IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i8p3654-d1637312.html
   My bibliography  Save this article

Artificial Intelligence as Enabler for Adoption of Sustainable Nuclear-Powered Maritime Ships: Challenges and Opportunities

Author

Listed:
  • Miltiadis Alamaniotis

    (Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Konstantinos Ipiotis

    (SWECO UK Limited, Leeds LS7 4DN, UK)

Abstract

Decarbonization stands as one of humanity’s most pressing challenges, demanding collective efforts from multiple sectors to meet established goals. The transportation industry plays a pivotal role in this endeavor, with the maritime sector offering significant potential to reduce emissions. As a cornerstone of global goods and commodity transport, the maritime industry is uniquely positioned to contribute meaningfully to the global drive for lower carbon emissions. Artificial intelligence (AI), with its profound influence across diverse domains, is anticipated to play a vital role in supporting the nuclear shipping industry on its path to a decarbonized future. Specifically, AI provides tools to make nuclear power on ships a more economically viable solution while enhancing the safety and security of nuclear systems. This paper explores AI tools as an enabler for adopting nuclear-powered ships, delving into the challenges and opportunities associated with their implementation. Ultimately, it highlights AI’s role in fostering sustainable nuclear-powered maritime solutions, which align with and contribute to achieving global decarbonization goals.

Suggested Citation

  • Miltiadis Alamaniotis & Konstantinos Ipiotis, 2025. "Artificial Intelligence as Enabler for Adoption of Sustainable Nuclear-Powered Maritime Ships: Challenges and Opportunities," Sustainability, MDPI, vol. 17(8), pages 1-21, April.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3654-:d:1637312
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/8/3654/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/8/3654/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3654-:d:1637312. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.