The impact of mine ownership on trade of metal ores
Bibliographic record
Abstract
Metals are essential to the global economy, yet traditional criticality assessments, often based solely on the geographic concentration of mining production, overlook the corporate control dimension of risk. Here, we analyzed whether ownership structures affect trade patterns in critical minerals and examined how production and corporate control diverged from 2000 to 2022. We developed a comprehensive country-level dataset using S&P Capital IQ Pro for 12 key metals and metal ores, calculated Herfindahl–Hirschman indices (HHI) for production and ownership, and statistically tested the relationship between foreign mine ownership and international trade flows using logistic and fixed-effects regressions. Finally, we built scenarios for production and ownership in 2030 to match demand estimates from the International Energy Agency (IEA). Results showed only 2%–14% of global ore trade value overlaping with existing foreign ownership ties and no statistically significant relationship between foreign mine ownership and trade flows. Additionally, clear divergences emerged between ownership and production concentration: cobalt production was highly geographically concentrated (HHI of 4602 in 2022) but had dispersed corporate ownership (HHI of 1985), and high-income countries frequently held substantial ownership stakes despite declining shares of actual production. Projected scenarios indicated continued shifts, notably reduced cobalt and lithium production shares in traditional producer countries, offset by growing Canadian and Australian ownership. Although market dynamics do not appear to be influenced by ownership structures today, corporate control remains a potential lever for supply chain disruption. This underscores the need to incorporate ownership into criticality assessments for a more comprehensive understanding of supply risks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".