The choice of an appropriate number of clusters is a key issue in model-based clustering framework. The most popular approaches are based on the information criteria. However, often the latter may likely overestimate the number of clusters even though a good density estimation is possible. Here, we provide a dynamic model-based clustering approach to identify homogeneous Italian NUTS3 areas based on their equitable and sustainable well-being (BES) indicators from 2004 to 2019. In particular, the proposed model allows NUTS3 areas to move between clusters over time and a local dimensional reduction within each cluster. The empirical results show a high heterogeneity among the NUTS3 areas, leading to a high number of clusters. Possible strategies for merging similar NUTS3 clusters are investigated.