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首页> 美国卫生研究院文献>International Journal of Environmental Research and Public Health >Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables
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Evaluating the Mutual Relationship between IPAT/Kaya Identity Index and ODIAC-Based GOSAT Fossil-Fuel CO2 Flux: Potential and Constraints in Utilizing Decomposed Variables

机译：评估IPAT / KAYA身份指数和基于ODIAC的GOSAT化石燃料CO2通量的相互关系：利用分解变量的电位和约束

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摘要

The IPAT/Kaya identity is the most popular index used to analyze the driving forces of individual factors on CO emissions. It represents the CO emissions as a product of factors, such as the population, gross domestic product (GDP) per capita, energy intensity of the GDP, and carbon footprint of energy. In this study, we evaluated the mutual relationship of the factors of the IPAT/Kaya identity and their decomposed variables with the fossil-fuel CO flux, as measured by the Greenhouse Gases Observing Satellite (GOSAT). We built two regression models to explain this flux; one using the IPAT/Kaya identity factors as the explanatory variables and the other one using their decomposed factors. The factors of the IPAT/Kaya identity have less explanatory power than their decomposed variables and comparably low correlation with the fossil-fuel CO flux. However, the model using the decomposed variables shows significant multicollinearity. We performed a multivariate cluster analysis for further investigating the benefits of using the decomposed variables instead of the original factors. The results of the cluster analysis showed that except for the M factor, the IPAT/Kaya identity factors are inadequate for explaining the variations in the fossil-fuel CO flux, whereas the decomposed variables produce reasonable clusters that can help identify the relevant drivers of this flux.
机译：iPat / Kaya身份是最流行的指数，用于分析CO排放的个别因素的驱动力。它代表了作为因素的产物的共同排放，例如人口总体，GDP总产量，GDP的能量强度以及能量的碳足迹。在这项研究中，我们评估了IPAT / KAYA身份的因素及其与化石燃料CO助焊剂的分解变量的相互关系，如受温室气体观察卫星（GOSAT）所测量的。我们建立了两种回归模型来解释这一助焊剂;一个使用iPat / Kaya身份因子作为解释性变量，另一个使用其分解因子。 IPAT / KAYA身份的因素比其分解变量较少，与化石燃料CO通量相对低的相关性。然而，使用分解变量的模型显示出显着的多色性性。我们对进一步调查使用分解变量而不是原始因素的益处进行了多变量的聚类分析。聚类分析结果表明，除了M因素之外，IPAT / KAYA身份因子不充分用于解释化石燃料CO通量的变化，而分解变量会产生合理的集群，可以帮助识别此相关驱动因素助焊剂。

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