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Spatial Correlation Network Structure of Carbon Emission Efficiency of Railway Transportation in China and Its Influencing Factors

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  • Ningxin Zhang

    (School of Civil Engineering, Central South University, Changsha 410083, China)

  • Yu Zhang

    (School of Civil Engineering, Central South University, Changsha 410083, China)

  • Hanli Chen

    (School of Civil Engineering, Central South University, Changsha 410083, China)

Abstract

Railway carbon emissions reduction is of great significance. In this study, carbon emission efficiency in railway transportation in China’s 31 provinces is measured for 2006–2019 based on an unexpected output slack-based measure (SBM) model. A gravity matrix of the spatial correlation network for carbon emission efficiency is constructed using the modified gravity model, the spatial network structure is explored using social network analysis, and the factors influencing the spatial network are analyzed using the quadratic assignment procedure (QAP) model. Based on the results, several conclusions can be drawn: (1) the carbon emissions efficiency of railway transportation in China increased periodically during the study period, but there are still great differences between regions. (2) The carbon emission efficiency in railway transportation shows significant characteristics of spatial correlation networks. (3) The inter-provincial associations gradually increased, while there are still large regional differences in the spatial correlation network. (4) Differences in spatial adjacency, economic development and scientific and technological advancement have significant positive impacts on the spatial correlation network. This research will help policy makers formulate relevant policies to promote the regional coordinated development of low-carbon railway transportation.

Suggested Citation

  • Ningxin Zhang & Yu Zhang & Hanli Chen, 2023. "Spatial Correlation Network Structure of Carbon Emission Efficiency of Railway Transportation in China and Its Influencing Factors," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9393-:d:1168725
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    References listed on IDEAS

    as
    1. Xiaochun Zhao & Huixin Xu & Qun Sun, 2022. "Research on China’s Carbon Emission Efficiency and Its Regional Differences," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    2. 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).
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    Cited by:

    1. Marian Kampik & Krzysztof Bodzek & Anna Piaskowy & Adam Pilśniak & Marcin Fice, 2023. "An Analysis of Energy Consumption in Railway Signal Boxes," Energies, MDPI, vol. 16(24), pages 1-21, December.
    2. Changwei Yuan & Jinrui Zhu & Shuai Zhang & Jiannan Zhao & Shibo Zhu, 2024. "Analysis of the Spatial Correlation Network and Driving Mechanism of China’s Transportation Carbon Emission Intensity," Sustainability, MDPI, vol. 16(7), pages 1-23, April.
    3. Hailing Wu & Yuanjun Li & Kaihuai Liao & Qitao Wu & Kanhai Shen, 2024. "Structural Characteristics of Expressway Carbon Emission Correlation Network and Its Influencing Factors: A Case Study in Guangdong Province," Sustainability, MDPI, vol. 16(22), pages 1-20, November.

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