Classification of Attributes on Green Manufacturing Practices: A Systematic Review
Bibliographic record
Abstract
This systematic review article aimed to identify the classification of the firm behaviour attributes in influencing firm decision on green manufacturing (GM).Firms as profit-oriented entities must identify critical features in implementing an environmental-friendly business practice.However, investment in the GM initiative often leads to extra costs and benefits that require attention in making an optimal decision.Thus, it is necessary to highlight the attribute of GM as the potential prospect of advantages and disadvantages of sustainable manufacturing for the firm.The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was adapted to study related articles using two major databases, Scopus and Web of Science, from 2002 to 2021.The results revealed four classifications of firm behaviour attributes that influence firm decisions on GM, namely (i) type of green manufacturing initiative; (ii) environmental impact; (iii) operating costs; and (iv) legislative requirement.Also, we found that GM practices impacts on benefits and losses for certain attributes.The study's findings suggest further study to determine in-depth industries' decisions in implementing the green manufacturing practices based on the attributes are presented.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.029 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".