Estimating the demand and cost-effectiveness of a hydrogen-based decarbonization strategy for airports
Bibliographic record
Abstract
The aviation sector remains a significant source of emissions in Canada that must be decarbonized to meet its net-zero emissions target by 2050, as mandated by the Net-Zero Emissions Accountability Act. This study examines a 2050 scenario where 38 of Canada's largest airports operate as multimodal hydrogen airport hubs. An hourly, service-level modeling framework is developed to estimate hydrogen demand, addressing a key gap in the literature; it is applied to all 38 airports. A benefit-cost analysis evaluates the cost-effectiveness of this transition. Results show annual hydrogen demand of 5.6–8.2 MtH 2 by 2050, with aviation fuel comprising 98 % of the demand. Carbon abatement costs range from 320 to 2,130 CA$/tCO 2 airport-wide, and from 360 to 1,500 CA$/tCO 2 for aviation, suggesting hydrogen could compete with drop-in power-to-liquid synthetic aviation fuels under certain conditions. The broad range of estimates reflects significant uncertainties in hydrogen supply costs. • The study models hourly energy demand for multimodal hydrogen airport hubs. • Aviation comprises 98 % of hydrogen demand at major airports, 80 % at smaller ones. • The cost of carbon abatement (CCA) ranges from 320 to 2,130 CA$/tCO 2 . • Ground support equipment and heavy-duty vehicles achieve the lowest CCAs. • Hydrogen aircraft achieve a CCA of 360 to 1,500 CA$/tCO 2 , potentially rivaling those using sustainable aviation fuel.
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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 | 0.000 |
| 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".