IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i4p958-d138071.html
   My bibliography  Save this article

Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

Author

Listed:
  • Shuyu Dai

    (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, Beijing 102206, China)

  • Dongxiao Niu

    (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, Beijing 102206, China)

  • Yaru Han

    (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, Beijing 102206, China)

Abstract

Presently, China is the largest CO 2 emitting country in the world, which accounts for 28% of the CO 2 emissions globally. China’s CO 2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO 2 emissions is crucial to China’s emission reduction policy formulating and global action on climate change. In order to forecast the CO 2 emissions in China accurately, considering population, the CO 2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO 2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO 2 emissions. Finally, taking the forecasting value of the CO 2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO 2 emissions in China from 2018 to 2025.

Suggested Citation

  • Shuyu Dai & Dongxiao Niu & Yaru Han, 2018. "Forecasting of Energy-Related CO 2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability," Sustainability, MDPI, vol. 10(4), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:958-:d:138071
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/4/958/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/4/958/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
    3. 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.
    4. 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.
    5. Gallo, C. & Contò, F. & Fiore, M., 2014. "A Neural Network Model for Forecasting CO2 Emission," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 6(2), pages 1-6, June.
    6. Vinit Sehgal & Mukesh Tiwari & Chandranath Chatterjee, 2014. "Wavelet Bootstrap Multiple Linear Regression Based Hybrid Modeling for Daily River Discharge Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 2793-2811, August.
    7. 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.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Wenhao & Zeng, Bo & Wang, Jianzhou & Luo, Xiaoshuang & Liu, Xianzhou, 2021. "Forecasting Chinese carbon emissions using a novel grey rolling prediction model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Yanbin Li & Zhen Li, 2019. "Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm," Energies, MDPI, vol. 12(12), pages 1-20, June.
    3. Herui Cui & Ruirui Wu & Tian Zhao, 2018. "Decomposition and Forecasting of CO 2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model," Energies, MDPI, vol. 11(11), pages 1-19, November.
    4. Yuhong Zhao & Ruirui Liu & Zhansheng Liu & Liang Liu & Jingjing Wang & Wenxiang Liu, 2023. "A Review of Macroscopic Carbon Emission Prediction Model Based on Machine Learning," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    5. Wei Wu & Shengjuan Yue & Xiaode Zhou & Mengjing Guo & Jiawei Wang & Lei Ren & Bo Yuan, 2020. "Observational Study on the Impact of Large-Scale Photovoltaic Development in Deserts on Local Air Temperature and Humidity," Sustainability, MDPI, vol. 12(8), pages 1-14, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Safdarnejad, Seyed Mostafa & Hedengren, John D. & Powell, Kody M., 2018. "Performance comparison of low temperature and chemical absorption carbon capture processes in response to dynamic electricity demand and price profiles," Applied Energy, Elsevier, vol. 228(C), pages 577-592.
    4. Kang, Charles A. & Brandt, Adam R. & Durlofsky, Louis J. & Jayaweera, Indira, 2016. "Assessment of advanced solvent-based post-combustion CO2 capture processes using a bi-objective optimization technique," Applied Energy, Elsevier, vol. 179(C), pages 1209-1219.
    5. Yuzhuo Zhang & Xingang Zhao & Yi Zuo & Lingzhi Ren & Ling Wang, 2017. "The Development of the Renewable Energy Power Industry under Feed-In Tariff and Renewable Portfolio Standard: A Case Study of China’s Photovoltaic Power Industry," Sustainability, MDPI, vol. 9(4), pages 1-23, March.
    6. Sanjana Dhingra & Purvil Khakharia & Alexander Rieder & Ashleigh Cousins & Alicia Reynolds & Jacob Knudsen & Jimmy Andersen & Robin Irons & Jan Mertens & Mohammad Abu Zahra & Peter Van Os & Earl Goeth, 2017. "Understanding and Modelling the Effect of Dissolved Metals on Solvent Degradation in Post Combustion CO 2 Capture Based on Pilot Plant Experience," Energies, MDPI, vol. 10(5), pages 1-15, May.
    7. Lihui Zhang & Riletu Ge & Jianxue Chai, 2019. "Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm," Energies, MDPI, vol. 12(1), pages 1-19, January.
    8. Mojica, Jose L. & Petersen, Damon & Hansen, Brigham & Powell, Kody M. & Hedengren, John D., 2017. "Optimal combined long-term facility design and short-term operational strategy for CHP capacity investments," Energy, Elsevier, vol. 118(C), pages 97-115.
    9. Shuxia Yang & Xianguo Zhu & Shengjiang Peng, 2020. "Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory," Energies, MDPI, vol. 13(8), pages 1-17, April.
    10. Song, Chunfeng & Liu, Qingling & Ji, Na & Deng, Shuai & Zhao, Jun & Li, Yang & Kitamura, Yutaka, 2017. "Reducing the energy consumption of membrane-cryogenic hybrid CO2 capture by process optimization," Energy, Elsevier, vol. 124(C), pages 29-39.
    11. 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.
    12. Dai, Gaofeng & Zhang, Jiaye & Wang, Xuebin & Tan, Houzhang & Rahman, Zia ur, 2022. "Calcination and desulfurization characteristics of calcium carbonate in pressurized oxy-combustion," Energy, Elsevier, vol. 261(PA).
    13. Xindong Wang & Chun Yan & Wei Liu & Xinhong Liu, 2022. "Research on Carbon Emissions Prediction Model of Thermal Power Plant Based on SSA-LSTM Algorithm with Boiler Feed Water Influencing Factors," Sustainability, MDPI, vol. 14(23), pages 1-26, November.
    14. Abdulkerim Karaaslan & Kürşat Özgür Özden, 2017. "Forecasting Turkey’s Credit Ratings with Multivariate Grey Model and Grey Relational Analysis," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 15(3), pages 583-610, September.
    15. Zhihua Zhang, 2015. "Techno-Economic Assessment of Carbon Capture and Storage Facilities Coupled to Coal-Fired Power Plants," Energy & Environment, , vol. 26(6-7), pages 1069-1080, November.
    16. Kanwal, S. & Khan, B. & Ali, S.M. & Mehmood, C.A., 2018. "Gaussian process regression based inertia emulation and reserve estimation for grid interfaced photovoltaic system," Renewable Energy, Elsevier, vol. 126(C), pages 865-875.
    17. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    18. Chun Chih Chen, 2021. "The path to a 2025 nuclear-free Taiwan: An analysis of dynamic competition among emissions, energy, and economy," Energy & Environment, , vol. 32(4), pages 668-689, June.
    19. Adamczyk, Janusz & Dylewski, Robert, 2017. "The impact of thermal insulation investments on sustainability in the construction sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 421-429.
    20. Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:10:y:2018:i:4:p:958-:d:138071. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.