Reclamation of a resource extraction site: A differential game approach
Bibliographic record
Abstract
Abstract We study an extraction site reclamation problem in a two‐player differential game setting over a finite time horizon. Environmental regulation requires each firm to engage in reclamation efforts during the entire lifespan of the extraction site and to pay an abandonment reclamation fee at the end of its lease term for the unclaimed pollution caused by firms’ activities. Firms determine their reclamation efforts in order to minimize their reclamation cost. We analyze and compare individual firms’ choices and the pollution stock in the noncooperative and the cooperative cases by distinguishing between situations in which firms are homogeneous and heterogeneous. We study the case in which firms have different lease durations and different degrees of environmental liability. We show that the dynamics of the reclamation efforts may be substantially different under noncooperation and cooperation, and in both cases, it is mainly determined by how the rate of time preference and the growth rate of firms’ liabilities compare. Moreover, in all scenarios, the reclamation efforts generally rise with the degree of liability and fall with the lease duration, suggesting that in order to promote better environmental outcomes, the regulators should carefully determine the lease conditions by introducing intra‐term reclamation fees along with stringent environmental accountability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".