dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Dijkstra, H. A. | |
dc.contributor.advisor | Wieners, C. E. | |
dc.contributor.author | Zeegers, S. | |
dc.date.accessioned | 2019-08-20T17:00:38Z | |
dc.date.available | 2019-08-20T17:00:38Z | |
dc.date.issued | 2019 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/33459 | |
dc.description.abstract | In the last years economic climate modeling has attracted a lot of attention because of
its fundamental interest and applications to policy-making. The most famous economic
climate model is DICE, made by William Nordhaus. In his model he assumed that the
climate sensitivity is constant. Motivated by the results of the IPCC reports, which
over the years show big deviations in estimates of the climate sensitivity, this thesis
develops a framework to model the economy and the climate with uncertain climate
sensitivity. This is done by implementing Bayesian learning, the results in this thesis
show that the framework works and that Bayesian learning has a signi?cant impact on
the welfare. It is found that Bayesian learning will enhance the performance of a policy
by 476%. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 3772727 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Performance of Bayesian Learning Applied on the Climate Sensitivity in EconomicClimate Modeling | |
dc.type.content | Bachelor Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Bayesian learning, climate sensitivity, Economic Climate Model, DICE, Hot Small World, climate uncertainty | |
dc.subject.courseuu | Natuur- en Sterrenkunde | |