A Data-Driven Approach to Labour Market Alignment with Renewable Innovation in the UK and US

Abstract

As the development of renewable energy technologies accelerates, the need for a highly skilled workforce becomes increasingly critical. However, empirical evidence remains limited regarding whether current labour markets are adequately responding to this growing demand. This paper analyses the demand for skills in electrical engineering as the energy sector transitions to renewables. We focus on the United States (US) and the United Kingdom (UK) markets and employ the enhanced Latent Dirichlet Allocation model with incorporated hierarchical skill relationships and automated variation handling, enabling deeper insights into skill interactions. The findings highlight both shared global priorities, such as practical and technical competencies, and distinct regional differences shaped by national energy transitions, as well as an evident gap between the projected future skill requirements for the renewable energy sector and the current labour market demand. To further investigate the influence of professional skills within a specific occupational group in the energy industry on national innovation output, we used a Bayesian regression model. The results indicate a robust, positive relationship between skills and innovation, with consistent effects across both countries despite differing broader innovation ecosystems. The study contributes to understanding the role of occupational skill development in the national innovation systems.