How much natural gas does China need: An empirical study from the perspective of energy transition. (1st March 2023)

• Record Type: Journal Article
• Authors: Xie, Minghua; Yi, Xiangyu; Liu, Kui; Sun, Chuanwang; Kong, Qingbao
• Is Part Of: Energy. Volume 266(2023)
• Publication Date: 2023-03-01
• Subjects: Energy demand -- Aggregate effect -- Structural effect -- Markov model; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.126357
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: The proposed policy targets of "2030 carbon peaking" and "2060 carbon neutrality" further accelerate the substitution of natural gas for oil and coal. In this paper, we systematically considered the growth trend of natural gas demand from both the total amount and the structure of energy.On the basis of 60-year sample data, we built the HMM model innovatively to simulate the complementary and alternative structural effects between different energy. In addition, according to the different policy intensity, we simulated the history of energy demand and forecast the future trend in three scenarios. The empirical results show that the share of natural gas in primary energy consumption in China in 2035 is 16.5%, 17.3%, and 13.6%, respectively, translating into natural gas demand of 6975, 7316, and 5748 bcm. Therefore, policymakers should promote energy transformation in a steady and orderly manner based on national conditions and strengthen gas demand-side management on the premise of ensuring reasonable demand. Our prediction of natural gas demand from a new perspective can assist policymakers to formulate the natural gas demand-side policy and import plan, and also provide a reference for studies of achieving the goal of energy transition and "2060 carbon neutrality". Highlights: The overall and structural effects of gas demand growth. The evolution trend of energy demand structure is predicted through three ideas. Structure model is used to simulate the substitution of energy sources. The prediction results of hidden Markov model are more reasonable.