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Structural Equation Modeling in Forecasting Satisfaction of Biogas to a Rural Household: Examples from Nepal
In this paper benefit of use of biogas is quantified with structural equation modelling. A survey of 400 households of biogas consumers produced primary data. This primary data is used here. Questionnaire developed for this survey was structured and generated mostly ordinal data. This questionnaire...
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Published in: | BioPhysical Economics and Resource Quality 2020-12, Vol.5 (4), Article 16 |
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Main Author: | |
Format: | Article |
Language: | English |
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Citations: | Items that this one cites |
Online Access: | Get full text |
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Summary: | In this paper benefit of use of biogas is quantified with structural equation modelling. A survey of 400 households of biogas consumers produced primary data. This primary data is used here. Questionnaire developed for this survey was structured and generated mostly ordinal data. This questionnaire was also pretested and refined. Among several variables studied, distance covered for firewood before and after, source of firewood before and after, income status, educational background, frequency of visit to the hospital and family size were used. Response of consumers on direct benefits like reduced pollution, reduced fuel expenses, reduced physical hazard and reduced physical labour were also considered. Among several structural models explored, two most suitable Time Saved models are discussed here in detail; out of which one model is finalised. This is done using model specification, identification, estimation, testing, identification and validation procedures. Benefit of biogas to the two genders is quantified with odds ratio. It is seen quantitatively that the benefit of biogas to a woman far outweighs its benefit to a man. The direct benefit of biogas to a household in cooking and lighting is obvious, but various hidden benefits and interrelationships cannot be measured. Strong backbone of good quality official records are also missing for data validation. This paper measures and analyses these indirect and latent relationships. It also gives a holistic understanding of the interrelationships. |
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ISSN: | 2730-7190 2366-0112 2730-7204 2366-0120 |
DOI: | 10.1007/s41247-020-00082-3 |