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ORIGINAL RESEARCH article

Front. Environ. Sci.
Sec. Environmental Informatics and Remote Sensing
Volume 12 - 2024 | doi: 10.3389/fenvs.2024.1450354
This article is part of the Research Topic Air Quality: Observation, Remote Sensing, and Model Development - Volume II View all 3 articles

New discrete Fractional Accumulation Grey Gompertz model for Predicting carbon dioxide Emissions

Provisionally accepted
Jiang Jianming Jiang Jianming 1Ban Yandong Ban Yandong 1Zhang Ming Zhang Ming 1*Huang Zhongyong Huang Zhongyong 2
  • 1 Youjiang Medical University for Nationalities, Baise, Guangx, China
  • 2 Guangxi University of Science and Technology, Liuzhou, Guangxi Zhuang Region, China

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

    Predicting carbon dioxide emissions is crucial for addressing climate change and achieving environmental sustainability. Accurate emission forecasts provide policymakers with a basis for evaluating the effectiveness of policies, facilitating the design and implementation of emission reduction strategies, and helping businesses adjust their operations to adapt to market changes. Various methods, such as statistical models, machine learning, and grey prediction models, have been widely used in carbon dioxide emission prediction. However, existing research often lacks comparative analysis with other forecasting techniques. This paper constructs a new Discrete Fractional Accumulation Grey Gompertz Model (DFAGGM(1,1) based on grey system theory and provides a detailed solution process. The Whale Optimization Algorithm (WOA) is used to find the hyperparameters in the model. By comparing it with five benchmark models, the effectiveness of DFAGGM(1,1) in predicting carbon dioxide emissions data for China and the United States is validated.

    Keywords: Carbon dioxide emissions forecasting, Grey prediction model, DFAGGM(1,1) model, Whale optimization algorithm, Environmental sustainability

    Received: 18 Jun 2024; Accepted: 13 Aug 2024.

    Copyright: © 2024 Jianming, Yandong, Ming and Zhongyong. 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: Zhang Ming, Youjiang Medical University for Nationalities, Baise, 533000, Guangx, China

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