Damaged forests provide an opportunity to mitigate climate change
Bibliographic record
Abstract
Abstract British Columbia (BC) forests are estimated to have become a net carbon source in recent years due to tree death and decay caused primarily by mountain pine beetle (MPB) and related post‐harvest slash burning practices. BC forest biomass has also become a major source of wood pellets, exported primarily for bioenergy to Europe, although the sustainability and net carbon emissions of forest bioenergy in general are the subject of current debate. We simulated the temporal carbon balance of BC wood pellets against different reference scenarios for forests affected by MPB in the interior BC timber harvesting area using the Carbon Budget Model of the Canadian Forest Sector (CBM‐CFS3). We evaluated the carbon dynamics for different insect‐mortality levels, at the stand‐ and landscape level, taking into account carbon storage in the ecosystem, wood products and fossil fuel displacement. Our results indicate that current harvesting practices, in which slash is burnt and only sawdust used for pellet production, require between 20–25 years for beetle‐impacted pine and 37–39 years for spruce‐dominated systems to reach pre‐harvest carbon levels (i.e. break‐even) at the stand‐level. Using pellets made from logging slash to replace coal creates immediate net carbon benefits to the atmosphere of 17–21 tonnes C ha−1, shortening these break‐even times by 9–20 years and resulting in an instant carbon break‐even level on stands most severely impacted by the beetle. Harvesting pine dominated sites for timber while using slash for bioenergy was also found to be more carbon beneficial than a protection reference scenario on both stand‐ and landscape level. However, harvesting stands exclusively for bioenergy resulted in a net carbon source unless the system contained a high proportion of dead trees (>85%). Systems with higher proportions of living trees provide a greater climate change mitigation if used for long lived wood products.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".