Prediction and Analysis of the Price of Carbon Emission Rights in Shanghai: Under the Background of COVID-19 and the Russia–Ukraine Conflict

Qing Liu (), Huina Jin, Xiang Bai and Jinliang Zhang ()
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Qing Liu: School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China
Huina Jin: School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China
Xiang Bai: School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China
Jinliang Zhang: School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China

Mathematics, 2023, vol. 11, issue 14, 1-16

Abstract: In the spring of 2022, a new round of epidemic broke out in Shanghai, causing a shock to the Shanghai carbon trading market. Against this background, this paper studied the impact of the new epidemic on the price of Shanghai carbon emission rights and tried to explore the prediction model under the unexpected event. First, because a model based on point value data cannot capture the information hidden in inter-day price fluctuation, based on the interval price of Shanghai carbon emission rights (SHEA) and its influencing factors, an autoregressive conditional interval model with jumping and exogenous variables (ACIXJ) was established to explore the influence of the Russian–Ukrainian conflict and COVID-19 on the interval price of SHEA, respectively. The empirical results show that the conflict between Russia and Ukraine has no obvious influence on the price of SHEA, but COVID-19 led to a decline in the price trend of SHEA over four days before the city was closed, and the volatility changed significantly on the day before the city was closed. The price fluctuation was the strongest within 3 days after the city was closed; In addition, in order to accurately predict the interval data of SHEA against the background of COVID-19, based on the interval data decomposition algorithm (BEMD), a hybrid forecasting model of NDGM-ACIXJ/CNN-LSTM was constructed, in which the discrete gray model of approximate nonhomogeneous exponential series (NDGM) combined with the ACIXJ model is used to predict the high-frequency sub-interval, and the convolution neural network long-term and short-term memory model (CNN-LSTM) is used to predict the low-frequency sub-interval. The empirical results show that the prediction model proposed in this article has higher prediction precision than the reference models (ACIX, ACIXJ, NDGM-ACIXJ, BEMD-ACIX/CNN-LSTM, BEMD-ACIXJ/CNN-LSTM).

Keywords: carbon emission rights in Shanghai (SHEA); convolutional neural network long-short memory model (CNN-LSTM); interval data decomposition algorithm (BEMD); autoregressive conditional interval model (ACIXJ) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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