• Program Transportation & Infrastructure Transportation & Infrastructure
  • Type Discussion paper
  • Date 11 February 2025
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Abstract

Truck-hailing is a relatively new Uber-like business model that connects road freight carriers with shippers via mobile apps. First appeared around 2013, it has achieved fast market uptake in China, involving almost 8 million commercial trucks annually by the end of 2023. With China being one of the world’s largest road transport carbon emitters, it is crucial to understand the potential climate implications of this emergent trend. Here we utilize a large national proprietary truck-hailing sample and a transport-energy-emission model to explore the potential role of truck-hailing and logistics improvement under multiple scenarios. We found that under optimistic scenarios, logistics improvements as a potential result of high market penetration of truck-hailing services could significantly reduce road freight emissions in China, and there could be potential synergies between logistics improvements and technological advancement. We also found that operational performance limitations (range and capacity) of zero-emission vehicles could have moderate emission impacts.

Authors

Xun Xu

Fellow- Transportation & Infrastructure Xun Xu is a fellow working on freight transport energy demand and freight transport big data. His work has been…

Xun Xu is a fellow working on freight transport energy demand and freight transport big data. His work has been published in Energy Policy. Prior to joining KAPSARC, he worked at the East West Center and in the Natural Resources and Environmental Management department of the University of Hawaii. Xun received his Ph.D. in economics from the University of Hawaii in 2015.

Expertise

  • Freight Transport Big Data
  • Freight Transport Demand Modeling
  • Macroeconomics and Chinese Economy

Publications See all Xun Xu’s publications

Shiqi Ou

Shiqi Ou

Tianduo Peng

Tianduo Peng

Zhenhong Lin

Zhenhong Lin

Xunmin Ou

Xunmin Ou

Zhandong Xu

Zhandong Xu

Mi Gan

Mi Gan

Dandan Li

Dandan Li

Xiaobo Liu

Xiaobo Liu

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