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An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Author

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  • Yi-Chung Hu

    (College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    Department of Business Administration, Chung Yuan Christian University, Taoyuan 32023, Taiwan)

  • Peng Jiang

    (School of Business, Shandong University, Weihai 264209, China)

  • Jung-Fa Tsai

    (Department of Business Management, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Ching-Ying Yu

    (College of Management, Yuan Ze University, Taoyuan 32003, Taiwan)

Abstract

Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fractional-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.

Suggested Citation

  • Yi-Chung Hu & Peng Jiang & Jung-Fa Tsai & Ching-Ying Yu, 2021. "An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:587-:d:478901
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    References listed on IDEAS

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    Cited by:

    1. Pingping Xiong & Xiaojie Wu & Jing Ye, 2023. "Building a novel multivariate nonlinear MGM(1,m,N|γ) model to forecast carbon emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9647-9671, September.
    2. Huiping Wang & Zhun Zhang, 2022. "Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China," IJERPH, MDPI, vol. 19(9), pages 1-22, April.

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