ORIGINAL RESEARCH article
Front. Environ. Sci.
Sec. Environmental Economics and Management
Volume 13 - 2025 | doi: 10.3389/fenvs.2025.1611380
Carbon Emissions from Public Transportation in Major Chinese Cities: Spatiotemporal Analysis, Decoupling Trends, and Key Drivers
Provisionally accepted
- 1Anhui Normal University, Wuhu, China
- 2Anhui Size Technology Co., Ltd., wuhu, China
- 3SIPSG Information Technology Co. Ltd, suzhou, China
- 4Beijing Foreign Studies University, Haidian District, Beijing Municipality, China
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This study utilized public transport data from 28 major Chinese cities from 2018--2022 and employed methods such as carbon emission measurement, standard deviation ellipse analysis, the Tapio decoupling model, and the LMDI decomposition method to ana-lyse the temporal and spatial evolution, decoupling states, and driving factors of public transport carbon emissions comprehensively. The results show that (1) total carbon emissions fluctuated markedly, and emissions dropped sharply in 2020 due to the COVID-19 pandemic, rebounded in 2021, and declined again in 2022 due to technological upgrades and policies. (2) The spatial distribution of carbon emissions follows a northeastern-southwestern pattern. The center of gravity shifted slowly southwards and slightly west-wards and was influenced by economic development and transportation policies. (3) The 28 cities were classified into four groups: Type I had high emissions but low intensity; Type II exhibited a positive decoupling trend; and Types III and IV showed weak decoupling. (4) Economic activities and line density were the main drivers of emission growth, whereas carbon emission intensity and transportation intensity increasingly inhibited emissions in recent years. On the basis of these findings, we propose differentiated low-carbon transportation policies, regional collaborative governance, and technology optimization to support urban transportation low-carbon transformation under the "dual-carbon" goal.
Keywords: Public transportation, carbon emissions, spatiotemporal evolution, Tapio decoupling model, LMDI decomposition
Received: 15 Apr 2025; Accepted: 27 May 2025.
Copyright: © 2025 Jia, Wang, Yang, Qian, Cao, Zhao and Lin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Luzhou Lin, Beijing Foreign Studies University, Haidian District, 100089, Beijing Municipality, China
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