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Carbon emissions trading price forecasts by multi-perspective fusion

Author

Listed:
  • Chong Zhang

    (Business School, Nanjing University, Nanjing, China)

  • Zhiying Feng

    (Business School, The University of Sydney, Sydney, Australia)

Abstract

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.

Suggested Citation

  • Chong Zhang & Zhiying Feng, 2024. "Carbon emissions trading price forecasts by multi-perspective fusion," Economic Analysis Letters, Anser Press, vol. 3(2), pages 13-25, June.
    • Handle: RePEc:bba:j00004:v:3:y:2024:i:2:p:13-25:d:266
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    References listed on IDEAS

    as
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