Learning in Green Hydrogen Production: Insights from a Novel European Dataset

The cost reduction of electrolysers is critical for scaling up green hydrogen production and achieving decarbonization targets. This study presents a novel and comprehensive dataset of electrolyser projects in Europe. It includes full cost and capacity details for each project and capturing project-specific characteristics such as technology type, location and project type for the period 2005–2030. We apply the learning curve methodology to assess cost reductions across different electrolyser technologies and project sizes. Our findings indicate a significant learning effect for PEM and AEL electrolysers in the last 20 years, with learning rates of 32.1% and 22.9%, respectively. While AEL cost reductions are primarily driven by scaling effects, PEM electrolysers benefit from both technological advancements and economies of scale. Small-scale electrolysers exhibit a stronger learning effect (25%), whereas large-scale projects show no clear cost reductions due to their early stage of deployment. Projections based on our learning rates suggest that reaching Europe’s 2030 target of 40 GW electrolyser capacity would require an estimated total investment of 14 billion EUR. These results align closely with previous studies and such predictions are closed to estimates from other organization. The dataset is publicly available, allowing for further analysis and periodic updates to track cost trends.