Clean energy investment decisions are getting more difficult to make due to public reactions. In order to support the policies in the field, analysis of the positive conditions is needed. This research aims to construct the positive scenarios for nuclear energy and renewable energy investments in the state of Oregon, USA. The Bayesian network technique will be used to create the scenarios. Oregon has a wide range of renewable energies; hence, investment is becoming more complex. Criteria affecting the decisions are taken from the literature, but were reviewed with energy authorities in Oregon in order to define the interactions.
机构:
Univ Calif Berkeley, Lawrence Berkeley Lab, Energy Anal Dept, Berkeley, CA 94720 USAUniv Calif Berkeley, Lawrence Berkeley Lab, Energy Anal Dept, Berkeley, CA 94720 USA
机构:
Seoul Natl Univ, New Energy Ind Convergence & Open Sharing Ctr, Seoul, South KoreaSeoul Natl Univ, New Energy Ind Convergence & Open Sharing Ctr, Seoul, South Korea
Kim, Soohyeon
Shin, Youngeun
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机构:
UAE Independent Climate Change Accelerators, Res & Policy Dept, Abu Dhabi, U Arab EmiratesSeoul Natl Univ, New Energy Ind Convergence & Open Sharing Ctr, Seoul, South Korea