Multi-Criteria Examination of Power-to-Gas Pathways under Stochastic Preferences
Bibliographic record
Abstract
Power-to-gas is an energy storage and vector technology which can utilize off-peak power, assist in the integration of renewable power and provide needed fuel for industry and transportation. Further, power-to-gas is a useful technology for balancing surplus baseload and renewable energy generation with demand. There are numerous applications of power-to-gas in Europe, where renewable power is used to generate hydrogen for numerous applications. Examining each of these power-to-gas pathways across quantitative and qualitative criteria, this paper utilizes the stochastic fuzzy analytic hierarchy process to determine criteria weights. These weights are then fed to a multiple criteria decision analysis tool to determine the viability of each pathway for investors and policy makers. A sensitivity analysis is carried out by reprioritizing the criteria and re-evaluating the multiple criteria analysis. The two pathways that score highest under multiple criteria rankings are power-to-gas to mobility-fuel and power-to-gas-to-power, due to their established technologies, lower costs and environmental performance. By extension, both of these power-to-gas pathways are the most appropriate ways for this technology to be implemented, due to their combination of public familiarity, emissions reductions, and developed, available technologies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".