An Integrated Evaluation Using Enhanced Panel Factor Model and Machine Learning: Assessing the Level and Structure of Regional Coordinated Development in the Guangdong-Hong Kong-Macao Greater Bay Area

Regional sustainable and coordinated development has become a central issue in the backdrop of a reshaped global economic landscape. Therefore, it is particularly important to evaluate the level of regional coordinated development effectively. This study aimed to validate and assess the effectiveness of machine learning algorithms and the Enhanced Panel Factor Model for evaluating regional coordinated development. To this end, based on panel data from 11 cities in the Guangdong–Hong Kong–Macao Greater Bay Area for 2005–2023, we constructed a four-dimensional composite indicator system covering economic growth, structural optimization, innovation-driven development, and social development. First, we employ a factor model to achieve dimensionality reduction and extract latent factors. SPSS and the JiekeLi platform are used for visualization, and finally, we combine LASSO regression with linear regression to build predictive models to verify the explanatory power of key factors for regional coordination. The findings indicate that the traditional factor model performs robustly in structural identification, whereas machine learning methods have advantages in variable selection and fitting accuracy. The empirical results show that the overall level of coordination in the Greater Bay Area has steadily improved; however, substantial disparities among cities remain. This study demonstrates a new pathway that integrates econometrics and machine learning for the comprehensive evaluation of regional development levels. It also conducts a comparative analysis of the applicability and effectiveness of these two methods, thereby offering significant theoretical and practical value.