Obtaining visual impacts

The first set of photographs is of Featherstone Kloof an area well known for its beauty and frequented by hikers, cyclists and other such activities. Figure 5.1A depicts the landscape as it currently stands and is characterised by an infrastructure free landscape and „picturesque‟ scenery. One large residence is present in the area and is prominent from much of Grahamstown, but likely detracts little from the view. Figure 5.1B is a photomontage of a hypothetical wind farm development. The first observation by participants regarded the relatively „pristine condition‟ the landscape was in, with no human interference clearly visible in the pictures. Respondents often recognised the area, stating that they frequently visited it on walks or drives and described it as „beautiful‟. The estimated average Willingness to Pay per person to relocate the wind farm from Featherstone Kloof to area D was R87. The valuation is indicative of the landscape‟s attractiveness, which many stated they enjoyed, while also being well recognised within the Grahamstown community.

Figure 5.1A: Vista of Featherstone Kloof as the landscape currently stands [Source: Self photographed]

Figure 5.1B: Featherstone Kloof with photo-montaged wind turbines in the landscape [Source: Self photographed and manipulated]

Makanaskop (figure 5.2A) was the second landscape to be valued and presents a more „busy‟ and urban setting. Residences within the Grahamstown East area are clearly visible, mainly displaying the historically black and poorer areas of town. In the foreground are power lines, the N2 highway with the Settlers‟ Monument falling about midway within the frame. Grey Dam, along with the highly vegetated area it occupies, is also present. A ridge was selected for placement of the hypothetical wind farm (figure 5.2B), nearby to the township residences at the start of Makanaskop, extending rightwards to the N2. The disruption to the landscape appears less severe than in Featherstone Kloof‟s case because the presence of urban structures seems to offset the visual addition of the wind farm.

Respondent comments confirmed that the area had already been partly aesthetically degraded; where the introduction wind turbines did not feel very “intrusive”. Some remarked that the wind farm was better suited for Makanaskop, rather than Featherstone Kloof, and simply asked why it had not been planned for this area initially. However, some were disturbed that the wind farm should be constructed nearby to people‟s living areas. A woman asked “why should they live with that?” while another revealed that she was “worried about the people” living just under the structures. One participant was relieved that he could not see the turbines from his house when placed at Makanaskop. Thus while respondents did find the location to make a lot more sense visually, concerns about human health and inequity made the site unattractive. Such reservations however, did not feature very strongly with the average Willingness to Pay estimate which amounted to R33 per person, considerably lower than what was arrived at for Featherstone Kloof.

Figure 5.2A: Photograph of Makanaskop which includes much of Grahamstown East [Source: Self photographed]

Figure 5.2B: Photograph of Makanaskop with the photo-montaged wind farm added [Source: Self photographed and manipulated]

Waainek, the third area valued, included two sets of photographs in order to provide a more comprehensive coverage of the scenery. The first pair of photographs depicts a landscape characterised by relatively flat land in the foreground accompanied by rolling hills in the background. Structures in the industrial area, as well as along the Highlands Road, are visible but they do not dominate the view to the extent seen in Makanaskop (Figure 5.3A). Urban disturbance is present but not prominent. Studying the second frame (Figure 5.3B), the installation of the wind farm does create a noticeable change on the area, especially due to the contrast of the turbines against the sky. As few structures occupy the landscape, the addition of the wind farm does seem to blend in better than was the case for Featherstone Kloof. The second set of photographs were taken south of Waainek, in the area near the Mariya uMama we Themba Monastery, along the Highlands Road, where two additional wind turbines will be constructed (Figure 5.4A). The second area appears more mountainous than the industrial area of Waainek and more closely related to Featherstone Kloof, which is located nearby, where both form part of the escarpment. Topographically, a ridge extends towards Faraway, with the land in the foreground sloping and dropping off into a valley. While the photograph does not immediately show any man-made structures in the landscape, there are some farm houses and power lines scattered in the vicinity, falling outside of the photo frame. Introducing the wind turbines into this particular area (Figure 5.4B) does provide a clear and stark change from the status quo, and may be inclined to offend those who frequent the site.

Figure 5.3A: Photograph of the Waainek area as seen from Hill 60 with the industrial area present in the mid-ground

[Source: Self photographed]

Figure 5.3B: Waainek area with photo-montaged wind farm in the landscape [Source: Self photographed and manipulated]

Figure 5.4A: Second photo south of the Waainek area where the remainder of the turbines would be installed

[Source: Self photographed]

Figure 5.4B: Area south of Waainek with photo-montaged images of wind turbines in the landscape

The average Willingness to Pay to relocate the wind farm away from Waainek amounted to R82, an unexpected result. Comments made on the site were very few, but generally indicated that it was suitable for the wind farm. It is unexpected that Featherstone Kloof, which appeared to be more pristine and scenic than Waainek, was valued only slightly higher at an average Willingness To Pay of R87. It is worth mentioning that the Waainek area is located in an area which is closer to residences than Featherstone Kloof. As proximity to wind farms was found to be an issue in the earlier findings addressed in this study, it could be argued that proximity costs have been included in the valuation of the scenery. The high values for Waainek may therefore reflect people‟s reluctance to live nearby the wind farm development.

Alternatively, inconsistent respondent valuations may be to blame. Data trimming is a recommended practice, but only for those outliers or extreme cases that may pose a problem for the estimates as a whole (Stevens, 1984). Two participants did display some behaviour which appeared incongruous with the general direction of their responses. For example, while one respondent found Waainek‟s scenery to be of medium/attractive quality, the installation of the wind turbines would improve the scenery significantly for him. When the same respondent was asked whether he would pay to remove the wind farm based solely on visual considerations, he replied “yes”, at odds with what had been stated before. The amounts he agreed to pay far exceeded his monthly expenditure on electricity, the bills of which he did not handle himself. Further, the individual was unemployed and earned less than R1000 a month, calling into question the reliability of the data provided. The best guess is that either acquiescence is to blame or the respondent simply did not understand the scenario and questions being asked. Thus this respondent was dropped from the sample.

The second respondent, a student, also provided inconsistent data according to her answers. To cite a few problems: she did not know the area well, she found wind turbines to be unappealing yet thought that the wind farm would improve the scenery at Waainek. Payments were refused for both Featherstone Kloof and Makanaskop, even where low offers were made. Another problem involved visual impacts, which did not rank at all as a problem for her concerning wind farms, yet she was still willing to pay to have them removed. Such responses prove problematic and may have been due to acquiescence bias; therefore the individual was also dropped from the Willingness to Pay estimates. The remaining respondents appeared rational in their selections and no further culling was required.

Willingness to Pay results for the revised dataset presented a significant change for only one of the three sites through the removal of the two anomalous respondents. For Featherstone Kloof, the average WTP dropped from R87 to R81 while the valuation for Makanaskop remained relatively stable at an average WTP of R30, a decrease of 3 Rand. The greatest change in average WTP was found for Waainek, which dropped from a high of R82 to R67, and was more in line with the initial expectations; an „improvement‟ on the first set of estimates. However, it may be observed that Waainek is still highly valued based on aesthetic value relative to Featherstone Kloof which is likely related to proximity. Based on the newly acquired estimates, the build site rankings for the wind farm, if based solely on visual considerations, would fall in the following order: Makanaskop, Waainek and then Featherstone Kloof.

The learning design Contingent Valuation Method employed in this study assumes a greater degree of uncertainty for „goods‟ valued earlier than those which occur successively (Bateman et al., 2008). Preference uncertainty produces monetary values which do not reflect true valuations and apply especially to non-use values which people are not used to quantifying. Further, the market institution or mechanism used to elicit values would further exacerbate the problem until respondents become accustomed to the procedure, only properly solved through successive „trials‟. Goods valued earlier are consequently less accurate than those values later on in the bidding process. Estimates may reflect upwards or downwards once respondents become more confident about their Willingness to Pay, depending on the changes in quality or the good itself and based on preference learning. The point is simply: estimates for Waainek are likely to be more reliable than those for Featherstone Kloof and Makanaskop because of the stabilising effect of preference learning. Perhaps then, preceding area estimates (Featherstone Kloof, Makanaskop) cannot be relied on as reference points for Waainek taking into account the respondent uncertainty for these values.

Interestingly, once respondents had gone through the entire valuation exercise, some asked if it would be possible to redo the section. Respondents were allowed to redo their WTP bids, which provided a useful method for further stabilising WTP, especially when people realised that first round estimates may have been exaggerated. Further, where future studies concentrate only on the WTP scenario, more elaborate learning methods could be employed to guarantee the accuracy of estimates. Theoretically, the estimates for Waainek, obtained through the learning design Contingent Valuation Method, should have stabilised sufficiently to provide a high degree of accuracy. As estimates are influenced by the design of the survey

instrument, diagnostics are required to check the integrity of the questionnaire, which is undertaken in the next section.