Can hydrogen win?: exploring scenarios for hydrogen fuelled vehicles
Bibliographic record
Abstract
This study explored the conditions under which hydrogen might succeed in Canada’s transportation sector in a carbon-constrained world. Long-run simulations were created using CIMS, a hybrid energy-economy model that is technologically explicit, behaviourally realistic, and incorporates drivers of technological change. A hydrogen supply submodel was built to simulate economies of scale in infrastructure. Capital costs, technology performance, infrastructure, fuel prices, and other conditions were varied in the simulations. All scenarios included a carbon tax and a vehicle emission standard that mandated a minimum market share for zero- and near-zero emission vehicles. Hydrogen was found to gain less market share than other options such as biofuel plug-in hybrids, but did well when biofuels were removed or priced excessively. Hydrogen fuel cells failed unless costs were assumed to descend independent of demand. However, hydrogen vehicles were shown to have little environmental advantage over other zero- and near-zero emission vehicles.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".