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How Do Energy-Economy Models Compare? A Survey of Model Developers and Users in Canada

Authors: Ekaterina Rhodes; Kira Craig; Aaron Hoyle; Madeleine McPherson;

How Do Energy-Economy Models Compare? A Survey of Model Developers and Users in Canada

Abstract

Governments at all levels rely on energy-economy models to design climate policy portfolios. Models vary in their purposes and methodologies, yet there is limited research comparing model characteristics and identifying models suitable for specific policy questions. We conduct a web-based survey of energy-economy model users and developers (n = 14) in Canada’s public, private, and non-profit sectors, to systematically compare seventeen models against the following characteristics: Technology representations, microeconomic and macroeconomic details, policy representations, treatment of uncertainty, high-resolution spatial and temporal representations, and data transparency. We find that for the most part, models represent technology, micro-, and macroeconomic characteristics according to the typology of bottom-up, top-down, and hybrid models. However, several modelling evolutions have emerged. To varying extents, top-down models can explicitly represent technologies and some bottom-up models incorporate microeconomic (non-financial) characteristics. We find that models differ in the types of policies they can simulate, sometimes underrepresenting performance regulations, government procurement, and research and development programs. All models use at least one method to explore uncertainty, rarely incorporate spatial and temporal representations, and most models lack publicly available methodological documentation. We discuss the implications of our comparative model analysis for climate policy projections and future research.

Keywords

energy-economy model, Environmental effects of industries and plants, climate policy projections, TJ807-830, model users, model assessment characteristics, TD194-195, Renewable energy sources, Environmental sciences, model developers, survey, GE1-350

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    2
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Top 10%
Average
Average
gold