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Climatic Change

, Volume 121, Issue 2, pp 143–160 | Cite as

Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach

  • John E. Bistline
  • John P. Weyant
Article

Abstract

Although emerging technologies like carbon capture and storage and advanced nuclear are expected to play leading roles in greenhouse gas mitigation efforts, many engineering and policy-related uncertainties will influence their deployment. Capital-intensive infrastructure decisions depend on understanding the likelihoods and impacts of uncertainties such as the timing and stringency of climate policy as well as the technological availability of carbon capture systems. This paper demonstrates the utility of stochastic programming approaches to uncertainty analysis within a practical policy setting, using uncertainties in the US electric sector as motivating examples. We describe the potential utility of this framework for energy-environmental decision making and use a modeling example to reinforce these points and to stress the need for new tools to better exploit the full range of benefits the stochastic programming approach can provide. Model results illustrate how this framework can give important insights about hedging strategies to reduce risks associated with high compliance costs for tight CO2 caps and low CCS availability. Metrics for evaluating uncertainties like the expected value of perfect information and the value of the stochastic solution quantify the importance of including uncertainties in capacity planning, of making precautionary low-carbon investments, and of conducting research and gathering information to reduce risk.

Keywords

Climate Policy Stochastic Programming Marginal Abatement Cost Expert Elicitation Stochastic Linear Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

J.E. Bistline would like to acknowledge support by the William K. Bowes, Jr. Stanford Graduate Fellowship. J.P. Weyant’s participation in the research reported here was supported by the US DOE, Office of Science, Office of Biological and Environmental Research, Integrated Assessment Research Program, Grant No. DE-SC005171.

Supplementary material

10584_2013_859_MOESM1_ESM.pdf (1.6 mb)
(PDF 1.61 MB)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Management Science and EngineeringStanford UniversityStanfordUSA

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