Pa ge 1 Pa ge 19 American Journal of Environmental Economics (AJEE) Fuelwood Supply Consumption and Dynamic on Forest Resource in Kakuma Refugee Camp, Turkana County Kenya Kipkemboi kandie1*, Wilson k. Kipkore1, P. O. Odwor2 Volume 3 Issue 1, Year 2023 ISSN: 2833-7905 (Online) DOI: https://doi.org/10.54536/ajee.v3i1.2042 https://journals.e-palli.com/home/index.php/ajee Article Information ABSTRACT Received: March 05, 2024 Accepted: April 15, 2024 Published: April 19, 2024 Forestry is a source of livelihood for many farmers and rural households in developing countries, especially in sub-Saharan Africa. However, the utilization of fuelwood in Africa contributes greatly to desert encroachment and consequently has implications with regard to climate change. Its, little to understand about the drivers and dynamics of fuelwood consumption in Kenya and other African countries. This study is to analyze determinants of refugee camp forest resource utilization efficiency Kakuma refugee camp Turkana county. It accomplishes two broad objectives:( 1) To analyse utilization of fuelwood from indigenous tree species supplied to the camp, the profitability of firewood supplied to the local market and a growing body of evidence on the influence of utilization of fuelwood in the camp in the refugee camp, as the host community get cash from the refugees and food ration in exchange with the resource (Fuelwood) in a household’s level. It embraced a mixed methods approach embedded with an explanatory research design for concurrent triangulation. The study interviewed a total of 296 respondents through HH questionnaires, the KII Tool and FGD Guide. Qualitative data was analyzed using thematic framework approach while quantitative data was analyzed using descriptive and inferential (correlations) statistics on SPSS. Results suggest that even with minimal investment in both productive and non-productive assets (M=2.40) and SD=0.99) from cash purchase of fuelwood from agencies, there is significant negative effect (r=0.139) and P=0.05) between asset accumulation and poverty reduction among targeted household. study reveals that the provision of alternative sources of energy has the potential to reduce overdependence on wood fuel by the refugees in Kakuma Kenya. Therefore, the government ought to support other cheaper energy alternatives like alternatives gadgets and energy-saving cooking technologies, while the local administrators should integrate the refugee needs in the development plans for equal distribution of resources at large Keywords Fuelwood, Supply, Refugee, Camp, Dynamic, Household 1 Department of Forestry and Wood Science, University of Eldoret, P. O. Box 1125-30100, Eldoret, Kenya 2 School of Economics, University of Eldoret, P. O. Box 1125-30100, Eldoret, Kenya * Corresponding author’s e-mail: kandy022005@yahoo.com INTRODUCTION Energy demand at the global level is continuously increasing due to the rapid population growth and the need to use more energy for domestic and industrial purposes (Yigezu and Jawo, 2021; Molina et al., 2022). Accordingly, forecast based on the International Energy Agency (EIA) and other energy sources, estimates that energy demand will rise by approximately 50-65% between 2020 to the year 2040 (Stanescu et al., 2021; Manandhar et al., 2022). Forests contribute immensely to economic and social development through formal trade in timber, environmental services, non-timber forest products, safety, net spiritual and aesthetic value. Despite varied sources of energy (Asadian et al., 2023), fuelwood accounts for 44.2-58.7% of all energy consumed globally (Paterson and Fleming, 2021; Rahman et al., 2021), subsequently benefiting the energy needs of atleast 1.7- 2.1 billion peoples (Avhad, 2023). Fuelwood is a key source of energy that has been used for millennia for cooking, boiling water, lighting and heating. Today, about 2.5billion people depend on biomass energy for cooking and heating with 87% of this energy being provided by wood. In sub-Saharan African, more than 90% of the population relies on wood fire, that is, firewood and charcoal as their primary source of domestic energy. Over 80% of urban householders and small industries use charcoal and firewood as their source of energy. Despite their numerous importance, Africa’s forest continues to decline rapidly due to increase in agricultural practices into forest lands, population growth and urbanization, increased poverty, high dependence on natural resources for subsistence and income through forest. Most of these are applicable in lighting, heating and cooking (Singh et al., 2021; Eakins et al., 2023). Between the year 2018 to 2022, fuelwood consumption was approximately 45 million m3 per year (Paudel, 2018; Johnston et al., 2022) and is projected to increase to 70 million m3 annually by the year 2030 (Romanach and Frederiks, 2021; Khan et al., 2022). Fuelwood is the dominant source of energy averaging about 58% of the energy supply, and account for more than 80% in some countries, such as Burundi (91%), Rwanda and the Central African Republic (90%), Mozambique (89%), Burkina Faso (87%), Benin (86%), Madagascar and Niger (85%) as well as Malawi (81%) (Sulaiman and Abdul-Rahim, 2020; Wassie et al., 2021; Sulaiman and Abdul-Rahim, 2022). (Omoju et al., 2020), majority of the rural dwellers still use fuelwood due to cultural preferences, availability, economic factors and perceived lack of alternative energy sources as well as widespread poverty (Sulaiman and Abdul-Rahim, 2020; Ali, 2021). Pa ge 20 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 In Kenya, fuelwood contribute about 68% to the total biomass energy need (Jepng’etich, 2020; Osano et al., 2020) and provides for more than 80% of rural household energy needs (Kariuki, 2021; Mbaka, 2021). It has been previously reported that Kenya uses 34.3 million tonnes of biomass for fuelwood (Kimutai and Talai, 2021; Takase et al., 2021). Majority of the rural dwellers still elect to use fuelwood due to low cost of obtaining the energy source, ease of availability, perceived lack of alternative energy sources (Osano et al., 2020). Problem Statement In areas with large influx of refugees there is a disturbance of the environment, the forest resources as sources of energy has resulted in over-utilization. The cutting of trees for fuelwood, by the refugees and the host community is very high indeed. In Turkana region where there is large conglomeration of refugees, the host community feels deprived of their livelihood from fuelwood sales. Main Objective The main objective of this study is to analyze fuelwood supply and consumption dynamics in Kakuma refugee camp Turkana County (Kenya). Research Questions I. What quantity of fuelwood was supplied from indigenous tree species to the refugee camp between 2015 and 2019? II. What is at the difference between fuelwood prices between the agencies (LOKADO) and local market vendors between 2015 and 2019? III. What is the profitability of fuelwood supplied by vendors to the local market and to the refugee camp between 2015 and 2019? IV. What are the alternative sources of energy for refugees in Kakuma Refugee camp? Theoretical Foundation This theoretical framework is based on the empirical analysis of fuelwood supply and consumption dynamics. Neoclassical economic theory focuses on supply and demand as the driving forces behind the production, pricing, and consumption of goods and services. Conceptual Framework Figure 1: Conceptual framework Pa ge 21 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 MATERIALS AND METHODS Study Area This study was conducted in Kakuma located in Turkana County within the Rift Valley Province in the north-west corner of Kenya near the South-Sudan border. Kakuma Refugee Camp, is the second largest shelter site for refugees in the country after Dadaab. It is situated 95 km south of Lokichogio about 120 km South of the Sudan/ Kenya border. It is located 3°42’59.99” N 34°51’59.99” E and almost 1,000 km Northwest of Nairobi. Figure 2: Map of Kenya and Kakuma (UNHCR, 2019) Source: Layout of Kakuma refugee camp. Research design The study involved collection of data fuelwood price, profitability of fuelwood, preference of tree species and the alternative source of energy therefore assumes both qualitative and quantitative research designs. Analysis of Fuelwood Supply of Dynamic Consumption During the study, two subsets of population were used. The first is the population of the host community. According to the latest census report, there are approximately is 168,053 (Kenya National Bureau of Statistics, 2010). From this population about 12,807 living within the area covered during this survey. It estimated that about 20% supply forest resources to the refugee camps in the region and therefore the sample size was determined from the formula (Ellen, 2012). Whereby: n = the desired minimum sample size, z = the standard normal deviation at set confidence interval (1.96), d = the acceptable range of error (0.05), p = the proportion of individuals supplying forest resources to the refugee camps (20%), and q = the proportion of individuals not supplying forest resources to the refugee camps = 1-p (80%). Therefore, the desired sample size was 246 local community members. The second batch of the sample size is for the refugees. The number remains variable depending on the economic conditions of their parent country and estimates indicate that upto 82% of the refugees can access forest resources in one way or the other. Therefore the sample fomula was used to arrive at the sample size as: was used to arrive at the sample size as Therefore, the desired sample size for the refugees was 227. Data Analysis Both descriptive and inferential statistics was employed in the analysis of the data. Data was analysed using SPSS 23.0 (IBM Corp., Armonk, NY, USA) and Microsoft Excel 2007 (Microsoft Corporation, Redmond, WA, USA). All data was analyzed for normality and appropriate transformation methods applied in case of a significant departure from normal distribution (ZAR, 1996). Table 1: Description of explanatory variables used as socio-economic factors in the binary logistic model Variable Description of the variables Gender (X1) Gender is 1 if the respondent is male, 0 otherwise Age (X1) Level is 1 = 18-25 years; 2 = 26-35 years; 3 = 36-55 year; > 55 years Pa ge 22 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 RESULTS Questionnaires Return Ratepredicting the Level of Survey- Based Research Participation, Defined by the Response Rate, is Often Difficult Due to A Variety of Impeding Factors (Woolf and Edwards, 2021). The overall response rate as well as the response rate recorded for the host and refugees were found to be suitable for analysis and making interpretations and conclusions for this study since response rate of 60-100% his considered adequate to validate any survey based studies (Meyer et al., 2022). Level of education (X3) Level is 1 = None; 2 = Primary; 3 = Secondary; 4 = Tertiary Occupation of the household head (X4) Occupation is 1 if the respondent is a farmer, 0 otherwise Household size (X5) Level is 1 = 3-5; 2 = 6-10; 3 = > 10 Land size (X6) Level is 1 = <2; 2 = 2-5; 3 = 5.1-10; 4 = >10 Farm household income (X7) Level is 1 = <5000; 2 = 5000-10000; 3 = 10,001-20000; 4 = > 20000- 50000; 5 = >50,000 Non-farm household income (X8) Level is 1 = <5000; 2 = 5000-10000; 3 = 10,001-20000; 4 = > 20000- 50000; 5 = >50,000 Table 2: Response rate for the host community and refugees during the study period Respondents Total Returned Response rate (%) H ost community 247 193 78.1 Refugees 227 192 84.5 Total 474 385 81.2 Socio-Economic Status of the Respondents Geographical Characteristics of the Respondents The geographical characteristics of the refugees and host community members are provided in Table 3. The respondents were sampled from mainly two regions in Kakuma: Kakuma and Kalobeyei. Most of the respondents from each category were obtained from Kakuma which has larger population than that of Kalobeyei (Betts et al., 2020). The vast majority of the refugees were Sudanese followed by Somalis and belonged to regugee families. Table 3: Respondents’ geographical characteristics of the refugees and host community members Variable Response category Host community Refugees Frequency Percent Frequency Percent Location Kakuma 121 62.7 150 78.1 Kalobeyei 72 37.3 42 21.9 Nationality Sudanese - - 53 27.6 Burundian - - 28 14.6 Rwandese - - 11 5.7 Ugandan - - 2 1.0 Somalia - - 44 22.9 Ethiopian - - 24 12.5 Congolese - - 21 10.9 Somali - - 9 4.7 Kenyan 193 100 - - Category Refugee family - - 171 89.1 Refugee minor - - 19 9.9 Host - - 2 1.0 Table 4: Socio-economic characteristics of the respondents Variable Response category Host community Refugees Frequency Percent Frequency Percent Age (years) 18-35 62 32.1 69 35.9 36-50 114 59.1 105 54.7 51-65 12 6.2 14 7.3 Above 65 5 2.6 4 2.1 Pa ge 23 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 Patterns of Fuelwood Utilization within the Kakuma Refugee In both cases higher percentage of the host and refuges used firewood (>87-91%) compared to the respondents using charcoal (48-53%). Gender Male 88 45.6 78 40.6 Female 105 54.4 114 59.4 Level of education None 142 73.6 102 53.1 Primary 41 21.2 40 20.8 Secondary 8 4.1 40 20.8 Tertiary 0 0.0 7 3.6 University 2 1.0 3 1.6 Household size <3 21 10.9 35 18.2 3-5 93 48.2 64 33.4 6-10 75 38.9 83 43.2 11-20 4 2.1 7 3.6 >20 0 0.0 3 1.6 Household income (pm) <5000 138 71.5 78 40.6 5000-10000 38 19.7 90 46.9 10001-20000 12 6.2 17 8.9 20001-50000 5 2.6 7 3.6 Occupation None 11 5.7 30 15.6 Salaried employment 13 6.7 4 2.1 Casual labour 39 20.2 70 36.4 Self employed 5 2.6 27 14.1 Pastoralist 103 53.4 1 0.5 Legal business 22 11.4 60 31.3 Illegal business 0 0.0 4 2.1 Figure 3: Proportion of fuelwood supplied and utilized in the camps Pa ge 24 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 Factors Influencing Fuelwood Utilization Patterns in the Refugee Camps The outcome of binary logistic regression on the relationship between socio-economic factors and supply of firewood to the camps are shown in Table 5. The selected socio-economic factors were significant (B = -4.534, Wald = 4.977, P = 0.026, Exp(B)[OR] = 4.011) in explaining supply of charcoal to the refugee camps (Maximum Likelihood ratio = 66.393; Negelkerker R2 = 0.678). Figure 4: Source of fuelwood supplied to the refugee camps Figure 5: Species preference of tree species for fuelwood supply among the host community members Table 5: Binary Logistic regression showing the influence of socio-economic factors on supply of firewood to the refugee camps Variables in the Equation B S.E. Wald df P-value Exp(B) Distance -1.783 0.53 11.317 1 0.001 0.168 Gender -0.753 0.61 1.523 1 0.217 0.471 Age 0.079 0.449 0.031 1 0.86 1.083 Level of education 0.634 0.438 2.09 1 0.048 1.885 Pa ge 25 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 Occupation 0.623 0.275 5.146 1 0.023 1.865 HHSize 0.128 0.49 0.068 1 0.794 1.136 HHIncome 1.853 0.464 15.966 1 0.002 6.378 Constant -4.534 2.033 4.977 1 0.026 4.011 Figure 6: Table 6: Variables in the Equation B S.E. Wald df P value Exp(B) Location 0.236 0.264 0.804 1 0.372 1.267 Gender 0.07 0.343 0.042 1 0.838 1.072 Age -0.085 0.269 0.1 1 0.752 0.918 Education -0.29 0.329 0.776 1 0.378 0.749 Occupation 0.033 0.147 0.051 1 0.821 1.034 HHSize -0.137 0.277 0.245 1 0.62 0.872 HHIncome -1.28 0.381 11.264 1 0.001 0.278 Constant 1.709 1.104 2.394 1 0.022 2.523 Table 7: Multiple linear regression analysis showing the relationship between socio-economic attributes and frequency of supply of firewood the refugee camps Regression Statistics Multiple R 0.421 R Square 0.177 Standard Error 0.952 Observations 183 Dependent Variable: How frequent do you supply firewood Predictors: (Constant), Location, Gender, Age, Level of education, Occupation, Household size, Household income ANOVA TSS df MSS F P-value Regression 34.422 7 4.917 5.421 0.0000 Residual 159.660 176 0.907 Total 194.082 183 Pa ge 26 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 Table 8: Multiple linear regression analysis showing the relationship between socio-economic attributes and quantity of firewood supplied to the refugee camps Regression Statistics Multiple R 0.724 R Square 0.524 Standard Error 69.686 Dependent Variable: How much firewood is supplied to the household Monthly Predictors: (Constant), Household income, Household size, Occupation, Level of education, Gender, Age, Location ANOVA TSS df MSS F P-value Regression 636676.4 7 90953.77 18.73 0.000 Residual 577880.5 119 4856.139 Total 1214557 126 Unstandardized Coefficients Standardized Coefficients B Standard Error Beta t Stat P value (Constant) 249.932 43.577 5.735 0.000 Location 8.05 12.446 0.048 0.647 0.519 Gender -53.965 13.073 -0.276 -4.128 0.000 Age 3.872 10.100 0.028 0.383 0.702 Level of education 24.435 12.998 0.125 1.88 0.063 Occupation -49.284 5.642 -0.621 -8.735 0.000 Household size 13.375 10.829 0.09 1.235 0.219 Household income 1.979 13.923 0.01 0.142 0.887 Correlations Collinearity statistics Zero-order Partial Part Tolerance VIF (Constant) Location -0.276 0.059 0.041 0.741 1.35 Gender -0.362 -0.354 -0.261 0.896 1.117 Unstandardized Coefficients Standardized Coefficients B Standard Error Beta t Stat P value (Constant) 2.735 0.482 5.702 0.000 Distance 0.389 0.111 0.312 3.494 0.001 Gender 0.023 0.153 0.011 0.147 0.884 Age 0.277 0.123 0.177 2.264 0.025 Level of education 0.487 0.135 0.28 3.598 0.000 Occupation -0.086 0.066 -0.102 -1.298 0.196 Household size 0.204 0.124 0.133 1.645 0.102 Household income -0.491 0.126 -0.299 -3.906 0.000 Correlations (Constant) Zero-order Partial Part Tolerance VIF (Constant) 0.16 0.255 0.239 0.587 1.702 Location 0.052 0.011 0.01 0.845 1.183 Gender 0.204 0.168 0.155 0.764 1.308 Age 0.082 0.262 0.246 0.772 1.295 Level of education 0.001 -0.097 -0.089 0.763 1.311 Occupation 0.213 0.123 0.112 0.711 1.407 Household size -0.132 -0.282 -0.267 0.8 1.25 Household income 0.16 0.255 0.239 0.587 1.702 Pa ge 27 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 Table 9: Multiple linear regression analysis showing the relationship between socio-economic attributes and quantity of charcoal supplied to the refugee camps Regression Statistics Multiple R 0.782 R Square 0.58 Standard Error 16.918 Dependent Variable: How much charcoal is supplied to the household monthly Predictors: (Constant), Household income, Household size, Occupation, Level of education, Gender, Age, Location ANOVA TSS df MSS F P-value Regression 7515.723 7 1073.675 3.751 .0100 Residual 5437.906 19 286.206 Total 12953.63 26 Unstandardized Coefficients Standardized Coefficients B Standard Error Beta t Stat P value (Constant) 9.602 30.464 0.315 0.756 Location -16.267 10.395 -0.339 -1.565 0.134 Gender 1.53 7.731 0.034 0.198 0.845 Age -3.089 4.679 -0.126 -0.66 0.517 Level of education 6.775 5.512 0.313 1.229 0.234 Occupation 4.831 3.496 0.308 1.382 0.0183 Household size -2.626 6.37 -0.098 -0.412 0.685 Household income 9.043 5.807 0.373 1.557 0.0136 Correlations Collinearity statistics Zero-order Partial Part Tolerance VIF (Constant) Location -0.264 -0.338 -0.233 0.471 2.125 Gender -0.066 0.045 0.029 0.735 1.361 Age -0.224 -0.15 -0.098 0.611 1.638 Level of education 0.557 0.271 0.183 0.341 2.933 Occupation 0.174 0.302 0.205 0.445 2.25 Household size -0.509 -0.094 -0.061 0.392 2.552 Household income 0.501 0.336 0.231 0.386 2.592 Age 0.067 0.035 0.024 0.742 1.348 Level of education 0.199 0.17 0.119 0.904 1.106 Occupation -0.636 -0.625 -0.552 0.79 1.265 Household size 0.168 0.113 0.078 0.749 1.334 Household income 0.059 0.013 0.009 0.89 1.124 Price Analysis of Fuelwood in Kakuma Camp The second objective of the study was to determine the pricing strategies and analysis of fuelwood at the refugee camps. First, the quantity of firewood and charcoal supplied to the refugee camps over the last five years are provided in Figure 7. There were significant differences in the quantity of firewood supplied during the last five years (F = 34.5523 df = 4, P = 0.0032). Demand of Firewood among the Refugees During the Study Supply and demand curve of firewood in refugee camp. Q=a+bp. where Q = Linear demand curve; b = Slope and p = Price Figure (7). Based on the curved, the equilibrium price was estimated at about Kshs 100 per kg. Table 7, Enterprise budget (in Kshs) of tree supplied to the refugee camps during the study. Profit=profitmargin ration= (gross operating or net) (profit/sales)/100. Breack even is ksh 64/- Profit=Total revenue-total expenses. Pa ge 28 https://journals.e-palli.com/home/index.php/ajee Am. J. Environ Econ. 3(1) 19-30, 2023 Figure 7: Supply and demand curve of firewood in refugee camps Table 10: Fuelwood supply parameters Parameters Fuelwood supply Total yield of trees (kgs/ha) 5,040 Unit cost/kg 300.00 Gross receipts 1,512,000 Variable costs Cost of harvesting 154,500 Cost of loading 145,670 Cost of transport 65,000 Cost of offloading 150,000 Miscellaneous 80,000 Sub-total variable costs 620,770 Interest on operating cost 99,323 Total variable cost (TVC) 720,093 Fixed costs Amortization 60,000 Interest on fixed cost 9000 Total fixed cost 169,000 Total cost (TC) 889,093 Net returns above TVC 791,620 Net returns above TC 622,620 Margins above TC (%) 142.84 Break even price 64.05 Alternative Energy Sources from Fuelwood for Refugees in Kakuma Camp The final objective of the study was to determine the alternative sources of energy within the Kakuma refugee camps. Among the local community members, the main alternative energy source was mud stove, ceramic jiko and kerosene. Meanwhile majority of the refugee used mud stove, portable maendeleo stove and Ceramic jiko as alternative energy source. CONCLUSIONS The study area remains highly populated with refugee settlements from South Sudan, meaning that pressure on wood fuel is still far from ending unless this situation is overturned in the near future. This study reports forth that the refugees used more firewood followed by charcoal, and other biomass fuels such as agricultural residues, husks, and grasses as cooking fuel. The household size categories that had a significant contribution to the collection and use of firewood were the large families. RECOMMENDATION The current consumption pattern is unsustainable given high dependence and inefficient use. This threatens the existence of the preferred wood species such as Salvadora persica, Acacia meliffera, and Dobera glabra trees and other dryland vegetation tree species as they are indiscriminately harvested to meet fuelwood needs and this will worsen the specter of the fragility of the ecosystem. Acknowlegement In preparing this thesis from the conception to the final write up, several people were involved and it is with all sincerity to acknowledge them for the great role they played. First and foremost, I wish to express my deepest gratitude to my academic supervisors Dr. Paul Okelo Odwori and Dr. Wilson Kipkore at the University of Eldoret for the professional guidance, encouragement, endless support and constructive criticism and timely supervision of this work to completion. REFERENCES Aukot, E. (2002). It is better to be a refugee than a Turkana in Kakuma: revisiting the relationship between hosts and refugees in Kenya. Refuge, 21, 73. Forest Products Journal, 70(1), 4-9. 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