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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Big Data, AI, and the Environment
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1513387
This article is part of the Research Topic AI and Data Analytics for Climate Data Management View all 9 articles

Novel Disctete Grey Bernoulli Seasonal Model with a Time Powter Term for Predicting Monthly Carbon Dioxide Emissions in the United States

Provisionally accepted
jiang jianming jiang jianming Ban Yandong Ban Yandong Nong Sheng Nong Sheng *
  • Youjiang Medical University for Nationalities, Baise, China

The final, formatted version of the article will be published soon.

    This study proposes a more efficient discrete grey prediction model to describe the seasonal variation trends of carbon dioxide emissions. The setting of the bernoulli parameter and the time power term parameter in the new model ensures that the model can capture the trend of nonlinear changes in the sequence. At the same time, the inclusion of dummy variables allows for the direct simulation of seasonal fluctuations in carbon dioxide emissions without the need for additional treatment of the seasonality in the sequence. The optimal search for the model’s hyperparameters is achieved using the MPA algorithm. The constructed model is applied to the monthly U.S. carbon dioxide emissions data from January 2003 to December 2022, a total of 240 months. The model is trained on 216 months of data from January 2003 to December 2020, and the monthly data from January 2021 to December 2022 is used for prediction, which is then compared with the actual values. The results show that the proposed model exhibits higher forecasting performance compared to SARIMA and other models. Therefore, this method can effectively simulate the seasonal variation trends in carbon dioxide emissions, providing valuable reference information for relevant departments to formulate more effective policies.

    Keywords: Carbon dioxide emissions forecasting, Grey system theory, Grey seasonal model, DSNGBM(1,1,tα)model;, Marine Predators Algorithm;

    Received: 21 Oct 2024; Accepted: 06 Dec 2024.

    Copyright: © 2024 jianming, Yandong and Sheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nong Sheng, Youjiang Medical University for Nationalities, Baise, China

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