The database–modeling nexus in integrated assessment modeling of electric power generation

Jeffrey C. Peters and Thomas Hertel

Energy Economics, 2016, vol. 56, issue C, 107-116

Abstract: Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

Keywords: Integrated assessment; Computable general equilibrium; Model validation; Matrix balancing; Cross-entropy; Database–modeling nexus (search for similar items in EconPapers)
JEL-codes: C61 C68 D57 D58 Q47 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: The database-modeling nexus in integrated assessment modeling of electric power generation (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:56:y:2016:i:c:p:107-116

DOI: 10.1016/j.eneco.2016.03.004

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