Improving carbon storage and greenhouse gas emissions avoidance through harvested wood products use
Bibliographic record
Abstract
Afforestation can mitigate climate change by creating new carbon sinks and increasing wood supply. However, climate change can impact the growth of trees in afforested areas and affect their characteristics, and the harvested wood products that can be manufactured from them. This study aimed to quantify to what extent the quality of the wood supply directed to primary processing is influenced by climate change and alters the carbon storage of wood products. A multi-model approach was used to estimate the carbon stocks in harvested biomass resulting from plantations of black spruce on open woodlands and hybrid poplar on abandoned farmlands in Québec (Canada) under a gradient of climate forcing projections. Results suggest that increased climate forcing negatively impacts the quality of the harvested wood product basket and influences the relative amount of lumber vs. pulpwood. However, according to our assumptions, the decay of solid wood products in landfills produced more methane emissions than paper, which may constrain their climate change mitigation potential in the absence of methane capture or flaring. The cascading use of solid wood products in bioenergy at the end of their service life significantly reduced overall emissions. This study highlights how comprehensive afforestation strategies can, in the long term, be used to maximize the carbon storage potential of harvested wood products sourced from new plantations, as long as these strategies also include better use of pulp-quality wood, improved cascading use at the end-of-life of wood products and, most importantly, the avoidance of methane emissions from landfilled wood.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".