Identifying energy model fingerprints in mitigation scenarios

Author:

Dekker Mark M.ORCID,Daioglou VassilisORCID,Pietzcker RobertORCID,Rodrigues RenatoORCID,de Boer Harmen-Sytze,Dalla Longa FrancescoORCID,Drouet Laurent,Emmerling Johannes,Fattahi Amir,Fotiou Theofano,Fragkos PanagiotisORCID,Fricko OliverORCID,Gusheva EmaORCID,Harmsen MathijsORCID,Huppmann DanielORCID,Kannavou Maria,Krey VolkerORCID,Lombardi FrancescoORCID,Luderer GunnarORCID,Pfenninger StefanORCID,Tsiropoulos Ioannis,Zakeri BehnamORCID,van der Zwaan BobORCID,Usher WillORCID,van Vuuren DetlefORCID

Abstract

AbstractEnergy 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.

Funder

EC | Horizon 2020 Framework Programme

Publisher

Springer Science and Business Media LLC

Subject

Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment,Electronic, Optical and Magnetic Materials

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Decoding energy model variations;Nature Energy;2023-11-15

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