Bureau-specific spatiotemporal carbon footprints and decarbonization pathways of China’s railways

China's industrialization and urbanization have driven differentiated railway development across regions, necessitating refined carbon footprint (CF) accounting at the bureau scale to address spatiotemporal heterogeneity beyond national-level analysis. Here we developed a bureau-specific CF model incorporating energy mix, technological efficiency, fleet structure, and transport capacity, paired with driver and efficiency identification models. Results show China's operational railway emissions dropped 25.5% during 2010–2022 but concentrated in northern bureaus, with Taiyuan (2.78 Mt CO 2 -eq), Shenyang (2.75 Mt CO 2 -eq), and Zhengzhou (2.52 Mt CO 2 -eq) as top emitters in 2022, primarily driven by energy intensity and transport volume variations. Electrification reduced emission intensity (electric locomotives outperforming diesel ones) but relies on low-carbon electricity. Emission efficiency improved alongside declining emissions, with Taiyuan railway bureau ranked first in both total CF and efficiency. These findings emphasize region-specific decarbonization strategies integrating renewable energy, technological upgrading, and operational optimization.

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Publication Details

Journal
iScience
Published
2026-07-17
DOI
https://doi.org/10.1016/j.isci.2026.116807
Primary Topic
Environmental Impact and Sustainability
Type
article
Field-Weighted Citation Impact
0.00

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article

Bureau-specific spatiotemporal carbon footprints and decarbonization pathways of China’s railways

Yulong Yan, Junjie Li, Xiaofan Wang, Lin Peng
iScience
Environmental Impact and Sustainability
article

Bureau-specific spatiotemporal carbon footprints and decarbonization pathways of China’s railways

Yulong Yan, Junjie Li, Xiaofan Wang, Lin Peng
article en

Abstract

China's industrialization and urbanization have driven differentiated railway development across regions, necessitating refined carbon footprint (CF) accounting at the bureau scale to address spatiotemporal heterogeneity beyond national-level analysis. Here we developed a bureau-specific CF model incorporating energy mix, technological efficiency, fleet structure, and transport capacity, paired with driver and efficiency identification models. Results show China's operational railway emissions dropped 25.5% during 2010–2022 but concentrated in northern bureaus, with Taiyuan (2.78 Mt CO 2 -eq), Shenyang (2.75 Mt CO 2 -eq), and Zhengzhou (2.52 Mt CO 2 -eq) as top emitters in 2022, primarily driven by energy intensity and transport volume variations. Electrification reduced emission intensity (electric locomotives outperforming diesel ones) but relies on low-carbon electricity. Emission efficiency improved alongside declining emissions, with Taiyuan railway bureau ranked first in both total CF and efficiency. These findings emphasize region-specific decarbonization strategies integrating renewable energy, technological upgrading, and operational optimization.

iScienceVol. 29(8)
Beijing Jiaotong University (CN), National Institute of Clean and Low-Carbon Energy (CN)
National Natural Science Foundation of China, Key Technologies Research and Development Program
Openalex Percentile: Top 15%
Environmental Impact and Sustainability
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