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Modeling the future costs of carbon capture using experts' elicited probabilities under policy scenarios

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  • Nemet, Gregory F.
  • Baker, Erin
  • Jenni, Karen E.

Abstract

We use expert elicitations of energy penalties and literature-derived estimates of basic cost parameters to model the future costs of 7 types of carbon capture technology applied to coal power plants. We conduct extensive sensitivity analysis to assess the effects of various parameters on additional levelized electricity costs ($/MWh) and costs of avoided CO2 emissions ($/tCO2) in 2025. Although the expert elicitation of energy penalties under various policy conditions spans a considerable range, we find that costs are more sensitive to assumptions about overnight capital costs and discounting. We run Monte Carlo simulations to specify a distribution of the minimum costs of capture across these 7 technologies and find that in 74% of cases, the minimum cost of capture is determined by one of three technologies. Despite these concentrated outcomes, we see benefits to technology portfolio diversification in that a full portfolio of technologies approximately doubles the likelihood of achieving a $60/tCO2 cost target versus focusing on a single capture technology.

Suggested Citation

  • Nemet, Gregory F. & Baker, Erin & Jenni, Karen E., 2013. "Modeling the future costs of carbon capture using experts' elicited probabilities under policy scenarios," Energy, Elsevier, vol. 56(C), pages 218-228.
    • Handle: RePEc:eee:energy:v:56:y:2013:i:c:p:218-228
    DOI: 10.1016/j.energy.2013.04.047
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    2. Liu, Guangmin & Qiao, Lina & Zhang, Hong & Zhao, Dan & Su, Xudong, 2014. "The effects of illumination factors on the growth and HCO3− fixation of microalgae in an experiment culture system," Energy, Elsevier, vol. 78(C), pages 40-47.
    3. Bistline, John E., 2016. "Energy technology R&D portfolio management: Modeling uncertain returns and market diffusion," Applied Energy, Elsevier, vol. 183(C), pages 1181-1196.
    4. Sakti, Apurba & Azevedo, Inês M.L. & Fuchs, Erica R.H. & Michalek, Jeremy J. & Gallagher, Kevin G. & Whitacre, Jay F., 2017. "Consistency and robustness of forecasting for emerging technologies: The case of Li-ion batteries for electric vehicles," Energy Policy, Elsevier, vol. 106(C), pages 415-426.
    5. Milford, James & Henrion, Max & Hunter, Chad & Newes, Emily & Hughes, Caroline & Baldwin, Samuel F., 2022. "Energy sector portfolio analysis with uncertainty," Applied Energy, Elsevier, vol. 306(PA).
    6. Erin Baker & Valentina Bosetti & Karen E. Jenni & Elena Claire Ricci, 2014. "Facing the Experts: Survey Mode and Expert Elicitation," Working Papers 2014.01, Fondazione Eni Enrico Mattei.
    7. Levihn, F. & Nuur, C. & Laestadius, S., 2014. "Marginal abatement cost curves and abatement strategies: Taking option interdependency and investments unrelated to climate change into account," Energy, Elsevier, vol. 76(C), pages 336-344.
    8. Gregory Nemet & Erin Baker & Bob Barron & Samuel Harms, 2015. "Characterizing the effects of policy instruments on the future costs of carbon capture for coal power plants," Climatic Change, Springer, vol. 133(2), pages 155-168, November.
    9. Du, Huibin & Li, Qun & Liu, Xi & Peng, Binbin & Southworth, Frank, 2021. "Costs and potentials of reducing CO2 emissions in China's transport sector: Findings from an energy system analysis," Energy, Elsevier, vol. 234(C).
    10. Akbilgic, Oguz & Doluweera, Ganesh & Mahmoudkhani, Maryam & Bergerson, Joule, 2015. "A meta-analysis of carbon capture and storage technology assessments: Understanding the driving factors of variability in cost estimates," Applied Energy, Elsevier, vol. 159(C), pages 11-18.

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