Spatiotemporal Patterns and Drivers of Urban Traffic Carbon Emissions in Shaanxi, China

Yongsheng Qian (), Junwei Zeng, Wenqiang Hao, Xu Wei, Minan Yang, Zhen Zhang and Haimeng Liu ()
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Yongsheng Qian: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Junwei Zeng: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Wenqiang Hao: Operations Branch, Xi’an Rail Transit Group Company-Limited, Xi’an 710016, China
Xu Wei: School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China
Minan Yang: School of Architecture and Urban Planning, Lanzhou Jiaotong University, Lanzhou 730070, China
Zhen Zhang: Beijing Institute of Ecological Geology, Beijing 100120, China
Haimeng Liu: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Land, 2025, vol. 14, issue 7, 1-21

Abstract: Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO 2 emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO 2 emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies.

Keywords: urban traffic; carbon emission; spatiotemporal pattern; impact mechanism; spatial and temporal geographically weighted regression (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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