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Trend Forecasting of Public Concern about Low Carbon Based on Comprehensive Baidu Index and Its Relationship with CO 2 Emissions: The Case of China

Author

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  • Wenshuo Dong

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Renhua Chen

    (School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Xuelin Ba

    (School of Public Health, Lanzhou University, Lanzhou 730000, China)

  • Suling Zhu

    (School of Public Health, Lanzhou University, Lanzhou 730000, China
    Big Data Research Center, Lanzhou University, Lanzhou 730000, China)

Abstract

Climate change is harmful to ecosystems and public health, so the concern about climate change has been aroused worldwide. Studies indicated that greenhouse gas emission with C O 2 as the main component is an important factor for climate change. Countries worldwide are on the same page that low-carbon development is an effective way to combat climate change. Enhancing public concern about low-carbon development and climate change has a positive effect on universal participation in carbon emission reduction. Therefore, it is significant to study the trend of public concern about low carbon and its relationship with C O 2 emissions. Currently, no related studies are available, so this research explores the relationship between the public concern about low carbon and C O 2 emissions of China, as well as the respective trends of each. Based on the daily data of Baidu-related keyword searches and C O 2 emission, this research proposes the GMM-CEEMD-SGIA-LSTM hybrid model. The GMM is utilized to construct a comprehensive Baidu index (CBI) to reflect public concern about low carbon by clustering keywords search data. CEEMD and SGIA are applied to reconstruct sequences for analyzing the relationship between CBI and C O 2 emissions. Then LSTM is utilized to forecast CBI. The reconstructed sequences show that there is a strong correlation between CBI and C O 2 emissions. It is also found that CBI affects C O 2 emissions, with varying effect lag times for different periods. Compared to LSTM, RF, SVR, and RNN models, the proposed model is reliable for forecasting public concern with a 46.78% decrease in MAPE. The prediction results indicate that public concern about low carbon shows a fluctuating upward trend from January 2023 to January 2025. This research could improve understanding of the relationship between public concern about low carbon and C O 2 emissions to better address climate change.

Suggested Citation

  • Wenshuo Dong & Renhua Chen & Xuelin Ba & Suling Zhu, 2023. "Trend Forecasting of Public Concern about Low Carbon Based on Comprehensive Baidu Index and Its Relationship with CO 2 Emissions: The Case of China," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12973-:d:1227217
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    References listed on IDEAS

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    1. Yan, Zichun & Tian, Fangzhu & Sun, Yuying & Wang, Shouyang, 2024. "A time-frequency-based interval decomposition ensemble method for forecasting gasoil prices under the trend of low-carbon development," Energy Economics, Elsevier, vol. 134(C).

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