Integrated assessment model diagnostics: key indicators and model evolution

Mathijs Harmsen*, Elmar Kriegler, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Gunnar Luderer, Ryna Cui, Olivier Dessens, Laurent Drouet, Johannes Emmerling, Jennifer Faye Morris, Florian Fosse, Dimitris Fragkiadakis, Kostas Fragkiadakis, Panagiotis Fragkos, Oliver Fricko, Shinichiro Fujimori, David Gernaat, Celine Guivarch, Gokul Iyer, Panagiotis KarkatsoulisIlkka Keppo, Kimon Keramidas, Alexandre Koberle, Peter Kolp, Volker Krey, Christoph Kruger, Florian Leblanc, Shivika Mittal, Sergey Paltsev, Pedro Rochedo, Bas J. van Ruijven, Ronald D. Sands, Fuminori Sano, Jessica Strefler, Eveline Vasquez Arroyo, Kenichi Wada, Behnam Zakeri

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
63 Downloads (Pure)


Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change 90 45-61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

Original languageEnglish
Article number054046
Number of pages12
JournalEnvironmental Research Letters
Issue number5
Publication statusPublished - May 2021
MoE publication typeA1 Journal article-refereed


  • diagnostics
  • integrated assessment models
  • climate policy
  • 6th Assessment Report IPCC
  • renewable energy
  • mitigation
  • AR6


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