A hybrid MCDA-LCA approach for assessing carbon foot-prints and environmental impacts of China’s paper producing industry and printing services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 CO 2 e 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it