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An Inquiry into the Characteristics of Carbon Emissions in Inter-Provincial Transportation in China: Aiming to Typological Strategies for Carbon Reduction in Regional Transportation

Author

Listed:
  • Yuhao Yang

    (School of Architecture, Tianjin University, Tianjin 300072, China)

  • Fengying Yan

    (School of Architecture, Tianjin University, Tianjin 300072, China)

Abstract

The low-carbon development of the transportation sector is crucial for China to achieve its national goals of carbon peaking and carbon neutrality. Since China is a vast country with unbalanced regional development, there are considerable differences in the levels of carbon dioxide emissions from the transportation sector across regions. Therefore, revealing the influencing factors that shape the characteristics of transportation carbon dioxide emissions (TCO 2 ) can inform tailored sub-national carbon reduction strategies based on local conditions, which is an important technical approach for achieving national goals. Based on an extended Kaya identity, we derived indicators of the impacts on provincial TCO 2 from factors such as economic development, population density, energy structure, transportation efficiency, technology research and development (R&D), infrastructure construction, transportation operation conditions, and residents’ transportation behavior. Using a multi-indicator joint characterization method, we explored the characteristics of provincial TCO 2 in China in 2019. By applying Ward’s method to hierarchical clustering, the thirty provinces of China were classified into six characteristic types (Types I to VI). Based on the total TCO 2 (TC), the intensity of TCO 2 (TI), and the per capita TCO 2 (TP) calculated for each province in 2019, the priority control directions and indicators for carbon reduction in each type were obtained through relative relationships with provincial averages and correlation analysis with the indicators. Specifically, Type I and Type IV can be categorized as TP-controlled, Type II and Type III as TC-controlled, and Type V and Type VI as TI-controlled. Finally, we provided typological strategies and key performance indicators (KPIs) relevant to local governments to better achieve carbon reduction goals in each provincial type. It can promote cooperative development and collaborative governance in carbon reduction across regions and the unified implementation of China’s dual-carbon goals.

Suggested Citation

  • Yuhao Yang & Fengying Yan, 2023. "An Inquiry into the Characteristics of Carbon Emissions in Inter-Provincial Transportation in China: Aiming to Typological Strategies for Carbon Reduction in Regional Transportation," Land, MDPI, vol. 13(1), pages 1-24, December.
    • Handle: RePEc:gam:jlands:v:13:y:2023:i:1:p:15-:d:1303954
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    References listed on IDEAS

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    1. Fei Ma & Yixuan Wang & Kum Fai Yuen & Wenlin Wang & Xiaodan Li & Yuan Liang, 2019. "The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective," IJERPH, MDPI, vol. 16(12), pages 1-23, June.
    2. Changzheng Zhu & Meng Wang & Yarong Yang, 2020. "Analysis of the Influencing Factors of Regional Carbon Emissions in the Chinese Transportation Industry," Energies, MDPI, vol. 13(5), pages 1-20, March.
    3. Xie, Rui & Fang, Jiayu & Liu, Cenjie, 2017. "The effects of transportation infrastructure on urban carbon emissions," Applied Energy, Elsevier, vol. 196(C), pages 199-207.
    4. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    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).
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