Identifying Key Drivers of Natural Gas Demand in Sichuan Province Using a Hierarchical Analysis Framework

Mi Han; Xintong Lv; Ke Bai; Hongbing Li

doi:10.54097/5zt59464

Authors

Mi Han Southwest Petroleum University, Chengdu 610500, China
Xintong Lv Southwest Petroleum University, Chengdu 610500, China
Ke Bai Cost Center of Southwest Oil & Gas Field Branch, PetroChina, Chengdu 610500, China
Hongbing Li Aba Teachers College, Wenchuan County, Sichuan Province 623002, China

DOI:

https://doi.org/10.54097/5zt59464

Keywords:

Natural gas demand; Driver identification; GRA; Fuzzy DEMATEL-ISM; Sichuan Province.

Abstract

Natural gas plays a critical role in the low-carbon energy transition, yet its demand drivers remain insufficiently understood due to complex factor interactions. This study aims to identify and hierarchically classify the key drivers of natural gas demand to better forecasting natural gas demand. Taking Sichuan Province as a case study, we develop an integrated framework combining grey relational analysis, fuzzy DEMATEL, interpretive structural modeling, and MICMAC to examine fourteen candidate factors using data from 2008 to 2023. Results show that all candidate factors have grey relational degrees above 0.82. However, fuzzy DEMATEL reveals that only five factors—population size, technological level, economic development, urbanization, and carbon constraint—are causal drivers, while highly correlated variables such as gas-using population and pipeline length are effect factors. ISM further decomposes the driver system into five hierarchical levels, with population size identified as the root driver, validated by MICMAC. We conclude that statistical association does not equate to causal dominance, and correlation-based variable selection alone is insufficient for natural gas demand forecasting.

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