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The security of natural gas supply and demand has always been an important topic, and as a result, analyzing and predicting the supply and demand of natural gas has received considerable attention from researchers. Numerous studies have extensively researched the supply and demand of natural gas. The main purpose of this study is to explore the supply‐demand balance mechanism for natural gas security. A system dynamics model is constructed to assess the supply and demand of natural gas in Sichuan Province, China. A new system dynamics model based on domestic gas, production gas, ecological environment and natural gas supply is proposed. The study considers four scenarios: natural development, economic priority development, environmental priority development and coordinated development. The results show that the rapid economic development has led to an excessive environmental carrying capacity, a larger gap between natural gas supply and demand, a decline in the quality of life of residents, and even affected the energy security of Sichuan Province and China. The findings also show that people's overprotection of the environment can lead to slow economic development, which prevents it from achieving its desired goals. The coordinated development scenario in this paper not only ensures the production of natural gas and the quality of life of residents, but also is an optimal scheme with low energy consumption and pollution. According to the research conclusions, considering the overall situation of China's energy security and the situation in Sichuan Province, the author proposes reforms from the supply and demand sides of natural gas. [ABSTRACT FROM AUTHOR]

Energy consumption is closely related to industrial structure, economic prosperity, and population. Because of the Granger causal relationship between GDP and energy consumption, many researchers consider the regression method to predict energy consumption using economic variables. However, some other researchers regard the time series method (forecast) to estimate for energy consumption. To address the advantages and disadvantages of two methods in energy consumption prediction, we performed the deep learning (DL) based on the case of China. The experiment results show that the accuracy of time series forecast is much higher than regression methods. In addition, we also explored the relationship between industry, economy, population, and energy consumption. We found that GDP, population, secondary sector, and tertiary sector were significant to energy consumption. A tertiary sector can help to reduce energy consumption to some extent. This could give suggestions about changing industry structure. More and more attention has been paid to energy intensity. The strategic approach to sustainable development requires reducing energy intensity while developing the economy. Since China is committed to achieving carbon emission peak by 2030, this paper predicts the energy intensity of China in the next decade based on a DL algorithm. The prediction results show that energy intensity would slightly decrease in the next decade. Different types of energy represent the industrial structure behind economic growth. Therefore, this paper was conducted using ridge regression (RR) to explore the relationship between different types of energy and energy intensity. In addition, the effect (positive or negative) of different energy sources in reducing energy intensity has also been explored. We found that the best way of developing the economy and reducing energy consumption is to use natural gas and hydroelectric instead of coal. This can guide policy development about changing energy source structure. [ABSTRACT FROM AUTHOR]