International eia Law and Geoengineering: Do Emerging Technologies Require Special Rules?
Bibliographic record
Abstract
This article explores the adequacy of the international rules on environmental impact assessment to contribute to geoengineering governance, with a focus on three fundamental challenges. First, the near-universal trigger for eia is the likelihood of significant environmental impact, which may prove to be insufficiently precautionary in light of current risk preferences surrounding geoengineering. Second, the scope of eia has traditionally focused narrowly on the assessment of direct physical impacts; however, many of the concerns that geoengineering research raises relate to environmental and social risks associated with downstream technological implications. A third and related challenge is the consultation requirements under eia laws, which focus on affected states and affected members of the public. Because many geoengineering activities are anticipated to impact the global commons, there is no clear institutional mechanism for implementing notification and consultation. Additionally, the broader sets of concerns that geoengineering raises are spatially unbounded, again making the identification of consultation partners uncertain. The article concludes with a discussion of the implications of the challenges and limitations of the rules of eia for geoengineering.
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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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.025 | 0.028 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.023 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".