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The Optimal Length of an Agricultural Carbon Contract

2005· article· en· W2161576547 on OpenAlexafffundvenue
Sumeet Gulati, James Vercammen

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaBIOCAP Canada
KeywordsContract farmingOffset (computer science)Marginal valueEconomicsMarginal costCarbon fibersWelfareAgricultureMicroeconomicsCarbon offsetSocial costSocial discount rateValue (mathematics)Natural resource economicsSocial contractAgricultural economicsCost–benefit analysisGreenhouse gasMathematicsEcologyMarket economyProduction (economics)Computer scienceBiologyStatistics

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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.161
Teacher spread0.151 · 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.

Study designObservational
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

Citations22
Published2005
Admission routes3
Has abstractyes

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