Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks
This article analyzes the sustainability of China’s transport sector by examining the relationship between energy consumption (and CO₂ emissions), transport modes, and macroeconomic variables. Principal Component Analysis (PCA) and Neural Networks (NN) are combined using monthly data from January 1999 to December 2017. The goal is to propose a model that links China’s transport footprint to key macroeconomic factors, while simultaneously controlling for each transport mode.
Inflation and credit policies show relatively weak effects on the explained variable. In contrast, trade and investment in fixed assets, as well as monetary and fiscal policies, demonstrate a positive and significant impact. The use of waterways and airways plays a key role in sustainable development compared to highway usage.
Researcher: Peter Wanke et al.
Brazilian School of Public and Business Administration (FGV EBAPE)