Implementing a provincial-level universal daily industrial carbon emissions prediction by fine-tuning the large language model

Zhengyuan Feng, Yuheng Sun, Jun Ning, Shoujuan Tang, Guangxin Liu, Fangtao Liu, Yang Li and Lei Shi

Applied Energy, 2025, vol. 383, issue C, No S0306261925001023

Abstract: Accurate daily predictions of industrial carbon emissions can improve our understanding of industrial activities. In this paper, we propose a novel approach to predict daily industrial carbon emissions by fine-tuning pre-trained language models. This method constructs a general carbon emissions model applicable to all provinces, providing accurate predictions in natural language format and outperforming traditional carbon emissions models. It achieves Mean Absolute Error (MAE) of 0.005757, Root Mean Squared Error (RMSE) of 0.008982, and Mean Absolute Percentage Error (MAPE) of 5.485714 %. The results suggest that fine-tuned T5 with prompt text significantly improves the predictive accuracy for daily industrial carbon emissions compared with typical multi-layer perception and long short-term memory numerical models. A single model can be adapted to the prediction of industrial carbon emissions in 31 provinces of China.

Keywords: Carbon emissions prediction; Time series analysis; Large language model (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125372

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