Hierarchical Multi-Scale Decomposition and Deep Learning Ensemble Framework for Enhanced Carbon Emission Prediction

Yinuo Sun, Zhaoen Qu, Zhuodong Liu and Xiangyu Li ()
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Yinuo Sun: School of Economics and Management, Ningxia University, Yinchuan 750021, China
Zhaoen Qu: School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
Zhuodong Liu: School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
Xiangyu Li: Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Mathematics, 2025, vol. 13, issue 12, 1-34

Abstract: Carbon emission prediction is critical for climate change mitigation across industrial, transportation, and urban sectors. Traditional statistical and machine learning methods struggle to capture complex multi-scale temporal patterns and long-range dependencies in emission data. This paper proposes a hierarchical multi-scale decomposition and deep learning ensemble framework that addresses these limitations. We integrate complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose carbon emission time series into intrinsic mode functions (IMFs) capturing different frequency bands. Each IMF is processed through a hybrid convolutional neural network (CNN)–Transformer architecture: CNNs extract local features and transformers model long-range dependencies via multi-head attention. An adaptive ensemble mechanism dynamically weights component predictions based on stability and performance metrics. Experiments on four real-world datasets (133,225 observations) demonstrate that our CEEMDAN–CNN–Transformer framework outperforms 12 state-of-the-art methods, achieving a 13.3% reduction in root mean square error (RMSE) to 0.117, 12.7% improvement in mean absolute error (MAE) to 0.088, and 13.0% improvement in continuous ranked probability score (CRPS) to 0.060. The proposed framework not only improves predictive accuracy, but also enhances interpretability by revealing emission patterns across multiple temporal scales, supporting both operational and strategic carbon management decisions.

Keywords: carbon emission forecasting; multi-scale decomposition; CEEMDAN; CNN–Transformer; adaptive ensemble learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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