Link Between Sustainability and Industry 4.0: Trends, Challenges and New Perspectives
Bibliographic record
Abstract
The increasing number of studies that underline the relationship between industry 4.0 and sustainability shows that sustainability is one of the pillars of smart factories. Through a bibliometric performance and network analysis (BPNA), this research describes the existing relationship between industry 4.0 and sustainability, the strategic themes from 2010 to March 2019, as well as the research gaps for proposing future work. With this goal in mind, 894 documents and 5621 keywords were included for bibliometric analysis, which were treated with the support of Science Mapping Analysis Software Tool (SciMAT). The bibliometric performance analysis presented the number of publications over time and the most productive journals. The strategic diagram shown 12 main research clusters, which were measured according to bibliometric indicators. Moreover, the network structure of each cluster was depicted, and the patterns found were discussed based on the documents associated to the network. Our findings show the scientific efforts are focused to enhance economic and environmental aspects and highlights a lack of effort relating the social sphere. Finally, the paper concludes the challenges, perspectives, and suggestions for the potential future work in the field of study relating to industry 4.0 and 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".