Spatiotemporal evolution and spatial differentiation of carbon emission intensity in the Chinese transport sector

Spatiotemporal evolution and spatial differentiation of carbon emission intensity in the Chinese transport sector | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spatiotemporal evolution and spatial differentiation of carbon emission intensity in the Chinese transport sector Yongping Tang, Huiyuan Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8409143/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Accurately identifying the spatiotemporal evolution and spatial differentiation of carbon emission intensity in the transport sector is essential for formulating region-specific carbon reduction policies. From a static perspective, this study applies the Dagum Gini coefficient to examine spatial disparities and their sources of transport carbon emission intensity. From a dynamic perspective, the kernel density estimation method is used to depict its evolutionary trajectory, and the spatial Markov chain model is further introduced to capture locking effects and spillover effects of spatial variation. The empirical results indicate that (1) The carbon emission intensity of the transport sector in China presents an overall declining trend with significant spatial heterogeneity among provinces. Regional disparities have expanded, with the largest gap occurring between the eastern and western regions, where inter-regional differences contribute an average of 47.374% to the total disparity, representing the main source of variation. (2) The carbon emission intensity in the national, eastern, and central regions tends to converge gradually, while the western region shows a pattern of initial convergence followed by renewed divergence. Within each region, several provinces maintain carbon emission intensity levels significantly higher than the average, forming a clear spatial gradient structure. (3) The traditional Markov chain analysis reveals evident hierarchical solidification and club convergence in transport carbon emission intensity. The spatial Markov chain analysis further indicates that the state of neighboring regions exerts a significant influence on local transition probabilities, demonstrating strong spatial spillover and path dependence effects. Hypothesis testing confirms the necessity of incorporating spatial dependence into the analysis. Finally, regionally differentiated carbon reduction strategies are proposed based on the research findings. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental social sciences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Jan, 2026 Reviews received at journal 15 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 06 Jan, 2026 Reviewers invited by journal 06 Jan, 2026 Editor invited by journal 04 Jan, 2026 Editor assigned by journal 20 Dec, 2025 Submission checks completed at journal 20 Dec, 2025 First submitted to journal 19 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8409143","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":570616159,"identity":"089d026a-f34a-4587-841a-6d5415f4cbea","order_by":0,"name":"Yongping Tang","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yongping","middleName":"","lastName":"Tang","suffix":""},{"id":570616160,"identity":"c2fb7511-173c-44e0-9f2f-424748dbb0a0","order_by":1,"name":"Huiyuan 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From a static perspective, this study applies the Dagum Gini coefficient to examine spatial disparities and their sources of transport carbon emission intensity. From a dynamic perspective, the kernel density estimation method is used to depict its evolutionary trajectory, and the spatial Markov chain model is further introduced to capture locking effects and spillover effects of spatial variation. The empirical results indicate that (1) The carbon emission intensity of the transport sector in China presents an overall declining trend with significant spatial heterogeneity among provinces. Regional disparities have expanded, with the largest gap occurring between the eastern and western regions, where inter-regional differences contribute an average of 47.374% to the total disparity, representing the main source of variation. (2) The carbon emission intensity in the national, eastern, and central regions tends to converge gradually, while the western region shows a pattern of initial convergence followed by renewed divergence. Within each region, several provinces maintain carbon emission intensity levels significantly higher than the average, forming a clear spatial gradient structure. (3) The traditional Markov chain analysis reveals evident hierarchical solidification and club convergence in transport carbon emission intensity. The spatial Markov chain analysis further indicates that the state of neighboring regions exerts a significant influence on local transition probabilities, demonstrating strong spatial spillover and path dependence effects. Hypothesis testing confirms the necessity of incorporating spatial dependence into the analysis. 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