Article: The FET Standard between Treaty Reform and ISDS Practice: An Analysis of the Modernized ECT
Bibliographic record
Abstract
The Modernization of the Energy Charter Treaty (ECT) counts as a prominent attempt to better incorporate climate change considerations into an investment treaty that is highly impactful from a climate change perspective. Among other initiatives, reform efforts in the ECT Modernization have led to amendments of key standards of treatment providing the substantive legal basis for claims by foreign investors. Focusing on the Fair and Equitable Treatment (FET) standard, this article contrasts the new treaty provision under Article 10 of the ‘modernized’ ECT with consolidated interpretations of the FET standard in previous arbitral practice. Building on such a jurisprudential analysis, this article evaluates the extent to which reformed standards of investment protection in the Modernized ECT can lead to an increased likelihood of climate-aligned outcomes in investor-State disputes, thereby providing an analytical assessment as to the potential of the newly introduced ECT provisions on FET – which may also serve as benchmark for further reforms of international investment agreements (IIAs) – to expand regulatory space for host States and support the adoption of more stringent climate policies. Fair and Equitable Treatment, Energy Charter Treaty, Modernization, Investor-State Dispute Settlement, Climate Change, Regulatory Space, Investment Arbitration, Fossil Fuels, Legitimate Expectations, Treaty Reform
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".