Modeling the Diffusion of Carbon Capture and Storage Under Carbon Emission Control and Learning Effects

This paper examines the interrelationship of learning e ffects and emission control on the diffusion of carbon capture and storage (CCS). We introduce a dynamic model in which an electricity producer maximizes pro ts subject to emissions control. All technologies are characterized by specific linear marginal costs and CO2 emissions which need to be covered by limited emission permits. The fossil fuel-based plants can be replaced by the CCS technology which is associated with higher capital costs and a lower system efficiency. Both parameters improve due to learning effects if the technology is applied. The model is formulated as a non-linear optimization problem, and solved using GAMS. Given the assumed technological data, the results for a data set of Germany show that CCS is essential to reach carbon emission goals. However, particularly in the case of learning, CCS can help renewable technologies to become competitive