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. 2021 Jun 28;2(7):100301.
doi: 10.1016/j.patter.2021.100301. eCollection 2021 Jul 9.

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

Jann M Weinand  1 , Russell McKenna  2 , Max Kleinebrahm  1 , Fabian Scheller  3 , Wolf Fichtner  1
Affiliations

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

Jann M Weinand et al. Patterns (N Y). .

Abstract

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.

Keywords: CO2 emissions; Gaussian process regression; cluster analysis; cost efficiency; energy system analysis; landscape esthetics; linear optimization; onshore wind; public acceptance; scenicness.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Mean scenicness quality in 11,131 German municipalities The original scenicness values are taken from Roth et al. The colors in the map of Germany quantify the beauty of the landscapes according to the coloring of the scenicness values in the histogram.
Figure 2
Figure 2
Methodology of the case study In the first step, representative clusters for the 11,131 German municipalities are identified. The results of the cluster analysis (upper map of Germany) are from Weinand et al. The colors in the histogram refer to the clusters in the map. In the next step, the energy systems of the cluster centers of these clusters are optimized in the energy system optimization model RE³ASON. The transfer of optimization results to all municipalities is performed in the third step, using a regression model. The lower map of Germany shows the regression results for the LCOEs in the Reference Scenario. The colors of the municipalities refer to the colors in the histogram.
Figure 3
Figure 3
Overview of the two parts of the RE³ASON model
Figure 4
Figure 4
Electricity supply mix and specific CO2 emissions for the ten cluster centers The diagrams show the results for the Reference Scenario (A) and the NoWind Scenario (B) in 2021–2050. The entire quantity of generated electricity is taken into account for the generation technologies, regardless of whether it is curtailed, or fed into electricity storages or the grid (exports).
Figure 5
Figure 5
Distribution of ΔLCOEs resulting from the application of the regression model for the 11,131 municipalities The ΔLCOEs in comparison with the Reference Scenario result for specific municipalities, in which the onshore wind potential is restricted. The colors of the municipalities in the map on the left side of the figure correspond to the values in the histogram on the right side. In (A), onshore wind is excluded in all municipalities with a mean scenicness quality of at least 4.15, in (B) for a mean scenicness quality of at least 4.98, and in (C) for a scenicness quality of at least 5.86.
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