This document is a research project submitted by Mutambu Dominic Mwanzia in partial fulfillment of the requirements for a Bachelor's degree in environmental science from Kenyatta University. The research project investigates the adoption of clean energy solutions for cooking and lighting in rural households of Kyuso, Kitui County. It includes a declaration, list of figures and tables, abstract, introduction, literature review, methodology, results and discussion, conclusion and recommendations. The study aims to identify the major forms of domestic energy used for cooking and lighting and the socioeconomic factors limiting access to clean energy in the study area.

1. ADOPTION OF CLEAN ENERGY SOLUTIONS FOR COOKING AND
LIGHTING IN RURAL HOUSEHOLDS OF KYUSO, KITUI COUNTY
BY
MUTAMBU DOMINIC MWANZIA
REG. NO: N38/3111/2014
A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILMENT OF THE
REQUIREMENTS OF THE AWARD OF BACHELOR’S DEGREE IN
ENVIRONMENTAL SCIENCE OF KENYATTA UNIVERSITY
NOVEMBER, 2017

2. ii
DECLARATION
This research project is my original work and has not been presented for a degree in any other
university or any other award.
Signed: …………………...………… Date: ...........................………………
Mutambu Dominic Mwanzia
Department of Environmental Sciences
Kenyatta University
I confirm that the work reported in this research project was carried out by the candidate
under my supervision.
Signed: …………………...………… Date: ...........................……………….
Dr. Gathu Kirubi
Department of Environmental Sciences
Kenyatta University

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List of Figures and Tables
Figure 1.1 Conceptual framework .............................................................................................4
Figure 3.1: map of the study area.............................................................................................13
Figure 4.1: Gender of the respondents.....................................................................................16
Figure 4.2: Showing Respondents' Age distribution ..............................................................16
Figure 4.3: Respondent’s Level of Education..........................................................................18
Table 4.1: Distribution of respondents.....................................................................................15
Table 4.2: Household Size.......................................................................................................17
Table 4.3 income level.............................................................................................................19
Table 4.4: Type of cooking fuel used ......................................................................................20
Table 4.5: Cooking fuel per Gender ........................................................................................22
Table 4.6: Consumption of cooking fuel .................................................................................23
Table 4.7: Lighting by gender of the respondents ...................................................................23
Table 4.8a: Cooking Energy by Household Size.....................................................................24
Table 4.8b Household size and Lighting energy .....................................................................24
Table 4.9a: Cooking Fuel and Education Level.......................................................................25
Table 4.9b: Lighting fuel and Education Level .......................................................................26
Table 4.10a: Cooking and Income level ..................................................................................26
Table 4.10b: Lighting and Income Level.................................................................................27
Table 4.11: Model Parameters .................................................................................................28

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LIST OF ACRONYMS AND ABBREVIATIONS
ERC: Energy Regulatory Commission
GDC: Geothermal Development Company
GLI: Global Legal Insights
GoK: Government of Kenya
GTF: Global Tracking Framework
GWh: Gigawatt Hour
ICBED: International Conference on Business Economic Development
IEA: International Energy Agency
KCIDP: Kitui County Integrated Development Plan
KENGEN: Kenya Electricity Generating Company
KNBS: Kenya National Bureau of Statistics
KPLC: Kenya Power and Lighting Company
LPG: Liquefied Petroleum Gas
MWh: Megawatt Hour
OECD: Organization for Economic and Cooperation Development
TWH: Terawatt Hour
WHO: World Health Organization

5. v
Table of Contents
Cover Page……………………………………………………………………………………i
Declaration.................................................................................................................................ii
list of Figures ........................................................................................................................... iii
List of Acronyms and Abbreviations........................................................................................iv
Abstract.................................................................................................................................. viii
CHAPTER ONE: INTRODUCTION........................................................................................1
1.1 Background to the problem ____________________________________________1
1.2 Problem statement and justification______________________________________2
1.3 Research Questions __________________________________________________2
1.4 Objectives of the study________________________________________________3
1.6 Research Hypotheses _________________________________________________3
1.7 Significance of the study ______________________________________________3
1.8 Conceptual framework __________________________________________________3
1.9 Definition of terms _____________________________________________________4
CHAPTER TWO: LITERATURE REVIEW............................................................................5
2.1 Overview_____________________________________________________________5
2.2 Major Forms of Energy__________________________________________________6
2.2.1 Electricity _________________________________________________________6
2.2.2 Wind _____________________________________________________________6
2.2.3 Biomass __________________________________________________________7
2.2.4 Biogas ____________________________________________________________7
2.2.5 Solar _____________________________________________________________7
2.2.6 Petroleum _________________________________________________________7
2.3 Social and Economic Factors _____________________________________________8
2.3.1 Level of Education __________________________________________________8
2.3.2 Level of Income ____________________________________________________9
2.3.3 Fuel price _________________________________________________________9

6. vi
2.3.4 Gender __________________________________________________________10
2.3.5 Level of Awareness ________________________________________________10
2.3.6 Proximity to the energy source________________________________________10
2.5 The Energy Ladder Hypothesis___________________________________________11
2.6 Fuel Stacking Model ___________________________________________________11
2.7 Gaps of Knowledge____________________________________________________12
CHAPTER THREE: METHODOLOGY ...............................................................................13
3.1 Description of the Study Area____________________________________________13
3.2 Research Design ______________________________________________________13
3.3 Population and sample _________________________________________________14
3.4 Data collection procedures ______________________________________________14
3.5 Data analysis _________________________________________________________14
CHAPTER 4: RESULTS AND DISCUSSIONS ....................................................................15
4.0 Overview____________________________________________________________15
4.1 Respondents Characteristics and Relationships to Households’ Lighting and Cooking
Energy _________________________________________________________________15
4.2 Households Lighting and Cooking Energy__________________________________20
4.2.1 Cooking fuels _____________________________________________________20
4.2.2 Lighting fuels _____________________________________________________20
4.3 Socioeconomic Factors Barring Households Access to Clean Energy_____________21
4.3.1 Gender __________________________________________________________21
4.3.2 Age _____________________________________________________________22
4.3.3 Household Size____________________________________________________23
4.3.4 Level of Education _________________________________________________25
4.3.5 Level of Income ___________________________________________________26
4.4 Cooking and Lighting Energy Adoption____________________________________27
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS ......................................29

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5.1 Conclusion___________________________________________________________29
5.2 Recommendations_____________________________________________________30
References................................................................................................................................31
APPENDICES .........................................................................................................................34
APPENDIX 1: BUDGET __________________________________________________34
APPENDIX 2: Time Schedule ______________________________________________35
APPENDIX 3 ___________________________________________________________36
Household Questionnaire __________________________________________________36

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ABSTRACT
In Kitui County, access to clean energy particularly electricity is very low with only 4% of
the total number of households connected to the national grid, leaving about 96% of the
population relying on biomass energy sources. High reliance on biomass energy is known for
causing environmental, health and economic harms, for instance; about 10% of the total death
burden in Kitui County is caused by respiratory diseases due to indoor air pollution which
mainly result from domestic biomass energy. This study was investigating key factors
limiting access to clean energy. The main objectives were to: i) identify the major forms of
domestic lighting cooking and energy options in the study area, and ii) outline various social-
economic factors limiting access to clean energy in the study area. The outcome of the study
is an eye opener to the local community on benefits of using clean energy compared to
biomass energy and will help in proposing feasible policy interventions to facilitate rural
access to clean energy. The study was a descriptive research using survey research design.
Primary data was collected by questionnaires while Secondary data was gathered from
research papers, books, journals and internet materials. The study was carried out in Kyuso
Sub-County; a remote off-grid area in Kitui County where a sample size of 99 respondents
was randomly taken to provide the required data. The collected data was analysed for
descriptive statistics and inferential statistics and the results presented by tables, charts and
graphs. It was found that firewood and kerosene are the main cooking and lighting fuels
respectively. Hypothesis test confirmed that socioeconomic factors are the major barriers to
adoption of clean cooking and lighting fuels.

9. 1
CHAPTER ONE: INTRODUCTION
1.1 Background to the problem
The world’s total energy supply is dominated by oil at 31%, coal 29%, natural gas 21% and
nuclear energy 5% (IEA, 2015). Meanwhile, in this energy supply the largest fraction is
consumed by the industrial sector at 37%, transport 28%, residential sector 28%, commerce
and public sector 8%, forestry and agriculture 2%, and other sectors 2% (IEA, 2015).
Electricity access globally has been estimated at 85.3%. Roughly, around 1.06 billion people
still live without electricity despite the fact that 86 million people are connected to electricity
every year (GTF, 2016). Access to electricity in Africa is not growing as rapidly as its
population, but countries like Kenya, Malawi, Sudan, Uganda and Rwanda have particularly
increased their level of electrification by about 2 - 4% in the period 2012-2015 (World Bank,
2017).
About 3.4 billion people have no access to clean fuels and cooking technologies, majority
living Asia and Africa where cooking does not appear to be given a policy priority (IEA,
2015). This situation is more critical in Africa where population grows by 20 million people
per year while access to clean energy increases by only 4 million people annually.
More than 70% of the rural households in sub-Sahara Africa rely on fuel wood, charcoal,
kerosene, oil and wood waste, and this dependence is linked to various environmental harms
associated with tree clearing and land degradation hence raising sustainability issues (IEA,
2006; World Energy Council, 1999). Burning biomass energy is also associated to indoor air
pollution (Muchiri et al., 2000). Additionally, WHO (2006) estimates that about 1.5 million
people die prematurely due to indoor air pollution from biomass fuels. Projections by
International Energy Agency, (2017) indicate that 91% of the world will have electricity by
2030 while only 72% will have access to clean energy.
In Kenya, the total energy mix is generated from three major sources: biomass, petroleum and
electricity. Electricity energy mix is generated by hydropower 49%, geothermal 15%, wind
0.3%, cogeneration 2.3%, medium speed diesels 2.7%, Gas turbines 3.6%, High speed diesels
1.1% and emergency power plants 1.9% (NEPP, 2015).
A survey by FinAccess Kenya in collaboration with Kenya National Bureau of Statistics in
2006 revealed that 74% of the rural households used kerosene as their main lighting energy
and 65% used firewood for cooking. 10 years later, in a similar survey they realised that the

10. 2
proportion of households using kerosene for lighting had dropped to about 44% and fuel
wood dropped to 57% (KNBS, 2016). Energy consumption for lighting in rural areas is as
follows: 2% firewood, 5% dry cells, 12% solar, 34% electricity and 44% kerosene. While
cooking, firewood accounts for 57%, charcoal 14%, kerosene 11%, LPG 10% and electricity
0.3% (FinAccess, 2016).
In Kitui County, the main source of fuel is firewood and charcoal, although kerosene,
electricity and LPG are still used. About 4% of the households in the County are connected to
electricity but the level of rural electrification is less than 1% (KNBS, 2009).
Energy is the cornerstone for environmental conservation and economic growth; with reliable
access to energy, little CO2 will be released to the atmosphere, small businesses will thrive
and little air pollution is done, and hospitals can work efficiently in saving lives. Choices of
global energy that are made by the households are able to influence environmental
conservation and sustainable development (Africa et al., 2016).
1.2 Problem statement and justification
Kitui County has a population of about 1,200,000 people (KNBS, 2012). Out of this, about
less than 4% are connected to the national grid, leaving about 96% of the population relying
on biomass energy sources. High reliance on biomass energy is known to cause
environmental, health and economic harms (WHO,2016); for instance, about 10% of the total
death burden in Kitui County is caused by respiratory diseases mainly due to indoor air
pollution (KIDP, 2013) which mainly result from burning of domestic biomass energy.
It was therefore very essential to carry out this study in order to clarify to the locals on the
factors that limit them to access clean energy and help them open up their thinking towards
their domestic energy options, thus helping reduce environmental and health harms
emanating from various types of fuels used.
1.3 Research Questions
The following were the research question for this study:
1. What are the major sources of lighting and cooking energy for households in the study
area?
2. What are the major social-economic factors limiting access to clean energy in the study
area?

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1.4 Objectives of the study
The study objectives were to:
1. Identify the current major energy options for lighting and cooking in households of study
area.
2. Identify various social-economic factors limiting households’ access to clean energy
lighting and cooking energy in the households of the study area.
1.6 Research Hypotheses
The following are the research hypothesis for the study:
1. Firewood is likely to be the major source of domestic cooking energy.
2. Socio-economic factors are the major barriers to households’ access to clean lighting and
cooking energy in the study area.
1.7 Significance of the study
The outcome of the study is very crucial since it is informing the local households on various
forms of clean energy that are clean and safe than the current options, mainly biomass. With
relevant information they can evaluate the costs and benefits of using cleaner lighting and
cooking energy to biomass energy.
The outcome also provides some baseline information for the county administration that can
be reliable in domestic energy policy formulation. The county government’s role in
promoting clean energy adoption can be facilitated since there is baseline information.
The study will also ensure information about the situation of clean energy options in the
study area is available for scholars who may be interested to study further on clean energy
situation in the study area or somewhere else where such information will seem relevant.
1.8 Conceptual framework
Households’ energy option refers to the form of energy used for domestic purposes such as
cooking and lighting. Lighting energy sources include: kerosene, solar, electricity biogas and
firewood. Whereas, cooking fuels include: charcoal, kerosene LPG and electricity. The
independent variables in this study are; education, income, cost of fuel, proximity, awareness
and size of the household. The dependent variable is domestic energy option for lighting and
cooking while, intervening factors are government policies such as taxation and subsidies.

12. 4
Education level increases the understanding of pollution levels for different energy choices.
A household with high level of education is expected to use cleaner energy like electricity
more often than firewood.
For household size, a household with many family members will have a high likelihood of
using firewood or charcoal, as compared to electricity and LPG.
Modern energy, the presence or absence of modern energy options, will translate to little or
no use of such energy options.
Figure 1.1 Conceptual framework
1.9 Definition of terms
Biomass energy: refers to all categories of fuels obtained from plants and animal matter or
their derivatives.
Clean energy: Energy form that release little or no pollutants to the environment.
Domestic energy option: Refers to fuel that is used for residential or household chores only.
House-head: The one who makes decisions for the family (family head)
Household’s
Energy choice for
cooking and
lighting.
Taxation
Subsidies
.
Family size
Availability of
modern forms of
energy
Income level
Cost of fuel
Distance

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CHAPTER TWO: LITERATURE REVIEW
2.1 Overview
In developing countries, about 2.5 billion of rural residents rely on biomass energy such as
wood, charcoal, agricultural waste and animal dung (IEA, 2006). In many of these countries,
biomass account for 90% domestic energy consumption. In absence of new policies,
International Energy Agency, (2006) estimates that the number of people relying on biomass
energy may increase to 2.7 billion by 2030 due of population growth.
Use of biomass is not the center of concern; however, unsustainable exploitation of energy
resources and inefficient energy conversion technologies has had serious effects on the
environment, economy and public health. For instance, about 1.5 million people die
prematurely due indoor air pollution from biomass fuels (WHO, 2016).
Too much time is wasted in firewood collection instead of working to generate income. A
study in south Asia on gender and livelihood impact on clean cook stoves reports that women
spend approximately 374 hours annually collecting firewood (Global Alliance, 2014). In sub-
Sahara Africa, there is low level of rural electrification rate. 68 developing countries have set
rural electrification policy as a critical goal towards improving the level of access to
electricity to rural residents (Gunnar et al., 2011).
In Kenya, domestic energy mix is composed of biomass, petroleum, natural gas and
electricity (IEA, 2014). Some sources of electricity include: hydro, geothermal, biogas,
municipal waste, solar and wind which are renewable and clean energy sources.
The major energy supplies in Kenya are mainly petroleum and electricity, although fuel wood
use dominates in rural communities, the urban poor and the informal sector. However, there
is inadequate data on fuel wood consumption (Energypedia, 2015). Choice of fuel is
determined by its local availability, transactions, opportunity cost for obtaining the fuel rather
than budget constraints, price and cost (Farsi et al., 2005).
Despite Kenya relying more on biomass energy, its role in national energy mix is not well
appreciated. Many rural households rely on firewood and charcoal burning as their main
source of livelihood, although charcoal burning is illegal and its consumption is legal (GTZ,
2017).
According to Energy Regulatory Commission, the main challenges facing installation and
utilization of biomass technologies in Kenya include:

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i. high installation cost
ii. high technology failure
iii. inadequate post installation support
iv. poor management and maintenance
v. inadequate technology awareness
vi. scarce promotional activities
Following the contribution of biomass to national energy mix, it is necessary to develop a
private or government agency with such roles as facilitating data collection, issuing policy
guidelines on firewood, charcoal and modern biomass use, mapping the existing biomass
resources to facilitate sustainable conservation and management, raising revenue to support
sustainable biomass production and consumption, and assessing energy potential and use of
biomass residues.
2.2 Major Forms of Energy
Major sources of energy in Kenya are: biomass 69%, petroleum 22% and electricity 9%.
Biomass energy is mainly in the form of wood fuel and charcoal, and is extensively used in
poor rural areas for cooking and lighting. Kenya’s over reliance on biomass energy is due to
poor access to clean energy whereby 80% of the rural Kenyans rely on biomass energy
(Global Legal Insights, 2016).
2.2.1 Electricity
Electricity access in Kenya is low despite the government’s ambitious target to increase
electricity from current 15% to 65% by 2022 (Netherlands Development Organization, 2015).
Kenya has installed large scale hydropower which is about 743MW. Small scale hydro is
estimated to be 3000MW, of which less than 30MW have been exploited with just 15MW
supplying to the national grid (ERC, 2015). Energy Regulatory Commission enumerates the
following factors as the major impediments towards exploitation of small scale hydro:
i. High installation costs averaging to US$ 2,500 per KW.
ii. Inadequate hydrological data.
iii. Effects of climate change.
iv. Limited local capacity to manufacture hydropower components.
2.2.2 Wind

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Kenya’s installed wind capacity of 5.1MW at Ngong’ Hills. It is operated by KenGen (ERC,
2015). Many potential areas for wind generation in Kenya are located far away from the grid
and load centres, hence requiring high capital investment for transmission lines.
2.2.3 Biomass
In Kenya, biomass energy is derived from forests, farmlands, plantations, agricultural and
industrial residues and it includes wood fuel and agricultural residues. Wood fuel remains the
highest supplier of household energy consumption in rural Kenya. In addition, industries like
the cottage industry including tea factories rely heavily on wood for their energy needs. This
implies that wood production as a source of energy will be intensified so as to be made
sustainable.
The Kenya Energy Sector Environment and Social Responsibility Program (KEEP) within
the energy sector has initiated growing of trees as a source of energy. However, this effort
can only be sustained through collaboration with key sectors like forestry and agriculture.
Equally, sustainable production of other biomass requires similar collaboration because of the
integrated nature of land use system.
2.2.4 Biogas
Biogas potential in Kenya has been identified in Municipal waste, sisal and coffee
production. The total installed electric capacity potential of all sources ranges from 29-
131MW, generating 202 to 1,045 GWh which is about 1.3% - 5.9% of the total electricity
purchased in the system (GIZ, 2010).
2.2.5 Solar
Kenya has great potential for solar energy due to its strategic location along the equator with
insolation of about 4 - 6 kWh/day (ERC, 2015). In Kenya the amount of solar energy
generated annually from rural households, stands at about 9GWh and is projected to rise to
22GWh by 2020. However, this is not sufficient considering that there are over 4 million
Kenyans in the rural areas not connected to electricity in the national grid (ERC, 2010). The
same report estimates that Kenya’s rural areas have an area of 106,000 km2
with potential of
generating solar energy up to 638,790 TWh.
2.2.6 Petroleum
Currently, Kenya imports 100% of her petroleum needs. However, economically exploitable
oil deposits were discovered in north-western Kenya in 2012. Africa Oil and its partner
Tullow Oil, who made the discovery, may be able to start small-scale production of crude oil,

16. 8
transported by road and rail to the Kenyan port of Mombasa, in 2017 ( GLI, 2017). However,
low oil prices and Uganda’s recent decision to withdraw support from Kenya, and partner
with Tanzania instead, in the construction of a port and transport corridor known as
LAPSSET (The Lamu Port and South Sudan Ethiopia Transport) may impede Kenya’s
establishment as a major oil exporter. Major uses of petroleum products in rural area are
cooking, lighting, and powering water pumps. Main forms of petroleum products are diesel,
kerosene (paraffin) and LPG.
2.3 Social and Economic Factors
Energy access is a key indicator of socio-economic development of a country. Some of the
factors limiting adoption of clean energy in rural setting include: level of education of the
households, level of income, fuel price, gender, culture, and proximity.
2.3.1 Level of Education
The level of education of the households determines the choice of cooking fuels and also
influences the level of exposure of an individual (ICBED, 2016). A study by Adepoju (2012)
in rural households of Ogun state in Nigeria, reported that house heads that were not formally
educated had a higher likelihood of using firewood and charcoal as domestic energy than
their educated counterparts.
Another study in Bolivia, demonstrated that households with a high school degree or
additional schooling had a low likelihood of using firewood as their primary energy (Debra,
2002). Bisk et al., (2016), in their study in Bauchi, Metropolis in Nigeria on households’ level
of education on energy choice showed that wood, coal and kerosene declined with increasing
level of education while electricity utilization increased with increasing level of education.
Additionally, Aina (2001) also found that irrespective of educational background, economic
status was important in determining the choice of fuel by the household.
Solar adoption tended to rise with increasing level of education, and then drastically dropped.
In the study by Bisk et al, (2016) regression analysis showed a strong correlation between
energy choice and level of education.

17. 9
2.3.2 Level of Income
Ng’eno (2014) in her study in Kajiado County where majority of the people showed irregular
incomes and lack of savings accounts, she noticed that adoption of solar was very low. A
study by Pozzolo et al. (2011) in China indicated that the consumption of biogas increased
with the level of income increase except in some cases where the biogas consumption
decreased with income increase. Most of the residents would switch to other cleaner and
renewable fuels like LPG with higher increase in incomes level.
Bisu et al., (2016), in their study in Bauchi State, Nigeria noticed that firewood consumption
decreased with increase in the level of income of the households. On charcoal and kerosene,
consumption rose gradually and then at about middle-level income, it began to drop. LPG,
electricity and solar showed a gradual increase with increase in income.
It has been disagreed that households in developing countries tend to switch to modern
energy technologies as their level of income raises, instead they tend to integrate both
traditional and modern energy technologies such as solar, electricity and LPG (World Bank,
2012). In addition to this literature, energy households demand and supply has showed that
low income households tend to heavily rely on biomass energy (wood and charcoal) whereas
those with high income rely on cleaner energy such as electricity and solar (Heltzberg, 2005)
2.3.3 Fuel price
Bardhan et al., (2001), in their study on households’ firewood collection in rural Nepal,
indicated that when price increases the demand for wood decreases due to commodity
inferiority among other energy options. Although there is no direct correlation between
commodity price and consumption of LPG and electricity, consumption of wood, charcoal
and kerosene are directly linked to their prices (Bisu et al., 2016). These studies imply that
consumption of wood, kerosene and charcoal is a function of price while that of electricity
and LPG is independent.
Although literature concerning the adoption of domestic solar power systems is limited.
According to a report of ETSU (Flaherty et al 2001), it is technology that is being pushed by
policy, but has failed to be adopted as it is too expensive and while solar power systems are
attractive at a national level as a means of reducing carbon emissions, they remain
unattractive to individual households (Timilsina 2000). Research has suggested that to be

18. 10
attractive in simple financial terms, solar technologies would need to cost approximately
£1000 at 2003 UK prices (BRECSU 2001).
According to Peng et al., (2008) found that affordability influenced household fuel choices.
Since firewood was readily available than electricity many households had a high likelihood
of using it than electricity and solar.
2.3.4 Gender
According to Adepoju (2012), there is a lower likelihood of fuel wood use in male-headed
households than in female-headed households. This can be attributed to the traditional role of
women in firewood gathering, a livelihood for rural women. In another study by Bisu et al.
(2016), they realised that male-headed households’ consumption of kerosene is at 31% while
in female-headed households, it is at 28%. Charcoal consumptions in male house heads stood
at 45% while for their female counterparts was at 37%. Moreover, 19% male-headed
households and 29% female-headed households used LPG for cooking respectively.
2.3.5 Level of Awareness
A study in Kitengela, in Kajiado county by Ng’eno (2014) noted that majority of residents
were aware of availability of clean energy source, particularly solar in the study area, but the
decision of adopting it was commenced by individual’s driven precedent conditions for
instance, need to involved in innovative technology.
2.3.6 Proximity to the energy source
Adepoju (2012) reports that availability of oil and kerosene, and electricity payment points in
a distance that can be walked increased their likelihood for their consumption. Additionally,
Aina (2001) noted that availability is an important issue on domestic energy demand thus
higher likelihood for their consumption.
Studies in china stress that house location is the main reason for the availability and
accessibility of various fuels (Gao, 2009, Chen and Zheng, 2009; Wu et al., 2012; Qiao,
2010, Wang et al., 2007). In most remote areas, the consumption of traditional fuels like
firewood are very large while a high price and the difficult in the transport of cleaner fuels
like coal briquette and LPG preventing the use of these fuels in daily lives.
Availability, accessibility and reliability of energy supplies were found to influence
household fuel choice. This was justified as households that indicated electricity as main
source of fuel were influenced by household size and distance of family house to the power

19. 11
lines (Peng et al., 2008). Households with fewer members tended to use more electricity than
households with more members.
Additionally, households located far from the electricity grid were less likely to be connected
to electricity (ESMAP, 2003). In another study in Nakuru municipality households located far
away from the market show low interest to using kerosene and enhanced high utilisation of
charcoal (Langat et al, 2016).
2.5 The Energy Ladder Hypothesis
This model explains the consumption of energy from traditional to modern energy options
with respect to socioeconomic status of the household. This model assumes that households
will move from traditional to a modern energy option as their income increases (Hosier et al,
1987). Fuels in the model are characterized by cleanliness, ease to use, cooking speed and
efficiency (Horst et al, 2008).
The ladder is divided into three distinct phases: primitive, transition and the advanced phase
(Schlag et al, 2008). Primitive phase is characterized by fuels such as: firewood, agricultural
and animal waste, transition phase is composed of fuels like: charcoal, kerosene and coal,
while the advanced level fuels are electricity and LPG.
The processes of climbing up the ladder is described by linear movement, hereby introducing
another concept in the model, fuel switching. It refers to displacement of s previous fuel by
another advanced one. The model explains that fuel choices and switching in relation to
increase in socioeconomic status (Hertzberg, 2005).
The model portrays firewood as an “inferior good”, i.e. fuel of the poor, however in
developing countries firewood is a significant fuel for both poor and the rich (Hosier et al,
2008). This means that correlation between income level and fuel choice is not as strong as
indicated by the energy ladder model. This has led to critique of the model for
oversimplification and subsequent development of mixed energy model or the fuel stacking
model (Hiemstra -Van - der, 2008).
2.6 Fuel Stacking Model
According to Elias et al (2005), increase in income leads to adoption of new fuels and
technologies that partially substitute traditional fuels, but do not perfectly replace them.
Additionally, Foley (1995) that energy ladder model is a ladder model for energy demand
rather than energy preferences, depending on fuel utility.

20. 12
Masera et al (2000) states that, “practically there is nothing like fuel switching. Instead
households combine fuels from the three different phases of the energy ladder.” This process
is called Fuel Stacking.
Multiple fuel model has gained immense support from energy economics researchers (For
instance: Hertzberg 2005, Mekonnen et al 2008 and Mirza et al 2009). Various reasons have
been given for fuel stacking behavior for instance, Davis (1998) argues that fuel stacking is
an inherent behavior for the rural and urban poor because of their irregular and variable
income levels. Additionally, cultural habits also prevent households from completely
switching to modern fuels (Masera et al 2000).
2.7 Gaps of Knowledge
Following what has been done by other researchers on factors limiting adoption of clean
energy, there is some inconsistency in the energy ladder concept for instance, and increase in
income is thought lead to switching to clean energy. However, in practice households with
high income levels integrate several energy options instead of purely adopting clean energy
such as solar or biogas. Therefore, there is need to study question why do households fail to
entirely switch to clean energy even with high levels of income.
Policy gaps are also evident in that, there is no policy on price ceilings for various clean
energy sources such as LPG and this makes its price to periodically go up making it
unaffordable to the rural households.
Kenyan government does not reward investors in renewable energy technologies for instance,
tax exemption and this limits the willingness of households to adopt clean energy
technologies. No precise policy on domestic energy; the National Energy and Petroleum
Policy does not explicitly touch on domestic energy needs.
With the current era of devolution, research has not sufficiently addressed what devolution
can do to remove barriers to clean energy in rural areas of Kenya. Therefore, this study is a
necessity in the devolved Kenya.

21. 13
CHAPTER THREE: METHODOLOGY
3.1 Description of the Study Area
This research was carried out in Kyuso Sub-county, Mwingi North constituency, Kitui
County; an arid area which lies in latitude 00° 33' 00" S and longitude 38° 13' 00" E. The area
is indigenously composed of the Kamba community. Though in the shopping centres, there
are other tribes such as kikuyu, meru, tharaka and luos (KCIDP, 2013). Annual rainfall
ranges between 500-1040mm (Kyuso District Development Plan, 2008). Annual
temperatures range between 140
C – 340
C, and altitude of 400-1747 m above the sea level.
Figure 3.1: map of the study area
3.2 Research Design
The study used Survey Design. It was mainly a descriptive research concerned with finding
out the what are the main forms of households’ lighting and cooking energy options, it was
chosen because it could enable the researcher to generalize the findings to a larger population
(Cooper et al., 2003). Primary data collection method was entirely administration of
questionnaires. Secondary data collection methods were: review of books, journals,
government publications, magazines and online materials.

22. 14
3.3 Population and sample
The population of study was the residents of Kyuso Sub-County. The Sub-county has a
population of approximately 40,500. (KNBS, 2012). The area is divided into five locations;
Kyuso, Kimangao, Gai, Kathumula and Ngaaie. Kyuso Sub-County has household population
of about 266 (GoK, 2012). It was assumed that 50% of the total population of study could be
available for the study and would give a positive response the questionnaires. In this study
the results were projected to have a confidence level of 95%, and marginal error of 10%. The
sample for the study was determined by the following formula according to Krejcie et al
(1970) as below:
𝑺 =
𝑿 𝟐
𝑵𝑷(𝟏 − 𝑷)
𝒅 𝟐(𝑵 − 𝟏) + 𝑿 𝟐 𝑷(𝟏 − 𝑷)
Where, S= Sample size, d=Marginal error, P= Proportion of the population that will respond
to the questionnaires, N= Population of study and X = Z-Value of the significance level of the
results. The Z-values for Significance levels are: 2.71 for 90%, 3.84 for 95% and 6.64 for
99%.
In the study X= 3.84, N= 133, P= 0.5 and d=10%. Substituting the formula;
𝑺 =
𝟏𝟒. 𝟕𝟒𝟓𝟔𝒙𝟏𝟑𝟑𝒙𝟎. 𝟓 𝟐
{(𝟎. 𝟎𝟏𝒙𝟏𝟑𝟐) + 𝟏𝟒. 𝟕𝟒𝟓𝟔𝒙𝟎. 𝟓 𝟐}
= 99 Respondents.
3.4 Data collection procedures
The Main data collection tool was questionnaires. 99 respondents were administered with
questionnaires. The illiterate respondents were assisted to fill the questionnaires. The
questionnaires were filled by the househeads or any other mature person who could give
correct information about the family.
Secondary data was gathered by: reviewing published data to provide further knowledge on
factors limiting adoption of clean energy sources as observed by other researchers.
3.5 Data analysis
The collected data was analysed for descriptive statistics to show; the measures of central
tendency (mode, median and means) and measures of dispersion (standards deviation,
variance and interquartile range). Additionally, hypotheses tests was carried to determine
their applicability to the study.

23. 15
CHAPTER 4: RESULTS AND DISCUSSIONS
4.0 Overview
This chapter discusses the demographic, social and economic characteristics of the
households in the study area. These characteristics are the ones known to influence them
negatively or positively towards a certain fuel against the other. They include age, marital
status, household size, level of education, source of livelihood and income level. The chapter
also discusses how these socioeconomic factors are a barrier to households’ adoption to clean
energy options.
4.1 Respondents Characteristics and Relationships to Households’ Lighting and
Cooking Energy
4.1.1 Demographic, social and economic characteristics
This section provides a summary of the demographic, social and the economic characteristics
of the respondents. These characteristics are location, gender, age, household size, education,
source of livelihood and level of income.
4.1.1.1 Location
The respondents of the study were randomly taken from different locations in the study area.
Their location is critical because it shows the approximate distance in which the travel to
reach the min shopping Centre. It is in this shopping center where most services are available
including kerosene pumps.
According to Table 4.1 distribution of respondents per location was; Kimu 12 (12.1%), 7
(7.1 %) Kyuso, Kimangao 30 (30.3%), Kathumula 10 (10.1%), Gai 26 (26.3%) and Ngaaie
14 (14.1%)
Table 4.1: Distribution of respondents
Location Frequency percentange
Kimu 12 12.1
Kyuso 7 7.1
Kimangao 30 30.3
Kathumula 10 10.1
Gai 26 26.3
Ngaaie 14 14.1
Total 99 100

24. 16
4.1.1.2 Gender
The gender of the respondents represented in figure 4.1 shows that 59 (59.6%) male and 40
(40.4%) female respondents were involved in the e study. This means majority of househeads
in the study area are men.
Figure 4.1: Gender of the respondents
4.1.1.3 Age
The age composition for the respondents as shown in figure 4.2, 5.1% were below 25 years,
33.35% aged between 26 to 35 years, 30.3% aged between 36 to 47 years and 31.3% were
over 48 years. Descriptive statistics show that majority of the respondents were aged over 26
years. Mean age for the respondents in between 36 to 47 years but majority of them were
between 26 to 35 years.
Figure 4.2: Showing Respondents' Age distribution
59
40
0
10
20
30
40
50
60
70
MALE FEMALE
Gender of the respondents
5
33
30
31
Age of the respondents
<25 26 to 35 36 to 47 Over 48

25. 17
4.1.1.4 Household Size
According to table 4.2, 53.5% of the households had less than 5 members, 40.4% had
between six to nine members while 6.1% had above ten members. Generally, the mode of
households’ size was found to be below 5 members while average household size was
between 7 to 8.
Table 4.2: Household Size
Household
size Frequency Percent (%)
< 5 53 53.5
6 to 9 40 40.4
> 10 6 6.1
Total 99 100.0
4.1.1.5 Education Level
Figure 4.3 below shows the highest level of education the respondents in which 46 (46.5%)
had only studied up to primary level while 26 (26.3%) had studied up to secondary level.
Lastly, 19 (19.2%) and 8 (8.1%) had reached college and university respectively. This shows
the area of study still has high illiteracy level. The proportion of people who reached
university level of education to those who reached primary level is 8:46, almost six times
lower than the latter.

26. 18
Figure 4.3: Respondent’s Level of Education
4.1.1.6 Source of Livelihood
Source of livelihood means the main economic activity of the house head. Househeads are
involved various activities in order to meet their needs; farming, charcoal burning,
employment, and business.
It was observed that 38 (38.1%) and 38 (38.1%) of the respondents relied on employment and
farming respectively. Moreover ever 13 (13.1%) and 10 (10.1%) were involved in charcoal
burning and business respectively. According to these findings it is evident that about 61.6%
of the respondents were involved in faming small-scale business and charcoal burning both
which can hardly guarantee a reasonable income to the family. Farming in the area is
subsistence and rain fed agriculture is dominant not forgetting that rains in the area of study is
erratic and unpredictable.
46
26
19
8
0 5 10 15 20 25 30 35 40 45 50
PRIMARY
SECONDARY
COLLEGE
UNIVERSITY
Respondents' Level of Education

27. 19
Figure 4.4 Households source of livelihood
4.1.1.7 Income
The findings in table 4.3 indicate that 44.4% of the respondent had a monthly earning of
between Kshs 5001- 15000, while other 28.3% earned below Kshs 5000. Additionally, 19.2
% earned between Kshs 15001 to 30000 while, only 8.1% earned above Kshs 30000.The
findings also show that 72.7% of the respondents earned below Kshs 15000.
Table 4.3 income level
Income level Frequenc
y
Percent Cumulative
Percent
< 5000 28 28.3 28.3
5001 to 15000 44 44.4 72.7
15001 to 30000 19 19.2 91.9
30001 to 50000 7 7.1 99.0
> 50000 1 1.0 100.0
Total 99 100.0
38
38
10
13
23
Livelihood
employment Farming Business charcoal Burning

28. 20
4.2 Households Lighting and Cooking Energy
This section will present the various types of households’ lighting and cooking energy
options as realized during the study. Cooking energy options include firewood, kerosene,
LPG and electricity.
4.2.1 Cooking fuels
The main cooking fuel in the households of the study area is firewood at 89.9%, followed by
charcoal 7.1%, LPG 2% and kerosene 1%. According to the reviewed literature there are
various models which have been developed by researchers in order to elaborate on various
energy consumption behaviors. According to table 4.4, the cooking energy consumption
supports energy ladder hypothesis and it does not support Multiple Energy Model (fuel
stacking model). According to energy ladder model the households of the study area can be
classified to be at primitive level. This is because firewood is the dominating coking fuel.
Consequently, 8.1% of the respondents are at transition phase of the energy ladder model
(consuming charcoal and kerosene) at 7.1% and 1 % consecutively. This model portrays
firewood as an inferior good or “energy for the poor”, signifying high poverty level.
However, Hosier et al (2008) argues that, studies in developing countries show that firewood
is an essential fuel for both poor and the rich.
Table 4.4: Type of cooking fuel used
Fuel Frequency Percent
(%)
Cumulative
Percent (%)
Firewood 89 89.9 89.9
Charcoal 7 7.1 97.0
LPG 2 2.0 99.0
Kerosene 1 1.0 100.0
Total 99 100.0
4.2.2 Lighting fuels
The main types of lighting fuels observed in the study are: kerosene, firewood, dry cells, solar
and electricity. Figure 4.5 shows the distribution of lighting energy. The dominating lighting

29. 21
fuel is kerosene at 70.7%, followed by firewood at 15% then solar, electricity and dry cells
are the least used energy sources.
Figure 4.5: Percentage distribution of Lighting energy options
4.3 Socioeconomic Factors Barring Households Access to Clean Energy
This section looks at how various socio-economic factors of the households in the study area
are an incentive to consumption of traditional fuels and thus being a barrier to adoption of
modern clean fuels for lighting and cooking. Modern clean fuels for cooking are LGP and
Electricity. Whereas, for lighting include solar, dry cells and electricity. Major social factors
barriers barring the households from adopting clean energy for lighting and cooking in the
study area include household size, gender of the household, age of the househeads, income
level of the household, cost of the fuel and distance.
4.3.1 Gender
The study observed a high number of male headed households than their female counterparts
at 59.6% and 40.4% respectively. Out of 89 respondents who used firewood, 58.4% the male
headed while 41.6% were female headed, these results differ with Adepoju (2012) where he
found that male headed households had a lower likelihood of using firewood than female
headed households. Consequently, 57.1% of the respondents who used charcoal for cooking
were male while 42.9% were female headed. A similar observation is reported by Bisu et al
(2016), where he notes charcoal consumption by male households stood higher than in female
2
70.7
11.1
1
15
Lighting Energy
Electricity Kerosene Solar Dry Cells Firewood

30. 22
counterparts at 45% and 37% respectively. This means male headed household have a higher
likelihood of using charcoal than firewood.
No observed female households who used either LPG, electricity and kerosene. Two out of
ninety-nine respondents who used LPG for cooking were male headed. This show male
headed households have a higher affinity to cleaner fuels than female headed households,
thus gender composition is an important factor influencing domestic cooking fuel adoption.
The table below shows the type of cooking fuel used per gender.
Table 4.5: Cooking fuel per Gender
type of cooking fuel used Male female
Firewood 52 37
Charcoal 4 3
LPG 2 0
Electrcity 0 0
Kerosene 1 0
4.3.2 Age
The study grouped age of respondents into four categories whereby 5.1% of the respondents
were aged below 25 years, 33.3% between 26 and 35 years, 30.3% aged between 36 to 47
years and 31.3% aged over 48 years. According to table 4. 6 below, all househeads aged
below 25 years used firewood as the cooking fuel only, 31 out of 33 households aged
between 26 to 35 years used firewood only while the remaining 2 used LPG. Additionally, all
households with the househeads aged between 36 to 47 used firewood as well as all
househeads aged above 48 years. This data shows firewood as the dominating cooking fuel.
The population of respondents below 35 years of age shows mixed consumption of cooking
fuel (firewood and LPG). This is partially supported Mekonnen (2008), who notes that liquid
fuels are more likely adopted by young households.
According to the findings in table 4.6, all households aged above 36 used purely firewood as
the cooking energy. Similar findings were reported in others studies (Mekonnen et al 2008,
and Pundu et al 2003). They reasoned out that age influences fuel choices, where old people
are more likely to use firewood than other fuels. This was attributed to their loyalty to
traditional fuel options and their preference for solid fuels than liquid fuels.

31. 23
Table 4.6: Consumption of cooking fuel
Cooking Fuel Age of the family head
< 25 26 to 35 36 to 47 > 48
firewood 4 27 29 29
charcoal 1 4 1 1
LPG 0 2 0 0
electrcity 0 0 0 0
kerosene 0 0 0 1
The results on lighting fuel are in the table 4.7 below, show that kerosene was found to be the
main lighting fuel male and female head households. Solar has higher consumption in male
respondents than in female. Whereas, more female respondents reported to use firewood as
lighting fuels than male respondents.
Table 4.7: Lighting by gender of the respondents
Gender of the respondent electricity Kerosene solar Dry
cells
firewoo
d
male 1 42 9 1 6
female 1 28 2 0 9
4.3.3 Household Size
The number of family members were determined per household and grouped into: below 5
members, 6 to 9 members and above 10 members. Smaller households showed consumption
of mixed cooking fuel, where there were those who used firewood, charcoal and LPG.
Households above ten members used firewood only. In a study by Heltzberg (2005), he found
a relationship between household size and firewood size. He argues that it is cheaper to cook
for many people using firewood than other purchased fuels.
The findings in table 4.8a is supported by his literature because 100% of the respondents with
family size of above 10 members use firewood. Main reason behind this observation is that

32. 24
firewood in the study area is collected for free unlike other fuels (Charcoal and LPG) which
are purchased.
Consumption of charcoal and LPG is only witnessed in smaller households. This can be due
to the little labour inputs required for such fuels unlike firewood which requires high labour
input in firewood collection.
Table 4.8a: Cooking Energy by Household Size
Type of cooking fuel used < 5 6 to 9 > 10 Total
Firewood 48 35 6 89
Charcoal 3 4 0 7
LPG 2 0 0 2
Electricity 0 0 0 0
Kerosene 0 1 0 1
Kerosene consumption was as follows 67.9%, 70% and 100% at age categories below 5, 6 to
9 and above 10 members respectively. Electricity and solar was at 9.4 %, 20% and 0.00%
across the categories, below 5, 6 to 9 and 10 members respectively.
The findings in table 4.8b show a trend of increased kerosene consumption with increase in
the number of household size and high likelihood of consumption clean lighting energy in
lower household size.
Table 4.8b Household size and Lighting energy
Number of family members in the
household
Type of fuel used for lighting Frequency
Observed Percentage (%)
< 5
Electricity 1 1.9%
Kerosene 36 67.9%
Solar 4 7.5%
Dry cells 1 1.9%
Firewood 11 20.8%
6 to 9
Electricity 1 2.5%
Kerosene 28 70.0%
Solar 7 17.5%
Dry cells 0 0.0%

33. 25
Firewood 4 10.0%
> 10
Electricity 0 0.0%
Kerosene 6 100.0%
Solar 0 0.0%
Dry cells 0 0.0%
Firewood 0 0.0%
4.3.4 Level of Education
The highest level of firewood consumption was in primary level, secondary, college and
university consecutively. Respondents with only primary level education did not report to use
charcoal, LPG and Electricity. Only one case reported to use kerosene as a cooking fuel.
According to Pundo et al (2003) high education level improves one’s knowledge of fuel
attributes, tastes and preferences. Additionally, Wuyuan et al (2010) explains that when a
respondent’s education is high they use less biomass because the opportunity cost of biomass
fuels collection is high. These results show education as a significant factor determining
households’ adoption of clean energy according to table 4.9a below.
Table 4.9a: Cooking Fuel and Education Level
Type of cooking fuel used primary secondary college University Total
Firewood 45 25 17 2 89
Charcoal 0 1 2 4 7
LPG 0 0 0 2 2
Electricity 0 0 0 0 0
Kerosene 1 0 0 0 1
Households with primary level education reported to have the highest level of kerosene and
firewood for lighting purposes. It is evident that kerosene consumption dropped with increase
in the level of education of the respondent. People with college and university level education
of solar energy consumption. This means higher education improves one view of fuel and
considers less polluting fuels.

34. 26
Table 4.9b: Lighting fuel and Education Level
Type of fuel used for lighting Primary Secondar
y
College Universit
y
Electricity 0 1 0 1
Kerosene 30 24 15 1
Solar 1 0 4 6
Dry cells 1 0 0 0
Firewood 14 1 0 0
Total 46 26 19 8
4.3.5 Level of Income
Table 4.10a below show consumption of firewood is high among small income earners who
were seen to heavily rely on firewood only for cooking. Similar observations were made by
Hertzberg (2005), who noted that households with low incomes heavily relied on biomass
energy such as wood and charcoal, while those with higher incomes consumed cleaner energy
such as LPG. This shows agreement with energy ladder hypothesis where families choose
fuels according to their budget constraints. The findings are also supported by a study by Bisu
et al (2016) in their study they found that firewood consumption decreased with increased
income level. Similarly, consumption of charcoal increases at middle income level as
reported by the same study by Bisu et al (2016).
Table 4.10a: Cooking and Income level
Type of cooking fuel used < 5000 5001 to
15000
15001 to
30000
30001 to
50000
> 50000
Firewood 27 43 15 3 1
Charcoal 0 1 4 2 0
LPG 0 0 0 2 0
Electrcity 0 0 0 0 0
Kerosene 1 0 0 0 0
Lighting energy source across the income margins is seen to be dominated by kerosene,
however income earners above 50,000 Kshs have not reported any consumption of kerosene
as a lighting fuel. Firewood is also a significant source of lighting energy among the low-
income earners as shown in table 4.10b below.

35. 27
Table 4.10b: Lighting and Income Level
Type of fuel used for lighting < 5000 5001 to
15000
15001 to
30000
30001 to
50000
> 50000
Electricity 0 0 0 1 1
Kerosene 17 36 15 2 0
Solar 0 3 4 4 0
Dry cells 0 1 0 0 0
Firewood 11 4 0 0 0
4.4 Cooking and Lighting Energy Adoption
To ascertain the connection between various socioeconomic factors and the type of fuel used,
hypothesis test was done using Multinomial Logistic Regression. The two hypotheses that
were tested are i) Firewood is likely to be the major source of cooking energy in the
households of the study area, and ii) Socio-economic factors are the main barrier to adoption
of clean lighting and cooking energy in the study area.
Model assumptions are: the model is correctly specified, meaning i) the true conditional
probabilities are a logistic function of the independent variables, no important variables are
omitted, no extraneous variables are included and the independent variables are measured
without error, ii) the cases are independent and the independent variables are not linear
combinations of each other. Generally, it models how multinomial variable Y depends on a
set of X explanatory variables. The explanatory variables are discrete or continuous and, are
linear parameters.
The general Multinomial equation
𝑙𝑜𝑔𝑖𝑡 (𝑌 = 1) = log [
𝑝(𝑌 = 1)
(1 − (𝑃(𝑌 = 1))]
〗 = 𝛽0 + 𝛽1. 𝑋1 + 𝛽2 . 𝑋2 + 𝛽3. 𝑋3 + ⋯ + 𝛽 𝑛 . 𝑋 𝑛
Where, p (y=1) is the probability of the desired event happening, 1- {p(y=1}is the probability
of desired event not happening, β is the coefficient of the predictor, 𝑋1 is predictor variable 1,
𝑋2 is predictor variable 2 etc.
The null hypothesis (H0) was socioeconomic factors are the main barriers to the adoption of
clean lighting and cooking energy, While the alternative hypothesis (Ha)was socioeconomic

36. 28
factors are not the main barrier to adoption of clean cooking and lighting energy in the study
area.
To test hypothesis that socioeconomic factors are the main barriers to the adoption of clean
lighting and cooking energy, multinomial regression analysis for various factors of interest
was performed. Significance level was set at 5% and confidence level at 95%. The main
factors considered are age, household size, income level, gender and education. They were all
found to be not statistically significant at p<0.05.
This means the hypothesis that socioeconomic factors are the main barriers to adoption of
clean energy is accepted. Accepting the hypothesis entails that the considered factors
influence households’ decision on the kind of fuel to use for various domestic purposes such
as cooking and lighting. Table 4.11 below shows likelihood ratio test used for the above
hypothesis.
Table 4.11: Model Parameters
Likelihood Ratio Tests
Effect Model Fitting
Criteria
Likelihood Ratio Tests
-2 Log
Likelihood of
Reduced Model
Chi-
Square
df Sig.
Intercept 11.863 .000 0 .
X1- Age 17.974 6.111 9 .729
X2- HH Size 21.292 9.429 6 .151
X3-Income 27.982 16.119 12 .186
X4-Gender 16.606 4.743 3 .192
X5-Education 26.469 14.605 9 .102

37. 29
CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion
This chapter outlines the summary of the study and gives recommendations. The study was
centered at examining the main factors barring adoption of clean energy for cooking and
lighting in rural household of Kyuso subcounty, Kitui. There are many factors that influence
households to much towards a certain fuel, thus hindering their likelihood to consider another
fuel.
The major types of cooking fuels used by the households of the study area are firewood,
LPG, charcoal and kerosene. Whereas, the lighting energy is provided by kerosene, solar,
firewood and electricity. In most of the reviewed literature, the most evident factors are
household size, gender of the househeads, their level education, their income level, proximity
to the source of energy and their awareness about the existence of clean.
According to the descriptive statistics performed from the data, firewood was found to be the
main cooking fuel while kerosene is the major lighting fuel. Consequently, modern fuels or
rather clean energy options are given insignificant attention. Although there is no single
factor to attribute to this scenario, it can be assumed as a result of interplay of various
socioeconomic factors.
The high consumption of biomass energy can be linked to the proximity to households
whereby the longest distance which is covered to collect firewood is less than a kilometer.
Majority of households are farmers in the study area, yet farming in is quite challenged by
erratic and unpredictable rains. This makes their level of income to be low, thus they are
unable to afford clean fuels which are characterized by high upfront costs.
Most households are large meaning that cooking for them using LPG is uneconomical and
expensive, therefore many households prefer using freely fetched firewood. Additionally,
large households provide enough labour needed for collection of firewood.
Hypothesis test for the factors barring adoption of clean lighting and cooking energy was
carried out and all factors found statistically not significant, thus failing to reject the null
hypothesis. The null hypothesis stated that socioeconomic factors are the main barriers for
household’s adoption of clean cooking and lighting energy.

38. 30
5.2 Recommendations
After the study, the researcher has the following recommendations to make regarding ways in
which various mechanisms can be enacted to facilitate adoption of cleaner, less polluting
fuels in the study area. The recommendations are directed to various actors including; the
county governments, individuals, self-help organizations, suppliers, charity organizations and
public benefit organizations.
• Both the national and the county government should formulate policies which
facilitate consumption and adoption of clean energy options. Such policies may
include making feed in tariffs and net metering policies clearer and unbureaucratic.
This will enable many people in the region which is characterized by high insolation
to venture in solar energy and be able to supply the local community.
• The national government should consider increasing rural electrification in the study
are because more than 99.5% of the households are not connected to the national grid.
• The local public benefit and Charity organizations in the study area can help the locals
switch to cleaner lighting energy by supplying the local community with cheap d-light
lamps like the way Compassion International is doing in some area
• Individuals should consider behavioral change, to switching to cleaner, less polluting
fuels. This can be achieved by intensive awareness creation on opportunity costs
associated with overreliance on biomass energy.
• For the existing self-help groups, to contribute money together and form a pool of
funds from where they can conveniently purchase modern options for one another.

39. 31
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APPENDICES
APPENDIX 1: BUDGET
S/N ITEMS No. of units Cost @ unit
(Kshs)
Subtotals
(Kshs)
1. Pens 40 25 1000
2. Note book 2 100 200
3. Back Pack-Bag 1 1500 1,500
4. Printing 50 50 2500
5. Photocopies 100 50 5000
6. Transport 20 1000 20,000
7. Food 30 200 6,000
8.
Total Time spent (in hours)
30 500 15,000
9. Laptop 1 40,000 40,000
10 Miscellaneous expenses - - 10,000
Total 111,200

43. 35
APPENDIX 2: Time Schedule
ACTIVITY TIME IN MONTHS
Jan Feb Mar Apr May Jun July Aug Sept Oct Nov
Proposal
development
Submission for
review by
supervisor
Data collection
Data analysis
Development of
final project

44. 36
APPENDIX 3
Household Questionnaire
I’m Mutambu Dominic, from Kenyatta University in the school of Environmental
Studies. I’m undertaking a research study on households’ clean energy solutions for
cooking and lighting, and I hereby request you to fill this questionnaire. Information
provided and your identity will be kept confidential. I will really appreciate for your
time and effort. Thank you!
Serial no…..… Date…../…../…..
Q1. Location_________________
Q2. Gender
i) Male ii) Female
Q.3 Age
i) Below 25
ii) 26- 35
iii) 36- 47
iv) Over 48
Q. 4 How many are you in your family?
a) Below 5
b) 5- 9
c) Above 10
Q.5 What is your highest level of education?
a) Primary
b) Secondary
c) Tertiary
Q6. What is your source of livelihood?
i. Employment
ii. Farming
iii. Business
iv. Charcoal burning

45. Q7. What is your average level of income per month in Kshs?
i. Below 5,000
ii. 5,001-15,000
iii. 15001 – 30,000
iv. 30,001- 50,000
v. Above 50,000
Q8. What source of lighting and cooking fuel do you use? (Put a Tick)
iii) In what units do you buy these fuels? ______________________
iv) What is the cost of the above volume? ______________________
v) How long does each unit last? __________________________
Q9. What is the approximate distance from your homestead do you cover to get these
fuels?
S/NO Fuel Appx.
Distance
1 Electricity
2 Kerosene
3 Solar
4 Dry cells
5 Firewood
6 Charcoal
7 Charcoal
Lighting Yes No Cooking Yes No
1. Electricity 1. 2. Firewood
2. Kerosene 3. 4. Charcoal
3. Solar 5. 6. LPG
4. Dry cells 7. 8. Electricity
Firewood 9. 10. Kerosene
5. Others
(specify)
11. 12. Others
(specify)