Comparative life cycle assessment of bamboo-containing and wood-based hygiene tissue: Implications of fiber sourcing and conversion technologies
Bibliographic record
Abstract
This study assesses the environmental impact of producing consumer bath tissue (CBT) using Brazilian bleached eucalyptus kraft (BEK) and Canadian northern bleached softwood kraft (NBSK) market pulps in the United States, in comparison to bamboo-based CBT from China. Additionally, the analysis includes considerations of soil organic carbon (SOC) sequestration, and the biogenic global warming potential (GWPbio) based on biomass rotation periods. Results indicate a cradle-to-grave carbon footprint (CF) of 1,824 kg CO 2 eq/air-dry ton (ADt) for US CBT (70% BEK/30% NBSK) using Light Dry Creped (LDC) technology. Substituting BBK for BEK increases CF to 2,041 kg CO 2 eq/ADt, with Chinese CBT at 2,400 kg CO 2 eq/ADt. Using Creped Trough Air Drying (CTAD), CF rises to 2,531 and 2,739 kg CO 2 eq/ADt for BEK-NBSK and BEK-BBK mixtures, respectively. Including SOC factors do not change the overall picture, while the GWPbio factors are highly dependent on the time horizon considered. These results emphasize production technologies’ critical role in tissue sustainability and challenge bamboo’s perceived environmental advantages. • Conversion technology drives the environmental impact of consumer bath tissue. • Bamboo-based tissue shows a higher carbon footprint than wood-based alternatives. • Soil carbon sequestration and biogenic carbon cannot counteract tissue's 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".