FACTORS AFFECTING ADOPTION OF SOLAR POWER ENERGY PROJECTS AMONG HOUSEHOLDS IN BARINGO COUNTY, KENYA
FACTORS AFFECTING ADOPTION OF SOLAR POWER ENERGY PROJECTS AMONG HOUSEHOLDS IN BARINGO COUNTY, KENYA
Snowy Jepkoech - Master Student, Mount Kenya University, Kenya
Dr. Ruth Winnie Munene - Lecturer, Faculty of Business and Management Sciences, Mount Kenya University, Kenya
ABSTRACT
Kenya being on the equator experiences enough solar energy of between 4-6 KWh/M2 which provides excellent opportunity for solar energy development. Kenya envisions transforming itself into a newly-industrializing, middle-income country by 2030, with a globally competitive and prosperous economy and high quality of life in a clean and secure environment. To achieve this vision, energy is identified as one of the foundations and enablers of the socio-economic transformation envisaged in the country. Nonetheless, only 49% of Kenyans have access to Grid Electricity meaning Solar energy provides Kenyan government with the opportunity to address energy challenges without the need for expensive power generation projects, transmission and distribution networks despite the huge potential the country possesses. There has been a lot of criticism, from various quarters, on the way the county government of Baringo, Kenya solar projects are managed. The purpose of the study was to establish the factors affecting adaptation of solar energy projects among households in Baringo County, Kenya. The study was guided by the following objectives: To establish the influence of alternative sources of energy and level of household income on adaptation of solar energy projects in Baringo County, Kenya. The study was based on resource dependence theory and public participation theory. The study adopted descriptive research design. The population for this study were 364 respondents comprising of solar project managers, community leaders, and community representatives. A sample size of 225 was selected from the target population using stratified random sampling technique. After data collection, it was analyzed. Descriptive statistics was computed and presented in form of frequencies, percentages, mean and standard deviation. Inferential data analysis was e presented using multiple correlation and regression analysis to show the relationship between the variables. Based on the findings the study concluded that there was a moderate positive and statistically significant correlation between alternative sources of energy and adoption of solar power energy. (r = 0.463; p < 0.05). There was a moderate positive and statistically significant correlation between income of households and adoption of solar power energy (r = 0.476; p < 0.05). Based on the findings of the study, the researcher recommended alternative sources of energy that are ecofriendly should be encouraged in this case solar power energy in Baringo county, Kenya.
