Impact of Hydrogen Availability at Airports on the Potential Environmental Benefits of Hydrogen-Kerosene Dual-Fuel Business Jets Using ADS-B Data
Bibliographic record
Abstract
Business aviation’s operational flexibility is characterized by its ability to access less congested airports. This flexibility is also evident in the ability of these aircraft to carry out long-haul missions despite a high frequency of short-haul flights. This unique characteristic distinguishes business aviation from other sectors. However, with 5 to 14 times higher per-passenger emissions than commercial flights and a growing increase in business aircraft deliveries, this sector is under pressure to contribute to global net-zero emissions targets. A hydrogen-kerosene dual-fuel propulsion system offers a balanced solution by maintaining operational flexibility while significantly reducing greenhouse gas emissions. This propulsion system is designed to rely on liquid hydrogen for the most frequent missions, typically short-range flights, while retaining kerosene to maintain the long-range capability needed for less frequent but critical missions. However, the environmental benefits of this approach depend heavily on hydrogen availability at airports. Unlike commercial aviation, which operates mainly from large hubs, business aviation frequently uses smaller airports with lower traffic volumes. This network of airports represents a major challenge for the deployment of hydrogen infrastructure, as an investment in storage, refueling, and logistics infrastructure is likely to focus initially on large hubs, leaving smaller regional airfields with limited or no access. This paper investigates the impact of hydrogen availability on the environmental benefits of the dual-fuel propulsion concept applied to a super-midsize business aircraft. A statistical analysis of ADS-B flight data from a 762 different Challenger 300 aircraft fleet over one year offers insights into the airports commonly visited by business jets and their travel distances. A hydrogen deployment scenario is created in which large commercial hubs have access to hydrogen and can supply hydrogen at a nearby airport within a circle of a specified radius centered on the large commercial hub. This analysis links the hydrogen deployment scenario with the actual flight data to evaluate the potential reduction in greenhouse gas emissions from the dual-fuel concept. A performance model is applied to the operational data to calculate the energetic and environmental performance of the dual-fuel aircraft. The results indicate that the dual-fuel concept is environmentally viable at the fleet level, provided that large commercial hubs can supply hydrogen to nearby airports within a radius of 10 miles or more. However, the dual-fuel aircraft consumes 8.8% more energy than the reference aircraft for this hydrogen deployment radius. This concept could significantly facilitate the energy transition in business aviation.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".