Carbon Footprint of Copying Paper: Considering Temporary Carbon Storage Based on Life Cycle Analysis
Bibliographic record
Abstract
In the era of climate change, life cycle analysis (LCA) has been acknowledged as a useful method for examining carbon footprints of products or services., Once captured and stored by trees and other plants, biogenic CO2 would re-enter the atmosphere sooner or later after the use phase of the product. And many LCA studies did not calculate the temporary carbon storage in biogenic carbon. Thus, this paper proposed a hybrid LCA approach to provide a structured methodology for evaluating carbon footprint of copying paper in consideration of temporary carbon storage. The developed method was then applied to a paper mill of China. It is indicated that the hybrid LCA method could provide a comprehensive methodology for accounting carbon footprints as well as assessing effects of delaying GHG emissions. The results shows that the carbon footprint of 1000 kg copying paper was 647.89 kg CO2 under scenario 1 and -5094 kg CO2 under scenario 2. Concurrently, the effect of delaying the emission of the temporarily stored carbon in copying paper was 7.67%, 15.52%, or 23.58% for a certain period of time (i.e., 10, 25, or 30 years).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".