Complete lithium export restrictions from major producers could raise BESS system costs by more than 20% under baseline assumptions, according to a research brief from MIT Leaders for Global Operations. Copper and aluminum disruptions have smaller system impacts. A likelihood-weighted index ranks the risks for developers planning procurement at multi-gigawatt scale.
Click here for a quick project snapshot
MIT Leaders for Global Operations, a US-based dual-degree engineering and MBA programme, has published a research brief that models how disruptions in upstream mineral supply feed into BESS costs. The brief, authored by Matthew Hoel of the programme's Class of 2026, finds that lithium creates the largest exposure. In the model, complete export restrictions from major producers could increase system costs by more than 20% under baseline assumptions.
Copper and aluminum disruptions would produce large commodity price increases but smaller system impacts, because both metals hold a lower cost share within battery cells. The brief notes that lithium-ion BESS deployment is increasing reliance on globally concentrated supplies of lithium, copper, aluminum and graphite, while export restrictions and trade disruptions are becoming more frequent.
The deterministic model uses global export concentration, disruption likelihood and price elasticities to calculate post-disruption mineral prices. Those price changes are then passed through a cost structure that runs from raw materials to cells, battery packs and full systems. Inputs come from UN COMTRADE trade flows and export shares, US Geological Survey disruption likelihood estimates, published price elasticities of supply and demand, and internal company data on BESS cost structure and material cost shares.
A likelihood-weighted disruption index combines probability and impact into a single metric. The index tells rare, severe events apart from risks that persist in the supply chain. Developers can then rank mitigation work by likelihood-adjusted impact, and not by the worst case alone. An interactive tool with user-defined parameters simulates disruption scenarios. At multi-gigawatt deployment scale, low single-digit percentage cost increases translate into significant capital exposure across a project portfolio.
The brief states that the model should be used to compare relative risk, not to predict exact outcomes. It relies on short-run assumptions and simplified market behaviour, and the trade data lacks granularity. Future work could add inventory drawdown, substitution across battery chemistries, production capacity expansion and multi-country disruptions.