Socioeconomic Determinants of Household Solar Energy Adoption in Trans-Nzoia County, Kenya
Karani Oliver Ray *
Moi University, Kenya.
Kongo Yabesh
Moi University, Kenya.
Matundura Erickson
Moi University, Kenya.
Nganai Simon
Moi University, Kenya.
*Author to whom correspondence should be addressed.
Abstract
The solar energy systems adoption constitutes an important integration to Kenya’s transition towards sustainable economic development and the realization of Vision 2030. However, rural adoption remains low, largely due to a knowledge gap regarding the socioeconomic factors that influence uptake. This study investigated the effects of household income, access to credit facilities, awareness of the benefits of solar energy, and education level on the adoption of solar energy in Trans-Nzoia County. The study was anchored on four theoretical frameworks: the Diffusion of Innovations Theory, the Theory of Planned Behavior, the Economic Theory of Consumer Choice, and the Social Cognitive Theory. A cross-sectional research design was adopted, targeting a stratified random sample of 385 households in the county. Primary data were collected through structured questionnaires and analyzed using descriptive statistics and binary logistic regression to estimate the probability of solar energy adoption based on the identified socioeconomic factors. The regression analysis results revealed that education level (β = 0.035, p < 0.05), access to credit facilities (β = 0.272, p < 0.05), and awareness of solar energy benefits (β = 0.213, p < 0.05) were statistically significant predictors of the likelihood of adoption of solar energy. In contrast, household income (β = 0.024, p > 0.05) was not a statistically significant predictor of adoption. These findings suggest that the decision to use solar energy is influenced by factors other than income, such as educational attainment, financial inclusion, and access to information. Households with higher levels of education, better access to credit, and increased awareness were more likely to adopt solar technology, indicating that non-income factors, particularly financial accessibility and information dissemination, play an important role in shaping energy decisions in rural areas. The robustness of the model was affirmed through diagnostic tests, including the Hosmer-Lemeshow goodness-of-fit test. County governments and energy stakeholders implement public awareness campaigns on solar energy in selected wards on an annual basis, with measurable indicators such as the number of households reached and changes in solar adoption rates. Financial institutions should introduce tailored solar financing such as pay-as-you-go credit for low- and middle-income households before 2030, with success measured by loan uptake and repayment rates. Also, the Ministry of Education, should integrate basic renewable energy modules into primary and secondary school curricula before 2030, with monitoring based on curriculum rollout and student participation. Future research, should conduct multi-county comparative studies involving at least three counties within the next 3-5 years to examine regional variations in solar adoption. They also should systematically assess cultural attitudes, grid and off-grid infrastructure readiness, and county-level policy and regulatory frameworks, using standardized indicators to enable comparability across diverse Kenyan contexts.
Keywords: Solar energy adoption, rural households, socioeconomic factors, financial inclusion, Kenya