Futuristic Challenges in Circular Economy: Transforming Challenges into Opportunities in Industry 5.0

Gayatri Rathi () and Samta Kathuria ()
Additional contact information
Gayatri Rathi: Swami Vivekanand Subharti University
Samta Kathuria: Parul Institute of Law, Parul University

A chapter in Sustainable Economy Models in the Age of Industry 5.0, 2025, pp 229-251 from Springer

Abstract: Abstract Sustainability through a circular economy using artificial intelligence within the context of Industry 5.0 is therefore a viable line of development. Industry 5.0 focuses on the placement of people alongside smart technology to work together and best fit circular economy guidelines, including waste reduction, better resource utilization, and recycling. This study also explores whether AI has a role in making linear industries circular by offering cutting-edge technological applications for waste and resource management, as well as supply chain traceability utilizing predictive modelling, self-acting sorting, and digital replicas. The research methodology of the study is done after reviewing high-quality research reviews and articles to draw evidence from existing literature and case studies to establish the use of AI in the circular economy to overcome these challenges. Further, the absence of clear legislation to address the use of AI in circular models hinders the advancement of circular economy applications, particularly in developing nations that have poor technological environments. To overcome these challenges, this study proposes a behavioural systems approach. Finally, outlining the state-of-the-art advancements of AI, the paper appeals to industries, policymakers, and communities for cooperation to realize AI’s potential and pursue the Industry 5.0 goals for developing a sustainable and resilient economy worldwide.

Keywords: Circular Economy (CE); Industry 5.0; Artificial Intelligence (AI); Waste management; Resource optimization (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-96-4104-8_14

Ordering information: This item can be ordered from
http://www.springer.com/9789819641048

DOI: 10.1007/978-981-96-4104-8_14

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().