Open Access Journal Article

Carbon emissions trading price forecasts by multi-perspective fusion

by Chong Zhang a,*  and  Zhiying Feng b
Business School, Nanjing University, Nanjing, China
Business School, The University of Sydney, Sydney, Australia
Author to whom correspondence should be addressed.
EAL  2024, 53; 3(2), 53;
Received: 17 October 2023 / Accepted: 4 December 2023 / Published: 15 June 2024


The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.

Copyright: © 2024 by Zhang and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Zhang, C.; Feng, Z. Carbon emissions trading price forecasts by multi-perspective fusion. Economic Analysis Letters, 2024, 3, 53.
AMA Style
Zhang C, Feng Z. Carbon emissions trading price forecasts by multi-perspective fusion. Economic Analysis Letters; 2024, 3(2):53.
Chicago/Turabian Style
Zhang, Chong; Feng, Zhiying 2024. "Carbon emissions trading price forecasts by multi-perspective fusion" Economic Analysis Letters 3, no.2:53.
APA style
Zhang, C., & Feng, Z. (2024). Carbon emissions trading price forecasts by multi-perspective fusion. Economic Analysis Letters, 3(2), 53.

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  1. Atsalakis, G. S. (2016). Using computational intelligence to forecast carbon prices. Applied Soft Computing, 43, 107–116.
  2. Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271.
  3. Bi, H., Xiao, H., & Sun, K. (2019). The impact of carbon market and carbon tax on green growth pathway in China: a dynamic cge model approach. Emerging Markets Finance and Trade, 55(6), 1312–1325.
  4. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & et al. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877–1901.
  5. Byun, S. J., & Cho, H. (2013). Forecasting carbon futures volatility using GARCH models with energy volatilities. Energy Economics, 40, 207–221.
  6. Canakoglu, E., Yahsi, M., & Agrali, S. (2019). Carbon price forecasting models based on big data analytics. Carbon Management, 10(2), 175–187.
  7. Carl, J., & Fedor, D. (2016). Tracking global carbon revenues: A survey of carbon taxes versus cap-and-trade in the real world. Energy Policy, 96, 50–77.
  8. Chen, W., Xu, H., Jia, L., & Gao, Y. (2021). Machine learning model for Bitcoin exchange rate prediction using economic and technology determinants. International Journal of Forecasting, 37(1), 28–43.
  9. Cui, H-Y., & Dou, X-S. (2018). Carbon price forecasts in Chinese carbon trading market based on emd-ga-bp and emd-pso-lssvm. Operations Research and Management Science, 27(7), 133.
  10. Fan, X., Li, S., & Tian, L. (2015). Chaotic characteristic identification for carbon price and a multi-layer perceptron network prediction model. Expert Systems with Applications, 42(8), 3945–3952.
  11. Fauvel, K., Lin, T., Masson, V., Fromont, E., & Termier, A. (2021). Xcm: An explainable convolutional neural network for multivariate time series classification. Mathematics, 9(23), 3137.
  12. Fawaz, H. I., Lucas, B., Forestier, G., Pelletier, C., Schmidt, D. F., Weber, J., ... Petitjean, F. (2020). Inceptiontime: Finding alexnet for time series classification. Data Mining and Knowledge Discovery, 34(6), 1936–1962.
  13. Graves, A., Alex. (2012). Long short-term memory. Supervised sequence labelling with recurrent neural networks, pages 37–45.
  14. Hao, Y., Tian, C., & Wu, C. (2020). Modelling of carbon price in two real carbon trading markets. Journal of Cleaner Production, 244, 118556.
  15. Ji, L., Zou, Y., He, K., & Zhu, B. (2019). Carbon futures price forecasting based with arima-cnn-lstm model. Procedia Computer Science, 162, 33–38.
  16. Karim, F., Majumdar, S., Darabi, H., & Chen, S. (2017). Lstm fully convolutional networks for time series classification. IEEE access, 6, 1662–1669.
  17. Koop, G., & Tole, L. (2013). Forecasting the European carbon market. Journal of the Royal Statistical Society Series A: Statistics in Society, 176(3), 723–741.
  18. Li, S., Goel, L., & Wang, P. (2016). An ensemble approach for short-term load forecasting by extreme learning machine. Applied Energy, 170, 22–29.
  19. Liu, H., Dai, Z., So, D., & Le, Q. V. (2021). Pay attention to mlps. Advances in Neural Information Processing Systems, 34, 9204–9215.
  20. Liu, X., Wang, C., Wu, H., Yang, C., & Albitar, K. (2023). The impact of the new energy demonstration city construction on energy consumption intensity: Exploring the sustainable potential of China’s firms. Energy, 283(6), 128716.
  21. Lu, H., Ma, X., Huang, K., & Azimi, M. (2020). Carbon trading volume and price forecasting in China using multiple machine learning models. Journal of Cleaner Production, 249, 119386.
  22. Myerson, J., Green, L., & Warusawitharana, M. (2001). Area under the curve as a measure of discounting. Journal of the experimental analysis of behavior, 76(2), 235–243.
  23. Sanin, M. E., Violante, F., & Mansanet-Bataller, M. (2015). Understanding volatility dynamics in the eu-ets market. Energy Policy, 82, 321–331.
  24. Sun, W., & Zhang, C. (2018). Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm. Applied Energy, 231, 1354–1371.
  25. Tan, C. W., Dempster, A., Bergmeir, C., & Webb, G. I. (2022). Multirocket: multiple pooling operators and transformations for fast and effective time series classification. Data Mining and Knowledge Discovery, 36(5), 1623–1646.
  26. Wang, C., Liu, X., Li, H., & Yang, C. (2023). Analyzing the impact of low-carbon city pilot policy on enterprises' labor demand: Evidence from China. Energy Economics, 124, 106676.
  27. Wang, J., Cui, Q., & Sun, X. (2021). A novel framework for carbon price prediction using comprehensive feature screening, bidirectional gate recurrent unit and Gaussian process regression. Journal of Cleaner Production, 314, 128024.
  28. Wang, J., Wang, Z., Li, J., & Wu, J. (2018). Multilevel wavelet decomposition network for interpretable time series analysis. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2437–2446.
  29. Wang, M., Zhu, M., & Tian, L. (2022). A novel framework for carbon price forecasting with uncertainties. Energy Economics, 112, 106162.
  30. Wang, S., Zhang, N., Wu, L., & Wang, Y. (2016). Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and ga-bp neural network method. Renewable Energy, 94, 629–636.
  31. Yang, C., Chen, L., & Mo, B. (2023). The spillover effect of international monetary policy on China’s financial market. Quantitative Finance and Economics, 7(4), 508-537.
  32. Yang, S., Chen, D., Li, S., & Wang, W. (2020). Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm. Science of the Total Environment, 716, 137117.
  33. Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., & Eickhoff, C. (2021). A transformer-based framework for multivariate time series representation learning. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, 2114–2124.
  34. Zhang, F., & Wen, N. (2022). Carbon price forecasting: a novel deep learning approach. Environmental Science and Pollution Research, 29(36), 54782–54795.
  35. Zhou, F., Huang, Z., & Zhang, C. (2022). Carbon price forecasting based on ceemdan and lstm. Applied Energy, 311, 118601.
  36. Zhu, B., Chevallier, J., Zhu, B., & Chevallier, J. (2017a). Carbon price forecasting with a hybrid arima and least squares support vector machines methodology. Pricing and Forecasting Carbon Markets: Models and Empirical Analyses, 87–107.
  37. Zhu, B., Han, D., Wang, P., Wu, Z., Zhang, T., & Wei, Y-M. (2017b). Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. Applied Energy, 191, 521–530.
  38. Zhu, B., Ye, S., Wang, P., He, K., Zhang, T., & Wei, Y-M. (2018). A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting. Energy Economics, 70, 143–157.