![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
Identification of best Fit Models for Fuelwood Consumption in Foothills of Western Himalayas
Subscribe/Renew Journal
A survey of household fuelwood consumption was carried out in Samba district of Jammu and Kashmir, India in the year 2011. Different models for fuelwood consumption (social, economic, alternate, biophysical and composite) were prepared to identify best fit models. The households surveyed covered heterogeneous population belonging to different income, educational and social groups. There was more availability and utilization of fuelwood as energy resources in domestic sector as compared to the commercial fuels. Dung cakes, crop residues and fuelwood were found to be the three main fuels used for cooking, though LPG was also used along with biomass fuels. But complete conversion to cleaner fuels has not taken place yet even in households that had been using LPG for many years. Income was an important factor determining the choice of fuel for cooking, but there were some socio-cultural factors which were equally important in making fuel preferences at household level. Based on logistic regression analysis, out of five models (alternate, social, economic, biophysical and composite) only three (social, economic and composite) were valid in predicting fuelwood consumption. Economic and composite models were equally accurate in predicting fuelwood consumption. The composite model implies that the economic factors dominated fuelwood consumption.
Keywords
Fuelwood, Consumption, Households, Models, Regression.
Font Size
User
About The Authors
Subscription
Login to verify subscription
Information
Submissions
- Bose R.K. (1985). Biomass energy resource assessment through rural energy surveys — a common approach. In: Commonwealth Science Council Technology Publication Series No. 188, C.Y. Wereko Brobby, London, pp. 85–123.
- Digest of Statistics (2010). Directorate of Economic and Statistics. Government of Jammu and Kashmir.
- Greenhouse J.B., Bromberg J.A. and Fromm D. (1995). An introduction to logisticregression with an application to analysis of language recovery following a stroke, J. Communication Disorders, 28: 229-246.
- Joshi V., Bose R.K., Puri C. and Bhattacharya T.K. (1987).Renewable energy potential and its development in a tribal village of Uttar-Pradesh: village Bishanpur in Bahraich district, Tata Energy Research Institute (TERI), New Delhi.
- Menard S. (2002). Applied Logistic Regression Analysis. Thousand Oaks, CA:Sage.
- NCAE (1985). National Council of Applied Economic Research. Domestic fuel survey with special reference to kerosenevols. I and II, Universal Advertising Press, New Delhi.
- Puri C. (1988). Analysis of energy consumption and production pattern. In: Dhanwar: A survey report. Discussion Paper No. 02/88, Tata Energy Research Institute (TERI), New Delhi.
- Ravindranath N.H. and Hall D.O. (1995). In: Biomass energy and environment. A developing country perspective from India, Oxford University Press, Oxford, pp. 368.
- Tabachnick B.G. and Fidel L.S. (1996). Using multivariate Statistics. pp.880. Happercollins College Publishers, New York.
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 249
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 0