Prospects for the 2020 Iron Ore Market. Quantitative Analysis of Market Dynamics and Risk Mitigation Strategies

Similar documents
Influence of Industry Environment on Firms 9 Characteristics

Service Business Costing

RECENT MARKET DEVELOPMENTS

Florian Mueller. Sales Management Control Strategies in Banking

Stefan Worm. Branded Component Strategies

Lars Thomann. Steps to Compliance with International Labour Standards

Familienunternehmen und KMU

Eva-Maria Bauer. Top Executives Work Relationship and Work-Family Balance

GLOBAL WARMING AND ECONOMIC DEVELOPMENT

Familienunternehmen und KMU

Nino Grau Constanta-Nicoleta Bodea (Editors) ISO Project Management Standard Characteristics, Comparison and Implementation

Global Freight & Shipping: Where are prices heading?

The Influence of Risk on CDM Cost Effectiveness from the Perspective of EU-ETS Compliance Investors

Iron Ore Markets Forecast Till 2025

Taking the Pulse of Economic Development

Marija Ugarković Profit Sharing and Company Performance

Successful Project Management. Second Edition

International Marketing and Purchasing

RAW MATERIALS AND IRONMAKING. Raw materials improvement in the steel industry

A Quick Look on the Steel Industry in the Arab World ============================================

AN INVESTIGATION INTO THE RISKS OF OUTSOURCING OF KNOWLEDGE RICH, SUPPLY CRITICAL ELEMENTS WITHIN SUPPLY NETWORKS: A SRI LANKAN CONTEXT

Global Coal Services. Prepared by: Dan Watkins 1 February Copyright 2002 Global Insight, Inc.

DYNAMIC CHANGE PROCESS: HOW DO COGNITIVE READINESS DRIVERS INFORM CHANGE AGENTS ON EMPLOYEE BEHAVIOURAL CHANGE INTENTION.

Customer Relationship Management

PRICING STRATEGY AUDITING FOR GARMENT MANUFACTURING COMPANIES IN SRI LANKA

Michael A. Stolle From Purchasing to Supply Management

Successful Project Management. Third Edition

Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and

TABLE OF CONTENTS 6 MEDIUM-TERM COAL MARKET REPORT 2016

INTELLECTUAL CAPITAL IN INDONESIA:

WF VAN DER VYVER

A STUDY OF SELECT DIMENSIONS OF DEVELOPMENT OF THE SME SECTOR OF SRI LANKA

Freight Investor Services presents. Freight (FFA) & Iron ore swaps (IOS) trading overview. Singapore - November 2009

Research & Management Science

Autonomous agent negotiation strategies in complex environments

INFORMATION SECURITY MANAGEMENT MATURITY: A STUDY OF SELECT ORGANIZATIONS

6 TH Basic Materials Seminar Exane BNP Paribas. London March 22, 2011 Edwin Eichler, Executive Board Member ThyssenKrupp AG

China s Iron Ore Boom

A Study of Job Satisfaction and Employee Productivity in Selected Organisations With Special Reference to SIDCUL, Udham Singh Nagar.

Load distribution factors of straight and curved steel concrete composite box and I girder bridges

The Economics of Twoway Interconnection

The Economics of Evaluating Water Projects

Information Age Economy. Editorial Board H. U. Buhl W. König R. M. Lee H. Mendelson A. Picot B. Schmid R. Wigand

Buy-outs in Family Businesses

Managing Multiple Projects

Market Orientation and Business Performance: Empirical Evidence from Thailand

A FRAMEWORK FOR THE IMPLEMENTATION OF TOTAL QUALITY MANAGEMENT IN THE SOUTH AFRICAN AIR FORCE JACOBUS JOHANNES OSCHMAN

Applications of Sensor-based Sorting in the Raw Material Industry

Using local intelligence to actively monitor. distributed data sources in decision making. architectures

Raw Material Demand and Availability Seaborne market perspective. Kees Gerretse, Group Director Supplies and Transport Tata Steel Group

THE POTENTIAL IMPACT OF A LEVY ON BUNKER FUELS ON DRY BULK SPOT FREIGHT RATES N.T. Chowdhury and J. Dinwoodie

PART ONE: THE CHALLENGE FOR TRANSPORT

Ökonomische Analyse des Rechts

The largest producing companies in Brazil are: Vale with 84.52%, CSN with 5.45%, Samarco with 6.29%, MMX with 2.03%, and Usiminas with 1.71%.

Disaster Relief Logistics

Ck TECHNISCHE UNIVERSITÄT

European Union, Trade in goods with NAFTA (North American Free Trade Agreement)

The Contribution of Human. Capital towards Economic. Growth in China. John Joshua

Management Accounting

EFFECT OF FIRM SIZE ON THE RELATIONSHIP BETWEEN STRATEGIC PLANNING DIMENSIONS AND PERFORMANCE OF MANUFACTURING FIRMS IN KENYA

Contents Contents... ii List of Tables... vi List of Figures... viii List of Acronyms... ix Abstract... x Chapter 1: Introduction...

Realizing Business Model Innovation

THE MICRO-FOUNDATIONS OF DYNAMIC CAPABILITIES, MARKET TRANSFORMATION AND FIRM PERFORMANCE. Tung-Shan Liao

ASSESSING THE IMPACT OF SERVICE BENEFITS ON EMPLOYEE PERFORMANCE IN GAUTENG DEPARTMENT OF EDUCATION. TJ Poopa MASTER OF ARTS. in the.

Data Mining and Applications in Genomics

STRATEGIC LEADERSHIP COMPETENCIES: A COMPARATIVE STUDY

European Union, Trade in goods with Israel

MIPLC Studies. Hyewon Ahn. Second Generation Patents in Pharmaceutical Innovation. Nomos

Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and

MATERIALS MANAGEMENT

For more information, please contact

A STUDY OF INDIVIDUAL CONSUMER LEVEL CULTURE IN B2C E-COMMERCE THROUGH A MULTI-PERSPECTIVE itrust MODEL

IMPACT OF MONETARY AND NON-MONETARY REWARDS TOWARDS EMPLOYEE MOTIVATION : CASE STUDY OF GARMENT INDUSTRY IN SRI LANKA

4-6 JUNE 2013 AT SWISSOTEL, THE BOSPHORUS ISTANBUL, TURKEY

Organisation de Coopération et de Développement Economiques Organisation for Economic Co-operation and Development

Service Delivery and Management

China Trends and forecasts Iron ore International dry bulk trade. Dry Bulk Shipping Steel products. Coking coal. Iron ore resources

Controlling. Concepts of Management Control, Controllership, and Ratios

A new era for the global steel industry

European Union, Trade in goods with ASEAN (Association Of South-East Asian Nations)

Revenue Management in Manufacturing

Q1 FY15 Investor Update Presentation

European Union, Trade in goods with Hong Kong

Global Mindset and Leadership Effectiveness

Diagnosing and Changing Organizational Culture

Raw materials improvement in the steel industry

... M arketing strategies and.performance of agricultural marketing firms

The application of information systems in marketing: a study of empowerment in electronic commerce

Cross-market Behavior Modeling

Steel Price Forecasts To 2023 European Union

THE STRATEGICALLY NETWORKED ORGANIZATION. Leveraging Social Networks to Improve Organizational Performance

Research Compendium. Coal Sector in India 2013 A comprehensive data and information update

Dry Bulk Freight Market: Prospects For Recovery

Time Series Models for Business and Economic Forecasting

Leibniz Universität Hannover. Masterarbeit

AN EMPIRICAL ANALYSIS OF THE IMPACT OF TRADE ON PRODUCTIVITY IN SOUTH AFRICA S MANUFACTURING SECTOR CHARLES AUGUSTINE ABUKA.

GAPS IN TECHNOLOGY: PLASTICS

FUNDAMENTALS OF HYDROLOGY

Copyright is owned by the Author of the thesis. Permission is given for a copy to be downloaded by an individual for the purpose of research and

"Dimensions of Employer Brands"

Transcription:

Marc P. Bielitza: Prospects for the 2020 Iron Ore Market. Quantitative Analysis of Market Dynamics and Risk Mitigation Strategies Schriften zu MANAGEMENT, ORGANISATION UND INFORMATION, hrsg. von Hagen Lindstädt, Band 36, ISBN 978-3-86618-679-8, ISBN 978-3-86618-779-5 (e-book pdf), Rainer Hampp Verlag, München u. Mering 2012, 321 S., 29.80 As the key ingredient for steelmaking, iron ore is integral to the global economy and represents the world's second-largest natural resource market by value. The overall structure and dynamics of the iron ore market have recently undergone various radical changes. The global demand shock caused by the rapid industrialization of China has sent market prices skywards and triggered a collapse of the 40-year-old benchmark pricing system. The newly adopted quarterly index-based pricing mechanism has brought an abrupt end to the traditional stability of the iron ore market, with price dynamics now experiencing greater volatility. Against the backdrop of these disruptive changes, this dissertation proposes a deterministic nine-region spatial equilibrium model of the 2020 iron ore market based on the fundamental characteristics and mechanics of the market. Key features of the model are its bottom-up structure, modeling supply volumes and costs on an individual mine level as well as the simulation of interregional trade flows using a linear optimization approach. In analyzing a number of relevant scenarios, this dissertation projects the key characteristics of the 2020 global iron ore market in terms of regional production volumes, demand, iron ore trade flows and market prices. It quantifies market dynamics based on changes in available production capacity, demand and freight rates. From a demand perspective, the dissertation proposes several strategies to mitigate the risk arising from higher and more volatile iron ore prices. Of these strategies, the captivity of mines and the introduction of iron ore export taxes are analyzed in greater detail with respect to their effectiveness and influence on the iron ore market. Key words: Iron ore, mining, steel, steelmaking, China, market simulation, industry cost curve, pricing, linear optimization, trade flows, market dynamics, volatility, risk, freight, captivity, export taxes Dr. Marc P. Bielitza was born in 1981 in New York, NY. He studied Business Engineering at Universität Karlsruhe (TH) in Germany and Università Commerciale Luigi Bocconi in Milan (Italy) with a focus on Production Technology and Industrial Dynamics. In 2007 he joined McKinsey & Company as a management consultant specializing in basic materials industries. From 2009 to 2011 he took an educational leave to write his doctoral thesis under the supervision of Prof. Dr. Hagen Lindstädt at the Karlsruhe Institute of Technology (KIT).

Schriften zu MANAGEMENT, ORGANISATION UND INFORMATION Herausgegeben von Hagen Lindstädt Band 36

Marc P. Bielitza Prospects for the 2020 Iron Ore Market Quantitative Analysis of Market Dynamics and Risk Mitigation Strategies Rainer Hampp Verlag München, Mering 2012

Bibliographic information published by the Deutsche Nationalbibliothek Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de. ISBN 978-3-86618-679-8 (print) ISBN 978-3-86618-779-5 (e-book) Schriften zu Management, Organisation und Information: ISSN 1612-1767 DOI 10.1688/9783866187702 First published 2012 Von der Fakultät für Wirtschaftswissenschaften des Karlsruher Instituts für Technologie (KIT) genehmigte Dissertation. Tag der mündlichen Prüfung: 13. Januar 2012 Referent: Prof. Dr. Hagen Lindstädt Korreferent: Prof. Dr. Frank Schultmann 2012 Rainer Hampp Verlag München und Mering Marktplatz 5 86415 Mering, Germany www.hampp-verlag.de All rights reserved. No part of this publication may be reprinted or reproduced or utilized in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publisher. In case of complaints please contact Rainer Hampp Verlag.

Acknowledgments This dissertation was written during my time as a PhD student at the Karlsruhe Institute of Technology (KIT), during an educational leave from McKinsey & Company. A number of people have contributed significantly to this dissertation and I would like to take this opportunity to extend my sincere thanks to them. Foremost, I express my profound gratitude to Prof. Dr. Hagen Lindstädt, for giving me the opportunity to work in such a stimulating research context and for allowing me to learn from his impressive theoretical, methodological and empirical knowledge. The inspiring discussions with him as well as his constructive criticism significantly widened my scientific understanding of oligopolies and market modeling. Also, I would like to thank Prof. Dr. Frank Schultmann for taking on the role of co-referee. My special thanks go to Dr. Ilse Kenis from the McKinsey Basic Materials Institute, for serving as a sounding board for my ideas and for letting me benefit from her vast knowledge of the global iron ore and steel industries. I would also like to thank the McKinsey GEM leadership, particularly Dr. Peter Feldhaus, Dr. Heiner Frankemölle, Dr. Lorenz Jüngling, Dr. Sigurd Mareels, Dr. Stefan Rehbach, Dr. Carsten Sürig and Dr. Benedikt Zeumer, for their guidance and support. Furthermore, I am grateful for the experience of being a part of the vibrant Fellow community in the Munich office. I thank all colleagues for the many helpful discussions as well as their insightful comments. There is no telling how much my dissertation has profited from working in such an environment that is at the same time friendly and professional. I will always remember the good times we had. Great appreciation goes to my parents for their continuous support in many different ways. They have laid the foundation for this dissertation by providing the upbringing and education that make up much of who I am today. Finally, I would like to thank Christina for her relentless encouragement and affectionate support which is so strongly coupled with the success of this dissertation. Marc P. Bielitza Karlsruhe, January 2012

vii Contents List of Figures List of Tables Abbreviations Symbols xi xv xvii xix 1 Introduction 1 1.1 Motivation.............................. 1 1.2 Ambition.............................. 4 1.3 Structure of the present paper................... 7 1.4 Relevant previous research on the iron ore market........ 8 2 Technical introduction to iron ore 13 2.1 Nature and occurrence of iron and iron ores........... 13 2.1.1 Natural iron resources and iron ore reserves....... 13 2.1.2 Depletion of iron ore.................... 14 2.1.3 Differentiating factors of natural iron ores........ 16 2.2 Iron ore value chain from mine to market............. 17 2.2.1 Discovery of iron ore deposits............... 17 2.2.2 Buildup of mining infrastructure.............. 18 2.2.3 Mining of iron ore...................... 18 2.2.4 Beneficiation and agglomeration of iron ore........ 19 2.2.5 Transportation of iron ore to the market......... 20 2.3 Significance of iron ore for steelmaking.............. 22 2.3.1 Overview of iron- and steelmaking process routes.... 22 2.3.2 Quality of iron ores with respect to their use for steelmaking 26 3 Structure and characteristics of the 2009 iron ore market 29 3.1 Introductory comments regarding iron ore market data and statistics............................... 30 3.1.1 Units of measure for volume data............. 31

viii Contents 3.1.2 Units of measure for price data.............. 32 3.2 Iron ore demand.......................... 32 3.2.1 Geography of iron ore demand............... 33 3.2.2 Chinese steel industry as key source of iron ore demand. 34 3.3 Iron ore supply........................... 39 3.3.1 Geography of iron ore supply............... 39 3.3.2 Corporate control in iron ore mining........... 43 3.4 Dry bulk freight rates........................ 50 3.5 Iron ore trade flows and market mechanism............ 51 3.5.1 Global iron ore trade flows................. 51 3.5.2 Iron ore market mechanism................ 55 3.6 Public policy............................ 60 4 Analysis of recent market developments and derivation of risk mitigation strategies 63 4.1 Recent developments and trends in the iron ore market..... 63 4.1.1 Increased iron ore market price.............. 64 4.1.2 Increased amount of iron ore capacity expansion projects 67 4.1.3 Increased iron ore market price volatility......... 69 4.1.4 Increased freight rates and freight rate volatility..... 73 4.2 Impact of iron ore market developments and trends on the steel industry............................... 75 4.2.1 Impact of increased iron ore prices............ 75 4.2.2 Impact of increased iron ore price volatility........ 78 4.3 Potential risk mitigation strategies for demand-side players... 79 5 A model of the 2020 iron ore market 85 5.1 Anatomy and mechanisms of the model.............. 85 5.1.1 Definition of geographic regions.............. 87 5.1.2 Key input components................... 89 5.1.3 Market mechanism..................... 120 5.1.4 Output............................ 134 5.2 Technical implementation of the model.............. 136 5.3 Validation of the model vs. 2009 actuals............. 138 5.3.1 Validation of the allocation mechanism.......... 138 5.3.2 Validation of the pricing mechanism............ 139 6 Quantitative analysis of the 2020 iron ore market and risk mitigation strategies 141 6.1 Definition of scenarios based on research questions........ 142 6.1.1 Definition of 2020 base case and determination of scenarios for sensitivity analysis................. 143

Contents ix 6.1.2 Definition of 2020 scenarios for evaluation of selected risk mitigation strategies.................... 146 6.2 Description of methodology and scope of analyses........ 156 6.3 Results of quantitative analyses.................. 158 6.3.1 Quantitative analysis of structure and dynamics of the 2020 iron ore market.................... 159 6.3.2 Evaluation of risk mitigation strategies for demand-side players in 2020....................... 213 6.4 Discussion of results........................ 238 6.4.1 Discussion of results of quantitative analysis of structure and dynamics of the 2020 iron ore market........ 238 6.4.2 Discussion of evaluation of selected risk mitigation strategies.............................. 243 6.4.3 Discussion of results from a European perspective.... 250 7 Wrap-up and outlook 255 7.1 Review of goal attainment and conclusion............ 255 7.2 Critical assessment and areas for future research......... 259 Appendix 265 I Alphabetical list of countries/territories and geographic regions 265 II Model input data by scenario................... 270 III Overview of formulae and mechanisms of the market model... 273 Bibliography 275

xi List of Figures 2.1 Classification of natural and marketable iron ores......... 20 2.2 Process routes for the production of crude steel.......... 23 3.1 Framework defining the iron ore market structure......... 30 3.2 Geographic distribution of top ten iron ore consuming countries, 2009.................................. 35 3.3 Chinese crude steel production and share of world total, 1980 2009 36 3.4 Geographic distribution of top ten iron ore producing countries, 2009 40 3.5 Top ten interregional iron ore trade flows, 2009........... 54 3.6 Iron ore price levels under changing pricing regimes, 2008 2010.. 58 3.7 Key players active in the development and trading of iron ore derivatives............................... 60 4.1 Global crude steel production and average annual iron ore market price, 1980 2010............................ 65 4.2 Global crude steel production and average annual iron ore market price, 1980 2009............................ 69 4.3 Estimation of iron ore derivatives trading volumes vs. physical trading volumes 2009 2020...................... 72 4.4 Monthly Capesize voyage freight rates for routes from Tubarão and Port Hedland to Qingdao, 1999 2010................ 74 4.5 Development of hot-rolled coil raw materials basket for Western European steelmaker, 2000 2010................... 76 4.6 Shift in EBITDA profit pools from steelmaking to iron ore mining, 1995 2009............................... 77 4.7 Sensitivity of European steel industry s profitability to iron ore price 79 5.1 Structure of proposed global iron ore market model........ 86 5.2 Definition of geographic regions and ports............. 88 5.3 Values of parameters defining 2020 iron ore production capacity levels.................................. 95 5.4 Example of standardization of production capacity, Minas do Meio (Brazil)................................ 96

xii List of Figures 5.5 Split of FOB cost along the iron ore value chain.......... 97 5.6 Split of mining cost by cost type for seven mines.......... 100 5.7 Example of standardized FOB cost calculation from mining cost, Corumbá mine (Brazil)........................ 106 5.8 Derivation of 2009 average Capesize freight rate per route using linear OLS regression......................... 109 5.9 Exemplary derivation of 2020 Capesize freight rate scenarios for route Oceania China........................ 110 5.10 Top-down methodology of long-term apparent finished steel demand modeling............................ 113 5.11 Top-down methodology of long-term iron ore demand modeling. 117 5.12 Network representation of the allocation mechanism........ 124 5.13 Illustrative iron ore mining cost curve................ 129 5.14 Illustrative overview of market mechanism............. 134 5.15 Comparison of iron ore trade flows from 2009 model run to 2009 actuals................................. 139 5.16 Comparison of actual spot market price CFR China in 2009 with calculated price from 2009 model run................ 140 6.1 Definition of 2020 base case and scenarios............. 145 6.2 Structure of results of quantitative analyses............ 158 6.3 Scenario A: 2020 base case Analysis of production, net trade and demand by region........................... 160 6.4 Scenario A: 2020 base case Analysis of interregional trade flows 161 6.5 Scenario A: 2020 base case Analysis of market prices, procurement cost and profit contribution of miners by region....... 163 6.6 Scenario A: 2020 base case Analysis of individual profit contributions of miners........................... 165 6.7 Scenario B: 2020 low capacity Analysis of production, net trade and demand by region........................ 167 6.8 Scenario B: 2020 low capacity Analysis of interregional trade flows168 6.9 Scenario B: 2020 low capacity Analysis of market prices, procurement cost and profit contribution of miners by region..... 169 6.10 Scenario B: 2020 low capacity Analysis of individual profit contributions of miners.......................... 170 6.11 Scenario C: 2020 high capacity Analysis of production, net trade and demand by region........................ 172 6.12 Scenario C: 2020 high capacity Analysis of interregional trade flows173 6.13 Scenario C: 2020 high capacity Analysis of market prices, procurement cost and profit contribution of miners by region..... 174

List of Figures xiii 6.14 Scenario C: 2020 high capacity Analysis of individual profit contributions of miners.......................... 175 6.15 Scenario D: 2020 low demand Analysis of production, net trade and demand by region........................ 177 6.16 Scenario D: 2020 low demand Analysis of interregional trade flows178 6.17 Scenario D: 2020 low demand Analysis of market prices, procurement cost and profit contribution of miners by region....... 179 6.18 Scenario D: 2020 low demand Analysis of individual profit contributions of miners.......................... 181 6.19 Scenario E: 2020 high demand Analysis of production, net trade and demand by region........................ 183 6.20 Scenario E: 2020 high demand Analysis of interregional trade flows184 6.21 Scenario E: 2020 high demand Analysis of market prices, procurement cost and profit contribution of miners by region..... 185 6.22 Scenario E: 2020 high demand Analysis of individual profit contributions of miners.......................... 187 6.23 2020 global capacity surplus and utilization per scenario..... 188 6.24 Scenarios A E Split of supply to China by source........ 190 6.25 Scenarios A E Market prices by region.............. 191 6.26 Scenarios A E Split of total iron ore procurement cost..... 192 6.27 Scenarios A E Split of miner s profit contribution by player.. 194 6.28 Scenario F: 2020 low freight rates Analysis of production, net trade and demand by region..................... 196 6.29 Scenario F: 2020 low freight rates Analysis of interregional trade flows.................................. 197 6.30 Scenario F: 2020 low freight rates Analysis of market prices, procurement cost and profit contribution of miners by region... 199 6.31 Scenario F: 2020 low freight rates Analysis of individual profit contributions of miners........................ 200 6.32 Scenario G: 2020 high freight rates Analysis of production, net trade and demand by region..................... 202 6.33 Scenario G: 2020 high freight rates Analysis of interregional trade flows.................................. 203 6.34 Scenario G: 2020 high freight rates Analysis of market prices, procurement cost and profit contribution of miners by region... 205 6.35 Scenario G: 2020 high freight rates Analysis of individual profit contributions of miners........................ 206 6.36 Scenarios F, A, G Split of supply to China by source...... 208 6.37 Scenarios F, A, G Market prices by region............ 210 6.38 Scenarios F, A, G Split of total iron ore procurement cost.... 210 6.39 Scenarios F, A, G Split of miner s profit contribution by player. 211

xiv List of Figures 6.40 Scenario H: 2020 no captivity ArcelorMittal Analysis of production, net trade and demand by region................ 214 6.41 Scenario H: 2020 no captivity ArcelorMittal Analysis of interregional trade flows........................... 215 6.42 Scenario H: 2020 no captivity ArcelorMittal Analysis of market prices, procurement cost and profit contribution of miners by region216 6.43 Scenario H: 2020 no captivity ArcelorMittal Analysis of individual profit contributions of miners.................. 217 6.44 Scenario H: 2020 no captivity ArcelorMittal Analysis of procurement cost of ArcelorMittal...................... 219 6.45 Scenario I: 2020 no Chinese captivity abroad Analysis of production, net trade and demand by region................ 220 6.46 Scenario I: 2020 no Chinese captivity abroad Analysis of interregional trade flows........................... 221 6.47 Scenario I: 2020 no Chinese captivity abroad Analysis of market prices, procurement cost and profit contribution of miners by region223 6.48 Scenario I: 2020 no Chinese captivity abroad Analysis of individual profit contributions of miners.................. 224 6.49 Scenario I: 2020 no Chinese captivity abroad Analysis of procurement cost of China........................ 225 6.50 Scenario J: 2020 no export taxes India Analysis of production, net trade and demand by region................... 227 6.51 Scenario J: 2020 no export taxes India Analysis of interregional trade flows............................... 228 6.52 Scenario J: 2020 no export taxes India Analysis of market prices, procurement cost and profit contribution of miners by region... 230 6.53 Scenario J: 2020 no export taxes India Analysis of individual profit contributions of miners.................... 231 6.54 Scenario K: 2020 introduction export taxes Brazil Analysis of production, net trade and demand by region............ 233 6.55 Scenario K: 2020 introduction export taxes Brazil Analysis of interregional trade flows....................... 234 6.56 Scenario K: 2020 introduction export taxes Brazil Analysis of market prices, procurement cost and profit contribution of miners by region................................ 236 6.57 Scenario K: 2020 introduction export taxes Brazil Analysis of individual profit contributions of miners.............. 237 6.58 Influence of 2020 Chinese and South American iron ore demand on European market price........................ 252 6.59 Influence of 2020 Brazilian and Indian iron ore export tax on European market price.......................... 253

xv List of Tables 1.1 Chronological overview of relevant previous research on the iron ore market............................... 9 2.1 World iron ore reserves by country as of January 2011....... 15 3.1 Iron ore demand by country, 2009.................. 34 3.2 Iron ore production by country, 2009................ 39 3.3 Iron ore production by controlling entity, 2009........... 45 3.4 Iron ore exports and imports by country, 2009........... 52 3.5 Interregional iron ore trade flows, 2009............... 53 3.6 Overview of three main spot-based iron ore indices........ 59 3.7 Overview of tax types affecting iron ore mining.......... 62 I.1 Alphabetical list of countries/territories and geographic regions.. 269 II.1 Input data 2009 capacity and demand................ 270 II.2 Input data 2020 capacity....................... 270 II.3 Input data 2020 demand....................... 270 II.4 Input data 2009 freight rates..................... 271 II.5 Input data 2020 freight rates..................... 272