Abstract
In the context of the energy transition, municipalities are increasingly attempting to exploit renewable energies. Socio-energetic data are required as input for municipal energy system analyses. This Data Descriptor provides a compilation of 40 indicators for all 11,131 German municipalities. In addition to census data such as population density, mobility data such as the number of vehicles and data on the potential of renewables such as wind energy are included. Most of the data set also contains public data, the allocation of which to municipalities was an extensive task. The data set can support in addressing a wide range of energy-related research challenges. A municipality typology has already been developed with the data, and the resulting municipality grouping is also included in the data set.
Highlights
Background & SummaryNational targets in energy policy are leading to a radical change in the energy sector
The associated expansion of renewable energies is mainly decentralised, which applies to the owners and operators of energy plants: private individuals increasingly invest in renewable energy systems or form so-called citizen-energy cooperatives[1]
In the course of the growing interest in renewable energy systems, an increasing number of energy system analyses for the development of climate protection plans are conducted at the municipal level
Summary
National targets in energy policy are leading to a radical change in the energy sector. More and more municipalities are striving to exploit renewable decentralised energy generation. In the course of the growing interest in renewable energy systems, an increasing number of energy system analyses for the development of climate protection plans are conducted at the municipal level. Many municipalities lack both the financial resources and the expertise to determine the potential for renewables or develop effective climate protection plans[3]. These municipalities would benefit from studies on their suitability for decentralised energy systems. The dataset[9] enables energy researchers to conduct studies at municipal and national levels without having to obtain and synthesize a large amount of data. The cluster composition can help to transfer results from energy system analyses of individual municipalities to other, similar municipalities
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