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Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale

Author

Listed:
  • Chao Ding

    (Lawrence Berkeley National Laboratory)

  • Jing Ke

    (Lawrence Berkeley National Laboratory)

  • Mark Levine

    (Lawrence Berkeley National Laboratory)

  • Nan Zhou

    (Lawrence Berkeley National Laboratory)

Abstract

Artificial intelligence has emerged as a technology to enhance productivity and improve life quality. However, its role in building energy efficiency and carbon emission reduction has not been systematically studied. This study evaluated artificial intelligence’s potential in the building sector, focusing on medium office buildings in the United States. A methodology was developed to assess and quantify potential emissions reductions. Key areas identified were equipment, occupancy influence, control and operation, and design and construction. Six scenarios were used to estimate energy and emissions savings across representative climate zones. Here we show that artificial intelligence could reduce cost premiums, enhancing high energy efficiency and net zero building penetration. Adopting artificial intelligence could reduce energy consumption and carbon emissions by approximately 8% to 19% in 2050. Combining with energy policy and low-carbon power generation could approximately reduce energy consumption by 40% and carbon emissions by 90% compared to business-as-usual scenarios in 2050.

Suggested Citation

  • Chao Ding & Jing Ke & Mark Levine & Nan Zhou, 2024. "Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50088-4
    DOI: 10.1038/s41467-024-50088-4
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    References listed on IDEAS

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    Cited by:

    1. Jiang, Wei & Hu, Yanhui & Zhao, Xiangyu, 2025. "The impact of artificial intelligence on carbon market in China: Evidence from quantile-on-quantile regression approach," Technological Forecasting and Social Change, Elsevier, vol. 212(C).

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