Predicting greenhouse gas Emissions in Shipping: A Case Study Of Canada
Bibliographic record
Abstract
Shipping plays a vital role in global trade, facilitating over 90% of international trade by volume. However, it also contributes to significant environmental pollution, accounting for around 3% of global greenhouse gas (GHG) emissions and substantial amounts of nitrogen oxides (NOx), sulfur oxides (SOx), particulate matter (PM), black carbon (BC), and methane (CH4). These pollutants negatively impact both the climate and public health, particularly in coastal areas. In response, the International Maritime Organization (IMO) has set ambitious targets for reducing shipping emissions, with the goal of achieving near-zero GHG emissions by 2050. Interim goals include a 20% reduction by 2030 and 70-80% by 2040. As part of these efforts, the IMO introduced regulations such as the Energy Efficiency Existing Ship Index (EEXI) and the Carbon Intensity Indicator (CII) to monitor energy efficiency and emissions. To support these regulatory efforts, we propose a machine learning framework to predict GHG emissions from vessels navigating the Saint Lawrence River. By utilizing AIS data, vessel-specific details, and meteorological data, our approach will create a detailed emissions inventory. Using deep learning models, the system will predict emissions based on individual vessel activities. This scalable approach will contribute to more accurate environmental monitoring and support Canada’s broader efforts to reduce emissions from maritime transportation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".