THE ROLE OF EXPROPRIATION CLAUSES IN PROTECTION AND PROMOTION OF FOREIGN INVESTMENTS IN RENEWABLE ENERGY: AN ESSENTIAL BUT OVERLOOKED LEGAL CONSIDERATION
Bibliographic record
Abstract
Today the world is tackling climate change. The global threat of energy poverty along with the growing need for energy has escalated this crisis. The promotion of renewable energy sources is widely known as the main solution to this challenge. Many International and regional agreements address various aspects of renewable energy development such as trade, transit, security, and investment. Foreign investment is recognized as a crucial prerequisite for the global deployment of renewable energy since not all States have the financial and technological potential to develop this sector. Various investment agreements are signed to facilitate and promote investments. These instruments contain a mixture of obligations that have direct or indirect effects. Expropriation provisions which are often crystallized in the form of 'a duty not to expropriate' are among these obligations. This article analytically describes the legal aspects of this standard and proposes the trends that can better protect the foreign investments in this sector; a factor without which the foreign investors would normally be reluctant to invest. It concludes that restricted police power, guarantees of transfer, and a full compensation standard that entails the payment of compound interest are the prominent legal features that can best perform this task.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".