A life cycle assessment of environmental and economic balance of biochar systems in Quebec
Bibliographic record
Abstract
A life cycle analysis (LCA) for pyrolysis biochar systems was carried out to determine greenhouse gas balance, carbon cycling, and the economics of biochar production from different agricultural residues and wastes. Investigating a range of feedstocks (forest residues, corn stover, etc.) provided insight into the use of biomass residues rather than bioenergy crops as biochar production substrates and the resulting energy and climate change impacts. The analyses were conducted based on various optimized pyrolysis parameters for corn fodder and forest residue. The observed reductions of greenhouse gas (GHG) emissions (CO 2 equivalent per Mg dry feedstock) for both corn fodder and forest residue were mainly contributed by the stable carbon in the biochar. Corn fodder showed a greater reduction in emissions than forest residue, indicating the corn fodder’s greater economic potential for soil sequestration of stable carbon. The relative GHG emission analysis found that the optimization of a biomass pyrolysis system for biochar production is better suited for soil sequestration of stable carbon than as a fuel source. The economic viability of the pyrolysis-biochar system is largely dependent on the costs of feedstock production, pyrolysis, and the value of C offsets. The LCA reported in this study can be instrumental in assessing the environmental potential of biochar production and its application in the region.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".