Predicting and Analyzing CO2 Emissions Based on an Improved Least Squares Support Vector Machine
Wei Sun 1  
,   Hongyuan Jin 1  
,   Xiumei Wang 2  
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Department of Business Administration, North China Electric Power University, Baoding, China
Engineering Training Center, North China Electric Power University, Baoding, China
Hongyuan Jin   

North China Electric Power University, Baoding, North China Electric Power University, Baoding, 071000 Baoding,Hebei, China
Submission date: 2018-07-07
Final revision date: 2018-08-16
Acceptance date: 2018-08-26
Online publication date: 2019-08-01
Publication date: 2019-09-17
Pol. J. Environ. Stud. 2019;28(6):4391–4401
Carbon dioxide (CO2) is the main cause of the greenhouse effect. With the rapid development of the economy in China, CO2 emissions have increased dramatically. To reduce CO2 emissions, ensure the sustainability of China’s economy and implement the Paris International Convention, it is important to investigate the main factors affecting CO2 emissions and use those factors to accurately forecast CO2 emissions. In order to achieve accurate prediction of CO2, this paper proposes a CO2 emission prediction model based on principal component analysis (PCA) and particle swarm optimization for least squares support vector machine (PSO-LSSVM). Through data 1990-2016 in Hebei Province of China, this paper identifies 24 influencing factors though the bivariate correlation analysis. After applying PCA to reduce the dimensions of the influencing factors, two principal components were extracted as input variables. Then the parameters of the LSSVM model are obtained by PSO and the forecast model is established. By comparing the prediction results with actual values, it is proved that the prediction error of the PSO-LSSVM prediction model is 0.663%, which is smaller than that of the traditional BPNN and LSSVM models.