Environmental and Cost Assessments of Marine Alternative Fuels for Fully Autonomous Short-Sea Shipping Vessels Based on the Global Warming Potential Approach
Bibliographic record
Abstract
This research paper presents an effective approach to reducing marine pollution and costs by determining the optimal marine alternative fuels framework for short-sea shipping vessels, with a focus on energy efficiency. Employing mathematical models in a Python environment, the analyses are tailored specifically for conventional and fully autonomous high-speed passenger ferries (HSPFs) and tugboats, utilizing bottom-up methodologies, ship operating phases, and the global warming potential approach. The study aims to identify the optimal marine fuel that offers the highest Net Present Value (NPV) and minimal emissions, aligning with International Maritime Organization (IMO) regulations and environmental objectives. Data from the ship’s Automatic Identification System (AIS), along with specifications and port information, were integrated to assess power, energy, and fuel consumption, incorporating parameters of proposed marine alternative fuels. This study examines key performance indicators (KPIs) for marine alternative fuels used in both conventional and autonomous vessels, specifically analyzing total mass emission rate (TMER), total global warming potential (TGWP), total environmental impact (TEI), total environmental damage cost (TEDC), and NPV. The results show that hydrogen (H2-Ren, H2-F) fuels and electric options produce zero emissions, while traditional fuels like HFO and MDO exhibit the highest TMER. Sensitivity and stochastic analyses identify critical input variables affecting NPV, such as fuel costs, emission costs, and vessel speed. Findings indicate that LNG consistently yields the highest NPV, particularly for autonomous vessels, suggesting economic advantages and reduced emissions. These insights are crucial for optimizing fuel selection and operational strategies in marine transportation and offer valuable guidance for decision-making and investment in the marine sector, ensuring regulatory compliance and environmental sustainability.
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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 | 0.001 | 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".