Energy Consumption in a

Semi-Urban Settlement:

A Case Study of Ajibode

Community in Nigeria

Centre for Petroleum Energy Economics and Law (CPEEL),

Working Paper Series CPEEL/WPS/2015/2

Energy Consumption in a Semi-Urban Settlement: A Case Study of Ajibode Community in Nigeria

Adeola AdenikinjuOlusanya Elisa Olubusoye

Patricia AjayiAdebisi Adewole

Adedoyin A AdelekeLucy Dilinna Nwobi

Michael Oluwaseun Sodipe

October 2015

Centre for Petroleum Energy Economics and Law (CPEEL), University of Ibadan

1.0 INTRODUCTION 3

1.1 Background to the Study 3

1.2 Aim and Objectives of Study 4

1.3 Justification for the Study 5

1.4 Organization of the Study 5

2.0 LITERATURE REVIEW 6

3.0 METHODOLOGY 9

3.1 Description of the Study Area 9

3.2 Sample Design 11

3.3 Mobile Survey Technology 11

3.4 Field Administration 17

3.5 The Challenges 18

4.1 PRELIMINARY ANALYSIS AND DISCUSSION OF RESULTS 19

4.1 Household Structure 19

4.2 Economic Profile 21

4.3 Energy mix 21

4.4 Household Electricity consumption pattern 22

4.5 Energy Saving Behaviour 23

4.6 Determinants of energy consumption 23

4.7 Energy use 24

5.0 CONCLUSION 24

References 25

Questionnaire

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CONTENTS

1.0 INTRODUCTION

1.1 Background to the StudyThe importance of energy in the growth and development of a country cannot be over emphasized. Energy demand, supply and pricing exert great impact on social and economic development as well as the living standards and overall quality of life of the population. Energy use also affects environmental quality through deforestation associated with unsustainable biomass energy dependence and greenhouse gas emission from fossil fuel use resulting in global warming. One important aspect of energy consumption is the household demand for it. There are multiple reasons why the study of household energy consumption pattern and energy requirements is important especially for a densely populated developing country like Nigeria. First, households are major consumers of energy and to a large extent they contribute to the amount of total energy use in the country. Second, household spending and consumption patterns give detail information on the lifestyle of the people. Furthermore, household energy requirements at a disaggregated level help to provide information on the lifestyles of households at the individual level, their energy use and overall standard of living. It also helps to know the extent of penetration of modern energy sources (renewable energy) in the household for heating, cooling and lighting.Nigeria is endowed with vast natural resources both renewable and non renewable, yet it is observed that modern energy services are neither available nor affordable for a high percentage of the population (Akinwumi et al 2009). Nigeria is among the fifteen largest exporters of crude oil in the world and yet ranks high among nations with poor living standards. In Nigeria, the household sector is the largest energy consuming sector in the economy and accounts for about a quarter of total commercial energy and over 80% of traditional fuel use (Adegbulugbe and Akinbami, 1995; Dayo, 2004). Nigeria has a population of over 140 million and an annual growth rate of about 2.9 percent per annum. Urban population as a percentage of total population has grown tremendously over the years. This has led to large variations in the types of energy used and patterns of consumption among households. Oftentimes it is expected that urbanization and increased per capita income will result in greater use of modern fuels, such as gas and electricity and a fall in the share of traditional biomass (IEA, 2005), this has not been the case for many African cities (Chambwera and Folmer, 2007),

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Nigeria inclusive. Although Nigeria is endowed with a variety of clean energy sources, such as hydropower, geothermal, wind, and solar energy, both rural and some sections of urban households in Nigeria rely heavily on biomass fuel for their energy needs. Poverty and lack of access to cleaner and modern fuels has also contributed to heavy dependence on less clean fuels and less efficient appliances. To better understand the factors influencing household energy consumption for various activities, a micro level analysis is imperative. For this reason, the Centre of Petroleum Energy Economics and Law (CPEEL), University of Ibadan decided to embark on a systematic gathering of household energy related data with the ultimate aim of having a credible, useable and regularly updated database for its staff and students to conduct rigorous research on micro level analysis of energy consumption. In this first unique exercise, a survey was conducted for a cross section of households covering the entire enumeration areas of Ajibode community a semi-urban area in Ibadan, Oyo state, Nigeria. The choice of the community is largely due to limited funding available and its proximity to the university environment. Also, perhaps most significantly is the absolute lack of micro level survey designed purposely to collected wide range of data on household energy consumption information in Nigeria. Therefore, the fundamental questions confronting this study are: what are the determinants of energy consumption in Ajibode Community? To what extent has the modern energy alternatives penetrated this community? Who are the suppliers of these alternative energy sources? What is the level of awareness of this community with regard to energy saving? What is the nexus between modern energy use and income level of consumers?

1.1 Aim and Objectives of StudyFollowing the above research questions, the broad aim of this study is to understand the general structure and pattern of household energy consumption in a typical semi-urban settlement in Nigeria. The specific objectives include: i. to investigate the key determinants of energy consumption in the semi-urban settlement; ii. to investigate the extent of penetration of modern energy sources in the semi-urban settlement;

iii. to investigate the nexus between modern energy sources and income levels of consumers.

1.1 Justification for the StudyThe Centre for Petroleum, Energy Economics and Law (CPEEL), University of Ibadan is undertaking a project aimed at profiling the energy consumption among households in Ibadan. The first stage of this project is focusing on Ajibode, a semi-urban settlement because of its mix of rural and urban features. The outcome of the study will enable the centre to characterize energy consumption in the community, the factors driving it as well as analyze the extent of penetration of modern energy into the consumption profiles of residents of the community.The centre also intends to develop an energy information system to collect, manage and analyze data as well update the data annually thereby building up a large energy data base for energy consumption in Nigeria. In addition, undertaking micro level survey will help CPEEL build its students' capacity in the use of modern technology for data gathering for energy research. More specifically, the essence of this initiative is threefold. First, energy consumption pattern in the household sector has long been a subject of research both in the developed and developing countries. It is therefore important to replicate such studies in Nigeria. Second, considering the influx of new energy sources to Nigeria, the need to ascertain the origin (source) and how deep they have penetrated at the household level is important. Finally, the third reason is to build a database for household energy consumption to aid research and help in the effort to design evidence based policy interventions for both government and non governmental agencies working on energy and energy related issues.

1.2 Organization of the StudyThis report is structured as follows: In section 2, a comprehensive literature review is undertaken. Section 3 discusses the survey methodology, sample selection, sample size and a brief description of the study area. Detailed discussion of the mobile survey technology used as well as general field administration is discussed. Section 4 presents and discusses the preliminary results of the survey on household energy consumption. Section 5 concludes the report.

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2.0 LITERATURE REVIEWHousehold energy consumption can be described by the theory of consumer behavior. The basic hypothesis of consumer behaviour is that a rational consumer will always choose a most preferred bundle of goods from a set of affordable alternatives (Varian 1990). The theory of consumer behavior assumes that each consumer has a stable preference system which can be described by means of a utility function. Thus the consumer seeks to maximize his utility subject to income. Therefore the consumer will select goods and services which maximize his utility given his level of income. This implies that households make decisions on the type of fuel to use based on some factors such as economic, social, demographic, cultural and psychological. Household fuel choice which is often viewed through the lens of the 'energy ladder' places heavy emphasis on income in explaining fuel choice and fuel switching. The energy ladder model envisions a three stage fuel switching process. The first stage is marked by dependence on biomass. In the second stage, households move to transition fuels such as kerosene and charcoal in response to higher incomes and factors such as deforestation and urbanization. In the third stage, households switch to Liquefied Petroleum Gas (LPG), natural gas or electricity due to marked increase in their income. The energy ladder seems to suggest that the main driver affecting the movements up the ladder is income and relative fuel prices (Leach, 1992; Barnes and Floor, 1999). Although the energy ladder model appears to imply that a move up to a new fuel is simultaneously a move away from fuels used before. However, this is usually not the case, as most households still retain old fuel appliances as a way of energy security. This multiple fuel use has been the norm in most households (Karekezi and Majoro, 2002: Hosier and Kipondya, 1993). This explains why despite rapid urbanization, the transition to more sophisticated fuels has progressed slowly (Brouwer and Falcao, 2004). The energy ladder model emphasizes income as a determinant for fuel choice it does not provide guidance on other factors that may influence household energy consumption. Studies on household energy fuel choice supports the view that the energy ladder theory is simplistic and looks rather too straight forward, and there are other factors that determine fuel choice (Masera et al, 2000; Campbell et al, 2003; Barnes, 2004). An earlier study by (Hosier and Dowd, 1987) on household fuel choice in Zimbabwe applied a multinomial logit formulation to household energy data. They showed that apart from economic status, other factors such as

ecological zone and relative price or availability of different fuels also determine fuel choice. Other studies in this field includes (Karekezi and Majoro, 2002; Campbell et al, 2003; Heltberg, 2005; Quedraogo, 2006 and Pachauri and Jiang, 2008). These studies showed that a lot of other factors besides income and price affect fuel choice. Some of such factors include the level of education of the head of household, size of the household, type of dwelling, fuel availability and energy access. Evidence from empirical studies on household fuel choice in Nigeria (Adegbulugbe and Akinbami, 1995; Maconachie et al, 2009; Akinwumi et al, 2009) have shown that income, price of the energy good, price of other alternatives and level of education play a key role in determining fuel choice amongst households. However, Abd'razack et al, (2013) shows that due to economic downturn and increased price of petroleum products many households have reverted to the use of fuel wood. Maconacchie, et al, (2009) identified prices and availability of modern fuels as the main factors influencing household fuel choice in Kano state, Nigeria. As economic conditions worsened the price of petroleum products increased thereby compelling many households especially the low and middle income earners to revert to the use of fuel wood. In like manner, Naibbi et al, (2013) corroborate the findings of Maconachie et al, (2009) and also revealed that Northern Nigeria, especially Kano state still relies heavily on traditional fuels. This implies that most Northern states are descending the energy ladder while the Southern states seem to be ascending the energy ladder. In addition the proportion of households' using fuel wood in Minna has also increased (Abd'razack, et al, 2013). Nnaji et al, (2012) also showed that fuel wood is the main energy used for cooking by households in the Eastern zone. They found household size, level of education, occupation of households' and type of cooking appliance to greatly influence the type of energy used by households. This can be attributed to dwindling real incomes of households (Karekezi, et al, 2002) as well as non availability of modern fuels for domestic use. A reassessment of the energy ladder model and its implication for household fuel choice showed that the model seems to have a significant weakness in the fuel transition process because it assumes a switch away from traditional and less efficient fuels to modern fuels (Hosier and Kipondya, 1993; Davies, 1998; Karekezi and Majoro, 2002; Campbell, et al, 2003; Greg Hiemstra-van der Horst, et al, 2008).

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In addition, access to electricity supply has been found to be an important determinant of energy transition (Campbell et al, 2003; Davis 1998; Quedraogo, 2006). Babatunde and Shuaibu, (2011), found residential electricity consumption is a normal good whose expenditure increases as income rises. They also showed that socioeconomic variables, such as household size, number of rooms in the household and hours of power supply were some of the determinants of electricity demand in Nigeria. In conclusion the available body of literature for Africa and Nigeria has mainly focused of the factors that influence energy consumption by households. There appears to be a dearth of literature on the structure and pattern of energy consumption, the extent of modern energy penetration as well as the nexus between income levels and energy consumption of households.

3.0 METHODOLOGY

3.1 Description of the Study Area

Ajibode town was established as a resettlement site for the original

inhabitants of the site on which the University of Ibadan presently sits. It is a

semi-urban settlement, bordered by the Nigeria Institute for Social and

Economic Research (NISER) and University of Ibadan (UI). It is located in

Akinyele Local Government Area of Ibadan in Oyo state, southwest Nigeria.

The GPS coordinates of Ajibode is 7° 39' 0" N, 3° 47' 60" E. By virtue of its

location in the southwest of Nigeria, Ajibode experiences a double-maxima

rainfall season running from April till October and a dry season from

November to March.

Majority of the inhabitant are artisans and farmers. While some are public

servants working either in UI or NISER. The town is undergoing

transformation from rural to semi-urban due mainly to the influx of UI and

NISER staff members who are transforming the landscape of the

community with new buildings and other physical development. The

community is headed by a Baale in the person of Chief Ajibode and for the

purpose of administration and security, it is divided into eleven zones each

headed by a zonal chairman who reports to the Baale of Ajibode.

The study area had been sub-divided into enumeration areas by the National

Population Commission for ease of access and coverage during the census

conducted in 2005. The delineation of the enumeration areas done by the

NPC was utilized for the study. A total of 24 enumeration areas were covered

with GPS coordinates of each area taken. As much as circumstances

permitted, the coordinates were taken from the midpoint of each

enumeration area. A Garmin GPS device was used in taking the readings of

the coordinates (see Figure 3.1)

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Satellite image of Ajibode, as shown in Figure 3.2 below, was downloaded with the aid of the software 'Google Earth'. Here, the area was located and the high resolution image was saved alongside the georeference file. This was done to enable the editing of geo-spatial work on the image. The coordinates were converted from 'degrees, minutes, seconds' to 'decimal degrees' using the formula 'degree + (minute/60) + (second/3600). The computed result is imported into the MS Excel worksheet. This served as the primary data used in plotting the GPS coordinates on the high resolution image. The conversion was necessary in order to align the geographic unit of the primary data with that of the secondary data, that is, the high resolution satellite image. The GIS software ArcGIS was used for plotting the coordinates and rendering the high resolution satellite image. In order to achieve this, the high resolution satellite image was imported into the software's workspace alongside the converted coordinates from MS Excel. The software automatically identifies and plots the coordinates as points on the pre-loaded image. For ease of access and manipulation however, the .xls (excel) file was converted to a shape file (.shp). This

Figure 3.1 A Garmin GPS device being used to take GPS coordinates of an EA

made secondary manipulation easier. The icon, icon size, icon colour, etc., were selected according to preference and compatibility.3.2 Sample DesignA representative sample of 384 households was selected for the study. The sample covered the entire population residing in the non-institutional dwelling units in the community. The community was stratified on the basis of the enumeration areas (EAs). The demarcation of the EAs was made easy through the support of the National Population Commission. The Commission provided the map for each of the EAs and the officials assisted in the identification of the EA boundaries. Allocation of sample size to EAs was proportional to the number of listed households in each of the EAs and the sampled households were drawn randomly from each EA. The Global Positioning System (GPS) receivers were used to capture the coordinates at the center of each of the 24 EAs. 3.3 Mobile Survey TechnologyThe limited accessibility of domestic survey data in emerging economies like Nigeria has been a major impediment to research and development. However in recent times, there have been concerted efforts to improve statistical capacity and more detailed focus on significant variables both in individual and aggregate levels. Statistical production tools are struggling to meet the ever increasing demand for precise indicators required for analysis and hypothesis verification. With the rapid advancement in information communication technology (ICT), there are now more promising software techniques for data gathering, as well as crunching numbers concerning pointers with greater efficiency. Such software include datawinners®, QuickTapSurvey® and Survey monkey mobile applications. With a pool of available technologies with distinctive array of functionalities and features, asserting the objectives for the project was the major determinant of the choice of ICT tools employed. The project team decided on the Harvest your data mobile application because it clearly met the defined project objectives, budget, and data collection process. Particularly, its unique ability to collect data offline and the result options in CVS or SPSS (see figure 3.2) makes it best fit as it makes the project unaffected by the uncertainty of internet access. The mobile application boosts the speed of data collection and facilitates data accuracy at an affordable price. It allows offline collection and submission of data on the field thus requiring

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internet access only for uploading of the collected data. Conventional surveys may cost less at the initial stages however their credibility and input error in the end make them less efficient. There has been surge of mobile phone based methods for data collection and analysis. These involve some of the usual techniques of information gathering such as questionnaire and interview while maintaining the integrity of the responses at the points of data entry and collation. The results of this process are vital for policy formulation and legislation. Improved statistics produced through these new techniques is critical to improving data-based research results which are a true representation of the sample population.

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The survey instrument contained 88 questions covering the following subjects:· Socioeconomic background· Awareness of various domestic energy sources· Usage of various domestic energy sources· Household expenditure on power supply alternatives· Electricity consumption profile· Accessibility and satisfaction derived from modern energy useThe questions were structured within the software to ensure readability, comprehension and credible data from the respondents. The question styles used are:· Single Select: only on answer can be chosen e.g gender of respondent· Multiple-select: more than one answer can be chosen e.g reasons for

choice of cooking energy· Numeric: only accepts inputs within 0-9 without special characters e.g

quantity of electricity· Drop-down: has a scroll feature view question's response question. · Grid Single Select: these questions require ranking of responses e.g.

“agree”, “strongly agree” etc

Figure 3.2: Map of Ajibode showing the Enumeration Areas

Figure 3.2: Unique features of harvestyourdata Software

See the Illustrations on Figure 3.6 showing some of the question styles.The first graphical user interface (GUI) of the application displays the centre's logo and the aims and objectives of the survey in brief. Each page of the questionnaire (see appendix) depicts the Center's name and logo. The software allowed for GPS, audio and video data capture. However, the team opted for none of these as they would consume the battery power and reduce the number of surveys conducted before the Mobile device battery power is depleted.

Some special features of this mobile data survey include: LOCK SCREEN: which is activated during a survey after a period o f

inactivity HIDE STATUS BAR: of the mobile device to provide a wider view a n d

avoid distractions of incoming alerts during the survey exercise. PROGRESS BAR: is located at the top of the screen and moves from

left to right showing percentage of the survey that has been completed

REVIEW RESPONSE: feature was disabled to increase the credibility of the responses

AUTO-NEXT: tool was enabled to switch to the next question once t h e current one has

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RANDOMIZED QUESTIONS: for each survey set to caution during the exercise.

REPHRASED QUESTIONS: to verify previous responses and increase survey credibility especially for questions regarding periodic income and expenditure.

The software allows the questions to follow a logical sequence. The flow (depicted in figure 3.4) of the questions is arranged by the survey developer indicating which questions precedes others and which ones to skip based on respondents' answer to previous ones.

been answered.

Figure 3.3: Designing the Survey Questions

INFO -PAGES: Figure 3.2 displays the design of an information page which precede each group of questions briefly describing the intent of the questions on the next pages.

Figure 3.4: Flow of questions showing precursors and successors The mobile survey can be previewed and the question set exported in pdf format. At this stage the questions is now downloaded on mobile devices and run to execute the survey (See Figure 3.5). The figure below shows snapshot of the mobile device view of the questions and subsequent page to up load the survey result.

3.4 Field AdministrationSixty graduate students of the centre were engaged as field workers. Forty-eight of them served as enumerators while twelve served as supervisors. To prepare the team for the survey, training was conducted to orientate the field workers (enumerators and supervisors) on their duty and questionnaire administration procedure. It was facilitated by the officials from the National Population Commission (NPC) and National Bureau of Statistics (NBS). The field workers were trained on delineation and identification of enumeration areas and how to conduct survey interview. It also entailed how to approach and start up an interview with respondents and how to strategize on how to finish the survey within stipulated time. Training was also conducted on the use of the mobile application (harvestyourdata mobile application). Both trainings which lasted one hour each, equipped the field workers with the necessary skills for the survey. Training materials were also provided by the resource persons for the field workers for reference purposes.The field work commenced with sensitization activities which took the team first to the local government headquarters of Akinyele LGA where a formal letter about the survey project was submitted to the chairman and the director of planning, research and statistics. Then a visit was made to the Baale of Ajibode, Chief Ajibode to inform and seek his assistance in getting the community to cooperate with our field workers who will be administering the

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questions. This was followed by a sensitization meeting with each of the zonal executive committee of the eleven zones that made up the community.

Figure 3.5 Snap shot of the Mobile software showing the download page

Figure 3.6 Showing the various styles of survey questions

Figure 3.7 Cross section of the project team member during the survey

exercise

The fieldworkers were divided into twenty-four groups each consisting of

two people. Each group was assigned to an enumeration area (EA). Each of

the twelve supervisors was assigned to cover two EAs (See Figure 3.7 showing

some members of the team). In order to anticipate and address issues that

may come up during the main survey, a pretest was carried out a week

preceding the actual survey. A number of issues relating to logistics, the

questionnaire and the electronic mobile application were identified and

addressed prior to the actual survey.

3.5 The Challenges

A number of challenges were encountered in the course of executing the

project. Some of which are

i. Overlapping enumeration areas: There were some streets that fell into

more than one enumeration area. This was due to the outdated and

incorrectly drawn maps of the community provided by the National

population commission.

ii. There were issues with the electricity bill such as bill misplacement or

lost estimated billing.

iii. Inadequate mobile and compatible devices (mobile phones, tablets,

etc.) for the use of the fieldworkers recruited for the project.

iv. A major challenge was that device batteries could not last the survey

period and power banks were needed to recharge flat phone batteries.

v. There were few cases of resistance and outright refusal on the part of

some selected households to participate in the survey.

4.1 PRELIMINARY ANALYSIS AND DISCUSSION OF

RESULTS

This preliminary analysis is basically descriptive and covers a wide range of

issues such as: Household Structure; Economic Profile; Energy Mix;

Household Energy Consumption Pattern; Energy Saving Behaviour;

Determinants of Energy Consumption; and energy use. The following

subsections provide brief summary of the findings.

4.1 Household Structure

In Figures 4.1.1 and 4.1.2, although 57% of the respondents are household

heads, 73% of those that are not household heads are either spouses or

relatives thus giving high degree of confidence on the quality of information

provided. 76% of the household heads are married and 84% are of Yoruba

decent. 60% of them have a university degree or its equivalent. The family

composition shows that on average, there are 4 adults and 3 children per

household. A further analysis of the family composition by EAs reveals that

Ademola, Community Health Centre and Mr Faturoti EAs have the largest

average number of adults (7) per household.

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43.083%

56.917%

0

20

40

60Percentage of Respondents

Not Head

Head

Figure 4.1.1: Distribution of The Respondents' Status

4.2 Economic Profile

In Figure 4.2.1, the distribution of the major sources of income of the

respondents is shown. Most of the respondents, precisely 58.5% are on paid

employment. The average monthly income of the households is

NGN74,279. Most of them are involved in small businesses (70%). A good

proportion of the households live in bungalows (49%) and flats (21%). 60%

either built or bought their homes. It can be deduced that majority of the

households are in the middle class economic ladder.

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5% biofuel lamp, and 10% solar home system. This explains the non utilization of these alternative sources by households as only less than 1% of the respondents owned any of them. Indeed and worthy of note is a particular household that is not connected to national grid for over 10 years.

4.4 Household Electricity consumption pattern

Presently, electricity consumers are billed either on prepaid or postpaid options. The bills paid for consecutive three months of June, July and August 2015 were captured in the survey. As shown in Figure 4.4.1, 71% of the respondents use the post-paid billing system which implies they are billed after they consume. The household on prepaid option spent an average of NGN2679.76 on electricity bill for the three months observed in the study. Similarly, households on the post-paid platform spent an average of N14,827.64 for the same period observed. This category includes those without electricity meters but who are given estimated monthly bills. This billing method has been a controversial in recent times as most electricity consumers often complaint of receiving bills for what they did not consume. It not clear if this could be responsible for the huge difference between households operating the two systems.

41.5%

58.5%

Paid employment Self employment

Figure 4.2.1: Major Sources of Income of Respondents

4.3 Energy mix

Surprisingly, awareness of modern alternative energy sources appears to be lacking. The analysis shows that %8 of household heads are aware of solar cooker, 5% biofuel gel, 15% Solar lantern,

4.5 Energy Saving BehaviourThe culture of saving energy seems to be very high in this community as 39% of the respondents make use of energy-saver bulbs. The question as to whether they turn off appliances when not in use indicates an overwhelming proportion, 94%, of the respondents showing “yes”. A quick explanation for this may be to reduce cost of energy but further empirical investigation is necessary.

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4.7 Energy use

The analysis investigates the energy types and their uses among the sampled

households in the community. For cooking: 47% of households depend on

Kerosene, 31% depend on cooking gas, 19% depend on firewood and 13%

depend on Electric Stove. For lighting: 52% depend on electricity, 39% on

generator, 17% on kerosene lamp and 5% on inverter. This corroborates the

findings in 4.6 that kerosene is devoted for cooking and electricity devoted for

lighting.

5.0 CONCLUSION

The project was aimed at generating a reliable and useable database on energy

information particularly at the micro level for researcher at CPEEL and

elsewhere. In particular, the study profiled the energy consumption pattern

of a semi-urban settlement namely, Ajibode community of UI and also

examined the penetration of modern energy sources in the community.

Sixty percent of the sampled household heads hold first university degrees

which depicts a significantly high level of literacy in the community. However,

contrary to expectation, majority were unaware of the modern energy

alternatives talk less of their utilization. On a positive note, energy saving

practice was found to be quite high in the community although the

penetration of modern energy alternatives (renewable energy source) was

observed to be very low. The study also established that the key driving factor

for household choice of energy alternatives is availability rather cost as one

would have expected.

4.6 Determinants of energy consumption Here attempt is made to investigate the driving factors responsible for the households' choice of energy source for lighting and cooking. In both cases, the key factor is availability, 31% for lighting and 37% for cooking. Other factors such as cost, quality and reliability, environmental friendliness, safety and ease of use carry less weight. The implication of this is that, households are driving more by the availability factor, not even cost to meet their energy needs for lighting and cooking. This is indeed a window of opportunity to energy suppliers if their product can be made available then the people are willing to consume them.

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Varian, H.R. (1992), Microeconomic Analysis, Third Edition. W.W. Norton and Company Inc. New York.Yonas Alem, Abebe D. Byere, Gunnar Kohlin and Alemu Mekonnen (2013),

“Household fuel Choice in Urban Ethiopia: A Random Effects Multinomial logit Analysis. Environment for Development (Discussion Paper).

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Survey Name: Final Draft CPEEL Ajibode Mobile Data Survey ProjectDescription: UNIVERSITY OF IBADAN CENTRE FOR

PETROLEUM, ENERGY ECONOMICS AND LAW (CPEEL) QUESTIONNAIRE ON HOUSEHOLDS ENERGY CONSUMPTION PROFILE

Introduction: The Centre for Petroleum, Energy Economics and Law (CPEEL), University of Ibadan is undertaking a research to profile energy consumption among households, in Ibadan. The outcome of the study will enable the Centre to characterize energy consumption in Ibadan as well as factors driving it. In addition, it will help to study the extent of penetration of new energy into the consumption profiles of residents of the city. This research is for academic purpose only and the information supplied will be treated with utmost confidence.

Conclusion: Thank you for completing the survey. CPEEL, UI IbadanSurvey Created by: Centre for petroleum energy economics and Law UISurvey Created on: 9/3/2015 5:33:57 AM

Survey Questions and Flow

Question Setting Statements Answers/Ratings Next Question

Question

Type

1: Interview’s Full Name AnswerRequired

2

2: InformationThis section Provides data about your placeof residence andSocioeconomic Characteristics

3

Text

3: Enumeration Area (As Stated on the Map) AnswerRequired

4

Text

4 : Address of respondent including Building Number and Owner's name AnswerRequired

5

Text