Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets
Open accessAbstract
Carbon pricing is regarded as a crucial enabler for an accelerated low-carbon energy economy transformation to achieve temperature control targets. This paper studies carbon price forecasting considering historical carbon price series as an influencing factor. A hybrid model of a kernel-based extreme learning machine (KELM) optimized by the bat optimization algorithm based on wavelet transform is proposed. Firstly, the wavelet transform is used to eliminate the high-frequency components of the previous day’s carbon price data to improve the accuracy of prediction. Then, the partial auto-correlation function (PACF) is applied to analyse the correlation among historical carbon prices to select the inputs for the forecasting model. Additionally, adding a kernel function improves to some extent the fitting accuracy and stability of the traditional extreme learning machine. Finally, the parameters of the KELM model are optimized by the bat optimization algorithm. Two types of carbon prices in the China ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results show that the proposed hybrid methodology is more robust than other comparison models for carbon price forecasting.
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ML can be used to analyze prices within cap-and-trade markets, for example by predicting prices via supervised learning or analyzing the main drivers of prices via hierarchical clustering.
“ML can be used to analyze prices within these markets, for example by predicting prices via supervised learning [769,834,890,892] or analyzing the main drivers of prices via hierarchical clustering [891].”
From Tackling climate change with machine learning (2023) · cites this paper as [834]
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Tackling climate change with machine learning
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- Authors
- Wei, S., Chongchong, Z., & Cuiping, S.
- Affiliations
- Department of Business Administration, North China Electric Power University, Baoding 071000, China
- Venue
- Carbon Management
- Publisher
- Informa UK Limited
- Published
- 2018-11-02
- Volume
- 9
- Issue
- 6
- Pages
- 605-617
- ISSN
- 1758-3004 (ISSN-L)
- 1758-3012
- OpenAlex
- W2912766632
- Cited by
- 66
- References
- 26
- Citations by year
- 2026: 3 · 2025: 9 · 2024: 8 · 2023: 10 · 2022: 14 · 2021: 10 · 2020: 9 · 2019: 3
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Carbon Management (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- carbon price forecasting, wavelet transform, kernel extreme learning machine, bat algorithm, hybrid forecasting model, low-carbon transition
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