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A Predictive Analysis of China’s CO 2 Emissions and OFDI with a Nonlinear Fractional-Order Grey Multivariable Model

Author

Listed:
  • Hang Jiang

    (School of Business Administration, Jimei University, Xiamen 361021, China)

  • Peng Jiang

    (School of Business, Shandong University, Weihai 264209, China
    International Institute of Tourism Science, Shandong University, Weihai 264209, China)

  • Peiyi Kong

    (Ph.D Program in Business, Chung Yuan Christian University, Taoyuan 32023, Taiwan
    School of Economics and Management, Xiamen Nanyang University, Xiamen 361101, China)

  • Yi-Chung Hu

    (Department of Business Administration, Chung Yuan Christian University, Taoyuan 32023, Taiwan)

  • Cheng-Wen Lee

    (Department of International Business, Chung Yuan Christian University, Taoyuan 32023, Taiwan)

Abstract

Since the implementation of the Belt and Road Initiative, China’s total amount of outward foreign direct investment (OFDI) has increased each year and this has caused its relationship with carbon emissions (CO 2 e) to receive great attention recently. Forecasting China’s CO 2 e accurately by considering the impact of OFDI is important since the government can use it to formulate an appropriate emissions plan to fulfill its carbon reduction commitments. Because the relationship between OFDI and CO 2 e has nonlinear characteristics, a nonlinear grey multivariable model with fractional-order accumulation (NFGM(1,N)) was proposed in this study. To enhance the prediction accuracy, an optimization process was used to determine the parameters. The outcomes of the variable fractional order showed that fractional-order accumulation can better extract the grey information hidden in the original data, which confirmed the principle of new information priority. The result of the power coefficient indicated a nonlinear relationship between the CO 2 e and OFDI. Based on the prediction performance, the prediction accuracy of the NFGM(1,N) model was proven to be superior to those of the ARMA model, linear regression model, the GM(1,1), GM(1,N), and FGM(1,N) models. The empirical results also revealed that OFDI increased the CO 2 e in China. The relationship between the CO 2 e and OFDI exhibits a U-shaped development based on further predictions for the 2018–2030 period. Finally, some suggestions for long-term CO 2 e reduction plans were provided in this paper.

Suggested Citation

  • Hang Jiang & Peng Jiang & Peiyi Kong & Yi-Chung Hu & Cheng-Wen Lee, 2020. "A Predictive Analysis of China’s CO 2 Emissions and OFDI with a Nonlinear Fractional-Order Grey Multivariable Model," Sustainability, MDPI, vol. 12(10), pages 1-14, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:10:p:4325-:d:362695
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    References listed on IDEAS

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    2. Qianxiao Zhang & Syed Asif Ali Naqvi & Syed Ale Raza Shah, 2021. "The Contribution of Outward Foreign Direct Investment, Human Well-Being, and Technology toward a Sustainable Environment," Sustainability, MDPI, vol. 13(20), pages 1-29, October.

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