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

Research output: Contribution to journalReview articlepeer-review

316 Downloads (Pure)

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 languageEnglish
Pages (from-to)783-816
Number of pages34
JournalEnvironmental Chemistry Letters
Volume23
Issue number3
Early online date21 Mar 2025
DOIs
Publication statusPublished - Jun 2025

UN SDGs

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

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • artificial intelligence
  • building carbon emissions
  • construction industry
  • sustainable development
  • real-time monitoring
  • calculation and prediction

Fingerprint

Dive into the research topics of 'Artificial intelligence for calculating and predicting building carbon emissions: a review'. Together they form a unique fingerprint.

Cite this