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There is an increasing pressure that enhanced and novel energy technologies are swiftly adopted by the market to ensure meeting the energy and climate targets. An important issue with such novel developments is their risk to be stuck in the 'valley of death', i.e. that their transition to the market is delayed or unsuccessful. Publicly supported demonstration projects could help to bridge the valley of death by reducing barriers to the adoption caused by missing information and perceived risks. A challenge for technology demonstrations in the industrial context is their often high investments that are required to prove their real-world benefits. Given the magnitude of such investments, it becomes crucial that public funding focuses on the most promising demonstration proposals. Structured evaluation processes can help to facilitate the identification of promising proposals and to improve the quality and transparency of decisions. This paper deals with a corresponding multi-staged multi-criteria decision support system (DSS) suggested to the German Federal Ministry for Economic Affairs and Energy. It deals with the evaluation of demonstration proposals across three stages: The first stage represents a filtering stage to identify those proposals relevant for further considerations. The second stage comprises a multi-criteria scoring method drawing on an evaluation against nineteen criteria. The final third stage serves to critically review the need for public funding of well-scored proposals. This contribution outlines the development of the DSS and its design and thus provides insights on proposal evaluating in energy research.

Zur Erreichung der Pariser Klimaziele ist es notwendig, die industrielle Produktion auf klimaneutrale Prozesse umzustellen. Hierfür werden gegenwärtig eine Reihe von Technologien erforscht oder bereits getestet. Auf dem Weg vom Labor- zum Demonstrationsmaßstab wurden diese bisher überwiegend anhand von techno-ökonomischen Kriterien bewertet. Für eine nachhaltige Entwicklung sind jedoch auch zahlreiche andere Umwelt- und Nachhaltigkeitsaspekte relevant, wie in den seit 2015 weltweit gültigen Sustainable Development Goals (UN 2019) zum Ausdruck kommt. Deshalb ist eine ganzheitliche Betrachtung angebracht, bei der neben techno-ökonomischen Kriterien auch ökologische, soziale und systemische Aspekte berücksichtigt werden. Im nachfolgenden Bericht wird hierfür eine Methode entwickelt, die in drei Schritten angewandt wird. Ihr Kernstück ist ein Kriterienraster mit zugehörigen Indikatoren. Abschließend wird die Methode beispielhaft für Technologien im Stahl- und Zementsektor getestet, sie ist jedoch auch auf andere Technologien übertragbar.

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies' impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether insights can be used beyond the initial case-studies. We conclude that agentbased modelling has high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial. ; 218