Occupational Transitions into Clean Energy: A Workforce Development Approach Using Occupational Similarity and Unsupervised Clustering

The transition to clean energy needs rapid workforce development. Short-term retraining can fulfill workforce development needs for many clean energy occupations in the Occupational Information Network (ONET) database. The authors assessed the utility of unsupervised clustering to cluster clean energy occupations for resource-efficient retraining. Occupations to retrain using text similarity-based occupational similarity metrics are also identified. The authors found that the network-based approach to organizing occupations using text similarity can identify more occupations to retrain compared to standard occupational groupings, thus improving trainees’ employability and job quality prospects. This study demonstrates the utility of the ONET database as a reconnaissance framework for clean energy workforce development programs with equity and justice considerations. These approaches can also be adapted to workforce development for different sets of occupations to identify other occupations for retraining and designing cluster-wise workforce training programs.