Climate change mitigation in British Columbia’s forest sector: GHG reductions, costs, and environmental impacts
Bibliographic record
Abstract
Abstract Background The potential contributions from forest-based greenhouse gas (GHG) mitigation actions need to be quantified to develop pathways towards net negative emissions. Here we present results from a comparative analysis that examined mitigation options for British Columbia’s forest sector. Mitigation scenarios were evaluated using a systems perspective that takes into account the changes in emissions and removals in forest ecosystems, in harvested wood product (HWP) carbon stocks, and in other sectors where wood products substitute for emission-intensive materials and fossil fuels. All mitigation activities were assessed relative to a forward-looking ‘business as usual’ baseline for three implementation levels. In addition to quantifying net GHG emission reductions, we assessed economic, and socio-economic impacts as well as other environmental indicators relating to forest species, age class, deadwood availability and future timber supply. We further considered risks of reversal for land-based scenarios, by assessing impacts of increasing future wildfires on stands that were not harvested. Results Our spatially explicit analyses of forest sector mitigation options demonstrated a cost-effective portfolio of regionally differentiated scenarios that directed more of the harvested wood to longer-lived wood products, stopped burning of harvest residues and instead produced bioenergy to displace fossil fuel burning, and reduced harvest levels in regions with low disturbance rates. Domestically, net GHG emissions were reduced by an average of -9 MtCO 2 e year −1 over 2020–2050 for a portfolio of mitigation activities at a default implementation level, with about 85% of the GHG emission reductions achieved below a cost of $50/tCO 2 e. Normalizing the net GHG reduction by changes in harvested wood levels permitted comparisons of the scenarios with different ambition levels, and showed that a 1 MtCO 2 increase in cumulative harvested stemwood results in a 1 MtCO 2 e reduction in cumulative emissions, relative to the baseline, for the Higher Recovery scenario in 2070. Conclusions The analyses conducted in this study contribute to the global understanding of forest sector mitigation options by providing an integrated framework to synthesize the methods, assumptions, datasets and models needed to quantify mitigation activities using a systems approach. An understanding of economically feasible and socio-economically attractive mitigation scenarios along with trade offs for environmental indicators relating to species composition and age, helps decision makers with long-term planning for land sector contributions to GHG emission reduction efforts, and provides valuable information for stakeholder consultations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".