Wind Turbine Syndrome: The Impact of Wind Farms on Suicide

This new report examines how locations where industrial wind turbines were erected near residences experienced measurable upticks in suicide. The researcher identifies three indirect tests of the role of low-frequency noise exposure including those most vulnerable to the noise, prevailing wind direction and potental of greater noise impacts, and turbine noise resulting in sleep deficiency. The abstract and conclusion of the paper are provided below. The full report can be accessed by clicking the links on this page.


Current technology uses wind turbines’ blade aerodynamics to convert wind energy to electricity. This process generates significant low-frequency noise that reportedly results in residents’ sleep disruptions, among other annoyance symptoms. However, the existence and the importance of wind farms’ health effects on a population scale remain unknown. Exploiting over 800 utility-scale wind turbine installation events in the United States from 2001-2013, I show robust evidence that wind farms lead to significant increases in suicide. I explore three indirect tests of the role of low-frequency noise exposure. First, the suicide effect concentrates among individuals who are vulnerable to noise-induced illnesses, such as the elderly. Second, the suicide effect is driven by days when wind blows in directions that would raise residents’ exposure to low-frequency noise radiation. Third, data from a large-scale health survey suggest increased sleep insufficiency as new turbines began operating. These findings point to the value of noise abatement in future wind technology innovations.

Discussion and Conclusion

I conclude the paper with a back-of-envelop calculation of the external costs of wind farms as the result of suicides. Given the findings on the age profile of suicide effects, life years lost (LYL) are computed as the summation of age-specific effects across age groups:

𝐿𝑌𝐿 = ∑𝑎 ∑𝑐 𝛽𝑎 × 𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑐𝑎 × 𝐿𝑖𝑓𝑒𝐸𝑥𝑝𝑎

where 𝛽𝑎 is the age group specific effect of a wind farm on the suicide rate obtained from equation (5). This is multiplied by population in county c of age group a (𝑃𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛𝑐𝑎) and expected remaining years of life (𝐿𝑖𝑓𝑒𝐸𝑥𝑝𝑎) to obtain excessive life years lost in the county. 𝐿𝑖𝑓𝑒𝐸𝑥𝑝𝑎 is computed as the difference between average Social Security Administration life expectancy and average age at suicide for individuals in age group a. 110This calculation concludes that, from 2001-2013, new wind farms are responsible for 997 excessive suicides in the first year following their installation. This amounts to 33,939 life years lost. 

I now contrast this number with life years that would have been lost in the one-year window had the energy instead been generated by coal. I use the estimate of the social cost of coal-generated electricity at $178 per megawatt hour (MWh) from Epstein et al (2011).132Applying their adopted value of statistical life (VSL) of $7.66 million and an average remaining life years of 15.2 years per death, this number is converted to 0.00035321 LYL per MWh coal electricity. New wind farms generated a total of 148.7 million MWh wind power within the first year of operation, which implies a total of 52,523 avoided life years lost had the power been generated entirely from coal. Of course, these numbers do not immediately inform welfare; however, they do suggest that wind farm-related suicides potentially reduce the overall value, even though the technology offers a renewable source of energy, providing an alternative to fossil fuel-based sources, which contribute to greenhouse gas emissions and detrimentally affect air quality.

This study has important limitations that bear mention. First, estimates of this paper reflect the effect of exposure to wind farms. While I have shown a number of tests that support the view that noise exposure plays a role in wind farms’ effect on suicides, more direct evidence is needed to establish the causal effect of noise. Ambient noise monitoring data would be particularly useful. Such data could be used to better measure the noise profile of wind farms, and, in combination with medical data, could enhance understanding of any potential effects on those living in proximity to turbines. In addition, such data could be used to test for a potential dosage relationship, to determine a possible threshold at which noise exposure is likely to affect health. Second, this paper’s analysis relies upon county-level suicide data. The growing availability of administrative data on health outcomes may provide more granular information regarding location of related health outcomes. This may benefit the study of wind turbine syndrome in multiple ways. For example, finer geographical data would help identify effects on individuals who live in the immediate vicinity of wind farms - the situation that provided the initial motivation for this literature’s area of inquiry. Greater geographic detail would also be particularly useful for studies that use changes in wind directions as quasi-experiments to pinpoint the effects of noise. Third, while the analysis focuses on suicide as the key outcome of interest, it likely captures only the most severe consequence of wind farm exposure. Other health outcomes, such as emergency room visits and hospitalization, may also be important to provide a richer characterization of the health effects that may stem from living or working in close proximity to wind turbines, and to shed light on the full related costs. 

Finally, it is perhaps most important to emphasize that this study estimates wind turbine syndrome clearly as a result of the way wind energy is captured with today’s technology. It is clear that wind energy, together with other renewable sources, will play a significant role in combating climate change. As noted earlier, this research may bring a new perspective to the value of noise abatement in wind technology innovations. 

Turbine Zou201710

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OCT 30 2017
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