Technological Advancements in Reducing Carbon Emissions in Maritime Transportation: A Literature Survey
Bibliographic record
Abstract
This research examines technological advancements designed to reduce greenhouse gas (GHG) emissions in the maritime industry, offering recommendations for researchers and practitioners to promote sustainability.The study's motivation is the shipping sector's significant contribution to global carbon dioxide emissions, presenting a challenge to achieving carbon neutrality by 2050.The research analyzed 210 papers published between 2011 and 2024 from Scopus using bibliometric techniques and VOSviewer software.By applying the modularity community detection algorithm, seven key themes were identified: alternative fuels like LNG and ammonia, hybrid and electric power systems, renewable energy sources (solar, wind, wave), energy-efficient engine technologies, integration of alternative power systems, carbon capture and storage (CCS), and ship-network optimization.Key findings highlight the increasing focus on renewable energy and alternative fuels since 2018, particularly ammonia, hydrogen, and electricity.CCS technologies, gaining attention since 2021, show potential for reducing CO2 emissions through long-term storage.The research underscores the need for integrating diverse technologies and optimization algorithms to reduce GHG emissions effectively while balancing efficiency and costs.Governments, businesses, and communities are encouraged to prioritize renewable energy, explore advanced technologies like AI and blockchain, and expand efforts in CCS to ensure a sustainable future for the shipping industry.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".