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University of the Witwatersrand
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.
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Name : Senzo Fortune Mokoena
Course : Bcom Honours
Due date : 17/10/2014
Supervisor : Professor Chriss Malikane
Research topic : The energy intensity of the South African economy
.
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Plagiarism declaration
I Senzo Fortune Mokoena (Student number: 769107) Iam a student registered for Honours in
Development theory and Policy in the year 2014 I hereby declare the following:
I am aware that plagiarism (the use of someone elses work without their permission
and/or without acknowledging the original source) is wrong.
I confirm that the work submitted for assessment for the above course is my own
unaided work except where I have explicitly indicated otherwise.
I have followed the required conventions in referencing the thoughts and ideas of
others.
I understand that the University of the Witwatersrand may take disciplinary action
against me if there is a belief that this is not my own unaided work or that I have
failed to acknowledge the source of the ideas or words in my writing.
Signature: Date: 17/10/2014
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Acknowledgements
Italways seem impossible until I take actionNelson Mandela
A special thanks to my supervisor professor C Malikane for the precious time that he gave
me towards executing this research paper. Moreover the valuable advices that I got from
him. I also thank my brother Thaphelo Makonyane for his encouragement during hard times
and the department of trade and industry to finance my studies. Furthermore, I dedicate
this research to my late grandmother Linha Batukeni Mokoena who taught me hard work,
discipline and dedication at a very tender age.
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Table of content
Page
Introduction .. 5
Literature review .. 6
Methodology . 10
Analysis of data 12
Interpretation of results and discussions. 15
Conclusion and policy recommendation .. 16
References .. 17
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Abstract
Energy is crucial in the South African economy for sectors to operate and industrial sector to
produce commodities. This research paper measures the energy intensity of the South
African economy. In addition, this paper outlines and examines various economic methods
that are used to measure energy intensity, there are at least 11 economic methods.
However Input-output economic method is used in this research paper to measure energy
intensity of the South African economy. Using data provided by statistics SA for natural
resource accounts, 1995-2001, supply and use tables report, 2002. The results of this
research show that manufacturing sector consume more energy to produce each unit of
GDP, mining sector is also energy intensive. Furthermore, the results of this research show
that consumption of energy by construction and agricultural sector is efficient. However the
is a clear evidence that energy intensity of the South African economy is high. In addition,
this research paper provides policy recommendations to reduce the high level of energyintensity in the South African economy.
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1. Introduction
This paper measures the energy intensity of the South African economy. Moreover, this paper
outlines and examines different economic methods that are used to measure energy intensity.
Ziramba (2009) shows that South Africa sources 68% of its energy from coal and 19% of its
energy from crude oil. Consumption of energy in South Africa is driven by resourceextraction and connected economic activities termed mineral energy complexby Fine and
Rustomjee (1996). These interrelated activities comprise iron and steel, non-ferrous metals,
non-metallic minerals, rubber, plastics, industrial and other chemicals and mining industries.
In addition, Winkler (2003) states that economic structure and large share of economic
activities influence energy consumption in the South African economy. According to Lotz
and Pouris (2012), low energy prices and lack of public awareness has caused energy
consumption in the South African economy to rise. Given the degree of concentration around
production of the energy intensive sectors, it is crucial that policymakers understand the level
of energy intensity of the economy for long-term planning.
Measuring the energy intensity of the economy is significant given the extreme problem ofglobal warming. The South African economy in particular relies heavily on coal for power
generation. In addition, Woulde-Ruafael (2009) shows that coal constitute 95% of electricity
in the South African economy. Furthermore, Blignaut and Lautz (2011) noted that gas
emissions occur from coal consumption for power generation. Empirical studies show that
gas emissions have an adverse effect to the environment such as air pollution. However,
Menyah and Woulde-Rufael (2010) document that energy sector accounts for more than 15%
to the South African GDP. Furthermore, Marquand and Winkler (2009) say that industrial
sector use more energy to produce each unit of GDP within the South African economy.
According to Meyer and Oladiran (2007), energy has to be used efficiently, save costs and
prevent gas emissions. In addition, empirical studies show that measuring energy intensity
reduces cost of production for industrial sector, gas emissions and improve energy efficiencyin a country.
The gap that exists in the body of knowledge is the use of input - output method to measure
energy intensity of the South African economy. Furthermore, the are few studies in South
Africa that examine different methods that are used assess energy intensity. Blignaut and
Lautz (2012) used sectoral analysis method to examine energy intensity among various South
African industries. As a result they found that energy consumption per unit of GDP produced
is higher than OECD on average. Furthermore, Blignaut and Lautz (2011) used
decomposition method to measure energy intensity of the South African economy, they found
that change of economic structure contributes to the increase in energy intensity. According
to Ziramba (2008), high energy consumption per unit of GDP in South Africa indicates rapidenergy consumption growth rates as compared to economic growth rates. In addition,
aggregate energy consumption efficiency can be achieved through assessing energy intensity.
However I acknowledge various economic methods used by various South African authors to
assess energy intensity of the economy.
The contribution of this paper is the use of input-output method to measure energy intensity
of the South African economy. Furthermore to examine different economic methods that are
used to measure energy intensity and provide policy recommendations to reduce energy
intensity. According to Hu and Zhang et al (2014), input- output method is the substantial
tool that is used to determine total energy consumption by the industrial sector.In addition,Gama and Sloan et al (2011) suggested that input-output economic model is capable to detect
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energy intensity of a country. Furthermore, Ang and Zang (2000) document that sectoral-
energy intensity includes amount of energy used to produce certain level of output within the
sectoral level. Decomposition analysis method requires low amount of data to assess energy
intensity. However Ang (2004) says the weakness that arises from index decomposition
analysis method is the appropriateness to examine only economic structure and incapability
to assess aggregate energy consumption in relation to output. This statement underpins thatindex decomposition method is suitable to investigate changes that occur within the structure
of the economy.
Other authors used granger causality technique to assess relationship between energy
consumption and level of gas emissions within the South African economy. In addition, it is
significant for a country to monitor overall energy consumption of the economy to determine
factors that influence energy intensity. Menya and Rufael (2010) used granger causality test
to measure energy intensity of the South African economy. As a result, they found that in
order for South Africa to mitigate level of gas emissions it has to reduce economic growth
and energy consumption within the economy. However, Lean and Smyth (2009) criticize
granger causality test to omit variables that influence energy consumption. Furthermore,Chen and Guo (2006) noted that granger causality test lead to invalid results if time series
data used is not constant. According to Esposito and Kayser et al (2009), the strength of
granger causality technique is to provide sufficient input that can improve estimations.
Arithmetic decomposition economic method is used to breakdown aggregate energy data of a
country. Cornilie and Frankhouser (2004) used arithmetic decomposition method to
decompose aggregate energy data of transition countries within central and east Europe from
1992 -1998. Moreover, they found a decline in energy intensity within central and east
Europe from 1992-1998. As a result, change of economic structure contributes to the
decrease in energy intensity, which is the shift from high energy intensive sectors to less
energy intensive sectors within the economy. Furthermore, Raddy and Ray (2009) document
that arithmetic decomposition method is capable to identify structural change and economic
activity in a country. However Ang and Choi (2003) document that arithmetic decomposition
method is connected to index number problem. In addition, Index number problem means a
difficulty that arises to make estimation regarding inputs if it changes overtime. In addition,
index number problem have a negative effect regarding calculations used to determine energy
required to produce commodities in a country.
Other authors used carbon index method to assess energy intensity in relation to climate
change. Carbon index method is used to investigate the relationship between energy intensity
and gas emissions. Ang (1999) noted that gas emissions are interrelated to carbon factor andenergy intensity. Furthermore, Carbon factor is energy related carbon emissions given
energy consumption per GDP. Ang (1999) examined developing and industrialized countries
concerning climate change using carbon index method. As a result, energy intensity is the
good indicator to examine industrialized and developing countries concerning climate
change. Moreover, Sun (2003) noted that carbon index method can lead to contradictions
regarding countrys energy policy. Energy policy is significant to curb energy intensity
complications that are faced by a certain country. Furthermore, the strength of carbon index
method is the capability to investigate effects of energy consumption especially to a country
that rely on coal for power generation.
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Panel granger causality test economic method has been used to examine the relationship
between energy intensity and economic growth for G7 countries. In addition, G7 countries
include Canada, Germany, France, Japan, Italy, United States and UK. Furthermore, theeconomy of the countries is developed. Narayan and Smith (2007) used panel and granger
causality method to measure energy intensity of G7 countries. As a result, they found that
high energy consumption increases the level real GDP. Furthermore, empirical studies showthat most developing countries increase real GDP by increasing consumption of energy.
According to Konya (2006), panel granger causality test is capable to use extra information
from a given data. In addition, Wang and Zhou et al (2011) document that panel granger
causality model is efficient to assess the relationship between economic growth and energy
consumption. However, panel granger causality technique is linked to omitted variable bias
problem (Hooi and Smyth: 2010). Moreover, empirical studies show that omitted variable
bias problem can negatively affect results and estimations in some aspects, for an example
the results for assessing energy intensity in a country.
The transition of less energy intensive sectors to high energy intensive sectors within the
economy has an influence to energy intensity of a country. Energy per unit of GDP changesovertime due to cyclical variations of economic activities within the economy. Ma and Zhao
et al (2009) used logarithmic mean divisa index method to analyse the change in industrial
energy consumption from 1998-2006 in China. As a result, they found that energy intensity
increased from 1998-2006, fast expansion of energy intensive industries drives the economy
towards the increase in energy intensity. Furthermore, the increase of industrial output and
low energy prices contributes to the increase in energy intensity. According to, Ang and
Choi (2003) state that logarithm mean devisa method is capable to provide consistent
measurements. However, Ang and Liu (2007) say that logarithmic method complicate
measurements if data set contains zero values. In addition, Choi and Wang (1997) say that
data containing zero values lead to computational problems in some decomposition methods.
Laspayers index model is capable to measure trends in energy consumption within the
industrial sector. Furthermore, Deur and Howarth (1991) applied laspayers index method to
measure amount of energy used by the manufacturing sector in OECD countries. They found
that energy intensity decreased from 1973-1987. In addition, the findings reflect that
technological change and increase of energy prices contribute to the decrease in energy
intensity. Furthermore, they found that industrial sector accounts for more than 80% of
energy use in the OECD countries. According to Ang and Liu (2006), residual value is
associated with laspayers index method. In addition, Altan and Beck et al (2014) say that
residual value is used to test significance of each coefficient. As a result, large residual value
can influence interpretation of results that are obtained to make projections. Furthermore,large residual value is the weakness that is associated with laspayers index method. However,
the strength of laspayers index model is the capability to assess percentage change in energy
consumption (Wang: 2004).
Sectoral analysis method is used to measure energy intensity within the industrial sector.
According to Ang and Zang (2000), Sectoral energy intensity entails the amount of energy
desired by industrial sector to produce commodities at sectoral level. Bowden and Payne
(2010) used sectoral analysis method to assess energy intensity for industrial sector in
relation real GDP in USA. As a result, they found that non-renewable energy consumption
and real GDP by industrial sector supplements one another. Furthermore, Ang and Lin (2003)
document that energy consumption by the industrial sector influence aggregate energyintensity of a particular country. According to Ang and Zang (2000), the strength of sectoral-
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analysis method is the capability to investigate energy improvements regarding energy
intensity. However, the weakness of sectoral analysis method is the incapability to reflect
aggregate impact on final demand in relation to energy use by consumers and environmental
influence of energy usage by the industrial sector (Dowlatabadi and Bin: 2005).
Energy consumption has the effect to change industrial output, empirical studies show thatdeveloping and industrial countries consume more energy in order to increase the level of
output. Furthermore, Fowowe (2012) used panel co-integration test to investigate the effect of
energy consumption to GDP for 14 sub Saharan African countries. As a result, the author
found that there is no firm interdependence between energy consumption and real GDP in the
long run. However, as the economy develops, consumption of energy also increases. Ziramba
(2008) says the strength of panel co-integration test is the capability to explain variables that
depend on economic model. In addition, panel co-integration test method is capable to assess
causal relationship between energy consumption and GDP. However, Feng and Sun et al
(2009) noted that using panel co-integration test method to assess energy intensity has a
weakness to keep linear combination of 2 variables constant. Moreover, variables that
represents energy consumption and output influence each other, they change overtime.
Input - output method reflect the relationship between industries through supplying inputs for
the output of the economy. Furthermore Geng and Liu et al (2012) used input- output
economic model to measure energy intensity of the Chinese economy. They found that
manufacturing sector uses both direct and indirect energy to produce commodities. In
addition, the construction sector consumes more of indirect energy in China. According to
Gama and Sloan (2011), input-output method has explicit boundaries to measure energy
intensity. Furthermore, input- output method is integrated to the economy and industrial
sectors energy consumption. In addition, Ang (2004) documents that input- output method
entails the use of input- output tables to estimate aggregate energy consumption for a country.
The complex part of using input - output tables entails the long process that is followed to
assess energy intensity of the economy.
Several authors from various countries used input - output method to assess the energy
intensity of the economy. Mongelli and Notarnicola et al (2004) used input output model to
calculate greenhouse emissions and energy intensity of the Italian economy. Garbaccio and
Ho et al (1999) used input output method to assess the decline in energy intensity for the
Chinese economy. Fan and Liang (2005) used input-output method to forecast energy
requirements and energy intensity for Chinese economy from 1997-2020. Machado and
Schaeffer (2001) used input-output method to evaluate the impact of international trade on
energy use and carbon emissions for the Brazilian economy. Williams (2004) used input-output model to measure energy intensity for computer manufacturing in the Chinese
economy. Lanzen (1998) used input- output method to assess gas emissions and total energy
requirements for the production of final goods in Australia. According to Machado and
Schaffer et al (2004), input- output method is appropriate to measure resources that are
embodied on goods and services within macroeconomic scale.
Given different methods used to measure energy intensity including strength and weakness of
each economic method. However, the contribution of this research paper to the body of
knowledge is the use input-output economic method to measure energy intensity of the South
African economy. According to Castler and Wilbur (1984), input-output model considers
both direct and indirect energy for the production of commodities. Furthermore, Input-outputeconomic model comprise tables that reflect industrial sector and other sectors that contribute
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to the change in energy intensity to a particular country. Empirical Studies show that input-
output model is internationally recommended to be used to assess energy consumption.
Furthermore the economic model is capable to estimate quantity of commodities to be
produced by the industrial sector to meet final demand of output. The purchase of
commodities by consumers entails final demand of output to a particular country. Moreover,
energy embodied on commodities is in-direct energy consumption to households. Theadvantage of input-output economic method is to consider both direct and indirect energy
consumption by the industrial sector to the production of commodities.
Section 3: Methodology
Arbex and Perobelli (2009) noted that Input - output method was established by Wassily
Leontief in the early 1930s. Furthermore, Input - output method is the powerful tool that is
used to analyse production activity in relation to energy consumption within a country. For an
example, to determine energy requirements to produce commodities. According to Mongelli
and Notarnicola (2004), input output method is capable to do analysis at micro and macrolevel. Moreover, Liu and Xie (2010) say that input output method considers the link
between the economy and its energy intensity. According to Tiwari (2000), input output
economic method is suitable to determine the level of output for a particular country. In
addition, Guo and Liu et al (2009) document that input-output model comprise of Leontief
inverse matrix that is used to determine energy requirements. To the production of
commodities, energy requirements entail the amount of inputs required to produce a certain
quantity of output.
Input- output methodology has assumptions concerning the production of commodities and
energy consumption by the industrial sector. In addition, Chiang and Wainwright (2005)
outline some of the assumptions concerning input-output method, the assumptions are as
follows - each industry use mixed factors of production to produce output and the increase of
inputs used by the industrial sector leads to equivalent increase of output in the economy and
each industry produce same product as compared to other industries in the economy.
However, they also document that assumptions concerning input-output economic method
are not rational.
The following economic input-output economic used in this paper is from Liu and Xie et al
(2010), the impact of Chinese economic growth and energy consumption of global financial
crises: an input output analysis. According to Tiwari (1999), equation (1) reflects that
aggregate production of any sector is equivalent to the product that is used by all sectorswithin the economy including final demand of output by consumers. Equation (1) also show
the intersesectoral relations and final demand of output for sector -
(1) - Represents the purchase of products by sectorfrom sector as an input. Products thatare purchased by sector from sector are used to support the production of othercommodities to sector
.
is also known as the intermediate use of energy by the sectors of
the economy.
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- Represents final demand of products of sector within the economy. - Is the aggregate output in the economy.Assuming that = / , then is the purchase of products as direct input fromsector
by sector
. Furthermore, it shows direct input that is required by sector
to produce
commodities. In addition, the purchase of products from sectorby sectoris also called thetechnical coefficient of production.Using matrix notation and for the entire economy, equation 1 can be rewritten as follows:
AX+ Y = X (2)
Where A = , X [] and Y =[]
A in equation (2) is the direct input coefficient of input- output matrix, it entails the scale ofresources utilized to generate one unit of GDP in each sector in the economy.
Solving for X, we get the gross output
X= (I - A)-1Y (3)
I - represents the identity matrix
Y- Is the final demand of output in the economy.
Where (1- A)-1is called the Leontief inverse matrix. According to Tiwari (1999), elements of
inverse matrix represent total direct and indirect energy that is required by sectors to produce
each unit of GDP to meet the final demand. In addition Liu and Xie (2010) noted that
Leontief inverse matrix shows total production in terms of direct and indirect input used to
produce commodities to fulfil final demand of output in the economy.
, is the element in the Matrix (IA)-1, it represents the increase of output that sector produces if the demand of products by sectorincreases by one unit.The following equation reflects that production of energy depend to the connection between
sectors of the economy and the amount of energy consumed by final demand of output.Energy consumed by final demand entails the purchase of final products by household,
government, investments, exports minus imports.
(4) Is the consumption of energy by sector. Is the consumption of energy by final demand.E is the aggregate energy consumption within the economy.
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/, represent direct energy consumption by sector . In matrix notation and for the entireeconomy, Equation 4 is shown as follows:
DX + = E (5)Where D = , X = D in equation (5) represent direct energy consumption matrix by the sectors of the economy.Based on equation (3) and (5), D can be obtained as follows:
D (IA) -1Y+ = E (6)Where D (I A) -1 Y represents aggregate production by the energy consuming sectors
including both direct and indirect energy.
is the household energy consumption by final demand of output. Households consumeenergy by purchasing finished products produced by the industrial sector in the economy.Section 4 : Analysis of data
Descriptive analysis
The data used in this research paper is from statistics South Africa. Furthermore, the data is
showed in the natural resource accounts, 1995-2001, supply and use tables report, 2002.
Input-output and energy use tables for South African sectors is comprised in the data by
statistics South Africa., input-output table includes intermediate use of inputs, final demand
and total output. In addition, energy use table document energy used by South African sectorsfor further production. The are 9 sectors outlined in the energy use tables , the sectors
comprise agriculture and fishing, Mining and quarrying, manufacturing, electricity, gas and
steam production, construction, transport, storage and communication, commercial sector and
community services. Furthermore, Input-output table in 2002 shows that South African
sectors are interrelated, each sector purchases products from other sectors as direct input .
Energy use table for South Africa,2001 shows aggregate energy use by sectors which include
crude oil, nuclear, coal, petroleum products, gas to users, electricity, hydro-electricity,
renewables and waste energy. However, in this research paper I will use the value of
electricity to compute energy consumption by the South African sectors. Furthermore, energy
used by the sectors is measured in terajoule including electricity. Moreover, Using the datafrom statistics South Africa for natural resource accounts, 1995-2001 and supply and use
tables report for 2002, input-output table on energy is constructed, the difference between the
sectors in the inputoutput table and the sectors that are denoted in the energy consumption
table should be recreated and combined as indicated in table 1. In addition, A matrix is
computed according to table 1, the value of matrix A is showed in table 2. Furthermore
Leontief inverse Matrix is computed using values in table 2, the values of Leontief inverse
matrix is shown in table 3.
D matrix represents direct energy consumption to produce each unit of GDP by production
sectors, direct energy consumption values are showed in table 4. Furthermore, D matrix is
computed based on energy use table, 1995-2001 for South Africa. Moreover, value of D is
calculated using amount of electricity consumed by each sector in the South African -
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economy. The value of direct energy requirements by South African sectors is showed in
table 4. In addition, the value of D matrix shows total energy required by South African
sectors in the economy, table 5 denote electricity use by South African sectors.
Table 1. Input output table (2002)
Output per sector
1 2 3 4
Sectors Total output (Final use) Agriculture Mining Manufacturing Electricity
Agriculture 102613 3296 25 48011 8
Mining 205748 245 425 73111 6832
Manufacturing 1340 754 287727 30174 419508 6692
Electricity 52625 632 4217 12587 6196
Construction 110048 347 1602 00 2724
Transport 239496 4944 26705 23300 969
Commercial 476404 2145 5084 55367 2772
Community 414608 1480 8081 21346 86
Output per sector
5 6 7 8
Sectors Total output (final use) Construction Transport Commercial Community
Agriculture 102 613 3 2 671 281
Mining 205748 1917 247 109 540
Manufacturing 1340 754 38165 64020 64824 63690
Electricity 52625 232 3171 4723 1751
Construction 110048 23873 1391 8520 2594
Transport 239496 1855 33927 51677 12381
Commercial 476404 8848 34141 160463 31450
Community 414608 969 1457 7466 28579Statistics South Africa: Supply and use tables (2002)
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Table 2 - A Matrix
Sectors
1 2 3 4 5 6 7 8
0.03212 0.00024 0.46788 7.79628E-05 2.92361E-05 1.94907E-05 0.00654 0.00274
0.00119 0.00206 0.35534 0.03320 0.00932 0.00120 0.00053 0.00262
0.02143 0.022505 0.31289 0.00499 0.02846 0.04775 0.04835 0.04750
0.01201 0.080133 0.23918 0.11774 0.00441 0.06026 0.08975 0.03327
0.00315 0.01456 0 0.02475 0.21693 0.01264 0.07742 0.02357
0.02064 0.11150 0.09729 0.00404 0.00774 0.14166 0.21577 0.05169
0.00450 0.01067 0.11621 0.00582 0.01857 0.07166 0.33682 0.06601
0.00356 0.01949 0.05148 0.00021 0.00234 0.00351 0.01800 0.06893
Table 3Leontief inverse Matrix
Sectors
1 2 3 4 5 6 7 8
1 1.05187 0.02599 0.75815 0.00705 0.03069 0.05075 0.08812 0.05194
2 0.01608 1.02546 0.57720 0.04352 0.03550 0.04299 0.06823 0.04207
3 0.03833 0.05240 1.56244 0.01412 0.06250 0.10264 0.15963 0.09908
4 0.30987 0.12414 0.55108 1.14448 0.03537 0.13257 0.24531 0.09511
5 0.00824 0.03101 0.07462 0.03900 1.28505 0.04072 0.17552 0.05255
6 0.03724 0.15404 0.36711 0.01746 0.03808 1.22433 0.43566 0.11973
7 0.01934 0.04706 0.34577 0.01649 0.05303 0.15507 1.5960 0.14154
8 0.00703 0.02606 0.10977 0.00246 0.00873 0.01452 0.04360 1.08393
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Table: 4 D
Agriculture 0.14648
Mining 0.55440
Manufacturing 0.23179
Electricity 0.07386
Construction 0.00131
Transport 0. 08336
Commercial 65 882.38
Community 0.37793
Table 5 : Reflect the electricity use by South African sectors
Agric Mining Manufact Electr Constr Trans Commerce Commu
0.018262 0.62298 0.95398 0.11768 0.05685 0.19391 0.37961 0.50020
Interpretation of results and discussions
This paper used input-output method to assess energy intensity of the South African
economy. Furthermore, 8 sectors used from the data provided by statistics South Africa.
As a result, the research found that manufacturing and mining sector consume moreenergy in the South African economy. Manufacturing sector comprises of rubber,
plastic, steel, iron and non-ferrous metals industries. In addition, mining sector extract
non-metallic and metallic minerals. In addition, the structure of the South African
economy is in line with the idea of Fine and Ramstomjie (1996), South Africa comprise
of energy intensive industries termed Mineral energy complex. Furthermore, this
research shows that manufacturing sector consumes 0.95398 TJ of electricity to produce
each unit of GDP in the South African economy. Moreover, mining sector consume
0.62298 TJ of electricity. As a result, manufacturing sector consume more energy as
compared to other sectors in the South African economy. The increase of energy
intensity in the South African economy is mainly attributed to the processing of raw
materials and extracting mineral resources by the energy intensive sectors. In addition,economic activities by energy intensive sectors requires high amount of energy to carry
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daily operations. However, construction and agricultural sector is found to be less energy
intensive. Moreover, construction sector consume 0.05685 TJ electricity and agricultural
sector consume 0.018262 TJ of electricity in the South Africa economy. Based on the
findings of this research, construction and agricultural sector consume energy efficiently.
Furthermore, consumption of energy efficiently by the construction and agricultural
sector is due to high labour intensity to the sectors. The results of this research are thesame regarding energy consumption in the South Africa by Blignaut and Lotz (2012),
they also found that Mining and manufacturing are most energy intensive sectors in the
South African economy. Furthermore energy intensity is high in the South African
economy.
Electricity sector consume 0.11768 TJ of electricity, including storage and
communication. Furthermore, the findings shows that electricity sector consume less
energy than community service, commerce and Transport. The reason for electricity
sector to be less energy intensive is the fact that it does not entail the production of
commodities. Moreover, the results of this research paper show that energy consumption
by sectors in the South African economy is high especially manufacturing and miningsector. The increase in energy consumption in the South African economy is caused by
the shifting of low energy intensive sectors to high energy intensive sectors, low prices
of energy also contribute to the increase in energy consumption to the South African
economy.
Energy requirements for sectors is showed in table 4, furthermore the results of this
research reveal that most of the sectors in the South African economy use more energy
than the predicted figures of energy consumption. Moreover, the results of the predicted
energy consumption show that mining is supposed to consume 0.55440 TJ and
manufacturing 0.23179 TJ of energy. However the findings of this research indicate that
energy use for each sector is more than the estimated figures of energy requirements.
Comparing estimated energy requirements table for South African sectors with the
energy use table as denoted by table 5, the is a clear evidence that energy intensity of
the South African economy is high.
Conclusion and policy recommendation
This paper used input-output economic method to measure energy intensity of the South
African economy. Moreover, different methods used by various authors to measure
energy intensity are outlined and examined in this paper. However input-output
economic method is used in this paper to measure energy intensity of the South Africaneconomy. Furthermore, data in the natural resource accounts 1995-2001, supply and
energy use tables report, 2002 by statistics South Africa is used in this paper. The results
of this research indicates that majority of sectors use more energy in the South African
economy. Moreover manufacturing and mining sector are more energy intensive
Moreover, manufacturing sector consumes more energy as compared to all the sectors.
The results of this research also indicate that sectors that are labour intensive use
electricity efficiently such as, agriculture and construction sector. Given high level of
energy intensity in the South African economy, it is crucial for energy intensity to be
assessed to improve efficiency in energy consumption. The government must set strict
measures to curb level of energy intensity such as regulating industrial sector and mining
regarding consumption of energy. Furthermore energy prices must be increased todiscourage sectors that consume more energy in the South African economy.
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