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Record W3126110983 · doi:10.22004/ag.econ.15843

THE OPTIMAL LENGTH OF AN AGRICULTURAL CARBON CONTRACT

2005· preprint· en· W3126110983 on OpenAlexafffund
Sumeet Gulati, James Vercammen

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2005
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaBIOCAP CanadaUniversity of Victoria
KeywordsContract farmingMarginal valueOffset (computer science)EconomicsMarginal costAgricultureWelfareCarbon fibersSocial costCarbon offsetMicroeconomicsNatural resource economicsSocial discount rateValue (mathematics)Contract managementSocial contractAgricultural economicsCost–benefit analysisGreenhouse gasMathematicsEcologyMarket economyProduction (economics)Biology

Abstract

fetched live from OpenAlex

In this paper we present the economic determinants of the optimal length of a carbon offset contract. We find that because of a declining capacity of the soil to sequester carbon, the optimal length of the carbon contract is finite (the marginal benefit of remaining in the contract is declining over time, whereas marginal opportunity cost is rising). We also explore the effect of varying key parameter values on the optimal length in the contract. If the contract requires the farmer to sequester at a higher rate, the farmer chooses the contract for a shorter length of time, and this may decrease rather than increase social welfare. If society places a higher value on carbon accumulation, the contract is chosen for a longer length of time. Finally, if both the farmer and society have a higher discount rate, the model provides a somewhat surprising result. The overall time in the contract, and benefits from carbon accumulation are higher when the common discount rate is higher.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.081
GPT teacher head0.244
Teacher spread0.163 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2005
Admission routes2
Has abstractyes

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Same venueAgEcon Search (University of Minnesota, USA)Same topicClimate Change Policy and EconomicsFrench-language works237,207