The role of digital twins in realising circular economy in the era of Industry 4.0 and Industry 5.0
Bibliographic record
Abstract
Technological advancements contribute to increased waste and greenhouse gas emissions through e-waste, energy-intensive manufacturing and infrastructure, and increased consumption. However, technology also offers solutions for mitigating these impacts through improved efficiency, waste management optimisation and environmental monitoring. This chapter explores the transformative role of digital twins (DTs) in realising the circular economy (CE) within the paradigms of Industry 4.0 (I4.0) and Industry 5.0 (I5.0). It begins by examining the advancements brought by I4.0, characterised by automation, IoT, AI and cyber-physical systems, aimed at enhancing efficiency and productivity. Transitioning to I5.0, the narrative shifts towards a human-centric and sustainable industrial approach, emphasising collaboration between humans and machines. DTs, as virtual replicas of physical systems or processes, emerge as pivotal enablers of CE by facilitating real-time monitoring, predictive maintenance and optimisation. The integration of DTs with CE principles supports resource efficiency, waste reduction and lifecycle extension. Key technologies such as AI, IoT, 5G and edge computing enhance the potential of DTs, enabling applications like supply chain transparency, energy efficiency and product lifecycle management. The chapter delves into the synergy between DTs and I5.0, highlighting how DTs empower mass customisation, personalised production and ethical sustainability. Real-world case studies, spanning aerospace, automotive and urban planning, demonstrate the practical impact of DTs in driving CE objectives. Despite challenges like data security, high implementation costs and standardisation gaps, the chapter identifies promising opportunities in predictive maintenance, energy optimisation and healthcare innovations. Concluding, the chapter asserts that DTs represent a cornerstone of sustainable industrial practices, bridging physical and digital realms to foster a resilient, human-centred and eco-friendly future. Their integration with evolving technologies ensures their relevance in shaping industries and achieving CE goals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".