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

Guanyu Lu, Hajime Katayama, Toshi H. Arimura, Shohei Morimura, Tomoichi Ishiwatari and Tetsu Iwasaki
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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.

RIEEM Discussion Paper Series from Research Institute for Environmental Economics and Management, Waseda University

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.

Keywords: Artificial intelligence energy management system; electricity consumption; difference in differences; energy savings (search for similar items in EconPapers)
JEL-codes: C23 Q41 Q48 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2026-01
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