition is driving unprecedented demand growth for materials like lithium, cobalt, nickel, graphite, and rare earths, while supply responses are constrained by long development timelines, permitting hurdles, processing bottlenecks, and geopolitical risks. Accurate modeling of supply and demand balances is essential for investment decisions, policy planning, procurement strategy, and project development. Yet the complexity of these markets, the uncertainty surrounding technology adoption curves, and the opacity of key data sources make forecasting inherently difficult.

Demand-Side Modeling

Demand forecasting for critical minerals begins with end-use analysis. Analysts identify the major applications for each mineral, such as electric vehicle batteries for lithium or permanent magnets for rare earths, and then project growth in each application using a combination of policy scenarios, technology roadmaps, and market penetration curves. For battery metals, demand models typically start with forecasts for global electric vehicle sales, then apply assumptions about battery chemistry mix (NMC 811, NMC 622, LFP, solid-state, sodium-ion), average battery capacity per vehicle, and mineral intensity per kilowatt-hour of battery capacity.

These demand projections are layered with forecasts for other applications. Lithium demand, for example, includes not only EV batteries but also consumer electronics, grid-scale energy storage, industrial greases, ceramics and glass, and air treatment. Rare earth demand models must account for wind turbine generators, electric vehicle motors, consumer electronics, defense systems, and industrial catalysts. The challenge lies in modeling how technology shifts and substitution dynamics will affect mineral intensity over time. A shift from NMC to LFP battery chemistry, for instance, significantly reduces cobalt and nickel demand per vehicle while increasing lithium iron phosphate demand.

Supply-Side Modeling

Supply forecasting requires detailed, asset-level analysis. Analysts build databases of existing mines, expansion projects, and development-stage projects for each mineral, then assess the probability and timing of each project reaching production. Key inputs include announced capacity, construction status, permitting progress, financing arrangements, and the track record of the project developer. Historical analysis of mine development timelines reveals systematic optimism bias: projects typically take longer and cost more than initial estimates, and a significant percentage of announced projects never reach production.

For critical minerals, supply modeling must also account for the processing and refining stage, which is often the binding constraint. Even if sufficient ore is mined, global supply of battery-grade lithium hydroxide or cobalt sulfate depends on the availability and location of chemical processing capacity. China's dominance in midstream processing means that supply models must track not only mine output but also the conversion capacity in China and the progress of new facilities being built in Europe, North America, and other regions. Similarly, rare earth supply models must differentiate between mining rare earth ores and separating them into individual oxide products, as separation capacity is even more geographically concentrated than mining.

Market Balance and Price Implications

The core output of supply-demand modeling is a market balance, the projected surplus or deficit between supply and demand for each year in the forecast horizon. A projected deficit signals potential price increases and supply security risks, while a projected surplus suggests price pressure and potential project deferrals. However, interpreting market balance projections requires nuance. Small projected deficits may be resolved through inventory drawdowns, destocking, or demand destruction at higher prices. Projected surpluses may not materialize if producers cut output in response to falling prices, as occurred in the lithium market during 2023-2024 when several high-cost producers curtailed or delayed operations.

Some analysts extend market balance models into price forecasting by applying cost curve analysis. The supply cost curve ranks all current and potential production sources by their operating cost, and the price is assumed to settle at the cost of the marginal producer needed to balance the market. For lithium, this approach suggests that long-run incentive prices must be high enough to justify investment in new hard-rock mining and brine extraction projects with varying cost structures. Cost curve approaches work best in markets with many producers and transparent cost data, but are less reliable for highly concentrated markets like rare earths or cobalt, where a single producer's strategic decisions can override cost-based price signals.

Scenario Analysis and Uncertainty

Given the uncertainties inherent in critical mineral markets, responsible supply-demand modeling relies heavily on scenario analysis. Organizations like the International Energy Agency (IEA), BloombergNEF, Wood Mackenzie, Benchmark Mineral Intelligence, and Roskill (now part of Wood Mackenzie) publish multi-scenario forecasts that typically include a reference case, a high-demand scenario aligned with aggressive decarbonization targets, and a low-demand scenario reflecting slower technology adoption or policy backsliding. The spread between these scenarios can be enormous: IEA projections for lithium demand in 2040, for example, vary by a factor of three or more depending on the assumed speed of the energy transition.

Scenario analysis also applies to the supply side, where geopolitical disruptions, regulatory changes, and technological breakthroughs can alter supply trajectories. Models may include scenarios for Chinese export restrictions on processed materials, the impact of new direct lithium extraction (DLE) technology on brine-based supply, or the effect of Indonesian resource nationalism on nickel availability. The value of scenario modeling lies not in predicting the future with precision but in identifying the key variables that drive market outcomes and stress-testing strategies against a range of plausible futures.

Key Institutions and Data Sources

Several institutions produce widely referenced critical mineral supply-demand models. The IEA's annual Critical Minerals report provides global market balances and scenario projections for key energy transition minerals. Benchmark Mineral Intelligence maintains detailed lithium-ion battery supply chain models covering mine-to-cell material flows. Wood Mackenzie and S&P Global offer asset-level supply databases and integrated market models. CRU Group provides analysis across a range of metals and mining commodities. Government agencies including the U.S. Geological Survey (USGS), Geoscience Australia, and the European Commission's Joint Research Centre also publish supply-demand assessments for critical minerals on their respective national lists.

For market participants, the choice of which models and data sources to rely on is itself a strategic decision. Different providers use different methodologies, cover different scopes, and may have different commercial incentives. Triangulating across multiple sources, understanding their assumptions, and developing internal analytical capabilities are best practices for organizations with significant exposure to critical mineral markets. See Benchmarks and Price Reporting Agencies for more on the data infrastructure that supports these models, and Trade Flows and Customs Codes for information on the trade data used to calibrate supply-demand estimates.