Relating R&D and Investment Policies to CCS Market Diffusion
A prime challenge for the 21st century is the limiting of global warming to 2°C through the reduction of anthropogenic greenhouse gas emissions, formulated as a central result in the joint Accord of the Copenhagen Conference on Climate Change in 2009. There are several CO2 emission reduction targets addressing this challenge, such as the EU’s commitment to a 20–30% target by 2020 or the announced 17% target of the USA.
Carbon capture and storage (CCS) is widely seen as a major opportunity to continue fossil-fuel-based generation, while at the same time contributing to CO2 abatement. The IEA’s World Energy Outlook 2009 in its 450 ppm scenario predicts that 150 GW of CCS coal capacity will be in operation by 2030 within OECD countries alone. Whereas the expectations placed on CCS are very high, the capture technology has still not been widely proven at full scale, and technological progress has been limited in recent years, with only a few CCS pilot power plants being operational. This presents a key obstacle to the anticipated large-scale CCS deployment. 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 is the main focus of this research project.
TWO-FACTOR LEARNING CURVES FOR CCS
One method to estimate the relationship between R&D funding and technological improvement is “two-factor learning curves” (2FLC). This approach is based on “technological learning”, the phenomenon that the unit costs of a specific technology decrease along with its cumulative deployment, but is extended by the additional consideration of cumulative R&D efforts. As no empirical data is available because CCS deployment has not yet started, we empirically derive the 2FLC for the flue gas desulfurization, a SO2 scrubber technology which is similar to and hence comparable with the CO2 scrubber technology used for CCS power plants. To this end, we use historical cost and technology deployment and R&D levels for the time period 1970 - 2000.
We find that the learning-by-doing rate is 7.1% and the learning-by-researching rate 6.6%. This effectively means that the cost reduction caused by a doubling of installed capacity is roughly the same as for a doubling of R&D efforts. Other technologies that are currently promoted through subsidies and other funding types, such as solar power and wind power, have learning-by-researching rates that are 2–5 times higher than their learning-by-doing rate, indicating advantages for R&D-driven policies over capacity-additiondriven policies. This conclusion cannot be drawn for CCS, however, the observed learning rates are very similar, a fact that should be considered when comparing or even applying wind and solar promotion policies to potential CCS promotion policies. The values observed are also in line with expectations found in some of the existing literature.
THE IMPACT OF R&D AND INVESTMENT SUBSIDIES ON CCS DIFFUSION
To analyze market deployment of CCS in dependence of stimulation policies, we use HECTOR, a model that simulates the European electricity market bottom-up with two policies: One provides R&D funding, addressing learning-by-researching progress. The other one provides a subsidy for new CCS plants, reducing the investment costs by a certain percentage. This demand-side policy promotes diffusion and addresses learning-by-doing. Simulations of variations in overall policy spending levels for these two policies are run for two scenarios, covering CO2 prices of 25 €/t and 38 €/t. The results are plotted in the Figure.
At high CO2 prices, both policies only slightly improve the diffusion of CCS technology, and the policy type – i.e. R&D or investment subsidies – only plays a secondary role as their effectiveness is relatively similar, with slight advantages for an R&D-based policy. At lower CO2 prices, the impact of the investigated policies rises and they provide a suitable method for improving CCS diffusion. However, even a massive policy budget cannot compensate for CO2 prices as a key driver for CCS success. Even if €5 billion are spent annually on CCS after 2015, the technology will not reach the capacity needed to reach commercial readiness of 21–22 GW by 2020, regardless of the policy type. In a direct comparison between both policies, their effectiveness is similar at a budget below €0.5 billion p.a. Beyond that, investment subsidies are the more effective policy type. This is due to the logarithmic impact of R&D effort on investment costs, which cannot compensate for the linear reduction of investment costs by the investment cost subsidy.
The overall project results show a difficult situation for policy-makers: If CO2 prices are sufficiently high, no diffusion stimulation policies are needed in the first place. If they are low, opportunities for specific CCS promotion policies exist and do improve the situation, but their impact will never outweigh the unfavorably low CO2 prices, unless extraordinarily high budgets are allocated for CCS. Aggressive GHG reduction policies with high CO2 prices are, therefore, of prime relevance for CCS. If CCS policies are deployed at relatively low CO2 prices (such as 25 €/t), the impact of R&D and investment subsidy policies on CCS diffusion is about equally effective below €0.5 billion p.a.; beyond that, R&D policy effectiveness stagnates, compared to a continued linear growth for investment subsidies. In summary, we can therefore conclude that both effects on technological learning – R&D and capacity diffusion – are very similar for CCS, suggesting a simultaneous and balanced two-way policy.
The project results described were presented at the 33th IAEE International Conference in Rio de Janeiro (June 6-9, 2010) and published as an FCN Working Paper. This research is part of a larger research project on the economics of CCS that forms the doctoral dissertation of Richard Lohwasser, and in the course of which so far two other FCN Working Papers could be realized (Nos. 6/2009 and 7/2009).
Lohwasser R., Madlener R. (2010). Relating R&D and Investment Policies to CCS Market Diffusion Through Two-Factor Learning, FCN Working Paper No. 6/2010, Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, June.
Lohwasser R., Madlener R. (2009). Impact of CCS on the Economics of Coal-Fired Power Plants: Why Investment Costs Do and Efficiency Doesn’t Matter, FCN Working Paper No. 7/2009 , Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Lohwasser R., Madlener R. (2009). Simulation of the European Electricity Market and CCS Development with the HECTOR Model, FCN Working Paper No. 6/2009 , Institute for Future Energy Consumer Needs and Behavior, RWTH Aachen University, November.
Prof. Dr. Reinhard Madlener
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Dipl. Wirt.-Inf. Richard Lohwasser
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