Environmental Discrimination in International Investment Law
Bibliographic record
Abstract
One emerging tension between environmental law and international investment law stems from the differential treatment of foreign investors required by environmental regulation which may violate the non-discrimination clause in investment treaties. This article identifies three groups of environmental differentiations: impact-based, jurisdiction-based and treaty based. Impact-based differentiation refers to the polluter/non-polluter distinction, which includes the differential treatment of private actors based on their relative environmental impacts, location, size, public opposition, and administrative feasibility. However, foreign investors with similar environ mental impacts may be subject to differential treatment for other reasons, including (1) jurisdiction-based differentiation, meaning the multi-jurisdictional environmental governance in the host state leads to different environmental standards enacted by federal, state, and local authorities; and (2) treaty-based differentiation, arising from states’ obligations under international environmental treaties to accord differential treatments to private actors based on their nationalities. All three types of differentiations may violate the non-discrimination clause in investment treaties. The article proposes a real tension test to reconcile the tension between environmental protection and non-discrimination through a three-step analysis. In its conclusion, this article applies the real tension test to the recent Bilcon v. Canada case as an illustration of the test’s application in arbitration practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".