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Study on Transportation Green Efficiency and Spatial Correlation in the Yangtze River Economic Belt

Author

Listed:
  • Yangzhou Li

    (College of Air Transportation, Shanghai University of Engineering Science, Shanghai 201600, China)

  • Cheng Li

    (College of Air Transportation, Shanghai University of Engineering Science, Shanghai 201600, China)

  • Dongni Feng

    (College of Air Transportation, Shanghai University of Engineering Science, Shanghai 201600, China)

Abstract

The Yangtze River Economic Belt (YREB), a crucial transportation corridor spanning China’s east and west and linking coastal and inland regions, is not only pivotal in the nation’s strategic development but also drives regional economic and social progress through its transportation industry. Despite rapid growth, the industry faces challenges such as low efficiency, resource supply–demand imbalances, and environmental issues. To advance green and sustainable progress, this study establishes a regional transportation green efficiency evaluation system. Using principal component analysis (PCA) to refine input data, the undesirable super-SBM model quantitatively assesses green transportation efficiency (GTE) in YREB provinces and cities, revealing regional disparities. The study also explores spatial correlations and distribution characteristics of GTE. Results indicate that ① YREB’s GTE shows a U-shaped trend, with significant differences between upper, middle, and lower reaches, being stronger in the east and weaker in the west (lower > middle > upper reaches); ② GTE exhibits spatial correlation in YREB regions, with clear clustering; and ③ cold and hot spots of GTE in the middle reaches are relatively stable, with upstream areas generally cold or sub-cold, and hot spots mainly downstream.

Suggested Citation

  • Yangzhou Li & Cheng Li & Dongni Feng, 2024. "Study on Transportation Green Efficiency and Spatial Correlation in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 16(9), pages 1-25, April.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3686-:d:1384863
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    References listed on IDEAS

    as
    1. 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).
    2. Rehman, Faheem Ur & Islam, Md. Monirul & Miao, Qing, 2023. "Environmental sustainability via green transportation: A case of the top 10 energy transition nations," Transport Policy, Elsevier, vol. 137(C), pages 32-44.
    3. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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