Environmental Policy Frameworks for Ports: In Search of a Gold Standard
Bibliographic record
Abstract
Abstract Ports play a vital role in reducing greenhouse gas emissions and a broad range of other tranportation-related environmental impacts. This article examines the critical elements of an effective environmental policy frame-work for ports that could be considered a “gold standard.” An exploratory analysis is conducted by delving into the academic literature and analyzing existing international and regional policy frameworks for ports. Fifteen in-terviews with subject-matter experts were conducted to identify sustaina-bility initiatives of two case study ports: Rotterdam and Long Beach. The article extends a previously developed framework for ports developed by Christodoulou et al., which identifies the following spheres of influence: the ports themselves, the role of ports in supporting shipping and inland transportation, and the geographical area surrounding the port. The current study extends the framework from its initial focus on GHG emission reduc-tion-related initiatives to other environmental sustainability initiatives un-dertaken by ports. It is clear that a “gold standard” requires a proactive and participatory approach to sustainability initiatives for the power itself and effective processes for collaboration with the shipping sector, inland trans-portation, and the geographic area surrounding ports to enable the port to be a constructive partner in all its spheres of influence and for the frame-work to evolve over time to continue to improve on an ongoing basis.
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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 | 0.067 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.037 | 0.044 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".