Carbon price fluctuation prediction using a novel hybrid statistics and machine learning approach

Dawei Shang, Yudan Pang and Haijie Wang

Energy, 2025, vol. 324, issue C

Abstract: This study adopts a novel hybrid statistics and machine learning approach to predict carbon price fluctuations. We propose a framework integrating DILATED convolutional neural networks (CNN) and a long short-term memory (LSTM) neural network (NN) algorithm. We adopt the L2 parameter norm penalty as a regularization method based on statistics to make predictions based on the DILATED CNN-LSTM framework. Given the high correlation between the carbon price indicator and independent variables, we primarily include indicators related to blockchain information through the regularization process. We establish a dataset for carbon-price predictions. The experimental results indicate that the DILATED CNN-LSTM framework is superior to the traditional CNN-LSTM architecture and that blockchain information is associated with the carbon price. Compared to other approaches, the proposed RR-DILATED CNN-LSTM can effectively and accurately predict the fluctuation trend of carbon prices. The new forecasting methods and theoretical ecology proposed herein can provide a new basis for trend prediction and digital asset policy-making, represented by carbon prices, for both academia and practitioners.

Keywords: Carbon price; Energy prices; Price prediction; Driving factors; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:324:y:2025:i:c:s036054422501223x

DOI: 10.1016/j.energy.2025.135581

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