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The analysis of demographics, environmental and knowledge factors affecting prospective residential PV system adoption: A study in Tehran
Renewable and Sustainable Energy Reviews 81 (2018) 3131–3139
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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
The analysis of demographics, environmental and knowledge factors affecting prospective residential PV system adoption: A study in Tehran
MARK
Ali Bashiri, Sasan H. Alizadeh⁎ Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords: Prospective PV adopters Residential PVs Tehran
The identification of potential adopters has a vital role in developing renewable energies. Photovoltaic systems, as a clean power generation technology, provide a great potential advantageous for the environment and especially for families. This study aims to evaluate those factors which affect adoption of photovoltaic systems by taking into consideration Tehran's unique circumstances, air pollution and high-density of population, low price of energy and governmental financial supports. Moreover, families who are more likely to adopt these systems are identified. For this purpose, we employed a binary logistic regression model to analyze the adoption probability. The empirical results suggest that low and moderate-income level homeowners who live in low-rise multi-unit residential buildings have more tendency towards photovoltaic systems. In general income shows a negative impact on adoption. Environmental concerns, knowledge of renewable energies, innovativeness and number of households, either of these factors positively increases the probability of adoption individually. Results of this study help policy-makers and renewable energy marketers to make energy-related decisions.
1. Introduction In recent decade, energy and saving environment have been two contradict issues. However, scientists have been trying to rectify this by producing clean energy. Some notable examples of these clean energies are power produced by photovoltaic and hydropower systems. Clean energies are not only more environment-friendly but also have some financial advantages for countries as they reduce the indirect costs of environmental pollution. Given these advantages, the unanswered question is why renewable energies are not commonplace in some countries. The low price of fossil fuels is the first imperative reason that comes to mind. Taking the limitation of fossil fuels into consideration, some countries, even oil rich ones like Iran, see their energy future in renewable energies. The other reason may be the hardship of persuading or imposing the industries to relinquish the cheap and reliable power produced by fossil fuels and to use expensive green power. Government subsidies, grants, and financial supporting mechanisms are the best ways to cope with this problem. Residential electricity consumers as a huge electricity consumption sector seem to be an alluring target for renewable energies. This sector has its challenges and having a good understanding of market's needs and potentials is vital. In this study, we try to shed light on this market and the focus is on families as the main customers of photovoltaic systems. Iran, with approximately 300 sunny days per year, and by receiving
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Corresponding author. E-mail address: [email protected] (S.H. Alizadeh).
http://dx.doi.org/10.1016/j.rser.2017.08.093 Received 12 April 2017; Received in revised form 7 August 2017; Accepted 30 August 2017 Available online 06 September 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.
4.5–5.5 Kwh/m2 per day has a good potential to benefit from solar energy [1]. Fig. 1 shows solar radiation in Iran. As it shows, Iran and the area of the study, Tehran, has a great radiation potential for utilizing solar energy. Although Iran benefits from other types of renewable energies slightly, like geothermal, wind and hydropower to produce power, solar energy which is the most available, cheapest and cleanest energy source amongst them play a little role in producing energy. According to Renewable Energy Organization of Iran in 2015, only 23 Mw power of photovoltaic system was achieved, from which roughly 14.5 Mw was from private sectors [2]. Alongside the potentials of renewable energies, particularly solar energy in Iran, the high rate of energy consumption is noteworthy. In 2014, household electricity consumption accounted for 32% of total electricity consumption in Iran [3] and it was the largest power sector consumption. Currently, Iran consumes electricity more than three times of world average. In a mega city like Tehran, the capital of Iran, 45% of electricity consumption of whole electricity sector can be observed. As shown in Fig. 2a sharp increase in electricity consumption occurred continuously between 2011 and 2015. In 2015 residential electricity consumption dominated all of other sectors. Baring it in mind, families have high potential to be targeted for using renewable energies. Currently, customers are one of the most important elements for which organizations and companies are concerned. The key to success of leading organizations is their focus on customer and product quality.
Renewable and Sustainable Energy Reviews 81 (2018) 3131–3139
A. Bashiri, S.H. Alizadeh
Fig. 1. Solar radiation potential in Iran [59].
likely to purchase a product or a service. This group of customers has similar characteristics such as age, gender, income, lifestyle, location, and perceptions of product on which marketers focus their marketing efforts [7]. Considering the increase in household electricity consumption and its environmental benefits, households can be considered as an alluring target market for photovoltaic systems. In Iran, these systems as a new and innovative product begin to enter domestic electricity generation market. However, purchase, installation, and maintenance of these systems can be very costly for families and even an obstacle for development of these products [8]. Considering the advantages of these systems, governments have set financial incentive mechanisms to promote the use of renewable technologies in houses [9]. These mechanisms may include subsidies used to reduce initial cost of the system, guaranteed purchase tariffs which allow the owner to earn money, low-interest loans and tax credits. In Iran, financial incentive mechanisms such as subsidies and guaranteed electricity purchase contract have been provided for development of these systems, which has led to a good return on the investment. However, the factors which play a role in adoption of these systems and potential customers are unclear. In general, target customers of this product are families who have accepted this technology or, in other words, families who tend to purchase and use these systems. Therefore, this study identifies families who have accepted these systems as potential customers, because these families are more likely to purchase photovoltaic systems. To identify potential customers and adoption factors, this study used demographic and psychological variables. These variables provides a more accurate identification of potential customers. Moreover, variables used in this study fit to local conditions of the studied region. while recent studies have just highlighted the financial information of these systems [10]. In this study, Respondents are given detailed information on costs and revenues of the system to reduce their uncertainty about the profitability of the system. Accordingly, some
Fig. 2. Electricity consumption trends by two sectors [3].
Because of the importance of customers and their important effects in the success of organizations, the question always arises that how different levels of customers are identified and how certain market priorities are considered for each of these customers. The success of any organization is dependent on a proper planning; it is essential to determine target market in order to determine a strategic plan which facilitates organizational goals [4]. Identification of target market is particularly important for a new product. Many studies, particularly those which identify potential customers, have recently focused on marketing of new innovative products. Predicting the reaction and relative behavior of potential customers for a new product, businesses will be able to make plans for their sales tactics and market penetration strategies [5]. Moreover, identification of potential customers determines target market and leads to focus on customers who are more likely to buy [6]. Target market refers to a group of people who are 3132
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diffusion theory by zooming on the effect of financial incentive to explain PV adoption in Australia. All of the aforementioned theories tell the story of adoption from different points of view. Wolske et al. [24] believed that a model encompassing several theories led to a better understanding of adoption process. They used a combination of theory of planned behavior, Innovation diffusion theory and value-belief-norm theory to assess adoption of residential PV in the US. Moreover, they asserted that households have a different point of view towards PV systems; adopters saw PV as an environmental, innovative or consuming product and because of these different points of view, several theories were needed to model PV adoption. Bergek and Mignon [25] believed that although many factors affect adoption decisions, adoption motivations could be significantly different between renewable energy adopters. Because of that, different financial incentives and business models were needed to escalate adoption rate. Along with these studies which addressed customer adoption behavior, some studies merely identified potential customers as well as effective factors on adoption by using data related to families [26,27]. Some information about families such as demographic variables or data related to their house (area, number of rooms, number of units in a house, ownership) might be included in this data. Many studies have used demographic variables to identify potential customers of renewable energies [13,28–30]. Sommerfeld et al. [27] examined characteristics of people who adopted domestic photovoltaic systems using demographic data in Australia. Based on their results, ownership, the age greater than 55 years, living in houses with more than three bedrooms and type of residence were the most effective factors on adoption. In another study conducted in Greece, Sardianou et al. [26] used demographic variables and some variables related to financials as well as other variables related to efficiency and incentives of domestic renewable energies to address adoption of these systems. High-level income, well-educated, and middle-age families were more likely to adopt renewable energies. Moreover, gender, marital status, and ownership had no effect on adoption. Despite accessibility and accuracy of demographic data and its suitable application in customer segmentation, diverse studies obtained different variables. Some authors believed that cognitive behavioral and psychological variables had a more significant effect on eco-friendly behaviors [31]. In addition, adoption of photovoltaic systems could be considered as an eco-friendly behavior. Some studies suggested that psychological variables brought about more reliable results than demographic variables [7,32]. Bearing the advantageous of two aforementioned variables in mind, the presence of both these variables can increase the accuracy of prediction. Photovoltaic systems are innovative products which could be useful for the environment. Therefore, green and eco-friendly consumers, as well as people who are interested in innovative products, are expected to install photovoltaic systems to fulfill their social and moral duties or satisfy their innovativeness desires. Recent studies have focused on environmental concerns or environmental problems, eco-friendly behaviors, values, beliefs and environmental attitudes. Moreover, innovativeness, knowledge, and awareness about the environment and renewable energies have been highlighted in many studies [17,30,33]. Environmental concerns refer to values, beliefs, and concerns about the environment. In another word, environmental concerns refer to beliefs such as environmental pollution and the exploitation of natural resources. Ahn et al. [33] claimed that environmentally conscious people are more likely to use eco-friendly products or services. In some cases, environmental concerns can lead to a motivation for purchase or acceptance of eco-friendly products [34]. Considering advantages of photovoltaic systems for environmental protection, acceptance of photovoltaic systems can be considered as an environmental behavior. Therefore, it can be helpful to evaluate the effect of environmental variables on adoption of these systems. Customer innovativeness refers to one's rush in adoption of an innovation compared to others. This concept is related to Rogers' theory
findings obtained in this study reveals new dimensions of adoption of these. In addition, identification of potential customers of these systems is a new subject on which very few studies have been conducted in the context of Iran. Unfortunately, low energy prices, high upfront costs of PV's equipment and household's uncertainty of PV's power production might not give a positive outlook on adoption. The low price of energy has encouraged the power industries to focus on gray power production and consequently has made renewable energies overshadowed. Albeit, informing people about renewable energy technologies can be a viable solution, a little announcement or advertising about PV systems has been made. Moreover, environment pollution and municipal lifestyle of households living in Tehran have risen many concerns. To alleviate these problems, the governments have set financial incentive to promote adoption of renewable technologies in Iran. The objective of this research is mainly to evaluate the effects of environmental, demographics and knowledge factors on early adoption of PV systems taking into account of government's incentives in Tehran. The rest of this paper is structured as follows. In Section 2, it presents a summary of literature review on renewable energy adoption specifically PV adoption. Section 3 offers the methodology, data collection and used variables to assess the adoption. The results are presented in Section 4 and Section 5 describes how our findings confirm or contradict other studies. Finally, the conclusion of the analysis is discussed in Section 6. 2. Literature review Based on literature, identification of potential customers is associated with identification of factors affecting adoption. In other words, potential customers can be identified by psychological and demographical factors. According to studies on adoption of renewable energy innovations, adoption occurs in three political, social and market dimensions [11,12]. This study addressed adoption in market dimension and from customer perspective. Based on this, the role of customer shifts from consumer to investor. In recent studies, many methods and theories have been applied to assess the adoption of residential renewable energy technologies. Cognitive-behavioral theories are one of these theories addressing adoption of domestic renewable energy technologies. Cognitive behavioral theories are based on studies in social psychology and are widely used to evaluate behaviors related to adoption of eco-friendly products. These models claim that there is a direct relationship between adoption behavior and attitude towards the behavior [13]. Theory of reasoned action developed by Ajzen and Fishbein [14] and theory of planned behavior developed by Ajzen [15] are the most important cognitive behavioral theories [16]. These theories have been used in a wide range of studies on adoption of photovoltaic [9,16,17]. In addition to above theories, technology acceptance model is another theory used in adoption of renewable technologies [18]. This theory was an extended form of the theory of reasoned actions [19]. Diffusion of innovations is another theory which is commonplace between scientists and practitioners to assess how, why and through which channels an innovation, idea or technology are spread. Based on this theory adopters are classified in five categories of innovators, early adopters, early majority, late majority and laggards [20]. Most of the studies have focused on innovators and early adopters, because these adopters are the first group of people who use new technology and their attitude toward innovations can hinder or foster the diffusion process [21]. Nygrén et al. [22] assess the motivations and barriers of sustainable energy adoption between innovators and early adopters in Finland. Based on this study, these two group of adopters have different motivation including environmental and financial benefits and different barriers including lack of information and poor financial incentives. In another study, Simpson and Clifton [23] utilized an innovation 3133
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throughout 22 districts of Tehran. In order to reduce confusions of respondents, introductory remarks on photovoltaic systems were included at the beginning of the questionnaire. Descriptions included information such as purchase cost, maintenance costs, income from electricity guaranteed purchase contract, system lifetime, and other technical and financial information. The studied sample population included household decision-makers living in Tehran. Those respondents who were directly involved in making financial decisions were asked to fill the questionnaire. Totally, 450 face-to-face interviews were conducted in 22 districts of Tehran. After omitting uncompleted and equivocal responses, 345 questionnaires were selected for analysis.
of innovation adoption. People with a higher degree of innovativeness can better deal with the uncertainty of new products and have a higher tendency for adoption [20]. This variable have been used in studies which had addressed the acceptance of a new innovative product among society. Innovativeness had also been used in some studies related to renewable energies [17,33]. Since photovoltaic systems had recently targeted household market of some countries, this could be considered as an innovative product for families. Knowledge on the performance of a technology can influence adoption of an innovation directly or indirectly by influencing one's perceptions about innovations [12]. Many studies have examined the relationship between knowledge and adoption. Knowledge as a continuous process was presented at all stages of the decision-making process for adoption of an innovation; this decision-making process begins with prior knowledge about an innovation [20]. Lack of knowledge can cause uncertainty about technology and thus a rejection of technology [35]. Rundle et al. [7] considered the lack of knowledge on renewable energies as one of the obstacles to promotion and development of renewable energies. Rai et al. [36] claimed that the cost of photovoltaic systems is not the only barrier to photovoltaic adoption, insufficient knowledge of costs, environmental problems and advantages of these systems lead to non-monetary costs for potential customers. Another study in this way considered the lack of sufficient knowledge on costs and promotions as a reason for which potential customers did not tend to use and purchase photovoltaic systems [10]. Simpson and Clifton [23] found that the majority of PV adopters in Australia have considered PV systems for its financial benefits. However, for evaluating the benefits and costs of PV systems they needed more details and education. Although environmentalists and technology lovers have intrinsic motivation towards using PV systems, the lack of financial and technical supports hinder them to adopt. Many studies have addressed the effects of these barriers on adoption. Qureshi et al. [37] claimed that intermittent electricity shortages suffered households and consequently the necessity of PV systems was inevitable but the lack of financial incentives, trustworthy vendors and engineers were the main barriers to adoption of PV systems in Pakistan. Perceived economic returns can be alluring for people while technology barriers have a negative effect on adoption [38]. In a study, Fleib et al. [39] asserted that the main motivation for adoption in Australia was financial benefits rather environmental benefits. In a long term, even environmentalist may return to gray electricity in absence of a satisfactory financial support [39]. Because of the importance of financial supports and monetary aspects of PVs in adoption, many studies incorporated these variables with other factors to assess adoption. Recently, Agent Based Modeling has gained increased attention between scientists. Many studies tried to model adoption of PV systems in presence of financial incentives using ABMs [40,41].
3.2. Statistical analysis This study considers the tendency to adopt photovoltaic systems interchangeably with the tendency to use these systems. The tendency to adopt is a binary variable. This variable is given one if the respondent tended to adopt photovoltaic systems; otherwise, it is given zero. If dependent variable was a binary, results of linear regression would be unreliable, because this rejects the hypothesis on the linear relationship between dependent and independent variable [42]. Therefore, binary logistic regression was used to examine the effective factors on adoption of photovoltaic systems. Instead of predicting dependent variable value through independent variables, logistic regression predicts the probability of an event based on independent variables. The logistic regression equation for the event Y is written as:
P(Yi = 1) =
1 1+e−(b0 +βXi)
(1)
P(Y) i denotes the probability of the event Y by the respondent I; b0 is a constant value; β represents regression coefficients; and Xi is a vector of independent variables for the respondent i. Odds are an important concept in interpreting results of logistic regression. Odds refers to the probability of the event Y to the probability of the lack of that event. Let P(Yi = 1) be the probability of tendency to adopt photovoltaic systems and P(Yi = 0) be the probability of the lack of tendency to adopt; odds of adopting these systems will be P(Yi = 1) [42]. The odds ratio represents P(Yi = 0) the division of odds after changing 1-unit of a numeric variable or changing the level of a nominal variable by odds without changes. It is easier to interpret odds ratio rather than logarithmic β coefficients. Logit model is in fact a logarithmic transformation of the odds ratio, as shown in Eq. (2). L i denotes logarithm of the odds ratio or the same logit; b0 is a constant value; X1 to Xk are independent variables; β1 to βk are the estimated parameters proportional to independent variables and Ɛ is random error. P(Yi = 1) ⎞ L i = Ln ⎛ = b0 + β1 X1 + β2 X2 +…+ βk Xk +Ɛ ⎝ P(Yi = 0) ⎠ ⎜
⎟
(2)
Constructs with a set of items were used to measure some psychological variables. To reduce data dimension, factors were rated by using principal components analysis with Varimax rotation. Then factor scores of customer innovativeness, environmental problems and knowledge of renewable energies were selected as dependent variables.
3. Data collection and methodology This study was objectively conducted throughout 22 districts of Tehran, in 2015, right after implementation of the guaranteed purchase tariffs in Iran. Tehran was selected for the study because of its characteristics in relation to environmental pollution, population density and high household consumption of electricity.
3.3. Variable description
3.1. Questionnaire and data collection
The tendency to adopt photovoltaic systems can be influenced by a variety of factors, regarding alternative technology acceptance, tendency to pay for green power and consumption of green products [13,30,33]. However, selection of variables partially depends on particular circumstances of the studied area. As Tehran suffers from environmental pollution, environmental concerns were included in this study. Demographic variables such as age, gender, income, house ownership, family size, and number of units of the residential building, type of residence and level of development are considered in this study.
Data was directly obtained using a structured questionnaire. This questionnaire was developed by reviewing recent literature on profiling of green electricity customers and adoption of renewable energies. To verify the validity and reduce uncertainty, a pilot questionnaire was distributed through social networks; then, experts and university professors evaluated the questionnaires and responses. After reviewing and making necessary modifications, we distributed the questionnaire 3134
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Table 1 variables with descriptions. Variable
Variable description
Deviance
Median
Age Household size Education Income Gender Ownership
Age of responder (up to 30 = 1, 31–50 = 2, above of 51 = 3) Total members of family living together Up to diploma = 1, Bachelor degree = 2, Master degree = 3, Ph.D. degree = 4 Income per Tomans Up to 2M = 1, 2–4M = 2, above of 4M = 3 Male = 1, Female = 2 Ownership status of the house in which responder lives (Owner = 1, rental = 2) Total Number of units of the house in which the responder lives Apartment = 1, Private = 2, villa = 3 Level of development of the region in which the responder lives Developed = 1, semi-developed = 2, low developed = 3
0.74 1.70 0.84 0.74 0.50 0.47
1.63 3.53 2.53 1.74 1.48 0.66
9.6 0.68 0.86
5.50 1.45 2.06
Number of units House type Level of Development
factor and all questions of the questionnaire. Cronbach's alpha for the whole questionnaire (α = 0.925) and for each factor are shown in Table 2. Based on these results, the questionnaire was reliable. Bartlett test supports fit of data for the factor analysis (X2 = 3159, KMO = 0.909). None of the items were changed or removed. The main objective of factor analysis was using factor scores and rectifying the correlation between data. In addition, with factor scores, we can use the score of a factor rather than total items of that factor as an independent variable in regression models. Varimax rotation with regression method was used to calculate rating factors. Table 1 lists the demographic variables. In general, 345 families were studied. The sample included families in which decision-makers aged 30–50 years and there were 3–4 members, which was similar to Tehran's Population and Housing Census' report [45]. Also, the income of the sample was average (2–4 million Tomans). Moreover, the gender of sample was similar to the population and housing statistics of Tehran (49% women and 51% men). According to reports, 62% of Tehran population owned a house, which was not significantly different from the studied sample. Each building in which respondents lived encompasses 5–6 units revealing the high density of population in Tehran. Fig. 3 depicts demographic characteristics of the respondent sample. Having a general view on the answers, we can conclude that most of the respondents believe in environmental values and have a sensitivity to environmental problems, but they have little knowledge on renewable energies (Fig. 4).
Table 2 Constructs with reliability and validity tests. Constructs and items
Environment problems (α=0.880) Human is the most effective factor of environmental changes Environmental changes bring about declining in human’s Quality of life Changing the environment and air pollution is jeopardizing human life Paying to save the environment is a good idea I am willing to overlook some facilities of my life in favor of environment I agree with some strict rules and taxes in favor of saving the environment I’m aware that natural resources are limited Natural resources have been overused by human Novelty seeking (α=0.847) I would like to use the newest products I always check the newest and high-tech products as soon as possible Before buying new products I usually gather comprehensive information about them Knowledge about renewable energies and benefits ( KNOWL) (α=0.779) I am familiar with renewable energies. I know how photovoltaic systems work and what is the benefit of using them
Loading factor
0.654 0.740 0.783 0.779 0.683 0.721 0.753 0.707 0.787 0.819 0.703
0.688 0.824
4.1. Effective factors on acceptance of photovoltaic systems
Cumulative factor loading = 64%, KMO = 0.909. Bartlett test ( χ 2 = 3159, df = 153, ρ = 0.001).
The regression model showed that the model fits the observed data. According to Omnibus test, the final model, which included all variables, was totally significant (ρ = 0.01, X2 = 172). Hosmer and Lemeshow test is a suitable tool to evaluate the fit of the model [46]. Given X2 = 4.88 and P = 0.77, the null hypothesis was rejected and fit of the model was confirmed. Cox and Snell’ and Nagelkerke's coefficients of determination (56.9% and 39.6%, respectively) supported fit of the model. These two coefficients of determination are similar to the coefficient of determination in linear regression; however, their values are underestimated in comparison to the coefficient of determination in linear regression [47]. Fig. 5 shows classification plot of observed and predicted the probability of PV adoption. This plot depicts a visual demonstration of true or false predictions. On the other word, this plot shows the accuracy of our prediction. X axis is predicted probability and separation point is 0.5 which means if the predicted occurrence falls before 0.5 the cases would be categorized as non-adopter otherwise they would be categorized as adopter. The more “a” representing adopters on the right-hand side of separation point and more “n” representing non-adopters on the left-hand side of separation point, the more accuracy of prediction. As we can see in this plot adopters has been predicted accurately. Although, only 28% of respondents were unwilling to adopt PV systems, the model predicted this number of nonadopters accurately. Seven variables (family size, education, house ownership, number of residential units, environmental problems,
Using a study conducted in the University of Tarbiat Modarres, 22 districts of Tehran were categorized as underdeveloped, developing, and developed areas [43]. Development of a district refers to the level of welfare and accessibility to utilities. We expected that households living in low-level developed districts were more willing to use PV systems because of some power voltage problems and power outages. PV system along with prevalent electricity can be more reliable. Customer innovativeness was measured by a variety of methods and concepts. In general, there is no exact measurement for this construct; in a general sense, however, the tendency to search for innovative and different things is measurable [44]. Photovoltaic systems are complex systems and their outcomes and performance can be complicated for the respondent. Hence, knowledge of renewable energies was included in this study.Table 1 lists demographic and geographical variables used in logistic regression and Table 2 lists psychological variables used in the logistic model and its factor loadings. Items of these variables were measured using Likert scale ranging from strongly disagree (1) to strongly agree (5). 4. Results Prior to analysis, Cronbach's alpha reliability test was done for each 3135
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Fig. 3. Demographic characteristics of respondent sample.
Fig. 4. Psychological factors influencing adoption of PV systems.
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Fig. 5. Classification plot of predicted probabilities.
adoption of renewable energies. For example, the increase in income increased tendency for green electricity acceptance [13,28], while some studies have rejected the effect of income on green electricity adoption [48,49]. Similar results have been reported on adoption of photovoltaic systems [16]. The negative effect of income on adoption can be attributed to profitability and income derived from photovoltaic systems. These systems are very cost-effective due to financial incentive policies, such as guaranteed purchase tariffs and subsidies. Hence, low-income people are more likely to accept these systems because of their profitability. Furthermore, gender had a significant effect on the adoption. Females are more likely to adopt these systems. In general, females are more aware of environmental problems than males are; this awareness leads to higher tendency to accept renewable energies [50]. Also, family size was an important variable associated with the use of electricity. A growth in family size leads to an increase in power consumption [51,52]; this can influence the increase in the probability of adoption of photovoltaic systems. According to the results of this study, a rise in family size increases the probability of adoption of these systems; this finding is consistent with previous studies [27,53]. Moreover, education is another demographic variable used in many studies to address adoption of renewable energies [13,54,55]. Based on these results, an increase in education has led to rising in the tendency for adoption. On the other hand, multiple displacements of tenants and cost of disassembling, transporting, and reassembling these systems reduce the tendency to install these systems [56]. House ownership is recognized as an important factor in the ability to install photovoltaic systems. Generally, property owners have fewer obstacles to install photovoltaic systems. House ownership has been examined in several studies to address the adoption or to investigate barriers to acceptance [27,28,57] The number of residential units is a variable which has not been addressed in the literature, based on our knowledge. The increase in the number of residential units reduces available space for installation of photovoltaic systems. Moreover, these systems are usually installed on roofs; in Tehran, sufficient space is not allocated to each residential unit to install these systems. Thus, the increase in the number of units reduced the tendency to install photovoltaic systems. Type of houses was not significant, while those who live in singlefamily houses were expected to have higher tendency to install these systems because of their higher authorities and sufficient space for installing the systems. Further studies are required to explain this behavior. However, this finding is consistent with Sommerfeld [27]. The level of development was slightly significant. Those families who lived in developing areas were more likely to adopt these systems rather than people living in developed areas. Moreover, people who lived in underdeveloped areas were more likely to accept these systems compared to people who lived in developed ones. Underdeveloped and
Table 3 Binary logistic regression results. Variable
Wald
Sig
STD Error
B
ODD’s ratio
Age Household size Education Income Gender (Male) Ownership (rental) Number of units House type Apartment Single family house Level of development Developed Semi developed Environment problems Novelty seeking KNOWL Constant
0.45 27.28 15.03 5.39 3.81 32.62 14.38 5.13 1.89 0 4.63 0.01 3.92 13.09 8.62 10.22 1.09
0.49 0.000 0.000 0.02 0.051 0.000 0.016 0.177 0.169 0.989 0.099 0.90 0.049 0.000 0.003 0.001 1.03
0.26 0.13 0.22 0.27 0.35 0.35 0.01 – 0.65 0.70 – 0.39 0.47 0.7 0.17 0.17 1.03
−0.18 0.67 0.85 −0.62 −0.69 −2.04 −0.2 – 0.90 −0.01 – 0.04 0.94 0.69 0.51 0.54 −1.08
0.83 1.97 2.35 0.53 0.50 0.13 0.67 – 2.47 0.99 – 1.05 2.58 1.85 1.67 1.72 0.34
innovation and knowledge of renewable energies and its outcome) were highly significant (P < 0.01). Table 3 shows results of binary logistic regression. The odd ratio is mentioned in Table 3, indicating the effect of variations in one variable on the probability of adoption. If the odd ratio was equal to one, variations in that variable were not effective on the probability of adoption. One unit increase in age of respondents reduced the odd ratio by 0.17. In other words, the increase in age reduced the probability of acceptance (P (Yi = 1) ). One-unit increase in family size increased the odd ratio by 0.97; in other words, the increase in family size increased probability of acceptance. Accordingly, the increase in education, environmental concerns, knowledge and innovativeness increased probability of adoption of photovoltaic systems. The increase in income reduced the odd ratio by 47%. Therefore, lower levels of income were associated with higher probability of adoption. High-income people were less likely to install these systems. Moreover, an increase in number of units in multi-unit residential buildings reduced the tendency to adopt these systems. The change in ownership from property-owner to tenant considerably reduced the odd ratio. In other words, the change in the ownership from property owner to tenant reduced the tendency to adopt photovoltaic system. In the findings of this study, gender and age were a very weak predicting variable. 5. Discussion This study evaluates effective factors on household's photovoltaic systems adoption in Tehran. According to the obtained results from the study, the more income, the less tendency for PV system adoption. In the previous studies, the effect of income was different from the 3137
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Branch, Islamic Azad University for providing useful suggestions that improved this paper.
developing areas have weaker electricity transmission infrastructure; hence, they have higher potential to install photovoltaic systems. Environmental concerns were well significant (P < 0.01). According to the results, people who were concerned about the environment and felt that the environment was in danger, were more interested in these systems. These people believe that photovoltaic systems reduce environmental pollution by generating clean electricity. Based on findings of this study, people with high environmental concerns are more likely to accept these systems. This finding is consistent with previous studies [17,33]. Innovativeness was known as an important variable in studies related to the adoption of an innovation. People with higher innovativeness are more likely to accept innovative products and ignore uncertainty about these products [20]. In some studies, innovativeness had a positive effect on adoption of photovoltaic systems. Results of this study are consistent with the findings in previous studies [17,33]. Awareness and knowledge of innovative products can reduce uncertainty about performance and profitability of products. Therefore, awareness of technology prior to acceptance is vital. Moreover, being aware of costs and promotions converts potential customers to actual ones [10] and changes the attitudes towards renewable energies [18]. In general, the increase in awareness of people about renewable energies increases the probability of adoption. Therefore, successful development of renewable energies depends on public support and awareness of families about advantages of these energies. Children's Education is of great importance. Nowadays children have not enough knowledge of renewable energies, although they seem to be responsible to the environment [58]. So by educating children and informing them about the importance of renewable energies, we can guarantee the future of sustainable energy.
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6. Conclusion Considering the profitability of PV systems, financial supports of Iran's government and unique characteristics of Tehran, the objective of this study was to gain an overview of effective factors on PV adoption and to identify potential customers of residential photovoltaic systems. Two groups of demographic and psychological variables were utilized to evaluate PV adoption in this study. Once the data was gathered, a binary logistic analysis was used to evaluate effective factors on the adoption of PV systems. One of the most important results of this study is the negative effect of income on the probability of adoption. Profitability and income resulting from financial mechanisms can be a good motivation to stimulate low-income families to install photovoltaic systems. This finding proves that current financial supports are effective. Environmental concerns significantly affect adoption. This finding shows that households living in Tehran, are seriously concerned about their polluted environment and looking for a concrete solution to dwindle pollutants. Roughly the half of residents of this mega city are tenants and as the results revealed, the tenants have less motivation in order to adopt these systems. By renting the photovoltaic systems to tenants, as leasing business models, and providing free maintenance services, this group of households will have more tendency to adopt. The level of development of different part of the city has no effect on propensity of adoption but number of units in an apartment significantly affect adoption. Since innovativeness is recognized as an important factor in this study and innovative people are more likely to accept photovoltaic systems, this group of people can be attracted by advertisements, which focuses on innovative and technological aspects of these systems. Results of this study help policy-makers and renewable energy marketers to make energy-related decisions. Acknowledgements The authors would like to thank Dr. Khunsiyavosh from Qazvin 3138
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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser
The analysis of demographics, environmental and knowledge factors affecting prospective residential PV system adoption: A study in Tehran
MARK
Ali Bashiri, Sasan H. Alizadeh⁎ Faculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords: Prospective PV adopters Residential PVs Tehran
The identification of potential adopters has a vital role in developing renewable energies. Photovoltaic systems, as a clean power generation technology, provide a great potential advantageous for the environment and especially for families. This study aims to evaluate those factors which affect adoption of photovoltaic systems by taking into consideration Tehran's unique circumstances, air pollution and high-density of population, low price of energy and governmental financial supports. Moreover, families who are more likely to adopt these systems are identified. For this purpose, we employed a binary logistic regression model to analyze the adoption probability. The empirical results suggest that low and moderate-income level homeowners who live in low-rise multi-unit residential buildings have more tendency towards photovoltaic systems. In general income shows a negative impact on adoption. Environmental concerns, knowledge of renewable energies, innovativeness and number of households, either of these factors positively increases the probability of adoption individually. Results of this study help policy-makers and renewable energy marketers to make energy-related decisions.
1. Introduction In recent decade, energy and saving environment have been two contradict issues. However, scientists have been trying to rectify this by producing clean energy. Some notable examples of these clean energies are power produced by photovoltaic and hydropower systems. Clean energies are not only more environment-friendly but also have some financial advantages for countries as they reduce the indirect costs of environmental pollution. Given these advantages, the unanswered question is why renewable energies are not commonplace in some countries. The low price of fossil fuels is the first imperative reason that comes to mind. Taking the limitation of fossil fuels into consideration, some countries, even oil rich ones like Iran, see their energy future in renewable energies. The other reason may be the hardship of persuading or imposing the industries to relinquish the cheap and reliable power produced by fossil fuels and to use expensive green power. Government subsidies, grants, and financial supporting mechanisms are the best ways to cope with this problem. Residential electricity consumers as a huge electricity consumption sector seem to be an alluring target for renewable energies. This sector has its challenges and having a good understanding of market's needs and potentials is vital. In this study, we try to shed light on this market and the focus is on families as the main customers of photovoltaic systems. Iran, with approximately 300 sunny days per year, and by receiving
⁎
Corresponding author. E-mail address: [email protected] (S.H. Alizadeh).
http://dx.doi.org/10.1016/j.rser.2017.08.093 Received 12 April 2017; Received in revised form 7 August 2017; Accepted 30 August 2017 Available online 06 September 2017 1364-0321/ © 2017 Elsevier Ltd. All rights reserved.
4.5–5.5 Kwh/m2 per day has a good potential to benefit from solar energy [1]. Fig. 1 shows solar radiation in Iran. As it shows, Iran and the area of the study, Tehran, has a great radiation potential for utilizing solar energy. Although Iran benefits from other types of renewable energies slightly, like geothermal, wind and hydropower to produce power, solar energy which is the most available, cheapest and cleanest energy source amongst them play a little role in producing energy. According to Renewable Energy Organization of Iran in 2015, only 23 Mw power of photovoltaic system was achieved, from which roughly 14.5 Mw was from private sectors [2]. Alongside the potentials of renewable energies, particularly solar energy in Iran, the high rate of energy consumption is noteworthy. In 2014, household electricity consumption accounted for 32% of total electricity consumption in Iran [3] and it was the largest power sector consumption. Currently, Iran consumes electricity more than three times of world average. In a mega city like Tehran, the capital of Iran, 45% of electricity consumption of whole electricity sector can be observed. As shown in Fig. 2a sharp increase in electricity consumption occurred continuously between 2011 and 2015. In 2015 residential electricity consumption dominated all of other sectors. Baring it in mind, families have high potential to be targeted for using renewable energies. Currently, customers are one of the most important elements for which organizations and companies are concerned. The key to success of leading organizations is their focus on customer and product quality.
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Fig. 1. Solar radiation potential in Iran [59].
likely to purchase a product or a service. This group of customers has similar characteristics such as age, gender, income, lifestyle, location, and perceptions of product on which marketers focus their marketing efforts [7]. Considering the increase in household electricity consumption and its environmental benefits, households can be considered as an alluring target market for photovoltaic systems. In Iran, these systems as a new and innovative product begin to enter domestic electricity generation market. However, purchase, installation, and maintenance of these systems can be very costly for families and even an obstacle for development of these products [8]. Considering the advantages of these systems, governments have set financial incentive mechanisms to promote the use of renewable technologies in houses [9]. These mechanisms may include subsidies used to reduce initial cost of the system, guaranteed purchase tariffs which allow the owner to earn money, low-interest loans and tax credits. In Iran, financial incentive mechanisms such as subsidies and guaranteed electricity purchase contract have been provided for development of these systems, which has led to a good return on the investment. However, the factors which play a role in adoption of these systems and potential customers are unclear. In general, target customers of this product are families who have accepted this technology or, in other words, families who tend to purchase and use these systems. Therefore, this study identifies families who have accepted these systems as potential customers, because these families are more likely to purchase photovoltaic systems. To identify potential customers and adoption factors, this study used demographic and psychological variables. These variables provides a more accurate identification of potential customers. Moreover, variables used in this study fit to local conditions of the studied region. while recent studies have just highlighted the financial information of these systems [10]. In this study, Respondents are given detailed information on costs and revenues of the system to reduce their uncertainty about the profitability of the system. Accordingly, some
Fig. 2. Electricity consumption trends by two sectors [3].
Because of the importance of customers and their important effects in the success of organizations, the question always arises that how different levels of customers are identified and how certain market priorities are considered for each of these customers. The success of any organization is dependent on a proper planning; it is essential to determine target market in order to determine a strategic plan which facilitates organizational goals [4]. Identification of target market is particularly important for a new product. Many studies, particularly those which identify potential customers, have recently focused on marketing of new innovative products. Predicting the reaction and relative behavior of potential customers for a new product, businesses will be able to make plans for their sales tactics and market penetration strategies [5]. Moreover, identification of potential customers determines target market and leads to focus on customers who are more likely to buy [6]. Target market refers to a group of people who are 3132
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diffusion theory by zooming on the effect of financial incentive to explain PV adoption in Australia. All of the aforementioned theories tell the story of adoption from different points of view. Wolske et al. [24] believed that a model encompassing several theories led to a better understanding of adoption process. They used a combination of theory of planned behavior, Innovation diffusion theory and value-belief-norm theory to assess adoption of residential PV in the US. Moreover, they asserted that households have a different point of view towards PV systems; adopters saw PV as an environmental, innovative or consuming product and because of these different points of view, several theories were needed to model PV adoption. Bergek and Mignon [25] believed that although many factors affect adoption decisions, adoption motivations could be significantly different between renewable energy adopters. Because of that, different financial incentives and business models were needed to escalate adoption rate. Along with these studies which addressed customer adoption behavior, some studies merely identified potential customers as well as effective factors on adoption by using data related to families [26,27]. Some information about families such as demographic variables or data related to their house (area, number of rooms, number of units in a house, ownership) might be included in this data. Many studies have used demographic variables to identify potential customers of renewable energies [13,28–30]. Sommerfeld et al. [27] examined characteristics of people who adopted domestic photovoltaic systems using demographic data in Australia. Based on their results, ownership, the age greater than 55 years, living in houses with more than three bedrooms and type of residence were the most effective factors on adoption. In another study conducted in Greece, Sardianou et al. [26] used demographic variables and some variables related to financials as well as other variables related to efficiency and incentives of domestic renewable energies to address adoption of these systems. High-level income, well-educated, and middle-age families were more likely to adopt renewable energies. Moreover, gender, marital status, and ownership had no effect on adoption. Despite accessibility and accuracy of demographic data and its suitable application in customer segmentation, diverse studies obtained different variables. Some authors believed that cognitive behavioral and psychological variables had a more significant effect on eco-friendly behaviors [31]. In addition, adoption of photovoltaic systems could be considered as an eco-friendly behavior. Some studies suggested that psychological variables brought about more reliable results than demographic variables [7,32]. Bearing the advantageous of two aforementioned variables in mind, the presence of both these variables can increase the accuracy of prediction. Photovoltaic systems are innovative products which could be useful for the environment. Therefore, green and eco-friendly consumers, as well as people who are interested in innovative products, are expected to install photovoltaic systems to fulfill their social and moral duties or satisfy their innovativeness desires. Recent studies have focused on environmental concerns or environmental problems, eco-friendly behaviors, values, beliefs and environmental attitudes. Moreover, innovativeness, knowledge, and awareness about the environment and renewable energies have been highlighted in many studies [17,30,33]. Environmental concerns refer to values, beliefs, and concerns about the environment. In another word, environmental concerns refer to beliefs such as environmental pollution and the exploitation of natural resources. Ahn et al. [33] claimed that environmentally conscious people are more likely to use eco-friendly products or services. In some cases, environmental concerns can lead to a motivation for purchase or acceptance of eco-friendly products [34]. Considering advantages of photovoltaic systems for environmental protection, acceptance of photovoltaic systems can be considered as an environmental behavior. Therefore, it can be helpful to evaluate the effect of environmental variables on adoption of these systems. Customer innovativeness refers to one's rush in adoption of an innovation compared to others. This concept is related to Rogers' theory
findings obtained in this study reveals new dimensions of adoption of these. In addition, identification of potential customers of these systems is a new subject on which very few studies have been conducted in the context of Iran. Unfortunately, low energy prices, high upfront costs of PV's equipment and household's uncertainty of PV's power production might not give a positive outlook on adoption. The low price of energy has encouraged the power industries to focus on gray power production and consequently has made renewable energies overshadowed. Albeit, informing people about renewable energy technologies can be a viable solution, a little announcement or advertising about PV systems has been made. Moreover, environment pollution and municipal lifestyle of households living in Tehran have risen many concerns. To alleviate these problems, the governments have set financial incentive to promote adoption of renewable technologies in Iran. The objective of this research is mainly to evaluate the effects of environmental, demographics and knowledge factors on early adoption of PV systems taking into account of government's incentives in Tehran. The rest of this paper is structured as follows. In Section 2, it presents a summary of literature review on renewable energy adoption specifically PV adoption. Section 3 offers the methodology, data collection and used variables to assess the adoption. The results are presented in Section 4 and Section 5 describes how our findings confirm or contradict other studies. Finally, the conclusion of the analysis is discussed in Section 6. 2. Literature review Based on literature, identification of potential customers is associated with identification of factors affecting adoption. In other words, potential customers can be identified by psychological and demographical factors. According to studies on adoption of renewable energy innovations, adoption occurs in three political, social and market dimensions [11,12]. This study addressed adoption in market dimension and from customer perspective. Based on this, the role of customer shifts from consumer to investor. In recent studies, many methods and theories have been applied to assess the adoption of residential renewable energy technologies. Cognitive-behavioral theories are one of these theories addressing adoption of domestic renewable energy technologies. Cognitive behavioral theories are based on studies in social psychology and are widely used to evaluate behaviors related to adoption of eco-friendly products. These models claim that there is a direct relationship between adoption behavior and attitude towards the behavior [13]. Theory of reasoned action developed by Ajzen and Fishbein [14] and theory of planned behavior developed by Ajzen [15] are the most important cognitive behavioral theories [16]. These theories have been used in a wide range of studies on adoption of photovoltaic [9,16,17]. In addition to above theories, technology acceptance model is another theory used in adoption of renewable technologies [18]. This theory was an extended form of the theory of reasoned actions [19]. Diffusion of innovations is another theory which is commonplace between scientists and practitioners to assess how, why and through which channels an innovation, idea or technology are spread. Based on this theory adopters are classified in five categories of innovators, early adopters, early majority, late majority and laggards [20]. Most of the studies have focused on innovators and early adopters, because these adopters are the first group of people who use new technology and their attitude toward innovations can hinder or foster the diffusion process [21]. Nygrén et al. [22] assess the motivations and barriers of sustainable energy adoption between innovators and early adopters in Finland. Based on this study, these two group of adopters have different motivation including environmental and financial benefits and different barriers including lack of information and poor financial incentives. In another study, Simpson and Clifton [23] utilized an innovation 3133
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throughout 22 districts of Tehran. In order to reduce confusions of respondents, introductory remarks on photovoltaic systems were included at the beginning of the questionnaire. Descriptions included information such as purchase cost, maintenance costs, income from electricity guaranteed purchase contract, system lifetime, and other technical and financial information. The studied sample population included household decision-makers living in Tehran. Those respondents who were directly involved in making financial decisions were asked to fill the questionnaire. Totally, 450 face-to-face interviews were conducted in 22 districts of Tehran. After omitting uncompleted and equivocal responses, 345 questionnaires were selected for analysis.
of innovation adoption. People with a higher degree of innovativeness can better deal with the uncertainty of new products and have a higher tendency for adoption [20]. This variable have been used in studies which had addressed the acceptance of a new innovative product among society. Innovativeness had also been used in some studies related to renewable energies [17,33]. Since photovoltaic systems had recently targeted household market of some countries, this could be considered as an innovative product for families. Knowledge on the performance of a technology can influence adoption of an innovation directly or indirectly by influencing one's perceptions about innovations [12]. Many studies have examined the relationship between knowledge and adoption. Knowledge as a continuous process was presented at all stages of the decision-making process for adoption of an innovation; this decision-making process begins with prior knowledge about an innovation [20]. Lack of knowledge can cause uncertainty about technology and thus a rejection of technology [35]. Rundle et al. [7] considered the lack of knowledge on renewable energies as one of the obstacles to promotion and development of renewable energies. Rai et al. [36] claimed that the cost of photovoltaic systems is not the only barrier to photovoltaic adoption, insufficient knowledge of costs, environmental problems and advantages of these systems lead to non-monetary costs for potential customers. Another study in this way considered the lack of sufficient knowledge on costs and promotions as a reason for which potential customers did not tend to use and purchase photovoltaic systems [10]. Simpson and Clifton [23] found that the majority of PV adopters in Australia have considered PV systems for its financial benefits. However, for evaluating the benefits and costs of PV systems they needed more details and education. Although environmentalists and technology lovers have intrinsic motivation towards using PV systems, the lack of financial and technical supports hinder them to adopt. Many studies have addressed the effects of these barriers on adoption. Qureshi et al. [37] claimed that intermittent electricity shortages suffered households and consequently the necessity of PV systems was inevitable but the lack of financial incentives, trustworthy vendors and engineers were the main barriers to adoption of PV systems in Pakistan. Perceived economic returns can be alluring for people while technology barriers have a negative effect on adoption [38]. In a study, Fleib et al. [39] asserted that the main motivation for adoption in Australia was financial benefits rather environmental benefits. In a long term, even environmentalist may return to gray electricity in absence of a satisfactory financial support [39]. Because of the importance of financial supports and monetary aspects of PVs in adoption, many studies incorporated these variables with other factors to assess adoption. Recently, Agent Based Modeling has gained increased attention between scientists. Many studies tried to model adoption of PV systems in presence of financial incentives using ABMs [40,41].
3.2. Statistical analysis This study considers the tendency to adopt photovoltaic systems interchangeably with the tendency to use these systems. The tendency to adopt is a binary variable. This variable is given one if the respondent tended to adopt photovoltaic systems; otherwise, it is given zero. If dependent variable was a binary, results of linear regression would be unreliable, because this rejects the hypothesis on the linear relationship between dependent and independent variable [42]. Therefore, binary logistic regression was used to examine the effective factors on adoption of photovoltaic systems. Instead of predicting dependent variable value through independent variables, logistic regression predicts the probability of an event based on independent variables. The logistic regression equation for the event Y is written as:
P(Yi = 1) =
1 1+e−(b0 +βXi)
(1)
P(Y) i denotes the probability of the event Y by the respondent I; b0 is a constant value; β represents regression coefficients; and Xi is a vector of independent variables for the respondent i. Odds are an important concept in interpreting results of logistic regression. Odds refers to the probability of the event Y to the probability of the lack of that event. Let P(Yi = 1) be the probability of tendency to adopt photovoltaic systems and P(Yi = 0) be the probability of the lack of tendency to adopt; odds of adopting these systems will be P(Yi = 1) [42]. The odds ratio represents P(Yi = 0) the division of odds after changing 1-unit of a numeric variable or changing the level of a nominal variable by odds without changes. It is easier to interpret odds ratio rather than logarithmic β coefficients. Logit model is in fact a logarithmic transformation of the odds ratio, as shown in Eq. (2). L i denotes logarithm of the odds ratio or the same logit; b0 is a constant value; X1 to Xk are independent variables; β1 to βk are the estimated parameters proportional to independent variables and Ɛ is random error. P(Yi = 1) ⎞ L i = Ln ⎛ = b0 + β1 X1 + β2 X2 +…+ βk Xk +Ɛ ⎝ P(Yi = 0) ⎠ ⎜
⎟
(2)
Constructs with a set of items were used to measure some psychological variables. To reduce data dimension, factors were rated by using principal components analysis with Varimax rotation. Then factor scores of customer innovativeness, environmental problems and knowledge of renewable energies were selected as dependent variables.
3. Data collection and methodology This study was objectively conducted throughout 22 districts of Tehran, in 2015, right after implementation of the guaranteed purchase tariffs in Iran. Tehran was selected for the study because of its characteristics in relation to environmental pollution, population density and high household consumption of electricity.
3.3. Variable description
3.1. Questionnaire and data collection
The tendency to adopt photovoltaic systems can be influenced by a variety of factors, regarding alternative technology acceptance, tendency to pay for green power and consumption of green products [13,30,33]. However, selection of variables partially depends on particular circumstances of the studied area. As Tehran suffers from environmental pollution, environmental concerns were included in this study. Demographic variables such as age, gender, income, house ownership, family size, and number of units of the residential building, type of residence and level of development are considered in this study.
Data was directly obtained using a structured questionnaire. This questionnaire was developed by reviewing recent literature on profiling of green electricity customers and adoption of renewable energies. To verify the validity and reduce uncertainty, a pilot questionnaire was distributed through social networks; then, experts and university professors evaluated the questionnaires and responses. After reviewing and making necessary modifications, we distributed the questionnaire 3134
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Table 1 variables with descriptions. Variable
Variable description
Deviance
Median
Age Household size Education Income Gender Ownership
Age of responder (up to 30 = 1, 31–50 = 2, above of 51 = 3) Total members of family living together Up to diploma = 1, Bachelor degree = 2, Master degree = 3, Ph.D. degree = 4 Income per Tomans Up to 2M = 1, 2–4M = 2, above of 4M = 3 Male = 1, Female = 2 Ownership status of the house in which responder lives (Owner = 1, rental = 2) Total Number of units of the house in which the responder lives Apartment = 1, Private = 2, villa = 3 Level of development of the region in which the responder lives Developed = 1, semi-developed = 2, low developed = 3
0.74 1.70 0.84 0.74 0.50 0.47
1.63 3.53 2.53 1.74 1.48 0.66
9.6 0.68 0.86
5.50 1.45 2.06
Number of units House type Level of Development
factor and all questions of the questionnaire. Cronbach's alpha for the whole questionnaire (α = 0.925) and for each factor are shown in Table 2. Based on these results, the questionnaire was reliable. Bartlett test supports fit of data for the factor analysis (X2 = 3159, KMO = 0.909). None of the items were changed or removed. The main objective of factor analysis was using factor scores and rectifying the correlation between data. In addition, with factor scores, we can use the score of a factor rather than total items of that factor as an independent variable in regression models. Varimax rotation with regression method was used to calculate rating factors. Table 1 lists the demographic variables. In general, 345 families were studied. The sample included families in which decision-makers aged 30–50 years and there were 3–4 members, which was similar to Tehran's Population and Housing Census' report [45]. Also, the income of the sample was average (2–4 million Tomans). Moreover, the gender of sample was similar to the population and housing statistics of Tehran (49% women and 51% men). According to reports, 62% of Tehran population owned a house, which was not significantly different from the studied sample. Each building in which respondents lived encompasses 5–6 units revealing the high density of population in Tehran. Fig. 3 depicts demographic characteristics of the respondent sample. Having a general view on the answers, we can conclude that most of the respondents believe in environmental values and have a sensitivity to environmental problems, but they have little knowledge on renewable energies (Fig. 4).
Table 2 Constructs with reliability and validity tests. Constructs and items
Environment problems (α=0.880) Human is the most effective factor of environmental changes Environmental changes bring about declining in human’s Quality of life Changing the environment and air pollution is jeopardizing human life Paying to save the environment is a good idea I am willing to overlook some facilities of my life in favor of environment I agree with some strict rules and taxes in favor of saving the environment I’m aware that natural resources are limited Natural resources have been overused by human Novelty seeking (α=0.847) I would like to use the newest products I always check the newest and high-tech products as soon as possible Before buying new products I usually gather comprehensive information about them Knowledge about renewable energies and benefits ( KNOWL) (α=0.779) I am familiar with renewable energies. I know how photovoltaic systems work and what is the benefit of using them
Loading factor
0.654 0.740 0.783 0.779 0.683 0.721 0.753 0.707 0.787 0.819 0.703
0.688 0.824
4.1. Effective factors on acceptance of photovoltaic systems
Cumulative factor loading = 64%, KMO = 0.909. Bartlett test ( χ 2 = 3159, df = 153, ρ = 0.001).
The regression model showed that the model fits the observed data. According to Omnibus test, the final model, which included all variables, was totally significant (ρ = 0.01, X2 = 172). Hosmer and Lemeshow test is a suitable tool to evaluate the fit of the model [46]. Given X2 = 4.88 and P = 0.77, the null hypothesis was rejected and fit of the model was confirmed. Cox and Snell’ and Nagelkerke's coefficients of determination (56.9% and 39.6%, respectively) supported fit of the model. These two coefficients of determination are similar to the coefficient of determination in linear regression; however, their values are underestimated in comparison to the coefficient of determination in linear regression [47]. Fig. 5 shows classification plot of observed and predicted the probability of PV adoption. This plot depicts a visual demonstration of true or false predictions. On the other word, this plot shows the accuracy of our prediction. X axis is predicted probability and separation point is 0.5 which means if the predicted occurrence falls before 0.5 the cases would be categorized as non-adopter otherwise they would be categorized as adopter. The more “a” representing adopters on the right-hand side of separation point and more “n” representing non-adopters on the left-hand side of separation point, the more accuracy of prediction. As we can see in this plot adopters has been predicted accurately. Although, only 28% of respondents were unwilling to adopt PV systems, the model predicted this number of nonadopters accurately. Seven variables (family size, education, house ownership, number of residential units, environmental problems,
Using a study conducted in the University of Tarbiat Modarres, 22 districts of Tehran were categorized as underdeveloped, developing, and developed areas [43]. Development of a district refers to the level of welfare and accessibility to utilities. We expected that households living in low-level developed districts were more willing to use PV systems because of some power voltage problems and power outages. PV system along with prevalent electricity can be more reliable. Customer innovativeness was measured by a variety of methods and concepts. In general, there is no exact measurement for this construct; in a general sense, however, the tendency to search for innovative and different things is measurable [44]. Photovoltaic systems are complex systems and their outcomes and performance can be complicated for the respondent. Hence, knowledge of renewable energies was included in this study.Table 1 lists demographic and geographical variables used in logistic regression and Table 2 lists psychological variables used in the logistic model and its factor loadings. Items of these variables were measured using Likert scale ranging from strongly disagree (1) to strongly agree (5). 4. Results Prior to analysis, Cronbach's alpha reliability test was done for each 3135
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Fig. 3. Demographic characteristics of respondent sample.
Fig. 4. Psychological factors influencing adoption of PV systems.
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Fig. 5. Classification plot of predicted probabilities.
adoption of renewable energies. For example, the increase in income increased tendency for green electricity acceptance [13,28], while some studies have rejected the effect of income on green electricity adoption [48,49]. Similar results have been reported on adoption of photovoltaic systems [16]. The negative effect of income on adoption can be attributed to profitability and income derived from photovoltaic systems. These systems are very cost-effective due to financial incentive policies, such as guaranteed purchase tariffs and subsidies. Hence, low-income people are more likely to accept these systems because of their profitability. Furthermore, gender had a significant effect on the adoption. Females are more likely to adopt these systems. In general, females are more aware of environmental problems than males are; this awareness leads to higher tendency to accept renewable energies [50]. Also, family size was an important variable associated with the use of electricity. A growth in family size leads to an increase in power consumption [51,52]; this can influence the increase in the probability of adoption of photovoltaic systems. According to the results of this study, a rise in family size increases the probability of adoption of these systems; this finding is consistent with previous studies [27,53]. Moreover, education is another demographic variable used in many studies to address adoption of renewable energies [13,54,55]. Based on these results, an increase in education has led to rising in the tendency for adoption. On the other hand, multiple displacements of tenants and cost of disassembling, transporting, and reassembling these systems reduce the tendency to install these systems [56]. House ownership is recognized as an important factor in the ability to install photovoltaic systems. Generally, property owners have fewer obstacles to install photovoltaic systems. House ownership has been examined in several studies to address the adoption or to investigate barriers to acceptance [27,28,57] The number of residential units is a variable which has not been addressed in the literature, based on our knowledge. The increase in the number of residential units reduces available space for installation of photovoltaic systems. Moreover, these systems are usually installed on roofs; in Tehran, sufficient space is not allocated to each residential unit to install these systems. Thus, the increase in the number of units reduced the tendency to install photovoltaic systems. Type of houses was not significant, while those who live in singlefamily houses were expected to have higher tendency to install these systems because of their higher authorities and sufficient space for installing the systems. Further studies are required to explain this behavior. However, this finding is consistent with Sommerfeld [27]. The level of development was slightly significant. Those families who lived in developing areas were more likely to adopt these systems rather than people living in developed areas. Moreover, people who lived in underdeveloped areas were more likely to accept these systems compared to people who lived in developed ones. Underdeveloped and
Table 3 Binary logistic regression results. Variable
Wald
Sig
STD Error
B
ODD’s ratio
Age Household size Education Income Gender (Male) Ownership (rental) Number of units House type Apartment Single family house Level of development Developed Semi developed Environment problems Novelty seeking KNOWL Constant
0.45 27.28 15.03 5.39 3.81 32.62 14.38 5.13 1.89 0 4.63 0.01 3.92 13.09 8.62 10.22 1.09
0.49 0.000 0.000 0.02 0.051 0.000 0.016 0.177 0.169 0.989 0.099 0.90 0.049 0.000 0.003 0.001 1.03
0.26 0.13 0.22 0.27 0.35 0.35 0.01 – 0.65 0.70 – 0.39 0.47 0.7 0.17 0.17 1.03
−0.18 0.67 0.85 −0.62 −0.69 −2.04 −0.2 – 0.90 −0.01 – 0.04 0.94 0.69 0.51 0.54 −1.08
0.83 1.97 2.35 0.53 0.50 0.13 0.67 – 2.47 0.99 – 1.05 2.58 1.85 1.67 1.72 0.34
innovation and knowledge of renewable energies and its outcome) were highly significant (P < 0.01). Table 3 shows results of binary logistic regression. The odd ratio is mentioned in Table 3, indicating the effect of variations in one variable on the probability of adoption. If the odd ratio was equal to one, variations in that variable were not effective on the probability of adoption. One unit increase in age of respondents reduced the odd ratio by 0.17. In other words, the increase in age reduced the probability of acceptance (P (Yi = 1) ). One-unit increase in family size increased the odd ratio by 0.97; in other words, the increase in family size increased probability of acceptance. Accordingly, the increase in education, environmental concerns, knowledge and innovativeness increased probability of adoption of photovoltaic systems. The increase in income reduced the odd ratio by 47%. Therefore, lower levels of income were associated with higher probability of adoption. High-income people were less likely to install these systems. Moreover, an increase in number of units in multi-unit residential buildings reduced the tendency to adopt these systems. The change in ownership from property-owner to tenant considerably reduced the odd ratio. In other words, the change in the ownership from property owner to tenant reduced the tendency to adopt photovoltaic system. In the findings of this study, gender and age were a very weak predicting variable. 5. Discussion This study evaluates effective factors on household's photovoltaic systems adoption in Tehran. According to the obtained results from the study, the more income, the less tendency for PV system adoption. In the previous studies, the effect of income was different from the 3137
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Branch, Islamic Azad University for providing useful suggestions that improved this paper.
developing areas have weaker electricity transmission infrastructure; hence, they have higher potential to install photovoltaic systems. Environmental concerns were well significant (P < 0.01). According to the results, people who were concerned about the environment and felt that the environment was in danger, were more interested in these systems. These people believe that photovoltaic systems reduce environmental pollution by generating clean electricity. Based on findings of this study, people with high environmental concerns are more likely to accept these systems. This finding is consistent with previous studies [17,33]. Innovativeness was known as an important variable in studies related to the adoption of an innovation. People with higher innovativeness are more likely to accept innovative products and ignore uncertainty about these products [20]. In some studies, innovativeness had a positive effect on adoption of photovoltaic systems. Results of this study are consistent with the findings in previous studies [17,33]. Awareness and knowledge of innovative products can reduce uncertainty about performance and profitability of products. Therefore, awareness of technology prior to acceptance is vital. Moreover, being aware of costs and promotions converts potential customers to actual ones [10] and changes the attitudes towards renewable energies [18]. In general, the increase in awareness of people about renewable energies increases the probability of adoption. Therefore, successful development of renewable energies depends on public support and awareness of families about advantages of these energies. Children's Education is of great importance. Nowadays children have not enough knowledge of renewable energies, although they seem to be responsible to the environment [58]. So by educating children and informing them about the importance of renewable energies, we can guarantee the future of sustainable energy.
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6. Conclusion Considering the profitability of PV systems, financial supports of Iran's government and unique characteristics of Tehran, the objective of this study was to gain an overview of effective factors on PV adoption and to identify potential customers of residential photovoltaic systems. Two groups of demographic and psychological variables were utilized to evaluate PV adoption in this study. Once the data was gathered, a binary logistic analysis was used to evaluate effective factors on the adoption of PV systems. One of the most important results of this study is the negative effect of income on the probability of adoption. Profitability and income resulting from financial mechanisms can be a good motivation to stimulate low-income families to install photovoltaic systems. This finding proves that current financial supports are effective. Environmental concerns significantly affect adoption. This finding shows that households living in Tehran, are seriously concerned about their polluted environment and looking for a concrete solution to dwindle pollutants. Roughly the half of residents of this mega city are tenants and as the results revealed, the tenants have less motivation in order to adopt these systems. By renting the photovoltaic systems to tenants, as leasing business models, and providing free maintenance services, this group of households will have more tendency to adopt. The level of development of different part of the city has no effect on propensity of adoption but number of units in an apartment significantly affect adoption. Since innovativeness is recognized as an important factor in this study and innovative people are more likely to accept photovoltaic systems, this group of people can be attracted by advertisements, which focuses on innovative and technological aspects of these systems. Results of this study help policy-makers and renewable energy marketers to make energy-related decisions. Acknowledgements The authors would like to thank Dr. Khunsiyavosh from Qazvin 3138
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