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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".