Carbon emissions trading price forecasts by multi-perspective fusion

Chong Zhang and Zhiying Feng
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Chong Zhang: Business School, Nanjing University, Nanjing, China
Zhiying Feng: Business School, The University of Sydney, Sydney, Australia

Economic Analysis Letters, 2024, vol. 3, issue 2, 13-25

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.

Keywords: Carbon Emissions Trading Prices; Multi-Perspective Fusion; Random Volatility; Non-Stationary haracteristics; Deep Neural Networks (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (2)

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