On the socio-technical potential for onshore wind in Europe: A response to Enevoldsen et al. (2019), Energy Policy, 132, 1092-1100

Abstract

A recent article in this journal claimed to assess the socio-technical potential for onshore wind energy in Europe. We find the article to be severely flawed and raise concerns in five general areas. Firstly, the term socio-technical is not precisely defined, and is used by the authors to refer to a potential that others term as merely technical. Secondly, the study fails to account for over a decade of research in wind energy resource assessments. Thirdly, there are multiple issues with the use of input data and, because the study is opaque about many details, the effect of these errors cannot be reproduced. Fourthly, the method assumes a very high wind turbine capacity density of 10.73 MW/km² across 40% of the land area in Europe with a generic 30% capacity factor. Fifthly, the authors find an implausibly high onshore wind potential, with 120% more capacity and 70% more generation than the highest results given elsewhere in the literature. Overall, we conclude that new research at higher spatial resolutions can make a valuable contribution to wind resource potential assessments. However, due to the missing literature review, the lack of transparency and the overly simplistic methodology, Enevoldsen et al. (2019) potentially mislead fellow scientists, policy makers and the general public.

Full text

1
On the socio-technical potential for onshore wind in
Europe: a response to Enevoldsen et al. (2019),
Energy Policy, 132, 1092-1100
McKenna
a
, R., Ryberg
b
, D. S., Staffell
c
, I., Hahmann
d
, A. N., Schmidt
e
, J, Heinrichs
b
, H.,
Höltinger
e
, S., Lilliestam
g
, J., Pfenninger
f
, S., Robinius
b
, M., Stolten
b
, D., Tröndle
g
, T., Wehrle
e
,
S., Weinand
h
, J. M.
a
Corresponding author: Energy Systems Analysis, DTU Management, Technical University of Denmark, Lyngby,
Denmark, rkenna@dtu.dk; School of Engineering, University of Aberdeen, Scotland, UK
b
Institute for Techno-economic Systems Analysis (IEK-3), Forschungszentrum Jülich GmbH, Jülich, Germany
c
Centre for Environmental Policy, Imperial College London, London, UK
d
Resource Assessment Modelling Group, Department of Wind Energy, Technical University of Denmark, Roskilde,
Denmark
e
Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna,
Austria
f
Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland
g
Institute for Advanced Sustainability Studies, Potsdam, Germany
h
Chair of Energy Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany
Abstract
A recent article in this journal claimed to assess the socio-technical potential for
onshore wind energy in Europe. We find the article to be severely flawed and raise
concerns in five general areas. Firstly, the term socio-technical is not precisely defined,

2
and is used by the authors to refer to a potential that others term as merely technical.
Secondly, the study fails to account for over a decade of research in wind energy
resource assessments. Thirdly, there are multiple issues with the use of input data and,
because the study is opaque about many details, the effect of these errors cannot be
reproduced. Fourthly, the method assumes a very high wind turbine capacity density of
10.73 MW/km
2
across 40% of the land area in Europe with a generic 30% capacity
factor. Fifthly, the authors find an implausibly high onshore wind potential, with 120%
more capacity and 70% more generation than the highest results given elsewhere in the
literature. Overall, we conclude that new research at higher spatial resolutions can
make a valuable contribution to wind resource potential assessments. However, due to
the missing literature review, the lack of transparency and the overly simplistic
methodology, Enevoldsen et al. (2019) potentially mislead fellow scientists, policy
makers and the general public.
Keywords
Onshore wind; resource assessment; public acceptance; barriers; feasibility
1 Introduction
Resource assessments for renewable energy is an active field of research driven by the
worldwide push towards more sustainable energy systems. Significant attention has
been devoted to this area in research and literature over the past decades, leading to
substantial methodological improvements and more reliable resource estimates. One

3
area which has seen particular methodological focus is improving the ways in which
such studies account for non-technical (e.g. social) constraints for renewable resources
like onshore wind (e.g. Jäger et al. 2016, Höltinger et al. 2016, Harper et al. 2019,
Eichhorn et al. 2019).
Against this background, a recent paper in this journal seemed upon first impression
to be a welcome contribution. It presents a resource assessment for onshore wind in
Europe, purporting to evaluate the socio-technical potential for this technology
(Enevoldsen et al. 2019). Indeed, the article received intensive media attention upon its
publication in July 2019, partly due to the enormous European onshore wind potential
implied
1
.
A closer reading of the article, however, reveals five severe shortcomings, which we
address in the following section:
1. Potential definitions: the paper employs the term socio-technical without clearly
defining or differentiating it from related terms (section 2.1).
2. Lack of a literature review of the state of the art: the paper fails to account for
substantial progress in this area and ignores the body of recent literature (section
2.2).
3. Opaque and incorrect use of input data: the contribution lacks transparency in
its application of existing datasets and in some cases is demonstrably incorrect
2
.
1
For example a search on 26.11.19 for “wind potential Europe” revealed the following online articles amongst the top fifteen results
on Google: Wind it up: Europe has the untapped onshore capacity to meet global energy demand
(https://www.sussex.ac.uk/news/media-centre/press-releases/id/49312); Study shows huge potential of Europe’s onshore wind
(https://www.theengineer.co.uk/onshore-wind-untapped-europe/); Europe's 52.5TW onshore wind potential revealed
(https://renews.biz/54837/europes-525tw-onshore-wind-potential-revealed/); Europe could power the world with onshore wind
(https://www.anthropocenemagazine.org/2019/08/europe-could-power-the-world-with-onshore-wind/); Europe Could Power The
Entire World With Onshore Wind Farms Alone (https://www.sciencealert.com/europe-could-power-the-entire-world-with-enough-
onshore-wind-farms); Turning Europe into a giant wind farm could power the entire world
(https://www.weforum.org/agenda/2019/08/europe-giant-wind-farm-could-power-entire-world/).
2
A recent corrigendum (Enevoldsen et al. 2020) corrects the citation and description of the data, but not its application as outlined in
section 2.3.2.

4
Given the incomplete description of how data were used, the full extent of the
error introduced by incorrect data usage is difficult to estimate (section 2.3).
4. Oversimplified methods without validation: the paper employs overly
simplistic methods which are substantially behind the state of the art, are not
validated and, in most areas, impose considerable bias on the results (section
2.4).
5. No consideration of other recent results: the controversial results are only
compared with a single outdated study, and not put into the context of the larger
body of recent work on wind resource assessments (section 2.5).
The remainder of this response addresses these five points in turn before closing with an overall
conclusion.
2 The five weak points of Enevoldsen et al.
2.1 Potential definitions
In the field of resource assessments for renewable energies, it is common to distinguish
between different kinds of potential. For example, five different potential categories can
be defined as shown in Table 1 (Hoogwijk et al. 2004, Jäger et al. 2016). The table shows
these potential terms alongside possible definitions and examples of energy policy
relevance.

5
Table 1: Overview of different potential definitions and examples of their policy relevance
Potential term
Defined as…
Policy relevance
, e.g.
Theoretical
potential
…the total energy content of wind globally. Generally irrelevant
Geographical
potential
…(the amount of wind energy across) the total area
available for wind turbines
Generally irrelevant
T
echnical
potential
…the electricity that can be generated from wind turbines
within the geographical potential with a given turbine
technology (e.g. current, future).
Wind industry R&D,
innovation and market
dynamics
Economic
potential
…a subset of the technical potential that can be realized
economically.
Energy-political
frameworks
Feasible
potential
…reflecting non-technical constraints. Public acceptance,
market barriers, inertia
Within this context, it is difficult to situate Enevoldsen et al.’s (2019) approach combining
a “1) common wind atlas methodology centering on information about wind resources,
with 2) high resolution exclusion of areas where wind project development is hampered
by socially centered constraints to siting”. A problem with this second claim is that it blurs
technical constraints (e.g. exclusion criteria for infrastructure, as in the geographical
potential definition in Table 1) with some constraints relating to social acceptance (more
details in section 2.3). Enevoldsen et al. claim that “the erosion of public support and siting
increasing costs [sic] coupled with the emergence of promising innovations in offshore
foundations […] have tempered onshore wind growth projections” and call for a “more
qualitative, refined socio-technical dimension” in the assessment of wind power
potentials, which should consider that “public opposition is complex, and it often stems
from visual (aesthetic), environmental, and socioeconomic concerns, especially in regard

6
to onshore wind projects.” However, they fail to define exactly what their socio-technical
potential is and, based on the presented information, we must conclude that they do not
(attempt to) consider any of these complex social constraints in their approach; thus their
potential should be considered a technical one, despite the title of the paper.
2.2 Literature review and state of the art
Enevoldsen et al. (2019) contains 13 self-citations from a total of 29 references (i.e.
45%), which is extremely high for a peer-reviewed original research article (van
Noorden & Chawla 2019). The authors overlook more than a decade of research in
resource assessments for onshore wind. Instead they repeatedly stress the novelty of
their study, especially regarding its continental application and supposed use of high-
resolution data. The authors directly claim that “none [of the preceding literature] exhibit
the level of aggregation that [their] model represents.” Whilst Enevoldsen et al. (2019) is
indeed one of the first studies to employ the high-resolution Global Wind Atlas V2 (cf.
section 2.3), it is by no means the first to provide results at their level of aggregation for
the whole of Europe (cf. e.g. Ryberg et al. 2019a, Bosch et al. 2017, McKenna et al.
2015). Figure 1 gives an overview of the main results from other exemplary literature
with a similar spatial scope (large-scale international studies in a European context).
Selected studies have also recently attempted to frame social acceptance issues in
the context of wind power potential studies, which Enevoldsen et al. (2019) do not. For
example, Höltinger et al. (2016) present a participatory approach with key stakeholders
to consider the effect of socio-political and market acceptance on techno-economic
potentials for wind in Austria. In a study of the feasible wind energy potential for the

7
Baden-Württemberg region of Germany, Jäger et al. (2016) analysed the public’s views
with respect to their aesthetic appreciation of the landscape. Considering rules of local
planning and the level of social acceptance of wind in specific landscapes resulted in a
feasible potential at around 50% of the previously-determined technical potential and a
substantial shift in the location of this potential due to different wind park spacing and
size assumptions. Also, Harper et al. (2019) present a Multi-Criteria Decision Analysis
(MCDA) approach that considers technological, legislative and social constraints in a
British context. Finally, Eichhorn et al. (2019) developed a sustainability assessment
framework for possible wind sites, including environmental, social, technical and
economic asepects, and applied it to Germany.
Missing this literature means that Enevoldsen et al. (2019) fail to embed both their
methodology (section 2.4) and results (section 2.5) into the broader scientific discourse.
Especially during the last half decade, a thread of research has emerged that focuses
on developing and applying methods to assess the impact of social constraints on wind
resource assessments. For a contribution aiming to assess the socio-technical
constraints for onshore wind energy in Europe, these (or similar) studies are a
necessary point of reference.
2.3 Data
2.3.1 Geospatial data and land-use constraints
Enevoldsen et al.’s (2019) stated aim was to determine “how much wind power potential
[Europe has] after infrastructure, built-up areas, and protected areas are factored in”.
This implies that at least these three considerations (infrastructure, built-up areas, and

8
protected areas) must be addressed in detail for all of the countries included in their
analysis. However, such a detailed analysis is not possible without further geospatial
data sources not mentioned in the paper.
The first geospatial dataset of importance is OpenStreetMap (OSM), which the
authors have used to represent all infrastructure (including roads, waterways, airports,
and railways) as well as all buildings (including residential, industrial, military, public,
and existing wind turbines). The OSM database is constructed by means of user-
volunteered input, which naturally calls into question its completeness. Validation of
OSM data shows that, while the completeness of street data is high (>95%) for most
Western European countries, for other European countries such as Turkey (79%),
Albania (75%), and, most notably, Russia (47%) it is significantly lower (Barrington-
Leigh et al. 2017). In comparison, the completeness of buildings in OSM is often found
to be much less; example estimates include 23% for Saxony, Germany in 2013 (Hecht
et al. 2013) and 57% for Lombardy, Italy (Broveli & Zamboni 2018). In addition to
missing a large portion of real buildings, another issue with Enevoldsen et al.’s use of
OSM for building data is that of filtering. When using the same OSM extract source as
Enevoldsen et al. (geofabrik 2019), it becomes clear that 33% of buildings in Germany,
as an example, are unlabeled; meaning that without the use of additional data sources it
is impossible to distinguish buildings in the manner implied by the authors. Ultimately,
when evaluating geospatial exclusions from infrastructure and buildings across Europe,
Enevoldsen et al. (2019)’s reliance on OSM alone is not sufficient for a detailed wind
energy potential estimate.

9
The second geospatial data source employed by Enevoldsen et al. (2019) is the
Natura2000 dataset (EEA 2016), which the authors claim to have used to determine the
geospatial positioning of “castles, monuments, areas protected by Natura2000, Special
Protection Area, Flora Fauna Habitat, etc”. However, the Natura2000 (EEA 2016)
dataset describes itself as “a network of core breeding and resting sites for rare and
threatened species, and some rare natural habitat types … across all 28 EU countries”.
This raises two issues: firstly, the Natura2000 database only contains data for protected
areas relating to birds and endangered species, and thus is not suitable for locating
castles and monuments (EEA 2016); and secondly it only covers the EU28, and thus
has no representation in many of the countries that Enevoldsen et al. (2019) claim to
have evaluated, including Norway, Iceland, Switzerland, Ukraine, Belarus, Moldova,
Serbia, Albania, Montenegro, Turkey, Georgia, and Russia. In total, these missing
countries make up 59% of the area the authors evaluated. Once again, when evaluating
geospatial exclusions from protected areas and historical sites across the whole of
Europe, Enevoldsen et al. (2019)’s sole reliance on the Natura2000 dataset is far from
sufficient.
Furthermore, Enevoldsen et al. (2019) claim to exclude areas for wind turbine
siting with the vague concept of “socially centered constraints”. Yet the subsequently
applied constraints include a mixture of around 16 constraints (located using OSM and
Natura2000), which are largely of a technical or legal nature. Notably, they have not
included those factors contributing to social acceptance identified in their own previous
publication (Enevoldsen & Sovacool 2016, Tables 2 and 3). Additionally, missing or
contradictory descriptions of the buffer distances applied to the set of employed

10
constraints hinder understanding and reproducibility of the study. Enevoldsen et al.
(2019) write that “proxies of 200 m (infrastructure) and 1000 m (buildings)” are used,
while in the Supplementary Material (Figure 3) half of the width of waterways, rivers,
riverbanks and lakes is defined as the buffer distance. It remains unclear which buffer
was applied to castles and monuments, which belong to the restriction type “Protected
Areas” and for which only “longer distances from historical landmarks” is stated. Overall,
no explanation or comparison to literature is given for the chosen constraints or buffer
distances.
2.3.2 Wind data
As presented in Figure 1, many studies have analyzed the resource potential for
onshore wind in Europe and its sub-regions. All these analyses rely on high quality
meteorological datasets relating to both long- and short-term wind resource
availabilities. Common methodologies involve estimating wind turbine performance from
static wind-resource maps (e.g. NEWA Consortium 2019, DTU 2019) and from climate
model reanalysis products (Olauson 2018, Hersbach et al. 2019, Nuño et al. 2018).
Well-known advantages and challenges are associated with either approach (Ramon et
al. 2019, Sanz Rodrigo et al. 2016, Staffell & Pfenninger 2016), resulting in a recent
trend towards combining aspects of both for energy system modelling (Ruiz et al. 2019;
Bosch et al. 2018; Gruber et al. 2019, Ryberg et al. 2019b).
In comparison, the description of wind data sources in Enevoldsen et al. (2019) is
highly opaque and does not make any reference to these trends in the wind energy
literature
2
. The source they reference when describing their input data analyzes wind
energy resources in western Iran (Noorollahi et al. 2016), with no apparent use of the

11
Global Wind Atlas that Enevoldsen et al (2019) seem to refer to when stating that their
dataset was created by the World Bank and the Technical University of Denmark. This
is surprising, as Enevoldsen and colleagues claim to have used a dataset with (a)
spatial resolution of approximately 1 x 1 km and (b) hourly temporal resolution. In
contrast, the GWA (GWA V2.1 at the time of the publication of the manuscript, since
updated to GWA V3: DTU 2019) had (a) a horizontal resolution of 9 km x 9 km, while
the microscale downscaling of the GWA2 was available at a spatial grid spacing of 250
m x 250 m. Moreover, the GWA is (b) not available in hourly resolution but represents
the wind climatology of the past decade reporting a 10-year means of hourly wind
speeds. This is also relevant in the context of the validation results reported in Table 3
of Enevoldsen et al. (2019). As information on the validation sample is largely missing,
validation might be compromised due to different underlying time periods. Finally, their
use of the dataset to estimate the energy outputs (by combining the power curve with
the site-specific wind speed distribution) is not described – instead a constant capacity
factor seems to have been employed as discussed in the following section.
In summary, the utilized datasets and/or their description are inadequate or
incorrect in parts. The completeness of the OSM database and the content of the
Natura2000 dataset make them inappropriate to be employed for wind resource
assessments over the spatial domain investigated by Enevoldsen et al. (2019) without
further analysis or validation, and the application exclusion zones and buffers on these
datasets are not well justified or described. Finally, the employed GWA2 wind data
provides high-resolution annual average wind speeds, but not hourly time series data as
stated in the paper. Without further assumptions, it is therefore not possible to estimate

12
energy yields from onshore wind turbines across this domain. In total, these points have
the following consequences: (a) resource potentials are estimated at too high levels
because availability of land-area is overestimated, as the incomplete coverage and
detail within the data sources will arbitrarily increase land availability, (b) due to partly
incomplete definitions of exclusion zones and buffers, it is hard to compare results to
other, similar studies, and (c) the confusion on the data sources used for estimating
wind power output makes validation through comparison with the results of others
impossible without further information.
2.4 Methodology
To determine the capacity potential, Enevoldsen et al. (2019) assume a single capacity
density value (in MW/km
2
) which is multiplied by the total available land of each country.
This is far simpler than the methods used elsewhere and demonstrably leads to errors
due to overlooking important techno-economic characteristics of turbines, especially the
dimensions, the power curve and the costs (McKenna et al. 2014).
The employed capacity density value is not stated in Enevoldsen et al. (2019),
but can be back-calculated from the Supplementary Material as 10.73 MW/km
2
. While
the implied capacity density is high, it is technically possible and similar capacity
densities have been used in other studies (e.g. Ryberg et al. 2019b, McKenna et al.
2015).
More problematic is the application by Enevoldsen et al. of one capacity factor of
30% for all of Europe. Global average capacity factors for onshore wind have indeed
increased from 27% in 2010 to 34% in 2018 (IRENA 2019), and will most likely continue

13
to do so. But these vary strongly by location: for example, in 2018 the 5-year running
mean capacity factor in Germany was 19% (Fraunhofer IEE 2018) compared to 25% in
the UK (BEIS 2018). Employing a single capacity would also seem to defeat the object
of using high spatial resolution wind speed data, as outlined in the preceding section.
The assumed capacity factor also stems from the specific power (ratio of
generator capacity to rotor swept area) of the chosen turbine, the Chinese Envision 4.5-
148, at 262 W/m
2
. There is a weak downward trend in the mean specific power of
European onshore turbines, from 300 W/m
2
in 2014 to 280 W/m
2
in 2018 (WindEurope
2018). So the selected turbine’s specific power is well below average for the European
stock.
For comparison, the 160 turbine power curves available in the Renewables.ninja
software (Pfenninger & Staffell 2016) have an average specific power of 380±88 W/m
2
.
A simulation using the WTPC model (Saint-Drenan et al. 2019) shows that the Envision
4.5-148 would yield a capacity factor of 26.1% for a location in central Germany,
compared to 17.5% for a more average turbine with 380 W/m
2
specific power. Scaling
this result to the whole of Europe would imply an energy potential one-third lower than
given by Enevoldsen et al. (2019).
The dense packing of wind turbines implied by the assumptions of Enevoldsen et
al. overlooks the negative relation between capacity density and capacity factor, as the
capacity factor depends on rotor size relative to nameplate capacity. Rotor size, in turn,
affects the distances at which turbines can be installed without causing array losses due
to wake effects. Using the proposed configuration (turbines in a large array spaced at

14
4.375D X 4.375D) would incur massive array losses (Volker et al. 2017), possibly even
up to around 90% (Gustavson 1979).
In addition, previous work has shown the importance of validating simulated
outputs against measured wind power production (the energy domain) rather than wind
speeds (the meteorological domain) (Staffell & Pfenninger 2016). It is laudable that the
authors validated their input wind data (the Global Wind Atlas) against meteorological
observations from wind masts (main manuscript, Section 3 and Supplementary
Material). However, it is unclear how this builds upon the validation already performed
by the creators of the atlas (DTU Wind 2019). It is also unclear how this validation is
relevant to the study, as it appears as though the Global Wind Atlas is not used to
derive any of the final results. As noted above, the authors simply assume 30% capacity
factor across the entire continent of Europe when calculating the energy production
potential (main manuscript, Table 4).
While there is potential for improving capacity factors, much of this is based on
moving to taller turbines and lower power densities, some technical improvements
moving turbine efficiency closer to the Betz’ limit and better locations offshore
(Caglayan et al. 2019, Staffell & Pfenninger 2016). There is a more limited potential for
improving onshore capacity factors as a result of restrictions on turbine height (due to
social constraints such as visual impact) and lower wind resource availability. The
combination of high turbine density and capacities with low specific power, without
accounting for the associated array losses, mean that Eneveldsen et al.’s (2019)
method greatly overestimates the generation potential.

15
2.5 Results
For the reasons outlined above, Enevoldsen et al. (2019) determine a very high
potential for onshore wind in Europe: a total area of almost 4.8 million km
2
(2.8 million
km
2
excluding Russia), an installed capacity of 53 TW (29 TW) and an annual
generation of 138 PWh (77 PWh). The Supplementary Material from Enevoldsen et al.
(2019) includes results at the country level, which can be employed for comparison.
Figure 1 illustrates some of these results in comparison with 13 other studies, for the
whole of Europe excluding Russia.
Overall, Enevoldsen et al. (2019) derive a potential for Europe that is at least
120% higher in terms of installed capacity and 70% higher in terms of generation, than
all previous results in this area (Figure 1). In their own discussion of the results, the
authors limit their comparison to those of the EEA (2009) study, which has a much
higher area (5.4 million km
2
) and the highest installed capacity across Europe (13 TW)
of all studies reviewed here. We note that in section 5 of the manuscript, the authors
also refer to the Natura2000 database as European Commission 2018 for a value of 39
PWh, but this reference must be incorrect (the cited database relates to protected
areas, cf. section 2.3.1 in this response) and also contains a dead link.
When compared to the mean of all 13 studies, the results from Enevoldsen et al.
(2019) suggest 35% more area, 150% more installed capacity, 266% more generation
and 17% higher full load hours. Their upper ceiling for wind power generation assumes
that wind turbines would cover 40% of Europe’s land area, which is surprising given that
this potential is supposed to reflect a socio-technically feasible one. More concerning is
the lack of contextualisation and critical discussion of these results, especially as

16
research in recent years (e.g. Jäger et al., 2016; Scherhaufer et al. 2018; Harper et al.
2019) has made progress in deriving more meaningful, feasible potentials for renewable
energies through integrated assessments that include some market, organizational and
social barriers. Instead the authors restrict their commentary on these findings to
questioning “the academic and industrial concern of land use being a major constraint
for renewable energy” and suggesting that “national scenarios ought to be recalibrated
appropriately”.
Figure 1: Comparison of results from Enevoldsen et al. (2019) with 13 other studies for
the European continent excluding Russia. Dotted lines give the mean of the other 13
0
1
2
3
4
5
6
Available area (million km2)
Mean = 1.91
0
5
10
15
20
25
30
Installable capacity (TW)
Mean = 8.12
0
10
20
30
40
50
60
70
80
Annual generation (PWh)
Mean = 16.27
0
500
1000
1500
2000
2500
3000
Full load hours (per year)
Mean = 2214

17
studies. Not all 13 studies report all results, which accounts for the different samples in
the four panels. For sources see text and references.
3 Conclusions and policy implications
In the sense that it is one of the first studies to employ the latest Global Wind Atlas to
assess European onshore wind potentials, Enevoldsen et al. (2019) is a valuable
contribution. However, as outlined in the preceding section, the paper suffers from
fundamental flaws. While we agree that a novel, social dimension needs to be included
in the assessment of wind power potentials, we fail to see how the chosen
methodological approach stands up to such demands. Even if it could, it remains
unclear whether a specific offset distance is effective in reducing social conflicts towards
the expansion of wind energy. Visibility is only partly related to distance; other
environmental or socio-economic concerns towards wind energy are much more
complex.
In terms of the employed data, the author’s claims to have evaluated the onshore
wind land eligibility of Europe with “a more qualitative [and] refined socio-technical
dimension” do not appear to be well-founded. The two datasets used to represent socio-
technical points of interest are not nearly complete enough to achieve the paper’s stated
objective across the whole regional scope. Moreover, the authors do not discuss the
suitability of the chosen datasets for use in their analysis and therefore seem unaware
of their limitations. The authors also employ the Global Wind Atlas as the main source
for their annual average wind speeds, but both refer to this dataset incorrectly
2
and

18
make a very simplified turbine capacity factor assumption of 30%, which demonstrates
a fundamental misunderstanding of onshore wind resources.
The paper overlooks more than ten years of research in this field and therefore
employs a methodology that is outdated, inaccurate, and in some cases simply
incorrect. The land use constraints are incomplete and rely on partly unreliable input
data. The one selected turbine is not representative of the European stock and the
assumed 30% capacity factor is unrealistic, both of which bias the results in a particular
direction. Finally, the employed method is neither validated nor accompanied by a
sensitivity analysis, both standard practice for a modelling exercise such as this one.
Due to the inferior method employed, the results are impractical. The authors
reach very large potentials for onshore wind energy in Europe, which require covering
around 40% of the continent’s area in wind parks with a very high capacity density.
These results are not only much higher than most other previous studies, they cannot
credibly be interpreted as “socio-technical”, even in the broadest sense.
In the context of energy transitions, wind potential assessments provide
indispensable inputs to energy modelling and energy policy development, and can
potentially contribute to a cost-efficient realization of climate and energy targets.
Research in this field that breaks new ground by improving methodologies, considering
additional aspects (e.g. public acceptance) and/or exploiting new datasets stands to
contribute to this ongoing scientific and energy-political discussion. As for any scientific
research, transparency, reproducibility and openness should be embraced as
cornerstones (Allison et al. 2016, Nosek et al. 2015), especially as energy modelling is
noted for lagging behind other disciplines (Pfenninger et al. 2017). Overall, then, we

19
conclude that new research at higher spatial resolutions can make a valuable
contribution to this field, but due to the missing literature review, the lack of
transparency and the inferior methodology, Enevoldsen et al. (2019) instead potentially
misleads fellow scientists, the general public and policy makers.
4 Acknowledgements
S.W. and J.S. received funding from the European Research Council (ERC) under the
European Union's Horizon 2020 research and innovation programme (reFUEL, grant
agreement No. 758149). J.L. and T.T. received funding from the European Research
Council (ERC) under the European Union’s Horizon 2020 research and innovation
programme (grant agreement no. 715132). The usual disclaimer applies.
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