Substitution benefits of British Columbia’s mitigation strategies in the bioeconomy
Bibliographic record
Abstract
Abstract Globally, efforts to increase land sector contributions to net-zero emissions are pursued. Harvested wood products may retain carbon, and substitute emission-intensive products. The emission reductions achieved through substitution, or substitution benefits, can inform the design of climate-effective wood-use strategies. Mitigation analyses of a wood-based bioeconomy therefore need to include substitution to evaluate the mitigation outcomes across sectors. Substitution benefits can be estimated using displacement factors, which quantify the emissions avoided per unit of wood use. Here, we calculated the displacement factors of timber constructions and wood-derived biofuels to be around 1.03 and 0.45 tCO2e/tCO2e, respectively. Assuming substitution was achieved when changes in human behavior increased the share of wood use relative to the reference market share, we added the substitution benefits to a previous analysis that focused on biogenic emissions in British Columbia, Canada. At projected declining harvest rates, the theoretical maximum reduction that forest products can contribute over the period 2016 to 2050 is 66 MtCO2e·year−1 with an uncertainty range of 45–79 MtCO2e·year−1, relative to the baseline, by focusing on long-lived, high-displacement construction applications. However, because construction uses of wood in foreign markets are not guaranteed, and constrained by market access, the practical strategy that combines construction and biofuel uses can achieve 17.4 MtCO2e·year−1, equivalent to 30% of British Columbia’s 2050 target. Although a transformation of the bioeconomy may help achieve both climate and socio-economic benefits, potential conflict exists between maximizing regional and global benefits. How and where wood will be used can influence the desired mitigation outcomes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".