Predicting and decoupling analysis of transportation peak carbon emissions in Guanzhong Plain urban agglomeration based on panel data modeling
Using data on carbon emissions from the transportation industry(CT)from 2007 to 2021,a random-effects model with Driscoll-Kraay standard errors was employed to fit the extended STIRPAT model and predict CT in the coming years.Then,the decoupling situation of the added value of the transportation industry(TGDP)and CT was analyzed with a decoupling model during 2007 to 2035.The results indicated that transportation energy intensity,TGDP,and private vehicle ownership were the main factors driving CT growth in the Guanzhong Plain urban agglomeration;On the contrary,the proportion of renewable electricity and the intensity of transport fixed assets investment posed negative effects on CT,of which the proportion of renewable electricity was the main inhibitory factor.The CT reached its peak by 2030 based on the low-carbon scenario.The decoupling e value between CT and TGDP fluctuated between-0.62 and 3.01 from 2007 to 2013,then stabilized,primarily indicating weak decoupling.A strong decoupling between CT and TGDP was achieved after 2030.Overall,the study suggests optimizing the energy structure by increasing the proportion of renewable energy and controlling private vehicle ownership to achieve the transportation"emission peak"goal on schedule in the Guanzhong Plain urban agglomeration.
万方数据
维普
