Multi-ETS carbon prices forecasting based on EMD-SVM model

Xiaoli Yang,Huizheng Chen,Zixuan Zhang,Shanglei Chai,R Weerasinghe,J Wu,C.-H Weng

doi:10.1051/e3sconf/202019404052

Abstract

With the widespread attention of governments around the world on climate issues, carbon pricing-related policies have been gradually adopted by countries to deal with climate change. Among these policy tools, the carbon emissions trading system (ETS) is the most widely used. Carbon price plays a crucial role in this trading system, not only determining the trading activity, but also affecting the market stability. Therefore, carbon price prediction is so significant that we are motivated to study it. However, carbon price presents complex nonlinear dynamic characteristics, which makes some existing methods inaccurate. To address it, this paper combines empirical mode decomposition (EMD) and support vector machine (SVM) to predict carbon prices. The original carbon prices are signal-decomposed by using EMD and the decomposed signal is predicted by using SVM. Based on the EMD-SVM model, this paper conducts empirical analysis on the carbon prices of multi-ETS, including European Union ETS and China ETS pilots. The results of analysis show that the EMD-SVM model has better overall forecasting ability, and carbon prices forecasting performance of China ETS pilots is better than that of the EU ETS, while the short-term forecasting results of the model show the opposite conclusion. The proposed EMD-SVM model is advisable in carbon prices forecasting for market participants and regulatory authorities of multi-ETS.

Highlights

Since the industrial revolution, global climate change caused by excessive carbon emissions has had a serious impact on the environment and human life, and experience has shown that it is difficult to achieve the desired goal by government compulsory requirements or voluntary reduction of economic entities
The results showed that the hybrid model was better than single model
Empirical Mode Decomposition (EMD) is an adaptive signal decomposition method for nonlinear, nonstationary signals proposed by Huang et al (1998)

Summary

Introduction

Global climate change caused by excessive carbon emissions has had a serious impact on the environment and human life, and experience has shown that it is difficult to achieve the desired goal by government compulsory requirements or voluntary reduction of economic entities. Carbon prices time series has its own nonlinear and non-stationary characteristics[5], which makes traditional prediction models in poor performance. The use of a single machine learning model does not allow high-precision prediction of complex nonlinear carbon prices predictions. The above literature rarely compared the prediction effects of different carbon emission systems with a hybrid model containing machine learning model, and studied the applicability of the models under different trading systems. This paper used EMD-SVM hybrid model to forecast carbon prices of the EU ETS and China ETS pilots, in order to compare the prediction effects of the hybrid models in these markets. When forecasting carbon prices, this paper uses EMD-SVM hybrid model to predict the carbon prices and observed the prediction performance of the three carbon markets. That we can find a more suitable carbon prices forecasting market for this model

Methodology

Data description

Empirical results

Error Evaluation

Conclusion