Modeling Carbon Sequestration and Economic Returns Using 3-PG in the FSC-Certified Simcoe County Forest
Bibliographic record
Abstract
In this study, we developed three forest management scenarios for Simcoe County Forest in Southern Ontario, Canada, using the Physiological Principles Predicting Growth (3-PG) model to simulate future forest growth and carbon dynamics. The focus was on four main species: Red pine (Pinus resinosa), White pine (Pinus strobus), Sugar maple (Acer saccharum), and Red oak (Quercus rubra). We parameterized, calibrated, and validated parameters of the 3-PG model for these four species and applied the model to evaluate the performance of management scenarios incorporating timber and carbon values in Simcoe County Forest. The first scenario, “business as usual,” maintained the existing management plan for the forest, ensuring stable timber income (531.2 CAD/hectare) and moderate carbon sequestration. The second scenario aimed to optimize management for the highest timber Net Present Value (NPV), with half of the trees harvested before 2030, followed by gradual thinning over 15 years. This approach yielded the highest financial returns (1634.1 CAD/hectare) but the lowest carbon sequestration potential. The third scenario integrated carbon certification, emphasizing the retention of sugar maple stands over the next 20 years. This scenario produced financial returns (580.2 CAD/hectare) higher than the “business as usual” scenario, while saving 49.33 tons of biomass per hectare. Overall, this study provides a reference for the implementation of carbon sequestration projects.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".