Uniformly constrained land eligibility for onshore European wind power

Renewable Energy 146 (2020) 921e931

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Uniformly constrained land eligibility for onshore European wind power David Severin Ryberg a, b, *, Zena Tulemat a, Detlef Stolten a, b, Martin Robinius a a

Institute for Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., D-52428, Germany Chair for Fuel Cells, RWTH Aachen University, C/o Institute for Electrochemical Process Engineering (IEK-3), Forschungszentrum Jülich GmbH, WilhelmJohnen-Str., D-52428, Germany b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 June 2018 Received in revised form 15 May 2019 Accepted 21 June 2019 Available online 27 June 2019

When and where renewable energy sources such as onshore wind turbines generate energy depends heavily on their spatial distribution. This distribution, however, derives from the preferences and restrictions imposed by local stake-holders and dictates the overall onshore wind land eligibility. Unfortunately, due to inconsistent analysis methods and a shifting sociotechnical landscape, current understanding of land eligibility is insufficient. Therefore the Geospatial Land Availability for Energy Systems (GLAES) model, a general framework for land eligibility investigation, is used to conduct a uniformly-constrained pan-European investigation of onshore wind land eligibility in which 31 socially and technologically driven constraints are imposed. A detailed characterization of the average wind resource and current land usage within the eligible areas is then discussed. Constraint sensitivity is then evaluated at both the European and national levels including the construction of a detailed sensitivity trend for all constraints. Ultimately, it is found that 26.24% of land is eligible across Europe, with the highest shares possessed by Spain, France and Sweden. On average across Europe, onshore wind land eligibility is most sensitive to the minimal wind speed, the maximal terrain slope, the maximal distance from power lines, and the minimal distance from settlements. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Land eligibility Renewable energy Onshore wind energy Wind energy policy Policy implications Wind energy land usage

1. Introduction In order to effectively reduce carbon footprints at the national and international scales, a multitude of energy system development pathways must be evaluated in order to contend with the uncertainties of climate change and evolving sociotechnical landscapes [1]. Yet for researchers and policymakers to reach informed conclusions regarding which actions to take, extensive exploration of the possible pathways is required to find an effective global solution. Despite the complex landscape of this task, progress is nevertheless being made via energy system design models and similar analyses that serve to evaluate the potential pathways [2]. Without a doubt, many of the pathways of interest will include a contribution from variable renewable energy sources (VRES) in the total energy mix. Amongst these sources, spatially-distributed technologies such as onshore wind turbines will certainly be

* Corresponding author. Institute for Electrochemical Process Engineering (IEK3), Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Str., D-52428, Germany. E-mail address: [email protected] (D.S. Ryberg). https://doi.org/10.1016/j.renene.2019.06.127 0960-1481/© 2019 Elsevier Ltd. All rights reserved.

present. Although onshore wind energy has been the focus of intense research, and issues such as the intermittent and spatiallysensitive production are well known and commonly discussed, the extent to which sociotechnical criteria (such as distances from settlement areas, terrain suitability, and conservation efforts) can affect the available distribution of wind turbines across a region has not received the same attention [3]. As the pathways under consideration progress towards larger spatial contexts, the spatial distribution quality quickly becomes a crucial consideration since the final distribution of wind turbines directly affects where and when energy is generated. Navigating this space is a particularly challenging endeavor, however, given that the relevant criteria are dependent not only on the social, political, and economic qualities of the region in question [4], but can also change over time alongside evolving stakeholder preferences and technological advancements [5]. Given the complexity of this concept, it is clear that the proper evaluation of pathways involving onshore wind turbines fundamentally depends on a methodological application of the sociotechical criteria governing where they can be installed. Land eligibility, the process by which a plot of land is deemed eligible for installing a particular technology according to a given

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set of exclusion constraints, constitutes a fundamental and commonly employed process by which geospatial criteria influence distribution across a region [6]. When conducted in reference to onshore wind turbines, the results of a land eligibility analysis are typically used to estimate total installable capacity for the region in question. In the simplest of cases a land coverage factor is assumed, typically around 7 MW km
2 [7], which maps total land availability to installable capacity. These potentials can be used to inform energy system design studies such as that of Robinius et al. [8] and Welder et al. [9], or else can be used within a multi-criteria decision analysis approach to estimate a likely distribution of turbine €fer et al. [10], Atici et al. [11] placements, such as in the work of Ho and Vasileiou et al. [12]. Across the European scale, several previous evaluations of onshore wind land eligibility have been carried out. However, each of these studies consider different sets of exclusion constraints and, at times, consider additional steps such as applying suitability factors to the eligible areas. The European Environmental Agency [13], for example, performed a simple onshore wind land eligibility evaluation where only the avoidance of protected areas are considered as a constraint, leading to a stated availability of 82% across Europe. Hoogwijk [14] considered four land eligibility constraints, excluding locations that: have average 10 m wind speeds below 4.0 m s
1, have an altitude above 2000m, are heavily urbanized, or are within a bioreserve. Hoogwick also applied suitability factors between 50 and 90% depending on land cover type, and ultimately found that about 10% of Europe remains eligible. More involved onshore wind eligibility analyses include that of McKenna et al. [15], Bosch et al. [16] and Eurek et al. [17], who each also use suitability factors inspired by the work of Hoogwijk. Although the explicit exclusion constraint thresholds used by these authors differs between one another, they each employ constraints based on protected areas, terrain slope, elevation, forests, settlement areas, water bodies, and agricultural areas. Compared to the others, the study of McKenna et al. [15] is evaluated at the highest spatial resolution and also considers the exclusion of buffered areas around settlements, roads, railways, harbors and airports. In the end, McKenna et al. [15] find that 23% of the European landmass is available, while Bosch et al. [16] find 25%1 and Eurek et al. [17] find 40%. From these examples, it is clear that there is not a clear consensus in the literature in regards to onshore wind land eligibility in Europe. When comparing the methodologies of these studies it is also clear that significant differences are present in regards to criteria definitions,2 exclusion thresholds,3 fundamental geospatial dataset,4 or the evaluation method5; which, all together, make it difficult to identify the underlying cause of differing land eligibility outcomes. Furthermore, since all of these sources present their results “as is”, they generally do not characterize their land eligibility result or perform a sensitivity analysis to complement their primary eligibility outcome. Thus it is often not clear how these eligibility results could be best used to inform the decisions of policy makers and stakeholders, nor is it possible to predict how the results could change in response to alternative exclusion constraint scenarios or to indicate which constraints are most the most impactful. To shed light on this topic, Ryberg et al. [3] previously discussed

1 This value only includes generation locations which exceed a 15% capacity factor. Bosch et al. do not report their unrestricted land eligibility potential. 2 Such as, ‘what constitutes a protected area?’ 3 Such as, ‘how much of an exclusive buffer area should be added around a settlement area?’ 4 Such as, ‘what land cover data source is used?’ 5 Such as the spatial resolution or, even, the employed spatial reference system.

the general application of geospatial criteria in the form of exclusion constraints for land eligibility analyses, and found that a very strong spatial dependency exists regarding where in Europe particular constraints are impactful. In order to perform this analysis, Ryberg et al. developed the Geospatial Land Availability for Energy Systems (GLAES) model [18], a fully programmatic and open-source land eligibility model, which was then applied to the typical expression of 36 exclusion constraints across multiple VRES technologies and spatial domains. Although this study provides an indication of how relevant a given constraint is to a European region, and moreover in regards to how the inclusion of additional constraints could interact with the given constraint, it does not explicitly specify a land eligibility result when evaluated for the particular case of onshore wind turbines. Therefore, in this work the GLAES model is utilized to evaluate a uniformly-constrained onshore wind land eligibility analysis across Europe6 which is more detailed than similar results previously available in the literature. The outcome of this eligibility analysis is then characterized in terms of the wind resources available within the eligible areas, as well as in terms of the current land use of these areas. Following this, the automized nature of the GLAES model is utilized to perform a detailed sensitivity analysis of European and national land eligibility results to reveal the effect of singularly varying exclusion constraint thresholds from the primary result; an analysis which, to the authors knowledge, has not been previously performed at the European scale. The primary eligibility results can serve as a reference scenario of European and national onshore wind land eligibility for use in further onshore wind investigations. Meanwhile, the subsequent characterization and sensitivity analyses can inform both the research and political communities on how the available land for wind turbines, and by extension the available onshore wind capacity and generation potential, can be affected by future policies and technological advancements.

2. Methodology 2.1. Framework description As discussed in deeper detail by Ryberg et al. [3], a land eligibility evaluation framework has been developed as a precursor to this study which is capable of performing eligibility analyses in any geographical scope and can incorporate the vast majority of geospatial data formats. Since a complete description of this framework is beyond the scope of this study, only a brief overview is given here. The overall purpose of this framework is to help alleviate many of the inconsistencies which currently limit the land eligibility literature; including inconsistent criteria definitions, uncertain analysis techniques, and data availability. In order to accomplish this, the framework consists of three main features: a set of general VRES-related criteria definitions, a programmatic and fully transparent land eligibility model (GLAES), and finally a set of normalized geospatial datasets that realize the majority of the VRES-related criteria over the European context. In this section, each of these features will be shortly described. The general VRES-related criteria definitions provide a set of criteria which are typically used in the literature when the land eligibility of a VRES technology is sought. These definitions were

6 Which, for the purpose of this study, will include: Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, the Czech Republic, Croatia, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Montenegro, the Netherlands, Northern Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, and the United Kingdom.

D.S. Ryberg et al. / Renewable Energy 146 (2020) 921e931

found by Ryberg et al. [3] by reviewing over 50 studies containing a land eligibility analysis for common VRES technologies and recording the manner and frequency by which criteria were defined. As a result of this effort, 28 general criteria were identified including, for example, the distance from settlement areas and the distance from protected areas. These criteria were also separated into 4 groups depending on the underlying motivation for their consideration, these were Sociopolitical, Physical, Conservation, and Pseudo-economical. The general criteria where in some cases also broken down into multiple sub-criteria, for example the distance from urban settlements vs. the distance from smaller and less dense rural settlements. The land eligibility model contained within this framework, the Geospatial Land Availability for Energy Systems (GLAES) model, has been developed in the Python 3 programming language and has only open source dependencies. When performing a land eligibility analysis, GLAES's work flow is fairly simplistic. First the region over which the eligibility analysis is to take place must be given along with the spatial reference system and resolution of the desired eligibility result. During an initialization step, an availability matrix is created which is simply a matrix filled with boolean values that each represent the availability of one location of the study region. The availability matrix is initially filled with ‘True’ values, indicating that 100% of the land is available. After this, repetitive exclusion procedures are performed, each of which set indicated cells in the availability matrix to ‘False’, dictating that the associated locations are no longer available. For each exclusion procedure, a geospatial dataset is given along with parameters governing how the dataset should be manipulated in order to identify which cells should be indicated for exclusion. These procedures can be repeated any number of times, and can handle the most common geospatial dataset formats. When the exclusions are complete, the resulting availability matrix is written as a raster file, formulating the final land eligibility result. Finally, a collection of normalized datasets, referred to by Ryberg et al. [3] as Priors, were produced which realize the majority of the general VRES-related criteria definitions over the European context. In total, 45 Priors were created which each represent the values of one criterion (or sub-criterion) across Europe. Each are expressed in the EPSG:3035 spatial reference system at 100 m by 100 m spatial resolution. Although this coordinate system does not map directly to longitude and latitude, the extent of the chosen context generally ranges latitudinally from 34 to 72+ N and longitudinally from
12 to 32+ W, constituting roughly 1.5 billion individual pixels to be processed for each Prior dataset. Due primarily to a lack of data, Ukraine, Belarus, Moldova and Russia are not included in these datasets. Additionally, several smaller nations and island nations are not included, such as, for example, Malta, Liechtenstein, and San Marino. The exact production method, including the underlying datasets used, of each Prior dataset is detailed by Ryberg et al. [3]. As a final note, the entirety of the described framework is made freely available to the research community. The GLAES model can be found on GitHub under the project [18] named GLAES, where the version 1.0 corresponds to the version of the code at the time of this writing. Lastly, the Prior datasets can be retrieved by following the instructions detailed on the GLAES GitHub page [18], or by contacting the lead author of this work. In this way, all results regarding onshore wind land eligibility in Europe found in this study can be easily reproduced or adjusted. Nevertheless, all results of this study will also be made available in the supplementary material of this work. 2.2. Onshore wind land eligibility set-up Although

the

land

eligibility

framework

investigating any VRES technology in Europe (with the Prior datasets) or anywhere else in the world (when appropriate datasets can be found), the focus of the current work concerns onshore wind turbines on the European continent. Therefore in this section the set-up for the primary onshore wind land eligibility investigation of this work is described. As the vast majority of the investigation was performed using the Prior datasets, the characteristics of the study match to their construction; as in, using the EPSG:3035 spatial reference system, having a pixel resolution of 100 m by 100 m, and with the same set of included countries. By relying on the Prior datasets and the GLAES model, the 31 constraints shown in Table 1 are then enforced across the entire study area. All of these constraints are explained in detail by Ryberg et al. [3], nevertheless a brief overview is give here as well. In the Sociopolitical motivation group, 13 constraints are used. Settlements refers to an exclusion distance from all settlement areas to reduce visual and audible disruptions to the local population. Similarly Urban Settlements refers to an exclusion distance from dense urban settlements, where these disruptions may be a larger concern. Commercial Airports and Airfields refer to exclusion distances from large airports (having greater than 150,000 annual passengers) and small airfields (less than 150,000 annual passengers) in order to avoid turbines and airplanes being affected by each others wake. Primary Roadways and Secondary Roadways both refer to an exclusion distance from the indicated roadways in order to avoid issue which may be caused to drivers by the structural failure of a turbine, ice throws, or other anomalous events. Railways and Power Lines refer to an exclusion distance from crucial infrastructure for similar reasons. Leisure Areas, Camp Sites and Touristic Areas refer to an exclusion distance from recreational areas for similar

Table 1 Applied constraints for onshore wind turbines in Europe. The Prior datasets [3] are used to represent all criteria involved except for the constraint based on wind speed. Nevertheless, the stated source indicate the underlying data source used in each case.

Sociopolitical

Physical

Conservation

Eco

is

capable

of

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Constraint

Threshold

Source

Settlements Urban Settlements Commercial Airports Airfields Primary Roadways Secondary Roadways Railways Power Lines Leisure Areas Camp Sites Touristic Areas Industrial Areas Mining Sites Slope Water Bodies Rivers Coast Lines Wetlands Elevation Sandy Areas Bird Areas Habitats Biospheres Wildernesses Landscapes Reserves Parks Natural Monuments Access Connection Wind Speed

>800 m >1.2 km >5.0 km >3.0 km >300 m >200 m >200 m >200 m >1.0 km >1.0 km >1.0 km >300 m >200 m <17 + >400 m >200 m >1.0 km >400 m <2.0 km >1.0 km >1 km >200 m >0 m >0 m >0 m >0 m >0 m >500 m <5.0 km <20 km <4.0 m s
1

CLC [19] EuroStat [20] EuroStat [21] EuroStat [21] OSM [22] OSM [22] OSM [22] OSM [22] OSM [22] OSM [22] OSM [22] CLC [19] CLC [19] EuroDEM [23] CLC [19] EuroStat [24] CLC [19] CLC [19] EuroDEM [23] CLC [19] WDPA [25] WDPA [25] WDPA [25] WDPA [25] WDPA [25] WDPA [25] WDPA [25] WDPA [25] OSM [22] OSM [22] GWA [26]

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reasons as those stated for settlements. Industrial Areas and Mining Sites also indicate an exclusion distance in order to avoid disruption to the operation of these areas as well as to minimize danger posed to employees or equipment. Seven constraints were used in the Physical motivation group. Slope indicates a maximal terrain slope, above which construction of the turbine is deemed unsound. Wetlands, Water Bodies and Rivers indicate exclusion distances from wetlands, large water bodies, and rivers in order to prevent the degradation of riparian zones, avoid water contamination during turbine construction, and to minimize the likelihood of water damage to the turbine in rainy periods. Coast Lines is included for similar reasons, but is given a larger exclusion distance to account for the corrosive effects of sea spray on turbine components. Elevation indicates a maximal elevation above which air density decreases and construction locations become increasingly inaccessible. Finally, Sandy Areas indicates an exclusion distance from areas dominated by sand coverage, as the sand can be carried by the wind and can accelerate erosion of turbine components. The Conservation group is characterized by eight constraints, all of which are aligned with the protected areas category guidelines described by the International Union for Conservation of Nature (IUCN) [27]. In general, the constraints included in this group enforce an exclusion distance around the indicated areas and serve to protect the fluara, fauna and facade of the land. Included in the Habitats constraint are the sites identified by the European Union's Habitat Directive of 1992 [28] and incorporated into the Natura 2000 network of protected sites. Similarly, the Bird Areas constraint includes the sites identified by the European Union's Bird Directive of 2009 [29] which were also incorporated into the Natura 2000 network. Only three constraints are considered in the final motivation group, Pseudo-Economic. Access is concerned with a maximal distance from all roadways (including tertiary, service and unclassified roadways) and is included since constructing turbine in locations which are far from preexisting roadways would incur high costs in order to make them accessible for construction and maintenance. Likewise, Connection refers to a maximal distance from any point on the electrical grid and is included since constructing turbines too far from the preexisting grid would incur high connection costs. Finally, the Wind Speed constraint relates to the average wind speed7 seen at each location and removes those locations where the typical wind resource is not strong enough to justify the installation of a wind turbine. To represent wind speeds, the unprocessed Global Wind Atlas (GWA) [26] dataset produced by the Denmark Technical University will be used. This is a global raster dataset with a spatial resolution of 1 km by 1 km, and provides time-averaged wind speed values over the last few decades. Furthermore, since the average wind speed is a smoothly-varying quantity, the GWA data is warped, with cubic resampling, to the 100 m by 100 m resolution and EPSG:3035 spatial reference system used for the primary eligibility analysis prior to applying the exclusions. Besides this, however, the Prior datasets discussed by Ryberg et al. [3] are used to represent all other exclusion constraints employed in this study. For clarity, the fundamental sources on which these Priors are built are also briefly described. The Corine Land Cover [19] (CLC) raster dataset describes the predominant land cover of each 100 m by 100 m patch of land across Europe and was primarily used to indicate features visible from satellites; such as settlement areas, mining sites, and open water bodies. In total, the CLC dataset indicates 44 different land cover categories. An extract of the OpenStreetMap [22] (OSM)

7

At a height of 50 m above ground.

dataset is employed in vector format to represent human-centric features such as roadways, power lines, touristic and leisure areas. The EuroDEM [23] dataset is a digital elevation raster dataset with a pixel resolution approximating 30 m and was used to determine the elevation and slope at all locations. The World Database on Protected Areas [25] (WDPA), another vector dataset, was used to indicate designated protected areas of all types, but was filtered differently for each of the conservation constraints according to internationally recognized categories [27]. Finally, three datasets originating from EuroStat were used to: differentiate large/commercial airports from smaller airfields [21], trace likely routes of rivers and streams [24] too small to register in the CLC raster, and to identify settlements areas qualifying as urban [20]. As mentioned, a more detailed discussion of how these datasets were manipulated, in particular concerning how the OSM and WDPA were filtered in order to extract the relevant features, can be found in the publication of Ryberg et al. [3]. 2.3. Eligibility characterization and sensitivity The analysis described in the previous section presents a highly detailed look at onshore wind land eligibility in Europe, however, to build upon this, the primary eligibility results are further characterized and tested for sensitivity. The approaches taken to perform these evaluations will be described in this section. In the context of this work, characterization refers to identifying the land cover category shares and the average wind resource within eligible areas over Europe as well as for each evaluated country. Land cover shares will be determined by counting the occurrence of each of the 44 land cover categories within the CLC [19] dataset which remain in an eligible area. Similarly, average wind speed in the eligible regions will be determined simply by averaging all values from the GWA [26] which are in an eligible location. The distribution of all average wind speeds within each regional scope will also be determined. The sensitivity of the primary land eligibility result is investigated in a two-level analysis. As a first step, all but one of the constraint thresholds from Table 1 are held constant while, for the free constraint, the threshold value is changed by ± 20%.8 This is repeated for each of the 31 constraints used in the primary eligiblity evaluation, and in each case the overall percentage by which the resulting land eligibility total changes from the base case is recorded. For constraints with a zero-valued threshold (indicating that only the features themselves are excluded without a buffer area), a 50 m buffer is used for the þ20% case and nothing is evaluated for the
20% case. This procedure is evaluated for the entire European study area as well as specifically for Germany and Spain, as they are the current European leaders in terms of installed onshore wind capacity [30]. In the second stage of the sensitivity analysis, a similar sensitivity procedure is repeated wherein all constraints of Table 1 are held constant except for a chosen constraint which is altered and the percent difference of the primary land eligibility result is observed. In this instance, however, the chosen constraint's threshold is scanned across a continuous range of values reflecting the range observed in the literature; corresponding to the ranges used by when constructing the Prior datasets [3]. By logical deduction, the sensitivity of these constraints should describe a monotonically-decreasing function originating from the total eligibility found at the lowest exclusion threshold value (where the constraint has a minimal effect) and summarily transitioning

8 To note, ± 20% is arbitrarily chosen since the overarching goal with this step is to identify the most sensitive constraints.

D.S. Ryberg et al. / Renewable Energy 146 (2020) 921e931

asymptotically to no available land as the exclusion threshold approaches the highest exclusion limit. This sensitivity procedure is evaluated for all constraints over the entire study area, as well as for each country. 3. Results 3.1. Uniformly-constrained onshore wind land eligibility result Fig. 1 displays the remaining eligible areas after applying the exclusion constraints of Table 1 across the European landscape. In total 1,298,395 km2 is found to be available across Europe, constituting 26.24% of the study region. Compared to the previous studies of onshore wind land eligibility in Europe it is clear that the total eligibility value found here is more or less in the middle of these other estimates. Hoogwijk [14], who estimated 10.8% availability at the European level, and the European Energy Agency [13], who reported 82% are of course significantly different from the result of this work. However, the constraints and methodology used in both of these studies differ significantly from the current one, as does the study region, and therefore this comparison cannot serve to invalidate any result. The McKenna study [15], on the other hand, who used the most similar constraints and datasets compared to those used here, reported as land eligibility result of 23%, which constitutes only a
12% difference from the result here. Table 2 summarizes the results for each country within the study area as ordered by the their total available land. It can immediately be seen from Fig. 1 that available land in many central European nations, as well as the UK and Greece are greatly dispersed across the countryside, while other countries such as Spain, Ireland, France and the Nordic states show much larger contiguous patches of available land. Spain is found to possess the highest share of available land, with 165,349 km2, followed by

925

Sweden (160,601 km2) and then France (147,847 km2). Compared to the whole study area, these countries constitute 12.73, 12.37, and 11.39%, respectively, of all the land in Europe available for onshore wind turbines. Germany is ranked 10th in the current study, which is interesting considering its leading position in terms of European installed onshore wind capacity [30]. Unsurprisingly, smaller countries such as Luxembourg and several Baltic nations rank towards the bottom of the list. If ordering were defined by the percentage available, Bosnia would be first, with 44.20% of land available, followed by Lithuania (42.32%) and Montenegro (40.70%). However, especially in the Balkan states, it is possible that this high availability result is in part due to incomplete infrastructure and conservation data which, once included, would certainly reduce the available land. The Netherlands, Belgium and significant portions of Germany are characterized by highly dispersed settlement areas and, as such, are heavily impacted by minimal proximities from these features. As a result, all of these countries have a relatively low availability percentage: Belgium is last, with 4.40%, Germany is sixth from last, with 14.67%, followed closely by the Netherlands at 15.63%. Many of the previously mentioned studies of European land eligibility only provide a value for the whole of Europe, however McKenna et al. [15], Bosch et al. [16] and Eurek et al. [17] each reported their values at the national level, allowing a direct comparison against the findings of this study. Fig. 2, therefore, shows the percent difference in land eligibility results compared against this work for each country. When taking the total-area-weighted average of these differences, it is seen that the outcome of Eurek et al. [17] differs from the this work by þ62.5%, McKenna et al.‘s [15] result differs by
26.2%, and Bosch et al.‘s [16] result differs by þ16.2%. The largest national discrepancy is seen in Belgium, where Eurek et al.‘s and Bosch et al.‘s result are nearly 806% and 960% of the result found here. Following this, Luxembourg and the Netherlands also show large discrepancies of at most 598% and 377%, respectively. These large discrepancies are a result of Eurek et al.‘s and Bosch et al.‘s lack of an exclusive buffer distance around settlement areas, roads, railways, and water bodies; which are all present in this work and are mostly present in the work of McKenna et al. [15]. 3.2. Land eligibility characterization In addition to the total land eligibility results, Table 2 also summarizes a few wind speed characterizations of the available area in each regional context. For example, across Europe it is seen that an average wind speed9 of 5.42 m s
1 can be expected over all locations which are eligible for wind turbine placement. In Germany and Spain, the average wind speed is 5.03 and 5.21 m s
1, respectfully. Since these values are both below the European average, this suggests that these two countries, which together represent 44% of the total wind energy capacity in Europe [30] do not actually correspond to the regions with the greatest wind energy generation potentials. In contrast, Ireland, which is found to have the highest average wind speed in the eligible locations of 7.72 m s
1, only currently possess 2% of Europe's overall wind energy capacity [30]. Of course, only considering the average wind speeds across each country does not fully characterize their wind energy potential since it does not accurately reflect the upper tier of average wind speeds which would correlate to the turbine installation locations most likely to be utilized. Therefore Fig. 3 shows the observed distributions of location-specific average wind speed values within

Fig. 1. Available areas in the European application case. A full-resolution rasterversion of this outcome is available in the supplementary data.

9

Measured at 50 m from the surface.

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Table 2 Land eligibility results per country.

Available Area

European

Share of Availability Found In:

Mean Wind Speed

Share

Agriculture

Forests

In Eligible Area

[km2]

[%]

[%]

[%]

[%]

[m s
1]

Europe Spain Sweden France Finland Poland Norway Romania United Kingdom Italy Germany

1298395 165,349 160,601 147,847 109,043 71,615 65,224 60,156 59,231 53,141 52,450

26.24 32.67 35.69 26.90 32.36 22.95 20.06 25.24 24.13 17.67 14.67

100.00 12.73 12.37 11.39 8.40 5.52 5.02 4.63 4.56 4.09 4.04

48.82 59.37 7.72 68.17 10.11 67.71 4.63 74.73 70.83 70.55 67.11

37.49 17.26 78.96 27.60 76.85 30.34 51.46 22.42 11.58 18.16 32.14

5.42 5.21 5.31 5.56 5.45 5.58 6.22 4.75 6.71 4.88 5.03

Serbia Portugal Hungary Lithuania Ireland Latvia Bulgaria Greece Bosnia and Herz. Estonia

31,507 29,731 28,393 27,512 26,387 25,807 25,506 24,597 22,571 17,630

40.27 32.36 30.51 42.32 37.50 39.88 22.86 18.55 44.20 38.76

2.43 2.29 2.19 2.12 2.03 1.99 1.96 1.89 1.74 1.36

66.95 47.81 76.41 63.13 86.12 39.89 71.86 51.26 33.60 32.59

24.20 26.11 18.14 31.71 6.80 42.85 20.51 12.20 44.81 57.49

4.68 5.08 4.87 5.75 7.72 5.52 4.69 4.87 5.08 5.52

Czech Republic Croatia Austria Denmark Slovakia Netherlands N. Macedonia Montenegro Albania Switzerland

17,481 14,325 11,813 10,459 8326 5888 5706 5428 4200 3432

22.19 25.10 14.09 24.24 16.96 15.63 22.98 40.70 14.62 8.32

1.35 1.10 0.91 0.81 0.64 0.45 0.44 0.42 0.32 0.26

51.83 40.22 43.59 87.54 53.41 89.75 50.65 13.61 30.58 48.03

46.30 41.29 50.49 10.54 42.54 8.61 19.60 40.79 24.04 41.63

5.26 4.92 5.40 6.57 4.86 6.15 4.56 5.29 4.85 4.90

Kosovo Slovenia Belgium Luxembourg

2782 2713 1351 187

25.48 13.58 4.40 7.23

0.21 0.21 0.10 0.01

38.38 44.00 73.35 45.59

44.93 55.17 25.66 53.88

4.61 4.58 5.55 4.78

Fig. 2. Comparison of national onshore wind land eligibility results to those reported by McKenna et al. [15], Bosch et al. [16] and Eurek et al. [17].

the eligible areas in Europe as well as in each country. Note that summing the counts of all bins10 in these plots returns to the total available areas reported in Table 2. For Europe, it is clear that the average wind speed of 5.42 m s
1 does not accurately reflect the significant portion of eligible locations with average wind speeds in

10

Which are each of 0.05 m s
1 width.

the range of 7e9 m s
1. This same effect is also seen for Germany and Spain, although their upper tier of high average wind speeds only reaches to 7 m s
1. As indicated by the averages reported earlier, wind speeds in Ireland are clearly far superior to many of the other countries, as seen by the fact that nearly one third of its wind-eligible locations have an average wind speed above 8 m s
1. Nevertheless, Norway is seen to surpass Ireland with its most windiest locations for which average wind speeds of 9e10 m s
1 are observed. The outcome of investigating land cover category shares within the eligible regions are also summarized in Table 2, where only the total shares of eligible forests and eligible agricultural areas are reported. Only these two groups are reported in Table 2 since they clearly make up the vast majority of the onshore-wind eligible regions in Europe. For example, across the whole of Europe 48.82% of the eligible land is currently used by some sort of agricultural area, and 37.49% of the available land is currently some sort of woodland. While this share is closely split at the European level, some countries are seen to heavily favor one or the other. This is seen in Sweden and Finland where over three quarters of the eligible land is currently woodland. Similarly, 86.12, 87.54, and 89.75% of the eligible land in Ireland, Denmark, and the Netherlands, respectfully, are currently used for some sort of agriculture. The CLC dataset breaks both of these land cover categories down into sub categories, and furthermore specifies other categories, and

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Fig. 3. Distributions of location-specific average wind speeds within the eligible regions of each country in the study area.

so Fig. 4 provides a heat map of these shares for all CLC land cover categories. Note that this figure only shows CLC categories that make up at least 1% of the eligible area in at least one country. These distinctions show that the agricultural use in Ireland is almost entirely attributed to pastures, while for Denmark it is instead completely attributed to non-irrigated agricultural land. Similarly, Bulgaria, Germany, Hungary, Poland, and Romania are also seen to have over half of their eligible areas within areas currently used for non-irrigated agricultural land. Besides agricultural areas and forests, Norway appears to have between 15 and 20% of its available land in both sparsely vegetated areas as well as moors and heathlands, and about 2.5% in bare rock areas. Greece and Albania both have a significant portion of their available land in sclerophyllous

vegetation areas, as does, to a less degree, Spain and Italy. 3.3. Land eligibility sensitivity Fig. 5 displays the result of investigating the ±20% sensitivity of each constraint in the context of Europe, Germany and Spain. It is seen that the Wind Speed constraint has the largest impact on total land availability for all three regional scopes, but only for increases in the constraint threshold. Next to this, the Slope constraint is the second most sensitive constraint over Europe and is third-most impactful in Spain, but has a relatively small impact on Germany. Meanwhile, Settlements, the third-most impactful constraint across Europe, is clearly dominant in Germany, yet is ranked 7th for

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Fig. 4. Land cover category shares of onshore wind eligible land in Europe and constituent countries.

Fig. 5. Sensitivity of total resulting land eligibility for Europe, Germany, and Spain to each input constraint. Constraints not displayed have sensitivities less than those shown.

Spain. The Access and Connection constraints both show significance in the general European sense, with a higher significance for Spain, yet have almost no significance in Germany. Conversely, increases to the Protected Landscapes constraint has a small, but nevertheless noticeable, effect on Germany while having almost no effect on either Spain or the whole of Europe. Airports are notable considering that they constitute one of the most considered Sociopolitical constraints in the land eligibility literature [3], but consistently contribute only a small impact. Despite having a lower proximity threshold (3 km vs 5 km), Airfields are seen to have a much larger impact compared to Airports. Lastly, constraints based on touristic, industrial, mining, and sandy areas, as well as

protected parks, national monuments, reserves, wildernesses, and biospheres show next to no impact for all three regional scopes; which is more or less in line with the criteria value determination performed by Ryberg et al. [3]. Fig. 6 show the results of the more detailed sensitivity investigation for Europe and various nations in the study area. Since not all countries can be shown in this figure, only Sweden, France, Italy, Austria, Denmark, the United Kingdom, Germany, Spain, and the whole of Europe will be discussed on account of their interesting and representative sensitivity behavior. Similarly, only the Wind Speed, Slope, Settlement, Connection, Secondary Roads, Water Bodies, Protected Habitats, and Airfields constraints are shown due to their high ranking impact in one or more regional contexts. Nevertheless, the full sensitivity trends for all countries and constraints is provided in this work's supplementary material. Each line shown in these plots displays the resulting percentage of available land for the respective region as a function of the variable constraint, which was applied after the other constraints shown in Table 1. Furthermore, the vertical line indicates the threshold value used in the primary eligibility evaluation. The predicted behavior of a low-exclusion starting point, monotonic decent and asymptotical approach to zero is generally found to hold, which is exemplified especially well by the Wind Speed sensitivity trends. Quite clearly, however, the specific characteristics of this general behavior strongly depends on both the constraint in question as well as the region in which it is evaluated. For example, all regional contexts show a strong sensitivity trend for the Wind Speed constraint, although the point at which this constraint begins to take effect appears to be different for each context; and naturally corresponds to the eligible average wind speed distributions of Fig. 3. In comparison, the Settlement constraint has a much more varied regional response; where Germany, France and Austria are strongly impacted for even small proximity thresholds, while Sweden and Spain require much larger

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Fig. 6. Detailed Sensitivity trends for various constraints. The Wind Speed, Slope, Settlement, Connection, and Secondary Road constraints are shown as they are the most sensitive in the European context, while the Water Body, Airfield, and Habitat constraints are shown on account of their interesting behavior.

thresholds before this constraint becomes significant. Countries that are notoriously flat, such as Denmark, show a rapid saturation to the low-exclusion percentage for the Slope threshold (which in this case is also asymptotically approaching 0% at 0+), while countries that have mountainous features, Austria and Italy, appear to increase nearly linearly. Proximity constraints from Water Bodies and Habitats both share a characteristic wherein they show consistent low-sensitivity for most countries, but then a remarkably strong sensitivity for one. For instance, the exclusion of protected habitats alone reduces Denmarks land eligibility from greater than 30% to around 24%. Meanwhile, the Water Body constraint appears to have a very strong impact in Sweden, while having limited sensitivity in other countries or in Europe as a whole. A similar, albeit less pronounced, behavior is also seen in France for the Secondary Road constraint. Finally, the discrepancy between Airports and Airfields is explained. Neither of these constraints are large contributors to land eligibility until large thresholds are considered, however deviation from the low exclusion point is observed much sooner for airfields than for airports. Since airfields are more numerous and have a greater likelihood of occupying remote locations than airports, exclusions from airfields tend to have less overlap with exclusions from other constraints compared to exclusions from airports. As a result, airfields are found to be more significant despite having a significantly lower exclusion distance.

4. Conclusion When applying the uniform exclusion constraints scenario proposed in this study, it appears that almost all European countries have a significant amount of land that can be utilized for new wind parks; in total summing to 26.24% of the land in Europe. Countries like Spain, France, and Sweden, who possess the most available land, may find it easier than other countries to continually utilize new high-quality areas when expanding their wind fleet due to their expansive availability. Although all countries will benefit from economy-of-scale effects in the future wind industry, these high-availability countries would likely benefit more from policies and advancements which reduce costs in the existing manufacturing, transportation, and construction processes. In comparison, other countries may experience a higher incentive to take full advantage of their prime wind locations as a result of their relatively smaller percentage of available land, and are therefore more likely to be benefited by turbine performance advancements and policies to reduce deconstruction costs; which can both increase the value gained when replacing aging wind parks. Depending on their land cover characterizations, each country will also likely need to employ a unique policy strategy in order to achieve large installations of onshore wind capacity. For example, a country like Ireland, where onshore wind available land is predominantly used for pastures, may need to pursue turbine designs

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which are less disruptive to grazing animals or provide attractive incentives to farmers. Conversely, countries like Sweden and Finland, where most available land is found in forests, will likely need to favor modular turbine designs, use careful construction techniques, and establish clear environmental regulations which can all minimize deforestation and other negative effects on the environment. As the wind speed threshold is seen as the most sensitive constraint across Europe when the limit of 4 m s
1 is specified, then it stands to reason that wind turbines which are specifically designed for these lower wind speed locations would have ample area available to them. Therefore, although turbines in these locations might not produce electricity as cheap as turbines in higher average wind speed locations, they might nevertheless face less social opposition when installed in large quantities. Besides this, the average terrain slope threshold is seen as the second most sensitive criteria across Europe, which suggests advancements in turbine designs that improve suitability amidst steeper slopes could open up large quantities of currently unsuitable locations; such as by engineering turbines to be able to better withstand turbulent winds or to remain erect despite potentially unstable ground. Meanwhile, in the context of adapting to requirements of a high terrain slope, countries like Denmark, where the slope constraint is not impactful unless a very small threshold is chosen, can likely continue to use conventional turbine and foundation designs for the foreseeable future. On the other hand, Denmark's strong sensitivity to constraints related to protected habitats clearly showcases how future Danish policies related to habitat availability can directly affect their onshore wind energy potential. The same is true for Sweden in the case of policies related to distance from water bodies, meaning that future wind farms in Sweden may need to make extra considerations compared to other countries in order to ensure that water bodies aren't negatively impacted by the wind farm's presence (and vice versa). In Spain, where a high sensitivity to a maximum connection distance and a relatively low sensitivity to settlement proximity is observed, an unintended increase of onshore wind available land could occur as a result of urban expansion and population growth; and so city planners may want to take this dynamic into consideration when directing future expansion. Conversely, Germany's extremely low sensitivity to a maximal connection distance and maximal access distance suggest that there are virtually no locations in Germany which are not already reasonably accessible or grid-connectible. As a result, Germany's onshore wind land eligibility could be improved via social policies which, for example, permit turbines in certain conservation areas to limited degree. Of course, the results presented here should also be understood in context. As mentioned before, the concept of land eligibility is, at the moment, largely unregulated and is always subject to change. Due to the specific attitudes of local policy makers and stakeholders, buffer distances and other threshold values would certainly differ in small regional contexts from the uniform assumptions made in the primary eligibility analysis of this study. Furthermore the constraints considered here will likely not cover all the relevant criteria for some areas, where special considerations may need to be made. Finally, considering the large spatial context, ensuring the completeness and utter accuracy of all datasets used would involve a large collective effort from the research community which goes far beyond the scope of this work; and therefore the emergence of new or updated datasets could also change the resulting land eligibility outcomes. As a result of these considerations, the primary eligibility result should be viewed as more of a suggestion of how onshore wind land eligibility might look as opposed to precisely how it does look. Moreover, land eligibility is a concept which should never be considered solved,

but instead should be expected to change and should be frequently re-evaluated at the regional level to reflect new data and shifting constraints. For this reason, it is highly beneficial when local policy makers and stake holders clearly define and communicate their preferred constraints such that they can be directly incorporated into land eligibility evaluations; and, by extension, energy system design studies. Nevertheless, the primary eligibility result, as well as all plotting data, is provided in the supplementary data of this study. It is the hope of the authors that future researchers will use these results to inform their own onshore wind implementation studies, or at the very least as a point of comparison for their own land eligibility analyses. Moreover, the underlying open source GLAES model [18] used for this study can be used by any interested party who may wish to perform a land eligibility evaluation for onshore wind turbines or any other spatially-constrained application. Lastly, the characterization results provides an initial intuition on how onshore wind available land is distributed in Europe, and the sensitivity results provide a first-order approximation of how the land eligibility of these countries can respond to criteria shifts; both of which can be useful for policy makers and stake holders who seek to predict how their actions can directly impact the future of wind energy in their regions. Addendum. Map data copyrighted OpenSteetMap contributors and available from https://www.openstreetmap.org. This work was supported and funded by the Helmholtz Association under the Joint Initiative EnergySystem 2050A Contribution of the Research Field Energy. Furthermore, it was supported by funding of the Virtual Institute for Power to Gas and Heat by the Ministry of Innovation, Science and Research of North Rhine-Westphalia. The authors would like to thank the numerous master and bachelor students who contributed to the METIS packages and Christopher Wood for editing this paper. Competing Interests The authors declare that they have no competing financial interests. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.renene.2019.06.127. References [1] European Comission, A Roadmap for Moving to a Competitive Low Carbon Economy in 2050, Europese Commissie, Brussel. [2] S. Al-Yahyai, Y. Charabi, A. Gastli, A. Al-Badi, Wind farm land suitability indexing using multi-criteria analysis, Renew. Energy 44 (2012) 80e87, https://doi.org/10.1016/j.renene.2012.01.004. [3] D. S. Ryberg, M. Robinius, D. Stolten, Evaluating land eligibility constraints of renewable energy sources in europe, Energies 11 (5). doi:10.3390/ en11051246. URL http://www.mdpi.com/1996-1073/11/5/1246. [4] C. Klessmann, A. Held, M. Rathmann, M. Ragwitz, Status and perspectives of renewable energy policy and deployment in the European UnionWhat is needed to reach the 2020 targets? Energy Policy 39 (12) (2011) 7637e7657. https://doi.org/10.1016/j.enpol.2011.08.038. http://www.sciencedirect.com/ science/article/pii/S0301421511006355Uhttps://doi.org/10.1016/j.enpol. 2011.08.038. [5] B. Moller, Spatial analyses of emerging and fading wind energy landscapes in Denmark, Land Use Policy 27 (2) (2010) 233e241, https://doi.org/10.1016/ j.landusepol.2009.06.001. [6] M. Iqbal, M. Azam, M. Naeem, A.S. Khwaja, A. Anpalagan, Optimization classification, algorithms and tools for renewable energy: a review, Renew. Sustain. Energy Rev. 39 (2014) 640e654, https://doi.org/10.1016/ j.rser.2014.07.120. [7] P. Denholm, M. Hand, M. Jackson, S. Ong, Land Use Requirements of Modern Wind Power Plants in the United States, Tech. Rep., NREL, Golden, Colorado. USA, 2009 https://doi.org/10.2172/964608.

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