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Record W2037873969 · doi:10.1139/x09-023

Impact of carbon value on the profitability of slash pine plantations in the southern United States: an integrated life cycle and Faustmann analysis

2009· article· en· W2037873969 on OpenAlexvenueno aff
Puneet Dwivedi, Janaki R.R. Alavalapati, Andres Susaeta, G. Andrew Stainback

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsSlash PineProfitability indexPaymentAfforestationCarbon fibersBiomass (ecology)Slash (logging)ForestryCarbon sequestrationClearingAgroforestryCarbon stockValue (mathematics)BusinessNatural resource economicsAgricultural economicsEconomicsEnvironmental sciencePinus <genus>GeographyMathematicsClimate changeFinanceEcologyCarbon dioxideBotanyBiologyStatistics

Abstract

fetched live from OpenAlex

The emergence of voluntary carbon markets provides an additional income opportunity to nonindustrial private forest (NIPF) landowners because of the carbon sequestered in forest biomass. This study integrates life cycle analysis and the modified Faustmann model to assess the impact of carbon payments on the optimum rotation age and profitability of 1 ha of privately owned but intensely managed slash pine ( Pinus elliottii Engelm.) plantation in the southern United States. Guidelines of the Chicago Climate Exchange were followed for carbon payments. When carbon payments were included, land expectation values (LEVs) were found to be about $1384·ha –1 and $1063·ha –1 for the with and without thinning scenarios, respectively. When payment for carbon sequestered in the live forest biomass was included, LEVs increased to about $2807·ha –1 and $2765·ha –1 for the aforementioned scenarios, respectively. No significant change in the optimum rotation age was observed in the presence of carbon payments. Results suggest that voluntary carbon markets could play a key role in improving the financial returns to NIPF landowners.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.037
GPT teacher head0.334
Teacher spread0.297 · 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 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

Citations41
Published2009
Admission routes1
Has abstractyes

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