State Responsibility for Environmental Harm from Climate Engineering
Bibliographic record
Abstract
Some have proposed that climate-engineering methods could be developed to offset climate change. However, whilst some of these methods, in particular a form of solar radiation management referred to as stratospheric aerosol injection (SAI), could potentially reduce the overall degree of global warming as well as some associated risks, they are also likely to redistribute some environmental risks globally. Moreover, they could give rise to new risks, raising the issue of legal responsibility for transboundary harm caused. This article examines the question of international accountability of states for an increased risk of environmental harm arising from a large-scale climate intervention using SAI, and the legal consequences that would follow. Examination of the applicability of customary rules on state responsibility to SAI are useful for understanding the limitations of the existing accountability framework for climate engineering, particularly in the context of global environmental problems involving risk-risk trade-offs and large uncertainties.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".