Assessing the economic tradeoffs of various forest management activities to enhance carbon sequestration efforts in Pennsylvania and Maryland
Bibliographic record
Abstract
Forests play a vital role in mitigating climate change by sequestering and storing atmospheric carbon dioxide. State forestry agencies in the United States can enhance these benefits through sustainable management practices on public lands and by supporting private landowners with technical assistance and financial incentives. Furthermore, understanding the implications of shifting incentives for climate-smart forest management is essential to improving the efficacy of landowner support. This study evaluates the financial tradeoffs of various forest management strategies compared to a business-as-usual (BAU) scenario using modeled outputs from the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3) and two associated harvest wood product models in Maryland and Pennsylvania. Results show that scenarios like afforestation, restocking, and silvopasture provide higher net present value (NPV) when carbon revenue is included, despite initially lower returns compared to BAU. Scenarios such as controlled deer browsing and silvopasture outperformed BAU in NPV when accounting for carbon sequestration. Altering rotation lengths showed higher economic tradeoffs compared to other management strategies. Active forest management with diversified strategies, such as a portfolio approach incorporating multiple management strategies simultaneously, produced the best balance between economic and ecological goals. These strategies enhance carbon sequestration, improve NPV, and support climate change mitigation more effectively than BAU or single management approaches. Actively managed forests with diverse prescriptions produce superior benefits for both climate mitigation and forest productivity. The findings underscore the importance of active management and collaboration among policymakers, foresters, and stakeholders to optimize forest carbon benefits while maintaining ecological and economic balance.
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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 | 0.000 | 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".