Economics of the Global Steam Coal Market - Modeling Trade, Competition and Climate Policies

Technische Universität Berlin

School VII Economics and Management

Economics of the Global Steam Coal Market

-Modeling Trade, Competition and Climate Policies

vorgelegt von Clemens Haftendorn

Von der Fakultät VII - Wirtschaft und Management der Technischen Universität Berlin

zur Erlangung des akademischen Grades Doktor der Wirtschaftswissenschaften

Dr. rer. oec. genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr. Radosveta Ivanova-Stenzel

Berichter: Prof. Dr. Christian von Hirschhausen (TU Berlin)

Prof. Dr. Claudia Kemfert (Hertie School of Governance) Prof. Dr. Marco Runkel (TU Berlin)

Tag der wissenschaftlichen Aussprache: 17.10.2012 Berlin 2012

Steam coal is one of the main pillars of global electricity generation and its con-sumption is increasing, mainly driven by the growing Asian economies. Since the early 2000s the global steam coal trade underwent some significant changes. The international seaborne trade flows are growing rapidly and on the supply side a process of mergers and acquisitions increased market concentration, fueling fears that an oligopolistic market structure would develop similarly to the other fossil fuel markets (oil and natural gas). Concerning environmental externalities, the continued use of coal poses a serious problem especially because of the high carbon dioxide emissions that exacerbate climate change. This thesis presents several numerical partial equilibrium models to analyze these main issues affecting the market. In a first step the international seaborne trade of steam coal in the years 2005 and 2006 is investigated using the COALMOD-Trade model. At the time when market concentration on the supply side was the highest, we find no evidence of non-cooperative strategic behavior. The international trade flows and prices are better represented by a competitive model. However, a further analysis of the more mature and liquid Atlantic sub-market between 2003 and 2006 finds some evidence of market power exertion in this region. Yet we conclude that there is no structural market power problem in the mid-term for the global steam coal market. The large scale COALMOD-World model is thus based on the assumption of perfect competition and computes yearly trade flows until 2030 including both international trade and domestic markets as well as endogenous investments in mining and transport infrastructure. This model replicates actual trade flows for the base year 2006. One main finding is that, if global demand continues to increase after 2015, global supply costs may rise due to the large need of investments. COALMOD-World is also used to analyze the interactions between climate policies and the global steam coal market. We find that the effectiveness of regional policies reducing coal demand can be undermined by demand shifting to countries with less stringent climate goals.

This thesis contributes to the energy economics literature in several respects. An updated analysis of market power in the global steam coal market is performed. Further-more, a contribution is made concerning the use of conjectural variations to represent strategic interactions on a market, especially how conjectural variations models may fail to represent Nash equilibria. Thus, an alternative for the modeling of oligopoly markets with a competitive fringe is proposed. The COALMOD-World model contains novelties such as an endogenous cost mechanism that have not yet been used in other energy and resources market models. Finally, while the mechanisms of carbon leakage through en-ergy markets are well understood in theory, we are able to provide a quantification for the order of magnitude using the COALMOD-World model.

Keywords: global steam coal market, partial equilibrium numerical modeling, market power modeling, climate policy

Zusammenfassung

Kesselkohle ist ein wichtiger Pfeiler der weltweiten Stromversorgung, dessen Ver-brauchswachstum hauptsächlich von der wirtschaftlichen Entwicklung Asiens getrieben wird. Seit den frühen 2000er Jahren befindet sich der globale Kesselkohlehandel im Um-bruch. Die Handelsflüsse wachsen stetig und es hat ein Konzentrationsprozess auf der Anbieterseite stattgefunden. Dies hat Befürchtungen über eine oligopolistische Markt-struktur geweckt. Die weitere Nutzung von Kesselkohle verursacht zudem erhebliche Umweltexternalitäten, insbesondere auf das Klima durch den Ausstoß von Kohlendioxid. Diese wichtigen Einflussfaktoren des Kesselkohlemarktes werden in der vorliegenden Dis-sertation mithilfe von numerischen partiellen Gleichgewichtsmodellen analysiert.

Zuerst wird der internationale Kesselkohlehandel in der Jahren 2005 und 2006 mit dem COALMOD-Trade-Modell untersucht. Zum Zeitpunkt der höchsten Anbieterkon-zentration in diesen Jahren kann kein Anhaltspunkt für strategisches Verhalten gefunden werden. Die Handelsflüsse und Preise im globalen Markt werden durch ein wettbewerbli-ches Modell besser abgebildet. Im liquideren atlantischen Markt, der in einer genaueren Untersuchung von 2003 bis 2006 analysiert wird, gibt es zwar Anzeichen einer punk-tuellen Marktmachtausübung. Dennoch kann für die folgende Modellentwicklung davon ausgegangen werden, dass Marktmacht mittelfristig kein strukturelles Problem im glo-balen Kesselkohlemarkt darstellt. Das umfangreiche COALMOD-World-Modell simuliert daher jährliche Handelsflüsse bis 2030 für den internationalen Handel und einheimische Märkte mit endogenen Investitionen in Abbau- und Transportinfrastruktur. Das Modell kann die Handelsflüsse für das Basisjahr 2006 nachbilden. Im Fall eines weiteren globalen Nachfrageanstiegs jenseits von 2015 kann es zu Transportengpässen sowie steigenden Be-schaffungskosten kommen. Dies ist auf den großen Investitionsbedarf zurückzuführen, der nur schwer der Nachfrageentwicklung folgen kann. COALMOD-World wird im Weiteren genutzt, um das Wechselspiel zwischen Klimapolitiken und globalem Kesselkohlemarkt zu untersuchen. Eines der Ergebnisse ist, dass die Effektivität von regionalen Politiken erheblich beeinträchtigt werden kann, falls es zur Verschiebung der Nachfrage in Länder, die weniger oder keine klimapolitischen Anstrengungen tätigen, kommt.

Diese Dissertation trägt zur energieökonomischen Literatur in vielerlei Hinsicht bei. Es wird eine aktuelle Marktmachtanalyse des internationalen Kesselkohlemarktes durch-geführt. Die Methodenforschung wird durch einen Beitrag zur Anwendung von “conjectu-ral variations” zur Abbildung strategischen Verhaltens bereichert. Diese Modelle können problematisch sein, da sie nicht in jedem Fall eine Nash-Lösung abbilden. Deswegen wird ein Alternative für die Modellierung von Oligopolen mit einem wettbewerblichen Rand vorgeschlagen. Des Weiteren beinhaltet COALMOD-World mehrere methodische Neue-rungen, z.B. einen endogenen Kostenmechanismus. Zudem ermöglicht das Modell eine Quantifizierung des theoretisch viel erforschten “Carbon Leakage” Effekts, hier über den globalen Kesselkohlemarkt.

Schlüsselwörter: Globaler Kesselkohlemarkt, partielle numerische Gleichgewichtsmo-dellierung, MarktmachtmoGleichgewichtsmo-dellierung, Klimapolitik

First I would like to thank Professor Christian von Hirschhausen for giving me the oppor-tunity to write this dissertation as well as for the motivation and incentives that helped me finish this project successfully. Christian introduced me to the world of economic re-search and to the fascinating world of global energy markets. The knowledge and passion for the energy markets he helped me gain during those research years will always be with me.

I would like to give a very special thanks to Franziska Holz for her continuous support and for the very fruitful cooperation on many research projects over the years. She introduced me to numerical modeling, accompanied my dissertation project from the very beginning and was always available for discussion. Franziska’s support and friendship was fundamental for the successful completion of this dissertation. Another dear friend and colleague I must thank is Daniel Huppmann for a lot of interesting discussions on modeling and other topics and for his knowledge that he is always happy to share and help with.

I am grateful to the DIW Graduate Center, Professor Claudia Kemfert and the en-tire department of Energy, Transportation and Environment for providing me with an excellent research environment.

I would also like to thank all the wonderful people I have met in the various research teams I have been in contact with over the years and that have contributed to the success of this research project: the old Dresden team, the TU Berlin WIP group and of course the team in the department of Energy, Transportation and Environment at DIW Berlin. I am also indebted to Professor Steven Gabriel and to Ruud Egging for the many interesting workshops that have helped improve my modeling skills. Many thanks also to Professor Ben Hobbs for hosting me for a research stay at Johns Hopkins University in Baltimore. Last but no least I would like to thank my friends, especially those in the 2009 DIW GC cohort, my family and Catalina for their continuous moral support and for the fun and laughter during those research years.

Workin’ in a coal mine Goin’ down down down Workin’ in a coal mine Whop! about to slip down

Abstract 2 Zusammenfassung 3 Acknowledgements 4 Table of Contents 6 List of Tables 9 List of Figures 11 1 Introduction 13

1.1 Trying not to “carry coal to Newcastle” . . . 13

1.2 Market Power . . . 14

1.2.1 Overview . . . 14

1.2.2 Numerical modeling of imperfect competition . . . 15

1.2.3 Market concentration in the international steam coal trade . . . 19

1.2.4 Research questions and modeling challenges . . . 20

1.3 Trade and Investments Dynamics under Rising Demand . . . 21

1.3.1 Rising demand in Asia . . . 21

1.3.2 A complex supply side . . . 22

1.3.3 Research questions and modeling challenges . . . 23

1.4 Climate Change and Policy . . . 24

1.4.1 Stand or fall by coal . . . 24

1.4.2 Climate policy must not neglect global market dynamics . . . 25

1.4.3 Research questions and modeling challenges . . . 25

1.5 Overview of the Thesis . . . 25

1.5.1 Chapter 2: Modeling and analysis of the international steam coal trade: is the market competitive? . . . 27

1.5.2 Chapter 3: Atlantic steam coal market power: theory and applica-tion of oligopoly models with a competitive fringe . . . 29

1.5.3 Chapter 4: A techno-economic analysis using the COALMOD-World model: the end of “cheap coal”? . . . 30

Contents 1.5.4 Chapter 5: Climate policies and the global steam coal market:

interactions until 2030 . . . 33

1.6 Concluding Remarks and Outlook . . . 35

2 Modeling and Analysis of the International Steam Coal Trade 37 2.1 Introduction . . . 37

2.2 State of the Literature . . . 39

2.3 The COALMOD-Trade Model . . . 40

2.3.1 Description of the analytical model . . . 40

2.3.2 Data . . . 42

2.4 Results . . . 44

2.4.1 Market structure analysis . . . 44

2.4.2 Pricing and export restrictions in a temporal perspective . . . 48

2.4.3 Spatial price discrimination . . . 50

2.5 Conclusions . . . 51

2.A Appendix . . . 53

3 Atlantic Steam Coal Market Power 58 3.1 Introduction . . . 58

3.2 Literature Review . . . 59

3.3 Critique of Conjectural Variation Models . . . 60

3.3.1 Theory . . . 60

3.3.2 Partial equilibrium modeling applications . . . 61

3.4 Developments in the Atlantic Steam Coal Market in the Early 2000s . . . 65

3.5 Market Power Models with Dominant Players and a Competitive Fringe . 68 3.5.1 Modeling the competitive fringe . . . 68

3.5.2 Cournot oligopoly as Stackelberg leader . . . 69

3.5.3 Stackelberg-Cartel model . . . 70

3.5.4 Nash-Bargaining model . . . 70

3.6 Application to the Atlantic Steam Coal Market . . . 71

3.6.1 Model specification and data . . . 71

3.6.2 Results . . . 72

3.7 Conclusions . . . 74

3.A Appendix . . . 76

3.A.1 Proofs . . . 76

3.A.2 Market data . . . 79

4 A Techno-economic Analysis using the COALMOD-World Model 80 4.1 Introduction . . . 80

4.2 Equilibrium Modeling of Energy Resource Markets . . . 83

4.3 The COALMOD-World Model . . . 85

4.3.2 The producers’ problem . . . 87

4.3.3 The exporters’ problem . . . 93

4.3.4 Other model equations . . . 94

4.4 Model Specification and Data . . . 95

4.4.1 Countries and nodes definition . . . 96

4.4.2 Production, costs and reserves . . . 97

4.4.3 Land transport . . . 99 4.4.4 Export ports . . . 100 4.4.5 Freight rates . . . 101 4.4.6 Demand . . . 101 4.4.7 Investments . . . 104 4.5 Results . . . 106

4.5.1 General assumptions and base year results . . . 106

4.5.2 Increasing demand scenario . . . 108

4.5.3 Stabilizing demand scenario . . . 112

4.5.4 Results comparison with Hubbert-method based models . . . 113

4.5.5 Model evaluation and criticism . . . 115

4.6 Conclusions . . . 115

4.A Appendix . . . 117

4.A.1 Mathematical Formulation of the Model . . . 117

4.A.2 Nodes of COALMOD-World . . . 119

4.A.3 Data of COALMOD-World . . . 121

4.A.4 Results of COALMOD-World . . . 122

5 Climate Policies and the Global Steam Coal Market 125 5.1 Introduction . . . 125

5.2 Assessment of Positive Modeling Approaches . . . 126

5.2.1 Overview of possible modeling approaches . . . 126

5.2.2 Advantages of partial equilibrium models . . . 128

5.2.3 Description of the COALMOD-World model . . . 129

5.3 Climate Policy Scenarios with the COALMOD-World Model . . . 130

5.3.1 Worldwide climate policy . . . 132

5.3.2 Unilateral European climate policy . . . 133

5.3.3 Yasuní-type supply-side policy in Indonesia . . . 134

5.3.4 CCS fast roll-out . . . 136

5.3.5 Scenario combination: hedging of negative market adjustments . . 138

5.4 Conclusions and Policy Recommendations . . . 139

5.A Appendix . . . 141

List of Tables

1.1 Models specifications in the chapters . . . 27 2.1 Share of imports in total consumption and share of imported steam coal

in total electricity generation of major steam coal consuming countries 2006 38 2.2 Units used for the COALMOD-Trade and COALMOD-Trade-Energy models 41 2.3 Countries in the COALMOD-Trade model . . . 43 2.4 Coal quality by exporter . . . 44 2.5 Average FOB prices of Australian steam coal exports to importing

coun-tries in 2005 and 2006, in USD per ton . . . 51 2.6 Simulated import prices for the CMT model and reference CIF prices for

2005, in USD per ton . . . 54 2.7 Steam coal trade flows in the 2005 perfect competition scenario for the

CMT model, in million tons (Mt) . . . 54 2.8 Steam coal trade flows in the 2005 Cournot scenario for the CMT model,

in million tons (Mt) . . . 54 2.9 Actual steam coal trade flows in the base year 2005, in million tons (Mt) 54 2.10 Simulated import prices for the CMT model and reference CIF prices for

2006, in USD per ton . . . 55 2.11 Steam coal trade flows in the 2006 perfect competition scenario for the

CMT model, in million tons (Mt) . . . 55 2.12 Steam coal trade flows in the 2006 Cournot scenario for the CMT model,

in million tons (Mt) . . . 55 2.13 Actual steam coal trade flows in the base year 2006, in million tons (Mt) 55 2.14 Simulated import prices for the CMT-E model and converted reference

CIF prices for 2005, in USD per Gigajoules . . . 56 2.15 Steam coal trade flows in the 2005 perfect competition scenario for the

CMT-E model, in Petajoules (PJ) . . . 56 2.16 Steam coal trade flows in the 2005 Cournot scenario for the CMT-E model,

in Petajoules (PJ) . . . 56 2.17 Actual steam coal trade flows in the base year 2005, converted in Petajoules

2.18 Simulated import prices for the CMT-E model and converted reference CIF prices for 2006, in USD per Gigajoules . . . 57 2.19 Steam coal trade flows in the 2006 perfect competition scenario for the

CMT-E model, in Petajoules (PJ) . . . 57 2.20 Steam coal trade flows in the 2006 Cournot scenario for the CMT-E model,

in Petajoules (PJ) . . . 57 2.21 Actual steam coal trade flows in the base year 2006, converted in Petajoules

(PJ) . . . 57 3.1 Producer data: marginal cost parameters (intercept and slope) in USD/t

and production capacities in million tons per year . . . 71 3.2 Modeling results for the traded quantities in Mt and the prices in USD/t

in 2004 and 2005 for the four market structure scenarios. . . 72 3.3 Modeling results for the traded quantities in Mt and the prices in USD/t

in from 2003 to 2006 for the Cournot oligopoly and the perfect competition cases. . . 73 3.4 Steam coal trade flows to Europe in million tons and import market share

of South Africa and Colombia . . . 79 3.5 Export volumes in million tons and export market share of the “Big Three” 79 4.1 Reserves of major countries in COALMOD-World in Million tons . . . 98 4.2 Energy content and quality κf of coal by production nodes . . . 99

4.3 World Energy Outlook demand projections for coal for power generation in the reference scenario converted to Petajoules . . . 102 4.4 Nodes of the COALMOD-World Model . . . 120 4.5 Data and assumptions for the endogenous cost mechanism . . . 121 4.6 Data assumptions for the per 5-years capacity expansion limitations in Mtpa121 4.7 Results of COALMOD-World: Domestic trade flows in Mtpa for the

in-creasing demand and the stabilizing demand scenarios . . . 122 4.8 Results of COALMOD-World: International trade flows in Mtpa for the

increasing demand and the stabilizing demand scenarios (part 1/2) . . . . 123 4.9 Results of COALMOD-World: International trade flows in Mtpa for the

increasing demand and the stabilizing demand scenarios (part 2/2) . . . . 124 5.1 EU demand reduction in the Unilateral European Climate Policy scenario

compared to the reference scenarios in percentage . . . 133 5.2 Assumed installed capacities of coal power plants with CCS for the CCS

scenario in GW . . . 136 5.3 Demand elasticities . . . 141

List of Figures

2.1 Imported quantities in the perfect competition (PC), Cournot scenario (CO), and reference data (RE) in 2005, in million tons (Mt) for the CMT model . . . 45 2.2 Imported quantities in the perfect competition (PC), Cournot scenario

(CO), and reference data (RE) in 2005, in million tons (Mt) converted from Petajoules for the CMT-E model . . . 46 2.3 CIF Prices in the perfect competition (PC), Cournot scenario (CO), and

reference data (RE) in 2005 for the CMT and CMT-E models, USD per ton 47 2.4 CIF Prices in the perfect competition (PC), Cournot scenario (CO), and

reference data (RE) in 2006 for the CMT and CMT-E models, USD per ton 47 2.5 CIF Prices in the perfect competition (PC) and Cournot scenario (CO)

model results for different elasticity values, and reference data (RE) in 2005 and 2006 for the CMT-E model, USD per ton . . . 48 2.6 Historical steam coal prices: CIF(cost insurance freight) spot price in ARA

(Amsterdam-Rotterdam-Antwerpen) and price of delivered steam coal at the German border . . . 49 2.7 Imported quantities in the perfect competition (PC), Cournot scenario

(CO), and reference data (RE) in 2006, in million tons (Mt) for the CMT model . . . 53 2.8 Imported quantities in the perfect competition (PC), Cournot scenario

(CO), and reference data (RE) in 2006, in million tons (Mt) converted from Petajoules for the CMT-E model . . . 53 3.1 Price developments in the Atlantic steam coal market in the 2000s . . . . 66 3.2 Market structure of the coal market in South Africa and Colombia in the

early 2000s . . . 67 3.3 Costs and merit-order of suppliers in the Atlantic steam coal market in

2004/05 . . . 68 4.1 Model players on the steam coal value-added chain . . . 86 4.2 COALMOD-World model structure . . . 86 4.3 Endogenous cost mechanism in relation with short and long-run marginal

4.4 Producer’s quality definition relative to its reserves . . . 94 4.5 Countries included in the COALMOD-World database . . . 96 4.6 FOB costs for all export countries implemented into COALMOD-World . 100 4.7 Linear regression of average freight rates between 2002 and 2009 . . . 101 4.8 Capacity and investment costs for all production nodes in the base year . 104 4.9 Capacity and investment costs for all export nodes in the base year . . . . 105 4.10 Increasing demand scenario results 2006: seaborne trade flows (in Mt) . . 107 4.11 Increasing demand scenario results 2010: seaborne trade flows (in Mt) . . 107 4.12 Increasing demand scenario results 2020: seaborne trade flows (in Mt) . . 107 4.13 Increasing demand scenario results 2030: seaborne trade flows (in Mt) . . 108 4.14 Increasing demand scenario: aggregated consumption and imports (in Mt) 110 4.15 Investments in additional mining capacity and capacity losses of producers

between 2006 and 2025 (in Mtpa) . . . 110 4.16 Increasing demand scenario: computed average prices representing the

marginal costs of supply of selected regions for all model years (in 2006 USD/t) . . . 111 4.17 Stabilizing demand scenario results 2020: seaborne trade flows (in Mt) . . 112 4.18 Stabilizing demand scenario results 2030: seaborne trade flows (in Mt) . . 113 4.19 Stabilizing demand scenario: aggregated consumption and imports (in Mt) 113 4.20 2020 FOB costs for all export countries calculated endogenously in the

stabilizing demand scenario . . . 114 4.21 Comparison of production projections in COALMOD-World and Mohr

and Evans (2009) for bituminous coal (in Gt per year) . . . 114 5.1 Projected carbon emissions from fossil fuels and amounts in the ground . . 128 5.2 Scenario space . . . 131 5.3 Annual carbon dioxide emissions from steam coal consumption in the six

reference scenarios . . . 132 5.4 Worldwide emissions reductions and adverse market adjustments in the

Unilateral European Climate Policy model scenario . . . 134 5.5 Worldwide emissions reduction in the Indonesian supply-side policy scenario135 5.6 Worldwide emissions in the CCS scenario . . . 138 5.7 Unilateral European Climate policy results with and without additional

Chapter 1

Introduction

1.1

Trying not to “carry coal to Newcastle”

Coal in its physical form has almost disappeared from our daily lives. We don’t use it to cook or heat our homes anymore and the trains we take to travel are not pulled by steam locomotives. However, coal remains present in our languages through idioms. “Carrying coal to Newcastle” describes a pointless undertaking as Newcastle in Northern England was the main coal export port during the industrial revolution. In French “aller au charbon” means executing a cumbersome and laborious task while in colloquial German “Kohle machen” means making money.

Before I started my research, my experience with coal was more on the historical and literary level. On the one hand, it is easy to have a “romanticized” view of the age of the industrial revolution as an age of great scientific discoveries and technical progress where coal played a preeminent role. Indeed, coal was not only the fuel of the industrial revolution but can also be seen as its trigger: the purpose of the first industrial steam engine invented by Newcomen in 1712 was to pump water out of coal mines.1 _{On the}

other hand, there was a much darker side to coal associated with the inhuman working conditions of coal miners causing great social unrest as thematized in the novel Germinal by Zola (1885).

This ambivalence of coal still exists today and increases as we are experiencing a “renaissance” of steam coal2_{, the coal type used for electricity generation that is the}

main subject of investigation in this thesis. The use of steam coal is increasing in the fast growing economies of Asia and helps millions of people to reach higher standards of living through affordable electrification. International trade of steam coal is steadily growing and this trend is expected to continue. But there are also some major environmental concerns associated with the use of coal such as local pollution and, foremost, climate change due to high intensity of carbon dioxide emissions that occur when burning coal.

In this thesis the global steam coal market is investigated using numerical modeling 1

For a complete account of the formidable history of coal I recommend reading the book: “Coal: A Human History” by Freese (2004).

2

methods that combine industrial organization with the applied mathematics of operations research. I identify three main issues or subjects of investigation that I will develop hereafter: the issue of market power, rising demand associated with trade and investments dynamics, and climate change and policy. The outcomes of my modeling give insights into the complexity and dynamics of the global steam coal market and make contributions to numerical market modeling methods, and thus, I believe, reach the goal of creating something original, or, in other words, not to “carry coal to Newcastle”.

1.2

Market Power

1.2.1 Overview

Market power is a common feature of our economies but at the same time it is complex and difficult to grasp. Its technical definition is of utter simplicity as it is defined by prices that are above marginal costs (Tirole, 1999). This, however, means that we fall out of the paradigm of the perfect market and break the first fundamental theorem of welfare economics: Pareto optimality. This situation is caused by the firms’ ability to raise prices above marginal costs, which they often can. As Schmalensee (1982) puts it: “absolutely perfect competition is rarely encountered outside textbooks. Almost all firms have some market power” and what is relevant is the “importance” of market power.

Revealing and measuring market power holds an important place in economics due to its relevance to antitrust proceedings and regulation. Kaplow and Shapiro (2007) give an overview of the means of inferring market power. One can try to measure or estimate the price-cost margin. Other methods have the goal to estimate factors that may indicate or facilitate market power such as a low elasticity of demand, high market shares or the rivals’ supply responses. These factors suggest that the industry structure is relevant to understand market power and since we are often between the two well understood extremes of perfect competition and monopoly there is a need for other theoretical models of oligopolies. The distinctive feature of such oligopoly models is that there are a few firms in the market and that these are strategically linked (Friedman, 1983). I describe some of these models in more detail in the next section.

One empirical method used to infer market power is based on econometrics. Bresna-han (1989) gives an overview of methods and studies of market power in industries. One method described there, and widely used since, estimates a conduct parameter based on the concept of conjectural variations. This model of conjectural variations, that will be discussed in parts of this thesis, aims at describing a continuum of imperfectly compet-itive situations from perfect competition to collusion through the variation of a single parameter. This thesis will highlight the theoretical and numerical problems associated with this approach. In empirical studies, Corts (1998) argues that the use of conjectural variation in the conduct parameter method is problematic because it is not valid to infer market power with a method that is not based on a properly specified model of imperfect competition.

1.2. Market Power In this thesis, market power is dealt with in a different way, not using econometric methods but numerical modeling methods with properly specified models of oligopoly. This approach, initiated by Kolstad and Abbey (1984), uses numerical market modeling based on market fundamentals such as production costs, capacities and demand and compares different model specifications regarding competition and strategic behavior of players with actual market outcomes.

1.2.2 Numerical modeling of imperfect competition

There exist various models of oligopoly. Competition can be based, for example, on setting quantities like in the Cournot model or on setting prices like in the Bertrand or Edgeworth models and the models can be static or dynamic. The starting point for the numerical models in this thesis is the oldest model of imperfect competition: the model by Cournot (1838). In this model, the firms decide independently what quantity they supply to the market to maximize their profits given the output of the other firms. When the output of any firm represents the best response to all the other firms’ output, a Nash equilibrium is reached, the central concept in static game theory. The Cournot model allows for the representation of market power in a non-cooperative way as well as perfect competition (Vives, 2000).

Since the Cournot model is based on profit maximization we can use mathematical programming which solves problems of choosing values within an allowed set in order to optimize an objective function.3 _{In particular we can use the Karush-Kuhn-Tucker}

condi-tions to find an optimal solution.4 _{For example let us consider the following minimization}

problem where we want to minimize an objective function f(x) subject to equality and inequality constraints:

min

x∈RN f (x)

s.t. gj(x) ≤ 0 ∀j = 1, . . . , m (λj) (1.1)

hi(x) = 0 ∀i = 1, . . . , n (µi)

The following Karush-Kuhn-Tucker (KKT) conditions are necessary for optimality: ∇_xf (x) + m X j=1 λj∇xgj(x) + n X i=1 µi∇xhi(x) = 0 (1.2) gj(x) ≤ 0, λj ≥ 0, gj(x) · λj = 0, ∀j = 1, . . . , m (1.3) hi(x) = 0, µif ree, ∀i = 1, . . . , n (1.4)

If the objective function f and the inequality constraints gj are continuously

differ-entiable convex functions and the equality constraints hi are affine functions, then the

3

As we will see later in this section there are other types of mathematical problems that do not optimize functions but try to find an equilibrium state for a problem.

4

necessary KKT conditions are sufficient for optimality. Equation (1.2) of the KKTs is the stationarity condition and ensures that the gradient (or first-order derivative) of the function is zero and thus an optimal point is reached. This equation is the gradient of the Lagrange function which includes both the objective function and constraints shown in (1.1). Equations (1.3) and (1.4) are feasibility conditions. The variables λj and µi are

the dual variables associated to each constraint.

In order to solve large scale problems or problems with multiple profit maximizing players we can use the logic of the KKT to find an optimal solution but in a different setting. We want to find not only the maximum profit for all the players involved but also an equilibrium state of the market that results from the competition between the firms. For that we need equilibrium programming that “refers to the modeling, analysis and computation of equilibrium via the methodology of mathematical programming”.5

Since the beginning, the development of mathematical programming and economic the-ory have been interrelated and the method used here is called the variational inequal-ity/complementarity approach to equilibrium programming. The variational inequality problem is a general form that comprises the complementarity problems and can be defined as follows (from Harker, 1993):

Let X be a nonempty subset of RN and F be a mapping from RN into itself. The variational inequality problem, denoted by V I(X, F ), is to find a vector x∗ ∈ X such that:

F (x∗)T(y − x∗) ≥ 0for all y ∈ X (1.5) A special case of the V I(X, F ) is the nonlinear complementarity problem NCP (F ):6

Let F be a mapping from RN into itself. The nonlinear complementarity problem, denoted by N CP (F ), is to find a vector x∗ _{∈ R}N₊ such that:

F (x∗) ∈ RN+ and F (x ∗

)Tx∗ = 0 (1.6)

When F (x) is an affine function of x, say F (x) = q + Mx for some given vector q ∈ RN

and matrix M ∈ Rn×n _{, the problem NCP (F ) reduces to the linear complementarity}

problem, which is denoted by LCP (q, M):

x ≥ 0, q + M x ≥ 0, xT(q + M x) = 0 (1.7) The name complementarity comes from the third condition in (1.7) which requires that at least one of the expressions in the multiplication of xT _{with q + Mx should be equal}

to zero in the solution of the problem (Billups and Murty, 2000).7

It is important to recognize that the Karush-Kuhn-Tucker conditions of a quadratic 5

Harker (1993): p. 3

6

Harker (1993): p. 11

7

An alternative notation (used in the subsequent chapters) employs the perpendicular sign instead of the multiplication. (1.7) would then read 0 ≤ q + M x ⊥ x ≥ 0

1.2. Market Power program are a mixed LCP. This is also true for the Nonlinear Program (NLP) and the Mixed Nonlinear Complementarity Problem (mixed NCP or MCP), a generalization of the LCP, as shown by the following example.8 _{If we take problem (1.1), considering it}

as a generic nonlinear program with KKTs (1.2)-(1.4) we get a mixed NCP as follows:

F    x λ µ   =    ∇xf (x) +Pmj=1λj∇xgj(x) +Pni=1µi∇xhi(x) −g_j(x), ∀j = 1, . . . , m hi, ∀i = 1, . . . , n    (1.8)

This can be expressed as follows: ∇xf (x) + m X j=1 λj∇xgj(x) + n X i=1 µi∇xhi(x) = 0 x f ree (1.9) gj(x) ≤ 0, λj ≥ 0, gj(x) · λj = 0, ∀j = 1, . . . , m (1.10) hi(x) = 0, µif ree, ∀i = 1, . . . , n (1.11)

We recognize the conditions (1.9)-(1.11) of the mixed NCP to be exactly the KKT con-ditions (1.2)-(1.4) of optimization problem (1.1). By solving the mixed NCP we obtain the optimal solution of the optimization problem. The constraint qualifications (CQ) ensure that the KKT solution is an optimal solution (Bazaraa et al., 2006). Typical CQs are linearity and linear independence. In the case of a minimization problem with convex objective function whose feasible region is defined by affine equality conditions and convex inequalities, the KKT conditions are necessary and sufficient for an optimal solution.

Complementarity problems help us to solve large scale equilibrium problems like mar-ket equilibria with multiple profit maximizing players. In order to add equality conditions to a model like demand functions or market clearing conditions we have to formulate the model in the form of a mixed nonlinear complementarity problem or simply mixed com-plementarity problem (MCP) as it is often referred to in the modeling literature. MCP is a generalization of the NCP9 _{and thus comprises other problems like linear and}

nonlin-ear systems of equations, LCP, NCP and nonlinnonlin-ear programs. The profit maximization problems of the different players included in an equilibrium model are nonlinear programs (NLP), as we want to include nonlinear functions, for example for the cost functions. The

8

See Gabriel (2009): Slide 12

9

MCP is defined as:10 Given: f : RN → RN, l, u ∈ RN, Find: z, w, v ∈ RN, (1.12) s.t: F (z) − w + v = 0, l ≤ z ≤ u, w ≥ 0, v ≥ 0, wT(z − l) = 0, vT(u − z) = 0

with −∞ ≤ l ≤ u ≤ +∞ and F being continuously differentiable. We recognize the complementarity relationship stated before, w and v being the dual variables of the lower bound l and upper bound u, respectively.

The MCP format is also the format understood by the GAMS modeling language and the PATH solver used for MCP.11 _{GAMS stands for General Algebraic Modeling}

System and was developed in the 1980s to help economists at the World Bank to make quantitative analyzes of economic policies. Since 1987 this system has been further de-veloped by the GAMS Development Corporation. Today GAMS can resort to plenty of different solvers to solve various mathematical programming problems such as linear programming, non-linear programming, integer programming, and of course complemen-tarity programming. A description of the PATH solver with an example of a GAMS model code is provided by Ferris and Munson (2000).

So far I have considered one-stage representations of equilibria of imperfect markets such as the Cournot model solved in the MCP format that represents the core of the modeling work in this thesis. But two-stage games are also relevant for this thesis and for a better understanding and representation of market power. One of the first examples of two-stage games found in the literature is the model by Stackelberg (1934) with two firms where one firm, the leader, chooses her optimal output first, knowing what the follower firm will do in all possible cases. The Stackelberg equilibrium represents a subgame perfect Nash equilibrium of a two-stage perfect information game (Vives, 2000). This model can be expressed as a mathematical program with equilibrium constraints (MPEC). An MPEC encompasses an optimization problem that can represent the leader firm’s profit maximization and an equilibrium problem that can represent one or multiple followers competing in a Cournot market. MPECs are generally hard to solve since normal constraint qualifications do not hold due to the non-convexity of the feasible region (Leyffer and Munson, 2010). However, it is possible to solve them in GAMS as an MPEC class of models (Dirkse and Ferris, 1999), but being careful as the solution delivered might only be local.

In order to achieve a more realistic market representation we may want to include more than one player on the upper level problem. This problem is called an equilibrium problem with equilibrium constraints (EPEC) since we have to consider an upper level

10

See Rutherford (1995): p. 1301 et seq.

11

1.2. Market Power equilibrium, for example a Cournot model, where the players also need to take into account the actions of the players of a subsequent lower level equilibrium. These model are even harder to solve and are at the current research frontier. First methods for solving EPECs are currently under development, for example by Leyffer and Munson (2010). 1.2.3 Market concentration in the international steam coal trade To motivate the use of numerical models with market power representation we first have to look at the market concentration in the international steam coal trade. Steam coal used for electricity generation is primarily a domestically produced and consumed fuel due to relatively high transport costs. However, in the wake of the oil crisis of the 1970s, and the subsequent move away from oil as main fuel for industry and electricity generation, international seaborne steam coal trade flows started growing. What followed in the next 30 years was a period of tremendous growth in the international steam coal trade with a doubling of trade volumes every 10 years from 78Mt in 1980 to 597 Mt in 2010. The share of international seaborne trade in global steam coal consumption also rose, from 3% in 1980 to 10% in 2000 and stabilized around 11% until 2010 (IEA, 2011a). In this period of time the structure of trade also underwent several changes regarding the actors involved.

In the early 1980s the main exporters were countries with a large domestic production and consumption base such as the US, Poland and South Africa. From 1985 on, more export driven players emerged such as Australia, Colombia and Indonesia. After the year 2000 high growth continued, especially of Indonesian exports and new players entered the market such as China and Russia (EPRI, 2007).

The demand side of the international steam coal trade also underwent some significant changes. Traditionally the international steam coal market can be separated into two sub-markets: the Atlantic basin with Europe as main importing region and the Pacific basin with the main East Asian economies as importers. The separation is due to the fact that most of the trade flows occur between suppliers and importers of one basin due to the long transport distances and associated freight costs. However, trade between basins occurs and recent studies show that the regional markets are more and more integrated (Li et al., 2010, Zaklan et al., 2009). In the early 1980s the volumes in the Atlantic steam coal market represented around 90% of total seaborne trade but the Pacific steam coal trade grew continuously and overtook the Atlantic market in the early 1990s. Until 2000 the market shares were around 45% and 55% for the Atlantic and Pacific basins respectively. In the early 2000s the Pacific market grew faster and expanded tremendously after 2005. In 2010, the Pacific steam coal market made up for 73% of the international seaborne trade (IEA, 2011a).

In the 1980s long-term contracts of up to 10 years were dominant in the international steam coal trade. In the Atlantic steam coal market the nature of contracts changed radically towards short term contracts which typically represent the delivery of a cargo or series of cargoes in the next 3 months. These spot transactions grew from 14% of the

Atlantic market in 1983 to 80% in 2003 (Ekawan and Duchêne, 2006). In the Pacific market there was a move towards long-term contracts with annual price negotiation or annual contracts and some spot trade (Ekawan et al., 2006). By 2010 the Atlantic steam coal market had developed into a highly liquid market with a significant amount of paper trade whereas in the Pacific market long-term contracts are still dominant with shares of more than 80% for most importers (IEA, 2011c).

In the 1980s and 1990s the international steam coal market was characterized by low levels of market concentration on the supply side with a constant oversupply that lead to low import prices around 35 USD/t with a slightly declining trend. Many relatively small mining companies were active in the market as well as some oil majors that left the market in the late 1990s, early 2000s. In the same time period, roughly from 1998 to 2002 a major consolidation of the industry took place in South Africa, Australia and Colombia that led to the emergence of four multinational mining giants, the “Big Four”: Anglo American, Rio Tinto, Xstrata/Glencore and BHP Billition. During that time the share of these companies in the global steam coal seaborne trade grew from 29% to 33% (Couser and Goldsack, 2001). In 2005 this share was 32% and the position of the Big Four has not grown stronger since; in 2007 it had fallen back to 27% (Kopal, 2007, Rademacher, 2008). The position of the Big Four in the Atlantic market is stronger: in the period 2002 to 2008 they were responsible for 80% of the exports from South Africa and Colombia. However, the share of the Big Four in the total Atlantic market has been steadily declining from 49% in 2002 to 43% in 2004, less than 40% after 2006 and 33% in 2008.12

1.2.4 Research questions and modeling challenges

The market consolidation and the increased concentration on the international seaborne steam coal market in the late 1990s and early 2000s lead to concerns about the use of market power by the dominant suppliers (Couser and Goldsack, 2001). The fear about collusion and the development of a cartel of coal suppliers, a “COALPEC” similarly to the OPEC on the oil market was also present (EPRI, 2007, Haftendorn et al., 2008b).

The abuse of market power could be one determining factor in the sudden rise of import prices from around 35 USD/t before 2003 to almost 80 USD/t in 2004, remaining on average higher than 60 USD/t in 2005 and 2006. Since the peak in the share of the “Big Four” in the international steam coal market was around 2005 it is very relevant to investigate the global market structure around that time to see with a partial equilibrium model if the trade flows and price levels are in line with a competitive market or if market power is present.

Furthermore, a closer look into the Atlantic steam coal market might reveal strategic behavior. The market share of the “Big Four” on this market is significantly higher than on the global market and the market is more liquid with a majority of short-term trades which gives more room for strategic behavior than long-term contracts.

12

1.3. Trade and Investments Dynamics under Rising Demand From a technical modeling point of view the representation of non-cooperative Cournot equilibria as well as competitive markets is well understood. However, for intermediary cases, for example with a few dominant players, the concept of conjectural variations is often used as a basis for the market power representation. These conjectural variations need to be better understood as they can lead to counter-intuitive results. Better models of dominant oligopolies with a competitive fringe need to be developed.

1.3

Trade and Investments Dynamics under Rising Demand

1.3.1 Rising demand in Asia

The last decade from the years 2000 to 2010 has been a “coal decade”. During that period the incremental use of coal as primary energy was equivalent to that of gas, oil and renewables, all put together, as highlighted in the latest World Energy Outlook by the International Energy Agency (IEA, 2011c). The share of coal in primary energy grew from 23% in 2000 to 28% in 2010. Most of this growth took place in Asia where steam coal is the dominant fuel for electricity generation and electricity demand is strongly correlated with GDP growth.

In 2010 Asia accounted for 72% of global steam coal consumption followed by America with 17% and then Europe including Russia with 7%. China alone accounted for 52% of global steam coal consumption. This rising demand in Asia also led to rising imports and growth of the Pacific market as discussed before. In 2010 the first five importers of steam coal are Asian countries: Japan and China with 129 Mt each, South Korea with 91 Mt, and then Taiwan and India around 59 Mt each. The Asia-Pacific region accounts for 67% of world imports and the first European importers Germany and the United Kingdom are far behind with import levels of 38 and 29 Mt respectively.

China has a particularly important influence on the world market as the size of its internal market is more than three times the size of the global international trade. China, a net exporter in the early 2000s, saw its exports steadily declining from a maximum of 80 Mt in 2004 to less than 20 Mt in 2010. On the other hand, imports grew. After 2008 and a doubling of import levels China became a net importer of steam coal for the first time and is expected to remain one. The reduction in exports can be explained by the increase in internal demand that is hardly satisfied by internal supplies and the increasing imports by the attractivity of imported steam coal, especially from Indonesia, in the coastal areas of Southern China that are far away from the coal basins in the North.

In India steam coal is also the dominant fuel for electricity generation and its con-sumption almost doubled since the year 2000 to reach the level of 560 Mt, the third place after China and the USA. The development of imports was even more dramatic, rising from 10 Mt in 2000 to almost 60 Mt in 2010 (IEA, 2011a). The rise of imports can be explained by structural problems in the Indian coal mining sectors with a low productivity, low quality of coal and and high transport costs which makes imported coal

especially attractive in the highly populated coastal areas (Haftendorn et al., 2008a). In the latest World Energy Outlook by the International Energy Agency (IEA, 2011c) more than two-thirds of the projected increased coal demand in the Current Policies and New Policies scenarios will come from China and India. The first scenario represents the business as usual case whereas in the second some degree of climate policy is implemented. In the Current Policies scenario global coal demand is projected to rise by 64% from 2009 to 2035 and by 24% in the New Policies scenario. Most of the growth will come from Asia and some coal-rich countries such as Russia and South Africa. Demand from the OECD is projected to rise marginally in the Current Policies scenario and to decrease in the New Policies scenario.

The trend observed in the decade 2000 to 2010 with an increase in coal consumption in Asia is likely to continue. In certain projections this increase will be very strong and this opens the question of the potential supply sources to serve that demand.

1.3.2 A complex supply side

Coal is the result of a biological and then geological process that transformed trees and other plants over millions of years by trapping them under sediments in the earth’s crust. Pressure, temperature and time increase the quality of coal towards higher carbon contents. This quality parameter measured in energy unit per unit of mass is also the main driver behind the economics of coal (Minchener, 2009). Higher quality coals are called hard coal. They are traded internationally and can be transported over long distances. The majority of produced hard coal is steam coal for electricity generation and a smaller fraction is higher quality coking coal used for steel production. On the other hand, lower quality brown coal is only used domestically for electricity generation near the mines as it is not economical to transport it over large distances.13

Coal is the most abundant of all fossil resources. Measured in energy values the reserves of hard coal with 18 ZJ14 _{are higher than the reserves of all conventional and}

unconventional oil and gas resources of 16 ZJ (DERA, 2011). Reserves can by defini-tion be extracted with current technologies at current market prices. Resources include quantities that may be extracted in the future but are not yet economical or geologically proven. The hard coal resource base is even more abundant with an estimated 426 ZJ which is more than four times the resources of oil and gas and more than twenty times the resources of conventional oil and gas. Hard coal resources are also more evenly dis-tributed on the surface of the globe with large resource bases in North America, Eurasia and the Asia-Pacific region. However, if we look at the distribution on a per country basis we find that 82% of the reserves are found in only five countries: the US, China, India, Russia and Australia (DERA, 2011).

Coal may be abundant but the the share of resource that is technically accessible and 13

One can also find the terms “metallurgical coal” for coking coal and “lignite” for brown coal in the literature.

14

1.3. Trade and Investments Dynamics under Rising Demand can be economically brought to the market is significantly smaller since a capital-intensive infrastructure is needed to bring the coal to the final users. The value-added chain of steam coal starts at the mine where the coal is extracted. It is then transported overland either directly to the final customer or to a coal export terminal where it is loaded on bulk transport vessels that ship the coal overseas. On each step of the value chain, different technologies might be used given the physical and geological characteristics of the deposit and its location and this heterogeneity also affects the cost structure. For example, mining can occur underground which is more labor and capital intensive. In opencast mining large draglines are cheaper to remove the overburden and extract the coal and the machinery can be powered by electricity. In less favorable terrain the truck and shovel techniques uses a lot of explosives, tires and diesel (IEA, 2011b). The overland transport can be done by barge, by truck over short distances or by train. Coal export terminals require large investment sums and years of planning, even for the expansion of ports already in place.

As mining and transport infrastructure investments may take up to four years to come online, a certain commodity cycle could be observed in the past. Moreover, the coordination of the expansion of the different supply chain components is difficult and can lead to short to medium term bottlenecks (IEA, 2011b). An indicator for the tightness of the market is the mine capacity utilization rate that has varied between 70% and 95% in the past 20 years and remained higher than 85% from 2003 to 2009 due to the high Asian demand (Rademacher and Braun, 2011). Investments remain strong as in 2008 and 2009 the 30 leading coal companies invested around 15 billion USD in mining projects and 16 billion USD in 2010 (IEA, 2011c).

1.3.3 Research questions and modeling challenges

In view of the expected increase in demand in Asia in the mid- and long-term and a supply side that is operating close to its capacity limit, the main issue concerns the investments. How much investments are needed in different demand scenarios? Can certain capacity and investment restrictions affect the market adversely? On what stages of the value-added chain will investments be necessary and specifically in what mining basins, transport links or export terminals? Minchener (2009) gives an overview of the possible future coal supply prospects. These are diverse and include for example bringing coal to the market from new countries with high potential such as Mozambique or connecting the Powder River Basin, the biggest coal mining basin of the US, to the Pacific market by expanding railways and export terminals.

In this thesis I will investigate the future of the global steam coal market and answer the above questions using a partial equilibrium numerical model. To do so the value-added chain of steam coal has to be represented properly. Important characteristics such as the quality of coal or the endogenous evolution of costs have to be represented. This representation has to be detailed enough to be able to draw conclusions about the components of the value added chain such as transport links or export terminals but also

aggregated enough so that the size of the model and data collection remain manageable. Data for a numerical model is a challenge by itself. There exists no central database for the coal market and data has to be collected from a wide range of heterogeneous sources and aggregated, disaggregated, calculated or estimated to fit the specifications of the model.

1.4

Climate Change and Policy

1.4.1 Stand or fall by coal

Unmanaged climate change represents a risk for humankind on an unprecedented scale. As Stern and Rydge (2012) put it, a business as usual could lead to an increase of carbon dioxide concentrations to a level of 750ppm and a warming of more than 5°C to temperature levels never seen on earth since 30 million years and thus never experienced by human societies. Disruptions to local habitats and economies through floods, droughts and water scarcities would have far reaching consequences on a global level.

Climate change mitigation requires a response on a global level to decarbonize the energy sector. This response is usually framed around two lines: the deployment of renewable energy technologies and increased energy efficiency. However, another view is possible: it is the imperative to move away from coal as a source of energy. Indeed, in the different climate policy scenarios of the latest World Energy Outlook (IEA, 2011c) the share of coal in world primary energy demand is the most sensitive to different levels of climate policy compared to the other fossil fuels. In the most stringent climate policy scenario that aims at stabilizing carbon dioxide level at 450ppm the consumption of coal significantly drops in 2035 compared to 2008 levels whereas for oil and gas it is more or less constant.

The criticality of coal to climate change mitigation is due to two factors: its high carbon dioxide emission factor and the low efficiency of steam coal power plants. The emission factor that describes how much carbon dioxide is released by burning one energy unit of a fuel is twice as high for coal as for gas (DEHSt, 2005). Also the energy efficiency, that is how much of the energy contained in the fuel can be transformed into electricity, is relatively low for coal power plants. As of 2010, 75% of the worldwide installed capacity were sub-critical coal power plants with energy efficiencies of less then 40% (IEA, 2011c). In comparison combined cycle natural gas power plant can reach efficiencies of up to 60%.

If a stringent climate policy is implemented, coal will lose its competitiveness in the power market. Sijm et al. (2005) show that natural gas power plants become more competitive than coal in the merit-order curve at carbon dioxide prices as low as 18.5 Euro per ton. It then comes to no surprise that coal is only mentioned twice in the Energy Roadmap 2050 of the European Commission (EC, 2011): first as a fuel that will be substituted by the “transition fuel” natural gas and second with the remark that coal could continue to play a role in Europe only with the use of the carbon capture and

1.5. Overview of the Thesis storage technology. This technology to capture carbon dioxide at the power plant and sequestrate it underground was once seen as the great hope for a sustainable use of coal (IPCC, 2005). However, recent developments show that the promise of “clean coal” on a large scale is fading. Numerous demonstrations projects have been put on hold or canceled in Europe (Hirschhausen et al., 2012b).

1.4.2 Climate policy must not neglect global market dynamics

After the failure of the climate conference in Durban to reach a globally binding climate agreement and the announcement there that China might only consider such an agree-ment after 2020 the outlook for climate policy on the global level is a quite unfavorable in the mid-term. This means that for the time being, only national or regional climate policies will be implemented. This creates a series of problems such as free-riding, since the benefits of climate policies are shared but the costs are local. On top of that there is the problem of leakage. This can happen in the goods market where investments may be re-located to countries with no carbon costs and then the goods will be exported to countries with climate policies. A second effect goes through the fossil fuel markets where the reduced use in a country with climate policy incentivizes countries with no climate policy to consume more due to a lower world price for the resource (Tirole, 2012).

While the first type of carbon leakage has been the most studied in the literature, Böhringer et al. (2010) find that there is a significant share of leakage in energy markets that can undermine policies targeting carbon leakage in goods such as border tax adjust-ments. Hence, there is an urgent need to better understand and quantify carbon leakage in energy markets in a world where regional or national climate policies are likely to be the only type of climate policies implemented in the mid-term.

1.4.3 Research questions and modeling challenges

The mechanism of carbon leakage in the global steam coal market is a reality derived from the basic economic relation between price and demand. But what needs to be assessed is the relevance and strength of this effect in the actual market context. The effects of the size of the players expressed by their share in global exports or imports and the magnitude of the quantity effects need to be taken into account. Regional policies around the world should also be taken into consideration.

From a modeling perspective, the challenge is to find meaningful and realistic speci-fications for the global conditions as well as for the regional climate policies that might induce carbon leakage in that context. The scenarios shall deliver valuable insights into the effects of carbon leakage we can expect in the global steam coal market.

1.5

Overview of the Thesis

This thesis is based on four original research articles that are presented in Chapters 2 to 5. The following overview organizes the papers according to the three research issues

identified in the first part of this introduction and provides information regarding my contribution and the state of publication. I am the main author of Chapters 2, 4 and 5 and the sole author of Chapter 3.

Market power:

Chapter 2: Modeling and analysis of the international steam coal trade: is the market competitive?

◦ This chapter is joint work with Franziska Holz. We jointly developed the COALMOD-Trade model. I had the main responsibility for the data search and writing.

◦ This work was published in The Energy Journal, Vol. 21, No. 4, 2010, pp. 205-229. This is also the version of the paper used in the thesis.

Chapter 3: Atlantic steam coal market power: theory and application of oligopoly models with a competitive fringe

◦ This chapter is my independent research.

◦ This work was published as DIW Discussion Paper 1185, 2012. This is also the version of the paper used in the thesis. This paper was distinguished by the “Student Paper Award” at the 35th _{IAEE International Conference 2012 in Perth, Australia}

and accepted at the EEA 2012 conference in Málaga.

Trade and investment dynamics under rising demand:

Chapter 4: A techno-economic analysis using the COALMOD-World model: the end of “cheap coal”?

◦ This chapter is joint work with Franziska Holz and Christian von Hirschhausen. I had the main responsibility for building the COALMOD-World model, for most of the data search, calculations and for the writing.

◦ This work was published as DIW Discussion Paper 1067, 2010, and PESD Stanford University Working Paper 96, 2010. An updated version has been accepted for publication in the journal FUEL. This is the version of this chapter complemented by additional sections that describe all the data sources.

Climate change and policy:

Chapter 5: Climate policies and the global steam coal market: interactions until 2030

◦ This chapter is joint work with Claudia Kemfert and Franziska Holz. I had the main responsibility for the scenario definition, the data search and model runs as well as the writing.

◦ This work was published as DIW Discussion Paper 1146, 2011. An updated version has been accepted for publication in the journal Energy Policy. It is the basis for this chapter that also includes an additional scenario.

A major contribution of this thesis lies in the development of numerical partial equi-librium models to investigate the global steam coal sector. Table 1.1 gives an overview

1.5. Overview of the Thesis Table 1.1: Models specifications in the chapters

Chapter Model name Time Coverage Market structure Format/Solver

2 COALMOD static International Perfect competition, MCP/PATH

-Trade 2005-06 seaborne trade Cournot competition

3 n/a static Atlantic market Oligopoly as Cartel, MPEC/MPEC

2003-06 seaborne trade Cournot competitors, MCP/PATH

Nash bargainers, with NLP/CONOPT

a competitive fringe

4 - 5 COALMOD Multi International Perfect competition MCP/PATH

-World -period seaborne and

2006 overland trade

-2030 and major

domestic markets

over the different specifications and coverage of the models developped in this thesis. All models are implemented in GAMS. The format is the type of model used from the theory of operations research while the solver is the algorithm that solves the model numerically. The modeling of the international steam coal after pioneering work by Kolstad and Abbey (1984) has again found interest in the literature recently. Paulus and Trüby (2011a) analyze the effects of Chinese infrastructure decision on the global trade flows while Paulus and Trüby (2011b) and Paulus et al. (2011) focus their investigation on strategic behavior and market power especially in the Pacific Market. The market power analysis with the COALMOD-Trade model of Chapter 2 of this thesis predates those works and the focus of Chapter 3 is on the Atlantic market and also on the theoreti-cal approaches to market power modeling. Chapter 4 presents the COALMOD-World model for the investigation of long-term developments and Chapter 5 concentrates on an application of this model to climate policy issues.

1.5.1 Chapter 2: Modeling and analysis of the international steam coal trade: is the market competitive?

In the early 2000s a movement of market concentration on the global steam coal market led to the emergence of the “Big Four” multinational companies in Australia, South Africa and Colombia. In the other major exporting countries such as Indonesia, Russia, China and the US, only a few national champions dominate the export market. In this situation market power may emerge. In Chapter 2, we analyze the international seaborne trade for steam coal in a similar way as was done by Kolstad and Abbey (1984) for the market in the early 1980s using partial equilibrium numerical modeling and countries as strategic players.

While we are aware that companies are the active players on the global market, we choose to use a country set-up. A country specification is easier to implement from a data perspective and can give us first valuable insights into the market structure. Company data, especially in the Pacific market, is hard to find and it would not have

been possible to properly specify the model. Also, nationalistic resource policies through taxes and export restrictions are at work in certain countries such as Indonesia and China as identified by Paulus et al. (2011). This also speaks in favor of a country set-up as a first approach.

Chapter 2 develops a numerical partial equilibrium model of the international trade of steam coal, the COALMOD-Trade model, based on the mixed-complementarity (MCP) approach. The model consists of eight profit maximizing exporters constrained by pro-duction and export capacities that sell coal to 10 importers represented by an inverse demand function specified for the years 2005 and 2006. The exporters can be modeled as Cournot players or as competitive players. Furthermore, two different versions of the model are tested, one where mass quantities of coal are traded and the other one where it is the energy content of the coal expressed in energy units.

For both model specifications, mass and energy, the simulation of perfect competition better fits the observed real market flows and prices. The prices are in the range of the real prices while the Cournot prices are significantly higher. A sensitivity analysis with respect to the price elasticity of demand also shows that the price results of the perfect competition case are robust while the Cournot prices vary by more than 20%. The total traded quantities are in line with the perfect competition case while in the Cournot case there is more diversification as observed in reality. However, trade flow diversification by itself is not an argument for market power as there are other reasons for diversification such as security of supply or historical trade relations. This leads to the main finding of this chapter that we reject a Cournot market structure on the international steam coal market for the years 2005 and 2006.

Furthermore, we find that the energy specification of the model leads to a better representation of the trade flows. The customers in fact want the energy in the coal and this is also reflected in the market trade flows and prices. The energy specification of the model also considers the mass of the coal through a conversion factor for the capacity constraints and productions and transport costs.

Based on the modeling results, Chapter 2 also examines more closely the prices and pricing mechanisms. While the modeled prices in the perfect competition case are in the range of the real market prices, the variation of the real prices between countries is significantly higher than the computed prices. Possible reasons for these higher variations can be price discrimination, historical trade relations, the influence of domestic markets or the nature of contracts and pricing that can negatively affect the flexibility and reactions of the market.

In that context the timing issue of contracts is subject to scrutiny in this chapter. Spot contracts are for deliveries three months ahead and long-term contracts with yearly price adjustments are dominant in the Pacific market. Adding the relative intransparency and lack of liquidity in the spot market as there are no central commodity exchanges but just bi-weekly price indices based on quoted trades, a time-lag can arise in the pricing-in of capacity constraints. This delays and weakens the signal that the price is sending to

1.5. Overview of the Thesis incentivize new investment. This may have contributed to the run-up in prices observed in 2007 and 2008 as the increased demand from China and India was not met by sufficient global investments in production and export capacities.

1.5.2 Chapter 3: Atlantic steam coal market power: theory and appli-cation of oligopoly models with a competitive fringe

Having found in the previous chapter that the international market for steam coal in the time of the peak in global market concentration in the years 2005 and 2006 is better represented by a competitive model, we analyze the market conduct in the Atlantic steam coal market where a higher market concentration and market liquidity may have favored strategic behavior. The Atlantic steam coal market in the early 2000s was characterized by three dominant companies active in two countries, South Africa and Colombia, and by what we can describe as a competitive fringe composed of price-taking players from countries such as Russia and the US. In order to represent such a market in a numerical partial equilibrium model we cannot resort to the standard Cournot approach since we assume to have some competitive price-taking players alongside a dominant oligopoly.

In order to represent imperfect competition in markets that lie between the Cournot and perfect competition assumptions the literature has often resorted to the concept of “conjectural variations” (Figuières, 2004). This concept is derived from the Nash-Cournot model and describes the expected or “conjectured” reaction of players to the action of other players. It is often stated that in a Nash-Cournot equilibrium players do not have an incentive to deviate from their strategies, hence they will not react to changes of other players and their conjectural variation is zero. This is an interpretation that may be valid in equilibrium, however, the conjectural variation approach misinterprets and translates this into a model that describes how to get to an equilibrium. Chapter 3 provides an overview of the theoretical literature of conjectural variations that came to discredit the approach described above due to the ambiguity of its equilibrium concept.

However, in the numerical applications in models of energy and resources, the con-jectural variations concept is still widely used. For example, the gas market model of the PRIMES model (E3MLab, 2011) that was used for the recent Energy Roadmap 2050 of the EU (EC, 2011) uses a conjectural variation approach to model competition be-tween upstream producers. While the conjectural variation approach may be used as an approximation when there is not enough information about the market structure of an imperfectly competitive market, it must be used carefully as it can lead to counterintu-itive results. In particular, a dominant player may in a conjectural variation model be worse off than if he had exercised less market power, because he is assumed to have a naive view regarding the reaction of the competitive players.

Chapter 3 strives to provide a better understanding of the concept of conjectural variations from the theoretical basis to its implementation in numerical models. We use an analytical duopoly model in different settings to show that the best response of a player can be in conflict with the assumptions used in numerical partial equilibrium