The social costs and benefits of wind turbines

(by Sebastian Wehrle, reFUEL researcher)

This is work in progress, latest findings are presented at NoeG and EGU2020.

In Austria, social conflict around the installation of new wind turbines is intensifying. Opponents of wind energy mourn about the “verspargelung” (literally: “asparagus-ization”) of landscape and the related economic impacts, such as declining house prices in the vicinity of wind turbines [for a survey on negative externalities see Zerrahn, 2017]. As a result, some Austrian provinces restricted the areas available for the installation of wind turbines, while other regions never saw any expansion at all up to today. At the same time, Austrian policy makers have set the goal of meeting 100% of annual electricity consumption from “national renewable sources” by 2030. [mission 2030] (On a side note, “100%” is actually just 90% of total electricity consumption, as industrial self-consumption and balancing energy are exempt.) In addition, Austria seeks to reduce emissions from the transport sector by 7.2 million tons CO2 by 2030. If this is goal is to be achieved by electrification, we estimate the additional electricity consumption at 12.5 TWh.

Yet, not only wind turbines are contested. Austrians largely oppose nuclear power (against which they voted in a 1978 referendum), as well as many (large) hydro power projects. The adoption of biomass for energy generation is hindered by its lack of environmental and economic sustainability. In effect, solar PV today seems to be the only realistic, large-scale substitute for wind energy in Austria. (However, solar PV in Austria mostly exists on roof tops, also because subsidies for open-space PV are lower than for rooftop PV.)

While a large body of literature identifies wind energy as the dominant energy source in European power systems designed to minimize system cost, such analyses neglect negative externalities of wind turbines. Several papers seek to quantify these negative externalities, for example through the impact of wind turbines on house prices. Results are mixed, however. Some studies, mostly for the US, do not find significant impact of wind turbines on property prices [e.g. Hoen et al., 2011; Lang et al. 2014]. Others find the negative impact of wind turbines to be depending on the proximity to and visibility of wind turbines. Two recent papers [Sunak and Madlener, 2016; Kussel et al, 2019] estimate the negative impact of wind turbines in Germany at close to 7% of the property values within 2 km distance to the turbine.

To derive some insight on the socially optimal deployment of wind turbines (that is including all external costs and benefits), we complement these finding with an assessment of the benefits of wind turbines compared to their next best alternative, solar PV. For this purpose, we use the energy system model medea, which is set up to resemble a competitive Austrian electricity market in 2030 in combination with the coupled generation of district heat, either through combined heat and power (CHP) plants (including waste incineration), heat pumps, or electric or natural gas fired boilers. In addition, medea also incorporates the rapidly changing German electricity system with its projected status in 2030 (i.e. including nuclear exit, partial coal exit, renewables expansion as laid out in EEG 2017). This allows us to study the effect of interlinkages through electricity trade with Austria’s largest electricity trading partner. We abstract from any subsidy schemes, so that we can study the actual economic value generated by the analyzed policies.

A large strand of literature on the system cost minimal mix of renewable generation technologies suggests that relatively small shares of solar PV are cost minimizing in the European power system [e.g. Rodriguez et al. 2015; Reichenberg 2018]. Our analysis of a system cost-minimal renewables expansion in Austria confirms this result. Given the policy goal of meeting 100% of consumption from renewables sources on annual average, the system cost-minimal implementation suggested by our model is to add 16.3 GW of onshore wind turbines, backed by around 1.2 GW of conventional, natural gas-fired generators. Additional flexibility is provided by adding approximately 350 MW of heat pumps, which can be used to generate heat from electricity.

At an intermediate CO2 price of 50 €/t, this results in total system costs of 1.8 bn € at an annual electricity generation of 105.1 TWh or an average system cost of 17.05 €/MWh. According to our estimates, producer surplus amounts to 2.45 billion € in this setting, while 9.4 million tons of CO2 (corresponding to an average CO2 intensity of 0.09 t/MWh) are emitted from the power generation sector.

Starting from this least-cost set-up, we gradually restrict the capacity of wind turbines that can be added to the system, while maintaining the “100% renewables” policy. The effects of this on system cost, CO2 emissions and producer surplus are illustrated in Figure 1.

Figure 1: Effects of substituting wind with solar PV in the Austrian electricity system, at 50€/t CO2 price

For each GW of wind power substituted by solar photovoltaics, system costs increase by approximately 1.7%. This effect is strongest up to the point where about one-third of wind power gets substituted and levels off to some extent for very high penetration of solar PV. Likewise, emissions of climate-damaging carbon dioxide increase by a considerable 0.7% for each of the first 10 GW of solar PV deployed instead of wind power. Further deployment of solar PV is less damaging to the climate. Finally, the economic surplus of Austrian producers increases particularly strong for the first 10 GW of solar PV deployed instead of wind power. Above 20 GW solar PV penetration, producer surplus starts to fall back towards initial levels.

As a direct consequence of this, producers have an incentive to overinvest in solar PV at the expense of rising system cost and increasing greenhouse gas emissions. In this case, producers’ incentives align with opposition against local wind power projects. Yet, residents in the vicinity of wind turbines face actual economics impacts from wind turbines that might lower the value of their property. Using the case of Germany, as we are lacking data for Austria, the value of the average affected house could decline by up to 19 500 Euros once wind turbines are situated nearby . [Kussel et al, 2019].

From a societal perspective, however, these negative impacts need to be complemented with the benefits of wind power. Depending on the level of wind turbines deployment, wind turbines save between 29 000 Euro and 38 500 Euros per year and MW installed capacity in system costs compared to solar PV. Thus, over its lifetime, a standard 3.5 MW wind turbines generates system cost saving worth between 1.5 and 2.3 million Euros at the time of turbine installation. Hence, between 80 and 120 property owners could, on average, be fully compensated for accepting a wind turbine in their vicinity.

As a corollary, our analysis suggests that wind turbines should ideally be grouped in larger wind farms that are situated on or near property with below-average value. Compensating residents for living near wind turbines might increase social acceptance and counterbalance adverse impacts on the distribution of wealth that would otherwise arise from such an allocation of wind turbines.

However, these results hinge on several assumptions taken in the literature on negative local externalities on wind turbines. First, if impacts are not proportional to property values but constant, situating turbines in low-value regions is no longer indicated. Second, one would need to carefully account for the marginal negative externality of adding an additional wind turbine to a wind farm. It might be reasonable to expect larger negative impacts from a huge wind farm than from a small turbine in the surrounding.

Federal Ministry for Sustainability and Tourism, Federal Ministry for Transport, Innovation, and Technology, 2018. #mission 2030. Austrian Climate and Energy Strategy.

Hoen, B., Brown, J., Jackson, T., Thayer, M., Wiser, R., Cappers, P., 2015. Spatial Hedonic Analysis of the Effects of US Wind Energy Facilities on Surrounding Property Values. J. Real Estate Finance Econ. 51, 22–51. https://doi.org/10.1007/s11146-014-9477-9

Kussel, G., Frondel, M., Vance, C., Sommer, S., 2019. Local Cost for Global Benefit: The Case of Wind Turbines, in: Beiträge Zur Jahrestagung Des Vereins Für Socialpolitik 2019 - Session: Environmental Economics I, No. A15-V1. Presented at the Annual Meeting of the German Economic Association.

Lang, C., Opaluch, J., Sfinarolakis, G., 2014. The windy city: Property value impacts of wind turbines in an urban setting. Energy Econ. 44, 413–421. https://doi.org/10.1016/j.eneco.2014.05.010

Rodriguez, R.A., Becker, S., Greiner, M., 2015. Cost-optimal design of a simplified, highly renewable pan-European electricity system. Energy 83, 658–668. https://doi.org/10.1016/j.energy.2015.02.066

Schlachtberger, D.P., Brown, T., Schramm, S., Greiner, M., 2017. The benefits of cooperation in a highly renewable European electricity network. Energy 134, 469–481. https://doi.org/10.1016/j.energy.2017.06.004

Scholz, Y., Gils, H.C., Pietzcker, R.C., 2017. Application of a high-detail energy system model to derive power sector characteristics at high wind and solar shares. Energy Econ. 64, 568–582. https://doi.org/10.1016/j.eneco.2016.06.021

Sunak, Y., Madlener, R., 2016. The impact of wind farm visibility on property values: A spatial difference-in-differences analysis. Energy Econ. 55, 79–91. https://doi.org/10.1016/j.eneco.2015.12.025

Zerrahn, A., 2017. Wind Power and Externalities. Ecol. Econ. 141, 245–260. https://doi.org/10.1016/j.ecolecon.2017.02.016