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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.

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