Forecast of China’s natural gas demand based on the double-logarithmic model with stepwise regression method

Abstract

ABSTRACT In the current world’s energy system, natural gas, as a clean energy source, is gradually becoming the main energy source. However, the volume of natural gas demand could be able to be influenced by numerous factors, in order to forecast the future trend of the requirement more precisely, it is necessary to further explore effective driving factors influencing the volume of demand and construct a brand new function model of forecasting natural gas demand. In this study, the relative gray relational degree is used to quantitatively describe the correlation among variables, diagnosing whether there are serious multicollinearity problems among these factors. According to the principle of correlation degree: first and then small, a stepwise regression analysis is adopted to refine the analysis of factors influencing natural gas demand. On the basis of statistical significance testing, in-depth studying is conducted on the effective influencing factors of natural gas demand. This helps to develop a more accurate prediction model. In this study, we find that: (1) China’s energy consumption structure and economic development influence its natural gas demand. (2) The stepwise regression double logarithmic demand function model based on the effective drivers has good forecasting performance. Researchers can use it to forecast the natural gas demand for medium- and long-term in different regions, and the forecasting results can be considered an important reference basis for scientific natural gas policy formulation. (3) Over the coming 15 years, Natural gas demand in China will continue to grow. It will reach about 6910 × 10 8 m 3 by 2035. It is concluded that the development of China’s natural gas demand would be promoted in an increasing scale trend by striving to promote China’s high-quality economic development, carefully building a new pattern of high-quality energy development, and increasing the proportion of natural gas consumption.