Global uranium market dynamics: analysis and future implications
Bibliographic record
Abstract
This paper analyzes the global uranium market and assesses whether future supply can meet growing demand through 2050, focusing on market and geopolitical drivers. A dual approach was used, combining econometric analysis and uranium supply curve modeling. The econometric analysis examines the long-term relationship between prices and production volumes, using multiple methods to ensure robustness. Supply curve modeling shows how uranium availability changes with price, which helps gauge market resilience under various conditions. The main finding is a significant gap between projected uranium supply and demand, particularly in medium and high-demand scenarios, with a potential shortage emerging as early as 2035. By 2050, Kazakhstan and Canada are expected to dominate the uranium export market. Political and energy security concerns may lead to new global alliances and trade routes to meet the growing demand for nuclear energy. The study also highlights the International Atomic Energy Agency’s outlook, emphasizing that primary mining will remain the dominant source of uranium, despite contributions from secondary sources. For policymakers, the study stresses the need for strategic interventions, including re-evaluating production and export policies in uranium-rich nations and developing effective strategies to secure supply. Findings offer key insights into market dynamics and ensure nuclear energy's sustainability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".