Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks
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Author (Corporation)
Publication date
2023
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Collections
Type
01A - Journal article
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Editor (Corporation)
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Parent work
Transportation Planning and Technology
Special issue
DOI of the original publication
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Series
Series number
Volume
46
Issue / Number
5
Pages / Duration
573-598
Patent number
Publisher / Publishing institution
Routledge
Place of publication / Event location
London
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Version
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Practice partner / Client
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.
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ISBN
ISSN
1029-0354
0308-1060
0308-1060
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Hybrid
License
Citation
Wanke, P. F., Yazdi, A. K., Hanne, T., & Tan, Y. (2023). Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks. Transportation Planning and Technology, 46(5), 573–598. https://doi.org/10.1080/03081060.2023.2198517