Forecasting fuel combustion-related CO2 emissions by a novel continuous fractional nonlinear grey Bernoulli model with grey wolf optimizer
نویسندگان
چکیده
Foresight of CO2 emissions from fuel combustion is essential for policy-makers to identify ready targets effective reduction plans and further improve energy policies plans. A new method forecasting the future development China’s proposed in this paper by using grey theory. Although existing fractional nonlinear Bernoulli model (denoted as FNGBM(1,1)) has been theoretically proven enhance adaptability diverse sequences, its fixed integer-order differential derivative still impairs performance some extent. To end, a varying-order introduced into equation enable more flexible structure, thus improving prediction ability FNGBM(1,1). Specifically, because advantages conformable accumulation, traditional first replaced derivative. As consequence, continuous (hereinafter referred CCFNGBM(1,1)) proposed. increase validity model, metaheuristic algorithm, namely Grey Wolf Optimizer (GWO), then applied search optimal emerging coefficients model. Two real examples are considered verify effectiveness newly experimental results show that outperforms other benchmark models terms accuracy. The finally employed forecast 2023, accounting 10,039.80 million tons. Based on forecasts, several policy suggestions provided curb emissions.
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ژورنال
عنوان ژورنال: Environmental Science and Pollution Research
سال: 2021
ISSN: ['1614-7499', '0944-1344']
DOI: https://doi.org/10.1007/s11356-021-12736-w