Lead: Karlsruher Institut für Technologie
Description and Objectives
Energy has become one of the major social challenges and causes for societal conflict in the last five years. The energy transition requires a huge transformation of society and the accustomed way of life. Therefore, research projects addressing this process need to incorporate strategies to engage and communicate with the public. As a consequence, the same holds true for a scientific research data service that - among other goals - is intended to provide guidelines on good research practices for academics in the energy domain. Therefore, an energy system research data service should provide the possibility for non-professionals to become informed and it should communicate scientific results in a way that allows for a universal understanding. Furthermore, citizens’ preferences should also be considered as part of this data. In this regard, it is of the essence to ensure that the target group is as broad as possible so that information on the energy transition is not reserved to an engaged minority or small elite, which might either support or oppose the energy transition. This requires research projects to develop a strategy for the corresponding communication for which guidelines are currently not available. Additionally, no infrastructure is given that would allow researchers to publish and discuss appropriate guidelines with this regard.
Today, energy system models are becoming increasingly powerful and detailed regarding techno-economic parameters, but they rarely include social and political factors, even though we know that these factors are strong determinants for the optimal design of energy systems. For example, the most detailed power system modelling is irrelevant if public opposition makes it impossible to construct wind farms or power lines. Availability and accessibility of robust data on social and political factors are essential elements for policy-relevant energy modelling, but presently such data is scarce. In addition, the decentralised character of the energy transformation makes the local level and its specifics an important factor. Hence integrating qualitative and quantitative data of the decentralised energy transformation (including aspects of acceptance) constitutes an imperative for effective system modelling. We will draw on the findings of the project Sustainable Energy Transition Laboratory (SENTINEL) (EU Horizon 2020 project co-led by IASS) which established a great knowledge base for better understanding the needs and expectations in energy modelling.
In this TA, we explore best practices for incorporating social and political drivers and constraints of the energy transition in the research and transfer cycle of energy system research. Therefore, we generate and link relevant data, and prepare it for incorporation into the developed community services and data infrastructure. The aim of this TA is further to co-design the NFDI4Energy platform and key services, which will feed into new or existing energy models to help inform the public and political decision makers to determine socially acceptable energy pathways of the future. In addition, we will involve citizens during the project lifetime in the development of the platform that enables and incentivises the active participation of public stakeholders in energy system research.
Task Area 2 targets the following objectives:
- [O2.1] Identify public stakeholders, learn from local conditions and involve citizens in the service development process
- [O2.2] Collect regulatory data and identify political preferences and logics to feed into the service development
- [O2.3] Explore public acceptance as well as social trends and link them to political preferences and regulatory frameworks
- [O2.4] Examine the status quo of incorporating societal and political factors into energy system models and develop guidelines and data sources for future energy research
- [O2.5] Enable active participation of citizens in energy system research based on the developed services
- [O2.6] Communicate data and model results to different audiences from society and policy using developed best practices
TA2 Lead:
Christina Speck
KIT Karlsruher Institut für Technologie
Researcher
View Profile: https://im.iism.kit.edu/team_3495.php