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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kongo, Yabesh Ombwori | - |
| dc.contributor.author | Kitum, Lydia Jeptoo | - |
| dc.contributor.author | Ndambiri, Hillary | - |
| dc.date.accessioned | 2026-07-27T07:26:56Z | - |
| dc.date.available | 2026-07-27T07:26:56Z | - |
| dc.date.issued | 2026-07 | - |
| dc.identifier.uri | file:///home/systems/Downloads/19.pdf | - |
| dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/10398 | - |
| dc.description.abstract | Renewable energy is important for enhancing energy accessibility, supporting broader development goals, and ensuring environmental sustainability. This study investigates the influence of four types of renewable energy, namely solar, wind, hydroelectric, and geothermal energy, on energy accessibility in Kenya, guided by the Energy Justice Theory and Energy Stacking Theory. An explanatory research design was adopted using annual secondary data from 1990 to 2023 sourced from the World Bank, IEA, KNBS, and EPRA. Hypotheses were tested through an Autoregressive Distributed Lag (ARDL) model with lag order selected using information criteria. The estimated ARDL (4,4,4,1,4) model reveals that renewable energy sources significantly influence energy accessibility in both the short and long run. Long-run results show that solar energy exerts a positive and statistically significant effect on energy accessibility (β = 73.8975, p = 0.002), hydroelectric energy shows a positive and significant effect (β = 0.8807, p < 0.001), and geothermal energy similarly contributes positively (β = 0.1360, p < 0.001). In contrast, wind energy exhibits a negative and statistically significant long-run relationship (β = -0.2623, p = 0.023), attributed to grid integration challenges and intermittency. The error correction term is negative and significant (β = -5.472, p < 0.001), confirming rapid adjustment to long-run equilibrium. Diagnostic tests confirm that the model is well specified, showing normality, no multicollinearity, homoscedasticity, and no serial correlation. The model explains 97.57% of variation in energy accessibility (R² = 0.9757). The study recommends strengthening pay-as-you-go solar models, upgrading transmission infrastructure in wind-rich regions such as Turkana, modernising hydroelectric infrastructure with climate-smart planning, and accelerating geothermal exploration through concessional financing and private partnerships | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Economics, Finance and Management Studies | en_US |
| dc.subject | Solar energy | en_US |
| dc.subject | ARDL Model | en_US |
| dc.subject | Energy accessibility | en_US |
| dc.subject | Renewable energy | en_US |
| dc.title | Renewable energy consumption and energy accessibility in Kenya | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | School of Business and Economics | |
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