Economics of Carbon Capture and Storage for Small Scale Hydrogen Generation for Transit Refueling Stations
Bibliographic record
Abstract
Refueling infrastructure for early adopters of hydrogen vehicles finally appears to be imminent. There is a consensus among long haul trucking and transit agencies that hydrogen fuel cell electric vehicles are likely to be the most cost-effective strategy for transitioning to low or zero emission fuels, especially in cold weather climates. Hydrogen refueling stations will require careful planning to ensure costs are low and that carbon dioxide emissions are minimized. Until such time that refueling stations are commonplace, the most likely scenario for mitigating both costs and carbon intensity will be local, on site hydrogen generation at the refueling stations. This study was undertaken on behalf of Stark Area Regional Transit Authority (SARTA), which currently has a hydrogen refueling station on its campus in Canton, Ohio, to support a fleet of hydrogen fuel cell buses and paratransit vehicles (17 by 2021). The refueling facility is expected to require 500 kg/day of hydrogen to maintain this fleet, and could grow higher depending upon future fleet replacement. Currently, SARTA has liquid hydrogen delivered by truck from a large steam methane reformer in Ontario, Canada. The life cycle carbon dioxide emissions, while significantly lower than that from burning diesel, is relatively high from this strategy. SARTA seeks to identify, and if practicable, implement lower carbon emission strategies. Accordingly, SARTA commissioned this study through the Renewable Hydrogen Fuel Cell Collaborative to examine alternative scenarios to mitigate carbon emissions from hydrogen delivery.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".