Bilateral Investment Treaties — A Potential Trap for Developing Economies: A Lesson From Thailand
Bibliographic record
Abstract
There is a growing concern amongst both developing and developed countries concerning the potential impact of Investor-State Dispute Settlement (ISDS) awards on the ability of a government to act in the best interest of its citizens. ISDS clauses are included in Bilateral Investment Treaties (BITs) and increasingly, but not always, in Free Trade Agreements (FTAs). The potential impacts on the ISDS clauses may considerable and affect the decision-making ability of the government. Unfortunately, the government officers making those decisions may not be aware of the potential conflict with the requirements of a BIT or FTA. This paper focuses on the litigation between Walter Bau AG (in liquidation) and the Government of Thailand in relation to a concession agreement to design, construct, operate and maintain the Don Muang Tollway in Bangkok. Walter Bau alleged the lack of Fair and Equitable Treatment (FET) in relation to its investment due to the Thai government reducing tolls; continuing to improve roads in the vicinity of the toll road thus affecting traffic volumes and subsequently closing the Bangkok International Airport at Don Muang. Arbitral proceedings were conducted in Switzerland and resulted in a significant award to Walter Bau which was unsuccessfully challenged by Thailand. It describes the circumstances that led to the government’s actions and the lessons that have been learnt from them. It also discusses how these issues have been addressed in Investor State Dispute Settlement in recent Free Trade Agreements entered into by Thailand and its trading partners, including Australia. Walter Bau provides a significant lesson for government’s developing Public Private Partnership (PPP) projects which can have multiple investors at both the construction and operations stages. These investors are often foreign companies who have no other interest other than the return on capital from their investment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".