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dc.contributor.authorDalby, Peder A.O.
dc.contributor.authorGillerhaugen, Gisle R.
dc.contributor.authorHagspiel, Verena
dc.contributor.authorLeth-Olsen, Tord
dc.contributor.authorThijssen, Jacco J.J.
dc.date.accessioned2019-01-11T10:03:55Z
dc.date.available2019-01-11T10:03:55Z
dc.date.created2018-07-31T09:53:58Z
dc.date.issued2018
dc.identifier.citationEnergy. 2018, 161 1262-1281.nb_NO
dc.identifier.issn0360-5442
dc.identifier.urihttp://hdl.handle.net/11250/2580306
dc.description.abstractMany countries have introduced support schemes to accelerate investments in renewable energy (RE). Experience shows that, over time, retraction or revision of support schemes become more likely. Investors in RE are greatly affected by the risk of such subsidy changes. This paper examines how investment behavior is affected by updating a subjective belief on the timing of a subsidy revision, incorporating Bayesian learning into a real options modeling approach. We analyze a scenario where a retroactive downward adjustment of fixed feed-in tariffs (FIT) is expected through a regime switching model. We find that investors are less likely to invest when the arrival rate of a policy change increases. Further, investors prefer a lower FIT with a long expected lifespan. We also consider an extension where, after retraction, electricity is sold in a free market. We find that if policy uncertainty is high, an increase in the FIT will be less effective at accelerating investment. However, if policy risk is low, FIT schemes can significantly accelerate investment, even in highly volatile markets.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleGreen investment under policy uncertainty and Bayesian learningnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber1262-1281nb_NO
dc.source.volume161nb_NO
dc.source.journalEnergynb_NO
dc.identifier.doi10.1016/j.energy.2018.07.137
dc.identifier.cristin1599104
dc.relation.projectNorges forskningsråd: 268093nb_NO
dc.description.localcode© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 31.7.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,60,25,0
cristin.unitnameInstitutt for industriell økonomi og teknologiledelse
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal