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Forecasting CO2 emissions in Hebei, China, through moth-flame optimization based on the random forest and extreme learning machine

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References

2016Forecasting Chinese carbon emissions from fossil energy consumption using non-linear grey multivariable models264 cited
2016A comparative study on decoupling relationship and influence factors between China's regional economic development and industrial energy–related carbon emissions222 cited
2017A novel method for carbon dioxide emission forecasting based on improved Gaussian processes regression207 cited
2018What matters for carbon emissions in regional sectors? A China study of extended STIRPAT model199 cited
2016Prediction and analysis of the three major industries and residential consumption CO2 emissions based on least squares support vector machine in China156 cited
2017Factor analysis and forecasting of CO2 emissions in Hebei, using extreme learning machine based on particle swarm optimization143 cited
2009Decomposition analysis and mitigation strategies of CO2 emissions from energy consumption in South Korea119 cited

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24 cited