Open Access Journal Article

Carbon emissions trading price forecasts by multi-perspective fusion

by Chong Zhang a,*  and  Zhiying Feng b
a
Business School, Nanjing University, Nanjing, China
b
Business School, The University of Sydney, Sydney, Australia
*
Author to whom correspondence should be addressed.
EAL  2024, 53; 3(2), 53; https://doi.org/10.58567/eal03020002
Received: 17 October 2023 / Accepted: 4 December 2023 / Published Online: 15 June 2024
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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;
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. https://doi.org/10.58567/eal03020002
AMA Style
Zhang C, Feng Z. Carbon emissions trading price forecasts by multi-perspective fusion. Economic Analysis Letters; 2024, 3(2):53. https://doi.org/10.58567/eal03020002
Chicago/Turabian Style
Zhang, Chong; Feng, Zhiying 2024. "Carbon emissions trading price forecasts by multi-perspective fusion" Economic Analysis Letters 3, no.2:53. https://doi.org/10.58567/eal03020002
APA style
Zhang, C., & Feng, Z. (2024). Carbon emissions trading price forecasts by multi-perspective fusion. Economic Analysis Letters, 3(2), 53. https://doi.org/10.58567/eal03020002

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