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Prediction of carbon dioxide emissions based on principal component analysis with regularized extreme learning machine: The case of China
2017
Environmental Engineering Research
Nowadays, with the burgeoning development of economy, CO2 emissions increase rapidly in China. It has become a common concern to seek effective methods to forecast CO2 emissions and put forward the targeted reduction measures. This paper proposes a novel hybrid model combined principal component analysis (PCA) with regularized extreme learning machine (RELM) to make CO2 emissions prediction based on the data from 1978 to 2014 in China. First eleven variables are selected on the basis of Pearson
doi:10.4491/eer.2016.153
fatcat:zhvbck3bivhjzl7fyhtbljurya