Influencing factors and predictions of carbon emissions for the chemical industry in China

被引：0

作者：

Wang, Weiru ^{[1
]}

Hu, Fan ^{[2
]}

Li, Mengzan ^{[1
]}

Shi, Xincong ^{[2
]}

Liu, Xinyuan ^{[1
]}

机构：

[1] The department is Power Grid Technology Center, State Grid Shanxi Electric Power Research Institute, Taiyuan

[2] State Grid Shanxi Electric Power Company, Taiyuan

来源：

Frontiers in Energy Research | 2024年 / 12卷

关键词：

carbon emission; carbon prediction; influencing factor decomposition; LMDI; STIRPAT model;

D O I：

10.3389/fenrg.2024.1442106

中图分类号：

学科分类号：

摘要：

As global warming increases the frequent occurrences of natural disasters, the reduction of carbon emissions has become an important issue around the world. The chemical industry is an important source of carbon emissions in China. The carbon emissions of the chemical industry are calculated from 2000 to 2019 by using the emission factor method. The logarithmic mean divisia index (LMDI) method is exploited to analyze the factors that influence carbon emissions, and the emissions variations are attributed to the contributions of carbon intensity, energy structure, energy intensity, industrial value-added rate, per capita industrial output value, and industrial scale. The results of decomposition show that per capita industrial output value is the main driving factor, and energy intensity is the main inhibiting factor of the chemical industry’s carbon emissions. In order to quantify the variation of carbon emissions, the extended stochastic impacts by regression on population, affluence, and technology (STIRPAT) model is constructed and examined. Using the STIRPAT model, the basic scenario and energy intensity control scenario are set, and the carbon emissions are predicted, which shows that under a strict energy intensity control scenario, carbon emissions may reach a peak around 2031. The factors influencing the decomposition and prediction of carbon emissions should be helpful in reducing the carbon emissions of the chemical industry in China. Copyright © 2024 Wang, Hu, Li, Shi and Liu.

引用

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