TY  - JOUR
PY  - 2023//
TI  - Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks
JO  - Transportation planning and technology
A1  - Wanke, Peter Fernandes
A1  - Yazdi, Amir Karbassi
A1  - Hanne, Thomas
A1  - Tan, Yong
SP  - 573
EP  - 598
VL  - 46
IS  - 5
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0308-1060
UR  - http://dx.doi.org/10.1080/03081060.2023.2198517
ID  - ref1
ER  -