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Influencing the Variable Selection and Prediction of Carbon Emissions in China

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  • Zhiyong Chang

    (School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China)

  • Yunmeng Jiao

    (School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China)

  • Xiaojing Wang

    (School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471023, China)

Abstract

In order to study the changing rule of carbon dioxide emissions in China, this paper systematically focused on their current situation, influencing factors, and future trends. Firstly, the current situations of global carbon dioxide emissions and China’s carbon dioxide emissions were presented via a visualization method and their characteristics were analyzed; secondly, the random forest regression model was used to screen the main factors affecting China’s carbon emissions. Considering the different aspects of carbon emissions, 29 influencing factors were determined and 6 main influencing factors were determined according to the results of the random forest regression model. Then, a prediction model for carbon emissions in China was established. The BP neural network model, multi-factor LSTM time series model, and CNN-LSTM model were compared on the test set and all of them passed the test. However, the goodness of fit of the CNN-LSTM model was about 0.01~0.02 higher than the other two models and the MAE and RMSE of the CNN-LSTM model were about 0.01~0.03 lower than those of the other two models. Thus, it was selected to predict China’s carbon dioxide emissions. The predicted results showed that the peak of China’s carbon emissions will be around 2027 and the peak of these emissions will be between 12.9 billion tons and 13.2 billion tons. Overall, the paper puts forward reasonable suggestions for China’s low-carbon development and provides a reference for an adjustment plan of energy structure.

Suggested Citation

  • Zhiyong Chang & Yunmeng Jiao & Xiaojing Wang, 2023. "Influencing the Variable Selection and Prediction of Carbon Emissions in China," Sustainability, MDPI, vol. 15(18), pages 1-15, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13848-:d:1242049
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    References listed on IDEAS

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    1. Vaninsky, Alexander, 2014. "Factorial decomposition of CO2 emissions: A generalized Divisia index approach," Energy Economics, Elsevier, vol. 45(C), pages 389-400.
    2. Mideksa, Torben K. & Kallbekken, Steffen, 2010. "The impact of climate change on the electricity market: A review," Energy Policy, Elsevier, vol. 38(7), pages 3579-3585, July.
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    1. Xiangqian Li & Keke Li & Yaxin Tian & Siqi Shen & Yue Yu & Liwei Jin & Pengyu Meng & Jingjing Cao & Xiaoxiao Zhang, 2024. "Decision Support for Carbon Emission Reduction Strategies in China’s Cement Industry: Prediction and Identification of Influencing Factors," Sustainability, MDPI, vol. 16(13), pages 1-17, June.
    2. Nan Xu & Yaoqun Xu & Haiyan Zhong, 2023. "Pricing Decisions for Power Battery Closed-Loop Supply Chains with Low-Carbon Input by Echelon Utilization Enterprises," Sustainability, MDPI, vol. 15(23), pages 1-30, December.
    3. Irina Georgescu & Ionuț Nica & Camelia Delcea & Cristian Ciurea & Nora Chiriță, 2024. "A Cybernetics Approach and Autoregressive Distributed Lag Econometric Exploration of Romania’s Circular Economy," Sustainability, MDPI, vol. 16(18), pages 1-26, September.
    4. Joanna Michalowska, 2023. "Model of a Predictive Neural Network for Determining the Electric Fields of Training Flight Phases," Energies, MDPI, vol. 17(1), pages 1-27, December.

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