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Labour Allocated to Fuel Wood Collection
Unlike other studies related to rural energy, we were able to classify study areas based on forest cover using GIS information, allowing us to consider the possibility that forest cover affects the quantity of labour allocated to fuel wood collection. The household labour allocation towards fuel wood collection was estimated separately for degraded forest areas (LFC), and less degraded forest areas (HFC). A Chow test for pooling across this measure of forest cover was also applied, and the results rejected the hypothesis that the estimates could be pooled, at a one per cent confidence level (F(16, 545) = 2.04, p-value = 0.0001).
Table 2.3 presents the regression results of fuel wood collection labour inputs for the LFC, HFC and pooled samples, where the labour input is measured as the natural log of total household time, in hours, allocated to fuel wood collection. In line with many similar studies, the shadow price (collection time per kg of fuel wood collected) in the pooled regression is positive and statistically significant at the 5% level.22 For households in close proximity to degraded forests, the shadow price is positive and significant at the 10% level; however, for households living near higher quality forests, the shadow price is not a significant determinant of total collection time. Therefore, as forest resources become increasingly scarce in an already degraded area, rural households respond by increasing total fuel wood collection time. Any attempt to generalize the responsiveness of demand or production of fuel wood to increasing forest scarcity, without taking into account the forest status of the study area, therefore, would be misleading.
The impact of community level variables related to forest stock, forest access and local institutions are also included in order to examine their influence on fuel wood collection. In the analysis, forest access, as measured by population density, is positively and significantly correlated with fuel wood collection time in LFC areas, but the correlation is insignificant for HFC areas. This result is similar to Heltberg et al. (2000), in that households respond by increasing their collection time in areas where population density is relatively high. Similar to both Heltberg et al. (2000) and Palmer and MacGregor (2009), we find that forest stock, measured by the number of people per hectare of forest, is negatively correlated with the time spent collecting fuel wood in the pooled regression. We find a similar result in the HFC regression, but there is no significant influence on LFC households. In terms of the community level knowledge dummy variable, we find that households that are aware of forestry rules and regulations undertake significantly more hours to collect fuel wood in the pooled regression, although it is not significant in either the HFC or LFC regions.
Household characteristics, such as age, sex and the education level (except for the HFC) of the household head have no impact on fuel wood collection labour inputs, irrespective of the status of forest cover. In contrast to Heltberg et al. (2000), the number of female household members aged 10 years old and older was found to be an insignificant determinant of fuel wood collection time. The number of children is also insignificant in both the HFC and LFC regions, although it is positive and significant for the pooled regression. In contrast, the number of male household members negatively impacts collection time in LFC regions, although the relationship is insignificant within HFC regions and for the pooled sample.
Other Biomass Production and Consumption Activities
As previously uncovered, the fuel wood labour input elasticity is affected by the quality of the forest cover accessible by these households, and the results suggest that households in highly degraded forests must either increase their labour input further or cut their fuel wood consumption or turn to other sources of energy. In Table 2.4, we report the total production function of dung and residues, because the Chow test fails to reject the null hypothesis that the coefficients are the same in both equations (LFC vs HFC areas) for dung, though it is different for crop residues. Note also that only 27 households participated in the fuel wood market. Of these, 12 households collect fuel wood from private or common sources, while the rest depend on purchased fuel wood only. Because of the small numbers of market participants, we do not distinguish between collecting and purchasing households, as was the case in Palmer and MacGregor’s (2009) Namibian study.
In order to examine the effect of fuel wood scarcity on the consumption of other biomass energy sources (dung and crop residues), selection regressions of dung collection and crop residue collection activities was also undertaken.The sign and significance of the fuel wood shadow price in the dung and residue functions suggest the nature of the relationship (substitutability or complementarity) between these two types of biomass energy sources and fuel wood. Here the results are not statistically significant.
CHAPTER 1: Introduction
CHAPTER 2: Rural Households Coping Mechanisms to Fuel Wood scarcity in Ethiopia
CHAPTER 3: Property Rights, institutions and choice of fuel wood sources in rural Ethiopia
CHAPTER 4: Non-Timber Forest Products Dependence, Property Rights and Local Level Institutions: Empirical Evidence from Ethiopia
CHAPTER 5: Clean fuel saving technology adoption in urban Ethiopia
CHAPTER 6: Summary and Conclusion
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