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

Xiaofan Wang, Junjie Li, Yulong Yan, Lin Peng

Abstract read

In one paragraph

Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Xiaofan WangEngineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Carbon Neutrality and Environment, Beijing Jiaotong University, Beijing 100044, China.

Junjie LiEngineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Carbon Neutrality and Environment, Beijing Jiaotong University, Beijing 100044, China.

Yulong YanEngineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Carbon Neutrality and Environment, Beijing Jiaotong University, Beijing 100044, China.

Lin PengEngineering Research Center of Clean and Low-carbon Technology for Intelligent Transportation, Ministry of Education, School of Carbon Neutrality and Environment, Beijing Jiaotong University, Beijing 100044, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

carbon footprintcarbon mitigationChinadieselelectricitylife cycle assessmentLMDIrailwaySBM-DEA

Identifiers

PMID42502395

DOI10.1016/j.isci.2026.116807

PMCPMC13401036

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.