RESEARCH ARTICLE
MCDM analysis of wind energy in Turkey: decision making based
on environmental impact
Sinem Değirmenci
1
& Ferhat Bingöl
2
& Sait C. Sofuoglu
1,3
Received: 4 October 2017 /Accepted: 11 April 2018 /Published online: 8 May 2018
#
Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
Development of new wind energy projects require complex planning process involving many social, technical, economic,
environmental, political concerns, and different agents such as investors, utilities, governmental agencies, or social groups.
The aim of this study is to develop a tool combining Geographic Information System (GIS) and Multi-Criteria Decision-
Making (MCDM) methodologies, and its application for Turkey as a case study. A variety of constraints and criteria were
identified based on a literature review and regulations gathered from variety of agencies, use of which resulted in determination
of infeasible sites. Then, pairwise comparisons were carried out using analytic hierarchy process as the MCDM method to
estimate relative importance of the criteria, and to visualize a suitability map with three classes. As the final stage, decision
making was carried out based on environmental impact where 45.5% of the Turkish territory was found as infeasible area. Sixty
percent of the remaining area are covered by the moderate suitability class, followed by the highly suitable area (20.3%) and low
suitable area (19.8%). The output of this study can be used by energy planners to estimate the extent that wind energy can be
developed based on public perception, administrative, and environmental aspects.
Keywords Wind
.
Energy
.
Environmental impact
.
MCDM
.
AHP
.
GIS
Introduction
Electrical energy is required for economic growth and hu-
man populations. Although it is mainly obtained from con-
ventional sources such as coal, oil, and natural gas, envi-
ronmental impacts caused by conventional sources are
much worse than those brought about by the use of renew-
able energy sources (RES) (Góralczyk 2003; Weisser
2007; Kumar et al. 2016). Therefore, RES have become
promising alternatives to non-renewable sources. RES in-
clude natural sources such as wind, solar, thermal, photo-
voltaic, hydro, wave, tidal, biofuels, ocean, and geothermal
sources (Twidell and Weir 2015). Utilization of RES such
as wind reduces emission of CO
2
and other greenhouse
gases (GHG), and hazardous air pollutants, increases water
conservation; provides domestic job creation; landown er
revenue generation and rural tax revenue; and perhaps
most importantly, reduce reliance on fossil fuels for elec-
tricity generation (AWEA 2008). Specifically, the i mpacts
of wind energy are low, local, and manageable (Bilgili and
Simsek 2012).
Turkey is one of the fastest growing power markets in the
world and was positioned in the global wind energy market as
the 10th largest annual market in 2015. Turkey’s first com-
mercial wind farm was commissioned in Alaçatı,Çeşme, in
1998 with a capacity of 1.5 MW (Ilkılıçetal.2011). Even
though the first wind farm started operation in Turkey as early
as 1998, investments significantly increased after 2005 with
the adoption of BThe Renewable Energy Law of Turkey.^ The
market has grown from 20 MW in 2006 to 4700 MW in 2016
(TWEA 2016). Turkey’s primary energy reserves are not
Responsible editor: Philippe Garrigues
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s11356-018-2004-4) contains supplementary
material, which is available to authorized users.
* Ferhat Bingöl
ferhatbingol@iyte.edu.tr
1
Department of Environmental Engineering, Izmir Institute of
Technology, Gulbahce, Urla, 35430 Izmir, Turkey
2
Department of Energy System Engineering, Izmir Institute of
Technology, Gulbahce, Urla, 35430 Izmir, Turkey
3
Department of Chemical Engineering, Izmir Institute of Technology,
Gulbahce, Urla, 35430 Izmir, Turkey
Environmental Science and Pollution Research (2018) 25:19753–19766
https://doi.org/10.1007/s11356-018-2004-4
enough to meet the rising energy demand. Currently, there are
3.144-GW acquired wind farm licenses waiting to be built.
Europe, specifically the EU states, has a goal of at least 27%
renewable energy consumption in final energy consumption at
European level until 2030 (EWEA 2015). T urkey has a similar
target of increasing the installed capacity to 20 GW until 2023
(Dursun and Gokcol 2014). Since geographical limitation s, pub-
lic opposition, wildlife conservation, and electricity grid integra-
tion pose challenges for the investors, not much space is available
for the implementation of wind farms. These factors make plan-
ning of wind power plants to become a complex procedure,
involving the consideration of many different criteria and con-
straints. The most important consideration in site selection for
wind turbines is the wind energy density, but technical, econom-
ic, environmental, social, and political requirements have started
being considered efficaciously. Economic issues are associated
with maximizing economic benefit, and environmental concerns
aim to decrease the adver se effects of the wind farms.
Environmental concerns include impacts on humans (e.g., noise,
visual effect) and effects on ecosystems (e.g., the damage to the
wildlife, especially birds and bats, and habitat loss). The accep-
tance of wind farms and socio-political parameters are two diffi-
cult points that have been imbedded in the Environmental Impact
Constraints (EIC) and should be discussed by the developer and
other parties involved, after the output of MCDM tools results
are extracted.
The complex decision-making process requires a tool that can
incorporate a set of decision alternatives and the decision maker’s
preferences effectively. In a Geographical Information System
(GIS)-based decision-making process, the alternatives are evalu-
ated by preferences of individuals (decision makers, managers,
stakeholders, interest groups) with utilization of Multi-Criteria
Decision-Making (MCDM) that contributes a methodolo-
gy for guiding the decision maker, whereas GIS provides
processing of the geographic data. Therefore, GIS and
MCDM methods combine and transform geographic data
and t he decision maker’s preferences into a resultant de-
cision, and allow to reach optimal solutions for highly
complex spatial decision-making problems (Malcz ewski
2010). The ma jor advantages of using a GIS-based ap-
proach for siting is to reduce the time and cost of site
selection, and also to provide a digital data bank for
long-term monitoring of the site (Moeinaddini et al.
2010). Therefore, GIS is widely used in the decision and
management situations such as environmental planning
and management, transportation planning and manage-
ment, urban and regional planning, waste management,
hydrology and water resource, agriculture and forestry,
geology and natural hazard, and real estate and industrial
facility management (Malczewski 2010).
One of the first studies regarding the evaluation of the wind
energy potential has been carried out by Voivontas e t al.
(1998) for the island of Crete, Greece, using a 250-kW wind
turbine. In recent years, there are several studies of GIS ap-
plied to site selection of wind farms around the world includ-
ing Turkey. Studies differ from each other with respect to
choice of the study area, the criteria, and the methodologies
applied. Short list of studies compiled by the authors are from
several countries such as Austria (Gass et al. 2013), Belgium
(Lejeune and Feltz 2008), Cyprus (Georgiou et al. 2012),
Denmark (Hansen 2005), Germany (Höfer et al. 2016),
Greece (Te gou et al. 2010 ; Latinop oulos an d Kechagia
2015), Iran (Noorollahi et al. 2016), Oman (Al-Yahyai et al.
2012), Poland (Sliz-Szkliniarz and Vogt 2011), Spain
(Sánchez-Lozano et al. 2014; Schallenberg-Rodríguez and
Notario-del Pin o 2014), Thailand (Bennui et al. 2007),
Turkey (Aydin et al. 2010; Atici et al.
2015),
the UK (Baban
and Parry 2001; Watson and Hudson 2015), and the USA
(Van Haaren and Fthenakis 2011; Gr assi et al. 2012;
Gorsevski et al. 2013). While some of them focus on the
national scale (Al-Yahyai et al. 2012;Gassetal.2013), the
others were conducted on regional scale. Parameters of inter-
est vary from country to country, even sometimes region to
region based on the legal framework enforced by govern-
ments. MCDM ha ve been used by different metho ds like
Elimination and Choice Translating Reality (Sánchez-
Lozano et al. 2014; Atici et al. 2015), Stochastic
Multiobjective Acceptability Analysis (Atici et al. 2015),
Order Weighted Averaging (Baban and Parry 2001; Aydin
et al. 2010; Al-Yahyai et al. 2012), Analytical Hierar chy
Process (Baban and Parry 2001; Bennui et al. 2007; Tegou
et al. 2010; Al-Yahyai et al. 2012; Georgiou et al. 2012;
Latinopoulos and Kechagia 2015; Watson and Hudson
2015; Höfer et al. 2016), Simple Additive Weighting
(Hansen 2005;Georgiouetal.2012; Gorsevski et al. 2013),
and Weighted Index Overlay (Noorollahi et al. 2016).
This study presents a GIS-based multi-criteria decision-
making model applicable to diverse terrain and climatology
conditions, that accounts air density for wind speed, that can
be updated with changing regulations, that integrates environ-
mental impact into the selection at the last stage among those
remain after application of the technical/economic criteria,
and that has been validated by comparing locations of > 100
in-operation wind farms, which all give form to a unique study
with regard to the literature. Nevertheless, nationwide studies
are scarce in the literature and Turkey has not been studied
based on Turkish regulations or there is no such model in use
in Turkey for wind farm planning.
Theory and method
The proposed methodology for site selection of wind power plant
installation in T urkey, which is illustrated in Fig. 1, was handled
under four stages. In the first stage, decision-making criteria as-
sociated with energy generation of wind turbines were identified
19754 Environ Sci Pollut Res (2018) 25:19753–19766
based on a review of the literature and the regulations. Then,
boundaries and geographical coordinates of these factors were
collected and processed in GIS. In the next step, infeasible sites
were excluded depending on regulations and planning con-
straints. In the third stage, the remaining sites were evaluated
based on the economical and technical characteristics of the study
area. The evaluation at this stage was conducted with a multiple-
criteria decision-making method. In the last stage, the areas were
evaluated with the environmental objectives adopting a conser-
vative approach (i.e., implementing exclusion areas) to show
potentially problematic sites and suitable locations for wind
turbines.
Study area: Turkish territory
Turkey, located between 36
o
–42
o
North latitude and 26
o
–45
o
East longitude, is a large peninsula that bridges the
continents of Europe and Asia. The country has a small
part in Europe and a large area in Asia called Thrace
and Anatolia, respectively. Turkey is surrounded by sea
on three sides: the Black Sea, the Mediterranean Sea,
and the Aegean Sea. Turkey is divided into 81 prov-
inces in seven geographical regions. All the provinces
and regions mi ght app l y differ ent c ost and / or env ir on-
mental criteria. Its total area is 78 mill ion hectares of
which 21.7 million hectares are designated as forest area
(TUIK 2014).
Multi-criteria decision-making methods and AHP
The application of MCDM techniques is gaining popularity in
renewable energy management (Pohekar and Ramachandran
2004). The aim of traditional single criteria decision making in
energy investments was maximization of net benefits, in mone-
tary terms. As the energy management problems are getting more
complex, economic considerations are complemented with envi-
ronmental and social considerations, leading to multiple-criteria
decision making being used to deal with conflicting decision
problems.
There is a wide range of MCDM methodo logies, based
on their goals and application steps, and how the alterna-
tives are ranked. For energy planning applications, the
Analytical Hierarchy Pr ocess (AHP) is pop ular and rec-
ommended (Pohekar and Ramachandran 200 4) becau se
of its simplicity, flexibility, and especially its ability to
mix qualitative as well as quantitative criteria in the same
decision framework, which was followed in t his study.
The basic principles can be summarized a s follows; how-
ever, a detailed description of the methodology employed
in this study is provided in Supporting Document–1:
AHP, a hierarchy is a MDCM approach where criteria
are organized in a hierarchic structure in such a way that
each level ma y represent a different section at the prob-
lem. Once each of the levels is defined, the method derives
priority scales or weights through a series of pairwise compar-
isons (Saaty 1980). The pairwise comparisons are the core of
Stage 4. Environmental Impact Evaluation (Combination of Stage 2 and 4)
Stage 3. Criteria Evaluation
Determine Criteria Weight
Create Suitability Map
(Combination of Stage 2 and 3)
Stage 2. Exclusion of Infeasible Sites
Determine Constraints
Create Constraint Map
Stage 1. Data Collection and Processing
Review literature and
regulations
Collect datasets for analysis Convert to required formats
Fig. 1 Overview of the
methodology
Environ Sci Pollut Res (2018) 25:19753–19766 19755
the AHP process and they determine the relative importance
of one criterion over another (Saaty 2008). Evaluations can be
based on measurable quantities or people’s perceptions and
preferences, in which case the need and purpose of the deci-
sion and the effects on various stakeholders come strongly
into play. Finally, the alternatives are ranked in terms of com-
bination of the criteria scores. Additionally, the method has a
special provision for the consistency in the judgements of each
individual evaluator assigning scores.
Data collection and processing
All land with adequate or optimum wind energy resources may
not be equally suitable for wind energy development. For exam-
ple, certain areas may be declared as protected land by the gov-
ernmental regulations, or they may be located at a significant
distance from available roads, which significantly increases con-
struction costs. Therefore, all objectives/possible factors associ-
ated with site selection for wind turbines were identified based on
national legislation related to wind turbine development and lit-
erature research. A set of factors were finally selected and the
boundaries and geographical coordinates of those factors were
collected from government agencies, web-based datasets, scien-
tific studies, and a voluntary agency (Table 1) which were then
processed in GIS. Factors were classified into three categories,
based on a logical sequence of application (schematic diagram).
& Exclusion Parameters (political concern)
& Evaluation Criteria (economical and technical concern)
& Environmental Impact Constraints (environmental and so-
cial concern)
Data sources (and other information) are presented for each
factor in Table 1.
In addition, a detailed description of the rationale for the in-
clusion of each factor and the definitions of elements/classes/
thresholds are given in the Supporting Document–2. It should
be emphasized here that the inclusion of further factors in the site
suitability analysis was also considered, such as location of
protected forests, world heritage sites, natural sites, military dis-
tricts, bat habitats, and bird migration routes. However , these
Table 1 Spatial analysis layers
Step Layer Source of data
Exclusion Parameters (political concern) Radars Turkish Republic Official Journal (Number: 29033) (Legistration on
Prelicence of Wind and Solar Power 2014)
Airports CORINE 2002 Seamless Vector Data (CLC: 124),
Energy Market Regulatory Authority Announcement in 12th of May, 2016
Fault lines Kandilli Observatory and Earthquake Research Institute (KOERI 2015)
Urban areas CORINE 2002 Seamless Vector Data (CLC: 111, 112)
Protected areas Directorate of Nature Conservation and National Parks (Coordinates of
Special Environmentally Protected Areas 1990, 2000, 2004, 2007, 2010,
2013; Law on Wildlife Protection and Development Areas 2004),
UNESCO (UNESCO 2016a, b)
Altitude SRTM 4.1 DEM (Jarvis et al. 2008)
Evaluation Criteria (economical and technical
concern)
Air density ‘Adaptation of Uniform Wind Atlases’ (Bingöl 2016)
Wind speed Global Wind Atlas (DTU 2016)
Frozen period WorldClim (Hijmans et al. 2005)
Land cost The Revenue Administration (Turkey 2014)
Roads OpenStreetMap (OpenStreetMap 2015)
Grid capacities TEİAŞ (TEIAS 2015)
terrain Complexity ‘Adaptation of Uniform Wind Atlases’ (Bingöl 2016)
Evaluation Criteria and Environmental Impact
Constraints (economical and environmental
concern)
Forest CORINE 2002 Seamless Vector Data (CLC: 311, 312 and 313), (Regulation
for Article 18 of the 17/3 of the Forest Law 2014)
Environmental Impact Constraints (environmental
and social concern)
Agricultural lands CORINE 2002 Seamless Vector Data (CLC: 212)
Bird habitat DoğaDerneği (KusBank Veritabanı,Doğa Derneği 2016)
V
isual impact The study of ‘Identification of Visual Influence Zones of Wind Farms in
Lithuania’ (Abromas and Kamičaitytė-Virbašienė 2014)
Noise Regulation on Assessment and Management of Environmental Noise
(Regulation on Assessment and Management of Environmental Noise
2008)
To validate results Farms areas Energy Market Regulatory Authority (EMRA 2015)
19756 Environ Sci Pollut Res (2018) 25:19753–19766
datasets were either not accessible or not available, due to secu-
rity re gulations or were simply not shared with the public by the
authorities.
Generic wind turbines
An additional consideration in our study pertains to the different
wind potential calculation for differen t wind turbine models (cri-
terion BWind potential^ of step 2–Technical and economical
evaluation criteria). Given that more than 50 wind turbine models
have been developed over the last decade, it was deemed imprac-
tical to create a separate evaluation/decision-making tool for each
of them. Additionally, environmental impacts of bigger and
smaller turbines differ from each other (Tr emeac and Meunier
2009). Therefore two different wind turbines representing the
900-kW (50-m hub height) and 2.1-MW (100-m hub height)
classes, which are frequently used, were chosen for this study
(Table 2). Capacity factors o f the defined turbines were acquired
from Hughes (2012) who collected onshore wind datasets on
substantial installations. Capacities were calculated by taking
the averages of lifetime efficiency of the turbines that have the
same power rate with the generic turbines and found as 22% for
900 kW and 31% for 2.1 MW.
Data analysis
The data analysis was performed following the step-wise pro-
cessing algorithm described in the BTheory and method^ sec-
tion. In the first step (Exclusion of Infeasible Sites), layers
representing exclusion zones for wind farms were collated,
merged, and dissolved. In the second step (Criteria
Evaluation), technical and economical criteria were assigned
weights through pairwise comparisons, the values of the GIS
layers were scaled and used in the calculation of coefficients
for the final decision-making atlas. Finally, when the user
reaches the last step, a very few number of places are left
which can be compared among each other. Now, the user
can choose one against other (maybe between top two best
locations) based on environmental impact criteria to finalize
the case study.
An exclusion zone is an unsuitable zone for wind turbine
installation based on legal regulations and literature review . In
some cases, buffer zones are also taken into account to define the
minimum distance around those areas. The raw data, in the form
of GIS vector layers, were used to create exclusion zones by
adding buffers. Any site that is outside the below listed buffers
is assumed to be Ba technically feasible site^.
& Radar locations with 5-km buffer zones around them
(Legistration on Prelicence of Wind and Solar Power
2014),
& Airports with 3-km buffer zones around them (According
to the Turkish legislation prior to 2016 (Legislation for
Construction Criteria Around the Airports 2012))
& 2- and 15-km buffer zones around aeronautical stations
and n avigational aids, respe ctively (Energy Market
Regulatory Authority announcement on 12th of May,
2016)
& Fault lines with 150-m buffer zones around them
(Demirtaş 2005)
& Urban areas with 1-km buffer zones areas around them
(applicable to cities only) (Regulation on the Technical
Assessment of Applications related to Wind Power
Generation 2015)
& Protected areas (Forest Law 1956; Law on National Parks
1983; Law on Protection of Cultural and Natural
Properties 1983; Coordinates of Special Environmentally
Protected Areas 2004; Law on Wildlife Protection and
Development Areas 2004)
& Areas with an elevation above 1500 m (REPA 2007)
Each vector layer created in order to define the exclusion
zones was subtracted from the borders of T u rkey. As the regula-
tion regarding airports was changed during the study, two differ-
ent suit ability maps with the new and old regulati on were devel-
oped to illustrate the suitability levels within the feasible sites.
After assessing the technically feasible sites, economically
optimum sites were obtained in the next step using the pairwise
comparisons of criteria weights in combination with scaled nu-
merical values of each criterion/variable. Firstly, data of each
layer were converted from vector format to a raster format and
resampled to
1
120
°
cell sizes (around 700 × 900 m) to obtain raw
data of all criteria in grid format, and each of these grid cells was
considered as a potential location for installation of wind farms.
Secondly, the numerical values for each criterion were scaled
from 0 to 100 based on their maxima and minima. The calcula-
ti on for wind pow e r was repeate d for two generic wind turbines
definedinTable2. The scaling factor is applied to criteria as
Table 2 Properties of the generic wind turbines
Characteristic Generic turbine
1
Generic turbine
2
Hub height (m) 50 100
Diameter (m) 45 80
Swept area (m
2
) 1590.4 5026.5
Power (kw) 900 2100
Capacity factor (%) 0.22 0.31
Total turbine cost (€) 680,000 1.580.000
O&M cost (€) 680,000 1.580.000
Gross yearly income from electric
sale (€)
125.000 375.000
Net income annually (€) 25.000 175.000
Environ Sci Pollut Res (2018) 25:19753–19766 19757
