Valuing climate change uncertainty reductions for robust energy portfolios

9059 4 13273 1 22332 document 13273 XP-09-005.pdf application/pdf b1debb00d5d0e661eadd0e5de40378da MD5 184390 2016-01-15 08:42:59 https://pure.iiasa.ac.at/id/eprint/9059/1/XP-09-005.pdf 9059 1 1 text en public

XP-09-005.pdf

16121 2 27851 document 16121 lightbox.jpg image/png 47676c638b640854c6ca33d22f55c254 MD5 70214 2016-01-15 11:15:24 https://pure.iiasa.ac.at/id/eprint/9059/2/lightbox.jpg 9059 2 2 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/13273 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/13273 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/13273 archive 351 disk0/00/00/90/59 2016-01-15 08:42:59 2021-08-27 17:20:59 2016-01-15 08:42:59 conference_item show 1 Fuss S. 8259 Khabarov N. 1850 0000-0001-5372-4668 Szolgayova J. 1833 Obersteiner M. 7255 0000-0001-6981-2769 Valuing climate change uncertainty reductions for robust energy portfolios pub iis_for prog_for Real options; Energy; Policy uncertainty; Robust portfolios; Earth observations 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. 2009 published XP-09-005 In:; Proceedings from the 33rd International Symposium on Remote Sensing of Environment (ISRSE-33) "Sustaining the Millennium Development Goals"; 4-8 May 2009, Stresa, Italy [ISBN 978-0-932913-13-5] 101 151 305 219 public Proceedings from the 33rd International Symposium on Remote Sensing of Environment (ISRSE-33) "Sustaining the Millennium Development Goals" 4-8 May 2009 other TRUE 978-0-932913-13-5 FALSE info:eu-repo/semantics/conferenceObject