Cradle-to-gate life cycle analysis of slow pyrolysis biochar from forest harvest residues in Ontario, Canada
Bibliographic record
Abstract
Abstract Climate change mitigation technologies have been a focus in reducing atmospheric carbon levels for the past few years. One such mitigation technology is pyrolysis, where biomass feedstocks are combusted at elevated temperatures for varying durations to produce three main products: biochar, bio-oil, and biogas. While bio-oil and biogas are typically used to produce energy via further combustion, biochar can be used in several different applications. Furthermore, using forest harvest residues as a feedstock for biochar production helps use excess biomass from the forestry industry that was previously assumed unmarketable. In our study, we combined forest carbon analysis modelling with cradle-to-gate life cycle emissions to determine the greenhouse gas emissions of biochar produced from forest harvest residues. We examined three collection scenarios, spanning two harvesting methods in one forest management unit in northern Ontario, Canada. From our analysis, we observed immediate reductions (− 0.85 tCO2eq·tbiochar−1 in year 1) in CO2-equivalent emissions (CO2eq) when producing biochar from forest harvest residues that would have undergone controlled burning, without considering the end use of the biochar. For the forest harvest residues that would remain in-forest to decay over time, producing biochar would increase overall emissions by about 6 tCO2eq·tbiochar−1. Throughout the 100-year timeframe examined–in ascending order of cumulative emissions–scenario ranking was: full tree harvesting with slash pile burn < full tree harvesting with slash pile decay < cut-to-length/tree-length harvesting. Graphical Abstract
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".