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Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators

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  • Wei Meng
  • Daoli Yang
  • Hui Huang

Abstract

Sulfur dioxide is an important source of atmospheric pollution. Many countries are developing policies to reduce sulfur dioxide emissions. In this paper, a novel prediction model is proposed, which could be used to forecast sulfur dioxide emissions. To improve the modeling procedure, fractional order accumulating generation operator and fractional order reducing generation operator are introduced. Based on fractional order operators, a discrete grey model with fractional operators is developed, which also makes use of genetic algorithms to optimize the modeling parameter . The improved performance of the model is demonstrated via comparison studies with other grey models. The model is then used to predict China’s sulfur dioxide emissions. The forecast result shows that the amount of sulfur dioxide emissions is steadily decreasing and the policies of sulfur dioxide reduction in China are effective. According to the current trend, by 2020, the value of China’s sulfur dioxide emissions will be only 86.843% of emissions in 2015. Fractional order generation operators can be used to develop other fractional order system models.

Suggested Citation

  • Wei Meng & Daoli Yang & Hui Huang, 2018. "Prediction of China’s Sulfur Dioxide Emissions by Discrete Grey Model with Fractional Order Generation Operators," Complexity, Hindawi, vol. 2018, pages 1-13, January.
    • Handle: RePEc:hin:complx:8610679
    DOI: 10.1155/2018/8610679
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    References listed on IDEAS

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    1. Shuhua Ma & Zongguo Wen & Jining Chen, 2012. "Scenario Analysis of Sulfur Dioxide Emissions Reduction Potential in China's Iron and Steel Industry," Journal of Industrial Ecology, Yale University, vol. 16(4), pages 506-517, August.
    2. Mohajan, Haradhan, 2014. "Chinese Sulphur Dioxide Emissions and Local Environment Pollution," MPRA Paper 55953, University Library of Munich, Germany, revised 06 Apr 2014.
    3. García-Labiano, Francisco & de Diego, Luis F. & Cabello, Arturo & Gayán, Pilar & Abad, Alberto & Adánez, Juan & Sprachmann, Gerald, 2016. "Sulphuric acid production via Chemical Looping Combustion of elemental sulphur," Applied Energy, Elsevier, vol. 178(C), pages 736-745.
    4. Che-Jung Chang & Wen-Li Dai & Chien-Chih Chen, 2015. "A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1887-1894, November.
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    Cited by:

    1. Ma, Xin & Mei, Xie & Wu, Wenqing & Wu, Xinxing & Zeng, Bo, 2019. "A novel fractional time delayed grey model with Grey Wolf Optimizer and its applications in forecasting the natural gas and coal consumption in Chongqing China," Energy, Elsevier, vol. 178(C), pages 487-507.
    2. Ma, Xin & Deng, Yanqiao & Ma, Minda, 2024. "A novel kernel ridge grey system model with generalized Morlet wavelet and its application in forecasting natural gas production and consumption," Energy, Elsevier, vol. 287(C).
    3. Peng Li & Ju Liu & Cuiping Wei, 2019. "A Dynamic Decision Making Method Based on GM(1,1) Model with Pythagorean Fuzzy Numbers for Selecting Waste Disposal Enterprises," Sustainability, MDPI, vol. 11(20), pages 1-19, October.
    4. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.

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