Attitudes Towards the Visual Impact and Community Acceptability of Wind Farms in Australia and Britain: Findings from Three Surveys

Abstract

The paper summarises three studies of the visual impact of wind farms in Australia and Britain and draws findings from them that have applications in the establishment of wind farms and in future policies and research. The paper provides information about the global growth and scale of wind farms and then summarises the literature regarding their visual impact, planning for wind farms, guidelines, and the psychology of attitudes. The characteristics of the three surveys are described and their results are presented, covering the visual impact of wind farms on landscape ratings and the acceptability of wind farms. The threshold of acceptability, the influence of familiarity and of proximity to wind farms on ratings are examined together with the influence of environmental and wind farm factors on ratings. Significant findings from the three surveys are discussed. There are four novel features of this paper: first, it compares the visual impacts of wind farms across several countries using the same methodology; second, it assesses the acceptability of wind farms as perceived by residents in these countries; third, it identifies the enhancement that wind farms can provide in landscapes of low landscape quality; and fourth, it identifies a gap in the attitudes towards wind farms between elected and non-elected members of councils.

1. Introduction

The visual impact of wind farms occurs where they interact with the community. While advances in engineering have greatly increased the height, reliability and robustness of wind turbines, advances in communicating with the public and advancing the social licence of wind farm operators have not progressed at the same pace.

The purpose of this paper is to summarise three studies of the visual impact of wind farms in Australia and in Britain as these findings provide lessons for wind farm operators in their dealings with the community.

1.1. Scale

Globally, wind energy has grown remarkably since 2000 when it was virtually zero. Onshore wind energy has risen to 1136 GW by 2024, a growth rate over the past decade of 11% [1]. Of this, Australia contributes just over 12 GW while the United Kingdom’s figure is 15.6 GW. The largest contributors are China’s 478 GW, the United States’ 154 GW, Germany’s 63 GW, India’s 48 GW and Brazil’s 33 GW.

Australia has over 150 wind farms which in 2024 generated one third of the nation’s renewable energy electricity and 13.4% of its national electricity [2]. Britain has 296 onshore wind farms (>5 MW) generating 11.2% of total electricity [3].

A factor in the growing visual impact of wind farms is their size. The early wind turbines were less than 50 m in height to the top of the hub with 10–15 m blades. By 2024, turbines have grown to 300 m with 120 m blades [1].

1.2. Visual Impacts of Wind Farms

1.2.1. Before and After

Research has found that residents become adjusted to a wind farm in their vicinity and actually become positive about it. Residents living within 20 km of wind farms in Scotland were largely negative (48%) prior to their construction but this fell to 18% after construction [4]. In Ireland, a large survey of householders found that 67% were positive about wind farms compared with only 12% opposed to them [5]. Interestingly, 72% of those living closest, within a kilometre, were positive.

The most influential objectors to wind power developments in the UK are local authority planning departments, conservationists and the Ministry of Defence [6].

1.2.2. Planning for Wind Farms

In Australia, responsibility for approving wind farms rests with the State Governments, not the national government. In New South Wales and Victoria, both the State Government and local governments are involved in the assessment of wind farm proposals. However, classifying a wind farm as of State significance means that the State Government has the final say and this applies to the larger proposals, which in Victoria are those of 1 MW capacity or over. In South Australia, only the State Government approves wind farms with the local government providing local feedback.

In England, since 2016, all decisions have been made by local governments providing that the area is suitable for a wind farm and that the local community are happy with it. Few councils have identified suitable areas for wind farms and they prefer to treat each case based on its merits. The Green Belts around many of the major cities has been viewed as possible sites for wind farms and the government’s policy is that these areas should not be encroached upon other than in “very special circumstances.” Many councils have assessed the sensitivity and capacity of their landscapes for wind farms, rating them from low to high sensitivity. Few, if any, wind farms would be approved for areas of high sensitivity.

Pereković et al. [7] deployed a case-by-case assessment of wind farms which failed to consider the cumulative impact of many wind farms on a region resulting in a “rural landscape with wind turbines” being transformed into a “landscape of wind turbines”. They coined the term “visual overload” which occurred when the landscape’s absorption capacity was exceeded, and the landscape became “supersaturated (e.g., the entire horizon of the observer is filled with wind turbines).” In an area of Croatia on the Dalmatian coast near Split, the existing 11 wind farms were planned to be expanded by a further 30 thereby overwhelming the landscape. While environmental impact assessment is applied to each wind farm, there is no national strategy for cumulative assessment of many wind farms in a region. A report adopted by the Council of Europe proposed “the harnessing of wind energy be planned on a wide scale and that siting strategies be drawn up as far as possible in advance of any specific projects that may be submitted to local or regional authorities” [8].

Covering northern Israel, Ref. [9] used GIS and Multi-Criteria Decision Analysis to identify possible sites for wind farms, incorporating a range of ecological and anthropogenic criteria including visual impacts. They prepared maps showing the most suitable sites for each criterion and then a consolidated map of all criteria. They identified 79 potential sites for wind farms with an installed capacity of 273 MW.

Khanano et al. [10] applied the same tools to an area in Oklahoma and identified nearly 1400 sites that were suitable for wind turbines. They then evaluated the impacts of a particular wind farm on nearby towns.

In a project in the eastern Mediterranean coastal region of Turkey, Ref. [11] calculated potential visibility models for wind turbines in scenic areas in the region. Based on the results, he identified areas of lower potential visibility scores for wind turbine siting.

Guan [12] applied a landscape visual impact evaluation model covering landscape sensitivity, the visual impact of wind turbines, and viewer exposure in an area of Germany. The model improved wind farm planning and facilitated cooperation between planning agencies and stakeholders.

To overcome the limitations of Zone of Visual Influence mapping, Ref. [13] developed the concept of the Degree of Visible Change which calculated and mapped the horizontal and vertical view occupied by proposed wind turbines and combined them to show their visual prominence. It also showed the effect of distance attenuation on visibility. He applied this to a wind farm proposal in Queensland, Australia.

1.2.3. Guidelines

To assist in planning for wind farms, several planning authorities have issued guidance documents. These include [14,15,16,17]. Also, several consultancy firms have issued guidance including [18,19]. In New South Wales, the Oberon Citizen Science Network issued their own guidance [20].

Danesh et al. [21] described the use of free open-source software to create visualisations of proposed wind farms. Hurtado et al. [22] established the so-called Spanish Method which used five coefficients regarding the proposed wind farm that are then integrated into a single value. Manchado et al. [23] reviewed and updated this method and provided a link to an online program for use by consultants.

Consultants typically use 50 mm focal length images of the proposed wind turbines. Takacs and Goulden [24] tested the accuracy of this and included scenes taken at 75 mm and 90 mm of existing wind farms plus a photomontage of the wind farms. They found that the photomontages of 50 mm scenes underestimated the visual impact of wind turbines and found that the 75 mm images gave the more accurate results. They noted that the Scottish Natural Heritage now accepted 75 mm images. However, the draft technical bulletin on photography by the Landscape Institute (UK) [25] continued to promote the 50 mm lens.

In a similar vein, Ref. [26] examined the effect on the visibility of viewing the turbine blades at an angle instead of front on and found that simulation should show the rotor blades diverging up to 15 degrees or more on either side of the turbine.

1.2.4. Psychology of Attitudes Towards Wind Farms

Believing that moral judgments about wind turbines influence their aesthetic evaluation, Ref. [27] surveyed ratings of wind turbines along with a communications mast and an incinerator chimney, all three of which were similar visually. They found that the chimney had the largest landscape impact, the wind turbine had the least and the communication tower was in between. They asserted that “moral judgments on these structures are driven by their moral associations.” They also found that respondents in favour of wind energy gave lower negative impacts than opponents of wind energy.

Beer et al. [28] believed that age, educational level and proximity to wind farms influenced their acceptance. Surveying respondents confirmed the influence of these factors. They found that respondents who gained information from educational environments rather than mass media were more positive about wind farms. In a study in the Slovak Republic, a popular tourist area, they used questionnaires among members of hiking clubs and among the non-hiking public and found that hikers were more negative towards wind farms and that both groups rated negatively where the wind farms affected views of the mountains. A small percentage even stated that they would not visit the area with a wind farm again due to their visual impact.

For countries relying on tourism to generate income, the potential effect of wind farms on tourism can be crucial. To assess this, Ref. [29] interviewed tourists in five rural areas of Sweden and found that many were mindful of the need for sustainable energy and that rural landscapes were in the midst of change to provide sustainable energy futures.

1.2.5. Summary of the Visual Impact of Wind Farms

Early responses to wind farms in the UK and on the Continent were mostly negative, but once constructed, the community became more positive about them, finding that their initial fears were unfounded. A range of sophisticated methods and tools have been used to plan for wind farms and there has been concern of regions becoming “supersaturated” with wind farms. While several planning authorities have issued guidelines for wind farms, it is surprising that more authorities have not followed suit, particularly in identifying where wind farms may or may not be located. Compared with tall chimneys and communication towers, wind farms had the least landscape impact. Among the public, there are some who see wind farms as an expression of sustainable energy provision. Wind farms continue to offer interesting subjects of study for researchers and the number of studies does not appear to be diminishing.

2. Materials and Methods

Over sixteen years, the author carried out three community-based surveys of the visual impact of wind farms, in 2003 [30], 2018 [31] and 2019 [32]. The first was conducted in South Australia and covered hypothetical wind farms as few existed at the time, the second covered actual wind farms in three states of Australia, and the third covered actual wind farms in Britain which itself comprises three countries: England, Scotland and Wales. Resource constraints prevented surveying additional countries.

The surveys comprised photo images of sites with and without wind farms, the extant wind farm being removed digitally to depict the original landscape prior to the wind farm. Respondents were shown the scenes in random order and rated the landscape quality on a 1–10 scale (low–high). In the Australian and British surveys, following their rating of the landscape quality of a scene which contained a wind farm, the respondent was asked how acceptable they considered the particular wind farm was on a 5-category scale from very acceptable to very unacceptable. The survey focused solely on the visual impact of the wind farms and did not ask respondents about other issues associated with them such as noise, shadow flicker or bird mortality about which there is a considerable amount of study in the literature.

Figure 1 shows a scene of a hypothetical wind farm from the South Australian survey.

Figure 2a,b shows a typical scene from the British survey showing the original landscape without the wind farm and then the landscape with the wind farm. Note that the scenes were randomised so that the scene with the wind farm usually would not appear immediately after the scene without it.

The methodologies of the three surveys followed a similar pattern. Participants entered their demographic information: age, gender, education, and birthplace. The British survey also asked them to enter the first two letters of their postcode to identify their location. The South Australian survey asked their familiarity with the various regions of the State, and the Australian and British surveys asked their familiarity with wind farms and their attitude, positive or negative, towards them. Each of the surveys was introduced by viewing (without rating) a handful of up to 6 images from the survey to familiarise themselves with the scenes that were to follow. All surveys were anonymous. As the South Australian survey occurred when few wind farms were built and viewing scenes of them would be novel for respondents, the first ten scenes in the survey were fixed and were provided to familiarise respondents with wind farms. These scenes were repeated at the end of the survey and the ratings of the first ten scenes were discarded. Apart from these, the scenes in all surveys were randomised. In the Australian and British surveys, the rating of a wind farm scene was followed by the same scene and the respondent was asked about the wind farm’s acceptability on a 5-point Likert scale. At the end of each survey, the respondents were thanked for their participation, provided with the author’s email address for queries, given an opportunity to write comments about wind farms for the survey, and invited to provide their email address if they wished to receive a summary of the survey (many did).

As the surveys followed a similar methodology with additions to some, the results are comparable. At their core, each survey used the same 1–10 rating scale, scenes were randomised, scenes with and without wind farms were shown, and in the Australian and British surveys, respondents were asked in an identical manner about the acceptability of the wind farm.

The sample composition varied in each survey. The South Australian survey involved groups of respondents comprising tertiary students, planners and environmental professionals attending sessions to rate the scenes. In addition, a CD of the survey was distributed to South Australian Department for Environment and Heritage worksites, and the survey was placed on the Internet and individuals from a variety of backgrounds were invited to participate. For the Australian survey, the aim was to cover the country through involving rural local government personnel and participants in outdoor clubs including bushwalking, car, 4WD, caravan, gem and mineral clubs whose members travelled in the country and were likely to have seen a wind farm. In the British survey, the aim was to assess the involvement of local governments who, as the local planning authority, assessed wind farm proposals and were more familiar with them than the wider community. The survey was sent to councillors and senior council staff in England, Scotland and Wales. As large Metropolitan cities such as London, Birmingham and Manchester were unlikely to deal with wind farms, they were excluded. Each survey aimed to engage as many respondents as possible in completing the survey and their selection was governed as much by pragmatism as ensuring sufficient coverage by key groups.

In surveys such as these, a control group of, for example, transmission towers or solar panel arrays could be included to assess their visual impact in comparison with that of wind farms. In the 1980s and 1990s, prior to the presence of wind farms, surveys were conducted of the visual impact of transmission towers, e.g., [33,34]. It is noted, however, that a baseline is provided by the survey scenes without wind farms from which the scenes with the wind farms can be compared. These provided a valid basis for comparison. The inclusion of other infrastructure would extend the survey unduly without providing additional validity to the results. If considered necessary, alternative forms of infrastructure could be included in future research.

Table 1. Characteristics of wind farm surveys.

	Surveys
	#1. South Australia	#2. Australia	#3. Britain
Survey date	2003	2018	2019
Total scenes and scenes with/without turbines	Total 150 68/68 + 14	Total 98 49/49	Total 46 23/23
Location of scenes	South Australian coast 21 wfs, inland 47	New South Wales 8 wfs, Victoria 14, South Australia 17 + 10 survey scenes from 2003	England 8 wfs, Scotland 14, Wales 1
Survey instrument	Sessions, CDs, Internet	Internet	Internet
Randomised scenes	Yes	Yes	Yes
Demographics of respondents	Age, gender, education, birthplace	Age, gender, education, birthplace, resident State	Age, gender, education, birthplace, postcode (first 2 digits)
Familiarity with SA or with wind farms	Very familiar, familiar or not familiar with SA regions	Never seen one, seen few, seen many wfs	Never seen one, seen few, seen many wfs
Extra questions	Seen a wf, location	-	Councillor, work for council. Live near a wf. If so, how far away
Rating scale	1–10 (low–high)	1–10 (low–high)	1–10 (low–high)
Attitude	-	In favour, against, it depends	In favour, against, it depends
Acceptability of wind farms	-	Very acceptable, acceptable, neutral, unacceptable, very unacceptable	Very acceptable, acceptable, neutral, unacceptable, very unacceptable
Nos. photos taken	68 wfs	966, 27 wfs	530, 44 wfs
Photomontage	-	Standard 34 scenes, Photomontage 15 scenes	Standard 5 scenes, Photomontage 18 scenes
Scene preparation	Clouds removed, blue sky	SmartFix and haze removal, blue sky	SmartFix and haze removal
Extras	Distance, turbine colour	-	-
Respondents	University students, public servants	Local councils and relevant clubs in all states	District and County councillors, senior staff of councils
Days of survey	14	32	58
Invitations sent	-	Councils 1726, clubs 1703, personal 375. Total 3804	Councillors 15,897 Senior staff 539 Total 16,436
Respondents	454	848	Councillors 606 Senior staff 199
Useable response	311	527	782
Confidence interval	5.56	4.30	3.50

wf = wind farm.

summarises the vital statistics of the three surveys.

The methodologies of the Australian and British surveys are virtually identical and the results of the surveys will therefore reasonably accurately reflect the visual impacts of the wind farms as perceived by respondents.

Each survey asked respondents their age, gender, education, and birthplace. The relationship between these and the ratings were explored in detail. Figure 3 shows the mean ratings of scenes with wind farms by gender, age and education for the three surveys. Ratings in the Australian survey were higher than for the South Australian and British surveys.

3. Results

3.1. Three Surveys

The South Australian survey had many young respondents, with university students, while the Australian and British surveys had mainly middle age and older respondents. All three surveys had more male than female respondents. Well over half the respondents had degrees or higher degrees. Over 80% of respondents were born in the country where the survey took place.

Table 2. Mean ratings of wind farms for three surveys.

	South Australian Survey	Australian Survey	British Survey
Without wind farm	6.15	6.90	7.09
With wind farm	5.49	5.67	5.27
% decrease	10.73%	17.83%	25.67%
Paired samples t test	t = 7.77, df 1, 310, p < 0.000	t = −10.15, df 1, 48, p < 0.000	t = 13.13, df = 1, 22, p < 0.000
Nos. respondents	311	527	782

displays the average ratings of the three surveys with and without the wind farm. The third survey had the largest decrease in ratings, over 25% for the presence of a wind farm.

3.2. Ratings from the Three Surveys

3.2.1. Tests of Significance

Comparisons of the ratings of scenes with and without wind farms relied largely on the student t test to assess the significance of the difference. The paired samples t test was used and the null hypothesis was that there was no significant difference. The samples being compared had the same number of respondents. It was assumed that their distributions were also similar. In analysing the respondent data in the Australian survey, the chi square test ${\chi }^2$ was used to compare the survey respondents with the Australian population. In measuring the difference between staff and councillors in the British survey, the Analysis of Variance (ANOVA) was used.

In all three surveys, regression analysis was used to assess the contribution of each factor to the ratings. The factors included the participant’s characteristics (age, gender, education, birthplace) and also environmental and wind farm characteristics. Each analysis was accompanied by ANOVA to determine its significance.

3.2.2. Survey in South Australia, 2003

The first survey divided the responses by the coastal scenes and the inland scenes (

Table 3. South Australian survey: mean ratings of coastal and inland scenes with and without wind farms.

	Coastal Scenes	Inland Scenes
Without wind farm	7.61	5.47
With wind farm	6.09	5.21
% decrease	19.97%	4.75%
t test of difference	t = 14.06, df 1310, p < 0.000	t = 3.28, df 1310, p < 0.001

). Figure 4 displays the results for the coastal scenes arranged in descending order of the scenes without wind farms. Wind farms had the greatest impact where the landscape quality rating was high and the gap narrowed as the landscape rating decreased. Their impact on the coastal landscape was four times greater than on inland scenes.

Figure 5 displays the ratings for the inland scenes also arranged in descending order of the scenes without wind farms. Inland away from the sea, the ratings were lower than the coastal scenes. A feature was that when the landscape rating dropped below 5.1, the scene ratings with the wind farms actually increased the landscape ratings. This is evident on the right-hand side of the graph. The equations for the trend lines are as follows:

• Scenes without wind farms: y = −0.058x + 6.81, R² = 0.96
• Scenes with wind farms: y = −0.024x + 5.82, R² = 0.56

Figure 5. South Australian survey: inland scene ratings without wind farms in descending order.

Figure 5. South Australian survey: inland scene ratings without wind farms in descending order.

To the author’s knowledge, this was the first time that it had been demonstrated that wind farms can enhance landscape quality through increasing diversity and interest in an otherwise uninteresting landscape. It raises the possibility of preferentially locating wind farms in such landscapes. Coastal sites always rate higher than the inland scenes because of the presence of water and should be avoided for wind farms, and rather, inland sites on flat, barren land should be selected.

Figure 6a,b illustrates a site where the presence of the wind farm enhances the rating of its landscape quality.

3.2.3. Australia Survey 2018

The second survey covering Australian wind farms again displayed the widening gap in ratings as the landscape quality increases (Figure 7). Projecting the trend lines for these indicated that the cross-over point was 4.1 compared with the 5.1 in the first survey. While the first survey was of hypothetical wind farms, the second survey had actual wind farms. The equations for the trend lines are as follows:

• Scenes without wind farms: y = −0.059x + 7.64, R² = 0.96
• Scenes with wind farms: y = −0.036x + 6.26, R² = 0.72

The difference between scenes with and without wind farms was statistically significant. t = −10.15, df 48, p < 0.000.

Figure 7. Australian survey: rating of Australian inland scenes without wind farms, arranged in descending order. Note: Graph includes only inland scenes and excluded 10 hypothetical coastal scenes.

Figure 7. Australian survey: rating of Australian inland scenes without wind farms, arranged in descending order. Note: Graph includes only inland scenes and excluded 10 hypothetical coastal scenes.

3.2.4. British Survey 2019

Figure 8 displays the ratings in the third survey in Britain for scenes without wind farms arranged in descending order. A larger gap is apparent between the rating with and without wind farms than occurred in the Australian survey. Here, the cross-over point was very low, 3.4, a rating of landscape quality which is unlikely to occur in Britain. This means that all wind farms in Britain are likely to reduce landscape quality. The equations for the trend lines are as follows:

• Scenes without wind farms: y = −0.147x + 8.84, R² = 0.89
• Scenes with wind farms: y = −0.073x + 6.20, R² = 0.61

The difference in ratings between scenes with and without wind farms was significant: t = −18.77, df = 734, p < 0.000.

Figure 8. British survey: rating of British scenes without wind farms, arranged in descending order.

Figure 8. British survey: rating of British scenes without wind farms, arranged in descending order.

A difference between the Australian and British wind farms is that British wind farms tend to have fewer turbines. The survey, however, selected most sites with many turbines. Also, because they were often constructed earlier than the Australian wind farms, they tended to be lower in height.

3.3. Acceptability of Wind Farms

A primary purpose of the Australian and British surveys was to assess the acceptability of the wind farms in the landscape. Immediately after their rating of the wind farm in the survey, the respondent was asked about the acceptability of the wind farm. A five-level Likert scale was used to assess acceptability.

Table 4. Australian and British surveys: acceptability scores of scenes.

	Very Acceptable	Acceptable	Neutral	Unacceptable	Very Unacceptable
Australian survey *	34.32%	43.57%	11.61%	10.50%	00.00%
British survey	25.59%	32.47%	12.54%	14.44%	14.95%

* Excluded 10 hypothetical coastal scenes.

shows the scale and the percentage of respondents who chose each level for the Australian and British surveys. It is evident that most respondents—77% for the Australian survey, and 58% for the British survey—found the wind farms to be acceptable or very acceptable. A larger percentage voted that wind farms were acceptable than those that voted they were very acceptable. In the Australian survey, no one voted that any of the wind farms were very unacceptable, whereas in Britain, nearly 15% voted that they were very unacceptable.

The relationship between the respondent characteristics and their ratings of the scenes was examined, using the British survey results. Figure 9 shows the average total number of scenes selected by respondents and classified by their characteristics. It shows that across respondent characteristics, the very acceptable and acceptable categories dominate. While females had slightly more very acceptable scores than males, the number of very acceptable and acceptable ratings both increased with age and with education. Similar results were found in the Australian survey.

Figure 10 compares the acceptability of wind farms versus the respondent’s attitudes towards them from the Australian survey and shows a close correspondence. However, even among those who were against wind farms, some were rated as acceptable.

Table 5. British survey: respondent attitudes vs. acceptability of wind farms.

	In Favour	Against	It Depends	Do Not Know	Total
Very Acceptable	3327	41	160	4	3532
Acceptable	3665	20	727	55	4467
Neutral	1055	98	552	18	1723
Unacceptable	555	387	992	50	1984
Very Unacceptable	140	1385	491	38	2054
Total	8742	1931	2922	165	13,760

Note: Table shows the total number of wind farms classified by 782 respondents.

shows the results for the British survey and Figure 11 shows the results as percentages. While those against wind farms voted almost exclusively for their unacceptability, those in favour voted largely for their acceptability. The division between those in favour and those against wind farms was far more marked among the British respondents than in the Australian survey. Britain has had far longer experience with wind farms than Australia, and given its small size relative to Australia, its people are more likely than Australians to have become familiar with them and have long-formed attitudes regarding wind farms. In Australia, they still represent a relatively new and novel technology and are generally distant from population centres.

In the British survey, the highest ratings for the scenes without wind farms were found to be from respondents who were against them. Those in favour had lower ratings for these scenes (

Table 6. British survey: ratings vs. attitude.

	In Favour	Against	It Depends	Do Not Know
Without wind farm	6.9	7.8	7.1	7.3
With wind farm	6.3	2.1	4.3	3.4
Difference	0.54	5.75	2.8	3.9
Respondents	480	107	187	8
Paired t test p	<0.000	<0.000	<0.000	0.001

). For those against wind farms, however, the ratings of scenes with wind farms were lowest, and for those in favour, they were highest. The difference in ratings for those in favour was only 0.54 but the difference for those against was a large 5.75. This demonstrates conclusively that attitudes affect ratings.

The British survey divided the results by country—England, Scotland and Wales. The results are shown in

Table 7. British survey: respondent attitudes vs. acceptability of wind farms (%), in England, Scotland and Wales.

	Respondents in Favour			Respondents Against
	England	Scotland	Wales	England	Scotland	Wales
Very Acceptable	2944	235	59	4	20	0
Acceptable	3076	275	228	3	9	1
Neutral	843	112	84	46	42	4
Unacceptable	1676	76	46	137	150	4
Very Unacceptable	88	15	19	644	569	112
Total	8627	713	436	834	790	121
% of country total in favour/against	75.21%	37.43%	62.02%	7.27%	41.47%	17.21%

Note: Table shows the total number of wind farms classified by 782 respondents.

and Figure 12. While in all three countries the respondents in favour of wind farms voted them as very acceptable or acceptable, those who were against them voted strongly for their very unacceptability. This was particularly apparent for Scotland, where over 80% of respondents against wind farms voted them as very unacceptable. The figure for Wales was even higher, at 93%.

While 75% of respondents in England and 62% in Wales were in favour of wind farms, the figure for Scotland was only 37%. The differences between countries were significant for the very acceptable (F = 9.57, df = 2, 757, p < 0.000), acceptable and very unacceptable categories but not significant for the neutral (p = 0.44) or unacceptable (F = 1.66, df = 2, 757, p = 0.189) categories. In this context, it is noteworthy that while there is on average around 66 sq km per turbine in England, the figure for Wales was less than 22 sq km, and for Scotland was 18 sq km (

Table 8. British survey: density of wind farms in England, Scotland and Wales.

Country	Area sq km	Turbines	Sq km per Turbine
England	130,279	1979	65.8
Scotland	78,722	4339	18.2
Wales	21,218	972	21.8

). The high density of turbines in Scotland and Wales may contribute to their negative attitudes towards wind farms.

The British survey was sent to District and County councillors and senior staff of councils and 606 Councillors and 198 staff members responded. While 12% of local councillors and 7% of country councillors opposed wind farms, 31% of staff members opposed them (

Table 9. British survey: Councillors and staff attitudes toward wind farms.

Respondents	Against	In Favour	It Depends	Total
Senior staff	62	81	52	198
Local councillor	28	297	195	427
County councillor	21	109	46	176
Total	111	487	195	801
%	13.86%	60.80%	24.36%	99%

Plus 8 ‘do not know’ (1%).

, Figure 13). This difference in attitude between councillors and their staff was an important finding of the survey. The difference was significant (p < 0.000) for all but the unacceptable ratings (ANOVA F = 0.1, df = 2, 755, p = 0.90). Seventy percent of local councillors strongly supported wind farms and 61% of country councillors were similarly supportive. But of the staff, only 41% were in favour. Planning authorities who are aware of the difference between their elected and non-elected members should be sensitive to this and seek to maximise communication between them to reach a consensus. Further research should further explore the reasons for the gap in attitudes.

Measuring the difference between staff and councillors in the British survey found that only the unacceptable rating was not significant (ANOVA).

• Very acceptable F = 7.32, df 2, 755, p < 0.000
• Acceptable F = 10.64, df 2, 755, p < 0.000
• Neutral F = 34.20, df 2, 755, p < 0.000
• Unacceptable F = 0.105, df 2, 755, p = 0.90
• Very unacceptable F = 35.19, df 2, 755, p < 0.000

Though relatively small in area, Britain has enormous variety in its culture, industry, politics and population across the nation. This is reflected in variations in the level of acceptability of wind farms. Figure 14 shows that while many regions in England found them to be acceptable, those in Wales and particularly Scotland did not.

Public opinion polls by the UK Government have shown continued strong support for wind farms. When asked “Whether you would be happy for an onshore wind farm to be constructed in your local area”, over the past four years the average positive support has been 41.5% compared with 28.5% negative but also with a further 14% saying they would not mind either way, which suggests a clear majority in support (

Table 10. Attitude toward an onshore wind farm being constructed in their local area.

	2022	2023	2024	2025
Positive	43	43	43	37
I would not mind either way	27	28	28	31
Negative	12	12	14	18

) [35].

The strongest support came from people in the southwest of England and in Yorkshire and the Humber, and opposition came from people living in the east of England, and those older than 55, results which paralleled the findings shown in Figure 14.

In terms of policy regarding wind energy, in England all decisions have been devolved to local governments. However, in Scotland, the Scottish Government is responsible for the larger proposals, those over 50 MW, with local governments being responsible for the smaller proposals. A similar situation applies in Wales, where large wind farm proposals, classified as Developments of National Significance, are decided on by the Welsh Government and local councils are consulted.

The difference in attitude towards wind farms as shown in Figure 14 may be partly due to the greater say that communities in England have about proposals compared with those in Scotland and Wales, where the national governments are responsible and hence more remote from the communities that may be affected.

3.4. Threshold of Acceptability

To assess which point in landscape quality the community considers as the threshold of acceptability of wind farms, with this being the threshold at which the acceptance of wind farms changes from acceptable to unacceptable, the ratings for the Australian and British surveys were compared with the scores added together for acceptable and very acceptable (Figure 15). In both surveys, the acceptability ratings peaked and then declined as the landscape quality increased. The threshold in the Australian survey was reached at a rating of six, while in the British survey, the threshold was seven. This indicates that where the landscape quality is rated on a 1–10 scale, the threshold for acceptability lies between 6 and 7. Other factors such as the density of wind farms and their proximity will also affect their acceptability.

3.5. Familiarity with Wind Farms

Over half the respondents in the three surveys had seen many wind farms and nearly half had seen a few so they were reasonably familiar with them (

Table 11. Number of respondents who had seen wind farms.

	Never Seen One	Seen a Few	Seen Many	Total
South Australian survey		175		175
Australian survey	12	396	368	779
British survey
Staff	2	54	142	198
Local council	1	163	263	427
County council		46	130	176
Total	15	834	903	1755
%	0.85%	47.52%	51.45%	100.00%

). Surprisingly, a few indicated that they had never seen a wind farm.

Figure 16 examines the relationship between a respondent’s attitude towards wind farms and their familiarity with them. In the Australian survey, an equal number of those in favour had seen many or a few, while in the British survey, the number in favour increased for those who had seen many compared with those who had seen only a few. Those against were of a similar number in the Australian survey regardless of whether they had seen a few or many, while in the British survey, a much larger number of respondents against wind farms had seen many than had seen a few.

The British survey found 314 people who lived near a wind farm and 479 who did not.

Table 12. British survey: proximity vs. familiarity.

Familiarity	Live Near Wind Farm	Do not Live Near Wind Farm
Seen a few	57	204
Seen many	257	275
Total	314	479

combines their proximity with their familiarity with wind farms. Those close to a wind farm had seen many, while those living further away had similar proportions of who saw few or many. While 39 respondents lived within 2 km of a wind farm, 107 lived 5–10 km away.

3.6. Proximity vs. Attitude

Does living near a wind farm affect one’s attitude towards them? The answer appears to be ‘no’.

Table 13. British survey: proximity versus attitude (number of respondents who live near a wind farm).

	0–2 km	2–5 km	5–10 km	Total
Against	9	17	18	44
In favour	19	23	62	104
It depends	11	21	27	59
Total	39	61	107	207

Plus 1 ‘do not know.’

and Figure 17 show the relationship between proximity and attitude for those respondents who lived near a wind farm in the British survey. Of the 39 who lived within 2 km, 49% were in favour of wind farms and only 23% were against them. Living near a wind farm did not appear to unduly prejudice these residents against them.

3.7. Environmental and Wind Farm Factors

The ratings in the Australian and British surveys were analysed for the influence that four environmental factors (weather, landform, land use, vegetation) and three wind farm factors (turbine height, turbine numbers, distance) had on ratings

3.7.1. Environmental Factors

Table 14. Australian and British surveys: influence of environmental factors on ratings.

	Australian Survey		British Survey
Weather	Without wfs	With wfs	Without wfs	With wfs
Sunny	6.95	5.59	6.90	5.13
Scattered cloud	6.83	5.73	7.43	5.47
Heavy cloud	6.29	5.46	6.60	4.89
Landform
Flat	6.32	5.21	6.12	4.77
Undulating	6.97	5.70	7.57	5.33
Hilly	8.09	5.82	7.91	5.89
Ridge	6.81	5.77
Land use
Cropping	6.39	5.47	5.80	4.76
Pasture	6.23	5.51	7.32	5.17
Semi-natural	6.81	5.72
Natural	7.99	5.76	7.86	5.76
Mixed grazing and cropping			7.83	5.82
Pines			7.52	5.00
Vegetation
Barren	6.69	5.48	7.17	5.56
Scattered vegetation/low shrubs	6.88	5.81	4.86	4.15
Clumps or dense veg.	7.24	5.72
Low trees			7.33	5.25

summarises the ratings for environmental factors for the Australian and British surveys.

Although the effects on ratings were small, they were generally statistically significant. In the Australian survey, the largest reductions in landscape ratings due to the presence of wind farms were for hilly landforms (2.19) (F = 304.8, df = 1, 96, p < 0.000) and natural land use (2.16) (F = 514, df = 1, 96, p < 0.000). In the British survey, reductions in landscape ratings occurred for undulating and hilly landforms (2.24, 2.02) (F = 368, df = 1, 44, p < 0.000), pine and pasture land uses (2.52, 2.15) (F = 133.15, df = 1, 44, p < 0.000), and low trees (2.08) (F = 496.5, df = 1, 44, p < 0.000).

3.7.2. Wind Farm Factors

The number of turbines and their height had only a minor influence on ratings. In the Australian survey, the difference between 1–9 turbines and 40–49 was a decrease of only 0.2 in ratings (F = 55.25, df = 1, 96, p < 0.000), whereas in the British survey, it was a decrease of 0.75 (F = 36.87, df = 1, 44, p < 0.000). Ratings increased slightly with increasing height of turbines: in the Australian survey by 0.19 for an increase in height from 100 to 150 m (F = 2496, df = 1, 96, p < 0.000), while in the British survey, ratings increased by 0.63 (F = 243.3, df = 1, 44, p < 0.000). Greater distance increased ratings as the wind farm diminished in size with distance. Ratings increased in the Australian survey by 0.42 for distant wind farms compared with those nearby (F = 1201.3, df = 1, 96, p < 0.000), while in the British survey, ratings increased by only 0.09 (F = 223.3, df = 1, 44, p < 0.000).

4. Discussion

The three studies of the visual impacts of wind farms identified the following key findings.

The fear and anxiety of the unknown have a strong influence on the community prior to a wind farm’s construction. This is apparent from the literature and also from the difference in attitudes of Australians in the second survey compared with the first when wind farms were largely unknown. Fifteen years later when the second survey was conducted, the community had become familiar with them and was no longer anxious about them. Anxiety exaggerates the visual impact (and other impacts) of the wind farm. Following construction, however, and over time, anxiety abates, and the structures are regarded more objectively and positively. After the construction of a wind farm, although most people recognised that it does have a visual impact on the landscape, most generally find it acceptable and minimise its perceived visual impacts.

Acceptance of the wind farm occurs even for areas of high landscape value, contrary to expectations. This suggests that most in the community are very tolerant of their visual impact and do not view wind farms as having a negative impact on the landscape.

While 75% of respondents in England found wind farms to be acceptable, the figure was 62% in Wales and only 37% in Scotland. Only 7% of respondents in England found them to be unacceptable, 17% in Wales but as many as 41% in Scotland. Of the wind farms in Britain, 56% are in Scotland. There are indications that Scotland may be reaching the limit for the acceptability of wind farms. Future research should monitor the attitudes of the Scottish community about wind farms. In England, approvals for wind farms are dealt with by councils, whereas in both Scotland and Wales, the national government is responsible for approvals. This difference may help to explain the difference in attitude as residents of Scotland and Wales would have less influence on their national government than those in England on their local government.

Based on the visual impact of wind farms, the threshold of acceptability in the Australian survey was reached at a landscape quality rating of six while in the British survey the threshold was seven. This indicates that the threshold of acceptability lies between 6 and 7 where landscape quality is rated on a 1–10 scale. This does not take account of other factors such as the density of wind farms in a locality.

A respondent’s positive or negative attitudes towards wind farms shaped their ratings of scenes. Those opposed to wind farms rated scenes without them the highest and the scenes with them, the lowest, a difference in ratings of 5.75. Conversely for those in favour of wind farms, the difference in rating was only 0.54. This conclusively showed that attitude affects ratings.

Living near a wind farm does not appear to unduly prejudice these residents against them. Nearly half of those living within 2 km of a wind farm were in favour of them and less than a quarter were against them. While this is a generalised finding from the British survey, differences are likely to occur, for example, in Scotland where the density of wind farms is higher than in England.

Differences in opinion about wind farms occurred between the elected members and the professional staff of councils, with the elected members being more likely to accommodate the wind farm having little objection to it, whereas their advisors, the professional staff, highlighted the issues and were generally opposed to the wind farm. Close communication and respect for contrary views are essential to reach a consensus within councils. Further research should further explore the reasons for the gap in attitudes.

Wind farms do not necessarily have a negative visual impact in all landscapes; in some, they can lift ratings of the landscape. In areas of low landscape value, the presence of the wind farm can enhance the landscape value by introducing diversity and interest into an otherwise mediocre landscape. Such areas should be preferentially selected for locating wind farms, instead of areas of higher landscape quality such as near the coast. While respondents rated the scenic quality of landscapes with wind farms lower than scenes without them, wind farms had the greatest negative effect on landscapes perceived as highly scenic and progressively less effect on landscapes rated as lower in scenic quality.

Increasing the height and number of turbines had a minor impact on landscape quality ratings and distance to the wind farm had little effect.

In terms of policy and actionable outcomes, the paper supports the following.

• Councils should monitor the attitudes of their constituents, particularly those living near wind farms, to ascertain their acceptability of existing wind farms and their attitudes towards additional wind farms. The results may inform the Council’s position in the face of minority opposition and also assist the Council in reaching a consensus between elected and non-elected members.
• The wind farm industry should play its part in informing the community of its plans, involving them in the planning and layout of wind farms, and assisting Councils in monitoring community attitudes regarding wind farms.
• Governments should identify areas of lower landscape quality which may be zoned for wind farms.
• Researchers could analyse in depth the attitudes of residents towards wind farms, focusing particularly on those living with many wind farms in their vicinity, as well as the attitudes within councils.

There are limitations to the surveys covered in this paper. The samples of respondents comprised different segments of the community and did not represent the entire community, which may affect the generalizability of the results. The respondents were self-selected in that they chose whether or not to do the surveys. Only two countries were surveyed (although Britain comprises three countries). The wind farms selected in the British survey were dominated by those in Scotland, 14, compared with 8 in England and only 1 in Wales due to inclement weather. There were differences in the preparation of the scenes, with some containing only blue sky and no clouds, some having the haze removed, and some comprised photomontages with several images combined. These differences, however, are not considered to be of sufficient magnitude to affect the essential outcomes of the surveys.

5. Conclusions

The three surveys of wind farms, two in Australia and one in Britain, provided a wealth of information about their visual impact, some of which was contrary to expectations. Among the significant findings were the following. In otherwise mediocre landscapes, the presence of wind farms can actually enhance landscape ratings by introducing diversity and interest. Despite their obvious visual impact, most respondents found the presence of the wind farm acceptable, even in areas of high landscape quality. Nevertheless, the point may have been reached in Scotland that the acceptance of further wind farms is reaching its limit and opposition is likely to increase. There is a marked difference in attitude towards wind farms by councillors and senior council staff, with the former generally in favour and the latter generally opposed. The threshold of acceptance of wind farms appears to occur in landscapes that rate between 6 and 7 on a 1–10 rating scale. Respondents’ positive or negative attitudes towards wind farms strongly shaped their ratings of wind farms.

The novel features of this paper are four-fold: first, it compared the visual impacts of wind farms across several countries using the same methodology; second, it assessed the acceptability of wind farms as perceived by residents in these countries; third, it identified the enhancement that wind farms can provide in landscapes of low landscape quality; and fourth, it identified a gap in the attitudes towards wind farms between elected and non-elected members of councils.