Determining optimal forest rotation ages and carbon offset credits: Accounting for post‐harvest carbon storehouses
Bibliographic record
Abstract
Abstract Sequestering carbon in forest ecosystems is important for mitigating climate change. A major policy concern is whether forests should be left unharvested to avoid carbon dioxide (CO 2 ) emissions and store carbon, or harvested to take advantage of potential carbon storage in post‐harvest wood product sinks and removal of CO 2 from the atmosphere by new growth. The issue is addressed in this paper by examining carbon rotation ages that consider commercial timber as well as carbon values. A discrete‐time optimal rotation age model is developed that employs data on carbon fluxes stored in both living and dead biomass as opposed to carbon as a function of timber growth. Carbon is allocated to several ecosystem and post‐harvest product pools that decay over time at different rates. In addition, the timing of carbon fluxes is taken into account by weighting future carbon fluxes as less important than current ones. Using simple formulae for determining optimal rotation ages, we find that: (1) Reducing the price of timber while increasing the price of carbon will increase rotation age, perhaps to infinity (stand remains unharvested). (2) An increase in the rate used to discount physical carbon generally reduces the rotation age, but not in all cases. (3) As a corollary, an increase in the price of carbon increases or reduces rotation age depending on the weight chosen to discount future carbon fluxes. (4) Site characteristics and the mix of species on the site affect conclusions (2) and (3). (5) A large variety of carbon offset credits from forestry activities could be justified, which makes it difficult to accept any.
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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 | 0.001 |
| 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".