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Driving Factor Analysis and Forecasting of CO 2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

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  • Weijun Wang

    (Department of Economics and Management, North China Electric Power University, Baoding 071000, China)

  • Weisong Peng

    (Department of Economics and Management, North China Electric Power University, Baoding 071000, China)

  • Jiaming Xu

    (Department of Economics and Management, North China Electric Power University, Baoding 071000, China)

  • Ran Zhang

    (Shijiazhuang Power Supply Branch, Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Yaxuan Zhao

    (Hebei Electric Survey and Design Research Institute, Shijiazhuang 050000, China)

Abstract

With power consumption increasing in China, the CO 2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO 2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO 2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO 2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO 2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future.

Suggested Citation

  • Weijun Wang & Weisong Peng & Jiaming Xu & Ran Zhang & Yaxuan Zhao, 2018. "Driving Factor Analysis and Forecasting of CO 2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method," Energies, MDPI, vol. 11(10), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2709-:d:174884
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    References listed on IDEAS

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    1. 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.
    2. Zhao, Xiaoli & Ma, Qian & Yang, Rui, 2013. "Factors influencing CO2 emissions in China's power industry: Co-integration analysis," Energy Policy, Elsevier, vol. 57(C), pages 89-98.
    3. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
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