China's natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model
- Publication Type:
- Journal Article
- Renewable and Sustainable Energy Reviews, 2016, 53 pp. 1149 - 1167
- Issue Date:
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2015 Elsevier Ltd. All rights reserved. As fossil fuels reserves deplete rapidly and the low-carbon economy develops expeditiously, especially in China, natural gas as a clean and alternative energy is underway to help meet increased energy needs and climate needs. Therefore, accurate forecasts of natural gas production and consumption have been a necessary task for policy making in the coming years. This paper presents a review of natural gas forecasting models. The multicycle Hubbert model is employed to forecast China's annual nature gas production and to determine the peak production, the peak year and the future production trends based on several different URR scenarios. Moreover, a small-sample effective rolling GM(1,1) model is proposed for the first time to forecast exponential natural gas consumption with different lengths of data sets. Then, the grey relationship analysis is used to select the best consumption curve in correspond with different URR scenarios. The empirical result shows that the supply-demand gap will be larger and larger in the future, with a minimum gap of 22 bcm in 2011 and 225 bcm in 2050, with a maximum gap of 31 bcm in 2011 and 807 bcm in 2050, which indicates that the natural gas production in China cannot meet the rising consumption. Therefore, policy measures must be taken to ameliorate the situation, including expanding natural gas imports, increasing unconventional natural gas production, complementing the gap with other energy resources and combining energy saving with emission reduction. Accurate forecasting of natural gas production and consumption can provide the basis for decision making and help the government generate new significant policies.
Please use this identifier to cite or link to this item: