Climate obstruction and capital accumulation by feigned victimization: TC Energy and the political economy of investor-state dispute settlement
Bibliographic record
Abstract
Abstract The international investment regime provides generous protections for foreign investors against adverse legal changes in host states, and unusually strong procedural rights to enforce those protections in investor-state dispute settlement (ISDS). Scholars have observed that the regime enables corporate capital accumulation and raises the costs of climate action, potentially deterring states from adopting ambitious climate policies. Building on this literature, we locate a key source of these concerns in the asymmetric treatment of state and investor behavior in ISDS, which allows investors to depict themselves as innocent victims of “unfair” and “unforeseeable” “political” processes, despite themselves being active political players and sophisticated political risk managers—a tactic we call feigned victimization . This tactic is employed by fossil fuel companies to achieve capital accumulation and climate obstruction goals. We illustrate our argument through an empirical case study of TC Energy’s US$15 billion ISDS claim against the United States in relation to the Biden Administration’s revocation of a permit required to construct the Keystone XL oil pipeline. Our case study also illustrates a method by which states can expose feigned victimization tactics by investors and incorporate evidence of this into their legal defenses in ISDS.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".