Hybrid Prediction Model for the Interindustry Carbon Emissions Transfer Network Based on the Grey Model and General Vector Machine

• Main Authors: Ying Hu, Kangjuan Lv
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Hybrid prediction model; grey model; general vector machine; artificial fish swarm algorithm; carbon emissions transfer network
• Online Access: https://ieeexplore.ieee.org/document/8966250/

Through analysis of the carbon emissions transfer network formed by the exchange of intermediate products among industries, we can promote the realization of national carbon emissions reduction goals. Therefore, it is of great significance to build a prediction model of the carbon emissions transfer network for more accurate predictions. According to the characteristics of the random oscillation sequence (ROS) of interindustry carbon emissions transfer, a hybrid prediction model denoted as the ROGM-AFSA-GVM is proposed based on the grey model (GM) for ROS and the general vector machine (GVM) optimized by the artificial fish swarm algorithm (AFSA). The proposed model uses the ROGM model to predict the general ROS trend and relies on the AFSA-GVM model to predict the nonlinear law of ROS. The predicted values of the two parts are combined to obtain predicted interindustry carbon emissions transfer values. The proposed model is used to simulate the interindustry carbon emissions transfer network of China. The simulation results show that the ROGM-AFSA-GVM model can effectively resolve the prediction problem of ROS. Comparing the predicted networks with the actually measured networks, it is verified that the proposed model is suitable for simulating the interindustry carbon emissions transfer network and has a good prediction performance.