4.7 Article

Forecast of natural gas consumption in 30 regions of China under dual carbon target

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s11356-023-28762-9

关键词

Natural gas consumption; Carbon peaking; Carbon neutrality; Fractional cumulative grey model

向作者/读者索取更多资源

Natural gas is a clean energy source that can help China achieve its carbon peaking and carbon neutrality goals. The government's policies to encourage natural gas development make forecasting its consumption crucial. The fractional cumulative grey model is used in this study to forecast natural gas consumption in 30 areas of China from 2022 to 2030. The results show that, except for a few areas, consumption will continue to rise in recent years, and regional characteristics in consumption patterns are observed.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据