Modeling Energy Use and Emissions from North American Shipping: Application of the Ship Traffic, Energy, and Environment Model
Bibliographic record
Abstract
The waterway network ship traffic, energy, and environment model (STEEM) is applied to geographically characterize energy use and emissions for interport ship movement for North America, including the United States, Canada, and Mexico. STEEM advances existing approaches by (i) estimating emissions for large regions on the basis of nearly complete data describing historical ship movements, attributes, and operating profiles of individual ships, (ii) solving distances on an empirical waterway network for each pair of ports considering ship draft and width constraints, and (iii) allocating emissions on the basis of the most probable routes. We estimate that the 172 000 ship voyages to and from North American ports in 2002 consumed about 47 million metric tonnes of heavy fuel oil and emitted about 2.4 million metric tonnes of SO2. Comparison with port and regional studies shows good agreement in total estimates and better spatial precision than current top-down methods. In quantifying limitations of top-down approaches that assume existing proxies for ship traffic density are spatially representative across larger domains, we find that International Comprehensive Ocean-Atmosphere Data Set (ICOADS) proxy data are spatially biased, especially at small scales. Emissions estimated by STEEM for ships within 200 nautical miles of the coastal areas of the United States are about 5 times the emissions estimated in previous studies using cargo as a proxy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".