A novel fractional grey Riccati model for carbon emission prediction

Abstract

Greenhouse gas emissions bring severe challenges to the global climate, and CO2 emission prediction can provide decision support for atmospheric environmental governance and regional sustainable development. However, previous studies only focused on forecasting accuracy and neglected modeling mechanisms. To solve this problem, this paper proposes a novel fractional grey Riccati model (FGRM(1,1) model), which combines the Environmental Kuznets Curve hypothesis and differential information principle. The least-squares parameter estimation and mathematics analytical methods are utilized to obtain model parameters and the discrete response function, and the bare bone fireworks algorithm is introduced and designed to obtain the optimal fractional order. 20 data sets from M-competition are studied for confirming the effectiveness of the proposed model, and the performance test shows that it has higher stability and accuracy. Finally, the proposed model is employed to estimate and forecast the CO2 emissions of the United States, China, and Japan. Compared with three types of classical models without considering modeling mechanisms, the results show that the FGRM(1,1) model demonstrates better estimation in all cases and efficiency in short-term carbon emission forecasting. Results also indicated that these three countries all will reduce their carbon emissions gradually in future and the carbon emissions under current policies are estimated to be 0.31% (Japan), 4.52% (U.S.) and 4.49% (China) below 2020 levels by 2025, respectively.