| 基于扩展STIRPAT模型LMDI分解的碳排放脱钩因素 |
| 摘要点击 1529 全文点击 117 投稿时间：2023-04-23 修订日期：2023-06-25 |
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| 中文关键词 STIRPAT模型 Kaya恒等式 LMDI分解 Tapio脱钩模型 碳排放 |
| 英文关键词 STIRPAT model Kaya identity LMDI decomposition Tapio decoupling model carbon emission |
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| 中文摘要 |
| 为研究经济发展与碳排放的脱钩情况,常用对数平均迪式指数分解法（LMDI）结合Kaya恒等式和Tapio脱钩模型计算碳变化量和弹性脱钩指数.借鉴上述方法,将STIRPAT模型与LMDI分解法相结合,建立STIRPAT模型的回归系数与碳变化量和脱钩弹性指数之间的数量关系,研究影响碳排放各因素的脱钩状态.结果表明：①STIRPAT模型LMDI分解法能够避免满足Kaya恒等式的IPAT模型中使用LMDI分解法时增加新变量的情况,部分新增变量往往缺乏明确的经济学含义；②LMDI分解将STIRPAT模型中的统计回归系数的含义,由变量的变动引起碳排放量变动的弹性系数,扩展到变量的变动引起碳变化量的倍数；③STIRPAT模型LMDI分解法,将数据的统计结果通过统计回归系数纳入到各因素的碳变化量和弹性脱钩指数之中,使弹性脱钩指数能够反映数据的统计信息；④以重庆市2001~2019年碳排放数据为例,来说明STIRPAT模型LMDI分解法可以用于判定碳排放变量的脱钩状态,能够体现数据本身所包含的统计信息,更能反映研究对象的实际情况. |
| 英文摘要 |
| 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. |
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