Diverting residual biomass to energy use: Quantifying the global warming potential of biogenic <scp>CO<sub>2</sub></scp> (<scp>GWP<sub>bCO2</sub></scp>)
Bibliographic record
Abstract
Abstract To calculate the global warming potential of biogenic carbon dioxide emissions (GWPbCO2) associated with diverting residual biomass to bioenergy use, the decay of annual biogenic carbon pulses into the atmosphere over 100 years was compared between biomass use for energy and its business‐as‐usual decomposition in agricultural, forestry, or landfill sites. Bioenergy use increased atmospheric CO2 load in all cases, resulting in a 100GWPbCO2 (units of g CO2e/g biomass CO2 released) of 0.003 for the fast‐decomposing agricultural residues to 0.029 for the slow, 0.084–0.625 for forest residues, and 0.368–0.975 for landfill lignocellulosic biomass. In comparison, carbon emissions from fossil fuels have a 100GWP of 1.0 g (CO2e/g fossil CO2). The fast decomposition rate and the corresponding low 100GWPbCO2 values of agricultural residues make them a more climate‐friendly feedstock for bioenergy production relative to forest residues and landfill lignocellulosic biomass. This study shows that CO2 released from the combustion of bioenergy or biofuels made from residual biomass has a greenhouse gas footprint that should be considered in assessing climate impacts.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".