Rethinking the Function of Financial Assurance for End-of-Life Obligations
Bibliographic record
Abstract
This Article develops a new normative account of the function of financial assurance requirements (FARs) for end-of-life obligations in the energy sector. These obligations cover restoration of the site to its original condition or to a level that could accommodate another productive use once the energy project ends. FARs necessitate that operators evidence ability to pay for this. However, many FARs are failing across the United States, Canada, and the United Kingdom, posing serious implications for public funds and the environment and resulting in significant cost savings for operators that have the potential to distort trade. The authors argue that it is time to rethink FARs’ function, and that they ought to empower operators and regulators to discharge important legal responsibilities—or duties—ascribed to them prospectively. From the operator’s perspective, this is a duty to perform their end-of-life obligations. In contrast, the regulator’s duty is to protect the environment and human health by obtaining an appropriate guarantee from the operator that the works will be performed. The authors conclude that the empowering quality of FARs may be achieved most effectively through ensuring that fully funded capital reserves are “ring-fenced” from the claims of creditors prior to operations commencing.
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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 | 0.032 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.016 |
| 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".