Ship Energy Efficiency and Maritime Sector Initiatives to Reduce Carbon Emissions
Bibliographic record
Abstract
With stricter IMO regulations on CO2 taking effect in 2023 and ambitious goals to reduce carbon intensity by 2030, the maritime industry is scrambling to clean up its act. Conventional methods and equipment are now being reevaluated, upgraded or completely replaced. The difference between a short-term fix and a long-term sustainable option is how flexible vessels will be to use new energy sources or technology as they become viable. The review discusses the recent literature on renewable energy sources, technical and operational strategies for new and existing ships, technology maturity, and alternative fuels. It is found that the IMO’s targets can be met by combining two or three technologies, or via a radical technology shift which can provide innovative, high-efficiency solutions from an environmental and economic standpoint. It has also been noted that policies and enforcement are essential management instruments for mitigating the unfavourable environmental effects of marine transportation and directing the maritime industry toward sustainability on a regional, national, and international scale.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".