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$(\text{CO}_{2})$as the predominant emission, in addition to various air pollutants incuding NOx, SOx, CO, PM, and$\text{CH}_{4}$. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".