Graphite and manganese mining in the U.S.: Proposed projects and federal battery mineral policies
Bibliographic record
Abstract
Demand is increasing for the minerals used in energy transmission and storage, such as graphite and manganese in batteries. These two commodities are produced in relatively few locations around the world, and major consumers such as the European Union (EU) and the United States (US) are highly reliant on imports, especially for refined products from China. In the US, graphite and manganese are not currently mined. In efforts to avoid geopolitical supply chain disruptions, the U.S. federal government has implemented new policies promoting domestic mining and processing of critical minerals. This contribution reviews the geological resources and development status of graphite and manganese projects in the U.S. and examines the impacts of policies on U.S. mining and processing of these two commodities. Measured, indicated, and inferred resources of 20.9 Mt are known for graphite, including 3.7 Mt of reserves. Resources of 50.5 Mt are known for manganese, with no established reserves. Graphite exploration is in advanced stages in Alaska and Alabama and in early stages in Montana and New York. There is one operating processing plant for natural graphite in Louisiana and one is under construction in Alabama. Synthetic graphite is produced in one plant in New York, and synthetic graphite plants are under construction in Georgia and Tennessee. One manganese mine is in permitting review in Arizona, and manganese exploration is underway in Minnesota and Arizona. Historic manganese mines in Arkansas, Maine, Colorado, Nevada, and Montana are unlikely to reopen in the near term, and exploration for manganese on the seafloor is in very early stages. The potential for known resources to meet demand is modeled based on three energy transition scenarios and a range of battery share assumptions, showing that known graphite resources are likely sufficient to meet future U.S. demand for hundreds of years, whereas manganese resources are sufficient to meet only decades of projected U.S. demand. Supply bottlenecks will arise if these projects do not progress into development. Policies intended to spur domestic production include the Biden administration's Bipartisan Infrastructure Law of 2021, the 2022 authorization of the Defense Production Act, the 2022 CHIPS in Science Act, the 2022 Inflation Reduction Act, as well as the second Trump administration's 2025 executive orders on Unleashing American Energy and Unleashing America's Offshore Minerals and Resources. Potential shortfalls include overemphasis on processing compared to mining, insufficient incentives, and a contentious regulatory framework for both terrestrial and deep sea mining. • Graphite and manganese are geologically available in the United States. • New policies are aimed at increasing domestic production. • Federal investment and research are needed to enable responsible development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".