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Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM

Author

Listed:
  • Zhenggang Huo

    (College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China)

  • Xiaoting Zha

    (College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China)

  • Mengyao Lu

    (College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China)

  • Tianqi Ma

    (College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China)

  • Zhichao Lu

    (College of Civil Science and Engineering, Yangzhou University, Yangzhou 225127, China)

Abstract

To meet the twin carbon goals of “carbon peak” and “carbon neutrality”, it is crucial to make scientific predictions about carbon emissions in the transportation sector. The following eight factors were chosen as effect indicators: population size, GDP per capita, civil vehicle ownership, passenger and freight turnover, urbanization rate, industry structure, and carbon emission intensity. Based on the pertinent data from 2002 to 2020, a support vector machine model, improved by a genetic algorithm (GA-SVM), was created to predict the carbon peak time under three distinct scenarios. The penalty factor c and kernel function parameter g of the support vector machine model were each optimized using a genetic algorithm, a particle swarm algorithm, and a whale optimization algorithm. The results indicate that the genetic algorithm vector machine prediction model outperforms the particle swarm algorithm vector machine model and the whale optimization vector machine. As a result, the model integrating the support vector machine and genetic algorithm can more precisely predict carbon emissions and the peak time for carbon in Jiangsu province.

Suggested Citation

  • Zhenggang Huo & Xiaoting Zha & Mengyao Lu & Tianqi Ma & Zhichao Lu, 2023. "Prediction of Carbon Emission of the Transportation Sector in Jiangsu Province-Regression Prediction Model Based on GA-SVM," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3631-:d:1070387
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    References listed on IDEAS

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    Cited by:

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    2. Wu Bo & Kunming Zhao & Gang Cheng & Yaping Wang & Jiazhe Zhang & Mingkai Cheng & Can Yang & Wa Da, 2024. "Study on Transportation Carbon Emissions in Tibet: Measurement, Prediction Model Development, and Analysis," Sustainability, MDPI, vol. 16(19), pages 1-26, September.
    3. Yuhao Yang & Ruixi Dong & Xiaoyan Ren & Mengze Fu, 2024. "Exploring Sustainable Planning Strategies for Carbon Emission Reduction in Beijing’s Transportation Sector: A Multi-Scenario Carbon Peak Analysis Using the Extended STIRPAT Model," Sustainability, MDPI, vol. 16(11), pages 1-23, May.
    4. Maksymilian Mądziel, 2024. "Modeling Exhaust Emissions in Older Vehicles in the Era of New Technologies," Energies, MDPI, vol. 17(19), pages 1-18, October.

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