Research on Carbon Emission Decoupling Factors Based on STIRPAT Model and LMDI Decomposition

被引：0

作者：

Zhang J.-Y. ^{[1
]}

机构：

[1] Upper Yangtze River Economic Research Center, Chongqing Technology and Business University, Chongqing

来源：

Huanjing Kexue/Environmental Science | 2024年 / 45卷 / 04期

关键词：

carbon emission; Kaya identity; LMDI decomposition; STIRPAT model; Tapio decoupling model;

D O I：

10.13227/j.hjkx.202304203

中图分类号：

学科分类号：

摘要：

To study the decoupling between economic development and carbon emissions，the logarithmic mean Divisia index（LMDI）method is commonly used in conjunction with the Kaya identity and Tapio decoupling models to calculate carbon change and the elastic decoupling index. It was found that the STIRPAT model could obtain the carbon change in each variable through the LMDI decomposition method，and the regression coefficient was included in the carbon change and elastic decoupling index of each variable. In the LMDI decomposition of the Kaya identity，new variables were introduced to satisfy the identical equation，which often lacked clear economic meaning. The LMDI decomposition of the STIRPAT model could maintain consistency in selecting variables before and after without adding new variables. The LMDI decomposition extended the meaning of statistical regression coefficients in the STIRPAT model and the elasticity coefficient of carbon emissions caused by variable changes to the multiple of carbon emissions changes caused by variable changes. The LMDI decomposition of the STIRPAT model incorporated the statistical information of the data into the carbon change and elastic decoupling index of each variable through statistical regression coefficients so that the carbon change and elastic decoupling index could reflect the statistical information of the data. Taking the carbon emission data of Chongqing from 2001 to 2019 as an example，it was shown that the STIRPAT model LMDI decomposition could be used to determine the decoupling state of variables affecting carbon emissions，which was more comprehensive than the LMDI decomposition that satisfied Kaya identity to reflect the actual situation of the research object. © 2024 Science Press. All rights reserved.

引用

页码：1888 / 1897

页数：9

共 46 条

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