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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".