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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".