Forecasting natural gas demand in China: Logistic modelling analysis. (May 2016) - Shaikh, Faheemullah; Ji, Qiang
Highlights: Natural gas has emerged an important policy choice in China to reduce GHG emissions. We use logistic modelling approach to forecast China's natural gas demand. We use Levenberg–Marquardt Algorithm to estimate the parameters of logistic model. The employed modelling approach has shown good fit with sample and out sample data. The forecasting results are validated by comparing with other research studies. Abstract: Natural gas has increasingly appeared as an important policy choice for China's government to modify high carbon energy consumption structure and deal with environmental problems. This study is aimed to develop the logistic and logistic-population model based approach to forecast the medium- (2020) to long- (2035) term natural gas demand in China. The adopted modelling approach is relatively simple, compared with other forecasting approaches. In order to further improve the forecasting precision, the Levenberg–Marquardt Algorithm (LMA) has been implemented to estimate the parameters of the logistic model. The forecasting results show that China's natural gas demand will reach 330–370 billion m 3 in the medium-term and 500–590 billion m 3 in the long-term. Moreover, the forecasting results of this study were found close in studies conducted by the national and international institutions and scholars. The growing natural gas demand will cause significant increase in import requirements and will increase China's natural gas import dependency. The outcomes of this study are expected to assist the energy planners and policy makers to chalk out relevant natural gas supply and demand side management policies.