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Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks

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  • Peter Fernandes Wanke
  • Amir Karbassi Yazdi
  • Thomas Hanne
  • Yong Tan
Abstract
The paper analyzes the sustainability of the Chinese transportation sector by examining the relationship between energy consumption (and CO2 emissions), transportation modes, and macroeconomic variables. Principal Component Analysis (PCA) and Neural Networks (NN) are combined using monthly data from January 1999 to December 2017. Our goal is to propose a model that links China's transportation footprint to major macroeconomic factors while simultaneously controlling each mode of transportation. Inflation and credit policies exert relatively weak effects on the explained variable. In contrast, trade and fixed asset investments, as well as monetary and fiscal policies, show a positive and significant impact. The use of waterways and airways plays an imperative role in sustainable development compared to the use of roads.

Suggested Citation

  • Peter Fernandes Wanke & Amir Karbassi Yazdi & Thomas Hanne & Yong Tan, 2023. "Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks," Transportation Planning and Technology, Taylor & Francis Journals, vol. 46(5), pages 573-598, July.
    • Handle: RePEc:taf:transp:v:46:y:2023:i:5:p:573-598
    DOI: 10.1080/03081060.2023.2198517
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    Cited by:

    1. Cao, Sheng & Xu, Haicheng & Xu, Yan & Wang, Xiaoshen & Zheng, Yingjie & Li, Yanling, 2023. "Assessment of the integrated benefits of highway infrastructure and analysis of the spatiotemporal variation: Evidence from 29 provinces in China," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).
    2. Laurentiu Nicolae Pricope & Valentin Marian Antohi & Alina Meca & Angela Buboi (Danaila) & Costinela Fortea & Monica Laura Zlati, 2024. "The New European Development Scoreboard for SDG11 at the European Level," Sustainability, MDPI, vol. 16(17), pages 1-21, September.

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