Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration

Carbon trading is one of the pivotal means of carbon emission reduction. Accurate prediction of carbon prices can stabilize the carbon market, mitigate investment risks, and promote green development. In this study, firstly, the IVMD and ICEEMDAN are used to decompose carbon price quadratically; sec...

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Bibliographic Details
Published in:Journal of environmental management Vol. 362; p. 121253
Main Authors: Lan, Yuqiao, Huangfu, Yubin, Huang, Zhehao, Zhang, Changhong
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.06.2024
Subjects:
Carbon
carbon markets
Carbon price forecasting
Commerce
entropy
environmental management
Forecasting
hybrids
Improved variational mode decomposition
Machine learning
Models, Theoretical
prediction
Seagull optimization algorithm
Secondary decomposition
Carbon price forecasting
Improved variational mode decomposition
Seagull optimization algorithm
Secondary decomposition
Machine learning
ISSN:0301-4797, 1095-8630, 1095-8630
Online Access:Get full text
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Summary:Carbon trading is one of the pivotal means of carbon emission reduction. Accurate prediction of carbon prices can stabilize the carbon market, mitigate investment risks, and promote green development. In this study, firstly, the IVMD and ICEEMDAN are used to decompose carbon price quadratically; secondly, the Dispersion entropy is used to identify the sequence frequency, and then the SOA-LSSVM model and TCN model are used to predict the high-frequency and low-frequency sequences, respectively; finally, the prediction results are integrated by SOA-GRU. As a result, the hybrid IVMD-ICEEMDAN-SOALSSVM/TCN-SOAGRU model was constructed. This framework consistently performs best under two carbon markets, the CEEX Guangzhou and the EU ETS, compared with 21 comparative models, with MAPEs of 0.42% and 0.83%, respectively. The main contributions are as follows: (1) A novel IVMD-ICEEMDAN secondary decomposition method is proposed, which improves the problem of poorly determining the value of the decomposition modal number K in the traditional VMD method and improves the efficiency of the carbon price sequence decomposition. (2) A hybrid forecasting model of LSSVM and TCN is proposed, effectively capturing the features of different sequences. (3) Optimization for LSSVM and GRU using SOA improves the stability and adaptability of the model. The article provides governments, enterprises, and investors with novel and effective carbon price forecasting tool. •Propose a novel secondary decomposition-integration framework.•Compare 18 different hybrid frameworks and single models.•Validate that the secondary decomposition method is superior to the primary decomposition method.•Validate hybrid methods in frameworks are better than single methods.•Validate the effectiveness of the nonlinear integration and optimization method.
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ISSN:0301-4797
1095-8630
1095-8630
DOI:10.1016/j.jenvman.2024.121253