Environmental bonds and public liability for resource extraction site cleanup
Bibliographic record
Abstract
Governments have been left with large liabilities for cleanup at natural resource extraction sites after firms have declared bankruptcy. This research studies the impact of different forms of financial assurance on a firm’s optimal actions over the full life cycle of a hypothetical natural gas well, in a world of uncertain natural gas prices, when firm bankruptcy may shift cleanup costs to the government. A firm’s optimal control problem is described by an HJB equation with the natural gas price modelled as a stochastic differential equation. The firm is assumed to declare bankruptcy if the lease value is negative. The impact of financial assurance is examined in relation to firm investment decisions, the cleanup liability imposed on government, and resource taxation revenue. Sureties in the form of a Cash Deposit and a Surety Bond are contrasted with the case of no financial assurance requirement. Numerical results demonstrate that in the presence of distortionary taxes, there is a trade off between indemnifying the government against cleanup costs versus maintaining government tax and royalty revenues. A numerically plausible case is presented in which the total value of the natural gas well (to the firm and the government) is not increased by the imposition of a strict form of financial assurance. It is important to consider financial assurance in the context of the suite of royalties and taxes collected by governments from the oil and gas industry as well as any potential environmental damages from shut-in oil and gas wells.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".