Mitigating Climate Change by Planting Trees: The Transaction Costs Trap.” Land Economics 78(4
Bibliographic record
Abstract
ABSTRACT. Land-use change and forestry projects are considered a low-cost option for addressing climate change mitigation. In Canada, afforestation is targeted to sequester enough carbon to meet one- � fth of its international obligations, and at lower cost than emissions reduction. We examine economic aspects of the institutions and incentives needed to encourage landowners in Canada to adopt tree planting on a large scale. Based on data from a survey of landowners, the transaction costs of getting landowners to convert their land from agriculture to plantation forests appear to be a signi � cant obstacle, possibly increasing the costs of afforestation projects beyond what conventional economic analysis suggests. (JEL Q25) I. BACKGROUND Land-use change and forestry (LUCF) projects that result in greater carbon storage in terrestrial ecosystems are widely seen as a low-cost alternative to CO 2-emissions reduction for mitigating climate change (Obersteiner,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 source (direct Gemma or distilled Codex), 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".