Chen Huanyu, Yi Jizheng, Chen Aibin, Zhou Guoxiong
College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, China.
Math Biosci Eng. 2022 Sep 9;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 CO 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, CO 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 CO 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.
基于2003年至2017年中国的面板数据，本文将面板向量自回归（PVAR）模型应用于碳排放影响因素的研究。在对面板数据进行截面依赖性检验、单位根检验和协整检验之后，利用PVAR模型研究能源消耗、经济增长、城市化、金融发展与碳排放之间的动态关系。然后，我们使用脉冲响应函数工具，以更好地理解感兴趣的主要变量碳排放对其他四个因素的冲击反应。最后，通过对所有因素的方差分解，得出单个变量对其他内生变量的影响程度。总体而言，结果表明这四个因素对碳排放有显著的正向影响。此外，方差分解还表明，能源消耗和经济增长对碳排放有很强的解释力。这些结果表明，中国各省的金融、经济和能源部门在减少碳排放和改善环境质量方面的贡献仍然相对较弱。因此，提出并讨论了若干政策。
