Legal Issues Arising from the Feed-in Tariff of Renewable Energy: Controversial issues in investor-state dispute settlement (Japanese)
Bibliographic record
Abstract
This paper aims at analyzing several cases of investor-State dispute settlement (ISDS) which relate to the feed-in tariff (fiT) of the renewable energy sector, for the purpose of extracting the legal issues involved in them. On the basis of this analysis, it will bring some implications toward Japan, both from the investors' viewpoint and the host-State's perspective. first, in European and North American countries, there have been many cases in which foreign investors (claimants) submitted disputes, against the host-States, before the ISDS concerning the operation and abolishment of the fiT system. In particular, the main topic is to allege a violation of the fair and equitable treatment (FET) obligation stipulated in the applicable international investment agreement. Second, in cases against Spain, the Tribunal either admitted a breach of FET ( Eiser case) or did not ( Charanne case and Isolux case). The same applies in the cases against Canada. These situations require us to analyze the reason why there has been a difference of conclusions. Third, on the basis of the above analysis, it will become possible to evaluate the modified fiT law of Japan (2016) and present some implications about it.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".