The Role of Household Consumers in Adopting Renewable Energy Technologies in Kenya

environments

Article The Role of Household Consumers in Adopting Renewable Energy Technologies in Kenya

Eliud Kiprop 1,*, Kenichi Matsui 2 and Nicholas Maundu 3

1 Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan 2 Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan 3 Ministry of Energy, Nairobi 30582-00100, Kenya * Correspondence: [email protected]

Received: 2 July 2019; Accepted: 9 August 2019; Published: 13 August 2019

Abstract: In transition to a low-carbon economy, the adoption of renewable energy (RE) technologies by energy investors, power utilities and energy consumers is critical. In developing countries like Kenya with a high rate of urbanization, this transition requires urban and rural residents’ proactive responses to using renewable energy sources. In this regard, a better understanding of residents’ perceptions about renewable energy investment, RE sources availability, climate change, environmental conservation and other factors can lead to more eﬃcient and sustainable implementation of renewable energy policies. This study investigates the role Kenya’s household energy consumers in urban and rural areas can play in adopting renewable energy technologies. To achieve this, a questionnaire survey was administered among 250 household consumers in Nairobi County, Makueni County, and Uasin Gishu County. Our survey analysis shows that about 84% of the respondents were interested in adopting renewable energy for their entire energy consumption mostly because of solving frequent power outages and high energy cost from the grid system. This perception did not have any correlations with income levels or any other socio-economic factors we identiﬁed. Furthermore, about 72% of the respondents showed their interests in producing and selling renewable energy to the national or local grids if government subsidies were readily available. Rural residents showed strong interests in adopting renewable energy technologies, especially solar PV solutions. However, the main impediment to their investment in renewable energy was the high cost of equipment (49%) and the intermittent nature of renewable energy (27%) resources.

Keywords: 100% renewable energy; Kenyan electricity consumers; environmental conservation; adoption of renewable energy

1. Introduction Residential energy consumption reduction can play a signiﬁcant role in mitigating climate change [1]. Residential emissions particularly from urban areas, account for 30%–40% of the global greenhouse gases (GHGs) emissions [2,3]. Since 2012, Kenya’s residential power consumption has grown by 28%. This growth occurred mainly as a result of urbanization and improved electricity access in the rural areas [4]. Currently, the residential sector accounts for 31% of the total electricity consumption [5]. In 2018, hoping to dramatically change this energy consumption outlook, the Kenyan government announced that it would supply 100% of its energy from renewable resources. Despite this noble attempt, the question remains as to the extent to which household energy consumers are willing to adopt renewable energy technologies in developing countries like Kenya that has suﬀered from chronic poverty conditions but is endowed with a high renewable energy generation potential. Kenya’s renewable energy market was established as early as the 1970s mostly by foreign investments. The country soon became known as a “donor hub” for solar photovoltaic (PV) facilities [6].

Environments 2019, 6, 95; doi:10.3390/environments6080095 www.mdpi.com/journal/environments Environments 2019, 6, 95 2 of 13

Currently, however, only about 1.2% of Kenyan households have installed home solar systems [4]. Some communities have undertaken community-level hydro projects while other individuals have invested in wind power generation. In an international context, studies found that consumers’ decisions to adopt renewable energy technologies were influenced by motivational, contextual, and habitual factors [7]. Some studies emphasized that residents adopted renewable energy technologies because of prospects for economic benefits or social pressure from their peers and neighbors [7–9]. Others suggested that environmental motivations induced people to adopt renewable energy [9]. Some research found a strong link among household environmental attitudes, energy consumption and investment patterns [10]. Government incentives would also induce the adoption of renewable energy as the cost appears to be a strong barrier to adoption [7,10–13]. Although several studies examined residential consumers’ perceptions on renewable energy, these were mainly carried out in developed countries. This study attempts to investigate regional contexts of residential consumers’ decision-making for adopting renewable energy technologies in Kenya. Previous studies [14–16] demonstrated that consumer perceptions about renewable energy often vary by country. Even within a country studies [17,18] showed that those in rural areas are more likely to invest in renewable energy than those in urban areas. Other studies [19,20] noted that different lifestyles affect technological choices. In order to grow the building integrated photovoltaics (BIPV) market leading to economic and social progress, education of urban residential consumers’ is crucial [19]. Despite these studies, scant attention has been paid to residential consumers’ attitudes and willingness to adopt renewable energy technologies in Kenya. In addition, we know little about motivating factors and challenges that Kenya’s household consumers face in investing in renewable energy technologies.

2. Materials and Methods

2.1. Study Areas This study targets household electricity consumers in both urban and rural areas of Kenya. Considering climatic conditions, urbanization level, household power demand, economic activities, and renewable energy potential, we selected Nairobi County (urban), Makueni, and Uasin Gishu counties (both largely rural) (Figure1). These counties have di fferences in electricity access rates, population distribution, electricity usage, and climatic conditions. The government has focused on renewable energy projects for rural areas [20]. However, the grid extension has progressed relatively slowly in rural areas. The two rural counties of Makueni (south-east) and Uasin Gishu (north-west) are located on the opposite sides of the capital city (Nairobi) with different climatic conditions. The eastern part of the capital city generally receives less rainfall than the western part does. The three counties, therefore, are a good representation of urban and rural areas in Kenya. Nairobi County is the most populous county in Kenya with a population of 3,138,369, according to the 2009 census. Kenya’s capital is located here. It has a total surface area of 697 km2 and is classified as 100% urban. It receives an average annual precipitation of 926 mm with annual average maximum and minimum daily temperatures of 25.3 ◦C and 12.6 ◦C, respectively [21]. Currently, Nairobi County has the highest power consumption (45%) and is projected to remain so in the next 20 years. Nairobi county hosting the capital of Kenya has benefitted from a World Bank funded project of public/street lighting program [22]. In addition, it was the main beneficiary of slum electrification project [23]. Demographically, Nairobi residents can be clearly classified as low, middle-high-income groups by residential areas [24]. The main economic activities are manufacturing, tourism, commercial, and financial services. The average annual photovoltaic (PV) electricity output is 1530 kWh/kWp. The average annual wind power density ranges from poor to marginal (0–165 w/m2) although some spots are very good (425–615 w/m2)[25,26]. The County has no known hydro power generation potential [27]. EnvironmentsEnvironments 20192019,, 66,, 95x FOR PEER REVIEW 33 of of 13

Makueni County is one of the semi-arid counties located in the eastern part of Kenya about 130 km fromMakueni Nairobi County [28]. It is has one a oftotal the population semi-arid countiesof 884,527, located according in the to easternthe 2009 part census of Kenya [24]. It abouthas a 130total km surface from area Nairobi of 6806 [28 ].km It2 haswith a urban total populationareas covering of 884,527, about 1% according of the total to theland 2009 area. census It receives [24]. 2 Itan has average a total annual surface precipitation area of 6806 of km 596with mm urban with areasan annual covering average about maximum 1% of the totaland minimum land area. Ittemperatures receives an average of 28.2 °C annual and precipitation16.8 °C, respectively of 596 mm [29]. with Toan increase annual electricity average maximum access in andrural minimum areas, in temperatures2012, the government of 28.2 ◦C set and up 16.8 a ◦13.5C, respectively kWp solar [29plan]. Tot, battery increase storage, electricity and access canopy in rural at the areas, Kitonyoni in 2012, thevillage government trading center. set up This a 13.5 solar kWp project solar plant,supplies battery electricity storage, to about and canopy 3,000 residents at the Kitonyoni [28]. The village main tradingeconomic center. activities This solarhere are project agriculture supplies (especia electricitylly toanimal about husbandry) 3,000 residents and [commerce.28]. The main The economic average activitiesannual photovoltaic here are agriculture electricity (especially output is animal1573 kWh/kWp husbandry) [28]. and The commerce. average annual The average wind annualpower photovoltaicdensity ranges electricity from poor output to marginal is 1573 kWh(0–165/kWp w/m [282),]. although The average some annual spots windare classified power density as good ranges (275– 2 2 from425 w/m poor2) to [25,26]. marginal Given (0–165 the w /aridm ), and although semi-arid some nature spots are of classiﬁed the County, as good it has (275–425 little whydro/m )[ power25,26]. Givengeneration the arid potential and semi-arid [27]. nature of the County, it has little hydro power generation potential [27].

Figure 1. Map showing the study areas Source:Source: Ministry of Devolution and ASAL Areas.Areas.

Uasin Gishu County is located on a plateau in the westernwestern part of KenyaKenya about 310 km fromfrom Nairobi [[24].24]. It It has has a a total population of of 818,757 818,757,, which which is is evenly evenly distributed distributed across across the the County, County, according toto thethe 20092009 census.census. It has a total surface areaarea ofof 33513351 kmkm22 with urban areas covering about 6% ofof thethe County.County. It receivesreceives anan averageaverage annualannual precipitationprecipitation of 1100 mmmm withwith anan annualannual averageaverage maximum and minimumminimum temperaturestemperatures ofof 23.323.3 ◦°CC andand 11.311.3 ◦°C,C, respectivelyrespectively [[30].30]. The average annual photovoltaicphotovoltaic electricityelectricity output is 1793 kWhkWh/kWp/kWp [[28],28], whilewhile thethe average annual wind power density ranges from poor poor to to marginal marginal (0–165 (0–165 w/m w/m2) 2with) with some some high high density density spots spots (275–425 (275–425 w/m w2) /[25,26].m2)[25 This,26]. ThisCounty County also has also potential has potential for small for small hydroelectric hydroelectric generation generation and is and one is of one the of main the maincatchment catchment areas areasof Lake of LakeVictoria Victoria [27,31]. [27 ,31It ].benefited It beneﬁted from from a Wo a Worldrld Bank Bank funded funded project project of of public/street public/street lighting programprogram [[22].22].

2.2. Data Collection The primaryprimary data data was was collected collected through through the questionnairethe questionnaire survey survey that was that administered was administered between Octoberbetween andOctober November and November 2018. Prior 2018. to this,Prior a to preliminary this, a preliminary survey was survey conducted was conducted to makesure to make that thesure respondents that the respondents could understand could understand all the questions. all the questions. A revised A revised multiple multiple choice choice questionnaire questionnaire was thenwas then administered administered to 250 to household 250 household heads heads through thro randomugh random sampling sampling in the three in the counties three counties of Nairobi, of Makueni,Nairobi, Makueni, and Uasin Gishu.and Uasin We targeted Gishu. 50We household targeted heads50 household in each county heads evenly in each distributed county toevenly cover distributed to cover different parts of the country according to the local conditions. Considering

Environments 2019, 6, 95 4 of 13 different parts of the country according to the local conditions. Considering Nairobi County is very diverse, we obtained cooperation from 100 residents from both low-income households (Kibera and Mukuru kwa Reuben slums) and middle-high-income households (Roysambu, Karen and Westlands estates). In addition, we had 50 respondents from the Nairobi County city center. This ensured that the survey captured a representative sample of all income levels and diversities in the County. Since most household heads were either working or had some other errands during weekdays, the questionnaire was administered mainly during weekends except for shops and the Nairobi County city center respondents. The questionnaire was administered to adults only. The questionnaire was formulated after reviewing similar studies on perceptions of renewable energy [7,32–34], renewable energy acceptance and policies [35–37], and specific renewable energy technology studies [38–40]. In addition, an extensive review of government publications on rural and urban electrification projects [7,24], energy generation and transmission [41], energy planning [5], and reports on energy generation and supply [42,43] was carried out. The questionnaire was divided into six sections. The first section attempted to find out the socio-demographic characteristics of the respondents, including the current source of electricity. The second section focused on respondents’ concerns about environmental conservation, air pollution associated with diesel power generation plants, and the types of power they used. The third section investigated their willingness to accept 100% of their power supply from renewable energy sources, and their perceptions about government support for this purpose. The fourth section attempted to find out what renewable energy technologies the respondents would be willing to adopt and invest in. The fifth section looked into the motivations to invest in renewable energy technologies. The sixth section assessed the challenges residents faced in installing renewable energy technologies. The collected data were coded and entered into Microsoft Excel 2016. The data were analyzed by using Microsoft Excel Analysis ToolPak. The data was summarized using both descriptive and inferential statistics.

3. Results and Discussions

3.1. Socio-Demographic Characteristics The first part of the questionnaire on socio-demographic characteristics identified age, gender, household size, educational level, and monthly income (Table1). In addition, we asked the respondents to state their current electricity power source. Here we found that about 61% of the respondents were males. Three-quarters of the respondents fell within an age bracket of 20–39 years old. Only 3% of our respondents had no formal education and 89% had at least attained high school education. About 77% of those with postgraduate qualifications were from the middle and high-income category in Nairobi County. Those in Uasin Gishu, a rural county, tended to be less educated. Regarding the household size, 70% had four or less persons [21]. Those who had more than five persons tended to be in Uasin Gishu County (39%). About 63% of the respondents earned monthly income of between KES10,000 to 50,000 (US$1 = KES101.2986 on 1 January 2018). About 73% of those who earned less than KES20,000 per month lived in rural counties of Makueni (22%), Uasin Gishu (15%), and the low-income areas of Nairobi County (36%). On the question of their current electricity power source, about 74% of the respondents obtained their electricity solely from the national grid, while 16% did from the combined sources of the national grid and solar PV. About 7% had electricity from a solar mini-grid, while 3% had all their electricity supplies from household solar PV with battery. All the respondents in Nairobi County were connected to the national grid. The majority of those who got power supply from both the national grid and solar PV were from rural counties of Uasin Gishu (48%) and Makueni (32%). All those who received power from a solar mini-grid were from Makueni County. Three-quarters of the households who had all their electricity from household solar PV with battery were from Makueni County while the rest were from Uasin Gishu County. Environments 2019, 6, 95 5 of 13

3.2. Concern on Environment and Power Generation The second section of our survey focused on respondents’ concerns about environmental conservation, renewable energy sources and other power sources the respondents used. We asked the respondents about the extent to which they cared about (1) environmental conservation, (2) air pollution associated with diesel power generation, and (3) the type of power sources they used (renewable or non-renewable sources).

Table 1. Socio-demographic characteristics of the respondents in the three counties.

Characteristics N % Age Under 20 7 3 20–29 126 50 30–39 63 25 40–49 39 16 50–59 12 5 60 and above 3 1 Gender Male 152 61 Female 98 39 Household size 1–2 73 29 3–4 103 41 5–6 61 25 7–8 11 4 9–10 1 0.4 Over 10 1 0.4 Education Level No formal education 8 3 Primary 20 8 Secondary 75 30 Diploma 71 29 Bachelors’ 63 25 Postgraduate 13 5 Monthly income (KES) Less than 10,000 38 15 10,000–20,000 81 32 20,001–50,000 77 31 50,001–80,000 23 9 80,001–100,000 8 3 100,001–150,000 9 4 150,001–200,000 7 3 Over 200,000 7 3

The results show that about 98% of the respondents either strongly cared or cared about environmental conservation (Figure2). Similarly, about 96% either strongly cared or cared about air pollution that was associated with diesel power generation. About 84% of the respondents strongly cared or cared whether the energy they used was generated from renewable sources or not. Previous studies [44–46] similarly showed high environmental concerns among household consumers. These consumers depicted other pro-environmental behaviors like adopting green electricity and energy conservation for air pollution control. Environments 2019, 6, 95 6 of 13

Here we also tried to find if there are regional variations in response to these three factors. EnvironmentsAn analysis 2019, of6, x variance FOR PEER on REVIEW environmental conservation (p-value = 7.93 10 11), air pollution associated6 of 13 × − with diesel power generation (p-value = 1.75 10 8), and power generation source (p-value = 3.34 10 8) 10−8) showed significant statistical differences among× − the three study areas. Those who strongly cared× − showed significant statistical differences among the three study areas. Those who strongly cared about about environmental conservation were mostly from Makueni County (72%), low-income category environmental conservation were mostly from Makueni County (72%), low-income category (56%), (56%), middle and high-income category (54%), and Nairobi County city center (50%). In Uasin Gishu middle and high-income category (54%), and Nairobi County city center (50%). In Uasin Gishu County County 84% cared about environmental conservation. 84% cared about environmental conservation. The respondents who strongly cared about diesel power generated air pollution were from The respondents who strongly cared about diesel power generated air pollution were from Makueni County (64%), Nairobi County city center (56%), middle-high-income category (52%) and Makueni County (64%), Nairobi County city center (56%), middle-high-income category (52%) and low-income category (50%). About 94% of those in Uasin Gishu County stated that they cared about low-income category (50%). About 94% of those in Uasin Gishu County stated that they cared about it. Those who strongly cared about power generation sources were from Makueni County (54%), low- it. Those who strongly cared about power generation sources were from Makueni County (54%), income category (48%), middle-high-income category (44%), and Nairobi County city center (44%). low-income category (48%), middle-high-income category (44%), and Nairobi County city center (44%). In Uasin Gishu County 68% of the respondents cared about it and 30% said they were not sure. In Uasin Gishu County 68% of the respondents cared about it and 30% said they were not sure. These results show that the respondents in Makueni County strongly cared more about the three These results show that the respondents in Makueni County strongly cared more about the environmental issues compared to the other two counties. This can be understood from their three environmental issues compared to the other two counties. This can be understood from their geographical location in arid and semi-arid areas where the residents have experienced adverse geographical location in arid and semi-arid areas where the residents have experienced adverse environmental impacts like drought. In addition, their livelihoods (agriculture and livestock keeping) environmental impacts like drought. In addition, their livelihoods (agriculture and livestock keeping) are directly connected to the environment [21]. Although the respondents in Uasin Gishu County are directly connected to the environment [21]. Although the respondents in Uasin Gishu County also also depended on farming, this County received higher rainfall with more forest cover [30]. depended on farming, this County received higher rainfall with more forest cover [30].

Power Generation 38% 46% 14% 1% Source

Air pollution 45% 51% 3% 0%

Environmental 50% 48% 2% 0% Conservation

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Strongly care Care Not sure Do not care

Figure 2. Perceptions on environmental conservation and power generation source. Figure 2. Perceptions on environmental conservation and power generation source. Regarding responses from different regions, we additionally conducted a multiple regression analysisRegarding to findresponses the connection from different between regions, the we three additionally identified conducted environmental a multiple problems regression and the analysisrespondents’ to find socio-demographicthe connection between characteristics. the three Theidentified analysis environmental result shows thatproblems education and ( pthe-value respondents’= 2.42 10 socio-demographic2) and income (p characteristics.-value = 1.8 10The3 )analysis significantly result ashowsffected that their education perceptions (p-value about = air × − × − 2.42pollution × 10−2) and from income diesel (p-value power. =However, 1.8 × 10−3) thesignificantly two demographic affected their characteristics perceptions of about education air pollution and income fromhad diesel no significant power. However, effect on the their two perceptions demographic about characteristics environmental of conservation education and and income power had generation no significantsources. effect Interestingly, on their theperceptions household about size had environmental a significant conservation impact on perceptions and power of environmentalgeneration sources.conservation Interestingly, (p-value the =household2.19 10 size2) and had power a significant generation impact source on (perceptionsp-value = 2.85 of environmental10 2). However, × − × − conservationage and gender (p-value had = no2.19 significant × 10−2) and eff powerect on thegeneration respondents’ source perceptions. (p-value = 2.85 We found × 10−2). that However, the households age andwith gender less had than no three significant persons effect and thoseon the with respon moredents’ than perceptions. six persons showed We found their that strong the households concerns about withenvironmental less than three conservation persons and and those energy with sources more than whereas six persons the households showed withtheir threestrong to concerns five persons aboutcared environmental less about the conservation environment and and energy energy source issues.s whereas the households with three to five persons cared less about the environment and energy issues.

3.3. Power Supply from Renewable Energy Sources and Government Support The third part of the questionnaire aimed to assess the extent to which consumers perceived the importance of adopting renewable energy. We tried to find out if household energy consumers wanted to receive all their domestic electricity needs from renewable energy sources. We also sought

Environments 2019, 6, 95 7 of 13

3.3. Power Supply from Renewable Energy Sources and Government Support The third part of the questionnaire aimed to assess the extent to which consumers perceived Environments 2019, 6, x FOR PEER REVIEW 7 of 13 the importance of adopting renewable energy. We tried to find out if household energy consumers towanted find whether to receive these all theirconsumers domestic would electricity like to needssell energy from they renewable generated energy to the sources. national We or also local sought grid. Additionally,to find whether we these wanted consumers to know would how the like respondents to sell energy would they generatedrespond to to government the national subsidies or local grid. for householdAdditionally, customers’ we wanted renewable to know energy how the adoption. respondents would respond to government subsidies for householdThe result customers’ shows renewablethat about energy84% of adoption.the respondents either strongly agreed or agreed that they wantedThe to result receive shows all their that aboutelectricity 84% ofdemand the respondents from renewable either stronglysources agreed(Figure or3). agreed An analysis that they of variancewanted to(p receive-value = all 2.48 their × 10 electricity−4), indicated demand regional from variations, renewable in sources which (Figurewe found3). that An analysis92% of the of variance (p-value = 2.48 10 4), indicated regional variations, in which we found that 92% of the respondents in Makueni and× 100%− in Uasin Gishu demonstrated their positive responses. In Nairobi County,respondents middle- in Makueni and high-income and 100% inrespondents Uasin Gishu (86%) demonstrated showed more their positive positive response responses. compared In Nairobi to thoseCounty, in middle-low-income and ones high-income (66%) and respondents in the city (86%)center showed (76%). This more is positive probably response because compared among the to surveyedthose in low-income households onesin Makueni (66%) andCounty, in the about city 12% center had (76%). all their This electricity is probably demand because from amonghousehold the solarsurveyed PV with households battery while in Makueni about County,34% had about from 12%a solar had mini-grid. all their electricity In Uasin Gishu demand County, from household about 4% obtainedsolar PV withelectricity battery from while household about 34% solar had PV from whil ae solar44% from mini-grid. both the In Uasinnational Gishu grid County,and solar about PV. This4% obtained finding largely electricity corresponds from household with an Australian solar PV while study 44% [36], from in which both home the national ownership grid was and found solar toPV. be This an important finding largely factor corresponds for households with to an adop Australiant renewable study energy [36], insources. which A home multiple ownership regression was analysisfound to of be the an importantrespondents’ factor socio-demographic for households tocharacteristics adopt renewable of age, energy gender, sources. household A multiple size, education,regression analysisand income of the levels respondents’ did not show socio-demographic any significant characteristics effect on their of willingness age, gender, tohousehold obtain all theirsize, education,power demand and income from renewable levels did energy not show sources. any significant effect on their willingness to obtain all their power demand from renewable energy sources.

Government Support 31% 37% 13% 13% 6%

Sell the Energy 30% 42% 14% 12% 2% Generated from PV to KPLC

Want to Supply all Domestic 38% 46% 13% 3% 0% Power Needs from RE

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Strongly agree Agree Somehow agree Disagree Strongly disagree

Figure 3. Perception on power supply from renewable energy sources and government support. Figure 3. Perception on power supply from renewable energy sources and government support. Regarding the second question about respondents’ interests in selling energy to grids, about 72%Regarding of them either the second strongly question agreed about or agreed respondents’ to this idea interests while in about selling 12% energy disagreed. to grids, An about analysis 72% of variancethem either (p-value strongly= 2.53 agreed10 or7) ofagreed their responsesto this idea showed while regionalabout 12% and disagreed. income-level An dianalysisfferences. of × − varianceAll the respondents (p-value = 2.53 in Uasin× 10−7) Gishu of their County responses demonstrated showed regional their interests and income-level in selling differences. power to grids. All theIn Makueni respondents County, in Uasin about Gishu 72% showedCounty demonstrated their interests. their In Nairobi interests County, in selling about power 74% to of grids. those In in Makuenithe middle-high-income County, about 72% level showed showed their high interests. interests In while Nairobi about County, 64% of about those 74% in the of those low-income in the middle-high-incomelevel answered positively. level showed A multiple high regressioninterests while analysis about of 64% the respondents’of those in the socio-demographic low-income level answeredcharacteristics positively. of age, gender, A multiple household regression size, education, analysis and of income the respondents’ levels did not showsocio-demographic any significant characteristicseffect on their interestof age, togender, sell energy household to the grids.size, education, These findings and supportincome previouslevels did similar not show studies any in significantGermany, France,effect on Italy, their and interest Australia to sell [47 energy,48]. to the grids. These findings support previous similar studiesRegarding in Germany, the importance France, Italy, of government and Australia incentive [47,48]. for household consumers to invest in renewable energy,Regarding about 68% the eitherimportance strongly of agreedgovernment or agreed incentive while for about household 19% either consumers disagreed to or invest strongly in renewabledisagreed. Anenergy, analysis about of variance68% either (p-value strongly= 1.99 agreed10 or4) indicatedagreed while regional about variations. 19% either In ruraldisagreed counties or × − strongly disagreed. An analysis of variance (p-value = 1.99 × 10−4) indicated regional variations. In rural counties of Uasin Gishu and Makueni, 94% and 85% of the respondents showed their interests in government support, respectively. In Nairobi County, 56% of the middle-high-income respondents and 65% of low-income people wanted government support. In the city center 50% showed interests. Some facts can explain the reasons behind these differences. For example, in the past the government support on renewable energy projects (especially off-grid) has been in the rural areas. The multiple

Environments 2019, 6, 95 8 of 13

of Uasin Gishu and Makueni, 94% and 85% of the respondents showed their interests in government support, respectively. In Nairobi County, 56% of the middle-high-income respondents and 65% of Environments 2019, 6, x FOR PEER REVIEW 8 of 13 low-income people wanted government support. In the city center 50% showed interests. Some facts can explain the reasons behind these differences. For example, in the past the government support on regression analysis of these responses in connection to respondent’s socio-demographic renewable energy projects (especially off-grid) has been in the rural areas. The multiple regression characteristics indicates that education (p-value = 1.62 × 10−2) significantly affected their perceptions analysis of these responses in connection to respondent’s socio-demographic characteristics indicates about government support. that education (p-value = 1.62 10 2) significantly affected their perceptions about government support. × − 3.4. Renewable Energy Technology Choices 3.4. Renewable Energy Technology Choices The fourth section of the questionnaire asked the respondents about what renewable energy The fourth section of the questionnaire asked the respondents about what renewable energy technologies they would like to invest in. The question in this particular section targeted only those technologies they would like to invest in. The question in this particular section targeted only those households (74%) that obtained all their electricity needs from the national grid. The result shows households (74%) that obtained all their electricity needs from the national grid. The result shows that about 85% would invest in solar PV, while only 2% and 1% showed interests in wind and small that about 85% would invest in solar PV, while only 2% and 1% showed interests in wind and small hydro, respectively (Figure 4). About 6% would invest in the combination sources of solar PV, wind, hydro, respectively (Figure4). About 6% would invest in the combination sources of solar PV, wind, and small hydro. The rest had no interest in any form of renewable energy technologies. and small hydro. The rest had no interest in any form of renewable energy technologies.

3% 1% 2% Solar PV

1% Wind 2% 6% Small hydro

None

Solar PV, wind 85% Solar PV, Wind, Small hydro

Solar PV, Small hydro

Figure 4. Households’ renewable energy technology choices. Figure 4. Households’ renewable energy technology choices. Here we found different responses by regions. In terms of investment in solar PV, the respondents Here we found different responses by regions. In terms of investment in solar PV, the in Makueni (94%) and Uasin Gishu (90%) wanted to invest in solar PV systems. In Nairobi County, respondents in Makueni (94%) and Uasin Gishu (90%) wanted to invest in solar PV systems. In the higher percentage of the middle-high-income group (86%) showed interests in solar compared to Nairobi County, the higher percentage of the middle-high-income group (86%) showed interests in those in the low-income group (80%) and in the city center (72%). Those respondents who showed solar compared to those in the low-income group (80%) and in the city center (72%). Those interests in wind power were from Makueni County and the Nairobi County city center while all those respondents who showed interests in wind power were from Makueni County and the Nairobi who wanted to invest in small hydro were from Uasin Gishu County. The tendency of residents to show County city center while all those who wanted to invest in small hydro were from Uasin Gishu interests in solar energy can be found in other developing countries like Qatar [33] and Yemen [15]. County. The tendency of residents to show interests in solar energy can be found in other developing We found that respondents’ interests in investing in renewable energy was also influenced by countries like Qatar [33] and Yemen [15]. financial and climatic factors. In Kenya, solar PV is relatively easy and affordable technology compared We found that respondents’ interests in investing in renewable energy was also influenced by to small wind and hydro technologies. On the other hand, only limited areas in western, central, financial and climatic factors. In Kenya, solar PV is relatively easy and affordable technology and eastern parts of the country have potential for small and large hydro generation [31]. This explains compared to small wind and hydro technologies. On the other hand, only limited areas in western, why all respondents who showed interests in small hydro developments were from Uasin Gishu central, and eastern parts of the country have potential for small and large hydro generation [31]. County. Currently, most of the households who have invested in small hydro have done so through This explains why all respondents who showed interests in small hydro developments were from community projects which are relatively larger [31]. Uasin Gishu County. Currently, most of the households who have invested in small hydro have done so3.5. through Motivation community to Invest projects in Renewable which Energyare relatively Sources larger [31].

3.5. MotivationThe ﬁfth to section Invest in of Renewable the questionnaire Energy Sources attempted to understand residents’ motivations to invest in renewable energy technologies. The survey results revealed that the respondents were largely motivatedThe fifth to section prevent of electricitythe questionnaire supply problems, attempted such to understand as frequent residents’ power outages motivations (42%), highto invest power in costrenewable (37%), andenergy lack technologies. of connection The to the survey national resu gridlts revealed (11%) (Figure that 5the). Additionally,respondents environmentalwere largely motivated to prevent electricity supply problems, such as frequent power outages (42%), high power cost (37%), and lack of connection to the national grid (11%) (Figure 5). Additionally, environmental concerns (6%) and low cost of renewable energy equipment (4%) motivated some respondents. Previous study [6] depicted the three issues as the main challenges in Kenya’s energy sector. Within a regional context, we found that frequent power outage was an important factor for the respondents in the Nairobi County city center (48%), while the high cost of power was more problematic for the low-income respondents (52%) in Nairobi County. For the respondents in

Environments 2019, 6, 95 9 of 13 concerns (6%) and low cost of renewable energy equipment (4%) motivated some respondents. Previous study [6] depicted the three issues as the main challenges in Kenya’s energy sector. EnvironmentsWithin 2019 a regional, 6, x FORcontext, PEER REVIEW we found that frequent power outage was an important factor for9 of the 13 respondents in the Nairobi County city center (48%), while the high cost of power was more problematic forMakueni the low-income County, lack respondents of connection (52%) to in the Nairobi national County. grid Forwas thethe respondents main motivator in Makueni (30%). Interestingly, County, lack ofenvironmental connection to concerns the national were grid not was considered the main motivatoras a motivation (30%). factor Interestingly, by the respondents environmental of concernsMakueni wereCounty, not consideredUasin Gishu as aCounty, motivation and factor Nairobi by the County’s respondents low-income of Makueni group County, even Uasinthough Gishu 98% County, of the andrespondents Nairobi County’sindicated low-incomethat they strongly group cared even or though cared 98%about of environmental the respondents conservation. indicatedthat Previous they stronglystudies [44–46] cared or similarly cared about found environmental that pro-environmental conservation. concerns Previous did studiesnot always [44– translate46] similarly into foundactual thatactions. pro-environmental concerns did not always translate into actual actions.

Low cost of equipment

Environmental concerns

Lack of connection to the national grid

High power cost

Frequent power outages

0% 10% 20% 30% 40% 50% 60% High income City Center Low income Uasin Gishu Makueni

Figure 5. Motivation to invest in renewable energy sources. Figure 5. Motivation to invest in renewable energy sources. The regression analysis of respondents’ socio-demographic characteristics on motivations indicates The regression analysis of respondents’3 socio-demographic characteristics on motivations that income levels (p-value = 1.8 10− ) significantly affected their motivation. Low-income respondents × −3 wereindicates motivated that income by frequent levels (p power-value = outages 1.8 × 10 and) significantly high energy affected cost. their On themotivation. other hand, Low-income for the middle-high-incomerespondents were motivated respondents by frequent frequent power power outa outages,ges and high high energy energy cost. cost, On and the environmentalother hand, for concernsthe middle-high-income were equally worrisome. respondents frequent power outages, high energy cost, and environmental concerns were equally worrisome. 3.6. Challenges Facing Adoption of Renewable Energy Technologies 3.6. Challenges Facing Adoption of Renewable Energy Technologies The sixth section assessed the challenges the respondents faced in case they decided to install renewableThe sixth energy section technologies. assessed About the challenges 46% said thatthe theresp highondents cost offaced equipment in case wasthey the decided main challenge to install (Figurerenewable6). This energy was technologies. followed by intermittentAbout 46% naturesaid th ofat renewable the high energycost of sourcesequipment (31%) was and the lack main of qualifiedchallenge personnel (Figure 6). to This install was (16%). followed Previous by interm studiesittent on Kenya’snature of renewable renewable energy energy sector sources similarly (31%) foundand lack that of high qualified equipment personnel cost to was install one of(16%). the major Previous challenges studies foron KenyanKenya’s consumersrenewable energy [6]. sector similarlyIn terms found of that regional high equipment variations, cost those was in one the of middle-high-income the major challenges 58% for Kenyan found this consumers important, [6]. whereasIn terms 54% ofofthose regional in the variations, city center those and in 52% the of middle-high-income those in the low-income 58% found groups this showed important, their concernswhereas over54% of the those cost. in On the the city contrary, center and the cost52% wasof those less importantin the low-income in Makueni groups (38%) showed and Uasin their Gishuconcerns (30%). over In the these cost. two On rural the counties, contrary, the the intermittent cost was less nature important of renewable in Makueni energy (38%) was challenging and Uasin whereasGishu (30%). less thanIn these 30% two of rural the respondents counties, the ininterm all groupsittent nature of Nairobi of renewa Countyble energy indicated was this challenging as their challenge.whereas less The than lack of30% skilled of the persons respondents who can in install all groups solar PV of wasNairobi pronounced County inindicated Uasin Gishu this Countyas their (36%)challenge. and MakueniThe lack Countyof skilled (18%). persons In Nairobi who can County, install this solar is a PV challenge was pronounced for a certain in number Uasin ofGishu the middle-high-incomeCounty (36%) and Makueni group (14%). County (18%). In Nairobi County, this is a challenge for a certain number of the middle-high-income group (14%).

EnvironmentsEnvironments2019 2019,,6 6,, 95 x FOR PEER REVIEW 1010 of 1313

Intermittent nature of renewable energy sources

No interest in renewable energy

Lack of personnel to install

High cost of equipment

0% 10% 20% 30% 40% 50% 60%

High income City Center Low income Uasin Gishu Makueni

Figure 6. Challenges facing adoption of renewable energy resources. Figure 6. Challenges facing adoption of renewable energy resources. 4. Conclusions 4. Conclusions This paper examined Kenya’s household residents’ perceptions about renewable energy supplies. We alsoThis investigated paper examined factors that Kenya’s motivated household and hindered residents’ the adoption perceptions of renewable about energyrenewable technologies energy insupplies. three di ffWeerent also regions investigated of Kenya: factors Nairobi that (urbanmotivate county),d and hindered Makueni, the and adoption Uasin Gishu of renewable (rural counties). energy Eventechnologies though aboutin three 98% different of therespondents regions of Kenya: showed Nairobi their concerns (urban county), about environmental Makueni, and conservation, Uasin Gishu this(rural reason counties). alone didEven not appearthough toabout motivate 98% the of respondents the respondents to invest showed in renewable their energy.concerns Instead, about theenvironmental respondents conservation, mainly wanted this to reason secure alone the steadydid not energy appear supply to motivate as they the had respondents often experienced to invest powerin renewable outages. energy. The high Instead, and the fluctuating respondents energy mainly cost fromwanted the to grid secure system the steady also motivated energy supply them toas investthey had in renewable often experienced energy. In power rural outages. areas, the The respondents high and werefluctuating largely energy motivated cost byfrom lack the of grid connection system toalso the motivated national grid. them These to invest rural in residents renewable expressed energy. theirIn rural needs areas, to receive the respondents government were support largely in adoptingmotivated renewable by lack of energy connection technologies. to the national grid. These rural residents expressed their needs to receiveWhat government hindered the support respondents in adopting most inrenewable adopting energy renewable technologies. energy technologies was the high cost of equipmentWhat hindered and intermittent the respondents nature of most renewable in adopting energy renewable resources. energy The latter technologies reason was was particularly the high prevalentcost of equipment for rural residents.and intermittent About 96% nature of the of ruralrenewable respondents energy and resources. 76% of theThe urban latter respondents reason was preferredparticularly to haveprevalent all their for rural power residents. supplies About from renewable96% of the energyrural respondents sources. In and addition, 76% of about the urban 84% (86%respondents rural and preferred 63% urban) to have expressed all their their power wish supp to selllies electricityfrom renewable they generate energy sources. to the national In addition, grid. Theabout main 84% renewable (86% rural energy and technology63% urban) that expressed the respondents their wish preferred to sell electricity to invest in they was generate solar PV (85%).to the nationalOverall, grid. thisThe papermain renewable showed that energy Kenyan technology urban andthat the rural respondents residential preferred consumers to invest were highlyin was interestedsolar PV (85%). in renewable energy. Kenya’s national policy to have its 100% electricity supply from renewablesOverall, can this be furtherpaper expeditedshowed that with Kenyan better understandingurban and rural about residential regional diconsumersfferences inwere household highly consumers’interested in needs. renewable As the energy. past studies Kenya’s showed national the importance policy to have of understanding its 100% electricity regional supply differences from inrenewables terms of consumers’ can be further perceptions expedited about with adopting better renewableunderstanding energy, about this studyregional can differences shed light onin thishousehold topic through consumers’ a case needs. study As in the Kenya. past studies This paper showed also the demonstrated importance thatof understanding some of our findingsregional correspondeddifferences in terms with past of consumers’ studies about perceptions Europe andabou Australia.t adopting Within renewable a policy energy, improvement this study can context, shed thelight findings on this oftopic this through paper can a case better study inform in Kenya. Kenya’s This development paper also partners demonstrated like the that US, some EU, China,of our andfindings Japan. corresponded These countries with provide past studies funding about for majorEurope energy and Australia. projects in Within Kenya. a policy improvement context, the findings of this paper can better inform Kenya’s development partners like the US, EU, AuthorChina, Contributions:and Japan. TheseConceptualization, countries provide Design funding of Questionnaire, for major energy Visualization, projects Methodology, in Kenya. Formal Data Analysis, Writing—Original Draft Preparation, E.K.; Questionnaire Review and Revision, Writing—Review, Revision and Editing, Supervision, Project Administration, K.M.; Data Collection, Writing—Review and Editing, N.M.,Acknowledgements: E.K. Served as the We corresponding deeply acknowledge author. Allthe therespondents authors read who and took approved out time the to provide final manuscript. the relevant data by responding to the questionnaire. We equally thank the anonymous reviewers whose comments enriched the Funding: This research received no external funding. paper. Acknowledgments: We deeply acknowledge the respondents who took out time to provide the relevant data by respondingAuthor Contributions: to the questionnaire. Conceptualization, We equally thank design the of anonymous questionnaire, reviewers visualization, whose comments methodology, enriched formal the paper. data Conflictsanalysis, writing—original of Interest: The authors draft prepar declareation, no conflict E. K.; questionnaire of interest. review and revision, writing—review, revision and editing, supervision, project administration, K. M.; data collection, writing – review and editing, N. M., E. K. served as the corresponding author. All the authors read and approved the final manuscript.

Funding: This research received no external funding.

Environments 2019, 6, 95 11 of 13

References

1. Azizalrahman, H.; Hasyimi, V. A model for urban sector drivers of carbon emissions. Sustain. Cities Soc. 2019, 44, 46–55. [CrossRef] 2. Nejat, P.; Jomehzadeh, F.; Taheri, M.M.; Gohari, M.; Muhd, M.Z. A global review of energy consumption,

CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries). Renew. Sustain. Energy Rev. 2015, 43, 843–862. [CrossRef] 3. Hinnells, M. Technologies to achieve demand reduction and microgeneration in buildings. Energy Policy 2008, 36, 4427–4433. [CrossRef] 4. Energy Regulatory Commission. Least Cost Power Development Plan 2017–2037; Energy Regulatory Commission: Nairobi, Kenya, 2018. 5. The Kenya Power and Lighting Company Limited (KPLC). The Kenya Power and Lighting Company Limited: Annual Report and Financial Statements 2016/2017; Kenya Power and Lighting Company Limited: Nairobi, Kenya, 2017; p. 63. 6. Boampong, R.; Phillips, M.A. Renewable Energy Incentives in Kenya: Feed-in-Tariffs and Rural Expansion; University of Florida: Gainesville, FL, USA, 2016. 7. Kowalska-Pyzalska, A. What makes consumers adopt to innovative energy services in the energy market? A review of incentives and barriers. Renew. Sustain. Energy Rev. 2018, 82, 3570–3581. [CrossRef] 8. Zheng, M.; Meinrenken, C.J.; Lackner, K.S. Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response. Appl. Energy 2014, 126, 297–306. [CrossRef] 9. Palmer, J.; Sorda, G.; Madlener, R. Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation. Technol. Forecast. Soc. Change 2015, 99, 106–131. [CrossRef] 10. Masini, A.; Menichetti, E. The impact of behavioural factors in the renewable energy investment decision making process: Conceptual framework and empirical findings. Energy Policy 2012, 40, 28–38. [CrossRef] 11. Negro, S.O.; Alkemade, F.; Hekkert, M.P. Why does renewable energy diffuse so slowly? A review of innovation system problems. Renew. Sustain. Energy Rev. 2012, 16, 3836–3846. [CrossRef] 12. Khambalkar, V.P.; Katkhede, S.S.; Dahatonde, S.; Korpe, N.D.; Nage, S.M. Renewable energy: An assessment of public awareness. Int. J. Ambient Energy 2010, 31, 133–142. [CrossRef] 13. Qu, M.; Ahponen, P.; Tahvanainen, L.; Gritten, D.; Mola-Yudego, B.; Pelkonen, P. Chinese university students’ knowledge and attitudes regarding forest bio-energy. Renew. Sustain. Energy Rev. 2011, 15, 3649–3657. [CrossRef] 14. Halder, P.; Prokop, P.; Chang, C.Y.; Usak, M.; Pietarinen, J.; Havu-Nuutinen, S.; Pelkonen, P.; Cakir, M. International Survey on Bioenergy Knowledge, Perceptions, and Attitudes Among Young Citizens. BioEnergy Res. 2011, 5, 247–261. [CrossRef] 15. Baharoon, D.A.; Rahman, H.A.; Fadhl, S.O. Publics’ knowledge, attitudes and behavioral toward the use of solar energy in Yemen power sector. Renew. Sustain. Energy Rev. 2016, 60, 498–515. [CrossRef] 16. Balta-Ozkan, N.; Watson, T.; Mocca, E. Spatially uneven development and low carbon transitions: Insights from urban and regional planning. Energy Policy 2015, 85, 500–510. [CrossRef] 17. Balta-Ozkan, N.; Le Gallo, J. Spatial variation in energy attitudes and perceptions: Evidence from Europe. Renew. Sustain. Energy Rev. 2018, 81, 2160–2180. [CrossRef] 18. Hori, S.; Kondo, K.; Nogata, D.; Ben, H. The determinants of household energy-saving behavior: Survey and comparison in five major Asian cities. Energy Policy 2013, 52, 354–362. [CrossRef] 19. Tabakovic, M.; Fechner, H.; Van Sark, W.; Louwen, A.; Georghiou, G.; Makrides, G.; Loucaidou, E.; Ioannidou, M.; Weiss, I.; Arancon, S.; et al. Status and outlook for building integrated photovoltaics (BIPV) in relation to educational needs in the BIPV sector. Energy Procedia 2017, 111, 993–999. [CrossRef] 20. Bawakyillenuo, S. Deconstructing the dichotomies of solar photovoltaic (PV) dissemination trajectories in Ghana, Kenya and Zimbabwe from the 1960s to 2007. Energy Policy 2012, 49, 410–421. [CrossRef] 21. Kenya National Bureau of Statistics (KDHS). The 2009 Kenya Population and Housing Census. Counting Our People for the Implementation of Vision 2030. VOLUME IC Population Distribution by Age, Sex and Administrative Units; Kenya National Bureau of Statistics (KDHS): Nairobi, Kenya, 2010; Volume IC. 22. Kenya Power. Monthly Report to the MoE on the Power Supply Situation and Progress in Implementation of Priority Projects Coordinated by KPLC; Kenya Power: Nairobi, Kenya, 2018. Environments 2019, 6, 95 12 of 13

23. Kenya Power and Lighting Company Limited (KPLC). Kenya Electricity Expansion Project Slum Electrification Component Social Management Framework; The Kenya Power and Lighting Company Limited (KPLC): Nairobi, Kenya, 2016. 24. Kenya National Bureau of Statistics (KDHS). Nairobi County Statistical Abstract; Kenya National Bureau of Statistics (KDHS): Nairobi, Kenya, 2015. 25. Theuri, D. Kenya Country Report: Solar and Wind Energy Resource Assessment. Kenya Ctry. Rep. 2008, 1–61. 26. Ministry of Energy and Petroleum. Wind Sector Prospectus—Kenya; Ministry of Energy and Petroleum: Nairobi, Kenya, 2013. 27. Ministry of Energy and Petroleum. Hydropower Resources Atlas of Kenya with Emphasis on Small Hydropower Resources Department of Renewable Energy; Ministry of Energy and Petroleum: Nairobi, Kenya, 2015. 28. Heap, B. Smart Villages: New Thinking for Off-Grid Communities Worldwide; Essay Compilation: Banson, UK, 2015. 29. Brown, T.W.; Bischof-Niemz, T.; Blok, K.; Breyer, C.; Lund, H.; Mathiesen, B.V. Response to Burden of proof: A comprehensive review of the feasibility of 100% renewable-electricity systems. Renew. Sustain. Energy Rev. 2018, 92, 834–847. [CrossRef] 30. Heuberger, C.F.; MacDowell, N. Real-World Challenges with a Rapid Transition to 100% Renewable Power Systems. Joule 2018, 2, 367–370. [CrossRef] 31. Kiplagat, J.K.; Wang, R.Z.; Li, T.X. Renewable energy in Kenya: Resource potential and status of exploitation. Renew. Sustain. Energy Rev. 2011, 15, 2960–2973. [CrossRef] 32. Chapman, A.; Itaoka, K. Curiosity,economic and environmental reasoning: Public perceptions of liberalization and renewable energy transition in Japan. Energy Res. Soc. Sci. 2018, 37, 102–110. [CrossRef] 33. Abdmouleh, Z.; Gastli, A.; Ben-Brahim, L. Survey about public perception regarding smart grid, energy efficiency & renewable energies applications in Qatar. Renew. Sustain. Energy Rev. 2018, 82, 168–175. 34. Zografakis, N.; Sifaki, E.; Pagalou, M.; Nikitaki, G.; Psarakis, V.; Tsagarakis, K.P. Assessment of public acceptance and willingness to pay for renewable energy sources in Crete. Renew. Sustain. Energy Rev. 2010, 14, 1088–1095. [CrossRef] 35. Musall, F.D.; Kuik, O. Local acceptance of renewable energy-A case study from southeast Germany. Energy Policy 2011, 39, 3252–3260. [CrossRef] 36. Sommerfeld, J.; Buys, L.; Vine, D. Residential consumers’ experiences in the adoption and use of solar PV. Energy Policy 2017, 105, 10–16. [CrossRef] 37. Parkins, J.R.; Rollins, C.; Anders, S.; Comeau, L. Predicting intention to adopt solar technology in Canada: The role of knowledge, public engagement, and visibility. Energy Policy 2018, 114, 114–122. [CrossRef] 38. Herington, M.J.; van de Fliert, E.; Smart, S.; Greig, C.; Lant, P.A. Rural energy planning remains out-of-step with contemporary paradigms of energy access and development. Renew. Sustain. Energy Rev. 2017, 67, 1412–1419. [CrossRef] 39. Ozaki, R. Adopting sustainable innovation: What makes consumers sign up to green electricity? Bus. Strateg. Environ. 2011, 20, 1–17. [CrossRef] 40. Pickett-Baker, J.; Ozaki, R. Pro-environmental products: Marketing influence on consumer purchase decision. J. Consum. Mark. 2008, 5, 281–293. [CrossRef] 41. Government of the Republic of Kenya. 5000+ MW by 2016: Power to Transform Kenya; Government of the Republic of Kenya: Nairobi, Kenya, 2013. 42. Republic of Kenya. CBPR Database—Kenya; Republic of Kenya: Nairobi, 2016; pp. 1–8. 43. The Kenya Power and Lighting Company Limited (KPLC). The Kenya Power and Lighting Company Limited Five Year Corporate Strategic Plan; The Kenya Power and Lighting Company Limited (KPLC): Nairobi, Kenya, 2016; Volume 1, p. 80. 44. Gadenne, D.; Sharma, B.; Kerr, D.; Smith, T. The influence of consumers’ environmental beliefs and attitudes on energy saving behaviours. Energy Policy 2011, 39, 7684–7694. [CrossRef] 45. Dimeas, B.A.; Drenkard, S.; Hatziargyriou, N.; Karnouskos, S.; Kok, K.; Ringelstein, J. Smart Houses in the Smart Grid. IEEE Electrif. Mag. 2014, 2, 81–93. [CrossRef] 46. Morstyn, T.; Mcculloch, M.D.; Member, S. Multi-Class Energy Management for Peer-to-Peer Energy Trading Driven by Prosumer Preferences. IEEE Trans. Power Syst. 2018.[CrossRef] Environments 2019, 6, 95 13 of 13

47. Mondal, M.A.H.; Kamp, L.M.; Pachova, N.I. Drivers, barriers, and strategies for implementation of renewable energy technologies in rural areas in Bangladesh—An innovation system analysis. Energy Policy 2010, 38, 4626–4634. [CrossRef] 48. Yaqoot, M.; Diwan, P.; Kandpal, T.C. Review of barriers to the dissemination of decentralized renewable energy systems. Renew. Sustain. Energy Rev. 2016, 58, 477–490. [CrossRef]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).