This data set contains projections of capital investment costs of 9 low carbon energy technologies (40 subtechnologies) from 2015 to 2050, under different scenarios.
- Andreas Zucker
How to cite
Tsiropoulos, Ioannis; Tarvydas, Dalius; Zucker, Andreas (2018): Cost development of low carbon energy technologies. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-etri-10003
The excel file contains 8 sheets on low carbon energy technologies with results on their cost development over time
- Publications Office of the European Union, Luxembourg, Luxembourg
Future costs of low carbon energy technologies differ widely depending on assumptions and methods used. This report addresses this gap by presenting internally consistent trajectories of capital investment costs to 2050 for selected low carbon energy technologies. In order to do so, it combines global scenario projections of technology deployment with the one-factor learning rate method. Global scenarios are used to identify a range, based on potential deployment, in line with baseline assumptions and two long-term decarbonisation pathways. A sensitivity analysis is performed based on different learning rates and results are compared with literature. It is found that, depending on the technology, a 15 % to 55 % reduction in capital investment costs of offshore wind turbines, photovoltaics, solar thermal electricity and ocean energy may be achieved by 2030 compared to 2015. From then onwards, cost reduction may slow down yet remains substantial especially for photovoltaics and ocean energy. However, the assumed deployment pathway (global scenario) and learning rate influences the cost trajectories and cost reduction potential of these technologies. For onshore wind turbines, geothermal energy, biomass CHPs and CCS technologies cost reduction is less pronounced and results between scenarios do not differ significantly. The main aspects that deserve further research are firstly, the decomposition of technology cost-components and the distinction between the parts in the cost-structure that learning applies from those that need to be estimated with different methods and secondly, the influence of raw material prices in future cost trajectories of low carbon energy technologies.
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