Optimal timing of harzardous waste clean-up under an environmental bond an a strict liability rule
Bibliographic record
Abstract
Inadequate site clean-up and restoration by resource extraction firms leave a toxic legacy which must be dealt with by governments. This study compares the impacts of an environmental bond and a strict liability rule on a firm's incentives for cleaning up hazardous waste during resource extraction and upon termination. The firm's problem is modelled as a stochastic optimal control problem that results in a system of Hamilton Jacobi Bellman equations. The model is applied to a typical copper mine in Canada. The resource price is modelled as a stochastic differential equation, which is calibrated to copper futures prices using a Kalman filtering approach. A numerical solution is implemented to determine the optimal abatement and extraction rates as well as the critical levels of copper prices that would motivate a firm to clean up the accumulated waste under each policy. The paper demonstrates that an environmental bond provides stronger waste abatement incentives, implying that the waste is more likely to be cleaned up under the bond than the liability. The strict liability rule imposes sunk costs on a firm upon termination which would motivate it to remain inactive as a way to escape clean-up costs. However, the environmental bond raises funds ex ante for future clean-up costs and thus encourages site restoration.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".