The Role of Knowledge Development in Manufacturing Sustainability
Bibliographic record
Abstract
The propensity of industrial firms to build sustainability strategies based on knowledge development is an incentive for strategies driven by environmental, social, and economic criteria. However, although firms are increasingly committed to sustainability, poor engagement may lead to strategic drifts. Energy efficiency is one of the most important targets in industrial firms for reducing emissions primarily generated directly by the firm—emissions that are created by generating electricity or heat needed by firms. We focus on energy efficiency, which—along with technological advancement—is the most critical factor in industrial decarbonization and is a pathway for improving economic competitiveness and sustainability. Advances in technological innovation and stakeholders’ requirements provide a range of reasons for achieving environmental benefits. In our view, the outcomes of environmental strategies are influenced by the commitment of firms to sustainable business and their propensity for R&D. Our results show that 44.1% of firms claim to have an investment plan for energy efficiency. This percentage rises to 65% for firms investing more than 4% of their turnover in R&D. A similar trend can be noticed for investments in environmental, social, and governance (ESG) topics, in which 83.3% of the firms claim to have an investment plan of 4% of their turnover. We argue that ESG strategies require competencies and capability building among employees to be successful; otherwise, the risk of strategic drifts increases.
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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 | 0.016 |
| 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 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".