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Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises

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  • Chauhan, Chetna
  • Parida, Vinit
  • Dhir, Amandeep

Abstract

The circular economy (CE) has the potential to capitalise upon emerging digital technologies, such as big data, artificial intelligence (AI), blockchain and the Internet of things (IoT), amongst others. These digital technologies combined with business model innovation are deemed to provide solutions to myriad problems in the world, including those related to circular economy transformation. Given the societal and practical importance of CE and digitalisation, last decade has witnessed a significant increase in academic publication on these topics. Therefore, this study aims to capture the essence of the scholarly work at the intersection of the CE and digital technologies. A detailed analysis of the literature based on emerging themes was conducted with a focus on illuminating the path of CE implementation. The results reveal that IoT and AI play a key role in the transition towards the CE. A multitude of studies focus on barriers to digitalisation-led CE transition and highlight policy-related issues, the lack of predictability, psychological issues and information vulnerability as some important barriers. In addition, product-service system (PSS) has been acknowledged as an important business model innovation for achieving the digitalisation enabled CE. Through a detailed assessment of the existing literature, a viable systems-based framework for digitalisation enabled CE has been developed which show the literature linkages amongst the emerging research streams and provide novel insights regarding the realisation of CE benefits.

Suggested Citation

  • Chauhan, Chetna & Parida, Vinit & Dhir, Amandeep, 2022. "Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:tefoso:v:177:y:2022:i:c:s0040162522000403
    DOI: 10.1016/j.techfore.2022.121508
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