Predicting carbon futures prices based on a new hybrid machine learning: Comparative study of carbon prices in different periods.

Accurate prediction of carbon price is of great significance to national energy security and climate environment policies. This paper comes up with a new forecasting model variational mode decomposition, convolutional neural network, bidirectional long short-term memory, and multi-layer perceptron (VMD-CNN-BILSTM-MLP) to predict EUA carbon futures prices in two periods of five years before and after the introduction of emission reduction policies. The parameters of the VMD model are determined by genetic algorithm (GA) firstly, carbon futures prices are broken down into subsequences of different frequencies using the model. The MLP model is then applied to predict the highest frequency sequence. The CNN-BILSTM model is applied to predict other subsequences later. Finally, the predicted values of each subsequence are linearly added to obtain the final result of the entire model. The prediction effect of the model is mainly tested by root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R2) and the modification of Diebold-Mariano test (MDM). In both periods, the proposed model predicts better than the other models, and the prediction effect of carbon futures price in the first five years is a little better than that in the second five years. In general, the experiment of predicting carbon futures prices in two different periods, the experiment of changing the proportion of data set and the experiment of predicting the whole sample all prove that the mixed model proposed in this paper has good prediction effect.

Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine.

In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models.

Scenario simulation of the EU carbon price and its enlightenment to China.

Through exploring price characteristics of carbon futures products in EU ET, this paper aims to provide China's policy makers with meaningful materials and references for understanding how a carbon trading market can be established and well regulated. Based on the dataset comprising of multiple sources including Euro stoxx600 index, coal and crude oil prices, natural gas prices and European clean energy company stock prices, etc., this paper uses BP neural network model to simulate the long-term trends of carbon futures prices in six scenarios that represent the typical features of a carbon trading market. The results show that: (1) the magnitude of economic development's effect on carbon price is the largest among other factors, with the shortest duration; (2) in comparison, the effect of black energy consumption is weaker, but its lasting duration is the longest; (3) the impact of clean energy development on carbon price is similar to that of black energy, but the effect magnitude and lasting duration are relatively smaller. These findings suggest three viable directions for the development of China's carbon trading market in future i.e. adjusting total quotas in accordance with economic development, establishing market price stabilization mechanism, and developing clean energy. The novelty of this paper is to simulate the long-term trend of carbon prices by constructing a carbon price prediction system.