Abstract
Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks.
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The datasets analyzed during the current study are available from the first author on reasonable request.
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Funding
The study was supported in part by the Ministry of Education of the People’s Republic of China Humanities and Social Sciences Youth Foundation (Grant Nos. 21YJCZH148 and 22YJC910014), the Natural Science Foundation of Anhui Province (Grant No. 2108085MG239), the Social Sciences Planning Youth Project of Anhui Province (Grant No. AHSKQ2022D138), the National Natural Science Foundation of China under Grants (Grant No. 72071001), the Innovation Development Research Project of Anhui Province (Grant No. 2021CX053).
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Piao Wang: Conceptualization, Roles/Writing—original draft, Data curation. Muhammad Adnan Zahid Chudhery: Supervision; Visualization; Writing review & editing. Xin Zhao: Supervision, Writing review & editing, Formal analysis. Chen Wang: Conceptualization, Writing – review & editing. All authors contributed equally to this work.
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Roula Inglesi-Lotz.
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Wang, P., Chudhery, M.A.Z., Xu, J. et al. A two-stage interval-valued carbon price forecasting model based on bivariate empirical mode decomposition and error correction. Environ Sci Pollut Res 30, 78262–78278 (2023). https://doi.org/10.1007/s11356-023-27822-4
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DOI: https://doi.org/10.1007/s11356-023-27822-4