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

2021 ◽  
Author(s):  
Asmare Mossie Zeru ◽  
Dawit Diriba Guta
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Information Access ◽  
Farm Income ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Regional State ◽  
Solar Home System

Abstract 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 lighting. It is important to understand the demand and supply-side factors affecting adoption of technology. For this purpose, this study investigates the factors influencing household adoption of the solar home system (SHS).Methodology: The data used for the econometric model was collected from randomly selected 228 solar home system adopter and 143 non- adopter households in Baso Liben district, Amhara regional state of Ethiopia. The logistic regression model was applied to examine the factors affecting households’ willingness to adopt SHS. Results: The finding of this study shows significant variation in many of the socioeconomic and demographic characteristics between adopters and non-adopters. The result of the binary logistic regression model indicated that as income of a household increase, their propensity to adopt solar home system also increases. Likewise, participation in off-farm income activities, house type, educational status of the head, training access, media access, and prior knowledge of the technology positively correlated with the probability of adoption. On the other hand, the gender of the head (being male) and access to electricity were negatively associated with the adoption of SHS. Conclusion: Therefore, 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.

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

2020 ◽  
Author(s):  
Asmare Mossie Zeru ◽  
Dawit Diriba Guta
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Information Access ◽  
Educational Status ◽  
Farm Income ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Factors Influencing ◽  
Solar Home System

Abstract 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.

scholarly journals Modeling and Analyzing the Factors Affecting the Mortality of Pedestrians in Urban Accidents

2018 ◽  
Vol 14 (4) ◽  
pp. 10-21
Author(s):  
Ahmet Tortum ◽  
Alireza Motamadnia
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Motor Vehicle ◽  
Binary Logistic Regression ◽  
Education Level ◽  
Road Surface ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Surface Conditions

Abstract The nature of urban and rural accidents has been different from each other in some of the factors and even the severity of damage rate, mayhem, and death. In this research, using statistical methods and binary logistic regression model, we have addressed to analyze important parameters such as age, gender, education level, the color of the pedestrian dress, season of accident, time of accident, the speed of the vehicle colliding with pedestrians and road surface conditions at the time of accident on the way of death (at the scene of the incident or in the hospital) pedestrians who have been traumatized. After the creation of the binary logistic regression model, it was determined that only the parameters of speed and the accident time have been significant in the level less than 5%. And other parameters such as age, gender, the season of accident occurrence, the color of the pedestrian dress, road surface conditions and education level had no significant effect in terms of statistical on the incidence of mortality arising from a pedestrian accident with the motor vehicle. The results revealed that by adopting decisions related to the traffic calming, attention to passages lighting and brightness the mortality rate of a pedestrian due to the urban accidents can be reduced.

scholarly journals An Assessment of Factors Influencing Forest Harvesting in Smallholder Tobacco Production in Hurungwe District, Zimbabwe: An Application of Binary Logistic Regression Model

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Chipo Chivuraise ◽  
Tafireyi Chamboko ◽  
Godfrey Chagwiza
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Forest Harvesting ◽  
Natural Forest ◽  
Selling Price ◽  
Binary Logistic Regression Model ◽  
Factors Influencing ◽  
Tobacco Production

Deforestation is one of the major effects posed by the smallholder tobacco farming as the farmers heavily depend on firewood sourced from natural forest for curing tobacco. The research aims at assessing the factors that influence the harvesting of natural forest in the production of tobacco. Data is collected through the structured questionnaire from 60 randomly selected farmers. Binary logistic regression model is used to explain the significance of factors influencing natural forest harvesting. Results show that farmer experience, tobacco selling price, and agricultural training level negatively affect the harvesting of natural forests (to obtain firewood) for curing tobacco significantly (p<0.05). However, gender, size of the household, tobacco yield, and level of education are insignificant (p>0.05) in influencing natural forest harvesting. Though farmers are exploiting the environment and at the same time increasing foreign currency earning through tobacco production, there is therefore a need to put in place policies that encourage sustainable forest product utilization such as gum plantations, subsidizing price of coal, and introducing fees, as well as penalties or taxes to the offenders.

scholarly journals Prevalence and factors affecting the utilization of antenatal care in rural areas of Southwestern Ethiopia

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Assaye Belay ◽  
Tessema Astatkie ◽  
Solomon Abebaw ◽  
Bekele Gebreamanule ◽  
Wegayehu Enbeyle
Keyword(s):  
Logistic Regression ◽  
Antenatal Care ◽  
Regression Model ◽  
Rural Area ◽  
Rural Areas ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Factors Affecting ◽  
Southwestern Ethiopia

Abstract Background Antenatal care (ANC) is a health care intervention intended to ensure the safety of pregnancy. According to the World Health Organization, at least four ANC visits are recommended for a healthy pregnancy. However, whether this recommended number of visits was followed or not in the rural areas of Southwestern Ethiopia is not known. Therefore, the study aimed to investigate the prevalence of, and the associated factors of ANC utilization by pregnant women in the rural areas of Southwestern Ethiopia. Methods A community-based cross-sectional study design was used in three rural zones. The data were collected from n = 978 women through a structured questionnaire with face-to-face interview. The collected data were analyzed using descriptive statistics and a multiple binary logistic regression model. Results The results showed that 56% of women made the recommended minimum number of ANC visits and the remaining 44% of them underutilized the ANC service. The multiple binary logistic regression model identified zone, marital status of the woman, educational level of the husband, occupation of the husband, knowledge of danger signs of pregnancy, birth interval, source of information, timely visits, and transportation problem to be statistically significant factors affecting the prevalence of ANC visit utilization of women. Bench Maji zone had smaller odds ratio of ANC visit prevalence as compared to Kaffa zone. Women who lived in the rural area of Sheko zone are 2.67 times less likely to utilize ANC visit than those who lived in the rural area of Kaffa zone keeping other variables constant. Conclusion The study results highlight the need to increase the number of ANC visits, and the importance of using an appropriate model to determine the important socio-demographic factors that ANC service providers shall focus on to improve the health of the unborn baby and the mother during pregnancy.

scholarly journals Study of Factors Affecting the Entrepreneurship Behavior of Returned Migrants Using Binary Logistic Regression Model

2019 ◽  
Vol 3 ◽  
pp. 57-68
Author(s):  
Madhav Kumar Bhusal ◽  
Hari Prasad Pandey
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Home Country ◽  
Household Head ◽  
Binary Logistic Regression ◽  
Educational Status ◽  
Categorical Variables ◽  
Binary Logistic Regression Model ◽  
Factors Influencing

Background: Entrepreneurship or business ownership is a significant source of employment and economic growth. Many studies conducted by different researchers have shown that increase in entrepreneurial activities helps to reduce unemployment. Thousands of Nepalese youths exodus for foreign migration every year for employments due to lack of adequate working environment in Nepal. In this context, identification of significant factors influencing the entrepreneurship behavior of returned migrants could be useful for planner, decision makers, and other concerned authorities. Objective: To explore the entrepreneurship status of returned migrants and to ascertain the factors influencing the entrepreneurship behavior of returned migrants. Materials and Methods: This study was based on primary data of 393 returned migrants collected through convenience sampling in Sarawal Rural Municipality of Parasi district, Nepal. People who stayed abroad at least one year and returned during 2010 to 2017 were included in the study. On the basis of Industrial Enterprise Act, 2016a, Nepal, a person who has invested Nepalese rupees five lakh or more in business besides housing and land is considered as an entrepreneur. The response variable is entrepreneurship status and it is defined according to the aforementioned act. Both quantitative and categorical variables were used as predictor variables. Factors associated with entrepreneurship behavior were extracted using Chi-square test and binary logistic regression model. Results: Out of sample of 393 returned migrants, 137 (34.9%) were entrepreneur and rest 256 (65.1%) were non-entrepreneur. Results showed that for main occupation of household head odds ratio (OR) = 4.008 & confidence interval (CI) = 2.396 to 6.703. Similarly, for educational status of returned migrants OR = 2.650 & CI = 1.599 to 4.392. For the covariate skills learnt at abroad OR = 2.750 & CI = 1.654 to 4.573. Conclusion: The study revealed that majority of returned migrants were non-entrepreneur. The factors ‘main occupation of household head’, ‘educational status of returned migrant’, ‘remittance received at home per year’ and ‘skills learnt abroad’ are the major determinants behind the entrepreneurship behavior of returned migrants. It is suggested that higher education and adequate skills should be taken before departing from home country so that the migrants can earn more money which will help to start their own businesses once they get back to their home country.

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