When Industry 5.0 Meets the Circular Economy: A Systematic Literature Review
Bibliographic record
Abstract
Abstract This paper examines the convergence of Industry 5.0 and the circular economy, emphasizing the role of emerging technologies in promoting sustainability via human-centric approaches. In contrast to Industry 4.0, which prioritizes automation and digitalization, Industry 5.0 stresses the synergistic integration of technology, environmental sustainability, and human collaboration to enhance resource efficiency and minimize waste. Using co-word analysis and BERTopic modeling on 283 journal articles extracted from the Scopus database, this research identifies key trends and themes linking Industry 5.0 and the circular economy. The study findings demonstrate the use of automation, machine learning, and 3D printing in sustainable manufacturing, which aligns with circular economy principles by optimizing resource efficiency and reducing waste. The topic modeling analysis further demonstrates the role of blockchain, cybersecurity, and human-centric AI in enabling closed-loop systems while assuring transparency and accountability in circular production models. The collaboration between humans and machines emerges as a crucial topic highlighting the need for adaptive manufacturing systems to balance productivity and environmental responsibility. The findings indicate that Industry 5.0 increasingly aligns with circular economy goals, paving the way to more sustainable, resilient, and human-centric industrial processes. This study offers valuable insights for academics and practitioners, indicating that the confluence of technology, sustainability, and human involvement will propel the future of industrial innovation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".