What’s in a Right? Concretizing States’ Climate Change Mitigation Obligations under Human Rights Law
Bibliographic record
Abstract
Abstract States owe duties under human rights law to protect individuals from climate harm by mitigating climate change individually and collectively, in order to secure the Paris Agreement’s 1.5°C goal. It is, however, unclear what human rights law requires of states generally in terms of emissions reduction trajectories. This article elucidates that question, by looking at what reduction obligations can be deduced from scholarship and the work of human rights enforcement mandates. It argues that it is not possible to deduce individualized reduction obligations or methods to calculate such obligations from the current body of human rights law. The article then explores three different pathways to achieve such concretization: law-making, litigation, and monitoring bodies. The analysis provides a platform for human rights law to realize its potential in advancing state ambition on mitigating climate change at the norm-level by assessing the promise of the different pathways to concretization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".