IDEAS home Printed from https://ideas.repec.org/p/was/dpaper/2502.html
   My bibliography  Save this paper

Does an Artificial Intelligence Energy Management System Reduce Electricity Consumption in Japan’s Retail Sector?

Author

Listed:
  • Guanyu Lu

    (Faculty of Political Science and Economics, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050.)

  • Hajime Katayama

    (Faculty of Commerce, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku,in the Tokyo, 169-8050.)

  • Toshi H. Arimura

    (Faculty of Political Science and Economics, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050.)

  • Shohei Morimura

    (Research Institute for Environmental Economics and Management, Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo 169-8050.)

  • Tomoichi Ishiwatari

    (iGRID SOLUTIONS Inc., 3-7-4 Kojimachi, Chiyoda-ku, Tokyo 102-0083.)

  • Tetsu Iwasaki

    (iGRID SOLUTIONS Inc., 3-7-4 Kojimachi, Chiyoda-ku, Tokyo 102-0083.)

Abstract

This study examines the impact of “Enudge,†an artificial intelligence (AI) energy management system (EMS), on electricity consumption in the retail sector. As retail installations increasingly contribute to nonindustrial CO₂ emissions, conventional EMSs frequently fail to manage the complex and variable energy demands in these settings. By leveraging a difference-in-differences framework on store-level data from over 1,700 retail stores in Japan between November 2018 and December 2023, this study finds that installation of AI EMS-Enudge reduces electricity consumption by an average of 1.9%. However, this reduction effect declines over time, with electricity savings diminishing within five to ten months. This decay effect is consistent with the decrease in user interaction with the recommendations provided by AI, suggesting that user engagement may play a crucial role in reducing electricity consumption. Heterogeneity analyses reveal that the system’s performance varies across retail establishments and seasonal contexts. Moreover, a cost-benefit analysis aimed at exploring break-even tariffs and implied abatement costs highlights that the installation of an AI EMS can contribute to cost savings, especially under high tariffs and higher-carbon grids.

Suggested Citation

  • Guanyu Lu & Hajime Katayama & Toshi H. Arimura & Shohei Morimura & Tomoichi Ishiwatari & Tetsu Iwasaki, 2026. "Does an Artificial Intelligence Energy Management System Reduce Electricity Consumption in Japan’s Retail Sector?," RIEEM Discussion Paper Series 2502, Research Institute for Environmental Economics and Management, Waseda University.
    • Handle: RePEc:was:dpaper:2502
    as

    Download full text from publisher

    File URL: https://prj-rieem.w.waseda.jp/dp/dp2502.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    Statistics

    Access and download statistics

    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:was:dpaper:2502. 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: Takuro Miyamoto (email available below). General contact details of provider: https://prj-rieem.w.waseda.jp/en/discussion_papers .

    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.