Forecast for electricity consumption in the copper mining industry, 2018-2029
Bibliographic record
Abstract
This study forecast the copper mining industry’s electricity consumption in 2018-2029, it would increase from 22.6 to 31.9 TWh (~2.9% average annual growth). In this case, it isestimated that, in order to satisfy the copper mining industry’s demand, it would be necessary to add 1,336 MW in generating capacity between 2019 and 2029. The Antofagasta Region will continue to account for over half of the industry’s consumption, followed by the Atacama, Tarapacá and O'Higgins Regions. However, as from 2024, the share of the Atacama Region is expected to reach over 13% while that of the O’Higgins Region drops to 7%. The Tarapacá Region would show no significant change. The concentration is by far the most important source of expected consumption of electricity throughout the period, accounting for 58% in 2018 and 67% by 2029. The use of seawater is another process where electricity consumption is expected to show an important increase, rising from 4% of total consumption in 2018 to 10% in 2029 and emerging as the second most electricityintensive process. The existing operations in 2018 explain practically all the copper mining industry’s expected electricity consumption but, by 2029, potential, possible and probable projects would account for close to a quarter of its consumption. The new projects will acquire growing importance, accounting for 56% of expected consumption by 2029, up from 19% in 2018.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".