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Spatial analyses of emerging and fading wind energy landscapes in Denmark
Land Use Policy 27 (2010) 233–241
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Spatial analyses of emerging and fading wind energy landscapes in Denmark Bernd Möller Department of Development and Planning, Aalborg University, 9220 Aalborg, Denmark
a r t i c l e
i n f o
Article history: Received 9 February 2009 Received in revised form 28 May 2009 Accepted 6 June 2009 Keywords: Wind energy GIS Landscape Planning Visual impact Public acceptance
a b s t r a c t The development of wind energy in Denmark goes back 30 years, during which the technology was commercialised, up scaled and a series of planning systems were developed. After the millennium, the impact on landscapes increased, the planning regime failed and economic conditions were worsened with the removal of the fixed feed in tariff. The earlier forerunner country is left in the lee of the internationally boosting wind energy business. From a land use policy view it is interesting to analyse how this has happened and what impact the planning policy has had on the landscape effect of wind energy. In order to analyse the impact of wind turbine development through times and on the population of a region, the present paper analyses by means of geographical information systems and in time steps. The spatial relations between population, landscapes and the wind turbine development from 1982 to 2007 were modelled for the Northern Jutland region by means of proximity, density and visibility analyses. Results indicate that development was not continuous and impact on landscape and population was closely related to technology development. The paper concludes on the use of these methods and on the effectiveness of planning regimes. © 2009 Elsevier Ltd. All rights reserved.
Introduction There is a growing schism in the development of wind energy. On one hand, it is evident that wind energy can contribute significantly and on a global scale in solving problems such as climate change, the depletion of fossil fuel resources, as well as pollution (Hoogwijk et al., 2004; IEA, 2008). On the other hand, wind energy has grown to a scale that will undoubtedly alter natural and cultural landscapes as we know and appreciate them (Pasqualetti, 2000; Ek, 2005). An attempt will be made in this paper to document that both dimensions and their discourse are characterised by change over time and in relation to population and landscape. Denmark has been leading the development of modern wind power during the first part of its commercial history. The history of this technology is closely linked to several beneficial conditions: the beginnings of wind turbine manufacturing were characterised by many small producers, who were able to establish a competitive growth layer of industry. Good wind conditions, among the best in Europe, can be found on the West coast and in the North West of the rather densely populated country with its diverse glacial topography. Several forms of ownership have been developed for modern wind energy, and the development in terms of turbine location, planning and economic conditions is well documented. Wind energy has gone through an incommensurable evolution,
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from small scale symbol of iconic value to large scale industries and opposition against new installations (Hvelplund, 2006). It is therefore rather obvious to use the country or part of it for an analysis of emerging and fading wind energy landscapes. The present paper aims at analysing the quantitatively measurable changes to Danish landscapes due to the technological development of modern wind energy, which has brought along more and larger wind turbines through 30 years of commercial development. This development has been highly influenced by the reigning policies and planning targets, and public participatory planning has changed the way wind turbines are erected in landscapes. Possible feedbacks between technological development, wind utilisation and policy making may be important learning lessons in other regions. Historical development of wind energy The history of modern wind energy in Denmark begins in the 1970s after the significant impact of the oil crises on the Danish energy system, further stimulated by the anti-nuclear protests of that time. The first commercially available modern wind turbines were erected in 1977, their size limited to tens of kilowatts, at total heights in the order of 20 m. The first years were very much a period of pioneers, with wind turbines in the landscape an oddity after the tens of thousands of older wind mills and early wind generators had disappeared from the countryside following rural electrification as late as the 1950s. A boom of wind turbine export
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Fig. 1. Cumulative numbers and cumulative capacity of wind turbines erected in Denmark since 1982. Three periods of wind energy development can be identified: slow growth until 1995, accelerated growth until 2001 and stagnation and decline since 2001. Data source: Danish Energy Agency (2008).
to the United States in the early 1980s and the emerging European markets of the 1990s fuelled the further development of turbines to modern industrial products. Fig. 1 shows the cumulative numbers and capacity of wind turbines erected in Denmark since 1982. It can be seen that the first decade was characterised by a slow increase in numbers, while plants tripled their capacity. The second period until the year 2001 has seen wind energy growing to maturity in terms of quantity and size. It can also be noted that the number of turbines began decreasing after the millennium as older installations have been removed after ended useful lifetime and due to re-powering policies. The recent years have seen no further expansion except the erection of two large offshore developments, and a recent trend is that wind energy delivered to the energy system decreases, climatic variations levelled out. The current political dictum for the next 4 years aims at the installation of several large offshore parks in the order of 1000 MW in total, and 300–900 MW of new wind turbines to be erected on land in replacement of older turbines. Nevertheless, if the ageing fleet of turbines is taken into account, it seems that land based wind energy steers towards deconstruction. The development of modern wind energy in Denmark therefore is an interesting case for studies of emerging and fading wind energy landscapes. Quantitative studies of this kind are required to improve the understanding of the social construction of wind turbine siting (van der Horst, 2007). Wind energy policy and planning The development of wind energy has been accompanied by a continuous development of energy policy and planning practice, as the technology grew in size, extent and significance. While the first turbines were built almost everywhere given that simple distance rules were observed, in the mid-1990s planning restrictions came in place that basically reversed the concept of exclusion by means of buffers etc. to exclusive zoning. The result of this were specified areas on municipal and regional levels where new wind turbines could be built and old turbines could find their replacements through re-powering schemes. During this time, however, wind turbines continued to grow in capacity and size, making the majority of these exclusive zones obsolete. The added pressure on landscapes by higher visibility was one of the reasons why an increasing rate of wind power projects failed the environmental impact assessment, which became mandatory in the late 1990s. A major contributor to the diminishing numbers of new turbines, however, was of economic nature: the former fixed feed-in tariff was abolished after the year 2000, making wind energy invest-
ments increasingly dependant on volatile market prices (Agnolucci, 2007). After 2003, only very few locations achieved planning permissions and sufficient economic feasibility. Wind energy in Denmark generally enjoys a high public acceptance (Krohn and Damborg, 1999; Ladenburg, 2008). One of the corner stones for maintaining public acceptance on a national scale as well as in local areas with dense wind turbine development was ownership. Public regulation granted a proportion of the wind capacity to be erected by publicly owned utilities and, more importantly, legislation stimulated the formation of local wind energy cooperatives with limited ownership of shares in wind turbine projects within residents’ municipalities (Sovacool et al., 2008). Hereby widespread ownership was created, distributing income from wind energy to local areas. Many studies (McLaren Loring, 2007; Wolsink, 2007; Toke, 2005) agree that public participation and the economic involvement on equal terms guarantees local acceptance if planning otherwise is carried out consciously. Other studies (Warren et al., 2005) question the main paradigms still adhered to in planning: that turbines must be out of sight and remote in order to become accepted. Wind energy planning in Denmark has changed during times. In the early years and throughout the 1980s a planning permission was given by local authorities on simple distance rules, which from a present-day perspective has led to poor locating of many turbines in highly visible areas, close to areas of natural beauty and scenic value, or in disturbing geometric patterns. As turbines were rather small then, the effect of poor location with regard to visual impact, noise and shadow cast could be considered very local. As pressure on landscapes grew during the early 1990s, the call for a nationally coordinated planning became louder. In 1995 a system of municipal and regional wind planning zones was devised. Smaller municipal planning zones were of local character, while the former counties were to plan larger, coherent areas, where wind energy could be developed on a larger scale. Together these zones made up 600 km2 or 1.4% of the Danish land mass. In the years after this planning programme, most of the local zones and many regional zones disappeared from the land. The primary reason was technological development: as turbines grew in size and output, the smaller zones based on distance rules and fitted with prescriptions on the maximum height and capacity of turbines soon became obsolete. In the late 1990s environmental impact assessment became mandatory for groups of three turbines or more. The predominantly small planning zones, which did not grant sufficient distance to built-up areas, areas of scenic beauty or other sensitive land use, made most project proposals fails the impact assessment. The new market regulation for wind energy in the years after the millennium, abolishing the fixed feed in tariff in favour of market prices determined on the Nordic power market Nordpool plus a compensation for CO2 -free electricity, meant considerably lower income and greater uncertainty regarding the earnings of most proposed wind energy projects. In parallel, the public debate was increasingly characterised by a developing opposition and resistance against large scale projects that were brought about by the regional planning zones, as well as the technological transformation of wind energy from a small scale local technology to larger scale developments, which were increasingly the subject of private investments rather than cooperatives. Both the poorer economy of wind energy projects and the lack of planning grants for projects lead to an almost complete standstill of land based wind energy development in Denmark after 2003. Today only few municipalities are positive towards building new land based turbines and the location of new turbines is a sensitive issue in the public debate. Hence, during the 25 years of wind energy development the full circle has been made from exotic, popular and welcome small scale alternative to everyday, increasingly unpopular, and industrial scale
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development. In the general public the future of wind energy is seen off shore. What is often neglected in the debate is that local wind energy development, perhaps on a smaller scale, may bring major benefits to local communities in rural areas while being more acceptable. Even a rich country like Denmark has rural areas characterised by increased income inequalities, depopulation and other socioeconomic challenges. Wind energy has proven to increase income, maintain farmsteads and raise the economic activity of rural areas, where wind energy has been developed locally with predominantly local ownership. Objectives of the study It is believed that a quantification of the interaction between wind turbines, population and landscape by modelling visibility, proximity or density is achievable for the regional scale, and previous studies support this thesis (Tsoutsos et al., 2009; Bishop and Miller, 2007; Möller, 2006; Hurtado et al., 2003). The present study primarily aims at producing quantitative information essential to understand the historical development within a spatial context, and to learn important lessons for the future. Assuming that land based wind energy has a future in Denmark and in other countries, it is important to investigate the effectiveness of the planning regimes used during the past and to give input to forthcoming planning regulation. Hence as a secondary objective, the present paper intends to contribute to the development of quantitative methods for wind energy planning in Denmark and other regions of the World. In order to establish a quantitative relation between the development of wind energy through times and its effects on landscape a geographical model needs to be built of a part of the country, where wind energy has a long history and substantial contribution to local economy. As a suitable study area the region of Northern Jutland has been chosen, where wind conditions are very good, landscapes
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Table 1 Three types of spatial interaction between landscape, population and wind turbines. Type of interaction
GIS method
Data input
Visibility Proximity Density
Cumulative viewsheds Euclidean distance Kernel density
DEM, population, turbines Population, turbines Population, turbines
are very diverse (see Fig. 2) and wind energy was pioneered. While two thirds of the region is used by agriculture, large parts of the area are characterised by pristine coastal and cultural landscapes. Wind energy, in many ways, is closely related to geography. Wind energy potential and economy; visibility and visual impact; proximity to inhabited and conservation areas, land use forms, valued areas; as well as ownership of land can be put on maps. It should therefore be obvious and inviting to use numerical geography and in particular the analysis by means of geographical information systems (GIS) for studies of these relations. Special attention is currently on the impact on property value in rural Denmark. Since a new legislation came into place in 2008, neighbours to new wind turbines are eligible to claim compensation for the losses their real estate properties may suffer due to the development of wind turbines nearby. Three types of spatial interaction between landscape, wind turbines and population can be specified (see Table 1). The scale of these types of interaction is local to regional. Noise as a rather local effect has been excluded, as well as flicker, which is associated to visibility. The study will assess changes in visibility, proximity and density of wind turbines relative to population from 1982 to 2007 in order to identify those spatial interactions between landscape, population and wind turbines, which may be the cause for current problems. While viewshed and distance analysis is required for most wind energy projects, few attempts exist to model wind turbine impact on regions (Möller, 2006). It is believed that the regional scale of a study can include the important parameters of
Fig. 2. Topography as elevation, wind energy resources, population and existing wind turbines in the Northern Jutland region. Data sources: Danish Energy Agency (2008), KMS (2008), EMD (2008) and Statistics Denmark (2008).
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scale and time, in addition to taking into account composite effects of wind turbines built in groups and clusters. On the other hand, haze, sunlight exposure, contrast to background and other atmospheric effects have been excluded from the study. Neither have vegetation cover, buildings and other objects, which might obstruct the visibility of turbines, been used. Finally, the study does not take into account the movement of wind turbines. Therefore the study will produce “worst-case” results, which exclude the more subjective elements of wind turbine impact assessment needed in a planning situation, while reducing their applicability to the relative assessment year by year in a regional context, which is required for the present study. Methodology The raster data domain in a GIS comprises a range of analytical methods under the name map algebra, which includes surface, distance, logical and arithmetic overlay analysis. These analytical tools allow for continuous evaluation of space, location and flow. They are particularly suitable for the analysis of interdisciplinary objectives, where location is the common denominator for, e.g. landscape analysis, planning, and economical development (de Smith et al., 2007). A digital landscape model of the case study has been prepared, covering the North Jutland region expanded by a 30-km buffer in order to avoid border effects from within a distance at which wind turbines become virtually invisible under most conditions. The landscape model consists of a 10-m elevation grid (KMS, 2008), bilinearly resampled to 100 m grid resolution. A population grid of 100 m cell size was used (Statistics Denmark, 2008), based on the Danish national residence register for authoritative location of population by year. Finally, the national wind turbine register by the Danish Energy Agency (2008) was used to locate all wind turbines ever installed in Denmark, including their most important technical parameters. The geographical reference of the landscape model is the Datum EuRef89 (WGS1984) with UTM 32N projection. The grids are spatially aligned to the Danish Square Grid for spatially true representation of the population statistics used. Analysis of visibility The visibility of objects such as wind turbines in a landscape is determined by topography and by a number of subjective aspects that basically are in the eyes of the beholder. Commercial GIS packages such as ArcGIS 9.2 by ESRI include tools, which can calculate the viewshed or the visibility basin of one or many objects using a raster-representation of landscape elevation; a so-called digital elevation model (DEM). Several parameters such as the raster resolution, the scale of the study and landscape surface representations used influence the quality of these analyses. A number of variables can be used to adjust the calculated theoretical visibility to real situations. Firstly, visibility is hugely affected by the height of the wind turbines. An offset factor adjusts the viewshed to the total height of each turbine. Also the distance, at which a wind turbine is visible, is limited by its height; an outer radius of 150 times the total turbine height, as derived from studies such as (Shang and Bishop, 2000), effectively limits the viewshed. The output is a measure of maximum visibility likelihood. In the simplest way, a viewshed tells in binary form where an object may be visible and where not. A variation of viewshed analysis is the analysis of multiple viewsheds, which is the compound visibility of multiple objects, expressing a continuous level of likely visual impact. This paper uses multiple viewshed analysis for 9 consequent years with 3 years in between, covering the period from 1982 to 2007 (there is, however, a time
step of 4 years in between the last two calculation years, where little construction has happened anyway). For each year the number, location, size, etc. of actually existing wind turbines can be found in the highly authoritative database maintained by the Danish Energy Agency (2008). Analysis of turbine density Apart from turbine visibility and distance to the nearest wind turbine, the number of turbines in an area is an important measure of the cumulative effect of wind turbines in groups and clusters. The cumulative effect can either be progressive or regressive. A kernel density function in ArcGIS has been used, which weighs turbines at a closer distance higher than turbines located further away, thereby relating to Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970). The kernel density function has been applied for several search radii as well as different ways to populate the search radius. The choice fell on a search radius of 5 km, within which the total height of wind turbines is summarised in a continuous manner, cell by cell. The resulting density grids hence represent density including proximity and turbine size as proxies to clustering effects. Analysis of proximity The traditional Danish ownership model favoured local cooperatives as a third and very important form of organisation besides private and utility ownership. Residents could own a share in a cooperative within the same municipality based on their annual electricity consumption. Hence ownership and geographical distance are closely related. Because this relation has proven beneficial for the local acceptance of wind energy, distance and proximity are geographically analysed. Distance is calculated as the crow flies, i.e. the straight line distance, between wind turbine locations and populated places. The Euclidean Distance tool in ArcGIS is used to calculate the distance to each wind turbine continuously in a grid. Other types of distance perception might be included, e.g. the road network distance travelled by car to regional wind parks or by weighting distance by type of land use. Composite analysis One of the challenges in this type of analysis is to combine visibility, proximity and density with population in a way which allows for the assessment of regional trends. Cartographical visualisation may yield an overview of either of the parameters, but a composite numerical analysis will produce the quantitative basis called for in the present paper. One way to achieve this is by means of supply curves for each consecutive year, which describe the marginal change of visibility, proximity and density by cumulative population in a continuous fashion. Supply curves were produced by summarising population for zones of increasing visibility count, integer distance and numeral density. Using the Zonal Statistics as Table function in ArcGIS, tables were produced, which form the basis of cost supply analysis in MS Excel. Overlay analysis of visibility, proximity and density relative to population require the population to be mapped in an authoritative way. By means of the Danish residents’ register, a unique address system and a standard square grid it is possible to identify the number of residents at each location. But although each person can be located by residence, this method is far from reflecting the loca-
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Fig. 3. Number of turbines versus share of land mass and population in the North Jutland region located outside the viewshed of wind turbines. The graph indicates that wind energy early on has had high visibility both in terms of location of land and residence. Increase in the number of wind turbines has not added much to the likelihood of impact since the early 1990s. The decommissioning of turbines after 2000 has not reduced the regional visibility. Data source: Danish Energy Agency (2008).
tion of people while they feel exposure to wind energy by visibility, proximity or density. Results Visibility The visibility of wind turbines has changed dramatically during the period investigated. While wind turbines in the early 1980s, according to the analysis carried out here, only were visible in a minor part of the region and for a small proportion of population (see Fig. 3) soon wind turbines were likely to be seen in most
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Fig. 4. Marginal visibility, calculated as the number of turbines visible by population times population, and the number of turbines installed. The diagram shows that until the early 1990s visibility grew at a lower pace than the number of turbines. In the late 1990s this trend reversed to higher growth in visibility. Since the year 2000 the total number of turbines decreases, while visibility continues to grow at a low rate.
of the region and for most of its inhabitants. The results indicate that the visibility of turbines increased at a much faster pace than their number, which also follows from Fig. 4. Since the mid 1990s the increase in the number of wind turbines has added more to the likelihood of impact than the same increase in numbers in the first decade. When the number of wind turbines fell by 20% between year 2000 and 2003, this did not reduce the overall visibility but rather increase it. It appears from the visibility analysis carried out here that decommissioning not reduces overall visibility. Cumulative viewshed maps in a time series show how the likelihood of visibility changes spatially (see Fig. 5). Early turbines, because of their limited size, were only visible locally; and there were few of them. The first wind farms of the early 1980s can clearly be identified on the maps by their distinctive viewsheds. As turbines grew in size and numbers, their visibility became more
Fig. 5. Time series maps of the likelihood of visibility by means of cumulative viewshed analysis. The maps indicate that the highest increase in visibility occurred in the 1990s. Today only few areas are left without wind turbines theoretically visible.
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widespread and viewsheds began to overlap. Today there is hardly any place left without turbines to be seen; at least in the model used here, which is characterised by no obstructions and optimal visibility. Especially interesting is the spatial distribution of visibility: expectedly, in the areas around wind farm more turbines can be seen. But a broader pattern appears where the likelihood of visual impact is higher in some regions than in others. Large regional wind parks, a result of regional planning, bring along the highest visibility counts. Visibility alone does not tell how population is affected by possible visual impact. Composite analysis of viewsheds and population is therefore used to quantify the marginal visibility of wind turbines. Visibility is here calculated as the product of the number of wind turbines visible at a location times the number of people who have residence in this location. It is therefore an approximate measure of visual impact, assuming that visual impact is proportional to the number of turbines visible (this is questionable and will be discussed later). Marginal visibility is then shown by cumulative population (see Fig. 6). It can be seen from the diagram that visibility is not equally distributed among the population in the region. The inclination of the curves consequentially relates to how sensitive the issue of visibility is in a local area. A low inclination is equivalent to small increases of visibility by population count, which means that for this part of population there is little change in visual exposure in their neighbourhood. A high inclination on the other hand indicates significant marginal changes of visibility for a minority of population, which lives in an area where visibility changes at high rates, and where wind energy consequentially may be a controversial issue. It can be seen that until the year 1997 the curves are less inclined for lower values, which indicates that visibility issues are less critical than after the year 2000. On the other hand, it follows from Fig. 6 that inclination for higher values generally increases by years, except after 2003, when only few new turbines were built. The diagram also shows that the share of people with no turbines visible at their location of residence decreases, consistent with the findings from Fig. 3.
Fig. 6. The marginal visibility of wind turbines for cumulative population shows which proportions of population theoretically are exposed to wind turbine visibility. The inclination of the curves tells how sensitive the issue of visibility is. All curves show an increase in slope for the last 5–10% of population, indicating that these people have to suffer more than the majority.
Density The density of wind turbines in the region has followed a similar development as their visibility. But where visibility of turbines is a function of turbine height and topography, the density results presented here are characterised by a fixed search radius of 5 km, which significantly reduces the area of influence around wind turbines, and by neglecting topography and therefore proximity by visibility. Wind turbine height has been considered by using it as a weighing factor for the number of turbines. Thereby the number of turbines as well as their height is considered. As the Kernel density function used weighs turbine impact by the inverse distance squared, more emphasis is put on nearby turbines, while remote turbines have little influence on turbine density at a given location. Interpretation of the results is difficult on the grounds
Fig. 7. A series of density maps calculated for the selected years shows the turbine count within a search radius of 5 km, weighted by turbine height. Density is therefore a measure of wind turbine number and size within a neighbourhood. It follows from the maps that density increased with time, particularly until the year 2000. There are areas with high densities where wind turbines form clusters, and areas of low density remain in between. Data sources: Danish Energy Agency (2008) and KMS (2008).
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Fig. 8. A diagram representation of wind turbine density by population overlay. The Kernel density argument leaves around 40% of population outside areas with density >0, while the last 5–10% of population experience high increases in wind turbine density. Overall, density increases year by year until the year 2000, after which lower density is the result of re-powering schemes.
of these model conditions, which remain concealed in the model output, but it can be seen from Fig. 7 how density increases significantly until the year 2000, after which it appears constant on the maps. It is evident from the maps that larger proportions of the region remain unaffected by wind turbines, at least according to the chosen density criteria. In a few regions, however, clusters of wind turbine development evolve. These clusters indicate a higher presence of wind turbines in an area, but nothing is said about their likely impact, which again has to be seen relative to population distribution. The diagram in Fig. 8 shows the cumulative density development. Using the Kernel density function with the chosen parameters means that approximately 40% of population remain outside areas with measurable density values. This is about twice the amount compared to the viewshed criterion. For the last 5–10% of population high increases in wind turbine density are visible,
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higher than for visibility. This is a result of the weighted density, which includes turbine height and inverse distance to turbines and therefore puts higher emphasis on areas, where turbines are located in clusters. In other words, the spatial relation of population and large wind parks is underlined. It appears that only a minority of population experiences high wind turbine densities, while densities with the chosen parameters appear moderate to low for at least half the population in the years after the mid-1990s. Overall, density increases year by year until the year 2000, after which is appears to be reduced. While density is difficult to quantify for the first years, where only few wind turbines exist, the density criterion effectively finds those areas, where massive development of wind energy is going on during the 1990s. The lower density calculated for the years after the millennium must originate in the removal of many small, older turbines. The density criterion is therefore an effective means to quantify the effect of replacing small turbines. Proximity Proximity to wind turbines has been calculated using straight line distance to the nearest wind turbine (see Fig. 9). While the first turbines were erected in a few locations only, soon they were spread rather evenly across the region. Fig. 10 shows percentages of population for ranges of nearest distance to wind turbines. The results confirm that wind turbines are an integral part of landscape and the proximity to locations of residence is close for the overwhelming part of population. In 1982 about 75% of population were located more than 5 km to the nearest wind turbine. Only 6% of people in the region did live closer than 2 km to any of the earliest wind turbines. During the 1980s this rapidly changed. Since the year 1990 about 50% of the population have less than 2 km to the nearest turbine. About a quarter of the population live less than 1 km away from wind turbines. The removal of many of the older turbines since the year 2000 has led to fewer people with turbines in close proxim-
Fig. 9. Proximity to wind turbines is calculated as straight line distance from turbines and outwards. The maps show, similarly to the density maps, how fewer and fewer areas in the region are without influence from turbines. In the years after the mid-1990s, however, little change is visible. Data source: Danish Energy Agency (2008).
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Fig. 10. Proximity to wind turbines calculated as distance to nearest wind turbine by percentage of population. While in 1982 about 75% of population had a minimal distance of 5 km to the nearest turbine, this rapidly changed. Since the beginning of the 1990s about half of the population have less than 2 km to the nearest turbine. Deconstruction since the year 2000 has led to fewer people with turbines in close proximity.
ity, and more people are found to have turbines located very far away. Applicability in other regions As one of the objectives of the present paper is the contribution to the development of planning methods in an international context, the applicability of the results, in the form of the spatial model built and the analysis carried out, shall briefly be evaluated here. There are two main preconditions, which need to be in place in order to ensure a relevant and high-quality analysis of the spatial relations between wind turbines and their surrounding landscapes and population. Firstly, an analysis of this kind is only relevant where wind turbines exist, i.e. in an ex post fashion, but also where planning requires and ex ante approach. Secondly, there needs to exist a sufficiently good and available geographical data base, mainly comprised of elevation models, landscape forms and land use, high resolution population data and wind turbine locations. Methods, which include the regional distribution of turbines visibility, density and distance, will gain importance because there are an increasing number of regions in the World where there are problems of finding new locations for wind turbines in replacement of older installations. Regions like Northern Germany, California or Spain are characterised by the early installation of many turbines in patterns dictated by the planning regimes of the times when they were built. Since then, experience levels have increased and planning has moved along learning curves towards lower impact, better feasibility or better acceptance. In evaluating older planning prescriptions and regulations, their effectiveness could be measured against newer wind energy technology. Also, in regions such as parts of the UK where high pressure is put on the planning system, a set of methods like the one sketched here could be applicable to provide a better decision base for the public planning process. Regarding the availability of geographical data, the situation improves significantly in these years, as services such as GoogleMaps ©provide better data to a larger number of users, which until recently only were to be found among a few experts. Conclusions An assessment of the development of the spatial interaction between wind turbines, landscape and population has been carried out for the North Jutland region in Denmark, where wind energy has been pioneered and where development has culminated. Visibility,
density and proximity of wind turbines relative to population have been calculated in a GIS-based landscape model. The likely visibility of turbines has been modelled by means of cumulative viewsheds; proximity to wind turbines was assessed using a simple distance tool; and the density of wind turbine development was analysed using a Kernel density function, weighing groups of turbines higher than single turbines. The analysis was carried out for the period of commercial wind turbine development, using time steps of 3 years, and spanning from the year 1982 to 2007. The results are available as cartographic interpretations as well as geo-statistical representations by means of diagrams. The impact of wind turbines on landscapes and people clearly follows the technological development of wind energy. As turbine size has increased significantly in the period from about 30–150 m, higher visibility and higher turbine density has been calculated in the present study. The number of turbines has increased steadily until the year 2000, when re-powering schemes came into being, which replaced about 1000 older turbines out of the ca. 6000 turbines. Accordingly, turbines became more visible, appeared in higher densities and higher proximity to the Northern Jutland population. Alongside technical development, the planning paradigms have changed in the period, and the calculated spatial interaction is able to follow this. While in the first years few rules existed, the planning procedures became more elaborate, with a national system of municipal and regional planning areas in place in the mid-1990s. The term “poorly located wind turbines” referring to the oldest turbines was coined during the re-powering campaign, indicating that mistakes have been made in the location of many of the early turbines. The recent years after 2003 are characterised by a decline in wind turbine capacity. It was found that re-powering did not lead to lower overall visibility and density, but to higher distance for some of the inhabitants in the region. The massive increase in the number and size of turbines during the 1990s has led to increased visibility and density, and possibly to more polarisation: these parameters seem to pose a problem for a minority of people, as marginal visibility and density curves are steep for those 5–10% of population, who are most affected. It must also be mentioned that about 40% of population are located in areas, where visibility and density are unlikely to be serious problems, taking departure in this worst case analysis of visibility and density. One of the initial questions was if the change in planning policy is reflected in the spatial interaction between wind turbines, landscape and population. Clearly, when the new planning legislation became effective in the late 1990s, most wind turbines had already been built. The second important change in legislation brought about by the re-powering of old turbines, has had limited effect. The spatial model developed here is not free from assumptions and shortcomings, which reduce the credibility of results. First of all it must be accepted that a model is always simpler than reality. Ground testing of model results has not been carried out as they are considered impossible on a regional scale. Model results are therefore mostly suitable for comparison in between model years or scenarios. The visibility model is based on a terrain model characterised by completely “empty” landscapes without forests, hedges, buildings or other items that normally lead to significant reductions in visibility. The visibility figures produced here are therefore indeed worst case. Second, the criteria for calculating density may be arbitrary, and they are the result of trial and error rather than empirical findings. In particular the search radius, the kernel weight and the choice of total turbine height are subject of discussion. Third, distance to turbines may be subject to critical evaluation, as perceived distance would be a combination of straight line distance, visibility and density. Finally, all spatial interaction with population was modelled using maps of residence rather than the location of people at work, during commuting, leisure activities,
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etc., and without taking into account diurnal or seasonal variations. Also, it is a matter of extensive discussions if visibility is not spatially correlated to the number of turbines, to distance, and to type of landscape. Again, model results are useful for comparison in between scenario years rather than as absolute figures. To conclude on the effectiveness of planning regimes, a few findings of the present paper may be used. It is evident that the installation of wind turbines in wind parks, which is the idea favoured by regional planning, may be a matter of concern. Wind parks are here documented to cause higher visibility and density, particularly if density is attributed progressively. Smaller groups of wind turbines lead to lower regional visibility, but have economic disadvantages. But where larger developments typically are investment models, the more socially acceptable cooperatives are most efficient for smaller groups of wind turbines. Second, the installation of very large wind turbines has led to a higher visibility on a regional level, whereas the removal of many small turbines did not reduce overall visibility to a measurable extent. It must therefore be concluded that re-powering rather addresses economic issues than issues of bad location. It follows from the studies of proximity, visibility and density that there is a trade-off between small and large developments. Smaller turbines in small groups may conserve the notion of wind energy as a local, socially acceptable and welcome technology because of their more homogenous effect on landscapes. Planners may therefore reconsider the concept of a higher number of local wind parks to maintain acceptance and avoid increasing alienation of this technology. References Agnolucci, P., 2007. Wind electricity in Denmark: a survey of policies, their effectiveness and factors motivating their introduction. Renewable and Sustainable Energy Reviews 11 (5), 951–963. Bishop, I.D., Miller, D.R., 2007. Visual assessment of off-shore wind turbines: the influence of distance, contrast, movement and social variables. Renewable Energy 32 (5), 814–831. Danish Energy Agency, 2008. Available from: http://www.ens.dk. de Smith, M.J., Goodchild, M.F., Longley, P.A., 2007. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, 2nd edition. Matador, Leichester.
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EMD, 2008. Wind resource map for Denmark. Available from: http://www.emd.dk. Ek, K., 2005. Public and private attitudes towards “green” electricity: the case of Swedish wind power. Energy Policy 33 (13), 1677–1689. Hoogwijk, M., de Vries, B., Turkenburg, W., 2004. Assessment of the global and regional geographical, technical and economic potential of onshore wind energy. Energy Economics 26 (5), 889–919. Hurtado, J.P., Fernandez, J., Parrondo, J.L., Blanco, E., 2003. Spanish method of visual impact evaluation in wind farms. Renewable and Sustainable Energy Reviews 8, 483–491. Hvelplund, F., 2006. Renewable energy and the need for local energy markets. Energy 31 (13), 2293–2302. International Energy Agency (IEA), 2008. World Energy Outlook 2008 Edition. Paris. KMS, 2008. Danish national survey and cadastre. Available from: http://www. kms.dk. Krohn, S., Damborg, S., 1999. On public attitudes towards wind power. Renewable Energy 16 (1–4), 954–960. Ladenburg, J., 2008. Attitudes towards on-land and offshore wind power development in Denmark; choice of development strategy. Renewable Energy 33 (1), 111–118. Mclaren Loring, J., 2007. Wind energy planning in England Wales and Denmark: factors influencing project success. Energy Policy 35 (4), 2648–2660. Möller, B., 2006. Changing wind-power landscapes: regional assessment of visual impact on land use and population in Northern Jutland Denmark. Applied Energy 83 (5), 477–494. Pasqualetti, M.J., 2000. Morality, space, and the power of wind-energy landscapes. Geographical Review 90 (3), 381–394. Shang, H., Bishop, I.D., 2000. Visual thresholds for detection, recognition and visual impact in landscape settings. Journal of Environmental Psychology 20 (2), 125–140. Sovacool, B.K., Lindboe, H.H., Odgaard, O., 2008. Is the Danish wind energy model replicable for other countries? The Electricity Journal 21 (2), 27–38. Statistics Denmark, 2008. Available from: http://www.dst.dk. Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography 46 (2), 234–240. Toke, D., 2005. Explaining wind power planning outcomes: some findings from a study in England and Wales. Energy Policy 33 (12), 1527–1539. Tsoutsos, T., Tsouchlaraki, A., Tsiropoulos, M., Serpetsidakis, M., 2009. Visual impact evaluation of a wind park in a Greek island. Applied Energy 86, 546– 553. Van der horst, D., 2007. Nimby or not? Exploring the relevance of location and the politics of voiced opinions in renewable energy siting controversies. Energy Policy 35 (5), 2705–2714. Warren, C.R., Lumsden, C., O’dowd, S., Birnie, R.V., 2005. ‘Green on green’: public perceptions of wind power in Scotland and Ireland. Journal of Environmental Planning and Management 48 (6), 853–875. Wolsink, M., 2007. Planning of renewables schemes: deliberative and fair decision-making on landscape issues instead of reproachful accusations of noncooperation. Energy Policy 35 (5), 2692–2704.
Contents lists available at ScienceDirect
Land Use Policy journal homepage: www.elsevier.com/locate/landusepol
Spatial analyses of emerging and fading wind energy landscapes in Denmark Bernd Möller Department of Development and Planning, Aalborg University, 9220 Aalborg, Denmark
a r t i c l e
i n f o
Article history: Received 9 February 2009 Received in revised form 28 May 2009 Accepted 6 June 2009 Keywords: Wind energy GIS Landscape Planning Visual impact Public acceptance
a b s t r a c t The development of wind energy in Denmark goes back 30 years, during which the technology was commercialised, up scaled and a series of planning systems were developed. After the millennium, the impact on landscapes increased, the planning regime failed and economic conditions were worsened with the removal of the fixed feed in tariff. The earlier forerunner country is left in the lee of the internationally boosting wind energy business. From a land use policy view it is interesting to analyse how this has happened and what impact the planning policy has had on the landscape effect of wind energy. In order to analyse the impact of wind turbine development through times and on the population of a region, the present paper analyses by means of geographical information systems and in time steps. The spatial relations between population, landscapes and the wind turbine development from 1982 to 2007 were modelled for the Northern Jutland region by means of proximity, density and visibility analyses. Results indicate that development was not continuous and impact on landscape and population was closely related to technology development. The paper concludes on the use of these methods and on the effectiveness of planning regimes. © 2009 Elsevier Ltd. All rights reserved.
Introduction There is a growing schism in the development of wind energy. On one hand, it is evident that wind energy can contribute significantly and on a global scale in solving problems such as climate change, the depletion of fossil fuel resources, as well as pollution (Hoogwijk et al., 2004; IEA, 2008). On the other hand, wind energy has grown to a scale that will undoubtedly alter natural and cultural landscapes as we know and appreciate them (Pasqualetti, 2000; Ek, 2005). An attempt will be made in this paper to document that both dimensions and their discourse are characterised by change over time and in relation to population and landscape. Denmark has been leading the development of modern wind power during the first part of its commercial history. The history of this technology is closely linked to several beneficial conditions: the beginnings of wind turbine manufacturing were characterised by many small producers, who were able to establish a competitive growth layer of industry. Good wind conditions, among the best in Europe, can be found on the West coast and in the North West of the rather densely populated country with its diverse glacial topography. Several forms of ownership have been developed for modern wind energy, and the development in terms of turbine location, planning and economic conditions is well documented. Wind energy has gone through an incommensurable evolution,
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from small scale symbol of iconic value to large scale industries and opposition against new installations (Hvelplund, 2006). It is therefore rather obvious to use the country or part of it for an analysis of emerging and fading wind energy landscapes. The present paper aims at analysing the quantitatively measurable changes to Danish landscapes due to the technological development of modern wind energy, which has brought along more and larger wind turbines through 30 years of commercial development. This development has been highly influenced by the reigning policies and planning targets, and public participatory planning has changed the way wind turbines are erected in landscapes. Possible feedbacks between technological development, wind utilisation and policy making may be important learning lessons in other regions. Historical development of wind energy The history of modern wind energy in Denmark begins in the 1970s after the significant impact of the oil crises on the Danish energy system, further stimulated by the anti-nuclear protests of that time. The first commercially available modern wind turbines were erected in 1977, their size limited to tens of kilowatts, at total heights in the order of 20 m. The first years were very much a period of pioneers, with wind turbines in the landscape an oddity after the tens of thousands of older wind mills and early wind generators had disappeared from the countryside following rural electrification as late as the 1950s. A boom of wind turbine export
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Fig. 1. Cumulative numbers and cumulative capacity of wind turbines erected in Denmark since 1982. Three periods of wind energy development can be identified: slow growth until 1995, accelerated growth until 2001 and stagnation and decline since 2001. Data source: Danish Energy Agency (2008).
to the United States in the early 1980s and the emerging European markets of the 1990s fuelled the further development of turbines to modern industrial products. Fig. 1 shows the cumulative numbers and capacity of wind turbines erected in Denmark since 1982. It can be seen that the first decade was characterised by a slow increase in numbers, while plants tripled their capacity. The second period until the year 2001 has seen wind energy growing to maturity in terms of quantity and size. It can also be noted that the number of turbines began decreasing after the millennium as older installations have been removed after ended useful lifetime and due to re-powering policies. The recent years have seen no further expansion except the erection of two large offshore developments, and a recent trend is that wind energy delivered to the energy system decreases, climatic variations levelled out. The current political dictum for the next 4 years aims at the installation of several large offshore parks in the order of 1000 MW in total, and 300–900 MW of new wind turbines to be erected on land in replacement of older turbines. Nevertheless, if the ageing fleet of turbines is taken into account, it seems that land based wind energy steers towards deconstruction. The development of modern wind energy in Denmark therefore is an interesting case for studies of emerging and fading wind energy landscapes. Quantitative studies of this kind are required to improve the understanding of the social construction of wind turbine siting (van der Horst, 2007). Wind energy policy and planning The development of wind energy has been accompanied by a continuous development of energy policy and planning practice, as the technology grew in size, extent and significance. While the first turbines were built almost everywhere given that simple distance rules were observed, in the mid-1990s planning restrictions came in place that basically reversed the concept of exclusion by means of buffers etc. to exclusive zoning. The result of this were specified areas on municipal and regional levels where new wind turbines could be built and old turbines could find their replacements through re-powering schemes. During this time, however, wind turbines continued to grow in capacity and size, making the majority of these exclusive zones obsolete. The added pressure on landscapes by higher visibility was one of the reasons why an increasing rate of wind power projects failed the environmental impact assessment, which became mandatory in the late 1990s. A major contributor to the diminishing numbers of new turbines, however, was of economic nature: the former fixed feed-in tariff was abolished after the year 2000, making wind energy invest-
ments increasingly dependant on volatile market prices (Agnolucci, 2007). After 2003, only very few locations achieved planning permissions and sufficient economic feasibility. Wind energy in Denmark generally enjoys a high public acceptance (Krohn and Damborg, 1999; Ladenburg, 2008). One of the corner stones for maintaining public acceptance on a national scale as well as in local areas with dense wind turbine development was ownership. Public regulation granted a proportion of the wind capacity to be erected by publicly owned utilities and, more importantly, legislation stimulated the formation of local wind energy cooperatives with limited ownership of shares in wind turbine projects within residents’ municipalities (Sovacool et al., 2008). Hereby widespread ownership was created, distributing income from wind energy to local areas. Many studies (McLaren Loring, 2007; Wolsink, 2007; Toke, 2005) agree that public participation and the economic involvement on equal terms guarantees local acceptance if planning otherwise is carried out consciously. Other studies (Warren et al., 2005) question the main paradigms still adhered to in planning: that turbines must be out of sight and remote in order to become accepted. Wind energy planning in Denmark has changed during times. In the early years and throughout the 1980s a planning permission was given by local authorities on simple distance rules, which from a present-day perspective has led to poor locating of many turbines in highly visible areas, close to areas of natural beauty and scenic value, or in disturbing geometric patterns. As turbines were rather small then, the effect of poor location with regard to visual impact, noise and shadow cast could be considered very local. As pressure on landscapes grew during the early 1990s, the call for a nationally coordinated planning became louder. In 1995 a system of municipal and regional wind planning zones was devised. Smaller municipal planning zones were of local character, while the former counties were to plan larger, coherent areas, where wind energy could be developed on a larger scale. Together these zones made up 600 km2 or 1.4% of the Danish land mass. In the years after this planning programme, most of the local zones and many regional zones disappeared from the land. The primary reason was technological development: as turbines grew in size and output, the smaller zones based on distance rules and fitted with prescriptions on the maximum height and capacity of turbines soon became obsolete. In the late 1990s environmental impact assessment became mandatory for groups of three turbines or more. The predominantly small planning zones, which did not grant sufficient distance to built-up areas, areas of scenic beauty or other sensitive land use, made most project proposals fails the impact assessment. The new market regulation for wind energy in the years after the millennium, abolishing the fixed feed in tariff in favour of market prices determined on the Nordic power market Nordpool plus a compensation for CO2 -free electricity, meant considerably lower income and greater uncertainty regarding the earnings of most proposed wind energy projects. In parallel, the public debate was increasingly characterised by a developing opposition and resistance against large scale projects that were brought about by the regional planning zones, as well as the technological transformation of wind energy from a small scale local technology to larger scale developments, which were increasingly the subject of private investments rather than cooperatives. Both the poorer economy of wind energy projects and the lack of planning grants for projects lead to an almost complete standstill of land based wind energy development in Denmark after 2003. Today only few municipalities are positive towards building new land based turbines and the location of new turbines is a sensitive issue in the public debate. Hence, during the 25 years of wind energy development the full circle has been made from exotic, popular and welcome small scale alternative to everyday, increasingly unpopular, and industrial scale
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development. In the general public the future of wind energy is seen off shore. What is often neglected in the debate is that local wind energy development, perhaps on a smaller scale, may bring major benefits to local communities in rural areas while being more acceptable. Even a rich country like Denmark has rural areas characterised by increased income inequalities, depopulation and other socioeconomic challenges. Wind energy has proven to increase income, maintain farmsteads and raise the economic activity of rural areas, where wind energy has been developed locally with predominantly local ownership. Objectives of the study It is believed that a quantification of the interaction between wind turbines, population and landscape by modelling visibility, proximity or density is achievable for the regional scale, and previous studies support this thesis (Tsoutsos et al., 2009; Bishop and Miller, 2007; Möller, 2006; Hurtado et al., 2003). The present study primarily aims at producing quantitative information essential to understand the historical development within a spatial context, and to learn important lessons for the future. Assuming that land based wind energy has a future in Denmark and in other countries, it is important to investigate the effectiveness of the planning regimes used during the past and to give input to forthcoming planning regulation. Hence as a secondary objective, the present paper intends to contribute to the development of quantitative methods for wind energy planning in Denmark and other regions of the World. In order to establish a quantitative relation between the development of wind energy through times and its effects on landscape a geographical model needs to be built of a part of the country, where wind energy has a long history and substantial contribution to local economy. As a suitable study area the region of Northern Jutland has been chosen, where wind conditions are very good, landscapes
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Table 1 Three types of spatial interaction between landscape, population and wind turbines. Type of interaction
GIS method
Data input
Visibility Proximity Density
Cumulative viewsheds Euclidean distance Kernel density
DEM, population, turbines Population, turbines Population, turbines
are very diverse (see Fig. 2) and wind energy was pioneered. While two thirds of the region is used by agriculture, large parts of the area are characterised by pristine coastal and cultural landscapes. Wind energy, in many ways, is closely related to geography. Wind energy potential and economy; visibility and visual impact; proximity to inhabited and conservation areas, land use forms, valued areas; as well as ownership of land can be put on maps. It should therefore be obvious and inviting to use numerical geography and in particular the analysis by means of geographical information systems (GIS) for studies of these relations. Special attention is currently on the impact on property value in rural Denmark. Since a new legislation came into place in 2008, neighbours to new wind turbines are eligible to claim compensation for the losses their real estate properties may suffer due to the development of wind turbines nearby. Three types of spatial interaction between landscape, wind turbines and population can be specified (see Table 1). The scale of these types of interaction is local to regional. Noise as a rather local effect has been excluded, as well as flicker, which is associated to visibility. The study will assess changes in visibility, proximity and density of wind turbines relative to population from 1982 to 2007 in order to identify those spatial interactions between landscape, population and wind turbines, which may be the cause for current problems. While viewshed and distance analysis is required for most wind energy projects, few attempts exist to model wind turbine impact on regions (Möller, 2006). It is believed that the regional scale of a study can include the important parameters of
Fig. 2. Topography as elevation, wind energy resources, population and existing wind turbines in the Northern Jutland region. Data sources: Danish Energy Agency (2008), KMS (2008), EMD (2008) and Statistics Denmark (2008).
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scale and time, in addition to taking into account composite effects of wind turbines built in groups and clusters. On the other hand, haze, sunlight exposure, contrast to background and other atmospheric effects have been excluded from the study. Neither have vegetation cover, buildings and other objects, which might obstruct the visibility of turbines, been used. Finally, the study does not take into account the movement of wind turbines. Therefore the study will produce “worst-case” results, which exclude the more subjective elements of wind turbine impact assessment needed in a planning situation, while reducing their applicability to the relative assessment year by year in a regional context, which is required for the present study. Methodology The raster data domain in a GIS comprises a range of analytical methods under the name map algebra, which includes surface, distance, logical and arithmetic overlay analysis. These analytical tools allow for continuous evaluation of space, location and flow. They are particularly suitable for the analysis of interdisciplinary objectives, where location is the common denominator for, e.g. landscape analysis, planning, and economical development (de Smith et al., 2007). A digital landscape model of the case study has been prepared, covering the North Jutland region expanded by a 30-km buffer in order to avoid border effects from within a distance at which wind turbines become virtually invisible under most conditions. The landscape model consists of a 10-m elevation grid (KMS, 2008), bilinearly resampled to 100 m grid resolution. A population grid of 100 m cell size was used (Statistics Denmark, 2008), based on the Danish national residence register for authoritative location of population by year. Finally, the national wind turbine register by the Danish Energy Agency (2008) was used to locate all wind turbines ever installed in Denmark, including their most important technical parameters. The geographical reference of the landscape model is the Datum EuRef89 (WGS1984) with UTM 32N projection. The grids are spatially aligned to the Danish Square Grid for spatially true representation of the population statistics used. Analysis of visibility The visibility of objects such as wind turbines in a landscape is determined by topography and by a number of subjective aspects that basically are in the eyes of the beholder. Commercial GIS packages such as ArcGIS 9.2 by ESRI include tools, which can calculate the viewshed or the visibility basin of one or many objects using a raster-representation of landscape elevation; a so-called digital elevation model (DEM). Several parameters such as the raster resolution, the scale of the study and landscape surface representations used influence the quality of these analyses. A number of variables can be used to adjust the calculated theoretical visibility to real situations. Firstly, visibility is hugely affected by the height of the wind turbines. An offset factor adjusts the viewshed to the total height of each turbine. Also the distance, at which a wind turbine is visible, is limited by its height; an outer radius of 150 times the total turbine height, as derived from studies such as (Shang and Bishop, 2000), effectively limits the viewshed. The output is a measure of maximum visibility likelihood. In the simplest way, a viewshed tells in binary form where an object may be visible and where not. A variation of viewshed analysis is the analysis of multiple viewsheds, which is the compound visibility of multiple objects, expressing a continuous level of likely visual impact. This paper uses multiple viewshed analysis for 9 consequent years with 3 years in between, covering the period from 1982 to 2007 (there is, however, a time
step of 4 years in between the last two calculation years, where little construction has happened anyway). For each year the number, location, size, etc. of actually existing wind turbines can be found in the highly authoritative database maintained by the Danish Energy Agency (2008). Analysis of turbine density Apart from turbine visibility and distance to the nearest wind turbine, the number of turbines in an area is an important measure of the cumulative effect of wind turbines in groups and clusters. The cumulative effect can either be progressive or regressive. A kernel density function in ArcGIS has been used, which weighs turbines at a closer distance higher than turbines located further away, thereby relating to Tobler’s first law of geography: “everything is related to everything else, but near things are more related than distant things” (Tobler, 1970). The kernel density function has been applied for several search radii as well as different ways to populate the search radius. The choice fell on a search radius of 5 km, within which the total height of wind turbines is summarised in a continuous manner, cell by cell. The resulting density grids hence represent density including proximity and turbine size as proxies to clustering effects. Analysis of proximity The traditional Danish ownership model favoured local cooperatives as a third and very important form of organisation besides private and utility ownership. Residents could own a share in a cooperative within the same municipality based on their annual electricity consumption. Hence ownership and geographical distance are closely related. Because this relation has proven beneficial for the local acceptance of wind energy, distance and proximity are geographically analysed. Distance is calculated as the crow flies, i.e. the straight line distance, between wind turbine locations and populated places. The Euclidean Distance tool in ArcGIS is used to calculate the distance to each wind turbine continuously in a grid. Other types of distance perception might be included, e.g. the road network distance travelled by car to regional wind parks or by weighting distance by type of land use. Composite analysis One of the challenges in this type of analysis is to combine visibility, proximity and density with population in a way which allows for the assessment of regional trends. Cartographical visualisation may yield an overview of either of the parameters, but a composite numerical analysis will produce the quantitative basis called for in the present paper. One way to achieve this is by means of supply curves for each consecutive year, which describe the marginal change of visibility, proximity and density by cumulative population in a continuous fashion. Supply curves were produced by summarising population for zones of increasing visibility count, integer distance and numeral density. Using the Zonal Statistics as Table function in ArcGIS, tables were produced, which form the basis of cost supply analysis in MS Excel. Overlay analysis of visibility, proximity and density relative to population require the population to be mapped in an authoritative way. By means of the Danish residents’ register, a unique address system and a standard square grid it is possible to identify the number of residents at each location. But although each person can be located by residence, this method is far from reflecting the loca-
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Fig. 3. Number of turbines versus share of land mass and population in the North Jutland region located outside the viewshed of wind turbines. The graph indicates that wind energy early on has had high visibility both in terms of location of land and residence. Increase in the number of wind turbines has not added much to the likelihood of impact since the early 1990s. The decommissioning of turbines after 2000 has not reduced the regional visibility. Data source: Danish Energy Agency (2008).
tion of people while they feel exposure to wind energy by visibility, proximity or density. Results Visibility The visibility of wind turbines has changed dramatically during the period investigated. While wind turbines in the early 1980s, according to the analysis carried out here, only were visible in a minor part of the region and for a small proportion of population (see Fig. 3) soon wind turbines were likely to be seen in most
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Fig. 4. Marginal visibility, calculated as the number of turbines visible by population times population, and the number of turbines installed. The diagram shows that until the early 1990s visibility grew at a lower pace than the number of turbines. In the late 1990s this trend reversed to higher growth in visibility. Since the year 2000 the total number of turbines decreases, while visibility continues to grow at a low rate.
of the region and for most of its inhabitants. The results indicate that the visibility of turbines increased at a much faster pace than their number, which also follows from Fig. 4. Since the mid 1990s the increase in the number of wind turbines has added more to the likelihood of impact than the same increase in numbers in the first decade. When the number of wind turbines fell by 20% between year 2000 and 2003, this did not reduce the overall visibility but rather increase it. It appears from the visibility analysis carried out here that decommissioning not reduces overall visibility. Cumulative viewshed maps in a time series show how the likelihood of visibility changes spatially (see Fig. 5). Early turbines, because of their limited size, were only visible locally; and there were few of them. The first wind farms of the early 1980s can clearly be identified on the maps by their distinctive viewsheds. As turbines grew in size and numbers, their visibility became more
Fig. 5. Time series maps of the likelihood of visibility by means of cumulative viewshed analysis. The maps indicate that the highest increase in visibility occurred in the 1990s. Today only few areas are left without wind turbines theoretically visible.
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widespread and viewsheds began to overlap. Today there is hardly any place left without turbines to be seen; at least in the model used here, which is characterised by no obstructions and optimal visibility. Especially interesting is the spatial distribution of visibility: expectedly, in the areas around wind farm more turbines can be seen. But a broader pattern appears where the likelihood of visual impact is higher in some regions than in others. Large regional wind parks, a result of regional planning, bring along the highest visibility counts. Visibility alone does not tell how population is affected by possible visual impact. Composite analysis of viewsheds and population is therefore used to quantify the marginal visibility of wind turbines. Visibility is here calculated as the product of the number of wind turbines visible at a location times the number of people who have residence in this location. It is therefore an approximate measure of visual impact, assuming that visual impact is proportional to the number of turbines visible (this is questionable and will be discussed later). Marginal visibility is then shown by cumulative population (see Fig. 6). It can be seen from the diagram that visibility is not equally distributed among the population in the region. The inclination of the curves consequentially relates to how sensitive the issue of visibility is in a local area. A low inclination is equivalent to small increases of visibility by population count, which means that for this part of population there is little change in visual exposure in their neighbourhood. A high inclination on the other hand indicates significant marginal changes of visibility for a minority of population, which lives in an area where visibility changes at high rates, and where wind energy consequentially may be a controversial issue. It can be seen that until the year 1997 the curves are less inclined for lower values, which indicates that visibility issues are less critical than after the year 2000. On the other hand, it follows from Fig. 6 that inclination for higher values generally increases by years, except after 2003, when only few new turbines were built. The diagram also shows that the share of people with no turbines visible at their location of residence decreases, consistent with the findings from Fig. 3.
Fig. 6. The marginal visibility of wind turbines for cumulative population shows which proportions of population theoretically are exposed to wind turbine visibility. The inclination of the curves tells how sensitive the issue of visibility is. All curves show an increase in slope for the last 5–10% of population, indicating that these people have to suffer more than the majority.
Density The density of wind turbines in the region has followed a similar development as their visibility. But where visibility of turbines is a function of turbine height and topography, the density results presented here are characterised by a fixed search radius of 5 km, which significantly reduces the area of influence around wind turbines, and by neglecting topography and therefore proximity by visibility. Wind turbine height has been considered by using it as a weighing factor for the number of turbines. Thereby the number of turbines as well as their height is considered. As the Kernel density function used weighs turbine impact by the inverse distance squared, more emphasis is put on nearby turbines, while remote turbines have little influence on turbine density at a given location. Interpretation of the results is difficult on the grounds
Fig. 7. A series of density maps calculated for the selected years shows the turbine count within a search radius of 5 km, weighted by turbine height. Density is therefore a measure of wind turbine number and size within a neighbourhood. It follows from the maps that density increased with time, particularly until the year 2000. There are areas with high densities where wind turbines form clusters, and areas of low density remain in between. Data sources: Danish Energy Agency (2008) and KMS (2008).
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Fig. 8. A diagram representation of wind turbine density by population overlay. The Kernel density argument leaves around 40% of population outside areas with density >0, while the last 5–10% of population experience high increases in wind turbine density. Overall, density increases year by year until the year 2000, after which lower density is the result of re-powering schemes.
of these model conditions, which remain concealed in the model output, but it can be seen from Fig. 7 how density increases significantly until the year 2000, after which it appears constant on the maps. It is evident from the maps that larger proportions of the region remain unaffected by wind turbines, at least according to the chosen density criteria. In a few regions, however, clusters of wind turbine development evolve. These clusters indicate a higher presence of wind turbines in an area, but nothing is said about their likely impact, which again has to be seen relative to population distribution. The diagram in Fig. 8 shows the cumulative density development. Using the Kernel density function with the chosen parameters means that approximately 40% of population remain outside areas with measurable density values. This is about twice the amount compared to the viewshed criterion. For the last 5–10% of population high increases in wind turbine density are visible,
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higher than for visibility. This is a result of the weighted density, which includes turbine height and inverse distance to turbines and therefore puts higher emphasis on areas, where turbines are located in clusters. In other words, the spatial relation of population and large wind parks is underlined. It appears that only a minority of population experiences high wind turbine densities, while densities with the chosen parameters appear moderate to low for at least half the population in the years after the mid-1990s. Overall, density increases year by year until the year 2000, after which is appears to be reduced. While density is difficult to quantify for the first years, where only few wind turbines exist, the density criterion effectively finds those areas, where massive development of wind energy is going on during the 1990s. The lower density calculated for the years after the millennium must originate in the removal of many small, older turbines. The density criterion is therefore an effective means to quantify the effect of replacing small turbines. Proximity Proximity to wind turbines has been calculated using straight line distance to the nearest wind turbine (see Fig. 9). While the first turbines were erected in a few locations only, soon they were spread rather evenly across the region. Fig. 10 shows percentages of population for ranges of nearest distance to wind turbines. The results confirm that wind turbines are an integral part of landscape and the proximity to locations of residence is close for the overwhelming part of population. In 1982 about 75% of population were located more than 5 km to the nearest wind turbine. Only 6% of people in the region did live closer than 2 km to any of the earliest wind turbines. During the 1980s this rapidly changed. Since the year 1990 about 50% of the population have less than 2 km to the nearest turbine. About a quarter of the population live less than 1 km away from wind turbines. The removal of many of the older turbines since the year 2000 has led to fewer people with turbines in close proxim-
Fig. 9. Proximity to wind turbines is calculated as straight line distance from turbines and outwards. The maps show, similarly to the density maps, how fewer and fewer areas in the region are without influence from turbines. In the years after the mid-1990s, however, little change is visible. Data source: Danish Energy Agency (2008).
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Fig. 10. Proximity to wind turbines calculated as distance to nearest wind turbine by percentage of population. While in 1982 about 75% of population had a minimal distance of 5 km to the nearest turbine, this rapidly changed. Since the beginning of the 1990s about half of the population have less than 2 km to the nearest turbine. Deconstruction since the year 2000 has led to fewer people with turbines in close proximity.
ity, and more people are found to have turbines located very far away. Applicability in other regions As one of the objectives of the present paper is the contribution to the development of planning methods in an international context, the applicability of the results, in the form of the spatial model built and the analysis carried out, shall briefly be evaluated here. There are two main preconditions, which need to be in place in order to ensure a relevant and high-quality analysis of the spatial relations between wind turbines and their surrounding landscapes and population. Firstly, an analysis of this kind is only relevant where wind turbines exist, i.e. in an ex post fashion, but also where planning requires and ex ante approach. Secondly, there needs to exist a sufficiently good and available geographical data base, mainly comprised of elevation models, landscape forms and land use, high resolution population data and wind turbine locations. Methods, which include the regional distribution of turbines visibility, density and distance, will gain importance because there are an increasing number of regions in the World where there are problems of finding new locations for wind turbines in replacement of older installations. Regions like Northern Germany, California or Spain are characterised by the early installation of many turbines in patterns dictated by the planning regimes of the times when they were built. Since then, experience levels have increased and planning has moved along learning curves towards lower impact, better feasibility or better acceptance. In evaluating older planning prescriptions and regulations, their effectiveness could be measured against newer wind energy technology. Also, in regions such as parts of the UK where high pressure is put on the planning system, a set of methods like the one sketched here could be applicable to provide a better decision base for the public planning process. Regarding the availability of geographical data, the situation improves significantly in these years, as services such as GoogleMaps ©provide better data to a larger number of users, which until recently only were to be found among a few experts. Conclusions An assessment of the development of the spatial interaction between wind turbines, landscape and population has been carried out for the North Jutland region in Denmark, where wind energy has been pioneered and where development has culminated. Visibility,
density and proximity of wind turbines relative to population have been calculated in a GIS-based landscape model. The likely visibility of turbines has been modelled by means of cumulative viewsheds; proximity to wind turbines was assessed using a simple distance tool; and the density of wind turbine development was analysed using a Kernel density function, weighing groups of turbines higher than single turbines. The analysis was carried out for the period of commercial wind turbine development, using time steps of 3 years, and spanning from the year 1982 to 2007. The results are available as cartographic interpretations as well as geo-statistical representations by means of diagrams. The impact of wind turbines on landscapes and people clearly follows the technological development of wind energy. As turbine size has increased significantly in the period from about 30–150 m, higher visibility and higher turbine density has been calculated in the present study. The number of turbines has increased steadily until the year 2000, when re-powering schemes came into being, which replaced about 1000 older turbines out of the ca. 6000 turbines. Accordingly, turbines became more visible, appeared in higher densities and higher proximity to the Northern Jutland population. Alongside technical development, the planning paradigms have changed in the period, and the calculated spatial interaction is able to follow this. While in the first years few rules existed, the planning procedures became more elaborate, with a national system of municipal and regional planning areas in place in the mid-1990s. The term “poorly located wind turbines” referring to the oldest turbines was coined during the re-powering campaign, indicating that mistakes have been made in the location of many of the early turbines. The recent years after 2003 are characterised by a decline in wind turbine capacity. It was found that re-powering did not lead to lower overall visibility and density, but to higher distance for some of the inhabitants in the region. The massive increase in the number and size of turbines during the 1990s has led to increased visibility and density, and possibly to more polarisation: these parameters seem to pose a problem for a minority of people, as marginal visibility and density curves are steep for those 5–10% of population, who are most affected. It must also be mentioned that about 40% of population are located in areas, where visibility and density are unlikely to be serious problems, taking departure in this worst case analysis of visibility and density. One of the initial questions was if the change in planning policy is reflected in the spatial interaction between wind turbines, landscape and population. Clearly, when the new planning legislation became effective in the late 1990s, most wind turbines had already been built. The second important change in legislation brought about by the re-powering of old turbines, has had limited effect. The spatial model developed here is not free from assumptions and shortcomings, which reduce the credibility of results. First of all it must be accepted that a model is always simpler than reality. Ground testing of model results has not been carried out as they are considered impossible on a regional scale. Model results are therefore mostly suitable for comparison in between model years or scenarios. The visibility model is based on a terrain model characterised by completely “empty” landscapes without forests, hedges, buildings or other items that normally lead to significant reductions in visibility. The visibility figures produced here are therefore indeed worst case. Second, the criteria for calculating density may be arbitrary, and they are the result of trial and error rather than empirical findings. In particular the search radius, the kernel weight and the choice of total turbine height are subject of discussion. Third, distance to turbines may be subject to critical evaluation, as perceived distance would be a combination of straight line distance, visibility and density. Finally, all spatial interaction with population was modelled using maps of residence rather than the location of people at work, during commuting, leisure activities,
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etc., and without taking into account diurnal or seasonal variations. Also, it is a matter of extensive discussions if visibility is not spatially correlated to the number of turbines, to distance, and to type of landscape. Again, model results are useful for comparison in between scenario years rather than as absolute figures. To conclude on the effectiveness of planning regimes, a few findings of the present paper may be used. It is evident that the installation of wind turbines in wind parks, which is the idea favoured by regional planning, may be a matter of concern. Wind parks are here documented to cause higher visibility and density, particularly if density is attributed progressively. Smaller groups of wind turbines lead to lower regional visibility, but have economic disadvantages. But where larger developments typically are investment models, the more socially acceptable cooperatives are most efficient for smaller groups of wind turbines. Second, the installation of very large wind turbines has led to a higher visibility on a regional level, whereas the removal of many small turbines did not reduce overall visibility to a measurable extent. It must therefore be concluded that re-powering rather addresses economic issues than issues of bad location. It follows from the studies of proximity, visibility and density that there is a trade-off between small and large developments. Smaller turbines in small groups may conserve the notion of wind energy as a local, socially acceptable and welcome technology because of their more homogenous effect on landscapes. Planners may therefore reconsider the concept of a higher number of local wind parks to maintain acceptance and avoid increasing alienation of this technology. References Agnolucci, P., 2007. Wind electricity in Denmark: a survey of policies, their effectiveness and factors motivating their introduction. Renewable and Sustainable Energy Reviews 11 (5), 951–963. Bishop, I.D., Miller, D.R., 2007. Visual assessment of off-shore wind turbines: the influence of distance, contrast, movement and social variables. Renewable Energy 32 (5), 814–831. Danish Energy Agency, 2008. Available from: http://www.ens.dk. de Smith, M.J., Goodchild, M.F., Longley, P.A., 2007. Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, 2nd edition. Matador, Leichester.
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