Assessing the cost and resource requirements of converting carbon dioxide into synthetic aviation fuels
Bibliographic record
Abstract
Abstract Aviation requires energy-dense fuels and global coordination, making its emissions hard to abate. Here, we model a power-to-liquids process that produces sufficient aviation fuel to meet Canada’s monthly demand from 2025 to 2050. It begins with the steady-state production of syngas through the high temperature co-electrolysis of CO 2 and water. Established petrochemical processes are then used to preferentially produce aviation turbine fuel. The primary inputs into the system are a steady supply of CO 2 , water, and electrical energy. Resource requirements, costs, and net emissions are calculated and compared across six Canadian provinces with energy systems that differ in their electricity costs and grid emission factors. Several aviation demand scenarios are considered: in the highest-growth scenario, the process requires more electricity in 2050 (664 TWh) than Canada generated in 2019 (640 TWh). Water consumption in 2025 ranges from 12 to 22 Mt across scenarios and grows 1.2 to 1.6 times by 2050. Across scenarios, median levelized costs of jet fuel range from 30 to 70 CAD/gal if the plant is deployed today but sized to meet Canada’s 2050 aviation fuel demand. If the plant’s utilization rate is maintained at 85% throughout, these costs fall to the range of 20 to 42 CAD/gal. Our numbers are higher than existing studies which integrate projected costs for capital equipment, lower energy costs, by-product sales, and more established technologies. An electricity emission factor of 0.03 kgCO2 kWh −1 is required for a net-zero process, meaning that net-negative system emissions can be achieved when electricity provision is especially clean, such as that produced by some Canadian provincial grids.
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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".