Economic Dynamics of Tree Planting for Carbon Uptake on Marginal Agricultural Lands
Bibliographic record
Abstract
As a result of the 1997 Kyoto Protocol, afforestation of agricultural lands can be expected to take on an important role in the CO 2 emissions reduction policy arsenal of some countries. To date, identification of suitable (marginal) agricultural lands has been left mainly to foresters, but their criteria fail to take into account economic nuances. In this study, an optimal control model is used to determine the optimal level of afforestation in the western Canada. The results indicate that, while planting fast‐growing trees for carbon uptake on marginal agricultural land may be important, the path dynamics matter in determining whether Canada can rely on afforestation to meet its obligations under Kyoto. Sous l'impulsion duprotocole de Kyoto (1997), on peuts'attendre à voirle reboisement des terres agricoles prendre une place importante dans l'arsenal de mesures de réduction des émissions de CO 2 de certains pays. Jusqu'à présent, le choix des terres agricoles utilisables (c.‐à‐d. marginales pour l'agriculture) a été laissé principalement aux forestiers, mais les critères sur lesquels ces derniers se basent ne tiennent pas compte des aspects économiques. Nous utilisons ici un modèle de contrôle optimal pour déterminer le niveau optimal de reboisement qui conviendrait pour l'ouest du Canada. Il se dégage des résultats que, sans remettre en question l'importance de la plantation d'arbres à croissance rapide pour la capture du C dans les terres agricoles marginales, les décideurs devront tenir compte de la dynamique des sentiers avant que le reboisement puisse ètre la solution adoptée par le Canada pour honorer les engagements pris dans le cadre du Protocole.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".