Hydrogen economy transition plan: A case study on Ontario
Bibliographic record
Abstract
A shift towards a "hydrogen economy" can reduce carbon emissions, increase penetration of variable renewable power generation into the grid, and improve energy security. The deployment of hydrogen technologies promises major contributions to fulfilling the economy's significant energy needs while also reducing urban pollution emissions and the overall carbon footprint and moving towards a circular economy. Using the Canadian province of Ontario as an example, this paper prioritizes certain recommendations for near-term policy actions, setting the stage for long-term progress to reach the zero-emissions target by 2050. To roll out hydrogen technologies in Ontario, we recommend promptly channeling efforts into deployment through several short-, mid-, and long-term strategies. Hydrogen refueling infrastructure on Highway 401 and 400 Corridors, electrolysis for the industrial sector, rail infrastructure and hydrogen locomotives, and hydrogen infrastructure for energy hubs and microgrids are included in strategies for the near term. With this infrastructure, more Class 8 large and heavy vehicles will be ready to be converted into hydrogen fuel cell power in the mid-term. Long-term actions such as Power-to-Gas, hydrogen-enriched natural gas, hydrogen as feedstock for products (e.g., ammonia and methanol), and seasonal and underground storage of hydrogen will require immediate financial and policy support for research and technology development.
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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 | 0.000 | 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 | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".