Abstract
We develop a methodology with which to assess the effects of policy instruments on the long-term abatement and costs of carbon capture and storage (CCS) technologies for coal power plants. Using an expert elicitation, historical data on the determinants of technological change in energy, values from the engineering literature, and demand estimates from an integrated assessment model, we simulate ranges of outcomes between 2025 and 2095. We introduce probability distributions of all important parameters and propagate them through the model to generate probability distributions of electricity costs, abatement costs, and CO2 avoided over time. Carbon pricing has larger effects than R&D and subsidies. But much of the range of outcomes is driven by uncertainty in other parameters, such as capital costs and returns to scale. Availability of other low carbon technologies, particularly bioenergy with CCS affects outcomes. Subsidies have the biggest impacts when they coincide with expanding manufacturing scale of CCS components. Our results point to 4 parameters for which better information is needed for future work informing technology policy to address climate change: capital costs, demonstration plants, growth constraints, and knowledge spillovers among technologies.
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Notes
The use of the acronym ‘CCS’ throughout this paper refers to carbon capture installed on coal-fired power plants.
We use the following abbreviations for these 7 CCS technologies: Abs=absorption, Ads=adsorption, Mem=membranes, Oth=other post-combustion, Pre=pre-combustion, Oxy=oxyfuel, CLC=chemical looping combustion.
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Acknowledgements
This project was partially supported by the NSF Program on the Science of Science Policy under awards No. SMA-0960993 and SMA-0962100. Thanks also for comments from reviewers as well as those received in presentations at: APPAM, IEW, MIT, USAEE, and UT-Austin.
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Nemet, G.F., Baker, E., Barron, B. et al. Characterizing the effects of policy instruments on the future costs of carbon capture for coal power plants. Climatic Change 133, 155–168 (2015). https://doi.org/10.1007/s10584-015-1469-0
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DOI: https://doi.org/10.1007/s10584-015-1469-0