Electrifying Maritime Shipping: Evaluating the CO<sub>2</sub> Reduction and Viability of Future Battery-Powered Container Ships Calling at the Port of Los Angeles
Bibliographic record
Abstract
Abstract Maritime shipping plays a pivotal role in global logistics, influencing efficiency, emissions, and adaptability. This academic study envisions future electrified shipping fleets and evaluates the potential for CO2 reduction and the cost delta between grid electricity and marine fossil fuels using three types of container ships (5000-7999 TEU, 8000-11999 TEU, and 12000-14499 TEU). The scenario evaluated envisions an electrical charging infrastructure at the Port of Los Angeles, supported fully by wind and solar energy sources, to recharge future battery-powered ships. It is recognized that electric-powered ocean-going long-transit ships are not practical, but this study provides baseline groundwork to justify and support future development of hybrid or partially electric ships to help meet the International Maritime Organization’s (IMO) target of a 50% reduction in CO2 emissions from the world’s shipping by 2050. Additionally, a real case study analysis of all container ships at POLA was conducted using container vessel activity to further evaluate the practical implications and benefits of this transition. This analysis underscores the significant electrical infrastructure investments required and highlights the potential economic challenges as well as the environmental benefits of integrating electric power into maritime operations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".