Forecasting Energy-Related CO 2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China

Huiru Zhao, Guo Huang and Ning Yan
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Huiru Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Guo Huang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Ning Yan: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Energies, 2018, vol. 11, issue 4, 1-21

Abstract: Carbon dioxide (CO 2 ) 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 CO 2 emissions are multiplex and the relationships between factors and CO 2 emissions are complex and non-linear, a novel CO 2 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 CO 2 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 CO 2 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 CO 2 emissions forecasting. CO 2 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 CO 2 emissions of China in 2020 shows that structural factors significantly affect CO 2 emission forecasting results. The average annual growth of CO 2 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.

Keywords: CO2 emissions forecasting; influential factors; Salp Swarm Algorithm (SSA); least squares support sector machine (LSSVM); parameters optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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