Relating R&D and Investment Policies to CCS Market Diffusion Through Two-Factor Learning

Abstract

Carbon capture and storage (CCS) technologies have the potential to play a major role in the stabilization of anthropogenic greenhouse gases. To develop the capture technology from its current early pilot phase towards commercial maturity, significant public and private funding is directed towards R&D projects and pilot power plants. However, we know little about how this funding relates to the economics of CCS power plants and their market diffusion. This paper addresses that question. We initially review past learning effects from both capacity installations and R&D efforts for a similar technology, flue-gas desulfurization, using the concept of two-factor learning, and estimate the learning curve. We apply the obtained learning-by-doing rate of 7.1% and the learning-by-researching rate of 6.6% to CCS in the electricity market model HECTOR, which simulates 19 European countries hourly until 2040, to understand the impact of learning and associated policies on the market diffusion of CCS. Simulation results show that the individual impact of learning is similar for both learning rates, regardless of the CO2 price. We then evaluate the effectiveness of policies subsidizing CCS investment costs (addressing learning-by-doing) and of policies providing R&D grants (addressing learning-by-researching) by relating the policy budget to the realized CCS capacity. We find that policies promoting diffusion through subsidies are, at lower policy cost, about equally effective as policies providing R&D funding. At higher spending levels, diffusion-promoting policies are more effective. Overall, policy effectiveness increases in low CO2 price scenarios, but the CO2 price still remains the key prerequisite for the economic competitiveness of CCS, even with major policy support.

Keywords: Policy effectiveness, CCS, two-factor learning, electricity market

JEL Classification: C63; O30; Q47