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Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm

Author

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  • Xiwen Cui

    (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)

  • Shaojun E

    (School of Information Science and Technology, Hebei Agricultural University, Baoding 071001, 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)

  • Bosong Chen

    (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)

  • Jiaqi Feng

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China)

Abstract

As the global temperature continues to rise, people have become increasingly concerned about global climate change. In order to help China to effectively develop a carbon peak target completion plan, this paper proposes a carbon emission prediction model based on the improved whale algorithm-optimized gradient boosting decision tree, which combines four optimization methods and significantly improves the prediction accuracy. This paper uses historical data to verify the superiority of the gradient boosting tree prediction model optimized by the improved whale algorithm. In addition, this study also predicted the carbon emission values of China from 2020 to 2035 and compared them with the target values, concluding that China can accomplish the relevant target values, which suggests that this research has practical implications for China’s future carbon emission reduction policies.

Suggested Citation

  • Xiwen Cui & Shaojun E & Dongxiao Niu & Bosong Chen & Jiaqi Feng, 2021. "Forecasting of Carbon Emission in China Based on Gradient Boosting Decision Tree Optimized by Modified Whale Optimization Algorithm," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12302-:d:674199
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    References listed on IDEAS

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    Cited by:

    1. Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
    2. Rendao Ye & Mengyao Yang & Peng Sun, 2023. "Consumer Purchasing Power Prediction of Interest E-Commerce Based on Cost-Sensitive Support Vector Machine," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    3. Wentian Shang & Lijun Deng & Jian Liu & Yukai Zhou, 2023. "Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-25, April.
    4. Xuezhi Ren & Jianya Zhao & Shu Wang & Chunpeng Zhang & Hongzhen Zhang & Nan Wei, 2025. "Exploration of Dual-Carbon Target Pathways Based on Machine Learning Stacking Model and Policy Simulation—A Case Study in Northeast China," Land, MDPI, vol. 14(4), pages 1-31, April.

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