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Unpacking the modeling process for energy policy making.

Samuele Lo Piano1, Máté János Lőrincz1, Arnald Puy2

  • 1School of the Built Environment, University of Reading, Reading, Berkshire, UK.

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Summary
This summary is machine-generated.

Energy system modeling can limit policy options by creating false certainty. This study suggests quality assessment methods like Numeral Unit Spread Assessment Pedigree (NUSAP) to ensure more robust and inclusive energy research.

Keywords:
NUSAPenergy systems modelingnon‐neutrality of methodspostnormal sciencesensitivity auditinguncertainty

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Area of Science:

  • Energy Systems Analysis
  • Policy Modeling
  • Scientific Uncertainty

Background:

  • Energy system models are crucial for policy decisions, but can oversimplify complex systems.
  • Historical examples, like International Institute for Applied Systems Analysis (IIASA) global modeling in the 1980s, show how models can unduly narrow policy alternatives.
  • A perceived excess of certainty in model outputs can discourage exploration of diverse policy pathways.

Purpose of the Study:

  • To explore how energy system modeling can lead to premature closure of policy alternatives.
  • To introduce and illustrate quality assessment methodologies that enhance model reflexivity and robustness.
  • To encourage the adoption of these practices for more defensible and inclusive energy research.

Main Methods:

  • Retrospective case study analysis of International Institute for Applied Systems Analysis (IIASA) global modeling.
  • Discussion of quality assessment techniques: Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD).
  • Illustrative application of reflexive modeling in three contemporary cases: UK energy policy (ESME), negative emission technologies (NETs) in integrated assessment models (IAMs), and ecological footprint indicator.

Main Results:

  • Modeling can create an "excess of certainty" that limits consideration of alternative policy options.
  • Numeral Unit Spread Assessment Pedigree (NUSAP), diagnostic diagrams, and sensitivity auditing (SAUD) offer practical ways to assess and improve model quality.
  • Reflexive modeling practices, when applied, demonstrate potential for more nuanced and comprehensive policy analysis.

Conclusions:

  • Modelers should actively employ quality assessment tools to mitigate the risk of undue certainty and limited policy options.
  • Adopting NUSAP, diagnostic diagrams, and sensitivity auditing can lead to more robust, defensible, and inclusive energy modeling.
  • Enhanced model transparency and reflexivity are essential for effective energy policy-making and research.