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a b s t r a c tModel-based energy scenarios are a widely used tool for supporting economic and political decision makers. The results of energy modeling and the conclusions deduced therefrom, however, depend on the model input data derived from framework assumptions about future developments in the embedding society, which are deeply uncertain in the long term. The challenge to deal with this 'context uncertainty' in a systematic and comprehensive manner has only recently started to attract intensified attention in energy research; the search for appropriate methods is ongoing. This paper proposes a new concept for the construction of socio-technical energy scenarios, which combines familiar environmental modeling approaches with new developments in qualitative scenario methodology, and demonstrates the possible application of the concept in model-based energy scenario construction.

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Energy conversion is a major source of greenhouse gas (GHG) emissions, and energy transition scenarios are a key tool for gaining a greater understanding of the possible pathways toward climate protection. There is consensus in energy research that political and societal framework conditions will play a pivotal role in shaping energy transitions. In energy scenario construction, this perspective is increasingly acknowledged through the approach of informing model-based energy analysis with storylines about societal futures, an exercise we call "socio-technical energy scenario construction" in this article. However, there is a dispute about how to construct the storylines in a traceable, consistent, comprehensive, and reproducible way. This study aims to support energy researchers considering the use of the concept of socio-technical scenarios in two ways: first, we provide a state-of-the-art analysis of socio-technical energy scenario construction by comparing 16 studies with respect to five categories. Second, we address the dispute regarding storyline construction in energy research and examine 13 reports using the Cross-Impact Balances method. We collated researcher statements on the strengths and challenges of this method and identified seven categories of promises and challenges each.