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Green Transition: The Frontier of the Digicircular Economy Evidenced from a Systematic Literature Review

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  • Fabio De Felice

    (Dipartimento di Ingegneria Civile e Meccanica, Università degli Studi di Cassino e del Lazio Meridionale, 03043 Cassino, FR, Italy)

  • Antonella Petrillo

    (Dipartimento di Ingegneria, Università degli Studi di Napoli Parthenope, 80143 Napoli, NA, Italy)

Abstract

Today, the issue of economic circularity is certainly not a new concept. It represents an essential issue in any production system since it is an alternative to the current production and consumption model. The importance of the topic is confirmed worldwide. However, there is still a “circularity gap” that can be bridged in the short and medium term, probably with the use of innovative and digital technologies. In fact, many researchers agree that the sustainable future can be achieved in the long term thanks to digital technologies (i.e., IoT, artificial intelligence, quantum computing etc.) which, thanks to their speed of calculation, are able to identify the right solutions at the right time. The challenge, therefore, will be to develop innovative technologies and tools for the efficient use of resources in industries for sustainable production. Thus, the aim of this study is to define the current state of the art and future research developments in this very promising field. To achieve this goal, the integration of a “set” of tools, based on the AHP method and the PRISMA protocol, is proposed. The results aim to be a guideline for decision makers and researchers interested in this topic.

Suggested Citation

  • Fabio De Felice & Antonella Petrillo, 2021. "Green Transition: The Frontier of the Digicircular Economy Evidenced from a Systematic Literature Review," Sustainability, MDPI, vol. 13(19), pages 1-26, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:11068-:d:651139
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    4. Javier Maldonado-Romo & Mario Aldape-Pérez, 2021. "Sustainable Circular Micro Index for Evaluating Virtual Substitution Using Machine Learning with the Path Planning Problem as a Case Study," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
    5. Guoteng Xu & Shuai Peng & Chengjiang Li & Xia Chen, 2023. "Synergistic Evolution of China’s Green Economy and Digital Economy Based on LSTM-GM and Grey Absolute Correlation," Sustainability, MDPI, vol. 15(19), pages 1-29, September.

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