Logistics scale as trade catalyst: Principal component analysis of China-Korea bilateral trade drivers

Abstract

This study conducts an empirical analysis of the driving factors and development trends of China-Korea bilateral trade from the perspective of logistics, using IBM SPSS Statistics 27 for data processing and modeling. Based on annual panel data from 2013 to 2022, a multidimensional dataset of 18 core economic indicators was constructed, encompassing variables such as import and export volumes, logistics scale, enterprise expenditures, labor structure, exchange rates, and investment. To reduce dimensionality and address multicollinearity, a correlation analysis was first conducted to identify 9 key variables with strong explanatory power. Principal Component Analysis was then applied, extracting five principal components that effectively captured the underlying structure of the dataset. These components were used as independent variables in a principal component regression model to predict the China-Korea bilateral trade volume. Among them, the component representing Korea’s logistics industry scale and transportation efficiency (PC1) had the most significant positive impact on trade, highlighting the critical role of logistics in enhancing trade intensity and sustaining bilateral growth. The regression model exhibited strong goodness-of-fit and low prediction errors, through structural modeling and trend-based forecasting, this study confirms the foundational role of Korea’s logistics system in supporting bilateral trade and provides quantitative insights and policy-oriented recommendations for future China-Korea economic cooperation.