Forecasting Chinese carbon emissions using a novel grey rolling prediction model

Carbon dioxide is the crucial factor leading to global warming, which has brought great negative effects on economic development and human life. Accurate prediction of carbon dioxide emissions is of great significance for the evaluation of China's current low-carbon environmental protection policy. However, the traditional forecasting models only consider the regularity of historical data, and seldom consider the addition of new information on the development trend. Then, this paper proposes a novel grey rolling mechanism based on new information priority principle. An average weakening buffer operator is used to process the original sequence and the new information is mined before modeling. The modeling mechanism and steps of the new model are described in detail. Finally, the proposed model is used to predict and analyze the trend of carbon dioxide emissions in China. Compared with other two types of classical prediction models without considering new information priority, the experimental results show that the proposed model demonstrates better simulation and prediction performance and a high stability. The research results provide important reference value for China's relevant environmental sectors to make decisions on energy conservation and emission reduction policies, and also help to enrich and expand the development of grey system theory. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a novel grey rolling mechanism based on the principle of new information priority for predicting the trend of carbon dioxide emissions in China. Experimental results demonstrate that the proposed model shows better simulation and prediction performance, as well as high stability.