Economic and environmental impacts of public investment in clean energy RD&D

Learning curve theory has been adopted for investigating the relationship between technological learning and technology cost developments. The aim of this paper is to explore the impacts of public investment in clean energy research, development, and demonstration (RD&D) on future technology cost developments by using a two-factor learning curve approach. Learning-by-deploying and learning-by-researching were chosen as the main sources of learning. The focus is on onshore wind and solar photovoltaics in the United States of America. By using publicly available data, we estimated learning-by-deploying rates of 31.4% and 27.6%; and learning-by-researching rates of 2.3% and 4.7% for onshore wind and solar PV, respectively. By adopting a logistic curve approach, an additional $1322 and $819 mil. were forecast to be spent by 2050 in RD&D for onshore wind and solar PV, respectively. We explored the plausible long-term effects of diverse RD&D investment scenarios on electricity generation and greenhouse gas (GHG) emissions using a system dynamics model. The findings reveal that public investment in RD&D for clean energy technologies may play a key role in the pace of capital cost reductions and technology diffusion. However, relatively little long-term effects of RD&D efforts alone were found on market dynamics and GHG emissions.