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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).



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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



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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



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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



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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



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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



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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



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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



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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.



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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.

Bartl, D. (2019). Exponential utility maximization under 
model uncertainty for unbounded endowments. The 
Annals of  Applied Probability, 29(1), 577-612.

Betts, A., Omata, N. and Sterck, O. (2020). Self-reliance 
and social networks: explaining refugees’ reluctance 
to relocate from Kakuma to Kalobeyei. Journal of  



Pa
ge

 
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Refugee Studies, 33(1), 62-85.
Bhagat, A. (2020). Experimental financial inclusion 

as refugee management: shelter insecurities at the 
bottom of  the pyramid in Kenya. International Journal 
of  Housing Policy, 1-21.

Cakmakyapan, S. and Goktas, A. (2013). A comparison 
of  binary logit and probit models with a simulation 
study. Journal of  Social and Economic statistics, 2(1), 1-17.

Ceccherini, G., Duveiller, G., Grassi, G., Lemoine, G., 
Avitabile, V., Pilli, R. and Cescatti, A. (2020). Abrupt 
increase in harvested forest area over Europe after 
2015. Nature, 583(7814), 72-77.

Cox, D. R. (2018). Analysis of  binary data. Routledge.
Cramer, J. S. (1999). Predictive performance of  the binary 

logit model in unbalanced samples. Journal of  the Royal 
Statistical Society: Series D (The Statistician), 48(1), 85-94.

Davis, E. J., Hajjar, R., Charnley, S., Moseley, C., Wendel, 
K. and Jacobson, M. (2020). Community-based 
forestry on federal lands in the western United States: 
A synthesis and call for renewed research. Forest Policy 
and Economics, 111(102042).

Dutta, H. (2022). The Environmental Aspects of  Refugee 
Crises: Insights from South Asia, Middle East, and 
Sub-Saharan Africa. Journal of  International Migration 
and Integration, 1-26.

Eakins, J., Sirr, G. and Power, B. (2023). Informally 
sourced solid fuel use: Examining its extent and 
characteristics of  the users in the residential sector in 
Ireland. Energy Policy, 172(113293.

Ellen, S. (2012). Slovin’s Formula Sampling Techniques. 
Fort Worth: Dryden Press.

Fairhurst, C., Parkinson, G., Hewitt, C., Maturana, C., 
Wiley, L., Rose, F., Torgerson, D., Hugill-Jones, J., 
Booth, A. and Bissell, L. (2022). Enclosing a pen in 
a postal questionnaire follow-up to increase response 
rate: a study within a trial. NIHR Open Research, 2(53), 
53.

Grebner, D. L., Bettinger, P., Siry, J. P. and Boston, K. 
(2021). Introduction to forestry and natural resources. 
Academic press.

Harrell, F. E. (2015). Binary logistic regression, Regression 
modeling strategies. Springer. 219-274.

Hutton, J., Patenaude, G., Revéret, J.-P. and Potvin, 
C. (2017). The role of  indigenous peoples in 
conservation actions: a case study of  cultural 
differences and conservation priorities, Governing 
Global Biodiversity. Routledge. 159-176.

Jepng’etich, K. S. (2020). Sustainable Fuelwood 
Production In Kenya: Potential Role of  Community 
Forest Associations: 2020 International Conference and 
Utility Exhibition on Energy, Environment and Climate 
Change (ICUE). IEEE, 1-6.

Jiang, H., Zhang, Y., Lü, E. and Wang, C. (2015). 
Archaeobotanical evidence of  plant utilization in 
the ancient Turpan of  Xinjiang, China: a case study 
at the Shengjindian cemetery. Vegetation History and 
Archaeobotany, 24(1), 165-177.

Johnston, C. M., Guo, J. and Prestemon, J. P. (2022). US 

and Global Wood Energy Outlook under Alternative 
Shared Socioeconomic Pathways. Forests, 13(5), 786.

Kariuki, D. W. (2021). Socio-Economic Determinants 
of  Household Continued Use of  Solid Biofuels 
(Fuelwood and Charcoal) for Cooking Purposes in 
Sub-Saharan Africa-Kenya’s Situation. East African 
Journal of  Environment and Natural Resources, 3(1), 49-68.

Kariuki, P. M., Onyango, C. M., Lukhoba, C. W. and Njoka, 
J. T. (2018). The Role of  Indigenous Knowledge on 
Use and Conservation of  Wild Medicinal Food Plants 
in Loita Sub-county, Narok County. Asian Journal of  
Agricultural Extension, Economics & Sociology, 28(2), 1-9.

Karki, M.B. and Chowdhary, C.L. (2019). Non-timber 
Forest Products (NTFP) and Agro-forestry Subsectors: 
Potential for Growth and Contribution in Agriculture 
Development, Agricultural Transformation in Nepal. 
Springer. 385-419.

Kenya National Bureau of  Statistics. (2010). The 2009 
Kenya population and housing census. Kenya 
National Bureau of  Statistics.

Khan, I., Zakari, A., Dagar, V. and Singh, S. (2022). 
World energy trilemma and transformative energy 
developments as determinants of  economic growth 
amid environmental sustainability. Energy Economics, 
108(105884).

Kimutai, S.K. and Talai, S.M. (2021). Household 
Energy Utilization Trends in Kenya: Effects of  Peri 
Urbanization. European Journal of  Energy Research. 1(2), 
7-11. Kumi, R. A. K., & Owusu, E. (2023). 

Kungu, W., Agwanda, A. and Khasakhala, A. (2020). 
Trends and determinants of  contraceptive method 
choice among women aged 15-24 years in Kenya. 
F1000Research, 9(197), 197.

Kumi, R. A. K., & Owusu, E. (2023). Monetary Valuation 
of  the Unpaid Care Works and Experiences of  Some 
Women in the Upper East Region of  Ghana. American 
Journal of  Economics and Business Innovation, 2(1), 52-62.

Lumumba, L. G., Paul, N., Shanyisa, W. M. and Ndung’u, 
E. M. (2022). Relevance of  cash transfer programme 
on promoting nutrition security among refugees in 
Kakuma Camp, Kenya.

Lund, B. (2021). The questionnaire method in systems 
research: an overview of  sample sizes, response rates 
and statistical approaches utilized in studies. VINE 
Journal of  Information and Knowledge Management Systems, 
53(1), 1-10.

Maalim, S. A., Adwek, G. and Arowo, M. (2021). Shared 
energy parks as a solution to energy challenges for 
Dadaab Refugee Camps in Kenya. Scientific African, 
13(e00901).

MacDicken, K. G., Sola, P., Hall, J. E., Sabogal, C., 
Tadoum, M. and de Wasseige, C. (2015). Global 
progress toward sustainable forest management. 
Forest Ecology and Management, 352(47-56).

Manandhar, A., Mousavi-Avval, S. H., Tatum, J., Shrestha, 
E., Nazemi, P. and Shah, A. (2022). Solid biofuels, 
Biomass, Biofuels, Biochemicals. Elsevier. 343-370.

Mbaka, C. K. (2021). Spatial variation of  household 



Pa
ge

 
30

https://journals.e-palli.com/home/index.php/ajee

Am. J. Environ Econ. 3(1) 19-30, 2023

energy consumption across counties in Kenya. African 
Geographical Review, 1-32.

Meyer, V. M., Benjamens, S., El Moumni, M., Lange, J.F. 
and Pol, R.A. (2022). Global overview of  response 
rates in patient and health care professional surveys in 
surgery: a systematic review. Annals of  surgery, 275(1), 
e75.

Mikulewicz, M. (2018). Politicizing vulnerability and 
adaptation: On the need to democratize local 
responses to climate impacts in developing countries. 
Climate and Development, 10(1), 18-34.

Molina, A., Mendoza, A., Lozano, F. J., Serra-Barragán, L. 
and Ibarra-Yunez, A. (2022). Historical Context and 
Present Energy Use in the Global Economy, Energy 
Issues and Transition to a Low Carbon Economy. 
Springer, 1-29.

Molnár, Z. and Berkes, F. (2018). Role of  traditional ecological 
knowledge in linking cultural and natural capital in cultural 
landscapes. Reconnecting Natural and Cultural Capital: 
Contributions from Science and Policy; Paracchini, 
ML, Zingari, PC, Blasi, C., Eds: 183-193.

Monetary Valuation of  the Unpaid Care Works and 
Experiences of  Some Women in the Upper East 
Region of  Ghana. American Journal of  Economics and 
Business Innovation, 2(1), 52-62.

Mouzam, S. M. (2020). UNESCAP and UNCTAD, Asia-
Pacific Trade and Investment Report 2019: Navigating 
Non-tariff  Measures (NTMs) Towards Sustainable 
Development, United Nations Economic and Social 
Commission for Asia and the Pacific and United 
Nations Conference on Trade and Development. 
SAGE Publications Sage India: New Delhi, India.

Nato, G. N. (2020). Refugee-Environment Nexus: Socio-
Cultural Acceptability of  Eco-Friendly Options for 
Household Cooking in Kenyan Refugee Camps, 
Health in Diversity–Diversity in Health. Springer, 
121-133.

Nerfa, L., Rhemtulla, J. M. and Zerriffi, H. (2020). Forest 
dependence is more than forest income: Development 
of  a new index of  forest product collection and 
livelihood resources. World Development, 125(104689.

Odwar, F. N. A. (2020). Conflict influenced by geography: 
analysis of  Kenya’s geographical position to explain 
the Somali and North-Eastern Kenya’s unrest, Bursa 
Uludağ Üniversitesi.

Omata, N. (2021). Refugee livelihoods: a comparative 
analysis of  Nairobi and Kakuma Camp in Kenya. 
Disasters, 45(4), 865-886.

Omoju, O. E., Li, J., Zhang, J., Rauf, A. and Sosoo, V. E. 
(2020). Implications of  shocks in energy consumption 
for energy policy in sub-Saharan Africa. Energy & 
Environment, 31(6), 1077-1097.

Osano, A., Maghanga, J., Munyeza, C., Chaka, B., Olal, W. 
and Forbes, P. (2020). Insights into household fuel use 
in Kenyan communities. Sustainable Cities and Society, 
55(102039).

Pagdee, A., Kim, Y.-s. and Daugherty, P. J. (2006). What 
makes community forest management successful: a 
meta-study from community forests throughout the 
world. Society and Natural resources, 19(1), 33-52.

Pape, U., Beltramo, T., Fix, J., Nimoh, F., Sarr, I. and Rivera, 
L. A. R. (2021). Understanding the Socioeconomic 
Differences of  Urban and Camp-Based Refug


