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.
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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 | 0.001 |
| 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".