The impact of public acceptance on cost efficiency and environmental sustainability in decentralized energy systems

Jann M. Weinand*, Russell McKenna, Max Kleinebrahm, Fabian Scheller, Wolf Fichtner

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
14 Downloads (Pure)


Local resistance often hinders renewable energy technology developments, especially for onshore wind. In decentralized energy systems, the landscape impact of wind turbines or transmission lines is a key barrier to public acceptance. By using landscape scenicness as a proxy for public acceptance, we quantify its impact on the optimal energy systems of 11,131 German municipalities. In municipalities with high scenicness, it is likely that onshore wind will be rejected, leading to higher levelized costs of energy by up to about 7 €-cent/kWh. Onshore wind would be replaced mainly by solar photovoltaics and imports, and the cost-optimal energy systems would be associated with higher CO2 emissions of up to about 200 gCO2/kWh compared with an average of around 50 gCO2/kWh. The findings help to identify municipalities where public resistance to onshore wind could be particularly high and support the scientific and policy debate about the location of onshore wind farms.

Original languageEnglish
Article number100301
Number of pages16
Issue number7
Early online date28 Jun 2021
Publication statusPublished - 9 Jul 2021

Bibliographical note

Funding Information:
The authors would like to thank H.G. Schwarz-von Raumer as well as the German Federal Agency for Nature Conservation for providing the scenicness data. Large parts of the methodology of this study were developed during the first author's three-and-a-half year PhD study period. The first author would therefore like to thank the three funding bodies during this period: the German Federal Ministry of Education and Research (BMBF) within the Kopernikus Project ENSURE “New ENergy grid StructURes for the German Energiewende” (funding reference: FKZ 03SFK1N0 ), the PhD College “Energy and Resource Efficiency” ( ENRES ) from the Federal State of Baden-Wuerttemberg, as well as the German Federal Ministry for Economic Affairs and Energy (BMWi) within the TrafoKommune project (funding reference: 03EN3008F ).


  • cluster analysis
  • CO emissions
  • cost efficiency
  • DSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems
  • energy system analysis
  • Gaussian process regression
  • landscape esthetics
  • linear optimization
  • onshore wind
  • public acceptance
  • scenicness


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