Drivers, barriers and incentives to implementing environmental management systems in the manufacturing industry
Bibliographic record
Abstract
Industrial development has made major breakthroughs in the last decade in the wake of increased production, government policies and public demand. At the same time, problems related to environmental sustainability have become a major concern for producers. The unsatisfactory dimension of environmental protection has always been highlighted, because the environmental management system (EMS) is ignored and not consistently implemented in certain Tunisian companies. This increasing interest in environmental consciousness is pushing manufacturers to aspire to adopt successful environmental management strategies. Organizations are in fact increasingly responsible for monitoring and enhancing their environmental efficiency. The present work is intended to resolve these issues by establishing the essential factors and modelling their interrelationships in the Tunisian context. By reviewing literature and expert opinions, 14 critical factors have been identified which leads to responsive in the implementation of EMS- based ISO 14001. For better understanding, the MICMAC research was used to identify the critical variables according to their driving and dependence power. The present study highlights “Top management commitment and support” and “Government policies and legislation” as the most significant factors for ISO 14001 implementation. This research will facilitate organizations' readiness for implementation of ISO 14001 by providing a detailed understanding of mutual relationships among EMS factors based on ISO 14001.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".