Reducing logging intensity in north temperate rainforests for climate and economic benefits
Bibliographic record
Abstract
Interior temperate rainforest’s role as a global carbon sink conflicts with lucrative logging revenues. Yet, the relationship between the proportion of trees harvested and whole-ecosystem carbon flux is still unclear. In a large, replicated trial in western Canada, we monitored carbon in the ecosystem and wood products in response to logging of varying intensity: clearcuts, light and heavy partial-cuts, and no harvest. Averaged over the 26 years, net-CO 2 e yr −1 emissions increased linearly with logging intensity (R 2 =0.80 for mature stands). Unharvested stands were sinks (2.2 ± 1.9 MgCO 2 e ha −1 yr −1 ; mean ± standard deviation) and clearcuts were sources (-21 ± 11 MgCO 2 e ha −1 yr −1 ), whereas light partial-cuts almost recovered their pre-harvest carbon (-2.9 ± 2.7 MgCO 2 e ha −1 yr −1 ). An economic exercise suggests that partially or fully retaining mature trees in interior temperate rainforests can help meet 2050 ‘net-zero’ targets while still providing economic benefits greater than logging if emissions are priced above ∼CAD65 $ MgCO 2 e −1 . • A 26-year dataset of all temperate rainforest ecosystem and product carbon pools. • The first time for a long-term, empirical study on partial harvesting and carbon. • Forest regrowth did not recover carbon stocks in that time. • Reducing logging intensity and burning of residue will reduce emissions by 2050. • Compensating for the carbon benefits is relatively inexpensive.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".