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A novel fractional order Grey prediction modeland its application to Chinese carbon emissions

Hui Li, Zixuan Wu,Shuqu Qian,Huiming Duan

Research Square (Research Square)(2023)

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摘要
Abstract Carbon emissions have attracted widespread attention and become one of the most important research topics in the international arena. An objective and accurate prediction of carbon emissions can provide a theoretical basis for the Chinese government to set carbon reduction targets and policies, and also help China to explore a suitable carbon reduction pathway. Considering that the main source of carbon emissions is energy combustion, and the energy mix is constantly changing, new information is better able to characterize future trends. In this paper, a novel fractional-order grey multivariate forecasting model is established to analyze and forecast China's carbon emissions, reflecting the principle of new information priority. The model adds fractional order cumulative sequences to the traditional integer order cumulative sequences, uses the Gamma function to represent the fractional order sequences and the time response equation, and uses the particle swarm algorithm to find the optimal order of the cumulative. Finally, the modeling steps of the model are given. Then the new model is analyzed for its effectiveness from three different perspectives using 21 years of Chinese carbon emission data. The results of the three Cases show that the newly established particle swarm optimization fractional order model outperforms the original grey prediction model and the other three classical grey prediction models. It has stable characteristics for both simulation and prediction, and also shows high accuracy, and all three cases fully illustrate the effectiveness of the new model. Finally, this model is applied to forecast China's carbon emissions from 2022–2026, analyze the forecast results and make relevant recommendations.
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carbon emissions,prediction
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