A scoping review and bibliometric analysis on sustainability-related cobotics and targeted recommendations for the industry
Bibliographic record
Abstract
The clock is ticking. The industry should have been half-way to its duty to contribute to a sustainable future by 2030, which is the target date defined by the United Nations (UN) to achieve its 17 Sustainable Development Goals (SDGs). Sustainability is one of the three pillars of the Industry 5.0 concept. However, scientific reviews on cobots do not holistically address sustainability as their intent. The present study provides a comprehensive scoping review and bibliometric analysis on sustainability in the field of cobots, industrial robots inherently designed to collaborate with humans. The aim of this paper is to explore the following research questions: (i) which criteria, sub-criteria, and dimensions of sustainability does the scientific literature from 1996 to 2022 on cobots in an industrial context focus on?, (ii) to what extent are economic, social, and environmental dimensions considered holistically rather than separately?, (iii) which journals, institutions, countries, and authors are most influential in advancing sustainability-related cobot research?, and (iv) which SDG does sustainability-related cobot research align with? Findings show that the social dimension is most frequently studied (71.92%), followed by economic (51.34%), and environmental (1.87%). Only 0.53% of publications consider all three sustainability dimensions holistically. Although the field overall is experiencing exponential growth, particularly in social and economic aspects, the environmental dimension remains critically underexplored. Term analysis indicates a shift from technical and social concerns toward psychological issues. The bibliometric analysis further identifies key journals and opportunities for collaboration. This paper introduces an analytical framework that links sustainability criteria and sub-criteria to the SDGs, offering guidance for researchers in selecting impactful topics that contribute to global sustainability. It also emphasizes the need to expand sustainability assessments beyond the organizational level toward a global perspective. The sustainability-related cobot literature is linked mainly to SDGs 3 (good health and well-being), 8 (decent work and economic growth), 10 (reduced inequalities), and 5 (gender equality), respectively. Consequently, the paper provides targeted recommendations for both industry and researchers to: (1) balance the way sustainability is addressed with cobots; and (2) align with the SDGs that are deemed suitable for the field.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.009 | 0.064 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".