Biochar Affects Greenhouse Gas Emissions from Urban Forestry Waste
Bibliographic record
Abstract
Urban forests are vital to cities as they provide a range of ecosystem services, including carbon (C) sequestration, air purification, and urban cooling. However, urban forestry also generates significant quantities of organic waste, such as grass clippings, pruned tree branches, and fallen tree leaves and woody debris, contributing to the release of greenhouse gases (GHGs) if those organic wastes are improperly managed. We studied the effect of wheat straw biochar produced at 500 °C on GHG emissions from two common types of urban forestry waste: green waste (GW) and yard waste (YW), using a 100-day laboratory incubation experiment. Compared to YW, GW consistently emitted more CO₂ throughout the incubation period, but biochar addition reduced CO₂ emissions by 9.8% and 17.6% from GW and YW, respectively, by day 100. Biochar application increased CH₄ emissions from GW, peaking on day 70 at a rate of 80.2 mg C kg⁻¹. The YW and biochar-amended YW exhibited negative cumulative CH₄ emissions, acting as CH₄ sinks; however, the addition of biochar weakened the CH₄ sink of YW. Biochar addition increased N₂O emissions from GW by 94.3% but decreased its emissions from YW by 61.4% by day 100. The global warming potential was highest in GW added with biochar, at 125.3 g CO₂-eq kg⁻¹, resulting from biochar addition exacerbating GHG emissions from GW. Our findings emphasize the importance of evaluating the effect of biochar on GHG emissions for specific urban forestry waste. Different biochars need to be tested to find one that can mitigate GHG emissions from GW.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".