Abstract
Natural gas is an environmentally friendly and low-carbon clean energy. Its replacement of coal and other fossil energy sources will be important in China’s carbon peaking and carbon neutrality goals. The Chinese government has also introduced many policies to encourage the development of natural gas. Therefore, it is of great significance to forecast the natural gas consumption. The grey prediction model has the unique advantage that it can perform well in the case of inadequate sample size. In this paper, the fractional cumulative grey model (FGM(1,1)) is used to forecast the natural gas consumption of 30 areas (provinces, cities, and autonomous regions) in China from 2022 to 2030. According to the reasonable forecast results, except for a few special areas, the consumption in other areas of China will continue to rise in recent years. By analyzing the results, it can also be clearly concluded that the natural gas consumption has regional characteristics. The consumption in 19 regions shows a rapid growth trend, 8 regions show a steady growth trend, and 3 regions show a downward trend. The prediction results and analysis will provide some reference for different regions to formulate natural gas-related policies.
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Acknowledgements
The relevant researches are supported by the National Natural Science Foundation of China (71871084, U20A20316), the Young talent support scheme of Hebei Province (360-0803-YBN-7U2C), the key research project in humanity and social science of Hebei Education Department (ZD202211), the Natural Science Foundation of Hebei Province (E2020402074), and the Graduate Demonstration Course in Hebei Province (KCJSX2022095).
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Experimental design: He Zhang. Experiment and analysis: He Zhang. Data interpretation: Yuhan Xie. Writing and revision: He Zhang, Yuhan Xie, and Lifeng Wu. All authors read and approved the final manuscript.
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Zhang, ., Xie, Y. & Wu, L. Forecast of natural gas consumption in 30 regions of China under dual carbon target. Environ Sci Pollut Res (2023). https://doi.org/10.1007/s11356-023-28762-9
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DOI: https://doi.org/10.1007/s11356-023-28762-9