the energy intensity of the south african economy

8/10/2019 The energy intensity of the South African economy

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University of the Witwatersrand

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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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the energy intensity of the south african economy

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