Strategic deployment of hydrogen fuel cell buses and fueling stations: Insights from fleet transition models
Bibliographic record
Abstract
Establishing new hydrogen value chains is challenging, requiring economies of scale and balanced supply-demand dynamics. Municipalities can mitigate this risk through government support and deployment strategies. This study analyzes Edmonton's transition to zero-emission buses (ZEBs), focusing on hydrogen fuel cell electric vehicles (HFCEVs) and hydrogen fueling stations (HFSs). Using scenario-based modeling and S-curve models for technology diffusion, we project the adoption of battery electric vehicles (BEVs) and HFCEVs. Deploying over 1000 ZEBs by 2040 is necessary to meet Net-Zero targets, with 310–760 HFCEVs required for the municipal bus inventory. This results in an estimated hydrogen demand of 6.2–14.5 t-H 2 /day and a reduction of 0.4–1.0 Mt-CO 2 in tailpipe emissions by 2050. We use these scenario projections to develop a phased deployment strategy, optimizing fleet operations to reduce HFS costs by 50–60% from 8 to 9 C$/kg-H 2 to 3–4 C$/kg-H 2 . The study underscores the importance of strategic planning and infrastructure investment in realizing net-zero goals, providing a model applicable globally. • Public transit fleets can play leading role in breaking the vicious cycle between H 2 supply and demand. • For Edmonton, deployment of over 1000 Zero Emission Buses by 2040 is necessary. • Estimated hydrogen demand of 6.2–14.5 t-H 2 /day in 2050. • Reduction of 0.4–1.0 Mt-CO 2 in tailpipe eissions over the next 26 years. • Fleet transition models can be utilized to predict demand and minimize the cost of H 2 infrastructure. Phased deployment of H 2 stations and optimized fleet operations would reduce costs by 50–60%.
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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".