Assessment of carbon-abatement pricing to maximize the value of electrolytic hydrogen in emissions-intensive power sectors
Bibliographic record
Abstract
Electrolytic hydrogen can support the decarbonization of the power sector. Achieving cost-effective power-to-gas-to-power (PGP) integration through targeted emissions pricing can accelerate the adoption of electrolytic hydrogen in greenhouse gas-intensive power sectors. This study develops a framework for assessing the economic viability of electrolytic hydrogen-based PGP systems in fossil fuel-dependent grids, while considering the competing objectives of the electricity system operator, a risk-averse investor, and the government. Here we show that, given the risk-averse investor’s inherent pursuit of profit maximization, a break-even carbon abatement cost of at least 57 Canadian Dollars per tonne of CO₂ by 2030 from the government, with a shift in electricity market dispatch rules from sole system marginal price-reduction to system-wide emissions reduction, is essential to stimulate price discovery for low-cost hydrogen production and contingency reserve provision by the PGP system. This work can help policymakers capture and incentivize the role of electrolytic hydrogen in low-carbon power sector planning. Electrolytic hydrogen can play a role in the power sector by 2030 if its use is accompanied by a break-even abatement cost and if electricity market dispatch rules shift from minimizing costs to prioritizing emissions reductions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".