Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine

Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration

Carbon trading is one of the pivotal means of carbon emission reduction. Accurate prediction of carbon prices can stabilize the carbon market, mitigate investment risks, and promote green development. In this study, firstly, the IVMD and ICEEMDAN are used to decompose carbon price quadratically; secondly, the Dispersion entropy is used to identify the sequence frequency, and then the SOA-LSSVM model and TCN model are used to predict the high-frequency and low-frequency sequences, respectively; finally, the prediction results are integrated by SOA-GRU. As a result, the hybrid IVMD-ICEEMDAN-SOALSSVM/TCN-SOAGRU model was constructed. This framework consistently performs best under two carbon markets, the CEEX Guangzhou and the EU ETS, compared with 21 comparative models, with MAPEs of 0.42% and 0.83%, respectively. The main contributions are as follows: (1) A novel IVMD-ICEEMDAN secondary decomposition method is proposed, which improves the problem of poorly determining the value of the decomposition modal number K in the traditional VMD method and improves the efficiency of the carbon price sequence decomposition. (2) A hybrid forecasting model of LSSVM and TCN is proposed, effectively capturing the features of different sequences. (3) Optimization for LSSVM and GRU using SOA improves the stability and adaptability of the model. The article provides governments, enterprises, and investors with novel and effective carbon price forecasting tool.

Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting

Accurate carbon price forecasting can better allocate carbon emissions and thus ensure a balance between economic development and potential climate impacts. In this paper, we propose a new two-stage framework based on processes of decomposition and re-estimation to forecast prices across international carbon markets. We focus on the Emissions Trading System (ETS) in the EU, as well as the five main pilot schemes in China, spanning the period from May 2014 to January 2022. In this way, the raw carbon prices are first separated into multiple sub-factors and then reconstructed into factors of 'trend' and 'period' with the use of Singular Spectrum Analysis (SSA). Once the subsequences have been thus decomposed, we further apply six machine learning and deep learning methods, allowing the data to be assembled and thus facilitating the prediction of the final carbon price values. We find that from amongst these machine learning models, the Support vector regression (SSA-SVR) and Least squares support vector regression (SSA-LSSVR) stand out in terms of performance for the prediction of carbon prices in both the European ETS and equivalent models in China. Another interesting finding to come out of our experiments is that the sophisticated algorithms are far from being the best performing models in the prediction of carbon prices. Even after accounting for the impacts of the COVID-19 pandemic and other macro-economic variables, as well as the prices of other energy sources, our framework still works effectively.

Forecasting Carbon Price Using Double Shrinkage Methods

It is commonly recognized that setting a reasonable carbon price can promote the healthy development of a carbon trading market, so it is especially important to improve the accuracy of carbon price forecasting. In this paper, we propose and evaluate a hybrid carbon price prediction model based on so-called double shrinkage methods, which combines factor screening, dimensionality reduction, and model prediction. In order to verify the effectiveness and superiority of the proposed model, this paper takes data from the Guangdong carbon trading market for empirical analysis. The sample interval is from 5 August 2013 to 25 March 2022. Based on the results of the empirical analysis, several main findings can be summarized. First, the double shrinkage methods proposed in this paper yield more accurate prediction results than various alternative models based on the direct application of factor screening methods or dimensionality reduction methods, when comparing R2, root-mean-square error (RMSE), and root absolute error (RAE). Second, LSTM-based double shrinkage methods have superior prediction performance compared to LR-based double shrinkage methods. Third, these findings are robust with the use of normalized data, different data frequencies, different carbon trading markets, and different dataset divisions. This study provides new ideas for carbon price prediction, which might have a theoretical and practical contributions to complex and non-linear time series analysis.

An optimized decomposition integration framework for carbon price prediction based on multi-factor two-stage feature dimension reduction

The carbon trading market is an effective tool to combat greenhouse gas emissions, and as the core issue of carbon market, carbon price can stimulate the market for technological innovation and industrial transformation. However, the complex characteristics of carbon price such as nonlinearity and nonstationarity bring great challenges to carbon price prediction research. In this study, potential influencing factors of carbon price are introduced into carbon price forecasting, and a novel hybrid carbon price forecasting framework is developed, which contains data decomposition and reconstruction techniques, two-stage feature dimension reduction methods, intelligent and optimized deep learning forecasting with nonlinear integrated models and interval forecasting. Firstly, the carbon price series is decomposed into several simple and smooth subsequences using variational modal decomposition. The stacked autoencoder is then used to extract its effective features and reconstruct them into several new subsequences. A two-stage feature dimension reduction method is utilized for feature selection and extraction of exogenous variables. A bidirectional long and short-term memory model optimized based on the cuckoo search algorithm was used for prediction and nonlinear integration. Finally, Gaussian process regression based on a hybrid kernel function is applied to carbon price interval forecasting. The validity of the model was verified on seven real carbon trading pilot datasets in China. The methodology outperforms all benchmark models in the final simulation results, providing a novel and efficient forecasting method for the carbon trading industry.

Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine

Accurately forecasting carbon prices is key to managing associated risks in the financial market for carbon. To this end, the traditional strategy does not adequately decompose carbon prices, and the kernel extreme learning machine (KELM) with a single kernel function struggles to adapt to the nonlinearity, nonstationarity, and multiple frequencies of regional carbon prices in China. This study constructs a model, called the VMD-ICEEMDAN-RE-SSA-HKELM model, to forecast regional carbon prices in China based on the idea of ‘decomposition–reconstruction–integration’. The VMD is first used to decompose carbon prices and the ICEEMDAN is then used to decompose the residual term that contains complex information. To reduce the systematic error caused by increases in the mode components of carbon price, range entropy (RE) is used to reconstruct the results of its secondary decomposition. Following this, HKELM is optimized by the sparrow search algorithm and used to forecast each subseries of carbon prices. Finally, predictions of the price of carbon are obtained by linearly superimposing the results of the forecasts of each of its subseries. The results of experiments show that the secondary decomposition strategy proposed in this paper is superior to the traditional decomposition strategy, and the proposed model for forecasting carbon prices has significant advantages over a considered reference group of models.