Modelling the impact of future uncertainty in energy prices on aluminium decarbonization pathways
Bibliographic record
Abstract
Secondary aluminium production facilities typically consume 700–1,000 kWh of natural gas and 200–400 kWh of electricity per tonne of rolled sheets. To achieve environmental targets, the aluminium industry is exploring decarbonization strategies, including biomass gasification, carbon abatement and utilization, power-to-gas, direct electrification, and waste heat recovery, among others. While most of these technologies have lifetimes of a couple of decades, decisions on their installation must be made today. Biomass, electricity, and natural gas costs can be subject to unpredictable market variations, whereas carbon prices are related to environmental regulations and future market situations. Therefore, current decarbonization decisions must account for uncertainty in future energy prices. This study presents a systemic approach to incorporate energy price fluctuations into decarbonization planning for secondary aluminium production. A mixed integer linear programming (MILP) approach is used to generate a list of feasible system configurations under 4,000 combinations of energy prices and carbon taxes. Next, Monte Carlo simulations are applied to predict energy price trends and assess the resilience of favourable scenarios, from the MILP approach, under “stochastic” or “crisis” circumstances. Results show that decarbonization pathways are less costly than fossil CO 2 -emitting configurations in 50% of the price combinations. Among these decarbonization configurations, the pathway combining electricity and biomass is the most economical. However, its likelihood of outperforming the natural gas-driven baseline over a 25-year lifetime is estimated at 22%–37% under stochastic energy price profiles. Finally, resource diversification, such as biomass utilization, reduces risk during economic crises by 6% compared to complete electrification.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".