Life Cycle Assessment of Biochar Modified Bioasphalt Derived from Biomass
Bibliographic record
Abstract
This paper focuses on the life cycle assessment (LCA) of different types of biochar modified bioasphalt (BMBA) by considering greenhouse gas (GHG) emission and environmental pollution factors. Biochar and bio-oil were obtained from two types of biomass (waste wood and pig manure). The application of BMBA would not only improve the efficiency of biomass utilization but also enhance the environmental protection. Analyses were carried out by considering different stages which stem from the combination of material preparation, construction, use, maintenance, and demolition recovery. The GHG (CO 2 equivalent) and environmental pollutants (volatile organic compounds equivalent, VOCs) of BMBA were used for life-cycle inventory assessment. The results showed that three critical factors including material preparation and demolition recovery contribute to environmental impact. Bioasphalt species could significantly affect the energy consumption factor and reduce the environmental pollution. As biochar and bio-oil contents increase, GHG emissions decrease accordingly. The results indicated that material preparation had the biggest contribution in energy consumption. The findings highlighted the significance of bioasphalt species and content on VOCs decay pattern in life cycle assessment and global warming potential.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".