Toward a harmonization of sustainability criteria for alternative marine fuels
Bibliographic record
Abstract
The discussion around shipping decarbonization has accelerated rapidly in 2020 and 2021. The growing studies on alternative marine fuels based on different criteria are indicative of both the complexities involved in marine fuels evaluation and absence of a consistent framework for assessment of alternative marine fuels from a holistic perspective. There is a recent call for an integrated evaluation model for alternative marine fuels with respect to economic, environmental, and social criteria. In this study, we develop and present a comprehensive and integrated set of sustainability criteria that are relevant for evaluating alternative marine fuels. First, we provide an overview of different alternative marine fuel pathways and assess the current challenges associated with adopting alternative marine fuels. Second, we develop 18 sustainability criteria, identified through the academic and trade literature and validated through a multi-stakeholder participatory approach (based on the input from 70 maritime experts), for a systematic and consistent evaluation of marine fuels. Third, based on an in-depth survey, we evaluate maritime stakeholder perspectives on the importance of sustainability criteria. And finally, we provide a discussion of key policy implications and areas for future studies. Our analysis reveals the current degree of agreement amongst maritime stakeholders in the debate about the importance of multiple, and often conflicting, criteria for evaluating marine fuels; the top five most important criteria are regulatory compliance, life cycle GHG, fuel cost, air pollution, and occupational health and safety. The analysis also looks at the importance ranking of each criterion from the perspective of individual maritime stakeholder groups. These findings provide decision-makers with a platform to understand priorities and interests of maritime stakeholder groups for the choice of marine fuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.105 | 0.088 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".