Forecasting Carbon Price in China: A Multimodel Comparison

Houjian Li, Xinya Huang, Deheng Zhou, Andi Cao, Mengying Su, Yufeng Wang and Lili Guo
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Houjian Li: College of Economics, Sichuan Agricultural University, Chengdu 611130, China
Xinya Huang: College of Economics, Sichuan Agricultural University, Chengdu 611130, China
Deheng Zhou: College of Economics, Sichuan Agricultural University, Chengdu 611130, China
Andi Cao: College of Economics, Sichuan Agricultural University, Chengdu 611130, China
Mengying Su: College of Economics, Guangxi University for Nationalities, Nanning 530006, China
Yufeng Wang: College of Economics, Sichuan Agricultural University, Chengdu 611130, China
Lili Guo: College of Economics, Sichuan Agricultural University, Chengdu 611130, China

IJERPH, 2022, vol. 19, issue 10, 1-16

Abstract: With the global concern for carbon dioxide, the carbon emission trading market is becoming more and more important. An accurate forecast of carbon price plays a significant role in understanding the dynamics of the carbon trading market and achieving national emission reduction targets. Carbon prices are influenced by many factors, which makes carbon price forecasting a complicated problem. In recent years, deep learning models are widely used in price forecasting, because they have high forecasting accuracy when dealing with nonlinear time series data. In this paper, Multivariate Long Short-Term Memory (LSTM) in deep learning is used to forecast carbon prices in China, which takes into account the factors affecting the carbon price. The historical time series data of carbon prices in Hubei (HBEA) and Guangdong (GDEA) and three traditional energy prices affecting carbon prices from 5 May 2014 to 22 July 2021 are collected to form two data sets. To prove the forecast effect of our model, this paper not only uses Multivariate LSTM, Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Network (RNN) to forecast the same data, but also compares the forecast results of Multivariate LSTM with the existing research on HBEA and GDEA forecast based on deep learning recently. The results show that the MAE, MSE, and RMSE obtained by the Multivariate LSTM are all smaller than other prediction models, which proves that the model is more suitable for carbon price forecast and offers a new approach to carbon prices forecast. This research conclusion also provides some policy implications.

Keywords: multivariate long short-term memory; multilayer perceptron; support vector regression; recurrent neural network; carbon price forecasting (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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