Rehabilitation forestry and carbon market access onhigh-graded northern hardwood forests
Bibliographic record
Abstract
Decades of heavy-cutting and high-grading in the northeastern United States provide an opportunity for rehabilitation and increased carbon stores, yet few studies have examined the feasibility of using carbon markets to restore high-graded forests. We evaluated the effectiveness of rehabilitation on 391 ha of high-graded forest in Vermont, USA. Thirteen silvicultural scenarios were modeled over 100 years using the Forest Vegetation Simulator. Carbon offsets were quantified with the Climate Action Reserve (CAR) and American Carbon Registry (ACR) protocols and evaluated under voluntary and regulatory carbon price assumptions. Results indicate that management scenarios involving no harvest or low-intensity harvest yield the greatest incentives, yet these scenarios include a range of short-term rehabilitation options that provide flexibility for landowners. The choice of protocol also significantly influences results. Although ACR consistently generated more offsets than CAR for the same scenarios (p < 0.05), the protocols yielded similar net present values of US$121–US$256·ha −1 under high offset price assumptions. These returns are comparable to those generated from timber harvest alone under more intensive management scenarios. While timber will continue to be a primary source of revenue for many landowners, carbon markets may increasingly appeal as a new incentive for restoring high-graded forests.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".