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Combining a Genetic Algorithm and Support Vector Machine to Study the Factors Influencing CO 2 Emissions in Beijing with Scenario Analysis

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  • Jinying Li

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Binghua Zhang

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

  • Jianfeng Shi

    (Department of Economics and Management, North China Electric Power University, Baoding 071003, China)

Abstract

In recent years, Beijing has been facing serious environmental problems. As an important cause of environmental problems, a further study of the factors influencing CO 2 emissions in Beijing has important significance for the social and economic development of Beijing. In this paper, Cointegration and Granger causality test were proposed to select influencing factors of CO 2 emissions prediction in Beijing, the influencing factors with different leading lengths were checked as well, and the genetic algorithm (GA) was used to optimize the initial weight and threshold values of a support vector machine (SVM) and the new SVM optimized by GA (GA-SVM) was established to predict the CO 2 emissions of Beijing from 2016–2020 with scenario analysis. Through the comparison of 36 kinds of development scenarios, we found that economic growth, resident population growth and energy intensity enhancement were the major growth factors of carbon emissions, of which the contributions exceed 0.5% in all kinds of development scenarios. Finally, this paper put forward some reasonable policy recommendations for the control of CO 2 emissions.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1520-:d:113919
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    References listed on IDEAS

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    1. Laia Pié & Laura Fabregat-Aibar & Marc Saez, 2018. "The Influence of Imports and Exports on the Evolution of Greenhouse Gas Emissions: The Case for the European Union," Energies, MDPI, vol. 11(7), pages 1-14, June.
    2. 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.
    3. 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.

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