Greening Arctic Cruise Shipping Through Law and Technology: A Role for China
Bibliographic record
Abstract
Increased shipping in the Arctic will mean not only increasing tourism revenue for local communities but, more importantly in the long run, increasing health risks for local residents. The overwhelming majority of ships is powered with fossil fuels and concerns over emissions have led to the creation of Emission Control Areas, such as the Sulphur Emissions Control Area (SECA) in the Baltic Sea, the North Sea and along much, but not all, of the coasts of the United States and Canada. None of the existing SECAs includes areas north of the Arctic Circle. This means that coastal communities, in particular in cruise ship destinations, are put at risk from high emissions of SO2. The research presented here shows that China has the potential to play several roles in contributing to the protection of coastal communities in the Arctic and in safeguarding the human right to live in a healthy environment, which has long been recognized by the European Court of Human Rights. It will be shown that China has the potential to use international forms of cooperation in the context of the work of the International Maritime Organization in order to support the establishment of a SECA for the entire Arctic Ocean but can also profit from it in the long run, provided that China’s shipbuilding industry becomes able to meet the needs of more environment conscious ship buyers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".