Policy approaches to mitigate in-use methane emissions from natural gas use as a marine fuel
Bibliographic record
Abstract
Abstract Unregulated in-use methane emissions (or methane slip) can reduce or even eliminate the overall climate benefits of using liquefied natural gas as a marine fuel. We conduct critical review and expert interviews to identify methane slip mitigation measures, and then identify and evaluate potential policy instruments that could incentivize their uptake while considering the shipping sector’s climate targets. We find that regulatory instruments are expected to perform the best across a range of criteria when they are at the global level, include methane on a CO 2 -equivalent and lifecycle basis, promote polycentric approaches to climate governance, and allow flexibility in how the industry incorporates decarbonization measures. Market-based approaches and informational governance policies complement regulatory instruments by improving cost-effectiveness and increasing the availability of relevant information on emissions mitigation. The urgency and scale of shipping climate targets underscore the need for policy approaches that support planning for long-term decarbonization pathways and that can avoid locking into fossil-carbon intensive systems.
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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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".