a methodology for deriving a renewable energy market ... · a methodology for deriving a renewable...

A Methodology for Deriving a Renewable Energy

Market Competence Index with Application to

CSP Technology

By

Hatem Elsayed Hany Elrefaei

submitted to

Faculty of Engineering, Cairo University

and

Faculty of Electrical Engineering and Computer Science, University of Kassel

in partial fulfillment of the requirements for M.Sc. degree in Renewable Energy and

Energy Efficiency for the MENA Region

REMENA


Giza, Egypt

March 2012



CSP Technology

By

Hatem Elsayed Hany Elrefaei submitted to


and




REMENA

Under supervision of


Giza, Egypt

2012

Mohamed ElSobki Mohab Hallouda Professor

in Electrical Power and Machines Department Faculty of Engineering

Cairo University

Professor in Electrical Power and Machines Department

Faculty of Engineering Cairo University



CSP Technology By

Hatem Elsayed Hany Elrefaei submitted to


and




REMENA

Approved by the Examining Committee:

Dr. Hany Nokrashy, Member

Professor Dirk Dahlhaus, Member

Professor Mohamed ElSobki, Thesis Advisor

Professor Mohab Hallouda, Thesis Advisor


Giza, Egypt 2012

i

Contents

List of Tables............................................................................................................................. iv

List of Figures ........................................................................................................................... vii

List of Symbols ......................................................................................................................... viii

List of Abbreviations................................................................................................................ ix

Acknowledgment ...................................................................................................................... xi

Abstract ..................................................................................................................................... xii

1. Chapter 1: Introduction ..................................................................................................... 1

2. Chapter 2: Political, Economic, Social, and Energy Indices .......................................... 3

2.1 Introduction.................................................................................................................. 3

2.2 What is “Index”?.......................................................................................................... 3

2.2.1 Definition of the word “Index”........................................................................ 3

2.2.2 Other related terms: “Indicator” and “Coefficient” ......................................... 4

2.2.3 What is not an “Index”?................................................................................... 4

2.2.4 From Statistical Data to Indices....................................................................... 5

2.2.5 Score and Rank................................................................................................ 6

2.3 Important and Relevant Indices ................................................................................... 6

2.3.1 Political Indices ............................................................................................... 7

2.3.1.1 The Political Instability Index ........................................................ 7

2.3.1.2 Corruption Perceptions Index......................................................... 10

2.3.1.3 Freedom Country Index.................................................................. 12

2.3.1.4 Press Freedom Index ...................................................................... 14

2.3.2 Economics ....................................................................................................... 16

2.3.2.1 Gini Index....................................................................................... 16

2.3.2.2 Index of Economic Freedom .......................................................... 18

2.3.2.3 Global Competitive Index .............................................................. 20

2.3.3 Human development........................................................................................ 22

2.3.3.1 Human Development Index............................................................ 22

2.3.3.2 Global Gender Gap Index............................................................... 24

ii

2.3.3.3 Global Wellbeing Index ................................................................. 26

2.3.4 Energy.............................................................................................................. 29

2.3.4.1 Fossil Fuel Sustainability Index ..................................................... 29

2.3.4.2 Renewable Energy Countries Attractiveness Indices..................... 30

2.4 Conclusion ................................................................................................................... 31

3. Chapter 3: Index Construction ......................................................................................... 33

3.1 Introduction.................................................................................................................. 33

3.2 Gini Index .................................................................................................................... 34

3.3 Fossil Fuel Sustainability Index................................................................................... 35

3.4 Press Freedom Index.................................................................................................... 37

3.5 Renewable Energy Countries Attractiveness Indices .................................................. 39

3.6 Conclusion ................................................................................................................... 42

4. Chapter 4: Driving a Methodology for Renewable Energy Market Competence Index

.............................................................................................................................................. 43

4.1 Introduction.................................................................................................................. 43

4.2 Index Type and Score Range ....................................................................................... 43

4.3 Assumptions in Countries’ Governments .................................................................... 44

4.4 Addressed Countries .................................................................................................... 45

4.5 Renewable Energy Market Competence Index Hierarchy........................................... 46

4.6 Data Sources, Values, Scores, and Challenges ............................................................ 47

4.7 Political and Economic Indicators ............................................................................... 47

4.8 Energy Sector Indicators.............................................................................................. 49

4.8.1 Energy Intensity Indicator .............................................................................. 50

4.8.2 Non Electricity Final Indicator ....................................................................... 51

4.8.3 Electricity Consumption Growth Indicator .................................................... 53

4.8.4 Net Imported Electricity Indicator.................................................................. 54

4.8.5 Non-RE Electricity Production Indicator ....................................................... 56

4.8.6 Oil and Gas Insecurity Indicator..................................................................... 57

4.8.7 RE Target Indicator ........................................................................................ 61

4.9 Financial and Environmental Indicators (2 indicators)................................................ 64

4.9.1 Financial Indicator .......................................................................................... 64

iii

4.9.2 Environmental Indicator ................................................................................. 67

4.10 Logarithmic Scale of Some Indicators......................................................................... 68

4.11 Technology Specific Indicators .................................................................................... 73

4.11.1 Manufacturability Indicator ............................................................................ 74

4.11.2 Economic Potential Indicator ......................................................................... 74

4.11.3 Institute Indicator............................................................................................ 75

4.11.4 Technology Target Indicator .......................................................................... 75

4.11.5 Feed-in Tariff Indicator .................................................................................. 76

4.12 The Methodology of the Index..................................................................................... 77

4.13 Critique and Possible Improvements ........................................................................... 80

4.14 Conclusion ................................................................................................................... 80

5. Chapter5: A CSP Market Competence Index ................................................................. 82

5.1 Introduction.................................................................................................................. 82

5.2 CSP Technology Indicators ......................................................................................... 82

5.2.1 CSP Manufacturability Indicator.................................................................... 82

5.2.2 CSP Economic Potential Indicator ................................................................. 86

5.2.3 CSP Institute Indicator.................................................................................... 87

5.2.4 CSP Target Indicator ...................................................................................... 88

5.2.5 CSP Feed-in Tariff Indicator .......................................................................... 89

5.3 CSP Market Competence Index Score and Rank......................................................... 93

5.4 CSP Market Competence Index versus CSP Countries Attractiveness Index ............. 97

5.5 Conclusion ................................................................................................................... 102

Conclusion................................................................................................................................. 104

References ................................................................................................................................. 107

Appendix A ............................................................................................................................... 115

Appendix B................................................................................................................................ 120

Appendix C ............................................................................................................................... 121

Appendix D ............................................................................................................................... 131

......................................................................................................................................المستخلص 132

iv

List of Tables Table (2.1) Score and rank for RCREEE and 10 benchmark countries for the Political

Instability Index 2009/2010 [4]………………………………………………... 9

Table (2.2) Score and rank of RCREEE and 10 benchmark countries for the

Corruption Perception Index for 2010 [10]……………………………………. 11

Table (2.3) Categorization of country freedom status [12]………………………………… 12

Table (2.4) Score and status of RCREEE and benchmark countries for the

Freedom Country Index for 2010 [14] [15]……………………………………. 13

Table (2.5) Score and rank of RCREEE and benchmark countries for the Press Freedom

Index for 2010 [21].…………………………………….……………………… 15

Table (2.6) Score and rank of RCREEE and benchmark countries for the Gini Index

[23]……………………………………………………………………………... 17

Table (2.7) Score and rank of RCREEE and benchmark countries for the Index of

Economic Freedom for 2011 [26]……………………………………………... 19

Table (2.8) Score and rank of the RCREEE and benchmark countries for the

Global Competitive Index for 2010-2011 [30]………………………………… 21

Table (2.9) Score and rank of RCREEE and benchmark countries for the

Human Development Index for 2010 [33]…………………………………….. 23

Table (2.10) Score and rank of RCREEE and benchmark countries for the

Global Gender Gap Index for 2010 [34]………………………………………. 25

Table (2.11) Definition of Thriving, Struggling, and Suffering according to

Cantril Self-Anchoring Striving Scale [36]……………………………………. 26

Table (2.12) Score of RCREEE and benchmark countries for the

Global Wellbing Index for 2010 [36]………………………………………….. 28

Table (2.13) Score and rank of Algeria, Egypt and 9 benchmark countries according to

Fossil Fuel Sustainability Index for 2005 [37]………………………………… 30

Table (2.14) Score and rank Egypt, Morocco, and Tunisia along with 9 benchmark

countries for the Renewable Energy Countries Attractiveness Index for

November 2011 issue [39].…………………………………………………….. 31

Table (3.1) Goalpost for each indicator and fossil resource [35]…………………………... 37

Table (3.2) Percentage of technology indices in the final overall index…………………… 42

v

Table (4.1) Original scores, goalposts, and adjusted values for Global competitive Index

(for 2010), Political Instability Index (covering the period 2009-2010),

Corruption Perception Index (for 2010)………………………………………..

49

Table (4.2) Energy intensity values of the countries as reported by IEA, the goalposts, and

the Energy Intensity Indicator Score as calculated by equation (4.5)…………. 51

Table (4.3) TFC (in ktoe), total electricity consumption (TWh), non electricity final value

as given by equation (4.6), and the Non Electricity Final Indicator Score as

given by equation (4.7)………………………………………………………… 52

Table (4.4) Electricity consumptions in 2007 and 2009, average electricity consumption

growth at year 2008, and the Electricity Consumption Growth Indicator

Score…………………………………………………………………………… 54

Table (4.5) Net imported electricity values in Ktoe as reported by IEA [47], and the Net

Imported Electricity Indicator Score as calculated by equation (4.12)………... 55

Table (4.6) Non RE electricity production values in (GWh) and the Non RE Electricity

Production Indicator Score…………………………………………………….. 57

Table (4.7) Consumption and reserve values, the year of supply and Insecurity Indicator

Scores for oil…………………………………………………………………… 59

Table (4.8) Consumption and reserve values, the year of supply and Insecurity Indicator

Scores for natural gas………………………………………………………….. 60

Table (4.9) All required data for the calculation of RE Target Indicator Score……………. 64

Table (4.10) Countries’ scores for the different policies and the Financial Indicator Score

according to equation (4.24)…………………………………………………… 66

Table (4.11) CO2 emission per capita (ton CO2/capita) and the Environmental Indicator

Score…………………………………………………………………………… 67

Table (4.12) Non electricity final value in TWh as given by equation (4.6), its natural

logarithm values, and the Non Electricity Final Indicator Score using natural

logarithm values……………………………………………………………….. 69

Table (4.13) Average electricity consumption growth at year 2008, its natural logarithm

value, and the Electricity Consumption Growth Indicator Score using natural


Table (4.14) Net imported electricity values in Ktoe as reported by IEA [47], its natural

vi

logarithm value, and the Net Imported Electricity Indicator Score using

natural logarithm values………………………………………………………..

71

Table (4.15) Non RE electricity production values in (GWh), its natural logarithm value,

and the Non RE Electricity Production Indicator Score using natural


Table (4.16) Expected Annual new RE Production (GWh), its natural logarithm value, and

RE Target Indicator Score using natural logarithm values…………………….. 73

Table (4.17) Answers of the questionnaire for the Institute Indicator………………………. 75

Table (5.1) Point system of the CSP Manufacturability questionnaire-based Indicator…… 84

Table (5.2) CSP questionnaire values, and CSP Manufacturability Indicator Score………. 85

Table (5.3) CSP Economic Potential Values and Indicator Score…………………………. 87

Table (5.4) CEP Institute Indicator Score………………………………………………….. 88

Table (5.5) Initial and target years, current installed CSP capacity, targeted CSP capacity,

expected annual new installed CSP capacity, and CSP Target Indicator

Score.................................................................................................................... 89

Table (5.6) Point system to assess Feed-in Tariff law……………………………………... 90

Table (5.7) Feed-in Tariff merits and CSP Feed-in Tariff Indicator Scores……………….. 90

Table (5.8) All indicators used to develop CSP Market Competence Index………………. 92

Table (5.9) Scores of the 18 countries according to the CSP Market Competence Index…. 93

Table (5.10) Ranking of the 18 countries according to the CSP Market Competence

Index…………………………………………………………………………… 94

Table (5.11) X1, X2, and X3 of the 18 countries……………………………………………... 95

Table (5.12) CSP Index Scores for the 11 common countries as reported in the RE

Countries Attractiveness Index reports, their scores average, and their

ranking…………………………………………………………………………. 98

Table (5.13) Scores of the Market Competence Index and Countries Attractiveness

Index…………………………………………………………………………… 98

Table (5.14) Ranks of the Market Competence Index and Countries Attractiveness

Index…………………………………………………………………………… 98

Table (5.15) A comparison between CSP Market Competence Index and CSP Countries

Attractiveness Index…………………………………………………………… 102

vii

List of Figures

Figure (2.1) Relation between statistical data, indicators, and indices…………………….............. 5

Figure (2.2) World color map for the Political Instability Index covering the period 2009/2010,

Economist Intelligence Unit [5]…………………………………………………......... 8

Figure (2.3) World color map for the Corruption Perception Index for 2010 [9]………...……….. 10

Figure (2.4) World color map for the Freedom Country Index for 2011 [13]………...………........ 12

Figure (2.5) World color map for the Press Freedom Index for 2010 [20].……,,,…………........... 14

Figure (2.6) World color map for the Gini Index [24]…………...……....…………………............ 16

Figure (2.7) World color map for the Human Development Index for 2010 [32]…..…………....... 22

Figure (3.1) Lorenz curve and the Gini Index…………………………………………………....... 35

Figure (4.1) The hierarchy of the Renewable Energy Market Competence Index………………… 46

Figure (4.2) Illustration of the electricity consumption growth and linear expansion of electricity

production from RE resources to meet the announced target share at the target

year................................................................................................................................. 62

Figure (4.3) α-β relation…………………………………………………………………………… 79

Figure (5.1) Correlation between the data set of the Countries Attractive Index (far right column)

and each case of the Market Competence Index for both score and rank of Tables

(5.13) and (5.14) respectively………………………………........................................ 99

Figure (5.2) Score difference between CSP Market Competence Index and CSP Countries

Attractiveness Index for the two extreme cases α=0 and α=1……………………….. 100

Figure (5.3) Rank difference between CSP Market Competence Index and CSP Countries

Attractiveness Index for the two extreme cases α=0 and α=1………………….......... 101

viii

List of Symbols X1 Arithmetic average of the Economic Potential Indicator and Technology

Target Indicator.

X2 Arithmetic average of the Manufacturability Indicator, Institute Indicator, and

Feed-in Tariff Indicator.

X3 Arithmetic average of the 13 general indicators

α Weighting Parameter

γi Technology Weighting Parameters

ix

List of Abbreviations CE Carbon Emission

CLI Civil Liberties Index

CPI Corruption Perception Index

CSP Concentrated Solar Power

E&Y Ernst & Young Inc.

FCI Freedom Country Index

FFSI Fossil Fuel Sustainability Index

GCI Global Competitive Index

GGGI Global Gender Gap Index

GI Gini Index

GW Gega Watt

GWI Global Wellbeing Index

GWh Gega Watt Hour

HDI Human Development Index

IEA International Energy Agency

IEF Index of Economic Freedom

MENA Middle East and North Africa

MW Mega Watt

MWh Mega Watt Hour

NGO Non Governmental Organizations

PC Production to Consumption

PFI Press Freedom Index

PITF Political Instability Task Force

PV Photovoltaic

RP Reserve to Production

PRI Political Rights Index

RCREEE Regional Center for Renewable Energy and Energy Efficiency

SI Sustainability Index

t CO2 Tone CO2

TWh/y Tera Watt hour per year

x

UN United Nations

UNDP United Nation Development Program

USA United Stated of America

xi

Acknowledgment

Upon completion of this work, I would like to thank everyone participated in establishing

the REMENA program. I truly appreciate all the effort made by Cairo and Kassel universities to

make this bicultural master program an enjoyable journey. Many thanks go to Professor Adel

Khalil and Professor Sayed Kaseb from the Egyptian side and to Frau Anke Aref, Professor

John Sievers, and Prof. Dirk Dahlhaus from the German side.

I honestly appreciate Professor Mohamed Elsobky and Professor Mohab Hallouda for

their continuous support and valuable discussions during the course of developing this thesis. I

would like also to thank Mrs. Amel Bida, Executing Director of the Regional Center of

Renewable Energy and Energy Efficiency (RCREEE), who enriched the research work with her

valuable ideas and extensive discussions during the internship period in the center.

No doubt I would not have been able to complete this 18 month master program without

the full support of my close family; my wife Dr. Marwa Ragheb, and two kids, Omar and

Basent, so many thanks go to them.

xii

Abstract

A quantitative, objective, and analytically index that describes the competence of

renewable energy market of world countries is derived. The proposed index, Renewable Energy

Market Competence Index, is composed of 13 general indicators and 5 technology dependent

indicators. The index structure allows including more indicators for more accurate representation

of the relative competence among the countries. Though the index calculation depends on

weighted average between the average of the 13 general indicators and the average of 3 of the

technology dependent indicators, applying the index on Concentrated Solar Power (CSP)

technology for 18 countries shows very minor change of the index results (score and rank) with

the change of the weighting parameter. This is due to the high correlation of the general

indicators and the three technology indicators values, suggesting that even a simpler version of

the index that is composed only of 5 technology indicators is sufficient to give a very good

measure of the relative competence among the countries.

A comparison of the proposed index with CSP Countries Attractiveness Index, that is

developed by Ernst & Young Inc., shows a very good matching (correlation >85%) between both

score and rank of the two indices for the 11 common countries. Despite of the relatively large

difference in score between the two indices (17 to 22 points) for 3 countries (South Africa,

Greece, and Tunisia), the difference in the remaining 8 countries does not exceed 7 points. The

analysis of rank difference shows that 5 countries have the same rank in both indices, and that the

maximum rank change is only 3 places for only 2 countries.

The proposed Market Competence Index is simple one and thus needs much less human

resources to calculate compared to the Countries Attractiveness Index. In addition, it is an

objective and analytical index compared to the subjective, essay-based questionnaire of the

Countries Attractiveness Index which opens the way to make the proposed index programmable

and available to many users.

Chapter 1

Introduction Since the industrial revolution 300 hundred years ago, humanity experienced a huge

change in all aspects of life with the discovery of steam power, thanks to coal. This change has

touched every single dimension of life, reshaping the social life from real to virtual and

economic activities from agriculture, simple manufacturing, and mostly neighboring trading, to

mass production using highly advanced technologies, and International Corporation with a

globalization network of business. This could not have been happened without a similar change

in governing systems from monarchies to democracies across the globe. Though this change

looks like a general trend when looking on the life on earth collectively, still not every country

has experienced the same change with the same degree. Domination of scientific advancement,

military power, along with exploration and exploitation spirits in the 18th and 19th centuries by

some countries led to expanding economic, and social, and political system gaps among the

different countries in the world.

With increasing dynamics during the 20th century – thanks to the dense energy content

of oil that became the leading energy source till now – it becomes of great interest to different

bodies, though for different reasons, to clearly quantify these differences among the countries.

Different bodies devise quantitative methods (indices) to measure the specific counties’

qualities of its interest. International organizations like the United Nation (UN) and its

subsidiary organizations, whose mandate is spreading world peace and wellbeing of people in

different countries, have set up different kinds of indices that measure the level of education,

health, equality between genders and many other qualities of life. These indices help these

organizations recommend new policies to lacking-behind governments to improve their

situations, and know which countries deserve funds to improve what sectors. All of that serve

the main objectives of UN. Non governmental organizations that seek openness for

information and spreading of democracy (e.g Reporters without Boarders) make indices that are

able to identify – quantitatively – oppressing regimes and countries with harsh and

discriminating environment. Financial and investment companies and organizations are

interested to identify countries with excellent business conditions and opportunistic markets to

Chapter 1

2

divert their investments, or their clients’ investments, to them. Accordingly, they develop

indices that just match their purpose. In addition, as a side product, the annual announcements

of indices create internal pressure from the citizens of lacking-behind countries upon their

government to improve the living conditions of their countries.

Now, with the world energy crisis intensifying year after year with the depletion of

fossil fuel, especially oil, governments, investors, and research institutions are moving fast to

exploit the available potential of renewable energy resources worldwide. It becomes of interest

to governments that have large renewable energy potential, and have the need to exploit them to

show the huge market they have to attract investors. Equally important, governments would like

also to show – if exist – that they have very good industrial base, qualified personnel, and good

incentives for renewable energy projects which all make the renewable energy market of the

country very competent relative to other countries. On the other hand, it is also of interest to

investors to be able to identify countries that have large renewable energy potential with very

open and attractive business conditions. All of these demands by these different players can be

met with the existence of an index that can quantitatively measure the competence of

renewable energy market in different countries, which is the scope of the work presented in this

thesis.

This thesis is organized as follows; in Chapter 2, general overview of some definitions

that are used when describing indices are described, followed by a literature review of 12

different indices covering politics, economics, human development, and the energy sectors.

Chapter 3 gives a detailed description of how 4 different indices are made. These indices are

chosen to cover different ways used to device an index from a completely analytical index to a

completely questionnaire-based index. In Chapter 4, a methodology of the proposed

Renewable Energy Market Competence Index is derived along with all necessary general

indicators data that will be used in the final index. In Chapter 5, the proposed index is going to

be applied to the Concentrated Solar Power (CSP) technology, where all technology specific

indicators data are going to be presented, and the index score and rank is calculated for the

targeted countries. The chapter then concludes with a comparison of the proposed index score

and rank with the only currently existing renewable energy index; that is Renewable Energy

Countries Attractiveness Indices; developed by Ernst & Young (E&Y) Incorporation.

Political, Economic, Social, and Energy Indices

3

Chapter 2


2.1 Introduction

The aim of this Chapter is to give introduction of the world of indices. Different indices

have different structure, and different scoring and ranking method. In order to develop a new

index it becomes important to understand the general characteristics and rules (if any) that

governs the indices world.

This Chapter is organized as follows, in Section 2, definitions to identify what is meant

by the word “Index”, and general rules concerning score and rank will be given. In Section 3,

12 relevant and important indices covering politics, economics, social, and energy are detailed

and their components are explained. This gives a good literature review to learn how to make a

new index.

2.2 What is “Index”?

2.2.1 Definition of the word “Index”

The term “Index” has many usages in the different sciences and in normal life, one of

them is what this thesis refers to. And since a definition for the term “Index” with the meaning

under discussion in this work was not identifiable in literature, it is prefer to start with a

definition that allows anyone to separate the relevant items from the very wide spectrum of

items that are associate with the word “Index”. So, a proposition of a definition for the word

“Index” is as follows: It is a normalized and dimensionless scale that gives a quantitative

measure of a defined aspect of a country

A “normalized scale” means that the “Index” will have a minimum and a maximum values. In

most indices the minimum value is “0” and the maximum value is either “1” or “100”.

A “dimensionless scale” refers to its nature that the “Index” has no measuring unit.

Chapter 2

4

A “quantitative measure” indicates that the “Index” gives a number that represents the

strength (or weakness) of the aspect under measure.

An “aspect” refers to any societal behavior or quality that exists in a country in any domain

such as in economics, politics, education, gender differences and so on.

Finally, a “country” refers to the fact that the scope of work is to, solely, compare countries

among each other.

2.2.2 Other related terms: “Indicator” and “Coefficient”

The number of existing indices issued by different international organizations,

institutions, universities, Non Governmental Organizations (NGO), and companies are

enormous. Though the use of the word “Index” – as defined above – is dominant, very seldom

it is called “Coefficient” (e.g. Gini coefficient). As long as the term used follows the definition

mentioned previously, it shall not be excluded whether it is called index, or coefficient.

On the other hand, many times the word “Indicator” is used to express a dimensional

parameter that measures a certain aspect of a country (e.g. primary pupil to teacher ratio [1],

total fertility rate [2]). In that case, this “Indicator” doesn’t belong to the scope of study

mentioned here. On the other hand, developing an index usually involves the use of many

indicators, but still the difference between an “Index” and an “Indicator” is clearly distinct.

2.2.3 What is not an “Index”?

Sometime definitions become clearer when things that do not follow the definition are

well identified. There are 3 categories that don’t follow the definition mentioned above and thus

are out of the scope of this work.

• Ranking of countries based on mere statistical data for a certain aspect is not considered an

index. Census bureau in different countries collect data periodically; monthly, quarterly,

annually or every 5 years, or even on longer intervals depending on the measured aspect; to

measure a certain societal characteristic as function of time. This could be population

(usually every 10 years), number of death in a year, number of physicians per 1000


5

population, energy consumption in MWh, extra. These statistical figures, though very

important, are not considered as indices because they posses a certain unit and so don’t have

a normalized scale. Alternatively, in most cases they are called Indicators.

• Clearly any stock market index is not part of the scope of the study as they do not measure

counties’ characteristics but alternatively they measure the market value of group of

companies.

• Index that is related to one single market because it doesn’t cover the whole world and thus

is not used to compare different countries (e.g. Employment Cost Index used in USA [3]).

2.2.4 From Statistical Data to Indices

Though Chapter 3 will discuss in details how indices are made, from its original concept

till the creation of the final scores, here an overview of the indices hierarchy shall be presented

and is graphically shown in Figure (2.1). Indices start with either statistical data of a certain

aspect of the country (e.g. population, number of doctors, number of pupils at schools, total

Global Domestic Product GDP,..), or with a questionnaire to collect opinion or information

from international institutions (NGO’s, investment bodies, UN, … extra) or from representative

samples in each country.

Figure (2.1) Relation between statistical data, indicators, and indices.

Stat. data 8 or questionnaire

Indicator 1 Indicator 2 Indicator 3 Indicator 4 Indicator 5

Index 1 Index 2

Index 3








Chapter 2

6

From these data, indicators are constructed (e.g. GDP/capita, number of doctors per 1000

citizens, electrification ratio….). Then the indicator(s) are converted into an index as defined

previously. The index could be very simple, composed of only one indicator (e.g. Gini Index),

or it can be composed of multiple indicators (e.g. Fossil Fuel Sustainability index). Also indices

can be compounded of several indices to be able to evaluate a multidimensional situation of the

country (e.g. UN Human Development Index).

2.2.5 Score and Rank

According to the how the index is designed – as will be shown in Chapter 3 – every

country gets a score between the minimum and maximum value. Then the world countries and

territories are ranked according to this score. According to the nature of the index, some indices

rank the countries in a “forward” way, meaning that the rank of the country increases as the

score increases (e.g. Press Freedom Index). Some other indices rank the countries in a

“backward” way, meaning that the rank increases as the score decreases (e.g. Corruption

Perception Index, Gini Index, and Index of Economics Freedom).

2.3 Important and Relevant Indices

It is worth mentioning that this part of the thesis was written during June and July 2011,

so the indices values that were available at that time were those before the spring revolutions in

the Arab countries. These revolutions will – most probably – bring major changes (good or bad

is something no one knows yet) in these countries and in neighboring ones even if they do not

enjoy revolutions themselves.

It is not the aim of this chapter to collect all existing indices produced by all

international organizations, institutions, universities, NGO, and companies as they are

enormous, but rather to:

• Bring the attention to the most famous and relevant ones to the scope of this thesis.

• Give introduction to some indices that will be studied in detail in Chapter 3 to learn

how the indices are made.


7

In the following subsections, 12 indices are going to be discussed; 4 in politics, 3 in

economics, 3 in human development, and 2 in energy. Since indices vary in the way they are

developed, scale of ranking, and ranking order, and for the sake of unifying the way tables are

read, countries will be listed with the best country on the top and worst country at the bottom of

the list. The word “best” here means the more equal, fare, free, developed, and human

regardless of the index type under discussion. Also to be relevant to the main research work

carried out in Chapter 4 and 5, the tables in the following subsections will focus on 13 Arab

countries that are members of the Regional Center for Renewable Energy and Energy

Efficiency (RCREEE) (Algeria – Bahrain – Egypt – Iraq – Jordan – Lebanon – Libya –

Morocco – Palestine – Sudan – Syria – Tunisia – Yemen) in addition to another 10

benchmark countries (Brazil – Turkey – Spain – Greece – South Africa – Malaysia – India

– Chain – USA –Germany). In case a data of a country is not available, the country will not be

shown in the list.

2.3.1 Political Indices

2.3.1.1 The Political Instability Index

This index is developed by Economist Intelligence Unit from “The Economist”

magazine. It measures the threat level to governments by social unrest and protest. The Political

Instability Index is a type of a compound index that is made of only two indices [4]:

• Underlying Vulnerability Index.

• Economic Distress Index.

where the former index is composed of 12 indicators and the later one is composed of only 3

indicators. The overall Political Instability Index is made by taking a simple arithmetic average

of its two component indexes.

2ln IndexDistressEconomicIndexerabilityVugUnderlayinIndexyInstabilitPolitical +

=

(2.1)

Chapter 2

8

Figure (2.2) World color map for the Political Instability Index covering the period 2009/2010, Economist Intelligence Unit [5].

This index is a forward index that ranges from “0” (no vulnerability) to “10” (highest

vulnerability) where Norway, Denmark, Canada have lowest vulnerability with scores 1.2, 2.2,

and 2.8 respectively. The indices scores for 165 countries for 2009/2010 are shown as a color

map for the whole world in Figure (2.2) [5].

It is most interesting to see from the color map of Figure (2.2) that countries like Tunisia,

Libya, and Egypt are put on the same risk category as USA and China (moderate risk). This

reveals that in the 2009/2010 period no one had any expectations of the spring revolutions in

these countries. Qualitatively, Libya’s score is 4.3, and Tunisia is 4.6, even making them more

stable than China with a score 4.8, and USA with 5.3 as shown in Table (2.1) where RCREEE

Arab countries are shown in gray background. The author of the index admits that while it

relays on the work of the Political Instability Task Force (PITF) of George Mason University in

the US, this PITF has a success rate over 80% in identifying serious outbreak for data extending

back till 1955 [6].


9

Table (2.1) Score and rank for RCREEE and 10 benchmark countries for the Political Instability Index 2009/2010 [4].

Rank Country Political Instability Index

150 Germany 3.8

137 Libya 4.3

135 India 4.5

134 Tunisia 4.6

124 China 4.8

110 USA 5.3

106 Egypt 5.4

106 Jordan 5.4

105 Brazil 5.4

104 Spain 5.5

99 Bahrain 5.5

98 Morocco 5.6

94 Syria 5.8

79 Yemen 6.1

71 Greece 6.3

64 Malaysia 6.5

61 Algeria 6.6

55 Turkey 6.8

39 South Africa 7

39 Lebanon 7

6 Iraq 7.9

4 Sudan 8

Chapter 2

10

2.3.1.2 Corruption Perceptions Index

The Corruption Perception Index (CPI) is developed by Transparency International

which is a global NGO that works to globally fight corruption [7]. As the name stands, this

index ranks the countries “in terms of the degree to which corruption is perceived to exist

among public officials and politicians” [8]. The index is based on common questionnaire

assessment made by 10 reputable international organizations. Each question in the

questionnaire has a list of possible answers with certain number of points corresponding to each

answer. The score of the country is calculated by taking the simple arithmetic average of the

points of the same question coming from different surveys and then taking the simple

arithmetic average of all questions of the country (much more details of the data processing is

available in [8]).

The CPI is a backward index that ranges from “0” indicating a highly corrupted country

up to “10” for clean government and politicians where Denmark, New Zealand, and Singapore

are on the top of the list at scores 9.3 for all of them. Figure (2.3) shows a world color map for

the CPI for 2010 [9]. The result of that year indicates that only one fourth of the 178 countries

studied – mainly the developed countries – have score above five. This reveals that most of the

citizens in the world are governed by highly corrupted governments, and corruption is a

common practice across the globe.

Figure (2.3) World color map for the Corruption Perception Index for 2010 [9].


11

Table (2.2) shows the score and rank for RCREEE and benchmark countries in the CPI for 2010

which reveal that all RCREEE countries have score below “5”, indicating high corruption.

Table (2.2) Score and rank of RCREEE and 10 benchmark countries for the Corruption Perception Index for 2010 [10].

Rank Country CPI Score

15 Germany 7.9

22 USA 7.1

30 Spain 6.1

48 Bahrain 4.9

50 Jordan 4.7

54 South Africa 4.5

56 Malaysia 4.4

56 Turkey 4.4

59 Tunisia 4.3

69 Brazil 3.7

78 China 3.5

78 Greece 3.5

85 Morocco 3.4

87 India 3.3

98 Egypt 3.1

105 Algeria 2.9

127 Lebanon 2.5

127 Syria 2.5

146 Libya 2.2

146 Yemen 2.2

172 Sudan 1.6

175 Iraq 1.5

Chapter 2

12

2.3.1.3 Freedom Country Index

The Freedom Country Index (FCI) is developed by Freedom House and it measures the

freedom of individual citizen as they experience in their daily life [11]. The index relies on two

main indices:

• Political Rights Index (PRI).

• Civil Liberties Index (CLI).

The FCI is a forward index where the scale ranges from “1” (most free) to “7”, (least

free) on half point intervals. Finally the countries are categorized into one of three groups; Free,

Partly Free, and Not Free as shown in Table (2.3).

Table (2.3) Categorization of country freedom status [12].

Freedom Country Index Country Status

1.0 to 2.5 Free

3.0 to 5.0 Partly Free

5.5 to 7.0 Not Free

The FCI for 2010 reports 194 countries and 14 territories and are color mapped in

Figure (2.4) [13]. The status of the RCREEE and benchmark countries is listed in Table (2.4)

where they are ranked according to PRI first then CLI. While this table shows that all RCREEE

countries except Lebanon and Morocco are not free, it is believed that after the spring

revolutions and with fare and transparent elections, the statuses of the relevant countries will be

changed.

Figure (2.4) World color map for the Freedom Country Index for 2011 [13].


13

Table (2.4) Score and status of RCREEE and benchmark countries for the Freedom Country Index for 2010 [14] [15].

Country PRI CLI Country Status

Germany 1 1 Free

Spain 1 1 Free

USA 1 1 Free

Greece 1 2 Free

Brazil 2 2 Free

South Africa 2 2 Free

India 2 3 Free

Turkey 3 3 Partially Free

Malaysia 4 4 Partially Free

Lebanon 5 3 Partially Free

Morocco 5 4 Partially Free

Iraq 5 6 Not Free

Algeria 6 5 Not Free

Bahrain 6 5 Not Free

Egypt 6 5 Not Free

Jordan 6 5 Not Free

Yemen 6 5 Not Free

Palestine1 6 6 Not Free

Tunisia 7 5 Not Free

China 7 6 Not Free

Syria 7 6 Not Free

Libya 7 7 Not Free

Sudan 7 7 Not Free

1 The latest data for Palestine is that of 2009.

Chapter 2

14

2.3.1.4 Press Freedom Index

The Press Freedom Index (PFI) is developed by Reporters Without Borders which is a

French registered international non-profit organization [16]. The Index measures the freedom

enjoyed by journalists and news organizations in different countries, and thus it neither measure

the level of the freedom of the country itself, nor how oppressive the regime of the country

understudy. The index accounts for any emotion or physical violence that may be imposed on

journalists or from mere threats to imprisonment and murders. It also accounts for any freedom

violation imposed on media outlets such as censorship, confiscation of equipment, and

harassment [17].

The PFI is a questionnaire-based forward index that is calculated based on a survey of

43 questions [18]. A pointing system is devised to give certain number of points according to

the answer to each question. The score starts from “0” for most press freedom country and

counts up according to a point system that maps the answers of the questions [19]. The world

color map shown in Figure (2.5) is based on data collected from 178 countries between

September 2009 and September 2010 [20]. The questionnaire and the pointing system of the

Press Freedom Index will be discussed in much more details in Chapter 3. The score and rank

of the RCREEE and benchmark countries according to the PFI are listed in Table (2.5) [21].

Figure (2.5) World color map for the Press Freedom Index for 2010 [20].


15

Table (2.5) Score and rank of RCREEE and benchmark countries for the Press Freedom Index for 2010 [21].

Rank Country Press Freedom Index

17 Germany 4.25

20 USA 6.75

38 South Africa 12

39 Spain 12.25

58 Brazil 16.6

73 Greece 19

78 Lebanon 20.5

120 Jordan 37

122 India 38.75

127 Egypt 43.3

130 Iraq 45.58

133 Algeria 47.33

135 Morocco 47.40

138 Turkey 49.25

141 Malaysia 50.75

144 Bahrain 51.38

150 Palestine 56.13

160 Libya 63.5

164 Tunisia 72.5

170 Yemen 82.13

171 China 84.67

172 Sudan 85.33

173 Syria 91.5

Chapter 2

16

2.3.2 Economics

2.3.2.1 Gini Index

The Gini Index (GI) (some reports it as Gini coefficient) was originally developed by

the Italian statistician and sociologist Corrado Gini in 1912 [22]. Currently, it is calculated and

maintained by the Central Intelligent Agency [23]. The GI measures the degree of family

income inequality in the country. The Gini Index is type of indices that depend on direct

mathematical equations based on statistical data representing the wealth distribution of the

country. The description of the GI and the equation used to calculate it will be presented in

detail in Chapter 3. It is a backward Index where its scale ranges from “0” for perfect equal

income distribution among all families and “100” for perfect unequal income distribution where

all the income of the country is owned – theoretically – by only one family while all the rest

have zero income. The 2009 version lists 136 countries where Sweden, Hungary, and Norway

have the most equal income distribution with scores 23, 24, and 25 respectively. Figure (2.6)

presents a world color map for the Gini Index [24].

Figure (2.6) World color map for the Gini Index [24].


17

Table (2.6) Score and rank of RCREEE and benchmark countries for the Gini Index [23].

Rank Country Gini Index Date of Information

130 Germany 27 2006

106 Spain 32 2005

101 Greece 33 2005

92 Egypt 34.4 2001

90 Algeria 35.3 1995

83 India 36.8 2004

76 Yemen 37.7 2005

65 Jordan 39.7 2007

64 Turkey 39.7 2008

63 Tunisia 40 2005 (estimate)

58 Morocco 40.9 2007 (estimate)

53 China 41.5 2007

40 USA 45 2007

34 Malaysia 46.2 2009

13 Brazil 53.9 2009

3 South Africa 65 2005

Table (2.6) lists RCREEE and benchmark countries (Libya, Palestine, Syria, and

Lebanon data are missing from the original source) according to their GI. Unfortunately the

data are not available at the same year, and thus the rank does not exactly representative to the

current relative situation among these countries.

Chapter 2

18

2.3.2.2 Index of Economic Freedom

The Index of Economic Freedom (IEF) is developed by The Heritage Foundation and

the Wall Street Journal and it measures individual rights to have full control over his/her labor

and property [25]. Economically free countries are characterized by the freedom of its citizens

to work, produce, consume, and invest as they want without restriction from the government or

state, off course within the framework set by the state to protect the environment and without

infringing others liberties. The IEF combines the following 10 measures:

• Business freedom

• Trade freedom

• Monetary freedom

• Government size/spending

• Fiscal freedom

• Property rights

• Investment freedom

• Financial freedom

• Freedom from corruption

• Labor freedom

The index is a backward one with scale that ranges from “0”, meaning no freedom, up

to “100”, meaning maximum freedom. The 2011 index covers 183 countries where the leading

ones are Hong Kong, Singapore, and Australia with scores 89.7, 87.2, and 82.5 respectively.

Table (2.7) lists RCREEE and benchmark countries according to their IEF where Palestine

information is missing from the source.


19

Table (2.7) Score and rank of RCREEE and benchmark countries for the Index of Economic Freedom for 2011 [26].

Rank Country Index of Economic Freedom

9 USA 77.8

10 Bahrain 77.7

23 Germany 71.8

31 Spain 70.2

38 Jordan 68.9

53 Malaysia 66.3

67 Turkey 64.2

74 South Africa 72.7

88 Greece 60.3

89 Lebanon 60.1

93 Morocco 59.6

96 Egypt 59.1

100 Tunisia 58.5

113 Brazil 56.3

124 India 54.6

127 Yemen 54.2

132 Algeria 52.4

135 China 52

140 Syria 51.3

173 Libya 38.6

Chapter 2

20

2.3.2.3 Global Competitive Index

The Global Competitive Index (GCI) was developed by the World Economic Forum

under the supervision and leadership of Professor Xavier Sala-i-Martin in 2004 at Columbia

University [27] [28]. The World Economic Forum was founded as a non-profit organization in

1971 by Professor Klaus Schwab in Switzerland under the name European Management Forum

and changed its name to the current one in 1987 [27]. The GCI comprises 12 dimensions that

cover very wide spectrum of societal performance; from infrastructure, education, and health, to

labor market, and innovation.

• Institutions

• Infrastructure

• Microeconomic environment

• Health and primary education

• Higher education and training

• Good market efficiency

• Labor market efficiency

• Financial market development

• Technical readiness

• Market size

• Business sophistication

• Innovation

It is one of the most complex indices covered in this thesis. The latest GCI of 2010-2011

covers 139 countries. The index is a backward one with its scale ranges form “1” for the least

competitive economy to “7” for the most competitive one where Switzerland, Sweden, and

Singapore are the leading countries with scores 5.63, 5.56, and 5.48 respectively. Table (2.8)

lists RCREEE and benchmark countries according to their GCI where Palestine and Yemen

data are not reported for 2010-2011 results [30]. It is worth noticing that though Tunisia has a

relatively advanced place as an economically competitive society, Tunisia ranks badly in the

Economic Freedom (Table (2.7)), Gini Index (Table (2.6)), Press Freedom Index (Table (2.5)),

and Freedom Country Index (Table (2.3)). This reveals that being an economically competitive

society doesn’t necessarily mean that the market is free, the wealth is fairly distributed, or the

country is free from the media or political censorship. At the end, being economically

competitive did not save the old ruling regime in Tunisia a revolution that overthrew it.


21

Table (2.8) Score and rank of the RCREEE and benchmark countries for the Global Competitive Index for 2010-2011 [30].

Rank Country Global Competitive Index

4 USA 5.43

5 Germany 5.39

26 Malaysia 4.88

27 China 4.84

32 Tunisia 4.65

37 Bahrain 4.54

42 Spain 4.49

51 India 4.33


58 Brazil 4.28

61 Turkey 4.25

65 Jordan 4.21

75 Morocco 4.08

81 Egypt 4.00

83 Greece 3.99

86 Algeria 3.96

92 Lebanon 3.89

97 Syria 3.79

100 Libya 3.74

Chapter 2

22

2.3.3 Human development

2.3.3.1 Human Development Index

The Human Development Index (HDI) is developed by United Nation Development

Program (UNDP) and it combines three pillars into one measure [31]:

• Life expectancy.

• Educational attainment.

• Income.

HDI is calculating by taking the arithmetic average of the three components indices. As

so, it is a compound index, which scale range from “0”, for the least developed, to “1” for the

most developed country. A world color map for the HDI is shown in Figure (2.7) [32], while a

list of the RCREEE and benchmark countries with their score and rank is given in

Table (2.9) [33].

It is worth mentioning that the countries where the first 3 spring revolutions erupted in

the Arab world; namely Tunisia, Egypt, and Yemen, belong to 3 different levels under the HDI,

from High (Tunisia), Medium (Egypt), to low (Yemen).

Figure (2.7) World color map for the Human Development Index for 2010 [32].


23

Table (2.9) Score and rank of RCREEE and benchmark countries for the Human Development Index for 2010 [33].

Rank Country Human Development Index

4 USA 0.902

10 Germany 0.885

20 Spain 0.863

22 Greece 0.855

39 Bahrain 0.801

55 Libya 0.755

57 Malaysia 0.744

73 Brazil 0.699

81 Tunisia 0.683

82 Jordan 0.681

83 Turkey 0.679

84 Algeria 0.677

89 China 0.663

97 Palestine 0.645

101 Egypt 0.62


111 Syria 0.589

114 Morocco 0.567

119 India 0.519

133 Yemen 0.439

154 Sudan 0.379

Chapter 2

24

2.3.3.2 Global Gender Gap Index

The Global Gender Gap Index (GGGI) is developed by the World Economic Forum and

it was introduced for the first time in 2006. It aims to measure the differences between genders

in different countries based on four indices [34]:

• Economic participation and opportunity index.

• Educational attainment index.

• Health and survival index.

• Political empowerment index.

The index measures the disparity between different genders in how much they enjoy

these four aspects of life. Three important concepts were adopted for the Index to be

representative of the gap between genders; namely [34]:

• It measures the gaps between genders rather than absolute levels.

• It measures the gaps in the outcomes but not the gaps in the means.

• Country rank is based on gender equality not on women’s empowerment.

The GGGI is a backward one, with its scale ranges from “0” for the country of least

equality between genders up to “1” for the country if most equality. The results of 2010 analyze

134 countries where Iceland, Norway, and Finland are on the top of the list with scores 0.8496,

0.8404, and 0.8260 respectively. Table (2.10) lists the score and rank of RCREEE and

benchmark countries for the GGGI for 2010 [34]. It shows that the RCREEE countries are far

from the top 100 countries, where Yemen is at the bottom of the list. Investigating RCREEE

countries’ profiles in the final GGGI report, one notices that the lacking behind for these

countries is due to the large gap in economic and political factors, not in health or education.

Normally men earn more than women in Arab countries, and normally women are not

participating in the political life. This is understandable in the sense that in the Arab countries’

men are responsible for family wellbeing, and women “willingly” are more dedicated for house

keeping. If the gap in the economic and political factors were based on a societal discriminatory

behavior, it would have shown the same characteristic in the education and health indicators as


25

well, but this is not the case. At the end, the GGGI just measures the gap in the outcome rather

than the reasons for that.

Table (2.10) Score and rank of RCREEE and benchmark countries for the Global Gender Gap Index for 2010 [34].

Rank Country Global Gender Gap Index

11 Spain 0.7554


13 Germany 0.753

19 USA 0.7411

58 Greece 0.6908

85 Brazil 0.6655

98 Malaysia 0.6479

107 Tunisia 0.6266

110 Bahrain 0.6217

112 India 0.6155

116 Lebanon 0.6084

119 Algeria 0.6052

120 Jordan 0.6048

124 Syria 0.5926

125 Egypt 0.5899

126 Turkey 0.5876

127 Morocco 0.5767

134 Yemen 0.4603

Chapter 2

26

2.3.3.3 Global Wellbeing Index

The Global Wellbeing Index (GWI) is developed by Gallup Incorporation, an

international consultancy firm specialized in conducting surveys and offering studies about

people opinion, and living conditions that help decision makers in companies and government

make informed decisions [35]. The GWI is based on Cantril Self-Anchoring Striving Scale

which measures individual’s life satisfaction on a scale from “0”, indicating worst possible life,

up to “10” indicating best possible life. This scaling measure is carried for the current

individual situation and for how he/she thinks the future will be. Then individuals are

categorized into three categories; thriving, struggling, and suffering as shown in the Table

(2.11) [36].

Table (2.11) Definition of Thriving, Struggling, and Suffering according to Cantril Self-Anchoring Striving Scale [36].

Thriving Struggling Suffering

Current situation 7-10 4 7 0-4

Perception of the future 8-10 4 8 0-4

GWI reports also an average daily experience on a scale from “0”, meaning worst

experience, up to “10”, meaning best experience, based on 10 factors [34]:

• Feeling well-rested

• Being treated with respect

• Smiling/laughter

• Learning/interest

• Enjoyment

• Physical pain

• Worry

• Sadness

• Stress

• Anger

Table (2.12) lists the score of RCREEE and benchmark countries for the GWI for 2010

[36]. Unfortunately the original source and rank groups the countries on continental bases, so it

was difficult to get a rank for the RCREEE and benchmark countries in Table (2.12), but

instead they are ordered according to the daily experience score. It is interesting to see that

though the first country after Germany, the Sudan, has much more struggling and suffering and


27

less thriving people; the score of daily experience of the people in both Germany and the Sudan

are the same, 7.4. This can be attributed to the fact the many of Sudanese people – as in the rest

of the Arab countries – are more inclined to accept what life gives them as a will of God, and so

they tend to appreciate and enjoy what they have even if it is little. Their future prosperity is as

attached to God’s willing after doing the maximum efforts they do.

Chapter 2

28

Table (2.12) Score of RCREEE and benchmark countries for the Global Wellbing Index for 2010 [36].

Country Thriving (%) Struggling (%) Suffering (%) Daily experience

Malaysia 15 80 5 8.1

China 9 77 14 7.6

Brazil 58 40 2 7.5

Germany 43 50 7 7.4

Sudan 7 81 12 7.4

South Africa 21 71 8 7.3

USA 57 40 3 7.3

Bahrain 32 45 23 7

Greece 31 57 11 7

Morocco 10 80 10 7

Spain 36 58 6 7

India 10 69 21 6.9

Tunisia 14 77 9 6.8

Syria 10 66 24 6.8

Jordan 30 61 8 6.7

Algeria 18 77 6 6.7

Lebanon 21 64 15 6.3

Yemen 14 62 24 6.3

Egypt 10 71 19 6.1

Libya 24 68 8 6

Turkey 13 67 20 6

Palestine 14 70 15 5.8

Iraq 11 71 18 5.2


29

2.3.4 Energy

2.3.4.1 Fossil Fuel Sustainability Index

The Fossil Fuel Sustainability Index (FFSI) was developed by Volkan Ş. Ediger et al. to

introduce a criterion to measure the sustainability of fossil resources [37]. The proposed index

combines three indicators:

• The Reserve to Production ratio of the fossil fuel (RP ratio).

• The annual Production to annual Consumption of each fossil fuels (PC ratio).

• The Carbon Emission ratio (CE ratio).

The FFSI is a backward index with scale that varies from “0”, meaning worst possible

sustainability, up to “1”, meaning highest possible sustainability. The study covers 62 countries

where Qatar, Norway, and Kuwait are on the top of the list with scores 0.4393, 0.3972, and

0.3953 respectively. Unfortunately only two RCREEE countries (Algeria and Egypt) appear in

this index and are shown in Table (2.13) along with other 8 studied benchmark countries. This

index also is a very good example of an index that is solely based on statistical data and direct

application of equations. The construction of FFSI and the equations used will be studied in

more details in Chapter 3.

Chapter 2

30

Table (2.13) Score and rank of Algeria, Egypt and 9 benchmark countries according to Fossil Fuel Sustainability Index for 2005 [37].

Rank Country Fossil Fuel Sustainability Index

10 Algeria 0.1656

15 Brazil 0.101

20 Egypt 0.0763

21 Malaysia 0.0701

35 India 0.0467

45 USA 0.0336

49 Greece 0.0303

53 China 0.0233

55 Turkey 0.0155

59 Germany 0.0074

60 Spain 0.0072

2.3.4.2 Renewable Energy Countries Attractiveness Indices

These indices are developed by E&Y, an international consulting firm for business

development, which has been producing it since 2003 [38]. The indices track the attractiveness

of different countries markets across different renewable energy technologies namely; wind

(onshore and offshore), solar PV, solar CSP, biomass, geothermal, and Tidal and Waves. It also

has an index for infrastructure and another one that combines all of them together.

The indices are of the backward type with scale that ranges from “0”, for least attractive,

to “100”, for most attractive country. November 2011 issue contains 40 countries, where only

Egypt, Morocco, and Tunisia out of all RCREEE countries were included. The indices for these

three countries along with 9 studied benchmark countries are listed in Table (2.14) [39].


31

Table (2.14) Score and rank Egypt, Morocco, and Tunisia along with 9 benchmark countries for the Renewable Energy Countries Attractiveness Index for November 2011 issue [39].

Rank Country All Renewable

Wind Index

Onshore Wind

Offshore Wind

Solar Index

Solar PV

Solar CSP

Biomass /other Geothermal Infrastructure

1 China 70 76 78 70 61 66 47 58 51 75

2 USA 66 66 69 55 72 71 74 61 67 61

3 Germany 65 69 65 78 51 70 0 65 75 70

4 India 63 63 71 42 64 69 53 95 45 66

9 Spain 51 50 54 39 58 56 63 46 30 47

10 Brazil 50 53 57 40 42 46 32 51 23 49

21 Greece 43 44 48 33 46 51 33 34 25 32

23 South Africa 42 44 47 35 42 39 47 36 33 46

27 Egypt 40 41 45 32 41 39 45 35 25 34

30 Turkey 39 41 43 32 37 40 28 34 41 37

30 Morocco 39 38 42 25 48 47 52 35 21 42

34 Tunisia 34 35 38 27 45 44 48 19 27 41

The work presented in this thesis is compared to this index, and thus it will be discussed

in much more details in Chapter 3.

2.4 Conclusion

A definition for the word “Index” is presented, which will be used throughout the thesis.

In the context of this thesis, the word “Index” refers to political, economic, and social indices

that are commonly used to quantitatively express the relative ranking of countries for a certain

aspect. We explored 12 important indices from which few are also relevant to the scope of work

of this thesis. This exploration shows, in general, the different methods used to design and

calculate indices, thus provide a good background to be used in the formation of the proposed

Renewable Energy Market Competence Index. Scores and rank for RCREEE and benchmark

countries are shown for these various indices. From the 12 presented indices in this chapter, 4

indices are going to be discussed in details in Chapter 3; namely the Gini index, Fossil Fuel

Chapter 2

32

Sustainability Index, Press Freedom Index, and Renewable Energy Countries Attractiveness

Indices. These indices present different construction methods; either mere analytically based or

questionnaire based indices, from which lessons will be learned to design the proposed index in

this thesis.

Index Construction

33

Chapter 3

Index Construction

3.1 Introduction

In light of Figure (2.1) in Chapter (2), the process of constructing an index can be

summarized – in general – into 5 steps as follows:

• Statistical data or questionnaire: The starting point of an index could be either a mere

statistical data from which, through an analytical formula and some data processing, it is

converted into a normalized dimensionless index. Or alternatively, the stating point

could be a questionnaire with a set of predefined answers that cover all possible

situations where each answer corresponds to a certain points. Then, after possibly some

other steps, these points are aggregated to give the final index score.

• Data processing or questionnaire conversion process: Data processing could only be

a normalization procedure where goalposts (minimum and maximum values) are

introduced, or it may include more complicated operations like data fitting to a

mathematical function or some other mathematical operations. Alternatively, in

questionnaire-based index, the questionnaire answers are quantitatively expressed by a

predefined method.

• Indicator(s) formation: After the preceding quantitative expression step, indicators are

formed to compress data that measure various qualities. It is worth mentioning that in

some cases indicator formation step precedes the normalization step.

• Index formation: Indicators are merged together to form the required index. In most

cases this includes arithmetic of geometrical averaging. This aggregation of indicators

could be based on equal or on unequal weighting. This depends on the way the index

designer wants to value each quantity with respect to the other.

Some indices are very simple that they can be constructed in only 3 steps like the Gini

Index, while others may need some detailed operations (e.g. normalizing the standard deviation

Chapter 3

34

in Global Gender Gap Index). Here, the construction details of few indices that give good

representation of the different possible ways of index formation are going to be explored.

3.2 Gini Index

The GI is one of the simplest indices encountered in this study. The construction of the

GI is performed in only 3 steps as follows.

Statistical data: The GI is based on statistical data of the family income in a country.

Data processing: The number of families with certain income is divided by the total number of

families in the country and the family income is divided by the total income of all families in

the country. These normalized statistical data are arranged from the poorest families to the

richest ones. Then accumulation number of families and accumulation of family income are

calculated from the previous data [21]. Upon plotting these normalized and ordered

accumulative number of families on the abscissa and the accumulative income on the ordinate,

one gets what is called Lorenz curve as shown in Figure (3.1). The straight line of equality

represents the ideal case where all families in the country have the same income. The more the

Lorenz curve concavity increases the more it reveals the income inequality among the different

families. The most extreme case occurs when the curve is composed of two linear piecewise

segments; a horizontal line on the abscissa from 0 to 1, and then a vertical line at 1. This

represents the case where all families have zero income except one family that has all the

income of the country.

Index formation: The GI is defined as the ratio between the area “A” and the area “A+B”;

where the area “A” is the area between the equality line and the Lorenz curve, and the area “B”

is the area under the Lorenz curve as shown in Figure (3.1) [21].

BAAGI+

= (3.1)

Since the data are normalized, thus the area A+B=0.5

AGI 2= (3.2)

Index Construction

35

Figure (3.1) Lorenz curve and the Gini Index.

BGI 21−= (3.3)

If the data forming Lorenz curve can be fitted to a mathematical function F(x), then the GI can

by calculated using the formula

∫−=1

0

)(21 dxxFGI (3.4)

The resulted Gini Index has a maximum value of 1, but it can be renormalized to have a

maximum of 100.

3.3 Fossil Fuel Sustainability Index

This index is also based on statistical data, but it is much more complicated than the GI

and gives a very good example of data-based index. The FFSI is formed in 5 steps as follows:

Statistical data: The raw data needed to construct this index are:

• The amount of national reserve of oil, natural gas, and coal.

• The amount of national production of oil, natural gas, and coal.

• The amount of national consumption of oil, natural gas, and coal.

0% 0%

100%

100%

Accumulated population %

Accumulated income %

Line of equality

Lorenz curve

A

B

Chapter 3

36

Indicator formation: Based on these raw data for any country, three indicators are formed for

each fossil resource (oil, natural gas, and coal). These indicators are:

• The Reserve to Production ratio of the fossil fuel (RP) which is an indication of the

rate of recourse depletion. It is given by:

( ) ( )( ) )(

,,Pr,,Re,, years

CoalGasNaturalOiloductionCoalGasNaturalOilserveCoalGasNaturalOilRP = (3.5)

• The annual Production to annual Consumption of each fossil fuels (PC) which is an

indication of the national dependence on foreign imports. It is given by:

( ) ( )( )CoalGasNaturalOilnConsumptio

CoalGasNaturalOiloductionCoalGasNaturalOilPC,,

,,Pr,, = (3.6)

• The Carbon Emission ratio (CE) which is an indication of the country contribution to

global warming. It is given by:

( ) ( ) ( )CoalGasNaturalOilnConsumptioCoalGasNaturalOilFactorCoalGasNaturalOilCE

,,,,1,,

×=

(3.7)

Where the Factor for oil, natural gas, and coal are given by [35]:

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛=

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

08.164.084.0

CoalGasNatural

OilFactor (3.8)

Normalization step: After calculating the three indicators for all the countries under study, the

minimum and maximum values (goalposts) are found for each fossil energy resource. These

goalposts are used to normalize the actual indicator values according to

ValueMinimumValueMaximumValueMinimumValueActualValueNormalized

−−

= (3.9)

Volkan Ş. Ediger et al analyzed 62 countries and the goalpost they found are summarized in

Table (3.1).

Index Construction

37

Table (3.1) Goalpost for each indicator and fossil resource [35].

RP PC CE

Min 1 4.14×10-6 896×10-6 Oil

Max 114 15.6 0.336

Min 1 896×10-6 2.68×10-3 Natural Gas

Max 658 0.336 31.3

Min 1 27.2×10-6 968×10-6 Coal

Max 2300 66 926

So now, there are nine normalized (0 to 1) and dimensionless indicators which are shown in

Table (2) in [35].

Indices Formation: The arithmetic average of the three indicators (RP, PC, and CE) is taken

with equal weights to form Sustainability Index for each fuel as follows:

3),,( CEPCRPCoalNaturalGasOilSI ++= (3.10)

Index aggregation: Finally the FFSI is constructed by adding SI for Oil, Natural Gas, and Coal

(OSI, GSI, and CSI respectively) according to their percentage usage as fossil fuel energy in the

country as follows:

CSICoalGSIGasNaturalOSIOilFFSI ×+×+×= %%% (3.11)

3.4 Press Freedom Index

The PFI is a good example of questionnaire-based indices. Its formation can be

described in 3 steps:

The Questionnaire: The starting point of this index is a questionnaire that addresses all aspects

that will be included in the final index. The questionnaire investigates different kinds of

violations and restrictions imposed on journalists during their work, and the level of protection

Chapter 3

38

enjoyed by those who carried such aggressions [17]. It is composed of 43 different questions

which are grouped as follows [40]:

• 6 questions that address the Physical Violence confronted by journalists.

• 8 questions that address the number of journalists murdered, detained, physically

attacked or threatened, and role of authorities in these actions.

• 6 questions that address indirect threats, harassment and restriction to information

access.

• 5 questions that address censorship and self-censorship.

• 7 questions that address media control.

• 7 questions that address judicial, business and administrative pressure.

• 7 questions that address internet and new media.

The complete questionnaire is given in Appendix A [40]. For the 2010 PFI, the questionnaire

was sent to 15 different groups and to 140 correspondents around the world, and to journalists,

researchers, and human rights activists [17].

Questionnaire conversion process: The conversion of the questionnaire answers to numbers is

the first step in the quantification of the questionnaire-based index. In case of the PFI, the

answer of a question is:

• Either a “Yes” or “No”, where a “Yes” answer is given a certain point (1, 2, or 3 points

depending on the question) while a “No” answer is given zero points.

• Or a ranked answers where each rank is given a certain point.

Appendix B gives the conversion scheme used to convert answers into points [41]. The

questions are phrased in a why that a “No” answer and a low ranking of the questions indicates

a more safe environment for journalists and a better protection for their safety and rights to

access information. That is why the PFI is a forward index.

Index formation: Though literature review doesn’t show in details how the final index is

calculated, it is intuitive to understand that once a quantification conversion is achieved,

Index Construction

39

constructing a final index becomes a very easy process. The index may be constructed just by

adding the points of the questionnaire questions followed by a normalization step.

3.5 Renewable Energy Countries Attractiveness Indices

According to the general steps presented at the beginning of the chapter, the

development of Renewable Energy Countries Attractiveness Indices can be visualized to take

the following steps:

The Questionnaire: The first step starts with data gathering using the questionnaire shown in

Appendix C. The questionnaire consists of a general section that addresses general

characteristics of the energy sector of the country, followed by identical technology sections,

each one is dedicated to a separate RE technology type; namely: Onshore Wind, Offshore

Wind, Solar PV, Solar CSP, Biomass, Geothermal, Small Scale Hydro (<30MW), and Wave

and Tidal. The data gathered in each section is as follows:

• General Parameters Section: This section of the questionnaire is – mostly – of qualitative

nature and most of the answers will be of essay type ones. This section is divided into 5

different segments; namely:

Electricity Market Regulatory Risk (2 questions): This part investigates the potential

risks regarding RE projects, nature of the electricity market, the availability of support

mechanisms or financial incentives.

Political Risk (3 questions): This part investigates how strong the government is

committed to RE and what role it takes. It also asks if there are any delays from the

government side in preparing the market, and whether there is a legislation regarding RE

industry or not.

Planning Environment (6 questions): This part investigates the necessity to obtain

planning permissions, who does issue it, how much it cost, is there a legislation that

governs this process, how long it takes. It also investigates the necessity of

Environmental Impact Assessment, and how long it takes to conduct one. It also asks

whether it takes more or fewer days for planning compared to other countries, and about

local opposition to RE development by the citizens.

Chapter 3

40

Grid Connection Issues (4 questions): This part investigates the suitability of the grid

infrastructure for RE projects, incentives for grid providers, who carries the connectivity

cost, priority dispatch, how long grid connectivity takes, and restriction and regulation

for RE projects that are going to be connected to the national grid.

Access to Finance (4 questions): This part investigates who are the main financiers for

RE projects, is the finance equally available to all RE technologies, the availability of

easy/cheap finance, and the maturity of the RE financing market in the country.

• Technology Parameters Section: On contrary to the first section, this section of the

questionnaire is – mostly – of quantitative nature and most of the questions ask for specific

numbers. This section is divided into 7 different segments; namely:

Power Off take (2 questions): This part asks if there is any power off take incentives

for this specific technology, asks about prices, longevity, and conditions associated with

any support mechanism (feed-in tariff, green certificates, …)

Tax Climate (1 question): This part asks about the availability of tax credits for this

specific technology.

Grant / Soft Loan Availability (1 question): This part asks for details about any

grants, governmental backed loans, or any other financial support for this specific

technology.

Resource Quality (1 question): This question asks for the availability of the resource

quality for this specific technology. For example, for wind technology, it asks about

wind speed, and areas of strong wind. For PV and CSP, it asks about solar irradiation,

and number of sunny hours during the day. In general, any condition that is unlikely to

change over time.

Current Installed Base (2 questions): This part asks for the total installed capacity

(MW/GW), or generated energy (MWh/GWh), and “how does the installed capacity for

the specific technology compare to the total energy requirements of the country”,

Appendix C.

Index Construction

41

Market Growth Potential (3 questions): This part asks for the expected future

capacity, maximum estimated potential, government targets, and manufacturing base for

this specific technology.

Project Size (1 question): This part asks for the average project size for this

technology, with example of existing and/or planned projects.

Questionnaire conversion process: After gather the information and filling the questionnaire

by E&Y team, the questionnaire information is translated into quantitative ones in an excel

sheet. The data that are originally have quantitative nature, are simply put into the excel sheet,

while the qualitative data is converted via a subjective evaluation by E&Y team members. This

subjective evaluation is based on comparison with other countries. A direct communication

with E&Y team emphasized – in no doubt – this subjective nature of the index which is not

even based on a point-system questionnaire [42].

Index formation: Finally, as all entered data in the model are normalized and adjusted to the

index range, the calculation of the final score of the indices becomes possible. The RE

Countries Attractiveness Indices contains 10 indices:

• 1 index for the infrastructure.

• 6 indices for individual technologies; Wind Onshore, Wind Offshore, Solar PV, Solar CSP,

Biomass and others (most probably others include Small Hydro, and Wave and Tidal), and

Geothermal.

• 1 wind index, combing Wind Onshore and Wind Offshore indices.

• 1 solar index, combining both PV and CSP.

• 1 index that gives an overall value for all technologies and infrastructure.

The overall index is composed of weighted arithmetic average from 35% weight from

the infrastructure index and 65% of the technology indices. The technology indices are mixed

with weighted average according to the Table (3.2). It is worth mentioning that the RE

Countries Attractiveness Indices are published on quarterly bases; February, May, August,

November of each year since 2003 [43].

Chapter 3

42

Table (3.2) Percentage of technology indices in the final overall index.

Wind Onshore Index

Wind Offshore Index

Solar PV Index

Solar CSP Index

Biomass Index

Small Scale Hydro Index

Geothermal Index

Wave and Tidal Index

48% 17% 13% 5% 10% 3% 2% 2%

The subjective nature of the RE Countries Attractiveness Indices was the motive to

design a new index for the renewable energy technologies that is objective in its nature.

3.6 Conclusion

In conclusion, indices construction can be solely based on statistical data along with an

analytical formula(e) that can directly give the final index score. This can be as simple as the

Gini Index, or more complex as Fossil Fuel Sustainability Index. On the other hand, an index

can be completely based on a questionnaire (e.g. Press Freedom Index), and in such case the

final score depends on the point system that is devised for the answers of the questionnaire. An

intermediate scenario can be an index that is a mix between the two previously mentioned

cases, where some indicators are defined with the first method, while the others are defined

with a point system questionnaire. All these 3 types of indices have the property of being

completely objective where personal opinion and experience are not part of the evaluation

process.

Alternatively, the Renewable Energy Countries Attractiveness Indices is partially based

on personal judgment, experience, and comparing countries with each other to evaluate some

of its indicators. Such subjectivity in designing the questionnaire presents weakness in the full

index formation and must be avoided.

In all cases, some kind of averaging – usually arithmetic averaging, and sometimes a

weighted arithmetic averaging – is needed to combine the scores of the different indicators that

constitute the index.

Driving a Methodology for Renewable Energy Market Competence Index

43

Chapter 4

Driving a Methodology for Renewable Energy

Market Competence Index

4.1 Introduction The aim of this chapter is to derive a methodology for RE Market Competence Index

where, in light of the RE Countries Attractiveness Index described in Chapter 3, the proposed

index will be constituted from separate indices, each for a different RE technology.

With contrast with the RE Countries Attractiveness Index, which depends in many

aspects on subjective opinion, the goal here is to develop an index that is objective in nature, i.e.

as long as one knows the methodology of the index, he will reach the same countries’ scores and

ranks. No opinion-based quantification is used. In the following chapter, this proposed

methodology will be applied to CSP technology, and a comparison with CSP component of the

RE Countries Attractiveness Index will be performed.

This chapter is organized as follows, in section 2, the type and score range of the

proposed index and the involved indicators are presented, followed be section 3 where certain

assumptions in the countries’ governments are assumed and listed. In section 4, a list of the

analyzed countries is given followed in section 5 by the overall hierarchy of the proposed index.

Section 6 discusses the challenges in collecting the required data, and the sources of data that

were relied on. Sections 7, 8, and 9 gives details of 13 indicators (the group of general indicator),

including their design, values, and scores, followed by section 10 that discusses needed data

processing for specific indicators using logarithmic function. Then, section 11 gives general

description of the remaining 5 indicators (technology specific group), which is finally followed

by the methodology used to calculate the final RE Market Competence Index in section 12.

4.2 Index Type and Score Range As for the most famous indices that are presented in Chapter 2, the proposed RE Market

Competence Index is designed to be a backward index. With standardization to a 100 points, the

final index ranges from 0 meaning least competent (at the bottom in the country list), up to 100,

Chapter 4

44

meaning most competent (at the top in the county list). Accordingly, all indicators that constitute

the whole index are also designed to range from “0” up to “100” with the same meaning that “0”

is the least favored situation for the country and “100” for the most favored one. And for the

simplicity dealing with integer numbers instead of fraction ones, the final index score and all

indicators scores as well are rounded to the nearest digit.

This selection of the index type and score range also serves the purpose of being able to

compare the proposed index score and rank with that of RE Countries Attractiveness Index which

have this same type and score range.

4.3 Assumptions in Countries’ Governments When developing an indicator, first a value for the indicator is calculated which relates to

physical parameters, then this value is transformed into a score that matches the min/max range,

the type of the index as a backward one, and the objective of creating an index that represents the

competence of this market to RE business. Thus it becomes important to set a criterion by which

this value should relate to the final index. While in some cases these criteria are self explanatory,

in many of the proposed indicators these criteria relay on how the government reads these values

and thus how they are going to react to them. Accordingly, some properties had to be assumed in

the governments of the countries that are included in the index in order to direct the indicator

score to point towards the designed direction of the final index. These assumptions are:

1. Good governance: Each country’s government acts for the benefit of its citizens. Thus,

its choices, and future plans are made to meet the energy needs of the country to fulfill the

required economic growth and prosperity of its citizens.

2. Knowledge-based decisions: Each country’s government is well informed about fossil

fuel depletion (especially oil and natural gas), the hazardous effects associated with the

waste of nuclear fuel and thus switching to RE resources is an unavoidable decision.

3. Environmentally friendly government: Each country’s government actively and

responsibly tries to reduce its CO2 emission in order to avoid global warming effect.

The worldwide spreading of democracy, that finally reached the Arab world, justifies the

first assumption. The wide utilization of telecommunication system that allowed information


45

sharing and knowledge transfer among billions of citizens living on earth and accordingly the

democratically elected governments are also well informed which justifies the second

assumption. Finally, the continuing pressure from the world scientific community and civil

society on the governments of the leading developed countries will assure – with time – that

governments (developed and developing) will follow the way for environmentally friendly

governance. The leadership of the European Union in that direction is a role mode that sooner or

later will “hopefully” be followed by USA, China, India, and the rest of the world.

Off course by doing so an angelic view of the existing governments in the world is not

assumed nor defended, but at least these directions are currently persuaded, though still in

different speeds and commitments across the countries’ governments.

4.4 Addressed Countries Since a great deal of this research work was carried out when the author was residence at

the Regional Center for Renewable Energy and Energy Efficiency (RCREEE), all 13 member

countries in the RCREEE center (Algeria – Bahrain – Egypt – Iraq – Jordan – Lebanon –

Libya – Morocco – Palestine – Sudan – Syria – Tunisia – Yemen) were originally considered.

Unfortunately, due to data limitation, 13 indicators – out of total 18 – are missing for Palestine, 3

indicators are missing for Bahrain, Iraq, and Sudan, and 1 indicator is missing for Yemen.

Thus, the final list of countries excluded these 5 member states from the final list.

In addition, another 10 countries covering all continents, with various economical and

energy situation, various CSP installed capacity, potential, and policies were added as well. Their

addition was important to compare RCREEE countries with other benchmark countries, and also

to compare the score and rank of the index with the corresponding ones of the RE Countries

Attractiveness Indices. Thus, the list of 18 countries for which the indicators numbers are

gathered and the final index is created are:

8 RCREEE countries: Algeria, Egypt, Jordan, Lebanon, Libya, Morocco, Syria, and Tunisia.

10 benchmark countries: Brazil, Turkey, Spain, Greece, South Africa, Malaysia, India, Chain,

USA, and Germany.

Chapter 4

46

Technology Specific Indicators (5) General Indicators

Political and Economic Indicators (3)

Energy Sector Indicators (8)

Financial and Environmental Indicators (2)

RE Market Competence Index

4.5 Renewable Energy Market Competence Index Hierarchy

Figure (4.1) The hierarchy of the Renewable Energy Market Competence Index.

A country’s market competence towards RE projects involves many factors. Some of

these factors are common to all types of RE technologies and are related to the political,

economic, and financial situation of the country, and to the characteristics of the country’s energy

sector itself. On the other hand, each RE technology type has certain specific factors that also

determine the competence of the market (e.g. one of these specific factors is the existence of an

economic potential to be utilized by this specific technology). Thus, a visualization of the

components of a Market Competence Index for a certain RE technology type can be as shown in

Figure (4.1) where the General Indicators are divided into three groups:

1. Political and Economic Indicator (3 indicators).

2. Energy Sector Indicator (8 indicators).

3. Financial and Environmental Indicators (2 indicators).

where the numbers inside brackets represent the number of individual indicators that belong to

each group, with a total 18 indicators. For different RE technology types (e.g. CSP, PV, wind

one shore, wind off shore, biomass, geothermal), only the Technology Specific Indicators will be

changed. It is worth mentioning that E&Y RE Countries Attractiveness Index questionnaire is,

similarly, designed with section that describes common characteristics of the country, followed

by separate sections that describe each individual RE technology characteristic, see Appendix C.


47

4.6 Data Sources, Values, Scores, and Challenges While any index – as those shown in Chapter 2 – is always constructed on annual bases

where countries’ scores and ranks represent their situations at the end (or during) a specific year,

there were difficulties to collect all required data for the 18 indicators for a specific year. One

third of the indicators data (6 indicators) – 5 related to the energy sector indicators group and one

from the financial and environmental indicators group – are collected from the International

Energy Agency (IEA). The second main source of data is REN21-2011 global status report (3

indicators), followed by Central Intelligence Agency (CIA) World Fact Book (2 indicators),

DLR, World Bank, World Economic forum, Economic Intelligence Unit, Transparency

International, and Springer publisher (1 indicator each). In addition, tens of web-based

information are also gathered and analyzed.

The 6 indicators values of IEA are based on the available internet-free data for 2008,

while most of the rest indicators are those for 2010, and very few are for 2011. Thus, the final

index represents more the situation of the countries understudy during a period of time that

extends from 2008-2011, rather than an exact year.

In the following 3 sections the detailed design, value, and score of each indicator

belonging to each group under the general indicators will be presented. Then in section 4.11 a

description of the 5 proposed technology specific indicators will be given.

4.7 Political and Economic Indicators Since the general political and economical situation of the country shape the whole

business environment in the country and thus affect the investment activities in all sectors

including the RE one, it is important to included some indicators in the final index to incorporate

these effects. Thus, three existing indices that quantitatively measure these effects, which were

described in Chapter 2, are included in the proposed index. They are:

• Global competitive Index

• Political Instability Index

• Corruption Perception Index.

These 3 indices are annually published by the World Economic Forum, The Economist

magazine, and Transparency International respectively. In order to fulfill the index scoring rule

Chapter 4

48

where each indicator should range from “0” – least favored – to “100” – most favored –, the

reported scores are linearly mapped to this range. Since low scores for both Global Competitive

Index and Corruption Perception Index mean low competitiveness and low corruption

respectively, thus their mapping to the proposed index, which is backward in nature, takes the

form:

( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×

=

valueIndexeCopmetitivGlobalvalueIndexeCopmetitivGlobalvalueIndexeCopmetitivGlobalvaluecountryIndexeCopmetitivGlobal

AdjustedIndexeCopmetitivGlobal

minmaxmin)(100

)(

(4.1)

⎟⎟⎠

⎞⎜⎜⎝

⎛−

−×

=

)(min)(max)(min)(100

)(

valueIndexPerceptionCorruptionvalueIndexPerceptionCorruptionvalueIndexPerceptionCorruptionvaluecountryIndexPerceptionCorruption

AdjustedIndexPerceptionCorruption

(4.2)

On the other hand, since the low score for Political Instability Index means high stability, thus its

mapping to the proposed index takes the form:

⎟⎟⎠

⎞⎜⎜⎝

⎛−

−−×

=


)(

valueIndexyInstabilitPoliticalvalueIndexyInstabilitPoliticalvalueIndexyInstabilitPoliticalvaluecountryIndexyInstabilitPolitical

AdjustedIndexyInstabilitPolitical

(4.3)

In each of equations (4.1), (4.2), and (4.3), corresponding goalposts from the published scores of

each index are used. Table (4.1) shows the originally published scores of Global competitive

Index (for 2010-2011), Political Instability Index (covering the period 2009-2010), Corruption

Perception Index (for 2010), the goalposts, and their adjusted index score – according to the pre-

mentioned equations – that will be used in the final index.


49

Table (4.1) Original scores, goalposts, and adjusted values for Global competitive Index (for 2010), Political Instability Index (covering the period 2009-2010), Corruption Perception Index (for 2010).

Global Competitive

Index Political Instability

Index Corruption

Perception Index

Country Original

Value Adjusted

Score Original

Value Adjusted

Score Original Value

Adjusted Score

Algeria 3.96 13 6.6 13 2.9 12 Egypt 4.00 15 5.4 50 3.1 16 Jordan 4.21 28 5.4 50 4.7 44 Lebanon 3.89 9 7.0 0 2.5 5 Libya 3.74 0 4.3 84 2.2 0 Morocco 4.08 20 5.6 44 3.4 21 Syria 3.79 3 5.8 38 2.5 5 M

ENA

Cou

ntrie

s

Tunisia 4.65 54 4.6 75 4.3 37 Brazil 4.28 32 5.4 50 3.7 26 Turkey 4.25 30 6.8 6 4.4 39 Spain 4.49 44 5.5 47 6.1 68 Greece 3.99 15 6.3 22 3.5 23 South Africa 4.32 34 5.1 59 4.5 40 Malaysia 4.88 67 6.5 16 4.4 39 India 4.33 35 4.5 78 3.3 19 China 4.84 65 4.8 69 3.5 23 USA 5.43 100 5.3 53 7.1 86 B

ench

mar

k C

ount

ries

Germany 5.39 97 3.8 100 7.9 100

Min 3.74 3.8 2.2 Goalposts Max 5.43 7.0 7.9

4.8 Energy Sector Indicators Since the characteristics of the energy sector of the country has direct relation to the

possibility of developing RE projects, the following 8 indicators are included in the investigation

for RE Market Competence Index:

• Energy Intensity Indicator

• Non Electricity Final Indicator

• Electricity Consumption Growth Indicator

• Net Imported Electricity Indicator

• Non-RE Electricity Production Indicator

• Oil Insecurity Indicator

• Gas Insecurity Indicator

Chapter 4

50

• RE Target Indicator

In the following subsections, the reasons for including each of these indicators, how its

value is calculated, and how the indicator score is evaluated are going to be described.

4.8.1 Energy Intensity Indicator Since most economics of RE technologies are still more expensive than conventional

energy production in most of the countries, and since collectively the whole society will pay the

cost of the energy technology used, it is important to be sure that the energy that will be generated

by RE projects (mostly electricity) will results in good increase in the total GDP of the country.

Countries that efficiently use their energy to increase their GDP are more well-positioned to make

use of their RE potential and be able to pay back the investments of these RE projects.

A widely used measure that serves this purpose is the Energy Intensity Indicator which

measures the efficiency of using energy to generate wealth in the country, and thus is measured as

the ratio of the country’s Total Primary Energy Supply (TPES), measured in Tone Oil Equivalent

(toe), to the total GDP, measured in thousand U$, of the country taking Purchase Power Parity

(PPP) into account.

( ) ( )toe/1000U$int

)(PrAccountoPPPTakingGDPTotal

TEPSSupplyEnergyimaryTotalEIVValueIntensityEnergy =

(4.4)

We relied on IEA posted free values of the countries’ energy intensities for year 2008

which are given for the dollar value of year 2000 [44]. The posted indicator values should be

mapped to the indicator score such that countries with low energy intensity value should have a

higher energy intensity indicator score than countries with higher energy intensity values. Thus,

the energy intensity values are mapped to the indicator score scale from “0” (minimum Energy

Intensity Indicator Score, corresponding to maximum energy intensity value) to a maximum

“100” (maximum Energy Intensity Indicator Score, corresponding to minimum energy intensity

value) using the relation:

⎟⎟⎠

⎞⎜⎜⎝

⎛−

−−×=


valueEIVvalueEIVvalueEIVvaluecountryEIVIndicatorIntensityEnergy (4.5)

where goalposts are used. Table (4.2) shows the energy intensity values of the countries as

reported by IEA, the goalposts, and the Energy Intensity Indicator as calculated by equation (4.5).


51

Table (4.2) Energy intensity values of the countries as reported by IEA, the goalposts, and the Energy Intensity Indicator Score as calculated by equation (4.5).

Country TPES/GDP

(PPP) (toe/thousand

2000 U$)

Energy Intensity Indicator

score Algeria 0.17 53 Egypt 0.2 35 Jordan 0.21 29 Lebanon 0.23 18 Libya 0.25 6 Morocco 0.09 100 Syria 0.26 0 M

ENA

Cou

ntrie

s

Tunisia 0.1 94 Brazil 0.15 65 Turkey 0.12 82 Spain 0.13 76 Greece 0.11 88 South Africa 0.25 6 Malaysia 0.24 12 India 0.14 71 China 0.2 35 USA 0.19 41 B

ench

mar

k C

ount

ries

Germany 0.14 71

Min 0.09 Goalposts Max 0.26

4.8.2 Non Electricity Final Indicator Since in the coming few decades (2-3 decades) world oil and natural gas will be depleted,

and humanity will have to relay almost solely on RE resources, and since RE resources only

produce electricity except biomass that can produce electricity and liquid fuel, then the difference

between the Total Final energy Consumed (TFC) and the total electricity consumption represents

the size of the needed RE electricity production in the country in the coming near future. The

larger this difference now, the larger the market for RE projects. With the good governance

assumption in mind, this makes this country more open for local and foreign investments in the

RE sector. Accordingly, this indicator value is calculated as follows:

Chapter 4

52

( )( )TWhConsumedyElectricitTotalTFCConsumedenergyFinalTotal

NEFVValueFinalyElectricitNon−

=)(

(4.6)

Using goalposts, the Non Electricity Final Indicator is calculated using the relation:

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×=

vlaueNEFVvlaueNEFVvlaueNEFVvaluecountryNEFVIndicatorFinalyElectricitNon

minmaxmin100 (4.7)

Since, the published free data of IEA for TFC and total electricity consumption of the year 2008

are given in ktoe and TWh respectively [44], a conversion factor 0.01163 is used to convert ktoe

to TWh. The columns of Table (4.3) give TFC (in ktoe), total electricity consumption (TWh), non

electricity final value as given by equation (4.6) in TWh, and the Non Electricity Final Indicator

Score as given by equation (4.7) respectively.

Table (4.3) TFC (in ktoe), total electricity consumption (TWh), non electricity final value as given by equation

(4.6), and the Non Electricity Final Indicator Score as given by equation (4.7).

Country Total Final Consumed

(TFC) (Ktoe/year)

Total electricity

Consumption (TWh)

TFC - Total Electricity

Consumption (TWh)

Non Electricity

Final Indicator Score

Algeria 23447 32.9 239.78861 2 Egypt 48300 116.21 445.519 3 Jordan 4437 12.13 39.47231 0 Lebanon 3561 9.51 31.90443 0 Libya 8951 24.61 79.49013 0 Morocco 11313 23.25 108.32019 1 Syria 12099 31.31 109.40137 1 M

ENA

Cou

ntrie

s

Tunisia 6576 13.41 63.06888 0 Brazil 195378 428.5 1843.74614 13 Turkey 74381 170.6 694.45103 5 Spain 99065 287.71 864.41595 6 Greece 21186 64.31 182.08318 1 South Africa 64087 232.23 513.10181 4 Malaysia 43246 94.28 408.67098 3 India 407562 645.25 4094.69606 30 China 1370726 3252.28 12689.26338 92 USA 1542245 4155.92 13780.38935 100 B

ench

mar

k C

ount

ries

Germany 235673 587.01 2153.86699 15



53

4.8.3 Electricity Consumption Growth Indicator The more the electricity consumption increases year after year in a country the more it

needs to build electricity generating projects. And with the good governance and knowledge

based decision assumptions, this means an expanding market for RE projects in the country.

Thus, the higher the electricity growth value, the higher the index score should be. It is worth

mentioning that what actually matters is the size of the growth in TWh rather than the growth rate

itself, as real data of 2008 shows that the electricity consumption growth rate of Lebanon is 23%

this only corresponds to 2.1 TWh of new electricity growth relative to the previous year, while

for China the growth rate is only 7% but it corresponds to a market growth of 215 TWh.

Definitely for an international investor point of view, the Chinese market is more attractive than

the Lebanese market. The Electricity Consumption Growth value and Indicator Score are

calculated at year 2008 using the relations:

( )

( )TWhinnConsumptioyElectricitinnConsumptioyElectricitECGVVlaueGrowthnConsumptioyElectricit

220072009 −

= (4.8)

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×

=

valueECGVvalueECGVvalueECGVvaluecountryECGV

IndicatorGrowthnConsumptioyElectricit

minmaxmin100

(4.9)

respectively with the help of goalposts values, where electricity consumption is defined as:

Electricity consumption =

Gross production + imports – exports – transmission/distribution losses

(4.10)

The electricity consumption in 2007 and 2009 are available from IEA Key World Energy

Statistics reports [45][46] respectively. Electricity consumptions in these 2 years, average

electricity consumption growth at year 2008, and the Electricity Consumption Growth Indicator

are shown in Table (4.4). It is obvious how – by far – the Chinese market is large compared to

other world players.

Chapter 4

54

Table (4.4) Electricity consumptions in 2007 and 2009, average electricity consumption growth at year 2008, and the Electricity Consumption Growth Indicator Score.

Country Electricity

consumption in 2007 (TWh)

Electricity consumption

in 2009 (TWh)

Average Electricity Consumption

Growth in 2008 (TWh)

Electricity Consumption

Growth Indicator Score

Algeria 30.56 33.94 1.69 1 Egypt 110.82 123.45 6.315 3 Jordan 11.18 12.49 0.655 0 Lebanon 8.97 13.14 2.085 1 Libya 23.88 26.12 1.12 1 Morocco 22.08 23.9 0.91 0 Syria 29.49 31.32 0.915 0 M

ENA

Cou

ntrie

s

Tunisia 12.77 13.69 0.46 0 Brazil 412.69 426.34 6.825 3 Turkey 163.35 165.09 0.87 0 Spain 282.54 275.74 0 0 Greece 62.99 62.51 0 0 South Africa 238.56 223.52 0 0 Malaysia 97.39 101 1.805 1 India 609.74 689.54 39.9 19 China 3072.67 3503.4 215.365 100 USA 4113.07 3961.56 0 0 B

ench

mar

k C

ount

ries

Germany 591.03 555.19 0 0

Min 0 Goalposts Max 215.365

It is worth mentioning that if the electricity consumption is decreasing, the average electricity

consumption growth (5th column from the left in Table (4.4)) is set to “0”.

4.8.4 Net Imported Electricity Indicator With the good governance assumption in mind, the more the net imported electricity to a

country, the more this country’s market is in need for RE projects to provide the necessary

electrical energy for domestic consumption. The net imported electricity value and Net Imported

Electricity Indicator are calculated with the relations:

( ) ( )ktoeyElectricitExportedyElectricitportedNIEVValueyElectricitportedNet −= ImIm(4.11)


55

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×=

valueNIEVvalueNIEVvalueNIEVvaluecountryNIEVIndicatoryElectricitportedNet

minmaxmin100Im (4.12)

respectively where goalposts values are used. Table (4.5) gives the net imported electricity values

in Ktoe as reported by IEA [47], and the Net Imported Electricity Indicator as calculated by

equation (4.12). It is important to note that for net electricity exporting countries (Algeria, Egypt,

Libya, Tunisia, Turkey, Spain, South Africa, Malaysia, China, and Germany), their net imported

electricity values are set to “0”, and thus the min goalpost is “0” as well.

Table (4.5) Net imported electricity values in Ktoe as reported by IEA [47], and the Net Imported Electricity

Indicator Score as calculated by equation (4.12).

Country

Net Imported Electricity

(Ktoe)


Indicator Score Algeria -4 0 Egypt -77 0 Jordan 19 1 Lebanon 48 1 Libya -4 0 Morocco 366 10 Syria 0 0 M

ENA

Cou

ntrie

s

Tunisia -1 0 Brazil 3630 100 Turkey -29 0 Spain -949 0 Greece 483 13 South Africa -309 0 Malaysia -41 0 India 762 21 China -1101 0 USA 2833 78 B

ench

mar

k C

ount

ries

Germany -1729 0

Min 0 Goalposts Max 3630

Chapter 4

56

4.8.5 Non-RE Electricity Production Indicator If most of the electricity in a country is produced by non-RE resources (oil, natural gas,

coal, nuclear), thus there is a need for RE projects to substitute these conventional resource to

comply with the knowledge-based decision and environmentally friendly government

assumptions. Alternatively, if all the electricity generated in a country solely comes from RE

resources, then the only possible growth in RE projects will come from the growth of electricity

consumption. So, countries with high non-RE electricity production values will have higher

scores in the final index and vise versa. The non RE electricity production value is calculated

using the relation:

( )

( )GWhEnergyNuclearbyoducedyElectricitCoalbyoducedyElectricit

GasNaturalbyoducedyElectricitOilbyoducedyElectricit

NREEPVValueoductionyElectricitRENon

PrPrPrPr

Pr

++

+=

(4.13)

After determining min and max goal posts, the Non RE Electricity Production Indicator Score is

determined using the relation:

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×=

valueNREEPVvalueNREEPVvalueNREEPVvaluecountryNREEPVIndicatoroductionyElectricitRENon

minmaxmin100Pr

(4.14)

The breakdown of electricity generation by different primary energy is given by IEA statistical

data for year 2008 [48]. Table (4.6) gives the IEA reported data for non RE electricity production

values in (GWh) and the Non RE Electricity Production Indicator Score as well.


57

Table (4.6) Non RE electricity production values in (GWh) and the Non RE Electricity Production Indicator Score.

Country

NON-RE Electricity

Production (GWh)

NON RE Electricity Production

Indicator Score Algeria 39953 1 Egypt 115427 3 Jordan 13764 0 Lebanon 10253 0 Libya 28667 0 Morocco 19597 0 Syria 38151 1 M

ENA

Cou

ntrie

s

Tunisia 15234 0 Brazil 73100 2 Turkey 163920 4 Spain 248509 6 Greece 57143 1 South Africa 253946 6 Malaysia 89932 2 India 700098 18 China 2856113 72 USA 3938765 100 B

ench

mar

k C

ount

ries

Germany 536038 13


4.8.6 Oil and Gas Insecurity Indicator Though according to environmentally friendly government assumption all governments

should switch to RE resources, this switch is much more urgent for countries that don’t have

much oil and natural gas reserve to meet their energy needs in the near future than those countries

with much reserve. Large or small reserve of a country is a relative number and must be

measured with respect to its consumption. A simple measure of the country’s supply security of

oil and gas is defined by the number of years the current proven reserve can meet the country

needs assuming the same annual consumption in the future. This is given by the relation:

( ) ( ) ( )( ) ( )Years

GasOilnConsumptioAnnualCurrentGasOilserveovenGasOilYoSSupplyofYears

//RePr/ =

(4.15)

Chapter 4

58

Though this definition doesn’t take into account the futuristic national demand growth of oil and

gas consumption, nor the peak production theory where production will be declining after

reaching its maximum point [49], this simple measure gives an indication of the country’s

insecurity regarding oil and natural gas supply. The Oil/Gas Insecurity Indicator Score is

calculated such that countries with long period of supply will have lower scores and vice versa

using the relation:

( ) ( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−−×=

valueYoSvalueYoSvalueYoSvaluecountryYoS

GasOilIndicatorurityInGasOilGasOil

GasOilGasOil

minmaxmin

1100/sec//

//

(4.16)

Unfortunately data of oil and natural gas reserve and consumption are not available at

IEA website. Though the British Petroleum excel sheet of world historical energy statistical data

is comprehensive in covering all primary energy resources (consumption, production, and

reserve), it doesn’t have the required data for each of the 18 countries under study [51]. The oil

consumption and reserve data, and the natural gas consumption and reserve data are taken from

CIA world fact book [52][53][54][55] respectively. Unfortunately the only available data are for

year 2010, not 2008 like those from IEA. Table (4.7) and (4.8) list the consumption and reserve

values, the year of supply and Insecurity Indicator Score for oil and natural gas respectively.


59

Table (4.7) Consumption and reserve values, the year of supply and Insecurity Indicator Scores for oil.

Oil (data of 2010)

Country Oil Reserve 2010 (Billion

Barrel)

Oil Consumption 2010 (Billion Barrel/year)

Years of Supply (year)

Oil Insecurity Indicator

Score Algeria 12.2000 0.1139 50 0 Egypt 4.4000 0.2701 16.29026 67 Jordan 0.0010 0.0358 0.027956 100 Lebanon 0.0000 0.0387 0 100 Libya 46.4200 0.1055 50 0 Morocco 0.0007 0.0763 0.008914 100 Syria 2.5000 0.1066 23.45656 53 M

ENA

Cou

ntrie

s

Tunisia 0.4250 0.0307 13.86171 72 Brazil 12.8600 0.9687 13.27539 73 Turkey 0.2704 0.2359 1.146251 98 Spain 0.1500 0.5260 0.28519 99 Greece 0.0100 0.1355 0.073787 100 South Africa 0.0150 0.2018 0.074314 100 Malaysia 4.0000 0.2048 19.53459 61 India 5.6820 1.1614 4.892245 90 China 20.3500 3.3540 6.067409 88 USA 20.6800 6.9898 2.958618 94 B

ench

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k C

ount

ries

Germany 0.2760 0.9107 0.303072 99


Chapter 4

60

Table (4.8) Consumption and reserve values, the year of supply and Insecurity Indicator Scores for natural gas.

Gas (data of 2010)

Country Gas Reserve 2010 (Trillion Cubic Meter)

Gas Consumption 2010 (Trillion

Cubic Meter/year)

Years of Supply (year)

Gas Insecurity Indicator

Score Algeria 4.502 0.02986 50 0 Egypt 2.186 0.04437 49.26752 1 Jordan 0.006031 0.0031 1.945484 96 Lebanon 0 0 50 0 Libya 1.548 0.00601 50 0 Morocco 0.001444 0.00056 2.578571 95 Syria 0.2407 0.0071 33.90141 32 M

ENA

Cou

ntrie

s

Tunisia 0.06513 0.00485 13.42887 73 Brazil 0.3664 0.02513 14.58018 71 Turkey 0.006173 0.03812 0.161936 100 Spain 0.002548 0.03582 0.071133 100 Greece 0.0009911 0.003824 0.259179 99 South Africa 0.00002716 0.0054 0.00503 100 Malaysia 2.35 0.02907 50 0 India 1.074 0.06495 16.5358 67 China 3.03 0.1067 28.39738 43 USA 7.716 0.6833 11.29226 77 B

ench

mar

k C

ount

ries

Germany 0.1756 0.0995 1.764824 96

Min 0.00503 Goalposts Max 50

In case of oil, years of supply values according to equation (4.15) for Algeria, Iraq, Libya,

Sudan, and Yemen are 107, 454, 440,140, and 52 years respectively. Since this is a very long

period, and since most of their oil production is directed to exportation, and since collectively

world oil production will – most probably – be depleted long before that [50], these countries

years of supply value are set at 50 years, and thus the max goalpost as well. In case of natural gas,

the same happens for Algeria, Libya, and Malaysia. Concerning Lebanon, since it has zero

consumption of natural gas and zero reserve, and since equation (4.15) doesn’t apply, it is Gas

Insecurity Indicator Score is set to zero as it actually doesn’t depend on natural gas at all.


61

4.8.7 RE Target Indicator This indicator measures the expected annual new RE production of electricity based on

country’s announced target share of electricity to come from RE sources at a specific target year.

The larger this expected annual new RE production of electricity, the larger the RE market for

new players, and thus the more attractive it is. For example Egypt announced that 20% of its

electricity production will come from RE sources at year 2020. According to this mandate, it is

expected that – on average – certain amount of electricity shall be generated from new RE

projects annually. It is worth noting that the target percent (20% in case of Egypt) is based on the

target year electricity consumption (2020 in case of Egypt) and not on the electricity consumption

in the year when the index is calculated. Thus to be able to estimate the amount of electricity that

needs to be generated by RE in the target year, one has to take into account the growth rate of

electricity consumption till the target year.

To evaluate the amount of expected annual RE production of electricity, let’s define the

following parameters:

i: Initial year when index is evaluated

f: Final RE target year announced by the country

g: Average electricity consumption growth rate

Ei: Electricity consumption in initial year (i)

Ef: Expected electricity consumption in the final target year (f)

T: The target ratio of electricity production by RE announced by the county

REi: The total amount of electricity generated by RE resources in the initial year

REf: The amount of the needed electricity to be generated by RE resources in the target year.

According to these definitions, and assuming that the existing average electricity consumption

growth rate (g) will stay the same in the future from the initial year (i) till the final target year (f),

the electricity consumption at the final target year (Ef) will be given by:

( ) ifif gEE −+= 1 (4.17)

Since the amount of electricity generated by RE in the target year (REf) is related to the expected

electricity consumption in the final year (Ef) through:

ff ETRE ×= (4.18)

Chapter 4

62

Substituting from (4.17) into (4.18)

( ) ifif gETRE −+×=∴ 1 (4.19)

Thus, the expected annual new RE production of electricity during the remaining period (f-i),

assuming a linear expansion of RE electricity generating as shown in Figure (4.2), is given by:

ifRERE

productionREnewannualExpected if

−

−= (4.20)

Substituting from (4.19) into (4.20), the expected annual new RE production, which is used as the

measure for RE target indicator value, becomes

( ) ( )if

REgETEAREPoductionREnewAnnualExpected iif

i

−−+×

=∴−1Pr (4.21)

And using goalposts, the RE Target Indicator is calculated from

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×=

valueEAREPvalueEAREPvalueEAREPvaluecountryEAREPIndicatoretTRE

minmaxmin100arg (4.22)

While the initial year (i) is set to 2008, the rest of the data needed to apply equation (4.21) are

shown in Table (4.9) and are gathered from different resources as follow:

Figure (4.2) Illustration of the electricity consumption growth and linear expansion of electricity production

from RE resources to meet the announced target share at the target year.

f i

E

E f

RE i

RE f

Year

i

E f

Share of RE in final energy

Electrical consumption path

Renewable Energy path


63

• Most of the target year (f) and RE share percentage (T) are reported in Table (R8) in

REN21-2011 global status report [56]. If two target years with two target percentages are

reported for a country, the nearest year and its corresponding target percentage are taken in

evaluating the indicator value for this country. The reason for this choice is the assumption

that the electricity consumption growth rate is taken as a fixed value over the whole period,

which is more anticipated for a short futuristic period of time than for a longer one. In

addition, meeting a nearest mandate is more obligatory for the country’s government than a

farther one, which follows the nature of the countries’ governments’ assumptions.

• Target year and RE share percentage for Jordan, Lebanon, Syria, Greece, and Malaysia are

not available from the REN21-2011 report, and are collected from alternative sources

[57][58][59][60][61] respectively.

• Since USA is a federation country where each state puts its own plans and legislation, a

total summation for RE target in USA was difficult to evaluate and thus is set to zero.

Accordingly, one expects that the final total score and ranking of USA will not be

adequately representing its situation. Thus, it is recommended for future work that each

USA state be dealt with as a separate country by itself.

• The electricity growth rate (g) is calculated from the electricity consumption of 2007, 2009

[45][46], normalized to the consumption of 2007 according to the equation

( )

( )%20072

20072009innConsumptioyElectricit

innConsumptioyElectricitinnConsumptioyElectricitgRateGrowthnConsumptioyElectricit

×−=

(4.23)

• The total electricity consumption (Ei) and the electricity generated by RE resources (REi)

in the initial year data are brought from IEA available free data of year 2008 [48].

Chapter 4

64

Table (4.9) All required data for the calculation of RE Target Indicator Score.

Country Target Year

(f)

Electricity growth rate (g)

(%)

Target Percentage

(T) (%)

Electricity consumption in initial year

(Ei) (GWh)

Electricity generated by RE resources in the initial year (REi)

(GWh)

Expected Annual new

RE Production

(GWh)

RE Target

Indicator Score

Algeria 2017 5.53 5 40236 283 331.4076347 3 Egypt 2020 5.70 20 131040 15613 2945.846314 30 Jordan 2015 5.86 7 13838 74 195.5667989 2 Lebanon 2020 23.24 12 10626 373 1273.781293 13 Libya 2020 4.69 10 28667 0 414.0656797 4 Morocco 2020 4.12 20 21268 1671 436.2617936 4 Syria 2030 3.10 10 41023 2872 234.6706421 2 M

ENA

Cou

ntrie

s

Tunisia 2016 3.60 16 15311 77 396.8044107 4 Brazil 2020 1.65 16 463369 390269 0 0 Turkey 2023 0.53 30 198418 34498 1997.621585 20 Spain 2020 0.00 40 313746 65237 5021.783333 51 Greece 2020 0.00 18 63749 6606 405.735 4 South Africa 2013 0.00 4 258291 4345 1197.328 12 Malaysia 2020 1.85 11 97392 7460 491.1912372 5 India 2012 6.54 10 830126 130028 0 0 China 2020 7.01 3 3456910 600797 0 0 USA x 0.00 4369099 430334 0 0 B

ench

mar

k C

ount

ries

Germany 2030 0.00 50 637232 101194 9882.818182 100


4.9 Financial and Environmental Indicators (2 indicators) 4.9.1 Financial Indicator This indicator aims to measure the existence of the different means of financial incentives

to support, encourage and enable the expansion of RE market. REN21-2011 global status report

categorizes these means into 3 groups as follows [56]:

• Regulatory Policies

i. Quota Obligation

ii. Net Metering

iii. Tradable REC

• Fiscal Incentives

i. Capital Subsidy, Grant, Rebate


65

ii. Investment/prod. Tax Cr.

iii. Reduction in sales, energy, Tax

iv. Energy Production Payment

• Public Financing

i. Public Investments Loans, Grants

ii. Public Competitive Bidding

By nature, the data covered in this indicator includes legislative measures, laws, and

decrees that shapes the policies of the countries in favor of expanding RE production. Though,

the original REN21-2011 report includes 3 more policies under the Regulatory category, namely:

Feed-in Tariff, Biofuels Obligation, and Heat Obligation, they are not included here. Since the

Feed-in Tariff changes from a technology to another (a country with very good wind resources

and very low solar resources most probably will issue a Feed-in Tariff for wind energy project

but not for CSP ones), it is more appropriate to separately include it under technology specific

indicators (CSP in this case), which has been done as will be shown later on in this chapter.

Biofuels Obligation is also technology specific, so it was removed from the list, and to be added

separately when creating a Biofuel Market Competence Index. Finally, since the MENA region is

relatively very warm across the year compared to the benchmark countries, it was removed from

the list, so the final index will not be biased.

Table (2) of the REN21-2011 report qualitatively gives a status for the country regarding

these policies only by checking “ ” or leaving it blank for each policy [56]. This qualitative

measure is converted into a quantitative one by giving a score of “5” for a checked item, and “0”

for a blank item. Since there are 9 policy items, thus the total score of the country is given by

∑×=9

145100

iItemPolicyIndicatorFinancial (4.24)

Table (4.10) gives the countries’ scores for the different policies and the Financial Indicator

Score according to equation (4.24) where countries with “x” sign (Lebanon, Libya, and Syria)

indicate that they are not listed in REN21-2011 report, and thus their values are set to zero.

Chapter 4

66

Table (4.10) Countries’ scores for the different policies and the Financial Indicator Score according to equation (4.24).

Regulatory Policies Fiscal Incentives Public Financing

Country Quota Obligation

Net Metering

Tradable REC

Capital Subsidy, Grant, Rebate

Investment/ prod. Tax Cr.

Reduction in sales,

energy,…Tax

Energy Production Payment

Public Investments

Loans, Grants

Public Competitive

Bidding

Financial Indicator

Algeria 0 0 0 0 0 0 0 0 0 0 Egypt 0 0 0 5 0 5 0 5 5 44 Jordan 0 5 0 0 0 5 0 0 0 22 Lebanon x x x x x x x x x 0 Libya x x x x x x x x x 0 Morocco 0 0 0 0 0 0 0 5 0 11 Syria x x x x x x x x x 0 M

ENA

Cou

ntrie

s

Tunisia 0 0 0 5 0 5 0 5 0 33 Brazil 0 0 0 0 0 5 0 5 5 33 Turkey 0 0 0 0 0 0 0 0 0 0 Spain 0 0 0 0 5 5 0 5 0 33 Greece 0 5 0 5 5 0 0 5 0 44 South Africa 0 0 5 5 0 0 0 0 5 33 Malaysia 0 0 0 0 0 0 0 5 0 11 India 5 0 5 5 5 5 0 5 5 78 China 5 0 0 5 0 0 5 5 5 56 USA 0 0 5 5 5 5 5 5 5 78 B

ench

mar

k C

ount

ries

Germany 0 0 0 5 5 5 0 5 0 44


67

4.9.2 Environmental Indicator Based on the good governance and environmentally friendly government assumption,

countries with large CO2 emission per capita (ton CO2/capita) will have higher scores (and vice

versa) as they will be willing more to have more RE projects. Fortunately this number is directly

reported by IEA available free data of 2008 [44]. The Environmental Indicator is thus calculated

with the relation:

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×=

valueEmissionCOvalueEmissionCOvalueEmissionCOvaluecountryEmissionCO

IndicatortalEnvironmenminmax

min100

22

22

(4.25)

Table (4.11) lists the CO2 emission per capita (ton CO2/capita) and the Environmental Indicator

Score.

Table (4.11) CO2 emission per capita (ton CO2/capita) and the Environmental Indicator Score.

Country CO2/population

(t CO2/capita) Environmental Indicator Score

Algeria 2.56 8 Egypt 2.13 5 Jordan 3.12 11 Lebanon 3.68 14 Libya 7.15 34 Morocco 1.35 1 Syria 2.56 8 M

ENA

Cou

ntrie

s

Tunisia 2.01 4 Brazil 1.9 4 Turkey 3.71 14 Spain 6.97 33 Greece 8.31 41 South Africa 6.93 33 Malaysia 6.7 32 India 1.25 0 China 4.91 21 USA 18.38 100 B

ench

mar

k C

ount

ries

Germany 9.79 50


Chapter 4

68

4.10 Logarithmic Scale of Some Indicators For 5 of the developed indicators, it is obvious that there is huge difference among the

scores of the difference countries. These indicators are:

• Non Electricity Final Indicator

• Electricity Consumption Growth Indicator

• Net Imported Electricity Indicator

• Non-RE Electricity Production Indicator

• RE Target Indicator

By checking each indicator values (e.g. Table (4.3) for Non Electricity Final Indicator),

one notices that the ratio between the max and min values (the goalposts) is few orders of

magnitude (e,g. max value ≈13780 and min value ≈ 32 for Non Electricity Final Indicator). For

the first 4 indicators, the size of the industrial base, population, and energy demand of USA,

China, and Brazil make their indicators values much greater than most of all other countries. In

case of the RE Target Indicator, the very ambitious plan of Germany overshadows what other

countries are planning. This huge biasing in the indicators values and scores results in certain

countries dominating the index. In order to reduce the difference between the countries values

and scores while still appreciate the countries with greater scores with respect to other countries,

the natural logarithm of the indicators values are taken, then new goalposts are determined, and

finally the indicators scores are calculated using the same equations shown previously. Tables

(4.12) to (4.16) shows the new values and scores for the 5 mentioned indicators.


69

Table (4.12) Non electricity final value in TWh as given by equation (4.6), its natural logarithm values, and the Non Electricity Final Indicator Score using natural logarithm values.

Country

TFC - Total Electricity

Consumption (TWh)

TFC - Total Electricity Consumption

[Ln scale]

Non Electricity Final Indicator

Score [Ln scale]

Algeria 239.78861 5.4798 33 Egypt 445.519 6.0992 43 Jordan 39.47231 3.6756 4 Lebanon 31.90443 3.4627 0 Libya 79.49013 4.3756 15 Morocco 108.32019 4.6851 20 Syria 109.40137 4.6950 20 M

ENA

Cou

ntrie

s

Tunisia 63.06888 4.1442 11 Brazil 1843.74614 7.5196 67 Turkey 694.45103 6.5431 51 Spain 864.41595 6.7621 54 Greece 182.08318 5.2045 29 South Africa 513.10181 6.2405 46 Malaysia 408.67098 6.0129 42 India 4094.69606 8.3174 80 China 12689.26338 9.4485 99 USA 13780.38935 9.5310 100 B

ench

mar

k C

ount

ries

Germany 2153.86699 7.6750 69


Chapter 4

70

Table (4.13) Average electricity consumption growth at year 2008, its natural logarithm value, and the Electricity Consumption Growth Indicator Score using natural logarithm values.

Country Average Electricity

Consumption Growth in 2008 (TWh)

Average Electricity Consumption Growth

in 2008 [Ln scale]


Growth Indicator Score [Ln scale]


ENA

Cou

ntrie

s

Tunisia 0.46 6.1312 50 Brazil 6.825 8.8283 72 Turkey 0.87 6.7685 55 Spain 0 0.0000 0 Greece 0 0.0000 0 South Africa 0 0.0000 0 Malaysia 1.805 7.4983 61 India 39.9 10.5941 86 China 215.365 12.2801 100 USA 0 0.0000 0 B

ench

mar

k C

ount

ries

Germany 0 0.0000 0



71

Table (4.14) Net imported electricity values in Ktoe as reported by IEA [47], its natural logarithm value, and the Net Imported Electricity Indicator Score using natural logarithm values.

Country


(Ktoe)

Net Imported Electricity [Ln scale]

Net Imported Electricity Indicator

Score [Ln scale]

Algeria -4 0 0 Egypt -77 0 0 Jordan 19 2.9444 36 Lebanon 48 3.8712 47 Libya -4 0 0 Morocco 366 5.9026 72 Syria 0 0 0 M

ENA

Cou

ntrie

s

Tunisia -1 0 0 Brazil 3630 8.1970 100 Turkey -29 0 0 Spain -949 0 0 Greece 483 6.1800 75 South Africa -309 0 0 Malaysia -41 0 0 India 762 6.6359 81 China -1101 0 0 USA 2833 7.9491 97 B

ench

mar

k C

ount

ries

Germany -1729 0 0


Chapter 4

72

Table (4.15) Non RE electricity production values in (GWh), its natural logarithm value, and the Non RE Electricity Production Indicator Score using natural logarithm values.

Country

NON-RE Electricity

Production (GWh)

NON-RE Electricity Production [Ln scale]

NON RE Electricity Production Indicator

Score [Ln scale]

Algeria 39953 10.5955 23 Egypt 115427 11.6564 41 Jordan 13764 9.5298 5 Lebanon 10253 9.2353 0 Libya 28667 10.2635 17 Morocco 19597 9.8831 11 Syria 38151 10.5493 22 M

ENA

Cou

ntrie

s

Tunisia 15234 9.6313 7 Brazil 73100 11.1996 33 Turkey 163920 12.0071 47 Spain 248509 12.4232 54 Greece 57143 10.9533 29 South Africa 253946 12.4449 54 Malaysia 89932 11.4068 36 India 700098 13.4590 71 China 2856113 14.8650 95 USA 3938765 15.1864 100 B

ench

mar

k C

ount

ries

Germany 536038 13.1920 66



73

Table (4.16) Expected Annual new RE Production (GWh), its natural logarithm value, and RE Target Indicator Score using natural logarithm values.

Country

Expected Annual new RE

Production (GWh)

Expected Annual new RE Production

[Ln scale]

RE Target Indicator Score

[Ln scale]


ENA

Cou

ntrie

s

Tunisia 396.8044107 5.5977 61 Brazil 0 0.0000 0 Turkey 1997.621585 7.5997 83 Spain 5021.783333 8.5215 93 Greece 405.735 6.0057 65 South Africa 1197.328 7.0878 77 Malaysia 491.1912372 6.1968 67 India 0 0.0000 0 China 0 0.0000 0 USA 0 0.0000 0 B

ench

mar

k C

ount

ries

Germany 9882.818182 9.1986 100


4.11 Technology Specific Indicators While in the previous 3 sections the detailed designs, values, and scores of the general

indicators were developed, in the following subsections the designs of 5 technology specific

indicators will be presented; namely:

• Manufacturability Indicator

• Economic Potential Indicator

• Institute Indicator

• Technology Target Indicator

• Feed-in Tariff Indicator

Chapter 4

74

In Chapter 5, these indicators will be applied to CSP technology, their values and scores will be

determined, and finally a CSP Market Competence Index will be calculated based on the

methodology presented in section (4.12).

4.11.1 Manufacturability Indicator A country with a very good manufacturing base for the required components and

equipment of the technology under study is more attractive for investors as the economies of the

project becomes more favorable compared to other countries that lack these abilities. In order to

reach an objective method to calculate the Manufacturability Indicator, the following steps are

proposed:

• First, a breakdown of a RE project using the technology understudy to its basic industries is

performed. Taking the CSP technology as an example, a CSP project mainly involves the

glass, electrical and electronic, and steel industries.

• Second, each involved industry is broken down to its smaller ingredients.

• Third, a point system questionnaire is designed to quantify the level of each ingredient

component of each involved industry.

• Fourth, the points of each industry ingredients are added together to give a score for the

industry.

• Fifth, the scores of the industries are added together with weights that reflects the cost share

of this industry in the project.

The point system is designed such that the final indicator should be backward with “0” meaning

least favored, and “100” meaning most favored. Because each RE technology involves different

industries, a general questionnaire can not be designed. A detailed point system questionnaire for

CSP technology is detailed in Chapter 5 and applied to calculate CSP Manufacturability Index.

4.11.2 Economic Potential Indicator The larger the economic potential of a country for the specific technology understudy, the

larger the market available of that technology, and thus the more attractive it is from the business

point of view. Thus, the Economic Potential Indicator is designed to be proportional to the

existing economic potential, normally reported in TWh/y, according to the relation:


75

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×

=

valuePotentialEconomicvaluePotentialEconomicvaluePotentialEconomicvaluecountryPotentialEconomic

IndicatorPotentialEconomic

minmaxmin100

(4.26)

4.11.3 Institute Indicator The existence of an institution in a country dedicated to a specific RE technology plays an

important role in pushing that technology in the country. Such an institute becomes the driving

force in writing legislations, proposing certain measures, managing bidding for new projects,

commissioning of finished projects, and so on. A questionnaire based method is devised to

quantify the situation of each country. The questionnaire is based on answering the question

“What is the level of institution specialty that exists in the country regarding this specific

RE technology” where each answer is assigned a different score value as shown in Table (4.17)

Table (4.17) Answers of the questionnaire for the Institute Indicator.

Points Answer

0 No, there isn’t an institution for that specific technology

20 Yes, an institution exists but it is part of another one or a national research center

60 There is an institution that supports all RE technologies

100 Yes, there is an institution that solely supports this specific technology

4.11.4 Technology Target Indicator In addition to the general RE target that different countries set as percentage of total

electricity generated at a certain year, many countries make more detailed targets by setting a

mandate for the installed capacity (in MW) of the different RE technologies (PV, CSP, Wind,

Biomass, Hydro) at a certain year. The closer the target year and the larger the difference

between the target and current installed capacity of the technology understudy, the larger the

annual installed capacity that must be added and thus the larger the market for this technology.

Using the following definitions:

i: Initial year when index is evaluated

f: Final target year announced by the country

ICi: The amount of current installed capacity in the initial year.

Chapter 4

76

ICf: The amount of target installed capacity in the final target year.

the value of the expected annual new installed CSP capacity is given by

( ) yearMWifICIC

EAICCapacityInstallednewAnnualExpected if /−

−= (4.27)

With the use of goalposts, the Technology Target Indicator is calculated as:

( ) ( )( ) ( ) ⎟⎟

⎠

⎞⎜⎜⎝

⎛−

−×=

valueEAICvalueEAICvalueEAICvaluecountryEAICIndicatoretTyTechno

minmaxmin100arglog (4.28)

4.11.5 Feed-in Tariff Indicator Historically, the introduction of Feed-in Tariff has shown a booming effect for the

targeted RE technology, for example the Feed-in Tariff for wind, PV, biomass in Germany and

for CSP in Spain [62][63][64] respectively. To be able to quantitatively measure the relative

strength of Feed-in Tariff law in different countries, a quantitative criterion for comparison is

devised. At maximum, Feed-in Tariff laws addresses the following items:

• Maximum allowed installed capacity

• Purchase obligation period

• Inflation recovery

• Maximum allowance of hybrid power plants

• Tariff price compared to local electricity price

Based on these merits, a point system questionnaire is devised to assess the attractiveness of the

law in each country. The point system ranges form “0” (for worst evaluation) to “5” (best

evaluation) for each item, and the total score for Feed-in Tariff Indicator will be given by equal

weight summation of the scores of these 5 merits (such that the maximum will be 100 points)

according to:

∑=

×=5

14

iiMeritTariffinFeedIndicatorTariffinFeed (4.29)


77

4.12 The Methodology of the Index After selecting 13 general indicators and 5 technology specific indicators to represent the

strength of the market competence regarding specific technology, the aim now is to develop a

criterion to combine the scores of these indicators to give one final score to represent the market

competence of the technology understudy. This criterion is developed as follows:

• Countries that have low economic potential or have set small target to implement that

technology shall not be competent regardless whether they have very good situation

regarding all other indicators (general or technology specific) or not. Thus these 2

technology specific indicators should – collectively – have the ability to shape the score of

the final index up or down according to their collective scores. A way to mix these two

indicators together is to simply take their arithmetic average with equal weights as X1

2

arglog1

IndicatoretTyTechnoIndicatorPotentialEconomicX += (4.30)

• The 3 remaining technology specific indicators are averaged together with equal weights to

give X2

32IndicatorTariffinFeedIndicatorInstituteIndicatorabilityManufacturX ++

= (4.31)

• Similarly, the 13 general indicators are averaged with equal weights to give X3

∑=13

13 13

1 IndicatorsGeneralAllX (4.32)

• Now X2 and X3 are averaged together using weighting parameters to give the proper

representation for each of the 16 involved indicators. Thus, X4 is defined as

( ) 324 1 XXX αα −+= (4.33)

where the weighting parameter α should satisfy the inequality

10 ≤≤ α (4.34)

• In order to give the Economic Potential Indicator and Technology Target Indicator the pre-

mentioned preference, the final index is calculated as the geometric average between X1

and X4

( )[ ]321 1log XXXIndexCompetenceMarketSpecificyTechno αα −+×= (4.35)

Chapter 4

78

• Finally, after developing an index score for all countries for each technology separately

using (4.35), one can then calculate an index that represent the overall RE Market

Competence Index using weighting average:

[ ]

⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢

⎣

⎡

×

=

IndexCompetenceMarketTidalandWaveIndexCompetenceMarketGeothermal

IndexCompetenceMarketHydroScaleSmallIndexCompetenceMarketBiomass

IndexCompetenceMarketCSPIndexCompetenceMarketPV

IndexCompetenceMarketOffshoreWindIndexCompetenceMarketOnshoreWind

IndexCompetenceMarketRE

87654321 γγγγγγγγ

(4.36)

where the weighting parameters γi ,i=1:8 correspond to Wind Onshore, Wind Offshore, PV,

CSP, Biomass, Small Scale Hydro, Geothermal, and Wave and Tidal Market competence

Index respectively, where they have to satisfy the relation:

18

1=∑

=iiγ (4.37)

The weighting parameters values can be the same as the share of the corresponding

technology in the world market as it has been proposed by RE Countries Attractiveness

Index and shown in chapter 3 (γ1=0.48, γ2=0.17, γ3=0.13, γ4=0.05, γ5=0.10, γ6=0.03,

γ7=0.02, γ8=0.02).

For the technology specific index, given by (4.35), to give at least an equal or stronger

weight for each of the 3 technology specific indicators over each of the general indicators, and

referring to equations (4.31), (4.32), and (4.33), thus

131

3αα −

≥ (4.38)

Solving for α, one gets

1875.0163

=≥α (4.39)


79

Thus (for simplicity) starting from α=0.2 and higher, each of the 3 technology specific indicators

represented by X2 will have stronger effect than each individual general indicator, and vice versa.

More generally, if β represent the ratio between the weight of one of the 3 technology

specific indicators to one of the general indicators, thus

131

3αβα −

= (4.40)

Solving for α, one gets

ββα313

3+

= (4.41)

Equation (4.41) is plotted in Figure (4.3) which helps visualize the relation between the

ratio between the average of the 3 technology specific indicators,X2, and the average of all

general indicators,X3, (i.e. the parameter α), and the ratio between individual indicators from the

two groups (i.e. the parameter β).

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.1 1 10 100 1000

β

α

Figure (4.3) α-β relation.

Chapter 4

80

4.13 Critique and Possible Improvements Though 18 different indicators have been introduced in this work, and a methodology was

developed, it is believed that there is still room for improvement.

• Regarding the indicators, there are still some other important indicators that are believed to

give more complete picture about the overall situation of the energy sector and the market

competence regarding RE projects in the country. Such indicators are:

Country commitment to meet the RE target

Fossil fuel subsidy

Energy taxation

Details of financing and grants

Grid code existence

Grid connectivity

Effect on labor sector

Adding these indicators will bring this index closer to the RE Countries Attractiveness Index.

• X1, X2, and X3 given by (4.30), (4.31), and (4.32) were simply defined by using arithmetic

average with equal weights, despite the fact that each individual indicator in an average may,

in reality, have a different effect. For example, in calculating X2, most probably the Feed-in

Tariff has a stronger effect in pushing RE projects than the existence of good

manufacturability base, than the existence of an institute that support the technology.

Accordingly, X2 should be calculated using weighted average. The weighting parameters for

the 3 indicators can be determined with a feedback questionnaire from RE experts, RE system

manufacturers, and energy investors. This fine details, though could not be achieved in this

work, it could be persuaded in the future to refine the whole index.

4.14 Conclusion This chapter includes a new methodology to derive an index that quantitatively,

objectively, and analytically describes the competence of renewable energy market of a country.

The proposed index, Renewable Energy Market Competence Index, is composed of two types

of indicators. The first group is composed of 13 indicators that describe the general

characteristics of the country regarding political, social, economical situation in addition to the


81

conditions of the energy sector, fossil fuel sustainability, CO2 emission level of the country, and

the available financing for renewable energy projects. The second group is composed of 5

technology specific indicators that change from a technology to another and cover the

manufacturability of that technology in the country, economic potential, target, institution, and

the existence of Feed-in Tariff for that technology. This index score is calculated through a

simple mathematical formula that involves both weighted arithmetic average and geometric

average.

Chapter 5

82

Chapter 5

A CSP Market Competence Index

5.1 Introduction In the previous chapter a methodology for Market Competence Index for each RE

technology and a combined RE Market Competence Index were developed, equations (4.35) and

(4.37) respectively. Here, the proposed methodology is applied to calculate CSP Market

Competence Index for the countries understudy. Since the technology specific indicators were

described in Chapter 4, here the starting point is to calculate these indicators for CSP technology

in section 2. This is followed in section 3 by calculating the CSP Market Competence Index,

investigating the effect of varying the weighting parameters (α). Then in section 4 the scores and

ranking of the CSP Market Competence Index is compared with the CSP component of the

Countries Attractiveness Index.

5.2 CSP Technology Indicators 5.2.1 CSP Manufacturability Indicator Applying the proposed methodology for the manufacturability indicator described in

section (4.11.1) on CSP technology suggests the following:

• First, the CSP technology is broken down into its main industries; namely [65]:

Glass industry

Electronic and electrical equipment

Steel industry

• Second, each industry is broken down into more detailed ingredients as shown in the first 2

rows in Table (5.1).

• Third, a point system questionnaire is designed to cover all possible situations of the

countries with minimum score “0” and maximum score “5” as shown in rest of Table (5.1).


83

• Fourth, analyzing the situation of each country regarding each industry ingredient gives the

data shown in Table (5.2) where an averaging of every industry’s ingredients is shown in

the right hand side of the table (the average is out of 5).

• Fifth, to calculate the final CSP Manufacturability Indicator Score, the average score of

each industry category is weighted according to the industry contribution in the CSP

project. One can infer from the estimation of investment cost of an Andasol-like power

plant that is presented in Table (1.7) in [65] that the relative weightings among the glass,

electrical and electronics, and steel industries are 35%:15%:50% respectively. Accordingly,

the CSP Manufacturability Indicator is determined with the relation:

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

×+×+

××=

ScoreIndustryStealAverageScoreIndustrysElectronicAverage

ScoreIndustryGlassAverageScoreIndicaorabilityManufacturCSP

5.015.0

35.020

(4.26)

The most right hand side column gives the final value, out of 100, for CSP

Manufacturability Index. The details of each individual industry ingredient score is given in

Appendix D.

Chapter 5

84

Table (5.1) Point system of the CSP Manufacturability questionnaire-based Indicator. Glass Electronic and Electrical Steel Industry

Points Local

availability of raw

material

Float glass manufacturing

capacity

Glass Transformation

(mirror)

Glass bending

Glass coating

Cabling industry

Electronic and

electrical industries

R&D Steel

production capacity

R&D

Piping and

insulation capacity

0 NA

Far less than the current

needs of the country by more

than four 150,000 T/y plant each

NA NA NA NA NA <10 NA NA NA

1 Available but not

for CSP technology

10<=x<25 10<=x<25

2

Exist but not for

high power sector

25<=x<50 25<=x<50

3

More than the current national consumption by

less than 150,000 T/y

Available for CSP technology

with less than national demand

Available but not for

CSP application

Assembly lines of

electronic equipments

50<=x<75

Just sufficient

for current national demand

50<=x<75

Just sufficient

for current national demand

4

Exist for high power sector but capacity is less than demand

75<=x<100 75<=x<100

5 Yes

More than the current national consumption by

more than 150,000 T/y

Available for CSP technology with more than

national demand

Available

Available for CSP

application and with

necessary accuracy

Exist for high power sector but capacity is more than demand

(exporting)

Highly advanced (from SC

industry up to devices

and equipment)

>=100 More than national needs

>=100 More than national needs


85

Table (5.2) CSP questionnaire values, and CSP Manufacturability Indicator Score.

Glass Electronic and Electrical Steel Industry

Country Local

availability of raw

material

Float glass manufac-

turing capacity

Glass Transfo-rmation (mirror)

Glass bending

Glass coating

Cabling industry

Electronic and

electrical industries

R&D Steel

production capacity

R&D

Piping and

insulation capacity

Average Glass

Average electronics

Average steel

CSP Manufac-turability Indicator

Score

Algeria 5 5 0 0 0 0 3 0 0.17 0 5 2.00 1.00 1.72 34 Egypt 5 5 1 0 0 5 3 2 0.71 0 5 2.20 3.33 1.90 44 Jordan 5 0 0 0 0 0 0 0 0.23 0 5 1.00 0.00 1.74 24 Lebanon 0 0 0 0 0 0 0 0 0.00 0 0 0.00 0.00 0.00 0 Libya 5 0 0 0 0 0 0 0 1.66 0 0 1.00 0.00 0.55 13 Morocco 5 0 0 0 0 2 4 0 0.14 0 5 1.00 2.00 1.71 30 Syria 5 0 0 0 0 0 0 0 0.03 0 0 1.00 0.00 0.01 7 Tunisia 5 0 1 0 0 2 3 0 0.13 0 0 1.20 1.67 0.04 14 Brazil 0 5 5 5 3 5 5 0 1.58 0 5 3.60 3.33 2.19 57 Turkey 0 5 5 5 3 5 5 0 3.40 0 5 3.60 3.33 2.80 63 Spain 0 5 5 5 5 5 5 5 3.71 0 5 4.00 5.00 2.90 72 Greece 0 5 5 5 3 5 5 5 1.97 0 5 3.60 5.00 2.32 63 South Africa 0 5 5 5 0 5 5 0 1.51 0 5 3.00 3.33 2.17 53 Malaysia 0 5 5 5 3 5 5 0 2.10 0 5 3.60 3.33 2.37 59 India 0 5 5 5 5 5 5 0 0.42 0 5 4.00 3.33 1.81 56 China 0 5 5 5 5 5 5 5 3.39 0 5 4.00 5.00 2.80 71 USA 5 5 5 5 5 5 5 5 2.69 5 5 5.00 5.00 4.23 92 Germany 0 5 5 5 5 5 5 5 5.00 5 5 4.00 5.00 5.00 93

Chapter 5

86

5.2.2 CSP Economic Potential Indicator MED-CSP study made by DLR extensively analyzed the economic potential for the

Middle East and North Africa (MENA) region and southern European countries, thus it is relied

on to collect data for the 8 Arab countries in addition to Turkey, Spain, and Greece [66]. CSP

economic potential for Brazil is roughly estimated from reported data [67] [68]. CSP economic

potential for South Africa and USA are reported in [69] [70] respectively, while that for both

India and China in [71]. CSP economic potential data for Malaysia and Germany are not

available and are believed – from global solar map – to be very minimal and thus are set to zero.

The economic potential of CSP technology is reported in TWh/y, and the CSP Economic

Potential Indicator Score is accordingly calculated with equation (4.26) where both CSP

Economic Potential values and Indicator Score are listed in the middle columns in Table (5.3).

But since the CSP economic potential values (most left column in table (5.3)) varies by several

orders of magnitude, the natural logarithm of these values are taken then the CSP Economic

Potential Indicator Score is recalculated using equation (4.26) and shown in the most right

column in Table (5.3). In the final CSP Market Competence Index, the Scores using the natural

logarithm are being used.


87

Table (5.3) CSP Economic Potential Values and Indicator Score.

Country CSP Economic

Potentail TWh/year

CSP Economic Potential Indicator

Score

CSP Economic

Potentail [Ln scale]

CSP Economic Potential

Indicator Score [Ln scale]

Algeria 168972 100 12.0375 100 Egypt 73656 44 11.2072 93 Jordan 6429 4 8.7686 73 Lebanon 14 0 2.6391 22 Libya 139477 83 11.8457 98 Morocco 20146 12 9.9108 82 Syria 10210 6 9.2311 77 M

ENA

Cou

ntrie

s

Tunisia 9244 5 9.1317 76 Brazil 1702.9754 1 7.4401 62 Turkey 131 0 4.8752 41 Spain 1278 1 7.1531 59 Greece 4 0 1.3863 12 South Africa 52 0 3.9512 33 Malaysia 0 0 0 0 India 800 0 6.6846 56 China 3800 2 8.2428 68 USA 1000 1 6.9078 57 B

ench

mar

k C

ount

ries

Germany 0 0 0.0000 0

Min 0 0 Goalposts Max 168972 12.0375

It is worth mentioning that the CSP economic potential of Brazil is roughly estimated

based on the best solar irradiance [67] and 10% utilization of the area of Brazil based on its solar

map [68], and that of the USA is reported based on building CSP plants on only 3% of the

possible usable land of the best sites [70]. In case of India and Chain [71], their data may not

exactly follow the same definition as the majority of the data coming from DLR study. This may

result in some discrepancies in the final score of the index and thus the final ranking of these

countries.

5.2.3 CSP Institute Indicator According to the research carried to answer the questionnaire given in Chapter 4 suing the

point system devised in Table (4.17), the Institute Indicator Score is calculated and listed in Table

(5.4).

Chapter 5

88

Table (5.4) CEP Institute Indicator Score.

Country

CSP Institute Indicator

Score Algeria 60 Egypt 60 Jordan 60 Lebanon 60 Libya 60 Morocco 100 Syria 20 M

ENA

Cou

ntrie

s

Tunisia 60 Brazil 0 Turkey 0 Spain 20 Greece 0 South Africa 0 Malaysia 0 India 100 China 20 USA 100 B

ench

mar

k C

ount

ries

Germany 100

5.2.4 CSP Target Indicator Table (R9) in REN21-2011 report lists CSP capacity targets at certain years for the

countries that already had announced that commitment [56]. Table (5.5) shows the i, f, CSPi,

CSPf, the expected annual new installed CSP capacity, and CSP Target Indicator Score as

defined in Chapter 4. Countries with “X” sign in target year capacity column indicates that it is

not listed in the report, while countries with “0” indicates that the country made targets for other

RE technologies but not for CSP one. Applying equations (4.27) and (4.28), one gets the CSP

Target Indicator Score as shown in the far right column in Table (5.5).


89

Table (5.5) Initial and target years, current installed CSP capacity, targeted CSP capacity, expected annual new installed CSP capacity, and CSP Target Indicator Score.

Country Initial year capacity

(CSPi) (MW)

Initial year

(i)

Target year capacity

(CSPf) (MW)

Target Year

(f)

Expected annual new

installed CSP capacity (MW)

CSP Target

Indicator Score

Algeria 0 2008 170 2015 24.28571429 10 Egypt 0 2008 0 0 0 Jordan 0 2008 0 0 0 Lebanon 0 2008 X 0 0 Libya 0 2008 0 0 0 Morocco 0 2008 1000 2020 83.33333333 34 Syria 0 2008 225 2015 32.14285714 13 M

ENA

Cou

ntrie

s

Tunisia 0 2008 0 0 0 Brazil 2008 X 0 0 Turkey 2008 0 0 0 Spain 11 2008 500 2010 244.5 100 Greece 2008 X 0 0 South Africa 0 2008 50 2013 10 4 Malaysia 2008 0 0 0 India 0 2008 650 2013 130 53 China 0 2008 900 2020 75 31 USA 424 2008 X 0 0 B

ench

mar

k C

ount

ries

Germany 0 2008 0 0 0


5.2.5 CSP Feed-in Tariff Indicator Based on the 5 merits of Feed-in Tariff law as presented in Chapter 4, and on the

comparison made for CSP Feed-in Tariff laws that were issued in different countries [72], a

questionnaire based assessment is devised with point system and is shown in Table (5.6) to assess

the attractiveness of the law.

Chapter 5

90

Table (5.6) Point system to assess Feed-in Tariff law.

Points Power Period Inflation recovery Hybrid Plant

The price of electricity bought @ 1st year of the

law

0 <=5MW <=5 years Digression No

Less than 1.25 times the lower

electricity bracket price

1 5years<x<=10years <=10% 1.25-1.5 times the

lower electricity bracket price

2 5MW<x<=50MW 10years<x<=15years No 10%<x<=20% 1.5-2 times the lower electricity bracket price

3 15years<x<=20years 20%<x<=30% 2-2.5 times the lower electricity bracket price

4 50MW<x<=250MW 20years<x<=25years 30%<x<=50% 2.5-2.75 times the

lower electricity bracket price

5 Unlimited Unlimited Yes Yes >2.75 times the lower electricity bracket price

Table (5.7) Feed-in Tariff merits and CSP Feed-in Tariff Indicator Scores.

Country Power Period Inflation recovery

Hybrid Plant

The price of electricity

bought at 1st year of the law

CSP Feed-in Tariff

Indicator Score

Algeria 2 5 2 5 5 76 Egypt 0 0 0 0 0 0 Jordan 0 0 0 0 0 0 Lebanon 0 0 0 0 0 0 Libya 0 0 0 0 0 0 Morocco 0 0 0 0 0 0 Syria 0 0 0 0 0 0 M

ENA

Cou

ntrie

s

Tunisia 0 0 0 0 0 0 Brazil 0 0 0 0 0 0 Turkey 0 1 2 0 0 12 Spain 2 5 5 2 2 64 Greece 5 3 2 1 3 56 South Africa 5 3 2 2 5 68 Malaysia 0 0 0 0 0 0 India 5 4 2 0 5 64 China 2 4 2 0 5 52 USA 0 0 0 0 0 0 B

ench

mar

k C

ount

ries

Germany 5 5 0 0 0 40


91

Table (5.7) lists each county’s score for each of the five merits according to the point

system of Table (5.6). The CSP Feed-in Tariff Indicator Score is thus calculated with equation

(4.29) and is shown in the far right column in Table (5.7).

Finally, the indicators scores for the countries understudy that are developed throughout

Chapter 4 and this chapter are collectively listed in Table (5.8) where indicators scores come

according to the following list:

• Global Competitive, Political Instability, and Corruption Perception Indices from Table (4.1),

• Energy Intensity Indicator from Table (4.2),

• Non Electricity Final Indicator from Table (4.12),

• Electricity Consumption Growth Indicator from Table (4.13),

• Net Imported electricity Indicator from Table (4.14),

• Non RE Electricity Production Indicator from Table (4.15),

• Oil Insecurity Indicator from Table (4.7),

• Gas Insecurity Indicator from Table (4.8),

• RE Target Indicator from Table (4.16),

• Financial Indicator from Table (4.10),

• Environmental Indicator from Table (4.11),

• CSP Manufacturability Indicator from Table (5.2),

• CSP Economic Potential Indicator from Table (5.3),

• CSP Institute Indicator from Table (5.4),

• CSP Target Indicator from Table (5.5), and

• CSP Feed-in Tariff Indicator from Table (5.7).

Chapter 5

92

Table (5.8) All indicators used to develop CSP Market Competence Index.

Country Global

Competitive Index

Political Instability

Index

Corruption Perception

Index

Energy Intensity Indicator

Non Electricity

Final Indicator

[Ln]


Growth Indicator [Ln]

Net Imported electricity Indicator

[Ln]

Non RE Electricity Production Indicator

[Ln]

Oil Insecurity Indicator

Gas Insecurity Indicator

RE Target Indicator

[Ln]

Financial Indicator

Environmental Indicator

CSP Manufacturability

Indicator

CSP Economic Potential

Indicator (Ln)

CSP Institute Indicator

CSP Target

Indicator

CSP Feed-in

Tariff Indicator

Algeria 13 13 12 53 33 61 0 23 0 0 63 0 8 34 100 60 10 76 Egypt 15 50 16 35 43 71 0 41 67 1 87 44 5 44 93 60 0 0 Jordan 28 50 44 29 4 53 36 5 100 96 57 22 11 24 73 60 0 0 Lebanon 9 0 5 18 0 62 47 0 100 0 78 0 14 0 22 60 0 0 Libya 0 84 0 6 15 57 0 17 0 0 66 0 34 13 98 60 0 0 Morocco 20 44 21 100 20 55 72 11 100 95 66 11 1 30 82 100 34 0 Syria 3 38 5 0 20 56 0 22 53 32 59 0 8 7 77 20 13 0 Tunisia 54 75 37 94 11 50 0 7 72 73 61 33 4 14 76 60 0 0

Brazil 32 50 26 65 67 72 100 33 73 71 0 33 4 57 62 0 0 0 Turkey 30 6 39 82 51 55 0 47 98 100 83 0 14 63 41 0 0 12 Spain 44 47 68 76 54 0 0 54 99 100 93 33 33 72 59 20 100 64 Greece 15 22 23 88 29 0 75 29 100 99 65 44 41 63 12 0 0 56 South Africa 34 59 40 6 46 0 0 54 100 100 77 33 33 53 33 0 4 68 Malaysia 67 16 39 12 42 61 0 36 61 0 67 11 32 59 0 0 0 0 India 35 78 19 71 80 86 81 71 90 67 0 78 0 56 56 100 53 64 China 65 69 23 35 99 100 0 95 88 43 0 56 21 71 68 20 31 52 USA 100 53 86 41 100 0 97 100 94 77 0 78 100 92 57 100 0 0 Germany 97 100 100 71 69 0 0 66 99 96 100 44 50 93 0 100 0 40


93

5.3 CSP Market Competence Index Score and Rank Now, applying the methodology that was developed in Chapter 4 on the 18 indicators

given by Table (5.8), one can now calculate the CSP Market Competence Index Score for each of

the 18 countries understudy, provided the weighting parameter α is given. Table (5.9) and (5.10)

show the index score and rank, respectively, of the 18 countries for 6 different cases; α=0, 0.2,

0.4, 0.6, 0.8, and 1, where they are ranked with descending score according to the case α=0 with

gray background for RCREEE countries, and white background for the benchmark ones.

Table (5.9) Scores of the 18 countries according to the CSP Market Competence Index.

Score Country α=0 α=0.2 α=0.4 α=0.6 α=0.8 α=1 |∆ (max)|

Spain 66 65 65 65 65 64 1 India 56 58 59 60 62 63 7 Morocco 53 52 52 51 51 50 2 China 51 51 50 50 49 49 3 USA 45 45 44 44 43 43 2 Egypt 41 41 41 41 40 40 1 Tunisia 41 39 37 35 33 31 10 Jordan 39 37 36 35 33 32 7 Brazil 39 36 34 31 28 24 14 Algeria 34 40 44 48 52 56 22 Libya 33 33 33 34 34 34 2 Syria 32 30 28 26 23 20 12 Turkey 31 29 28 26 24 23 8 South Africa 29 28 28 28 28 27 2 Lebanon 17 16 16 16 15 15 2 Greece 17 16 16 16 15 15 2 Malaysia 0 0 0 0 0 0 0 Germany 0 0 0 0 0 0 0

Chapter 5

94

Table (5.10) Ranking of the 18 countries according to the CSP Market Competence Index. Rank

Country α=0 α=0.2 α=0.4 α=0.6 α=0.8 α=1 |∆ (max)| Spain 1 1 1 1 1 1 0 India 2 2 2 2 2 2 0 Morocco 3 3 3 3 4 4 1 China 4 4 4 4 5 5 1 USA 5 5 5 6 6 6 1 Egypt 6 6 7 7 7 7 1 Tunisia 7 8 8 8 10 10 3 Jordan 8 9 9 9 9 9 1 Brazil 9 10 10 11 11 12 3 Algeria 10 7 6 5 3 3 7 Libya 11 11 11 10 8 8 3 Syria 12 12 13 14 14 14 2 Turkey 13 13 14 13 13 13 0 South Africa 14 14 12 12 12 11 3 Lebanon 15 16 16 16 16 16 1 Greece 16 15 15 15 15 15 1 Malaysia 17 17 17 17 17 17 0 Germany 17 17 17 17 17 17 0

By inspecting the data in the 6 columns of the weighting parameter α, it is obvious that

– except for Algeria – the change in both score and rank is relatively very small across the whole

spectrum of α. Quantitatively, the correlation between the index score of the data sets of all

countries for α=0 and α=1 (the smallest correlation among any 2 columns) of Table (5.9) is

0.9143, and that of the index rank of Table (5.10) between the two cases α=0 and α=1 is 0.8975.

The reason for this stability of the results among various values of the weighting parameter α and

the high correlation among the scores and rank results is the high correlation between data sets of

X2 and X3. Table (5.11) shows X1, X2, and X3 of the 18 countries understudy where the

correlation between X2, and X3 is 0.6806. Without taking Algeria into account, this correlation

dramatically increases to 0.8476. Even by partitioning, the correlation between X2 and X3 values

for the RCREEE countries excluding Algeria is 0.7438 (0.1607 with Algeria), and between the

benchmark countries is 0.854.

This relatively high correlation between X2 and X3 may suggest a connection between the

general indicators and the three CSP indicator represented by X2. For example, countries that

have high Global Competitive Index Score; which means very good infrastructure, education,

technical readiness, business sophistication, and innovation will most probably have a high score


95

Table (5.11) X1, X2, and X3 of the 18 countries. Country X1 X2 X3

Spain 80 52 54 India 54 73 58 Morocco 58 43 47 China 50 48 53 USA 29 64 71 Egypt 47 35 37 Tunisia 38 25 44 Lebanon 11 20 26 Brazil 31 19 48 Algeria 55 57 21 Libya 49 24 22 Syria 45 9 23 Turkey 20 25 46 South Africa 18 40 45 Lebanon 11 20 26 Greece 6 40 49 Malaysia 0 20 34 Germany 0 78 69

in the CSP Manufacturability Indicator as well. Actually, the correlation between these two

indicators is 0.7465.

More generally, developed counties, in general, have high scores in the 3 Political and

Economic Indicators, high score for Energy Intensity Indicator (they well understand the role of

energy in economic development), high score for Oil and Gas Insecurity Indicators (as they are

energy demanding economies and their national productions are not sufficient for their

consumption), and consequently also high score for the Environmental Indicator (as they burn a

lot of fossil fuel). By nature of their advancement, these developed countries have a good

industrial base that covers many industries including those of CSP technology, and thus have

high CSP Manufacturability Index. Also by nature of their well-governance and advancement in

economic science, they invent and apply all necessary financial tools, including Feed-in Tariff, to

boost RE technologies which they have good economic potential. Thus these countries will have

a high Feed-in Tariff Indicator Score as well. Vise versa applies to developing countries.

According to equation (4.35), and in the limiting case when X2 equals X3 for a certain

country, its Technology Specific Market Competence Index Score becomes independent on α

and simply it will be given by:

21log XXIndexCompetenceMarketSpecificyTechno ×= (5.1)

Chapter 5

96

Similarly, when there is a high correlation between X2 and X3 data sets for the group of countries

as shown in Table (5.11), the final scores, and consequently their ranks, becomes very weakly

dependent on α.

The reason for the oddness of the case of Algeria is that it is one of the rare developing

countries that has a Feed-in Tariff law for CSP. Being a developing country correlate to the fact

that its X3 score is low (21), while having a Feed-in Tariff law gives it a high value for X2 (57).

Actually, because of the generous Feed-in Tariff in Algeria compared to all other counties that

issued the law, Algeria has the highest Feed-in Tariff Indicator Score of 75.

More quantitative analysis is shown in the far right column in Tables (5.9) which gives

the absolute difference between scores of the two extreme cases of α=0, and α=1. Except for

Algeria, Brazil, and Syria the absolute difference for the remaining 15 countries is less than or

equal to 10 points, and for 11 countries this difference is less than or equal to only 3 points.

Similarly for Table (5.10), between the two extreme cases of α=0, and α=1, the rank does

not change for 5 countries, changes by only 1 position for 7 countries, 2 positions for 1 country, 3

positions for 4 countries, and 7 positions for 1 country (Algeria).

From this comparison and analysis, one concludes that there is a good correlation

between the average value of 3 CSP indicators (X2) and the average value of the 13 general

indicators (X3). Accordingly it is sufficiently representative to calculate the CSP Market

Competence Index using only 5 indicators and apply the simple equation (5.1) instead of 18

indicators and equation (4.35). Though this has been concluded from the study of only 18

countries, the variety in the nature of these countries covering developed and developing

countries, and countries with various CSP potential and targets, it is believed that this conclusion

will apply to other counties in the world, though; further confirmation is still needed to be

performed by investigating more countries.

Similar investigation has also to be carried out for other RE technologies (wind, PV,

biomass, geothermal, wave and tidal) to check that similar behavior also exists.


97

5.4 CSP Market Competence Index versus CSP Countries

Attractiveness Index In order to compare the result of the proposed index with that of the Countries

Attractiveness Index, and for fare comparison, it is crucial to be sure that we compare similar

things against each other. Accordingly, the following few points have to be considered:

• Firstly, since the proposed index only studies 18 countries, while the Countries Attractiveness

Index studies more than 30 countries (30 countries in Nov 2010 issue, increasing to reach 40

countries in Nov 2011 issue), only common countries will be considered in the comparison.

From these common countries, USA has been excluded because of the low confidence in the

values of few of its indicators – as it has been mentioned before –; specifically RE Target

Indicator and CSP Economic Potential Indicator. Thus, the comparison is carried out for the

following 11 countries only: Egypt, Morocco, Tunisia, Brazil, Turkey, Spain, Greece,

South Africa, India, China, and Germany.

• Secondly, because the data used to calculate the proposed CSP Market Competence Index

Scores were not available for a single year but rather over a period of time that extends from

2008 to 2011, thus the final index score and rank measure the countries’ competence over that

period of time. On the other hand the CSP component of the Countries Attractiveness Index is

published on quarterly bases; February, May, August, November of each year [73]. Thus, the

comparison should be done with the average of the Countries Attractiveness Index taken over

the mentioned period from 2008 to 2011. Unfortunately only the available reports are those

published in Nov 2010, and Feb, May, August, and Nov of 2011 (the internet links of the

older reports are not working) [73]. It is worth mentioning that while most of the 11 common

countries exist in the 5 mentioned reports, Morocco only started to appear in May 2011 issue,

while Tunisia was just lately added to the index in Nov 2011 issue. Thus Morocco’s score is

averaged over 3 reports representing the period from May to Nov 2011, while the score of

Tunisia of Nov 2011 issue is taken as is.

• Thirdly, and accordingly, countries rank for the Countries Attractiveness Index is done for the

exclusive group of the 11 common countries understudy using their calculated average scores

not according to the averaging of their ranking in these reports.

Chapter 5

98

Table (5.12) CSP Index Scores for the 11 common countries as reported in the RE Countries Attractiveness Index reports, their scores average, and their ranking.

CSP Countries Attractiveness Index Score Country Nov-10 Feb-11 May-11 Aug-11 Nov-11 Average Score Rank

Spain 69 68 65 62 63 65 1 India 63 64 53 52 53 57 2 Morocco 51 49 52 51 3 Tunisia 48 48 4 Egypt 50 45 46 45 45 46 5 South Africa 45 46 46 46 47 46 6 China 40 40 48 47 47 44 7 Greece 41 41 40 33 33 38 8 Brazil 30 30 32 32 32 31 9 Turkey 27 29 30 29 28 29 10 Germany 22 0 0 0 0 4 11

Table (5.13) Scores of the Market Competence Index and Countries Attractiveness Index.

Market Competence Index Score

Country α=0 α=0.2 α=0.4 α=0.6 α=0.8 α=1

Countries Attractiveness

Index Score Spain 66 65 65 65 65 64 65 India 56 58 59 60 62 63 57 Morocco 53 52 52 51 51 50 51 China 51 51 50 50 49 49 44 Egypt 41 41 41 41 40 40 46 Tunisia 41 39 37 35 33 31 48 Brazil 39 36 34 31 28 24 31 Turkey 31 29 28 26 24 23 29 South Africa 29 28 28 28 28 27 46 Greece 17 16 16 16 15 15 38 Germany 0 0 0 0 0 0 4

Table (5.14) Ranks of the Market Competence Index and Countries Attractiveness Index.

Market Competence Index Rank

Country α=0 α=0.2 α=0.4 α=0.6 α=0.8 α=1

Countries Attractiveness

Index Rank Spain 1 1 1 1 1 1 1 India 2 2 2 2 2 2 2 Morocco 3 3 3 3 3 3 3 China 4 4 4 4 4 4 7 Egypt 5 5 5 5 5 5 5 Tunisia 6 6 6 6 6 6 4 Brazil 7 7 7 7 7 8 9 Turkey 8 8 9 9 9 9 10 South Africa 9 9 8 8 8 7 6 Greece 10 10 10 10 10 10 8 Germany 11 11 11 11 11 11 11


99

Table (5.12) shows the CSP Index Scores for the 11 common countries as reported in the

Countries Attractiveness Index reports, their scores average, and their ranking.

In order to compare the results of the CSP Market Competence Index and CSP Countries

Attractiveness Index, Table (5.13) and (5.14) list the scores and ranks – respectively – of both

indices for various values of the α parameter, where countries are listed according to the rank of

the case α=0.

Again, by inspection, the similarity of the score and rank results between both indices is

obvious revealing that the proposed CSP Market Competence Index gives a good representation

for the relative attractiveness of CSP markets among the countries.

Quantitatively, Figure (5.1) shows the correlation between the data set of the Countries

Attractive Index (far right column) and each case of the Market Competence Index for both score

and rank of Tables (5.13) and (5.14) respectively. The figure reveals that the correlation value for

the score ranges from 0.8772 to 0.8905, and for the rank between 0.8455 and 0.9091, both

moving monotonically from α=0 to α=1. Off course this stability of the correlation value against

the whole spectrum of α is understood in light of the result of the previous section.

In order to inspect from where the difference in the results between the two indices

comes, the score and rank values of the Countries Attractiveness Index are subtracted from the

corresponding values of the Market Competence Index for the countries understudy and plotted in

0

0.2

0.4

0.6

0.8

1

0 0.2 0.4 0.6 0.8 1

α

Cor

rela

tion

betw

een

the

valu

es o

f the

2

indi

ces

ScoreRank

Figure (5.1) Correlation between the data set of the Countries Attractive Index (far right column) and each

case of the Market Competence Index for both score and rank of Tables (5.13) and (5.14) respectively.

Chapter 5

100

Figures (5.2) and (5.3) respectively. In each figure, the two extreme cases of the Market

Competence Index; α=0 and α=1, are considered.

Figure (5.2) reveals that the difference is negative, meaning that the proposed Market

Competence Index Score is – in general – smaller than the Counties Attractiveness Index Score.

One of the reasons that may lead to that – in addition to the uncertainties in some of the data – is

that the Market Competence Index is based on geometric average while that of the Countries

Attractiveness Index is based on weighted arithmetic average. For the same numbers, geometric

average is always smaller than arithmetic average.

Figure (5.2) also shows that the greatest discrepancies in the score, for the 2 extreme cases

of α, are those of South Africa and Greece, followed by Tunisia only for the case of α=1. In all

these cases the differences exceed 15 points. For the rest of the cases, the absolute difference does

not exceed 7 points.

The reason that Greece has a very small score in the Market Competence Index is that the

reported CSP potential is very small (12 points), and that it has zero CSP target. This gives

Greece only 6 points for X1, and thus very small final Market Competence Index Score. Actually,

there are many dynamics pushing for CSP market in Greece. The government is planning a more

attractive Feed-in Tariff law, and many companies are applying for new CSP projects especially

in Crete and Rhodes Islands [74][75]. If the Greek government sets a clear CSP target, and if the

-25

-20

-15

-10

-5

0

5

10

Spai

n

Indi

a

Mor

occo

Chi

na

Egyp

t

Tuni

sia

Bra

zil

Turk

ey

Sout

h A

frica

Gre

ece

Ger

man

y

Scor

e D

iffer

ence

∆ (α=0)

∆ (α=1)

Figure (5.2) Score difference between CSP Market Competence Index and CSP Countries

Attractiveness Index for the two extreme cases α=0 and α=1.


101

Market Competence Index includes an indicator about the currently planned projects in pipeline

as in the Countries Attractiveness Index, the difference between the scores of the two indices may

be get smaller.

Similarly for South Africa, it has relatively small score for Economic Potential Indicator

(33 points), and very small score for CSP Target Indicator (4 points), leading to a small value of

X1 (18 points), and thus small final Market Competence Index Score (but relatively better than

Greece, where both have almost the same scores for X2 and X3, see Table (5.11))

Figure (5.3) shows that 5 countries (out of 11) have the same rank in both indices. The

greatest rank change is 3 places, and it happens for China for both extreme cases of α, and also

for South Africa in case of α=0. Generally speaking, putting in mind the uncertainties of some of

the data, the averaging of the Countries Attractiveness Index over a period of time that is not

exactly the same as that used for calculating the Market Competent Index, a maximum rank

change of only 3 places for 2 countries out of 11 is a very good match between the two indices.

Finally, Table (5.15) gives a comparison between the CSP Market Competence Index and

CSP Countries Attractiveness Index. This comparison emphasizes that the proposed index has

better merits regarding its objectivity, simplicity, fewer need for manpower, and the ability to

be programmed and accessed by many users using either the Internet or through software

packages.

-4

-3-2

-10

1

23

4

Spai

n

Indi

a

Mor

occo

Chi

na

Egyp

t

Tuni

sia

Bra

zil

Turk

ey

Sout

h A

frica

Gre

ece

Ger

man

y

Ran

k D

iffer

ence

∆ (α=0)

∆ (α=1)

Figure (5.3) Rank difference between CSP Market Competence Index and CSP Countries

Attractiveness Index for the two extreme cases α=0 and α=1.

Chapter 5

102

Table (5.15) A comparison between CSP Market Competence Index and CSP Countries Attractiveness Index.

CSP Market Competence Index CSP Countries Attractiveness Index

Item Backward Backward

Score range 0:100 0:100

Objectivity/Subjectivity Completely objective Has both objective and subjective

components.

Complexity • Relatively simple in its structure.

• Direct data collection.

• Simple point-system questionnaires.

• Only 5 quantitative indicators can still

give a very good representation.

• Complex.

• Direct data collection.

• Open essay questionnaire.

Manpower Can be calculated by one person in a

relatively short time (1 month).

Needs a team of high expertise to be able

to correctly evaluate the open essay

questionnaires.

Programmability The index can be programmed, and a

person with medium level experience

can fill in the data, and the index can be

directly calculated using a software

package.

Not applicable

Cooperative development

Can be available to individual users (or

community of users) to calculate the

index themselves through Web-based

application.

Not applicable

5.5 Conclusion The methodology that was developed in Chapter 4 is applied in this chapter to CSP

technology. A CSP Market Competence Index has been calculated for 8 RCREEE and 10

benchmark countries. The index scores show great stability across the possible range of the

weighting parameter α due to the high correlation between the average of the general indicators

(X3) and the average of 3 of the technology specific indicators (X2). Except for only 3 countries

(Algeria, Brazil, and Syria) the maximum absolute difference for the two extreme cases of the

weighting parameter α for the remaining 15 countries is less than or equal to 10 points, and for 11


103

countries this difference is less than or equal to only 3 points. Similarly for the two extreme cases

of the weighting parameter α, the rank does not change for 5 countries, changes by only 1

position for 7 countries, 2 positions for 1 country, 3 positions for 4 countries, and 7 positions for

only 1 country (Algeria).

The score and rank of the proposed index is also compared with those of the CSP

Countries Attractiveness Index for 11 common countries. The comparison shows a strong

correlation (>85%) between the results of the two indices for both the score and rank. Detailed

analysis for the difference between the score results reveals that the proposed Market

Competence Index Score is – in general – smaller than the Counties Attractiveness Index Score.

Despite of the relatively large difference in score (17 to 22 points) for 3 countries (South Africa,

Greece, and Tunisia), the difference in the remaining 8 countries does not exceed 7 points. The

analysis of rank difference shows that 5 countries have the same rank in both indices, and that the

maximum rank change is only 3 places for only 2 countries.

The high correlation for the score and rank of both indices with the advantage that the

Market Competence Index is completely objective, simpler, not human resource intensive as the

Countries Attractiveness Index makes it more attractive to be adopted and developed by different

organizations and countries.

Conclusion

104

Conclusion In conclusion of this thesis, a new methodology was driven for an index that

quantitatively, objectively, and analytically describes the competence of renewable energy

market of a country. The proposed index, Renewable Energy Market Competence Index, is

composed of two types of indicators; general indicators section and a technology specific

indicators one. The former group is composed of 13 indicators that describe the general

characteristics of the country regarding political, social, economical situation in addition to the

conditions of the energy sector, fossil fuel sustainability, CO2 emission level of the country, and

the available financing for renewable energy projects. The later group is composed of 5

technology specific indicators that change from a technology to another. They cover the

manufacturability of that technology in the country, economic potential, target, institution, and

the existence of Feed-in Tariff for that technology.

The proposed formula to calculate the index gives more privilege to the existence of

economic potential and a target set by the government for the renewable energy technology type

understudy. The proposed formula is simple, involving both weighted arithmetic average and

geometric average.

The developed methodology and formula is applied to CSP technology and a CSP Market

Competence Index is calculated for 8 RCREEE and 10 benchmark countries. The index scores

show great stability across the possible range of the weighting parameter α due to the high

correlation between the average of the general indicators and the average of 3 of the technology

specific indicators. Except for only 3 countries (Algeria, Brazil, and Syria), the maximum

absolute difference for the two extreme cases of the weighting parameter is less than or equal to

10 points, among them 11 countries have differences less than or equal to only 3 points. Similarly

for the two extreme cases of the weighting parameter α, the rank does not change for 5 countries,

changes by only 1 position for 7 countries, 2 positions for 1 country, 3 positions for 4 countries,

and 7 positions for only 1 country (Algeria).

The score and rank of the proposed index is also compared with those of the CSP

Countries Attractiveness Index quarterly published by E&Y for 11 common countries. The

comparison shows a strong correlation (>85%) between the results of the two indices for both the

Conclusion

105

score and rank. Analysis for the difference between the score results for both indices reveals that

the proposed Market Competence Index Score is – in general – smaller than the Counties

Attractiveness Index Score. Despite of the relatively large difference in score (17 to 22 points)

for 3 countries (South Africa, Greece, and Tunisia), the difference in the remaining 8 countries

does not exceed 7 points. The analysis of rank difference shows that 5 countries have the same

rank in both indices, 4 countries change their rank by 2 places, and that the maximum rank

change is only 3 places and happens only for 2 countries.

The high correlation for the score and rank between the proposed CSP Market

Competence Index and CSP Countries Attractiveness Index suggests that it is preferable to adopt

the simpler one since it gives almost the same result. The proposed index is relatively simpler in

its structure than the CSP Countries Attractiveness Index. It directly depends on statistical data

collection and simple point system questionnaires while that of the later strongly depend on open

essay questionnaire and subjective assessment. Analysis of CSP Market Competence Index

shows that even only 5 quantitative indicators can still give a very good representation of the

market and very similar results to that of CSP Countries Attractiveness Index.

In terms of manpower, the simplicity of the proposed index makes it less human power

intensive compared to CSP Countries Attractiveness Index which needs a team of experts to

collect data, fill questionnaire, and assess the information based on personal experience and on

comparing them with similar ones coming from different countries.

The simplicity and programmability of the proposed CSP Market Competence Index

opens the door for widespread use of the index and the ability to be calculated simultaneously for

different countries by different users on a web portal. Alternatively, the index can be programmed

and distributed as a computer application and be sold to interested organizations.

Though these results looks encouraging, the proposed index is still in its enfant stage of

development, and much more work still lies ahead. For example, other indicators need to be

added such as: country commitment to meet its RE target, fossil fuel subsidy, energy taxation,

details of financing and grants, effect on labor sector, grid code and grid connectivity. Another

point of improvement shall be related to the 3 arithmetic averaging taken of the three sets of

indicators (X1, X2, X3). More investigating should be carried in order to convert this into

weighted average where each indicator should be represented with a proportional weight for its

importance in making the technology market more competence. For example, in calculating one

Conclusion

106

of these averages, X2, which equally combines Feed-in Tariff Indicator with CSP

Manufacturability Indicator and the Institute Indicator, one should first evaluate the relative

strength of these 3 indicators in making the market more attractive and then use a weighted

average to calculate the average of this group. One way to do so is through feedback

questionnaire from technology experts, manufacturers, and energy investors. More over, though

applied to CSP, the proposed methodology should be also tested against other renewable energy

technology types to know the generality of the methodology across all renewable energy

technologies. Most preferably for the Arab region, is to apply the proposed index on wind

onshore, PV, and biomass and compare the results with the corresponding values of the

Countries Attractiveness Indices.

References

107

References Unless a different duration is mentioned, the following links were last checked during

the period December 2011 – January 2012.

[1] Primary Pupil to Teacher ratio http://data.worldbank.org/indicator/SE.PRM.ENRL.TC.ZS

[2] Total Fertility rate http://data.worldbank.org/indicator/SP.DYN.TFRT.IN

[3] Employment Cost Index http://www.bls.gov/news.release/eci.toc.htm

[4] Description, score, and rank of the Political Instability Index for 2009/2010

http://viewswire.eiu.com/site_info.asp?info_name=social_unrest_table&page=noads&rf=0

[5] Color map of the counties according to Political Instability Index Score of 2009/2010

http://viewswire.eiu.com/site_info.asp?info_name=instability_map&page=noads

[6] The detailed indicators used for the Political Instability Index

http://viewswire.eiu.com/index.asp?layout=VWArticleVW3&article_id=874361472

[7] Transparency International http://www.transparency.org/about_us

[8] Corruption Perceptions Index 2010, Long Methodological Brief

http://www.transparency.org/content/download/55903/892623/CPI2010_long_methodology

_En.pdf

[9] The full Corruption Perception Index report for 2010

http://www.transparency.org/content/download/55725/890310

[10] Corruption Perception Index for 2010, countries’ ranking and score

http://www.transparency.org/content/download/56231/898923/CPI+2010+results_pls_stan

dardized_data.xls

[11] Freedom Country Index (last viewed in June-July 2011)

http://www.freedomhouse.org/template.cfm?page=351&ana_page=363&year=2010

[12] (last viewed in June-July 2011)

http://www.freedomhouse.org/template.cfm?page=351&ana_page=364&year=2010

References

108

[13] Color map of Freedom Country Index (last viewed in June-July 2011)

http://www.freedomhouse.org/template.cfm?page=363&year=2011

[14] Combined Average Ratings – Independent Countries (last viewed in June-July 2011).

http://www.freedomhouse.org/uploads/fiw10/CombinedAverageRatings(IndependentCoun

tries)FIW2010.pdf

[15] Combined Average Ratings – Disputed Territories (last viewed in June-July 2011).

http://www.freedomhouse.org/uploads/fiw10/CombinedAverageRatings(RelatedandDisput

edTerritories)FIW2010.pdf

[16] Introduction to Reporters Without Borders organization

http://en.rsf.org/introduction-24-03-2011,32617.html

[17] How Press Freedom Index is compiled http://en.rsf.org/IMG/pdf/methodology.pdf

[18] Press Freedom Index questionnaire

http://en.rsf.org/IMG/pdf/cm_questionnaire_2010_gb.pdf

[19] Press Freedom Index point system http://en.rsf.org/IMG/pdf/bareme-2.pdf

[20] World color map of the Press Freedom Index http://en.rsf.org/IMG/pdf/carte-2011.pdf

[21] Table of Press Freedom Index for 2010

http://www.rsf.org/IMG/CLASSEMENT_2011/GB/C_AFRICA_GB.pdf

[22] About the Gini coefficient http://en.wikipedia.org/wiki/Gini_coefficient

[23] Central Intelligent Agency

https://www.cia.gov/library/publications/the-world-factbook/rankorder/2172rank.html

[24] World colored map of Gini Index. While the image says that the source is from The World

Fact book of the Central Intelligent Agency, I was not able to get it from the CIA website

http://upload.wikimedia.org/wikipedia/commons/3/34/Gini_Coefficient_World_CIA_Rep

ort_2009.png

[25] Index of Economic Freedom http://www.heritage.org/index/

[26] List of country for the Index of Economic Freedom

http://www.heritage.org/index/excel/2011/Index2011_Data.xls

References

109

[27] History of the World Economic Forum http://www.weforum.org/history-0

[28] The Global Competitive Index http://www.weforum.org/issues/global-competitiveness

[29] The full Global Competitive Index report

http://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2010-11.pdf

[30] List of country index

http://www3.weforum.org/docs/WEF_GCR_IndexRankingAndComparison_2010-11.xls

[31] Human Development Index page http://hdr.undp.org/en/statistics/hdi/

[32] World colored map of the Human Development Index http://hdr.undp.org/en/data/map/

[33] Human Development Index data

http://hdr.undp.org/en/media/Lets-Talk-HD-HDI_2010.pdf

[34] Global Gender Gap Index report

http://www3.weforum.org/docs/WEF_GenderGap_Report_2010.pdf

[35] Global Wellbeing Index page

http://www.gallup.com/poll/126977/global-wellbeing-surveys-find-nations-worlds-

apart.aspx

[36] Global Wellbeing Index report

http://www.gallup.com/poll/File/126965/Gallup-Global-Wellbeing.aspx

[37] Volkan Ş. Edigera , Enes Hoşgör, A. Neşen Sürmeli, Hüseyin Tatlıdil, “Fossil fuel

sustainability index: An application of resource management”; Elseveir; Energy Policy; 35

(2007) 2969–2977.

http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6V2W-4MHPHW0-1-

1&_cdi=5713&_user=4861060&_pii=S0301421506003806&_origin=na&_coverDate=05

%2F31%2F2007&_sk=999649994&view=c&wchp=dGLbVzW-

zSkzk&md5=e68bb0ea9c47be84d9fd9040aae90202&ie=/sdarticle.pdf

[38] Renewable Energy Country Attractiveness Indices

http://www.ey.com/GL/en/Industries/Oil---

Gas/Oil_Gas_Renewable_Energy_Attractiveness-Indices

References

110

[39] Renewable Energy Country Attractiveness Indices, November 2011 issue.

http://www.ey.com/Publication/vwLUAssets/Renewable_energy_country_attractiveness_i

ndices_-_Issue_31/$FILE/EY_RECAI_issue_31.pdf

[40] Questionnaire for compiling the 2010 press freedom index

http://en.rsf.org/IMG/pdf/cm_questionnaire_2010_gb.pdf

[41] How we scored each country http://en.rsf.org/IMG/pdf/bareme-2.pdf

[42] After studying E&Y questionnaire used in developing the Renewable Energy Countries

Attractiveness Index, it was not clear how they quantify many questions in the

questionnaire that have qualitative nature. The following is an email communication

between the author and one of the leading team members in E&Y who is responsible for

the index. This email communication shows clearly the high subjectivity of Renewable

Energy Countries Attractiveness Index and thus the need to create a mere objective one.

(P.S. the emails are put as original with their own spelling mistakes).

The author 15/Sept/2011: “Do you have any document that explains what value to give

for the data collected in the questionnaire? especially that many of these data are non-

quantitative ones.

For example: The “Electricity Market Regulatory Risk” section contains a question saying:

Potential risks inherent in generating renewable energy e.g. what type of electricity market

exists; is it fully deregulated, stable, and reliable? Who manages the electricity market in

the country?

Alternatively, if the questionnaires' answers of Germany, Egypt, Turkey, and Morocco

could be shared, I will be able, somehow, to relate the quantitative number you give in the

excel sheet to the qualitative answer in the questionnaire. Then I -myself- will be able to

give quantitative value for the questionnaires I am going to fill myself.”

E&Y leading team member 16/Sept/2011: “I'm afraid we cannot provide any further

information on the derivation of these scores - with the exception of certain scores which

are driven off quantitative data (e.g. wind capacity), all of the other scores are based on a

qualitative assessment of the research gathered, and by bench-marking the performance of

that country against other countries for each parameter. There is no formal / quantitative

References

111

methodology for converting the information in the questionnaire into scores - the process

is far more subjective and involves ongoing research, review and discussion by the CAI

team. Further, we will also look at the overall score/ranking of the country once the scores

have been entered into the model - if the country ranks far too high or low compared to

other countries and/or our expectations, this may indicate we have over or under

scored certain parameters, and we will re-review the scores we have allocated.

There is no guarantee we will get the score 100% correct every time(!), however, we

always make it clear that the CAI is essentially a quantitative assessment of country's

renewables markets and is not an exact science. Therefore - as we have to - you will need

to rely on the quality of your own research and your own judgement in scoring the

parameters, but the good news is there is no right or wrong answer! Rather, you will have

to judge whether the rankings arrived at are sensible based on your view of which

countries are more or less attractive.

On a separate but related point, when we add a new country, we use the questionnaire as a

template to guide our research and to ensure we are gathering the relevant information to

enable us to score the country. We then use the information in the questionnaire, plus any

additional research undertaken and/or any further insight provided by members of our

team, to generate the scores. Therefore, the questionnaire should help you formulate scores

based on the information you find to populate it, but there is no direct link / formal

methodology to move from the questionnaire to a score - it is still a subjective exercise

based on the research you have found. As such, we are unable to provide questionnaires for

Germany, Turkey, Egypt and Morocco for two reasons:

• The current questionnaire is a relatively new tool - Germany and Turkey have been in

the CAI for many years and previous team members may have used a different

methodology to incorporate these countries and allocate initial scores - no comparable

questionnaire is available.

• In respect Egypt and Morocco, the UK production team did use a similar questionnaire,

and other tools/information to generate the scores, but as set out above, this process is

extremely subjective. Therefore, it would be incredibly misleading to release these

References

112

questionnaires as a way for you to benchmark the scores, and we are simply not happy

to do so. Further, all the information in these questionnaires is in the public domain.”

[43] Renewable Energy Countries Attractiveness Index page

http://www.ey.com/GL/en/Industries/Oil---


[44] http://www.iea.org/stats/prodresult.asp?PRODUCT=Indicators

[45] http://www.iea.org/textbase/nppdf/free/2009/key_stats_2009.pdf

[46] http://www.iea.org/textbase/nppdf/free/2011/key_world_energy_stats.pdf

[47] http://www.iea.org/stats/prodresult.asp?PRODUCT=Balances

[48] http://www.iea.org/stats/prodresult.asp?PRODUCT=Electricity/Heat

[49] M. King Hubbert; Spring Meeting of the Southern District, Division of Production,

American Petroleum Institute, “Nuclear Energy and the fossil Fuel”; March 7-9, 1956

http://www.oilcrisis.com/hubbert/1956/1956.pdf

[50] Mikael Höök, Robert Hirsch, Kjell Aleklett; “Giant oil field decline rates and their influence

on world oil production”; Energy Policy, Volume 37, Issue 6, June 2009, Pages 2262-2272,

http://www.tsl.uu.se/uhdsg/Publications/GOF_decline_Article.pdf

[51] British Petroleum excel sheet of world countries energy statistical data

http://www.bp.com/assets/bp_internet/globalbp/globalbp_uk_english/reports_and_publication

s/statistical_energy_review_2011/STAGING/local_assets/spreadsheets/statistical_review_of_

world_energy_full_report_2011.xls

[52] Oil consumption data for 2010


[53] Oil reserve data for 2010


[54] Natural gas consumption data for 2010


[55] Natural gas reserve data for 2010


[56] REN21-2011, Renewable Energy Policy Network for 21st century (REN21); Renewables

2011: Global Status Report;

http://www.ren21.net/Portals/97/documents/GSR/REN21_GSR2011.pdf

References

113

[57] Target year and percent for Jordan

http://www.encharter.org/fileadmin/user_upload/Publications/Jordan_EE_rr_2010_ENG.pdf

[58] Target year and percent for Lebanon: Hussein Abaza, Najib Saab, Bashar Zeitoon; “Arab

environment 4 Green economy: Sustainable Transition in a changing Arab World”, 2011,

http://afedonline.org/Report2011/PDF/En/chapter%203%20Energy.pdf

[59] Eng. Ashraf Kraidy from RCREEE, communicated through email, where he mentioned the

original reference to be

“ 2011نتاج واستهالك الطاقة دليل إمكانات الدول العربية في مجاالت الطاقة المتجددة ورفع آفاءة إ ”.

[60] Target year and percent for Greece:

http://www.energy.eu/renewables/factsheets/2008_res_sheet_greece_en.pdf

[61] Target year and percent for Malaysia:

http://biz.thestar.com.my/news/story.asp?file=/2010/8/28/business/6921425&sec=business

[62] http://www.smidirect.net/published/feed-in.htm

[63] http://infinitybiopower.com/technology-2/weltec-biopower/german-biogas-industry/

[64] http://newenergynews.blogspot.com/2009/09/high-noon-for-spanish-lies-about-new.html

[65] “Middle East and North Africa Region Assessment of the Local Manufacturing Potential for

Concentrated Solar Power”, Jan 2011

http://siteresources.worldbank.org/INTMENA/Resources/CSP-Job-Study-Eng-Sum.pdf

[66] Concentrating Solar Power for the Mediterranean Region, German Aerospace Center (DLR),

Institute of Technical Thermodynamics, System Analysis and Technology Assessment

Section, April 2005.

http://www.dlr.de/tt/Portaldata/41/Resources/dokumente/institut/system/publications/MED-

CSP_complete_study.pdf

[67] Patricia Cordeiro; International Energy Agency (IEA), Solar Power and Chemical Energy

Systems, START Mission to Brazil; May 5-9, 1997.

http://www.solarpaces.org/_Libary/START_Brazil%20Start%20Mission%20Rept.pdf

[68] Brazilian solar map: http://www.solarpaces.org/News/Projects/Brazil.htm

[69] Max Edkins, Andrew Marquard, and Harald Winkler; “Assessing the effectiveness of

national solar and wind energy policies in South Africa”; Energy Research Centre,

University of Cape Town; June 2010.

http://www.erc.uct.ac.za/Research/publications/10Edkinesetal-Solar_and_wind_policies.pdf

References

114

[70] Cédric Philibert; “International Energy Technology Collaboration and Climate Change

Mitigation, Case Study 1: Concentrating Solar Power Technologies”; International Energy

Agency; 2004. http://www.oecd.org/dataoecd/25/9/34008620.pdf

[71] Kevin Ummel; “Concentrating Solar Power in China and India: A Spatial Analysis of

Technical Potential and the Cost of Deployment”; Center of Global Development; Working

Paper; July 2010.

http://www.cgdev.org/files/1424287_file_Ummel_ChinaIndiaCSP_FINAL.pdf

[72] M. Villarini, M. Limiti, and R. Impero Abenavoli, “Overview and Comparison of Global

Concentrating Solar Power Incentives Schemes by Means of Computational Models”;

Computational Science and Its Applications-ICCSA 2011, International Conference,

Santander, Spain, June 2011, Proceedings, Part IV, pp 258-269.

http://books.google.com.eg/books?id=3ivd-

NwNCN8C&pg=PA269&lpg=PA269&dq=CSP+target+turkey&source=bl&ots=0QjKN_Ed

1f&sig=0AfhYq-gKg0h7WCT-Lt7klGxi60&hl=en&ei=n3PgTu-

eLZHU4QTL863uBg&sa=X&oi=book_result&ct=result&resnum=4&ved=0CC4Q6AEwA

w#v=onepage&q=CSP%20target%20turkey&f=false

[73] http://www.ey.com/GL/en/Industries/Oil---


[74] http://social.csptoday.com/emerging-markets/greece%E2%80%99s-renewable-energy-bill-

panacea-troubled-csp-sector

[75] http://www.nurenergie.com/index.php?page=greece

[76] World Steal Organization http://www.worldsteel.org/dms/internetDocumentList/yearbook-

archive/Steel-statistical-yearbook-

2009/document/Steel%20statistical%20yearbook%202009.pdf

[77] United Nation

http://esa.un.org/unpd/wpp/Excel-

Data/DB02_Stock_Indicators/WPP2010_DB2_F01_TOTAL_POPULATION_BOTH_SEX

ES.XLS

Appendix A

115

Appendix A

Questionnaire for compiling the 2010 Press Freedom Index

The period runs from 1 September 2009 to 31 August 2010

Give as many examples as possible. Answers must be limited to events that took place during

this period.

Physical Violence

Answer Yes or No to each question. During this period, were there any cases of journalists:

1. Being tortured or mistreated during detention?

2. Being kidnapped or disappearing?

3. Being illegally detained (without an arrest warrant, for longer than the maximum period of

police custody, without a court appearance etc)?

4. Fleeing the country as a result of threats?

During this period, were there (Yes/No):

5. Armed militias or clandestine organisations regularly targeting journalists?

6. Journalists who had to have bodyguards or use security measures (such as wearing bullet-proof

vests or using armour-plated vehicles) in the course of their work?

Number Of Journalists Murdered, Detained, Physically Attacked Or

Threatened, And Role Of Authorities In This

During this period, how many journalists, media assistants or press freedom activists:

7. Were killed in connection with their work?

8. Were killed in situations in which authorities (police, soldiers, central or local government

officials, ruling party activists etc) were involved?

9. Were detained or jailed (for more than 24 hours)?

Appendix A

116

10. Were still in prison at the end of this period as a result of receiving a long jail sentence (more

than a year) for a press offence?

11. Were physically attacked or injured?

12. Did representatives of the state carry out any or all of these acts of violence?

- Yes

- No

13. In the above cases, did the authorities do their best to punish those responsible for these press

freedom violations? Give a score from 0 (no effort by the authorities to punish those

responsible) to 5 (determined efforts by the authorities to punish those responsible).

14. Or did the authorities take steps to prevent those responsible for these press freedom

violations from being prosecuted (for example, by blocking investigations or postponing

trials indefinitely)? Give examples.

Indirect Threats, Harassment And Access To Information

During this period, were there any cases of (Yes/No):

15. Surveillance of journalists by the state (were any journalists’ phones tapped, were any

journalists followed etc)?

16. Journalists employed by privately-owned media being forced to stop working because of

political pressure or threats?

17. Journalists being prevented from working because of their gender, origin, sexual orientation

or religion ?

18. Serious difficulty accessing public or official information (such as a refusal by officials to

provide information, information being provided selectively, according to the media’s

editorial position)?

19. Restrictions on access to or coverage of any region or regions in the country (including an

outright ban or strict government controls)?

20. Foreign journalists deported or prevented from entering the country?

Appendix A

117

Censorship And Self-Censorship

21. How many news media were censored, had issues seized or had their premises ransacked?

22. Were all the media subjected to systematic prior censorship (control before publication)? And

if so, name the body that exercised this censorship function: During this period, was there

(Yes/No).

23. Widespread self-censorship in the privately-owned media? Give a score from 0 (no self-

censorship) to 5 (a great deal of self-censorship) ?

24. Important news that was suppressed or not covered because of political or business pressure?

Give examples.

25. Frequent investigative reporting on sensitive subjects?

CONTROL OF MEDIA

26. a) Are there privately-owned TV stations in your country?

b) If so, are they free to determine their own editorial policies?

27. a) Are there privately-owned radio stations in your country?

b) If so, are they free to determine their own editorial policies?

28. Are there privately-owned printing and distribution companies? During this period, was there

or were there (Yes/No)

29. Government control of what the state-owned media publish or broadcast?

30. Unjustified dismissals of journalists in the state-owned media?

31. Opposition access to state-owned media? Give a score from 0 (no access at all) to 5 (free and

fair access).

Give an estimate of the number of:

32. Independent news media operating in the country (excluding media based abroad).

- 0

- 0 to 5

Appendix A

118

- 6 to 50

- More than 50

Judicial, Business And Administrative Pressure

During this period, was there or were there (Yes/No):

33. Unjustified or improper use of fines, summonses or legal action against journalists or media

outlets?

34. Violations of the confidentiality of journalistic sources (by such means as investigation,

interrogation or legal action)?

35. Use of the withdrawal of advertising to pressure media (in which the government or state

agencies stop buying advertising space or the authorities pressure private firms into doing

this)?

36. A requirement to obtain a government licence in order to start up a newspaper or magazine?

37. A transparent and fair process for allocating broadcast frequencies?

38. Serious threats to news diversity, including threats resulting from narrow ownership of media

outlets? Give a score from 0 (no threat) to 5 (very serious threat to media diversity).

39. A government takeover of any privately-owned media during this period, either directly or

through government-controlled firms?

Internet And New Media

40. Do the authorities control Internet service providers directly or indirectly? During this period,

was there or were there (Yes/No).

41. Cases of access to websites being blocked by filtering mechanisms or being closed down by

the authorities? Evaluate the level of this censorship on a scale of 0 (no censorship) to 5 (total

censorship).

42. Cases of cyber-dissidents or bloggers being detained for more than a day? How many?

43. Cases of independent websites being the target of cyber-attack or counter-information

campaigns?

Appendix A

119

Are there any points not included in this questionnaire that might be relevant for assessing the

press freedom situation in your country? Please mention them.

If there are questions that give rise to doubts on your part (about their applicability to your

country or the accuracy of your answer), please list them and say why (for example, lack of data,

wording that seems ambiguous or wording that does not correspond to the situation in your

country).

Appendix B

120

Appendix B

Appendix C

121

Appendix C COUNTRY ATTRACTIVENESS INDICES

NEW COUNTRY RESEARCH QUESTIONNAIRE

IMPORTANT– Each response should begin on the line underneath the ‘Response:’ heading for each question, and should automatically appear in blue font. The size of the response box DOES NOT provide an indicator of the length of the response or the level of detail required – this is purely a template. Your response should be as detailed as possible relative to the information available in respect of each parameter, and it is absolutely fine to go into multiple pages, since page breaks are in place to separate the sections. .

It is also important that responses include dates / sources where possible (in brackets after the relevant part of the response). If necessary, you can also create a separate document which lists the sources (e.g. report names / website links) which have been used throughout the research task.

NAME OF COUNTRY:

GENERAL PARAMETERS ELECTRICITY MARKET REGULATORY RISK Potential risks inherent in generating renewable energy; e.g. what type of electricity market exists; is it fully deregulated, stable, and reliable? Who manages the electricity market in the country? Response:

Outline what, if any, support mechanisms or financial incentives are available in excess of the wholesale electricity price for renewable energy, without going into technology-specific detail (covered in ‘Power-offtake’). Response: POLITICAL RISK

How strong is the government’s commitment to developing the local renewable energy industry? What role has government played so far in promoting and developing the industry?

Response:

Are there any delays from the government’s side that are stalling the market? Response:

What legislation is in place to govern the country’s renewable energy industry?

Response:

PLANNING ENVIRONMENT Is it necessary to obtain planning permission for new renewable projects? Please specify which technologies is it necessary to obtain planning permission for. Response: Who does the planning permission need to be obtained from? What are the costs involved in obtaining planning permission? What documentation needs to be submitted? Response:

Appendix C

122

Is there any legislation governing the granting of planning permission? Response: How long does it take to obtain planning permission for renewable energy projects? Response: Is an Environmental Impact Assessment (EIA) necessary? If so, for which technologies? How long does it take to conduct an EIA for the different technologies? Response: Overall, are the more or fewer planning delays and restrictions compared to other countries (if known, or any indications)? How strong is local opposition to RE development? Response: GRID CONNECTION ISSUES What is the coverage of suitable grid infrastructure? Does existing technology allow for effective connection of renewable energy projects to the national grid? Response: Are there incentives for grid providers? Who carries the cost for connection to the grid? Response: Does renewable energy have “priority dispatch”? Response: How long does it take to be connected to the grid? Are there any restrictions / minimum requirements placed on renewable energy projects that apply for connection to the national grid? Response: ACCESS TO FINANCE Who are the main providers of finance for renewable energy projects in the country? Response: Is finance equally available for all technologies? If not, which technologies are favoured by financiers? Response:

Are there easy and / or cheap financing opportunities from local / international banks?

Response: How mature is the renewable energy financing market? Response:

Appendix C

123

TECHNOLOGY PARAMETERS – ONSHORE WIND

POWER OFFTAKE Are there any power off take incentives /subsidies in place for this technology? (This excludes direct tax benefits or tax breaks granted for a given technology which cannot be sold on to another entity). Response:

Please give details of the price, longevity, and conditions associated with any support mechanism (e.g. feed-in tariff, green certificate, RPS mechanisms, etc.), and include in databook where appropriate. Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. wind speed, strong wind areas, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

124

TECHNOLOGY PARAMETERS – OFFSHORE WIND


Please give details of the price, longevity, and conditions associated with any support mechanism (e.g. feed-in tariff, green certificate, RPS mechanisms, etc.), and include in databook where appropriate. Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. wind speed, strong wind areas, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

125

TECHNOLOGY PARAMETERS – SOLAR PV


Please give details of the price, longevity, and conditions associated with any support mechanism (e.g. feed-in tariff, green certificate, RPS mechanisms, etc.), and include in databook where appropriate. Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. solar irradiation, hours of sun, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

126

TECHNOLOGY PARAMETERS – SOLAR CSP (only relevant for some countries – mainly southern)

POWER OFFTAKE Are there any power off take incentives /subsidies in place for this technology? (This excludes direct tax benefits or tax breaks granted for a given technology which cannot be sold on to another entity).

Response: Please give details of the price, longevity, and conditions associated with any support mechanism (e.g. feed-in tariff, green certificate, RPS mechanisms, etc.), and include in databook where appropriate.

Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. solar irradiation, hours of sun, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

127

TECHNOLOGY PARAMETERS – BIOMASS



Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. forest areas, indigenous feedstock, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

128

TECHNOLOGY PARAMETERS –GEOTHERMAL



Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g... geothermal fields, volcanic activity, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

129

TECHNOLOGY PARAMETERS –SMALL SCALE HYDRO (<30MW)



Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. .rivers, mountainous areas, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix C

130

TECHNOLOGY PARAMETERS –WAVE & TIDAL



Response: TAX CLIMATE What tax breaks or tax-related incentives are in place for the specific technology? (e.g. accelerated depreciation, no import duties on RE components etc) Response: GRANT / SOFT LOAN AVAILABILITY Please provide details of any grants, government backed loans or other financial support (e.g. low-interest loans) that is made available in respect of this technology. Response: RESOURCE QUALITY Details of the resource quality available for this specific technology (e.g. strength of waves / tidal movement, any other conditions making the country a suitable / non-suitable location for the development of this technology). [Note - Unlikely to change over time unless a new resource becomes available or following dramatic climate shifts] Response: CURRENT INSTALLED BASE What is the total current installed capacity (MW/GW and/or MWh/GWh)? Give figure as at end of previous calendar year, and any more recent updates if available. Enter figures in databook if appropriate. Response: How does the installed capacity for the specific technology compare to the total energy requirements of the country? What proportion of total energy / power does this technology capacity represent? Response: MARKET GROWTH POTENTIAL What is the expected future capacity (per targets) or estimated maximum potential (in MW/GW or MWh/GWh if possible)? Response: Provide details of any other indications / government targets, which indicate the future growth potential of the technology. What is the potential for large projects? Response: Is there an established supply / manufacturing base for the specific technology? Response: PROJECT SIZE What is the average project / facility size for this technology? If appropriate, give examples of operating and/or planned projects. Response:

Appendix D

131

Appendix D The scores shown in Table (5.2) are based on the following analysis:

• The score of local availability of raw material of glass, float glass manufacturing, and glass

transformation indicators are based on the analysis given in [65].

• The score of glass bending indicator is based on extensive internet survey about the existing

factories in the countries understudy that have the ability to perform such process.

• The score of glass coating, cabling industry, and electronic and electrical indicators are

based on both extensive internet survey and on [65].

• The scores of the R&D of the electronic and electrical industry are concluded from Fig A11

in [65].

• The score of the steel production capacity indicator is determined by devising an index based

on the amount of steal production in K tone per 1000 person of the country population using

the following two equations:

( ) CapitatoneKPopulation

oductionSteelSPCVValueCapacityoductionSteel 1000/PrPr = (D.1)

( ) ( )( ) ( )valueSPCVvalueSPCV

valueSPCVvaluecountrySPCVIndexCapacityoductionSteelminmax

min5Pr−−

×= (D.2)

where the values of countries’ steel production and population are brought from [76] and [77]

respectively.

• The score of R&D of the steel industry is concluded from Fig A13 in [65].

• The score of piping and insulation capacity extensive internet survey.

132

المستخلص

ويتكون المؤشر ، دول العالمفي سوق الطاقة المتجددة تنافسية يصف مؤشر آمي موضوعي تحليلي تم إشتقاق

ة ، و مؤشرات تكنولوجيخمسة و عام مؤشرثالث عشر من " المتجددةسوق الطاقةمؤشر تنافسية " و المسمى المقترح

. النسبية بين الدولللتنافسية لتمثيل أآثر دقة في المستقبلد من المؤشراتالمزيإضافة يسمح بالمقترح مؤشر لل يهيكلالترآيب ال

ثالث منومتوسطالعامة الثالثة عشر المؤشرات بين متوسط الموزون مؤشر يعتمد على المتوسط العلى الرغم من أن حساب و

يظهر تغييرلـثمانية عشر دولة ة المرآزة الطاقة الشمسي تكنولوجيا تطبيق المؤشر علىالمؤشرات التكنولوجية الخمسة فإن ا

هذا ويرجع ذلك . معامل التوازنمع تغيير ) سواء في مجموعة النقاط أو الترتيب النهائي للدول(مؤشر النتائج في جداا طفيف

حساب من الكافي أنه ، مما يشير إلىةتكنولوجي مؤشرات ثالثةالثالثة عشر و بين الإلى االرتباط الكبير من المؤشرات العامة

.ة فقط دون الحاجة لدراسة آل المؤشرات العامةمؤشرات تكنولوجيبداللة الخمسة المقترح المؤشر

تم تطويره ذي ال و، لتكنولوجيا الطاقة الشمسية المرآزة جاذبية البلدان مؤشرو مقارنة بين المؤشر المقترح إن عقد

األحد عشر و الترتيب النهائي للدول النقاتمجموع بين ) ٪85> ( جداةيونغ ، يظهر مطابقة جيد من قبل شرآة ارنست و

22 إلى 17و الذي يصل إلى ما بين بين المؤشرين النقات في على الرغم من الفرق الكبير نسبياالمشترآة في المؤشرين ، و

آذلك فإن . فقط نقاط7تجاوز يالمتبقية ال الثمان دول لفرق في ا فإن جنوب أفريقيا واليونان وتونسهي دول لعدد ثالث نقطة

في الترتيب يبلغ فقط تغييرالترتيب ، و أن أآبر دول لديها نفس خمس بين أن بين ترتيب الدول في المؤشرين يتحليل الفرق

.و يحدث لدولتين فقطأماآن ثالث

ذلك ه ولحساببشرية آبيرة حتاج إلى موارد فهو ال يلي وبالتامؤشر بسيط هو الطاقة المتجددة سوق تنافسية ومؤشر

بمؤشر جاذبية البلدان مقارنة موضوعي وتحليليهو مؤشر فباإلضافة إلى ذلك . األآثر تعقيدا بالمقارنة مع مؤشر جاذبية البلدان

حساب المؤشر مما يجعل م على ة القائذاتيخبرة و مقالي و آذلك على ستبيانللطاقة المتجددة و الذي يعتمد بشكل آبير على إ

.لكثير من المستخدمينأآثر موائمة لوضعة على شكل برنامج حاسب آلي و إتاحته المؤشر المقترح

منهجية إلشتقاق مؤشر تنافسية سوق الطاقة المتجددة مع تطبيقه

على تكنولوجيا مرآزات الطاقة الشمسية إعداد

يــي الرفاعــد هانـم السيـحات

القاهرة جامعة ، الهندسة ةيآل إلى مقدمة رسالة

و قسم الهندسة الكهربية و علوم الحاسب ، جامعة آاسيل

في الطاقة المتجددة و آفاءة الطاقة لدول الشرق األوسط و شمال الماجستر درجة على الحصول متطلبات من آجزء

(REMENA)أفريقيا

:يعتمد من لجنة الممتحنين

)آلية الهندسة ، جامعة القاهرة (عضو أمين مبارك/األستاذ الدآتور

)آلية الهندسة ، جامعة آاسيل (عضو ديرك دالهاوس /األستاذ الدآتور

)آلية الهندسة ، جامعة القاهرة (مشرف محمد السبكي/األستاذ الدآتور

)آلية الهندسة ، جامعة القاهرة (مشرف مهاب هلوده/األستاذ الدآتور

آلية الهندسة ، جامعة القاهرة

الجيزة ، جمهورية مصر العربية

2012


على تكنولوجيا مرآزات الطاقة الشمسية

دإعدا

حاتـم السيـد هانــي الرفاعــي



في الطاقة المتجددة و آفاءة الطاقة لدول الشرق األوسط و شمال الماجستر درجة لىع الحصول متطلبات من آجزء

(REMENA)أفريقيا

تحت إشراف

مهاب هلوده محمد السبكي أستاذ

الكهربية و اآلالتالقوىهندسة بقسم آلية الهندسة ، جامعة القاهرة

أستاذ بقسم هندسة القوى و اآلالت الكهربية




2012


على تكنولوجيا مرآزات الطاقة الشمسية

إعداد

حاتـم السيـد هانــي الرفاعــي



في الطاقة المتجددة و آفاءة الطاقة لدول الشرق الماجستر درجة على الحصول متطلبات من آجزء

(REMENA)األوسط و شمال أفريقيا



2012

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