A Multi-Strategy Integration Prediction Model for Carbon Price

Hongwei Dong, Yue Hu (), Yihe Yang and Wenjing Jiang
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Hongwei Dong: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Yue Hu: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Yihe Yang: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Wenjing Jiang: School of Science, Zhejiang University of Science and Technology, Hangzhou 310023, China

Energies, 2023, vol. 16, issue 12, 1-19

Abstract: Carbon price fluctuations significantly impact the development of industries, energy, agriculture, and stock investments. The carbon price possesses the features of nonlinearity, non-stationarity, and high complexity as a time series. To overcome the negative impact of these characteristics on prediction and to improve the prediction accuracy of carbon price series, a combination prediction model named Lp-CNN-LSTM, which utilizes both convolutional neural networks and long short-term memory networks, has been proposed. Strategy one involved establishing distinct models of CNN-LSTM and LSTM to analyze high-frequency and low-frequency carbon price sequences; the combination of output was integrated to predict carbon prices more precisely. Strategy two comprehensively considered the economic and technical indicators of carbon price sequences based on the Pearson correlation coefficient, while the Multi-CNN-LSTM model selected explanatory variables that strongly correlated with carbon prices. Finally, a predictive model for a combination of carbon prices was developed using Lp-norm. The empirical study focused on China’s major carbon markets, including Hubei, Guangdong, and Shanghai. According to the error indicators, the performance of the Lp-CNN-LSTM model was superior to individual strategy prediction models. The Lp-CNN-LSTM model has excellent accuracy, superiority, and robustness in predicting carbon prices, which can provide a necessary basis for revising carbon pricing strategies, regulating carbon trading markets, and making investment decisions.

Keywords: carbon price forecast; complete ensemble empirical mode decomposition with adaptive noise; convolutional neural network; long short-term memory networks; Lp-norm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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