A Sustainable Model for Forecasting Carbon Emission Trading Prices

Jiaqing Chen, Dongpeng Peng (), Zhiwei Liu, Lingzhi Wu and Ming Jiang
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Jiaqing Chen: School of Economics and Finance, Xi’an Jiao tong University, Xi’an 710061, China
Dongpeng Peng: School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China
Zhiwei Liu: School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China
Lingzhi Wu: School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China
Ming Jiang: School of Internet Economics and Business, Fujian University of Technology, Fuzhou 350014, China

Sustainability, 2024, vol. 16, issue 19, 1-16

Abstract: Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Keywords: carbon price prediction; ICEEMDAN; LSTM; Grey Wolf Optimizer; multi-scale entropy; sustainable development (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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