Toward the Decarbonization of Maritime Supply Chains: A Ship Emissions Prediction Framework
Bibliographic record
Abstract
Maritime transport is a vital component of international trade, yet the industry contributes substantially to greenhouse gas (GHG) emissions, with carbon dioxide <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\text{CO}_{2})$</tex> as the predominant emission, in addition to various air pollutants incuding NOx, SOx, CO, PM, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CH}_{4}$</tex>. These emissions pose serious risks to both the environment and public health. As the International Maritime Organization (IMO) establishes ambitious decarbonization objectives, the development of reliable methods to quantify and predict ship emissions has become increasingly important. This work introduces a data-driven methodology that combines Automatic Identification System (AIS) records, vessel characteristics, and meteorological parameters. Using advanced machine learning algorithms, the framework estimates emissions at the individual ship level and generates a dynamic emissions inventory. The proposed approach provides actionable insights to support emission monitoring, environmental assessment, and policy development. Moreover, the methodology can be adapted to other Canadian waterways and international contexts, offering a pathway toward more sustainable and low-carbon maritime transport.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 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".