Master Thesis

Photovoltaics in the Developing World: A System Dynamics Approach in the

Energy-Growth Relation

A thesis submitted to the School of Forest Science and Resource Management

Sustainable Resource Management Program In partial fulfillment of the requirements for the degree of

Masters of Science

written by:

J. Anwar Darwich del Moral Matr. Nr. 03649173

Submitted to:

Institute for Renewable and Sustainable Energy Systems Technische Universität München

Examiner:

Prof. Dr. Thomas Hamacher

Advisor:

Dipl.-Ing. (Univ.) Matthias Huber

February, 2015 TECHNISCHE UNIVERSITÄT MÜNCHEN

i

Abstract

Underdeveloped economies in rural areas continue to face the poverty trap due to the lack of

reliable and economically viable energy services. Today, solar photovoltaic (PV) technologies

have achieved a level of competitiveness which allows its implementation with the aims of

alleviating this issue. The goal of this thesis is theoretically understand and describe the

positive effects of the development of PV, in terms of cost reductions, in order to be used as a

source of electricity that can detonate economic growth in the developing world. Based on the

analysis of the theories on learning curves and the energy-growth nexus, a system dynamics

approach is implemented to construct a model representing the interrelation of these

mechanisms.

The learning-by-doing, learning-by-researching and input price effects obtained from the

increased installed capacity and investment in R&D are the main forces behind the decrease

of the levelized costs of electricity of PV. The combination of these effects initiate the

reinforcement of a PV-Energy-Growth Engine similar to the Salter Cycle, which explains the

positive outcome of the implementation of PV in developing economies: The decrease in

production costs of PV-generated electricity translates into lower electricity prices in these

regions. Increased demand for PV-generated electricity generates economic activity allowing

the investment in more PV capacity and cost-reducing techniques. The reinforcement of this

cycle is responsible for the improvement of the economic state and contributes to the

alleviation of poverty.

Keywords

Energy-Growth Relation; Economic Growth; Photovoltaics; Developing Countries, Energy

Economics; System Dynamics.

ii

Project Description

Photovoltaics in the Developing World: A System Dynamics Approach in the Energy-Growth Relation

The provision of energy is one of the key factors for economic growth and the wellbeing of

nations. People in developing countries lack access to reliable sources of energy and

electricity. The national grids are poor and unreliable, thus the communities often have to

supply their power with Diesel engines at very high costs. This lack of reliable and affordable

energy supply restricts economic development due to various aspects, e.g. the substitution of

labor through capital (machinery) requires energy, current form of energy supply even requires

high share of workforce (e.g. collecting fire wood), unreliable power supply restricts schooling

and medical service which in turn restricts development.

The subsidy induced large scale installations of PV in many industrialized countries have led

to decreasing costs for this source of energy. As a consequence, it is now possible to generate

electricity from PV at costs below the costs of a Diesel generator. Especially rural regions

where reliable energy was restricted, economic growth might now be able to start. The scope

of this Master Thesis is to quantify the positive effects that renewable subsidies can have on

the economic situation of development countries.

Objectives: Based on a short overview of literature on the energy and economic growth nexus,

the goal of this thesis is to theoretically describe the role of photovoltaic technologies in the

economic and social development with a focus on developing countries. In achieving this goal,

a general overview of the current state of the energy markets will be provided in order to

establish the main advances of the photovoltaic industry and its competitors. The main streams

of thoughts regarding the learning curves theories are evaluated. A review of the literature

dealing with relation between economic growth and energy will be discussed.

As a result, the following questions will be answered:

Why did prices for PV drop and what further price development can be expected

What is the link between energy and growth?

What is the role of alternative energy sources such as PV in economic growth?

How will the overall economy be influences by the implementation of new PV technologies?

Requirements: Interest in energy economics, development economics, renewable technologies

in developing countries, high motivation, ability to work independently

Supervisor: Prof. Hamacher, Dipl.-Ing. Matthias Huber

Duration: 6 months

Examiner: Prof. Dr. rer. nat. Thomas Hamacher

iii

Statement of Academic Integrity

I,

Last name: Darwich del Moral

First name: Jesus Anwar

ID No.: 03649173

hereby confirm that the attached thesis,

Photovoltaics in the Developing World: A System Dynamics Approach in the Energy

Growth Relation

was written independently by me without the use of any sources or aids beyond those cited,

and all passages and ideas taken from other sources are indicated in the text and given the

corresponding citation.

Tools provided by the institute and its staff, such as models or programs, are also listed.

These tools are property of the institute or of the individual staff member. I will not use them

for any work beyond the attached thesis or make them available to third parties.

I agree to the further use of my work and its results (including programs produced and

methods used) for research and instructional purposes.

I have not previously submitted this thesis for academic credit.

Munich, February 27th, 2015

......................................................

J. Anwar Darwich del Moral

iv

Acknowledgements

The culmination of my Master Thesis represents another step in the accomplishment of my

academic, professional and personal goals, therefore I would like to thank those who made

this possible:

I thank Prof. Hamacher for the opportunity to write my Master Thesis at the Institute for

Renewable and Sustainable Energy Systems.

I thank my thesis supervisor Matthias Huber for the constant advice and dialogue throughout

these months. I appreciate the guidance during our discussions and the valuable input

towards the development of the research.

I also thank Dr. Biber of the Chair of Forest Growth and Yield Science for his guidance and

helpful discussions regarding the implementation of the system dynamics approach in my

thesis.

And of course, I thank my family for their unconditional support and motivation, and also for

the understanding and sacrifice that the achievement of this goal represented over the last

year.

v

Contents

1. Introduction .................................................................................................................. 1

1.1 Implementation of PV for Economic Development ...................................................................... 1

1.2 Methods ........................................................................................................................................ 3

2. Energy Markets ............................................................................................................ 5

2.1 Non-renewables ............................................................................................................................ 5

2.2 Renewables ................................................................................................................................... 6

2.2.1 Drivers Renewables ................................................................................................................ 7

2.3 Solar PV Market ........................................................................................................................... 12

2.3.1 Drivers PV ............................................................................................................................. 14

2.3.2 Forecasts............................................................................................................................... 17

3. Experience and Learnings in Photovoltaics ..............................................................19

3.1 Levelized Cost of Electricity ......................................................................................................... 19

3.1.1 General formulation ............................................................................................................. 19

3.2 Learning curves............................................................................................................................ 22

3.2.1 One-factor learning curve .................................................................................................... 23

3.2.1 Two-factor learning curve .................................................................................................... 24

3.2.3 Multi-factor learning curves ................................................................................................. 26

4. Theories on the Energy-Growth Relation ..................................................................29

4.1 Basic Concepts ............................................................................................................................. 29

4.1.1 Energy in the context of economics ..................................................................................... 29

4.1.2 Growth and Development .................................................................................................... 30

4.2 Energy and Economic Growth ..................................................................................................... 33

4.2.1 The Production Function ...................................................................................................... 33

4.2.2 Mainstream Growth Models ................................................................................................ 34

4.2.3 Integration of Energy in Growth Models .............................................................................. 36

4.2.4 The Growth Engine ............................................................................................................... 39

4.2.5 Energy-Growth Causality ...................................................................................................... 42

vi

4.3 Energy on Development .............................................................................................................. 44

4.3.1 The Environmental Kuznets Curve ....................................................................................... 44

4.3.2 The Social Impact of Energy ................................................................................................. 46

4.3.3 Barriers to the Deployment of Renewable Technologies .................................................... 48

5. PV on Economic Growth .............................................................................................49

5.1 The System Dynamics Approach ................................................................................................. 49

5.1.1 The SD-Approach on the Energy-Growth Relation ............................................................... 51

5.2 The PV-Energy-Growth Engine .................................................................................................... 55

5.2.1 Description of the PVEGE ..................................................................................................... 55

5.2.2 Inside the PVEGE .................................................................................................................. 57

5.3 Application of the PVEGE: Case Analysis ..................................................................................... 61

5.3.1 Case 1: Developed Economy ................................................................................................ 62

5.3.2 Case 2: Developing Economy ............................................................................................... 62

6. Conclusions .................................................................................................................72

Bibliography .......................................................................................................................74

Annex ..................................................................................................................................82

vii

List of Figures

Figure 1: System Dynamics Methodology.............................................................................. 4

Figure 2: Renewable Energy Power Capacity in GW ............................................................. 7

Figure 3: Global New Investment in Renewable Energy by Technology, 2013 ...................... 9

Figure 4: Global New Investment in Renewable Power and Fuels, by Region, 2004-2013 .... 9

Figure 5: Comparison of LCOE of Renewables and Conventional Power Plants, 2013; .......12

Figure 6: Global PV Cumulative Installed Capacity; Source: EPIA (2014). ...........................13

Figure 7: Net Power Generation Capacities Added in the EU 28 in 2013 .............................14

Figure 8: World Policy Map: Feed-in-Tariffs and Targets for PV ...........................................15

Figure 9: PV Module Price Decrease ....................................................................................17

Figure 10: Relationships and Feedbacks for TFLC Models Including R&D, Production Growth

and Production Cost .............................................................................................................25

Figure 11: Relationships and Feedbacks for MFLC Models ..................................................26

Figure 12: The Capital-Labor-Energy-Creativity Model of Wealth Production in the Physical

Basis of the Economy ...........................................................................................................38

Figure 13: Salter Cycle .........................................................................................................40

Figure 14: Correlation Coefficients for OECD and Non-OECD Regions ...............................43

Figure 15: Environmental Kuznets Curves............................................................................45

Figure 16: Multiplier Economic Effects from Increased Energy Services Utilization ..............47

Figure 17: Types of Behavior in a System ............................................................................51

Figure 18: PV-Energy-Growth Causal Loop Diagram ...........................................................56

Figure 19: PVEGE System (View of the Learning and Input Price Effects) ...........................59

Figure 20: PVEGE System (View of the Price Effects) .........................................................60

Figure 21: Development of PV Cumulative Capacity over Time ............................................64

Figure 22: LCOE Development over Time due to Experience, Input Price and Knowledge

Effects ..................................................................................................................................65

Figure 23: Demand over Time for PV Energy Services.........................................................66

Figure 24: GDP Development over Time ..............................................................................66

Figure 25: Knowledge Development over Time ....................................................................67

Figure 26: Cumulative Capacity Development due to Policy Changes ..................................68

Figure 27: LCOE Development due to Policy Changes ........................................................69

Figure 28: GDP Development due to Policy Changes ..........................................................69

Figure 29: Causal Loop Diagram of the Coupled PVEGE .....................................................70

viii

List of Tables

Table 1: Search Strategy ....................................................................................................... 3

Table 2: PVEGE Values for a Developed Economy ..............................................................62

Table 3: PVEGE Values for a Developing Economy .............................................................63

Table 4: PV Policy Comparison ............................................................................................68

Table 5: LCOE Energy Technologies in USD/kWh ...............................................................82

Table 6: Country comparison for Selected Renewables .......................................................82

Table 7: Countries implementing Feed-in-tariffs and Targets for Solar PV ...........................84

Table 8: Barrier Classification ...............................................................................................86

ix

List of Abbreviations

APAC Asian-Pacific

CCGT Combined Cycle Gas Turbines

CO2 Carbon Dioxide

CSP Concentrated Solar Power

EKC Environmental Kuznets Curve

FIT Feed-in-Tariffs

GDP Gross Domestic Product

GDPPP Gross Domestic Product Purchase Power Parity

GNP Gross National Product

GW Gigawatt

GWh Gigawatt-hour

LBD Learning-by-Doing

LBI Learning-by-Interacting

LBR Learning-by-Researching

LBU Learning-by-Using

LCOE Levelized Cost of Electricity

LR Learning Rate

LTEP Lifetime Energy Production

MDG Millennium Development Goals

MENA Middle East and North Africa

MFLC Multi-factor Learning Curve

MVP Marginal Value Product

NRET Non-Renewable Technologies

OFLC One Factor Learning Curve

PR Progress Ratio

PV Photovoltaics

PVEGE Photovoltaics Energy-Growth Engine

RET Renewable Energy Technologies

RPS Renewable Portfolio Standards

SD System Dynamics

TFLC Two Factor Learning Curve

TLCC Total Lifecycle Costs

TW Terawatt

TWh Terawatt-hour

1

1. Introduction

1.1 Implementation of PV for Economic Development

Already in 2015, the year set as a deadline for Millennium Development Goals (MDG), energy

and the services it provides remain subjects of study due to their essential role in achieving

economic and social development. Dealing with issues about poverty, hunger, education,

gender equality, health and sustainability, the MDG do not refer explicitly to energy. However,

each and every single of the goals established at the World Summit on Sustainable

Development can be positively influenced by improving the access to reliable, affordable,

economically viable, socially acceptable and environmentally sound energy services (UN,

2002). As a result, even if it is not directly stated, energy must be treated as an issue of major

concern.

Defined by the UN as the greatest challenge faced today (UN, 2002), the goal of poverty

eradication aims to halve the amount of people living under the 1-dollar-a-day fringe. In this

regard, it is important to understand that, by no means, the achievement of other goals are

less transcendent than poverty. Nonetheless, they relate to poverty issues to the extent that

they are either originated by or associated to situations found in an environment of poverty.

Therefore, given the important role that the provision of energy services represents on the

promotion of sustainable development, thus the eradication of poverty, the implementation of

international cooperation, policy mechanisms and innovative techniques ought to be a crucial

aspect to be dealt.

Population without electricity

A world without the energy services provided by electricity would be unimaginable today. Not

only it enables us to make use of our appliances for lighting, heating or cooking, it can be used

for powering our vehicles, pumping water or for industrial and agricultural purposes needed to

generate social and economic development. Developed countries would not enjoy of their

favored wealth endowment without electricity. Unfortunately, not all corners in our globe can

benefit from its services. Only 77 percent of the people in developing countries have access

to it. Even after improvements in this field, the contrast among regions in the same continent

is alarming. In Africa itself, the difference between the northern and the Sub-Saharan regions

is large. Data from 2011 shows that 99 percent of the people in North Africa enjoy access to

electricity, whereas in Sub-Saharan Africa less than one third do (REN21, 2014).

Consequently, such disparity even within the same region denotes the need for better energy

sources.

2

How to better supply electricity: off-grid, mini-grid

The unsustainable depletion of natural services is another challenge worldwide. Wood remains

an essential resource especially for the poor countries. Around 2.6 billion people in developing

countries still depend on traditional biomass as a source for cooking and heating, most of them

in rural areas. Developing Asia concentrates most of these people, reaching 1.7 billion,

followed by Africa with 696 million, thus, calling for the improvement of the region (REN21,

2014). In this regard, modern renewables provide the possibility to increase the access to

alternative energy sources while also reducing the depletion of our forests through off-grid or

community-level mini-grid systems. Off-grid systems, as their name states, are autonomous

and independent systems whose competitive advantage lies in the ability to be installed in

remote regions where the infrastructure does not allow connection to the central electric-line

system. Given the economic and social development of many regions in Latin America, Asia

or Africa, such power sources result ideal for the provision of reliable electricity. In many cases,

villages or small communities in these places rely only in electricity produced by diesel

generators lacking maintenance and being outdated. Therefore, natural as well as economic

circumstances in many developing countries create a suitable environment for the

competitiveness among traditional and alternative sources of energy.

It is clear that new technologies, such as renewable energy systems, are the key to improving

human well-being. They can provide the access to better health services, education, heating,

cooking, refrigeration, telecommunications and transportation. If those services can be

effectively satisfied, there is a major chance to also increase economic activities and reduce

poverty (Modi, et al., 2005). Developing countries understand the importance of providing cost-

effective and secure energy services in order to achieve these goals. It is not a coincidence

that most of the new implemented renewable power plants can be found in these regions of

the world. Their governments have therefore sought to foster this transition through the

execution of diverse institutional, financial and legal mechanism. Moreover, international

organizations and countries in the developed world have provided aid in the form of

development programs and technology transfer. Still, many of these efforts have encounter

local barriers that impede the achievement of such goals.

Over the last years, solar PV systems have proved to be an alternative fulfilling the conditions

established by the UN in the battle against poverty: they represent a socially acceptable,

environmentally sound and now also economically viable source of energy. In remote areas

lacking infrastructure and access to the grid, or where fossil fuels and traditional biomass are

the only means of energy, PV displays a level of competitiveness that equals and even excels

mainstream sources. Notwithstanding the fact that other renewable energy technologies (RET)

3

such as onshore wind and hydropower have achieved better results than PV, natural

conditions surrounding most of the developing regions determines the preference for the

former technology. Most of the people lacking access to electricity conversely are located in

some of the regions with the highest solar radiation (Breyer & Gerlach, 2013). Therefore, its

implementation as a mean for electrification is more than convenient.

1.2 Methods

The goal of this thesis is to theoretically determine the extent by which PV technologies, as an

electricity source, can influence economic development of rural areas lacking access to

traditional energy services in developing countries. Under the premise that these technologies

have achieved a level of competitiveness which allows them to effectively provide energy

services in remote areas with large solar radiation potential, this goal will be achieved by

answering the following questions:

What is the relation between energy utilization (production and consumption) and

economic growth?

What are the mechanics influencing an economy relying on energy services provided

by PV technologies?

How will the overall economy be influenced by the availability of PV technologies?

For the assessment of these research questions, a combination of theoretical and empirical

approaches is implemented. The theoretical foundation, namely the search, filtering and

selection of the relevant literature for the thesis has been carried following a standard search

strategy (See Table 1). It consists on the selection of key aspects describing the topic of

discussion and the combination of them with appropriate synonyms. This procedure is carried

out iteratively in major literature databases in the field of economics, engineering and

environmental sciences (Web of Science, Scopus) and in search machines (Google, Google

Scholar).

Table 1: Search Strategy

Topic: The role of Photovoltaic technologies in the energy growth relation

Photovoltaics Economic Growth Energy Utilization

Solar Photovoltaics Economic Development Exergy Consumption

Solar PV Economic Progress Useful Work Production

Supply

The structure of this thesis consists of five main chapters. Firstly, a general evaluation of the

current state of the energy markets are provided in order to establish the main advances of the

photovoltaic industry and the development of its competitors. In doing so, special attention is

4

given to the current and new installation of electricity-generation capacity and its main drivers,

namely policy support. This chapter later concludes on the evaluation of the Levelized Costs

of Electricity (LCOE) of PV and its global forecast.

Secondly, the main streams of though regarding the learning curves theories are examined,

however only applied to the PV industry. Most of the theories on learning curves focus on the

experience obtained from the continuous production process, namely the one-factor learning

curve. Nevertheless, In order to have a better fit to the actual evolution of the industry, the

learning theory focused on the PV can also focus on more than one factor, such as the

influence of research and input price changes. These different views are presented and

discussed.

Thirdly, a review of the literature dealing with relation between economic growth and energy is

carried out. Starting with the definition of basic concepts, mainstream growth models are

introduced followed by those including energy as an intrinsic element of the production

function. Throughout this chapter, the energy-growth causality is also evaluated, as well as the

impacts of energy on economic and social development.

Fourthly, based on the previous three sections, a basic model combining these ideas is

developed using a system dynamics approach powered by the modeling software VENSIM.

For this purpose, the common methodology in system dynamics described in

Figure 1 is carried out.

Figure 1: System Dynamics Methodology; Source: (Biber, 2015)

Finally, this thesis it concludes with some last comments on the subject and the summary of

the findings.

Interpretation

Simulation of the model

Determination of initial values

Construction of the model

Establishment of the parameters

Mathematical representation of the model

Development of a verbal and graphical description of the model

Analysis of relationships

Establishment of system borders and determinaition of variables

Question

5

2. Energy Markets

Our world is constantly changing, evolving in each and every aspect. Currently with a

population ascending to the 7 billion, we are responsible for the effects nearly 30 billion tones

carbon dioxide (CO2) and other greenhouse gases have on our natural systems. To a great

extent, these emissions come as a result of the production of electricity necessary for our

countries’ economic activities. Half of this electricity is consumed by industrialized countries,

however, it is estimated that future demand will be driven by emerging markets, most of them

located in the Asian continent. China currently consumes 18.4 percent of the world’s electricity,

most of which is generated using coal as its main source and other fossil fuels, thus emitting

large amounts of CO2 (IEA, 2013). Other countries such as the United States, Japan, India

and Russia also belong to those high-consuming countries. However, this is not the only

element in common. All these countries coincide in their attempt to supply that increasing

demand through the implementation of alternative energy sources, and thus, prolong their

productivity.

2.1 Non-renewables

Fossil fuels are today, and will continue to be at least within the short and mid-term, the

mainstream source of energy in the globe. Coal, gas and oil represented more than half of the

total installed capacity. According to 2011 figures (World Energy Council, 2013), total installed

capacity reached 5,161 gigawatts (GW), from which coal accounted for 46 percent of total

electricity generation, followed by gas, hydro and nuclear, with 18, 15.7 and 13.1 percent

respectively. In In 2012, this dependency towards non-renewable technologies (NRET)

prevailed, however, the shares of coal and nuclear were reduced to 40 and 11 percent of total

production (IEA, 2012). Additionally, by end-2013, fossil fuels and nuclear energy remained as

the main source of global electricity production with a share of 78.9 percent (REN21, 2014).

Although a total reliance on NRET exists, these figures should not be considered a reason to

desist on the belief of a more sustainable future, but as a challenge to overcome.

Investment

Investment in power will significantly increase over the next decades due to the need to supply

emerging markets around the world. All energy sources will be subject to this increase,

however the slowly replacement of fossil by renewables will be the reason for the notable

difference investment between them. Accounting annually 106 billion USD, fossil fuels are

expected to rise nearly by 18 percent over the next 20 years (IEA, 2014). This difference is

perceivable already today, as investment in RET already surpasses that of mainstream

sources. Less than 40 percent of new investment in power supply is being aimed at fossil fuels.

Coal however, continues to accumulate the largest share. Nuclear, on the other hand, faces a

6

different trend. Although the annual 8 billion USD barely represents 3 percent, it is expected

to increase the share of total investment over the next 10 years to almost 14 percent (IEA,

2014). After that increase, forecasts predict again a reduction of total investment towards

nuclear.

Levelized Cost of Electricity of Non-renewables

Contrary to RET, traditional energy sources show an increasing LCOE. Whereas every day

electricity produced by fossils is becoming on average more expensive, RET are turning into

a more reliable and competitive source. Nuclear on the other hand, remains as a competitive

source of electricity, however the disadvantage does not rely on the cost side, but on security

and health aspects. Right after Fukushima’s nuclear disaster, countries around the world have

started to change their attitudes towards this technology, to the extent of progressively

phasing-out it out, as in the case of Germany (BMU, 2011). For this reason, a shift from NRET

to RET is expected.

Even when this transition has already begun, the preference for cost-effective sources dictates

the demand, thus the share by which electricity is produced. Global costs for electricity

generated by coal remain as the lowest within non-renewable sources. Being this the reason

for the large share of electricity capacity and generation worldwide. LCOE for coal ranged from

0.035 to 0.172 USD/kWh whereas LCOE for gas from 0.061 to 0.141 USD/kWh (World Energy

Council, 2013). Large hydro power plants were the only alternative with lower LCOE in some

places. They lie between 0.02 and 0.302 USD/kWh (World Energy Council, 2013; REN21,

2014). Finally, among the sources with the largest installed capacity, nuclear still counts as a

low-cost alternative with LCOE between 0.091 and 0.147 USD/kWh. Nonetheless, depending

on the size of the plant, sources such as geothermal, biogas or even Solar PV have also

reached the same level of competitiveness (See Table 5 in Annex). Their advantage however,

remains on the acceptance and phasing-out of the former.

2.2 Renewables

In 2013, our world has already exceeded the renewable power capacity of 1560 GW, meaning

that every moment more and more people around the globe are gaining access to energy for

transportation, heating, cooling and electricity due to solar, wind, biomass or geothermal

power. Alone last year, 56 percent of new net additions were just from renewables (REN21,

2014). The key to building a path towards a clean energy supply lied in fostering the

implementation of new technologies. Even when the estimated RET share of the world’s power

generating capacity still shows a high dependency towards fossil fuels, more than one fifth of

the global electricity has been supplied by renewables. China, the United States, Brazil,

Canada and Germany remain as the countries with most installed renewable electric capacity,

7

however there is an increasing interest in regions such as Latin America, the Middle East and

Africa (REN21, 2014).

Current capacity of renewables by technology

Between the period of 2004 and 2013, power capacity provided by RET rose by 80 percent.

Simply by the end of 2013, the increase of a year was 8 percent for all different technologies

(See Figure 2). The highest share remains hydropower with approximately 1000 GW, followed

by Wind power with 318 GW and Solar PV with 139. However, the development was dissimilar

for each of these technologies. The largest leap was taken by solar energies. PV increased in

capacity by 39 percent whereas Solar Thermal by 36 percent. Wind power increased as well,

nonetheless its development was not as remarkable as the former technologies.

Figure 2: Renewable Energy Power Capacity in GW; Source: REN21 (2014).

2.2.1 Drivers Renewables

Drivers favoring the implementation of new technologies among countries can be diverse,

however, there are similar elements which make it possible to classify them. In the broadest

manner, they can be either technical or non-technical. Technical drivers refer to those

machine-related innovations focusing on the physical components' quality, efficiency,

performance and suitability to specific conditions during the process of manufacturing,

distribution, installation and maintenance that enable the utilization of RET instead of other

sources. Non-technical drivers describe other aspects such as the availability of economic or

investment resources, the legal and political infrastructure, and the attitude towards the

diffusion of RET. Therefore, when studying the panorama by which projects are carried out

around the world, it is possible to observe that they are being driven by technical, political,

social, environmental, institutional or market forces (Marques, et al., 2010; Gan & Smith, 2011;

100088

12

139

3.4

318

Hydropower

Bio-power

Geothermal

Solar PV

Concentrating Solar Thermal

Wind power

8

Zahedi, 2011). Nonetheless, some of these forces may have a greater influence on the overall

implementation of these technologies.

One of the motivations for RET is the fact that they represent a socially, environmentally and

now also economically viable alternative for investing in the expansion of energy services.

They can reduce health and environmental impact caused by fossil fuels as well as improve

the provision of security, employment and education opportunities, poverty reduction and

economic development especially in rural regions of the world (IEA, 2014). For this reason, it

is common that investment in cleaner technologies is often an elemental topic in a country’s

governmental agenda. The former development of RET in the developed world can be

attributed, to a great extent, to policy measures in the form of regulations, fiscal incentives or

public financing. Regulatory policies are implemented as targets, feed-in tariffs (FIT), quotas,

net metering, certificates or obligations. In the same manner, incentives in many countries

come in the form of subsidies, tax exemptions, production payments, loans or grants (REN21,

2014).

Investment

Regardless of the impetus for the implementation of alternative energy sources over the last

decades, investment in renewable power, except for geothermal power, has shown an

important global decrease with respect to previous years. Although estimated investment

was around 214.4 billion dollars for all RET (See Figure 3), compared to 2012 there was a

reduction of 14 percent (REN21, 2014). Such downturn was not only observed in developed

countries. After continuous years of increasing efforts, the developing world also showed a

similar reduction of 13 percent (REN21, 2014). Nevertheless, despite the global negative

trend, namely in Europe and the United States, the era of renewables has just had a slight

pothole and the hope for improvement is already visible as several regions of the world

showed positive attitudes towards renewables (See Figure 4). The Asia-Oceania region

increased its investment by 47 percent over 2012, mostly due to Japan’s attitude towards PV

(REN21, 2014). Therefore, the expectation of other developing countries joining this

momentum is present, especially in the Sub-Saharan Region and Latin America.

Investment in R&D

One of the most important investments in RET has been observed in research and

development (R&D). Although it represents a small portion of the total new investment in the

sector, this stage is mostly responsible for the development of the industry. Without the proper

efforts of governments as well as the private sector, such crucial step in the energy generation

and distribution could not have been possible. In the same direction as total new investment

in RET, investment on technology research has displayed an increasing trend over the last

9

decade. Most of this however, is attributed to private firms. Even when there has been a

decrease of 6 percent with respect to investment in 2012, corporate R&D in 2013 accounted

for 4.7 billion USD, which also represents 2 percent more than investment from governments

in the same year (REN21, 2014; FS-UNEP-BNEF, 2014).

Figure 3: Global New Investment in Renewable Energy by Technology, 2013; Source: REN21 (2014).

Figure 4: Global New Investment in Renewable Power and Fuels, by Region, 2004-2013; Source: REN21 (2014).

74.8

36.0

5.7

0.5

3.6

2.0

0.1

38.9

44.0

2.3

4.6

1.3

0.5

0.0

0 10 20 30 40 50 60 70 80

Solar Power

Wind

Biomass & Waste-to-…

Hydro <50MW

Biofuels

Geothermal Power

Ocean Energy

billion USDDeveloping Countries Developed Countries

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

India 2.50 2.90 4.40 6.30 5.40 4.20 8.70 12.60 7.20 6.10

China 2.40 5.80 10.10 15.80 24.90 37.10 36.70 51.90 59.60 56.30

Asia & Oceania 6.80 8.20 9.00 10.90 11.40 12.90 20.70 25.30 29.50 43.30

Europe 19.70 29.40 39.10 61.80 73.40 75.30 102.40 114.80 86.40 48.40

Africa & Middle East 0.50 0.50 0.90 1.60 2.30 1.40 4.30 3.20 10.40 9.00

Brazil 0.60 2.60 4.60 11.00 12.20 7.80 7.70 9.70 6.80 3.10

United States 5.50 11.70 28.20 33.60 35.90 23.50 34.70 53.40 39.70 35.80

Americas 1.40 3.30 3.20 4.90 5.80 6.10 11.50 8.70 9.90 12.40

0.00

50.00

100.00

150.00

200.00

250.00

300.00Billion USD

10

Policy measures

Currently, there is a total of 98 countries which have implemented FIT mechanisms to foster

the use of renewables (REN21, 2014). Germany was one of the countries whose intensive

support through FIT fostered the implementation wind power and solar (Frondel, et al., 2009;

EU-Commission, 2011; OECD, 2012). This was under the intention to boost the industry and

benefit from the learning effects. However such support has been lately revised under the

Renewable Energy Act, as payments were perceived as too high. Other developing countries,

especially the sub-Saharan Africa of Kenya, Nigeria, Rwanda, Tanzania or Uganda, have also

implemented FIT mechanisms, however most of the countries (including developed as well)

favor the setting of targets or tax reductions as a mean to support RET (REN21, 2014). Gabon

has set the target of providing 70 percent of its electricity and 80 percent of primary and final

energy from renewables by 2020. Uganda as well has followed this path by setting a target of

61 percent by 2017. Currently they have succeeded in providing electricity, Gabon with 40

percent and Uganda with 79 percent (REN21, 2014).

On the other hand, 144 of them have already implemented targets to boost the maturation of

the markets. As it is to be expected, investment in renewables and the setting of targets follow

a similar direction. It occurs that regions whose energy agenda demands the increase of

capacity allocates large amounts of investment. The development observed over the Asian

continent and its outlook creates the expectation of more ambitious targets, thus even larger

investments. By 2015 it is planned the installation of 439 GW power capacity in China, from

which 13 GW will be biopower, 290 GW hydro, 35 GW PV, 1 GW concentrated solar power

(CSP) and 100 GW wind. Japan, India and Indonesia also target the increase of renewable

power in the near future, most of which are focused on the installation of PV and wind capacity

(REN21, 2014). In Europe, targets might not seem as aspiring as those in Asia. Still, the aim

remains on the provision of secure and cost-efficient energy (EU-Commission, 2007). Finally,

other targets that result outstanding are those from countries in Latin America. One of these is

Costa Rica, which already generating 92 percent of electricity just from RET, aims for the full

provision of electricity by 2021. Other countries in the region have also implemented similar

targets, therefore the presence of renewables fostered through national targets will continue

to be common worldwide.

Levelized Cost of Electricity of Renewables

Another decisive factor for the rise of RET is, of course, the global cost of producing one

kilowatt hour by each technology. Currently, the development of the industry has achieved a

level of competitiveness that allows RET to fully challenge in costs with mainstream

technologies (See Table 5 in Annex).

11

In some cases, LCOE of RET even out-tops fossils and nuclear, as it is for hydropower.

Onshore wind power also represent viable and low-cost alternatives for electricity. Still, on

average, they remain above the costs for fossil fuels. Similar cases are geothermal and biogas

power plants, which also offer low LCOE. Finally, Solar, either in the form of PV and CSP, and

Wind offshore also have achieved high levels of competitiveness with respect to the past.

However, LCOE for these technologies remain relatively high, if they are compared to hydro

power, and coal.

Several markets around the world have had a crucial influence over the LCOE of RET. It is

observed that less than a dozen countries dominate the industry, mainly due to their decision

to invest heavily in the installation of power capacity to supply their present and future needs

(World Energy Council, 2013; REN21, 2014; Fraunhofer-ISE, 2013). Due to strategic and

political reasons, support through different incentives such as FIT, subsidies or tax exemptions,

as well as the investment of firms in the development of technologies, have been responsible

for the current decrease of LCOE of RET against traditional sources in many countries. Efforts

in RET from the developing world such as those of the United States, the European Union or

Japan are responsible for the decrease of LCOE of wind, solar and biopower technologies.

By establishing ambitious policies and targets to foster new technologies, these countries have

also created a market from which developing countries can benefit. In addition to the incursion

of China and India, the market gains from the scaling effects created, thus reducing even more

LCOE for each technology. In this regard, besides large hydro and ocean power plants,

geothermal and onshore wind power have some of the most competitive costs around the

world. LCOE of course differ among developed and developing countries (See Table 6 in

Annex). India and China show the lowest LCOE for onshore wind, ranging from 0,047 to 0,113

USD/kWh. These costs differ greatly to those in the US, Europe or Japan, although installed

capacity is less. LCOE of biopower is also lower in China, however the difference with respect

to developed countries is not as large as for wind.

One of the most developed markets for RET in Europe is Germany. Almost by the end of 2013

some of these technologies proved to be as competitive as their fossil fuel counterparts

(Fraunhofer-ISE, 2013). Wind power located onshore is an example of competitiveness in this

aspect. With a LCOE between 0.060 and 0.142 USD/kWh it delivers lower costs than hard

coal (0.084 - 0.106 USD/kWh) and combined cycle gas turbines (CCGT) (0.100 – 0.130

USD/kWh) but still behind brown coal (0.050 – 0.070 USD/kWh). Solar PV power plants,

although not as low as onshore wind power, also improved in terms of LCOE. Depending on

the type of power plant and insolation (1000 – 1200 kWh/m2a), they offered better alternatives

than some CCGT and almost as good as hard coal. They ranged between 0.104 – 0.189

12

USD/kWh. Wind offshore and biogas power plants ranked behind PV and onshore wind,

however the potential for improvement remains. In general however, LCOE of RET in Germany

remain above world averages.

Figure 5: Comparison of LCOE of Renewables and Conventional Power Plants, 2013; Source: Fraunhofer-ISE (2013); REN21 (2014); World Energy Council (2013).

2.3 Solar PV Market

The development of the Solar PV Market over the last decade has been remarkable, enabling

the production of at least 160 terawatt hours of electricity annually (EPIA, 2014). To better

perceive its rapid evolution, it is only necessary to take a look at the increase in installed

capacity around the world. Global solar PV capacity in 2013 increased around 39GW, reaching

a total capacity of almost 140 GW (REN21, 2014; EPIA, 2014). Most of this increase comes

as a result of the expansion of the Asian-Pacific (APAC) region, as China and Japan became

the top installers worldwide, followed by the U.S., Germany and the U.K. China itself accounted

for 11.8 GW (12.9 GW if extra 1.1 GW are accounted), almost one third of the global

installations, whereas Japan 6.9 GW (REN21, 2014; EPIA, 2014). Moreover, it is in China

where the largest completed solar PV plant is located, with a 320 MW capacity (REN21, 2014).

This country has done much over the last few years and will continue to do so. Such attempts

come as a result of the increase in the economic activity and population observed over the last

decades, where unless new plants are being built, demand for electricity cannot be supplied,

thus hindering further growth.

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

PV Germany Wind onshoreGermany

Wind offshoreGermany

Biogas Germany Coal Germany Gas Germany

USD/kWh

13

Figure 6: Global PV Cumulative Installed Capacity; Source: EPIA (2014).

Nonetheless, regardless of the market’s downturn and the loss of a large amount of the global

share, Europe continues to lead in cumulative installed solar PV capacity with 81.5 GW (EPIA,

2014), enabling the provision of an estimated 3 percent of total consumption and 6 percent of

peak demand (REN21, 2014). Electricity produced from new generators in the EU 27 region

allows the provision of more than 19 TWh annually (EPIA, 2013). Germany, Italy and Spain

remain as the EU countries with most installed solar PV per capita, with 35.9 GW, 17.6 GW

and 5.6 GW respectively. Germany remains as the largest EU market, however the decrease

of new installed capacity from 7.6 to 3.3 GW made it loose three positions globally (EPIA,

2014). Still solar PV plays an important role for the electricity generation in this country, as it

meets 5 percent of the total annual electricity demand (REN21, 2014; EPIA, 2013).

Solar with respect to other renewables

Although Solar PV has shown a decrease in the average annual growth rate of installed

capacity (with respect to the period between 2008 and 2013), it remains as the third most

important renewable energy source in terms of global installed capacity (EPIA, 2014). Only

last year, solar PV accounted for about one third of the renewable power capacity added, being

the first year in which more solar PV has been added than wind power worldwide (REN21,

2014). In Europe, PV finished second in terms of new installed electricity capacity, following

hydropower, biomass and thermosolar. Still, the difference between wind power and PV was

minimal (EPIA, 2014).

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Europe 129 265 399 601 1306 2291 3289 5312 11020 16854 30505 52764 70513 81488

APAC 368 496 686 916 1198 1502 1827 2098 2628 3373 4951 7513 12159 21992

Americas 21 24 54 102 163 246 355 522 828 1328 2410 4590 8365 13727

China 19 24 42 52 62 70 80 100 140 300 800 3300 6800 18600

MEA 0 0 0 0 1 1 1 2 3 25 80 205 570 953

RoW 751 807 887 964 993 1003 1108 1150 1226 1306 1590 2098 2098 2098

0

20000

40000

60000

80000

100000

120000

140000

160000

MW

14

Figure 7: Net Power Generation Capacities Added in the EU 28 in 2013; Source: EPIA (2014).

2.3.1 Drivers PV

Policy measures

In order for PV to effectively be deployed, several conditions must be fulfilled. System

integration, market deployment, interaction with alternative sources, policies and the cost

competitiveness determine whether or not, PV projects will succeed in the provision of energy

solutions (EPIA, 2009). Similar to other RET, the momentum behind PV has been boosted by

governmental policies aimed towards the development of the local industry, thus influencing

innovation and the decrease of the LCOE. Countries around the world, especially developing,

understand the benefits of electricity access through PV and have implemented policy

incentives. Within the main drivers of the market in 2012, FIT schemes outstand with 61

percent. Other major drivers are direct subsidies and tax breaks 21 percent, self-consumption

12 percent, renewable portfolio standards (RPS) or quotas 4 percent, and net-metering 2

percent (IEA, 2013). As a result, investment in solar power represented the largest with 113.7

billion USD. This amount accounts also for 53 percent of total new investment in RET, from

which 90 percent went to solar PV (REN21, 2014).

In 2013, a total of 67 countries have shown interest in developing the capacity of PV for their

own market by implementing programs in the form of FIT or setting specific targets (See Table

7). In this regard, the top five installers of solar PV capacity in 2013 also belong to those making

use of FIT. Japan and the United Kingdom were the most generous in terms of FIT with 0.31

– 0.5485, 0.12 – 1.01, and 0.0963 – 1.0293 USD/kWh respectively. China and Germany on

10835 10335

1197 705 419 120 100 10 1

-2572 -2655

-5823-8000

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

12000MW

Wind PV Hydro Biomass CSP Nuclear

Waste Geothermal Ocean Fuel oil Gas Coal

15

the other hand, have also implemented FIT, however they are lower as those from the previous

countries.

Africa, the Middle East and some countries in the Pacific also favor the utilization of targets as

their main policy (See Figure 8). The Middle East and North Africa (MENA) region outstands

in the ambition of their targets (See Table 7). Just Saudi Arabia has established the installation

of 6 GW by 2020 and 16 by 2032. Other countries in the Africa and the MENA region also aim

for similar targets, although not in the same level. Therefore, if such policies are successfully

followed, it is expected that at least before the end of next two decades, more than 30 GW

could be available.

However, the initiative taken by the Asian countries results more remarkable. The high

installation capacity observed last year does not come only from the support of the tariffs, but

also from the combination of specific targets for PV (See Figure 8). China has determined the

addition of 10 GW in 2014 and 35 by 2015, whereas Japan also targets the installation of 28

GW by 2020. The result of such impetus towards solar is namely, the successful installation of

more capacity than other European countries that formerly dominated the market.

Figure 8: World Policy Map: Feed-in-Tariffs and Targets for PV; Source: Adapted from REN21 (2014); PV-Tech (2014).

Levelized Cost of Electricity of PV

PV’s competitiveness against other energy sources is as well measured mostly by its costs.

This, however, depends on natural conditions such as the hours of sun, the prices of electricity

for each location, availability of flexible electricity prices and, of course, the prices offered by

the system and its performance (EPIA, 2009). Some of these aspects are independent of the

development of PV, nonetheless some others can be influenced. In order to enhance its

16

competitiveness, governmental support has aimed to invest in the development of the

technology and its installed capacity. As a result, better manufacturing processes and

economies of scale will be achieved, thus decreasing the costs.

Almost identical to other RET, China and India display the lowest prices for PV (See Table 6

in Annex). Compared to Europe, Chinese and Indian LCOE outshines with ranges between

0.079 – 0.145 and 0.087 – 0.137 USD/kWh against 0.147 – 0.38 USD/kWh. Although many

regions have benefited from the large support from their governments, this level of

competitiveness has been achieved thanks to the scaling effects, low material costs and cheap

manufacturing processes. Consequently, the industry has faced the shift of the production

sites, moving from Europe and North America, to Asia, where now 87 percent of production

worldwide is located (REN21, 2014).

In the increase of competitiveness of PV, a large share of responsibility must be attributed to

the decreases in the capital expenditures of their systems. These expenditures correspond to

the investments needed for the purchase of its essential components, namely the modules.

Over the last 40 years, the average cost for silicon modules has displayed a decreasing trend,

which ultimately influenced the price for the installation of PV systems and its LCOE. Starting

in the early 1970s with prices around the 70 USD/Wp, modules have already dropped to levels

surpassing the 0.55 USD/Wp in South and Southeast Asia, although Chinese and German

modules still within the range between 0.64 – 0.82 USD/Wp (PV Magazine, 2014).

In this regard, further aspects have influenced the development of the module costs (See

Figure 9). First of all, input prices for PV-modules have a major repercussion on its costs, as it

has been observed through the changes caused by the polysilicon shortages in 2006 (IRENA,

2012). Additionally, increase cumulative production volume have allowed the industry benefit

from the knowledge obtained from the continuous production processes and translated it into

price reductions. It is estimated that every doubling of the installed capacity, there is a 20

percent reduction in the module prices (Yu, et al., 2011; DGS, 2012; IRENA, 2012). Therefore,

the larger the cumulative installed capacity, the lower the average price for PV modules.

17

Figure 9: PV Module Price Decrease; Source: Adapted from Hernández-Moro & Martínez-Duart (2013).

2.3.2 Forecasts

Within the near future, it is still expected an increase in the global PV market. Less optimistic

scenarios still forecast an installation of capacity between the 35 GW and 39 GW, whereas

other more optimistic predict an installation between 52 GW and 69 GW. Whether low or high

scenarios, by the end of this year it is already expected an increase of the total capacity ranging

the 174 GW and 190 GW, and by the end of 2018, between 321 GW and 430 GW. Such

forecasts assume a slowdown of the European market as well as an expansion of emerging

markets. China and the APAC region will still drive the increase of PV over the next five years.

Other countries such as Australia, Mexico, Chile or Brazil also show high attractiveness for

investment and the installation of PV due to the size of their markets and the competitiveness

of PV (EPIA, 2014). This only leads to the belief of an improvement of the overall capacity

around the world.

High expectations have been set for the “Sunbelt Region”, where solar radiation conditions

tend to favor PV technologies. With a population of more than 5 billion and an electricity

consumption of 7,000 TWh (EPIA, 2011), this region will be the focus for PV markets in the

next years. Currently most developed markets are located in less radiated regions, therefore

the fact that the countries located in this favored region in addition to the booming of their

economies, leads to the idea of an expected increase of PV share.

18

Furthermore, PV policies, increased volume and technological change will continue to drive

LCOE down. With an expected combined capacity of more than half TW in the Asian continent,

and depending on the plant size and operating hours, at the beginning of next decade LCOE

are expected to range between 0.07 and 0.09 USD/kWh, and by 2030 between 0.06 and 0.05

USD/kWh (EPIA, 2011). Other markets such as the Australian and Latin American will have

an influence, as cumulative capacity will be around 104 and 73 GW (EPIA, 2011).

The future performance among sources will be a determinant factor for developing countries

located in the Sunbelt Region, especially for rural areas. The competitive advantage of off-grid

mini-grid systems against fossil fuels, together with the expected price increase of the later will

represent the solution for current electrification problems. However, not only current trends will

be needed to solve the lack of electricity in these regions, as also current barriers against the

deployment of PV must be overcome. Only when both of these conditions are fulfilled,

economic and social development will be achieved in these countries.

19

3. Experience and Learnings in Photovoltaics

3.1 Levelized Cost of Electricity

Competitiveness of prices among energy sources is one of the most decisive factors for

projects related to the provision of electricity. In this regard, it has been observed a remarkable

improvement of the performance of RET, especially for wind and solar power. On the other

hand, NRET have displayed an opposite trend. Prices for electricity generated from fossil fuels

are expected to increase, as a result, a time will come in which the produced watt from NRET

will be more expensive. Fortunately, this time seems not to be far away from now, as in some

rural locations, off-grid or mini-grid RET already outperform diesel generators. In these

situations, the challenge remains in finding a common metric for deciding which technology to

implement. The most basic metric remains the price-per-watt. For PV, its calculation consists

on the evaluation of the capital expenditures and the price at which electricity is supplied.

Although costs per unit of electricity may seem as a reliable parameter for comparison, this is

very sensitive to changes in the price of its components (Bazilian, et al., 2013) and certainly

leaves out other aspects influencing the competitiveness of each source.

3.1.1 General formulation

Traditionally, competitiveness among energy sources is evaluated based on the concept of

grid parity. For those technologies with the ability to provide on-location energy, i.e. residential

systems, grid parity or socket parity is described as the point at which the LCOE is equal to

the average retail price for electricity (IEA, 2013), that is, the purchase of electricity at a relevant

residential or commercial tariff (Bazilian, et al., 2013). Different from just the cost of each

energy unit produced, the LCOE is usually defined as the price at which energy must be sold

to break even over the lifetime of the technology represented as the net present value in

monetary units per kilowatt-hour (Darling, et al., 2011). Having its foundations in the lifecycle

costing methods, the LCOE is used as a proxy to estimate grid parity among energy sources

(Branker, et al., 2011). It involves the total lifecycle costs (TLCC) incurred during the

implementation of the project. That is, the cost of installing (or capital expenditures),

maintaining and financing the system (or operation and maintenance expenditures). Moreover,

it also considers the output of the system over its life span (Hegedus & Luque, 2011). In other

words, it is the TLCC of the whole project over the lifetime energy production (LTEP) (Short,

et al., 1995), or

𝐿𝐶𝑂𝐸 =𝑇𝑜𝑡𝑎𝑙 𝐿𝑖𝑓𝑒𝑐𝑦𝑐𝑙𝑒 𝐶𝑜𝑠𝑡

𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛 (1)

20

Advantages

The reason for the acceptance of such metric is the simplicity for comparing different

technologies with diverse operation scales, investment or operating time periods, regardless

of being RET or NRET. LCOE displays the costs of producing one unit taking into consideration

the overall costs incurred during the whole project. Therefore the fact that costs are

represented in net present values allows a simple benchmarking between projects with 25, 30

or more years. The simplest form of the LCOE can be described by Eq. (1), however it can be

modified to include other considerations such as taxes, subsidies and other complexities

(Darling, et al., 2011; Branker, et al., 2011). Still, its basic principle as a tool for assessing the

cost-effectiveness and comparing different technologies remains, which represents a more

realistic estimation of the investment and the benefits obtained from that decision.

Disadvantages

Nonetheless, as for other metrics used for comparison, there are diverse number of

drawbacks. Critics of LCOE (Joskow, 2011; Ueckerdt, et al., 2012) usually acknowledge the

advantages of using it as a benchmarking tool for evaluating alternatives, however the

simplicity of its assumptions and omission of factors remain the main focus of discussion. First

of all, LCOE treats the electricity produced among sources as a homogeneous product.

Furthermore, it is said to disregards the intermittent nature of RET, namely, the fact that

different energy sources have various production profiles, which tend to vary over the course

of a year and their location (Joskow, 2011). In other words, it neglects the integration costs,

being these the costs of fully acknowledging the differences in variability, uncertainty and

location-specificity (Ueckerdt, et al., 2012). Although such critics point out basic details that

may cause bias, LCOE metrics do represent a better alternative for capturing the overall picture

of the development in energy projects, which is the reason for its widespread acceptance.

LCOE for PV

When calculating LCOE for PV, the general method needs to be adapted for the specific

conditions of the technology. One of this conditions is the scale of the system. Based on Eq.

(1), utility scales and residential PV systems may vary. An extended and modified version of

the equation calculating LCOE for utility-scale projects is well explained by Darling et al. 2011.

For residential PV however, calculations of LCOE can take the form of:

LCOE=

∑It+ Ot+Mt+Ft

(1+r)t

Tt=0

∑Et

(1+r)t

Tt=0

(2)

where

21

T Life of the project [years] t Year t Ct Net cost of the project for t [$] Et Energy produced for t [$] It Initial investment/cost of the system (construction, installation, etc.) [$] Mt Maintenance costs for t [$] Ot Operation costs for t [$] Ft Interest expenditure for t [$] r Discount rate for t [$] St Yearly rated energy output for t [kWh/year] d Degradation rate [%] (Branker, et al., 2011).

Even when the amount energy supplied and the design may differ, the basic elements

conforming the calculation of its LCOE remain. However, in order to efficiently capture a metric

that enables the comparison against other projects, special attention should be taken to

discount rates, average system costs, the lifetime and the degradation rate of the energy

generation (Branker, et al., 2011). This resides in the fact that some of the variables represent

larger costs due to the nature of the technology. First of all, discount rates are influenced by

the risk perception of investors as well as the consideration of inflation. Secondly, average

system costs depend on various aspects such as the design and installation, means for

financing and managing the system, as well as the fiscal regulations to which projects are

subject. Thirdly, another technical aspect, namely the state of innovation, has a large

repercussion on the average lifetime of the system. It will be responsible for the operation and

maintenance costs as well as the energy output, which also depends on the degradation rate.

This last aspect will dictate basically the overall lifetime of the system, however this is largely

influenced by natural conditions of the location.

One of the many benefits of PV technologies however, is the ability to generate electricity at

the location where it is needed. Distributed generation from residential PV and off-grid systems

represent an advantageous alternative, especially in rural and remote areas, due to its high

production of electricity at times of high demand, reduction of transmission investment,

avoidance of other distribution costs, and the social and environmental value gained as a

consequence of the energy services provided (Borenstein, 2011). Especially for developing

countries located at the Sunbelt Region, natural conditions enhance these benefits and

multiply the potential for the implementation of PV. Given the fact that most of the intended

projects aim to supply for rural areas with no grid access and relatively high stability in the solar

radiation, aspects such as the neglectfulness of integration costs should not influence the

reliability of LCOE metrics for the benchmarking of alternatives.

Throughout the development of PV-LCOE in different markets, the observed decreasing trend

will continue in the near future, at least until current RET are substituted by newer and better

22

sources. In order to forecast future developments in the industry, the importance of capital

expenditures namely silicon modules and other installation costs, must be understood. In this

regard, a relationship exists in which the further costs for the technology are reduced, the

higher the competitiveness of PV modules against its alternatives. In the same manner as for

the development of the renewables sector as a whole, there are different driving forces that

can improve the LCOE of PV (Breyer & Gerlach, 2013). This however should not be confused

with the elements affecting the sensitivity of the LCOE-forecast previously described. Access

to electricity and electricity prices are two of them. In developed countries, high infrastructure

levels allow most of their population the access to electricity from diverse sources, thus

integration costs and prices as a function of available supply and demand represent an

important role. The growth rate of the PV industry itself also influences the competitiveness of

LCOE. Due to the scarcity of resources and increasing prices of fossil fuels, investment and

policies fostering the implementation of PV-projects generate scaling effects, which

consequently reduce costs for modules. Similarly, by improving the performance and achieving

longer the lifetimes, energy output increases, thus reducing the LCOE. Finally, experience

obtained from the deployment of the technology and the overall interconnection with the other

factors in the industry can drive the LCOE down. This last one however, can be interpreted as

the most relevant driver.

3.2 Learning curves

Implementing the LCOE methodology in the decision-making process can certainly represent

great aid for project managers and policymakers. It serves as a reliable parameter for complete

projects and portrays as well, a good description of the decreases throughout time, thus the

increases in its competitiveness. Relevant for this competitiveness are mostly capital

expenditures i.e. silicon modules and other components. However, the decrease in costs is a

consequence of the technological change perceived for silicon modules. Such improvement

cannot be attributed solely to the change in input prices, as other effects have been involved.

For the PV-industry to achieve such development over the last 40 years, experience and

learning effects has been described as the key factors. Therefore, although learning effects do

not directly explain the decrease of LCOE, it does represent a reliable approach for

understanding the evolution of PV-components, which represent the largest costs in this

methodology.

Learning considered as a factor with economic implications was firstly discussed by Arrow

(1962). Its origins are the result of the debate against the idea of technological change being

an exogenous phenomenon in production. Under endogenous growth theories, learning was

intended to capture the fact that technical knowledge is the consequence of the experience

23

gained throughout repetition in the production process and not as a given factor, as it was

previously treated in the neoclassical view of production. This type of learning came to be

known as learning-by-doing (LBD), however, further types of learnings were identified

according to its focus: learning-by-researching (LBR), learning-by-using (LBU) and learning-

by-interacting (LDI) (Weiss, et al., 2010). LBR refers to the feedbacks occurred from the

investment in R&D and its influence on productivity; LBI involves the benefits of the exchange

of information and networking among actors involved in the process; and LBU accounts for the

increases in productivity due to the use of products, machinery and other inputs (Malerba,

1992).

3.2.1 One-factor learning curve

The most widely spread approach for explaining previous and future evolution of PV, i.e. its

diffusion and adoption, has been the one based on experience or learning curves (Neij, 1997;

Nemet, 2006; Yu, et al., 2011). Although very similar, experience curves have a more

macroeconomic orientation than learning curves. The former one relates to the development

of the industry, whereas the second on the firm. Adapted to the costs incurred in the

implementation of PV (labor, capital, administrative, R&D, etc.), these curves describe how the

cost of one unit declines by a constant percentage as total production doubles. This

relationship is represented by:

𝐶 = 𝐶𝑖 ∙ 𝑄𝑏 (3)

and its linear form

log 𝐶 = log 𝐶𝑖 − 𝑏 log 𝑄 + 휀 (4)

where

C Cost per unit of production, installed capacity or capital Ci Cost of the first unit installed or produced Q Cumulative capacity or output b Learning index or experience index.

In this relation, the most important variable is experience, denoted by b, and used to calculate

the relative cost reduction every time cumulative capacity or output Q doubles. Moreover, Eq.

(3) uncovers two further essential elements in the understanding of PV evolution over time.

The first one is the progress ratio (PR) denoted by the value 2b, expressing the progress of

cost reductions of PV. The second is the learning rate (LR) in the form of 1-2-b, describing the

percentage of change in cost. Using these values, it can be observed a further relation between

cost reductions and the diffusion and adoption of PV: as production is expanded, costs fall,

24

generating a feedback influencing the expansion of the market demand, thus increasing

additional production. According to such mechanism, firms improve their processes and

reduce costs due to the learning effects obtained from previous experiences.

Given its nature as an empirical research method and not a theory, the analyses derived from

the evaluation of the cost reduction and the increased capacity may differ (Yu, et al., 2011;

Neij, 2008). These analyses are based on bottom-up approaches. Therefore, the

establishment of system boundaries such as geographical areas, technical aspects of the

system and time periods, is commonly a source of bias and variability. As a consequence,

discrepancies between LR and PR can be perceived, which influence the exactitude and the

ability to forecast future developments. Moreover, the basic idea of learnings comes from the

microeconomic improvement through performance, where labor accumulates experience,

processes are improved and technical progress occurs (The Boston Consulting Group, 1972).

This correlation phenomenon previously described however, is commonly known as one-factor

learning curve (OFLC), as it represents the costs reductions of PV changed by only one factor,

being this cumulative capacity or production. This type of cost reduction comes as a result of

LBD, where the experience obtained is caused by the continuous performance of a certain

task. As for the general rule, simplicity in the design of the model may have both positive and

negative outcomes. On the one hand it allows a straightforward representation of reality

(Nemet, 2006). However this oversimplification can exclude important elements. In this case,

other variables such as R&D, technological change, scaling effects, knowledge spillovers and

changes in the prices of inputs are ignored (Yu, et al., 2011; De La Tour, et al., 2013). As a

result, it leaves space for possible bias when estimating further reductions.

3.2.1 Two-factor learning curve

In response to the critics on the explanation of experience through just increases in production

or cumulative capacity, learning curve models have been enhanced by the inclusion of more

factors (Yu, et al., 2011; Weiss, et al., 2010). In such cases, Eq. (3) is modified and represented

as:

𝐶 = 𝐶𝑖 ∙ 𝑄𝑏 ∙ 𝐾𝑆−𝛼 (5)

and its linear form

log 𝐶 = log 𝐶𝑖 − 𝑏 log 𝑄 − 𝛼 log𝐾𝑆 + 휀 (6)

in which KS is defined as

25

𝐾𝑆𝑡 = (1 − 𝜂)𝐾𝑆𝑡−1 + 𝑅𝐷𝑡 (7)

and where

KS Learning by researching -α Knowledge stock index η Annual depreciation rate RDt R&D expenditures at time t.

Under this extended model, the implication of knowledge as a stock improves the estimation

of the curve. In this case, cost reductions occur not only due to the learnings gained by doing,

but also by researching through a series of feedbacks (See Figure 10). Additionally, it is

assumed that knowledge or information can be gathered through time, where the longer the

process is carried out, the larger the amount of knowledge. Similar to the learning index b, -α

represents the elasticity of LBR. Furthermore, special attention is given to support on R&D in

the form of investment, contained in variable RDt, and which has a major influence on costs.

Nonetheless, it must be understood that such effect of investment cannot act immediately

(Berglund & derholm, 2006).

By expanding the model, other learning effects become visible. As there is a direct and indirect

relationship between LBD and LBR, the model is also influenced by the LBI effect. Such

relationship describes a case where changes in investment enhances or reduces the LBR

effect, thus influencing the cost reduction directly. On the other hand, the same changes in

investment influence the production cost reduction indirectly through the LBD effect. This

would be the case of improvements of technology or production techniques which have an

influence on output. As a result, R&D and the learning effects will have an expanded effect on

production costs.

Figure 10: Relationships and Feedbacks for TFLC Models Including R&D, Production Growth and Production Cost; Source: Adapted from Yu, et al., (2011).

The importance of the addition of more variables remains in the idea of improving the

estimation of the cost decrease. It is then intended that, the more elements considered,

26

therefore the more exact the forecast. Still, it is important as well not to forget the increase in

the complexity of the model. Eq. (5) describes the case of a two-factor learning curve (TFLC).

TFLC however, can be conformed either by other variables such as scaling effects, costs of

inputs or technology improvement (Yu, et al., 2011). Under TFLC models, the idea remains on

combining the effects of cumulative capacity with additional variables in order to achieve a

better explanation of costs development.

3.2.3 Multi-factor learning curves

More recent approaches expand even further and attempt combine the most relevant factors

influencing costs. One of this is the so-called multi-factor learning curves (MFLC). This model

was developed as an improvement to the existing OFLC and TFLC, in the sense that it includes

input costs as well as both scaling and learning effects. Here, it is worth to mention that these

two effects influence the cost curve differently: scaling effects are observed in the short run,

whereas learning effects are on the long run. The first describes the case of static economies,

in which the movement occurs along the curve, whereas the second represent the case of

dynamic economies causing a shift of the curve (Papineau, 2004; Yu, et al., 2011; Kahouli-

Brahmi, 2009). Nonetheless, the relationship among the variables is similar to other learning-

curves models (See Figure 11). Increasing returns gained from large-scale production foster

new investment in R&D, thus influencing the LBR and LBD effects. On the other hand, the cost

for inputs impact directly production costs in the same direction as the change experienced

(Yu, et al., 2011).

Figure 11: Relationships and Feedbacks for MFLC Models; Source: Adapted from Yu, et al. (2011).

Following the basic form for learning curves, MFLC are represented by the following equation

27

𝐶 = 𝑎𝑄𝑏 𝑟⁄ ∙ 𝑄𝑥(1−𝑟) 𝑟⁄ ∙ (𝐶𝑆𝑖

𝛿3𝐶𝐴𝑔𝛿4𝐶𝑂

5)1 𝑟⁄ (8)

and its linear form

log 𝐶 = log 𝛼1 + 𝑏1 log + 𝑛 log 𝑄𝑥 + 𝛿𝑆𝑖 log 𝐶𝑆𝑖 + 𝛿𝐴𝑔 log 𝑃𝐴𝑔 + 𝛿𝑂 log 𝐶𝑂 (9)

in which

𝛼 = 𝑟(𝛿3𝛿3𝛿4

𝛿4𝛿5𝛿5)−1 𝑟⁄ (10)

and

𝑟 = 𝛿3 + 𝛿4 + 𝛿5 (11)

where

Qx Output CSi Cost of silicon CAg Cost of silver CO Cost of other inputs δ3 Elasticity of silicon δ4 Elasticity of silver δ5 Elasticity of other inputs logα Remaining-factors effect b1 = b(n+1) LBD index α1 = α(n+1) LBR index n = (1−r)/r Scale index or the elasticity plant size δSi = δ3(n+1) Silicon cost index δAg = δ4(n+1) Silver cost index δO = δ5(n+1) Other input-cost index r Return-to-scale parameter

Throughout the development of the PV industry, studies on learning curves usually distinguish

three different phases, in which diverse factors play an important role (Nemet, 2006; Yu, et al.,

2011; Breyer & Gerlach, 2013). Although the level of influence on the model of each of the

factors may vary over time, authors have come to reduce the number to just a few. Commonly,

OFLC and TFLC usually employ size or plant capacity given the relevance played on the initial,

diffusion and mature stages of PV. Nonetheless, by the inclusion of more variables in MFLC,

there is a better fit in the curve in its linearized form (Yu, et al., 2011). Expanded models include

learning effects, such as LBD, LBR and learning-by-interacting (LBI); scaling effects achieved

28

by the size plant; input-price effects for crucial components such as silicon or silver; and other

less relevant such as subsidies or labor.

Finally, for fully evaluating the effectiveness of MFLC it is crucial to capture its strengths and

shortcomings. Notwithstanding the fact that adding explanatory variables to the learning curve

model has positive effects on the elimination of bias, as stated by De La Tour et al. (2013),

such addition to the model can create multicollinearity if the variables are highly correlated to

the other explanatory variables. As a result, in the attempt to increase the predictive power of

the model, exactly the opposite effect could be obtained.

As already described, learning curve models can certainly be a powerful approach for the

estimation of previous and future developments of system costs, being this last one the most

relevant argument. The ability to effectively predict the change of costs for electricity among

energy sources, and conversely the prices, provides decision-makers with the information

necessary in order to invest in the most convenient alternative. It should not be neglected that

this approach remains in the microeconomic scope, as the economic and social impact of PV-

projects is mostly local. However, as the PV-industry as a whole develops, the aggregated

impact of the cumulative capacity becomes macroeconomic. In this regard, learning curves

are considered to be part of a factor called technological change, which under endogenous

growth theories, represents an essential factor in the production function explaining economic

growth. Furthermore, under the macroeconomic scope, energy generated either by NRET or

RET such as PV, is considered as an input for production. Therefore, whenever technological

change in the energy sources generating it occurs, a change in productivity is observed.

Competitiveness in the form of cost decreases gained from experience and learnings, thus

technological change, influences productivity due to the shifts in the energy source. This

translates into an economic feedback, in which growth and social development will foster

further technological change that consequently will enhance the decreases in costs. In order

to better understand this feedback, next chapter focuses on the linkage between energy and

growth.

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4. Theories on the Energy-Growth Relation

4.1 Basic Concepts

4.1.1 Energy in the context of economics

Defining the concept of energy in context of economics requires first, the comprehension of

the essential role it represents for the modern world. Either being originated from those more

traditional sources, such as fossil fuels and coal, or in the form of photovoltaic modules capable

of transforming solar radiation into electricity, three major aspects should be considered when

capturing its relevance for our lives: it is a consumer good crucial for our everyday activities, it

has become a strategic resource which can cause conflicts across nations, and it is an

essential factor of production that detonates economic growth and social development

(Hanley, et al., 2013). Without it, given the pace our economies have chosen to follow, probably

none of today’s advances could have been achieved. Still, energy is much more than a good,

a resource or a factor of production. Its importance can be perceived as the world has directed

the focus towards its understanding in order to fully exploit its potential in the most efficient

way.

The term energy as we know it, has a different interpretation regarding the field and the focus

of study. In economics, when referring to energy it is usually meant “exergy”, the energy that

is available to perform a task or work (Rant, 1956) and to a lesser extent, entropy, which is

equivalent to the exergy loss. Although, even if the difference might just be a matter of

semantics, the distinction between energy and “useful work” becomes essential when dealing

with “raw energy” such as the one provided by the sun, and the “energy services” obtained

from using that energy and implementing it in favor of an activity. To the same extent, the

concept of useful work in the technical sense should be distinguished from the work which

performed by employees, as it can be the source of misunderstanding between the

engineering and economic sciences (Ayres, et al., 2007).

Moreover, whilst the explanation of such terms can be perceived as redundant, the fact that

the social and the natural sciences are interacting on a more often basis requires a brief

overview on the fundamental notions on thermodynamics. This approach is commonly used

(Ayres & Warr 2004, Ayres, et al., 2007; Stern, 2004; Stern, 2010; Kümmel, 2011) as every

economic activity requires, even to the minimum extent, of exergy to transform raw materials

(which are additionally carriers of energy) during the production process into what will result in

the delivery of a good or service. In other words, no product, service or idea can be created

without matter and energy being converted from its original form. This relates to the first law of

thermodynamics, or the mass-balance principle, where in order to produce a certain output, at

30

least the same amount of matter must be used as input. However, as in almost every

production process, there are losses, either of raw materials being disposed as waste or

energy being used inefficiently due to the lack of technological means. In regard to this, the

second law of thermodynamics states that there is a minimum amount of energy that must be

employed in order to perform certain task (Stern, 2004). This amount of energy can vary as

other inputs can be used to cover up for it, such as increased number of workers.

It has been established so far that energy, regardless of its origin or source, is relevant for

every economic activity due to the potential to perform a specific amount of work. However,

not all energy forms provide the same amounts of exergy, meaning that some of them can

provide a larger amount of work, thus being classified as high quality energy (Gundersen,

2009). Since no energy form can be transformed into useful work without having losses, when

a specific economic activity is being produced, it is important that energy efficiency is

maximized, being energy efficiency the ratio between useful energy output and energy input

(Patterson, 1996). Therefore, the implementation of high quality energy should be favored.

One example of this is electricity, which can be converted fully into work. Compared to

chemical energy, such as those obtained by burning fossil fuels, electricity represents a better

option due to the large exergy losses related to combustion reactions. It has been more than

30 years that economists state it as the reason why the increases in economic activity

observed over the last centuries (and examined over the next sections) are commonly

attributed to the switch to higher quality sources (Schurr, 1984).

For our purposes as economists, the study of energy is relevant namely because it represents

an essential factor for production. Traditional theories think of energy as an intermediary factor

(whereas capital, labor and land are primary), causing an idea of less significance to

production. Nonetheless, this conception been debated with strong arguments already in the

mid-80s (Schurr, 1984). Productivity has been improved in manufacturing as a result of the

implementation of electricity, which in return may have triggered technological transformation,

thereby causing economic growth. By reason of electricity being a high quality form of energy,

energy efficiency has generated productive efficiency. Being electricity therefore the motor of

current economic growth, the next step lies in the understanding of the means generating it.

To this extent, renewable energies offer the opportunity to explore the importance of energy in

economic growth.

4.1.2 Growth and Development

Once it has been clarified the concept of energy in the context of the economic sciences, it

follows to determine the same for growth. Again, it can lead to different, and almost always,

contrasting opinions. However, all of them consent to the idea of economic growth as a remedy

31

for alleviating poverty. In the most broad and simplistic manner, basic economics textbooks

define economic growth as just a measure of the country’s productive activity over time. This

corresponds to the overall value generated by its factors of production, regardless of the

location, as long as it is being owned by that country (Burda & Wyplosz, 2010; Samuelson &

Nordhaus, 2005). However, as much as it captures the core idea behind growth, it does leave

great opportunity for discussion regarding the fact that such ideology has caused that most of

the natural resources are being exhausted due to its orientation towards productivism and

overconsumption.

Environmental and resource economists stress a different aspect of economic growth.

Although still using the increases of the Gross National Product as a measure of improvement,

they do acknowledge the fact that materials and energy are limited resources employed in a

closed system (Turner, et al., 1993; Perman, et al., 2003; Common & Stagl, 2005). In such

case, a critical aspect arises. According to the first law of thermodynamics and given the

imperfect technological state, an everlasting growth of a country’s economic activity is limited

by two factors: the waste generated throughout its processes and the availability of its

resources, which in most cases are exhaustible. Therefore, an attempt to always achieve

improvements in the level of economic activity, in addition to the population increase and the

ability of the natural ecosystems to regenerate and assimilate waste has become a greater

challenge to overcome over the last years.

For this reason, the current notion of growth has experienced a partial change mainly due to

the acknowledgement of the limits of our ecosystems. This idea is by no means new, since the

concern of not being able to sustain everlasting growth has been already proposed in the

beginnings of the 18th century by Reverend Thomas Robert Malthus (Malthus, 1826).

Nonetheless, there have been numerous factors that have let economists still believe on the

possibility of continuing with current practices. These include the changes in technology

enabling increases in the productivity of resources, the discovery of more resources, or the

recycling and substitution of materials (Turner, et al., 1993). In spite of these breakthroughs,

the highlight of the proponents of the limits of growth is the switch of the common paradigms

about consumption and growth into one that can be endured by understanding the limitations

of the systems and efficiently utilizing its resources.

From the previously mentioned factors, one cannot determine which of them has been the

most decisive to enable economic growth to persist despite the known limitations. Certainly, it

has been the simultaneous interaction of all of them that has created momentum and fueled

our economies towards the current state. As a result, the world has placed special attention in

attempting to orientate these factors towards more sustainable practices. To this extent,

32

technological change such as new mechanisms to exploit solar energy more efficiently can

outstand by providing viable and cost effective alternatives to maintain this momentum in

economic growth, especially in those countries suffering from stagnation and

underdevelopment. In most of the cases, these counties not only lack in resources or the

knowledge to make use of the natural services of their regions, but also show large increases

in their population. Therefore, the implementation of traditional approaches may seem no

longer effective.

The idea of growth remains in increasing economic activity, accumulation of wealth and the

hypothesis that in the long run poor countries will converge towards the standards of the rich.

As the southern countries improve their conditions, their demand and consumption rise.

Unfortunately, the carrying capacity of the natural ecosystems cannot cope with it. This only

leads to a singular dilemma: on the one hand, economic activity generates increased wealth

(either aggregated or per capita), but on the other, there is an irreparable loss of natural

services. Moreover, the concept of growth described so far is based on the idea of “more is

always better” and does not involve other parameters of human well-being, such as happiness,

improved health, reduced inequality, better education, or protection of the environment

(Hanley, et al., 2013). In the end, under such conception, even when these countries would

benefit from the increases on the availability of income, it would only be a quantitative increase

in the physical dimensions of the economies and not qualitative. Under the classic approach

for growth, the benefits obtained from the increases in economic activity and income should

be greater than the losses of the natural ecosystems or the lack of time for leisure. However,

there are biophysical and ethico-social limits that hinder further expansion of the economy

(Daly, 1987). Therefore, in order to clear up this controversy and fully capture the goal of

improved well-fare, the concept of development should substitute that of growth, since it suits

better the idea of increased economic wealth while also offering improvements in the human

well-being.

Sustainable development goes beyond just economic development, as it includes economic

growth, social development and the natural ecosystems in order to provide future generations

with the same opportunities to enjoy human well-fare. Whenever it is being discussed, the use

of new energy sources in favor of its motion is always described as one of its medullar pillars.

Implementing renewable sources of energy such as solar, provides access to high quality

energy as a mean to boost productivity while simultaneously including the social and

environmental spheres. In this manner, those who thought of the limitations of the environment

are once more proven wrong, as technological change and innovation allow us to surpass

them. Being therefore the ultimate goal for this and the next generations, currently the concern

33

remains around achieving increased economic activity in order to later alleviate the social

aspects such as poverty in the developing world. For that reason, economists do place great

importance on determining how growth can be achieved and what forces drive it.

4.2 Energy and Economic Growth

4.2.1 The Production Function

The production function remains as the foundation for growth models, representing the

economic activity of an entity, firm or country. This is commonly defined as a theoretical

relationship between the output and the input of different factors (Burda & Wyplosz, 2010).

Although the economic sciences might not explicitly state the importance of physics in such

relation, the production function is another representation for the first and second laws of

thermodynamics. Production processes are therefore the transformation of matter or inputs,

some of which are defined as primary or secondary factors due to the fact that they are totally

or partially consumed during production. Mainstream growth models focus on primary factors,

namely capital, land and labor and omit intermediate factors such as energy. Although these

last are also relevant for the process, the argument that they are consumed entirely and might

not represent great relevance leaves them on a second plane (Stern, 2004). Nonetheless, as

the inclusion of energy in recent models has gained momentum, the argument whether energy

should be left out of the production function has been questioned.

In mainstream approaches, due to the assumption of scarcity and limited availability of

resources, the central idea of the production function is the set of input combinations that can

attain the maximum output (Varian, 2010). Given the technical constraints of an economic

entity during a certain period, two relevant concepts arise: marginal productivity and technical

substitution. Under the premise of an economy basing its production on the available stock of

capital and labor, the analysis of the productivity of either of the inputs is commonly performed

through aggregation of these and by holding the remaining factor constant. By rising the

amount of either of the factors, it is expected for output to increase, although under a

decreasing rate. Moreover, the common graphical depiction of these input combinations

illustrates the fact that there are different sets that can attain the same output, allowing

therefore the substitution or trade-off between factors. As a result, economic entities aiming

for profit maximization will strive for that combination that provides the maximum output level

under the least cost.

Production functions traditionally omit energy due to its classification as intermediate factor.

Alternative approaches (Ayres, 2002, 2007; Kümmel, 1989, 2011; Stern, 2003, 2004) however,

stress the importance of energy in production based on the first and second laws of

34

thermodynamics, since “all production processes involve the transformation or movement of

matter in some way and all such transformations require energy” (Stern, 2003). The first aspect

to consider in such production function would be the complementarity among factors, being

substitutability the second (Kümmel, 1989). With the inclusion of energy, the set of

combinations between capital, labor and energy, as well as the quantity of inputs needed to

attain the same level of output would differ. However, such approaches on production extend

even further the assumption of energy being an intermediate factor, namely because capital

and labor are reproducible factors of production while energy a non-reproducible (Stern, 2003,

2004). Consequently, the proposal of energy as the only factor arises. This other approach

borrowed from the natural sciences proposed by Hannon (1973) aims to explain production

based on the mechanics of energy transfer in the ecosystem. Here, capital and labor are

contained within the energy factor. Therefore, contrary to the idea of neoclassical approaches,

capital and labor become intermediate factors.

4.2.2 Mainstream Growth Models

Models are the simplified representation of the reality, seen through the eyes of statistical data

gathered during a specific period. Depending on the purpose, economists tend to derive

conclusions according to the relations they observe. When dealing with growth, Classical

Growth Theories employ a mathematical model consisting of the relationship between

production, savings and capital stock (Common & Stagl, 2005). This relationship is captured

by the production function

𝑌𝑡 = ƒ (𝐾𝑡, 𝐿𝑡) (12)

where

Y Size of the national income (for a given year) K Capital stock L Labor used.

In other words, it states that the output of an economy rises as a result of the rise of capital or

labor.

However, assuming an economy depending just on two factors is over simplistic (as all models

developed so far) and forgets about other similarly important aspects such as technological

change. Therefore, Neoclassical Growth Models explain growth in terms of the interaction

between two basic types of factors: technology and conventional inputs (Romer, 1996). It

includes a measure of the state of technology A (exogenously available) such that

𝑌𝑡 = 𝐴 ƒ (𝐾𝑡 , 𝐿𝑡) (13)

35

which reflects the impact of technology to the given levels of capital stock and employment

(Solow, 1956). An extended version of such model also includes the level of education and

skills of workers as well as the public infrastructure. This modifies the previous representation

to

𝑌𝑡 = 𝐴 ƒ (𝐾𝑡, 𝐿𝑡 , 𝐻𝑡, 𝐾𝑡𝐺) (14)

where H denotes human capital and KG the stock of public capital (Burda & Wyplosz, 2010).

Classical and Neoclassical Growth Models imply that the two main reasons for an economy to

grow is either by increasing its resources (demographically or by investing in new machinery)

or due to the changes in the productivity of these resources (Hanley, et al., 2013). The second

one however, places greater importance to A, which will cause an increase in Y, regardless of

the other factors remaining static. Moreover, it is understood not to be a factor of production

per se, since technological progress is considered as exogenous (Burda & Wyplosz, 2010).

Again, this theory differs considerably to reality in the sense that technology and innovation

are not truly available for everyone, therefore not able to be classified as public goods (Romer,

1996).

Technological change has been referred to be the total factor productivity or the engine fueling

growth in the long run (Burda & Wyplosz, 2010). Nonetheless, too little has been said about

what technology itself represents. Endogenous Growth Models propose the idea of technology

being a non-rival, partially excludable good originated from the investment of profit-maximizing

firms (Romer, 1990). In a more practical sense, it can be perceived either as “knowledge”

obtained by performing the same process during the daily routine, experimentation and

research; or as “innovation” in the form of new ideas, processes, products or techniques. Its

most remarkable characteristic is the spillover effect generating positive externalities. In such

case, the whole economy benefits as the incentive for the firm to invest and develop new

techniques grants it with profits from the temporary monopoly. In the simplest form of such

models, The AK Model described as

𝑌𝑡 = 𝐴 𝐾𝑡 (15)

technological progress and capital accumulation are linked, and the way to achieve long run

growth is through savings invested into financing technological progress (Aghion & Howitt,

2009).

Growth models focusing on technological change also grant attention to market incentives.

Whether as a new technique to transform wind power or solar radiation into electricity,

36

innovation is the result of investment efforts in R&D. Schumpeterian Growth Models attribute

economic growth to the boost generated by the creative and risky entrepreneurs seeking

business opportunities. Creative destruction is the term used to describe this mechanism,

because it is the process of invention and discovery of new ideas that make former innovations

obsolete (Schumpeter, 2013). Moreover, it is to be said that innovations occur either

stochastically or in long-term waves, being the second one the most accepted since ideas and

knowledge can trespass frontiers (Burda & Wyplosz, 2010). On the other hand, knowledge is

achieved by a continuous process denominated “learnings”. It not only refers to the fact that

experience is gathered through cumulative production, but also to the cost reduction achieved

throughout time (Arrow, 1962). Still, newer approaches applying the learning theory recognize

various types of learning focused on research, usage and interaction (Yu, et al., 2011).

The study of economic growth has become more specific with respect to the mechanisms by

which technology works. Alternative approaches in economics (Nelson & Sampat, 2001;

Beinhocker, 2006) borrow the concept of evolution from the natural sciences and adapt it to

describe the process of wealth accumulation and growth. They use the analogy of economy of

a country as an evolutionary system comprised by subclasses. Technological change and

entrepreneurship are still accounted responsibility for growth, nonetheless Coevolutionary

Growth Theories make a distinction between “Physical Technology” and “Social Technology”,

referring the second to the human interaction in the form of institutions (Nelson & Sampat,

2001). Under this thinking also institutions are involved in the process. Furthermore, it

emphasizes the importance of including energy and natural resources into the model. As a

result, the causal influences or feedbacks between the system factors (technology, institutions

and business strategies) are the ones that generate wealth as a byproduct of their coevolution

(Foxon, 2011; Foxon & Steinberger, 2013).

4.2.3 Integration of Energy in Growth Models

Growth models usually describe changes through time in economic activity as a function of

capital, labor and technological change. However, depending on the aim of the model, the

inclusion of further variables can portray a different approach on its analysis. This is the case

for models focusing on the influence of energy on the overall performance of an economy

(Kümmel, 1989; Toman & Jemelkova, 2002; Streising, et al., 2008; Costantini & Martini, 2009).

In a similar manner as traditional growth models, Eq. (16) describes this relation, where

𝑌𝑡 = ƒ (𝐾𝑡 , 𝐿𝑡 , 𝐸𝑡(𝑝𝑡)) (16)

Such approach recognizes the importance of energy inputs 𝐸𝑡, which conversely is also

dependent on energy prices 𝑝𝑡. In this regard, such model does not differentiate between the

37

sources of the energy inputs, therefore assigning a homogeneous characteristic to those

originated from NRET or RET. Secondly, energy inputs are considered to be strictly dependent

on energy prices in order to portray the idea of energy supply being affected by exogenous

elements represented by international prices or regulations.

Including the factor-independent output elasticities and linear homogeneity of the production

leads to a Cobb-Douglas function with constant returns to scale such that

𝑌𝑡 = 𝐾𝑡𝛼𝐿𝑡

𝛽𝐸𝑡

𝛾 (17)

which implies

𝛾 = 1 − 𝛼 − 𝛽 (18)

and the non-negativity of the elasticities of the constants (Streising, et al., 2008). In such case,

the model describes a typical development following the laws of returns to scale. If all factors

increase (decrease) by the same amount, output will consequently show a change by the same

proportion. On the other hand, if energy changes by a certain percentage while labor and

capital remain unchanged, final output will rise to a lesser extent (Toman & Jemelkova, 2002).

As it is expected, the extension of traditional growth models do have benefits. It has been

argued that a production function including energy as a third factor, can better trace the

changes in economic activity as its relevance is similar to that for labor and capital (Ayres, et

al., 2013). Based on the income allocation theorem, it is even inferred that, since the output

elasticity of energy is greater than its cost share, economic growth can be enhanced by the

increases in the usage of energy. As a consequence, it can be later understood not only that

energy as a factor of production should be considered in growth models, but that its increases

in utilization does influence output to a greater extent than capital or labor accumulation

(Streising, et al., 2008; Ayres, et al., 2013).

A similar, and yet extended growth model is described by Toman & Jemelkova (2002). In their

approach on the description of the energy growth relation, the idea of factor A representing

factor augmentation is considered. In the form of

𝑌 = 𝐹(𝐴𝐾𝐾𝑌, 𝐴𝐻𝐻𝑌, 𝐸𝑙 , 𝐸𝑛) (19)

the model encompasses the concepts of technological change in the form of R&D, knowledge

improvement or education, thus including the idea of endogenous growth. Furthermore, it

elevates the importance of energy as a traditional production factor into one providing multiplier

effects. In doing so, there is a distinction between factors El and En, representing energy in

38

which the difference in quality forms is stressed. This makes allusion to the fact that high- and

low-quality forms of energy offer different levels of productivity and thus, attain dissimilar costs.

Therefore, whenever costly high-quality forms perform better than its alternatives, there will be

a trade-off, which in the end, will represent increases in the overall welfare. As a consequence,

such model not only represents the effects of the different factors on economic activity, but it

extends its application to the social spheres, causing a change from growth towards one

representing development.

Kümmel (2011) discusses as well the importance of the consideration of energy in growth

models. However, his view of the economy is on a more resource-oriented basis. Following a

KLE model including creativity as an exogenous factor, the representation of the economy

describes the importance of the interrelations between each of the inputs (See Figure 12).

Figure 12: The Capital-Labor-Energy-Creativity Model of Wealth Production in the Physical Basis of the Economy; Source: Adapted from Kümmel (2011).

Further representations of the energy-growth relation extend the importance energy and take

another view for other factors from the model. Following the laws of thermodynamics, capital,

labor, and land are just another representation of energy, thus being this last one the only

factor. Such biophysical approach has been discussed by Stern (1999, 2004, 2010) and Ayres

& Warr (2002, 2004, 2012) and explained as an input-output model degrading energy. Based

on the natural sciences, energy, is treated as a stock that is exogenously determined, whereas

capital and labor become flows. Ayres & Warr (2002, 2004, 2012) explain this model through

the example of an economy consisting of successive conversion stages ruled by the laws of

thermodynamics, in which each stage represents the energy conversion efficiency of the inflow

into the next stage. In other words,

𝐺𝐷𝑃 = 𝑅 𝐼𝑂1

𝑅×

𝐼𝑂1

𝐼𝑂2×

𝐼𝑂2

𝐼𝑂3× ⋯ ×

𝐺𝐷𝑃

𝐼𝑂𝑛= 𝑅 × 𝑓1 × 𝑓2 × ⋯ 𝑔 (20)

39

where

R Resource (exergy) inflow IOn Intermediate output in the nth stage fn Conversion efficiency g Ratio of output to the last intermediate product.

Consequently, under a two-stage economy based on a single intermediate product, the

simplest mathematical representation takes the form

𝑌 = 𝑈𝑔 (21)

where U (containing capital and labor) denotes the useful work provided by energy.

4.2.4 The Growth Engine

Regardless of the consideration of energy as the only factor or in combination with capital and

labor, new growth theories complement the positive effects of capital accumulation through a

so-called energy-power feedback cycle (Ayres & Warr, 2002). As it has been portrayed in the

previous sections of this thesis, there has been a historical decrease of real prices for energy

sources as a consequence of improved techniques and decreasing costs, especially for RET

such as PV and wind. Moreover, the shift of energy quality forms (from low to high) and the

increase in demand for better energy services have been attributed to play a crucial role in

some of today’s developed countries. As a result, due to the combined effects that these

components conduct on each other, and thus growth, they have been said to conform the main

pillars of a system characterized by the positive feedback discussed by Ayres & Warr.

Contrary to mainstream growth models describing changes through the accumulation of capital

and labor, the Salter cycle (See Figure 13), named after Wilfred E. G. Salter, can be

summarized as follows: given the technological progress achieved by previous experiences

(learnings), economies of scale and innovations in energy conversion technologies, energy

services can be produced at a lower cost than previous periods. Being these services offered

in competitive markets, lower production costs are conversely translated into low prices. It

follows that according to the price elasticity for energy services, the decrease in its price will

cause an increase in its demand, and thus a substitution from less efficient sources towards

more efficient ones. Such increase in demand generates a further increase in the promotion

and investment on R&D for those more competitive energy technologies as a result of the

increases in the payments for the energy services. In order to close the cycle, it is implied that

as economic entities invest in R&D, there will be a positive effect on innovation and

technological progress, which are further translated into cost-reducing techniques (Ayres,

2000; Ayres & Warr, 2002, 2004; Ayres et al., 2007; Warr & Ayres, 2012). Finally, it is important

to mention the relevance of the cycle, not only in the efficiency improvements of energy

40

services, but also on other aspects such as the innovation and creation of commodities and

products, therefore having a spillover effect on the economy as a whole.

Figure 13: Salter Cycle; Source: Adapted from Ayres (2000).

To understand the complexity of the engine and its feedback, it is beneficial to explain into

detail each of the cogwheels conforming the process. Firstly, the price of the composite good

is assumed to be calculated as a proportion of the total costs incurred during its production,

represented as

𝑃 = 𝑚 𝐶 (22)

where m is assumed to be equal to 1, therefore obtaining a price equal to the cost. Secondly,

as it is inferred a change in the demand for products due to the change in its price, this

relationship can also be expressed as

𝜕𝑙𝑛𝑌

𝜕𝑡= −𝜎 (

𝜕𝑙𝑛𝑃

𝜕𝑡) (23)

or

𝜎 = −𝑃

𝑌

𝜕𝑌

𝜕𝑃 (24)

Furthermore, the simplistic assumption of Ayres (2000) on the Salter cycle is that, as demand

for the product increases, it will conversely affect the investment on R&D and the increase of

the physical capacity, causing an improvement on the experience in production and creating

learning effects. Experience N (t) is therefore given by

𝑁 = ∫ 𝑌 (𝑥) 𝑑𝑥𝑡

0

(25)

representing the fact that the higher the accumulated output of a specific period, the larger the

experienced gathered. On the other hand, the learning effects are assumed to be represented

41

as the decrease in the production costs. Similar to previous sections of this thesis, the Salter

cycle includes a TFLC in the form of

𝐶 = (𝐶0 + 𝑁)−𝑏 (26)

Within the Salter cycle, several important elements are observed: the output of an economy

denoted by 𝑌, the production costs for the energy services 𝐶 translated into the price 𝑃 when

offered on the market, the price elasticity of the demand 𝜎, and the learning parameter b

represented as the cost reduction per period. However, as described by Ayres & Warr (2000),

the importance of such feedback is contained within the two independent parameters 𝜎 and 𝑏,

because “technical change is restricted to the cost of production and demand is a function of

price (equals cost) alone. Growth results from the cyclic feedback between falling prices and

increasing demand (equated to output)”. As a result, depending on the empirical values

determined for these parameters, output will differ. Represented as

𝑌 = 𝑁0

1 − 𝑏𝜎𝑡

𝑏𝜎1−𝑏𝜎 (27)

output 𝑌 will grow linearly whenever the product 𝑏𝜎 =1

2. Conversely, a growth of output is a

quadratic function of time when 𝑏𝜎 = 2

3. It follows that as the product of such parameters

approaches 1, the exponent will become arbitrarily large, thus influencing significantly the

growth of 𝑌 (Ayres, 2000).

In the simplest case, it is further determined that whereas the price elasticity must decline

monotonically, the experience parameter must conversely increase over time. Such model

encompasses the possibility of sudden innovations that may accelerate the cycle, thus

generating technical change. In this regard, one of the most relevant aspects of the Salter

cycle, is its versatility with respect to other microeconomic mechanisms. Not only it combines

the learning curves methodology with the market forces in traditional economies, but it can

also be implemented to describe the development of fossil-fuel dependent countries and its

transition to improved technologies. In the same manner, such model displays consistency

with the interpretation of technological change and innovation according to the different stages

of the “product life-cycle curve” (Ayres, 2000) and the view of the economy as a complex

adaptive system (Foxon & Steinberger, 2013).

Regardless of the simplicity of the assumptions for the models, energy has been established

as an important driver for output. The Salter cycle provides a proper mechanism to better

understand the relevance of its inclusion and even proposes the idea of the transition towards

42

better energy sources and improved technologies. However, the acknowledgement of energy

in the development of an economy continues to be widely ignored in mainstream growth

models. One of the reasons for this is based on the cost-share theorem. To this extent,

materials and the energy services they provide cannot be properly accounted. Secondly, there

is still a constant debate around the causality between economic growth and energy utilization.

Even when economic growth is assumed to be strongly correlated with energy consumption,

such relation remains an assumption based on empirical research and does not imply

certainty. For this reason, different researchers (Ozturk, 2010; Payne, 2010) have attempted

to clarify this debate, nonetheless results have been in most cases not decisive.

4.2.5 Energy-Growth Causality

Acknowledging the linkage between energy and the economic activity of a country infers a

special focus on the understanding of two major concepts: energy intensity and energy

efficiency. The first one refers to the case where output (GDP per capita) is compared to the

levels of primary energy consumption per capita. Commonly, depending on the different

economic activities conforming the GDP of a country, the levels of energy consumed tend to

vary. The second refers to the ratio between the useful output of a process and the energy

input needed (Patterson, 1996). This is commonly referred as the energy-GDP ratio measured

by the GDP of a country divided by its energy consumption level, in which efficiency can be

increased either by the rise of output (being energy consumption static) or the decline of energy

consumed for the same amount of output produced. Later, these two concepts serve as

parameters to analyze the overall performance of a country.

One of the most recognized works focusing on the analysis of these parameters is that from

Schurr (1984). After identifying changes in the energy use and gross national product (GNP)

in the United States before and after World War I and during the 1970s energy crisis, he

attempted to explain this behavior by proposing two main arguments: the fact that the

composition of national output had shifted from a heavy to lighter manufacturing industry,

changing the intensity by which energy had been used; and the improvements in energy

efficiency as a result of the changes in the composition of energy supply towards more

electricity and fluid fuels. Throughout the period between 1920 and 1981 he observed that

increases in productivity showed an inverse relation with respect to energy intensity.

Furthermore, he highlighted the positive effects of available and low-cost energy (in the form

of electricity and fluid fuels), which influenced firstly technological change, productive efficiency

and later increased final output (similarly to the Salter cycle).

The concept of energy intensity is central for proving the causality between energy

consumption and economic growth, namely because it establishes a correlation between these

43

two. After gathering empirical data from more than one hundred countries between the years

1960 – 1995, Ferguson et al. (2000) took a similar approach to Schurr (1984) and extended

the evaluation of the relationship between electricity use and wealth creation. Although they

clearly state their inability to prove the causal relationship between electricity use and

economic development, they do acknowledge their simultaneous evolution. Within their

findings, their research concluded the following:

Wealthy countries have a stronger correlation between electricity use and wealth

creation than poor countries.

There is a stronger correlation between electricity use and wealth creation than there

is between total energy use and wealth.

The increase in wealth over time correlates with an increase in the proportion of energy

that is used in the form of electricity.

Figure 14: Correlation Coefficients for OECD and Non-OECD Regions; Source: Adapted from Ferguson et al., (2000).

Nevertheless, even when empirical data may imply a correlation between energy, namely

electricity, and output of an economy, uncertainties remain on the direction of the causal

relationship. Literature on the energy growth nexus recognize four different types of relations

in which causality may or may not be found (Ozturk, 2010; Payne, 2010). Firstly, it can be the

case of no causality, which implies the inexistence of a correlation between the variables. This

is also referred as the neutrality hypothesis. The second and third cases do acknowledge a

causality, however such relationship is unidirectional. The conservation hypothesis proposes

that economic growth is the driving force for energy consumption. In this case, the change in

output in an economy would have a change in the same direction as the consumption of

energy. On the other hand, the growth hypothesis proposes the idea of a causality running

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Electricity consumption per capitavs. GDPPPP per capita

Total primary energy supply percapita vs. GDPPPP per capita

Electricity-energy ratio vs.GDPPPP per capita

Middle East Africa Non-OECD Europe Latin America Asia OECD

44

from energy consumption to economic growth, therefore whenever energy supply is affected,

a similar impact would be observed on economic growth. Finally, it can be the case in which

causality runs in both directions, generating a feedback between energy and growth. Under

the feedback hypothesis, changes in either of the variables would be translated in further

changes in similar directions.

Current reviews on the subject (Ozturk, 2010; Ozturk et al., 2010; Payne, 2010a, 2010b)

continue to validate a relations energy consumption-economic growth and electricity

consumption-economic growth. Nonetheless, analyses of empirical data for different countries

have been unable to reach consensus for either of the hypotheses. According to Ozturk (2010),

data from low-income countries may suggest a causality running from GDP to energy

consumption, whereas middle-income countries a bi-directional causality. With respect to the

causality between electricity and growth, Ozturk et al. (2010) find a causality running from

electricity consumption to economic growth. Payne (2010a) however, reports a causality

favoring the feedback hypothesis for low-income countries and the growth hypothesis for

middle-income countries describing a relationship between energy consumption and growth.

Regarding electricity consumption and growth, causality is contrastingly running mostly from

GDP to electricity Payne (2010b). Such results further expand the uncertainty for causality

analyses and increase the debate on the subject. Explanations given by both authors are

based on the differences in the parameters considered for the data, the methodology for the

analyses, heterogeneity in climate conditions, energy consumption patterns or stages of

economic development of a country.

4.3 Energy on Development

4.3.1 The Environmental Kuznets Curve

Previous conclusions on the causality between energy and growth, differences in energy

consumption patterns and economic development, seem to be consistent with the findings by

Schurr (1984) and Ferguson et al. (2000). To some extent, the levels of economic development

influence the intensity, efficiency and energy forms used during the economic activities of a

country (Janosi & Grayson, 1970). Throughout his analysis, Schurr (1984) observed a change

in the energy-GDP ratio as income of the country improved attributed to the shift from output

depending mostly on heavy industrial activity towards one relying more on services. Similarly

there was a shift to more efficient energy forms such as electricity. Ferguson et al. (2000)

observed a strong relation between an increased amount of electricity consumption with

respect to the overall energy consumption and the income levels of a country. However, some

important aspects on these findings are the effects that the changes in energy consumption

patterns have on other spheres such as the environment or the society as a whole. This is

45

based on the linkage between high industrial activity, thus high energy consumption, and the

pollution generated as a byproduct of such processes.

Empirical approaches have described a similar relation between economic activity and

environmental quality called the “Environmental Kuznets Curve” (EKC). As countries transit

from undeveloped and agricultural-based to industrialized economies, energy intensity and

thus environmental quality tends to follow a “U-shaped” development as a result of the

dematerialization of their industries (Medlock & Soligo, 2001). Found in the early stages of

development, energy intensity is low. Similarly, the damage created to the environment is

minimal. However, an economy increasing its productivity will tend to consume more energy,

which as a result will contribute largely to the emission of pollutants. The original research of

Grossman & Krüger (1991), from which the basis for EKC-approach was taken, states that

economies in this transition will eventually start to reduce emission levels whenever they have

reached a certain stage of development (range of income). After this, already developed

countries will depict an opposite trend. Environmental quality will improve and energy intensity

will decrease, however the overall energy demand will continue increasing (See Figure 15).

Figure 15: Environmental Kuznets Curves; Source: Adapted from Dasgupta, et al. (2002) and Medlock & Soligo (2001).

While analyzing the relation between economic development and end-use energy demand,

Medlock & Soligo (2001) assert that energy consumption is represented in the form of

𝑒𝑐𝑡∗ = 𝑓(𝑦𝑡 , 𝑝𝑡 , 𝜏𝑡)

(28)

GDP Per Capita Y’’ Y’

Pollution/ Energy intensity

Revised EKC

Conventional EKC

46

describing a function of per capita output 𝑦𝑡, a vector of energy prices 𝑝𝑡 and technology 𝜏𝑡 for

a single sector. Therefore, the total final energy consumption should be the sum of each end-

use sector, thus

𝑒𝑐𝑡∗ = ∑ 𝑒𝑐𝑡,𝑗

∗

𝑗

(29)

Furthermore, in order to describe the long-run-U-shaped relation between energy demand and

income, energy demand is assumed to be in the form

𝑒𝑐𝑡∗ = 𝐴𝑝𝑡

𝑏1𝑦𝑡𝑏2+𝑏3 ln 𝑦𝑡 (30)

and its linear transformation

ln 𝑒𝑐𝑡,𝑗∗ = (𝑎𝑗 + 𝜃𝑡) + 𝑏1 ln 𝑝𝑡,𝑗 + 𝑏2 ln 𝑦𝑡 + 𝑏3(ln 𝑦𝑡)2 (31)

In this manner, the model includes the long-run income elasticity which declines as income

rises according to the stage of development of the country.

The shift in the “U-shaped” curve of EKC is not only attributed to the stages of development of

a country, but to the transition to high quality energy forms and the improvements in energy

efficiency due to better technologies (Alstine & Neumayer, 2010). Furthermore, it is inferred

that after a certain stage, environmental quality as a good becomes more relevant than

increases in income. Therefore, developed countries will struggle to implement more strict

standards and regulations, and switch to more environmental friendly technologies (Dasgupta,

et al., 2002). On the other hand, developing countries assign a lesser value to natural

resources and environmental quality in order to achieve economic growth in the form of

improved GDP. Under the conventional EKC, it would be expected for these countries to

increase their demand for energy services, thus increasing the amount of pollution and

environmental damage. Nevertheless, availability of better energy sources, as thus provided

from photovoltaics, can provide an alternative to avoid such path. As a result, a revised or

adjusted EKC can portray a new relationship between these factors (Auci & Trovato, 2011).

4.3.2 The Social Impact of Energy

In this regard, improved energy technologies can not only influence positively the environment

whenever energy demand increases. Further analyses on the availability of new energy

services describe also an improvement on the overall welfare of the society. Based on the fact

that energy availability can disproportionally stimulate growth, thus development, Toman &

Jemelkova (2002) stress the importance of the quality of energy used throughout the different

stages of development of a country. The quality of energy sources can also be evaluated by

47

the services provided such as the lightning services of kerosene or electricity lamps measured

in lumens. Under the assumptions of constrained supply of energy services, the marginal value

product (MVP) of high- and low- quality energy forms may differ. This relation is depicted in

Figure 16 for the case of a rural area in a developing economy. The usage of costly and low-

quality sources (associated with low-income countries) such as wood, depicted by schedule

MVP0 can be improved by the availability of electricity provided by off-grid systems, such as

PV, and represented by schedule MVP1. As a result, by switching to a low-price and high-

quality energy form, the availability of better energy sources generate a multiplier effect that

influences the overall economy, especially in countries with low levels of development with

high opportunity costs and benefits are higher.

Figure 16: Multiplier Economic Effects from Increased Energy Services Utilization; Source: Adapted from Toman & Jemelkova (2002).

The graphic description also incorporates the mechanisms of other models such as the Salter

cycle and other macroeconomic feedbacks. Given the availability of low-cost and high quality

sources, the first effect observed in a low-income country would be the increase in

consumption of energy services. This is described by the area 𝑎𝑏𝑑𝑒. Following the reasoning

of Toman & Jemelkova (2002), better energy services will improve the productivity in economic

activities and have positive spillover effects on education, creation of institutions and

modernization and provision for infrastructure for water, sanitation and communications. This

will enable countries to improve their income levels (schedule MVP1) which will later start the

previously mentioned growth engine, further reducing the costs of energy services, switching

for more efficient and environmentally friendly technologies and providing the basis for a more

sustainable development.

Lumens

Price

Unit cost per oil lamp

Unit cost per electric light MVP1

MVP0

h

i

a

d

b

c

g

e f

48

4.3.3 Barriers to the Deployment of Renewable Technologies

PV as well as other RET, have successfully proven to offer great potential due to its economic

viability, especially in rural areas of developing countries. However, despite the technological

advances of the last years and the awareness of its benefits, these technologies are still lacking

the expected deployment and adoption in these regions. Acknowledging this situation,

proponents of RET have been immerse in the task of establishing a proper framework for the

identification and understanding of the barriers hindering such projects (Painuly, 2001).

Barriers in the context of energy-related projects refer to the conditions limiting or influencing

to a certain negative extent, the full deployment of the potential services RET can offer.

Following the framework proposed by Painuly (2001), the analysis of these barriers can be

better performed when classifying them according to the type or main characteristic describing

its influence on the establishment of such projects. Such conception is based on the idea of

heterogeneity and dissimilarity among regions due to their economic, social or political state.

Therefore, whereas some projects might be hindered by a certain barrier, it cannot be assumed

as a universal impediment for other regions.

Over the years, similar analyses have been carried out over different countries aiming for the

understanding of these barriers (Jacobs & Darwich, 2015). Under a global scope covering

developing and developed countries where RET have been implemented, barriers perceived

during these energy-related projects deal with conditions regarding technical, financial,

institutional, policy, market capacity and social dimensions. In the same manner that these

barrier types do not apply for all countries, the extent by which each of them affect RET is also

different, thus the importance of the adaptation of the respective corrective measures for each

case.

Literature acknowledging the existence of barriers coincide on the prevalence of those types

dealing with the market capacity, institutions and financial conditions as the main aspects

hindering PV and other renewables. In these cases, the barriers could take the form of lack of

institutional mechanism or frameworks aimed at regulating or promoting these initiatives. The

lack of infrastructure enters in conflict as well with the social and economic conditions such as

the lack of mechanisms to incentivize or finance further projects. Contrastingly, whereas in

some cases of success, the main drivers for the deployment of renewables across the world

have been the governmental and financial support through mechanisms such as FIT, targets

and quotas, literature focusing on the understanding of barriers attribute the unsuccessful

implementation to the lack of such.

49

5. PV on Economic Growth

Throughout the previous chapters of this thesis, the evolution patterns of the energy sector

and its influence on the economic activity has been characterized for portraying similar

structures. In order to fully understand the mechanics by which both theories on learnings and

the energy-growth relation operate, it has been necessary to think of the factors influencing

such models as a form of dynamic system. This abstract appreciation of their interrelations not

only gives a better representation of these models, but also helps to identify each and every

possible impact that its factors have on the overall change.

Acknowledging the dynamic behavior of the global energy and economic systems has been

already performed in by Warr & Ayres (2012) and Dale et al., (2012a, 2012b). Again influenced

by the natural sciences, their biophysical approaches aim to interpret a complex and almost

living system which describes an economy based on energy. Its most basic assumption is that

economic processes must occur in a system with continuous cycles and not in a linear manner,

thus implying a model based on the concept of sustainability. Therefore, following some of the

basic ideas proposed by them, this chapter uses the system dynamics approach in order to

explain the relation between energy utilization and economic development, however giving a

special focus on the role of photovoltaic technologies and their production of electricity in the

developing world.

For this reason, this chapter is divided into three sections. A brief introduction into the basic

notions of system dynamics based on the work of J.W Forrester can help to highlight the main

patterns and behaviors observed in such systems. Afterwards, the application of the SD-

approach will serve as a tool for the understanding of the mechanisms behind the development

of the photovoltaic industry. Finally, in order to better portray the insights of the theories on

learnings and the energy-growth relation, the construction of a simple model including the

“growth engine” will be applied on different hypothetical cases of developed and developing

economies. In doing so, the aim will be to better describe the role of the availability of

photovoltaic energy sources and electricity on economic development.

5.1 The System Dynamics Approach

Economists usually observe social behavior and interpret it through abstract and in many

cases unquantifiable means. This is probably one of the most common difficulties that arise

when trying to translate complex mechanisms into mathematical functions and models. In

addition, they must explain with accuracy what seems to be intrinsic in human nature. Although

this problem has been acknowledged by almost all great economists, as in many other cases,

50

a different approach stemming from external sciences can often bring new insights that can

push through existing barriers and shift knowledge into a different path.

Proposed during the mid-50s by Jay W. Forrester and based on basic engineering concepts,

System Dynamics (SD) aims to determine the process by which social behavior and the

decisions of individuals and institutions cause changes through time in large and complex

systems. Although the SD-Approach describes the implementation of feedback loops and

stock and flows for the representation of systems, when applied specifically to social systems

it is more commonly referred to as Industrial Dynamics (Forrester, 1968). In any case, both

concepts relate to the study of causes and effects derived from decisions. However, the

viewpoint taken is “that the internal structure of the system is often more important than the

external events“ (Kirkwood, 2001).

For the study of economic systems, an SD-Approach employs three core elements that will

conform the structure of whatever model is constructed. The first of these are feedbacks or

feedback loops. As it names states, it represents a circular cause-effect phenomenon in

industrial dynamic systems where an input generates a decision process that further influences

the system (Forrester, 1968). In order to generate these feedbacks, such models make use of

different variables that will detonate the change or start a chain-reaction. The second element

comes in the form of stocks. They can appear in the form of materials, personnel, capital

equipment, orders, or money, however their most remarkable characteristic is that they

represent quantifiable physical entities over a specific period of time (Kirkwood, 2001). Finally,

flows are the representation of a change for stocks in a given direction and influenced by the

observed variables. Nevertheless, when using the SD-Approach, most of the focus is placed

on feedbacks and their characteristics, namely its order, nonlinearity, polarity, and loop

multiplicity as well as their patterns of behavior (Forrester, 1968).

The order of a system is equal to the number of accumulation to this extent, most common

systems refer to cases with first or second-orders. The degree of nonlinearity however, can be

defined based on the multiplication or division of variables or functions of variables contained

in the coefficient of the variables of a system. The feedbacks of a system can be described as

either positive or negative. The first case refers to a polarity in which a change in the system

will generate further changes in the same direction, thus producing more change or reinforcing

it. On the other hand, a negative polarity will cause a change in the opposite direction. In this

regard, it is commonly said that negative feedback loops are goal seeking, whereas positive

feedbacks are in charge for the description of cases with growth (Forrester, 1968; Sterman,

2000; Kirkwood, 2001).

51

Furthermore, within the implementation of SD, several types of behavior are observed. Each

of them is dominated by the previously described feedbacks (See Figure 17). Exponential

growth is the result of a positive self-reinforcing feedback represented most commonly by a

constantly increasing curve, whose most remarkable property is the constant doubling time.

Goal seeking behaviors are, on the other hand, influenced by negative feedback that aim for

a specific state, thus counteracting influences in the opposite direction. For such behavior,

when the rate of adjustment is proportional to the size of the gap to the desired goal, the

described behavior is the commonly known exponential decay. Conversely to the constant

doubling time in exponential growth, exponential decay is characterized by its half-life. Finally,

a third behavior is caused by the influence of both positive and negative feedbacks and an

alternation of their dominance on the system, thus causing an oscillation on the development

of stocks (Sterman, 2000).

Figure 17: Types of Behavior in a System; Source: Adapted from (Sterman, 2000).

5.1.1 The SD-Approach on the Energy-Growth Relation

As previously explained, the basic principles of SD can be used to interpret the observed

outcomes on energy markets and their influence on other economic indicators over time.

Following the same structures proposed under a systemic thinking, one can translate the

changes on the PV industry, such as the development of the installed capacity bi-directionally

influencing production costs and experience, into a language of stocks, flows and feedbacks.

Once this transformation is possible, one can easily assume the existence of a construct

representing the characteristics of a vivid network.

Energy Markets

The stock of global PV capacity over the last decade has described an ascending trend,

namely one of exponential growth. With an initial 1.3 GW capacity in year 2000, the possibility

to produce electricity from solar photovoltaic technologies is 100 times larger than it was 13

years ago (EPIA, 2014). Just in 2013, the PV stock worldwide increased by almost 40GW.

China and Europe contributed with more than half. Within the last years however, the increases

in the stock have shown slight reductions caused by global economic downturns. They have

Time

Exponential Growth

Time

Goal Seeking

Goal

Time

Oscillatory behavior

Goal

52

been reflected in the deceleration of the newly installed capacities in Europe, however total

installed capacity has never been as large as today.

New installed capacity in the context of SD corresponds to one of the major flows affecting the

accumulation of PV capacity. After becoming aware of its many benefits and the coming

challenges in the matter of energy supply, countries around the globe have aimed to influence

the development of their home industries through policy measures. By setting specific targets

or quotas, every year more capacity has been added. There is a strong relation between those

regions displaying constant growth rates in cumulative capacity and aggressive policy

schemes. Regardless of the decreases in the cumulative growth rate in Europe, other regions

such as China and the APAC still display exponential growth with an average growth rate of

85 and 38 %/year respectively. As a result, the PV stock has reached the global average

growth rate of more than 40 %/year (EPIA, 2014), an amount which supersedes and outruns

any depreciation of the lifetime of the already installed facilities.

The other relevant flow for cumulative capacity is represented by the depreciation of the PV

systems, or in other words, the reduction in efficiency caused by the system lifetime. A common

practice for the planning of a project is the assumption of a lifetime of 20 to25 years for c-Si

modules. They are used to calculate the LCOE of PV in order to reflect a proper benchmark

among other sources. However, after the system lifetime has reached its limit, it does not

represent the complete loss of efficiency. Such calculations only reflect the fact that a 100

percent conversion is not possible anymore, but one of approximately 85 to 90 percent on

average. As a consequence, there is an outflow rate of the installed capacity of 0.2 to

0.5%/year (Branker, et al., 2011; Breyer & Gerlach, 2013). Although it may seem low compared

to the annual addition of PV systems, the fact that this depreciation rate will continue even

during stagnation, it must be taken into consideration.

Conversely to the always-increasing cumulative capacity of PV, production costs have

displayed changes in an opposite direction. Whereas the GW-capacity exponentially increases

over time, over the last years, the LCOE of PV has been asymptotically decaying. This trend

on the stock of production costs occurs mostly as a consequence of the changes in related

variables. Even if at first glance costs should not be considered as a stock, under an SD-

approach it falls into such category namely because it is being treated as a level. Nevertheless,

compared to the interrelations of the stock of PV capacity, the mechanics behind production

costs are more complex.

On the one hand, the cost reduction of capital expenditures (the silicon module) have played

an important role in the current cost level of electricity. From the period between 2000 and

53

2011, the cost decay rate of silicon modules has averaged 7%/year (De La Tour, et al., 2013).

However, the experience gained by the constant addition of more capacity and the increases

in the knowledge stock from the investment in R&D, translate into further cost reductions, which

ultimately cause an equally important influence. Thus, the combined outflow of experience and

knowledge correspond to the learning effects of the industry translated into the cost reduction

of PV.

Under a systemic approach, there is also an abstract conception of experience and learnings

obtained as a result of R&D. Knowledge in reality cannot be easily quantified or represented

through a unitary measure. Nevertheless, it should be seen as a stock that increases as a

consequence of the investment on R&D. The effects of such investments can be perceived by

the innovations and cost-reducing techniques occurring thanks to the support of governmental

entities or the private sector. Therefore, the inflow of more than 113 billion USD in 2013 should

also be accounted as responsible for the cost reductions on that year.

Finally, after the relevant elements of the PV industry have been put under the systemic lens,

it could be possible to think of a closed loop. Previous years have been witnesses of the parallel

development of increasing PV capacity and decreasing costs. It may seem plausible to assume

a reinforcing relation between these two stocks, namely because the strong efforts towards

the expansion of PV have influenced the production costs largely. Conversely, the lower the

costs, the higher the incentive for project planners to further invest in more capacity. Such

construct might be incomplete, since it leaves further variables outside this reinforcement,

however, for the purposes of understanding the basic mechanics of the industry, it can be

accepted as true.

PV Salter Cycle

As described by Ayres and Warr throughout their research, the Salter cycle can be understood

as a dynamic system dominated by positive feedbacks and reinforcements within price

elasticities, learning effects and monetary stocks. Although the cycle has been discussed for

the utilization of fossil fuels as the main source of energy, the general principle of the model

does allow for its application in order to describe the transition to more alternative energy

sources such as PV. It is only then, when the interrelations between stocks in energy markets

appear to be just a part of a larger system. Under such scope, it is possible to complete the

unknown elements on feedback loop encouraging the increases in installed capacity and cost

decreases. Thus, the Salter cycle provides a proper framework for the combination of these

two concepts.

54

In order to continue with the analysis of the feedbacks in the energy markets using the Salter

cycle, it is important to highlight some of the assumptions of the model described on the

previous chapter. First of all, there should not be a distinction between the watts of electricity

or the energy services provided by the technologies. Furthermore, the factors used for the

production of these services also become irrelevant, therefore the overall economy must be

comprised by small competing producers providing identical products or services. Additionally,

the technical change generated throughout the process must be restricted to the cost of

production and the demand for energy services to the price function. Finally, although the

traditional model assumes experience from cumulative production, in this case it also takes

into consideration the learnings originated from investment efforts to the development of a

knowledge stock.

Once these assumptions have been set, the PV Salter cycle is nothing but a resemblance of

the traditional cycle for fossil fuels. Starting from the cost level variable, the fact that electricity

is generated from identical firms allows the calculation of unitary prices as a proportion of the

cost. Therefore, whereas the stock representing the LCOE of PV has decreased to a certain

level, the price stock must resemble a similar change in the same direction multiplied by the

proportional constant 𝑚 described previously. Later, price changes will influence the demand

for those services provided by PV.

The next stock under analysis is the one measuring the level of economic activity resulting

from the changes in the system, namely GDP. This variable is affected simultaneously by two

different flows: one representing the incoming dollar flow caused by the aggregated demand

in the system, and the other denoting the investment on R&D. The former corresponds to the

sum of individual demands influenced by the price demand effect and the current cumulative

capacity. In this case, the price demand effect is nothing but the price elasticity of demand 𝜎.

The outflow however, will be simply affected by a constant denoting the proportion of GDP

assigned towards R&D. It will therefore lead to the accumulation of a further stock comprising

the total USD/year of investment into the development of better technologies and cost

reduction techniques. Consequently, this last stock will be the cause for the increase in

experience and the cost reduction achieved by knowledge, thus the missing element for the

completion of the feedback. The relation intended between increasing RD expenditure and

knowledge is positive and relatively straightforward: the greater the budget for RD, the higher

the technological advances for reducing PV costs. It is worth to mention that there is no specific

parameter denoting the cost reduction per dollar invested since in many cases, innovation

occurs spontaneously. Therefore, the inflow between the RD expenditure and the knowledge

stocks can be interpreted freely.

55

5.2 The PV-Energy-Growth Engine

The combination of the PV Salter cycle and the endogenous forces contained within the

learning mechanisms of the industry lead to a PV-Energy-Growth Engine (PVEGE) similar to

the one proposed by Ayres & Warr (2000, 2002). Based on the premise of a continuous

feedback between economic output and PV-energy prices, this modification of the salter cycle

portrays the endogenous influences of the learning effects gathered through the investment in

R&D (LBR) and the cumulative increase of capacity, thus the continuous process of electricity

generation (LBD). Furthermore, it also represents the influence of input prices and scaling

effects.

Before deepening on the description of the inner elements of the model, it is important to define

some further assumptions and boundaries. First of all, the PVEGE pictures a generic economy

not distinguishing between the states of development of a country. It is as well intended that

there are no barriers hindering economic or knowledge transactions. In this regard,

technological change and innovations can be assumed to be available. Furthermore,

infrastructure, institutions and other market conditions shall be considered as under a level

such that could hinder the interrelations of the system. Nevertheless, under such assumptions,

it might be questioned for the igniting force behind. For this reason, the initial state should be

considered as a consequence of the efforts of developed countries through the implementation

of command-and-control mechanisms, FIT and any other form of government support. Finally,

as one of its most intrinsic characteristics, fuel costs of PV must be considered as constant.

Contrary to fossil fuels, solar radiation as a fuel is seen as an unlimited stock leading the

attention to the improvements in efficiency of the technology transforming it into electricity.

5.2.1 Description of the PVEGE

The interrelations of the PVEGE can be described through a causal loop diagram such as the

one in Figure 18. Here the positive and negative signs denote the direction of change inflicted

within the variables. Consisting of four different loops, each of the paths begins with a change

in the output/demand element and finalizes with a change in the opposite direction of the

element for cost/price. Regardless of the path taken, all of the four loops complement the

diagram with a counteracting influence of the cost/price variable on output/demand. This last

relation is responsible for the reinforcement of the system, thus the generation of a positive

feedback. The only variable not contained in any of the paths is the one corresponding to the

influence of input prices on cost/prices for energy. Although it can be argued that changes in

output/demand might have an influence on input prices, for simplicity this relation is omitted.

Therefore, input prices are in this case determined exogenously, however, its effect on the

completion of the loop prevails as one of great relevance.

56

The output/demand element is represented by the amount of Watt capacity for a given point in

time. It denotes not only how much it can be produced, but also the quantity demanded. There

is a duality for the units of this element: whereas output is represented as the Watt production

capacity and demand, in economic terms a monetary value can be assigned, corresponding

to the value of the energy demanded measured in USD. Similarly, the production cost/price

element corresponds to the price level for each unit demanded, in other words, the USD/kWh

level achieved at a given period of time. On the other hand, investment in R&D portrays the

dollar-amount originated from economic activity assigned to research activities and

development of cost-reducing techniques.

These previous three elements exemplify the cases of stocks. However, they also contain

further relevant variables (described later in this chapter), which will determine the level of

influence on the whole system. These three remaining elements correspond to the effects

obtained from the LBD, LBR, achievement of economies of scale and the change on input

prices for components. All of them take the form of a cost competitiveness increase, thus a

reduction for the production cost per unit, or LCOE, measured as the USD/kWh/year.

Compared to the previous stocks, they only represent a simplification of the general

mechanism influencing cost/price and include further relevant parameters and indexes

responsible for this change.

Figure 18: PV-Energy-Growth Causal Loop Diagram

57

Regarding the loops of the system, two of them correspond to paths with a length of two

elements before they complete the reinforcement. Loop A describes a path in which a change

on output will lead to a change in the same direction on the magnitude of the LBD effects.

Later, the LBD effects will influence cost/price in an opposite direction. These same form of

influences are observed for Loop B, where an increase/decrease in output/demand will

increase the influence of the scaling effects, further influencing cost/price negatively. Loops C

and Loop D share the same intermediate variable describing the influence of investment in

R&D. For C, a change on output/demand will generate change in a similar direction for the

investment in R&D, and later on the magnitude of influence of the LBR effect. This however

further affects cost/price negatively. Finally for Loop D, economies of scale are also affected

in the same direction of the change R&D investment, still the change inflicted on cost/price is

in opposite direction.

5.2.2 Inside the PVEGE

As commonly implemented by the SD-approach, the next step in the construction of the model

corresponds to the mathematical representation of its elements. According to the previously

presented relations, the relevant state variables of the model are the following:

Q Cumulative capacity Y Demand C Production costs KS Knowledge stock P Price GDP Gross domestic product RD R&D expenditure

each of them are further affected by the parameters

α Cumulative capacity growth rate δ System lifetime or depreciation rate β Experience index κ Knowledge stock index η Knowledge depreciation rate ρ Input price reduction rate σ Price demand elasticity

𝜄 Investment rate t Time.

Starting with the first part of the system (See Figure 19), PV cumulative capacity 𝑄 has been

described as the stock in Watts at a given period. Not only it increases by the new installed

capacity each year, but also is negatively influenced by the lifetime of the system. In other

words, the change in cumulative capacity is the difference between the new installed capacity

and the losses due to the limits in the lifetime of the system. These two relations on 𝑄 can

represented as

𝑑𝑄

𝑑𝑡= 𝛼 ∙ 𝑄 − 𝛿 ∙ 𝑄 (32)

58

or

𝑑𝑄

𝑑𝑡= 𝑄(𝛼 − 𝛿) (33)

where the parameters 𝛼 and 𝛿 correspond to the rates by which cumulative capacity annually

increases and depreciates. The former can be interpreted as a constant proportional to the

already-existing stock level. Nevertheless, the flexibility of the model does allow for an annual

change on the rate depending on the intended goal. Once seen as a change through time or

flow, the first part of Eq. (32) can portray the development of the PV industry driven by a specific

target or quota established by a policy institution. In a similar manner, the second parameter

can be adjusted each period, namely due to the fact that the extended system lifetime improves

as innovation becomes present. For simplicity, it must also be assumed as a constant rate,

measuring the average depreciation with respect to the previous periods. Furthermore,

depending on the values established for the terms in parenthesis in Eq. (33) the overall change

in 𝑄 can take three forms: no growth, for the case where 𝛼 = 𝛿, asymptotic decay for 𝛼 < 𝛿,

and exponential growth whenever 𝛼 > 𝛿.

The influence of 𝑄 is later observed on the learning effects acting on the electricity production

costs. Learnings throughout the development of the industry have been described as the cost

reductions not only as a result of the continuous installation of new systems and the provision

of energy services, but also as technological innovation takes place. The combination of these

two learnings and the cost reduction obtained takes the mathematical form

𝑑𝐶

𝑑𝑡= 𝐶𝑡 ∙ (1 − 𝑄−𝛽 ∙ 𝐾𝑆−𝜅) (34)

where 𝐶𝑡 denotes the cost at the previous period and the term in parenthesis corresponds to

the cost reduction from the LBD and LBR effects. More relevant in the context of learnings are

the empirically-determined parameters 𝛽 and 𝜅. The first one is responsible for characterizing

experience from the process in the cost reduction, whereas the later conversely portrays the

change in costs as a result of knowledge and research activities. In both cases, the parameters

range between zero and unity, thus the larger the values the greater the proportional negative

change on the production costs. Similarly to the parameters influencing 𝑄, both 𝛽 and 𝜅 can

be established as constants. This would account for simplicity in the simulation of the model,

nevertheless in reality, they are not fixed and can be adjusted for each period.

The PVEGE not only uses the learning effects to describe the reductions in electricity costs.

Capital expenditures and the price for essential components for modules infers the importance

to account for the influences that input prices have on the model. Contrary to other effects of

the system, the parameter responsible for the input price effects is determined exogenously.

The change on the cost level however also takes the form of an outflow represented by

59

𝑑𝐶

𝑑𝑡= −𝜌 ∙ 𝐶𝑡 (35)

iIn which 𝜌 is the annual reduction rate estimated as an average from previous periods.

Therefore, in combination with the changes through time from the learning effects, overall cost

reductions are mathematically represented as

𝑑𝐶

𝑑𝑡= 𝐶𝑡 ∙ (1 − 𝑄−𝛽 ∙ 𝐾𝑆−𝜅 − 𝜌) (36)

Figure 19: PVEGE System (View of the Learning and Input Price Effects)

The second part of the PVEGE corresponds to the macroeconomic influences of lower PV

prices on the overall economy (See Figure 20). In a single-sector economy similar to the one

described by Ayres (2000), the $/kWh price for electricity is equal or proportional to the costs

for its generation as it has been described by Eq. 11 in the previous chapters. Therefore, in

order to establish a true identity between 𝑃 and 𝐶 in the model, 𝑚 must be equal to unity. Under

this thinking, cost or price reductions have an influence on the quantity demanded, which later

is translated into economic growth.

60

The price elasticity of demand is responsible for a price demand effect, describing a change in

demand as a consequence of the changes in the $/kWh price. In such relation, an increase in

the electricity prices has a direct influence in the quantity demanded in the opposite direction,

thus

𝑑𝑌

𝑑𝑡= −𝜎 ∙ (

𝑑𝑃

𝑑𝑡) (37)

Contained in Eq. (37), 𝜎 can take the form of a constant. However, in order to describe a

negative change on demand, it must be considered as a value between minus unity and

zero (−1 < 𝜎 < 0).

Figure 20: PVEGE System (View of the Price Effects)

As portrayed by the Salter cycle, the PVEGE is completed when the investment in R&D and

the learning mechanism are linked to the changes of economic output. The relation between

these two elements is certainly straightforward: the better an economy performs during a

certain period, the larger the investment for the next period. Over the last years developed

countries such as Germany or Japan have been characterized by their large support in the

fields of R&D for the PV industry. The result has been the improvements in the knowledge

stock responsible for the current cost decreases. In a mathematical representation, this relation

is addressed as

𝑑𝑅𝐷

𝑑𝑡= 𝜄 ∙ 𝐺𝐷𝑃 (38)

In this part of the PVEGE, the proportion of GDP corresponds to the government support on

the R&D stock, also known as R&D budget. In practice, 𝜄 is determined by the responsible

61

authorities dealing with investment policies. Although it can be assumed as uncertain, for

simplicity it is also treated as a constant.

5.3 Application of the PVEGE: Case Analysis

This section of focuses with the implementation of the PVEGE into two different cases: Case

1 dealing with a developed economy and Case 2 with an underdeveloped or developing

economy. Both of them, similar to the generic form of the PVEGE model, are based on the

assumptions already described. It is worth to mention that both scenarios represent

hypothetical cases. Nevertheless, they do portray similarities to the current panorama for the

developed and developing world.

Previous to the simulation of the PVEGE, the establishment of the parameters for each of the

cogwheels is determinant for an appropriate description of the interrelations of the system.

These are calculated based on the information on the current state of the energy markets and

the theoretical framework provided over the last two chapters. Moreover, in order for the model

to portray consistency, further assumptions are taken into consideration. The parameters α, δ,

β, κ, η, ρ, and σ are assumed to be universal among the different economies tested. The

reasoning behind this relays in the idea of equal technologies implemented in both cases. This

involves not only the same physical technology (lifetime of the system, efficiency, components,

etc.), but also the knowledge and skill to operate them. The special case is parameter ι

representing the rate of investment on R&D. Among countries with different states of

development, investment in the forming of innovation and new technologies cannot be

considered equal. Although it can be established as a proportion of the overall GDP, the policy

agenda determines the support for certain fields, being infrastructure, education, security or

health fields with a greater priority.

On the other hand, the variables Q, C, KS and GDP can convey the idea of imparity between

states of development. It can be assumed that a discrepancy between the existing cumulative

capacity and wealth namely due to the initial stocks each country is given. These conditions

are not discussed into depth, however it can be assumed that some countries may have a

larger resource endowment which allows them to start in a better situation as others. For this

reason, economic activity and watt capacity should be lower in developing countries. The same

applies for the knowledge stock and the LCOE. In this regard, infrastructure will be responsible

for the dissimilarities. While developed countries have better institutions, communication

networks and better established education systems, the southern countries struggle in the

same areas. As a result, the initial stock of knowledge and the LCOE should differ.

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5.3.1 Case 1: Developed Economy

Case 1 describes a generic economy with an initial GDP of 4 billion USD which can be

considered as developed (See Table 2). As a result of previous efforts to create an industry

able to cope with the high electricity demand, PV capacity in 𝑡0 accounts for 5 GW. Throughout

the previous years, the average growth of the industry has displayed a rate of 20 %/year,

describing a strong policy towards the energy sector. Conversely to the established PV targets,

the investment in R&D of new cost-reducing techniques and innovation has characterized the

current agenda. Over the last years, a 0.15 percent of the annual GDP has been assigned

solely for the purpose of improving the knowledge in the field.

Table 2: PVEGE Values for a Developed Economy

Variable Description Value

Q Cumulative Capacity 5 GW

C Levelized Cost of Electricity 0.2 USD/kWh

KS Knowledge Stock 50

GDP Gross Domestic Product 4 Billion USD

α Cumulative Capacity Growth Rate 20 %/year

δ System Lifetime 0.5 %/year

β Experience Index 0.02

κ Knowledge Stock Index 0.01

η Knowledge Depreciation Rate 2 %/year

ρ Input Price Reduction Rate 0.2 %/year

σ Price Demand Elasticity -0.5

ι R&D Investment Rate 0.15 %/year

Due to the lack of barriers in the implementation of PV-related projects, the achieved LCOE in

the country has been calculated around the 0.2 USD/kWh. In this regard, it must be

acknowledge that the high infrastructure of the country, the skilled labor and the advanced

communication networks do play a role for the relatively low PV-electricity costs. On the other

hand, the current global state of art is to a large extent responsible for such improvements.

Over the last years, capital expenditures originated from the silicon components in the modules

have displayed an average reduction rate of 2 %/year. Similarly, increased efficiency in such

modules allows for a depreciation rate of 0.5 %/year, representing an overall efficiency of 90

to 95 percent after the expected lifetime of the system of 25 years is achieved. Furthermore,

the cost reduction rates from previous experiences as well as the current knowledge of the

industry are constantly changing. Nevertheless, they are influenced by the state of art of the

industry estimated as 0.02 and 0.01 respectively.

5.3.2 Case 2: Developing Economy

The second case evaluated represents an underdeveloped economy in a rural region of the

developing world, lacking access to energy services and many other resources (See Table 3).

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With a lower economic output than Case 1, the GDP accounts 0.5 billion USD. Regardless of

the large potential for the installation of PV capacity, namely due to its relatively high solar

irradiance, the public and private sectors are not able to support the energy industry. Current

PV capacity has reached the 1 GW due to an average 15 percent increase per year over the

last years originated from the establishment of specific targets in the energy sector. In a similar

form, investment in R&D for the PV industry has been determined to be a constant proportion

of 0.1 percent of the annual GDP.

In contrast to other regions of the world, underdeveloped economies are characterized by the

poor infrastructure, unskilled labor and lacking communication networks which contribute to

higher costs in the production of electricity. For this reason it is common for LCOE to be

significantly larger than in developed economies. In this case, it is assumed an LCOE of 0.4

USD/kWh. Nevertheless, other costs, such as those for inputs are considered to remain under

the same range. Technology and the innovations related to the efficiency of PV modules can

be perceived as universal among regions, thus assuming similar performance. This also

applied for the experience and knowledge indexes.

Table 3: PVEGE Values for a Developing Economy

Variable Description Value

Q Cumulative Capacity 1 GW

C Levelized Cost of Electricity 0.4 USD/kWh

KS Knowledge Stock 50

GDP Gross Domestic Product 0.5 Billion USD

α Cumulative Capacity Growth Rate 15 %/year

δ System Lifetime 0.5 %/year

β Experience Index 0.02

κ Knowledge Stock Index 0.01

η Knowledge Depreciation Rate 2 %/year

ρ Input Price Reduction Rate 0.2 %/year

σ Price Demand Elasticity -0.5

ι R&D Investment Rate 0.1 %/year

Simulation of the PVEGE

Previous chapters on learnings and economic growth theories have served as the theoretical

foundation for the PVEGE applied on both cases. Not taking into account the state of economic

progress, the adapted version of the Salter cycle aimed to represent the main relations

between decreasing costs translated into competitive prices, increased demand for energy

services and improvements in economic activity of a country (or region) causing multiplier

effects.

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The observed behavior for the cumulative capacity is properly described by one of exponential

growth (See Figure 21). This is mostly influenced by the proportional and constant new

capacity installed every year. Although it does account for a depreciation of 0.5 %/year, the

annual increases in PV systems overrule the reduction in efficiency related to the lifetime of

the systems. The differences between both cases is caused by the initial stock 𝑄 at 𝑡0 and the

parameter 𝛼. Although the trend in both stocks is increasing, Case 1 shows a faster doubling

time than Case 2. Even if the growth rates in new capacity installed were to be equated, the

initial endowment of the developed economy would still cause a steeper growth of the curve.

Economic development and wealth in these cases do represent an important factor for the

future evolution of both industries. It portrays how different two economies would perform even

if similar targets towards renewable energy supply are set into action. However this case only

describes one with proportional growth with respect to previous activities and not a specific

quota in the amount of Watt capacity.

Figure 21: Development of PV Cumulative Capacity over Time Contrary to the exponential growth in PV capacity, LCOE on both cases decreases

asymptotically (See Figure 22). Again, the development of the curve depends on the initial

state of the economy. Since both economies share the same technology, both curves are

affected similarly by the input price decreases of the industry, and to the same extent, by the

experience and knowledge indexes over time. Based on the profiles of these economies

however, it is important to highlight the reductions of each of the effects on the overall LCOE.

Cost reductions by experience are the main driver, although knowledge does portray a similar

decrease magnitude. Input prices in both cases influence to a lesser extent than the previous

effects. Nevertheless, the two former depend on a constant financial support to keep on the

installation of new capacity and the investment on R&D. This is mostly due to the assumption

of endogenous learnings and exogenous input prices.

65

Demand as a function of price is represented by Figure 23. In both cases, the price demand

elasticity α is assumed to be inelastic with a value of -0.5. Nevertheless, the discrepancy

between both economies is large. Initial prices and initial supply capacity are the main drivers

for the increasing economic activity in both cases. However, Case 1 depicts a faster growth in

the energy demand and therefore, a faster improvement in economic terms. Since a reinforcing

behavior exists in the system, increasing demand for energy services will generate further

demand within the next periods.

Figure 22: LCOE Development over Time due to Experience, Input Price and Knowledge Effects

Under the premise of energy supply generating its own demand, an assumption also

considered by the classical Salter cycle, the larger an economy is the more energy it is

demanded. This correlation between energy consumption and economic growth is later

represented by Figure 24. The exponential growth of economic activity is explained through

the unlimited fuel that solar radiation provides for the system. Whereas other sources of

electricity are bounded by their fuels, in both cases, energy supply is the result of the

66

technological limits of the industry. Similar to the AK models, technological change in the form

of learnings, experience and innovation are the main forces driving economic growth.

Figure 23: Demand over Time for PV Energy Services

Figure 24: GDP Development over Time

As the watt stock increases, the costs and thus the prices decrease. This further detonates

increasing demand for electricity and economic growth. As both economies perform better than

the previous periods, it is possible to save or invest in further activities, such as R&D of better

cost-reducing techniques. Since the amount invested is again determined as a constant

proportion of the current performance of the economy, the annual R&D stock increases in a

different manner for each case. As a result, the learnings occurring from innovation within the

process differ among cases.

67

The knowledge stock cares in this regard for conveying the relevance of investment in R&D.

Although it cannot be accurately quantified as it must be abstractly perceived, it can be inferred

that information and knowledge within the production of electricity can come in the form of cost

reducing techniques only acquired when time and financial support has been invested (See

Figure 25). The model does portray the idea that developed economies can afford larger

investments than other economies lacking in wealth.

Figure 25: Knowledge Development over Time

Special cases

The previous two cases represent scenarios in which the attitude of those in charge with the

government agenda have established a steady policy towards PV. As observed in the first

chapter of this thesis, economies around the world continuously change their attitude for PV in

the sense that specific targets or support mechanism are set into action in order to accelerate

its development. These policies can also be programmed for the PVEGE, therefore its

importance for the analysis of the model with respect to its reaction towards exogenous

changes (See Table 4).

Developing economies can pursue a rapid growth for PV, and thus of their overall economic

activity, by strongly investing in large capacity projects and in R&D. Applied to the economy in

Case 2, policymakers establish the target of increasing the annual installation rate to 20 %/year

over the next 5 years combined with the development of a 1 GW PV park and a further support

of 5 million USD for year 2015. As observed in Figure 26, the results of such policies might

have an instantaneous effect on the development of the industry. Although the boost in the

industry causes a significant change within the years those policies are implemented, as the

support stops, so does the acceleration of the cumulative capacity. On the contrary, if initial

68

support for the industry is stopped, the detrimental effects of the depreciation of installed

systems cause a lag which follow over the next years. As a result the importance of the

continuous support towards the industry is highlighted.

Table 4: PV Policy Comparison

Tight PV Policies Relaxed PV Policies

Cumulative Capacity Growth Rate

20%/year 2015-2020 10%/year 2015-2020

PV Target 1 GW in 2015 none

Investment in R&D 5 million USD in 2015 None in period 2015-2017

Figure 26: Cumulative Capacity Development due to Policy Changes

Similar to the changes in cumulative capacity, the increases in the knowledge stock are present

due to the large investment in R&D. Both economies diverge with respect to their current state

and the policies implemented, therefore, a proportional change in the LCOE is expected.

Nevertheless, the short-term character of an increase in GW capacity and financial support to

R&D does not have a significant change in the cost of electricity production (See Figure 27).

In both cases, it seems that the sudden change on GW capacity and financial support does

not represent a large influence on LCOE. Cost reduction depends not only in the accumulation

of capacity, but also on the experience indexes and the input prices. Furthermore, as already

discussed before, infrastructure deficits as a form of barrier continues to play an important role

for the discrepancies among economies, thus a call for attention towards this topic.

Given the nature of the PVEGE, economic activity is the result of the changes in prices.

Nevertheless, increases in energy supply will consequently create increases in energy

demand. This is further translated into changes in GDP, regardless of the policy support

observed in the industry. Described by the green curve in Figure 28, increases in economic

69

activity does not originate from changes in price, as these are not significant. Improvements in

GDP come as a result from increased energy supply occurring from more ambitious policies

towards the installation of capacity.

Figure 27: LCOE Development due to Policy Changes

Figure 28: GDP Development due to Policy Changes

Critiques and shortcomings of the PVEGE

The system described by the PVEGE is one linking energy from PV and economic growth of

individual economies. The hypothetical cases previously presented does allow for the analysis

of the effects caused by the learnings and price effects of the system on the overall

70

performance of an economy. However, most of the assumptions are over-simplistic and only

describe artificial situations which may differ from reality. In order to better portray these

economies, one must acknowledge the interconnection between them. Although the main

elements of the system still apply in the individual scope, a better construction of the PVEGE

is one aggregating the changes occurring in both economies.

The reasoning behind a coupling of the two systems relies on the fact that technological

innovations and learnings may be the result of the investment and development of single

economies, however due to global markets, these advances can become available to others.

As observed in Case 1, a developed economy deploys larger efforts on cumulative capacity

and R&D and consequently, its LCOE decrease to a greater extent than Case 2. Nevertheless,

on a global scale, such policies have further effects on less developed economies by

generating cost-reducing techniques, thus influencing global production costs (See Figure 29).

Therefore, economies as those represented in Case 2 benefit from the positive externalities

from developed economies.

Figure 29: Causal Loop Diagram of the Coupled PVEGE The observed cost reductions in PV electricity are therefore described by the joined efforts of

both the developed and developing world. The larger the aggregated PV capacity worldwide,

the greater the investments in R&D. Consequently, the learning effects generated will cause

even larger improvements in the competitiveness of global LCOE, thus providing with further

momentum in the trickling effect over economic growth.

However, the approximation to reality of the PVEGE can be questioned in terms of the

limitations of a never-ending exponential growth. As previously explained, the

71

oversimplification of the model assumes constant parameters (𝛽, 𝜅 and 𝜌). Furthermore, the

need for natural resources (land, raw materials, etc.) to build the required facilities may also

place a constraint. Although technological change in the form of innovations improving the

efficiency in these aspects may account to a small extent for some of these inconsistencies, it

must not be completely used as a strong argument. Nevertheless, the idea behind the model

is to portray the positive effects of the learning effects and input price effects into the reduction

of LCOE of PV in order to be implemented as a mean to achieve economic growth. In order to

achieve a better fit to reality, such parameters must be periodically estimated and included in

the simulation of the model.

72

6. Conclusions

Throughout the previous chapters, the goal of this thesis has been to determine the role of PV

for influencing economic growth. Following a systemic approach and based on the theories on

the energy-growth relation and experience curves, the PVEGE model developed has been a

useful tool in the provision of responses to the questions initially established. Therefore, the

idea behind this thesis is one focused on the understanding and description of the interaction

of the elements within such system.

The relation between energy utilization (production and consumption) and economic growth is

one driven by a series of feedbacks or reinforcements. Introduced and explained by the

existent literature, and later corroborated by the PVEGE model, the magnitude of an economy

is a determinant factor for the amount of energy needed to further engine growth. Therefore,

the larger the GDP stock, the greater the proportional increase in the stock of energy capacity.

Said in other words, underdeveloped countries lacking economic resources are also

characterized by its scarcity in energy sources.

Lack of energy services in rural areas of the southern countries represents a crucial aspect

hindering their development. Nevertheless, the progress achieved by the PV industry lets

these regions to believe in their implementation to supply electricity and thus campaign against

this issue. Most recent research in the rural areas of Sub-Saharan Africa already speak about

the competitiveness of PV achieving lower prices than traditional diesel-fueled systems. This

process leads to the idea of an economy based totally on PV-generated electricity to engine

further growth. However, in order to change the given state of scarcity, proper policies need to

be implemented.

The mechanics of an economy relying on energy services provided by PV technologies can

be described by the PVEGE model proposed in this thesis. Under such systemic approach,

the attention should be placed on the inner cogwheels described by the LDR, LBD, scaling

and input price effects responsible for the cost reductions in LCOE. These effects are

depending on the implementation of ambitious policies aiming for the strong investment in R&D

as well as on the establishment of targets and quotas. Unfortunately, as much as

underdeveloped economies strive for the tightening of their policies towards the production of

energy, their inherent limitations, such as their economic, political and social states, will

continue to be responsible for the gap between those endowed with more resources.

It must be understood that the aim for the implementation of PV technologies in rural areas of

developing countries is to allow for their economies to abandon the poverty trap. The

availability of PV technologies influences the overall economy by generating economic

73

prosperity through the multiplier effects and feedbacks previously described. Although the

exponential growth originated by the constant reduction in prices and the increased capacity

in the PVEGE only focuses on the investment in R&D for the industry, it does allow for the

interpretation of a larger monetary stock available for the investment in further sectors of the

economy. The PVEGE in this regard deals with the provision of the spark that can help

detonate economic growth. If well it has been determined that the future development of

countries is influenced by its current state, by improving the GDP stock, further feedbacks in

subsystems dealing with core institutions (education, health, telecommunications) will help in

the elimination of the barriers hindering the steepness of their growth.

For this reason, further considerations must be taken in order to achieve a fully PV-driven

economy. A more accurate representation of an economy based on the PVEGE must be one

in which developed and developing economies are coupled. Although the course of action for

the next years in the less favored corners of the world aims towards the implementation of PV,

it will take time until the results are visible. Therefore, developed countries shall continue with

their current support for RET and PV, since the benefit is mutual. Innovations and

improvements in the rich countries will help reduce even further the global costs of electricity.

In return, a transition from low-quality and unsustainable technologies (fossil fuels) to “green”

technologies will be facilitated.

Another important aspect is the elimination of technological, institutional, financial and social

barriers against the deployment of PV. For instance, in order to constantly deploy PV capacity,

there must be full availability of components and raw materials. In addition, the standardization

of the quality of the systems, its efficiency and performance, becomes similarly relevant. Full

availability of knowledge, information and “know-how” must still be solved, since the

endogenous principle of the model is one based on the constant learnings from previous

experiences, processes and interactions. Although these only represent some of the conditions

hindering PV around the world, they do account for some of the main causes for the

discrepancies in the implementation of such projects.

Finally, the exponential behavior of economic growth in a PVEGE economy might be plausible

only due to the assumption of unlimited solar radiation. Fueled by sun power, such economies

will have an advantage against traditional models in the long term. Even when technological

limitations will continue to represent a barrier, the idea behind the PVEGE is to initiate a self-

sustainable system driven by the momentum of its own elements.

74

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Annex

Table 5: LCOE Energy Technologies in USD/kWh

Energy source (REN21, 2014) (World Energy Council, 2013)

(Fraunhofer-ISE, 2013)

low high low high low high

Re

ne

wa

ble

s

Wind onshore 0,040 1,000 0,047 0,106 0,060 0,142

offshore 0,150 0,230 0,147 0,367 0,158 0,258

Solar PV small

0,160 0,440 0,079 0,145 0,104 0,189

PV utility

0,120 0,340 N/A

CSP 0,125 0,380 0,123 0,490 0,185 0,260

Biogas Biogas 0,060 0,190 0,050 0,140 0,179 0,286

Landfill gas

0,040 0,065 0,034 0,950 N/A

Geothermal Flash 0,050 0,130 0,039 0,201 N/A

Binary 0,070 0,140 0,089 0,276 N/A

Hydro Small 0,030 0,230 0,190 0,314 N/A

Large 0,020 0,120 0,024 0,302 N/A

Ocean Tidal 0,021 0,028 0,263 1,049 N/A

Fo

ssil

Coal Brown N/A 0,035 0,172 0,050 0,070

Hard N/A 0,084 0,106

Natural Gas N/A 0,061 0,141 0,100 0,130

Diesel Small scale

N/A N/A 0,173 0,226

Utility scale

N/A N/A 0,159 0,173

Nuclear N/A 0,091 0,147 N/A

Table 6: Country comparison for Selected Renewables

United States Wind Solar PV Biopower Hydropower

Onshore Offshore PV CSP

Capacity (GW) 61 12,1 0,9 15,8 78

Target / policy N/A

Historical Investment 2000-

2013 (Bn USD)

9 4 2 1

LCOE (USD/kWh) 0,061 - 0,136

N/A 0,117 -

0,239

0,156 -

0,490

0,045 - 0,210

N/A

Europe Wind Solar PV Biopower Hydropower

Onshore Offshore PV CSP

Capacity (GW) 117

80 2,3 35

124

83

Target / policy 20% final energy from renewables by 2020

Historical Investment 2000-

2013 (Bn USD)

17

23

8

3

LCOE (USD/kWh) 0,071 - 0,117

0,147 - 0,367

0,14 -

0,38

N/A 0,045 - 0,210

N/A

Germany Wind Solar PV Biopower Hydropower

Onshore Offshore PV CSP

Capacity (GW) 34 36 0 8,1 5,6

Target / policy 6,5 GW offshore by 2020, 15 GW by 2030

N/A N/A N/A N/A N/A

Investment (Bn USD)

17 23 8 3

LCOE (USD/kWh) 0,060 - 0,142

0,158 - 0,258

0,104 -

0,189

0,185 -

0,260

0,179 - 0,286

N/A

Japan Wind Solar PV Biopower Hydropower

Onshore Offshore PV CSP

Capacity (GW) N/A 13,6 N/A N/A N/A

Target / policy 5 GW by 2020, 8,03 GW offshore by 2030

28 GW by

2020

N/A 3,3 GW by 2020, 6 GW

by 2030

49 GW by 2020

Historical Investment 2000-

2013 (Bn USD)

0 4 0 1

LCOE (USD/kWh) 0,3 N/A 0,439 N/A N/A N/A N/A

China Wind Solar PV Biopower Hydropower

Onshore Offshore PV CSP

Capacity (GW) 91 19,9 N/A 6,2 260

Target / policy 100 GW by 2015, 200 GW by 2020

35 GW by

2015

1 GW by

2015, 3

GW by

2020

13 GW by 2015

290 GW by 2015

Historical Investment 2000-

2013 (Bn USD)

9 3 2 22

84

LCOE (USD/kWh) 0,049 - 0,093

N/A 0,079 -

0,145

N/A 0,034 - 0,83 N/A

India Wind Solar PV Biopower Hydropower

Onshore Offshore PV CSP

Capacity (GW) 20 2,2 0,1 4,4 44

Target / policy 15 GW added by 2017 10 GW added by

2017, 20 GW grid-

connected added by

2022, 2 GW off-grid

addded by 2020

2,7 GW added by

2017

2.1 GW added by

2017

Historical Investment 2000-

2013 (Bn USD)

3 0 1 3

LCOE (USD/kWh) 0,047 - 0,113

N/A 0,087 -

0,137

N/A 0,065 -

0,086

N/A N/A

Source: Adapted from Fraunhofer-ISE, 2013, REN21, 2014, World Energy Council, 2013.

Table 7: Countries implementing Feed-in-tariffs and Targets for Solar PV

Country FIT (USD/kWh) Target

Algeria 0.6836 25 MW by 2013, 241 MW by 2015, 946 MW by 2020, 2.8 GW by 2030

Argentina 0.2162 N/A

Armenia 0.0527 N/A

Australia 0.076 - 30.94 N/A

Austria 0.4458 - 0.5134 1.2 GW added 2010 - 2020

Bangladesh N/A 500 MW by 2015, 2.5 million units off-grid and rural by 2015

Bhutan N/A 5 MW by 2025

Belgium 0.0882 - 1.6103 N/A

Bulgaria 0.073 - 0.2634 80 MW park by 2014

Canada 0.4593 - 0.8241 40 MW by 2015

China 0.0006 - 0.1551 10 GW added in 2014, 35 GW by 2015

Croatia 0.3513 - 0.6214 N/A

Cyprus 0.2769 - 0.4863 N/A

Czech Republic 0.3107 - 0.6485 N/A

Denmark 0.073 - 0.1094 N/A

Djibouti N/A 30 % rural electification by 2017

Ecuador 0.4269 N/A

Egypt N/A 700 MW by 2017

France 0.0939 - 0.4268 N/A

85

Germany 0.13 - 0.19 N/A

Greece 0.104 - 0.156 2.2 GW by 2030

Guinea N/A 6% of electricity by 2025

Guinea-Bissau N/A 2% of primary energy by 2015

India 0.12 - 0.3918 10 GW added by 2012 - 2017, 20 GW grid-connected added by 2010 - 2022, 2 GW off-grid

added 2010 - 2020, 20 million solar lighting systems added 2010 - 2022

Indonesia N/A 156.8 MW by 2025

Iraq N/A 240 MW by 2016

Italy 0.1527 - 0.2715 23 GW by 2017

Japan 0.31 - 0.5485 28 GW by 2020

Jordan 0.17 - 0.19 300 MW by 2020

Kenya 0.2026 423 MW by 2016

Kuwait N/A 3.5 GW by 2030

Libya N/A 129 MW by 2015

Lithuania 0.5634 N/A

Luxembourg 0.4999 - 0.5674 N/A

Malaysia 0.2468 - 0.3567 N/A

Mongolia 0.15 - 0.3 N/A

Morocco N/A 2 GW by 2020

Mozambique N/A 82,000 solar home systems installed

Nepal N/A 3MW by 2013

Netherlands 0.4769 - 0.5174 N/A

Nigeria 0.497 - 0.579 75 MW by 2015, 500 MW by 2025

Pakistan 0.08 - 0.2 N/A

Palestina N/A 45 MW by 2020

Philippines 0.2526 284 MW added 2010 - 2030

Portugal 0.2905 - 0.5674 670 MW by 2020

Romania 0.27 - 0.48 N/A

Qatar N/A 1.8 GW by 2014

Saudi Arabia N/A 6 GW by 2020, 16 GW by 2032

Slovakia 0.3499 N/A

Slovenia 0.2972 - 0.6485 N/A

South Africa 0.166 N/A

Serbia N/A 150 MW by 2017

South Korea 0.4593 - 0.5134 2046 GWh by 2030

Spain 0.1655 - 0.3838 3% by 2020, 7.3 GW by 2020

Sudan N/A 350 MW by 2031

Switzerland 0.24 - 0.37 N/A

Syria N/A 45 MW by 2015, 380 MW by 2020, 1.1 GW by 2025, 1.8 GW by 2030

Taiwan 0.24 - 0.34 130 MW in 2013

Thailand 0.19 3 GW by 2021, 1 GW added in 2014

The Galapagos 0.4701 N/A

Tunisia N/A 1.9 GW by 2030

Turkey 1,2159 N/A

86

Uganda 0.3729 400 kW by 2012, 700 kW by 2017

Ukraine 0.3119 - 0.4204 N/A

United Kingdom 0.0963 - 1.0293 N/A

United States 0.12 - 1.01 N/A

Yemen N/A 4 MW by 2025

Source: Adapted from REN21, 2014.

Table 8: Barrier Classification

Barrier Type

Description Barrier Barrier Elements

Tech

nic

al

Refe

rs to t

hose m

ach

ine

-re

late

d c

onstr

ain

ts f

ocusin

g o

n the

physic

al com

pon

ents

' qua

lity,

eff

icie

ncy,

perf

orm

ance a

nd s

uita

bili

ty t

o s

pecific

cond

itio

ns d

urin

g the

pro

cess o

f m

an

ufa

ctu

ring,

dis

trib

ution,

insta

llatio

n a

nd m

ain

tenance. T

hey d

ea

l w

ith h

ard

ware

issues for

the d

escribed t

echnolo

gy a

nd leave

asid

e o

ther

such a

s k

now

-how

an

d info

rmation a

ware

ness.

Components & Raw Materials

Availability

Limited availability of RETs components and raw materials needed for production: Lack of rare metals; cadmium, tellurium, supply of silicon; lack of PV equipment supply

Physical Infrastructure

Underdeveloped RET industry capacities and challenges regarding technology and energy distribution and transportation: Lack of private sector capacity in manufacturing, distributions, installation, and maintenance; design of cities and other settlements, transport systems and utilities; flexibility in the adoption of alternative technologies and production systems, Grid unreliability, structural issues for existing buildings, scattered population, long-distance transmission requirements, limited rural infrastructure, challenges regarding system integration, need for bi-directional communication systems (smart grid),

Standardization

Lack of RETs technical standardization and variability of product quality: RET production quality inadequate appliance quality, lack of technical standards, inappropriate technical designs, ineffective quality controls and certificates, low installation standards, difficulties in installations

Efficiency & Performance

Deficits in RETs technical efficiency and performance; added technical challenges such as energy storage and production variability: Low quality RETs available, Limitations of the components, Variability and intermittency of radiation, Low technical efficiency, Need for backup/storage device, low generation capacity, high technical losses, weather/seasonal performance variability, technological immaturity, system performance validation (metering/billing), large O&M requirements; Intermittent nature of renewable energy

Market Compatibility &

Availability

RETs inability to meet varying energy market demands; inability of RETs to access certain market segments: Not available in location; affordability; suitability to local conditions; components not yet commercially tested; lack of industry-offered systems to meet consumer needs; low foreign technology transfer

87

Fin

an

cia

l

Refe

rs to o

bsta

cle

s c

oncern

ing th

e a

va

ilabili

ty o

f eco

nom

ic a

nd

investm

ent re

so

urc

es n

ece

ssary

for

the d

eve

lopm

ent

of th

e p

roje

ct.

Describe f

ina

ncia

l constr

ain

ts faced b

y e

ntr

epre

ne

urs

and investo

rs

when d

ea

ling w

ith c

redit institu

tio

ns a

nd

oth

er

financia

l

inte

rmed

iari

es.

Economic Viability

Inability of RETs to compete under market condition due to higher relative cost of energy production: High up-front costs, cost of BOS is not declining proportionally to the decline in module price, high transaction costs due to project scale, competition of land-use, high repair cost, high administrative costs, poverty and low-income household affordability, the "lock-in" effect; does not add value to property; lack of economic analysis

Availability of Capital

Deficit of accessibly funding for RET projects: Storage of capital, availability of foreign exchange, high cost of capital, lack of funds, availability of capital, high financing rates; slow rate of investment (ROI); lack of private funding due to inexperience

Availability of Credit

Inaccessibility or unacceptability of adequate RET project financing options: Access to affordable financing, lack of financial intermediaries, institutions, financial options, communication of information, lack of credit products, low finance; lack of competition among financial institutions

Financing Conditions

Inappropriate financial mechanisms; a lack of tailored financial products conducive to RET development: High financing due to high perceived risks, high payback period, high discount rates, financial dependency, donor dependency, RETs not viewed as an attractive investment option, long-lived capital, improper discounting;

Insti

tuti

on

al

Refe

rs to infr

astr

uctu

re c

on

str

ain

ts s

uch a

s lack o

f le

gal fr

am

ew

ork

or

institu

tions, as w

ell

as t

he ineff

icie

nt fu

nctio

nin

g o

f exis

ting o

ne.

They d

ea

l w

ith

the lack o

f exp

erie

nce a

nd k

now

ledge

in

the

deve

lopm

ent o

f R

ET

-pro

jects

as

well

as th

e d

issem

ination

of

info

rmation

an

d incentives. Framework

Development

Lack of institutional mechanisms and framework aimed at regulating and promoting RETs initiatives: Institutional framework to assure quality; lack of a legal/regulatory framework; support to R&D; professional institutions; plannification; O&M facilities; intellectual property right system; freedom of speech and information; ease of litigation; incompatible donor policies; lack of financial literacy; lack of local management; maintenance services and manufacturing facilities; lack of innovation capacity (institutions); difficulties in protecting intellectual property

Awareness and Incentives

Lack of institutions or mechanisms to disseminate information, lack of incentives; problems in realizing financial incentives; supply-side investment bias and energy tariffs discouraging energy-efficiency investments; institutions for RET promotion lack power; FIT rates not attractive for investors, project connection fees; misconceptions regarding system performance; lack of adequate fiscal incentives

Experience & Know-how

RETs knowledge gap for planners, developers, professionals, technicians, users, and financial community: Lack of knowledge of markets including energy needs of target groups; limited understanding among key national and local institutions of basic system and finance; lack of reflection on past lessons and experience gained; lack of clear responsibilities, lack of consistency between RET projects; fragilities of solar development partnerships; early departure of key partners; inefficient means of data collection and information interpretation; limited access to RE information; non-existence of central information point

88

Workforce Qualifications

Deficit in training facilities, programs, and initiatives resulting in a lack of trained professionals equipped to develop RET landscape: Inadequate workforce skills and training; technical proficiency and lack of personnel; lack of common language; lack of technical competence

Actor Participation

Lack of involvement or cooperation by stakeholders and private sector in project implementation, policy change, market development, and technology dissemination: clash of interest, unclear procedures and/or complex interactions and lack of coordination between the various authorities involved, fragility of solar development partnerships, failure of past projects (lack of confidence), lack of engagement from energy providers; lack of clarity between stakeholders as to what role they are to play

Bureaucratic Requirements

Added burdens to RET dissemination as a result of complications within the system of rules dictating government and business practices within the market: Legal and regulation constraints; excessive permission requirements and siting restrictions; inappropriate or unnecessary interconnection requirements set by utilities for small producers; excessive requirements for liability insurance; procedural problems among public sector agencies; limited entry of technology platforms into the grid; complicated procedures; lack of transparency; strong hierarchical structure; bribery

Po

lic

y

Refe

rs to a

ll th

ose g

overn

menta

l re

gula

tions, a

gre

em

ents

and

decis

ions

concern

ing R

ET

-rela

ted p

roje

cts

th

at h

ave n

egative e

ffects

on t

heir

imp

lem

enta

tion,

diffu

sio

n a

nd d

evelo

pm

ent. I

t in

volv

es the c

om

merc

ializ

ation,

trade,

access to the g

rid

an

d tariff

s to R

ET

as w

ell

as o

ther

conven

tiona

l

energ

y s

ourc

es.

Trade Regulations

Limitations of RETs availability due to political and economic barriers as a result of national and international policies and agreements: Lack of favorable arrangements on taxes and duties; protective tariffs; import duties; technology not available due to high costs; restrictions on foreign exchange (allocation)

Governmental Program

Consistency

Inconsistencies, insecurities, and inconformity with existing government policies, resulting in direct and in-direct barriers to entry for RETs: Lack of clear government policy; political inconsistency; uncertain governmental policies; unstable macro-economic environment; discontinuity/insufficient transparency of policies and legislation; political unrest; barring of foreign land ownership/joint-ventures; lack of co-investment

Political Bias

Advocatory government policies towards traditional energy production and inadequate political support for widespread RET transitions: Subsidies to conventional sources; lack of government policy supporting RET; price distortion; lower tax burdens on conventional sources; lack of political will; economic policies support extractive industries; unfavorable utility regulations for RET;

Capability & Experience

Lack of ability to achieve effective project execution due to factors such as institutional, market and organizational capacity: Capability to manage projects; market organization, bad policy design; lack of organizational capacity; lack of financial procurement and spatial planning systems; budget uncertainty due to competition with various national priority areas

89

Mark

et

Cap

acit

y

Refe

rs to a

ll th

e e

lem

ents

involv

ed w

ith th

e s

upp

ly a

nd d

em

an

d o

f th

e R

ET

as w

ell

as th

eir p

ote

ntial su

bstitu

tes in th

e e

nerg

y s

ecto

r. It

dea

ls

with t

he m

ark

et

itself: th

e in

tera

ctio

n b

etw

een p

roducers

, in

term

ed

iari

es a

nd c

onsu

mers

, an

d th

e e

xte

rnalit

ies a

nd d

isto

rtio

ns o

bserv

ed d

uring

this

pro

cess.

Market Bias

The skewing of a competitive market as a result of both existing market conditions, as well as, external market forces: Market liberalization favors existing technologies; RET taxation; high transmission costs (grid); inconsistent pricing structures disadvantaging RET; RET market infancy; lack of RET demand; grid companies' unwillingness to accept prices that do not reflect production costs; inadequate transmission and distribution; inability of existing institutional systems to change to accommodate new technology; poorly accrued articulated demand; lack of proven technology model;

Externalities

Lack of accounting for the positive and negative effects of utilizing RETs or traditional energy production plants; the accounting of only effects or actions that directly affect the entity producing energy: Degree of internalization of social and environmental concerns through taxes, subsidies; insurance etc.; free rider effects; inability to account costs and benefits; high burden of proof to realizing externalities, NIMBY phenomenon

Industry Information

Network forces direct future market; lack of RET education; lack of marketing; split incentives; dissemination and awareness, information asymmetry; lack of solar radiation data; lack of coordination between R&D institutes and manufacturers; misinformation and misconception by stakeholders; complexity of diffusion

Energy Sector Control

Low energy market product variability and high fixed market conditions resulting in a high burden of entry for RETs: Existence of fixed market conditions and advocatory Degree of competitiveness for RET; including oligopolistic practices or informal arrangements between government and private sector; lack of competition, high transaction cost; high investment requirements; incomplete market, inseparability of energy from products; competition with other sources; market power; monopolies; ease of market entry; top-down management of energy-sector, RETs viewed as direct threat to market share; existence of a "natural monopoly"; lack of willingness by utilities to yield to net-metering model; general abundance of conventional energy sources

Market Infrastructure

Lack of necessary market framework conducive for widespread RET development, production, investment, and dissemination: Lack of sufficient market base; market uncertainty; underdeveloped purchasing channels; restricted access to technology; insufficient commercialization; poorly developed industry; economies of scale; lack of incentives for local manufacturing; low productivity and little industry; lack of local entrepreneurship; lack of technical and marketing capacity; logistical problems; logistical problems with distribution; high transaction costs associated with reaching environmentally conscious consumers

90

Motivation to Switch

Lack of will or intention to switch to the production, allocation and consumption of RETs due to pre-existing political, social, and market conditions: Lack of actual intention to switch to green products; market acceptance; poor perception, involvement of the community; concerns about possible devaluation of asset value such as properties; competition with other interests in the geographic area; lack of innovation in industry; fear of losing traditional customers; traditional energy producers economically driven; RETs may be perceived as variation from organizational mission; added burden of project planning; intra-firm acceptance of RE innovation

So

cia

l D

imen

sio

ns

Refe

rs to a

ll sets

of str

uctu

res, att

itud

es a

nd d

yna

mic

s

chara

cte

ristic o

f a g

iven

so

cie

ty o

r culture

that

hin

der

the

diffu

sio

n o

f R

ET

. T

hey involv

es n

ot o

nly

th

e ind

ivid

uals

of th

e

socie

ty, bu

t a

lso o

ther

gro

ups a

nd institu

tions s

uch a

s

govern

ments

and loca

l com

mun

itie

s.

Socio-political Structures

The role of the public in decision-making and the allocation of power in institutional and social relationships: formal or informal alliances involving government; industry and the media; acceptance by key stakeholders and policy actors; lack of community choice

Cultural Dynamics

The establishment of social norms and ideals through the allocation of roles within households and communities: cultural diversity; tendencies towards competition and cooperation; conformity and distinction; heterogeneous interests, values, and world views; gender issues; problems in local participation and theft; weak maintenance culture; lack of money culture; lack of private property concept; affinity towards political patronage; high instances of vandalism; lack of ownership culture; nomadic culture

Attitudes Towards

Technology

The role of technology and material consumption in establishing individual identity; status and social bonds; awareness; understanding and attitudes relating to energy efficiency, its causes and potential impacts, and to changes in technology and lifestyles: esthetic considerations; changing of mind among customers; traditional household culture (electricity prohibited); physical absence of traditional energy production breeds apathy; social stigmas against RETs (aesthetic issues); Opinion of friends