Including soil alters the optimization of forestry with carbon sinks
Bibliographic record
Abstract
We integrate a carbon net sink and stand-level wood production to analyze their simultaneous optimization as an economic problem. Carbon is included in living trees, wood products, and forest soil. Forestry is specified by a size-structured model for optimizing thinning timing and intensity, rotation period, and the optimal choice of rotation versus continuous cover forestry. The optimal inclusion of a carbon net sink increases the carbon pool mainly in living trees and forest soil, while the effect on the product carbon pool remains minor. With a 3% interest rate, increasing the CO2 price to €40 per tCO2 increases the total steady-state carbon pool by 131% and the soil carbon accounts for ca. 60% of the increased carbon storage. Omitting soil carbon, as in previous studies, leads to underestimates of the carbon sink, significantly decreasing the optimal total CO2 net sink and achievable economic net gain from joint wood production and carbon management. The inclusion of soil carbon suggests that, in contrast to previous results, a higher CO2 price does not necessarily favor continuous cover forestry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".