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Forecasting China’s natural gas demand based on optimised nonlinear grey models

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  • Shaikh, Faheemullah
  • Ji, Qiang
  • Shaikh, Pervez Hameed
  • Mirjat, Nayyar Hussain
  • Uqaili, Muhammad Aslam

Abstract

Natural gas increasingly has become an important policy choice for China to modify its high carbon energy consumption structure. Natural gas is a low carbon energy option for China’s government to fulfil its volunteer commitments with the international community to mitigate greenhouse gas emissions. This study has constructed China’s natural gas consumption forecasting model by utilising two optimised nonlinear grey models: the Grey Verhulst Model and the Nonlinear Grey Bernoulli Model. Both of these models have precisely adapted China’s actual natural gas consumption and forecasted that the country’s natural gas demand will reach 315 billion m3 by 2020. In addition, the existing and projected natural gas supplies and the capacities of imports, such as liquefied natural gas and pipeline natural gas, have been evaluated to gain a better understanding of the supply-demand and import trends. Accordingly, it has been observed that China’s existing and planned natural gas supplies and LNG and PNG infrastructure will be sufficient to cope with the growing energy demand for the period 2014–2020. However, this situation will cause a significant increase in its import dependency.

Suggested Citation

  • Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    • Handle: RePEc:eee:energy:v:140:y:2017:i:p1:p:941-951
    DOI: 10.1016/j.energy.2017.09.037
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    1. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    2. Lin, Chiun-Sin & Liou, Fen-May & Huang, Chih-Pin, 2011. "Grey forecasting model for CO2 emissions: A Taiwan study," Applied Energy, Elsevier, vol. 88(11), pages 3816-3820.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Akay, Diyar & Atak, Mehmet, 2007. "Grey prediction with rolling mechanism for electricity demand forecasting of Turkey," Energy, Elsevier, vol. 32(9), pages 1670-1675.
    5. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.
    6. Lin, Boqiang & Wang, Ting, 2012. "Forecasting natural gas supply in China: Production peak and import trends," Energy Policy, Elsevier, vol. 49(C), pages 225-233.
    7. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    8. Li, Junchen & Dong, Xiucheng & Shangguan, Jianxin & Hook, Mikael, 2011. "Forecasting the growth of China’s natural gas consumption," Energy, Elsevier, vol. 36(3), pages 1380-1385.
    9. Wang, Jianliang & Feng, Lianyong & Zhao, Lin & Snowden, Simon, 2013. "China's natural gas: Resources, production and its impacts," Energy Policy, Elsevier, vol. 55(C), pages 690-698.
    10. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
    11. Bianco, Vincenzo & Scarpa, Federico & Tagliafico, Luca A., 2014. "Scenario analysis of nonresidential natural gas consumption in Italy," Applied Energy, Elsevier, vol. 113(C), pages 392-403.
    12. Yao, Albert W.L. & Chi, S.C. & Chen, C.K., 2005. "Development of an integrated Grey–fuzzy-based electricity management system for enterprises," Energy, Elsevier, vol. 30(15), pages 2759-2771.
    13. Gutiérrez, R. & Nafidi, A. & Gutiérrez Sánchez, R., 2005. "Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model," Applied Energy, Elsevier, vol. 80(2), pages 115-124, February.
    14. Melikoglu, Mehmet, 2013. "Vision 2023: Forecasting Turkey's natural gas demand between 2013 and 2030," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 393-400.
    Full references (including those not matched with items on IDEAS)

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