基于面板数据模型关中平原城市群交通碳排放峰值预测与脱钩分析

Predicting and decoupling analysis of transportation peak carbon emissions in Guanzhong Plain urban agglomeration based on panel data modeling

基于2007~2021年交通运输业碳排放量(CT)相关数据,在扩展STIRPAT模型基础上运用带有Driscoll-Kraay标准误差的随机效应模型拟合,并以此预测未来年CT;采用Tapio模型对2007~2035年交通行业增加值(TGDP)与CT进行脱钩分析.结果表明,关中平原城市群交通能源强度、TGDP、私人车辆拥有辆等是推动CT增长的主要因素;相反,可再生电力占比和交通固定资产投资强度对CT增长有抑制作用,其中可再生电力占比是最主要的抑制因素;按低碳情景发展,关中平原城市群CT可在2030年达峰;2007~2013年,CT与TGDP间脱钩e值在-0.62~3.01间波动,之后趋于相对稳定,主要表现为弱脱钩,2030年后实现较强脱钩.研究表明,关中平原城市群要如期实现交通“碳达峰”目标,需优化能源结构来提升可再生能源占比、控制汽车拥有辆.

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.