Estimation of carbon dioxide emissions from the cement industry in Beijing-Tianjin-Hebei using neural networks

Yaju Liu, Qianjian Xu, Zheng Wang, LiPing Qi and Jingzhao Lu

PLOS Climate, 2025, vol. 4, issue 3, 1-16

Abstract: This study develops a method to predict carbon dioxide (CO2) emissions from the cement industry in the Beijing-Tianjin-Hebei region using artificial intelligence-based neural networks. By analyzing data from the National Bureau of Statistics and the China Statistical Yearbook (2010–2021), we calculated CO2 emissions generated by fossil fuel combustion during cement production. The neural network model achieved robust predictive performance with a root mean square error (RMSE) of 0.05, a mean absolute error (MAE) of 2,640,769 tons, and a coefficient of determination (R2) of 0.9620. These results demonstrate the model’s effectiveness in identifying emission trends and supporting real-time strategies to mitigate CO2 emissions. Future research could expand this approach to other high-emission industries, providing valuable tools for global carbon reduction efforts.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pclm00:0000544

DOI: 10.1371/journal.pclm.0000544

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