Forest bioenergy harvesting changes carbon balance and risks biodiversity in boreal forest landscapes
Bibliographic record
Abstract
Climate solutions relying on forest bioenergy may be in conflict with carbon sequestration and storage by forests as well as conservation of biodiversity. We quantified effects of forest-residue harvesting for bioenergy on both forest carbon balance and biodiversity in a boreal forest landscape. Through a modeling framework, we simulated forest development in four real watersheds with three scenarios: (i) with and (ii) without forest-residue harvesting and (iii) set aside to study the conservation potential of these landscapes in the future without management. We simulated changes in the forest carbon stocks and in the quality and quantity of deadwood resources for 100 years and combined this information with the information on species habitat associations based on expert judgements. In this study, current practices of slash and stump harvesting reduced forest carbon stocks and deadwood volumes at the landscape scale and, consequently, halved the emissions savings that can be obtained with bioenergy. In addition, logging-residue harvesting reduced 15%–21% of the combined species conservation capacity of the landscape for red-listed, saproxylic species compared with forest management without bioenergy harvesting. Furthermore, the results indicated a potential conflict between areas of high bioenergy potential and high conservation potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".