What drives ports around the world to adopt air emissions abatement measures?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The reduction of Greenhouses gasses (GHG) and other air emissions represents a major challenge for ports. The world over, however, ports vary considerably in their efforts to reduce air emissions, and the causes for this variation remain under-researched. This paper examines the drivers for the adoption of air emissions abatement measures in a sample of 93 of the world’s largest ports, covering all continents and mobile emitters. We test five hypotheses with a Linear Probability Model to disentangle the impacts of key port characteristics on the current adoption of abatement measures and identify three key drivers for adoption: Population density, the port landlord business model, and a specialization in servicing container shipping. We also find that ports are more likely to implement specific bundles of measures, in particular combining pricing and new energy sources. Our work has implications for ports, as we suggest that they should coordinate abatement efforts to achieve effectiveness in their work.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 it