Climate policy uncertainty has decisive influence on energy sector strategies. Potential stranded climate-energy investments may be enormous. Remote sensing can improve our understanding of the climate system and thus better inform climate policy and reduce associated uncertainties. We develop an integrated energy-portfolio model to value these uncertainties. The operations of individual power plants are optimized using real options given scenarios of stochastically evolving CO2 prices mimicking observation-induced climate policy uncertainty. The resulting profit distributions are used in a portfolio optimization. The optimization under imperfect information about future CO2 prices leads to substantially lower profits for a given risk level when portfolios are to be robust across all plausible scenarios. A potential uncertainty reduction associated with an improved climate modeling supported by remote sensing will thus not only lead to substantial financial efficiency gains, but will also be conducive to steering investments into the direction of higher shares of renewable energy.

Author names: 
Fuss, S.
Khabarov, N.
Szolgayova, J.,
Obersteiner, M.

Fuss, S., Khabarov, N., Szolgayova, J., & Obersteiner, M. (2009). Valuing Climate Change Uncertainty Reductions for Robust Energy Portfolios. Presented at the Sustaining the Millennium Development Goals, Stresa, Italy.

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