Resource Documents: Europe (33 items)
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Author: Koster, Hans; and Dröes, Martijn
Countries that invest in renewable energy production face frequent opposition from local homeowners. Using a detailed housing transactions dataset covering the whole of the Netherlands since 1985, this column compares the overall impact that wind turbines and solar farms have on housing prices. It finds that tall wind turbines (over 150 metres) have a negative effect, and solar farms generate losses as well (2-3% for homeowners within a 1km orbit). This evidence should be factored into finding the optimal allocation of renewable energy production facilities.
Renewable energy is on the rise (Newbery 2018). While global demand is still strongly increasing amidst the Covid-19 pandemic, the demand for fossil fuels has steeply declined (IEA 2020). Wind turbines are an important source of renewable energy, with 30% of total capacity located in Europe and 17% in the US in 2018. China has invested especially heavily in wind energy, overtaking the EU in 2015 as the largest producer of wind energy. Currently, 36% of worldwide capacity is located in China (GWEC 2019). Wind turbines have become taller over time: turbines in the 1980s were still around 30 metres, while the newest generation of wind turbines are well above 100 metres.
A related trend is the commercial production of renewable energy via solar farms. The first solar farm was constructed in 1982 in California. Yet, with advances in technology, the commercial exploitation of solar farms has only become attractive in the last decade or so (Heal 2009). These solar farms have also become bigger over time; the largest solar farm currently is 40km2 and located in Bhadla, India.
Even though wind turbines comprise a larger part of renewable energy production, last year’s growth in solar photovoltaics capacity was about twice that of wind turbines (REN21 2020). Whether the current surge in the construction of tall wind turbines and large solar farms will continue remains to be seen, but some countries have already suggested that the economic recovery after Covid-19 should be a green one (Jordans 2020).
Wind turbines make noise, cast shadows, cause flickering, and visually pollute the landscape, typically leading to substantial opposition from the local population, including homeowners. A similar story applies for ground-mounted solar panels, as they reflect ambient sound, sunlight, create a buzzing sound, and are also not so great to look at. In line with a large literature on hedonic pricing, we would expect that such ‘external effects’ capitalise into local house prices. Increasing our understanding of these external effects is important to gaining insight into the optimal allocation of renewable energy production facilities.
In a recent study (Dröes and Koster 2020), we examine the effects of tall wind turbines and solar farms on residential property values. Using a detailed housing transactions dataset covering the whole of the Netherlands since 1985, and a difference-in-differences regression methodology, we compare changes to house prices in areas that will receive a turbine in the future to areas in which a turbine already has been built, taking into account a host of other factors determining house prices such as location, general economic trends, and housing quality. In this way, we ensure that we compare apples with apples (i.e. houses in areas that have a turbine compared to houses in near-identical areas without a turbine), rather than apples with oranges. A comparable approach is used to measure the effects of solar farms.
In Figures 1a and 1b, we plot the spatial distribution of (respectively) wind turbines and solar farms across the Netherlands. It is easy to observe that turbines are more common than solar farms.
Most solar farms have been built in recent years. Turbines are particularly common in coastal areas where wind is ubiquitous. Solar farms are mainly built in the northwest of the Netherlands, as more space is available to facilitate large solar farms.
With regard to the empirical results for wind turbines, residential property values are negatively impacted when properties are in close proximity to a wind turbine. In particular, the house prices of properties within a 2km radius decrease on average by 2% relative to comparable properties with no wind turbines nearby. However, we find considerable heterogeneity in the effect of turbines on house prices (see Figure 2). For example, a tall wind turbine (>150m tip height) generates a negative price effect of about 5% within 2km, while we do not find a significant effect for turbines below 50m. We show that our results are robust when (i) we allow for changes in perception to wind turbines, (ii) we look at removals of turbines rather than placements, and (iii) we allow for the effects of multiple turbines. Regarding the latter, we find that only the first turbine within 2km has an effect on property prices. From a policy standpoint, this suggest that it is preferable to cluster wind turbines into large wind farms. We also show that the impact area of turbines is essentially the same for turbines taller than 50m, while the effects are more localised for turbines under 50m.
Taking the empirical results at face value, we calculate the overall loss in housing wealth as a result of wind turbines and solar farms. It appears that just 25 turbines account for almost 50% of the total loss, which shows that it is very important to build turbines not too close to residential properties. Indeed, the median loss per turbine is much lower and about €166,000, or about €89 per megawatt hour (MWh). Given the construction costs of about €1.27 million per MW, and the median installed capacity of 3MW, the median loss in housing values is about 4.4% of the median construction costs. Interestingly, the median loss per MWh varies considerably across turbines of different heights. For example, because tall turbines generate more power, the median loss per MWh is about €10, while it is €844 for small turbines. Hence, despite the smaller effects of small turbines on house prices, the lower power output means it is not more efficient to build small turbines.
For solar farms the results are less convincing because the number of solar farms is much lower, making the estimated coefficients less precise. Still, we find evidence suggesting that solar farms lead to a house price decrease of about 2-3%. Unsurprisingly, the effect is more localized than the effect of turbines and confined to 1km. Because fewer solar farms are constructed, the total loss is just over €84 million. Here it also seems more informative to look at the median loss of a solar farm, which amounts to about €0.5 million – somewhat larger than the median loss for one turbine. However, this is mainly because solar farms are generally larger and generate more electricity. The median loss per MWh is €63, which is in the same order of magnitude as the median loss per MWh for wind turbines (i.e. €89 per MWh).
Producing energy in a sustainable way is an important step towards a climate-neutral economy with net-zero greenhouse gas emissions (Castle and Hendry 2020). Wind and solar energy are important sources of renewable energy. However, while reductions in CO₂ emissions benefits the whole population, external effects are borne only by households living close to production sites. Hence, insights into these external effects is paramount, as the size of external effects directly informs the local support for the opening of production sites, such as wind turbines and solar farms. Our study shows that the location of production sites of renewable energy matters, as a few sites cause the lion’s share of losses in housing values in the Netherlands. The results also highlight that when building tall turbines in the right locations, reductions in housing values are a relatively small share (<5%) of the total construction costs of turbines.
Hans Koster, Professor of Urban Economics and Real Estate, Vrije Universiteit Amsterdam
Martijn Dröes, Assistant Professor of Real Estate Finance, University of Amsterdam
20 September 2020
Jordans, F (2020), “Germany, Britain call for ‘green recovery’ from pandemic”, Associated Press Berlin, 27 April.
Castle, J and D Hendry (2020), “Decarbonising the Future UK Economy”, VoxEU.org, 4 June.
Dröes, M and H Koster (2020), “Wind turbines, solar farms, and house prices“, CEPR Discussion Paper 15023.
GWEC (2019), Global Wind Report 2018, Global Wind Energy Council.
Heal, G (2009), “Can Renewable Energy Save the World?” VoxEU.org, 29 October.
IEA (2020), Global energy review 2020.
Newbery, D (2018), “Evaluating the Case for Supporting Renewable Electricity”, VoxEU.org, 20 July.
REN21 (2020), Renewables 2020 Global Status Report.
A laboratory study on the effects of wind turbine noise on sleep: results of the polysomnographic WiTNES study
Author: Smith, Michael; Ögren, Mikael; Thorsson, Pontus; Hussain-Alkhateeb, Laith; Pedersen, Eja; Forssén, Jens; Ageborg Morsing, Julia; and Persson Waye, Kerstin
Study Objectives: Assess the physiologic and self-reported effects of wind turbine noise (WTN) on sleep.
Methods: Laboratory sleep study (n = 50 participants: n = 24 living close to wind turbines and n = 26 as a reference group) using polysomnography, electrocardiography, salivary cortisol, and questionnaire endpoints. Three consecutive nights (23:00–07:00): one habituation followed by a randomized quiet Control and an intervention night with synthesized 32 dB LAEq WTN. Noise in WTN nights simulated closed and ajar windows and low and high amplitude modulation depth.
Results: There was a longer rapid eye movement (REM) sleep latency (+16.8 min) and lower amount of REM sleep (−11.1 min, −2.2%) in WTN nights. Other measures of objective sleep did not differ significantly between nights, including key indicators of sleep disturbance (sleep efficiency: Control 86.6%, WTN 84.2%; wakefulness after sleep onset: Control 45.2 min, WTN 52.3 min; awakenings: Control n = 11.4, WTN n = 11.5) or the cortisol awakening response. Self-reported sleep was consistently rated as worse following WTN nights, and individuals living close to wind turbines had worse self-reported sleep in both the Control and WTN nights than the reference group.
Conclusions: Amplitude-modulated continuous WTN may impact on self-assessed and some aspects of physiologic sleep. Future studies are needed to generalize these findings outside of the laboratory and should include more exposure nights and further examine possible habituation or sensitization.
Michael G. Smith, Mikael Ögren, Pontus Thorsson, Laith Hussain-Alkhateeb, Eja Pedersen, Jens Forssén, Julia Ageborg Morsing and Kerstin Persson Waye
Department of Occupational and Environmental Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg; Division of Applied Acoustics, Department of Civil and Environmental Engineering, Chalmers University of Technology, Gothenburg; Akustikverkstan, Lidköping; and Department of Architecture and the Built Environment, Lund University, Lund, Sweden
Sleep, Volume 43, Issue 9, 1 September 2020, zsaa046, doi:10.1093/sleep/zsaa046
Download original document: “A laboratory study on the effects of wind turbine noise on sleep: results of the polysomnographic WiTNES study”
Bird collisions at wind turbines in a mountainous area related to bird movement intensities measured by radar
Author: Aschwanden, Janine; et al.
Bird collisions at wind turbines are perceived to be an important conservation issue. To determine mitigation actions such as temporary shutdown of wind turbines when bird movement intensities are high, knowledge of the relationship between the number of birds crossing an area and the number of collisions is essential. Our aim was to combine radar data on bird movement intensities with collision data from a systematic carcass search.
We used a dedicated bird radar, located near a wind farm in a mountainous area, to continuously record bird movement intensities from February to mid-November 2015. In addition, we searched the ground below three wind turbines (Enercon E-82) for carcasses on 85 dates and considered three established correction factors to extrapolate the number of collisions.
The extrapolated number of collisions was 20.7 birds/wind turbine (CI-95%: 14.3–29.6) for 8.5 months. Nocturnally migrating passerines, especially kinglets (Regulus spp.), represented 55% of the fatalities. 2.1% of the birds theoretically exposed to a collision (measured by radar at the height of the wind turbines) were effectively colliding.
Collisions mainly occurred during migration and affected primarily nocturnal migrants. It was not possible to assign the fatalities doubtlessly to events with strong migration. Fresh-looking carcasses were found after nights with both strong and weak bird movement intensities, indicating fatalities are not restricted to mass movement events (onshore). Rather, it is likely that an important factor influencing collision risk is limited visibility due to weather conditions. Local and regional visibility should be considered in future studies and when fine-tuning shutdown systems for wind turbines.
Janine Aschwanden, Herbert Stark, Dieter Peter, Thomas Steuri, Baptiste Schmid, Felix Liechti
Swiss Ornithological Institute, Switzerland
Volume 220, April 2018, Pages 228-236
Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations
Author: Schippers, Peter; et al.
1. The consequences of bird mortality caused by collisions with wind turbines are increasingly receiving attention. So‐called acceptable mortality limits of populations, that is, those that assume that 1%–5% of additional mortality and the potential biological removal (PBR), provide seemingly clear‐cut methods for establishing the reduction in population viability. 2. We examine how the application of these commonly used mortality limits could affect populations of the Common Starling, Black‐tailed Godwit, Marsh Harrier, Eurasian Spoonbill, White Stork, Common Tern, and White‐tailed Eagle using stochastic density‐independent and density‐dependent Leslie matrix models. 3. Results show that population viability can be very sensitive to proportionally small increases in mortality. Rather than having a negligible effect, we found that a 1% additional mortality in postfledging cohorts of our studied populations resulted in a 2%–24% decrease in the population level after 10 years. Allowing a 5% mortality increase to existing mortality resulted in a 9%–77% reduction in the populations after 10 years.
Peter Schippers, Ralph Buij, Alex Schotman, Jana Verboom, Henk van der Jeugd, Eelke Jongejans
Wageningen Environmental Research, Wageningen University & Research; Environmental Systems Analysis, Wageningen University; Vogeltrekstation – Dutch Centre for Avian Migration and Demography, Wageningen; Animal Ecology and Physiology, Radboud University, Nijmegen, The Netherlands
Ecology and Evolution. Published on line 04 June 2020. doi: 10.1002/ece3.6360
Download original document: “Mortality limits used in wind energy impact assessment underestimate impacts of wind farms on bird populations”