Litigating Climate Change in National Courts: Recent Trends and Developments in Global Climate Law
Bibliographic record
Abstract
This Article highlights the role that national judiciaries worldwide have played in developing the field of “climate law.” It focuses on some of the key lawsuits from civil and common-law jurisdictions that may influence climate law beyond their borders, including climate mitigation and adaptation cases as well as transnational climate cases. In particular, it considers the procedural tools and interpretive principles that judges have employed to decide novel legal issues presented by climate litigation. It concludes that judges are successfully adapting their traditional role of administration of justice to the challenges posed by climate change litigation, and holding their own governments accountable. While courts have thus far been unwilling to impose civil liability on private entities, emerging science may help address some of the causation and apportionment hurdles in these cases, and additional and collateral avenues for private-sector accountability may emerge.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".