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On choosing the resolution of normative models

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  • Merrick, James H.
  • Weyant, John P.

Abstract

Long time horizon normative models are frequently used for policy analysis, strategic planning, and system analysis. Choosing the granularity of the temporal or spatial resolution of such models is an important modeling decision, often having a first order impact on model results. This type of decision is frequently made by modeler judgment, particularly when the predictive power of alternative choices cannot be tested. In this paper, we show how the implicit tradeoffs modelers make in these formulation decisions, in particular in the tradeoff between the accuracy of representation enabled by the available data and model parsimony, may be addressed with established information theoretic ideas. The paper provides guidance for modelers making these tradeoffs or, in certain cases, enables explicit tests for assessing appropriate levels of resolution. We will mainly focus on optimization based normative models in the discussion here, and draw our examples from the energy and climate domain.

Suggested Citation

  • Merrick, James H. & Weyant, John P., 2019. "On choosing the resolution of normative models," European Journal of Operational Research, Elsevier, vol. 279(2), pages 511-523.
    • Handle: RePEc:eee:ejores:v:279:y:2019:i:2:p:511-523
    DOI: 10.1016/j.ejor.2019.06.017
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    5. Wang, Delu & Mao, Jinqi & Shi, Xunpeng & Li, Chunxiao & Chen, Fan, 2024. "A planning model for coal power exit scales based on minimizing idle and shortage losses: A case study of China," Energy Economics, Elsevier, vol. 138(C).
    6. John E. T. Bistline & James Merrick & Victor Niemeyer, 2020. "Estimating Power Sector Leakage Risks and Provincial Impacts of Canadian Carbon Pricing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(1), pages 91-118, May.
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