Driving Circular Economy through Digital Technologies: Current Research Status and Future Directions

Ziyuan Chi, Zhen Liu (), Fenghong Wang () and Mohamed Osmani
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Ziyuan Chi: School of Design, South China University of Technology, Guangzhou 510006, China
Zhen Liu: School of Design, South China University of Technology, Guangzhou 510006, China
Fenghong Wang: School of Design, South China University of Technology, Guangzhou 510006, China
Mohamed Osmani: School of Architecture, Building and Civil Engineering, Loughborough University, Loughborough LE11 3TU, UK

Sustainability, 2023, vol. 15, issue 24, 1-28

Abstract: The transition from a linear economy (LE) to a circular economy (CE) is not just about mitigating the negative impacts of LE, but also about considering changes in infrastructure, while leveraging the power of technology to reduce resource production and consumption and waste generation, and improve long-term resilience. The existing research suggests that digital technologies (DTs) have great potential to drive the CE. However, despite the exponential growth and increasing interest in studies on DTs and the CE from year 2016 onwards, few systematic studies on the application of DTs to enable the CE have been found. In addition, the current status and development direction of the DT-driven CE is unclear, and the potential of DTs to support CE implementation is under-researched. Therefore, the aim of this paper is to explore the potential of DTs to drive the CE. This paper set out to analyze the current status and development of the DT-driven CE and examine future development trends in the field. Using a systematic literature review approach, this paper is the first attempt to use a mixed method, i.e., to combine macro-quantitative bibliometric methods with a micro-qualitative content analysis method to explore the DT-driven CE. The results, which include the research background, co-occurrence clusters, research hotspots, and development trends of keyword co-occurrence network visualization and keyword burst detection, are presented from a macro perspective using two bibliometric analysis softwares. In addition, the use of 13 specific DTs in the CE is analyzed according to seven disciplinary areas (Environmental Sciences and Ecology, Engineering, Science and Technology and Other Topics, Business Economics, Computer Science, Operations Research and Management Science, and Construction and Building Technology) of greatest interest from a micro-qualitative point of view. Further, future trends and challenges facing DT-driven CE development are explored and feasible directions for solutions are proposed.

Keywords: circular economy; digital technologies; systematic review; bibliometric; Industry 4.0; big data; internet of things (IoT); Building Information Modeling (BIM); artificial intelligent (AI); digital twin (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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