Measuring Renewable Energy Externalities: Evidence from Subjective Well-being Data

Charlotte von Möllendorff and Heinz Welsch


Electricity from renewable sources avoids disadvantages of conventional power generation but often meets with local resistance. We use 324,763 observations on the subjective well-being of 46,678 individuals in Germany, 1994–2012, for identifying and valuing the local externalities from solar, wind, and biomass plants in respondents’ postcode district and adjacent postcode districts. We find significant well-being externalities of all three technologies that differ with regard to their temporal and spatial characteristics. The monetary equivalent of 1 MW capacity expansion of wind power and biomass installations is estimated to be 0.35% and 1.25% of monthly per capita income, respectively. (JEL D62, Q42)


Electricity generation from renewable sources is rapidly expanding in many countries around the world. In Germany, the share of electricity generated from renewable energy (RE) sources in total electricity consumption has increased from 4.6% in 1994 to 23.7% in 2012. In the European Union, we observe the same trend; by 2012, the share of RE sources in total electricity consumptions amounted to 23.5%.

The public attitude toward RE is typically favorable (VZBV 2013), because renewable power avoids the externalities associated with electricity from fossil fuels (air pollution and greenhouse gases) and nuclear power (nuclear risk and waste disposal). Consistent with voiced opinions, Welsch and Biermann (2014) found in a multicountry study that a higher share of solar and wind power in a country’s national electricity mix is associated with greater subjective well-being of its citizens.

Although they exhibit fewer transregional externalities as compared to conventional power generation (environmental pollution), renewable power facilities may induce externalities on the local scale, such as visual impairments in the case of solar and wind power and odor nuisance in the case of biomass plants. In fact, the installation of such facilities often meets with local resistance, reflecting the so-called not-in-my-backyard (NIMBY) issue (van der Horst 2007) and, more generally, social and community acceptance problems (Batel, Devine-Wright, and Tangeland 2013). In the case where RE installations are owned by local residents, however, the impairments originating from them trade off against financial and moral benefits (such as the warm glow).

This paper studies RE externalities from the point of view of local subjective well-being. Specifically, the paper addresses the following aspects of the relationship between RE facilities and residents’ well-being: (1) How does the well-being of residents change due to the local expansion of RE? (2) Are there spatial spillovers of RE externalities? (3) How do well-being effects of RE installations evolve over time? (4) Do wind, solar, and biomass installations differ in terms of those questions?1

We use panel data on reported well-being of 46,678 German citizens, 1994–2012, and the number and capacity of installed wind power, solar, and biomass plants in the postcode district where the respondents live, as well as in the neighboring postcode districts. For each type of RE technology we estimate several specifications of life satisfaction regressions that are designed to address the specific research questions mentioned above.

We find significant negative well-being externalities from all three types of RE, but they differ in several important ways. For solar power, we find no well-being effects in people’s own postcode district, but negative effects from increasing numbers and installed capacities in adjacent districts. The absence of effects in the own district suggests that at the level of the district, financial and moral benefits just balance the impairments from solar power installations. For wind power we find negative effects in people’s own district, whereas facilities in both the own district and in adjacent districts negatively affect well-beingin the case of biomass plants. Wind turbines and biomass plants differ in that the well-being effect of the former is transitory, whereas the effect of the latter is permanent. In addition, the effect per megawatt of installed capacity is almost four times as large in the case of biomass as in the case of wind power. As discussed below, these findings for the different technologies are explicable in the light of those technologies’ characteristics and respective channels of influence on residents’ well-being.

To our knowledge, this paper is the first to study the nature and extent of well-being externalities from solar, wind, and biomass energy in a comparative perspective. In relation to previous literature, a major advantage of the present study is the use of a rich set of spatially disaggregated and nationwide representative panel data. This allows us to conduct a longitudinal analysis of externalities associated with RE. The only similar analysis we are aware of was conducted by Krekel and Zerrahn (2015), but that paper focuses exclusively on wind energy.2


During the past decades, especially after the introduction of the Electricity Feed-In Act (Stromeinspeisungsgesetz) in 1991 and its successor, the Renewable Energy Act (Erneuerbare-Energien-Gesetz) in 2000, Germany has faced a wide expansion of RE technologies. By the end of 2012 Germany had 13,611 biomass plants, 21,500 wind turbines, and about 1.3 million solar installations falling under the regulations of the Renewable Energy Act, whereas the corresponding numbers for 1994 were 54 biomass plants, 1,118 wind turbines, and 1,850 solar installations (see Section III for data sources). Figure 1 displays the development of electricity production in Germany by energy carrier.


Electricity Production by Energy Carrier

Source: Data from AGEE-Stat (Arbeitsgruppe Erneuerbare Energien-Statistik [Working Group Renewable Energy Statistics], BMWi 2014)

Polls among German citizens yield considerable support for a transition of the energy system toward RE (see, e.g., VZBV 2013). However, in some regions the expansion of RE technologies has given rise to local opposition that in view of the strong general support of RE, provoked a debate on the so-called not-in-my-backyard (NIMBY) problem (van der Horst 2007). One source of the NIMBY issue consists of the local or regional externalities associated with the different RE technologies. While wind turbines can affect residents via their shadowing and noise as well as influence on the landscape and biodiversity (Drechsler et al. 2011), biogas plants are objected to mainly because of odor nuisance, visual impacts, or fear of declining tourism or property prices (Soland, Steimer, and Walter 2013).3 Regarding solar power, possible impairments include glare risks and visual impacts on buildings as well as on landscapes in the case of free-standing solar plants (Chiabrando, Fabrizio, and Garnero 2009).

The geographic scope of these effects differs by technology as well as by location. By showing computer-generated landscape photographs to survey respondents, some studies evaluate the visual effect of wind farms at varying distances and find a linear but slow decline to 12 km (Bishop 2011). Noise disturbance from wind turbines usually occurs within 500 m but depends on local conditions such as level differences (Pedersen and Waye 2007). The shadow effect of wind turbines usually ranges between 1.5 and 2 km (Hau 2014). With respect to biogas plants, Nicolas et al. (2013) find odor nuisance up to a distance of 600 m. The glare impact of solar installations highly depends on the geometric conditions (e.g., slope of solar panel, direction of solar irradiation), while the landscape impact of free-standing solar plants has to our knowledge not been assessed yet (Chiabrando, Fabrizio, and Garnero 2009).

Another issue that might be important for well-being externalities of RE technologies is their ownership structure. Private owners of RE installations may enjoy financial and moral benefits (warm glow), which trade off against the various impairments originating from nearby RE facilities. In the case of solar panels there may in addition be status effects (Sonnberger 2015). Any measured well-being effect of the presence of RE installations thus represents a net effect of the externalities generated and the benefits enjoyed by nearby owners of those installations.

Maron, Klemisch, and Maron (2011) provide data for 2010 that show private persons own 39.3% of installed solar capacity, followed by 21.2% owned by farmers and 19.2% by trade and industry. The share of private investment in wind energy is 51.5%; 36.8% and 7.4% are owned by external investors and by utilities, respectively. Private investment in solar and wind power often takes the form of citizen energy cooperatives (whose number increased from 66 in 2001 to over 700 in 2012 [Klaus Novy Institut 2014]), the vast majority being cooperatives operating solar parks in the region where people live. Biomass plants are largely owned by farmers (Maron, Klemisch, and Maron 2011).

Decisions on the siting of RE plants are driven by regional attributes such as suitability (e.g., wind speed) and legal restrictions (e.g., minimum distances to residential areas), as well as by financial incentives (feed-in-tariff). In addition to technical, legal, and economic factors, political science has focused on the issue of social acceptance (Wüstenhagen, Wolsink, and Bürer 2007), which depends on perceived benefits, fairness considerations, the availability of information, participation options, and trust in the operator, just to mention a few (for a review see Devine-Wright 2007). Citizen participation in the siting process is not mandatory provided legal siting restrictions are satisfied.

Several nonmarket valuation techniques have been used for quantifying the externalities from RE technologies. Numerous case studies, polls, and discrete choice experiments have been conducted to identify the underlying problems of siting conflicts and factors of influence for social acceptance, whereas the bulk of studies focus on wind energy development projects (van der Horst 2007). Due to differences in methodological designs, stated questions, and terms used (e.g., acceptance vs. support), the results present quite a mixed picture (Devine-Wright 2007).

In the case of wind power, stated choice experiments tend to show that there can be negative externalities arising from wind turbines, resulting in a positive willingness to pay (WTP) by respondents for an increase in the distance to the nearest wind turbine (for an overview see Meyerhoff, Ohl, and Hartje 2010). Drechsler et al. (2011) estimate in a choice experiment that external costs make up approximately 14% of the total investment costs.

Stated preference methods exhibit some drawbacks, as respondents could be inclined to respond strategically if they assume their answers would influence political decisions upon the expansion of RE technologies (Fujiwara and Campbell 2011). Moreover, respondents may give socially desirable answers due to the positive connotation of RE (van der Horst 2007) or misconceive the aspect of adapting and habituating to the impacts of RE technologies (the focusing illusion, see Kahneman and Thaler 2006). Meyerhoff (2013) provides evidence of residents’ misconception of adaptation and habituation to wind turbines; he finds that people who live farther away from wind turbines are more likely opponents of wind power development compared to people who have wind turbines in close proximity (Meyerhoff 2013).

Another strand in the literature on the valuation of nonmarket goods relies on revealed preference rather than stated preference methods. In the context of local effects of RE siting, the hedonic approach has been applied, which reverts to housing market data. Supposing that housing prices reflect the value of nonmarket goods present in the neighborhood (e.g., proximity to recreational sites, local infrastructure, air quality or noise) it is possible to compute the individual WTP for those goods (Fujiwara and Campbell 2011). Several studies have analyzed the effect of wind turbines on property values (see, e.g., Sunak and Madlener 2016; Jensen, Panduro, and Lundhede 2014; Hoen et al. 2015). While most of them find a negative effect of wind energy development on property values (e.g., Sunak and Madlener 2016 for the state of North Rhine-Westphalia/Germany, and Jensen, Panduro, and Lundhede 2014 for Denmark) others do not find property values to be affected by wind turbines—at least not significantly (see, e.g., Hoen et al. 2015 for the United States).

The present paper applies the experienced preference method (Welsch and Ferreira 2013), also referred to as the life satisfaction or happiness approach, in order to analyze effects of RE expansion on well-being and to measure local residents’ preferences with regard to the various RE technologies. This method of preference elicitation uses people’s reported life satisfaction as a proxy for experienced utility. It estimates the statistical association of life satisfaction to the nonmarket good (or bad) in question, as well as to people’s income. The implied utility-constant trade-off of income for the good is then used as a measure of the monetary value of the latter. The experienced preference method thus provides a tool for nonmarket valuation, in addition to the standard stated and revealed preference methods.

Life satisfaction data have been used in environmental economics (for surveys see Welsch and Kühling 2009; Frey, Luechinger, and Stutzer 2010; MacKerron 2012; Welsch and Ferreira 2013) and, to a smaller extent, with respect to energy issues. Experienced preference studies differ with respect to the spatial resolution, which ranges from whole nations (Welsch 2002; Rehdanz and Maddison 2005) to postcode districts (Levinson 2012) and GPS coordinates (MacKerron and Mourato 2013). With respect to energy, Welsch and Biermann (2014) used life satisfaction data to study European citizens’ preferences for alternative structures of their national electricity supply system and found people’s subjective well-being to be positively correlated with the share of solar and wind power in their national electricity mix. Welsch and Biermann (2016) investigated the relationship between Swiss citizens’ life satisfaction and the distance of their homes from the nearest nuclear power plant and found a statistically and economically significant satisfaction-distance gradient. Using spatially disaggregated data from Australia, Ambrey and Fleming (2011) found scenic amenity to affect well-being, a result that may be relevant for the well-being assessment of RE facilities. Krekel and Zerrahn (2015) studied the relationship between German citizens’ subjective well-being and the presence of wind turbines in their proximity and found well-being to be significantly lower in individuals who live within 4 km of wind turbines. However, they focus solely on wind energy and the years following the introduction of the Renewable Energy Act, thereby neglecting a large fraction of the variation. The advantage of our study as compared to Krekel and Zerrahn’s (2015) lies in studying three different renewable technologies (biomass, solar, wind) in a comparative framework over a longer time frame. Our analysis is conducted on the postcode level, because data on the geographic coordinates are not readily available.



Our data come from several sources. The data on life satisfaction along with information on the respondents’ socioeconomic situation are provided by the German Socioeconomic Panel Study (SOEP) of the German Institute for Economic Research (DIW); see Schupp et al. (2014). The SOEP survey has been conducted on a yearly basis since 1984. Annual waves of the survey include more than 20,000 individuals in about 11,000 households. With respect to the spatial dimension, SOEP data are identified by respondents’ postcode district from 1993 onward (Wagner, Frick, and Schupp 2007).4

An important attribute of the SOEP is its panel structure (i.e., that the same individuals are reinterviewed each year), which facilitates analysis of changes on the individual level and controlling for unobserved time-invariant characteristics (Andreß, Golsch, and Schmidt 2013). As respondents may join the panel at a later stage (late entry), or drop-out in a single wave (temporary nonunit response) or permanently (panel attrition) (e.g., due to refusal, death, or relocation), the set of respondents is slightly changing over time; that is, the data are unbalanced (Andreß, Golsch, and Schmidt 2013).

The dependent variable in our life satisfaction regressions is the answer to the following question: “How satisfied are you at present with your life, all things considered? Please respond using the following scale, where 0 indicates ‘not at all satisfied’ and 10 indicates completely satisfied’.” Figure 2 shows that life satisfaction exhibits no apparent trend over time.


Development of Happiness across Time

Source: Data from SOEPv29 (Schupp et al. 2014).

The dataset used in this paper refers to the waves 1994–2012 and includes 324,763 observations for 46,678 individuals. The variables of the SOEP used in this analysis are summarized in Table A2 in the Appendix.

As to the energy data, the four German transmission system operators (TSOs)—Amprion, 50Hertz, Tennet, and TransnetBW— provide data on all RE plants that come under the Renewable Energy Act.5 Though the four sources are different with regard to their comprehensiveness, they all give information about the postcode district (five-digit hierarchical system) where the plant was installed, its type of technology (wind, solar, biomass, hydro, geothermal energy, as well as landfill, mine, and sewage gas), the commissioning date, and the installed capacity. Unfortunately, there is no information that would help us to distinguish between rooftop solar and freestanding installations.6 Nor can we obtain information about the sort of biomass plant, which may have implications for odor nuisance because of different materials being combusted. For data on wind energy, a different source was used (BDB7), which gives concise information on construction of wind turbines in Germany.8 The energy variables used in this analysis are summarized in Table A3 in the Appendix. From the summary statistics we can tell that wind energy has the greatest installed capacity of the three considered RE technologies and that most respondents have solar installations in their neighborhood. The standard deviations indicate that there is a considerable variance across observations.

We matched the data on RE plants and the socioeconomic data of the SOEP on the basis of the respondents’ postcode district. Moreover, as the exact dates of the interview and the plant commissioning were available, we could identify for each respondent the number of plants and capacity installed per type of technology by the time of the interview.9 For each wave during 1994–2012 the final dataset gives information about the respondents’ socioeconomic situation (see Table A2) as well as the presence of RE technologies in the respondents’ postcode district (see Table A3). In Germany, there are about 8,200 postcode districts, comprising an area of 44 km2 on average.10 In order to take account of possible spillover effects, we widened the spatial scale to include RE plants of neighboring postcode districts. Information from the open source platform OpenStreetMap was used to identify for each postcode district the adjacent postcode districts.

General Methodological Issues

Our approach to measuring externalities from RE involves approximating utility by data on subjective well-being, specifically, life satisfaction. Though this approach relies on subjective data, a major feature of this method is that it does not rely on people’s stated attitude toward or stated evaluation of the issues under study. Instead, life satisfaction data are being elicited independently of those issues, and it is the purely statistical association between life satisfaction and the independently measured variables of interest that is taken as a measure of preference.

Assumptions necessary for using reported life satisfaction in longitudinal analysis are a positive monotonic relationship between life satisfaction and the underlying true utility, and ordinal intrapersonal comparability (see Ferrer-i-Carbonell and Frijters 2004; Faßhauer and Rehdanz 2015). This means that if the satisfaction score at time t is greater than at time t ′, this reflects the same ranking of underlying utility. If, more restrictively, it is assumed that differences in satisfaction scores are proportional to differences in underlying utility, life satisfaction can be treated as a cardinal variable. Using SOEP data, Ferrer-i-Carbonell and Frijters (2004) found that assuming the data to be ordinal or cardinal and applying the corresponding estimation methods has little effect on qualitative results. In particular, the ratios of coefficients are similar, which is important for monetary valuation. Similar results were obtained by many others. In addition, Ferrer-i-Carbonell and Frijters (2004) stress the importance of including individual fixed effects (thereby controlling for time-invariant characteristics such as personality traits), as their omission biases the results substantially. In contrast to estimators for cardinal data (least squares), there is no consensus as to appropriate methods for implementing individual fixed effects in ordered regression models (Baetschmann, Staub, and Winkelmann 2015).

Econometric Approach

We estimated microeconometric life satisfaction functions in which the self-reported life satisfaction (LS) of individual i in postcode district s and year t depends on indicators of renewable energy (RE) in her postcode district, income, and a standard set of time-variant sociodemographic controls (age squared, number of children in household, internal migration, living in property, health status, partner status, employment status, person in household needing care). Time-invariant factors are implicitly captured through person-specific fixed effects. In addition, we use region-year fixed effects involving the states (Bundesländer) of Germany. The estimating equation can be stated as follows: Embedded Image [1] where personi and region_yearrt denote person and region-year fixed effects, respectively, and εist denotes the error term. Person fixed effects control for unobserved time-invariant characteristics of the individual (such as personality traits), whereas region-year fixed effects control for time-varying unobserved factors common to a particular region-year (such as, e.g., non-RE power generation or regional unemployment rates). The income variable is specified as the net monthly household income adjusted for inflation and equivalized according to the OECD-modified scale. As is common in the well-being literature, income is included in logarithmic form to account for decreasing marginal utility.

Equation [1] is estimated for RE referring to wind power, solar power, and biomass, respectively. Several alternative indicators are used for RE. One indicator is a dummy variable that takes the value 1 if at least one of the respective RE plants exists in the respondent’s postcode district and 0 otherwise. Alternative RE indicators are the number of plants and the installed capacity. It should be noted that over time the RE dummy variable changes its value only once (from 0 to 1), at the time of the first installation (unless the first installation took place before the period of observation or there is none).11 In contrast to the number of units and the installed capacity, the coefficient of the dummy variable hence measures the effect of presence versus nonpresence of RE installations.

In addition to RE in a respondent’s own postcode district, we include the corresponding RE indicator in the neighboring (adjacent) postcode districts. This specification serves to measure the existence and strength of spatial well-being spillovers. Given that the presence of RE installations in the own district and in adjacent districts are correlated (at r = 0.87, r= 0.54, and r=0.66 in the case of solar, wind, and biomass, respectively), omission of the adjacent districts may imply that effects from adjacent districts are falsely attributed to the own district.

In order to study the dynamics of RE externalities, we extended Equation [1] to include dummy variables that represent leads and lags of the first installation of RE units. Specifically, letting T denote the year of the first installation, D(T + i) is a dummy variable that takes the value 1 in T + i and 0 otherwise, where i= ≤ -3, -2, -1, 0, 1, 2, and i ≥ 3. The analysis is restricted to those individuals who actually experienced an RE installation in their postcode district. This reduces the number of observations to 94,324 for solar, 29,830 for wind, and 71,252 for biomass.

We minimize the risk of omitted variable bias by controlling for the observed timevarying life satisfaction factors known to be relevant (see Dolan, Peasgood, and White 2008 for a review) as well as for unobserved person-specific factors (through the fixed effects modeling framework). Though life satisfaction is likely to be measured with error, there is no reason to expect that measurement error is correlated with our independent variables of interest. Finally, including person fixed effects is an effective way of dealing with reverse causation in life satisfaction regressions (Ferrer-i-Carbonnel and Frijters 2004).

Following Ferrer-i-Carbonell and Frijters (2004), we treat the dependent variable, 11-point life satisfaction, as a cardinal variable and estimate Equation [1] and variants thereof using least squares. We report robust standard errors corrected for clustering at the individual level.


Main Results

Table 1 presents the estimation results for the main variables of interest, whereas more detailed results, including those for the control variables, are reported in the Appendix.12 Three alternative specifications are reported that differ according to whether the RE installations are captured by a dummy variable, by their number, or by installed capacity. We note that the coefficient estimate for the dummy variable measures the difference between those district-years in which there was no RE and those in which there was. The coefficient thus picks up both the effect of the initial installation and also the effect of subsequent expansions.


Estimation Results

With respect to solar power, we find non-significant coefficients in the own district for the dummy specification as well as for the number and capacity, whereas the number and capacity in the adjacent districts have weakly significant and significant negative coefficients, respectively. The presence of solar installations (dummy specification) in adjacent districts has a weakly significant positive coefficient.

With respect to wind power, the coefficient on the dummy variable in the own district is significantly negative, whereas the number and capacity have weakly significant negative coefficients. The wind power variables in the adjacent districts are nonsignificant.

In the case of biomass, the presence of at least one biomass plant (dummy specification) in both the own district and in the adjacent districts has significant negative coefficients. The numbers of plants in the own and adjacent districts are insignificant, but the installed capacity in the own district as well as that in the adjacent districts have weakly significant negative coefficients. The coefficients on the dummy and the capacity in the own district are greater than their counterparts in the adjacent districts, and the coefficient on the capacity in the own district is almost four times as large as its counterpart for wind power.

We conducted a falsification test regressing capacity expansion in T+4 on life satisfaction in t using the same empirical model. The coefficients of the RE variables turn out insignificant. As this procedure reduces the number of observations, we tested whether the results of our original model hold up, which is the case.

We thus find that the well-being effects of the technologies considered—solar, wind, and biomass—differ qualitatively and quantitatively. In particular, there are differences (1) with regard to whether effects originating from the own or the adjacent districts matter, (2) with regard to whether the mere presence of installations or their number and capacity matter, and (3) with regard to effect sizes. These differences can be explained in terms of the following features of the technologies: (1) the specific impairments through which the various technologies affect well-being (spatial scope of impairments and the possibility to alleviate impairments by averting behavior), (2) the ownership structure, and (3) the timing of initial installations and expansion (bulk vs. continuous expansion). With respect to the latter point we note that on average, the number and capacity of both wind and biomass plants in a district increased by a factor of 1.6 within the first three years. The number of solar installations increased by a factor of 4.6, while capacity increased by a factor of 6.6.

For solar power, well-being effects arise only from solar installations in adjacent districts. The absence of effects from solar installations in one’s own district suggests that at the level of the postcode district, benefits to private owners (residents and farmers) may offset the negative externalities. Benefits to the owners may be of a financial (reduced electricity expenditures and/or feed-in revenues), moral (warm glow), and psychological nature (status effects associated with the presence of solar panels on ones’ rooftop, see Sonnberger 2015). With respect to adjacent districts, an unexpected finding is that the dummy specification yields a weakly significant positive coefficient. A possible explanation for this finding may relate to the fact that a considerable number of solar parks are owned by citizen energy cooperatives. Assuming that citizen-owned solar parks are located not only in people’s own district but also in adjacent districts, people may enjoy the financial, moral, and psychological benefits from those, while being less affected by the associated impairments than if the installations are in closer proximity. In contrast to the dummy, the number and capacity have (weakly) significant negative coefficients. This suggests that the dominance of benefits over impairments may be a matter of size, becoming reversed as the scale of installations gets bigger.

For wind power, well-being effects arise only from installations in people’s own district. There do not seem to be spatial externalities from neighboring postcode districts. These findings are explicable because wind power installations differ in important ways from solar installations: They are more often owned by external investors and utilities than solar installations, such that there are fewer local benefits than in the case of solar power. In addition, citizens’ participation in the decision process is often limited. Moreover, visual impairments can be avoided by averting behavior, in particular when installations are not in close proximity. This may explain the absence of spillovers from adjacent districts. Acoustic impairments depend on proximity and are thus also less liable to spatial spillovers. In addition, while the dummy specification yielded a significant coefficient, the coefficients on the number and capacity are only weakly significant. This is consistent with the fact that initial installations are bigger and the expansion rate after the initial installation is much slower in the case of wind power than in the case of solar power.

In the case of biomass, effects arise from plants both in the own district and in adjacent districts, and they are highly significant in the dummy specification but weakly significant with respect to the capacity and insignificant with respect to the number. The generally negative effect of biomass plants reflects the circumstance that benefits accrue only to a small number of farmers, whereas the odor nuisance affects everybody and is difficult to avoid by averting behavior. Moreover, depending on the wind conditions, odor nuisance may be less related to proximity than are the visual impairments from wind turbines. The result that the dummy variable is significant whereas the numbers are insignificant and the capacity is only weakly significant reflects the circumstance that expansion rates after the initial installation are low.

With respect to effect sizes, we found that 1 MW of installed capacity in one’s own district has an effect that is almost four times as large in the case of biomass than in the case of wind power. This finding may be related to odor nuisance being more difficult to avoid than the mainly visual impairments from wind turbines. This finding is consistent with the result obtained in a cross-national setup that electricity from biomass is the most disliked form of electricity generation not only in comparison to other renewable energies but also in comparison to fossil-fuel-based and nuclear electricity generation (Welsch and Biermann 2014).

Dynamics of Renewable Energy Externalities

Table 2 reports the estimation results for the dynamics of RE externalities. This analysis focuses on wind power and biomass plants because the presence of solar power in one’s district was found to have no significant well-being effect. The coefficients of the dummy variables D(T + i) indicate whether and how life satisfaction in the respective year differs from life satisfaction before T − 2, where T is the year of the first installation. The lead coefficients indicate well-being effects from people’s anticipation of the installation of RE facilities. Such effects may arise when the installation is publicly debated in advance. The lag coefficients represent a blend of two effects. One is hedonic adaptation, which implies that coefficients become smaller in magnitude and less significant over time. The other is the effect of expansions after the first installation, which implies that coefficients become bigger.


Dynamics of Renewable Energy Externalities

For wind power, the coefficients on D(T +i) before and after T + 1 are nonsignificant. The coefficient on D(T + 1) is weakly significantly negative. We thus find an absence of any lead effects and a dominance of hedonic adaptation over any effect from expansion after the first installation. In the case of biomass, there is a weakly significant lead effect in T − 1, followed by significant effects thereafter, which persist even after three years. An F-test of whether the six time dummies are equal to each other and to 0 can be rejected for biomass, but not for wind. Considering that the expansion rate of power from biomass is the same as that of wind power, this suggests that hedonic adaptation to the nuisance from biomass plants is weaker than adaptation to the nuisance from wind turbines. A possible explanation for this finding may be that the nuisance from biomass plants (odor) is more difficult to avoid by averting behavior than is the nuisance from wind turbines (mainly visual impairment).

Quantifying Renewable Energy Externalities

Table 3 presents an overview of the estimated well-being effects of RE both in units of life satisfaction and in monetary terms. The monetary values are based on the results presented in Table 1 and obtained by dividing the coefficients on the respective RE variables by the coefficient on log income, which yields the marginal rate of substitution between RE and log income or the percentage change of monthly equivalized income required to compensate for a one-unit increase in the RE variable.


Quantification of Renewable Energy Externalities

As reported in Table 3, the presence of wind energy plants in one’s own postcode district is associated with a reduction in the 11-point life satisfaction score of about 0.0346. This corresponds to about 7% of the effect of being unemployed, which is one of the most important adverse factors for life satisfaction (the coefficient being –0.52, see Appendix). In monetary terms it corresponds to 11% of equivalized income. Evaluated at the mean of equivalized monthly income (€1,648.266, see Table A2), the presence of at least one wind turbine in one’s postcode district is equivalent to a decrease in equivalized monthly income of about €181.9.

The presence of biomass plants in one’s own postcode district is associated with a reduction in 11-point life satisfaction score of 0.0347, corresponding to €182.4 of equivalized monthly income. The presence of biomass plants in adjacent districts is associated with a reduction in life satisfaction score of about 0.029, corresponding to €150.4.

Turning to RE capacities, an increase in installed wind power capacity of 1 MW is equivalent to 0.35% in equivalized monthly income, which is about €5.8. With respect to biomass, an increase in installed capacity of 1 MW is associated with a drop in life satisfaction of 0.039, which relates to 1.25% of income, or €20.5.

Since a major motivation of expanding renewable energies in Germany is the abatement of greenhouse gases, it is useful to compare the local externalities from RE to the benefit of CO2 reduction. Such a comparison depends on several parameters such as the number of full-load hours of the renewable power plants on the one hand, and the type of fossil-fuel technology being replaced on the other hand. Focusing on wind power, we note that the average installed capacity in a district-year in which at least one wind turbine exists is 10.9 MW. Assuming 1,600 full-load hours per year (BMWi 2014), we obtain an average yearly power production per district of 17,440 MWh. If the same amount of electricity were to be produced from lignite-fired plants (at a heat rate of 34.7%, implying 1.190 tons of CO2 per MWh, see Wagner et al. 2007), it would imply 20,753.6 tons of CO2. If it were to be produced from hard coal–fired plants (at a heat rate of 36.9%, implying 0.931 tons of CO2 per MWh, see Wagner et al. 2007) it would imply 16,236.64 tons of CO2. Applying a medium estimate of worldwide damage from CO2 of €50 per ton (Foley, Rezai, and Taylor 2013), the climate damage avoided in a district-year with mean wind power capacity comes down to €1,037,680 (lignite) and €811,832 (hard coal), respectively. Applying the same assumptions to the district year with the maximum installed wind capacity (378.671 MW) yields an estimated avoided climate damage worth €36,049,479 (lignite) and €28,200,623 (hard coal).

Comparing these estimates of avoided global climate damage with the externalities from having wind turbines in one’s postcode district (€2,182 of equivalized yearly income per affected person) depends on the population size of the respective postcode district, which is unknown to us. For a postcode district with average installed capacity (10.9 MW), the affected population should not be greater than 476 individuals (if wind power replaces lignite) and 372 individuals (if wind power replaces hard coal) for (local) externalities to be less than (global) avoided damage. For the postcode district with maximum installed wind power capacity (378.671 MW), the critical numbers are 16,521 individuals (lignite) and 12,924 individuals (hard coal). Since the average population in a postcode district is about 10,000 (81 million divided by 8,200 districts) and since wind turbines can be found more often in rural (sparsely populated) districts than in urban (densely populated) districts, it is likely that the local externalities created by wind parks are less than the climate damage that they avoid. It may be added that nonmarket values obtained using subjective well-being data are frequently deemed to be biased upward due to the estimated marginal utility of income being biased downward (see Welsch and Ferreira 2013 for a discussion). The estimated values of climate damage, though typically obtained using other techniques, are of course also subject to considerable caveats.

In addition to climate damage, renewable energies may also avoid traditional forms of pollution. We deem this to be less relevant in the case of Germany since all fossil-fueled power stations in Germany are equipped with scrubbers for particulates, sulfur dioxide, and nitrogen oxides. Using a multicountry dataset, however, Welsch and Biermann (2014) found that a higher share of renewable energies in the national electricity mix is associated with higher well-being. This finding may be the net effect of local externalities created and air pollution avoided by renewable energies. In addition, it may reflect avoided subjective nuclear risk when the share of wind power is greater and the share of nuclear power is smaller (Welsch and Biermann 2014).


This paper has used representative nationwide panel data on the life satisfaction of German citizens for identifying and valuing the local externalities from solar, wind, and biomass plants. We found that renewable power plants generate statistically and economically significant local externalities whose effects differ across the technologies considered, both qualitatively and quantitatively. Our qualitative findings on the well-being externalities of the different RE technologies are in line with those technologies’ characteristics and the channels of influence through which they affect well-being.

In relation to previous literature, a major advantage of the present study is the use of a rich set of nationwide representative panel data merged with spatially disaggregated data on the location and expansion of several types of RE technologies. This has allowed us to conduct a longitudinal analysis of externalities associated with RE.

A limitation of our study relates to our inability to differentiate solar plants into rooftop installations and free-standing installations. Such a differentiation might be important because the visual impairments from free-standing installations may differ from those of rooftop installations. Similar considerations apply to different varieties of biomass plants that we are unable to differentiate. Another issue to be noted is the fact that the spatial units we use (postcode districts) differ with regard to their size. Since wind turbines and biomass plants are usually constructed in less densely populated areas (where one postcode may well comprise several communities), it is noteworthy that we still find significant well-being effects from these types of plants.

We translated the estimated well-being effects into monetary equivalents but acknowledge that the results may be biased due to endogeneity of income. By instrumenting income, previous studies have shown that the income coefficients may increase by factors of 2 to 3, resulting in correspondingly lower monetary values of the nonmarket goods under study. However, there is no consensus on appropriate instruments (see, e.g., Faßhauer and Rehdanz 2015).

Our estimates imply that, all else equal, individuals would rather not have RE (in particular wind turbines and biomass plants) in their neighborhood. As to policy implications, however, the issue (in Germany) is not expansion of RE “all else equal,” but the substitution of RE for conventional (fossil and nuclear) power generation technologies. Since these technologies have externalities of their own (air pollution, greenhouse gases, nuclear risk), the externalities from RE expansion must be balanced against the avoided externalities from conventional power generation. In this perspective, our findings do not imply a dismissal of RE in general. Rather, to further increase local acceptance, monetary compensation of externalities might be contemplated.

Besides considering more differentiated RE technologies, future research may investigate local RE externalities in comparison with externalities from fossil and nuclear power plants and extend those analyses to countries other than Germany. Moreover, by using geocodes and an energy dataset that distinguishes between different types and sizes of solar and biomass plants, one could further refine the analysis.


We are grateful to Jürgen Bitzer, Erkan Gören, Philipp Biermann, and Daniel Lückehe for useful comments and support.



Detailed Estimation Results (Dependent Variable: Life Satisfaction)


Summary Statistics of Socioeconomic Data


Summary Statistics of Energy Data, 1994–2012


  • The authors are, respectively, Ph.D. student; and profesor, Department of Business Administration, Economics and Law, University of Oldenburg, Oldenburg, Germany.

  • 1 We restrict our analysis to those “new” types of RE. The expansion of hydro power has been very limited in Germany over the last decades.

  • 2 Krekel and Zerrahn (2015) study externalities from wind power by using proximity to wind turbines. Such information is not readily available and/or less adequate for the other renewable technologies. In particular, the number of solar installations surrounding people may be large and “proximity” may be difficult to define in such cases. The comparative advantage of our study lies in studying three different renewable technologies (solar, wind, and biomass) in a common framework involving not just the presence of RE installations but also their number and size.

  • 3 There are other possibilities for producing electricity from biomass (e.g., vegetable oil or wood) that may induce other externalities. However, most of the biomass-based electricity in Germany is generated from biogas.

  • 4 Changing postcodes have not been recoded in the SOEP, which is why we used a manual search to—if possible—adjust postcodes in case more than five observations were affected. The same has been done with the energy dataset, which included some wrong or outdated postcodes.

  • 5 Some RE plants exceeding a certain capacity are excluded from the Renewable Energy Act promotion. By the end of 2011 this concerned about one-third of the hydroelectric installations, while all of the other technologies were still eligible to achieve the Renewable Energy Act feed-in tariff (BVEW 2013, 19f).

  • 6 The size of the installations provides a poor hint for distinguishing between rooftop and free-standing installations because the former are often built on commercial or administrative buildings, which can be large.

  • 7 See the BDB database (Betreiberdatenbank für Windenergieanlagen, Jochen Keiler), available at (accessed 2013).

  • 8 This analysis is restricted to onshore wind energy. As opposed to the energy data of the four TSOs the date in the BDB database refers to the plant construction instead of the commissioning date.

  • 9 As for the wind data, we only know the month and year of construction, which is why we used the 15th as the date of reference.

  • 10 Our data refer to 2,967 postcode districts out of 8,200. Postcode districts may differ in size, but we do not have any information either on their size or on the number of inhabitants, as it is not freely available. Since we are unable to unambiguously match postcode districts to municipalities we cannot use community statistics for our analysis. We obtained the average area by dividing the area of Germany by the number of postcode districts.

  • 11 Unfortunately we do not have data on the decommissioning of RE units, but this can be considered to be of minor importance in the time frame considered, given the typical lifetime of RE installations.

  • 12 The results for the controls do not vary appreciably across the various specifications and are reported only for the main specifications. They correspond to those typically found in life satisfaction regressions for developed countries (Dolan, Peasgood, and White 2008): life satisfaction increases with health and income and is greater when having a partner and smaller if unemployed than for any other employment status.