Mitigation of climate change with biomass harvesting in Norway spruce stands: are harvesting practices carbon neutral?
Bibliographic record
Abstract
Biomass combustion is considered to be carbon neutral, but intensive biomass harvesting may negatively impact carbon stocks in forest soil and vegetation, which can offset the benefits of substituting fossil fuels with biomass. Here we evaluated conventional stem-only harvesting, whole-tree harvesting (WTH), and WTH excluding needles in terms of timber yield, biomass harvests, and forest carbon sequestration. We simulated harvest scenarios in current and changed climates with a process-based growth model (PipeQual) that was integrated with models describing soil decomposition (ROMUL) and soil water dynamics. Furthermore, we compared gains and losses of forest carbon with reductions in fossil-fuel emissions that result from using harvested biomass for energy production. WTH negatively affected stand growth, biomass, and soil carbon stock; negative effects on growth and biomass can be reduced by leaving nitrogen-rich needles behind during WTH. In a changed climate, organic-matter decomposition and nitrogen mineralization accelerated and tree growth was enhanced, increasing the carbon stock of trees and slightly decreasing the soil carbon stock. In the changed climate, WTH had less influence on forest growth and a similar influence on soil carbon sequestration than in the current climate. In the current climate, the WTH decreased the forest carbon stock by, on average, 26.8 Mg C·ha −1 over the rotation period. If harvested forest residues are used for energy production instead of fossil fuels, emissions decline by 19 Mg C·ha −1 (when WTH is applied over a rotation period). Thus, our analysis suggests that using forest residues for energy production leads to a net increase in carbon emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".