Identifying energy model fingerprints in mitigation scenarios

Mark M. Dekker; Vassilis Daioglou; Robert Pietzcker; Renato Rodrigues; Harmen-Sytze de Boer; Francesco Dalla Longa; Laurent Drouet; Johannes Emmerling; Amir Fattahi; Theofano Fotiou; Panagiotis Fragkos; Oliver Fricko; Ema Gusheva; Mathijs Harmsen; Daniel Huppmann; Maria Kannavou; Volker Krey; Francesco Lombardi; Gunnar Luderer; Stefan Pfenninger; Ioannis Tsiropoulos; Behnam Zakeri; Bob van der Zwaan; Will Usher; Detlef van Vuuren

doi:10.1038/s41560-023-01399-1

Identifying energy model fingerprints in mitigation scenarios

Mark M. Dekker, Vassilis Daioglou, Robert Pietzcker, Renato Rodrigues, Harmen-Sytze de Boer, Francesco Dalla Longa, Laurent Drouet, Johannes Emmerling, Amir Fattahi, Theofano Fotiou, Panagiotis Fragkos, Oliver Fricko, Ema Gusheva, Mathijs Harmsen, Daniel Huppmann, Maria Kannavou, Volker Krey, Francesco Lombardi, Gunnar Luderer, Stefan PfenningerIoannis Tsiropoulos, Behnam Zakeri, Bob van der Zwaan, Will Usher, Detlef van Vuuren

Research output: Contribution to journal › Article › Academic › peer-review

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.

Original language	English
Pages (from-to)	1395–1404
Number of pages	10
Journal	Nature Energy
Volume	8
Issue number	12
DOIs	https://doi.org/10.1038/s41560-023-01399-1
Publication status	Published - 6 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

Funding

This work was supported by the European Climate and Energy Modelling Forum (ECEMF, H2020 grant agreement number 101022622). We acknowledge and thank H. Henke, E. Fejzic, M. Lewarski and I. Tatarewicz for their input throughout the project and useful comments on the manuscript. This work was also supported by the Exploring National and Global Actions to reduce Greenhouse gas Emissions (ENGAGE, H2020 grant agreement number 821471).

Funders	Funder number
European Climate and Energy Modelling Forum
Exploring National and Global Actions	821471
Horizon 2020 Framework Programme	101022622

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

Dekker, M. M., Daioglou, V., Pietzcker, R., Rodrigues, R., Boer, H.-S. D., Longa, F. D., Drouet, L., Emmerling, J., Fattahi, A., Fotiou, T., Fragkos, P., Fricko, O., Gusheva, E., Harmsen, M., Huppmann, D., Kannavou, M., Krey, V., Lombardi, F., Luderer, G., ... Vuuren, D. V. (2023). Identifying energy model fingerprints in mitigation scenarios. Nature Energy, 8(12), 1395–1404. https://doi.org/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 {\textquoteleft}energy model fingerprints{\textquoteright}, which describe systematic model behaviour and contextualize model differences for future multi-model comparison studies.",

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Dekker, MM, Daioglou, V, Pietzcker, R, Rodrigues, R, Boer, H-SD, Longa, FD, Drouet, L, Emmerling, J, Fattahi, A, Fotiou, T, Fragkos, P, Fricko, O, Gusheva, E, Harmsen, M, Huppmann, D, Kannavou, M, Krey, V, Lombardi, F, Luderer, G, Pfenninger, S, Tsiropoulos, I, Zakeri, B, Zwaan, BVD, Usher, W & Vuuren, DV 2023, 'Identifying energy model fingerprints in mitigation scenarios', Nature Energy, vol. 8, no. 12, pp. 1395–1404. https://doi.org/10.1038/s41560-023-01399-1

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AU - Dekker, Mark M.

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AU - Rodrigues, Renato

AU - Boer, Harmen-Sytze de

AU - Longa, Francesco Dalla

AU - Drouet, Laurent

AU - Emmerling, Johannes

AU - Fattahi, Amir

AU - Fotiou, Theofano

AU - Fragkos, Panagiotis

AU - Fricko, Oliver

AU - Gusheva, Ema

AU - Harmsen, Mathijs

AU - Huppmann, Daniel

AU - Kannavou, Maria

AU - Krey, Volker

AU - Lombardi, Francesco

AU - Luderer, Gunnar

AU - Pfenninger, Stefan

AU - Tsiropoulos, Ioannis

AU - Zakeri, Behnam

AU - Zwaan, Bob van der

AU - Usher, Will

AU - Vuuren, Detlef van

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N2 - 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.

AB - 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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SP - 1395

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JO - Nature Energy

JF - Nature Energy

IS - 12

ER -

Identifying energy model fingerprints in mitigation scenarios

Abstract

Bibliographical note

Funding

UN SDGs

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