Research of Carbon Emissions (CE) in Inter-City Passenger Transportation of Urban Agglomerations (IPTUA) in Different Periods

This study aims to propose a comprehensive framework for measuring CE in IPTUA, taking into account both regular and holiday periods. The Low Emissions Analysis Platform (LEAP) is employed to model the CE and improvement methods for the LEAP are proposed. Using a measurement framework combined with Baidu migration data, the CE from inter-city passenger transportation (IPT) between five main cities in the Guanzhong Urban Agglomeration (GUA): Xi'an (XA), Tongchuan (TC), Baoji (BJ), Xianyang (XY), and Weinan (WN), are calculated for the year 2021. This includes the CE from private car travel, highway bus travel, and railway travel. A dual-layer prediction framework is used to forecast future CE, and carbon peak predictions for IPT in the GUA under different policy and technological improvement scenarios are conducted. The results indicate that carbon peak status will be achieved in all scenarios by 2029. Furthermore, both policy and technological scenarios have a significant impact on reducing total CE. Under the policy scenario, CE decreased from the actual value of 7,714,864.408 tons to 7,564,746.75 tons. Under the technological scenario, CE from regular national and provincial roads (RNPR) decreased by 236,542.567 tons with a 10% reduction in energy consumption, and CE from highway private car travel decreased by 528,427.9295 tons. Meanwhile, with an annual 1% reduction in energy consumption, CE from RNPR decreased by 189,234.05 tons, and CE from highway private car travel decreased by 422,742.3 tons. From the reduction perspective, a one-time 10% reduction in energy consumption for private cars results in a greater reduction in CE. However, gradually reducing private car energy consumption is more feasible in practice. This study provides a comprehensive and accurate assessment of CE in IPT during different periods, offering a scientific basis for formulating carbon emission reduction policies.