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Energies 2018, 11(4), 781; https://doi.org/10.3390/en11040781

Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

1,2
,
1,2,* and 1,2
1
School of Economics and Management, North China Electric Power University, Beijing 102206, China
2
Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Received: 14 February 2018 / Revised: 18 March 2018 / Accepted: 23 March 2018 / Published: 28 March 2018
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Abstract

Carbon dioxide (CO2) emissions forecasting is becoming more important due to increasing climatic problems, which contributes to developing scientific climate policies and making reasonable energy plans. Considering that the influential factors of CO2 emissions are multiplex and the relationships between factors and CO2 emissions are complex and non-linear, a novel CO2 forecasting model called SSA-LSSVM, which utilizes the Salp Swarm Algorithm (SSA) to optimize the two parameters of the least squares support sector machine (LSSVM) model, is proposed in this paper. The influential factors of CO2 emissions, including the gross domestic product (GDP), population, energy consumption, economic structure, energy structure, urbanization rate, and energy intensity, are regarded as the input variables of the SSA-LSSVM model. The proposed model is verified to show a better forecasting performance compared with the selected models, including the single LSSVM model, the LSSVM model optimized by the particle swarm optimization algorithm (PSO-LSSVM), and the back propagation (BP) neural network model, on CO2 emissions in China from 2014 to 2016. The comparative analysis indicates the SSA-LSSVM model is greatly superior and has the potential to improve the accuracy and reliability of CO2 emissions forecasting. CO2 emissions in China from 2017 to 2020 are forecast combined with the 13th Five-Year Plan for social, economic and energy development. The comparison of CO2 emissions of China in 2020 shows that structural factors significantly affect CO2 emission forecasting results. The average annual growth of CO2 emissions slows down significantly due to a series of policies and actions taken by the Chinese government, which means China can keep the promise that greenhouse gas emissions will start to drop after 2030. View Full-Text
Keywords: CO2 emissions forecasting; influential factors; Salp Swarm Algorithm (SSA); least squares support sector machine (LSSVM); parameters optimization CO2 emissions forecasting; influential factors; Salp Swarm Algorithm (SSA); least squares support sector machine (LSSVM); parameters optimization
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Zhao, H.; Huang, G.; Yan, N. Forecasting Energy-Related CO2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China. Energies 2018, 11, 781.

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