Environmental and Human Rights Justifications in Investment Arbitration: Probing the Limits of ISDS for the Adjudication of Climate-Related Disputes
Bibliographic record
Abstract
Abstract This article examines the capacity of investment arbitration to analyze cases concerning complex issues of public international law, focusing on the challenges connected to climate-related cases. Through the analysis of arbitral awards involving environmental and human rights justifications, this article evaluates ways in which non-economic arguments may be effectively raised in investor-State dispute settlement (ISDS) and the extent to which arbitral tribunals may consider them. It is shown that ISDS adopts a binary approach; since tribunals focus primarily on investors’ rights, non-economic interests are only relevant for being either in favor or against investment protection. A further consequence of this binary approach is that, in cases where multiple non-economic interests are involved, i.e. cases of ‘triangulation’, ISDS is ill-equipped to balance competing non-economic interests and, therefore, to handle the complexities of climate-related disputes. This raises concerns about the role of ISDS in addressing cases involving broader issues of public interest.
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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 | 0.123 | 0.225 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.009 | 0.054 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".