Artificial intelligence for calculating and predicting building carbon emissions: a review

Jianmin  Hua; Ruiyi  Wang; Ying Hu; Zimeng  Chen; Lin  Chen; Ahmed I. Osman; Mohamed  Farghali; Lepeng  Huang; Ji  Feng; Jun  Wang; Xiang  Zhang; Xingyang  Zhou; Pow-Seng  Yap

doi:10.1007/s10311-024-01799-z

Artificial intelligence for calculating and predicting building carbon emissions: a review

Jianmin Hua, Ruiyi Wang, Ying Hu, Zimeng Chen, Lin Chen^*, Ahmed I. Osman ^*, Mohamed Farghali, Lepeng Huang^*, Ji Feng, Jun Wang, Xiang Zhang, Xingyang Zhou, Pow-Seng Yap^*

^*Corresponding author for this work

School of Chemistry and Chemical Engineering

Research output: Contribution to journal › Review article › peer-review

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Abstract

The construction industry, being responsible for a large share of global carbon emissions, needs to reduce its high carbon output to meet carbon reduction goals. Artificial intelligence can provide efficient support for carbon emission calculation and prediction. Here, we review the use of artificial intelligence techniques in forecasting, management and real-time monitoring of carbon emissions, focusing on how they are applied, their impacts, and challenges. Compared to traditional methods, the prediction accuracy of artificial intelligence models has increased by 20%. Artificial intelligence-driven systems could reduce carbon emissions by up to 15% through real-time monitoring and adaptive management strategies. Artificial intelligence applications improve energy efficiency in buildings by up to 25%, while reducing operational costs by up to 10%. Artificial intelligence supports the establishment of a digital carbon management system and contributes to the development of the carbon trading market.

Original language	English
Number of pages	34
Journal	Environmental Chemistry Letters
Early online date	21 Mar 2025
DOIs	https://doi.org/10.1007/s10311-024-01799-z
Publication status	Early online date - 21 Mar 2025

Keywords

artificial intelligence
building carbon emissions
construction industry

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s10311-024-01799-zLicence: CC BY

Artificial intelligence for calculating and predicting building carbon emissions: a review
Copyright 2025 The Authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 1.91 MBLicence: CC BY

Cite this

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title = "Artificial intelligence for calculating and predicting building carbon emissions: a review",

abstract = "The construction industry, being responsible for a large share of global carbon emissions, needs to reduce its high carbon output to meet carbon reduction goals. Artificial intelligence can provide efficient support for carbon emission calculation and prediction. Here, we review the use of artificial intelligence techniques in forecasting, management and real-time monitoring of carbon emissions, focusing on how they are applied, their impacts, and challenges. Compared to traditional methods, the prediction accuracy of artificial intelligence models has increased by 20%. Artificial intelligence-driven systems could reduce carbon emissions by up to 15% through real-time monitoring and adaptive management strategies. Artificial intelligence applications improve energy efficiency in buildings by up to 25%, while reducing operational costs by up to 10%. Artificial intelligence supports the establishment of a digital carbon management system and contributes to the development of the carbon trading market.",

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AU - Hua, Jianmin

AU - Wang, Ruiyi

AU - Hu, Ying

AU - Chen, Zimeng

AU - Chen, Lin

AU - Osman , Ahmed I.

AU - Farghali, Mohamed

AU - Huang, Lepeng

AU - Feng, Ji

AU - Wang, Jun

AU - Zhang, Xiang

AU - Zhou, Xingyang

AU - Yap, Pow-Seng

PY - 2025/3/21

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N2 - The construction industry, being responsible for a large share of global carbon emissions, needs to reduce its high carbon output to meet carbon reduction goals. Artificial intelligence can provide efficient support for carbon emission calculation and prediction. Here, we review the use of artificial intelligence techniques in forecasting, management and real-time monitoring of carbon emissions, focusing on how they are applied, their impacts, and challenges. Compared to traditional methods, the prediction accuracy of artificial intelligence models has increased by 20%. Artificial intelligence-driven systems could reduce carbon emissions by up to 15% through real-time monitoring and adaptive management strategies. Artificial intelligence applications improve energy efficiency in buildings by up to 25%, while reducing operational costs by up to 10%. Artificial intelligence supports the establishment of a digital carbon management system and contributes to the development of the carbon trading market.

AB - The construction industry, being responsible for a large share of global carbon emissions, needs to reduce its high carbon output to meet carbon reduction goals. Artificial intelligence can provide efficient support for carbon emission calculation and prediction. Here, we review the use of artificial intelligence techniques in forecasting, management and real-time monitoring of carbon emissions, focusing on how they are applied, their impacts, and challenges. Compared to traditional methods, the prediction accuracy of artificial intelligence models has increased by 20%. Artificial intelligence-driven systems could reduce carbon emissions by up to 15% through real-time monitoring and adaptive management strategies. Artificial intelligence applications improve energy efficiency in buildings by up to 25%, while reducing operational costs by up to 10%. Artificial intelligence supports the establishment of a digital carbon management system and contributes to the development of the carbon trading market.

KW - artificial intelligence

KW - building carbon emissions

KW - construction industry

U2 - 10.1007/s10311-024-01799-z

DO - 10.1007/s10311-024-01799-z

M3 - Review article

SN - 1610-3661

JO - Environmental Chemistry Letters

JF - Environmental Chemistry Letters

ER -

Artificial intelligence for calculating and predicting building carbon emissions: a review

Abstract

Keywords

UN SDGs

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