Are Agricultural Values a Reliable Guide in Determining Landowners' Decisions to Create Forest Carbon Sinks?
Bibliographic record
Abstract
This research examines the effects of various factors on farmer participation in agricultural tree plantations for economic, environmental, social, and carbon‐uptake purposes, and potential costs of sequestering carbon through afforestation in western Canada. Using data from a survey of landowners, a discrete choice random utility model is used to determine the probability of landowners' participation in and corresponding mean willingness to accept (WTA) compensation for a tree‐planting program. WTA includes positive and negative benefits to landowners from planting trees, benefits not captured by foregone returns from agricultural activities on marginal land. Estimates of WTA are less than foregone returns, but even so average costs of creating carbon credits still exceed their projected value under a CO 2 ‐emissions trading scheme. La présente étude a examiné les effets de divers facteurs sur la participation des producteurs agricoles à la plantation d'arbres à des fins économiques, environnementales, sociales et d'absorption du gaz carbonique, ainsi que les coûts potentiels de la séquestration du carbone au moyen du boisement dans l'Ouest canadien. À l'aide des données d'un sondage effectué auprès de propriétaires fonciers, nous avons utilisé un modèle d'utilité aléatoire à choix discrets pour déterminer la probabilité de participation des propriétaires fonciers à un programme de boisement et leur consentement à recevoir (CAR) une compensation financière pour leur participation. Le CAR inclut les avantages favorables et défavorables que le boisement procure aux propriétaires fonciers, des avantages non saisis par les revenus sacrifiés des activités agricoles sur des terres marginales. Les estimations du CAR sont inférieures aux revenus sacrifiés, mais malgré tout, les coûts moyens de la mise en uvre de programme de crédits pour le carbone demeurent supérieurs à leur valeur prévue dans un scénario d'échange de droits d'émission de CO 2 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".