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Way Off: The Effect of Minimum Distance Regulation on the Deployment of Wind Power

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  • Jan Stede
  • Nils May

Abstract

Several countries and regions have introduced mandatory minimum distances of wind turbines to nearby residential areas, in order to increase public acceptance of wind power. Germany’s largest federal state Bavaria introduced such separation distances of ten times the height of new wind turbines in 2014. Here, we provide a novel monthly district-level dataset of construction permits for wind turbines constructed in Germany between 2010 and 2018. We use this dataset to evaluate the causal effect of introducing the Bavarian minimum distance regulation on the issuance of construction permits for wind turbines. We find that permits decreased by up to 90 percent. This decrease is in the same order of magnitude as the reduction of land area available for wind turbines. The results are in line with findings indicating that minimum distances do not increase the public acceptance of wind power, but harm the expansion of onshore wind power.

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  • Jan Stede & Nils May, 2020. "Way Off: The Effect of Minimum Distance Regulation on the Deployment of Wind Power," Discussion Papers of DIW Berlin 1867, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp1867
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    2. Peri, Erez & Tal, Alon, 2021. "Is setback distance the best criteria for siting wind turbines under crowded conditions? An empirical analysis," Energy Policy, Elsevier, vol. 155(C).
    3. Dehler-Holland, Joris & Okoh, Marvin & Keles, Dogan, 2022. "Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Stetter, Chris & Wielert, Henrik & Breitner, Michael H., 2022. "Hidden repowering potential of non-repowerable onshore wind sites in Germany," Energy Policy, Elsevier, vol. 168(C).
    5. Croonenbroeck, Carsten & Hennecke, David, 2021. "A comparison of optimizers in a unified standard for optimization on wind farm layout optimization," Energy, Elsevier, vol. 216(C).
    6. Oehlmann, Malte & Glenk, Klaus & Lloyd-Smith, Patrick & Meyerhoff, Jürgen, 2021. "Quantifying landscape externalities of renewable energy development: Implications of attribute cut-offs in choice experiments," Resource and Energy Economics, Elsevier, vol. 65(C).
    7. Jan-Niklas Meier & Paul Lehmann & Bernd Süssmuth & Stephan Wedekind, 2024. "Wind power deployment and the impact of spatial planning policies," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 87(2), pages 491-550, February.

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    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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