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

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  • Huiru Zhao

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China)

  • Guo Huang

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China)

  • Ning Yan

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing 102206, China)

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.

Suggested Citation

  • Huiru Zhao & Guo Huang & Ning Yan, 2018. "Forecasting Energy-Related CO 2 Emissions Employing a Novel SSA-LSSVM Model: Considering Structural Factors in China," Energies, MDPI, vol. 11(4), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:781-:d:138550
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    References listed on IDEAS

    as
    1. Muhammad, Shahbaz, 2012. "Multivariate granger causality between CO2 Emissions, energy intensity, financial development and economic growth: evidence from Portugal," MPRA Paper 37774, University Library of Munich, Germany, revised 31 Mar 2012.
    2. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    3. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    4. Martínez-Zarzoso, Inmaculada & Maruotti, Antonello, 2011. "The impact of urbanization on CO2 emissions: Evidence from developing countries," Ecological Economics, Elsevier, vol. 70(7), pages 1344-1353, May.
    5. Zhang, Xing-Ping & Cheng, Xiao-Mei, 2009. "Energy consumption, carbon emissions, and economic growth in China," Ecological Economics, Elsevier, vol. 68(10), pages 2706-2712, August.
    6. Dongxiao Niu & Shuyu Dai, 2017. "A Short-Term Load Forecasting Model with a Modified Particle Swarm Optimization Algorithm and Least Squares Support Vector Machine Based on the Denoising Method of Empirical Mode Decomposition and Gre," Energies, MDPI, vol. 10(3), pages 1-20, March.
    7. Jinying Li & Jianfeng Shi & Jinchao Li, 2016. "Exploring Reduction Potential of Carbon Intensity Based on Back Propagation Neural Network and Scenario Analysis: A Case of Beijing, China," Energies, MDPI, vol. 9(8), pages 1-17, August.
    8. Lin Boqiang & Kui Liu, 2017. "Using LMDI to Analyze the Decoupling of Carbon Dioxide Emissions from China’s Heavy Industry," Sustainability, MDPI, vol. 9(7), pages 1-16, July.
    9. Saboori, Behnaz & Sulaiman, Jamalludin, 2013. "CO2 emissions, energy consumption and economic growth in Association of Southeast Asian Nations (ASEAN) countries: A cointegration approach," Energy, Elsevier, vol. 55(C), pages 813-822.
    10. Wang, Zheng & Zhu, Yanshuo & Zhu, Yongbin & Shi, Ying, 2016. "Energy structure change and carbon emission trends in China," Energy, Elsevier, vol. 115(P1), pages 369-377.
    11. Jinying Li & Binghua Zhang & Jianfeng Shi, 2017. "Combining a Genetic Algorithm and Support Vector Machine to Study the Factors Influencing CO 2 Emissions in Beijing with Scenario Analysis," Energies, MDPI, vol. 10(10), pages 1-17, October.
    12. Talbi, Besma, 2017. "CO2 emissions reduction in road transport sector in Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 232-238.
    13. Wang, Zhaohua & Yin, Fangchao & Zhang, Yixiang & Zhang, Xian, 2012. "An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China," Applied Energy, Elsevier, vol. 100(C), pages 277-284.
    14. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    15. Hongze Li & Sen Guo & Huiru Zhao & Chenbo Su & Bao Wang, 2012. "Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm," Energies, MDPI, vol. 5(11), pages 1-16, November.
    16. Budzianowski, Wojciech M., 2012. "Negative carbon intensity of renewable energy technologies involving biomass or carbon dioxide as inputs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6507-6521.
    17. Safdarnejad, Seyed Mostafa & Hedengren, John D. & Baxter, Larry L., 2015. "Plant-level dynamic optimization of Cryogenic Carbon Capture with conventional and renewable power sources," Applied Energy, Elsevier, vol. 149(C), pages 354-366.
    18. Rina Wu & Jiquan Zhang & Yuhai Bao & Quan Lai & Siqin Tong & Youtao Song, 2016. "Decomposing the Influencing Factors of Industrial Sector Carbon Dioxide Emissions in Inner Mongolia Based on the LMDI Method," Sustainability, MDPI, vol. 8(7), pages 1-14, July.
    19. Ang, James B., 2007. "CO2 emissions, energy consumption, and output in France," Energy Policy, Elsevier, vol. 35(10), pages 4772-4778, October.
    20. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    21. Lotfalipour, Mohammad Reza & Falahi, Mohammad Ali & Ashena, Malihe, 2010. "Economic growth, CO2 emissions, and fossil fuels consumption in Iran," Energy, Elsevier, vol. 35(12), pages 5115-5120.
    22. Burnett, J. Wesley & Bergstrom, John C. & Dorfman, Jeffrey H., 2013. "A spatial panel data approach to estimating U.S. state-level energy emissions," Energy Economics, Elsevier, vol. 40(C), pages 396-404.
    23. Arouri, Mohamed El Hedi & Ben Youssef, Adel & M'henni, Hatem & Rault, Christophe, 2012. "Energy consumption, economic growth and CO2 emissions in Middle East and North African countries," Energy Policy, Elsevier, vol. 45(C), pages 342-349.
    24. Kumar, Subhash & Madlener, Reinhard, 2016. "CO2 emission reduction potential assessment using renewable energy in India," Energy, Elsevier, vol. 97(C), pages 273-282.
    25. Liang, Qiao-Mei & Fan, Ying & Wei, Yi-Ming, 2007. "Multi-regional input-output model for regional energy requirements and CO2 emissions in China," Energy Policy, Elsevier, vol. 35(3), pages 1685-1700, March.
    26. Gopan, Akshay & Kumfer, Benjamin M. & Phillips, Jeffrey & Thimsen, David & Smith, Richard & Axelbaum, Richard L., 2014. "Process design and performance analysis of a Staged, Pressurized Oxy-Combustion (SPOC) power plant for carbon capture," Applied Energy, Elsevier, vol. 125(C), pages 179-188.
    27. Kofi Adom, Philip & Bekoe, William & Amuakwa-Mensah, Franklin & Mensah, Justice Tei & Botchway, Ebo, 2012. "Carbon dioxide emissions, economic growth, industrial structure, and technical efficiency: Empirical evidence from Ghana, Senegal, and Morocco on the causal dynamics," Energy, Elsevier, vol. 47(1), pages 314-325.
    28. Pao, Hsiao-Tien & Tsai, Chung-Ming, 2011. "Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil," Energy, Elsevier, vol. 36(5), pages 2450-2458.
    29. Matthew A. Cole & Eric Neumayer, 2003. "Examining the Impact of Demographic Factors On Air Pollution," Labor and Demography 0312005, University Library of Munich, Germany, revised 13 May 2004.
    30. Safdarnejad, Seyed Mostafa & Hedengren, John D. & Baxter, Larry L., 2016. "Dynamic optimization of a hybrid system of energy-storing cryogenic carbon capture and a baseline power generation unit," Applied Energy, Elsevier, vol. 172(C), pages 66-79.
    31. Ghosh, Sajal, 2010. "Examining carbon emissions economic growth nexus for India: A multivariate cointegration approach," Energy Policy, Elsevier, vol. 38(6), pages 3008-3014, June.
    32. Soytas, Ugur & Sari, Ramazan & Ewing, Bradley T., 2007. "Energy consumption, income, and carbon emissions in the United States," Ecological Economics, Elsevier, vol. 62(3-4), pages 482-489, May.
    33. Haoran Zhao & Sen Guo & Huiru Zhao, 2017. "Energy-Related CO 2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm," Energies, MDPI, vol. 10(7), pages 1-15, June.
    34. Qunli Wu & Chenyang Peng, 2016. "A Least Squares Support Vector Machine Optimized by Cloud-Based Evolutionary Algorithm for Wind Power Generation Prediction," Energies, MDPI, vol. 9(8), pages 1-20, July.
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