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Energy system transition pathways to meet the global electricity demand for ambitious climate targets and cost competitiveness. [Dataset]


Arman Aghahosseini
Data Collector

A.A. Solomon
Data Collector

Christian Breyer
Data Collector

Thomas Pregger
Data Collector

Sonja Simon
Data Collector

Arnulf Jäger-Waldau
Data Collector


This research aims to explore and analyse various energy transition pathways through a uniform modelling environment. This is a research gap that has not yet been investigated, although it significantly enhances comparability and offers new insights into underlying narratives and systemic effects of energy models and scenarios. Data for countries or sub-regions were aggregated or weighted for 9 world regions.


AGHAHOSSEINI, A., SOLOMON, A.A., BREYER, C., PREGGER, T., SIMON, S., STRACHAN, P. and JÄGER-WALDAU, A. 2023. Energy system transition pathways to meet the global electricity demand for ambitious climate targets and cost competitiveness. [Dataset]. Applied energy [online], 331, article ID 120401. Available from:

Acceptance Date Nov 18, 2022
Online Publication Date Dec 5, 2022
Publication Date Feb 1, 2023
Deposit Date Dec 9, 2022
Publicly Available Date Dec 9, 2022
Publisher Elsevier
Keywords Energy scenarios; Transition pathways; Decarbonisation; 100% renewable energy; Zero CO2 emissions; Energy system model
Public URL
Related Public URLs
Type of Data XLSX file, PDF and accompanying TXT file.
Collection Date Sep 30, 2022
Collection Method The LUT scenarios are built on the LUT-ESTM, which is a linear optimisation model with hourly temporal resolution and an objective function of minimising the total annual system costs. The Teske/DLR and the IEA scenarios are based on simulation models in the source publications, which have been remodelled using the LUT-ESTM in hourly resolution over the time horizon from 2015 to 2050 in 5-year intervals. The results of the (re)modelling indicate that scenarios with lower shares of renewable energy can meet hourly demand with minimum storage requirements, whereas variability of solar PV and wind power in deep decarbonisation pathways must be balanced via additional storage capacity and throughput. Other flexibility options such as grid interconnections, demand-side management, sector coupling, and NETs were not considered for the analysed scenarios. The findings reveal that renewable energy technologies, particularly solar PV, and wind power, coupled with energy storage are the least-cost energy solutions and will emerge as the central pillars of the electricity sector despite the scenario configuration. Other renewable energy technologies that could complement the future electricity system include hydropower, geothermal, CSP, biomass and ocean energy. According to the scenario modelling, deep decarbonisation of the electricity sector is possible under conducive political and social circumstances from around 13 Gt CO2 in 2020 to 0–8 Gt CO2 in 2030 and further to zero by 2050, depending on the scenario variant. It is explicitly observed from the findings of all the analysed scenarios that a 100% renewable energy system is more efficient and cost competitive than a today's policy scenario relying on fossil and nuclear fuels even by 2050.