Assessing the feasibility of electrifying container ships for sustainable maritime transport
Bibliographic record
Abstract
Maritime shipping accounts for over 80 % of global trade and about 3 % of global CO₂ emissions due to its reliance on fossil fuels for propulsion and auxiliary power generation. This study assesses in detail the energy demand, CO₂ emissions, and renewable energy infrastructure required to electrify container ships operating from Los Angeles Harbor (LAH) across three representative capacity ranges: 5000–7999 TEU, 8000–11,999 TEU, and 12,000–14,499 TEU. The estimated annual propulsion energy demand for these vessel classes is 227,842 MWh, 253,884 MWh, and 253,055 MWh, respectively, reflecting the growing energy requirements with increased displacement and engine capacity. Larger vessels show higher total power consumption but greater transport efficiency, as energy demand per TEU decreases from 45.6 MWh/TEU for the smallest class to 21.1 MWh/TEU for the largest, representing a 54 % improvement in energy intensity and overall fuel economy. Average cruising speeds are 24.6, 23.9, and 23.8 knots for the three classes, respectively, indicating that scale optimization does not significantly compromise operational performance. Under heavy fuel oil (HFO) operation, annual CO₂ emissions reach 72,245 t, 92,797 t, and 96,845 t per vessel, while full battery-electric operation powered entirely by renewable sources nearly eliminates these emissions. Reducing sailing speed by 25 % lowers annual fuel consumption by roughly 30 %, decreasing CO₂ emissions to about 67,700 t for the smallest class, demonstrating the strong non-linear relationship between speed and fuel use. Renewable methanol reduces emissions to around 29.8 gCO₂/TEU-n.mile, LNG achieves approximately 59.6 gCO₂/TEU-n.mile, and low-sulfur HFO produces nearly 99 gCO₂/TEU-n.mile. Battery weight increases propulsion power by roughly 3 % for the largest ship, but remains a secondary factor compared with voyage energy requirements.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".