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Willingness to pay for renewable electricity: A contingent valuation study in Turkey
The Electricity Journal 32 (2019) 106677
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Willingness to pay for renewable electricity: A contingent valuation study in Turkey
T
Eyup Dogana,**, Iftikhar Muhammadb a b
Department of Economics, Abdullah Gül University, Sumer Campus, Kayseri, 38280, Turkey Department of Economics, Erciyes University, Turkey
A R T I C LE I N FO
A B S T R A C T
Keywords: Willingness to pay Turkey Renewable energy
Renewable energy sources are advised as an important alternative vehicle for dealing with a high rate of energy dependency and global warming. Turkey has also an ambitious national energy goal of minimizing energy import and producing 30% of electricity from renewable energy sources by 2023. However, it may not be easy to reach these goals. Willingness to Pay (WTP) thus plays a central role in directing appropriate policies for the country to realize its energy targets. This study reviews previous studies in the same literature as well as examines WTP of Turkish citizens for renewable electricity energy by using a stratified-sample and contingent valuation survey of 2500 households. The results from estimated models show that environmental conscience, membership to an environmental organization, age, education level, gender and income of households are significant determinants of WTP. In addition, the mean value of WTP for green electricity by Turkish households is estimated at around US$ 1 (with the exchange rate 5,3 TL/ US$) per month per household. A number of policy suggestions are further discussed.
1. Introduction Global energy needs have become an increasingly very important concern for all economies and the primary sources include fossil fuels such as oil coal and natural gas; all of which contribute over 80% of the global energy supply. These energy resources are dispersed across different parts of the world; some of which are concentrated in unstable geographical areas where their use is compromised. Since they emit greenhouse gases, their eventual effects on climate change especially in regard to threats to the environment and human health have become a matter of urgent concern. Attempts to seek remedial measures have greatly acknowledged the role of renewable energy sources in reducing the emission of greenhouse gases, which cause climate change (Lee and Heo, 2016; Lloyd and Subbarao, 2009). Renewable energy has been defined as energy harnessed from green electricity sources such as hydroelectric power, wind, tide, solar and biomass power systems. Considering the threats of climate change, sustainability has become an important issue and it calls for a reduction of the use of fossil fuels like oil, coal and natural gas (IPCC, 2007; Moriarty and Honnery, 2009). Renewables have been considered vital elements of energy security, dynamic economic development, and environmental protection and Green House Gas (GHG) emissions reduction efforts and have taken a new dimension of policy concern across many countries of the world ⁎
Corresponding author. E-mail address: [email protected] (E. Dogan).
https://doi.org/10.1016/j.tej.2019.106677
1040-6190/ © 2019 Elsevier Inc. All rights reserved.
(Nienhueser and Qiu, 2016; Carley, 2009; Johnstone et al., 2010; Marques et al., 2010). IEA energy forecasts project renewable energy as the fastest growing energy source in the world between now and 2030 with projections showing global electricity consumption of renewable energy at an average growth rate of 2.6% over 2007-2035. Eventually, the share of renewable energy in terms of electricity generation is to grow by 5% during the same period; meaning that it will grow from 18% in 2007 to 23% in 2035 (BP, 2012; EIA, 2006; EIA, 2010). The development of renewable energy sector will reduce foreign energy dependence and prevent reliance on current energy consumption which has resulted into destructive effects on the environment, thus promoting sustainable development objectives (Kaygusuz, 2007; Payne, 2012). The government has the responsibility of compensating producers of renewable energy and investigating consumers’ preference if it wants to develop renewable energy. Thus, it must consider issues such as; households’ willingness to pay for use of renewable energy and factors which are likely to affect their willingness to pay and also the ability of the government to support and subsidize renewable energy costs at both national and provisional levels. Turkey as a developing country has experienced rapid economic growth and its energy needs have been on top of their development agenda. Recent statistics indicate that Turkey’s Total Primary Energy Supply (TPES) was 129.7 million tons of oil equivalent (Mtoe) in 2015
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expand renewable energy sources. It is an internationally used method in measurement of green-energy. This method essentially involves interviewing respondents about the amounts they would be willing to pay for resource conservation and environmental benefits in a hypothetical market. This method is widely known as the “stated preference” and it became very famous in the US during the 1980s. Since it was the most commonly used method for the estimation of non-use values in the disciplines of ecological and environmental economics (Bennet and Blamey, 2001). This method has been also used in many countries which include: USA (Hite et al., 2008) where the results show that respondents are willing to pay 6.48$ per month for renewable energy (RE); Japan (Nomura and Akai, 2004) which shows that people are willing to pay 2000 Yen (17$) monthly; China (Zhang and Wu, 2012) indicates that respondents are willing to pay CNY 7.91–10.30 (approximately US$ 1.15–1.51) per month. Additionally, in other countries such as: United Kingdom (Batley et al., 2001) where the findings indicate that 34% of the respondents are willing to pay 16.6% of the expenses on renewable energy; South Korea (Kim et al., 2012) finds that the willingness to pay is 1.350$ per month per household; Ethiopia (Arega and Tadesse, 2017) indicates that the respondents are willing to pay Birr 12.5 (0.66 USD) monthly per household for the period of five years. Some other studies also used contingent valuation method for the analysis of WTP (Oerlemans et al., 2016; Koundouri et al., 2009). In modelling WTP for green electricity, scholars used various discrete choice methods which include; Logit, Probit, Tobit, random parameters model, hierarchical bayes model, multivariate regression model, heckman selection model among others, but Logit and Probit are the most commonly used methods (Guo et al., 2014; Kim et al., 2012; Hanemann et al., 2011; Komarek et al., 2011). However, the use of Tobit model has received very limited attention from scholars and the few available studies into this model give directional significance into further study of the model (Mozumder et al., 2011; Zoric and Hrovatin, 2012). Therefore, our study is founded on this limitation and we try to build on the literature available by studying the Tobit model in addition to Logit and Probit models for the estimation of WTP. WTP is determined by several social determinants, which have divergent effects ranging from positive to negative effects. For instance, studies indicate that WTP is positively related to income (Abdullah and Jeanty, 2011; Aldy et al., 2012; Koundouri et al., 2009; Oliver et al., 2011; Whitehead and Cherry, 2007; Zografakis et al., 2010; Xie and Zhao, 2018). However, other findings tend to dispute the idea of the positive relationship between WTP and income. For instance, findings by Sundt and Rehdanz (2015), and Ek (2005) indicates that WTP and income are negatively related hence gives an inconclusive outcome about the relationship between WTP and income. In education, positive relationship between two (education and WTP) has been found (Bolino, 2009; Akyazı et al., 2012; Guo et al., 2014; Kim et al., 2012; Zoric and Hrovatin, 2012). However, other studies (Mozumder et al., 2011; Yoo and Kwak, 2009; Lim et al., 2017) found a negative relationship between WTP and education. This similarly gives an inconclusive result between WTP and education. The relationship between Age and WTP does not also give concise idea because some studies found positive relationship while others found negative. For instance, Guo et al. (2014); Yoo and Kwak (2009); Lanzini et al. (2016); Xie and Zhao (2018) found positive relationship whereas Hanemann et al. (2011); Liu et al. (2013) found a negative relationship between WTP and age. Household size is positively related to WTP according to scholars (Mozumder et al., 2011; Oca and Bateman, 2006) whereas negative relationship has also been found between two variables (Batley et al., 2001; Grösche and Schröder, 2011). Present studies have made wider contributions with regards the two mainly used methodologies of contingent valuation (CV) and choice experiment (CE) in the case of many countries. However, such a study is relevant for a country like Turkey for which WTP has not been studied before. Therefore, our study adopts the Tobit, Probit and Logit models to measure the WTP of the Turkish citizens using the CV method.
and represent an increase of 54% compared to 2005 (IEA, 2016; DSI, 2015). Its energy sector has seen renewable energy constitutes 48.9% of total production with constituted as follows: hydro 17.9%, geothermal 14.8%, biomass 10.1%, wind 3.1% and solar 3% (IEA, 2016). Most of the country’s electricity demands are met by natural gas (38.6%) although a huge proportion of the natural gas is imported from different countries. Coal follows with 28.3% making up 67.7% of total generation from fossil fuels in 2015. The rapid increase in electricity demand with the annual approximation of 5% has also been witnessed (MMO, 2016). International Energy Agency (2012) data puts Turkey as one of the countries projected to use renewable energy technologies until 2017 and as a contending candidate to the European Union, the country is relentlessly investing in the energy sector as a strategy of meeting the clean energy requirements of the EU by 2023 when the share of renewable energy in total energy consumption is at least 20% (TNRAP, 2014). Turkey has also an ambitious national energy goal of minimizing energy import and maximizing domestic energy until 2023. This is based on its 2010–2014 action plan framed by the Ministry of Energy and Natural Sources which will see the country produce 30% of electricity from renewable energy sources (Kose et al., 2014; TNRAP, 2014). Willingness to Pay (WTP) plays a central role in directing appropriate policy for the country to realize its ambitious renewable energy targets. Therefore, considering this discrepancy our study provides a new specific country case by estimating the WTP of the Turkish citizens for green electricity and also establishes various factors affecting their WTP using the Contingent Valuation (CV) method. There is no previous study, to the best of our knowledge, focuses on WTP for renewable electricity in Turkey. Thus, this study intends to bridge this existing gap by investigating the WTP of the Turkish people based on social determinants variables such as age, income, marital status and education, and the applicability of a utility program in Turkey that enables citizens to support investment in renewable energy. This paper takes the following structural form. Section 2 makes reviews of previous discussions while Section 3 explains the research methodology followed and results presented in Section 4. Conclusions and policy implications are discussed in Section 5. 2. Literature review Willingness to pay has widely been discussed in the literature (Abdullah and Jeanty, 2011; Aldy et al., 2012; Bigerna and Polinori, 2011; Lee and Heo, 2016; Xie and Zhao, 2018). The most famous methods are the choice experiment (CE) method and contingent valuation (CV) method in measuring willingness to pay (WTP). Choice experiment examines willingness to pay for each type of renewable source separately. It has been widely discussed in the literature for the case of several countries. For instance, In Germany, Kaenzig et al. (2013) found that the customers are willing to pay a premium of about 16% for the electricity from RS. In Malaysia, Soon and Ahmad (2015) found the WTP of the people based on this method as US$ 7.16 per month. In addition, using this method, the willingness to pay according to: Nienhueser and Qiu (2016) in USA is $0.61 per hour for level 2 electric vehicle service equipment (EVSE) and $1.82 for direct current fast charges (DCFC) and Yoo and Kwak (2009) for the case of Korea found the mean WTP USD 1.8 by using parametric approach and USD 2.2 by using non-parametric approach. Furthermore, in countries like the USA, Borchers et al. (2007) indicate that WTP for generic green electricity is $1.3 per kWh and WTP varies with the type of green source: solar $19.03, wind $13.36, farm methane $10.54 and biomass $8.92. Some other studies also used choice experiment method in order to analyze the WTP (Komarek et al., 2011; Williams and Rolfe, 2017). The second method is Contingent Valuation. This method is employed in a valuation of goods and services whose market is non-existed (Arrow et al., 1993). It is concerned with additional costs required to 2
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3. Research methodology
function of the form V (I, Q, P, and Z) which is an increasing in I and Q and where decreasing in P and Z where I, Q, P, and Z represent household’s disposable income, quality of environmental good, prices of relevant goods and services and household characteristics respectively. WTP is considered a maximum monetary value that a household is willing to pay considering the changes in environmental degradation. Household’s willingness to pay therefore is a function of earnings, environment, the price of goods and services in addition to features of households. The following approach is used as an econometric model:
3.1. Survey design The method we adopted for the survey design is the Contingent Valuation (CV) method, which is used to prepare questionnaires to seek the views of respondents. This method has been previously successfully used by scholars such as: Nomura ve Akaiki (2004), Whitehead and Cherry (2007); Hite et al. (2008); Yoo and Kwak (2009); Koundouri et al. (2009); Zografakis et al. (2010); Hanemann et al. (2011), Zang ve Wu (2012), Kim et al. (2012) and Guo et al. (2014); Xie and Zhao (2018). In preparing the questionnaires, confidential identities of the respondents were considered and we basically structured our questions into 26 characteristic questions based on respondents’ age, gender, marital status, educational level, electricity consumption, monthly income and expenditure, number of households and their motives for and against renewable energy. In addition, 3 questions out of the 26 used to examine respondents’ willingness to pay for renewable energy. Through the help of a professional data collection firm, we were able to conduct face-to-face interviews and questionnaire administration to 2500 households in 12 major metropolitan cities in Turkey using a stratified random sampling technique to access their WTP. By adopting Özdamar (2003), we sampled the number of people planned to be interviewed using variables such as confidence intervals, sample errors, and population. Like our study, Adaman et al. (2011) held face-to-face interviews with 2500 using a stratified random sampling to investigate the WTP for carbon emissions in Turkey. On assumption that respondents do not have sufficient knowledge of renewable energy and environment, prior information is made to acquaint them with the issue being investigated. Contingent valuation method helps in economic valuation of nonmarketed products and this method has been used in this paper to access mean willingness to pay by the respondents. Respondents are, therefore, asked about how much they are willing to pay for a portion of (say 20%) environmentally friendly renewable energy drawn from the total energy consumption, in addition to their monthly electricity bill, having been informed on Turkey’s renewable energy targets. The respondents were later asked whether and how they would pay if the proportion of renewable energy were to be increased say from 20% to 30%. This question sets to aid in investigating sensitivity to scope in assessing the validity of survey responses (Oca and Bateman, 2006) and ascertain whether customers’ preferences are aligned with Turkey’s 30% renewable energy production target by 2023.
WTP%20* = Xβ + ε1
(1)
WTP%20 = max (WTP%20*, 0)
(2)
WTP%20, X, β, and ε1 represent household’s latent WTP, a vector of covariates, vector of relevant coefficients to be estimated and stochastic error terms respectively. In the equation above, WTP%20 is not observed under the censoring limit and it can only be identified in positive values meaning that WTP only takes into consideration households with nonnegative WTP as shown in Eq. (2). Tobit model, equally considered as censored regression (Tobin, 1958) approach will be used in the estimation of Eqs. (1) and (2) in order to evaluate willingness to pay for the censored part. This model is used in censored data where a dependent variable, that’s, WTP for green electricity is nil for a significant fraction of observations. As in the previous discussion, the amount of willingness to pay and whether respondents want electricity harnessed from more renewable sources is examined to account for the consistency and sensitivities of individuals. By adopting methodologies used by the scholars: Mozumder et al. (2011); Zoric and Hrovatin (2012), econometric modeling takes the following form: WTP%30* = Xβ + ε2
(3)
WTP%30 = WTP%30* if Y* = Z + ε3 > 0
(4)
The error terms are assumed to be normally distributed and having zero mean and constant variance [ε2 ̴ N(0,σε2), ε3 ̴ N(0,1)]. Additionally, observed payment initiative is valid for households that agree more payment (Y* > 0) for receiving electricity from more renewable sources. However, since Y* can’t be represented in real life, dummy variables are used in which 0 represent unwillingness while 1 represents willingness for electricity harnessed from renewable energy sources. Tobit model will be used in estimating Eqs. (3) and (4). This method makes it possible to introduce variables that affect WTP for renewable energy through multiple models. For instance, monetary value (dependent variable), which is WTP, can be estimated by adopting income, self-perception of environmental conscience, electricity invoices paid, gender, education, house ownership, age, number of people in the household and membership to environmental organizations. Additionally, other techniques (Probit and Logit) are also be estimated by using the same explanatory variables.
3.2. Analytical scope and estimation The assumption is that households’ benefit from consumption of a joint commodity considered as private good (electricity) and environmental good. Electricity does not vary from different energy types while environmental goods are dependent on the types of energy (Kotchen, 2006) and form our basis of discussion. Consider an indirect utility Table 1 Descriptive Statistics. Variable
Description
Obs
Mean
Std. Dev.
Min
Max
WTP20 WTP30 GREEN30 ENVC MEMBER AGE FEMALE EDU INC BILL HOUSE SIZE
Household's willingness to pay for 20% of renewable energy per month (in TL) Additional household's willingness to pay per month for increasing renewable energy from 20% to 30% (in TL) If the household would pay more for 30% renewable energy (1=Yes, 0=Otherwise) Self-perception of environmental conscience (0=Not conscious at all, 10=Very conscious) Member to energy/environmental organizations (1=Yes, 0=Otherwise) Household’s age (scale 1 to 6) If household is female (1=Female, 0=Otherwise) Household’s level of schooling (scale 1 to 8) Household’s income (scale 1 to 9) Household’s monthly electricity bill (in TL) If household is the owner of the house (1=Yes, 0=Otherwise) Number of people living in the house
2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500
4.35 1.68 0.32 6.56 0.05 3.02 0.43 5.04 3.70 75.69 0.55 3.76
5.67 2.55 0.47 1.96 0.22 1.06 0.50 1.19 1.51 27.25 0.49 1.23
0 0 0 1 0 1 0 1 1 5 0 1
25 10 1 10 1 6 1 8 9 170 1 10
3
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E. Dogan and I. Muhammad
4. Results
as reported in Table 2 that the coefficient of age is estimated to be negative and significant for both 20% and 30% share of renewable energy in total energy. Yoo and Kwak (2009) and Lim et al (2017) contradict our findings since they obtained a positive relationship between age and willingness to pay for renewable energy in Korea. Male households are more willing to contribute as compared to female given the fact that the estimated coefficient of female is insignificant for 20% renewable energy but negative and significant for 30%. Bolino (2009) and Ivanova (2012) find quite contrastable results for gender in Italy and Queensland, respectively. However, results of Arega and Tadesse (2017) for Ethiopia are in line with our findings because females are less likely to be empowered than male households in developing countries like Turkey. These differences may be attributed to the level of financial status, cultural status and development status in terms of WTP for renewable energy on the side of females. Our findings on income of the household lead positively for the increased share of green energy in total energy for both 20% and 30% scenarios. Our findings for Turkey are in line with some developing countries such as South Africa (Chan et al., 2011), China (Guo et al., 2014; Xie and Zhao, 2018) and Ghana (Twerefou, 2014). Our findings on the house ownership show estimated coefficient to be positive and significant for both 20% and 30% renewable energy, implying that people having their own houses are less likely to pay for the rise in electricity produced from renewable sources. Our result is similar to that of Abdullah and Jeanty (2011) for Kenya. The result on self-perceived environmental conscience is positive and significant in all the models. This essentially means that a higher level of perceived environmental conscience positively contributes to the willingness to pay for renewable energy. The findings show additionally that respondents with higher education levels are less likely to pay and respondents who are the members of energy/environmental organizations are ready to pay more for green electricity. Other variables are very instrumental in playing an important role in explaining the WTP for green electricity.
Table 1 provides descriptive statistics and detailed explanation of the variables adopted in the model. Measurement is based on a scale of 1–8 for Household’s Educational level (EDU) where 1 means illiterate, 2 for literate, 3 for attainment of primary education and 4, 5, 6, 7, 8 for attainment of middle school, high school, associate degree, undergraduate and postgraduate studies respectively. The variable ENVC indicates how respondents perceive environmental consciousness, where the respondents were asked to indicate on a scale from 1 (not conscious at all) to 10 (very conscious). In the sample we employed, most of the respondents perceive themselves to be environmentally conscious (the average perception rank is 6.5 on a 10 point scale). Income (INC) being sensitive information, respondents was requested to make a choice on the intervals provided out of nine intervals instead of providing the exact amount of their monthly income. The average income range is between 3,000 TL and 4,001 TL. Male respondents represented 57% while the average age was 35 years. Most respondents had attained an educational level of high school which corresponds well to the population. The average household size (SIZE) is 3.7 and 55% of the respondents own their place of residence (HOUSE). These statistics are pretty much in line with the population. Other variables used to control for respondents and household characteristics are household’s monthly electricity bill (BILL) and member to environmental organizations (MEMBER). Given the data from descriptive statistics presented in Table 1, the average household willingness to pay (WTP) is 4.35 TL per month for the provision of 20% renewable energy in the total energy mix. Approximately 32% of the respondents are willing to pay an additional amount of 1.68 TL per month if this share of renewable energy was to rise from 20% to 30%. Our findings in terms of the share of respondents willing to participate together with the average amount they would be willing to pay are consistent with findings of some similar studies, for instance, Kim et al. (2012), and Arega and Tadesse (2017). The WTP for green electricity is analyzed by employing the Tobit, Probit and Logit models that include a number of relevant explanatory variables. The estimation results of these three models are presented in Table 2. The results indicate that the WTP for renewable electricity energy was significantly impacted by self-reported environmental conscience, age, home ownership, membership to environmental organizations, education, gender and household’s income. However, the number of people living in the house and household’s monthly electricity bill were not significant factors determining willingness to pay. Our findings indicate that younger households have a higher WTP for the increased share of renewable electricity than older households
5. Conclusion and policy implications Turkey being a developing country has been on a path of rapid economic growth and its energy needs have been issues of main development concern. The country has an ambitious national energy goal of minimizing energy import and maximizing domestic energy until 2023. This is based on its 2010–2014 action plan framed by the Ministry of Energy and Natural Sources which will see the country produce 30% of electricity production from renewable energy sources. Comprehensive knowledge of consumers’ preferences is instrumental in
Table 2 Estimation results from Tobit model, Probit model and Logit model. 20% renewable energy
30% renewable energy
Variables
Tobit
Probit
Logit
Tobit
Probit
Logit
ENVC MEMBER AGE FEMALE EDU INC BILL HOUSE SIZE CONS Sigma (σ) Log L LR test Pseudo R2 N
2.47 (0.11)*** 1.69 (0.87)** −1.23 (0.22)*** 0.23 (0.38) −3.10 (0.25)*** 3.02 (0.21)*** 0.01 (0.01) 3.37 (0.44)*** −0.15 (0.16) −10.4 (1.88)*** 7.73*** −4681.88 1342.6*** 0.125 2500
0.39 (0.02)*** 2.05 (0.43)*** −0.22 (0.03)*** 0.01 (0.06) −0.56 (0.04)*** 0.47 (0.03)*** 0.01 (0.01) 0.54 (0.07)*** −0.02 (0.02) −1.30 (0.30)***
0.70 (0.03)*** 4.20 (1.05)*** −0.40 (0.07)*** 0.02(0.11) −1.01 (0.08)*** 0.85 (0.06)*** 0.01 (0.01) 0.89 (0.12)*** −0.04 (0.04) −2.20 (0.55)***
0.24 (0.02)*** 0.26 (0.14)* −0.14(0.03)*** −0.10 (0.06)* −0.35(0.03)*** 0.31 (0.03)*** 0.01 (0.00) 0.48 (0.07)*** 0.01 (0.02) −1.38(0.29)***
0.40 (0.03)*** 0.47 (0.24)** −0.26 (0.06)*** −0.16 (0.10)* −0.62 (0.06)*** 0.52 (0.05)*** 0.00 (0.00) 0.82 (011)*** 0.02 (0.04) −2.15 (0.50)***
−1017.57 1422.5*** 0.411 2500
−1010.85 1435.2*** 0.415 2500
1.15 (0.07)*** 1.07 (0.63)* −0.60 (0.16)*** −0.34 (0.27) −1.64 (0.18)*** 1.57 (0.14)*** 0.01 (0.02) 2.13 (0.32)*** −0.01 (0.10) −7.07 (1.37)*** 5.23*** −3246.2 718.9*** 0.099 2500
−1229.71 708.6*** 0.223 2500
−1234.42 699.2*** 0.220 2500
Notes: Standard errors in brackets. *** Significant at 1%. ** Significant at 5%. * Significant at 10%. 4
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Marques, A.C., Fuinhas, J.A., Pires Manso, J.R., 2010. Motivations driving renewable energy in Eurpean countries: a panel data approach. Energy Policy 38, 6877–6885. Ministry of Energy and Natural Resources, 2014. Turkey National Renewable Action Plan.
setting achievable targets and designing an effective program to increase the share of energy generated from renewable resources. Our study provides estimates of willingness to pay for renewable energy for different levels of provision (20% and 30%) and identifies factors affecting consumers’ preferences in Turkey. On the basis of this, we conducted face-to-face interviews and administered 2500 questionnaire households in 12 major metropolitan cities in Turkey using a stratified random sampling technique to access the willingness to pay for green electricity of the Turkish population. We employed Tobit, Probit, and Logit models to estimate Mean WTP and identify determinant factors for WTP. Our findings from the Tobit, Probit and Logit models indicate that income, self-perception of environmental conscience, member to environmental organizations, and home ownership significantly and positively impact the WTP for green electricity whereas age, education, and females were estimated to have significantly negative relationship with the WTP. A number of positively influencing variables (such as income and environmental conscience) are key to empowering decision-making. Income and self-perception of environmental conscience show the capacity that defines ability to pay and therefore they are instrumental in empowering willingness to pay decision. Carrying out research on these socio-economic characteristics that provide insightful perspective on the behavior of WTP could be an important output in receiving financial support from Turkish households for the increased share of renewable electricity in total. These factors could particularly be crucial in enabling households to share the cost of investment by the government in expanding renewable energy stations. No previous studies have been done to relate WTP in Turkey to address how responsive consumer’s WTP is to the specific shares of renewable energy in the total energy mix and how this is affected by increase in this share. In an attempt to fill this gap, our research presents evidence that Turkey residents are willing to pay 4.35 TL per month for a 20% share of renewable energy and an increase of 1.68 TL per month for a 30% share of renewable energy. Our findings present several important implications for the policymakers. Turkey’s policy objective of continuously increasing the share of renewable energy in the energy portfolio is in line with consumers’ preferences because they are willing to pay more for a higher share. On the basis of these findings, utility companies may tailor their marketing strategy to target consumers with higher income, own houses, member to environmental organizations, and higher environmental concerns, so as to gain financial support for an increase in the market share of renewable energy. Our findings indicate that majority of respondents were prepared to support a green electricity scheme. This is in favor of 2010–2014 action plan framed by the Ministry of Energy and Natural Sources which projects the country to produce 30% of electricity production from renewable energy sources by 2023. In conclusion, our results from this study stand preferable in offering useful insights to energy regulators, utility companies and various organizations concerned with design of effective mechanisms and therefore charge appropriate fee to support a larger share of renewable energy in the energy portfolio. Declaration of Competing Interest None of the authors have any competing interests. Acknowledgment This study was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK). Grant No: 116K727. References Abdullah, S., Jeanty, P.W., 2011. Willingness to pay for renewable energy: evidence from a contingent valuation survey in Kenya. Renew. Sustain. Energy Rev. 15, 2974–2983.
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comparison of ex-ante and ex-post hypothetical bias mitigation approaches. Resour. Energy Econ. 29, 247–261. Williams, G., Rolfe, J., 2017. Willingness to pay for emissions reduction: application of choice modeling under uncertainty and different management options. Energy Econ. 62, 302–311. Xie, B.C., Zhao, W., 2018. Willingness to pay for green electricity in Tianjin, China: based on the contingent valuation method. Energy Policy 114, 98–107. Yoo, S.H., Kwak, S.Y., 2009. Willingness to pay for green electricity in Korea: a contingent valuation study. Energy Policy 37, 5408–5416. Zhang, L., Wu, Y., 2012. Market segmentation and willingness to pay for green electricity among urban residents in China: the case of Jiangsu Province. Energy Policy 51, 514–523. Zografakis, N., Sifaki, E., Pagalou, M., Nikitaki, G., Psarakis, V., 2010. Assessment of public acceptance and willingness to pay for renewable energy source in Crete. Renew. Sustain. Energy Rev. 14, 1088–1095. Zoric, J., Hrovatin, N., 2012. Household willingness to pay for green electricity in Slovenia. Energy Policy 47, 180–187.
http://www.eie.gov.tr/duyurular_haberler/document/Turkiye_Ulusal_Yenilenebilir_ Enerji_Eylem_Plani.PDF. MMO, 2016. Chamber of mechanical engineers, Energy outlook of Turkey. Moriarty, P., Honnery, D., 2009. What energy levels can the earth sustain? Energy Policy 37 (7), 2469–2474. Mozumder, P., Vasquez, W.F., Marathe, A., 2011. Consumers’ preference for renewable energy in the southwest USA. Energy Econ. 33, 1119–1126. Nienhueser, I.A., Qiu, Y., 2016. Economic and environmental impacts of providing renewable energy for electric vehicle charging- a choice experiment study. Appl. Energy 180, 256–268. Nomura, N., Akai, M., 2004. Willingness to pay for green electricity in Japan as estimated through contingent valuation method. Appl. Energy 78, 453–463. Oca, G.S.Md, Bateman, I.J., 2006. Scope sensitivity in households’ willingness to pay for maintained and improved water supplies in a developing world urban area: investigating the influence of baseline supply quality and income distribution upon stated preferences in Mexico city. Water Resour. Resear. 42, 1–15 W07421. Oerlemans, L.A.G., Chang, K.Y., Volschenk, J., 2016. Willingness to pay for green electricity: a review of the contingent valuation literature and its sources of error. Renew. Sustain. Energy Rev. 66, 875–885. Oliver, H., Volschenk, J., Smit, E., 2011. Residential consumers in the Cape Peninsula’s willingness to pay for premium-priced green electricity. Energy Policy 39, 544–550. Özdamar, K., 2003. Modern bilimsel araştırma yöntemleri. Eskişehir: Kaan Kitabevi. Payne, J.E., 2012. The causal dynamics between U.S. Renewable energy consumption, output, emissions, and oil prices. Energy Sources Part B 7, 323–330. Soon, J.J., Ahmad, S.A., 2015. Willingly or grudgingly? A meta-analysis on the willingness-to-pay for renewable energy use. Renew. Sustain. Energy Rev. 44, 877–887. Sundt, S., Rehdanz, K., 2015. Consumers’ willingness to pay for green electricity: a metaanalysis of the literature. Energy Econ. 51, 1–8. Twerefou, K.D., 2014. Willingness to pay for improved electricity supply in Ghana. Mod. Econ. 5, 489–498. Whitehead, J.C., Cherry, T.L., 2007. Willingness to pay for a Green Energy program: a
Dr. Eyup Dogan is an Associate Professor of Economics in the School of Management and Leadership at Abdullah Gül University (AGÜ), Turkey. He received his Ph.D. in Economics from Clemson University, USA, in 2014. His teaching and research interests lie on Macroeconomics, Energy and Environmental Economics and Applied Econometrics. He has currently over 30 papers in peer-reviewed journals such as Renewable Energy, RSER, Energy, ESPR, Energy Sources Part B, Sustainable Cities and Society (> 1200 citations and H-index: 16). He is serving as member of Editorial Board and reviewer of numerous journals. He is currently an editor for Environmental Science and Pollution Research. Mr Iftikhar Muhammad, PhD scholar (Economics) at Ibn Haldun University, Istanbul, Turkey.
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Willingness to pay for renewable electricity: A contingent valuation study in Turkey
T
Eyup Dogana,**, Iftikhar Muhammadb a b
Department of Economics, Abdullah Gül University, Sumer Campus, Kayseri, 38280, Turkey Department of Economics, Erciyes University, Turkey
A R T I C LE I N FO
A B S T R A C T
Keywords: Willingness to pay Turkey Renewable energy
Renewable energy sources are advised as an important alternative vehicle for dealing with a high rate of energy dependency and global warming. Turkey has also an ambitious national energy goal of minimizing energy import and producing 30% of electricity from renewable energy sources by 2023. However, it may not be easy to reach these goals. Willingness to Pay (WTP) thus plays a central role in directing appropriate policies for the country to realize its energy targets. This study reviews previous studies in the same literature as well as examines WTP of Turkish citizens for renewable electricity energy by using a stratified-sample and contingent valuation survey of 2500 households. The results from estimated models show that environmental conscience, membership to an environmental organization, age, education level, gender and income of households are significant determinants of WTP. In addition, the mean value of WTP for green electricity by Turkish households is estimated at around US$ 1 (with the exchange rate 5,3 TL/ US$) per month per household. A number of policy suggestions are further discussed.
1. Introduction Global energy needs have become an increasingly very important concern for all economies and the primary sources include fossil fuels such as oil coal and natural gas; all of which contribute over 80% of the global energy supply. These energy resources are dispersed across different parts of the world; some of which are concentrated in unstable geographical areas where their use is compromised. Since they emit greenhouse gases, their eventual effects on climate change especially in regard to threats to the environment and human health have become a matter of urgent concern. Attempts to seek remedial measures have greatly acknowledged the role of renewable energy sources in reducing the emission of greenhouse gases, which cause climate change (Lee and Heo, 2016; Lloyd and Subbarao, 2009). Renewable energy has been defined as energy harnessed from green electricity sources such as hydroelectric power, wind, tide, solar and biomass power systems. Considering the threats of climate change, sustainability has become an important issue and it calls for a reduction of the use of fossil fuels like oil, coal and natural gas (IPCC, 2007; Moriarty and Honnery, 2009). Renewables have been considered vital elements of energy security, dynamic economic development, and environmental protection and Green House Gas (GHG) emissions reduction efforts and have taken a new dimension of policy concern across many countries of the world ⁎
Corresponding author. E-mail address: [email protected] (E. Dogan).
https://doi.org/10.1016/j.tej.2019.106677
1040-6190/ © 2019 Elsevier Inc. All rights reserved.
(Nienhueser and Qiu, 2016; Carley, 2009; Johnstone et al., 2010; Marques et al., 2010). IEA energy forecasts project renewable energy as the fastest growing energy source in the world between now and 2030 with projections showing global electricity consumption of renewable energy at an average growth rate of 2.6% over 2007-2035. Eventually, the share of renewable energy in terms of electricity generation is to grow by 5% during the same period; meaning that it will grow from 18% in 2007 to 23% in 2035 (BP, 2012; EIA, 2006; EIA, 2010). The development of renewable energy sector will reduce foreign energy dependence and prevent reliance on current energy consumption which has resulted into destructive effects on the environment, thus promoting sustainable development objectives (Kaygusuz, 2007; Payne, 2012). The government has the responsibility of compensating producers of renewable energy and investigating consumers’ preference if it wants to develop renewable energy. Thus, it must consider issues such as; households’ willingness to pay for use of renewable energy and factors which are likely to affect their willingness to pay and also the ability of the government to support and subsidize renewable energy costs at both national and provisional levels. Turkey as a developing country has experienced rapid economic growth and its energy needs have been on top of their development agenda. Recent statistics indicate that Turkey’s Total Primary Energy Supply (TPES) was 129.7 million tons of oil equivalent (Mtoe) in 2015
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expand renewable energy sources. It is an internationally used method in measurement of green-energy. This method essentially involves interviewing respondents about the amounts they would be willing to pay for resource conservation and environmental benefits in a hypothetical market. This method is widely known as the “stated preference” and it became very famous in the US during the 1980s. Since it was the most commonly used method for the estimation of non-use values in the disciplines of ecological and environmental economics (Bennet and Blamey, 2001). This method has been also used in many countries which include: USA (Hite et al., 2008) where the results show that respondents are willing to pay 6.48$ per month for renewable energy (RE); Japan (Nomura and Akai, 2004) which shows that people are willing to pay 2000 Yen (17$) monthly; China (Zhang and Wu, 2012) indicates that respondents are willing to pay CNY 7.91–10.30 (approximately US$ 1.15–1.51) per month. Additionally, in other countries such as: United Kingdom (Batley et al., 2001) where the findings indicate that 34% of the respondents are willing to pay 16.6% of the expenses on renewable energy; South Korea (Kim et al., 2012) finds that the willingness to pay is 1.350$ per month per household; Ethiopia (Arega and Tadesse, 2017) indicates that the respondents are willing to pay Birr 12.5 (0.66 USD) monthly per household for the period of five years. Some other studies also used contingent valuation method for the analysis of WTP (Oerlemans et al., 2016; Koundouri et al., 2009). In modelling WTP for green electricity, scholars used various discrete choice methods which include; Logit, Probit, Tobit, random parameters model, hierarchical bayes model, multivariate regression model, heckman selection model among others, but Logit and Probit are the most commonly used methods (Guo et al., 2014; Kim et al., 2012; Hanemann et al., 2011; Komarek et al., 2011). However, the use of Tobit model has received very limited attention from scholars and the few available studies into this model give directional significance into further study of the model (Mozumder et al., 2011; Zoric and Hrovatin, 2012). Therefore, our study is founded on this limitation and we try to build on the literature available by studying the Tobit model in addition to Logit and Probit models for the estimation of WTP. WTP is determined by several social determinants, which have divergent effects ranging from positive to negative effects. For instance, studies indicate that WTP is positively related to income (Abdullah and Jeanty, 2011; Aldy et al., 2012; Koundouri et al., 2009; Oliver et al., 2011; Whitehead and Cherry, 2007; Zografakis et al., 2010; Xie and Zhao, 2018). However, other findings tend to dispute the idea of the positive relationship between WTP and income. For instance, findings by Sundt and Rehdanz (2015), and Ek (2005) indicates that WTP and income are negatively related hence gives an inconclusive outcome about the relationship between WTP and income. In education, positive relationship between two (education and WTP) has been found (Bolino, 2009; Akyazı et al., 2012; Guo et al., 2014; Kim et al., 2012; Zoric and Hrovatin, 2012). However, other studies (Mozumder et al., 2011; Yoo and Kwak, 2009; Lim et al., 2017) found a negative relationship between WTP and education. This similarly gives an inconclusive result between WTP and education. The relationship between Age and WTP does not also give concise idea because some studies found positive relationship while others found negative. For instance, Guo et al. (2014); Yoo and Kwak (2009); Lanzini et al. (2016); Xie and Zhao (2018) found positive relationship whereas Hanemann et al. (2011); Liu et al. (2013) found a negative relationship between WTP and age. Household size is positively related to WTP according to scholars (Mozumder et al., 2011; Oca and Bateman, 2006) whereas negative relationship has also been found between two variables (Batley et al., 2001; Grösche and Schröder, 2011). Present studies have made wider contributions with regards the two mainly used methodologies of contingent valuation (CV) and choice experiment (CE) in the case of many countries. However, such a study is relevant for a country like Turkey for which WTP has not been studied before. Therefore, our study adopts the Tobit, Probit and Logit models to measure the WTP of the Turkish citizens using the CV method.
and represent an increase of 54% compared to 2005 (IEA, 2016; DSI, 2015). Its energy sector has seen renewable energy constitutes 48.9% of total production with constituted as follows: hydro 17.9%, geothermal 14.8%, biomass 10.1%, wind 3.1% and solar 3% (IEA, 2016). Most of the country’s electricity demands are met by natural gas (38.6%) although a huge proportion of the natural gas is imported from different countries. Coal follows with 28.3% making up 67.7% of total generation from fossil fuels in 2015. The rapid increase in electricity demand with the annual approximation of 5% has also been witnessed (MMO, 2016). International Energy Agency (2012) data puts Turkey as one of the countries projected to use renewable energy technologies until 2017 and as a contending candidate to the European Union, the country is relentlessly investing in the energy sector as a strategy of meeting the clean energy requirements of the EU by 2023 when the share of renewable energy in total energy consumption is at least 20% (TNRAP, 2014). Turkey has also an ambitious national energy goal of minimizing energy import and maximizing domestic energy until 2023. This is based on its 2010–2014 action plan framed by the Ministry of Energy and Natural Sources which will see the country produce 30% of electricity from renewable energy sources (Kose et al., 2014; TNRAP, 2014). Willingness to Pay (WTP) plays a central role in directing appropriate policy for the country to realize its ambitious renewable energy targets. Therefore, considering this discrepancy our study provides a new specific country case by estimating the WTP of the Turkish citizens for green electricity and also establishes various factors affecting their WTP using the Contingent Valuation (CV) method. There is no previous study, to the best of our knowledge, focuses on WTP for renewable electricity in Turkey. Thus, this study intends to bridge this existing gap by investigating the WTP of the Turkish people based on social determinants variables such as age, income, marital status and education, and the applicability of a utility program in Turkey that enables citizens to support investment in renewable energy. This paper takes the following structural form. Section 2 makes reviews of previous discussions while Section 3 explains the research methodology followed and results presented in Section 4. Conclusions and policy implications are discussed in Section 5. 2. Literature review Willingness to pay has widely been discussed in the literature (Abdullah and Jeanty, 2011; Aldy et al., 2012; Bigerna and Polinori, 2011; Lee and Heo, 2016; Xie and Zhao, 2018). The most famous methods are the choice experiment (CE) method and contingent valuation (CV) method in measuring willingness to pay (WTP). Choice experiment examines willingness to pay for each type of renewable source separately. It has been widely discussed in the literature for the case of several countries. For instance, In Germany, Kaenzig et al. (2013) found that the customers are willing to pay a premium of about 16% for the electricity from RS. In Malaysia, Soon and Ahmad (2015) found the WTP of the people based on this method as US$ 7.16 per month. In addition, using this method, the willingness to pay according to: Nienhueser and Qiu (2016) in USA is $0.61 per hour for level 2 electric vehicle service equipment (EVSE) and $1.82 for direct current fast charges (DCFC) and Yoo and Kwak (2009) for the case of Korea found the mean WTP USD 1.8 by using parametric approach and USD 2.2 by using non-parametric approach. Furthermore, in countries like the USA, Borchers et al. (2007) indicate that WTP for generic green electricity is $1.3 per kWh and WTP varies with the type of green source: solar $19.03, wind $13.36, farm methane $10.54 and biomass $8.92. Some other studies also used choice experiment method in order to analyze the WTP (Komarek et al., 2011; Williams and Rolfe, 2017). The second method is Contingent Valuation. This method is employed in a valuation of goods and services whose market is non-existed (Arrow et al., 1993). It is concerned with additional costs required to 2
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3. Research methodology
function of the form V (I, Q, P, and Z) which is an increasing in I and Q and where decreasing in P and Z where I, Q, P, and Z represent household’s disposable income, quality of environmental good, prices of relevant goods and services and household characteristics respectively. WTP is considered a maximum monetary value that a household is willing to pay considering the changes in environmental degradation. Household’s willingness to pay therefore is a function of earnings, environment, the price of goods and services in addition to features of households. The following approach is used as an econometric model:
3.1. Survey design The method we adopted for the survey design is the Contingent Valuation (CV) method, which is used to prepare questionnaires to seek the views of respondents. This method has been previously successfully used by scholars such as: Nomura ve Akaiki (2004), Whitehead and Cherry (2007); Hite et al. (2008); Yoo and Kwak (2009); Koundouri et al. (2009); Zografakis et al. (2010); Hanemann et al. (2011), Zang ve Wu (2012), Kim et al. (2012) and Guo et al. (2014); Xie and Zhao (2018). In preparing the questionnaires, confidential identities of the respondents were considered and we basically structured our questions into 26 characteristic questions based on respondents’ age, gender, marital status, educational level, electricity consumption, monthly income and expenditure, number of households and their motives for and against renewable energy. In addition, 3 questions out of the 26 used to examine respondents’ willingness to pay for renewable energy. Through the help of a professional data collection firm, we were able to conduct face-to-face interviews and questionnaire administration to 2500 households in 12 major metropolitan cities in Turkey using a stratified random sampling technique to access their WTP. By adopting Özdamar (2003), we sampled the number of people planned to be interviewed using variables such as confidence intervals, sample errors, and population. Like our study, Adaman et al. (2011) held face-to-face interviews with 2500 using a stratified random sampling to investigate the WTP for carbon emissions in Turkey. On assumption that respondents do not have sufficient knowledge of renewable energy and environment, prior information is made to acquaint them with the issue being investigated. Contingent valuation method helps in economic valuation of nonmarketed products and this method has been used in this paper to access mean willingness to pay by the respondents. Respondents are, therefore, asked about how much they are willing to pay for a portion of (say 20%) environmentally friendly renewable energy drawn from the total energy consumption, in addition to their monthly electricity bill, having been informed on Turkey’s renewable energy targets. The respondents were later asked whether and how they would pay if the proportion of renewable energy were to be increased say from 20% to 30%. This question sets to aid in investigating sensitivity to scope in assessing the validity of survey responses (Oca and Bateman, 2006) and ascertain whether customers’ preferences are aligned with Turkey’s 30% renewable energy production target by 2023.
WTP%20* = Xβ + ε1
(1)
WTP%20 = max (WTP%20*, 0)
(2)
WTP%20, X, β, and ε1 represent household’s latent WTP, a vector of covariates, vector of relevant coefficients to be estimated and stochastic error terms respectively. In the equation above, WTP%20 is not observed under the censoring limit and it can only be identified in positive values meaning that WTP only takes into consideration households with nonnegative WTP as shown in Eq. (2). Tobit model, equally considered as censored regression (Tobin, 1958) approach will be used in the estimation of Eqs. (1) and (2) in order to evaluate willingness to pay for the censored part. This model is used in censored data where a dependent variable, that’s, WTP for green electricity is nil for a significant fraction of observations. As in the previous discussion, the amount of willingness to pay and whether respondents want electricity harnessed from more renewable sources is examined to account for the consistency and sensitivities of individuals. By adopting methodologies used by the scholars: Mozumder et al. (2011); Zoric and Hrovatin (2012), econometric modeling takes the following form: WTP%30* = Xβ + ε2
(3)
WTP%30 = WTP%30* if Y* = Z + ε3 > 0
(4)
The error terms are assumed to be normally distributed and having zero mean and constant variance [ε2 ̴ N(0,σε2), ε3 ̴ N(0,1)]. Additionally, observed payment initiative is valid for households that agree more payment (Y* > 0) for receiving electricity from more renewable sources. However, since Y* can’t be represented in real life, dummy variables are used in which 0 represent unwillingness while 1 represents willingness for electricity harnessed from renewable energy sources. Tobit model will be used in estimating Eqs. (3) and (4). This method makes it possible to introduce variables that affect WTP for renewable energy through multiple models. For instance, monetary value (dependent variable), which is WTP, can be estimated by adopting income, self-perception of environmental conscience, electricity invoices paid, gender, education, house ownership, age, number of people in the household and membership to environmental organizations. Additionally, other techniques (Probit and Logit) are also be estimated by using the same explanatory variables.
3.2. Analytical scope and estimation The assumption is that households’ benefit from consumption of a joint commodity considered as private good (electricity) and environmental good. Electricity does not vary from different energy types while environmental goods are dependent on the types of energy (Kotchen, 2006) and form our basis of discussion. Consider an indirect utility Table 1 Descriptive Statistics. Variable
Description
Obs
Mean
Std. Dev.
Min
Max
WTP20 WTP30 GREEN30 ENVC MEMBER AGE FEMALE EDU INC BILL HOUSE SIZE
Household's willingness to pay for 20% of renewable energy per month (in TL) Additional household's willingness to pay per month for increasing renewable energy from 20% to 30% (in TL) If the household would pay more for 30% renewable energy (1=Yes, 0=Otherwise) Self-perception of environmental conscience (0=Not conscious at all, 10=Very conscious) Member to energy/environmental organizations (1=Yes, 0=Otherwise) Household’s age (scale 1 to 6) If household is female (1=Female, 0=Otherwise) Household’s level of schooling (scale 1 to 8) Household’s income (scale 1 to 9) Household’s monthly electricity bill (in TL) If household is the owner of the house (1=Yes, 0=Otherwise) Number of people living in the house
2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500 2500
4.35 1.68 0.32 6.56 0.05 3.02 0.43 5.04 3.70 75.69 0.55 3.76
5.67 2.55 0.47 1.96 0.22 1.06 0.50 1.19 1.51 27.25 0.49 1.23
0 0 0 1 0 1 0 1 1 5 0 1
25 10 1 10 1 6 1 8 9 170 1 10
3
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4. Results
as reported in Table 2 that the coefficient of age is estimated to be negative and significant for both 20% and 30% share of renewable energy in total energy. Yoo and Kwak (2009) and Lim et al (2017) contradict our findings since they obtained a positive relationship between age and willingness to pay for renewable energy in Korea. Male households are more willing to contribute as compared to female given the fact that the estimated coefficient of female is insignificant for 20% renewable energy but negative and significant for 30%. Bolino (2009) and Ivanova (2012) find quite contrastable results for gender in Italy and Queensland, respectively. However, results of Arega and Tadesse (2017) for Ethiopia are in line with our findings because females are less likely to be empowered than male households in developing countries like Turkey. These differences may be attributed to the level of financial status, cultural status and development status in terms of WTP for renewable energy on the side of females. Our findings on income of the household lead positively for the increased share of green energy in total energy for both 20% and 30% scenarios. Our findings for Turkey are in line with some developing countries such as South Africa (Chan et al., 2011), China (Guo et al., 2014; Xie and Zhao, 2018) and Ghana (Twerefou, 2014). Our findings on the house ownership show estimated coefficient to be positive and significant for both 20% and 30% renewable energy, implying that people having their own houses are less likely to pay for the rise in electricity produced from renewable sources. Our result is similar to that of Abdullah and Jeanty (2011) for Kenya. The result on self-perceived environmental conscience is positive and significant in all the models. This essentially means that a higher level of perceived environmental conscience positively contributes to the willingness to pay for renewable energy. The findings show additionally that respondents with higher education levels are less likely to pay and respondents who are the members of energy/environmental organizations are ready to pay more for green electricity. Other variables are very instrumental in playing an important role in explaining the WTP for green electricity.
Table 1 provides descriptive statistics and detailed explanation of the variables adopted in the model. Measurement is based on a scale of 1–8 for Household’s Educational level (EDU) where 1 means illiterate, 2 for literate, 3 for attainment of primary education and 4, 5, 6, 7, 8 for attainment of middle school, high school, associate degree, undergraduate and postgraduate studies respectively. The variable ENVC indicates how respondents perceive environmental consciousness, where the respondents were asked to indicate on a scale from 1 (not conscious at all) to 10 (very conscious). In the sample we employed, most of the respondents perceive themselves to be environmentally conscious (the average perception rank is 6.5 on a 10 point scale). Income (INC) being sensitive information, respondents was requested to make a choice on the intervals provided out of nine intervals instead of providing the exact amount of their monthly income. The average income range is between 3,000 TL and 4,001 TL. Male respondents represented 57% while the average age was 35 years. Most respondents had attained an educational level of high school which corresponds well to the population. The average household size (SIZE) is 3.7 and 55% of the respondents own their place of residence (HOUSE). These statistics are pretty much in line with the population. Other variables used to control for respondents and household characteristics are household’s monthly electricity bill (BILL) and member to environmental organizations (MEMBER). Given the data from descriptive statistics presented in Table 1, the average household willingness to pay (WTP) is 4.35 TL per month for the provision of 20% renewable energy in the total energy mix. Approximately 32% of the respondents are willing to pay an additional amount of 1.68 TL per month if this share of renewable energy was to rise from 20% to 30%. Our findings in terms of the share of respondents willing to participate together with the average amount they would be willing to pay are consistent with findings of some similar studies, for instance, Kim et al. (2012), and Arega and Tadesse (2017). The WTP for green electricity is analyzed by employing the Tobit, Probit and Logit models that include a number of relevant explanatory variables. The estimation results of these three models are presented in Table 2. The results indicate that the WTP for renewable electricity energy was significantly impacted by self-reported environmental conscience, age, home ownership, membership to environmental organizations, education, gender and household’s income. However, the number of people living in the house and household’s monthly electricity bill were not significant factors determining willingness to pay. Our findings indicate that younger households have a higher WTP for the increased share of renewable electricity than older households
5. Conclusion and policy implications Turkey being a developing country has been on a path of rapid economic growth and its energy needs have been issues of main development concern. The country has an ambitious national energy goal of minimizing energy import and maximizing domestic energy until 2023. This is based on its 2010–2014 action plan framed by the Ministry of Energy and Natural Sources which will see the country produce 30% of electricity production from renewable energy sources. Comprehensive knowledge of consumers’ preferences is instrumental in
Table 2 Estimation results from Tobit model, Probit model and Logit model. 20% renewable energy
30% renewable energy
Variables
Tobit
Probit
Logit
Tobit
Probit
Logit
ENVC MEMBER AGE FEMALE EDU INC BILL HOUSE SIZE CONS Sigma (σ) Log L LR test Pseudo R2 N
2.47 (0.11)*** 1.69 (0.87)** −1.23 (0.22)*** 0.23 (0.38) −3.10 (0.25)*** 3.02 (0.21)*** 0.01 (0.01) 3.37 (0.44)*** −0.15 (0.16) −10.4 (1.88)*** 7.73*** −4681.88 1342.6*** 0.125 2500
0.39 (0.02)*** 2.05 (0.43)*** −0.22 (0.03)*** 0.01 (0.06) −0.56 (0.04)*** 0.47 (0.03)*** 0.01 (0.01) 0.54 (0.07)*** −0.02 (0.02) −1.30 (0.30)***
0.70 (0.03)*** 4.20 (1.05)*** −0.40 (0.07)*** 0.02(0.11) −1.01 (0.08)*** 0.85 (0.06)*** 0.01 (0.01) 0.89 (0.12)*** −0.04 (0.04) −2.20 (0.55)***
0.24 (0.02)*** 0.26 (0.14)* −0.14(0.03)*** −0.10 (0.06)* −0.35(0.03)*** 0.31 (0.03)*** 0.01 (0.00) 0.48 (0.07)*** 0.01 (0.02) −1.38(0.29)***
0.40 (0.03)*** 0.47 (0.24)** −0.26 (0.06)*** −0.16 (0.10)* −0.62 (0.06)*** 0.52 (0.05)*** 0.00 (0.00) 0.82 (011)*** 0.02 (0.04) −2.15 (0.50)***
−1017.57 1422.5*** 0.411 2500
−1010.85 1435.2*** 0.415 2500
1.15 (0.07)*** 1.07 (0.63)* −0.60 (0.16)*** −0.34 (0.27) −1.64 (0.18)*** 1.57 (0.14)*** 0.01 (0.02) 2.13 (0.32)*** −0.01 (0.10) −7.07 (1.37)*** 5.23*** −3246.2 718.9*** 0.099 2500
−1229.71 708.6*** 0.223 2500
−1234.42 699.2*** 0.220 2500
Notes: Standard errors in brackets. *** Significant at 1%. ** Significant at 5%. * Significant at 10%. 4
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setting achievable targets and designing an effective program to increase the share of energy generated from renewable resources. Our study provides estimates of willingness to pay for renewable energy for different levels of provision (20% and 30%) and identifies factors affecting consumers’ preferences in Turkey. On the basis of this, we conducted face-to-face interviews and administered 2500 questionnaire households in 12 major metropolitan cities in Turkey using a stratified random sampling technique to access the willingness to pay for green electricity of the Turkish population. We employed Tobit, Probit, and Logit models to estimate Mean WTP and identify determinant factors for WTP. Our findings from the Tobit, Probit and Logit models indicate that income, self-perception of environmental conscience, member to environmental organizations, and home ownership significantly and positively impact the WTP for green electricity whereas age, education, and females were estimated to have significantly negative relationship with the WTP. A number of positively influencing variables (such as income and environmental conscience) are key to empowering decision-making. Income and self-perception of environmental conscience show the capacity that defines ability to pay and therefore they are instrumental in empowering willingness to pay decision. Carrying out research on these socio-economic characteristics that provide insightful perspective on the behavior of WTP could be an important output in receiving financial support from Turkish households for the increased share of renewable electricity in total. These factors could particularly be crucial in enabling households to share the cost of investment by the government in expanding renewable energy stations. No previous studies have been done to relate WTP in Turkey to address how responsive consumer’s WTP is to the specific shares of renewable energy in the total energy mix and how this is affected by increase in this share. In an attempt to fill this gap, our research presents evidence that Turkey residents are willing to pay 4.35 TL per month for a 20% share of renewable energy and an increase of 1.68 TL per month for a 30% share of renewable energy. Our findings present several important implications for the policymakers. Turkey’s policy objective of continuously increasing the share of renewable energy in the energy portfolio is in line with consumers’ preferences because they are willing to pay more for a higher share. On the basis of these findings, utility companies may tailor their marketing strategy to target consumers with higher income, own houses, member to environmental organizations, and higher environmental concerns, so as to gain financial support for an increase in the market share of renewable energy. Our findings indicate that majority of respondents were prepared to support a green electricity scheme. This is in favor of 2010–2014 action plan framed by the Ministry of Energy and Natural Sources which projects the country to produce 30% of electricity production from renewable energy sources by 2023. In conclusion, our results from this study stand preferable in offering useful insights to energy regulators, utility companies and various organizations concerned with design of effective mechanisms and therefore charge appropriate fee to support a larger share of renewable energy in the energy portfolio. Declaration of Competing Interest None of the authors have any competing interests. Acknowledgment This study was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK). Grant No: 116K727. References Abdullah, S., Jeanty, P.W., 2011. Willingness to pay for renewable energy: evidence from a contingent valuation survey in Kenya. Renew. Sustain. Energy Rev. 15, 2974–2983.
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Dr. Eyup Dogan is an Associate Professor of Economics in the School of Management and Leadership at Abdullah Gül University (AGÜ), Turkey. He received his Ph.D. in Economics from Clemson University, USA, in 2014. His teaching and research interests lie on Macroeconomics, Energy and Environmental Economics and Applied Econometrics. He has currently over 30 papers in peer-reviewed journals such as Renewable Energy, RSER, Energy, ESPR, Energy Sources Part B, Sustainable Cities and Society (> 1200 citations and H-index: 16). He is serving as member of Editorial Board and reviewer of numerous journals. He is currently an editor for Environmental Science and Pollution Research. Mr Iftikhar Muhammad, PhD scholar (Economics) at Ibn Haldun University, Istanbul, Turkey.
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