Joint Control of Emissions Permit Trading and Production Involving Fixed and Variable Transaction Costs
Bibliographic record
Abstract
The use of permit markets to mitigate harmful emissions is on the rise. When participating in such a market, an emitting firm needs to acquire from it permits that cover emissions resulting from production. Thus, it has to simultaneously cope with fluctuating permit prices and random demand, and also juggle between the activities of permit trading and permit‐consuming production. We shed light on this complex dynamic control problem, while confronting difficulties brought on by fixed as well as variable transaction costs associated with permit trading. We exploit K‐convexity variants that are suitable for two‐dimensional control, and achieve the partial characterization of optimal control policies. When the selling of permits is prohibited, we prescribe an ( s, S)‐type permit purchasing policy. For the more general case involving two‐way trading, we find it optimal to carry out trading in a three‐interval fashion. Heuristics, including one based on the uncoupling of trading and production activities, are introduced. Their effectiveness has been illustrated in computational studies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".