Evaluation of metals mining and processing energy consumption and potential metals supply–demand mismatch towards 2100
Bibliographic record
Abstract
Introduction: Metals are essential for the global energy transition. Energy is required to both extract and process those metals to use them in the global economy. Given the soaring demand for metals until the end of the century, it is unclear whether the mining and metallurgical industry across producing countries will be able to cope with such pressure on supply. We estimate the yearly surplus and deficit of 50 resources across three scenarios of evolution of society as well as global energy consumption for mining and processing those resources. Methods: We develop the Within Limits Metal Flows (WILMFlo) model which is a stock-driven dynamic material flow analysis of resources over their life cycle. WILMFlo links the extraction of major metals, such as copper, to their byproducts, such as tellurium, using byproduct-to-host ratios. By considering yearly mining capacity, it allows to identify potential discrepancies between demand and supply between 2025 today to 2100 in three socioeconomic scenarios, including a Net Zero scenario. We also explore the evolution of energy consumption for mining resources and for materials fabrication at the global level. Results: First, we quantify the potential discrepancies between demand and supply of resources, including yearly surplus and deficit. Second, we derive the global metal mining and processing energy cost between 2025 and 2100. In the Net Zero scenario, we identify a potential deficit of lithium between 2028 and 2038. In the three scenarios, currently classified critical elements such as indium, rhenium or gallium are in situation of oversupply over the 21st century. Discussions and conclusions: The substitution of some energy transition metals such as copper, cobalt and lithium will be key to meet the global demand for mobility and energy storage. Global mining energy consumption is likely to rise during the 21st century but will still remain minor compared to materials processing energy consumption which is two to six times higher than global mining energy consumption. The WILMFlo framework allows to deepen the understanding of energy transition metals potential supply limits and to identify the greatest potential to improve energy efficiency on the primary supply chain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".