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Circular Economy 2023, 2(2): 100040 https://doi.org/10.1016/j.cec.2023.100040
Research Article | Open Access | Issue | Published: 09 May 2023

Shaping the future of sustainable energy through AI-enabled circular economy policies

Show Author's Information Mir Sayed Shah Danish( ),  Tomonobu Senjyu
Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, 903-0213, Okinawa, Japan
Keywords:
Energy policy, Circular economy, Decarbonization, AI-enabled energy policy, Policy tools
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Danish MSS, Senjyu T. Shaping the future of sustainable energy through AI-enabled circular economy policies. Circular Economy, 2023, 2(2): 100040. https://doi.org/10.1016/j.cec.2023.100040
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Abstract Full text About this article

The energy sector is enduring a momentous transformation with new technological advancements and increasing demand leading to innovative pathways. Artificial intelligence (AI) is emerging as a critical driver of the change, offering new ways to optimize energy systems operations, control, automation, etc. Developing a competitive policy framework aligned with circular economy practices to adapt to the trends of the rapid revolution is crucial, shaping the future of energy and leading the sector in a sustainable, equitable, and impartial direction. This study aims to propose an AI-driven policy framework that aligns with the circular economy business model to address the transformation trend in the development of energy policies through a multidisciplinary approach. The study identifies key trends, various approaches, and evaluates the potential of AI in addressing the challenges. The AI-driven policy paradigm outlines a comprehensive framework and roadmap to harness the potential of AI through a forward-looking policy framework that considers the rapidly changing landscape and the essence of the circular economy. The proposed novel framework provides a roadmap for researchers, governments, and other stakeholders to navigate the future of energy and unlock the potential of AI for a sustainable energy future.


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About this article

Shaping the future of sustainable energy through AI-enabled circular economy policies

Show Author's information Mir Sayed Shah Danish( ), Tomonobu Senjyu
Department of Electrical and Electronics Engineering, University of the Ryukyus, 1 Senbaru, Nishihara, 903-0213, Okinawa, Japan

Abstract

The energy sector is enduring a momentous transformation with new technological advancements and increasing demand leading to innovative pathways. Artificial intelligence (AI) is emerging as a critical driver of the change, offering new ways to optimize energy systems operations, control, automation, etc. Developing a competitive policy framework aligned with circular economy practices to adapt to the trends of the rapid revolution is crucial, shaping the future of energy and leading the sector in a sustainable, equitable, and impartial direction. This study aims to propose an AI-driven policy framework that aligns with the circular economy business model to address the transformation trend in the development of energy policies through a multidisciplinary approach. The study identifies key trends, various approaches, and evaluates the potential of AI in addressing the challenges. The AI-driven policy paradigm outlines a comprehensive framework and roadmap to harness the potential of AI through a forward-looking policy framework that considers the rapidly changing landscape and the essence of the circular economy. The proposed novel framework provides a roadmap for researchers, governments, and other stakeholders to navigate the future of energy and unlock the potential of AI for a sustainable energy future.

Keywords: Energy policy, Circular economy, Decarbonization, AI-enabled energy policy, Policy tools

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Publication history
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Acknowledgements
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Publication history

Received: 25 February 2023
Revised: 07 April 2023
Accepted: 14 April 2023
Published: 09 May 2023
Issue date: June 2023

Copyright

© 2023 The Author(s).

Acknowledgements

Acknowledgements

We would like to sincerely express our gratitude to all those who contributed to completing this article. Your unwavering support, insightful comments, and invaluable guidance, especially during critical moments, have played an instrumental role in shaping this work. We are deeply grateful for your contributions and appreciate your willingness to join this journey.

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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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