Quantitative Study on Economic Driving Factors of Guangdong-Hong Kong-Macao Greater Bay Area Based on Principal Component Regression

The driving mechanism of economic development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is quantitatively analyzed in this study against the backdrop of accelerated regional economic integration. A principal component analysis-multivariate regression hybrid model is built by combining multidimensional panel data from 2000 to 2024 in order to methodically assess how nine factors—such as population, industry, and innovation—affect GDP. According to the study, there is a significant negative correlation between the growth of the tertiary industry and the total logistics volume, while every 1 trillion yuan increase in the secondary industry's output value can result in a 4.79 trillion yuan increase in GDP (p<0.001). An economic comprehensive factor that accounts for 98.3% of the variance is extracted by the principal component analysis. According to the regression model, the main governing factors for the next five to ten years will be logistics cost control, innovation transformation efficiency, and industrial structure optimization. The Greater Bay Area can use the study's quantitative foundation to create unique industrial policies and increase factor allocation efficiency.