Based on the panel data of China from 2003 to 2017, this paper applies the panel vector autoregressive (PVAR) model to the study of the influencing factors of carbon emissions. After the cross-section dependence test, unit root test and cointegration test of panel data, the dynamic relationship between energy consumption, economic growth, urbanization, financial development and CO2 emissions is investigated by using PVAR model. Then, we used the impulse response function tool to better understand the reaction of the main variables of interest, CO2 emissions, aftershocks on four factors. Finally, through the variance decomposition of all factors, the influence degree of a single variable on other endogenous variables is obtained. Overall, the results show that the four factors have a significant and positive impact on carbon emissions. In addition, variance decomposition also showed that energy consumption and economic growth strongly explained CO2 emissions. These results indicate that the financial, economic and energy sectors of China's provinces still make relatively weak contributions to reducing carbon emissions and improving environmental quality. Therefore, several policies are proposed and discussed.
Citation: Huanyu Chen, Jizheng Yi, Aibin Chen, Guoxiong Zhou. Application of PVAR model in the study of influencing factors of carbon emissions[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13227-13251. doi: 10.3934/mbe.2022619
Based on the panel data of China from 2003 to 2017, this paper applies the panel vector autoregressive (PVAR) model to the study of the influencing factors of carbon emissions. After the cross-section dependence test, unit root test and cointegration test of panel data, the dynamic relationship between energy consumption, economic growth, urbanization, financial development and CO2 emissions is investigated by using PVAR model. Then, we used the impulse response function tool to better understand the reaction of the main variables of interest, CO2 emissions, aftershocks on four factors. Finally, through the variance decomposition of all factors, the influence degree of a single variable on other endogenous variables is obtained. Overall, the results show that the four factors have a significant and positive impact on carbon emissions. In addition, variance decomposition also showed that energy consumption and economic growth strongly explained CO2 emissions. These results indicate that the financial, economic and energy sectors of China's provinces still make relatively weak contributions to reducing carbon emissions and improving environmental quality. Therefore, several policies are proposed and discussed.
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Figures(1) / Tables(7)
Huanyu Chen, Jizheng Yi, Aibin Chen, Guoxiong Zhou. Application of PVAR model in the study of influencing factors of carbon emissions[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 13227-13251. doi: 10.3934/mbe.2022619
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