Determinants of household adoption of solar energy technology in rural Ethiopia

Guta, Dawit Diriba

doi:10.1016/j.jclepro.2018.09.016

Cited by 133 publications

(115 citation statements)

References 32 publications

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“…He also revealed that female-headed households are less likely to adopt solar home system compare to male-headed households and it is statistically significant. Guta (2018) investigated the determinants of household adoption of solar home system. The finding showed that income of the household, landholding size, number of cattle, age of household head, family size, and education level of the head has a positive effect on solar home system adoption.…”

Section: Empirical Literature Reviewmentioning

confidence: 99%

“…The reason behind using this model in the study than the probit model is that the equations of the logit model are and it is directly interpretable as log-odds (Pindyck and Rubinfeld, 1981). This model is also used in previous studies by several researchers for the same issue, for instance, Guta (2018) and Yusuf and Adeyemi (2019). According to Park (2013), there are two models of logistic regression, binary logistic regression, and logistic regression models.…”

Section: Empirical Model Of Solar Home System Adoptionmentioning

confidence: 99%

“…Many studies have assessed the determinants of solar home system adoption at the household level in different countries. For instance, some of the studies (Keriri, 2013;Gitone, 2014;and Regina, 2016 [in Kenya]; Tahir, 2017 [pakistan]; Guta, 2018;Anteneh, 2019;and Legesse, 2016[Ethiopia]) studied the socio-economic and demographic factors affecting household adoption of the solar home system. These studies have applied a predominantly binary model.…”

Section: Introductionmentioning

confidence: 99%

“…Anteneh (2019) and Legesse (1016) revealed the effects of the level of education of the household, land size, number of cattle, level of income, and age of the head of household on solar home system adoption. Whereas Guta (2018) investigated the effects of much more variable in a broad sense such as, trees, and saving including the above-mentioned variables using logistic regression. Besides, other determinants such as institutional factors (Guta, 2018;Anteneh, 2019), level of knowledge, and awareness (Anteneh, 2019), an alternative source of energy (Legesse, 2016) have been investigated.…”

Section: Introductionmentioning

confidence: 99%

“…Whereas Guta (2018) investigated the effects of much more variable in a broad sense such as, trees, and saving including the above-mentioned variables using logistic regression. Besides, other determinants such as institutional factors (Guta, 2018;Anteneh, 2019), level of knowledge, and awareness (Anteneh, 2019), an alternative source of energy (Legesse, 2016) have been investigated. However, there is a research gap in these empirical studies in addressing institutional factors such as awareness creation training given to the households either formally or informally.…”

Section: Introductionmentioning

confidence: 99%

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Factors Influencing Household Adoption of Solar Home System in Baso Liben District, Amhara Regional State of Ethiopia

Zeru¹,

Guta²

2020

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Background: Although, solar energy is abundant, accessible, affordable, and ecologically and environmentally friendly, in rural Ethiopia the majority of households are still using pollutant kerosene for lightning. It is important to understand demand and supply-side factors affecting the adoption of the technology. For this purpose, this study investigates the factors influencing household adoption of solar home system (SHS). Methodology: The data used for the econometric model was collected from randomly selected 228 adopters and 143 non-adopter households in Baso Liben district, Amhara region of Ethiopia. The logistic regression model was applied to examine the factors affecting household adoption of solar home system. Results: The finding of this study shows significant variation in many socioeconomic and demographic characteristics between adopters and non-adopters of solar home system. The result of the binary logistic regression model indicated that as income of household increase, their propensity to adopt solar home system also increases. Likewise, participation in off-farm income activities, house type, educational status, training access, media access, and prior knowledge positively correlated with the adoption of SHS. On the other hand, gender and access to electricity are negatively associated with the adoption of SHS. Conclusion: Policy measures should create awareness through training, education, and information access or better media availability, and improving the economic status of households through creating lucrative off-farm income-earning opportunities to achieve enhanced adoption of the solar home system.

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Section: Empirical Literature Reviewmentioning

confidence: 99%

Section: Empirical Model Of Solar Home System Adoptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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