Identifying energy model fingerprints in mitigation scenarios

Dekker, M.M., Daioglou, V., Pietzcker, R., Rodrigues, R., de Boer, H.-S., Dalla Longa, F., Drouet, L., Emmerling, J., Fattahi, A., Fotiou, T., Fragkos, P., Fricko, O. ORCID: https://orcid.org/0000-0002-6835-9883, Gusheva, E., Harmsen, M., Huppmann, D. ORCID: https://orcid.org/0000-0002-7729-7389, Kannavou, M., Krey, V. ORCID: https://orcid.org/0000-0003-0307-3515, Lombardi, F., Luderer, G., Pfenninger, S., et al. (2023). Identifying energy model fingerprints in mitigation scenarios. Nature Energy 10.1038/s41560-023-01399-1.

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Abstract

Energy models are used to study emissions mitigation pathways, such as those compatible with the Paris Agreement goals. These models vary in structure, objectives, parameterization and level of detail, yielding differences in the computed energy and climate policy scenarios. To study model differences, diagnostic indicators are common practice in many academic fields, for example, in the physical climate sciences. However, they have not yet been applied systematically in mitigation literature, beyond addressing individual model dimensions. Here we address this gap by quantifying energy model typology along five dimensions: responsiveness, mitigation strategies, energy supply, energy demand and mitigation costs and effort, each expressed through several diagnostic indicators. The framework is applied to a diagnostic experiment with eight energy models in which we explore ten scenarios focusing on Europe. Comparing indicators to the ensemble yields comprehensive ‘energy model fingerprints’, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.

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