Optimal Harvest Decision Considering Carbon Stored in Forest and Wood Products, and Associated Fossil Fuel Carbon Emissions
Bibliographic record
Abstract
In developing incentives and protocols to reduce carbon emissions and increase carbon sequestration, one important omission stands out. Policy makers have ignored or minimized the importance of carbon storage in wood products and the associated secondary emissions. In this paper, I present the results from a discrete dynamic programming model used to determine the optimal harvest decision for a forest stand that provides benefits from timber harvest, carbon sequestered in forest and carbon storage in wood products. This study is distinguishable from previous studies because it considers varying levels of starting dead organic matter (DOM) and wood product stocks. This is important because it allows one to establish a threshold level for determining if a landowner is better off participating in the type of carbon market considered in this study. The results of the study suggest that the optimal decision to harvest is independent on the carbon stocks in the wood product pool but significantly affects economic returns to carbon management. The results also indicate that economic returns decrease with increasing initial levels of carbon in wood product and secondary carbon emissions have very little or no impact on the optimal decision to harvest. Contrary to the results from other studies, the results from this study reveal that increasing carbon price for a landowner to participate in the type of carbon market considered in this study will have the counterintuitive result of inducing the landowner to manage for carbon, if the wood product pool is considered.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".