Climate change mitigation strategies in the forest sector: biophysical impacts and economic implications in British Columbia, Canada
Bibliographic record
Abstract
Managing forests to increase carbon sequestration or reduce carbon emissions and using wood products and bioenergy to store carbon and substitute for other emission-intensive products and fossil fuel energy have been considered effective ways to tackle climate change in many countries and regions. The objective of this study is to examine the climate change mitigation potential of the forest sector by developing and assessing potential mitigation strategies and portfolios with various goals in British Columbia (BC), Canada. From a systems perspective, mitigation potentials of five individual strategies and their combinations were examined with regionally differentiated implementations of changes. We also calculated cost curves for the strategies and explored socio-economic impacts using an input-output model. Our results showed a wide range of mitigation potentials and that both the magnitude and the timing of mitigation varied across strategies. The greatest mitigation potential was achieved by improving the harvest utilization, shifting the commodity mix to longer-lived wood products, and using harvest residues for bioenergy. The highest cumulative mitigation of 421 MtCO 2 e for BC was estimated when employing the strategy portfolio that maximized domestic mitigation during 2017–2050, and this would contribute 35% of BC’s greenhouse gas emission reduction target by 2050 at less than $100/tCO 2 e and provide additional socio-economic benefits. This case study demonstrated the application of an integrated systems approach that tracks carbon stock changes and emissions in forest ecosystems, harvested wood products (HWPs), and the avoidance of emissions through the use of HWPs and is therefore applicable to other countries and regions.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".