Forecasting fuel combustion-related CO2 emissions by a novel continuous fractional nonlinear grey Bernoulli model with grey wolf optimizer

• DOI: 10.1007/s11356-021-12736-w
• Publication Date: 2021-03-16T10:03:06Z
• Authors: Wanli Xie; Wen-Ze Wu; Chong Liu; Tao Zhang; Zijie Dong
• Keywords: Computational Engineering, Finance, and Science (cs.CE); FOS: Computer and information sciences; China; Policy; Nonlinear Dynamics; 13. Climate action; Carbon Dioxide; Computer Science - Computational Engineering, Finance, and Science; 7. Clean energy; Algorithms; Forecasting; 12. Responsible consumption

Foresight of CO$_2$ emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and further to improve energy policies and plans. For the purpose of accurately forecasting the future development of China's CO$_2$ emissions from fuel combustion, a novel continuous fractional nonlinear grey Bernoulli model is developed in this paper. The fractional nonlinear grey Bernoulli model already in place is known that has a fixed first-order derivative that impairs the predictive performance to some extent. To address this problem, in the newly proposed model, a flexible variable is introduced into the order of derivative, freeing it from integer-order accumulation. In order to further improve the performance of the newly proposed model, a meta-heuristic algorithm, namely Grey Wolf Optimizer (GWO), is determined to the emerging coefficients. To demonstrate the effectiveness, two real examples and China's fuel combustion-related CO$_2$ emissions are used for model validation by comparing with other benchmark models, the results show the proposed model outperforms competitors. Thus, the future development trend of fuel combustion-related CO$_2$ emissions by 2023 are predicted, accounting for 10039.80 Million tons (Mt). In accordance with the forecasts, several suggestions are provided to curb carbon dioxide emissions.