Impact of Cleaner Production and Environmental Management Systems on Sustainability: The Moderating Role of Industry 4.0
Bibliographic record
Abstract
Abstract The study aims to establish a framework by integrating the emerging topics and assisting the manufacturing companies, government, and policy-makers to encourage product innovation through cleaner production, environmental management techniques, and Industry 4.0 towards attaining sustainable development goals. The paper will conduct a survey using a structured questionnaire to collect data from the manufacturing companies in different states in Malaysia. Simple random sampling will be used to collect responses, and Structural Equation Modeling (SEM) will be used for data analysis. Cleaner production and environmental management systems enable organizations to create innovative products and contribute to developing environmental sustainability. Accordingly, the emerging and eco-friendly technologies of Industry 4.0 will support organizations in greener and innovative product creation through the minimum consumption of natural resources and reduce environmental pollution. This study intends to explore the developing topics essential for the current manufacturing atmosphere, as environmental sustainability is currently a significant concern for society. Furthermore, inspired by the recent research gap, this study will measure the moderating effect of Industry 4.0 on the relationship between product innovation and environmental sustainability.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".