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Carbon Emissions Intensity of the Transportation Sector in China: Spatiotemporal Differentiation, Trends Forecasting and Convergence Characteristics

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  • Zhimin Peng

    (School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China)

  • Miao Li

    (Academic Affairs, East China Jiaotong University, Nanchang 330013, China)

Abstract

Effectively controlling the carbon emissions intensity of the transportation sector (TSCEI) is essential to promote the sustainable development of the transportation industry in China. This study, which builds upon trend analysis, the Dagum Gini coefficient, and spatial autocorrelation analysis to reveal the spatiotemporal differentiation of TSCEI, employs both traditional and spatial Markov chain to analyze the dynamic evolution of TSCEI and forecast its future development trend. Furthermore, econometric models are constructed to examine the convergence characteristics of TSCEI. The empirical results reveal the following key findings: (1) TSCEI in China has significantly declined, exhibiting a spatial distribution pattern of “higher in the north, lower in the south; higher in the west, lower in the east”. (2) Inter-regional differences are the main contributors to overall TSCEI disparities, with provincial TSCEI exhibiting positive spatial autocorrelation, primarily characterized by high–high and low–low agglomeration. (3) TSCEI tends to gradually shift from high- to low-intensity states over time, with an equilibrium probability of 90.98% for transferring to lower intensity state. Provincial TSCEI shows significant spatial spillover effects, influenced by neighboring provinces’ states. (4) TSCEI demonstrates convergence characteristics at national and regional levels, including σ convergence, absolute and conditional β convergence, with the transportation energy structure and technological progress playing a particularly prominent role in facilitating the convergence of TSCEI towards lower values. The policy implications of promoting TSCEI convergence and reducing spatial inequality are discussed.

Suggested Citation

  • Zhimin Peng & Miao Li, 2025. "Carbon Emissions Intensity of the Transportation Sector in China: Spatiotemporal Differentiation, Trends Forecasting and Convergence Characteristics," Sustainability, MDPI, vol. 17(3), pages 1-30, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:815-:d:1572312
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    References listed on IDEAS

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    1. Jobert, Thomas & Karanfil, Fatih & Tykhonenko, Anna, 2010. "Convergence of per capita carbon dioxide emissions in the EU: Legend or reality?," Energy Economics, Elsevier, vol. 32(6), pages 1364-1373, November.
    2. Jiang, Lei & Yang, Linshuang & Wu, Qingyang & Zhang, Xinyue, 2024. "How does extreme heat affect carbon emission intensity? Evidence from county-level data in China," Economic Modelling, Elsevier, vol. 139(C).
    3. Meng, Conghui & Du, Xiaoyun & Zhu, Mengcheng & Ren, Yitian & Fang, Kai, 2023. "The static and dynamic carbon emission efficiency of transport industry in China," Energy, Elsevier, vol. 274(C).
    4. Tan, Jiayi & Peng, Shanbi & Liu, Enbin, 2024. "Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China," Applied Energy, Elsevier, vol. 376(PB).
    5. Liu, Jiaguo & Li, Sujuan & Ji, Qiang, 2021. "Regional differences and driving factors analysis of carbon emission intensity from transport sector in China," Energy, Elsevier, vol. 224(C).
    6. Kounetas, Konstantinos E. & Polemis, Michael L. & Tzeremes, Nickolaos G., 2021. "Measurement of eco-efficiency and convergence: Evidence from a non-parametric frontier analysis," European Journal of Operational Research, Elsevier, vol. 291(1), pages 365-378.
    7. Chaohui Zhang & Xin Dong & Ze Zhang, 2023. "Spatiotemporal Dynamic Distribution, Regional Differences and Spatial Convergence Mechanisms of Carbon Emission Intensity: Evidence from the Urban Agglomerations in the Yellow River Basin," IJERPH, MDPI, vol. 20(4), pages 1-28, February.
    8. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    9. Liu, Xiaoxiao & Niu, Qian & Dong, Shuli & Zhong, Shuiying, 2023. "How does renewable energy consumption affect carbon emission intensity? Temporal-spatial impact analysis in China," Energy, Elsevier, vol. 284(C).
    10. Rodríguez-Benavides, Domingo & Andrés-Rosales, Roldán & Álvarez-García, José & Bekun, Festus Víctor, 2024. "Convergence of clubs between per capita carbon dioxide emissions from fossil fuels and cement production," Energy Policy, Elsevier, vol. 186(C).
    11. Du, Zhili & Xu, Jie & Lin, Boqiang, 2024. "What does the digital economy bring to household carbon emissions? – From the perspective of energy intensity," Applied Energy, Elsevier, vol. 370(C).
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