Carbon emissions in metal manufacturing productivity: A global analysis of aluminium smelting
Bibliographic record
Abstract
The aluminium industry generates over a billion tonnes of carbon dioxide (CO 2 ) annually, contributing to global emissions significantly. To become a part of the sustainable economy, this carbon-intensive industry needs improved productivity to reduce its carbon footprint. Given the ubiquity of aluminium usage in a wide array of consumer and industrial products and its high production energy requirements, assessing the carbon component of productivity can have significant climate mitigation impacts. This paper examines the cost efficiency of carbon emissions embedded in the aluminium smelting sector. It refers to the point at which a smelter minimizes its cost for a given output level by optimally using input resources. Specifically, it investigates the energy-specific technology gaps that affect aluminium sector productivity, utilizing unique global smelter-level data from 2004 to 2020. This is the first study comparing smelters' cost efficiency using different energy sources and technologies. It estimates the Meta-Technology-Ratio (MTR), which measures how close a smelter is to the frontier, representing the use of the best available technology while being environmentally efficient. The analysis underscores the potential for reducing the technology gap in coal- and gas-powered smelters through modernization and efficiency improvements while highlighting renewables and nuclear power as leading options for aligning with the most efficient, cutting-edge technologies in aluminium smelting.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".