To payment willingness to RENEWABLE ENERGY FACTORS IN THE CASE OF
TURKEY
ABSTRACT
Turkey has developed an action framework designed by the Ministry of Energy and Natural
Sources with a view of minimizing energy import and maximizing domestic energy and produce
30% of electricity production from renewable energy sources all the way through 2023. In order
to materialize its ambitious renewable energy targets, Willingness to Pay (WTP) plays the central
role in directing appropriate policy. On the basis of this discrepancy, this study intends to
investigate the willingness to pay (WTP) of the Turkish citizens for green electricity by using
One-way Analysis of Variance (One-way ANOVA) and interviews were conducted face-to-face
comprising of 2,500 households in 12 major metropolitan cities of Turkey based on contingent
valuation method consisting of a total of 26 questions. The results for 20% share of renewable
energy indicate that middle-income groups are willing to pay higher than lower and upperincome groups. Additionally, highly environmentally conscious people tend to pay high for 20%
share of green energy. On the other hand, the results for 30% share of renewable energy show
that high-income groups and old age groups indicated a positive and high willingness to pay for
renewable energy. Moreover, primary school and undergraduate educational groups recorded
highly significant results for willingness to pay. The results also indicate that Turkey residents
are willing to pay 9.25 Turkish Liras per month for a 20% share and 4.77 Turkish Liras per
month for a 30% share of renewable energy in total energy production. This study may provide
useful insights to the utility companies and government to prepare effective channels in order to
charge reasonable amount for a larger share of green energy in total energy production and to
achieve their targets.
Keywords: Willingness to pay, Renewable energy, Contingent valuation, One way ANOVA,
Turkey
SUMMARY
Turkey, to reduce energy imports to a minimum internal energy of Energy and Natural Resources
has developed a framework for action designed by the Ministry in order to maximize the level
and produce 30% of electricity production from renewable energy sources by 2023. Turkey's
YÖİS to carry out its ambitious renewable energy targets (for renewable energy willingness to
pay), plays a central role in the appropriate policy guidance. To this end, Turkey's 12
metropolitan households face to face interviews were conducted in 2500 and a questionnaire was
applied. One-way ANOVA and Post-Hoc tests were used to investigate the willingness of
Turkish citizens to pay for green electricity and to analyze social factors such as age, income and
education. According to the results of 20% share of renewable energy, middle income groups are
willing to pay higher than lower and upper income groups. However, highly environmentally
conscious participants tend to pay high for 20% of renewable energy. On the other hand, 30% of
renewable energy shows that high-income groups over the age of 65 have a high willingness to
pay for renewable energy. In addition, the results show that primary and undergraduate education
groups are willing to pay more for the 30% share of renewable energy. However, the results
show that Turkish people are willing to pay TL 9.25 per month for 20% and TL 4.77 per month
for 30% of renewable energy. This study can provide useful information to the government and
utility companies in designing effective mechanisms, charging an appropriate amount to support
a larger portion of renewable energy in the energy portfolio, and achieving their goals.
Keywords: Willingness to Pay, Renewable Energy, Contingent Valuation, One-way ANOVA,
Turkey
1. INTRODUCTION
In parallel with technological and economic development, human energy needs have increased
significantly in recent years. In addition, economic growth has become largely dependent on
energy. This reality creates energy dependency problems for countries that do not have sufficient
energy resources. In the eighteenth century, immediately after the industrial revolution, the
world's energy demand increased rapidly and sharply. Ultimately, the use and consumption of
energy resources has increased in parallel with the increase in world population.
According to the World Population Report published by the United Nations (UN), the world
population will reach 8.9 billion in 2050 (UN, 2004: 20). This increase, together with
technological advances, will create more energy demand in the next few decades. However, due
to population growth and the upward trend of world energy consumption, it is controversial
whether traditional fossil fuels (oil, coal and natural gas) are too much on existing reserves. In
addition, because of the use of fossil fuels and greenhouse effect, climate change has become a
major concern, especially in terms of threats to the environment and human health.
The oil crises of the past years and especially the global warming and climate change problems
that have gained importance in recent years have led many countries to pursue different policies
and strategies in order to reduce their energy dependence and have a cleaner environment. As for
the environment, the Paris climate conference is a good example. This conference is an
agreement which was incorporated in Turkey to especially to reduce the greenhouse gas
emissions that cause carbon dioxide gas of 196 countries signed under the auspices of the United
Nations (UNFCCC, 2015: 2). Within the scope of this agreement, one of the prominent and
necessary measures to reduce greenhouse gas emissions to the atmosphere is to turn to renewable
energy sources.
The recently published Shafiei and Salim (2014), Boluk and Mert (2015), Dogan and Seker
(2016a), and Dogan and Seker (2016b) use CO2 from the use of non-renewable energy sources,
although the use of renewable energy sources reduces carbon dioxide emissions (CO2). using
various econometric instruments.
Since renewable energy is clean and safe, it is a good solution to prevent or minimize all
problems with existing energy sources (Lee and Heo, 2016: 151). Renewable energy;
hydroelectric energy, wind, solar energy and biomass, such as green electricity sources are
defined as energy. Considering the threat of climate change, sustainability has become an
important issue and calls for reducing the use of fossil fuels such as oil, coal and natural gas
(UNFCCC, 2015: 2).
The development of the renewable energy sector will reduce external dependency and support
sustainable development objectives by preventing dependence on existing energy consumption,
which has devastating effects on the environment (Kaygusuz, 2007: 81). Renewable energy;
energy security, dynamic economic development, is seen as an important element in protecting
the environment and greenhouse gas efforts to reduce emissions and including Turkey bears a
new political concern size in many countries (Carley, 2009: 3072).
Making adequate investment in environmentally friendly renewable energy field in Turkey for
the reduction of energy imports and minimize environmental pollution are planned. More
precisely, for 2023, the share of renewable energy in total energy consumption is expected to be
at least 20% and its share in electricity generation to be at least 30% (TUYEEP, 2014: 9).
However, the cost of installation of renewable power plants and facilities is higher than that of
conventional energy. Status of the high price of renewable energy is only valid in other
countries, not to Turkey. Kotchen and Moore (2007) found that the market price of renewable
energy throughout the United States (US) is on average 10% -30% more expensive than the
market price of traditional sources.
Turkey's YÖİS to carry out its ambitious renewable energy targets (willingness to pay for
Yenelen renewable energy) plays a central role in the appropriate policy guidance and therefore,
in this study, the willingness to pay for renewable energy in Turkey and analyzes the factors
determining this willingness. Turkey's negotiations face to face with 2500 households in 12
metropolitan Questionnaires were applied in this direction. In addition, it tries to explain various
factors affecting HIS by using Conditional Valuation method, One-way ANOVA and Post-Hoc
test. There are no studies in the literature before dealing with YÖİS for Turkey. Therefore, it is
thought that fills this gap in the literature about this work YÖİS in Turkey. The following
chapters of the study consist of: literature review, data set, method and application, findings and
discussion, conclusions and policy implications.
1. LITERATURE SEARCH
The oil crises of the past years and especially the global warming and climate change problems
that have gained importance in recent years have led many countries to pursue different policies
and strategies in order to reduce their energy dependence and have a cleaner environment. As for
the environment, the Paris climate conference is a good example. This conference, which was
incorporated in Turkey to especially to reduce the greenhouse gas emissions that cause carbon
dioxide gas of 196 countries in the United Nations (UN) has signed an agreement under the roof
(UNFCCC, 2015: 2). Within the scope of this agreement, one of the prominent and necessary
measures to reduce greenhouse gas emissions to the atmosphere is to turn to renewable energy
sources.
Turkey is planned for sufficient investments as well as to reduce energy dependence on foreign
and environmentally friendly renewable energy to reduce environmental pollution as a country
that is candidate for European Union (IEA, 2012: 5). More precisely, for 2023, the share of
renewable energy in total energy consumption is expected to be at least 20% and its share in
electricity generation to be at least 30% (TUYEEP, 2014: 8).
However, the cost of installation of renewable power plants and facilities is higher than
conventional energy. According to Kaya and Koç (2015) research, the cost of installation of
natural gas-fired lignite power plant and coal-fired lignite plant is $ 917 \ kW and $ 3246 \ kW,
respectively; wind power plant, nuclear power plant, geothermal power plant and solar power
plant installation costs are respectively $ 6230 \ kW, $ 5530 \ kW, $ 4362 \ kW, $ 3873 \ kW
(Kaya and Koc, 2015: 65).
As you can see, the state of the high energy prices may yüksektir.yenilen from the unit cost of
energy produced from renewable sources, the sale price is valid only in other countries, not to
Turkey. Kotchen and Moore (2007) found that the market price of renewable energy throughout
the United States (US) is on average 10% -30% more expensive than the market price of
traditional sources. In addition to government support, household support has an important role
in the sustainability of renewable energy investments. Therefore, the willingness of households
to pay for financial support is important. The willingness to pay is defined as the maximum price
at which the buyer is ready to pay for a certain amount of product or service (Wertenbroch and
Skiera, 2002: 232).
In previous studies, willingness to pay for renewable energy has been developed using methods
such as Tobit, Probit, Logit, Multinomial probit models such as income, age, gender, education,
household size, environmental awareness and so on. Variables were examined. USA. In a study
on Mozumder et al. (2011) analyzed the factors affecting the NOS and the willingness of
households living in the state of New Mexico using the Tobit model. Empirical findings showed
that although education level and gender (female) negatively affected willingness to pay,
variables such as environmental awareness, income level and number of households had positive
effects. Aldy et al., One of the articles that add the number of households to econometric
analysis. (2012), in a study examining the support of American citizens to the National Clean
Energy Standards program targeted by the United States, found that the high number of
households, black people and elderly people gave relatively less support.
Zoric and Hrovatin (2012) found that the willingness of Slovenian households to pay for
renewable energy increased the level of education, gender, environmental sensitivity and income,
but reduced the number of households and age, using Tobit Model. According to the
questionnaire prepared using the conditional value determination approach on Italy, which is
among the developed countries, Bigerna and Polinori (2014) revealed that income level and
education level positively affect YÖİS, and the number of households, gender and age adversely
affect YÖİS. As can be seen, although the number of studies that make analysis by considering
the number of households is small, different results have been reached.
Aldy et al. (2012) found that by using the Logit Model, an average American public was willing
to pay an extra $ 162 per year for renewable energy. Bigerna and Polinori (2014) found that
using the Krinsky and Robb’s Simulation Model, Italian households' willingness to pay for two
months was € 12.76. In addition, Nomura and Akaiki (2004) found a monthly average
willingness to pay $ 17 for Japan as a result of a survey using a conditional valuation approach.
USA. Whitehead and Cherry (2007), using the Multinomial Logit Model, found that the
American public was willing to pay an extra $ 4.24 per month for renewable energy.
For South Korea, Kim et al. (2012) found that the willingness to pay monthly for renewable
energy using the Logit Model was $ 1.35. However, for Greece, Koundouri et al. (2009) found
the willingness to pay € 5.44 per month for renewable energy using the regression method. For
Spain, Hanemann et al. (2011) found that households' willingness to pay monthly was € 12.76
using Probit Model. Zhang and Wu (2012) for the Chinese city of Jiangsu found that the average
monthly willingness to pay was $ 1.15- $ 1.51.
Guo et al. (2014), as a result of the empirical study using the conditional valuation approach,
found that households in Beijing, China, were willing to pay monthly renewable energy between
$ 2.7 and $ 3.3.
The present studies, as in many countries, have made broader contributions to the methods of
conditional valuation (CD) and selection testing (SD), which are mainly used. According to the
research we have done and recently published meta-analysis of studies in the literature
willingness to pay for renewable energies of citizens to Turkey and did not find a study
investigating the impact on YÖİS determining factors of this willingness. Therefore, this study
adopts one-way ANOVA and conditional appraisal method to measure Turkish citizens' HIS. In
addition, age, income, education and so on. The aim of this study is to fill the existing gap by
investigating YÖİS based on the variables that determine social factors.
1. DATA SET, METHOD AND APPLICATION
Turkey's 12 major cities (Istanbul, Balikesir, Bursa, Izmir, Antalya, Erzurum, Ankara, Gaziantep,
Samsun, Trabzon, Kayseri, Van) 2500 households based on interviews with the people face to
face survey application was made. In this study, the conditional evaluation method was used for
the questionnaire. This method was previously described by Nomura and Akaiki (2004),
Whitehead and Cherry (2007), Hite et al. (2008), Yoo and Kwak (2009), Zhang and Wu (2012)
and Guo et al. (2014).
In preparing the questionnaires, our questions were based on 26 characteristic questions, mainly
based on the participants' age, gender, marital status, education level, electricity consumption,
monthly income and expenditure, number of households, and their views on and against
renewable energy . Of the 26 questions used to examine the willingness of the respondents to
share, only 3 are about the willingness to share for renewable energy.
The questionnaire consists of several items (depending on the answers). However, in order to
achieve this study, only education, 20% share of renewable energy, 30% share of renewable
energy environmental awareness, age and income are used as variables.
Variable income, which represents annual gross income per capita, covering nine income groups:
TL 1400 and below, TL 1401-2000, TL 2001-3000, TL 3001-4000, TL 4001-5000, TL 5001 6000, It is in the form of 6001-8000 TL, 8000-10000 TL, 10000 and above. However, their
numbers were then increased to 4 for 20% YOS (2001-3000TL, 3001-4000TL, 4001-5000TL,
5001-6000TL) and for 5% for 30% YOS (2001-3000TL, 3001-4000TL, 4001-5000TL) , 5001-)
6000TL 6001-8000TL) has been downloaded. However, household age was categorized into 6
age groups (18-24 years, 25-34 years, 35-44 years, 45-55 years, 55-64 years and 65 years and
over).
Household education levels were measured on a sequential scale, involving nine groups: not
literate, literate, primary, secondary, high school, associate degree, undergraduate and graduate /
doctorate. However, their numbers were then reduced to 6 (primary, secondary, high school,
associate degree, undergraduate and graduate / doctorate) for 20% YOS and 30% YOS.
Environmental awareness was measured on a scale of 1 to 10. The lowest row shows that he is
not conscious at all and the highest row is very conscious.
Of the 26 questions used to examine the willingness of the respondents to pay, 3 are questions
about their willingness to pay for renewable energy. In the first question, the participants were
asked how much they want to pay in addition to the monthly electricity bill for 20 percent of the
electricity they use to come from environmentally friendly renewable sources. In the second
question, if the ratio of renewable energy is increased from 20% to 30%, the participants are
asked whether they want to pay or not. In addition, the participants who said “yes da in the
second question were then asked how much they would pay if the rate of renewable energy was
increased from 20% to 30%.
Participants were asked two questions to measure their preference and preference for renewable
energy. On the first question, the participants' opinions about the impact of renewable energy,
and they prefer renewable energy and to prefer their four options (air reduction of pollution,
Turkey's energy dependency reduction, to avoid damaging the environment, the refusal of
renewable electricity) has been asked by selecting from. On the other hand, in the second
question, the participants were asked why they prefer and do not prefer renewable energy by
choosing from the following options: because it is expensive, it does not think about air
pollution, it does not think about energy dependence, it is the responsibility of the state and
demanding renewable electricity in any case.
Survey data were analyzed using SPSS 20 software. The analysis is carried out independently for
20% LBS and 30% LBS, ie the same analysis procedures are performed first for participants who
want to pay 20% of renewable energy in total energy production, and then for participants who
want to pay 30% of renewable energy in total energy production.
1.1. Descriptive Statistics
The questionnaire was completed by 2500 people appropriately. Of the 2,500 respondents, 1335
(53.4%) did not want to pay both 20% and 30% of renewable energy in total energy production.
1165 respondents (46.6% of 2500 people) wanted to pay 20% electricity from renewable energy
sources and 816 (32.64% of 2500 people) wanted to pay 30% electricity from renewable energy
sources. Table 1 shows the means, standard deviations and standard errors for YÖS.
Table 1. Means, Standard Deviations (SS) and Standard Errors (SH) for YOS
YOS 20%
YOS 30%
Number of observations (N)
1165
816
Average
9,25 TL
4,77 TL
Standard Deviation (SS)
4,85 TL
0,79 TL
Standard Error (SH)
0,14 TL
0,03 TL
1 TL
3 TL
25 TL
10 TL
Minimum
Maximum value
According to Table 1, the values of those willing to pay for 20% share of renewable energy in
total energy production are between 1.00 TL and 25.00 TL and the values of those willing to pay
for 30% share are 10 and 3.00 TL, It varies between 00 TL. This is an average of TL 9.25 for
YOIS 20% (standard deviation of 4.85 TL, standard error 0.14 TL) and average of TL 4.77 for
YOIS 30% (standard deviation of 0.79 TL, standard error of 0.03 TL) ).
Descriptive statistics of the two sample groups used in the study are shown in Table 2 and Table
3.
Table 2. Descriptive Statistics of First Group (20% willing)
Variables
Gender
Woman
Male
marital status
Single
The married
Widow / Divorced
Age
18-24
25-34
35-44
45-54
55-64
65+
Number
Percentage
of groups
464
701
39,8
62,2
81
1.065
19
7,0
91,4
1,6
57
360
443
227
42
36
4,9
30,9
38.0
19,5
3,6
3,1
Education
Primary school
Middle School
High school
Associate Degree
License
Master / Doctorate
108
159
645
29
209
15
9,3
13,6
55,4
2,5
17,9
1,3
Home Situation
Own Property
Rent
lodgings
876
277
12
75,2
23,8
1,0
The effect of renewable energy
Reduction of air pollution
Reducing energy dependence
No damage to the environment
243
243
679
20,9
20,9
58,2
2
4
1159
0,2
0,3
99,5
199
562
290
114
17,1
48,2
24,9
9,8
Renewable energy preference and justification
Expensive (not suitable for budget)
Not thinking that energy addiction is a problem
In any case, demanding renewable energy
Revenue
2.001-3.000 TL
3.001-4.000 TL
4.001-5.000 TL
5.001-6.000 TL
Table 3. Descriptive Statistics of the Second Group (30% willing)
Variables
Gender
Woman
Male
marital status
Single
The married
Widow / Divorced
Age
18-24
25-34
35-44
45-54
55-64
65+
Education
Number
Percentage
of groups
307
509
37,6
62,4
54
751
11
6,6
92,0
1,4
42
246
312
164
31
21
5,1
30,2
38.2
20,1
3,8
2,6
Literate
Primary school
Middle School
High school
Associate Degree
License
Master / Doctorate
Home Situation
Own Property
Rent
lodgings
The effect of renewable energy
Reduction of air pollution
Reducing energy dependence
No damage to the environment
Renewable energy preference and justification
Expensive (not suitable for budget)
Not thinking that energy addiction is a problem
In any case, demanding renewable energy
Revenue
2.001-3.000 TL
3.001-4.000 TL
4.001-5.000 TL
5.001-6.000 TL
6.001-8.000 TL
2
72
113
462
20
138
9
0,3
8,8
13,8
56,6
2,5
16,9
1,1
630
174
12
77,3
21,3
1,4
172
156
488
21,1
19,1
59,8
2
4
810
0,2
0,3
99,5
119
423
194
58
22
14,6
51,8
23,8
7,1
2,7
1.1. Empirical Practice
In this study, among the participants who want to pay for renewable energy, age, education,
environmental awareness, the participants' opinions about the effect of renewable energy, the
reasons for the preference of the renewable energy and the preference of participants and the
effect of income variable on YOS (renewable energy willingness to pay) is examined. First, it is
done by excluding participants' willingness to pay from the analysis with the highest and lowest
five percent in order to minimize the effects of excessive values.
After excluding outliers, one-way analysis of variance (Oneway-ANOVA) is used. OnewayANOVA categorical variables are used to investigate whether there are statistically significant
differences between the groups of means (Cobb, 1984: 121).
If statistically significant differences are found between groups of categorical variables and there
are more than two comparable groups, Post-hoc (Tukey) test is used. Post-hoc (Tukey) test
examines which group is different (Jarrell, 1994: 52). In the post-hoc test, if the probability value
associated with each group is less than 5%, the groups differ (Giloni and Seshadri, 2005: 34).
Since the variables used in the study violate the assumptions of Oneway-ANOVA, which are the
homogeneity of normality and variance, the non-parametric ANOVA analysis, known as the
Kruskak-Wallis test, is used to test for differences between the groups of variables using a P
value of less than 5%. it is used. Therefore, in this study, non-parametric Oneway-ANOVA
(Kruskal-Wallis test) and post-hoc test are used to obtain more valid results.
4. RESULTS AND DISCUSSION
4.1. Effect of Selected Factors on Willingness to Pay for 20% Share of Renewable Energy
(YÖİS 20%)
This section analyzes the impact of selected variables on willingness to pay for a 20% share of
renewable energy. These variables are as follows: income, education, age, environmental
awareness, opinions on the impact of renewable energy, the choice of renewable energy, and
justification for non-preference. Each variable is analyzed as follows using Kruskal-Wallis and
Post-Hoc test.
4.1.1. Revenue
Of the 2,500 respondents, 1165 (46.6%) wanted to pay for a 20% share of renewable energy in
total electricity generation. The Kruskak-Wallis test is used to test for differences between the
means of the following income groups using a P-value of less than 5%: 2001-3000 TL, 30014000 TL, 4001-5000 TL, 5001-6000 TL.
Considering the amount of participants' payment for renewable energy, it was found using the
Kuruskal-Wallis test that income from the 46.6% recorded above affects the extra amount the
participants are willing to pay and is shown in Table 4.
Table 4. Kruskal-Waliss Test Results of Income Variable for YOS 20%
YOS %20
7,587
3
0,044
1165
Chi-Square (χ )
df (degree of freedom)
P-value
N (number of observations)
2
The P-value in the Kruskal-Wallis test was found to be less than 5%, indicating statistically
significant differences between the averages of income groups in case of willingness to generate
20% electricity from renewable energy sources. However, Post-Hoc (Tukey) test is used to
determine which group is different. Table 5 summarizes the results of the Post-Hoc test.
Table 5. Post-Hoc Test Results of Income Variable for YOS 20%
(İ) Income
(J) Income
2001-3000 TL
3001-4000 TL
4001-5000 TL
5001-6000 TL
3001-4000 TL 2001-3000 TL
4001-5000 TL
5001-6000 TL
4001-5000 TL 2001-3000 TL
3001-4000 TL
5001-6000 TL
5001-6000 TL 2001-3000 TL
3001-4000 TL
4001-5000 TL
The average difference is significant at 0.05
Average
Difference
(İ-J)
-0,51
-0,60
0,83
0,51
-0,09
1,34*
0,60
0,09
1,43*
-0,83
-1,34*
-1,43*
P
0,577
0,528
0,462
0,577
0,993
0,036
0,528
0,993
0,037
0,462
0,036
0,037
Standard
Error
0,399
0,445
0,568
0,399
0,350
0,497
0,445
0,350
0,535
0,568
0,497
0,535
As shown in Table 5, the Post-Hoc test revealed significant differences between the following
groups: 5001-6000 TL (Mean = 8.1, SH = 0.45; SD = 3.96), 2001-3000 TL ( Mean = 8.94; SH =
0.34; SD = 4.72), 3001-4000 TL (Mean = 9.45; SH = 0.20; SS = 4.87), 4001-5000 TL (Average
= 9.54; SH = 0.28; SS = 5.15).
Figure 1 shows the estimated marginal averages of the willingness to pay for the 20% share of
renewable energy of the revenue variable.
Figure 1. Estimated Marginal Average of YOS 20% for Income Variable
As shown in Figure 1, the willingness to pay of the income groups of 5001-6000 TL and 20013000 TL is lower than the willingness of payment groups of 3001-4000 TL and 4001-5000 TL.
These findings demonstrate the willingness to pay more than 20% share of renewable energy in
total electricity generation of citizens who TL 3001-4000 and 4001-5000 per income groups in
Turkey.
4.1.1. Age
In terms of age, statistically significant differences in willingness to pay for 20% of renewable
energy between different age groups (18-24, 25-34, 35-44, 45-54, 55-64, 65+) Kruskal-Wallis
test using. Kruskal-Wallis test results are presented in Table 6.
Table 6. Kruskal-Waliss Test Results of Age Variable for YIS 20%
Chi-Square (χ2)
df (degree of freedom)
YOS %20
5,106
5
P-value
N (number of observations)
0,403
1165
The P-value in the Kruskal-Wallis test was found to be more than 5%, indicating that there was
no statistically significant difference between the mean age groups in case of willingness to
produce 20% electricity from renewable energy sources. Therefore, Post-Hoc testing is not
necessary in this case.
4.1.1. Education
In terms of education, statistically significant differences were not found between the different
educational levels of primary, secondary, high school, associate, bachelor, master / doctorate
willingness to pay for 20% share of renewable energy using Kruskal-Wallis test. Kruskal-Wallis
test results are presented in Table 7.
Table 7. Kruskal-Waliss Test Results of Education Variable for YOS 20%
Chi-Square (χ2)
df (degree of freedom)
P-value
N (number of observations)
YOS %20
3,050
5
0,692
1165
The P-value in the Kruskal-Wallis test was found to be greater than 5%, indicating that there
were no statistically significant differences between the mean levels of education in case of
willingness to generate 20% electricity from renewable energy sources.
4.1.1. Environmental awareness
Of the 2,500 respondents, 1165 (46.6%) wanted to pay for a 20% share of renewable energy in
total electricity generation. The Kruskak-Wallis test is used to test for differences between
groups 1 to 10 in the scale of environmental awareness using a P-value of less than 5%. The
lowest group (1) shows that he is not conscious at all and the highest group (10) is very
conscious.
Considering the amount of participants 'payment for renewable energy, it was found from 46.6%
above that the participants' environmental awareness affects the extra amount they want to pay
by using the Kuruskal-Wallis test and is shown in Table 8.
Table 8. Environmental Consciousness Kruskal-Waliss Test Results for YOS 20%
YOS 20%
Chi-Square (χ2)
29,559
df (degree of freedom)
8
P-value
0,001
N (number of observations)
1165
The P-value in the Kruskal-Wallis test was found to be less than 5%, indicating a statistically
significant difference between the means of different groups on the scale of environmental
awareness in case of willingness to generate 20% electricity from renewable energy sources.
However, Post-Hoc (Tukey) test is used to determine which group is different.
Table 9. Post-Hoc Test Results of Environmental Awareness Variable for YÖİS 20%
(İ) Çevre Bilinci (J) Çevre Bilinci
1
2
2
4
5
6
7
8
9
10
1
4
5
6
7
8
9
10
Ortalama Fark
(İ-J)
-0,38
-2,50
-1,50
-1,30
-0,53
-1,96
-2,87
-2,67
0,38
-2,12
-1,12
-0,92
-0,15
-1,57
-2,49
-2,29
P
1,000
1,000
1,000
1,000
1,000
1,000
0,995
0,997
1,000
1,000
1,000
0,998
1,000
0,927
0,504
0,626
Standart
Hata
3,570
4,775
3,995
3,390
3,388
3,391
3,394
3,396
3,570
3,570
2,429
1,197
1,192
1,198
1,209
1,121
4
5
6
7
8
9
10
1
2
5
6
7
8
9
10
1
2
4
6
7
8
9
10
1
2
4
5
7
8
9
10
1
2
4
5
6
8
9
10
1
2
4
5
6
7
9
10
1
2
4
5
6
7
8
10
1
2
4
5
6
7
8
9
Ortalama fark 0.05 seviyesinde anlamlıdır
2,50
2,12
1,00
1,20
1,97
0,54
-0,37
-0,17
1,50
1,12
-1,00
0,20
0,97
-0,46
-1,37
-1,17
1,30
0,92
-1,20
-0,20
0,77
-0,65
-1,57*
-1,36
0,53
0,15
-1,97
-0,97
-0,77
-1,42*
-2,33*
-2,13*
1,96
1,57
-0,54
0,46
0,65
1,42*
-0,91
-0,71
2,87
2,49
0,37
1,37
1,57*
2,33*
0,91
0,20
2,67
2,29
0,17
1,17
1,36
2,13*
0,71
-0,20
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
1,000
0,999
1,000
1,000
0,998
1,000
1,000
0,644
0,849
0,020
0,093
1,000
1,000
1,000
1,000
0,644
0,020
0,000
0,000
1,000
0,927
1,000
1,000
0,849
0,020
0,566
0,858
0,995
0,504
1,000
0,999
0,020
0,000
0,566
1,000
0,997
0,626
1,000
1,000
0,093
0,000
0,858
1,000
4,775
3,570
3,995
3,390
3,388
3,391
3,394
3,396
3,995
2,429
3,995
2,157
2,154
2,158
2,164
2,166
3,390
1,197
3,390
2,157
0,414
0,432
0,460
0,473
3,388
1,192
3,388
2,154
0,414
0,418
0,447
0,460
3,391
1,198
3,391
2,158
0,432
0,418
0,463
0,476
3,394
1,209
3,394
2,164
0,460
0,447
0,463
0,502
3,396
1,214
3,396
2,166
0,473
0,460
0,476
0,502
Post-Hoc testinde, her bir grupla ilişkili P-değeri %5'in altındaysa, gruplar farklılık
göstermektedir. Dolayısıyla, Tablo 9’da görüldüğü gibi, Post-Hoc testi, 6,7,8,9 ve 10 olan
grupların arasında önemli farklılıklar bulunmuştur.
Şekil 2’de çevre bilinci değişkenin yenilenebilir enerjinin %20'lik payı için ödeme istekliliğinin
tahmini marjinal ortalamarını gösterilmektedir.
Şekil 1. Çevre Bilinci Değişken için YÖİS%20’nin Tahmini Marjinal Ortalamarı
Çevre bilinci 1'den 10'a kadar olan bir ölçekle ölçülmektedir. Şekil 2'de görüldüğü gibi, çevre
bilincin ölçeğindeki yüksek sıralarda gelen 9 ve 10 olan grupların ödeme istekliliği değerleri 6. 7.
ve 8. sırada gelen grupların ödeme istekliliği değerlerinden daha fazladır. Bununla birlikte, çevre
bilincin ölçeğindeki en yüksek sıralarda gelen (9 ve 10) grupların toplam elektrik üretiminde
yenilenebilir enerji kaynakların %20'i payı için daha fazla ödeme istekliliğini göstermektedir.
4.1.1. Yenilenebilir Enerjinin Etkisi (YEE)
Yenilenebilir enerjinin %20'lik payı için ödeme istekliliğinde, farklı yenilenebilir enerjinin
etkisini gösteren seçenekler (hava kirliliğinin azaltılması, enerji bağımlılığının azaltılması,
çevrenin zarar görmemesi) arasında istatistiksel olarak anlamlı farklar Kruskal-Wallis testi
kullanılarak bulunmamıştır. YÖİS%20 için yenilenebilir enerji etkileri ile ilgi Kruskal-Wallis
testi sonuçları Tablo 10’da sunulmaktadır.
Tablo 10. YÖİS%20 için YEE’nin Kruskal-Waliss Testi Sonuçları
Ki-Kare (Chi-Square) (χ )
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
2
YÖİS%20
0.943
2
0.624
1165
Kruskal-Wallis testindeki P-değeri %5'ten fazla bulunmuştur ve bu da yenilenebilir enerji
kaynaklarından %20 elektrik üretimine istekli olma durumunda yenilenebilir enerji etkileri ile
ilgi grupların ortalamaları arasında istatistiksel olarak anlamlı farklılıklar olmadığını
göstermektedir.
4.1.2. Yenilenebilir Enerji Tercihi ve Tercih Edilmemesi Gerekçesi (TTEG-YE)
Yenilenebilir enerjinin %20'lik payı için ödeme istekliliğinde, yenilenebilir enerji tercihi ve
tercih edilmemesini gösteren seçenekler (pahalı, enerji bağımlılığı sorunu olduğunu
düşünmemesi, her halükarda yenilenebilir elektrik enerjisi istemesi) arasında istatistiksel olarak
anlamlı farklar Kruskal-Wallis testi kullanılarak bulunmamıştır. YÖİS%20 için yenilenebilir
enerji tercihi ve tercih edilmemesi ile ilgi Kruskal-Wallis testi sonuçları Tablo 11’de
sunulmaktadır.
Tablo 11. YÖİS%20 için TTEG-YE’nin Kruskal-Waliss Testi Sonuçları
Ki-Kare (Chi-Square) (χ2)
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
YÖİS%20
4,592
2
0,101
1165
Kruskal-Wallis testi grupların ortalamaları arasında istatistiksel olarak anlamlı farklılıklar
olmadığını göstermektedir.
4.2. Seçilmiş Faktörlerin Yenilenebilir Enerjinin %30'luk Payı için Ödeme İstekliliği
(YÖİS%30) Üzerindeki Etkisi
Bu bölüm, seçilmiş değişkenlerin yenilenebilir enerjinin %30'luk payı için ödeme istekliliği
üzerindeki etkisini analiz etmektedir. Bu değişkenler aşağıdakileri içermektedir; gelir, eğitim,
yaş, çevre bilinci, yenilenebilir enerjinin etkisi ile ilgili görüşler, yenilenebilir enerjinin tercihi ve
tercih edilmemesi için gerekçe. Her değişken, Kruskal-Wallis ve Post-Hoc testi kullanılarak
aşağıdaki gibi analiz edilmektedir.
4.2.1. Gelir
Yanıt veren 2500 kişiden 816’sı (%32,6) toplam elektrik üretiminde yenilenebilir enerjinin
%30'luk payı için ödemek istemiştir. Kruskak-Wallis testi, %5'ten daha düşük bir P-değeri
kullanılarak, aşağıdaki gelir grupların ortalamaları arasında farklılıklar olup olmadığını test
etmek için kullanılmaktadır: 2001-3000 TL, 3001-4000 TL, 4001-5000 TL, 5001-6000 TL,
6001-8000 TL.
Katılımcıların yenilenebilir enerji için ödeme tutarı göz önüne alındığında, yukarıda kaydedilen
%32,6'dan gelirin, katılımcıların ödemek istediği ekstra miktarı etkilediği Kuruskal-Wallis testi
kullanılarak bulunmuştur ve Tablo 12’de gösterilmiştir.
Tablo 12. YÖİS%30 için Gelir Değişkenin Kruskal-Waliss Testi Sonuçları
YÖİS%30
23,255
4
0,000
816
Ki-Kare (Chi-Square) (χ )
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
2
Kruskal-Wallis testi gelir grupların ortalamaları arasında istatistiksel olarak anlamlı farklılıklar
olduğunu göstermektedir. Ancak, gruplardan hangisinin farklı olduğunu incelemek için Post-Hoc
(Tukey) testi kullanılmaktadır.
Tablo 13. YÖİS%30 için Yapılan Gelir Değişkenin Post-Hoc Testinin Sonuçları
(İ) Gelir
2001-3000 TL
3001-4000 TL
4001-5000 TL
5001-6000 TL
6001-8000 TL
(J) Gelir
3001-4000 TL
4001-5000 TL
5001-6000 TL
6001-8000 TL
2001-3000 TL
4001-5000 TL
5001-6000 TL
6001-8000 TL
2001-3000 TL
3001-4000 TL
5001-6000 TL
6001-8000 TL
2001-3000 TL
3001-4000 TL
4001-5000 TL
6001-8000 TL
2001-3000 TL
3001-4000 TL
4001-5000 TL
5001-6000 TL
Ortalama Fark
(İ-J)
-0,11
-0,04
-0,35*
-1,03*
0,11
0,06
-0,25
-0,93*
0,04
-0,06
-0,31
-0,99*
0,35*
0,25
0,31
-0,68*
1,03*
0,93*
0,99*
0,68*
P
0,663
0,988
0,034
0,000
0,663
0,876
0,150
0,000
0,988
0,876
0,056
0,000
0,034
0,150
0,056
0,004
0,000
0,000
0,000
0,004
Standart
Hata
0,080
0,089
0,123
0,178
0,080
0,067
0,107
0,168
0,089
0,067
0,115
0,173
0,123
0,107
0,115
0,192
0,178
0,168
0,173
0,192
Ortalama fark 0.05 seviyesinde anlamlıdır
Tablo 13’te görüldüğü gibi, Post-Hoc testi, aşağıdaki gelir grupları arasında istatistiksel olarak
önemli farklılıklar bulunmuştur: 5001-6000 TL (Ortalama=5,0; SH=0,10; SS=0,00), 2001-3000
TL (Ortalama=4,6; SH=0,07; SS=0,73), 3001-4000 TL (Ortalama=4,75; SH=0,04; SS=0,73),
4001-5000 TL (Ortalama=4,69; SH=0,06; SS=0,79) ve 6001-8000 TL (Ortalama=5,68;
SH=0,16; SS=1,81).
Şekil 3 gelir değişkenin yenilenebilir enerjinin %30'luk payı için ödeme istekliliğinin tahmini
marjinal ortalamarını göstermektedir.
Şekil 2. Gelir Değişken için YÖİS%30’un Tahmini Marjinal Ortalamarı
Şekil 3'te görüldüğü gibi, 4001-5000 TL, 2001-3000 TL ve 3001-4000 TL olan gelir grupların
ödeme istekliliği değerleri 5001-6000 TL ve 6001-8000 TL olan gelir groupların ödeme
istekliliği değerlerinden daha düşüktür. Dolayısıyla, bu bulgular, Türkiye’de 5001-6000 TL ve
6001-8000 TL gelir grupları olan vatandaşlarının toplam elektrik üretiminde yenilenebilir enerji
kaynakların %30'u payı için daha fazla ödeme istekliliğini göstermektedir.
4.2.2. Yaş
Yaş açısından, yenilenebilir enerjinin %30'luk payı için ödeme istekliliğinde, farklı yaş grupları
(18-24, 25-34, 35-44, 45-54, 55-64, 65+) arasında istatistiksel olarak anlamlı farklılıklar KruskalWallis testi kullanılarak bulunmuştur. YÖİS%30 için Kruskal-Wallis testi sonuçları Tablo 14’te
sunulmaktadır.
Table 14. YÖİS%30 için Yaş Değişkenin Kruskal-Waliss Testi Sonuçları
YÖİS%20
Ki-Kare (Chi-Square) (χ2)
16,385
df (serbestlik derecesi)
5
P-değeri
0,006
N (gözlem sayısı)
816
Kruskal-Wallis testi grupların ortalamalar arasında istatistiksel olarak anlamlı farklılıklar
olduğunu göstermektedir. Ancak, gruplardan hangisinin farklı olduğunu incelemek için Post-Hoc
(Tukey) testi kullanılmakta ve sonuçları ise Tablo 15’te sunulmaktadır.
Tablo 15. YÖİS%30 İçin Yapılan Yaş Değişkenin Post-Hoc Testinin Sonuçları
(İ) Yaş
18-24
25-34
35-44
45-54
55-64
(J) Yaş
25-34
35-44
45-54
55-64
65+
18-24
35-44
45-54
55-64
65+
18-24
25-34
45-54
55-64
65+
18-24
25-34
35-44
55-64
65+
18-24
25-34
35-44
45-54
Ortalama Fark
(İ-J)
-0,08
-0,08
-0,11
-0,36
-1,36*
0,08
0,00
-0,03
-0,28
-1,28*
0,08
0,00
-0,03
-0,27
-1,27*
0,11
0,03
0,03
-0,24
-1,24*
0,36
0,28
0,27
0,24
P
0,988
0,985
0,953
0,332
0,000
0,988
1,000
0,988
0,366
0,000
0,985
1,000
0,998
0,367
0,000
0,953
0,998
0,998
0,551
0,000
0,332
0,366
0,367
0,551
Standart
Hata
0,127
0,125
0,131
0,177
0,200
0,127
0,065
0,077
0,141
0,169
0,125
0,065
0,073
0,139
0,168
0,131
0,077
0,073
0,145
0,173
0,177
0,141
0,139
0,145
65+
65+
18-24
25-34
35-44
45-54
55-64
-1,00*
1,36*
1,28*
1,27*
1,24*
1,00*
0,000
0,000
0,000
0,000
0,000
0,000
0,209
0,200
0,169
0,168
0,173
0,209
Ortalama fark 0.05 seviyesinde anlamlıdır
As shown in Table 15, the Post-Hoc test found statistically significant differences between the
following income groups: 18-24, 25-34, 35-44, 45-54, 55-64, 65+.
Figure 4 shows the estimated marginal average of the willingness to pay for the 30% share of
renewable energy of the age variable.
Şekil 3. Yaş Değişken için YÖİS%30’un Tahmini Marjinal Ortalamarı
As can be seen in Figure 4, the willingness of payment of the age group of 65+ is higher than the
willingness of payment of age groups of 18-24, 25-34, 35-44, 45-54, 55-64. These findings, 30%
of renewable energy sources in total electricity generation of citizens who are 65+ years in
Turkey shows more willingness to pay for the shares.
4.1.1. Education
In terms of education, there are statistically significant differences between different levels of
education in primary, secondary, high school, associate, bachelor, master / doctorate, and
willingness to pay for the 30% share of renewable energy. test and are shown in Table 16.
Table 16. YÖİS%30 için Eğitim Değişkenin Kruskal-Waliss Testi Sonuçları
YÖİS%30
37,937
5
0,000
816
Ki-Kare (Chi-Square) (χ2)
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
Kruskal-Wallis testi Eğitim grupların ortalamaları arasında istatistiksel olarak anlamlı farklılıklar
olduğunu göstermektedir. Ancak, gruplardan hangisinin farklı olduğunu incelemek için Post-Hoc
testi kullanılmaktadır. Tablo 17’de ise, Post-Hoc testinin sonuçları özetlenmiştir.
Tablo 17. YÖİS%30 için Yapılan Eğitim Değişkenin Post-Hoc Testinin Sonuçları
(İ) Eğitim (J) Eğitim
İlkokul
Ortaokul
Lise
Ön Lisans
Lisans
Yüksek Lisans/Doktora
Ortaokul
İlkokul
Lise
Ön Lisans
Lisans
Yüksek Lisans/Doktora
Lise
İlkokul
Ortaokul
Ön Lisans
Lisans
Yüksek Lisans/Doktora
Ön Lisans İlkokul
Ortaokul
Lise
Lisans
Yüksek Lisans/Doktora
Lisans
İlkokul
Ortaokul
Lise
Ön Lisans
Yüksek Lisans/Doktora
YL/Dok İlkokul
Ortaokul
Lise
Ortalama Fark
(İ-J)
0,19
0,29*
0,30
0,00
0,25
-0,19
0,10
0,11
-0,19
0,06
-0,29*
-0,10
0,01
-0,29*
-0,04
-0,30
-0,11
-0,01
-0,30
-0,05
0,00
0,19
0,29*
0,30
0,25
-0,25
-0,06
0,04
P
0,278
0,002
0,345
1,000
0,869
0,278
0,533
0,973
0,114
1,000
0,002
0,533
1,000
0,000
1,000
0,345
0,973
1,000
0,282
1,000
1,000
0,114
0,000
0,282
0,856
0,869
1,000
1,000
Standart
Hata
0,089
0,076
0,150
0,087
0,221
0,089
0,062
0,144
0,075
0,217
0,076
0,062
0,136
0,057
0,212
0,150
0,144
0,136
0,142
0,248
0,087
0,075
0,057
0,142
0,216
0,221
0,217
0,212
Ön Lisans
Lisans
Ortalama fark 0.05 seviyesinde anlamlıdır
0,05
-0,25
1,000
0,856
0,248
0,216
Tablo 17’de görüldüğü gibi, Post-Hoc testi, ilkokul, lise ve lisans olan eğitim seviyeleri arasında
istatistiksel olarak anlamlı farklılıklar bulunmuştur.
Şekil 5’te eğitim değişkenin yenilenebilir enerjinin %30'luk payı için ödeme istekliliğinin
tahmini marjinal ortalamarını gösterilmektedir.
Şekil 5. Eğitim Değişken için YÖİS%30’un Tahmini Marjinal Ortalamarı
Şekil 5'te görüldüğü gibi, ilkokul ve lisans olan eğitim seviyelerin ödeme istekliliği değerleri
diğer eğitim seviyelerin ödeme istekliliği değerlerinden daha fazladır. Dolayısıyla, bu bulgular,
Türkiye’de ilkokul ve lisans eğitime sahip olan vatandaşlarının toplam elektrik üretiminde
yenilenebilir enerji kaynaklarının %30'u payı için daha fazla ödeme istekliliğini göstermektedir.
4.2.3. Çevre Bilinci
Çevre bilinci açısından, yenilenebilir enerjinin %30'luk payı için ödeme istekliliğinde, çevre
bilincin ölçeğindeki 1'den 10'a kadar olan grupları arasında istatistiksel olarak anlamlı farklılıklar
Kruskal-Wallis testi kullanılarak bulunmamıştır. Kruskal-Wallis testi sonuçları Tablo 18’de
sunulmaktadır.
Tablo 18. YÖİS%30 için Çevre Bilincin Kruskal-Waliss Testi Sonuçları
YÖİS%20
1,872
5
0,867
816
Ki-Kare (Chi-Square) (χ )
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
2
Kruskal-Wallis testi çevre bilincin ölçeğindeki farklı grupların ortalamaları arasında istatistiksel
olarak anlamlı farklılıklar olmadığını göstermektedir.
4.2.4. Yenilenebilir Enerjinin Etkisi (YEE)
Yenilenebilir enerjinin %30'luk payı için ödeme istekliliğinde, farklı yenilenebilir enerjinin
etkisini gösteren seçenekler (hava kirliliğinin azaltılması, enerji bağımlılığının azaltılması,
çevrenin zarar görmemsi) arasında istatistiksel olarak anlamlı farklılıklar Kruskal-Wallis testi
kullanılarak bulunmuştur. Kruskal-Wallis testi sonuçları Tablo 19’da sunulmaktadır.
Table 19. YÖİS%30 için YEE’nin Kruskal-Waliss Testi Sonuçları
YÖİS%20
9,509
2
0.009
816
Ki-Kare (Chi-Square) (χ2)
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
Kruskal-Wallis testi yenilenebilir enerji etkileri ile ilgi grupların ortalamaları arasında
istatistiksel olarak anlamlı farklılıklar olduğunu göstermektedir. Ancak, gruplardan hangisinin
farklı olduğunu incelemek için Post-Hoc testi kullanılmakta ve sonuçları ise Tablo 20’de
sunulmaktadır.
Tablo 20. YÖİS%30 için Yapılan YEE’nin Post-Hoc Testinin Sonuçları
(İ) YEE
(J) YEE
Hava kirliliğinin azaltılması
Enerji bağımlılığının azaltılması
Çevrenin zarar görmemesi
Enerji bağımlılığının azaltılması
Hava kirliliğinin azaltılması
Çevrenin zarar görmemsi
Ortalama
Fark
(İ-J)
P
Standart
Hata
-0,22*
-0,03
0,22*
0,18*
0,011
0,848
0,011
0,009
0,075
0,060
0,075
0,062
Çevrenin zarar görmemsi
Hava kirliliğinin azaltılması
Enerji bağımlılığının azaltılması
Ortalama fark 0.05 seviyesinde anlamlıdır
0,03
-0,18*
0,848
0,009
0,060
0,062
Tablo 20’de görüldüğü gibi, Post-Hoc testi, hava kirliliğinin azaltılması, enerji bağımlılığının
azaltılması, çevrenin zarar görmemsi olan yenilenebilir enerji etkileri arasında istatistiksel olarak
anlamlı farklılıklar bulunmuştur.
Şekil 6’da YEE değişkenin yenilenebilir enerjinin %30'luk payı için ödeme istekliliğinin tahmini
marjinal ortalamarını gösterilmektedir.
Şekil 4. YEE Değişken için YÖİS%30’un Tahmini Marjinal Ortalamarı
As shown in Figure 6, the willingness to pay effect of renewable energy, which is the reduction
of energy dependence, is greater than the willingness to pay effects of other renewable energy.
Therefore, these findings according to Turkey citizen of electrical energy generated from
renewable energy sources in reducing the energy dependency Turkey shows that the most
effective. In addition, 30% of renewable energy sources in total electricity generation of Turkish
citizens in Turkey to reduce its energy dependency shows more willingness to pay for the shares.
4.1.1. Renewable Energy Preference and Reason for Non-Preference (TTEG-YE)
The Kruskal-Wallis test was not found to show statistically significant differences in the
willingness to pay for the 30% share of renewable energy, indicating the preference and
preference of renewable energy (not considering expensive expensive energy dependence, in any
case requesting renewable electricity). Renewable energy preference for 30% of YÖİS and nonpreferred Kruskal-Wallis test results are presented in Table 21.
Table 21. YÖİS%30 için TTEG-YE’nin Kruskal-Waliss Testi Sonuçları
Ki-Kare (Chi-Square) (χ )
df (serbestlik derecesi)
P-değeri
N (gözlem sayısı)
2
YÖİS%30
2,046
2
0,360
1165
Kruskal-Wallis testi yenilenebilir enerji tercihi ve tercih edilmemesi ile ilgi grupların
ortalamaları arasında istatistiksel olarak anlamlı farklılıklar olmadığını göstermektedir.
RESULT
Energy is an important input for people's housing, industry, agriculture and transportation needs.
Energy demand is increasing over time and is expected to increase in the future. Energy
production planning and increasing energy demand are among the most important elements of
the country's development plans. With rapidly growing economy and growing population,
Turkey is obliged to take measures to cope with the increasing demand for energy. Turkey is
heavily dependent on foreign energy sources. Energy consumption based on fossil fuels also
creates economic, environmental and political problems. For these reasons, Turkey should
evaluate domestic and clean energy resources to ensure the sustainable development and use.
As a developing economy, especially in the last twenty years, Turkey's energy demand has
increased rapidly. However, this increase in energy demand is met by traditional fossil fuels such
as oil, coal, lignite and natural gas. Because these energy sources is very limited in the country
and a significant amount of renewable energy production is, Turkey is entirely dependent on
imported energy sources. This is to detect the economic and social forces in the country and
undermine Turkey's political influence in the region. On the other hand, Turkey has massive
renewable energy sources. In this context, the use of renewable energy sources, to fight against
energy-related issues and is regarded as one of the best solutions to ensure sustainable
development for Turkey.
Turkey, reducing energy import at least until 2023 and has an ambitious domestic energy
resources to maximize the level of national energy goals. This is based on the 2010-2014 action
plan prepared by the Ministry of Energy and Natural Resources (the country generates 30%
electricity from renewable energy sources). A better understanding of consumers' preferences can
help in setting realistic goals and designing effective programs to increase the share of energy
generated from renewable energy sources.
This study offers estimates of the willingness to pay for 20% and 30% share of renewable energy
in the total energy in Turkey and factors affecting willingness to pay for the consumer of
renewable energy in Turkey (age, income, education, environmental awareness, the impact of
renewable energy renewable energy preference and the reason for not preferred. To this end,
Turkey's 12 metropolitan in using stratified random sampling technique to provide access to
household willingness to pay for green electricity in 2500 face to face interviews and
questionnaires were administered.
It is important to examine and understand the factors such as income, age, education, renewable
energy consumption, renewable energy preference and not justification and environmental
awareness which play an important role in the willingness to pay financial support from
households for the expansion of electricity generated from green energy sources. Therefore, in
this study, one-way ANOVA and Post-Hoc Tests were used to estimate the average willingness
to pay for renewable energy and to determine the factors determining willingness to pay for
renewable energy.
The findings of this study show that there are statistically significant differences in the
willingness to pay the 20% and 30% share of renewable energy among the respondents
examined. According to the results of 20% share of renewable energy, middle income groups are
willing to pay higher than lower and upper income groups. However, highly environmentally
conscious participants tend to pay high for 20% of renewable energy. However, age, education,
the effect of renewable energy, renewable energy preference and the reason for the nonpreference of the variable has no effect on YOS 20%.
In the analysis, it was found that willingness to pay for 30% share of renewable energy in total
energy had a positive relationship with age and income. However, the variable, which is
environmentally conscious and the reason for the preference and non-preference of renewable
energy, had no effect on the YIS 30%. The results, which make up 30% of renewable energy,
show that groups over 65 years of age and with high income have a high willingness to pay for
renewable energy. In addition, the results show that primary and undergraduate education groups
are willing to pay more for the 30% share of renewable energy.
According to the idea of Turkish citizens it shows more willingness to pay for 30% share of
renewable energy in total electricity generation of Turkish citizens to reduce energy dependence
on show is the most effective source to reduce Turkey's energy dependence of the electrical
energy produced from renewable sources.
made earlier about the willingness to pay for renewable energy in Turkey have been no studies
and consumers is how to respond to certain share of renewable energy in total energy production
of the willingness to pay for renewable energy and how to handle that affected the increase in the
denominator of. Therefore, this study is to fill this gap, for a 20% share of renewable energy to
the citizens of Turkey for the month and 9.25 per and 30% share of renewable energy shows that
they are willing to pay a monthly 4.77 TL.
The findings of this study have important implications for policy makers. Turkey's gradually
increasing the share of renewable energy in the energy portfolio towards policy objectives are
consistent with the preferences of consumers as they are willing to pay more for renewable
energy. Based on these findings, utility companies can make marketing strategies suitable for
targeting specific age, education and income groups to provide financial support for the increase
share of renewable energy.
The results of the analysis show that the majority of the participants were prepared to support the
green electricity plan. This results in favor of the 2010-2014 action plan framed by the Ministry
of Energy and Natural Resources (EDKB), which will see that the country generates 30% of its
electricity generation from renewable energy sources by 2023. The results of this study can play
an important role in shaping the government's energy policy. In addition, environmentalists,
similar research to gather the opinions of civil society organizations and local communities
should be designed and carried out and the results of this survey should be considered together to
decide about Turkey's energy policy.
This study can provide useful information to the government and utilities to design effective
mechanisms, to charge an appropriate amount to support a greater share of renewable energy in
the energy portfolio, and to achieve their goals.
THANK
In this study, Assoc. Dr. I took part as a fellow in the TUBITAK project number 116K727,
which was conducted by Eyüp DOĞAN and I carried out the empirical application part of the
study by using the survey data collected within the framework of the project with the permission
of the project director. Therefore, Assoc. Dr. I owe a debt of thanks to Eyüp Doğan.
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