Precautionary Environmental Impact Assessments under the BBNJ Agreement: More than a Minor or Transitory Effect on the Marine Environment?
Bibliographic record
Abstract
The Agreement on the Conservation and Sustainable Use of Marine Biological Diversity of Areas Beyond National (BBNJ Agreement) heralds a new era in the international legal framework to protect the ocean from harmful effects of human activities. One of its achievements is that it formalized global consensus on the imperative to consider conducting environmental impact assessments (EIAs) of planned activities that may have “more than a minor or transitory effect” on the ocean in areas beyond national jurisdiction or where the effects are “unknown or poorly understood.” A commendable feature of the EIA provisions is that they adopt core tenets of the precautionary principle—the foremost environmental planning principle that provides the theoretical underpinnings for strong environmental protection measures. The BBNJ Agreement thus provides an advanced EIA process—even more advanced than some national EIA processes in developed states with half a century of legislative EIA experience. This article examines the BBNJ Agreement’s provisions on EIA and their alignment with the precautionary principle and considers the potential of the BBNJ Agreement’s EIA procedures to improve the management of the marine environment in areas beyond national jurisdiction, and within.
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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 | 0.029 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".