A hybrid MCDA-LCA approach for assessing carbon foot-prints and environmental impacts of China’s paper producing industry and printing services
Bibliographic record
Abstract
Abstract Background Labeling of carbon foot-prints (CFPs) for products and services is regarded as a convenient and effective method for reducing greenhouse gas (GHG) emissions. Life cycle analysis (LCA) is a useful tool for examine CFP of relevant products and services. However, the corresponding standards for CFP of products and services can hardly be satisfactorily adopted. Also, most of the previous studies were based on an individual indicator, which can hardly reflect multiple dimensions of sustainable implications of products and services. Results Thus, in this research, a hybrid life cycle analysis (LCA) and multi-criteria decision analysis (MCDA) method was proposed for helping evaluate CFP of products and services under multiple environmental indicators. The results indicated: (a) Air pollution caused by coal consumption was the primary environmental impact in China’s paper-production industry, and (b) in printing industry, air pollution caused by VOC was the primary environmental impact in China. At the same time, CFP of 1,000 kg copying paper was 1,415.39 kg CO2e based on LCI data of a paper factory in China. CFP of printing services was varied from each printing activity. Conclusions When purchasing copying paper, consumers should pay attention on coal consumption of the product. In printing industry, VOC of printing services should be taken serious consideration in China.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".