Capturing natural resource heterogeneity in top-down energy-economic equilibrium models

Top-down energy-economic modeling approaches often use simplified techniques to represent heterogeneous resource inputs to production. We show that for some policies, such as feed-in tariffs for renewable electricity, detailed representation of renewable resource grades is required to describe the t...

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Bibliographic Details
Main Authors: Rausch, Sebastian (Author) , Zhang, Da (Author)
Format: Article (Journal)
Language:English
Published: 26 July 2018
In: Energy economics
Year: 2018, Volume: 74, Pages: 917-926
ISSN:1873-6181
DOI:10.1016/j.eneco.2018.07.019
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.eneco.2018.07.019
Verlag, lizenzpflichtig, Volltext: https://www.sciencedirect.com/science/article/pii/S0140988318302688
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Author Notes:Sebastian Rausch, Da Zhang
Description
Summary:Top-down energy-economic modeling approaches often use simplified techniques to represent heterogeneous resource inputs to production. We show that for some policies, such as feed-in tariffs for renewable electricity, detailed representation of renewable resource grades is required to describe the technology more precisely and identify cost-effective policy designs. We demonstrate the hybrid approach for modeling heterogeneity in the quality of natural resource inputs required for renewable energy production in a stylized computable general equilibrium framework. Importantly, compared to the traditional approach, the hybrid approach resolves near-flat or near-vertical sections of the supply curve and improves the precision of policy simulation. We then represent the shape of a resource supply curve based on more detailed data. We show that for the case of onshore wind development in China, a differentiated feed-in tariff design that can only be modeled with the hybrid approach requires less than half of the subsidy budget needed for a uniform feed-in tariff design to achieve the same installation targets.
Item Description:Gesehen am 24.04.2023
Physical Description:Online Resource
ISSN:1873-6181
DOI:10.1016/j.eneco.2018.07.019