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Record W2794125379 · doi:10.1111/poms.12875

Joint Control of Emissions Permit Trading and Production Involving Fixed and Variable Transaction Costs

2018· article· en· W2794125379 on OpenAlexaff
Quan Yuan, Jian Yang, Yun Zhou

Bibliographic record

VenueProduction and Operations Management · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsMcMaster University
FundersNational Natural Science Foundation of China
KeywordsHeuristicsExploitProduction (economics)ConvexityVariable (mathematics)Transaction costControl (management)PurchasingDatabase transactionVariable costFixed costEmissions tradingComputer scienceMicroeconomicsBusinessEconomicsIndustrial organizationOperations researchOperations managementGreenhouse gasFinanceComputer security

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.817
Threshold uncertainty score0.446

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.238
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2018
Admission routes1
Has abstractyes

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