Trade-Offs between Economic Gains and Carbon Stocks across a Range of Management Alternatives in Boreal Forests
Bibliographic record
Abstract
Boreal forests, storing approximately half of the global forest carbon (C), are key to the global C cycle and climate regulation. The sustainability of C stocks is adversely impacted by forest management. However, the economic gain–C stock relationship across forest management alternatives and diverse C pools remain unclear. Using empirical data, we examined the relationships between economic gains and total ecosystem C in response to the changes in rotation age and overstorey composition in boreal forests. We found that total ecosystem C increased initially, reached a maximum, and declined thereafter with increasing economic gains. The relationships between economic gains and C stocks of live biomass, deadwood, forest floor, and mineral soil followed similar trends with total ecosystem C. Path analysis showed that both rotation age and overstorey composition simultaneously drove economic gains and C stocks that led to their trade-off relationship. We further indicated that maximum economic gains (USD 5000/ha) could lead to approximately 40% loss of total ecosystem C, while the maximum total ecosystem C (320 Mg/ha) could be attained when giving up 50% of economic gains. These results provide broad guides for forest managers and decision-makers towards balancing economic and C objectives in forest management by integrating into a forest carbon market.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".