DETERMINANTS OF ENERGY EFFICIENCY IN BLACK
TEA PROCESSING FACTORIES
A CASE OF KENYA TEA DEVELOPMENT AGENCY
BY
JAPHETH BULALI SAYI
A RESEARCH PROJECT REPORT SUBMITTED IN PARTIAL
FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF
THE DEGREE OF MASTER OF ARTS IN PROJECT PLANNING
AND MANAGEMENT OF THE UNIVERSITY OF NAIROBI
2014
DECLARATION
This project report is my original work and has not been submitted for degree award in any
university.
Signature-------------------------
Date-------------------------------
JAPHETH BULALI SAYI
L50/66283/2010
This project report has been submitted for examination with my approval as university
supervisor.
Signature-----------------------------
Date--------------------------------
DR STEPHEN WANYONYI LUKETERO
LECTURER
SCHOOL OF MATHEMATICS
UNIVERSITY OF NAIROBI
i
DEDICATION
This research study is dedicated to my parents, Floice and the late Fanuel Sayi, who took
special interest and commitment in my education. It is as a result of their attitude towards the
value of education that I am able to accomplish academic tasks of this nature. May the
Almighty bless them.
ii
ACKNOWLEDGEMENT
I wish to acknowledge Dr Stephen Wanyonyi Luketero, my immediate supervisor, for
playing a leading role in guiding this research study. I would also like to appreciate Mr.
Chandi Rugendo, resident lecturer-Meru extra mural centre university of Nairobi during my
study period, for his comments. The contribution from the university of Nairobi extra mural
department research proposal interview panel in pointing out gaps in the research proposal
reflected the true spirit of the institution as a centre for academic excellence. It improved the
quality of my initial work. I was humbled by the patience of the library staff at the University
of Nairobi on the many occasions I went to seek their assistance. They helped me access
useful material for literature review. My special gratitude and love go to my wife Helen
Bulali, my sons Fanuel Sayi, Johnston Bulali, James Bulali and my daughter Rose Bulali for
providing me with an enabling environment to carry out my work and also relax. I sincerely
thank all those who lent their support but have not been acknowledged individually due to
limited space.
iii
TABLE OF CONTENT
DECLARATION.....................................................................................................i
DEDICATION........................................................................................................ ii
ACKNOWLEDGEMENT...................................................................................... iii
TABLE OF CONTENT.......................................................................................... iv
LIST OF FIGURES................................................................................................ ix
LIST OF TABLES.................................................................................................. x
ABBREVIATIONS AND ACRONYMS...............................................................xi
ABSTRACT..........................................................................................................xii
CHAPTER ONE: INTRODUCTION.................................................................... 1
1.1 Background to the study................................................................................... 1
1.2 Statement Of The Problem............................................................................... 3
1.3 Purpose of the study.......................................................................................... 4
1.4 Objectives of the study..................................................................................... 4
1.5 Research Questions........................................................................................... 5
1.6 Significance of the study...................................................................................5
1.7 Delimitations of the study................................................................................. 6
1.8 Limitations of the study.................................................................................... 7
1.9 Assumptions of the study.................................................................................7
1.10 Definition of Significant Terms as Used In the Study.................................... 8
1.11 Organization of the study................................................................................9
iv
CHAPTER TWO: LITERATURE REVIEW........................................................ 10
2.1 Introduction....................................................................................................... 10
2.1.1 Review of Energy Efficiency in Energy Demand Sectors.............................10
2.1.2 Energy Efficiency and Climate Change.........................................................11
2.1.3 Energy Efficiency and Technology............................................................... 12
2.1.4 Energy Efficiency and Economic Development............................................ 13
2.1.5 Energy Efficiency and Primary Energy Sources........................................... 14
2.1.6 Energy Efficiency and Human Behaviour..................................................... 15
2.1.7 Energy Efficiency and Capacity Utilization.................................................. 16
2.1.8 Energy Efficiency and State Policy............................................................... 17
2.1.9 Energy Efficiency Awareness........................................................................ 18
2.3 Determinants of Energy Efficiency in KTDA.................................................. 19
2.3.1 Technology and Energy utilization Efficiency............................................. 19
2.3.2 Capacity Utilization and Energy utilization Efficiency................................. 21
2.3.3 Technical staff awareness and Energy utilization Efficiency...................... 22
2.3.4 Energy Mix and Energy utilization Efficiency............................................. 24
2.5 Energy efficiency concepts, indicators and methodological issues.................. 26
2.6 Energy Intensity................................................................................................ 27
2.7 Theoretical Framework..................................................................................... 28
2.8 Conceptual Framework..................................................................................... 30
2.8.1 Interrelationship of Variables in the Conceptual Framework...................... 31
v
2.9 Summary of Chapter Two.................................................................................32
CHAPTER THREE: RESEARCH METHODOLOGY.....................................
34
3.1 Introduction....................................................................................................... 34
3.2 Research Design............................................................................................... 34
3.3 Target Population.............................................................................................. 34
3.4.1 Determination of sample size........................................................................ 35
3.4.2 Sampling Procedure...................................................................................... 36
3.6 Validity............................................................................................................. 38
3.7 Reliability.....................................................................................
38
3.8 Data Analysis.................................................................................................... 39
Operationalization of Variables.............................................................................. 40
CHAPTER FOUR: DATA ANALYSIS, PRESENTATION AND
INTERPRETATION.............................................................................................. 41
4.1 Introduction....................................................................................................... 41
4.2 Document Form Review................................................................................... 41
4.3Questionnaire Return Rate................................................................................. 42
4.4. Demographic Information................................................................................43
4.5 The extent of the influence of capacity utilization on energy efficiency..........47
4.5.2 Correlation of capacity utilization and energy efficiency variables.............. 48
4.6 The influence of Technology on energy efficiency.......................................... 49
4.7 Influence of technical staff awareness on energy efficiency........................... 52
4.7.1 Awareness of the term energy efficiency...................................................... 53
vi
4.7.2 Technical staff awareness of energy efficiency measures............................. 54
4.7.3 Sources of Awareness.................................................................................... 55
4.8 The influence of energy mix on energy efficiency in KTDA Tea Factories.... 56
4.8.2 Correlation of energy mix variables and energy efficiency........................... 57
4.9 A Summary of the Chapter............................................................................... 59
CHAPTER FIVE:SUMMARY OF FINDINGS, DISCUSSIONS, CONCLUSIONS
AND RECOMMENDATION............................................................................. 60
5.1 Introduction....................................................................................................... 60
5.3 Discussion of findings...................................................................................... 62
5.3.1 Influence of Capacity utilization on energy efficiency.................................. 62
5.3.2 The influence of technology of processing machinery on energy efficiency 63
5.3.3 The influence of technical staff awareness on energy efficiency.................. 64
5.3.4 The influence of energy mix on energy efficiency........................................ 64
5.4 Conclusions.....................................................................................................65
5.5 Recommendations............................................................................................. 66
5.6 Suggestions for further study............................................................................ 67
References............................................................................................................... 68
APPENDICES........................................................................................................ 72
APPENDIX ONE: KTDA BLACK TEA PROCESSING FACTORIES...............72
APPENDIX TWO: MACHINE TECHNOLOGY DOCUMENT FORM............. 74
APPENDIX THREE: CAPACITY DOCUMENT FORM (T003)........................75
APPENDIX FOUR: ENERGY MIX DOCUMENT FORM (T004)..................... 76
vii
APPENDIX FIVE: QUESTIONNAIRE TRANSMITTAL LETTER................... 77
APPENDIX SIX: QUESTIONNAIRE ................................................................. 78
APPENDIX SEVEN: SAMPLE SIZE SELECTION TABLE.............................. 81
viii
LIST OF FIGURES
Figure 1: Conceptual framework ……………………………………………………… 38
ix
LIST OF TABLES
Table 1 Operationalization of Variables…………………………………….…........
40
Table 2 Questionnaire Return Rate……………………………………........ ……..…...... 42
Table 3 Gender of respondents……………………………………………….…............... 43
Table 4 Ages of Respondents……………………………………………………............... 44
Table 5 Academic Qualification of Respondents…………………………………........... 45
Table 6 Duration of Work Experience……………………………………………............ 46
Table 7 Mean Capacity utilization and energy efficiency per cluster sample.............. 47
Table 8 Correlation of capacity utilization and energy efficiency. ……………............ 49
Table 9 Table of energy efficient machines ratio and energy efficiency...........
50
Table 10 Energy efficient technology machines and energy efficiency........................ 51
Table 11 Awareness of energy efficiency terminology…………….…..... …………....
52
Table 12 Definition of the term energy efficiency……………….. …………………..... 53
Table 13 Technical staff awareness of energy efficiency measures…........................
54
Table 14 Sources of awareness………………………………………………….…….......
55
Table 15 Energy Mix by Percent Proportion….................................................................
56
Table 16 Correlation of energy mix variables and energy efficiency……………........ 58
x
ABBREVIATIONS AND ACRONYMS
ACEEE:
AIS:
American Council for Energy Efficient Economy
Advanced Industrial Systems
ASHRAE: American Association of Heating, Refrigeration and Air Conditioning
Engineers
CTC:
Cut, Curl and Tear
DECC:
UK Department of Energy Conservation Commission
ECE:
Energy Consumption and Efficiency
ERC:
Energy Regulation Commission
GHG:
Green House Gases
IEA:
International Energy Agency
IPCC:
Intergovernmental Panel on Climate Change
KTDA:
Kenya Tea Development Agency
OECD:
Organization for Economic Co-operation and Development
R&D:
Research and Development
TRF:
Tea Research Foundation of Kenya
WEO:
World Energy Outlook
UNFCCC: United Nations Framework Convention on Climate Change
UNIDO:
United Nations Industrial Development Organization
xi
ABSTRACT
The purpose of this study was to examine determinants of energy efficiency in black tea
processing factories. It was guided by four main objectives which sought to determine the
influence of capacity utilization, energy efficient technology machines, technical staff awareness
and energy mix on energy efficiency. The target population for the study comprised sixty five
KTDA managed black tea processing factories. The researcher employed descriptive and
empirical research designs for answering the four research questions. The sample size for the
study of technical staff awareness was obtained by using the Yamane (1967) simplified formula
for calculating sample size. It yielded two hundred and twelve technical staff out of a target
population of four hundred and twenty likely respondents. A representative sample for
questionnaire administration from the seven regions was achieved through cluster sampling and
balloting. The study of the remaining variables involved document review from the sixty five
target factories. The researcher adopted a census approach to minimize sampling errors.
Research questionnaires for technical staff awareness were developed, evaluated, pilot tested and
revised with the assistance of KTDA technical staff before being mailed to regional information
technology coordinators who were engaged to administer them in the field. A reliability test was
carried out and a reliability coefficient of 0.85 obtained using the split half technique.
Questionnaire respondents were guaranteed confidentiality through an introduction letter. A high
response rate of 96% was achieved. Collected data was analyzed using the internet based free
statistics software for social scientists. Results from the study show that capacity utilization has a
weak to moderate positive influence on energy efficiency, energy efficient technology machines
showed mixed results on their influence on energy efficiency. Although technical staff energy
efficiency awareness was high, above 95% in all the study samples, it showed no influence on
energy utilization efficiency. According to research findings, energy mix had the greatest
influence on energy efficiency. Among the energy mix semi- variables, fuel wood accounted for
the highest proportion at 91.1% and was negatively correlated to energy efficiency. Electrical
energy accounted for 8.6% and is positively correlated to energy efficiency. Boiler furnace oil
had the least percentage by proportion at 0.3% with mixed results in its correlation relationship
with energy efficiency among the different regions. These findings may assist the local tea
industry to prioritize energy efficiency improvement measures starting with fuel wood, electrical
energy and lastly furnace oil. Energy policy makers, the energy regulatory commission and Green
House Gas emission advocates may use the findings to plan and focus their activities towards
high impact results areas. The study recommends that more focus should be directed towards fuel
wood management and the establishment of an elaborate system to assess energy efficiency
performance of newer machinery technologies. The researcher has recommends further research
to be carried out in order to establish why fuel wood has a negative influence on energy
efficiency. This is important since this form of energy accounts for over 90% of current energy
needs.
xii
CHAPTER ONE:
INTRODUCTION
1.1 Background to the study
Energy is a basic factor for industrial production. Globally, growing population,
industrialization and rising living standards have substantially increased dependence on
energy (Ines, 2010). As a result, the development of conventional resources, the search
for new or renewable energy sources, energy conservation (using less energy) and energy
efficiency (same service or output, less energy) have become unavoidable topics.
Researchers have argued that the world population’s current ecological footprint far
exceeds the planet’s long-term capacity (Kamper et al. 2000).
Energy generation and industrial activity contribute significantly to the overall emission
of green house gases which are thought to be the key drivers of global warming. A
reduction of manufacturing energy consumption is therefore relevant for the limitation of
overall green house gas emissions. In this context, an understanding of the energy inputs
consumed by the available manufacturing processes is critical.
In the period 1990 – 2005, global primary energy supply increased by 30% while
Worldwide energy demand is projected to almost double between 2005 and 2050
(International Energy Agency, 2007). It is estimated that by exploiting the technical
potential for energy efficiency improvement in energy demand sectors, this growth can
be limited to 8% (Graus et al, 2010).Energy consumption is often used as a measure of
the level of economic development of a nation.
In 1995, the tea industry brought USD 342 million into the country and Kenya became
the largest exporter of black tea in the world then. Tea is now among the three leading
1
foreign exchange earners contributing up to 26% of the total foreign exchange earnings.
The sector provides substantial investment opportunities in areas of tea growing,
manufacturing and value addition (Lelgo et al, 2010). The industry is heavily dependent
on energy for the manufacturing process. This is mainly for tea withering and drying and
to run machinery and fuel for transport. Gesimba et al (2003) identified stagnated dollar
price, the rise in basic wages, unreliable electricity and high costs of fuel as some of the
forces that threaten the tea industry in Kenya while observing that tea factories have been
hardest hit by the ban on procurement of wood fuel from forests. Factories have therefore
been forced to procure fuel from farms where trees are rare and therefore sold at
exorbitant prices. The imported furnace oil alternative is too costly to sustain the tea
business due to a steady increase in world oil prices.
According to the Tea Research Institute of Sri Lanka and the Tea Research Foundation of
Kenya, this energy cost constitutes close to 30% of the cost of production at factory level.
In the year 2000 petroleum products and crude oil imports, the main sources of energy for
the tea industry at the time, accounted for 26% or Ksh 63 billion of Kenya’s import bill,
consuming half of the foreign exchange that the country generated from tea (Energy
Alternatives Africa, 2003). With the worldwide increase in energy costs, the cost of
production of tea has increased significantly resulting into reduced earnings from the
industry. According to De Silva (1993), the total energy required to produce one
kilogram of made black tea is about 25MJ where the main energy sources are imported
and expensive petroleum fuel and fuel wood. In comparison, the KTDA managed tea
factories recorded an average performance of 34.0 MJ for each of the last three years,
2009, 2010 and 2011, which is thirty six percent higher. Exploiting the benefits from
improvements in energy efficiency has the potential to provide immediate, least cost,
environmentally friendly and a sustainable alternative reprieve from the high energy
costs.
2
1.2 Statement Of The Problem
Globally, growing population, industrialization and the rise in living standards have
substantially increased dependence on energy (Ines, 2010). As a result; there has been an
increase in Green House Gas (GHG) emissions that are believed to be responsible for
global warming. The Green House Gas emissions result from many of the industrial,
transportation, agricultural and other activities through population growth, fossil fuel
burning and de forestation.
The International Energy Agency report (IEA, 2007) indicates that global energy supply
increased by 30% between 1995 and 2005. The Agency also projected that the worldwide
energy demand is to double between 2005 and 2050. There is now a broad consensus that
the continued increase in Green House Gas emissions impact on climate change will
affect human life on earth (AIS, 2002). In response, world organizations and
governments have developed various initiatives to address the potential problem. United
Nations Framework Convention on Climate Change (UNFCCC) has established funds to
aid developing countries to build capacity and the transfer of energy efficient and
environmentally sound technological measures. It can therefore be observed that energy
efficiency is among the tools that have been identified in the fight against global
warming.
There has been significant energy related changes on the global scene in the last twenty
years with reduced energy intensity (improved energy efficiency) in some countries.
Between 1980 and 2001, the OECD’s energy intensity declined 26%, the group of
seven’s (G-7) fell by 29%; and the US dropped by 34% (IEA, 2007). No similar
information is currently available from the African region; maybe due to lack of research
studies on energy efficiency. Results from previous studies on the influence of capacity
utilization, technology, awareness and fuel mix on energy efficiency have been mixed.
3
Baumers et al (2010) established empirically that the effect of capacity utilization on
energy efficiency varies across different sectors. Santosh (2009) carried out experimental
studies in Indian manufacturing industries that established a positive correlation between
the size of the firm and energy efficiency. The study does not mention the aspect of
capacity utilization as observed by Baumers et al (2010).
A review of the KTDA strategic business plan for the period 2010/2014 shows the
organization’s bench mark (key performance indicator) is set at 23.0 MJ. However, the
average recorded performance to date of 34MJ has consistently fallen below this target.
Energy intensity is inversely related to efficiency; the higher the value the lower the
efficiency. The average performance is thus 48% above the organization’s benchmark.
According to Lelgo et al. (2010), the tea sector provides substantial investment
opportunities in areas of tea growing and value addition. However, Gesimba et al. (2009)
identifies unreliable electricity and high cost of fuel as threats to this industry in Kenya.
The problem of this study was therefore to determine to what extent determinants of
energy efficiency influence energy efficiency and therefore energy consumption in black
tea processing factories.
1.3 Purpose of the study
The study sought to examine determinants of energy efficiency in black tea processing
factories.
1.4 Objectives of the study
The study was guided by the following objectives:
i. To determine the extent of the influence of capacity utilization on energy efficiency in
black tea processing factories.
4
ii.
To determine the extent of the influence of energy efficient technology machines on
energy efficiency in black tea processing factories.
iii. To determine the extent of the influence of technical staff awareness on energy
efficiency in black tea processing factories.
iv. To determine the extent of the influence of energy mix on energy efficiency in black
tea processing factories.
1.5 Research Questions
i) What is the extent of the influence of capacity utilization on energy efficiency in
black tea processing factories?
ii) What is the extent of the influence of the energy efficient technology machines on
energy efficiency in black tea processing factories?
iii) What is the extent of the influence of technical staff awareness on energy efficiency
in black tea processing factories?
iv) What is the extent of the influence of energy mix on energy efficiency in black tea
processing factories?
1.6 Significance of the study
Better understanding of the influence of energy efficiency determinants has the potential
to assist organizations in the development of strategies to manage the dwindling energy
resources and also control high energy prices. This would eventually lead to a reduction
in energy demand and reduce Green House Gas emissions. Factory managers, industrial
process manufacturing technology developers, energy policy makers, and the local and
global energy environment research institutions may find the information useful for
identifying high impact determinants of energy efficiency. This would enable them
5
prioritize allocation of resources to areas that promise high gains. Factory managers
could use the findings to improve processing facilities design, develop better strategies to
improve energy efficiency awareness among employees, sourcing of better technologies
and planning production to maximize energy efficiency. Research findings could be used
by technology developers in their research and development activities to develop more
energy efficient facilities and equipment. Government ministries such as the ministry of
energy and environment may find results from the research study useful in the
development and adoption of appropriate instruments and intervention measures to
promote energy efficiency as mandated by the Energy Act 2006.
The success of such interventions is likely to lead to a reduction in the importation of
expensive fossil fuels and save the country foreign exchange. As a result, capital
investment in alternative and new energy sources to meet expected growth in business
would also fall and put less strain on the company’s financial resources. The burning of
less fuel and by extension reduced green house gas emissions would contribute in
mitigating the effects of climate change. Tea farmers and industry stakeholders are likely
to benefit from improved earnings due to lower production costs.
1.7 Delimitations of the study
The study was delimited to KTDA managed black tea processing factories that have been
manufacturing black tea for at least three years. Questionnaires developed for studying
technical staff awareness were administered only to factory employees who posses at
least three years of working experience at their current work station. Research questions
were delimited to energy efficiency measures recommended and approved by the KTDA
Technical services department. The data utilized for document analysis was obtained
from the KTDA centralized production information system and the five year business
strategic plan documents. The aim was to make the findings relevant and enhance internal
6
validity by using data that has already been reviewed and where necessary corrected.
Energy efficiency results can only be associated with employees who have been involved
in the process prior to the measurements and that systems were up and running at least
one year after initial commissioning of the manufacturing process.
1.8 Limitations of the study
The results of this study are limited by the suitability of the study sample size to represent
the population, the ability of questionnaire respondents being willing to participate and to
provide accurate and honest answers. These limitations were mitigated by cluster
sampling and the use of proven techniques in determining a representative sample size.
Research participants were assured confidentiality through an introduction letter and had
the option of not disclosing their identities.
1.9 Assumptions of the study
The study assumed that the theoretical framework established to measure energy
efficiency provided an accurate measure of determinants of energy efficiency and energy
efficiency. It assumed that the sample size, statistical test and analysis are sufficient to
detect significant differences/relationships that exist in the population. Research
participants were assumed to have been willing to participate and to provide honest and
accurate answers to the research questions. The study also assumed that the results are
generalizable beyond the sample being studied and are also meaningful and utilizable to
the identified stakeholders.
7
1.10 Definition of Significant Terms as Used In the Study
The following definitions apply to the terms as used throughout this study.
Capacity utilization factor: The ratio of the amount of tea in kilograms processed at the
factory to installed withering floor area measured in square feet.
Design Capacity: The optimum amount of tea in kilograms that the factory has been
designed to process per square foot of withering floor space over a given period of time.
Energy Efficiency: The less the amount of energy required to perform a service, the
greater the efficiency.
Mega Joule (MJ): A standard unit for measuring energy.
Energy Intensity: The amount of energy expressed in mega joules required to process
one kilogram of tea or a unit measure of product or service.
Biomass Fuel: The fuel obtained from plant matter such as fuel wood, corn cobs or
baggasse.
Energy Efficient Technology Machines: This term is coined to refer to specific energy
intensive machines that were procured on the basis of their proclaimed superior energy
efficiency. These comprised Vibro fluid bed dryers, waste heat recovery boilers and
continuous fermenting machines.
Energy Mix: The ratio of each of the different forms of energy used; electricity from the
national grid or own generation, purchased fuel wood, generator fuel, boiler furnace oil
expressed in mega joules to the total sum of the energy consumed within a defined period
of time.
Black Tea: Tea that undergoes through the fermentation process before drying as
opposed to green tea which is not fermented prior to drying.
8
1.11 Organization of the study
Chapter one consists of background information on energy and energy efficiency as
relates to the research topic from a global, regional and local perspectives. The chapter
covers the research problem statement, purpose, objectives, research questions and an
explanation of the significance of the study to potential stakeholders. Chapter two deals
with energy efficiency literature review. The chapter is subdivided further into sub
headings of determinants of energy efficiency variables of technology of process
machinery, capacity utilization, technical staff awareness and energy mix. The theoretical
literature on energy efficiency and methodological issues, definitions for energy
efficiency and energy intensity , the neoclassical theory of energy efficiency and the
conceptual framework that depicts the relationships between dependent and independent
variables. The research methodology is explained and discussed in chapter three.
Research design, target population, determination of sample size, sampling procedure,
methods of data collection, issues of reliability and validity and methods of data analysis
are all addressed under chapter three. Chapter four contains data analysis, presentation
and interpretation. Research findings, discussions, conclusions and recommendations
have been summarized in chapter five. References used throughout the study and the
appendices are attached at the back of the research proposal. The appendices comprise
sample forms of data collection tools and a questionnaire introduction letter.
9
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
The literature review for energy efficiency study is based on secondary and primary
sources of information. Secondary information on energy efficiency focuses mainly on
energy efficiency concepts, indicators and methodological issues. Energy intensity has
been used as a statistical measure for energy efficiency. The literature further explores the
neo-classical theory of energy efficiency alongside its nemesis “the X-efficiency
literature”. The section provides a theoretical framework for the understanding of energy
efficiency. However, the greater portion of the literature review comprises primary
information from previous work contained in research articles and journals. Technology,
behavior, capacity utilization, fuel type and mix, awareness, economy, weather and
environmental issues have emerged as the most recurring research themes in the study of
energy efficiency. Further review of energy efficiency literature in the tea industry has
been carried out under the four determinants of energy efficiency of capacity utilization,
Awareness, Energy Mix and Technology.
2.1.1 Review of Energy Efficiency in Energy Demand Sectors
Energy is a basic factor for industrial production. Studies have established the existence
of a strong relationship between economic development and energy consumption (EIA,
2006). Globally, growing population, industrialization, and rising living standards have
substantially increased dependence on energy (Ines, 2010). The development of
conventional energy resources, the search for new or renewable energy sources, energy
conservation(using less energy) and energy efficiency(same service or output, less
energy) have therefore become un avoidable topics.
10
2.1.2 Energy Efficiency and Climate Change
After the industrial revolution, anthropogenic greenhouse gas emissions have been
increasing and a broad consensus has emerged that human life will be affected by earth’s
climate change (AIS, 2002).
The green house gas (GHG) emissions result from many of the industrial, transportation,
agricultural and other activities through population growth, fossil fuel burning and
deforestation. The economic and social consequences of GHG imply that they should be
addressed on a global scale. In a joint action under the United Nations Framework
Convention on Climate Change (UNFCCC), developed countries committed themselves
to reduce their anthropogenic emissions of GHG.To address these issues in developing
countries, UNFCCC established funds for their benefits in terms of capacity building and
transfer of energy efficient and environmentally sound technological measures. Kieran
and Torgsa (2011) in their research study on supply-side Determinants of Energy
Consumption and Efficiency (ECE) innovations concluded that the unrealized
innovations in energy efficiency are estimated to have significant potential for reducing
global greenhouse gas emissions and improving firm and industry competitiveness. Their
results further suggest that enhancing organizational capabilities may be one means of
obtaining further efficiencies in energy use sectors facing technological constraints, and
that acquisition of external knowledge and technology is an important factor.
Researchers have also argued that the world population’s current ecological foot- print
far exceeds the planet’s long-term capacity (West Kamper et al., 2000; Jovane et al.,
2008).According to the UK department of energy conservation commission (DECC,
2010), energy generation and industrial activity contribute significantly to the overall
emission of green house gases which are thought to be the key driver of global warming.
A reduction of manufacturing energy consumption would thus be highly relevant for the
limitation of overall green house gas emissions. In this context, an understanding of the
energy inputs consumed by the available manufacturing processes is critical. Foran et al.,
(2005) remarked that “if you can’t measure, you can’t manage”.
11
2.1.3 Energy Efficiency and Technology
In the period 1990-2005, global primary energy supply increased by 30% while
worldwide energy demand is projected to double between 2005 and 2050 (IEA,2007).
Graus et al., 2010 estimate that by exploiting the technical potential for energy efficiency
improvement in energy demand sectors, this growth can be limited to 8 %.
Any improvement in energy utilization has a crucial role at firm level (cost reduction),
local level (less deforestation, low fuel imports) and GHG Mitigation at global level
(AIS, 2002). In Kenya, over 600 billion Kenya shillings are needed to fund new power
projects over the next four years (ERC, 2012). Due to high capital investment required
for additional power plants and the desire to meet green house gas emission targets, many
countries have now incorporated energy efficiency in their energy policy. In Kenya, the
Energy Act 2006, section 104(2) I, gives the minister for energy powers to enhance
energy efficiency and conservation by making it mandatory, in collaboration with the
Kenya Bureau of Standards, the importation of energy efficient but cost effective
technologies.
Although the KTDA has been investing resources in energy efficiency improvement
over the last ten years, no study has been carried out to assess the real impact of these
measures. Manufacturing costs, especially the energy component is on a steady rise and
this is a major threat to the sustainability of tea business. Locally, the tea industry is a
major source of rural economic and social livelihoods and other development activities.
According to World Energy Outlook (WEO, 2007), lack of access to modern energy
services is a hindrance to economic and social development and must be overcome if the
UN Millennium Development Goals (MDGs) are to be achieved.
There has been a significant technology related energy efficiency change on the global
scene in the last twenty years with reduced energy intensity in the World’s developed
12
countries. Between 1980 and 2001, the OECD’S energy intensity declined 26%, the
Group of Seven’s (G-7) fell 29%; and the U.S. dropped by 34 % (IEA, 2007).
2.1.4 Energy Efficiency and Economic Development
According to Van (2008), energy consumption in developing countries will rise more
rapidly than in the developed economies. His findings further suggest that there will be a
serious challenge from economic and environmental problems in developing countries
due to an increase in green house gas emissions arising from energy use and excessive
pressure on existing energy resources. Lermit and Jollands (2001) analysis seems to show
that energy efficiency in the industrial sector deteriorated in periods of low economic
growth and increased in periods of high economic growth. Ines et al. (2010) observed that
energy consumption in the manufacturing industrial sector is influenced by the behavior
of several economic variables such as high energy prices or constrained energy supply
which motivates industrial facilities to try to secure the amount of energy required for
operations at the lowest price.
The tea industry is likely to be among those affected most due to its extensive
dependence on unsustainable energy sources from fuel wood and fossil fuels for its
manufacturing process. This is mainly for withering, drying tea and to run machinery.
According to De Silva (1993), this energy cost is about 30% of the cost of production at
the factory level. Both thermal (heat) and electrical energy are used mainly for withering
and drying while electrical energy is used to run machinery. The main sources here are
the imported expensive fossil fuels and the locally sourced wood fuel.
Historically, energy prices have been low, so the energy costs of operating inefficient
machines have not been significant (World Watch 2009, Vol 122). The KTDA black tea
processing factories have been using furnace oil and fuel wood as convenience
demanded. The rise in imported fossil fuels prices has changed this scenario. According
13
to Gesimba et al. (2009), unreliable electricity and high cost of fuel are a threat to the tea
industry in Kenya. They note that tea factories have been hit hardest by the ban on
procurement of wood fuel from the forest. Factories are now forced to procure fuel wood
from firms where trees are rare and therefore sold at exorbitant prices.
2.1.5 Energy Efficiency and Primary Energy Sources
Global energy use (2010 – 2011) data indicates that wood is not the main source of fuel
in most parts of the world. However, according to FAO, wood fuels still play a major role
in meeting energy demand in Africa. The tea processing factories have taken special
interest in firewood storage preparation and combustion equipment in order to obtain
maximum energy from it. The calorific value of any woody material ranges from 35004900 kcal/kg depending on the moisture content (De Silva, 1993). Moisture decreases the
heat content per Kg of fuel. It increases heat loss due to evaporation and super heating of
vapour. Factories build firewood sheds to season the wood fuel and lower the moisture
content for better efficiency. They split and size it into billets to improve maximum heat
energy extraction.
Furnace oil can be combusted more efficiently than firewood and release a greater
proportion of their calorific value due to the lower inherent moisture content (less than
1%) and more advanced boiler technologies. Firewood is still selected on the net cost
basis where it is still cheaper. More modern technology to improve energy extraction
from fuel wood has been embraced in several factories. Boilers with heat recovery
systems (air pre heaters and economizers) have been installed in several factories to make
better use of fuel wood. Wood fuel combustion efficiency requires a specific amount of
oxygen for perfect combustion and some additional (excess) air is required for ensuring
combustion (www.energyefficiencyasia.org). According to this article, too little or too
much air will result in efficiency losses. Annual or semi – annual boiler combustion tests
14
are needed to obtain optimal efficiency from the available sources. By virtue of practical
achievable combustion efficiency, fossil fuels normally give better energy efficiency
performance but cost more. It is therefore crucial to take into account the fuel type or mix
while comparing energy utilization efficiency across different factories. Very few studies
have been conducted in Kenya on energy efficiency and those that focus on determinants
of energy efficiency in particular. Reference has therefore been made to several energy
efficiency related literature and research studies from different parts of the world. India
and Sri Lanka share similar tea industry experience with Kenya and have been the major
sources of industry specific literature. Both countries are at near similar economic
development stage with Kenya which renders them more relevant to the Kenyan case.
Research findings from literature review indicate that energy efficiency predictors vary
across industry platforms which make the findings not generalize able across different
industry types. There is therefore a need to study the determinants of energy efficiency
within the KTDA managed black tea processing factories.
2.1.6 Energy Efficiency and Human Behaviour
Behavioral aspects in energy efficiency studies have been centered on demographic
investigation using bivariate analyses of age, income, education as potential predictor
variables for energy efficiency. According to Semenik, Russel Belk and John Painter
(1982), greater understanding of conserver and non conserver groups can be achieved
with a broader set of predictor variables. Despite the fact that a number of studies have
been directed at finding correlates of energy conservation attitudes and behavior their
findings have generally been weak and often contradictory. Based on general indices or
questions about energy conservation behavior some studies have found positive
associations between energy conservation and income (Grier, 1976; Talarzyk and Omura,
1974) and between energy conservation and class (Bultena, 1976).However,
(Cunningham and Lapreato, 1977; Opinion Research Corporation, 1975c) while (Gottlieb
and Maitre, 1975) found negative associations between energy conservation and income
as well as between energy conservation and social class. Other studies still report no
15
significant relationship between energy conservation and income (Hogan, 1976; Bartel,
1974). The general expectation would be that education and conservation would be
positively associated. Studies have found mixed results. As expected, the largest numbers
of studies have obtained a positive association between education and conservation
actions (Ropper, 1977b; Survey Research Laboratory, 1977; Rezeinstein and Bernaby,
1976; Thompson and MacTavish, 1976; Gallup, 1977a). The exceptions consist of
findings of a negative relationship(Opinion Research Corporation,1974a,1975a,1975c),
and findings of no significant education/conservation relationship(Murray, et
al.,1974;Hogan,1976c).Age has also failed to act as a consistently good predictor of
energy conservation. Hogan (1976), Kileary (1975) and Bartel (1974) found no
significant association between age and energy conservation).
2.1.7 Energy Efficiency and Capacity Utilization
According to Baumers et al (2011), the effect of capacity utilization on energy efficiency
varies strongly across different platforms and efficiency improves with an improvement
in capacity utilization. According to Ines et al. (2010) all variables of economic factor are
important, but the most relevant are improvement in plant capacity utilization and
improvement in levels of production. This finding concurs with Tholander et al., (2007)
who identified the non priority of energy investments and lack of access to capital
especially in small and medium enterprises as main barriers to increased energy
efficiency in the developing countries in contrast with the situation in developed
countries. Moreover, manufacturing industries in developing countries prefer traditional
investments like expansion of industrial plants or power generation.
Sahu et al. (2009) found a positive relationship between energy intensity and firm size.
They also found out that foreign owned firms exhibit a higher level of technical
efficiency and so are less energy intensive. The results of the study further reveal that
R&D activities are important contributors to the decline in firm level energy intensity.
16
Within KTDA managed black tea processing factories, poor energy efficiency is
recorded during high crop seasons corresponding to maximum capacity utilization within
the tea processing factories.
2.1.8 Energy Efficiency and State Policy
According to Jeffries & Elizabeth (2009), California’s official energy policy gives
efficiency the highest priority because it is far cheaper than developing solar, wind and
other renewable power or construction of natural gas fired power plants. In California, it
is estimated that energy efficiency initiatives have saved rate payers about $36 billion. It
has also helped California to keep its per capita energy consumption flat over the last
thirty
years,
while
the
rest
of
the
country’s
power
demand
grew
50%
(marc.lifsher@ltimes.com).
Within the USA, the current climate of opinion in both the residential and commercial
sectors for new and existing stock, the government gives a prominent role to energy
efficiency as a policy tool. Executive and legislative branches of government at both the
state and federal levels are considering and adopting policy options to valorize energy
efficiency in the service of anything from National security to curbing global warming to
creating a green economy (Taylor & Kipp, 2010). According to Zhivov &Deru,( 2009)
the 2005 USA energy policy requires that federal facilities be built to achieve at least
30% energy savings over the 2004 ASHRAE standard.
In Kenya, the Energy Act 2006 empowers the minister for energy to enhance energy
efficiency and conservation, by making it mandatory, in collaboration with Kenya Bureau
of Standards, the importation of energy efficient but cost effective technologies. Utlu
&Hepbaslic (2006) underscore the necessity of planned studies towards increasing
17
renewable energy utilization efficiency in the subsectors they studied and the critical role
of policy makers in establishing effective energy efficiency delivery mechanisms
throughout Turkey.
Technology analysts assert that carefully designed regulatory interventions can simulate
actions that yield simultaneous reductions in energy use and the effective use of energy
services. The USA National Academy of Sciences (1991), for example, identified the
potential to improve energy efficiency by up to 37 % at zero economic cost. The
intergovernmental panel on climate change (IPCC, 1996) concluded that global carbon
dioxide emissions could be cut by 10-30 % through the accelerated diffusion of least cost
energy technologies. Mc Mahon et al.,( 1990) and Geller ( 1997) estimate that the US
Appliance Efficiency Standards will save some 24 hex joules of energy and $46 billion
between 1990 and 2015 by mandating the adoption of least cost design features.
According to Kane (2009), the cheapest way to cut carbon footprint is to reduce energy
demand. It increases the bottom line almost immediately yet few people or organizations
have been prepared to invest in it without a push.
2.1.9 Energy Efficiency Awareness
Crosbie and Baker (2009), noted that the level of information provided in energy
efficiency projects is inadequate, in some cases overly complex and in other cases
nonexistent. Therefore, there are chances that the success of energy efficiency measures
could be improved with an increase in awareness levels. Individuals and firms need to be
informed and made aware of the benefits to be derived from energy efficiency activities
and energy efficient technologies. Studies have shown that a wide range of clean room
energy efficiency exists, and facility managers may not be aware of how efficient their
clean room facility can be relative to other clean room facilities with the same
requirements (Paul, Tschudi et al, 2010).according to Chakavarti (2005), energy
18
efficiency success requires continuation of the National Campaign on Energy
Conservation awards in order to create awareness and motivate industrial and commercial
establishments to save energy.
Crosbie &Baker (2010) recommend promotion of energy efficiency interventions in
terms of the direct benefits they can bring to the users or participants’ lifestyles and
practices if a significant level of participants is to be achieved.
2.3 Determinants of Energy Efficiency in KTDA
In this section, the study examines and discusses the major determinants of energy
efficiency in the KTDA. The discussion focuses on black tea processing factories since it
was not possible to examine energy efficiency within all the processes in the organization
within the limits of time and available resources. Black tea manufacturing process is
outlined in appendix 2.3. Determinants of energy efficiency in KTDA are quite similar to
other manufacturing plants elsewhere in the world. More parallels were drawn from India
and Sri Lanka which partner with Kenya as the world’s top three producers and exporters
of black tea. The bulk of black tea processing machinery in these three countries is
sourced from India. The main determinants of energy efficiency in KTDA black tea
processing factories based on available literature and industry experience is captured as
capacity utilization, technology, staff awareness, fuel mix, weather and Government
policy. The extent of their individual influence on energy efficiency has been determined
and the goal of filling this knowledge gap accomplished.
2.3.1 Technology and Energy utilization Efficiency
Average Prices for primary energy supplies within KTDA tea processing factories have
increased eight fold for wood fuel, fivefold for residual fuel oil and four fold for electrical
19
energy purchased from the national grid in the last twenty five years as generalized from
particulars found in KTDA factories annual accounts reports for the period 1987 to 2012.
Global primary energy supply increased by 30% between 1990 and 2005 and the world
wide demand is projected to double by 2050 according to the International Energy
Agency (IEA, 2007) report. Graus et al. (2010) estimate that by exploiting the technical
potential for energy efficiency improvement in energy demand sectors, this growth can
be limited to 8%.
KTDA embarked some eight years ago on building modern energy efficient factories.
The Agency is also undertaking modernization projects for factories that were built
before 2000. A majority of the newer factories have been equipped with more efficient
three pass steam generating boilers, high efficiency motors, energy saving lighting
systems, energy efficient withering air flow fans and of late boilers with heat energy
recovery systems such as air pre heaters and economizers. The newer technology is
expected to provide the same or better service with less energy inputs. Variable speed
drive/ variable frequency drives are being studied as a more efficient means of operating
processes where energy demand varies during the process.
In practice, the process operates at fixed energy demand irrespective of the actual
process needs leading to waste. Economic and business competitiveness has been the key
motivator in embracing technology as a way of managing the continuing rise in energy
costs. Scientific theory and research supports technology as a viable means of improving
energy efficiency. According to Kieran and Torga (2011), unrealized innovations in
energy efficiency are estimated to have significant potential for reducing global
greenhouse gas emissions and improving firm and industry competitiveness.
20
Kenya recognizes the role of energy efficient technologies through the Energy Act 2006.
The act has empowered the minister for energy to enhance energy efficiency and
conservation by making it mandatory in collaboration with the Kenya Bureau of
Standards (KBS) the importation of energy efficient but cost effective technologies.
KTDA therefore has improved access to energy efficiency technologies through
cooperation and compliance with the ministry of energy. Technology analysts further
assert that carefully designed regulatory interventions can stimulate actions that yield
simultaneous reductions in energy use and effective use of energy services. Research
findings by the USA National Academy of Sciences (1991), Intergovernmental Panel on
Climate Change (1996), McMahon et al. (1990) and Geller (1990) all agree on the
importance and relevance of technology as a means to improve energy efficiency. One of
the objectives of this study was to analyze and examine the influence of these technology
applications on energy efficiency within the KTDA black tea processing factories.
2.3.2 Capacity Utilization and Energy utilization Efficiency
Full capacity utilization results in lower specific energy consumption as empirically
demonstrated by Baumers et al. (2011). However, the size of this saving was found to
vary heavily across different industry platforms.
The sixty plus KTDA managed black tea manufacturing factories operate at varying
capacity levels due to
factors that include seasonal weather patterns, installed
manufacturing equipment availability, energy supply , government policy on employee
working hours , production planning and process equipment production rate among
others. Although theoretical production rates have been set, available data shows a wide
range of capacity utilization within individual processing plants. From a theoretical
perspective, the degree of capacity utilization has a positive impact on process energy
efficiency. Within the KTDA manufacturing process, like other similar processes, there is
21
a fixed energy base load that will always be expended irrespective of the quantity of
product being processed. It comprises the skin losses on heating equipment, the energy
required to start machinery from rest and the heat energy required to bring heating and
drying equipment up to their operating temperatures. Therefore, the energy consumed is a
function of the number of hours the equipment has been in operation and the energy
required to bring it into service. Since energy efficiency is measured by the energy use
rate, the energy intensity, it follows that higher production rates will result into higher
energy efficiency because the fixed energy component becomes insignificant. This is
expected within KTDA processing plants but no research has been carried out to establish
the extent to which capacity utilization influences energy efficiency when all other
intervening factors have been taken into consideration.
The main driving force behind processing capacity expansion has been occasioned by
increases in Greenleaf intake. Energy efficiency has not been previously considered as a
likely benefit to be derived from enhanced capacity utilization. The study conducted at
Wuppertal University by Ines et al (2010) to examine factors influencing energy
efficiency in German and Columbian industries cautions against undertaking energy
efficiency research without taking into consideration capacity utilization. The energy
efficiency benchmarking process in the KTDA black tea processing factories does not
take capacity utilization into consideration while comparing performance among the sixty
plus factories. An analysis that considers this factor may generate some new information
that could be useful in improving energy utilization efficiency.
2.3.3 Technical staff awareness and Energy utilization Efficiency
Studies conducted by the American Council for Efficient Energy Economy (ACEEE,
2009) indicate that although people are often aware of the benefits of using energy more
efficiently, a variety of social, cultural, and economic factors often prevent them from
22
doing so. Even when high efficiency technologies have been installed, 30% or more of
the energy savings that could potentially be realized through such technologies is lost.
KTDA black tea processing factories are spread across the country in geographical
clusters called regions. The different regions exhibit different cultures with varying
economic backgrounds. There is evidence of differences in energy efficiency
performance within factories that operate under similar working conditions. One of the
most likely causes of the difference may be associated with employee behavior. The
Building and Energy Conservation Support Unit (BRESCU) and the Sustainable Energy
Authority of Victoria in Australia have developed an Energy management Matrix that can
be useful in evaluating structures that influence behavior towards energy efficiency. The
matrix cites the establishment of formal policy and management system, action plan and
regular review with commitment of senior management as part of corporate strategy. This
requirement is adequately captured in the KTDA corporate strategy. However, it is not
yet understood how well it is reflected within the sixty plus black tea processing
factories.
Best practice requires energy management to be fully integrated into the management
structure with clear delegation of responsibility for energy use. Energy managers should
use formal and informal channels of communication so as to create awareness and
inculcate positive behavior towards energy efficiency. As in any other organization,
success depends on management effectiveness and therefore results will vary across the
factories. Training and awareness contribute to positive behavior towards energy
efficiency while lack of it may have negative effects. We do not currently know whether
staff at the factories to be studied has been trained and are aware of energy efficiency
improvement initiatives, importance to them as individuals and also to the organization as
a whole.
23
According to Chakavarti (2005), energy efficiency requires continuation of National
campaign on energy conservation awards in order to create awareness and motivate
individual and commercial establishments. Motivation influences behavior and its
absence or presence is likely to determine how staff work towards achieving set energy
targets. The motivation level within the different factories is governed by a variety of
factors resident within the factory establishment. Behavioral effects on energy efficiency
are not limited to the lower cadre employees alone. Positive discrimination by senior
managers in favour of energy saving schemes with detailed investment appraisal of all
new and plant improvement opportunities are a show of positive behavior towards energy
efficiency improvements. KTDA management has set targets for energy consumption.
Energy consumption is regularly monitored and costs quantified. We will need to find out
what the management at the different factories does with information on consumption
and costs tracking that are provided at the end of the month. The action taken based on
available information will be considered as behavior towards energy efficiency. Crosbie
and Baker (2010) recommend promotion of energy efficiency interventions in terms of
the direct benefits they can bring to the users or participants lifestyles and practices if
significant level of participants is to be achieved. We may therefore pose the question,
what is in it for the staff if improvements in energy efficiency are realized? Do they
expect to have some form of recognition at the end of the performance evaluation?
Rewards and recognition schemes will be considered as motivation indicators and used to
gauge performance across the different factories.
2.3.4 Energy Mix and Energy utilization Efficiency
The tea industry is heavily dependent on a variety of energy sources for its manufacturing
process. This is mainly for withering, drying tea and to run machinery. Currently, this
energy cost is about 30% of the cost of production at the factory level (De Silva 1993).
Both thermal (heat) and electrical energy could be used mainly for withering and drying
while electrical energy is also used to run machinery. The total energy requirement to
24
produce one kilogram of made tea is about 25MJ .The main sources here are the imported
expensive fossil fuels and the locally sourced wood fuel.
Historically, energy prices have been low, so the energy costs of operating inefficient
machines have not been significant World Watch (2009, Vol 122). The KTDA black tea
processing factories could use furnace oil or firewood as convenience demanded. The rise
in the price of imported fossil fuels has changed this scenario. According to Gesimba et
al. (2009), in Kenya, some of the several adverse forces that threaten the tea industry are
unreliable electricity and high cost of fuel. They note that factories have been hit hardest
by the ban on procurement of wood fuel from the forest. They have now been forced to
procure fuel wood from firms where trees are rare and therefore sold at exorbitant prices.
Available Global energy use (2010 – 2011) notes that data from energy use by source
wood is not the main source of fuel in most parts of the world. However, according to
FAO research findings, wood fuels still play a major role in meeting the energy demand
in Africa. De Silva, (1993) makes similar observations for tea factories in Sri Lanka.
The tea processing factories have taken special interest in firewood storage preparation
and combustion equipment in order to obtain maximum energy from it. The calorific
value of any woody material ranges from 3500-4900 kcal/kg depending on the moisture
content (De Silva, 1993). Moisture decreases the heat content per Kg of fuel. It increases
heat loss due to evaporation and super heating of vapour. Factories build firewood sheds
to season the wood fuel and lower the moisture content for better efficiency. They split
and size it into billets to improve maximum heat energy extraction. From a theoretical
perspective, one liter of furnace oil can be combusted more efficiently than firewood and
release a greater proportion of their calorific value due to the lower inherent moisture
content (less than 1%) and more advanced boiler technologies. Firewood is still selected
on the net cost basis where it is still three times cheaper based on the prevailing prices.
25
More modern technology to improve energy extraction from fuel wood has been
embraced in several factories. Boilers with heat recovery systems (air pre heaters and
economizers) have been installed in several factories to make better use of fuel wood.
There is a need to compare energy efficiency performance of factories while taking into
consideration the number of firewood sheds constructed and the boiler technology in use.
Wood fuel combustion efficiency requires a specific amount of oxygen for perfect
combustion and some additional (excess) air is required for ensuring combustion
(www.energyefficiencyasia.org). Too little or too much air will result in efficiency losses.
Annual or semi – annual boiler combustion tests are needed to optimal efficiency from
the available sources. By virtue of practical achievable combustion efficiency, fossil fuels
normally give better energy efficiency performance but would cost more. It is therefore
crucial to take into account the fuel type or energy mix while comparing energy
utilization efficiency across different factories.
2.5 Energy efficiency concepts, indicators and methodological issues
Increases in energy efficiency take place when either energy inputs are reduced for a
given level of service or there are increased or enhanced services for a given amount of
energy inputs. Energy efficiency is also defined as the relative thrift or extravagance with
which energy inputs are used to provide goods or services. The terms “energy efficiency”
and “energy efficient” are used in conjunction with other terms such as “energy intensity”
or “energy intensive” in describing the mathematical relationship between energy use and
service output. The intensity component, the energy use rate, is the commonly used basis
for measuring and assessing energy efficiency.
26
2.6 Energy Intensity
Energy intensity is the ratio of energy consumption to a unit of measurement. Intensity is
inversely related to efficiency for a given service, that is, the less energy required to
perform a given service, the greater the efficiency. It follows that a decrease in energy
intensity over time may correspond to an increase in energy efficiency depending on the
level of structural and behavioral effects. Measuring energy efficiency change in energy
use over time may be driven by a combination of efficiency, weather, behavior and
structural effects that may be only partially separable and may differ among energy
services. Therefore the task of measuring and assessing energy efficiency and its change
over time consists of the following: deciding which effects should be considered as
inherent in efficiency measurement and which are due to other factors such as weather,
behavioral, and structural changes to be eliminated, or at least recognized in the
measurement. The process involves creating an appropriate categorization of energy
services that provides the best framework of efficiency measures and combining these
statistical measures into meaningful and understandable assessment of energy efficiency
and its trends. There are two main approaches to measure energy efficiency trends; the
market basket and the comprehensive approach. The market basket approach is based on
consistent measures of consumption per service unit for a bench mark set of energy
services while the comprehensive approach attempts to take all energy use into account
(http://www.eia.gov/emeu/efficiency/ee-ch2.htm). It is almost practically impossible to
conduct a research by taking all energy use into account since a universally all
encompassing bench mark may not be readily available. The market basket approach is
therefore more feasible in my research study because there already exists a bench mark
set of energy services in a well defined industry(the tea industry) and energy
services(machinery running, withering and drying operations) to which the study could
be focused. Although there are other energy services which could have been considered
as well, their overall impact on production costs is minimal and therefore less important
to potential users of potential findings.
27
2.7 Theoretical Framework
Energy efficiency theory is based on the neo-classical theory of the firm which represents
firms as well informed, rational actors that systematically maximize profits subject to the
constraints imposed by technology, public policy and the prevailing market condition. It
forms a central framework in the economics of energy efficiency and the environment
(Decanio, 1993).In this perspective, firms enjoy informational advantages over
government regulators concerning the technological and economic aspects of energy use
and pollution abatement. Hence, market –based policies that equate marginal costs and
benefits of energy utilization may be used to implement specified levels of environmental
quality at minimum social cost. Direct regulatory interventions, in contrast, induce
inefficiencies by failing to exploit firm’s expertise and motivation to cut costs.
Applications of this framework have led to significant innovations in policy formulation.
The neo-classical theory of energy efficiency faces a major challenge from the outcome
of sustained direct regulatory interventions as has been witnessed in the state of
California in the USA. Rather than induce inefficiencies, regulatory measures have
continued to have a positive impact on both the environment and the state economy in
comparison to other states that have not followed a similar approach.
According to the “X-efficiency” literature initiated by Lebenstein (1996) and (Frantz,
1997), firms are better understood as networks of individuals than as monolithic, profitmaximizing actors. In this respect, success in energy efficiency improvements has been
realized through the neo-classical firm theory as well as through direct regulatory
interventions. Both state and business firms have varying interests in energy efficiency
and no single actor is bound to act in the interest of the other. This fact is illustrated by
the importance almost every state is now placing on energy efficiency through policy and
direct regulatory interventions. According to (Howarth et al., 2000), the role of policies to
promote the adoption of energy efficient technologies is a matter of great importance
28
given the links between energy use and global environmental change. It therefore follows
that the argument from the neo classical theory of the firm in energy efficiency
improvements does not hold true in all areas. Calculated government regulation and
technology are already yielding tangible results where they have been implemented.
There is currently no proof of better performance in energy efficiency where there is little
or less government regulation. The views of technology analysts are supported by a rich
and detailed literature on the engineering, social and psychological dimensions of energy
use. Economists, however, remain skeptical about the findings of this literature and its
relevance to policy formulation. (Sutherland, 1991) notes that the functioning of normal
markets provide powerful incentives for consumers and firms to exploit investments in
energy efficiency that yield accompanying cost savings. Government mandate might
impair economic efficiency to the detriment of society. Technology analysts are also
accused of overstating the energy savings generated by fuel-efficient technologies,
including a rebound effect that would offset the gains made through enhanced equipment
energy efficiency by misgauging the behavioral consequences of equipment performance.
A technology that yields time cost savings would stimulate the demand for energy
services, including a rebound effect that would partially offset the direct energy savings
from enhanced energy efficiency (Khazzoom, 1980). In extreme cases, the resulting
growth in energy services might more than offset the direct effects of enhanced
technologies so that improved energy efficiency paradoxically would lead to increased
energy use (Brooke, 1990, Saunders, 1992). Since the world population has kept on
increasing, there will be sustained demand for more energy. It does not matter whether
energy efficient technologies are developed or not. Maintaining the business as usual
approach will only assist in aggravating the energy deficit while worsening the global
warming situation. One can therefore debate on the extent of the contribution of
technology but not its relevance as its contribution is now well documented. Some of
these technologies have not been widely appreciated and exploited due to lack of
29
awareness among potential users. Awareness campaigns are likely to complement other
energy efficiency measures such as policy and technology. Research literature already
exists in the contribution from improved awareness to the success of energy efficiency.
2.8 Conceptual Framework
The study was guided by the conceptual framework shown in figure 1.
Moderating variable
Independent variable
Government Policy on
Energy Efficiency
Capacity Utilization
Capacity Utilization
Factor: Ratio of
Actual production
(kgs) to Design
Production Capacity
(sq ft)
Dependent variable
Energy Efficient
Technology
Machines
Energy
Utilization
Efficiency
Energy efficient machines
ratio
(Intensity)
MJ/Kg
Technical Staff
Awareness
Intervening Variable
Energy Efficiency
Awareness Score.
Weather
Energy Mix
1-temperature (oc)
Ratio of fuel wood
to total energy use
Ratio of furnace oil
to total energy
Ratio of Electrical
energy to total
energy
FIGURE 1: CONCEPTUAL FRAMEWORK
Behaviour
30
2-rainfall (mm)
2.8.1 Interrelationship of Variables in the Conceptual Framework
Capacity utilization as an independent variable is a measure of the ratio of the actual
production capacity in terms of kilograms of processed tea divided by the installed
capacity in square feet.
The second independent variable of technology was measured by the age of major energy
consuming equipment, the number of specific energy efficient equipment installed in the
last three years within the withering, drying and steam generation facilities.
Staff awareness as an independent variable was measured by the number of correct
answers to questionnaire questions derived from the recommended energy efficiency
measures.
The last independent variable defined as the Fuel mix was assessed by working out the
ratio of fuel wood, furnace oil and electrical energy fuels to total energy consumed. All
fuel types were converted into the same units; the mega joules (MJ), before being used to
work out the fuel mix ratio. The dependent variable, energy efficiency was obtained by
computing energy intensity whose units are mega joules per kilogram of processed black
tea calculated by converting all the energy used into mega joules and dividing the total by
processed black tea measured in kilograms. The influence of weather intervening
variables of temperature and rainfall shall be compared across the various geographical
regions to cater for their suspected influence encountered from the theoretical literature
review on energy efficiency. Government policy as a moderating variable applies to all
the factories in the case study and could only affect the general trend in energy efficiency.
31
2.9 Summary of Chapter Two
Section 2.4 under theoretical literature discusses energy efficiency concepts, indicators,
and methodological issues. Energy efficiency is realized when increases in energy
efficiency take place when either energy inputs are reduced for a given level of service or
there are increased or enhanced services for a given amount of energy inputs. Energy
intensity is defined here as the ratio of energy consumption to a unit of measurement and
is used for measuring and assessing energy efficiency.
The Neo-classical theory of energy efficiency and related research critique based on the
“X-efficiency” literature are examined under section 2.4.3. Energy efficiency theory is
based on the neo- classical theory of the firm which represents firms as well informed
rational actors that systematically maximize profits subject to the constraints imposed by
technology, public policy and the prevailing market condition. The “X-efficiency”
literature initiated by Lebenstein (1996) and Frantz (1997), argues that firms are better
understood as networks of individuals than as monolithic, profit maximizing actors.
Part 2.2 of the review contains previous work on energy efficiency by different
researchers and publishers. There is broad consensus that the major drivers for the
interest in energy efficiency can be classified into economic, energy security and climate
change factors. The three have attracted both private and state actors. Technological
issues, capacity utilization, humanly behavior, government policy and fuel mix have
emerged as recurrent themes as reflected by the findings among researchers and
publishers. (Crosbie & Baker, 2009; Chakavarti, 2005; Tschudi et al, 2010) and the
United States Environmental and Energy Study Institute (EESI, 2009) support the study
of human behavior as a key component in energy efficiency programs. Graus et al, (2011)
and the International Energy Agency (2007) estimate that it is possible to limit worldwide
Energy demand by exploiting the technical potential for energy efficiency improvement.
32
Baumers et al, (2011) and the German Wuppertal institute (2010) have shown empirically
that the effect of capacity utilization on energy efficiency varies strongly across different
platforms. Mc Kane et al, (2008) found out that high energy supply prices motivate
industrial facilities to try to secure the amount of energy required for operation at the
lowest price.
Further contribution to fuel mix determinant has been found in the works of (De Silva,
1993; Gesimba et al, 2009) and Global Energy use (2010 – 2011, www.energy
efficiency.asia.org). Contributions to energy efficiency literature by (Jeffries & Elizabeth,
2009; Taylor & Kipp, 2007; Zhivov & Deru, 2009; Utlu & HepBasic, 2006; Kenya
Energy Act 2006; UK Department of Energy Conservation Commission (DECC) and the
USA Academy of Sciences (1991) illustrate Governments’ adoption of energy efficiency
as a policy tool to achieve energy security and also meet the global Green house gas
emission obligations. section 2.3 discusses determinants of energy efficiency in KTDA.
A lot of attention on energy efficiency has been focused on technology as found from the
existing literature. Despite the fact that we have new purpose built factories with latest
energy efficient equipment, we have not been able to isolate and quantify their influence
on energy efficiency. The influence from other determinants highlighted in the literature
will be useful in developing strategies for improving energy efficiency. My research
study therefore aims at filling this knowledge gap and generating additional knowledge
which may eventually lead to improved performance in energy efficiency projects.
33
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
Chapter three is a presentation of the methodology used by the researcher to find answers
to the research questions. It covers the research design, target population, sample size
determination, sampling procedure, and methods of data collection, issues of reliability,
validity, methods of data analysis and the conceptual framework. It also explains how the
data was analyzed to answer research questions.
3.2 Research Design
The research employed a descriptive and empirical survey to study the influence of
determinants of energy efficiency on energy efficiency in black tea processing factories.
It adopted a quantitative approach in the study of the identified research variables.
3.3 Target Population
The target population for the study was made up of sixty five Kenya Tea Development
Agency managed black tea processing factories in Kenya. The factories are spread across
the country in the counties of Meru, Tharaka Nithi, Embu, Nyeri, Murang’a, Thika,
Kericho, Bomet, Nandi, Nyamira, Kisii, Vihiga and Tranzoia. These factories are further
grouped into seven regional clusters starting from Region one up to Region seven each
headed by a regional manager. These management administrative units represent
geographical, social and climatic diversity.
Individual factory units are headed by a factory unit manager. Both the factory unit
managers and regional managers are employees of the managing agents; Kenya Tea
34
Development Agency. The Agency hires, deploys and transfers its management staff on a
regular basis. The remaining majority of factory employees are recruited from the local
communities within which the factories have been established. This group of employees
provides low to medium level skills needed by the factories. All the factories, by virtue of
having one management agent, operate under similar management policy guidelines and
technology adoption due to the centralized nature of policy formulation and technology
sourcing.
3.4.1 Determination of sample size
The research study employed both cluster sampling and census. Cluster sampling was
used to study staff technical awareness of energy efficiency. This approach was adopted
in order to obtain a representative sample encompassing the diverse geography of factory
locations. Each factory has seven technical staff. The target population for this research
objective yielded four hundred and fifty five possible respondents from the targeted sixty
five factories. A sample size of two hundred and twelve was obtained from the seven
regional clusters using the Yamane (1967) simplified formula for calculating sample size
with a precision of 5% and 95% confidence level. capacity utilization, technology of
processing machines and energy mix variables were investigated by taking a census in
line with Yamane (1967) recommendation of taking a census when the population size is
less than two hundred. It involved analysis of documented monthly tea production and
energy consumption data covering the period from October 2010 to June 2012. A census
of the type of processing machinery technology and their corresponding age was also
analyzed.
n=N/ (1+N (e) 2)
Where n= sample size
N= Population size
e=Level of precision.
35
3.4.2 Sampling Procedure
The study of capacity utilization, technology of process machines and energy mix
involved taking a census of eighteen months manufacturing data in all the target factories
that met the set delimitation criteria of factories that have been in operation for at least
three years prior to the study. All the geographical regions were therefore represented.
However, the investigation of technical staff awareness involved cluster random
sampling to obtain a representative sample of all the seven regions under study. The
procedure involved simple balloting whereby each of the factories within a region was
assigned a number from one up to the last. The numbers were written on pieces of paper
which were later folded and mixed up in a container. The pieces were then randomly
picked and opened to reveal the identity of factories to be involved in the study.
Technical staff awareness questionnaires were delivered to the technical staff under the
employment of these factories through the recruited regional information coordinators.
36
3.5 Data Collection Procedure
Data
collection
procedure
employed
document
review
and
questionnaires.
Documentation review was used to gather data on capacity utilization, technology of
processing machines and fuel mix. This information was accessed after seeking formal
permission to access and use management records web site (KTDA website). The data of
interest covered the period from and inclusive of October 2010 to June 2012. Strategic
business plans covering the same period were also used. These plans contain details of
the factory’s design capacity, type and age of installed machines as well as planned and
executed factory plant up grades. The information was extracted and transferred to the
already prepared document forms ready for analysis.
Data collection on staff awareness involved the use of questionnaires. Questionnaires
were administered to technical staff at each factory company participating in the study.
Workers were drawn from sections that are directly involved in the operation of major
energy consuming and generating equipment. Their knowledge and attitude is believed to
have a direct influence on efficient energy use. They comprised production managers,
plant technicians, senior factory electricians, boiler supervisors, dryer attendants and
withering fans supervisors. Questionnaires were mailed to the factory field services
administrator who printed and issued them to the participants. The field services
administrator then scanned the filled questionnaire forms and returned them for analysis.
This arrangement was preferred because it is inexpensive and has a quick turnaround.
37
3.6 Validity
Validity is the degree by which the sample of test items represents the content the test is
designed to measure. Content validity which was employed by this study is a measure of
the degree to which data that was collected represents staff awareness of recommended
energy efficiency measures. The researcher sought the input of subject matter
professionals and supervisor as an additional measure to enhance validity.
Pilot testing was applied as a measure of validity. It involved sending the questionnaire to
seven respondents at one factory in order to rectify the contents and ensure that they are
understood by the respondents as intended by the researcher. The response was good
where all the seven questionnaires were returned with few issues which were addressed.
Some of the questions were found to have multiple answers which were not initially
anticipated. The questionnaire was therefore revised to accommodate more than one
answer in some questions where a single choice was found to be limiting the possible
responses. The convergence of findings from document review analysis among diverse
geographical, climatic and cultural settings re affirmed the validity of the findings from
the study.
3.7 Reliability
A measure is considered reliable if a person’s score on the same test given twice is
similar. The test was split into a first half and a last half, and then correlated. Responses
were divided using odd numbers for one set and even numbers for the other set. The
reliability coefficient was then calculated using the reliability calculator for the odd-even
split using the formula.
38
The value of 0.85 that was obtained was considered good enough for the research study.
Document review of official records was used to measure the concepts of capacity
utilization, technology, and fuel mix. These concepts have been investigated before as
revealed in the literature review. They are also supported by scientific principles and
standard definitions. The adopted document form analysis procedure is reproducible.
3.8 Data Analysis
The received questionnaires were first checked for errors and completeness. Incomplete
questionnaires were discarded. Information that was captured in document review forms
was similarly checked for errors and any missing entries. Both sets of information were
coded and organized in tabular form in readiness for analysis. Statistical analysis was
performed on the dependent and dependent variables using the internet based free
statistics software calculator for social science. A two sided Pearson correlation
coefficient with a p-value of .05 statistics was used to measure the strength and direction
of the relationship between the dependent and independent variables. The four research
question variables were analyzed successfully and the extent and direction of their
relationship.
39
Table 1
Operationalization of Variables
Objective
Variable
Type of
Variable
1.
2.
3.
4.
Variable
Indicator
Unit of
Measurement
Measurement
Scale
Data Collection
Instrument
Data
Analysis
Capacity
Utilization
Factor
Energy Intensity
Percentage
(Kgs/ft2)
Ratio
Document
Analysis
Pearson
correlation
MJ/kg
Ratio
Document
Analysis
Capacity
utilization
Independent
Energy
Efficiency
Dependent
Energy Efficient
Technology
Machines
Independent
Number of
machines
Number
Ratio
Document
Analysis
Energy
Efficiency
Dependent
Energy Intensity
MJ/kg
Ratio
Document
Analysis
Technical Staff
Awareness
Independent
Awareness
Number of
Correct Scores
Ordinal
Questionnaire
Pearson
correlation
Energy
Efficiency
Energy Mix
Dependent
Energy Intensity
MJ/kg
Ratio
Independent
Ratio of Energy
Type to Total
energy
Percent (%)
Ratio
Document
Analysis
Pearson
correlation
Dependent
Energy Intensity
Energy
Efficiency
40
MJ/kg
Ratio
Pearson
Correlat
ion
CHAPTER FOUR
DATA ANALYSIS, PRESENTATION AND INTERPRETATION
4.1 Introduction
Chapter four covers analysis, presentation and interpretation of collected data and
information for the purpose of answering the four research questions. The research has
employed document form analysis to answer three of the research questions and
questionnaire to answer the research question on technical staff energy efficiency
awareness. A structured questionnaire with closed ended questions was used to assess
technical staff energy efficiency awareness. The set of questions are based on energy
efficiency measures recommended by the KTDA technical services department. Data from
Document form review was used to answer the other three research questions that sought
to establish the extent to which capacity utilization, the technology of processing machines
and energy mix influence energy efficiency in KTDA managed black tea processing
factories.
4.2 Document Form Review
Document review was contacted for the three research questions that sought to establish
the influence of capacity utilization, technology of processing machines and energy mix
on energy efficiency. Monthly energy records from sixty factories covering the period
October 2010 to December 2012 were analyzed. The researcher was able to access all the
factory tea production and energy consumption records as envisaged from the KTDA
factories data hoisted on the organization’s local area network. Additional information on
factory capacity and technology of processing machines was obtained from the factories
business strategic plans (2010-2014) within the KTDA technical services department. The
data was analyzed to establish the influence of the above variables on energy efficiency
through Pearson correlation coefficient two sided test.
41
4.3Questionnaire Return Rate
Questionnaire forms were issued to one hundred and ninety eight participants that formed
fifty percent of the target population. They comprised factory sub ordinate, supervisory
and management staff that had served the processing factory in their respective capacity
for a minimum period of three years prior to the survey. One hundred and ninety one
responses were received from the targeted two hundred and thirty research participants.
This represented a response rate of 96% which is very good according to FrankfortNachmias &Nachmias ;( 1996) .Respondents were of varying education levels and drawn
from the seven geographical clusters. This accommodated diverse social and cultural
perspectives in the responses. The researcher maintained close telephone and e-mail
contact with the KTDA Regional information coordinators which contributed to receiving
filled questionnaire forms on time. The table below shows the response rate from the
different regional geographical clusters.
Table 2
Questionnaire Return Rate
Regional Cluster
Sample Size
Number of
Respondents
Return Rate (%)
A
36
36
100
B
30
29
96
C
24
24
100
D
24
24
100
E
36
31
86
F
36
35
97
G
12
12
100
Total
198
191
96
From the table the return rate varied from 86% to 100% among the respondents in the
seven regional clusters. This high response rate can be attributed to the multiple contact
42
approach used by involving the regional field co coordinators and the field service
administrators at the regional and factory levels respectively.
4.4. Demographic Information
Section A of the questionnaire sought background information on gender, age, working
experience and the highest level of education among the respondents. Results from the
completed questionnaires are summarized in the tables below. The researcher was
interested in understanding how the above factors are manifested within the diverse social
set ups and their possible influence on research findings.
Table 3
Gender of Respondents
CLUSTER
A
B
C
D
E
F
G
TOTAL
MALE
FEMALE
TOTAL
COUNT
31
5
36
% OF TOTAL
86.1
13.9
100
COUNT
25
4
29
% OF TOTAL
86.2
13.8
100
COUNT
19
5
24
% OF TOTAL
79.2
20.8
100
COUNT
22
2
24
% OF TOTAL
91.7
8.3
100
COUNT
27
4
31
% OF TOTAL
87.1
12.9
100
COUNT
33
2
35
% OF TOTAL
94.4
5.7
100
COUNT
12
..
12
% OF TOTAL
100
..
100
COUNT
168
23
191
% OF TOTAL
88.0
12
100
43
The researcher was interested in establishing the existing gender balance among the target
population and the likely influence it could have on research findings. According to the table,
there are more males than females in all the regional clusters. Males represent 88% while
females constitute 12% overall among the technical staff surveyed.
Table 4
Ages of Respondents
Below
25-35
36-45
46-55
Above
25yrs
yrs
yrs
yrs
55 yrs
COUNT
1
5
20
10
0
36
% OF TOTAL
2.8
13.9
55.6
27.8
0
100
COUNT
2
5
15
6
1
29
% OF TOTAL
6.9
17.2
51.7
20.7
3.4
100
COUNT
0
7
11
6
0
24
% OF TOTAL
0
29.2
45.8
25.0
0
COUNT
3
6
9
4
2
24
% OF TOTAL
12.5
25.0
37.5
16.7
8.3
100
COUNT
0
13
13
5
0
31
% OF TOTAL
0
41.9
41.9
16.1
0
COUNT
2
5
18
10
0
% OF TOTAL
5.7
14.3
51.4
28.6
0
COUNT
0
3
5
4
0
% OF TOTAL
0
25.0
41.7
33.3
0
COUNT
8
44
91
45
3
191
% OF TOTAL
4.2
13.4
44.2
26.8
11.4
100
CLUSTER
A
B
C
D
E
F
G
TOTAL
Total
35
12
Research participants were asked to choose their age bracket. The researcher wished to
know whether the age of technical staff could have had an influence on technical
awareness. The most prevalent age bracket in all the regions surveyed was found lie
44
between 25-35 and 36-45. Only two regional clusters had less than 10% of their technical
staff over 55 years. This is most likely to be attributable to retirement age policy of at 55
years.
Table 5
Academic Qualification of Respondents
CLUSTER
Primary
COUNT
Secondary
University
Other or
or College
None
9
12
15
0
25.0
33.0
41.7
0.0
8
9
12
0
27.6
31.0
41.4
0.0
4
10
10
0
16.7
41.7
41.7
0.0
6
7
10
1
25.0
29.2
41.7
4.2
5
13
13
0
16.1
41.9
41.9
0.0
5
15
15
0
14.3
42.9
42.9
0.0
4
3
5
0
33.3
25.0
41.7
0.0
41
69
80
1
21.5
36.1
41.9
0.5
Total
36
A
% OF TOTAL
COUNT
29
B
% OF TOTAL
COUNT
24
C
% OF TOTAL
COUNT
24
D
% OF TOTAL
COUNT
31
E
% OF TOTAL
COUNT
35
F
% OF TOTAL
COUNT
12
G
% OF TOTAL
COUNT
191
TOTAL
% OF TOTAL
The researcher analyzed the level of education of the respondents because it was felt that
it has the potential to affect awareness levels among staff. From the table, 78% of the
respondents have had at least secondary level education. This figure represents a high
45
percentage of employees with the ability to comprehend organizations’ performance
improvement initiatives.
Table 6
Duration of Work Experience
CLUSTER
A
B
C
D
E
F
G
TOTAL
Below 3 yrs
3-6 yrs
COUNT
2
8
17
9
% OF TOTAL
5.6
22.2
47.2
25.0
COUNT
0
5
21
3
% OF TOTAL
0.0
17.2
72.4
10.3
COUNT
0
3
19
2
% OF TOTAL
0.0
12.5
79.2
8.3
COUNT
0
6
11
7
% OF TOTAL
0.0
25.0
45.8
29.2
COUNT
1
7
21
2
% OF TOTAL
3.2
22.6
67.7
6.5
COUNT
0
4
26
5
% OF TOTAL
0.0
11.4
74.3
14.3
COUNT
0
8
4
0
% OF TOTAL
0.0
66.7
33.3
0.0
100.0
COUNT
3
38
119
28
191
% OF TOTAL
1.6
19.9
62.3
14.7
7-15 yrs
Above 15 yrs
Total
36
100.0
29
100.0
24
100.0
24
100.0
31
100.0
35
100.0
12
100.0
Research questionnaire administration was delimited to employees who had been working
for the target processing factory for a minimum period of three years prior to the research
survey. The researcher therefore found it important to establish the number of years the
respondents have been engaged by the company in their current positions.
46
From the table only 1.6 % of the respondents were established to have less than three
years working experience that was needed to participate in the survey. Their filled
questionnaires were therefore not included in the final analysis.
4.5 The extent of the influence of capacity utilization of energy efficiency
Data obtained from the seven geographical regions was used to calculate capacity
utilization factor in terms of kilograms of processed tea per square foot of installed green
leaf withering space at each processing factory. Energy efficiency was calculated by
converting all the energy forms into mega joules. This energy was later used to calculate
energy intensity (I) which is the internationally accepted unit of measuring energy
efficiency. The two variables were later used by the researcher to determine the extent of
the influence of capacity utilization on energy efficiency using the Pearson’s two sided
correlation coefficient.
Table 7
Mean Capacity utilization and energy efficiency
Regional
Capacity Utilization(Kg/ft2
Energy Efficiency Ratio(Kg/MJ)
Cluster
Percentage(%) of
Percentage(%) of
Percentage(%) of
Percentage(%) of
Factories Above
Factories Below
Factories Above
Factories Below
KTDA average
KTDA average
KTDA average
KTDA average
A
58
42
50
50
B
11
89
50
50
C
12.5
77.5
62.5
37.5
D
62.5
37.5
62.5
37.5
E
63.6
36.4
27.3
72.7
F
9
91
18
82
G
25
75
75
25
34.5
65.5
49.3
51.0
KTDA
47
From the table, 58% and 50% of factories within geographical cluster A scored above the
KTDA average capacity utilization and energy efficiency respectively. 11% of Factories
within cluster B scored above average capacity utilization while 50% of them scored
above average on energy utilization efficiency. There is varied performance in the seven
geographical regions. Factories within clusters A, D, F and G although geographically
dispersed, do show direct correspondence between above average performance for both
capacity utilization and energy efficiency. However, factories within clusters B, C and E
an inverse relationship in terms of average performance. The overall results show a weak
relationship between capacity utilization and energy efficiency within the study
population. Findings from the analysis are mixed when we consider the individual
regional cluster samples. According to Baumers et al. (2010), in the chapter two literature
reviews, full capacity utilization results in lower specific energy consumption. The study
also noted that the size of savings varied heavily across industry platforms. The variation
in our case has been observed across the geographical regions since the target population
involved a single industry platform, the Tea Industry. There is therefore a need to
undertake further research to establish the cause of divergent results in the two clusters.
4.5.2 Correlation of capacity utilization and energy efficiency variables
The researcher performed a correlation analysis of capacity utilization and energy
efficiency for every regional cluster sample based on twenty eight month data sets from
October 2010 to December 2012. The analysis employed the two sided Pearson
correlation coefficient test statistic r-value with a p-value of 0.05. The purpose was to
establish the extent to which capacity utilization influences energy efficiency. The results
of correlation between capacity utilization; the independent variable and energy
efficiency; the dependent variable were summarized as shown below.
48
Table 8
Correlation between capacity and energy efficiency.
Regional Cluster
Pearson correlation coefficient -r
Significance(2-tailed)- p
CLUSTER A
0.17 0
0.617
CLUSTER B
0.410
0.273
CLUSTER C
0.060
0.089
CLUSTER D
0.250
0.550
CLUSTER E
-0.490
0.126
CLUSTER F
-0.570
0.050
From the results of correlation analysis clusters B and E showed a moderate positive
correlation between capacity utilization and energy efficiency, A,C and D showed a weak
positive correlation while F showed a moderate negative correlation between capacity
utilization and energy efficiency. More than 70% of the clusters had a moderate to weak
correlation. However, none of the clusters correlation was significant on the basis of the
census and the two tailed point zero five Pearson correlation test.
4.6 The influence of Energy efficient Technology machines on energy efficiency:
The researcher used the age of tea processing machines to assess the extent of the
influence of technology on energy efficiency. The approach was informed by the
49
deliberate adoption of energy efficient technology in the strategic business plan for new
factories as well as in the replacement of old machines. Document review was carried out
on all the sixty five tea processing machines and the total age of the identified major
energy intensive machines tabulated against energy efficiency data for the period from
October 2010 to December 2012 for each of the factories within the regional clusters.
Table 9
Energy efficient technology machines and energy efficiency
Regional
Cluster
Average age of process machines(yrs)
Energy Efficiency Ratio(Kg/MJ)
Percentage(%) of
Percentage(%) of
Percentage(%) of
Percentage(%) of
Factories Above
Factories Below
Factories Above
Factories Below
average ratio
average ratio
average efficiency
average efficiency
A
54.5
45.5
50
50
B
100
0
50
50
C
75
25
62.5
37.5
D
50
50
62.5
37.5
E
57
43
27.3
72.7
F
73
27
18
82
G
25
75
75
25
KTDA
62
38
49.3
51.0
Factories with clusters A, B,C and D appear in the group whose average energy efficient
technology machines ratio are above the average of machines within the KTDA.The same
factories also appear among those that have registered above average performance in
energy efficiency among the KTDA tea processing factories. Factories within cluster E
and F appear among those above the average ratio and also constitute the highest
percentage 72.7 and 82 respectively among those whose energy efficiency performance
level is below the recorded KTDA average over the study period. The influence of the
energy efficient technology processing machines on energy efficiency among the regional
50
clusters is not apparent according to the census data. Although this may not be apparent
depending on other influencing factories, the researcher recommends that objectively
verifiable ways of assessing the contribution to energy efficiency by new machinery and
equipment purchases. The current findings are at variance with previous studies and the
body of knowledge obtained in the literature review in chapter two. Graus et al. (2010)
and Kieran & Torga (2011) underscore this fact. According to the latter researchers,
unrealized innovations in energy efficiency are estimated to have a significant potential
for reducing GHG emissions and improving firm and industry competitiveness.
Table 10
Energy Efficient Technology Machines Ratio and Energy Efficiency.
Regional Cluster
Pearson Correlation Coefficient
P-Value
CLUSTER A
0.22 0
0.516
CLUSTER B
-0.280
0.466
CLUSTER C
-0.170
0.687
CLUSTER D
- 0.040
0.925
CLUSTER E
-0.28
0.466
CLUSTER F
-0.66
0.020
There were mixed results from all the regional clusters on the correlation between energy
efficient technology machines ratio and energy efficiency. Clusters A, C and D showed a
weak positive correlation between the two variables. A weak negative correlation was
51
observed in clusters B and E, both situated in far flung geographical regions. However, a
moderate positive correlation was observed among the factories within cluster F. This
moderate negative correlation was also found to be significant at the Pearson correlation
test statistic p-value of zero point zero five (0.05). The researcher recommends further
research work aimed at establishing reasons for this particular cluster from the general
norm among factories under similar management structures and technology selection.
4.7 Influence of technical staff awareness on energy efficiency
Table 11
Awareness of Energy Efficiency Terminology among Technical Staff
Cluster
A
Count
Subordinate
Staff
Supervisory
Management
Staff
Staff
Yes
Yes
10
No
0
No
16
0
Yes
10
No
0
%
B
Count
Count
8
0
13
0
8
0
Count
7
0
10
0
7
0
Count
7
0
10
0
7
0
Count
9
0
13
0
9
0
Count
10
0
15
0
10
0
Count
%
0
24
0
24
0
31
0
35
0
100.0
3
0
6
0
3
0
%
Total
29
100.0
%
G
0
100.0
%
F
36
100.0
%
E
No
100.0
%
D
Yes
100.0
%
C
Total
12
0
100.0
54
100.0
0
83
0
100.0
52
54
100.0
0
191
100.0
0
In order to establish technical staff energy efficiency awareness levels, research
participants were assessed on their awareness of energy efficiency definition as well as
energy efficiency measures recommended by the KTDA.
4.7.1 Awareness of the term energy efficiency
The researcher was interested in conducting further assessment of those who indicated
that they have heard about the term energy efficiency. They were given four definitions of
energy efficiency to select from.
Table 12
Definition of the term energy efficiency
Research
A
Question
A:Using less
Count
energy
%
B:Using more
Count
energy
B
C
D
E
F
G
Total
28
21
16
19
26
25
7
142
77.8
72.4
66.7
79.2
83.9
71.4
58.3
74.3
0
0
0
0
0
0
0
0
%
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
C:Conserving
Count
30
27
24
23
29
32
10
175
energy
%
83.3
93.1
100.0
95.8
93.5
91.4
83.3
91.6
0
0
0
0
0
0
0
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Count
D:I am not sure
%
Obtained responses indicate that technical staff from the entire regional clusters defined
the term energy efficiency as conserving energy or using less energy.
53
4.7.2 Technical staff awareness of energy efficiency measures
The researcher, with the aim of establishing factory technical staff awareness of the
recommended energy efficiency measures, provided a list of these measures and asked
the respondents to select those they were not aware of. Results of the findings are
summarized in table 13.
Table 13
Technical staff awareness of energy efficiency measures
Awareness Score
Energy Efficiency
(%)
Ratio(Kg/MJ)
36
100
0.0287
B
30
97
0.0281
C
24
96
0.0327
D
24
96
0.0321
E
36
100
0.0290
F
36
97
0.0269
G
12
95
0.0305
Regional Cluster
Sample Size
A
From the findings, respondents in all the clusters scored above 95% on the awareness of
the recommended energy efficiency measures. They were able to detect non
recommended measures and only selected the recommended ones. The researcher was
able to conclude that the level of awareness among technical staff on recommended
energy efficiency measures was high. The results of correlation analysis between the two
variables indicated a very weak negative Pearson correlation(r=-0.0669). The researcher
felt that further statistical test of significance were not necessary and concluded that the
apparently high level of awareness among technical staff has no influence on the energy
efficiency among the target population.
54
4.7.3 Sources of Awareness
The researcher was interested in establishing how the respondents came to know about
the recommended energy efficiency measures in order to further test concurrence on
awareness scores recorded by the participants.
Respondents were asked how they came to hear about the energy efficiency measures and
provided with a list of choices.
Table 14
Source of Energy Efficiency Awareness among Technical Staff
Research
A
B
C
D
E
F
G
Total
22
20
16
18
15
3
125
17.6
16.0
12.8
14.4
12.0
2.4
100.0
B: From the Unit Count 29
15
14
17
13
17
4
109
Manager
%
13.8
12.8
15.6
11.9
15.6
3.7
100.0
C: From KTDA
Count 25
13
11
22
19
24
7
119
engineers
%
10.9
9.2
18.5
16.0
20.2
5.9
100.0
D: From training Count 10
5
12
3
4
2
0
36
seminar I attended %
13.9
33.0
8.3
11.1
5.6
0.0
100.0
Count 2
1
3
0
0
2
0
8
%
12.5
37.5
0.0
0.0
25.0
0.0
100.0
Question
A:
From
my Count 31
supervisor
E: Other sources
%
24.8
26.6
21.0
27.8
25.0
The majority of staff surveyed indicated that they had heard of energy efficiency from
their supervisors (125), the KTDA engineers (119), the factory unit manager (109),
training (36) and lastly from other sources (8).
55
4.8 The influence of energy mix on energy efficiency in KTDA Tea Factories
Document analysis was contacted for the three research questions that sought to establish
the extent of the influence of energy mix on energy efficiency. A set of one thousand six
hundred and eighty monthly energy data records covering the period between October
2010 to October 2012 were analyzed. The researcher was able to access all the factory tea
production and energy consumption records as envisaged from the KTDA factories data
hoisted on the factories website. Additional information on factory capacity and age of
processing machines was obtained from the factories business strategic plans (2010-2014)
within the KTDA technical services department. The data was analyzed to establish the
extent of the influence of the above variables on energy efficiency through Pearson
correlation coefficient two sided test.
Table 15
Energy Mix by Percent Proportion
Regional
Cluster
Types of Energy Source(Energy Mix)
Fuel wood (%)
Count(N) Mean
Efficiency
Furnace Oil Electrical
(%)
Ratio
Energy (%)
(Kg/MJ)
A
90.7
2.3
7.6
336
0.0287
B
90.8
2.1
7.1
252
0.0281
C
89.0
3.7
7.3
204
0.0327
D
89.6
1.8
8.6
204
0.0321
E
92.2
0.5
7.3
308
0.0290
F
91.5
1.5
7.1
308
0.0269
G
91.1
0.3
8.8
112
0.0305
The table obtained from the analysis of census data of all the regional clusters shows that
fuel wood accounts for more than 89% of all the energy consumed by the factories. It is
56
followed by electrical energy that represents between 7 to below 9% of the energy
consumed in each of the seven clusters. The use of furnace oil, an imported fossil fuel
product, accounts for between 0.3% to below 4.0% of all the energy consumed in all
regional clusters. Fuel wood accounted for 91.1%, electrical energy 8.8% and furnace oil
0.3% of the energy consumed by all the KTDA managed black processing factories for
the period covered in the study.
4.8.2 Correlation of energy mix variables and energy efficiency
The researcher performed a correlation analysis between the three energy source variables
and energy efficiency so as to answer the energy mix research question. The objective for
this research question was to establish to what extent energy mix influences energy
efficiency. The three energy mix sub-independent variables percent proportion were
correlated with the mean energy efficiency ratio (kilograms of processed black tea per
unit of energy consumption measured in MJ).This ratio is the inverse of energy intensity.
The researcher preferred it in the analysis because it is directly related to energy
efficiency as opposed to the energy intensity which has an inverse relationship with
energy efficiency. The latter’s interpretation is therefore simpler and direct.
57
Table 16
Correlation of energy mix variables and energy efficiency
Regional
Cluster
Fuel wood
Furnace Oil
Electrical Energy
A
r=-0.5255
p=0.0793
r=-0.3341
p=0.2885
r=+0.6877
p=0.01344
B
r=+0.3586
p=0.34329
r=-0.4908
p=0.1797
r=+0.6110
p=0.0805
C
r=-0.7512
p=0.0316
r=+0.5014
p=0.2055
r=+0.7914
p=0.01929
D
r=-0.3475
p=0.39899
r=+0.2360
p=0.57365
r=+0.6433
p=0.0852
E
r=-0.4680
p=0.1465
r=-0.4742
p=0.14058
r=+0.8682
p=0.000528
F
r=-0.2041
p=0.524595
r=-0.3766
p=0.227568
r=+0.7178
p=0.00857
G
r=-0.9849
p=0.01510
r=0.0403
p=0.95970
r=+0.5538
p=0.44620
Overall
r=-0.4840
p=0.000
r=+0.13378
p=0.29618
r=+0.71152
p=0.000
58
Correlation analysis between fuel wood and energy efficiency results indicate a moderate
to strong negative correlation six out of seven regional clusters. The findings for clusters
G and C are significant for two tailed and one tailed respectively. The analysis shows
mixed results for furnace oil use where four out of seven show a weak negative
correlation while three show a weak positive correlation with non being significant at the
two tailed test for significance(p=0.05). However, all the seven clusters show a moderate
to strong correlation for electrical energy use. Four of the clusters, A, C, E and F are
significant at P=0.05 while the remainder three are significant at p=0.1. The literature
review in chapter two supports the observations for the correlation between fuel wood
and energy efficiency. According to De Silva (1993), wood fuel combustion is inefficient
compared to fossil fuels, but it is still selected on the basis of net costs. The researcher
therefore recommends regular comparison of the net basis value due to the steady rise in
fuel wood costs.
4.9 A Summary of the Chapter
Data analysis, discussion and presentation in tables for the investigation of the four
research objectives were successfully realized. Document review was used to attempt to
find answers to three of the research questions. This involved taking a census data for all
the KTDA black tea processing tea production and energy consumption that covered the
period between October 2010 and December 2012. The fourth research question was
investigated through questionnaire administration. Demographic data covering age,
education level, work duration and gender was captured from filled questionnaires and
analyzed. The second section of the questionnaire provided information on energy
efficiency awareness among technical staff. This was also analyzed and presented in table
form. Statistical analysis technique employed Pearson correlation coefficient two sided
test and at 0.05 significance.
59
CHAPTER FIVE
SUMMARY OF FINDINGS, DISCUSSIONS, CONCLUSIONS AND
RECOMMENDATION
5.1 Introduction
Chapter five consists of a summary of findings, discussions, conclusions and
recommendations for further research. Observations from the research findings have been
compared with those from previous studies and a similar body of knowledge as obtained
from the literature review in chapter two. The purpose of the research study was to
determine the extent of the influence of determinants of energy efficiency on energy
efficiency in KTDA managed black tea processing factories. It was guided by four key
objectives. Three of the four research objectives were investigated through document
review while the fourth involved questionnaire administration.
5.2 Summary of Findings
Seven cluster samples were analyased.Clusters B and E showed a moderate positive
correlation between capacity utilization and energy efficiency. A, C and D showed a
weak positive correlation while F showed a moderate negative correlation between
capacity utilization and energy efficiency. More than 70% of the clusters had a positive
moderate to weak correlation. However, none of the clusters correlation was significant
on the basis of the census and the two tailed point zero five Pearson correlation test. The
overall results show a weak positive relationship between capacity utilization and energy
efficiency within the study population. Findings from the analysis are mixed when we
consider the individual regional cluster samples.
There were mixed results from all the regional clusters on the correlation between age of
processing machines and energy efficiency. Clusters A, C and D showed a weak positive
correlation between the two variables. A weak negative correlation was observed in
clusters B and E, both situated in far flung geographical regions. However, a moderate
negative correlation was observed among the factories within cluster F. This moderate
60
negative correlation was also found to be significant at the Pearson correlation test
statistic p-value of zero point zero five (0.05).
The influence of technical staff awareness on energy efficiency
From the findings, respondents in all the seven geographical clusters scored above 95%
on the awareness of the recommended energy efficiency measures. They were able to
detect non recommended measures and only select from among the recommended ones.
The researcher was able to conclude that the level of awareness among technical staff on
recommended energy efficiency measures was considerably high. The results of
correlation analysis between the two variables indicated a very weak negative
correlation(r=-0.0669). The researcher decided further statistical test of significance were
not necessary and concluded that the apparently high level of awareness among technical
staff has no influence on the energy efficiency among the target population.
The influence of energy mix on energy efficiency.
The analysis of census data of all the regional clusters shows that fuel wood accounts for
more than 89% of all the energy consumed by the factories. It is followed by electrical
energy that represents between 7 to below 9% of the energy consumed in each of the
seven clusters. The use of furnace oil, an imported fossil fuel product, accounts for
between 0.3% to below 4.0% of all the energy consumed in all regional clusters. Fuel
wood accounted for 91.1%, electrical energy 8.8% and furnace oil 0.3% of the energy
consumed by all the KTDA managed black processing factories for the period covered in
the study.
Correlation analyses between fuel wood and energy efficiency results indicate a moderate
to strong negative correlation in six out of seven regional clusters. The findings for
clusters G and C are significant for two tailed and one tailed tests respectively. The
analysis shows mixed results for furnace oil use where four out of seven show a weak
negative correlation while three show a weak positive correlation with non being
61
significant at the two tailed test for significance(p=0.05). However, all the seven clusters
show a moderate to strong correlation for electrical energy use. Four of the clusters, A, C,
E and F are significant at P=0.05 while the remainder three are significant at p=0.1.
5.3 Discussion of findings
Determinants of energy utilization efficiency in KTDA managed black tea processing
factories were evaluated successfully. They comprise capacity utilization, the age of
process machines, technical staff energy efficiency awareness and energy mix. Energy
mix is the composition of energy from the different sources of fuel wood, furnace oil and
grid supplied electrical energy.
5.3.1 Influence of Capacity utilization on energy efficiency.
The study sought to determine the extent of the influence of capacity utilization on energy
efficiency. Seven out of ten of the target study population showed a moderate positive
correlation between capacity utilization and energy efficiency. The result supports
findings encountered in the literature review. The overall results show a weak positive
relationship between capacity utilization and energy efficiency within the study
population. Full capacity utilization results in lower specific energy consumption as
empirically demonstrated by Baumers et al. (2011). However, the size of this saving was
found to vary heavily across different industry platforms. The variation in our case has
been observed across the geographical regions since the target population involved a
single industry platform, the Tea Industry. There is therefore a need to undertake further
research to establish the cause of divergent results in the two clusters that displayed a
moderate negative correlation.
Although we were able develop a measure for capacity utilization in our study, it was not
possible to establish the true value of capacity utilization for the target population. The
data obtained from document analysis encompassed periods of low as well as high
production and can therefore be treated as an average representation of capacity
62
utilization. This scenario is not possible in the medium to long term measure due to the
seasonal nature of tea production. More accurate results can be obtained by collecting and
comparing data for periods when the factories are operating at established capacity levels
of say 20%, 30%, and 70% up to 100% and the corresponding energy efficiency values.
5.3.2 The influence of the age of processing machinery on energy efficiency
The researcher sought to determine the extent of the influence of the age of processing
machines on energy utilization efficiency in KTDA managed black tea processing
factories. This involved taking a census of data for the earmarked period of study. There
has been a deliberate effort to procure energy efficient machines in the last ten years both
for new factories as well as those undergoing modernization. The age of machinery in a
factory offers an indirect means of assessing energy efficiency performance among more
modern and less modern technologies.
There were mixed results from all the regional clusters on the correlation between age of
processing machines and energy efficiency. Clusters A, C and D showed a weak positive
correlation between the two variables. A weak negative correlation was observed in
clusters B and E, both situated in far flung geographical regions. However, a moderate
negative correlation was observed among the factories within cluster F. The age of
processing machines that represent modern technology did not have visible influence on
energy efficiency compared to older factories which performed relatively better in terms
of energy utilization efficiency. The results point to the possibility that the age of
processing machines is insignificant as a determinant of energy efficiency within the
study population. Chapter two literature reviews observes that global primary energy
supply increased by 30% between 1990 and 2005 and the world wide demand is projected
to double by 2050 according to the International Energy Agency (IEA) 2007 report. It is
estimate that by exploiting the technical potential for energy efficiency improvement in
energy demand sectors, this growth can be limited to 8% Graus et al. (2010). The mixed
results could be an indicator to low technology innovation in the tea machinery. World
63
watch (2009, vol 122) observes that since energy prices have been historically low, the
energy costs of operating inefficient machines have not been significant.
5.3.3 The influence of technical staff awareness on energy efficiency
The research study sought to determine the extent of the influence of technical staff
energy awareness on energy efficiency. Respondents in all the seven geographical
clusters scored above 95% on the level awareness on the energy efficiency measures
recommended by KTDA. The results of correlation analysis between the two variables
yielded a very weak negative correlation(r=-0.0669). It was concluded that the apparently
high level of awareness among technical staff has no influence on the energy efficiency
among the target population. Several previous studies found no significant relationships
in their study of energy efficiency on similar variables. According to Semenik et
al.(1982), relationships between energy efficiency behavior and awareness have generally
been weak and often contradictory. Both Hogan (1976) and Bertel (1974) found no
significant relationship between energy efficiency awareness and energy efficiency. It is
generally expected that education and energy conservation and efficiency would be
positively correlated. However, a majority of studies have found mixed results. More
research needs to be carried out to establish why the two variables are not positively
correlated as expected.
5.3.4 The influence of energy mix on energy efficiency
The research sought to determine the extent of the influence of energy mix on energy
efficiency in KTDA managed black tea processing factories. Document form analysis was
used to study census data from the target population. The analysis of census data of all the
regional clusters shows that fuel wood accounts for more than 89% of all the energy
consumed by the factories. It is followed by electrical energy that represents between 7 to
below 9% of the energy consumed in each of the seven clusters. The use of furnace oil, an
imported fossil fuel product, accounts for between 0.3% to below 4.0% of all the energy
consumed in all regional clusters. Fuel wood accounted for 91.1%, electrical energy
64
8.8% and furnace oil 0.3% of the energy consumed by all the KTDA managed black
processing factories for the period covered in the study.
Correlation analyses between fuel wood and energy efficiency results indicate a moderate
to strong negative correlation in six out of seven regional clusters. The findings for
clusters G and C are significant for two tailed and one tailed tests respectively. The
analysis shows mixed results for furnace oil use where four out of seven show a weak
negative correlation while three show a weak positive correlation with non being
significant at the two tailed test for significance(p=0.05). However, all the seven clusters
show a moderate to strong correlation for electrical energy use. Four of the clusters, A, C,
E and F are significant at P=0.05 while the remainder three are significant at p=0.1. The
energy mix variable seems to have the greatest influence on energy efficiency among all
the variables studied. Fuel wood constitutes the highest percentage of three forms of
energy in use. It is also negatively correlated with energy efficiency. The most probable
reason for its choice is price. This is captured by Lermit &Jollands (2001) who noted the
quality effect while studying energy efficiency in New Zealand. They observed that the
quality effect captures the potential energy extracted from each fuel source. Electrical
energy is positively correlated with energy efficiency but constitutes less than ten percent
of the energy mix due to its corresponding higher purchase price. There is minimal use of
furnace oil; a more efficient source but more expensive than fuel wood. Greater focus
requires to be put on improving the fuel wood efficiency for two reasons. It supplies over
ninety percent of the factories energy requirements and it is also facing sustainability
challenges.
5.4 Conclusions
There exists a weak to moderate positive influence by capacity utilization on energy
efficiency in KTDA managed black tea processing factories.
There are mixed results on the extent of the influence of the age of process machines on
energy efficiency in KTDA managed black tea processing factories.
65
The apparent high level of technical staff energy efficiency awareness has no influence on
energy efficiency in KTDA managed black tea processing factories.
There exists strong influence of energy mix on energy efficiency in KTDA managed
black tea processing factories. The fuel wood semi variable has a moderate to strong
negative influence on energy efficiency among the target population. Electrical energy
has a moderate to strong positive influence on energy efficiency among the population
under study
5.5 Recommendations
The study examined determinants of energy efficiency in KTDA managed tea processing
factories. It sought to determine the extent of the influence of these variables on energy
efficiency. The following are recommendations that arose from the findings.
1. KTDA needs to place more focus for energy efficiency improvement on fuel wood
management. Fuel wood constitutes over 90% of the energy consumed within the tea
processing factories, has a negative influence on energy efficiency and is faced with
challenges of sustainability.
2.The KTDA should devise an elaborate way of assessing, monitoring and evaluating the
stated efficiency of new machinery and equipment to ensure that they match those of the
existing ones as a minimum. Results from this study did not show differences in energy
efficiency performance that could be associated with newer machines which are
expected to be more energy efficient.
66
3.The KTDA needs to develop a system of monitoring and reporting individual machinery
capacity utilization so as to realize improved energy efficiency and the resulting gains in
productivity.
5.6 Suggestions for further study
The initial purpose for this study was to determine the extent of the influence of
determinants of energy efficiency on energy efficiency. The target population for the study
was the KTDA managed black tea processing factories. During the study, the researcher
noted some areas that were felt to merit further investigation as captured below.
1. The influence of validity and reliability of fuel measurement instruments on energy
efficiency. These instruments were assumed to be valid and reliable in the foregoing
study.
2. The influence of capacity utilization based on the total installed electrical load on
electrical energy utilization efficiency.
67
References
ACEEE (American Council for Energy Efficient Economy), 2007: Consumer Guide to
Home Energy Savings-Cooling Equipment
ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers,
Inc (1985): Weather Year for Energy Calculations
Baumers, M. Tuck, C., Hague, R., Wildman, R. Ashcroft, I, 2010: A comparative study
of metallic additive manufacturing power consumption. Proceedings of the Solid
Freedom Fabrication Symposium, 2010, AUSTIN, USA.
Baumers, C.Tuck, R.Wildman, I. Ashcroft and R.Hague: 2011, Energy Inputs to additive
manufacturing, Does Capacity Utilization matter?
Briggs and Smith (2003): “Determinants of energy efficiency, a theoretical and empirical
investigation: NZ Institute of Economic Research (Inc.)
Brookes, L. (1990). “The green house effect”’ the fallacies in the energy efficiency
solution. Energy Policy 18(2).
Bultena, Gordon L. (1976), Public Response to the Energy Crisis: A Study of Citizens'
Attitudes and Adaptive Behaviors, (Ames, Iowa: Iowa State University).
Chakavarti K.K (2006): Promoting End – use Energy efficiency in India (Case Study)
Crosbie .T, & Keith Baker: Energy Efficiency Interventions in housing: Learning from
the inhabitants: - Research paper; Building Research & Information (2010), 38(1), 7079(UK).
Cunningham et al (1977): Energy Use and Conservation Incentives, New York Praeger
Publishers).
Decanio (1993): Scenarios for a clean energy future: US Dept of Energy Inter laboratory
working Group on Energy – Efficient and Clean Energy Technologies.
De Silva (1993): Some Energy Saving Achievements of the Tea Industry in Sri Lanka.
DECC (2010): UK Climate change sustainable Indicator: 2008 greenhouse gas emissions
final figures
(http://www.decc.gov.ik/en/content/cms/statistics/climatechange/gg/emisssions/uk-)
68
Foran, B., Lenzen, M., Dey, C. and Bilek, M. , 2005: Integrating Sustainable chin
management with triple bottom line accounting. Ecological Economics 52(2), pp 143
– 157.
Frankfort-Nachmias, C., &Nachmias, D. (1996).Research methods in the social sciences
(5th ed.).New York.St. Martin’s.
Frantz, R.S, (1997): X- Efficiency Theory, Evidence and Applications, Kluwert
Academic Publishers, Boston.
Gesimba R.M, Langat, M.C., Liu, G. & Wolukau, J.N: The Tea Industry in Kenya; The
challenges and the positive Developments.
Gottlieb et al (1975): “Conceptions of Energy Shortages and Energy Conservation
Behaviour”, Presented at the 70th Annual Meeting of the American Sociological
Association, San Francisco.
Graus, W., Blomen, E., and Worrell: Global Energy efficiency Improvement in the longterm: a demand – and supply – side perspective: Energy Efficiency (2011) 4:435 –
463
Hogan, Janice M. (1976), Energy conservation, house hold practices, and contextual
variables, PhD dissertation, East Lansing, Michigan State University.
Howarth R.B (1997): Energy efficiency and economic growth, Contemporary economic
policy 15(4), 1-9.
IEA (2009), “Implementing Energy Efficiency Policies. Are IEA Member Countries on
Track? OECD/IEA, Paris
IEA (2009): World Energy Outlook, Executive Summary, International Energy Agency.
IEA (2008): Energy Efficiency Policy Recommendations. In support of the G8 plan of
action (http://www.ies.org/G8/2008/G8-EE)
Ines ,C. (2010): Factors influencing energy efficiency in the German and Colombian
Manufacturing Industries.
International Energy Agency (IEA) (2007): Tracking Industrial Energy Efficiency and
Co2 Emissions. In Support of the G8 plan of action. Energy Indicators.
International Energy Agency (2009): World Energy OUTLOOK, 009: Executive
summary, IEA.
69
Jeffries & Elisabeth: (2009): Energy Efficiency Rediscovered, World Watch Jan/Feb
2009. Vol. 22 Issue 1, P 22-27.
Jovane, F.Yoshikawa, H.Ahing, L.Boer, C, R. West kamper, E.Williams, D. Tseng,
M.Seliger, G.Paci, A.M; 2008: The incoming global technological and resolution
towards competitive sustainable manufacturing.
Kamper. W, E.Alting and Arndt G, 2000: Life cycle Management and assessment:
Approaches and visions towards sustainable Manufacturing (Keynote paper) CIRP
Annals – Manufacturing Technology, 49(2), PP.501-526.
Kenya Energy Act. 2006: Kenya Gazette (Acts No. 12), 2’nd Jan. 2007.
Kieran & Nuttaneeya Torgsa (2011): Research paper, supply – side Determinants of
Energy consumption and Efficiency (ECE) innovations (Journal of business
chemistry).
Kumar. And M. Saqib (1996): “Firm Size, Opportunities for Adaption, and In-house
R&D Activity in Developing Countries: The Case of Indian Manufacturing.”
Research Policy, 25, pp.712-722.
Khazzoom D.J (1980). Economic implications of mandated efficiency in standards for
house hold applications, energy journal 1(4), 21-39.
Lermit, Jonathan and Nigel Jollands (2001): “Energy Efficiency trends in New Zealand:
Divisia decomposition results” in Energy Monitoring Quarterly, Issue 17, June
2001(EECA).
Mc Kane A., Pricel. , Rue S. (2008). Policies for promoting Industrial Energy Efficiency
in Developing countries and Transition Economies, UNIDO.
Sahu, Santosh, K.K, Narayanan (2009), “Determinants of Energy Intensity: A preliminary
Investigation of Indian Manufacturing Industries”.
Semenik. R, Russell Belk, and John Painter (1982): “A study of Factors Influencing
Energy Conservation Behaviour,” in Advances in Consumer Research Volume 09,
Association for Consumer Research.
Sutherland R.J (1991): Market Barriers to energy efficiency investments, Energy Journal
12(3).
70
Ted. B, (1974): "The Effects of the Energy Crisis on Attitudes and Life Styles of Los
Angeles Residents," presented at the 69th annual meeting of the American
Sociological Association, Montreal.
Thollander P., Danestig. , Rhodin P. (2007): Energy policies for increased industrial
energy efficiency, Evaluation of a local energy programme for manufacturing SME’s.
Tschudi, A., William, Sartor, Dale, Beasley, James: Clean Room Energy Efficiency,
ASHRAE Journal, OCT. 2010, Vol 5 issue 10 (USA).
UNIDO (2007): Policies for Promoting Industrial Energy Efficiency in Developing
Countries and Transition Economies.
http://www.unido.org/filadmin/media/documents/pdf/Energy-Environment/indenergy-efficiencyEbookV2.pdf
Vanden K.F, Jeferson G.H. ,Hangmei L., Quan T.(2002), “ What is Driving China’s
Decline in Energy Intensity,” weber.ucsd.edu/-carsonvs/papers/787.pdf.
Wuppertal Institute, 2008: Greenhouse Gas Mitigation in industry in developing countries
Final Report (GTZ). http://www.wuppper.ist.org/en/projects/proj/index
Zafer Utlu & Arif – Hepbasli: (2006), Analyzing the energy utilization efficiency of
Renewable Energy Resources (Turkey)
Zhivov. A, Dale Herron, Michael Deru: Achieving Energy Efficiency & Improving
Indoor Air Quality in Army Maintenance facilities (2009) Vol. 115, part. 2
71
APPENDICES
APPENDIX ONE: KTDA BLACK TEA PROCESSING FACTORIES
REGION-1
REGION-2
REGION-3
FACTORY
GACHARAGE
GACHEGE
IKUMBI
KAGWE
KAMBA
KURI
MAKOMBOKI
MATAARA
NDUTI
NGERE
NJUNU
THETA
FACTORY
CHINGA
GATHUTHI
GATUNGURU
GITHAMBO
GITUGI
IRIAINI
KANYENYAINI
KIRU
RAGATI
FACTORY
KANGAITA
KATHANGARIRI
KIMUNYE
MUNGANIA
MUNUNGA
NDIMA
RUKURIRI
THUMAITA
72
RANDOM SAMPLE






RANDOM SAMPLE





RANDOM SAMPLE




REGION 4
REGION-5
REGION-6
REGION-7
FACTORY
IGEMBE
KIEGOI
MICHIMIKURU
GITHONGO
IMENTI
KIONYO
KINORO
WERU
FACTORY
CHELAL
KAPKATET
KAPKOROS
KAPSET
KOBEL
LITEIN
MOGOGOSIEK
MOMUL
ROROK
TEGAT
TIRGAGA
TOROR
FACTORY
EBEREGE
GIANCHORE
ITUMBE
KEBIRIGO
KIAMOKAMA
NYAMACHE
NYANKOBA
NYANSIONGO
OGEMBO
RIANYAMWAMU
SANGANYI
TOMBE
FACTORY
CHEBUT
KAPSARA
KAPTUMO
MUDETE
73
RANDOM SAMPLE




RANDOM SAMPLE






RANDOM SAMPLE






RANDOM SAMPLE


APPENDIX TWO: ENERGY EFFICIENT TECHNOLOGY MACHINES
DOCUMENT FORM
The following machinery and equipment are used in tea manufacturing and constitute
the greatest proportion of energy consuming devices.
Factory Name: ……………………..
Region: ……………………..……...
Year Established: …………………..
Energy Efficient Technology Machines Document Form
EQUIPMENT
QUANTITY
YEAR
OF AGE
ACQUISITION
FUELWOOD BOILER
------------------No.
FBD DRYER
-------------------No
VFBD DRYER
------------------No.
WITHERING FANS
-------------------No.
FURNACE OIL BOILER
--------------------No.
WEIGH FEEDER
--------------------No.
BOILER APH
-------------------No.
74
(YEARS)
APPENDIX THREE: CAPACITY UTILIZATION DOCUMENT FORM (T003)
Region…………………..
Capacity Utilization Document Form
FACTORY
DESIGN
ACTUAL
CAPACITY(ft2)
PRODUCTION(Kgs) FACTOR (kg/ft2)
75
CAPACITY
APPENDIX FOUR: ENERGY MIX DOCUMENT FORM (T004)
Region …………………..
Energy Consumption
Energy Mix Document Form
FACTORY
WOODFUEL
FURNACE
ELECTRICAL
ELECTRIC
M3
OIL (LTS)
ENERGY(KWh) GENERATOR
DIESEL(LTS)
76
APPENDIX FIVE: QUESTIONNAIRE TRANSMITTAL LETTER
Dear Sir/Madam,
My name is Japheth Bulali Sayi. I work as a maintenance manager for Kenya Tea
Development Agency Power Company. I am undertaking a research project on energy
use in KTDA managed tea processing factories. I need your assistance to accomplish this
task. I am therefore requesting you to kindly answer the following questions accurately
and honestly by putting a tick in the box against your choice. I will keep the responses
you provide confidential. You may write your name in the space provided or you can
choose to leave it blank.
Thank you
Japheth Bulali Sayi
77
APPENDIX SIX: QUESTIONNAIRE ON TECHNICAL STAFF ENERGY
EFFICIENCY AWARENESS
This questionnaire consists of eleven multiple choice questions. You are requested to
provide your honest answers to each question by placing a tick in the box after your
choice. Responses will be kept confidential so that it is not possible to associate them
directly with you.
SECTION A
Background information:
B1. Select your job placement from the list below:
Gender: Male
Female
Date of Birth: Month/Year
Production Office (Management Staff) M1…………………..
Production Office (Management Staff) M2……………….….
Supervisory Staff(Maintenance-Mechanical)S1……………….
Supervisory Staff(Maintenance-Electrical)S2…………………
Supervisory Staff(Withering Section)S3………..……………
Dryer Attendant (DA) ……………….……………….………
Boiler Attendant (BA) ……………….…………………….
B2. When were you engaged by the company to work in your current position?
Year……………….……………….
Month……………….……………
B3. Highest education level attained.
Primary School……………….……
Secondary School……………….……………….
78
College or University……………….…………….
Other (please specify) ……………….………….
None ……………….……………….……………
SECTION B
Energy efficiency awareness
Q1. Have you heard of the term energy efficiency?
Yes……………….……………….
No……………….……………….
If your answer is No skip to question 3.
Q2. How would you define the term energy efficiency?
A: Using less energy…………….
B: Using more energy ………….
C: Conserving energy………….
D: I am not sure ……………….
Q3. Among the following list of recommended energy efficiency measures, which ones
have you NOT heard of?
3.1 lagging and insulating steam distribution pipes. ……………….…………
3.2 Timely repair of leaking steam, air and water pipes……………….………
3.3 Regular cleaning of boiler fire tubes……………….………………………
3.4 Keeping the firewood boiler feeding door closed most the time…………
3.5 Keeping fire wood under shade……………….
3.6 Installing air pre heaters on firewood boilers…
3.7 Using capacitor banks to maintain the factory power factor above 0.9.
79
3.8 Buying and using energy saving lighting ……………….……………
3.9 operating machines close to their design capacity ……………….
3.10 Switching off idle machines ……………….…………………..
3.11 Using only billeted firewood in the boiler. ……………………
3.12 Procurement of high efficiency motors ……………………….
3.13 Trapping and re using flash steam in tea dryers ……………….
3.14 Regular inspection and repair of leaking steam traps …………
Q4. How did you get to hear about the energy efficiency measures that you did NOT
select from the above list?
A: From my supervisor ……………….……………….
B: From the Unit Manager……………….……………
C: From KTDA engineers ……………….……………
D: From training seminar I attended …………………
E: Other sources (Name the source) …………………
THANK YOU
END
80
APPENDIX SEVEN: SAMPLE SIZE SELECTION TABLE
Sample Size for ±5%, ±7% and ±10% Precision Levels where Confidence Level is 95%
and P=.5.
Sample Size (n) for Precision (e) of:
Size of Population
±5%
±7%
±10%
100
81
67
51
125
96
78
56
150
110
86
61
175
122
94
64
200
134
101
67
225
144
107
70
250
154
112
72
275
163
117
74
300
172
121
76
325
180
125
77
350
187
129
78
375
194
132
80
400
201
135
81
425
207
138
82
450
212
140
82
Table 3.1 is derived from Yamane (1967) simplified formula for calculating sample sizes.
n=N/ (1+N (e) 2)
Where n= sample size
N= Population size
81