Using bioeconomic models to assess research priorities: a case study on afforestation as a carbon sequestration tool
Bibliographic record
Abstract
We have developed a spatial cost–benefit afforestation model that includes the tracking of five carbon pools. In this application we represent three possible afforestation strategies that could be implemented in Canada using plantations of hybrid poplar, hardwoods, and softwoods with average expected growth rates of 12–14, 5–7, and 5–7 m3· ha–1·year–1respectively. The model provides spatially explicit insights into the cost effectiveness of afforestation as a carbon sequestration tool. Here we develop an elasticity metric and experiment to assess model sensitivity and use the results to make recommendations about research priorities. The most important biological variables across all scenarios include site suitability, which is related to refining the spatial estimates of potential yields, biomass to carbon ratios, and wood density. The most important economic variables include refinement and lowering of establishment costs and agricultural opportunity costs. Parameters that have a low impact on the break-even carbon price, suggesting refinements in knowledge in these areas would be relatively less beneficial, include decay rates for forest products, stand senescence age (the age when stand mortality reaches its maximum), bioenergy and pulpwood prices, and mean residual time for leaf litter. Less importance was also placed on the proportions of forest products in the total harvest and refining a fossil fuel substitution coefficient.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".