Can Peak Oil Fuel the Sub-Saharan Aids Epidemic?

Home , Ghawar Field

A COMPLEX SYSTEMS APPROACH TO SUSTAINABILITY:

CAN PEAK OIL FUEL THE SUB-SAHARAN AIDS EPIDEMIC?

CRAIG PHILIP ATZBERGER

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Advisor:

Professor N. Sreenath

Department of Electrical Engineering and Computer Science

CASE WESTERN RESERVE UNIVERISTY

January, 2007 CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the dissertation of

______

candidate for the Ph.D. degree *.

(signed)______(chair of the committee)

______

(date) ______

*We also certify that written approval has been obtained for any proprietary material contained therein.

Table of Contents

TABLE OF CONTENTS III

LIST OF FIGURES VII

ACKNOWLEDGEMENTS XIII

LIST OF ABBREVIATIONS XIV

ABSTRACT XV

CHAPTER 1: INTRODUCTION 1

1.1 OVERVIEW 1 1.1.1 SUSTAINABLE DEVELOPMENT 3 1.2 DISSERTATION CONTRIBUTIONS 4 1.2.1 USEFULNESS FOR POLICY MAKERS 6 1.3 DISSERTATION ORGANIZATION 6

CHAPTER 2: BACKGROUND OF STUDY 8

2.1 INTRODUCTION 8 2.1.1 CHAPTER ORGANIZATION 10 2.2 TRANSITION TO THE POST-PEAK OIL ERA 10 2.2.1 HOW MUCH OIL IS THERE? 12 2.2.2 WHEN WILL OIL PRODUCTION PEAK? 17 2.2.3 THE LINK BETWEEN OIL CONSUMPTION AND ECONOMIC GROWTH 30 2.3 THE GLOBAL HIV/AIDS EPIDEMIC TODAY 37 2.3.1 THE PROBLEM IN SUB-SAHARAN AFRICA 39 2.3.2 THE PROBLEM IN BOTSWANA 44 2.3.3 PREVENTION INTERVENTIONS 44 2.3.4 CURRENT STATE OF PREVENTION INTERVENTIONS 45 2.3.5 TREATMENT INTERVENTIONS 47 2.3.6 CURRENT STATE OF TREATMENT INTERVENTIONS 48

CHAPTER 3: METHODOLOGY 57

3.1 INTRODUCTION 57 3.1.1 CHAPTER ORGANIZATION 58 3.2 ASPECTS OF THE CYBERNETIC PARADIGM FOR THE HUMAN DIMENSION 58 3.3 HUMAN AS A SUB-MODEL 64

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3.4 INTEGRATED VERSUS MULTI-LEVEL MODELING 67 3.5 MANAGEMENT OF COMPLEXITY VIA A MULTI-LEVEL HIERARCHICAL APPROACH 71 3.6 REASONING SUPPORT TOOL: GLOBESIGHT 81

CHAPTER 4: IMPACT OF PEAK OIL AND THE POST-PEAK OIL ERA 96

4.1 INTRODUCTION 96 4.1.1 CHAPTER ORGANIZATION 97 4.2 THE WORLD OIL MODEL 98 4.3 WHEN WILL IT PEAK? SCENARIOS FOR PEAK OIL: 2010, 2015, 2025 104 4.4 THE SECOND LEVEL: REGIONAL OIL DEMAND WITH ECONOMIC FEEDBACK 111 4.5 ANTICIPATORY POLICY FOR PEAK SHIFT FROM 2015 TO 2025 115 4.6 REGIONALIZED OIL DEMAND PROJECTIONS BY SECTOR TO 2025 121 4.7 CONCLUSIONS 139

CHAPTER 5: DEMOGRAPHIC IMPACT OF HIV/AIDS 144

5.1 INTRODUCTION 144 5.1.1 CHAPTER ORGANIZATION 145 5.2 MODELING OF THE VIRUS IN A POPULATION 145 5.2.1 EXISTING MODELS IN LITERATURE 146 5.2.2 RELATION OF CURRENT MODEL STRUCTURE TO PRIOR HIV/AIDS MODELS 149 5.3 THE 3RD LEVEL HIV/AIDS MODEL AND BAU PROJECTIONS TO 2050 150 5.3.1 MODEL FORMULATION 150 5.3.2 BAU PROJECTIONS FOR SUB-SAHARAN AFRICA 163 5.3.3 BAU PROJECTIONS FOR BOTSWANA 179 5.4 THE 2ND LEVEL HIV/AIDS MODEL 187 5.5 THE 1ST LEVEL HIV/AIDS MODEL 191 5.6 ADVANTAGES OF MULTILEVEL DESIGN 194 5.7 CONCLUSIONS 194

CHAPTER 6: SOCIO-ECONOMIC IMPACT OF HIV/AIDS 196

6.1 INTRODUCTION 196 6.1.1 CHAPTER ORGANIZATION 196 6.2 THE ECONOMIC MODEL 197 6.3 BAU SCENARIO ECONOMIC PROJECTIONS 198 6.3.1 ECONOMIC MODEL RESULTS FOR SUB-SAHARAN AFRICA 199 6.3.2 ECONOMIC MODEL RESULTS FOR BOTSWANA 200 6.4 CONCLUSIONS 201

CHAPTER 7: OPTIMISTIC VISION- ACHIEVING THE UN MILLENNIUM DEVELOPMENT GOAL FOR HIV/AIDS 202

7.1. INTRODUCTION 202 7.2 THE MILLENNIUM DEVELOPMENT GOALS 204 7.2.1 THE GOALS 204 7.2.2 THE MILLENNIUM PROJECT 206 7.2.3 THE MILLENNIUM PROJECT TASK FORCE ON HIV/AIDS 206 7.3 TARGETS 207 7.3.1 TARGETS FOR PREVENTION 209 7.3.2 TARGETS FOR TREATMENT 210 7.4 IMPACT OF ACHIEVING TARGETS 211 7.5 COST OF THE MILLENNIUM PROJECT FOR HIV/AIDS 212 7.5.1 COSTS FOR SUB-SAHARAN AFRICA 214 7.5.2 COSTS FOR BOTSWANA 217 7.6 ANALYSIS OF THE MILLENNIUM PROJECT SCENARIO USING GLOBESIGHT 217 7.6.1 FEASIBILITY OF REACHING THE GOAL ON HIV/AIDS BY 2015 218 7.6.2 RESOURCES: INTERNATIONAL AID AND THE 0.7 PERCENT GNI TARGET 218 7.6.3 MODELING PREVENTION INTERVENTIONS 219 7.6.3.1 MOTHER-TO-CHILD TRANSMISSION 80 PERCENT COVERAGE TARGET 219 7.6.3.2 INTEGRATED PREVENTION AND TREATMENT 220 7.6.4 MODELING TREATMENT INTERVENTIONS 221 7.6.4.1 ANTIRETROVIRAL THERAPY 75 PERCENT COVERAGE TARGET 221 7.6.4.2 HIV INFECTIVITY REDUCTION 221 7.6.4.3 OTHER TREATMENT AND PREVENTION INTERVENTIONS 222 7.6.5 DEMOGRAPHIC IMPACT OF THE MILLENNIUM PROJECT 222 7.6.5.1 DEMOGRAPHIC IMPACT: SUB-SAHARAN AFRICA 222 7.6.5.2 DEMOGRAPHIC IMPACT: BOTSWANA 230 7.6.6 SOCIO-ECONOMIC IMPACT OF THE MILLENNIUM PROJECT 238 7.6.6.1 SOCIO-ECONOMIC IMPACT: SUB-SAHARAN AFRICA 238 7.6.6.2 SOCIO-ECONOMIC IMPACT: BOTSWANA 239 7.7 IMPACT OF MILLENNIUM PROJECT CONTINUATION THROUGH 2050 240 7.7.1 IMPACT OF CONTINUATION THROUGH 2050: SUB-SAHARAN AFRICA 240 7.7.2 IMPACTS OF CONTINUATION THROUGH 2050: BOTSWANA 249 7.8 CONCLUSIONS 255

CHAPTER 8: IMPACT OF THE POST-PEAK OIL ERA ON THE SUB- SAHARAN HIV/AIDS EPIDEMIC 258

8.1 INTRODUCTION 258 8.1.1 CHAPTER ORGANIZATION 258 8.2 IMPACTS OF THE OIL DEFICIT ON ODA AND THE HIV/AIDS EPIDEMIC 259 8.3 ADDITIONAL AIDS DEATHS PER BARREL OF OECD OIL DEFICIT 267 8.4 INTEGRATED SCENARIOS THROUGH YEAR 2050 270

CHAPTER 9: CONCLUSIONS & RECOMMENDATIONS 273

9.1 CONCLUSIONS AND RECOMMENDATIONS 273

9.2 FUTURE RESEARCH 275

APPENDIX 1: MATHEMATICAL MODEL EQUATIONS 276

VARIABLE NAMING CONVENTION FOR MODEL EQUATIONS 276 1ST LEVEL POPULATION EQUATIONS 277 2ND LEVEL POPULATION EQUATIONS 277 3RD LEVEL POPULATION EQUATIONS 278 1ST LEVEL OIL EQUATIONS 280 2ND LEVEL OIL WITH ECONOMIC FEEDBACK EQUATIONS 281 1ST LEVEL HIV/AIDS POPULATION EQUATIONS 283 2ND LEVEL HIV/AIDS POPULATION EQUATIONS 284 3RD LEVEL HIV/AIDS POPULATION EQUATIONS 285 1ST LEVEL ECONOMIC EQUATIONS 292

APPENDIX 2: GLOBESIGHT VARIABLE DECLARATIONS (XML) 293

POPULATION PROJECT 293 OIL TRANSITION PROJECT 301 HIV/AIDS POPULATION & ECONOMIC PROJECT 312

APPENDIX 3: GLOBESIGHT MODEL CODE (JAVA LANGUAGE) 335

1ST LEVEL POPULATION MODEL 335 2ND LEVEL POPULATION MODEL 336 3RD LEVEL POPULATION MODEL 337 1ST LEVEL OIL MODEL 341 2ND LEVEL OIL MODEL 344 1ST LEVEL HIV/AIDS MODEL 350 2ND LEVEL HIV/AIDS MODEL 351 3RD LEVEL HIV/AIDS MODEL 353 1ST LEVEL ECONOMIC MODEL 361

APPENDIX 4: MODEL DATA 363

APPENDIX 5: INSTRUCTIONS FOR 3RD LEVEL POPULATION MODEL 377

BIBLIOGRAPHY 381

List of Figures

Figure 1.1: Integrated Assessment Structure______2 Figure 2.1: DAC members’ bilateral and multilateral aid to HIV/AIDS control, average commitments 2000-02, millions of USD [Source: OECD DAC, 2004]_ 9 Figure 2.2: World Primary Energy Demand by Fuel [Source: IEA, WEO 2004] _ 11 Figure 2.3: USGS vs. Campbell/Laherrere Ultimate Recovery Estimates [Source: DoE EIA] ______13 Figure 2.4: Ultimate Recovery Estimates from 65 Different Organizations [Source: ASPO, 2004]______14 Figure 2.5: Giant Oil Field Discoveries per Decade 1850 – 2000 [Source: Uppsala Hydrocarbon Depletion Group, 2004] ______14 Figure 2.6: Addition to World Proven Oil Reserves from the Discovery of New Fields and Production [Source: ASPO, 2004] ______16 Figure 2.7: New Field Wildcats vs. Cumulative Added Volume [Source: ASPO, 2004]______16 Figure 2.8: Hubbert Curve for Annual Production [Source: Laherrere, 1998] ___ 18 Figure 2.9: Oil and Gas Liquids Global Production Aggregate [Source: Campbell, 2004]______20 Figure 2.10: USGS 2000 Ultimately Recoverable Oil and NGL Resources [Source: USGS, 2000]______21 Figure 2.11: USGS Ultimately Recoverable Oil vs. Trend in Discovery [Source: Campbell, 2005]______22 Figure 2.12: Saudi Aramco Fields and Percentage of Reserves Produced [Source: Saudi Aramco, 2004]______23 Figure 2.13: Proven Oil Reserves by Region per BP Statistical Review [Source: BP, 2004] ______24 Figure 2.14: Global (minus ME & FSU) Peak Year Production by Region [Source: ASPO, 2004]______25 Figure 2.15: Discovery and Production for FSU 1930-2050 [Source: ASPO, 2004]______26 Figure 2.16: World Oil Demand by Region and Sector 1980-2030 [Source: Exxon Mobile, 2004] ______27 Figure 2.17: IEA Estimates for 2001 & 2025 OPEC Production Required [Source: DoE EIA, 2004] ______28 Figure 2.18: Saudi Aramco Maximum Sustainable Capacity 2000-2050 [Source: Saudi Aramco, 2004]______29 Figure 2.19: Global Decline in Oil Supply and Required New Production [Source: Exxon, 2003] ______30 Figure 2.20: OECD Oil Consumption vs. GDP Growth 1961-2002 [Data Source: World Bank/EIA, 2006] ______31 Figure 2.21: Global Oil Supply Disruptions 1951-2004 [Data Source: EIA, 2005]_ 33 Figure 2.22: GDP vs. Vehicle-Miles of Travel 1960-2000 [Source: US Department of Transportation, 2002]______34 Figure 2.23: U.S. Transportation Fleet Replacement Cost [Data Source: Bezdek, 2005]______36

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Figure 2.24: Penetration of Major U.S. Transportation Infrastructure [Source: Marchetti, 1985; Ausubel, 1996] ______36 Figure 2.25: Adults and Children Living with HIV as of end 2005 [Source: UNAIDS, 2006] ______38 Figure 2.26: Regional Statistics for Global HIV & AIDS as of end 2005 [Data Source: UNAIDS, 2006] ______39 Figure 2.27: Map of Sub-Saharan Africa [Source: Maps.com, 2003] ______40 Figure 2.28: Sub-Saharan African Adults Aged 15-49 HIV Prevalence % from Recent Population-based Surveys [Source: UNAIDS, 2006] ______41 Figure 2.29: HIV/AIDS Infections and Prevalence in Sub-Saharan Africa [Source: UNAIDS, 2006] ______42 Figure 2.30: Regional Statistics for Sub-Saharan Africa HIV/AIDS end 2005 [Source: UNAIDS 2006] ______44 Figure 2.31: Percent of Those in Need of Prevention Interventions Reached [Source: UN Millennium Project, 2004]______47 Figure 2.32: Coverage of Antiretroviral Therapy [Source: WHO, 2004] ______50 Figure 3.1: Global Change System ______59 Figure 3.2: Human Dimension______59 Figure 3.3: Multi-Layered Decision Hierarchy ______63 Figure 3.4: Traditional Computer Approach for Scenario Generation ______65 Figure 3.5: Scenario Generation Using the Integrated Human/Computer Modeling Process ______67 Figure 3.6: Global Change as a Multi-level (Stratified System)______70 Figure 3.7: First Level Population Model Input/Output Diagram______73 Figure 3.8: Second Level Population Model Input/Output Diagram______75 Figure 3.9: Third Level Population Model Input/Output Diagram______77 Figure 3.10: GLOBESIGHT Architecture ______83 Figure 3.11: First Level Population Model GLOBESIGHT Block Diagram _____ 85 Figure 3.12: First Level Population Data for the World 2000-2050 [Source: UN World Population Prospects, 2004]______86 Figure 3.13: GLOBESIGHT First Level Population Output for the World______87 Figure 3.14: Second Level Population Model GLOBESIGHT Block Diagram ___ 88 Figure 3.15: Second Level Population Data for the World 2000-2050 [Source: UN World Population Prospects, 2004]______89 Figure 3.16: First vs. Second Level Population & Growth Rate for the World ___ 89 Figure 3.17: Third Level Population Model GLOBESIGHT Block Diagram ____ 90 Figure 3.18: Population Pyramid for the World 2005______91 Figure 3.19: Population Pyramid for the World 2050______92 Figure 3.20: Population Pyramid for Japan in 2005 ______93 Figure 3.21: Population Pyramid for Japan in 2050 ______93 Figure 4.1: U.S. Lower 48 Oil Production 1930-2000 [Source: Blanchard, 2000] _ 99 Figure 4.2: Hubbert Production Curves for Different Peak Production Levels__ 102 Figure 4.3: Input/Output Diagram for the 1st Level Oil Model______103 Figure 4.4: Block Diagram for the 1st Level Oil Model in GLOBESIGHT _____ 104 Figure 4.5: BaU World Oil Supply, Demand, & Deficit - Peak 2010______105 Figure 4.6: BaU World Oil Supply, Demand, & Deficit - Peak 2015______106

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Figure 4.7: BaU World Oil Supply, Demand, & Deficit - Peak 2025______107 Figure 4.8: Years Until Supply Reaches 50% of Demand – BaU Demand ______108 Figure 4.9: Years Until Supply Reaches 50% of Demand – High Demand______108 Figure 4.10: Years Until Supply Reaches 50% of Demand – Low Demand _____ 109 Figure 4.11: Years Until Supply Reaches 50% of Demand – BaU Demand, High Ultimate Recovery______110 Figure 4.12: BaU World Oil Supply, Demand, Deficit – Peak 2010, 2015, 2025__ 115 Figure 4.13: World Oil Spare Production Capacity [Source: DoE EIA, 2005]___ 117 Figure 4.14: OECD Oil Use – Peak Shift Scenario ______120 Figure 4.15: World Energy Demand by Fuel Type 2000-2025 [Source: OPEC, 2004] ______122 Figure 4.16: World Oil Demand by Region 2000-2025 [Source: OPEC, 2004]___ 123 Figure 4.17: Annual Growth in Oil Demand by Region 2000-2025 [Source: OPEC, 2004] ______124 Figure 4.18: Annual Growth in OECD Oil Demand by Sector 2000-2025 [Source: OPEC, 2004] ______125 Figure 4.19: Annual Growth in D.C. Oil Demand by Sector 2000-2025 [Source: OPEC, 2004] ______126 Figure 4.20: Annual Growth in Trans. Econ Oil Demand by Sector 2000-2025 [Source: OPEC, 2004]______126 Figure 4.21: Vehicle Ownership in 2000 [Source: OPEC, 2004] ______127 Figure 4.22: Vehicle Ownership 2000 and Vehicle Growth 1970-2000 [Source: OPEC, 2004] ______128 Figure 4.23: OPEC Vehicle Ownership & Growth Projections to 2025 [Source: OPEC, 2004] ______129 Figure 4.24: Vehicle Scenario – Population and Vehicle Ownership, Growth and Efficiency Assumptions for India and China to 2025 ______131 Figure 4.25: Average Annual Growth in Oil Use per Vehicle 1970-2025 [Source: OPEC, 2004] ______132 Figure 4.26: Average Annual Growth in Oil Use per Vehicle 1970-2025 ______133 Figure 4.27: Vehicle Scenario Annual Growth Rate in Oil Demand 2005-25____ 133 Figure 4.28: Vehicle Scenario (Low): Based on Low Annual Growth Rate in Global Oil Demand 2005 - 2025 ______134 Figure 4.29: Vehicle Scenario Global Oil Supply, Demand, Deficit______135 Figure 4.30: Vehicle Scenario (Low) Global Oil Supply, Demand, Deficit ______135 Figure 4.31: High, Vehicle Scenario, Low Growth Global Oil Supply ______136 Figure 4.32: High, Vehicle Scenario, Low Growth Rate Global Oil Supply _____ 137 Figure 5.1: Modeling Techniques in the Early 1990’s [Source: Ljung, 2002]____ 147 Figure 5.2: Diagram of HIV/AIDS Progression______151 Figure 5.3: 3rd Level HIV/AIDS Input/Output Diagram ______162 Figure 5.4: 3rd Level HIV/AIDS Block Diagram ______163 Figure 5.5: Number of Years with Cumulative Percent Dying from AIDS [Source: UNAIDS, 2005] ______166 Figure 5.6: Age-specific AIDS Mortality Rates [Source: Ljung, 2002] ______167 Figure 5.7: BaU Model Results for Sub-Saharan Africa 2005, 2025 & 2050 ____ 168 Figure 5.8: UN BaU HIV Pyramid for Sub-Saharan Africa in 2005 ______170

Figure 5.9: UN BaU HIV/AIDS Pyramid for Sub-Saharan Africa in 2005______171 Figure 5.10: UN BaU HIV Pyramid for Sub-Saharan Africa in 2025 ______173 Figure 5.11: UN BaU HIV Pyramid for Sub-Saharan Africa in 2050 ______174 Figure 5.12: UN BaU Population Pyramid with and w/o Effects of AIDS: Sub- Saharan Africa in 2050______176 Figure 5.13: UN BaU Population with and w/o Effects of AIDS: Sub- Saharan Africa 2000-2050 ______177 Figure 5.14: UN BaU Population Pyramid with AIDS Losses from Cohort: Sub- Saharan Africa in 2050______178 Figure 5.15: BaU Scenario Results for Botswana 2005, 2025 & 2050 ______179 Figure 5.16: UN BaU HIV Pyramid for Botswana in 2005______180 Figure 5.17: UN BaU HIV/AIDS Pyramid for Botswana in 2005 ______181 Figure 5.18: UN BaU HIV Pyramid for Botswana in 2025______182 Figure 5.19: UN BaU HIV Pyramid for Botswana in 2050______183 Figure 5.20: UN BaU Population Pyramid with and w/o Effects of AIDS: Botswana in 2050 ______184 Figure 5.21: UN BaU Population with and w/o Effects of AIDS: Botswana 2000- 2050______185 Figure 5.22: UN BaU Population Pyramid with AIDS Losses from Cohort: Botswana in 2050______186 Figure 5.23: 2nd Level HIV/AIDS Input/Output Diagram ______190 Figure 5.24: 2nd Level HIV/AIDS Block Diagram ______191 Figure 5.25: 1st Level HIV/AIDS Input/Output Diagram ______193 Figure 5.26: 1st Level HIV/AIDS Block Diagram______193 Figure 6.1: BaU Scenario Economic Results for Sub-Saharan Africa 2005, 2025 and 2050 ______199 Figure 6.2: BaU Scenario Economic Results for Botswana 2005, 2025, 2050 ____ 200 Figure 7.1: Regional HIV/AIDS Network for Tanzania [Source: UN Millennium Project, 2005]______203 Figure 7.2: Targets for Millennium Project’s Working Group on HIV/AIDS [Source: UN Millennium Project, 2005] ______208 Figure 7.3: Official MDG Indicators and Millennium Project Working Group on HIV/AIDS Prevention/Treatment Targets [Source: UN Statistics, 2003] ___ 209 Figure 7.4: Benefits of Achieving MDGs for Sub-Saharan Africa [Source: UN Millennium Project, 2005] 212 Figure 7.5: DAC Member’s ODA in 2003 and 2004 [Source: OECD, 2005]_____ 213 Figure 7.6: DAC Member’s ODA in 2003 and 2004 [Source: OECD, 2005]_____ 214 Figure 7.7: Per Capita HIV Expenditures by Country Income Level 2000-05 [Source: UNAIDS, 2006] ______215 Figure 7.8: HIV/AIDS per Capita Expenditures Trend from 2001-2005 [Source: UNAIDS, 2006] ______216 Figure 7.9: Funding Sources for Expanded AIDS Response in Low and Middle- Income Countries from 2005-2007 [Source: UNAIDS, 2005] ______219 Figure 7.10: BaU versus MDG Scenario for Sub-Saharan Africa 2005 & 2015__ 223 Figure 7.11: BaU Scenario HIV Pyramid for Sub-Saharan Africa: 2015 ______224 Figure 7.12: MDG Scenario HIV Pyramid for Sub-Saharan Africa: 2015______225

Figure 7.13: BaU vs. MDG Scenario HIV Pyramid, Sub-Saharan Africa: 2015 _ 226 Figure 7.14: Total HIV/AIDS Population, BaU vs. MDG Scenario Sub- Saharan Africa: 2000-2015______227 Figure 7.15: HIV Prevalence Ages 15-49, BaU vs. MDG Scenario Sub- Saharan Africa: 2000-2015______228 Figure 7.16: HIV Prevalence Age 15-24, UN/WHO BaU vs. MDG Scenario, Sub- Saharan Africa: 2000 - 2015______229 Figure 7.17: HIV+ Births, BaU vs. MDG Scenario, SS. Africa: 2000-2015______230 Figure 7.18: BaU versus MDG Scenario for Botswana 2005 & 2015______231 Figure 7.19: BaU versus MDG Scenario HIV Pyramid for Botswana: 2015 ____ 232 Figure 7.20: BaU Scenario Population Pyramid for Botswana in 2015______233 Figure 7.21: MDG Scenario Population Pyramid for Botswana in 2015______234 Figure 7.22: Total HIV/AIDS Population, BaU vs. MDG Scenario Botswana: 2000-2015 ______235 Figure 7.23: HIV Prevalence Age 15-49, UN/WHO BaU vs. MDG Scenario, Sub- Saharan Africa: 2000-2015______236 Figure 7.24: HIV Prevalence Age 15-24, BaU vs. MDG, Botswana: 2000-2015 __ 237 Figure 7.25: HIV+ Births, BaU vs. MDG Scenario, Botswana: 2000–2015______238 Figure 7.26: MDG Scenario Economic Indicators for Sub-Saharan Africa _____ 239 Figure 7.27: MDG Scenario Economic Indicators for Botswana______239 Figure 7.28: BaU versus MDG Scenario for Sub-Saharan Africa 2005 & 2025__ 240 Figure 7.29: BaU versus MDG Scenario for Sub-Saharan Africa 2005 & 2050__ 241 Figure 7.30: BaU vs. MDG Scenario HIV Pyramid, Sub-Saharan Africa: 2025 _ 243 Figure 7.31: BaU vs. MDG Scenario HIV Pyramid, Sub-Saharan Africa: 2050 _ 244 Figure 7.32: Total HIV/AIDS Population, BaU vs. MDG Scenario ______245 Sub-Saharan Africa: 2000-2050______245 Figure 7.33: HIV Prevalence Age 15-49, BaU vs. MDG Scenario Sub- Saharan Africa: 2000-2050______246 Figure 7.34: HIV Prevalence Ages 15-24, BaU vs. MDG Scenario Sub- Saharan Africa: 2000-2050______246 Figure 7.35: New HIV Infections, BaU vs. MDG Scenario, SS. Africa: 2000-50 _ 247 Figure 7.36: HIV Infected Births, BaU vs. MDG Scenario, SS. Africa: 2000-50 _ 248 Figure 7.37: AIDS Deaths, BaU vs. MDG Scenario, SS. Africa: 2000-2050 _____ 249 Figure 7.38: BaU versus MDG Scenario for Botswana 2005 & 2025______250 Figure 7.39: BaU versus MDG Scenario for Botswana 2005 & 2050______250 Figure 7.40: BaU vs. MDG Scenario HIV Pyramid, Botswana: 2025 ______252 Figure 7.41: BaU vs. MDG Scenario HIV Pyramid, Botswana: 2050 ______253 Figure 7.42: BaU Scenario Population Pyramid for Botswana in 2050______254 Figure 7.43: MDG Scenario Population Pyramid for Botswana in 2050______255 Figure 8.1: World Price of Oil 1977-2005 [Source: Davis & Diegel, 2004] ______259 Figure 8.2: ODA Cut Scenario Results, Sub-Saharan Africa 2005, 2025, 2050 __ 262 Figure 8.3: ODA Cut Scenario Economic Results Sub- Saharan Africa 2005, 2025, 2050 ______262 Figure 8.4: BaU, MDG, and ODA Scenario Results, Sub-Saharan Africa 2050__ 263 Figure 8.5: BaU, MDG, and ODA Scenario HIV/AIDS Population Sub- Saharan Africa 2000-2050 ______264

Figure 8.6: BaU, MDG, and ODA Scenario HIV Prevalence Ages 15-49 Sub- Saharan Africa 2000-2050 ______264 Figure 8.7: BaU, MDG, and ODA Scenario HIV Prevalence Ages 15-24 Sub- Saharan Africa 2000-2050 ______265 Figure 8.8: BaU, MDG, and ODA Scenario HIV Infected Births Sub- Saharan Africa 2000-2050 ______265 Figure 8.9: BaU, MDG, and ODA Scenario AIDS Deaths Sub- Saharan Africa 2000-2050 ______266 Figure 8.10: BaU, MDG, and ODA Scenario Total Population Sub- Saharan Africa 2000-2050 ______266 Figure 8.11: Additional AIDS Deaths per Billion Barrels of OECD Oil Deficit__ 268 Figure 8.12: Additional AIDS Deaths per Billion Barrels of OECD Oil Deficit and AIDS Deaths Averted Due to Increased Funding ______269 Figure 8.13: Additional AIDS Deaths per Billion Barrels of World Oil Deficit and AIDS Deaths Averted Due to Increased Funding ______270 Figure 8.14: MDG vs. ODA Cut Scenario - Additional AIDS Deaths from Oil Crisis and Deaths Averted due to Adequate Funding of MDGs ______271

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Acknowledgements

I sincerely thank my wife Jenni for all her patience, care and understanding throughout the completion of this investigation. I am also grateful to my parents for all their support and to my mother Maureen who enjoyed and/or endured countless hours of babysitting for Kaia and Hayden. In addition, I extend my personal thanks and gratitude to all those who have inspired and helped me along the way.

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List of Abbreviations

AIDS Acquired Immuno Deficiency Syndrome

ART Antiretroviral Therapy

ARVs Antiretrovirals

ASPO Association for the Study of Peak Oil & Gas

BaU Business-as-usual bbls billions of barrels

EIA Energy Information Administration (division of U.S. DoE)

GDP Gross Domestic Product

GNI Gross National Income

HIV Human Immunodeficiency Virus

IEA International Energy Agency mb/d millions of barrels per day

MDGs Millennium Development Goals mtct mother-to-child transmission mtoe million tones of oil energy

ODA Official Development Assistance

OECD Organization for Economic Cooperation and Development

UN United Nations

UNAIDS Joint United Nations Programme on HIV/AIDS

UNDP United Nations Development Programme

US DoE United States Department of Energy

WHO World Health Organization

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A Complex Systems Approach to Sustainability:

Can Peak Oil Fuel the Sub-Saharan AIDS Epidemic?

Abstract

CRAIG PHILIP ATZBERGER

The focus of this dissertation is development of a systems methodology to

examine large scale interrelated complex global systems governing natural

resource use, population, economy and global health. The study investigates questions regarding the individual disciplines and their integration as a system.

Integrated assessment (IA) examines if the looming peak in world oil production, and the post-peak oil era, can intensify the HIV/AIDS epidemic. Other questions

include:

• When could the world reach peak oil production?

• What are the economic implications for HIV/AIDS funding in the post-

peak oil era?

• What is the potential humanitarian cost in lives lost per barrel of oil

deficit?

A range of models have been developed and integrated in a decision support future assessment system as a reasoning support guide. An interactive

cybernetic approach incorporating the global earth/human dimensions is applied

to manage the many aspects of complexity and uncertainty. IA is enhanced by a

decision-making paradigm that utilizes real data, a family of multi-level

hierarchical models, and a human-in-the-loop approach. “Corner scenarios” envelope the scope of future development and hypothetical scenarios demonstrate possible futures within the envelope.

Results show oil production may peak by 2015. Without an alternative to fill the gap left by declining oil supplies, economic growth, closely correlated with oil consumption, will slow or decline. Affluent countries of the Organization for

Economic Cooperation and Development may become unable to provide Official

Development Assistance (ODA) funding for needy countries. Without ODA,

HIV/AIDS preventive/treatment programs in sub-Saharan Africa will likely disappear causing a spike in prevalence, higher mortality and a reduction in economy. Conversely, if the impending oil crisis is averted via strategic planning and alternative energy development, then ODA adequate to achieve the United

Nations Millennium Development Goals (MDGs) could save millions of lives by preventing initial infection and providing antiretroviral therapy. IA through 2050 demonstrates that reductions in ODA lead to an increase from 6.1 to 15 percent

HIV/AIDS prevalence in sub-Saharan Africa, as opposed to 1 percent prevalence if the MDGs are achieved; total population falls by over 450 million and Gross

National Income growth drops by 30 percent.

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Chapter 1: Introduction

1.1 Overview

The central focus of this dissertation is development of a systems

methodology to examine large scale interdisciplinary problems. Specifically, we

examine whether the looming peak in world oil production, and the subsequent

post-peak oil era, can intensify the HIV/AIDS epidemic. The problem is clearly interdisciplinary requiring a complex systems approach. A range of models have been developed and integrated in a decision support future assessment system.

An interactive cybernetic approach incorporating the global earth/human

dimensions of interest is applied in order to manage the many aspects of

complexity and uncertainty. Integrated assessment of the resource and health

aspects is enhanced through the use of a decision-making paradigm that utilizes

real data, a family of multi-level hierarchical models, and a human-in-the-loop

approach. The analysis consists of the following steps. First, the approaching

world oil crisis is established. Next, global supply and demand for oil is linked to

sustainable development in sub-Saharan Africa at the social, economic, and

resource level. The comprehensive, systems-based approach developed is

employed to investigate the complex interrelationships between the impending

peak in world oil production, the HIV/AIDS epidemic, population, and economy.

A block diagram representing the integrated assessment structure is shown in

Figure 1.1. The arrows represent linkages between the various sub-systems,

demonstrating the indirect connection between resource consumption and global

health. The solid arrows represent hard coded links between the sub-systems.

The dashed arrows show connections in which the human acts as a sub-system,

interpreting outputs and determining inputs to form the linkages.

Per Capita Population Subsystem Link to GDP Economic Subsystem Sub-Saharan Africa OECD India / China Sub-Saharan Africa Transportation Health Link Official Growth in to Population Behavior Development India & China Assistance Oil Subsystem Oil Use Global Health Subsystem Link to World GDP HIV/AIDS Epidemic OECD / non-OECD Sub-Saharan Africa

Hard coded link “Human as subsystem” link

Figure 1.1: Integrated Assessment Structure

The figure demonstrates the coded link between per capita output and

GDP, transportation growth in India and China and world oil consumption, oil consumption and growth in GDP, and global health and population. “Human as a sub-system” links are used to form a connection between population and global health and between economy and global health due to the quantifiably uncertain nature of this relationship; since the impact of population on global health involves the behavior and personal choices by individuals which directly affect

HIV/AIDS transmission and the effect of economy on Official Development

Assistance, relief aid contributed by the Organization for Economic Cooperation and Development (OECD), is unprecedented in terms of the ramifications of reaching peak oil production.

Global and regional analysis based on dominant relations is used to

assess the effect of policy alternatives and demonstrate possible future outcomes. Transparency of analysis and realistic scenarios incorporating the most current data and statistics available provide insight for making critical decisions that affect both present and future generations. The post-peak oil era is shown to have potentially devastating effects on health, especially in sub-

Saharan Africa, and on worldwide economic prosperity. The likely reduction and elimination of Official Development Assistance (ODA) from the OECD donor countries for relief aid programs acts as a catalyst that increases prevalence of the HIV/AIDS epidemic and threatens any possible hopes for future sustainability or improved living standards for the massive, indigenous sub-Saharan population. Specifically, the analysis will inspect global oil resource depletion and comprise regional economic and HIV/AIDS impacts for sub-Saharan Africa and the included sub region of Botswana.

1.1.1 Sustainable Development

There are more than one-hundred different definitions of sustainable development, but the most often referred to definition comes from the report Our

Common Future, also called the Brundtland Report:

"Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs."1

Another definition from the Sustainable Development Communications Network

states:

“Sustainable development focuses on improving the quality of life for all of the Earth's citizens without increasing the use of natural resources beyond the capacity of the environment to supply them indefinitely. It requires an understanding that inaction has consequences and that we must find innovative ways to change institutional structures and influence individual behaviour. It is about taking action, changing policy and practice at all levels, from the individual to the international.”2

Both definitions are used since together they relate the current and future needs of society to their quality of life, consider the use of natural resources and convey the understanding that both action and inaction have consequences which must be addressed at both the individual and international level.

1.2 Dissertation Contributions

The analysis answers several complex questions using the most recent available data including HIV/AIDS estimates and projections from the UNAIDS

2006 Global AIDS Report released May 2006 (GAR 2006), which represent a significant departure from prior reports with respect to the epidemic in sub-

Saharan Africa. Questions answered include:

1) When could the world reach peak oil production?

2) What are the economic implications for HIV/AIDS relief aid in the post-peak

oil era?

a. Can the post-peak oil era inflame the HIV/AIDS epidemic?

b. How can such detrimental effects of HIV/AIDS best be mitigated?

3) What is the potential humanitarian cost in lives and livelihood lost per barrel

of oil deficit?

In addition to answering these questions, other significant contributions of the dissertation include:

1) Application of developed reasoning and methodology to explore possible

futures and demonstrate the global-problematique regarding the

interdependence and connectedness between:

a. Peak Oil and Economy

b. Peak Oil, Economy and Official Development Assistance for

HIV/AIDS

c. HIV/AIDS, Population and Economy

d. Peak Oil, Economy, Population and HIV/AIDS

2) Long-term time horizon up to the year 2050. Projections of this type are

currently unavailable in the literature and are outside the recommended

time scope of currently offered HIV/AIDS modeling and assessment

packages3.

3) Construction of interactive systems which integrates the human and multi-

level hierarchical models with the most current, reliable data to generate

scenarios that can be used to guide policy decision making for:

a. Oil Recovery

b. Population

c. HIV/AIDS

d. Economy

The term global-problematique here refers to the Club of Rome’s Global Systems

Center’s meaning; “The exponential growth of human populations and the impact of human activities on the natural resource base threaten the capacity of the earth to sustain future generations.”4 This is precisely the issue to be addressed,

which shows the real humanitarian and economic toll that may be paid if these issues are not explicitly dealt with on a global scale.

1.2.1 Usefulness for Policy Makers

The scenarios and results presented are established in an intuitive and transparent manner which can be easily understood by both scientists and policy makers alike. Transparency of analysis is of paramount importance as the problems and issues must be truly understood in order to make informed, strategic decisions. This work is not prescriptive in terms of what policies should be used to achieve desired outcomes, rather it provides insight and reasoning support for what is possible under certain assumptions. Policy makers can use the results to determine appropriate methods and programs necessary to provide conditions for beneficial or desired outcomes. This type of analysis can be persuasive in terms of justification for long-term energy resource planning, social and health care programs, and HIV/AIDS prevention and treatment funding, among others.

1.3 Dissertation Organization

The dissertation is organized by first discussing the background and relationship between global oil production, economic relief aid, and the HIV/AIDS epidemic in Chapter 2. Methodology is explained and cybernetic systems incorporating the use of a multi-level hierarchical approach are developed in

Chapter 3. Chapter 4 presents oil recovery and oil transition scenarios to illustrate the importance of oil for economic expansion enjoyed by the relief aid donor countries that currently subsidize a majority of HIV/AIDS prevention and

treatment programs and how they will be affected by impending world peak oil production. Chapter 5 assesses the current and possible demographic impacts of the epidemic in sub-Saharan Africa and Botswana. This is followed by an economic analysis which assesses the related monetary costs of HIV/AIDS in

Chapter 6. Chapter 7 analyzes the requirements and benefits of enacting the globally supported UN Millennium Development Goals (MDGs) with respect to

HIV/AIDS. The interrelationship between oil consumption, economic growth,

ODA and the HIV/AIDS epidemic and the foreseeable humanitarian costs are explicated in Chapter 8. Conclusions and recommendations for future work are offered in Chapter 9.

Works Cited

1 World Commission on Environment and Development (WCED). Our Common Future (Oxford: Oxford University Press, 1987), 43.

2 Sustainable Development Communications Network. SDGateway, n.d. http://www.sdgateway.net/introsd/definitions.htm#1 (accessed: January 3, 2006).

3 Joint United Nations Programme on HIV/AIDS (UNAIDS) and World Health Organization (WHO). Estimating national adult prevalence of HIV-1 in Generalized Epidemics (Geneva: UNAIDS, March 9, 2005), 54. http://data.unaids.org/Topics/Epidemiology/Manuals/EPP_GeneralizedEpidemic_ 05_en.pdf (accessed: August 9, 2006).

4 The Club of Rome – Global System Centre. “The Global Problematique.” n.d. http://www.robbert.ca/gsc/problem.html (accessed: January 3, 2006).

Chapter 2: Background of Study

2.1 Introduction

Since the beginning of the industrial era, oil has played a vital role in the

robust economic growth experienced by the worlds more affluent countries.

Economic growth rates are shown in this analysis to demonstrate a strong

correlation with oil consumption growth rates. Even stronger correlation is

demonstrated to exist between the number of vehicle miles traveled and the

gross domestic product for countries such as the United States and the United

Kingdom1. The related prosperity enjoyed by some countries, such as OECD

members, has provided an atmosphere in which humanitarian relief funding for

countries in need is in large part donated in the form of Official Development

Assistance (ODA). The OECD Development Assistance Committee (DAC)

members’ total average yearly bilateral and multilateral aid from 2000 through

2002 is approximately 2.3 billion dollars per year, as shown by region and type of

aid in Figure 2.1.2 Among other humanitarian assistance programs, HIV/AIDS

prevention and intervention programs are greatly dependent on ODA contributions. Historically, economic growth in OECD countries has stopped and even declined under past oil supply disruptions such as the Arab oil embargo from October 1973 through March of 1974 or the outbreak of the Iran-Iraq War

from October through December of 1980.3 Fortunately, over the years, supply disruptions have always ceased and demand has again been satisfied. But, once peak oil production is reached, there will not be a cessation in the oil deficit due to a real physical supply shortage. This study shows that all major oil fields

have either already reached peak production or are currently at capacity. In

addition, discovery of new fields has been in decline for some years. Climbing

demand for a resource with declining supply will cut economic growth and force

countries which previously had enough capital to donate funds to focus on trying

to remain fiscally viable in a hostile global market. Thus, ODA funding could

decrease and even halt in the wake of a global energy crisis.

Figure 2.1: DAC members’ bilateral and multilateral aid to HIV/AIDS control, average commitments 2000-02, millions of USD [Source: OECD DAC, 2004] One region that will be hardest hit by the cut in ODA from the OECD DAC

members will be sub-Saharan Africa. The current plight suffered by the sub-

Saharan people will only worsen in the absence of global relief. Soaring health

costs, additional deaths, higher prevalence rates of HIV/AIDS, increases in other

diseases such as Tuberculosis, increased poverty, and a reduction in per-capita

incomes, are just some examples of the possible human toll that will be extracted

by the coming global economic slump of the post-peak oil era. The concentration

of this analysis examines effects on sustainability in the post-peak oil era within

sub-Saharan Africa and the included sub-region of Botswana, which is home to

one of the world’s most severely afflicted HIV/AIDS populations.

2.1.1 Chapter Organization

In Chapter 2, Section 2.2 reviews critical data and indicators affecting

world oil consumption. Section 2.2.1 examines ultimate recovery quantities and

the potential for new discoveries. Section 2.2.2 examines the question: “When

will production peak?”. The link between oil consumption and growth in GDP is

established in Section 2.2.3. Section 2.3 is an overview of the state of the

HIV/AIDS epidemic in sub-Saharan Africa and Botswana. The current status of

preventive interventions and treatment programs enabled by the OECD

countries’ DAC contributions to ODA is assessed in Section 2.4.

2.2 Transition to the Post-Peak Oil Era

The exhaustion of oil resources, in consideration of current usage, future

reserve growth, consumption rates, and other variable factors, is inevitable. This is simply due to the fact that it is a finite resource that takes millions of years to form. In order to understand the importance of this event, Figure 2.24 shows that

oil has been and would continue to be, if available, the largest sector satisfying

world energy demands. Thus, in light of historic and projected fuel type consumption, a global oil deficit will occur once its’ demand surpasses supply.

Figure 2.2: World Primary Energy Demand by Fuel [Source: IEA, WEO 2004]

The questions of interest are when and how quickly the oil will run out and what the possible consequences may be for global health, economy, and the future sustainability of current relief aid programs. Variable factors such as ultimate recovery, projected consumption, and peak production years will all influence the eventual depletion of this most essential commodity. Historical supply and

demand data are available from various sources and are fairly consistent and

reliable. Some dispute remains over the year of peak production but this is

linked to the main issue of debate which rests on ultimate recovery quantities.

Ultimate recovery is defined as the assumed or estimated total amount of oil

which can be extracted from the planet. Clearly higher ultimate recovery

amounts of oil will provide more time until depletion. But, this analysis will confirm by scenario analysis in Chapter 4 that even increasing ultimate recovery

by more than the largest known oil field ever discovered, would only delay the impending peak in production by less than a decade. Thus, the looming production peak is inevitable and global consequences of the post-peak oil era must be considered.

2.2.1 How Much Oil Is There?

Ultimate recovery is defined as the assumed or estimated total amount of oil which can be extracted from the planet5. Different ultimate recoverable amounts are based on a recovery factor of the original oil in place resource base.

The recovery factor refers to the amount of oil that can be produced or retrieved from the planet. The rest is considered unrecoverable. The total original oil in place resource base is approximately 6000 billion barrels. Figure 2.36 shows a very optimistic 50 percent recovery factor for the United States Geological

Survey (USGS) and a 30 percent recovery factor from experts Campbell and

Laherrere. It should be noted that the USGS estimate for “undiscovered” and

“Reserves Growth” is exceedingly high as will be discussed in the next section,

Section 2.2.2. However, the unavoidable truth is that either estimate will result in peak production during the first half of this century assuming a continuation of current global demand.

Figure 2.3: USGS vs. Campbell/Laherrere Ultimate Recovery Estimates [Source: DoE EIA] Since there are a vast number of different estimates as to the total amount

of oil that can be recovered, this analysis will use an average value of 1930

billion barrels (bbls.). This estimate is taken from 65 past estimates, shown in

Figure 2.47, by major oil companies, research institutions and the USGS among others. While there may be considerable debate over the exact amount recoverable, the scope of this work will use an average value for ultimate recoverable of oil and will analyze, through the use of scenario analysis in

Chapter 4, the effect of adding an additional recovery equal to the world’s largest

oil field ever discovered to date, the Ghawar field in Saudi Arabia.

Figure 2.4: Ultimate Recovery Estimates from 65 Different Organizations [Source: ASPO, 2004]

Figure 2.5: Giant Oil Field Discoveries per Decade 1850 – 2000 [Source: Uppsala Hydrocarbon Depletion Group, 2004]

Figure 2.58 shows historic data for oil field discovery and number of fields by decade from the year 1850 through 2000. Extrapolating the trend of addition

to world proven reserves from discovery of new fields and production, Figure 2.69

shows a rough estimate of probable new finds. Another way of viewing how

much more might be discovered is through the use of wildcat drilling data.

Wildcat boreholes are drilled to look for oil. They either find or do not find oil. A

recent Association for the Study of Peak Oil (ASPO) paper presented the data

graphically in Figure 2.710. Clearly the remaining discoveries, especially for large

fields which contain a majority of proven reserves to date, are diminishing.

State-of-the-art technology, advanced 3D imaging, seismic location

techniques and other methods of searching for oil have been used and improved

for decades and there is, relative to earlier decades, little return on this

investment, as the wildcat boreholes representation displays. Thus the amount of oil recoverable is not the correct question when considering transition to the post-peak oil era. The transition will undisputedly come to pass, sooner or later.

Thus, the question of interest is: “When will oil production peak?” The

answer to this question will provide a time frame within which policy and action

must occur in order to deal with the approaching oil deficit. The costs of delay or

inaction will be analyzed in Chapter 4.

Figure 2.6: Addition to World Proven Oil Reserves from the Discovery of New Fields and Production [Source: ASPO, 2004]

Figure 2.7: New Field Wildcats vs. Cumulative Added Volume [Source: ASPO, 2004]

2.2.2 When Will Oil Production Peak?

The peak year question is causing considerable debate among scientists,

corporations, and policy makers. The timing of this event is of primary concern for many reasons. This is due to the fact that once peak oil is reached; the world will be faced with an energy deficit which, in the face of increasing demand, will have to be satisfied by some alternative source which has not yet been realized or made available in commercially viable quantities. In addition to the energy deficit faced in the post-peak oil era, Section 2.2.3 reviews the closely tied relationship between the rate of oil consumption in a country or region and economic growth in GDP.

The era after peak production could be a time of extreme oil crisis due to deficits in supply. The years after peak production will supply less oil each year thereafter. This peaking in production has to do with the physical extraction of a finite resource from the earth and the approximate bell-shape of the production versus time curve. The exhaustion of a finite resource concept for oil was first properly applied to U.S. oil production in the 1950’s by Geologist M. King

Hubbert. Hubbert claimed, much to the protest of the majority of experts, that

U.S. oil production would peak in the early 1970’s, which it did. Hubbert’s

“natural” rate of extraction defines that the cumulative depletion of a finite resource follows the logistic curve, and the annual extraction (first derivative of the logistic curve) follows the bell shaped curve shown in Figure 2.811.

The Hubbert curve is a derivative of the logistic curve which was introduced in 1845 by the Belgian mathematician Verhulst as a law of population growth. It is based on the following relation : CP = U/(1+EXP(-b(t-tm)) CP is Cumulative Production; U is an asymptote representing Ultimate Recovery; tm is the inflexion point, namely peak time of annual production

logistic curve and its derivative (Hubbert)

80 U cumulative=logistic 70 CP=U/(1/+EXP(-b(t-tm)))

60 annual=Hubbert x10 U=80 P=2Pm/(1+COSH(b(t-tm))) 50 b=0.15 c=5/b=33.3 40 tm=50 Pm=Ub/4=3 30 Pm

10 tm 0 0 102030405060708090100 c year

The equation of the Hubbert curve for annual production P (being ∆ CP/∆ t) is simple when related to peak annual production Pm occurring in year tm

P = 2Pm/(1+COSH(-b(t-tm)))

The constant b is equal to 4Pm/U and also 5/c where c is the half width of the curve on the time axis when production started and has fallen to a very low level (Pm/100 as LN(100)

Figure 2.8: Hubbert Curve for Annual Production [Source: Laherrere, 1998]

In order to gauge when peak oil will occur, several factors must be taken into account simultaneously. The factors for oil include: ultimate recovery, global demand, supply capacity, price per barrel, and the state of world trade, political policies and affairs. Any one of these factors can influence and change the

timing of peak oil production. As such is the case, this study uses reliable

scientific estimates for maximum production quantities, current real data, and

expert opinion to determine the peak year. Since the timing of this event is

sensitive to several factors, scenario analysis in Chapter 4 considers a range of

peak years that effectively envelopes the problem within a minimum and

maximum time frame.

In order to establish the peak year, it is illustrative to look at the aggregate

of oil and condensate production curves for all global suppliers. Figure 2.912 illustrates the amount of resource and indicates a peak year in approximately

2008. Clearly all production has peaked by 2010 which then ushers in the post- peak oil era. This figure is widely accepted by many reputable research and science foundations including the ASPO and the United States Army Corp of

Engineers. The US Army Corp of Engineers in September of 2005 released

Energy Trends and Their Implications for U.S. Army Installations, in which they assert that the peak in world oil production is estimated to occur between 2005 and 202013. The Army Corp report went on to cite several expert opinions in the following excerpt:

“Petroleum experts Colin Campbell, Jean Laherrere, Brian Fleay, Roger Blanchard, Richard Duncan, Walter Youngquist, and Albert Bartlett (using various methodologies) have all estimated that a peak in conventional oil production will occur around 2005. The corporate executive officers (CEOs) of Agip, ENI SpA (Italian oil companies), and Arco have also published estimates of a peak in 2005. These reliable estimates all project that conventional oil peak production will occur within the next few years (Campbell and Laherrere 1998; Youngquist 1997; Campbell 2004). Reduced demands caused by high prices may delay the peak slightly, but the peak is certainly within sight. Note that the peaking of conventional oil should not be confused with total oil production. Total oil production includes such commodities as natural gas liquids, deep water oil, and

polar oil. Inclusion of these will delay the peak to 2008 (Aleklett 2004). Estimates of peak production are not without controversy.”14

Figure 2.9: Oil and Gas Liquids Global Production Aggregate [Source: Campbell, 2004] In contrast to the preceding estimates for year of peak oil production, the

United States Department of Energy’s (DoE) Energy Information Administration

(EIA) and the International Energy Agency (IEA) claim that the peak will not occur before 2030. In the IEA’s World Energy Outlook 2004 (WEO2004) the summary states that, “Production of conventional oil will not peak before 2030 if the necessary investments are made”15. It turns out that the DoE’s EIA and the

IEA have premised their results on the United States Geological Survey (USGS)

2000 mean estimate of ultimate recoverable of 2628 bbls, displayed in Figure

2.10. But, the IEA’s WEO2004 makes explicit the point that peak year production

could occur by 2015 or before if their ultimately recoverable estimate is too high.

Figure 2.10: USGS 2000 Ultimately Recoverable Oil and NGL Resources [Source: USGS, 2000] With regard to the USGS estimate for ultimate recovery, it seems quite

unlikely that it will be as large as indicated for several reasons. The reasons

include: trend in past discoveries and projections for future discoveries, decline in production of existing major fields, and production limitations of Saudi Aramco,

the supplier of Saudi Arabian oil.

Prospects for significantly large new discoveries of oil are dwindling, as

was shown in Figures 2.5, 2.6, and 2.7. Further evidence of the USGS’s

overestimate for ultimate recovery, shown in Figure 2.1116, was presented in

November of 2005 at an ASPO conference in Denver, Colorado. Oil expert

“Jean Laherrere made an assessment of the USGS report and concludes that:

The USGS estimate implies a five-fold increase in discovery rate and reserve addition, for which no evidence is presented. Such an improvement in performance is in fact utterly implausible, given the great technological achievements of the industry over the past twenty years, the worldwide search, and the deliberate effort to find the largest remaining prospects.”17

“Low hanging Fruit” Trend

Figure 2.11: USGS Ultimately Recoverable Oil vs. Trend in Discovery [Source: Campbell, 2005] The next consideration indicating the approaching peak is the state of

maturity of current major oil fields. Production decline is being experienced by

almost all major oil fields including the Ghawar field in Saudi Arabia which

reached peak production in 198118. Matthew Simmons author of “Twilight in the

Desert: The Coming Saudi Oil Shock and the World Economy” states in his book

that many believe that once Saudi Arabia peaks, the world peaks19.

The Ghawar field, considered the “King of Kings” among oil fields, was

discovered in 1951, and produced “55 billion barrels of high-quality Arab Light

crude.”20 This largest producer of Saudi Oil is represented to have pumped 48 percent of reserves by Saudi Aramco in Figure 2.1221. It should also be noted

that production becomes increasingly difficult and costly as the oil is depleted.

Figure 2.12: Saudi Aramco Fields and Percentage of Reserves Produced [Source: Saudi Aramco, 2004] The world’s second largest oil field, the Cantarell oil field in Mexico, is now in decline. According to a March 2006 report:

“Cantarell is second only to Saudi Arabia's Ghawar oilfield and has been pumping millions of barrels of light crude a day since 1976. According to Carlos Morales, production manager for Mexico's state owned oil company, Pemex, Cantarell's projected output will be 6 percent lower this year at 1.9 million barrels per day and down to 1.43 million barrels by 2008, the level of production in 2000…The Cantarell field accounts for 60 percent of Mexico's total production. To make up for the anticipated decline of 500,000 bpd will be difficult to achieve and definitely more expensive if even possible. Mexico is the second-largest supplier of oil to the U.S. market. The decline will intensify America's dependence on Middle East oil.”22

Thus not only is supply in decline, but the bulk of remaining resources are concentrated in the chaotic political climate of the Middle East, as shown in

Figure 2.1323.

Figure 2.13: Proven Oil Reserves by Region per BP Statistical Review [Source: BP, 2004] Further evidence of the mature state of world oil fields is the recent peak

in some Kuwaiti oil production. The Kuwait Oil Company in January of 2006

released the news that their super giant Burgan oil field has peaked.24 In

addition to this news, Figure 2.1425 shows actual year of peak production and remaining regions set to peak for the world minus the Organization for Petroleum

Exporting Countries (OPEC) and the Former Soviet Union (FSU).

Figure 2.14: Global (minus ME & FSU) Peak Year Production by Region [Source: ASPO, 2004] The FSU is estimated by the ASPO to follow the discovery and production

pattern displayed in Figure 2.15.26 Thus even though the FSU discovered the largest field found within the last 10 years, a 10 Gb oil field in Kazakhstan in

2000, this quantity of oil could only satisfy world demand for four months, since

current yearly global demand is 30 Gb.27 Therefore, the FSU cannot be

expected to become the next major global supplier of oil. This leaves OPEC as

the only real source for increasing production to meet the increasing oil demands

of the United States, China, India, Europe, and the rest of the World.

Figure 2.15: Discovery and Production for FSU 1930-2050 [Source: ASPO, 2004] Therefore the question of import becomes:

“Can the Middle East turn up production to satisfy global demand?”

Unfortunately, according to the Saudis’ themselves, they can not. Global

demand for oil is expected to increase anywhere between 2 and 4 percent yearly

depending on several variable factors including: price, global economic growth, and an increase in Chinese and Indian demand to name a few, see Figure

2.1628.

World Oil Demand MBD: million barrels per day By Region By Sector MBD MBD 140 140 The “China” Factor 120 120 Power ME/AF 100 100 LA 80 80 Emerging Asia 60 60 Transportation Japan/Aus/NZ Russia/Caspian 40 40 Europe

20 20 IndustrialIndustrial North America Res/Comm 0 0 80 90 00 10 20 30 80 90 00 10 20 30

Figure 2.16: World Oil Demand by Region and Sector 1980-2030 [Source: Exxon Mobile, 2004] World oil demand ranged from 28.1 bbl per year in 2000 up to 30.1 bbl per year by the end of 2004.29 The EIA states in their International Energy Outlook

2004 (IEO2004) report:

“Total world oil consumption is expected to increase by 1.9 percent per year over the projection period, from 77 million barrels per day in 2001 to nearly 121 million barrels per day in 2025. The transportation sector is the largest component of worldwide oil use today, and it is expected to account for an increasing share of total oil consumption in the future.”30

The IEA in their WEO2004 report suggests the increased demand will be substantially satisfied by OPEC according to production quantities displayed in

Figure 2.1731. The main problem with this expectation is that the majority of new

capacity is supposed to come from Saudi Arabia which already declared a maximum sustainable supply capacity which is far below the 22.5 million barrels per day.

Figure 2.17: IEA Estimates for 2001 & 2025 OPEC Production Required [Source: DoE EIA, 2004] In fact, representatives of Saudi Aramco gave a presentation in Washington D.C.

in which they specified that they could produce a maximum sustainable supply of

12 million barrels per day from 2016 until 2033, as shown in Figure 2.18.32 Thus, since Saudi Arabia currently supplies approximately 10 million barrels per day and their maximum sustainable supply capacity is 12 million barrels per day, they can only turn up production 2 million barrels per day. This amount of production increase will clearly not meet the growing global demand.

Figure 2.18: Saudi Aramco Maximum Sustainable Capacity 2000-2050 [Source: Saudi Aramco, 2004] Somewhat surprising, but perhaps inevitable, is the acknowledgement on

the part of some oil companies that massive new discoveries or growth in

reserves will be required to supplement declining production. Figure 2.1933 comes from an Exxon shareholders publication, The Lamp. Since discoveries and reserve growth of this magnitude are implausible, the message is really a wake-up call to the fact that peak-production is only a heartbeat away.

Figure 2.19: Global Decline in Oil Supply and Required New Production [Source: Exxon, 2003] 2.2.3 The Link between Oil Consumption and Economic Growth

One undeniable fact regarding oil is that the relatively cheap and reliable energy it has provided has enabled strong economic growth throughout the developed world, growth which has been seen to increase as usage rates increase. Thus historically, there exists a strong link between a country’s oil consumption and its’ growth in GDP. While the exact relationship may not be easily determined, the dominant properties of this relation are shown in Figure

2.2034 35 36. This historical data illustrates the effects of notable past oil supply crises on GDP in OECD countries. The time frame from 1961-2002 will be used.

This time frame captures both the normal trend which will be defined as being

during times when supply equals demand and impacts of supply disruptions as

experienced in the 1973 Arab Oil Embargo, the Iranian revolution in 78-79, or the

outbreak of the Iran-Iraq War at the end of 1980.

Relationship Between OECD Oil Consumption and GDP Growth 1961-2002

12.00%

10.00%

8.00%

6.00%

4.00%

2.00% Growth

0.00%

3 7 965 987 1981 1983 1985 1 1989 1991 1993 1995 1997 1999 2001 -2.00%1961 1963 1 1967 1969 1971 19 1975 1977 1979

-4.00%

-6.00%

-8.00% Year

Oil Consumption GDP

Figure 2.20: OECD Oil Consumption vs. GDP Growth 1961-2002 [Data Source: World Bank/EIA, 2006] For time periods when supply is equal to demand (i.e. no significant supply disruptions) the GDP growth rate roughly follows the oil consumption growth rate. Dates, duration, supply shortfall, and reason for significant oil supply disruptions since 1951 are listed in Figure 2.2137. Periods of oil deficit

from supply disruptions have resulted in approximately a 1 percent loss in GDP growth per 2 percent loss in oil supply growth. From the Arab Oil Embargo, GDP growth dropped from just over 6 percent in 1973 to ½ percent by 1976 and oil

consumption growth dropped from 7 percent to -3 percent in the same period.

This yields a ratio of 5.5 to 10 for loss in growth of GDP to oil consumption. The

ratio for the Iranian revolution and Iran-Iraq War covering the period from 1977 to

1983 is 4.5 to 11.5 for loss in growth of GDP to oil consumption. Loss in GDP

here was probably slightly less than it may have been due to nuclear power

coming online during the same years. Clearly the relation is not exact but it does

illustrate the property that growth in oil consumption has historically promoted

economic growth while deficit in oil supplies results in a corresponding loss in

GDP. This is the dominant relation of concern in the face of physical supply

shortages.

Duration (Months Average Gross Date of Oil Supply Reason for Oil Supply of Supply Supply Shortfall Disruption Disruption Disruption*) (Million B/D) Iranian oil fields nationalized May 3/51-10/54 44 0.7 1, following months of unrest and strikes in Abadan area. 11/56-3/57 4 2.0 Suez War 12/66-3/67 3 0.7 Syrian Transit Fee Dispute 6/67-8/67 2 2.0 Six Day War Libyan price controversy; damage 5/70-1/71 9 1.3 to Tapline Algerian-French nationalization 4/71-8/71 5 0.6 struggle Unrest in Lebanon; damage to 3/73-5/73 2 0.5 transit facilities October Arab-Israeli War; Arab 10/73-3/74 6 2.6 oil embargo Civil war in Lebanon; disruption 4/76-5/76 2 0.3 to Iraqi exports 5/77 1 0.7 Damage to Saudi oil field 11/78-4/79 6 3.5 Iranian revolution 10/80-12/80 3 3.3 Outbreak of Iran-Iraq War 12/02-2/03** 3 2.1 Venezuela strikes and unrest. 3/03-8/03 6 0.3 Nigeria unrest. 3/03-9/04*** 19 1.0 Iraq war and continued unrest.

*Note: "Supply disruption" generally refers to a loss of oil from a particular country or group of countries relative to a preceding month or months. The full extent and impact of a disruption or loss depends on a variety of factors, including: a) replacement production from other, unaffected, countries; b) the level of oil inventories; and c) level and growth rate of demand. Definitions of "oil supply disruptions" are not entirely consistent from one case to the next, in part due to differing views of such events over time and amongst analysts.

**Venezuelan total oil production fell from 3.3 million barrels per day in November 2002 to under 700,000 barrels per day in January 2003, increased to 2.6 million barrels per day in March 2003, and has now stabilized at around 2.8 million barrels per day. Although Venezuelan output has not returned to pre-strike levels, for purposes of this table the "disruption" period is defined as the period between December 2002 and February 2003, when the crisis was at its peak.

***As of September 2004, Iraqi oil output has not yet recovered to pre-war levels (2.5 million barrels per day in February 2003). In April 2004, Iraqi production reached 2.3 million barrels per day, but since then has not exceeded 2.0 million barrels per day in any month through August 2004. Due to the continued instability in Iraq, the "disruption" is considered as continuing, although certainly the peak of the losses from Iraq were experienced during the spring and summer of 2003. From April 2003 through August 2003, the oil supply disruption from Iraq averaged about 2 million barrels per day. In contrast, since the beginning of 2004, the oil supply disruption from Iraq has averaged around 0.5 million barrels per day.

Figure 2.21: Global Oil Supply Disruptions 1951-2004 [Data Source: EIA, 2005]

An even more powerful demonstration of economic dependence on oil is the relationship between GDP and vehicle-miles traveled (VMT), Figure 2.2238.

This phenomenon is extremely important as sectors such as transportation are almost wholly dependent on oil powered vehicles. For example, the transportation sector in the United States is 97 percent dependent on oil39. A

2004 OPEC publication notes the critical importance of oil for transportation:

“Road and air transportation is by far the most important sector for oil demand, at 34.5 million barrels of oil equivalent per day, accounting for 47 per cent of world oil demand in 2001. This dominance is particularly marked in OECD countries, where the share averages almost 55 per cent, while, in non-OECD, it lies below 40 per cent.”40

The pattern of correlation shown in Figure 2.22 clearly shows striking similarity between GDP and VMT including during the years of oil supply

Figure 2.22: GDP vs. Vehicle-Miles of Travel 1960-2000 [Source: US Department of Transportation, 2002]

disruptions in 1973 and the late 1970’s and early 1980’s. This fact is of particular concern since there is currently not a practicable alternative replacement for oil in the transportation sector.

While there are currently many novel ideas on exactly what the future energy of transportation might be, none are available on a large enough scale that could quickly, cost-effectively replace the existing fleet of vehicles in use today. Figure 2.2341 shows the types of transportation, number of vehicles,

median life, and cost to replace the fleet for the United States. The magnitude of the problem is further compounded by the fact that new infrastructures take years

to implement and mature. Figure 2.24 shows the percentage of growth and length of time in years for penetration of major transportation infrastructures in

the United States. The time constants involved in all of these implementations

range from thirty to seventy years. The length of time to achieve infrastructure

implementation could be a major impediment to a quick fix for the transportation

crisis. Even if a hydrogen fuel cell vehicle, or other alternative fuel transportation,

was available, the infrastructure needed to support the fueling, maintenance, and

service is years away.

Most importantly, a severe economic downturn for the prosperous

countries of the OECD DAC could, in the case of sustained economic losses,

very well spell the end of relief aid for those countries in desperate need.

Programs, health care, preventions and interventions aimed at combating HIV

and AIDS among other preventable diseases and social conditions will fail and

eventually disappear as funding is diminished. Globally, severe health

consequences will emerge. Sub-Saharan Africa is one region which could become an even worse humanitarian disaster without proper action and strategic planning.

Median Life Cost to replace Fleet Size (years) the fleet (2003 $)

Automobiles 130 million 17 $2.6 trillion

Light trucks, 80 million 16 $2 trillion SUVs, etc.

Heavy trucks, 7 million 28 $3 trillion buses

Aircraft 8,500 22 $0.5 trillion

Figure 2.23: U.S. Transportation Fleet Replacement Cost [Data Source: Bezdek, 2005]

Figure 2.24: Penetration of Major U.S. Transportation Infrastructure [Source: Marchetti, 1985; Ausubel, 1996]

2.3 The Global HIV/AIDS Epidemic Today

There is little doubt that the progression of the HIV/AIDS virus throughout the world today has changed the face of human history. According to the Joint

United Nations Programme on HIV/AIDS (UNAIDS) 2004 Report on the Global

AIDS Epidemic, “In 2003, an estimated 4.8 million people became newly infected with HIV. This is more than in any one year before. Today, some 37.8 million people are living with HIV, which killed 2.9 million in 2003, and over 20 million since the first cases of AIDS were identified in 1981.”42 The disease has associated costs far beyond the individual’s loss of life. Socio-economic ramifications include the loss of premium age workers, millions and millions of orphans, breakdown of family structure and related decline in education.

Since 1981, HIV/AIDS has been pandemic in nature and has spared virtually no one group. The global distribution of the virus is illustrated pictorially in Figure 2.25.43 Worldwide, statistics for the disease by region are shown in

Figure 2.26.44 Since 2003, lower revisions of prevalence rates by UNAIDS and the World Health Organization (WHO) indicate that the epidemic may have stabilized somewhat, but is still the 4th leading cause of death worldwide and thus merits further research and prevention efforts.45

Western & Eastern Europe Central Europe & Central Asia North America 720 000 1.5 million 1.3 million [550 000 – 950 000] [1.0 – 2.3 million] East Asia [770 000 – 2.1 million] 680 000 Caribbean North Africa & Middle [420 000 – 1.1 million] East 330 000 South & South-East [240 000 – 420 000] 440 000 [250 000 – 720 000] Asia Sub-Saharan Africa 7.6 million Latin America 24.5 million [5.1 – 11.7Oceania million] 1.6 million [21.6 – 27.4 million] [1.2 – 2.4 million] 78 000 [48 000 – 170 000]

Total: 38.6 (33.4 – 46.0) million

Figure 2.25: Adults and Children Living with HIV as of end 2005 [Source: UNAIDS, 2006] The socio-economic cost of HIV/AIDS is tremendous. The disease disproportionately affects people in the 15-49 year old age group. This is the age range for peak economic productivity and family responsibility provisions.

According to Avert.org,

“Around half of the people who acquire HIV become infected before they turn 25 and typically die of the life-threatening illnesses called AIDS before their 35th birthday. This age factor makes AIDS uniquely threatening to children. The associated orphan problem has become staggering in nature. By the end of 2003, the epidemic had left behind 15 million AIDS orphans, defined as those having lost one or both parents to AIDS before reaching the age of 18. These orphans are vulnerable to poverty, exploitation and themselves becoming infected with HIV. They are often forced to leave the education system and find work, and sometimes to care for younger siblings or head a family.”46

In addition to the orphan problem, the effects of the disease on the family are numerous. Loss of income and medical expenses are incurred by the ailing family member. Often other family members must act as caretakers and also lose income or, in the case of child caretakers, miss out on an education. The

toll from the grief associated with their losses cannot be quantified and often results in depression and feelings of hopelessness.

Adults & Adults & Adult Deaths of Children Children Region Infection Adults & Living with Newly Rate (%) Children* HIV/AIDS* Infected Sub-Saharan Africa 24.5 2.7 6.1 2.0 North Africa & 0.44 .064 0.2 0.037 Middle East Asia 8.3 .93 0.4 0.6 Oceania 0.078 .0072 0.3 0.0034 Latin America 1.6 .14 0.5 0.059 Caribbean 0.33 0.037 1.6 0.027 Eastern Europe & 1.5 0.22 0.8 0.053 Central Asia North America, Western & Central 2.0 0.065 0.5 0.03 Europe Global Total 38.6 3.9 1.0 2.8

* millions

Figure 2.26: Regional Statistics for Global HIV & AIDS as of end 2005 [Data Source: UNAIDS, 2006] 2.3.1 The Problem in Sub-Saharan Africa

The large area in Africa below the Saharan Desert is known as sub-

Saharan Africa, shown in Figure 2.27. It consists of 42 mainland regions, one of which is Botswana, where about 1 in 4 adults aged 15-49 have the HIV/AIDS virus. Sub-Saharan Africa is home to the largest percentage of HIV/AIDS cases in the world. According to the UNAIDS 2004 report:

“Sub-Saharan Africa has just over 10% of the world’s population, but is home to close to two-thirds of all people living with HIV—some 25 million (range:

23.1–27.9 million). In 2003 alone, an estimated 3 million people (range: 2.6– 3.7 million) in the region became newly infected, while 2.2 million (range: 2.0– 2.5 million) died of AIDS. Among young people 15–24 years of age, 6.9% of women (range: 6.3–8.3%) and 2.1% of men (range: 1.9–2.5%) were living with HIV by the end of 2003.”47

Source: Maps.com 2003 Figure 2.27: Map of Sub-Saharan Africa [Source: Maps.com, 2003]

The latest UNAIDS report, GAR 2006, has been updated with better data

and information collected from population-based surveys which indicates a

slightly lower prevalence for sub-Saharan Africa than previously estimated. The

prevalence for adults aged 15-49 has been revised down from 7.5 to 6.1 percent, as of the release of the GAR 2006 at the end of May 2006. As shown in Figure

2.28, prevalence estimates from antenatal clinics, data, and assumptions used in combination with the UNAIDS and Futures Group modeling packages produced estimates which now seem to be, in some cases such as Botswana, overstated by as much as 50 percent. This fact does not indicate an error in their models or analyses; it simply demonstrates the uncertainties in modeling that exist due to inadequate data or subtle differences in estimated versus actual parameters.

Median HIV 2003 HIV prevalence(%) prevalence(%) Adjusted 2003 Population- 2005 HIV among women reported in HIV based survey prevalence(%) Trend in attending 2004 Report prevalence(%) Country prevalence(%) in current prevalence antenatal on the global in current (year) report clinics 2003- AIDS report 2004* epidemic Botswana 38.5 25.2 (2004) 38.0 24.0 24.1 Stable Burkina Decline in 2.5 1.8 (2003) 4.2 2.1 2.0 Faso urban areas Decline in Burundi 4.8 3.6 (2002) 6.0 3.3 3.3 capital city Cameroon 7.3† 5.5 (2004) 7.0 5.5 5.4 Stable Decline in Ethiopia 8.5 1.6 (2005) 4.4 (1.0–3.5) (0.9–3.5) urban areas Ghana 3.1 2.2 (2003) 3.1 2.3 2.3 Stable Guinea 4.2 1.5 (2005) 2.8 1.6 1.5 Stable Lesotho 28.4 23.5 (2004) 29.3 23.7 23.2 Stable Decline in Rwanda 4.6 3.0 (2005) 5.1 3.8 3.1 urban areas Senegal 1.9 0.7 (2005) 0.8 0.9 0.9 Stable Sierra 3.0 1.5 (2005) – 1.6 1.6 Stable Leone South 29.5 16.2 (2005) 20.9 18.6 18.8 Increasing Africa United Republic of 7.0 7.0 (2004) 9.0 6.6 6.5 Stable Tanzania Uganda 6.2‡ 7.1 (2004–5) 4.1 6.8 6.7 Stable *WHO Africa (2005). HIV/AIDS epidemiological surveillance report for the WHO African region, 2005 Update. Harare. †Estimate based on country report for 2002 (2003). Ministry of Public Health Cameroon. National HIV sentinel surveillance report 2002. ‡Estimate based on country report 2002 (2003). Ministry of Health Uganda. STD/HIV/AIDS surveillance report. STD/AIDS control programme. Kampala. Figure 2.28: Sub-Saharan African Adults Aged 15-49 HIV Prevalence % from Recent Population-based Surveys [Source: UNAIDS, 2006] The historical progression of prevalence rates in the area is shown in

Figure 2.29. Prevalence in sub-Saharan Africa as a region is substantially less than in some of its’ most severely afflicted sub regions, such as Botswana where

prevalence is more than 24 percent, but the many regions that comprise sub-

Saharan Africa demonstrate a great deal of variability, as shown for year 2005, in

Figure 2.30.48

Figure 2.29: HIV/AIDS Infections and Prevalence in Sub-Saharan Africa [Source: UNAIDS, 2006] According to these estimates, the total number of deaths in Sub-Saharan

Africa due to AIDS was 2.0 million and the number of orphans was 12 million, or

just over 80% of the total world AIDS orphan population. Figure 2.30 illustrates

the multitude of regional epidemics that are taking place within the bigger

framework of sub-Saharan Africa. So even in light of the fact that some

stabilization of prevalence has occurred, UNAIDS maintains that:

“Stabilized infection levels in an epidemic often result from rising death rates from AIDS, which conceal a continuing high rate of new infections. Even when HIV prevalence falls, as in Uganda, the number of new infections can remain high. Therefore careful planning and policy

measures must be enacted to try and curb the deadly consequences of this pandemic.”49

Adult People AIDS Country 15-49 Women Children AIDS deaths with HIV Orphans Prev % Angola 320,000 3.7 170,000 35,000 30,000 160,000 Benin 87,000 1.8 45,000 9,800 9,600 62,000 Botswana 270,000 24.1 140,000 14,000 18,000 120,000 Burkina Faso 150,000 2 80,000 17,000 12,000 120,000 Burundi 150,000 3.3 79,000 20,000 13,000 120,000 Cameroon 510,000 5.4 290,000 43,000 46,000 240,000 Central African Republic 250,000 10.7 130,000 24,000 24,000 140,000 Chad 180,000 3.5 90,000 16,000 11,000 57,000 Comoros <500 <0.1 <100 <100 <100 - Congo 120,000 5.3 61,000 15,000 11,000 110,000 Côte d'Ivoire 750,000 7.1 400,000 74,000 65,000 450,000 Dem. Republic of Congo 1,000,000 3.2 520,000 120,000 90,000 680,000 Djibouti 15,000 3.1 8,400 1,200 1,200 5,700 Equatorial Guinea 8,900 3.2 4,700 <1,000 <1,000 4,600 Eritrea 59,000 2.4 31,000 6,600 5,600 36,000 Ethiopia 0.42-1.3mil 0.9-3.5 0.19-0.73mil 0.03-0.22 0.038-0.13mil 0.28-0.87mil Gabon 60,000 7.9 33,000 3,900 4,700 20,000 Gambia 20,000 2.4 11,000 1,200 1,300 3,800 Ghana 320,000 2.3 180,000 25,000 29,000 170,000 Guinea 85,000 1.5 53,000 7,000 7,100 28,000 Guinea-Bissau 32,000 3.8 17,000 3,200 2,700 11,000 Kenya 1,300,000 6.1 740,000 150,000 140,000 1,100,000 Lesotho 270,000 23.2 150,000 18,000 23,000 97,000 Liberia* - 2.0-5.0 - - - - Madagascar 49,000 0.5 13,000 1,600 2,900 13,000 Malawi 940,000 14.1 500,000 91,000 78,000 550,000 Mali 130,000 1.7 66,000 16,000 11,000 94,000 Mauritania 12,000 0.7 6,300 1,100 <1,000 6,900 Mauritius 4,100 0.6 <1,000 - <100 - Mozambique 1,800,000 16.1 960,000 140,000 140,000 510,000 Namibia 230,000 19.6 130,000 17,000 17,000 85,000 Niger 79,000 1.1 42,000 8,900 7,600 46,000 Nigeria 2,900,000 3.9 1,600,000 240,000 220,000 930,000 Rwanda 190,000 3.1 91,000 27,000 21,000 210,000 Senegal 61,000 0.9 33,000 5,000 5,200 25,000 Sierra Leone 48,000 1.6 26,000 5,200 4,600 31,000 Somalia 44,000 0.9 23,000 4,500 4,100 23,000 South Africa 5,500,000 18.8 3,100,000 240,000 320,000 1,200,000 Swaziland 220,000 33.4 120,000 15,000 16,000 63,000 Togo 110,000 3.2 61,000 9,700 9,100 88,000 Uganda 1,000,000 6.7 520,000 110,000 91,000 1,000,000 United Rep. of Tanzania 1,400,000 6.5 710,000 110,000 140,000 1,100,000 Zambia 1,100,000 17 570,000 130,000 98,000 710,000

Adult People AIDS Country 15-49 Women Children AIDS deaths with HIV Orphans Prev % Zimbabwe 1,700,000 20.1 890,000 160,000 180,000 1,100,000 Total sub-Saharan Africa 24,500,000 6.1 13,200,000 2,000,000 2,000,000 12,000,000 Figure 2.30: Regional Statistics for Sub-Saharan Africa HIV/AIDS end 2005 [Source: UNAIDS 2006] 2.3.2 The Problem in Botswana

The current status of the disease in Botswana is devastating. “In an

address to the UN General Assembly in 2001, the President of Botswana, Festus

Mogae, said 'we are threatened with extinction. People are dying in chillingly high

numbers. It is a crisis of the first magnitude.'”50 Botswana is one of the most

severely afflicted countries with HIV/AIDS. Mortality has sky-rocketed and HIV

prevalence rates are estimated to be 24.1% according to UNAIDS/WHO. Life

expectancies have plummeted as a result. Life expectancy is only 39 years,

while it would have been 72, if it were not for AIDS.51 The number of AIDS deaths for 2005 alone in Botswana is estimated at 18,000 and the number of orphans is estimated at 120,000.52 Current trends indicate prevalence rate

stabilization, but this should not be construed as an indication that the disease is in check.

2.3.3 Prevention Interventions

“There is now substantial if incomplete agreement on a set of prevention measures that can stem the spread of HIV infection when carried out as part of a comprehensive plan backed by committed leadership.”53-54 “Some national

programs based on these approaches have achieved considerable success, and

it has been estimated that making these basic prevention measures available by

2005 would prevent 29 million new infections by 2010.”55 The set of accepted

prevention methods include:

• Education and communication campaigns conveying basic facts about

HIV/AIDS and its transmission, promoting behavior change, and

combating harmful myths and stigma.

• Programs focused on vulnerable groups.

• Access to the technical means of prevention: male and female condoms;

sterile needles and syringes.

• Voluntary testing and counseling

• Control of sexually transmitted infections.

• Prevention of mother-to-child transmission

• Precautions to prevent transmission in healthcare settings56

The preventions modeled in this analysis include: education and

information campaigns, condoms and other technical means, increased

detection, and prevention of mother-to-child transmission.

2.3.4 Current State of Prevention Interventions

One focus of current prevention interventions is prevalence among young people 15-24 years of age. In Uganda, recent studies have shown a substantial

reduction in prevalence among this group, attributed to delaying the start of

sexual activity, reduction in the number of partners, and increased condom

usage.57 One program of notable success in South Africa is the highly

advertised, youth prevention program, LoveLife. “LoveLife combines

sophisticated media campaigns aimed at influencing sexual behavior and

promoting “healthy life styles” with adolescent-friendly health services and

outreach and support programs.”58 Recent statistics indicate a positive response to LoveLife with 85% of 15-24 year olds having heard of LoveLife by 2003, and more than a third having participated in one of its programs.59

Another current method of prevention is the use of short-course

antiretroviral drug therapy to prevent mother-to-child transmission. A recent

clinical trial in the developing world showed “it is possible to decrease

transmission by 50 percent with a single shot of the antiretroviral drug Nevirapine

administered to the mother at the onset of labor and another given to the infant

within the first three days of life.”60 Currently in Africa, less than 4% of pregnant

women in need of services for preventing mother-to-child transmission receive

it.61

Overall, HIV/AIDS prevention services reach a small proportion of the population. Figure 2.3162 illustrates percentage of populations in need in

developing countries that are actually reached.

Figure 2.31: Percent of Those in Need of Prevention Interventions Reached [Source: UN Millennium Project, 2004] Another element of critical importance to prevention is accessibility and

knowledge of prevention methods. Data indicate that access to prevention

services in Africa is shockingly low. “Only 8 percent of out-of-school youth in

Africa have access to prevention programs.”63 Projected coverage for condom

distribution is also very small. The Policy Project estimates coverage at just 19%

in Africa.64

2.3.5 Treatment Interventions

One key element for receiving treatment for HIV/AIDS is knowledge of having the infection by the infected individual. Even in some of the most severely

afflicted countries, detection statistics indicating awareness of being infected are

disturbingly low. “In Botswana, for instance, it was estimated in 2003 that of

perhaps 270,000 (adjusted to GAR 2006 report for 2003 data) HIV-positive

people, only about 8 percent knew that they were infected.”65 Three primary

reasons for this phenomenon are:

1. Inability to afford services. Motivation to be tested is low if treatment is

unavailable.

2. Lack of qualified personnel to detect and subsequently perform pre and

post screening counseling.

3. Fear of social stigmatization and subsequent discrimination

Recognition of this factor is often under rated but in the January 13, 2006

issue of Health News former U.S. President Bill Clinton is noted as saying, “It’s

long past time when there should be any stigma attached to AIDS and also long

past time when we can just look away, knowing that 90 percent of the people

who are infected don't know it.”{Does this need a footnote?}

2.3.6 Current State of Treatment Interventions

In response to the low level of knowledge of infection, some governments

and other entities have enacted broader detection testing. “In November of 2003

Botswana became the first country in Africa to adopt a policy of routine offer of

testing in clinical settings.”66 This policy has been quite successful showing a

four-fold increase in the testing rate as a result.67

More recently, The William J. Clinton Foundation HIV/AIDS Initiative negotiated lower prices for rapid HIV testing. On January 12, 2006 they announced an agreement under which, “four companies will offer the tests for 49 cents to 65 cents apiece, slicing the cost of a diagnosis in half.”68

Currently, the search for a vaccine has been elusive but still merits research and funding as it could provide a potential cure for the problem. However, other methods of treatment must be considered in parallel in an attempt to lessen the suffering and improve the quality and duration of life for the 39 million people worldwide and the 25 million in sub-Saharan Africa presently carrying the disease.

Antiretroviral therapy (ART) has been utilized effectively in the more prosperous countries but has been prohibitively expensive for wide scale distribution in low and even some middle-income countries. “At more than

$10,000 per person per year in the first years after its introduction, this therapy was far beyond the reach of national health services and the great majority of individuals…In the past few years, however, differential pricing by research- based manufacturers, generic competition, partial easing of intellectual property restrictions, and aggressive negotiation by the advocacy and development communities have caused the price of triple-drug therapy to fall drastically to less than $0.55-$1.00 per person per day in some countries.”69 This savings, however, has not been broadly realized as a 2004 WHO 3 by 5 report estimated costs of $1.00 to $1.50 as typical.70 Even lower prices have currently been negotiated by the William J. Clinton Foundation HIV/AIDS Initiative. “Four companies will provide the antiretroviral drugs efavirenz and abacavir at prices about 30 percent less than the current market rates, former U.S. President

Clinton said.71 Clinton also stated that, “We can and must do more to stop the spread of AIDS by doing more to treat people who already have it.”72 There is

little uncertainty in the urgency for ARV therapy, especially in Africa, which

suffers 70 percent of the HIV/AIDS epidemic while providing access to only 4

percent of those in need (Figure 2.32).73

Figure 2.32: Coverage of Antiretroviral Therapy [Source: WHO, 2004]

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45 World Health Organization (WHO). “WHO-UNAIDS HIV Vaccine Initiative.” Initiative for Vaccine Research (IVR), n.d. http://www.who.int/vaccine_research/diseases/hiv/en/ (accessed: August 10, 2006).

46 Avert.org. “Worldwide HIV & AIDS Statistics Commentary.” Updated Aug. 1, 2006. Data source: 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition. http://www.avert.org/worlstatinfo.htm (accessed: August 9, 2006).

47 2004 report on the global AIDS epidemic: 4th global report, 30.

48 Avert.org. “Sub Saharan Africa HIV & AIDS Statistics.” Updated Aug. 2, 2006. Data source: 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition. http://www.avert.org/subaadults.htm (accessed: August 9, 2006).

49 2004 report on the global AIDS epidemic: 4th global report, 31.

50 Farley F. "At AIDS Disaster's Epicenter, Botswana Is a Model of Action; During U.N. conference, leader speaks of national 'extinction,' but country plans continent's most ambitious programs", Los Angeles Times, 27 June 2001.

51 United States Agency for International Development (USAID). “Life Expectancy will drop worldwide due to AIDS.” Press Release, July 8 2002, www.usaid.gov/press/releases/2002/pr020708.html (accessed: February 2, 2005).

52 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition, 320.

53 Kaiser Family Foundation Global HIV Prevention Working Group. Global Mobilization for HIV Prevention: A Blueprint for Action. 2002. www.kff.org/hivaids/200207-index.cfm (accessed: August 9, 2006).

54 Joint United Nations Programme on HIV/AIDS (UNAIDS). Report on the Global HIV/AIDS Epidemic (Geneva: UNAIDS, 2002).

55 Stover, J., N. Walker, G.P. Garnett, and others. 2002. “Can We Reverse the HIV/AIDS Pandemic with an Expanded Response?” The Lancet 360 (9326): 74.

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57 Bessinger, R. and P. Akwara. “Sexual Behavior, HIV and Fertility Trends: A Comparative Analysis of Six Countries. Phase I of the ABC Study.” University of North Carolina at Chapel Hill, Carolina Population Center, Measure Evaluation Project, 2002.

58 Stadlet, J. Looking at LoveLife, the First Year. Preliminary Monitoring and Evaluation Findings of the First Year of LoveLife Activity: September 1999- September 2000. Soweto, South Africa: Chris Hani Baragwanath Hospital, Reproductive Health Unit, 2001.

59 Pettifor, A., H.V.Rees, A.Steffenson, et al. “HIV and Sexual Behavior among Young South Africans: A National Survey of 15-24 Year Olds.” University of the Witwatersrand, Reproductive Health Research Unit, Johannesburg, 2004.

60 Guay, L.A., P.Musoke, T. Fleming, and others. 1999. “Intrapartum and Neonatal Single-Dose Nevirapine Compared with Zidovudine for Prevention of Mother-to-Child Transmission of HIV-1 in Kampala, Uganda: HIVNET 012 Randomized Trial.” The Lancet 354 (9181): 795-802.

61 Futures Group. Coverage of Selected Services for HIV/AIDS Prevention, Care and Support in Low and Middle Income Countries in 2003. Washington, D.C.: USAID, UNAIDS, WHO, UNICEF and the POLICY Project, June 2004. http://www.FuturesGroup.com (accessed: August 8, 2006).

62 Combating AIDS in the Developing World, 40.

63 Kaiser Family Foundation Global HIV Prevention Working Group. Access to HIV Prevention: Closing the Gap. 2003. http://www.kff.org/hivaids/200207- index.cfm (accessed: August 9, 2006).

64 Combating AIDS in the Developing World, 42.

65 KaiserNetwork.org. “Routine HIV Testing Initiative in Botswana Aims to Get More People into Treatment Program.” Daily HIV/AIDS Report. November 10, 2003. http://www.kaisernetwork.org/dailyreports/hiv (accessed: November 10, 2003).

66 “Routine HIV Testing Initiative in Botswana Aims to Get More People into Treatment Program.”

67 KaiserNetwork.org. “New York Times Profiles Botswana’s Mandatory HIV Testing Policy.” Daily HIV/AIDS Report. June 14, 2004. www.kaisernetwork.org/dailyreports/hiv (accessed: August 9, 2006).

68 Daily News Central. “Clinton Brokers Deal for Lower-Priced AIDS Tests, Drugs.” Health News. Jan. 13, 2006. http://health.dailynewscentral.com/content/view/2052/63 (accessed: February 1, 2006).

69 Combating AIDS in the Developing World, 77.

70 World Health Organization (WHO) and Joint United Nations Programme on HIV/AIDS (UNAIDS). 3 by 5 Progress Report December 2003 through June 2004 (France: UNAIDS/WHO, 2004).

71 “Clinton Brokers Deal for Lower-Priced AIDS Tests, Drugs.”

72 Clinton Foundation. http://www.clintonfoundation.org/index.htm (accessed March 16, 2006).

73 Combating AIDS in the Developing World, 78.

Chapter 3: Methodology

3.1 Introduction

Two defining characteristics in the evolution and functioning of global

systems and human issues are complexity and uncertainty. These two

characteristics are distinct and should not be considered interchangeable. For example, a very complicated and complex system could be absolutely certain.

Whereas, an uncomplicated one line relation defining a system can be highly

uncertain. Thus, determining the part that humans can play in relation to global

resource use, economic growth, relief aid, etc. is of fundamental importance in

the development of policy and programs benefiting humankind and improving the

overall human condition.

Often the “human dimension” is used to address the role humans can

play. This method considers two sets of indicators. They are:

• The impact of anthropogenic activities on the environment

• The impact of environmental change on humans1

The problem here is that the relationship of how the human system behaves as the state of the global system changes in time is missing. The important link between these two is largely ignored. This is due to the “human dimension” not explicitly recognizing the human as a natural subsystem just like any other global subsystem such as: water, air, land, etc.

The methodology developed here proposes a cybernetic paradigm for enhancing the human dimension. Two requirements for this are:

• A correct formulation of the interaction process between the natural

system and the humankind system; and

• Explicit recognition of the specific and unique character of humans

functioning as a system.2

These aspects provide a framework for developing multilevel hierarchical models

which rely on a human-in-the-loop approach. This methodology is applied to the

global resource, economic, and health systems indicated in Chapter 2 to gain

insight into possible futures up to the year 2050.

3.1.1 Chapter Organization

The aspects of the cybernetic paradigm for the human dimension are

discussed in Section 3.2. Section 3.3 outlines the necessity of having the human

as a sub-model. Next, Section 3.4 discusses shortcomings of integrated

modeling and defines multi-level hierarchical modeling. Then, Section 3.5 shows how management of complexity is provided through the use of a multi-level architecture. Section 3.6 demonstrates need to use integrated assessment as an interactive process. Finally, Section 3.7 is a review of the GLOBESIGHT reasoning support tool used for scenario generation.

3.2 Aspects of the Cybernetic Paradigm for the Human Dimension

The first aspect is the relationship between mankind and nature. It must be realized that humankind is changing nature while being simultaneously changed by nature. That is to say that man is changing in response to nature just as nature has been changed by man. This implies a kind of feedback from the one subsystem to the other. Thus in trying to understand global systems,

humankind must try to understand how they behave and interact as a part of the

whole. This presents some difficultly in obtaining a truly objective view of some

problems since humankind is in fact a part of the system it is assessing. An

example of this loss of objectivity is to imagine that you are walking on a Mobius

strip. From your perspective, you can walk forward or backwards but the strip

has only one side. But, an observer external to the system can observe that the

strip has two sides that are in fact distinct. The natural and humankind

subsystems are consequently related in a reflexive relationship represented by

Figure 3.1, rather than the traditional human dimension view illustrated in Figure

3.2.

Figure 3.1: Global Change System

Figure 3.2: Human Dimension

The reflexivity of the nature/humankind system assures that the impact on humans and human impact are undeniably connected. The Global Change

System shown in Figure 3.1 is in a state of continuous feedback. The subsystems constantly interact and evolve together. Understanding the Global

Change System and the linkages between the subsystems is essential in providing insight into the role that humankind can play.

The second aspect is an apposite depiction of the precise character of human beings and the part it plays in global change. This requires a new paradigm unlike the standard input/output or state transition paradigm used primarily thus far in the study of global change. The state transition paradigm is assumed to be completely predictable given: current state of the system, state transfer functions or mappings, and current system input. The physical sciences are largely dominated by this input/output, sometimes referred to as “Newtonian mechanics”3, paradigm. This paradigm is completely deterministic and only a

lack of data or insufficient knowledge prevents the ability to know the future state

with absolute confidence. Unlike the case for global systems, the state transition

paradigm is appropriate for systems which are not characterized by uncertainty.

The benefits and limitations of the state transition paradigm are described in the

United Nations Education and Scientific Cooperative Organization’s (UNESCO)

Global-problematique Education Network Initiative (GENIe) handbook as:

“The state transition (input/output, stimuli/response) view can be useful under limited circumstances in the representation of humankind as a subsystem but erroneous if overextended. Using this paradigm, models (economic, energy, integrated, etc.) are developed in terms of differential (or difference) equations with or without equilibrium processes. It has been observed that the problem with such models is not that their

predictions are wrong, but that they are right most of the time except when the predictions are really needed. If the time horizon is short and “business-as-usual” prevails, the prediction using input/output paradigms does not go wide from the mark. It is when the change is sufficiently large and the consequences are felt over a sufficiently long period of time that the input/output paradigm breaks down.”4

It is therefore necessary to use an alternative paradigm that accounts for

the reflexive property of man changing nature as nature changes man. A goal-

seeking or decision-making paradigm has been developed to deal with this

aspect. This archetype has its’ origins rooted in biological sciences and the

exploration of human behavior. Two things that characterize the operation of a

goal-seeking system are:

• Goal(s) of the system

• Methods and capabilities of the system to act in response to

environmental and natural factors and to pursue overall system goal(s).

Mathematical general systems theory, as described by Mesarovic5 6 and

Takahara, provides a concise and explicit representation of the goal-seeking paradigm. Several essential items are necessary in addition to a statement governing system operation. The goal-seeking paradigm requires:

• A set of various decisions, Ddd= , ,..., d, that can be chosen from { 12 n}

depending on the current or expected future state of the system.

• A set of uncertainties, Uuuu= , ,..., , which is anticipated to occur { 12 m}

in response to the decision chosen from D .

• A set of results, R = r ,rr ,..., , determined by the specific decision { 12 l }

chosen from D .

• An evaluation set, Vvv= v , ,..., , which assigns a value, v , to the { 12 l } i

result ri such that given rq and rs the system has a ranking.

• An expectation mapping,

ED: ⊗→ U R (3.1)

which is the system’s expectation of what result ri will occur from

selecting decision di and undergoing correctly anticipated uncertainty

ui .

• A preference mapping,

P:RD⊗ → V (3.2)

that determines between decision/result (drii, ) and decision/result

⎛⎞ ⎜⎟dr, which one is best or preferred. ⎝⎠kk

• A tolerance function,

TD: ⊗ U→ V (3.3)

which assigns a value vi to decision di after uncertainty ui occurs.

• The goal of the system is to:

Find a decision ()dD$ ∈ such that:

⎛⎞⎛⎞ ⎛⎞ P⎜⎟Edu⎜⎟, , d ≤ Tu ⎜⎟ , d , ∀∈ u U. ⎝⎠⎝⎠iii ⎝⎠

Thus, in words, select a decision from the given set of decisions such that the expected outcome is tolerable regardless of the uncertainty encountered. The multi-layered decision hierarchy can be represented as shown in Figure 3.3.

Multi-Layered Decision Hierarchy

Self-organization

E, P Structure

Adaptation

U Uncertainty

Decision Selection

d Decision

Figure 3.3: Multi-Layered Decision Hierarchy

The goal statement is critical in that it incorporates both uncertainty and tolerance; two key aspects inherent in global systems. The idea is that as long as the system is operating within a certain tolerance limit, it will be deemed

acceptable to the system. This construct differs greatly from the optimization techniques employed in operations research and economic theory. In the global arena, system degradation may occur for a long period before anything is done

(i.e. decisions made) to mitigate the effects. This has long been the case in air, water, and ground pollution problems which are often largely ignored until they become so bad human health is negatively impacted. A local example of system tolerance was the Cuyahoga River fire of 1969. Ohio cities had so badly polluted this tributary to Lake Erie with petrochemicals that it actually caught on fire. It was only then, after national embarrassment from such reckless environmental abuse, that Congress enacted the Clean Water Act and that local officials made concentrated clean-up efforts.

3.3 Human as a Sub-model

One important property of the goal-seeking paradigm that must be integral

in any assessment or modeling process is the reflexive relationship between man and environment. In order to represent this relation, the human or decision- maker must become a distinct subsystem within the overall model. Thus, the computer and the human portion of the model are linked in a manner that requires the user to play the interactive role of humankind in terms of decisions to be made in order for the computer portion to progress to the next time step.

The human and computer walking “hand-and-hand” through time is a necessary condition for achieving meaningful scenario generation in evaluating global change systems. Traditionally, computer modeling approaches have followed an if-then approach. This approach, illustrated in Figure 3.4, assumes certain policy options and decisions, applies the computer model to the conditions specified, and yields the outputs or consequences over the entire time horizon.

IF THEN (Assumptions (Consequences at the and Policy MODEL End of the Entire Options for the Policy Time Period) Entire Time Period)

Figure 3.4: Traditional Computer Approach for Scenario Generation

One major downfall of the traditional technique is the generation of infeasible or impossible scenarios. For example, if halfway through a model run the outputs indicate that a certain decision is not yielding a desired consequence then the above model keeps making that same decision even though it is not working. This is inconsistent with the actual process of policymaking. In which it has been asserted that:

“…policy decisions are mired in an incremental approach and that policymakers tend to muddle through and redefine goals when expectations are unrealized. In such approaches a range of policy alternatives are considered, resulting in a set of satisfactory (acceptable) policies (bounded rationality) rather than the ”best” policy. It is then left up to the decision–maker (user) to decide which of the alternative policies to pursue on the basis of risk aversion, rules-of-thumb, conflict avoidance and the likes.”7

In order to remedy the limitations of the traditional approach, the time

interactive archetype uses the human at each model decision stage. This

prevents a mechanistic projection of past trends into the future. While simple

trend projection has its’ application, global change systems contain a level of

uncertainty and tolerance which must be accounted for if meaningful

assessments of possible futures are desired. The following is an example of how

the process works.

• Take the time horizon of interest, say 2000 through 2050, and break it up

into time increments of relatively short duration after each of which the

state of the system is assessed and the system requires input from the

human to proceed to the next time frame.

• Make certain assumptions and policy decisions for the first time increment,

say 2000 through 2015.

• Run the computer portion of the model for the first time increment only.

• Evaluate the consequences and effectiveness of decisions made and

policies enacted.

• Make certain assumptions and policy decisions for the second time

increment, say 2015 through 2025.

• Run the computer portion of the model for the second increment only.

• Continue in this manner through 2050

This process is illustrated in Figure 3.58. It is an interactive symbiotic development of scenarios which could potentially evolve.

66 COMPUTER PROGRAM PORTION OF (OVERALL) MODEL

Then Then 1 2 Consequences at the Consequences at the End of the First Time End of the Second Increment Time Increment 2010 2025 2000 . . . etc.

If1 If 2 Assumptions Assumptions and Policies for the and Policies for the First Time Second Time Increment Increment

HUMAN AS A COMPORTMENT OF THE (OVERALL) MODEL

Figure 3.5: Scenario Generation Using the Integrated Human/Computer Modeling Process

3.4 Integrated versus Multi-level Modeling

The interdisciplinary nature of global change requires development of models that can integrate the behaviors within and between the individual disciplines. This integration is often the most difficult and least understood step in the modeling process. Linking models between disciplines requires knowledge of the exact nature of the linkages connecting one disciplinary model to the other.

For, it may be the case that within each discipline the models are correct but that once they are integrated the results are no longer a faithful representation of the

real system. The linkages may not be modelable merely by state-transition and thus the outcome is distorted.

The following example illustrates how even the simplest, fully-determinate subsystems can become completely indeterminate by integration. Consider the two equations: 3 Discipline A: x() t+=− 1 ax() t − x () t + y () t (3.4)

Discipline B: y( t+ 1) = bx( t ) (3.5)

The two disciplines are separate but the variables they refer to are the same.

Each of the sub-models are completely determinate in that their trajectories are fully predictable. Whereas for the integrated model, the values a=2.1 and b=0.04016 cause the system to become chaotic. This is due to the complexity contained within the sub-models.

“When the sub models are themselves complex, it is not possible with any degree of certainty to know whether the resulting integrated model produces a fundamentally different behavior from that observed in real life. Even a simple and weak linkage (as given in the above example) can fully destroy the faithfulness of the overall model in spite of the sub models being consistent with reality”9

In this analysis, multi-level modeling will be used as an alternative to the integrated approach. This type of modeling requires:

• A goal-seeking paradigm which uses a “human-in-the-loop” approach

• Models from each discipline

• Linkages between models

• Use of a multi-level, conceptual architecture which specifies the

connection linkages necessary and the relative position of the disciplines

The multi-level framework used in this study, illustrated in Figure 3.610, has been developed under the direction of the GENIe Program in the Systems

Engineering department at Case Western Reserve University. While there are many different possible architectures, the importance rests on the development of a multi-level structure that can incorporate and simulate the complexities inherent in the integrated system under investigation. This step should be the starting point for producing realistic integrated modeling of complex systems.

On the highest level, individual values, cultural aspects, personal needs are characterized. The next level is the group level representing government organizations, private sector interests, non-governmental organizations. The middle level is an economic and demography level. It accounts for pecuniary and informal (underground, subsistence) economy. The next level is for technology and resources as a representation in physical terms of human activities. Finally, the lowest level, environment/ecology, encompasses the natural world processes of global change.

It is the relationships between the levels that require the human to become a sub-model. The links and connections that are not modelable under the state transition paradigm must be left to the human user to specify a decision before the model can proceed to the next time step. This is especially important on the higher levels, individual and group, where the state transition paradigm is seldom

applicable. The human in the model enables the goal-seeking behavior and resolves uncertainty by allowing assessment of alternatives in order to discern the most probable relationship for the given conditions.

Individual level: Values,Cultural Human as a determinants, Human needs Submodel

t r o Group level: Government p p organization, Private sector, NGO u S k n r o o i w is e c e m Economic/Demography level: a D r Monetary Economy, Informal F (Underground, Subsistence) Economy

Computer Model Technology/Resources Level: Representation of Human Activities in Physical Terms

Environment/Ecology level: Physical Aspects of Global Change

Figure 3.6: Global Change as a Multi-level (Stratified System)

3.5 Management of Complexity via a Multi-level Hierarchical Approach

The developed multi-level approach is used to manage the complexity

involved in modeling. Further complexity is added when models are integrated

for interdisciplinary investigations. The integration requires more information and

data and is subject to a far greater degree of uncertainty due to the linkages

between disciplines. The complex system formed by the integration of multi-

disciplinary models must not obscure real life behavior of the overall system.

Further, if policy procedures are being developed, the results and model must be

intuitively understood to be used as justification for such.

The mathematical theory of general systems provides a framework within

which a complex system will be defined. First, a system is defined as a relation

among objects or items. Thus a system, S, composed of items, I, can be

represented by:

SI⊂⊗⊗⊗ I... I (3.6) 12 n . A complex system will be defined as a system of systems or as a relation among

systems and can be written:

SS⊂⊗⊗⊗ S... S (3.7) 12 n . This is clearly the condition for multi-disciplinary global models. The objects

which form the complex system through union have their own individually

identifiable behavior and identity but function as part of the total integrated

system. An example of this is the human body which has many subsystems: organs, bones, tissue, etc., each performing their own individual functions, but

together, they compose the overall system which is the human.

Thus, the behavior of a complex system can be considered on at least two levels. They are the:

• Overall system level; and

• Subsystem level

Therefore, a hierarchical system having two or more levels can justifiably be considered a complex system. In addition, the subsystem level may also be characterized by complexity and uncertainty which can be better understood through the use of multi-level hierarchical modeling.

Multi-level hierarchical modeling can help aid transparency of modeling and comprehension of what actually drives the system. The highest level of the hierarchy represents the overall system function while the details of how the system functions is contained within lower level analysis. An example of multi- level hierarchical modeling for population is developed here to clearly outline the modeling methodology used in this study.

One mandatory consideration in model construction is availability of required input data for system parameters. While there are many valid relational representations for global change, models which cannot be propagated with real data or valid estimations are virtually useless. All models created within this investigation contain real and verifiable data available from reputable sources including: United Nations World Population Prospects Database, U.S. Census

Bureau International Database, U.S. Census Bureau HIV/AIDS Surveillance

Database, World Health Organization (WHO) Country Fact Sheets, The World

Bank Group Developmental Database, among others as referenced. Therefore,

all equations and relations developed in the system representation process will

use real data and projections from expert sources with the exception of uncertain or unreliable data which will be handled through the use of an interactive

“human-in-the-loop” approach as indicated.

Consider a population from one year to the next. On the overall level, let the population and growth rate for region r at time t be represented respectively:

popfrt, and rpopfrt,

Then, the population relation from one year to the next is given by:

⎛⎞rpopfrt, popf=×+ popf 1 (3.8) rt, rt,1− ⎜⎟ ⎝⎠100

This simple one line model for population is the first level in the population

structure. It requires knowledge of only the number of people and the growth rate. A block diagram representation is shown in Figure 3.7.

st rpopfr,t 1 Level Population popfr,t Model popfr,t-1

Figure 3.7: First Level Population Model Input/Output Diagram

The population representation in equation 3.8 may contain enough information for some purposes but a natural question is:

What is the process that determines the growth rate?

The answer to this question brings us to the second level of the population hierarchy. Let the population, number of births, crude birth rate per 1000 of population, number of deaths, and crude death rate in region r at time t be represented respectively by:

popsrt,,, brts rt, crbrts rt , , dthsrt,,, crdths rt

The population for region r at time t is the population at time t-1 plus the number of births minus the number of deaths. This relation is given by:

pops= pops+− brts dths (3.9) rt,,rt,1− rt rt,

In order to calculate the number of births, the crude birth rate per 1000 of population is used. This leads to the equation:

popsrt, brts=× crbrts (3.10) rt,, rt 1000

Similarly for the number of deaths:

popsrt, dths=× crdths (3.11) rt,, rt 1000

Equation 3.9 can be re-written as:

popsrt,, pops rt pops=+×−× pops crbrts crdths rt,,,rt,1− rt1000 rt 1000

⎛⎞crbrtsrt,,− crdths rt =+pops pops ×⎜⎟ rt,1−− rt ,1⎜⎟1000 ⎝⎠

rpopsrt, =+pops pops × rt,1−− rt ,1 100

⎛⎞rpopsrt, =×+pops ⎜⎟1 rt,1− ⎜⎟100 ⎝⎠

Therefore, population on the second level can be re-written:

⎛⎞rpopsrt, pops=+ pops ×⎜⎟1 (3.12) rt, rt,1− ⎜⎟100 ⎝⎠

The second level population model is described by equations 3.9 – 3.11.

The input/output diagram for the second level of the population hierarchy is

shown in Figure 3.8. Equation 3.12 shows that the population rate, which was an

input on the first level, is simply an output of the second level analysis. On the

second level, the fact that population growth is determined by births minus

deaths is straightforward. But on the first level, the information is unavailable as

to the number of births or deaths. These operations or processes are located

deeper within the structure of the system than the first level can “see”.

crbrtsr,t rpopsr,t 2nd Level Population pops Model r,t crdthsr,t

brtsr,t

popsr,t-1

dthsr,t

Figure 3.8: Second Level Population Model Input/Output Diagram

One important aspect to recognize is that as the level of detail increases, the requisite amount of data and assumptions increases as well. The proliferation of required knowledge of the exact operating nature of system

processes and increased uncertainty of lower level analysis is often prohibitive to

the creation of a credible representation of the system. Only processes

modelable through the state transition paradigm should be included in the

computer component of the model. All uncertain, unknown, or goal-seeking

behavior should be handled by the human component of the model.

On the next level of the hierarchy, a great deal of information is available

in relation to the demographic indicators for the population system. The model

uses single-year age-cohorts, male and female categories, age-specific fertility

rates, and age-specific mortality rates. Outputs of the model include: total

population, population per cohort, total male and female population, male and

female population per cohort, total births, births per cohort, total deaths, deaths

per cohort, crude birth rate, crude death rate, and population growth rate.

Variables are also included to represent age groupings which are significant to

global change, such as number of children, working age population, elderly

population, etc. They include: five year population cohort aggregates (i.e. age 0-

4, 5-9, …, 95-99, 100+), population cohort aggregates for ages 0-20, 21-65, 65-

100+, college going population, and ages 80 and over. The cost of the

information comes in the form of the additional input data. The input/output

diagram for the third level population model is shown in Figure 3.9.

rpopr t

poprt frtcr, j,t popcrjt 3rd Level crbrtrt mfratior,t Population Model crdthrt

frtcrjt mfratio_frtr,t brtcrjt

brtrt mrtcr, j,t

mrtcrjt

dthcrjt popar, k,t=0

dthrt

pop malert

popcmalerjt

pop femalert popc_female r, j,t

Figure 3.9: Third Level Population Model Input/Output Diagram

The first step in the third level approach is greater detail in the information pertaining to births. On the second level, births are simply computed from the crude birth rate times the overall population, as shown in Equation 3.10. In fact, births occur by women of child bearing age, considered to be ages 15 through

49. An age-specific fertility rate is used to determine the number of births per age-cohort. Let the population per age, age-specific fertility rate, male-to-female ratio for fertile age group 15-49, and number of births per cohort for region r, cohort j (j=15…49), at time t be given respectively by:

popcrjt,,, frtcrjt,,, mfratio_ frtrt, , brtcrjt,,.

The relation for the number of births in a cohort is equal to the number of females

in that cohort, times the age-specific fertility rate as shown in Equation 3.13. frtc rjt,, mfratio_ frtrt, brtc=× popc × (3.13) rjt,, rjt,,− 1 1000 100

So the total number of births in region r at time t is simply the sum of the births in each cohort for ages 15 through 49, given by:

49 brtrt, = ∑ brtc (3.14) j=15 rjt,,

The next consideration in determining cohort population is death or mortality. Age-specific mortality and deaths per cohort for region r, age j=0 to

100, and time t are represented respectively:

mrtcrjt,, and dthcrjt,,.

Then, the number of people of a given age who die each year in a given region is

equal to the population of the cohort times the age-specific mortality of that

cohort, as shown in the following equation.

mrtcrjt,, dthc= popc × (3.15) rjt,, rjt,,− 1 100

Next, the total number of deaths for the entire population in a given region for

year t is the summation of the deaths in each age cohort from age 0 through 100,

as shown next.

100+ dthrt, = ∑ dthc (3.16) j=0 rjt,,

In terms of population level per cohort, except for the first cohort j=0 (i.e. newborns), the population in a cohort at time t for age j is equal to the number of people who were j-1 at time t-1 minus the number of people who died between times t-1 and t in cohort j-1. This relation is shown in the subsequent equation.

popc= popc− dthc (3.17) rjt,, rj,1,1− t−− rj ,1, t

Therefore total population is the summation of the population in each age cohort,

written:

100+ poprt, = ∑ popc (3.18) j=0 rjt,,

Male population, female population, and male-to-female sex ratio (i.e.

males per 100 females) for region r, age j, at time t are given respectively by:

popc_ malerjt,,, popc_ femalerjt,,, mfratiort,

Male population per cohort is the total population in that cohort times the ratio of

men, which can be written:

mfratiort, popc_ malerjt,,=× popc rjt ,, (3.19) mfratiort, +100

Similarly for the female population per cohort the relation is given by:

popc_ femalerjt,,= popc rjt ,,− popc_ male rjt ,, (3.20)

The total male and female population respectively for region r at time t is

given by:

pop_ malert, and pop_ femalert, .

Therefore the total male population is the summation of all the males in every age from newborn through 100. Similarly, the same is true for the female population and these two equations are written:

100+ pop__ malert, = ∑ popc male (3.21) j=0 rjt,, 100+ pop__ femalert, = ∑ popc female (3.22) j=0 rjt,,

Crude birth rate, an input on the second level, is an output of third level analysis. The equation is:

brtrt, crbrtrt, =×1000 (3.23) poprt,

Crude death rate, another input of the second level which is an out put of the third, equals the total number of deaths divided by the total population times

1000 to scale it to represent deaths per 1000 people:

dthrt, crdthrt, =×1000 (3.24) poprt,

Lastly, the population growth rate, shown in Equation 3.12 for the second level model, which was a first level input, is written on the third level as:

⎛⎞poprt, rpop =100×− 1 (3.25) rt, ⎜⎟ ⎝⎠poprt,1−

The first, second and third level variables for population respectively are:

popfrt, , popsrt, , and poprt, .

They represent the same population of course and the carry the f suffix for the first level, the s suffix for the second, and no suffix for the third level. Thus the different levels have their own distinct behavior but simulate the same phenomena of population. On the highest level, the process of population growth is rate based determined on absolute difference in magnitude from one year to the next. On the second level, population growth is further detailed to show that the population growth rate is actually determined by the number of births and deaths. Providing greater detail still, the third level shows exactly what ages and how many people died in addition to how many babies were born in each child bearing cohort.

These vastly different representations all have applicability depending on the required level of detail. Some investigations might only be concerned with the number of deaths, thus a second level analysis is appropriate. Whereas problems such as aging and health care for the baby boom generation will require third level knowledge of how many people reside in each cohort. The population structure emerges completely in terms of individual age cohorts only on the third level of detail. Therefore, the desired level of detail should dictate

the structure and processes included in the model.

3.6 Reasoning Support Tool: GLOBESIGHT

To investigate integrated assessment as a process, a reasoning support

system called GLOBESIGHT11, short for GLOBal forESIGHT, has been designed

by the System Group at Case Western Reserve University. GLOBESIGHT is a

5th generation, Java based application. It belongs to a class of active decision

support systems investigated by Takahara1. Models of dynamic systems in

GLOBESIGHT are based on difference equations. “Real & physical” variables

that are quantifiable are used for processes represented by the state transition

paradigm. Features include: data interpolation tools (includes exponential –

linear – constant growth methods) native to the GLOBESIGHT Graphical User

Interface (GUI), ease of model modifications, ability to integrate models, ability to

run multiple scenarios that are directly comparable, graphs using tailored graphic

plug-ins designed for specialized issues of global change (such as population

pyramids), incorporation of cybernetic paradigm allowing the user to interact and make decisions during and throughout the model run for goal-seeking behaviors.

These features enable a blending of reason with vision in an effort to bridge the

gap between the scientist and the policy-maker by the creation of an intuitively

understandable methodology and results. The approach enables model

transparency permitting easier understanding for policy making staff.

The cybernetic paradigm for global change and GLOBESIGHT have been

used in several alternative circumstances including: water issues of the Nile

River Basin, Terra 2000 Report covering global economy and global warming,

world carrying capacity analysis, and integrated world food models studying effects of China food demand on sub-Saharan Africa food supply, to name a few.

The models presented in this study are all constructed within the framework of the GLOBESIGHT decision support software.

1 Yasuhiko Takahara, et. al., “A Hierarchy of Decision Making Concepts – Conceptual Foundation of DSS”. Journal of General Systems Theory, 1994.

The GLOBESIGHT architecture includes:

• Information Base

• Models Base

• Issues Base

• Functionalities Base

A representation of the GLOBESIGHT structure is shown in Figure 3.10.

Information Models Base Base ANALYSIS SUPPORT SYSTEM Issues Functionalities Base Base

Human

Figure 3.10: GLOBESIGHT Architecture

The Information Base is comprised of numerical time series data and non- numerical information that can be used as justification for assumptions and policies enacted. The models base contains numerous procedures, functional and relational, to consider alternative futures based on realistic assumptions and consequences of policy decisions. The Functionalities Base allows the human to

engage in the modeling process interactively through time. Finally, the issues base is a cache of assumptions with results already conducted to indicate further research needed, for comparative purposes, and for future reference.

Real data is used for scenario generation and the approach shows general features of possible futures and compares their feasibility and desirability. A baseline or business-as-usual (BaU) scenario is first established, and represents a possible future based on apparent current trends. Then other potential scenarios can be created and evaluated by varying selected parameters from their baseline values, determined from considerations of what real-world actions would translate into such parameter variations. Key outputs can then be compared and contrasted as an aid to decision-making.

Mathematical equations for global system representations are given in

Appendix I; The GLOBESIGHT variables are listed in Appendix II; GLOBESIGHT

Java code for all models developed is in Appendix III; Appendix IV is a listing of the GLOBESIGHT XML project file. The GLOBESIGHT block diagram for first level population is shown in Figure 3.11. The time dimension is not present in the block representation since the computer code for GLOBESIGHT projects, written in the Java programming language, does not make explicit reference to the time dimension. This is due to the use of difference equations which calculates changes in system parameters from one year directly to the next.

rpopf_b [r] ● rpopf [r] ● popf [r] rpopf_m [r]

spopf [r] D

= Scenario = Data, BaU = Feedbacks; One Year

Figure 3.11: First Level Population Model GLOBESIGHT Block Diagram

The variables are all from time t except those in blue, which are from time t-1 (i.e.

the prior year). Variables in yellow represent BaU data taken from widely

accepted sources, including but not limited to the ones already mentioned. The

variables in pink are scenario variables. These are explicitly included in the

model to enable robust scenario generation for comparative evaluation. The blue

variables represent system feedback processes. These variables are preceded

by an s in the computer code representation such that they are kept distinct from

the current year values. System performance is validated against publicly

available population data and projections from the United Nations World

Population Prospects (UNWPP) Database, located on the internet at

http://esa.un.org/unpp/ .

The first, second, and third level population projects will use data for world population from the UNWPP Database to illustrate their validity. Initial population and population growth rate for years 2000 through 2050 are given in Figure 3.12.

World Population growth rate (%) and Population Medium variant

Population Population Period Start Period growth rate (thous.) 2000-2005 1.21 6085572 2005-2010 1.14 6464750 2010-2015 1.07 6842923 2015-2020 0.97 7219431 2020-2025 0.85 7577889 2025-2030 0.73 7905239 2030-2035 0.63 8199104 2035-2040 0.56 8463265 2040-2045 0.47 8701319 2045-2050 0.38 8907417 2050 0.38 9075903 Figure 3.12: First Level Population Data for the World 2000-2050 [Source: UN World Population Prospects, 2004] Population in the year 2000 was just over 6 billion and is expected to grow to

2 over 9 billion by 2050. The world population growth rate is cut by over the fifty 3 year period, dropping from 1.21 percent per year in 2000 to 0.38 percent per year in 2050. The reduction is due mainly to a decline in birth rates in developing countries, which are the primary source of growth. Generally, increases in a country’s wealth to developed standards eventually (over at least a generation) translates into a reduction in birth rates due to lower child mortality rates from better health care infrastructure, increase in amount of women who choose to work instead of having children, higher education levels of parents doing more child bearing and family size planning, and widespread, affordable availability of contraceptive methods including condoms and the birth control pill, in addition to other reasons to numerous to cite and elaborate upon.

The data is entered in GLOBESIGHT and the output graph for population

and growth rate for the world is shown in Figure 3.13. Project outputs for

population, variable name popf, are within 1 percent of projected values for the

BaU or Medium variant. Thus, the project output is virtually the same as projections from the UNWPP Database and the model is therefore a consistent

and credible representation for population using growth rate as a driver. Model validation for equations for all three population level models was carried out using Excel to compute outputs from the model equations and the UNWPP population dataset for the medium variant from 2000-2050. The results are verified to be consistent with values published by the UNWPP.

Figure 3.13: GLOBESIGHT First Level Population Output for the World

The GLOBESIGHT block diagram for second level population is shown in

Figure 3.14. Crude birth and death rate data for the second level approach is given in Figure 3.15. Figure 3.16 validates the second level representation for

population by simulating BaU results which are nearly identical to UNWPP projections. Population growth rate, a second level output, is compared to the first level input for growth rate and population overall is also compared across the two levels. Both levels are within 1 percent of UNWPP projection statistics.

● crbrts_b [r] brts [r] ● crbrts [r] crbrts_m [r]

+ rpops [r] ● pops [r]

crdths_b [r] ● crdths [r] spops [r] D crdths_m [r]

● dths [r] = Scenario Variable = Data, BaU Inputs = Feedbacks; One Year Delay

Figure 3.14: Second Level Population Model GLOBESIGHT Block Diagram

Analysis on the second level reveals that the reason for the drop in the population growth rate is not from a dramatic increase in deaths, which rose from

9.1 to 10.4 deaths per 1000 of population; rather it is from a decrease in births, which drops from 21.3 to 13.7 births per 1000 of population. Thus, information that was unavailable on level one is available here. Other advantages of the second level approach include the provision of information including the total number of births and deaths. The cost of this information is an enlargement of the required dataset to include crude birth and death data and projections.

World: Crude Birth/Death rate (per 1,000) Medium Variant

Crude birth Crude death Period rate rate 2000-2005 21.3 9.1 2005-2010 20.4 9 2010-2015 19.5 9 2015-2020 18.4 9.1 2020-2025 17.2 9.2 2025-2030 16.3 9.3 2030-2035 15.5 9.5 2035-2040 14.9 9.8 2040-2045 14.3 10.1 2045-2050 13.7 10.4 Figure 3.15: Second Level Population Data for the World 2000-2050 [Source: UN World Population Prospects, 2004]

Figure 3.16: First vs. Second Level Population & Growth Rate for the World

The third level of the population hierarchy is the most complex but also the most powerful in terms of providing a wealth of demographic indicators. The

third level model uses single-year age cohorts, age-specific fertility and mortality rates, and male/female categories. Outputs of the model include all variables

(inputs and outputs) included so far in the first two models in the hierarchy plus population per age cohort, population for aggregate age cohorts 0-4, 5-9, etc., male and female population per cohort, total male and female population, births per age-cohort of mother, and deaths per cohort. The GLOBESIGHT block diagram for third level population is shown in Figure 3.17.

= Scenario Variable mfratio [r] ● popc_male [r, j], j=0…100 = Data, BaU Inputs = Feedbacks; One Year Delay 100 ∑ pop_male [r] j=0

- 100 ∑ pop_female [r] j=0 frtc_b [r, j], j=15…49 ● frtc [r, j], j=15…49 popc_female [r, j], j=0…100 frtc_m [r] 49 mfratio_frt [r] ● brtc [r, j], j=15…49 ∑ brt [r] ÷ crbrt [r] j=15

100 + popc [r, j], j=0…100 ∑ pop [r] j=0

spopc [r, j], j=0…100 D spop [r] ÷ rpop [r] mrtc_b [r, j], j=0…100 ● mrtc_m [r] 100 mrtc [r, j], j=0…100 ● dthc [r, j], j=0…100 ∑ dth [r] ÷ crdth [r] j=0

Figure 3.17: Third Level Population Model GLOBESIGHT Block Diagram

Input data for the third level model is tabled in Appendix 5; data is from the

UNWPP 2002 Revision. Detailed instructions for obtaining, entering, and validating the data for the third level model are given in Appendix 6. The third level model’s accuracy at producing reliable outputs and projections for demographic indicators is confirmed by running within 1 percent of UNWPP

Projections. The third level model calculates indicators for population growth

rate, crude birth rate, and crude death rate. With this information, both levels of

the hierarchy above the third can be propagated with data. The specific

operating processes of the system on the third level are the drivers behind the

representations of the system on the first and second level and can therefore

recreate their operational indicators.

One major advantage of the third level approach is the use of population

pyramids to view the structure of the population in terms of age-cohorts. Figure

3.18 shows the population pyramid for the world in 2005.

Figure 3.18: Population Pyramid for the World 2005

The arrow head shape of the figure indicates that the majority of the population is younger with the largest cohort being the 0-4 age-cohort at the bottom. The projected population pyramid for the UN BaU Scenario for 2050, Figure 3.19, shows a bulging of the pyramid in the center with a slight tapering off towards the

bottom as population growth begins to decline after the 20-24 year old age-

cohort. This demonstrates the transition to a more developed world as a whole.

The massive population growth experienced due to the large contribution from

less developed countries diminishes as these countries become more developed.

Figure 3.19: Population Pyramid for the World 2050

Another interesting use of the population pyramid is for social and

healthcare planning for the elderly related to the increase in life spans and the

growing percentage of elderly relative to workforce age individuals. This phenomenon is well-illustrated in Japan. Figure 3.20 shows the population

pyramid for Japan as of 2005. The pyramid shows the loss in population in the

35-54 year old age group due to direct and indirect effects of World War II. The

2005 pyramid also shows a dramatic decline in population less than 30 years of age.

Figure 3.20: Population Pyramid for Japan in 2005

Figure 3.21: Population Pyramid for Japan in 2050

The effects of this structure on the future shape of population in the UN BaU

scenario are demonstrated by the year 2050 pyramid shown in Figure 3.21. A serious consequence of this cohort structure is the ability of the working age population to provide civil, financial, and healthcare support to such a large number of people, living to be older than ever before in history.

Other aspects and development of the third level population model will be discussed in Chapter 5 as the third level approach is used as a starting point for representation of the HIV/AIDS epidemic as a system.

Works Cited:

1 Mesarovic, M.D., McGinnis, D.L. & West D.A. “Cybernetics of Global Change: Human Dimension and Managing of Complexity.” Management of Social Transformations, Policy Paper 3, (Paris: UNESCO, 1996), 9.

2 “Cybernetics of Global Change: Human Dimension and Managing of Complexity”, 10.

3 “Cybernetics of Global Change: Human Dimension and Managing of Complexity”, 12.

4 United Nations Educational Scientific Cooperative Organization (UNESCO). GENIe: Global-problematique Education Network Initiative (GENIe) (Spain: UNESCO Chair on Technology, Sustainable Development, Imbalances and Global Change, 2000), 17.

5 Mesarovic, M.D. & Takahara Y. Mathematical Theory of General Systems. New York: Academic Press, 1974.

6 Mesarovic, M. and Takahara Y, eds. Thoma, M. and Wyner, A. Abstract Systems Theory, Lecture Notes in Control and Information Sciences. Berlin, Heidelberg: Spring-Verlag, 1989.

7 “Cybernetics of Global Change: Human Dimension and Managing of Complexity”, 16.

8 “Cybernetics of Global Change: Human Dimension and Managing of Complexity”, 19.

9 “Cybernetics of Global Change: Human Dimension and Managing of Complexity”, 23.

10 “Cybernetics of Global Change: Human Dimension and Managing of Complexity”, 25.

11 Sreenath, N. “Global Modeling and Reasoning Support Tools.” Integrated Global Models of Sustainable Development, ed. Akira Onishi, in Encyclopedia Technology, Information, and Systems Management Resources, part of Encyclopedia of Life Support Systems (EOLSS). Oxford ,UK: Developed under the Auspices of the UNESCO, EOLSS Publishers, 2001, http://www.eolss.net (accessed: February 14, 2005).

Chapter 4: Impact of Peak Oil and the Post-Peak Oil Era

4.1 Introduction

The analysis in this chapter will focus on assessment of when global oil

production will peak, regional impact of oil deficit on OECD economic growth,

and potential consequences on relief aid systems that help sustain third world countries, such as sub-Saharan Africa. It should be emphasized that current conditions in sub-Saharan Africa are quite bleak and that any further losses, economic or human capital oriented, will only further exacerbate the humanitarian disaster ravaging its’ people.

The investigation will first develop the reasoning and processes included in the representation for world oil supply, demand, and ultimate recovery.

Scenario generation illustrates a realistic range of possible futures for oil production and when peak production will occur using a first level world oil model. A second level oil transition model is constructed to evaluate the regional impacts of the post-peak oil era on OECD countries’ economic growth. The goal- seeking paradigm is used to create a scenario that provides sufficient time to transition to an alternative fuel while still achieving economic growth by delaying the peak-year of production by 10 years. Policies enacted through a participatory democratic process and trade agreements between the major oil suppliers and consumers would be a pre-requisite for this scenario to occur. Potential economic costs of the impending oil deficit are discussed.

Ramifications for OECD DAC ODA are analyzed in light of the looming economic crisis. When the resource that has fueled the greatest economic and

technological expansion man has ever undergone reaches a physical supply

shortage (i.e. not enough oil to meet demand), world trade and economies will

change in an unprecedented way. The supply shortage will not cease this time;

a yet-to-be determined alternative will have to emerge. The transition will be

quite costly and global aid on the whole could end all together. Some experts

predict severe energy wars and other apocalyptic predictions but these will not

be investigated in this analysis. The end of ODA in itself will prove to be

disastrous for global health and economic security, demonstrated via scenario

analysis in Chapter 8.

4.1.1 Chapter Organization

In order to gain an understanding of how current and future global supply

and demand of oil may develop, Section 4.2 presents the methodology and interactions employed in the First Level Oil Model. Section 4.3 is an analysis of when and why peak year production will occur; scenario analysis is used to support reasoning for selected peak years under low, medium, and high oil consumption growth. Section 4.4 relates world oil supply and demand to regional

oil supply, demand, deficit, etc. and links the impact of an oil supply deficit on

economic growth; a second level model is constructed to handle the

regionalization of the world system and to provide system feedback from

economic growth on the level of oil demand. Next, Section 4.5 develops an

interactive scenario in which an anticipatory policy is practiced in order to

minimize the negative effects of the impending oil deficit; the adopted policy

shifts the peak from 2015 to 2025 in order to gain time to solve and implement

the problem and to avoid the potential supply collapse resultant from peaking in year 2025 as illustrated in Section 4.3. Since the transportation sector is a major

portion of the increase in demand, Section 4.6 investigates the growth in oil

demand by examining the vehicle ownership and projected growth in ownership for the regions of the United States, Europe, India, China, and the rest of the world. Projections for growth in the transportation sector are extremely dependent upon assumptions, thus an interactive approach is used to evaluate the results in five year increments. Finally, Section 4.7 provides a summarization of emergent properties regarding oil consumption, relationship of scenarios to global health issue and conclusions.

4.2 The World Oil Model

One of the first considerations in creating the computer portion of the world oil model is an accurate representation of the system behavior. The system will be defined to have one region, the world. This is consistent with the global market that exists for petroleum trade. Historically, supply has met demand for oil except in cases of supply disruption which have always been resolved in the past due to the extreme thirst and real need for oil.

After the year of peak production, supply will be assumed to follow the

Hubbert curve, Figure 2.8, for annual supply during the post-peak oil era. This decline method is appropriate for this finite resource extraction modeling (i.e. oil) as it has been proven historically accurate for production during decline in other post-peak production regions such as the United States (lower 48 states). Figure

4.11 shows oil production for the U.S. (lower 48) from 1930 through the year

2000. The approximate bell-shape of the curve corresponds to the fact that as oil is discovered, production starts low due to insufficient production infrastructure, production then increases as facilities and technologies improve. Light, sweet crude oil is extracted during early field pumping. Then, as the field matures and production peaks, the heavy oil left is more expensive to pump and thus less desirable as a commodity. As the well is depleted, a point comes when the amount of energy it takes to recover, transport, and refine the oil is barrel for barrel a net energy sink (i.e. the oil being retrieved provides less energy than it takes to get, move, and process it).

Figure 4.1: U.S. Lower 48 Oil Production 1930-2000 [Source: Blanchard, 2000]

Demand for world oil will follow the rate based trajectory given in Equation

4.1. The relation states that world oil demand at time t is equal to world oil demand in the previous year times the quantity one plus the rate of oil demand growth, as a percent, divided by 100.

⎛⎞rwrd__ oil dm wrdoildmwrdoildm__=×+ __⎜⎟ 1 t (4.1) t t−1 ⎜⎟100 ⎝⎠

World oil supply up until and including the peak year will equal demand, as shown in Equation 4.2.

wrd_ oil___ sptt= wrd oil dm (4.2)

Cumulative world oil supply or the amount of oil already consumed,

Equation 4.3, is equal to the summation of the oil supplied each year over the number of years supplied. t wrd___ oil sp cmltx= ∑ wrd __ oil sp (4.3) x=2000

World oil supply during the year of peak production is equal to supply that year, given by:

wrd_ oil__ sp peak= wrd _ oil _ sp (4.4) t peakyear

After the year of peak production, supply will follow the Hubbert curve for decline of annual production. The relation states that world oil supply in year t, for t greater than peak year, equals two times the world oil supply during the peak year, divided by the quantity, one plus the hyperbolic cosine of the quantity b, the Hubbert constant, times the number of years since the peak, as written in

Equation 4.5. wrd_ oil__ sp peak wrd__ oil sp =× 2 (4.5) t ⎛⎞⎛⎞ 1cosh+×−⎜⎟btt⎜⎟ ⎝⎠⎝⎠peakyear

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The Hubbert constant, b, given in Equation 4.6, is a ratio between peak year

production and the asymptote for ultimate recovery quantity in the peak year. wrd_ oil__ sp peak b =×4 (4.6) wrd____ oil sp ult asym t peakyear

The equation for the Hubbert asymptote, Equation 4.7, is given by:

wrd____ oil sp ult asymtt=× 2( wrd ___ oil sp ult − wrd ___ oil sp cml ) (4.7)

Equations 4.5, 4.6, and 4.7 are used to compute production during the decline.

The importance of this equation comes from the property that the higher the

production peak, the quicker production diminishes, assuming a finite resource base. This is illustrated in Figure 4.2 which shows Hubbert curves for four different peak production quantities, given the same ultimately recoverable amount, over a one-hundred year period with the peak occurring in the fiftieth

year. The graphs show the potential production crash that may occur if oil

production peaks at a high level relative to the overall quantity of recoverable (i.e.

area under the curve).

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Figure 4.2: Hubbert Production Curves for Different Peak Production Levels

Once the peak year is reached, production in subsequent years will decline in the face of increasing demand. The difference between the demand and supply is defined to be the oil deficit. Equation 4.8 defines oil deficit for the world at time t to be equal to world oil demand at time t minus world oil supply at time t.

wrdoildfwrdoildmwrdoilsp______ttt= − (4.8)

The Input/Output diagram for the first level oil model is shown in Figure

4.3. The model is quite informative considering the rather small amount of

required data. The importance of the quality of data is much more important than

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the quantity of data. Larger models are not in themselves necessarily more

accurate, especially if the assumptions or underlying processes are obscured or

distorted due to inclusion of uncertain or unreliable data in order to propagate the database with values so the model can run.

rwrd__ oil dmt wrd__ oil dmt

st 1 Level wrd__ oil sp iwrd__ oil dmt t 0 Oil Model wrd___ oil sp cml t wrd___ oil sp ult

wrd___ oil sp peak peakyear wrd__ oil dft

Figure 4.3: Input/Output Diagram for the 1st Level Oil Model

A block diagram showing the GLOBESIGHT representation of the model

is given in Figure 4.4. Similar to the population block diagrams, variables in

yellow are assigned BaU values, variables in pink are the scenario drivers, and

the blue variables represent system feedback from the previous year, t-1, to the

present year, t. Project equations, variable definitions, Java model code, XML

variable declarations, and data with sources are listed in Appendices 1 through 5, respectively.

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rwrd___ oil dm b t ● rwrd__ oil dmt rwrd__ oil dm _ mt ● wrd__ oil dmt - wrd__ oil dft

wrd__ oil dm t−1 D

peakyear if t ≤ peakyear = if t > peakyear wrd___ oil sp cml t −1

wrd___ oil sp ult Hubbert’s wrd__ oil sp + D Curve t Model wrd___ oil sp cml = Scenario t = Data, BaU = Feedbacks; One Year

Figure 4.4: Block Diagram for the 1st Level Oil Model in GLOBESIGHT

As the block diagram shows, oil demand growth rate, peak year of production and ultimate recovery are the determining factors for future supply.

The uncertainty regarding peak year, demand growth rates, and ultimate recovery are investigated using scenario analysis in sections 4.3 through 4.5.

4.3 When Will It Peak? Scenarios for Peak Oil: 2010, 2015, 2025

In order to create the BaU scenario for the oil project, the average or expected data and projections should be used. Data for ultimate recovery indicates an average value of 1930 bbls as derived from the information in Figure

2.3. Average growth in global oil demand in the BaU scenario is 3 percent. This estimate is in line with historical demand, 2 percent since the 1990’s, plus the increased demand projected for India and China. The EIA projects a much more conservative growth rate of 2 percent, while other experts claim that the increased consumption in India and China will drive growth to 4 percent. These

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growth rate uncertainties are investigated with high and low growth scenarios in

Section 4.4. The peak years of interest for realistic scenario generation are

2010, 2015, and 2025.

The ASPO, Colin Campbell, J. Laherrere, and dozens of other

independent organizations and oil experts assert that oil will peak around 2008 or

so, perhaps slightly later if high oil prices dampen demand. This analysis will use

a peak year of 2010 for scenario analysis of this estimate since oil prices have

been hitting record highs this July of 2006, which very well may dampen demand.

The last piece of data required is initial world oil demand which was 28.1 bbls. in

the year 2000 according to the U.S. DoE EIA2.

Figure 4.5: BaU World Oil Supply, Demand, & Deficit - Peak 2010

Figure 4.5 shows a graph for supply, demand, and deficit from 2000 through 2050 for this scenario. As the graph indicates, by the year 2022, there is

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only half as much supply as would be needed for demand. This means that half of the entire global infrastructure dedicated to petroleum based commodities could be obsolete in little over a decade after oil peaks in the year 2010. If the peak is shifted by 5 years to 2015, the amount of time for supply to meet only 50 percent of demand is 8 years, Figure 4.6.

Figure 4.6: BaU World Oil Supply, Demand, & Deficit - Peak 2015

Dramatically, if the peak in oil production is shifted to 2025 in the BaU scenario, the supply collapses within 1 year and is depleted by 2027, Figure 4.7. This last scenario would prove disastrous for all countries of the world that rely on petroleum as it leaves almost no time whatsoever to transition to a new fuel.

Hopefully an alternative fuel will become viable soon as a replacement for oil since the time constants for penetration of major transportation in the U.S. was shown to range from 30 to 70 years, which at the earliest, implies if a new

106

infrastructure were started today, it wouldn’t be ready until approximately 2035.

Clearly a problem exists in terms of time left for infrastructure implementation if

BaU demand continues.

Figure 4.7: BaU World Oil Supply, Demand, & Deficit - Peak 2025

Similar scenarios are run for peak years of 2010, 2015, and 2025 under high demand growth of 4 percent and low demand growth of 2 percent.

The results for amount of time until supply reaches 50 percent of demand for the

BaU, high, and low growth demand scenarios are given in Figures 4.8, 4.9, and

4.10, respectively.

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Years Until Oil Supply Reaches 50% of Demand, BaU Demand Growth

2025

2015 50% Peak Year 2010

0 5 10 15 Years

Figure 4.8: Years Until Supply Reaches 50% of Demand – BaU Demand

Years Until Oil Supply Reaches 50% of Demand, High Demand Growth

2025

2015 50% Peak Year 2010

0 5 10 15 Years

Figure 4.9: Years Until Supply Reaches 50% of Demand – High Demand

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Years Until Oil Supply Reaches 50% of Demand, Low Demand Growth

2025

2015 50% Peak Year 2010

0 5 10 15 20 Years

Figure 4.10: Years Until Supply Reaches 50% of Demand – Low Demand

An important feature to note regarding all three demand scenarios is the short window of opportunity that exists to find an alternative source of fuel. The best case scenario with respect to length of time until supply is only half of demand is realized by the 2 percent growth in demand scenario with peak production in the year 2010. This case would provide 15 years of decline time until supply is 50 percent of demand (which it should be noted is an arbitrary, yet

indicative, value for comparison). In the worst case scenario, production follows

demand until 2025, if this is even technically feasible, and then in the case of

high demand, collapses completely by crashing in the year 2026. This scenario

would halt the productivity of the entire petroleum economy, ostensibly overnight.

Forty percent of the global energy supply mix would just dry up, and in the

absence of a replacement, leave the world “stranded by the side of the road” in

an energy crisis of unprecedented magnitude.

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One uncertainty affecting the year of peak production is the amount of

ultimate recovery. To address this uncertainty, an additional amount equal to the

world’s largest field ever discovered is added to the average ultimate recovery to

demonstrate the significance of a find of such magnitude. As mentioned, the

largest field is the Ghawar oil field in Saudi Arabia which, as reported by Saudi

Aramco, was 48 percent depleted after producing 55 billion barrels of oil. This

implies that the entire field is approximately 115 billion barrels. The next

scenario, ‘High Ultimate’, uses a value of 1930 bbls. plus 115 bbls. which is 2045

bbls. total for ultimate recovery. The results in terms of years until supply reaches 50 percent of demand are illustrated in Figure 4.11. The scenario uses a BaU demand profile for growth and investigates peak production in 2010, 2015 and 2025. As the figure shows, the addition of another Ghawar oil field will only buy about 1-2 years of extra time due to the magnitude of yearly global demand.

Years Until Oil Supply Reaches 50% of Demand, BaU Demand Growth High Ultimate Recovery

2025

2015 50% Peak Year 2010

0 5 10 15 Years

Figure 4.11: Years Until Supply Reaches 50% of Demand – BaU Demand,

High Ultimate Recovery

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Consequently even if discoveries of new fields and development of existing ones proves a somewhat higher value for ultimate recovery than the

average value in this investigation, the magnitude of such additions cannot stave

off the inevitability of oil depletion under the continuation of current trends and

increase in global consumer market base (i.e. India and China).

4.4 The Second Level: Regional Oil Demand with Economic Feedback

A second level in the hierarchy will be necessary to examine regional

effects of peak oil. Economic indicators are included to track Gross National

Income (GNI), rate of GNI growth, GNI per person, amount of ODA from GNI,

approximate effects on GNI of constricting oil supply in the period preceding the

peak year. Post-peak year economic behavior is not included in the second level

oil model since all traditional economic activity and modeling of that economy will

change in an unknown, unprecedented way. The relations for post-peak year

economy will not be considered modelable due to the great number of uncertainties involved and the fact that there is no historical basis on which to build hypotheses.

The second level model requires a regionalization for oil consumption in terms of OECD versus non-OECD countries. This regionalization will allow assessment of the economic indicators, especially percent of GNI for ODA, for

the OECD DAC countries which provide a large source of relief aid and funding

for regions including sub-Saharan Africa and its’ HIV/AIDS prevention and

intervention programs.

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The oil consumption for each region is calculated by multiplying total world consumption times the given regions’ percentage use of the total. Oil demand for a region at time t and percentage of global supply consumed for region r at time t are represented respectively by:

oil__ dmrt,,and oil use rt.

The equation relating regional supply and global supply is given by:

oil_ usert, oil___ dm=× wrd oil dm (4.9) rt, t 100

The regional demand for oil is rate based using this approach. Regional demand is derived by splitting up world demand, which is calculated on the first level. The amount of demand is assumed to be met so long as oil remains and has not peaked just as in the first level analysis. Thus, supply is equal to demand for a given region in year t, such that t is before production peaks, as given by:

oil_ sprt,,= oil_ dm rt (4.10) . An indicator for oil supply growth rate is given by:

⎛⎞oil_ sprt, roil_ sp =100×−⎜⎟ 1 (4.11) rt, ⎜⎟oil_ sp ⎝⎠rt,1−

Equation 4.11 states that the growth rate of oil supply in a region this year is equal to 100 times the quantity of this years’ oil supply divided by the previous years’ supply minus 1.

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After the year of peak production, the second level model assumes a

global Hubbert decline as developed in the first level model. The equation

relating regional post-peak year supply for region r in year t is written:

oil_ usert, oil___ sp=× wrd oil sp . (4.12) rt, t 100

This equation distributes global supply according to each regions’ percentage of

overall supply available after production has peaked.

While the model developed so far is an accurate regionalization of the first

level model, it is similar in the method of computing oil demand exogenously

without regard to economic growth of the region. This analysis is informative but

it cannot allow exploration of the regional effect of oil supply reduction on GNI.

Economically, growth in oil supply has led to growth in GNI. Conversely,

reducing oil supply growth could reduce GNI growth; thus a negative feedback

exists from oil supply reductions to GNI growth. A level of energy consumed or

energy intensity per unit of GNI exists which implies that a reduction in the

energy consumed will lead to a decrease in GNI assuming efficiency standards remain constant. This feedback is incorporated in the model to investigate the

effects of delaying a peak in production on economic growth.

The described relation between oil supply and GNI is represented in the

model by letting oil demand for year t for region r through the peak year equal the

projected GNI, times the oil demand intensity per unit of GNI, times the

percentage of oil used, as shown in the following equation.

oil_ usert, oil___ dm= gni×× oil dm gni . (4.13) rt,, rt rt, 100

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A switch is used in the actual model to determine whether to use the exogenous

rate based method for oil demand as in the first level or to use the feedback

relationship just described.

The feedback from level of economic activity to level of oil demand

mandates the inclusion of variables and relations for GNI. GNI is calculated

using a rate based methodology where the GNI for region r in year t is equal to the GNI for the region in the previous year times the quantity of 1 plus the growth rate in GNI. This relation is given by the equation:

⎛⎞rgnirt, gni=×+ gni ⎜⎟1 . (4.14) rt, rt,1− ⎜⎟100 ⎝⎠

Another indicator of importance is GNI per capita. The equation for this is:

gnirt, gni_ pcrt, =× 1000 . (4.15) popfrt,

The equation defines GNI per capita equal to 1000 times GNI divided by

population for region r in year t. Population comes from the first level model

developed in Chapter 3 since the overall level is the value needed (i.e. a second

or third level approach is not warranted if all that is needed is total population).

Variables have been added to track amount earmarked for ODA from the

OECD DAC countries. The amount of ODA donated is equal to the total amount

of GNI times the percentage of GNI for ODA. This relation is given by:

gni_ odart, oda=× gni . (4.16) rt,, rt 100

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The share of ODA for sub-Saharan Africa is simply the total ODA times the percentage for sub-Saharan Africa, as given by:

oda_ ssafrt,,,= oda__ ssaf per rt× oda rt. (4.17)

4.5 Anticipatory Policy for Peak Shift from 2015 to 2025

There exists an urgent need to consider means for delaying the peak year in order to gain time for developing alternative energies and implementing related infrastructure(s) for the alternate(s) selected. The BaU scenarios generated thus far for oil indicate that a global production peak in the years 2010 or 2015, producing 36 and 42 bbls. per annum respectively, provide a much more gradual decline than the supply collapse suffered by ramping up production to 56 bbls. by the year 2025 to meet demand, as shown in Figure 4.12.

Supply equal to one-half demand, peak year 2015

Supply equal to one-half demand, peak year 2010 Supply equal to one-half demand, peak year 2025

Figure 4.12: BaU World Oil Supply, Demand, Deficit – Peak 2010, 2015, 2025

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One way to achieve a positive shift in the peak year of production would be to reduce usage rates. The questions are:

1. What level of production is realistic in terms of actual capacity?

2. How can the shift in peak year be realized?

3. What are the costs for shifting the peak in production by a decade?

To address the first question, the OPEC 2004 publication, Oil Outlook to

2025, will be used to assess what the industry deems a plausible production level. The OPEC World Energy Model (OWEM) is the tool used for supply and demand projections for oil by OPEC. Their model asserts that world oil production could reach a level of 114.5 bbls. per day.3 This converts to a yearly

quantity of about 42 bbls., which according to Figure 4.12 is reached in the BaU

demand scenario by the year 2015.

This scenario will assume that the 42 bbls. per year production forecast by the OWEM model is a production maximum. In reality, 42 bbls. of oil production in a year could only become feasible with a substantial amount of production and development investment, if in fact it is possible. The following figure, Figure 4.13, shows the current world spare production capacity available. In 2005 there was about 1.3 mbpd, equal to about 0.5 bbls. over a year, of extra or spare capacity, which dropped down to 0.5 mbpd as of 2006 due to increased demand from

2005 to 2006.4 Clearly production capacity must increase in line with demand

growth if future supply is going to meet demand; otherwise an oil deficit will exist

due to production limitations.

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Projection 5.

2. Million Barrels per Day

0. 1991 1998 1999 2000 2001 2002 2003 2004 2005 2006 1997

Average

Figure 4.13: World Oil Spare Production Capacity [Source: DoE EIA, 2005]

The goal in shifting the peak is to gain time for alternate resource

development and implementation while still achieving economic growth.

Economic growth is a cornerstone of sustainability and should be carefully considered in constructing realistic scenarios. The production level in the year

2015 for the BaU Scenario is the same as the production capacity forecast by

OPEC. But, continuing along the path of the BaU demand scenario until production peaks in 2025 results in immediate global supply deficits of 20, 40, and 60 bbls. in the years 2026, 2027 and 2028 respectively. The BaU demand scenario that peaks in the year 2025 suffers an oil production collapse due to depletion just a couple years after the peak. Thus, if the peak comes in 2015, the decline is more gradual but consequently only 10 years of time exist to find a solution and implement the infrastructure required. If the peak production level in

2015 could somehow be shifted by a decade to the year 2025, it would allow 20

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years of time to find and implement a solution. This is twice as long as what

currently may occur if trends continue and peak production is reached in 2015

due to capacity restrictions. This scenario would require cooperation from the

world community involved, and the OECD countries to create an anticipatory

policy that would reduce consumption such that the world would reach peak oil at

42 bbls. of production in the year 2025.

The scenario assumes that as of year 2007, the OECD countries will

reduce growth in consumption from approximately two percent down to one

percent. So the current usage is not diminished, but the growth must be reduced. The OECD countries must maintain this cut in consumption until 2025.

This will provide an oil demand from OECD countries in 2025 equal to the

projected 2015 BaU demand level. At which point in time it is assumed that a viable alternative resource is filling the deficit left in the wake of declining oil

supplies.

The possible routes for cutting consumption come in the form of increases in efficiency and decreases in non-essential use. For example, the book, “Factor

Four: Doubling Wealth and Cutting Resource Use in Half” by Weizaker describes methods for increasing energy efficiency such that resource use can be cut in half yielding greater total wealth. Clearly if oil intensive machinery and processes

could produce the same outputs with only half the resource input oil demand

growth would be less than if the efficiencies were not achieved. Therefore some

demand will be diminished by increasing gas mileage of cars, improving leaner

oil consumption methods in the industrial sector, and improving efficiencies of

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remaining oil intensive activities. The other cut in demand will be the reduction or

elimination of non-essential oil use. This might involve: not driving long distances

for leisure on the weekend or vacation, moving closer to work, buying a hybrid

vehicle (note: Fuel Cell Vehicles are not for sale to the general public at this time, if ever), elimination of gas powered hand tools – lawn mowers – etc., and other

forms of conservation that lessen total use.

Unfortunately, historical data indicates that a supply constriction will have

negative effects on the economy. The interaction between economic growth and

oil demand must be considered to assess the potential monetary costs of reducing consumption in order to delay the peak. The next figure shows the BaU

demand scenario peaking in 2015 and 2025 and an interactive scenario which

requires less consumption and results in a peak level in the year 2025 equal to

the level reached by peaking along the BaU demand scenario in 2015. The

graph shows peak production for the BaU Peak 2015 Scenario and the Peak

Shift Scenario at 22.5 bbls. per year which equals 62 mbpd.

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BaU Growth Peak Year 2025 Result: Cost to Delay Peak from 2015 to 2025 is 1% Loss in GDP Growth

Peak Shift BaU Growth Scenario - Peak Peak Year 2015 Shifted from 2015 to 2025

Figure 4.14: OECD Oil Use – Peak Shift Scenario

The interaction accounted for by Equation 4.13 between oil demand and

GNI can be solved for oil demand per level GNI for region r at time t as:

oil_ dmrt, oil__ dm gnirt, = (4.18) gnirt,

The values for oil_ dm_ gnirt, in the BaU demand scenario will be

considered the baseline value for level of oil demand per level of GNI.

The oil supply profile in the Peak Shift scenario negatively impacts the rate

of GNI in that scenario relative to the BaU scenario. The reduction in GNI has a

feedback causing a decrease in oil demand. Oil supply growth rate for the

OECD drops from just above 2 percent in the BaU scenario to about 1 percent in

the Peak Shift scenario. The impact on growth in GNI drops from 3 percent in

the BaU scenario to 2 percent in the Peak Shift scenario. Therefore, economic

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growth is still happening but the rate of growth has been cut by one-third. This represents a potential decrease in year 2025 OECD GNI from just over 50 trillion in the BaU demand scenario to just under 42 trillion dollars in the Peak Shift scenario.

The benefits gained for the initial monetary loss include: avoidance of an oil supply collapse which could halt and even reverse economic growth altogether, 10 years of extra time for concentrated global efforts at finding and implementing an alternative such that a smooth transition without economic depression can occur, and continued ability of OECD countries to provide relief aid, especially ODA, for world health programs since their economies would still experience growth, albeit slightly less growth during the period of Peak Shift policies from 2007 through 2025. It should be noted however that if the Peak

Shift scenario realizes the goal of finding and implementing an alternative fuel, mankind as a whole will be liberated from decades of oil dependency and all the political and economic turmoil that results from its’ use. This alone could be justification for launching such a bold initiative.

4.6 Regionalized Oil Demand Projections by Sector to 2025

While the scenario presented in Section 4.5 has many positive advantages, it is based on a BaU demand pattern for oil. Low, BaU or high demand growth rates of 2, 3, and 4 percent respectively, as presented in Section

4.3, may envelope the possible evolution of demand, but should be thoroughly investigated to reveal the drivers behind the aggregate parameters. In order to dig deeper into the possible futures for oil supply and demand, the demand

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pattern is regionalized and the regionalization is broken down into sectors. This

enables investigation of oil demand growth in specific regions including the

U.S.A., Western Europe, India, China, and the rest of the world, among others.

The sectorization of demand within the regions allows explicit analysis of the

sectors, including transportation, which is the primary source of increases in oil

demand. A scenario is developed to investigate the sensitivity of growth in oil

consumption with respect to growth in vehicle ownership, especially in India and

China, impacting oil demand for transportation.

The importance of oil in the energy mix is shown in Figure 4.155. Its’

share of the total was just over 40 percent in year 2000 and is projected to nearly

hold its’ stake still claiming nearly 37 percent of the total mix by 2025.

World Energy Demand - Reference Case Levels (mtoe) Growth % p.a. Fuel Share % p.a. 2000 2010 2020 2025 2000–10 2010–20 2020–25 2000 2010 2020 2025 Oil 3,614 4,225 5,059 5,492 1.6 1.8 1.7 40.1 38.7 37.6 36.9 Solids 2,341 2,818 3,4353,750 1.9 2 1.8 26 25.8 25.525.2 Gas 2,101 2,800 3,808 4,453 2.9 3.1 3.2 23.3 25.7 28.3 29.9 Hydro nuclear 953 1,065 1,153 1,195 1.1 0.8 0.7 10.6 9.8 8.6 8 renewables Total 9,008 10,908 13,455 14,890 1.9 2.1 2 100 100 100 100 Figure 4.15: World Energy Demand by Fuel Type 2000-2025 [Source: OPEC, 2004] However, this growth projection from OPEC is quite conservative as it assumes

less than 2 percent global demand growth on average through 2025 and relies on the USGS estimate for ultimate recovery. Their estimates for growth may be

low due to an underestimate of vehicle ownership intensity or as an attempt to

match the U.S. DoE projections. The scenario developed here determines a

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plausible rate of growth in oil demand in relation to historical trends and projected and potential vehicle intensity rates for the major regions driving oil consumption.

In order to investigate oil demand growth by region, historical data and projections for the world are disaggregated as shown in Figure 4.166.

World oil demand outlook in the reference case (mb/d) Region 2002 2005 2010 2015 2020 2025 North America 24.2 25.0 26.1 27.2 28.3 29.4 Western Europe 15.1 15.4 15.9 16.3 16.6 16.8 OECD Pacific 8.5 8.8 9.2 9.4 9.5 9.6 OECD 47.7 49.3 51.2 52.9 54.5 55.8 Oil-importing DCs Latin America 3.2 3.3 3.8 4.4 5.1 5.7 Middle East and Africa 1.7 1.9 2.1 2.5 2.8 3.2 South Asia 2.6 3.1 4.1 5.5 7.1 9.1 South-East Asia 3.2 3.4 4.1 5.0 5.7 6.5 China 5.0 6.0 7.6 9.4 11.4 13.5 Oil-exporting DCs OPEC 6.2 6.5 7.3 8.1 9.0 9.9 Other 2.7 2.8 3.2 3.6 4.1 4.6 DCs 24.7 26.9 32.3 38.5 45.3 52.5 FSU 3.8 4.1 4.4 4.7 5.0 5.2 Other Europe 0.7 0.8 0.9 0.9 1.0 1.1 Transition economies 4.5 4.8 5.3 5.7 6.0 6.3 World 77.0 81.0 88.7 97.1 105.8 114.6 World (bbls/yr) 28.1 29.6 32.4 35.4 38.6 41.8 Figure 4.16: World Oil Demand by Region 2000-2025 [Source: OPEC, 2004] North America, Western Europe, South Asia (includes India), China, and the rest of the world are the regions investigated in this scenario as they comprise the major sources of consumption and/or growth in use. North America tops the list for demand using as much in 2002 as all the developing countries put together.

This projection attributes growth in North America to stronger economic and population growth relative to the OECD as a whole.7 Western Europe has little or

no growth over the projection period. This is probably due in part to the fact that

their population is only expected to increase from 183 million in 2000 to 189

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million in 2025. Thus, essentially the same number of people will be driving around in 2025 as there are today, unless for a yet-to-be-known reason there is a change in vehicle intensities. Demand growth in India and South Asia more than triples over the projection period. Demand in China also nearly triples. The transportation sectors in India and China are a main force behind the growth in demand due to the projected growth in vehicle ownership rates in these regions.

The remaining countries also are expected to increase their demand. Figure

4.178 shows the current and projected annual growth in oil demand by region from 2000 through 2025.

Figure 4.17: Annual Growth in Oil Demand by Region 2000-2025 [Source: OPEC, 2004] Next, Figure 4.18 demonstrates the magnitude of growth in the transportation sector relative to the Industry, Household/Commercial/Agricultural, and Electricity Generation sectors. The figure clearly shows the importance of the transportation sector. The transportation sector is the source of 78 percent of the growth in oil demand from 2000 to 2025 in OECD countries, almost the entire

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increase in transition economies and represents close to half of the expected rise in oil demand in developing countries which of course includes India and China.

Figures 4.18, 4.19, and 4.209 shows the annual growth in oil demand by sector for OECD, developing countries and transition economies respectively.

Figure 4.18: Annual Growth in OECD Oil Demand by Sector 2000-2025 [Source: OPEC, 2004]

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Figure 4.19: Annual Growth in D.C. Oil Demand by Sector 2000-2025 [Source: OPEC, 2004]

Figure 4.20: Annual Growth in Trans. Econ Oil Demand by Sector 2000-2025 [Source: OPEC, 2004]

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This scenario examines increases in vehicle intensities and is referred to as the “Vehicle Scenario”. In order to determine the evolution of vehicle ownership rates, an interactive process that requires user input for vehicle ownership growth every five years is used. Initial vehicle ownership in year 2000 per 1000 of the population for many different countries is displayed graphically in

Figure 4.2110.

Figure 4.21: Vehicle Ownership in 2000 [Source: OPEC, 2004] In light of the high ownership rates, especially in affluent countries, and the fact that the transportation sector is almost entirely dependent on oil, it is not surprising that road transportation accounted for about 38 percent of world oil

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consumed in 2001.11 Figure 4.21 shows the enormous potential for growth in vehicle ownership in India and China as they currently reflect only about 10 and

12 vehicles per 1000 of population respectively in contrast with the U.S.A. at over

770 vehicles per 1000 of population. Figure 4.22 lists the number of vehicles per

1000 of population, population, total number of vehicles and historical growth in vehicle ownership from 1970 through year 2000 for selected regions.

Vehicle Ownership in 2000 Vehicle Growth % yearly Veh. Cars Veh. Cars Region per per Pop mill. 70-80 80-90 90-00 mill. mill. 1000 1000 North America 620 384 415 257 159 4.0 2.1 1.7 USA 774 472 282 218 133 3.7 1.9 1.4 Mexico 189 107 98 18 10 12.0 5.8 6.4 Western Europe 444 395 515 229 203 5.1 3.5 2.6 OECD 516 395 1,126 582 445 4.8 2.9 2.2 India 10 6 1,016 10 6 2.9 10.7 10.3 China 12 7 1,275 16 9 15.9 19.6 15.7 Total DCs 36 24 4,574 164 109 8.8 6.8 6.5 Trans. economies 163 138 352 57 49 3.5 3.5 3.9 World 133 100 6,051 803 602 5.0 3.4 3.1 Figure 4.22: Vehicle Ownership 2000 and Vehicle Growth 1970-2000 [Source: OPEC, 2004] Projections for growth in vehicle ownership rates from OPEC are displayed in Figure 4.2312. Vehicle ownership growth in India and China is an

uncertainty which could become a significant driver for oil demand. The

ownership rates per 1000 of population for India and China, according to the

OPEC projection in Figure 4.23, both experience growth either at or below 10 percent over the projection period. This estimate may be too conservative in light of the fact that historically, several countries including Spain, Turkey, South

Korea and Mexico have over the past few decades experienced growth rates in

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vehicle ownership in excess of 10 percent yearly; growth due in large part to the

low initial vehicle intensities, similar to that of India and China13.

Vehicle Growth Vehicle Ownership to 2025 per 1000 % Yearly Region 2000 2010 2020 2025 2000-10 2010-25 North America 620 648 675 687 1.6 1.2 Western Europe 444 523 583 604 2.0 1.1 OECD 516 572 614 631 1.6 1.0 India 10 21 46 63 9.7 8.7 China 12 30 57 74 10.0 6.8 Total DCs 36 51 77 92 5.0 5.1 Trans economies 163 237 304 336 3.7 2.1 World 133 152 178 191 2.6 2.4 Figure 4.23: OPEC Vehicle Ownership & Growth Projections to 2025 [Source: OPEC, 2004] In fact, assumed saturation levels for the countries shown are quite a bit higher than any of the 2025 vehicle intensities projected. The assumed saturation rates are: 850 vehicles per 1000 for North America, 700 per 1000 in Western Europe,

600 per 1000 in transition economies, and 425 vehicles per 1000 for developing countries14.

The Vehicle Scenario assumes that the rate of vehicle ownership will

increase from the initial level in year 2005 to the assumed rate of saturation by

2025 for the regions of India and China (several justifications for this increase are

presented toward the end of this section). The rest of the world is assumed to

remain in line with presented projections. The scenario assumes that from 2000 to 2005 the growth rate in vehicle ownership followed the OPEC projection

displayed in Figure 4.23. From 2005 through 2010 growth will begin to increase gradually. From 2010 to 2015 growth will increase more quickly as used car markets and economies of scale contribute to lower total costs resulting in more

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end users and owners. As more cars enter the market and more 2nd and 3rd

owner mobiles become prevalent, growth continues to quicken over the period

from 2015 through 2020. Growth in vehicle ownership then continues to grow to

the level of saturation for India and China by 2025.

The total number of vehicles for a given region is the product of the

number of people (in thousands) in the region and the number of vehicles owned per 1000 of the population. The road oil demand, number of vehicles per 1000

people, population, total number of vehicles, and oil demand for road

transportation are summarized for the Vehicle Scenario projection period from

year 2000 through 2025 in Figure 4.24.

The global oil demand growth rate in the Vehicle Scenario changes every

five years in response to assumed vehicle ownership levels per 1000 and oil use

per vehicle, as listed in Figure 4.24. The amount of oil use per vehicle improves

by almost a factor of two for both India and China over the twenty-five year

projection period. This advance is inline with expected improvements in

efficiency and decline in oil use per vehicle as projected by the OPEC World

Energy Model and has been summarized by region in Figure 4.2515.

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Assumptions for India Indicators 2000 2005 2010 2015 2020 2025 Road Oil Demand 1.0 1.51 4.43 10.70 20.35 33.73 mboe/d Vehicles per 1000 10 15.5 49 128 253 425 Population (mill.) 1021 1103 1183 1260 1332 1395 Vehicles (mill.) 10 17 58 161 337 593 Oil Use per Vehicle 0.100 0.088 0.076 0.066 0.060 0.057 boe/d mboe/d – million barrels oil equivalent per day Assumptions for China Indicators 2000 2005 2010 2015 2020 2025 Road Oil Demand 0.9 1.46 3.53 7.23 14.32 22.40 mboe/d Vehicles per 1000 12 21 53 121 255 425 Population (mill.) 1274 1316 1355 1393 1424 1441 Vehicles (mill.) 16 28 72 169 363 613 Oil Use per Vehicle 0.056 0.053 0.049 0.043 0.039 0.037 boe/d Figure 4.24: Vehicle Scenario – Population and Vehicle Ownership, Growth

and Efficiency Assumptions for India and China to 2025

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Figure 4.25: Average Annual Growth in Oil Use per Vehicle 1970-2025 [Source: OPEC, 2004] Global oil demand growth for the Vehicle Scenario is the same as all other scenarios until 2005, as that period is now history. Global oil demand for the

Vehicle Scenario is compared with the BaU Scenario for oil production peaking in the year 2025 in Figure 4.26. The growth rate for oil demand in the BaU

Scenario is assumed to be 3 percent over the period from year 2005 through

2025. In contrast, the growth rate for global oil demand changes every 5 years over the period from year 2005 to 2025.

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Vehicle Scenario vs. BaU Peak 2025 - Global Oil Demand Vehicle Scenario Year BaU Demand (bbls) Demand (bbls) 2005 31 31 2010 36 37.5 2015 41.7 46.3 2020 48.3 58.4 2025 56 73.2 Figure 4.26: Average Annual Growth in Oil Use per Vehicle 1970-2025

This is due to the increase in regional demand from India and China in the

Vehicle Scenario as summarized in Figure 4.24. Subsequent growth in global oil

demand for the Vehicle Scenario from year 2005 through 2025 is given in Figure

4.27.

Vehicle Scenario Global Oil Demand Year Growth Rate 2005 - 2010 3.9% 2010 - 2015 4.3% 2015 - 2020 4.8% 2020 - 2025 4.6% Figure 4.27: Vehicle Scenario Annual Growth Rate in Oil Demand 2005-25

The growth rates for global oil demand in the Vehicle Scenario range from an average of 3.9 percent from year 2005 through 2010 to as high as 4.8 percent from year 2015 through 2020. If these rates seem high, it could be the fact that the increase in oil consumption due to higher vehicle intensities in India and

China is added to the BaU demand growth scenario which assumes average growth in global oil demand at 3 percent. If the growth in vehicle intensities is instead added to the low growth in oil demand scenario, which assumes an average annual growth rate of 2 percent, the consequent oil demand growth rates are given for the projection period in Figure 4.28; this scenario is

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henceforward referred to as the Vehicle Scenario (Low). The growth in demand

climbs from 2.9 percent on average over the years 2005 to 2010, up to 3.4

percent from 2010 to 2015 and continues at 4.1 percent on average over the

period from 2015 through 2025. Although this level of growth is certainly less

than in the Vehicle Scenario based on BaU growth, both vehicle scenarios

demonstrate the potential impact that India and China may have on global oil

demand if their car markets continue to flourish.

Vehicle Scenario (Low) Global Oil Demand Year Growth Rate 2005 - 2010 2.9% 2010 - 2015 3.4% 2015 - 2020 4.1% 2020 - 2025 4.1% Figure 4.28: Vehicle Scenario (Low): Based on Low Annual Growth Rate in Global Oil Demand 2005 - 2025 Global oil supply, demand and deficit for the Vehicle Scenario are displayed graphically by Globesight in Figure 4.29. Figure 4.30 shows that even if the increase in vehicle ownership is added to the low growth in global oil demand profile, a global oil deficit is delayed by only 5 years relative to the

Vehicle Scenario shown in Figure 4.29. Both vehicle scenarios leave very little time (5 years or less) until supply is only one-half of demand, indicated by the

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intersection of the deficit and demand curves in the graphs, Figure 4.29 and 4.30.

Figure 4.29: Vehicle Scenario Global Oil Supply, Demand, Deficit

Peak Year 2025

World Oil Demand Oil Deficit

World Oil Supply

Supply equals one-half Out of Oil 2030 of demand

Figure 4.30: Vehicle Scenario (Low) Global Oil Supply, Demand, Deficit

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In order to assess the results from Vehicle Scenario (Low), they are compared and contrasted with the high and low oil demand growth scenarios, with a peak year in 2025, already developed. The high and low oil demand growth scenarios are assumed to envelope the scope or range of potential future demand patterns. The first scenario for vehicle growth added to the BaU demand pattern is assumed to be an overestimate as it does not lie within the envelope created by the High and Low Growth Scenarios; thus the name Vehicle

Scenario will henceforward stand for the scenario in which increase in oil demand due to growth in vehicle ownership is added to the Low Growth

Scenario. Thus, as illustrated in Figure 4.31, the world oil supply for the Vehicle

Scenario follows a trajectory just below that of the High Growth Scenario but above the Low Growth Scenario.

High Growth Scenario

Vehicle Scenario Growth

Low Growth Scenario

Peak Year 2025

Figure 4.31: High, Vehicle Scenario, Low Growth Global Oil Supply

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High Demand Growth Rate

Vehicle Scenario Growth Rate

Low Demand Growth Rate

Figure 4.32: High, Vehicle Scenario, Low Growth Rate Global Oil Supply

Figure 4.32 demonstrates the changing, unassumed growth rate of oil demand

resultant from the vehicle ownership assumptions used in the Vehicle Scenario.

Although the rate peaks slightly above the High Growth Scenario after year 2020, the supply and demand in the Vehicle Scenario remain within the high and low

envelope justifying its’ validity, as depicted in Figure 4.31.

While the growth in vehicle ownership may seem high in the Vehicle

Scenario, several recent articles and country indicators support the increases in

ownership and subsequent increases in oil demand portrayed here. The BBC

News pointed out in February of 2006 that as China’s economy has been surging

forward at 10 percent yearly; it has gone from importing no oil just fifteen years

ago to becoming the number two importer of oil as of 2005 and may have more

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private cars than the U.S. by the year 202016. This projection is in line with the

Vehicle Scenario as the number of vehicles in China catches up to the U.S. level

by 2020. In July of 2006 the New York Times posted an article indicating that the

total number of miles of highway has more than doubled in China since 2001 and

is now only second to the U.S., the number of passenger cars on the road since

2000 has more than tripled from about 6 million to 20 million, and every day 1000

new cars and 500 used ones are sold in Beijing alone.17 National Geographic

has noted that, “China is the worlds fastest growing auto market” and car sales in

2003 showed an 80 percent increase over those of 2002; in addition they point

out that “While most car owners in China today are urban and wealthy, experts

say cheaper models and growing used car markets in the future are likely to

expand car ownership to consumers with more moderate incomes.”18

Similar patterns of very strong growth in vehicle ownership rates have

been observed in India. A recent BBC News article about India reports that:

• Over a million cars were sold in India in 2005

• Both foreign and domestic car producers are ramping up production to

meet Indian demand

• Hyundai has cited India as its’ fastest growing car market and is therefore

planning to double production

• Ford Motors became the fastest growing automotive firm in India as of

January 2006.19

Dr. Pawan Goenka, president of the Mahindra & Mahindra’s auto sector in India states several reasons for strong growth in the automotive sector. The reasons

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include the fact that the economy has been growing at 7 to 8 percent yearly and

it has boosted consumer confidence, available financing and low interest rates, wide selection of models, and a shift in the Indian mindset; Goenka stated that,

“We used to be a nation of savers, but now we’re willing to spend.”20 The BBC

article on India further asserts that the Indian car market is expected to grow by

10 percent in 2006 which has boosted demand for oil and has thus sent demand

for all energy skyrocketing; energy demand is set to increase so much so that by

2020 the region may have to import all of its’ energy needs and that its’ current 3

percent share of global oil demand is expected to climb to 10 percent by 2030.21

Thus the projections for ownership in the Vehicle Scenario may be higher

than OPEC’s, but they may ring true as the actual evolution of vehicle intensity in

light of the incredibly strong economic growth and low initial intensities in India

and China. The Vehicle Scenario demonstrates that the evolution of foreign car

markets, especially in India and China, may significantly impact global oil

demand. While many factors national and international affect how vehicle

ownership rates will unfold, comprehension of the scope and magnitude of the

transportation sector’s role in global oil demand must be at hand in order to

understand and gauge possible future resource consumption.

4.7 Conclusions

While there is no way to predict the future of oil prices, demand, supply, exact peak year of production, etc., the developed scenarios provide a range

within which the evolution may occur. Several properties are emergent across all

the scenarios, most important of which may be the fact that global demand, even

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under low growth, will deplete supplies within the first one-third to one-half of this

century. Demand may outpace production capacity as most of the existing fields

are either peaking or in decline and extraction becomes economically prohibitive

as wells age past their peak years. Another common feature between the

various scenarios is the short amount of time available after the year of peak

production until supply levels can only meet fifty percent of demand, which will of

course signal a period of physical supply deficit and related economic consequences.

The developed scenarios demonstrate several possible outcomes which

relative to the others are optimistic, a continuation of business-as-usual, and

pessimistic in terms of dealing with the oil deficit while sustaining economic

support from the affluent and developed countries of this world to the needy,

afflicted, and impoverished billions of our fellow human beings. The most

optimistic projection for dealing with the impending oil deficit comes from the

Peak Shift Scenario in which countries must work together to delay the peak in

order to avoid an oil supply collapse, gain an extra 10 years to find and

implement an alternative solution and continue economic expansion, albeit at a

slightly lesser pace. The cooperation that must take place between countries in

order to achieve the Peak Shift Scenario is assumed to also provide economic

growth and goodwill which could fund the Millennium Development Goals

(MDGs) presented in Chapter 7. The MDG Scenario developed in Chapter 7 is

linked to the Peak Shift Scenario in Chapter 8 which demonstrates the benefits of

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achieving the MDGs in number of deaths averted due to adequate funding achieved via ODA contributions.

The BaU Peak 2025 Scenario for oil is linked to the BaU Scenario for

AIDS developed next in Chapter 5. The pessimistic scenario, ODA Cut Scenario developed in Chapter 8, which results in a temporary elimination of ODA assistance from 2015 through 2025 is tied to the BaU Peak 2015 Scenario. This is due to the fact that the demand level in the BaU Peak 2025 Scenario from

2015 through 2025 is above probable supply capacities and would require

substantial investment adequate of providing additional production capacity

greater than the sum of all current capacity. The costs would be astronomical if

production capable of meeting supply in the BaU Peak 2025 Scenario is even

physically possible. The assumed effect on oil prices are an increase in per

barrel prices such that economic generosity from the OECD countries via ODA

assistance is temporarily suspended from 2015 through 2025 in light of a global

oil deficit and related economic recession. The BaU and ODA Cut Scenarios for

HIV/AIDS both assume that funding continues or returns at BaU levels after year

2025. The multi-level hierarchical models, relationships and BaU Scenario for

HIV/AIDS are developed and investigated next in Chapter 5.

Works Cited:

1 Blanchard, Roger. “The Impact of Declining Major North Sea Oil Fields upon Future North Sea Production.” Hubbert Peak of Oil Production, Jan. 2000. http://www.hubbertpeak.com/blanchard/ (accessed: August 7, 2006).

2 United States Department of Energy (DoE) Energy Information Administration (EIA). International Petroleum Monthly Online Data and Statistics, http://www.eia.doe.gov/emeu/ipsr/source2.html (accessed: May 13, 2004).

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3 Oil Outlook to 2025, 12.

4 United States Department of Energy (DoE) Energy Information Administration (EIA). Short Term Energy Outlook 2005 (Washington D.C.: DoE/EIA, May 2005) http://www.eia.doe.gov/emeu/steo/pub/outlook.html (accessed: June 10, 2005).

5 Oil Outlook to 2025, 6.

6 Oil Outlook to 2025, 7.

7 Oil Outlook to 2025, 8.

8 Oil Outlook to 2025, 8.

9 Oil Outlook to 2025, 10.

10 Oil Outlook to 2025, 16.

11 Oil Outlook to 2025, 16.

12 Oil Outlook to 2025, 19.

13 Oil Outlook to 2025, 17.

14 Oil Outlook to 2025, 17.

15 Oil Outlook to 2025, 24.

16 Wingfield-Hayes, R. “Satisfying China’s Demand for Energy.” BBC News, February 16, 2006, http://news.bbc.co.uk/2/hi/asia-pacific/4716528.stm (accessed: March 17, 2006).

17 Conover, T. “Capitalist Roaders.” The New York Times Magazine, 07/02/2006, http://travel2.nytimes.com/2006/07/02/magazine/02china.html (accessed: July 6, 2006).

18 Handwerk, B. “China’s Car Boom Tests Safety, Pollution Practices.” National Geographic News, 06/28/2004, http://news.nationalgeographic.com/news/2004/06/0628_040628_chinacars.html. (accessed: July 1, 2006).

19 Vaswani, Karishma. “Soaring Energy Demand Spark Indian Fears.” BBC News, February 16, 2006, http://news.bbc.co.uk/1/hi/business/4715980.stm (accessed: March 17, 2006).

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20 “Soaring Energy Demand Spark Indian Fears”, 2.

21 “Soaring Energy Demand Spark Indian Fears”, 3.

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Chapter 5: Demographic Impact of HIV/AIDS

5.1 Introduction

In order to analyze the potential consequences of ODA increases,

reductions or possible elimination on sustainability within sub-Saharan Africa and

Botswana, a comprehensive representation of the involved demographic and

economic systems is constructed. This chapter defines the representation of

demographic aspects and Chapter 6 then examines the related economic

features. Several hierarchical models have been developed to examine the

effects of HIV/AIDS on key population and epidemiologic indicators. A BaU

Scenario is created from UNAIDS data, projections, and their Estimation and

Projection Package (EPP) for the epidemic. The developed methodology and models are used to assess the future course of the virus and its’ impacts through

2050 for the BaU Scenario. Optimistic projections enabled by adoption and

implementation of the Millennium Development Goals are investigated in Chapter

7. Worst case possibilities for the future course of HIV/AIDS in sub-Saharan

Africa are then examined in the ODA Cut Scenario developed in Chapter 8, which then links the global oil deficits and subsequent economic recession to increased AIDS deaths and reduced sustainability within sub-Saharan Africa.

Once again it should be emphasized that the results are not intended for numerical accuracy or prediction; rather they show the general features of possible futures for given assumptions.

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5.1.1 Chapter Organization

First, Section 5.2 addresses the basic problem of modeling the epidemic

in a population. Section 5.2.1 provides an overview of current and previously

used modeling approaches. Then, Section 5.2.2 relates the current model to

former attempts. A third level HIV/AIDS model is developed with a BaU scenario

in Section 5.3. Sections 5.4 and 5.5 develop the second and first levels in the

hierarchy respectively. The advantages of the multilevel hierarchy formulated for

the HIV/AIDS virus are set out in Section 5.6. Finally, Section 5.7 presents

conclusions from model results and observed dominant relations.

5.2 Modeling of the Virus in a Population

One technique of gaining insight into how the HIV/AIDS virus progresses

and affects population is through the use of modeling. The first consideration in

creating the model is to determine the cause and effect nature of the system.

Here the system is a population infected with a disease. The dominant relations

of the system define the basic characteristics of operation. For example,

infections occur through exposure to another infected individual’s body fluids

through (usually) sexual contact, infected blood transfusions, or infection through

birth by exposure to an infected mother. But, not all exposure results in

contracting HIV. There is an associated HIV infection rate. Thus one

relationship to capture is that of exposure and possible infection. Further, by

discerning the dominant relations of the system, policy making and analysis can

become more transparent. For example, if it can be shown that limiting exposure limits infections, then condom and preventative programs can be justified as a

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method to combat the disease. Also, scenario analysis is enhanced through

understanding of how the system responds to changes in basic parameters.

The next consideration must be the level of detail. Hierarchical models

are used to understand the purpose of the overall system on the highest level,

while running down the hierarchy the model represents the functioning within the

system. This multi-level design offers various levels of detail. The scope of the

models becomes increasingly more complex from one level to the next. A simple

“Big Picture” model might be sufficient for some purposes, while other demands can require in-depth information about the exact operating mechanisms contained only within lower level analysis.

5.2.1 Existing Models in Literature

The models within this project are demographic/behavioral models. In the

early 1990’s, four significant models of this form were developed by Bulatao

(1991), Bongaarts (1990), Palloni (1991), and the Interagency Working Group

(Stanley et al., 1991). The methods comprise a demographic model coupled with

an epidemiologic model by Bulatao1, a Markovian approach by Bongaarts2, a

differential equation procedure by Palloni3, and a different differential equation

model by the Interagency Working Group (IWG)4. The major characteristics of

these previous demographic/behavioral models are summarized in Figure 5.15.

146 Bulatao (1991) Bongaarts (1990) Palloni (1991) IWG (Stanley, 1991) Markov chain (time differential step: 1 month), equation using differential equation, Demographic model probability of Structure/ instantaneous allows choice of linked with infection calculated Mathematics transition rates various numerical epidemiologic model by weighted average between disease solution methods over various stages distributions * heterosexual * heterosexual * homosexual male * heterosexual * heterosexual Transmission * perinatal * perinatal * perinatal * perinatal modes * transfusion * transfusion * transfusion * transfusion * medical injections * shared needle * up to 6 stages between infection and AIDS * 4 stages between * HIV to AIDS infection and AIDS

Disease transition is duration- * allows for natural * 3 basic states * 3 basic states Progression dependent, with immunity and people

different functions that never progress to for pediatric and AIDS adult

* demographic *single year cohorts model single year * infection rate and cohorts * single year cohorts uninfected * epidemiological * sexual activity, mortality are age- model adults and incubation period, dependent * treated as continuous Age structure children (no cohorts) non-AIDS mortality, * HIV to AIDS variable * adult AIDS deaths and fertility depend transition, HIV and assigned to 5 year on age AIDS mortality are cohorts by user- age- and duration- specified distribution dependent * inactive M,F * single * monogamous with * married to infected like partner M,F *married to uninfected * heterosexual M,F *monog. with highly * monogamous *rural and urban * homosexual M mobile partner F M,F populations Population/ * bisexual M * high mobility M,F * non-monogamous * subgroups: partner behavior groups * up to 10 behavioral * distribution over M, F acquisition rate, subgroups in each groups used in * prostitutes (F) migration status, group determining condom use probability of frequency, other infection, groups not STDs, individual tracked separately transmissibility * proportion of * distrib. of sexual * monogamous * allows for circular adults may change activity rates within choose like partners and permanent behavior subgroups, each behavior group * non-monog. M migration (migrant with random * distribution among choose prostitutes acts as “single” while Behavior redistribution and behavior within 50% of the time away from home) characterization * probabilities of groups varies by age * dist. of age prefs. * allows for polygamy choosing partners * partners from same used to calculate * age of partners from among cohort, except distrib. of couples determined by male subgroups prostitutes by age of partners preferences * disease stage * gender * gender Factors * condom usage * disease stage * duration of * handled according to affecting * other STDs * condom usage infection population subgroups transmission * one user-defined * other STDs * condom use factor Figure 5.1: Modeling Techniques in the Early 1990’s [Source: Ljung, 2002]

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More recently, the Joint United Nations Programme on HIV/AIDS

(UNAIDS) Reference Group on Estimates, Modeling, and Projections in conjunction with the World Health Organization (WHO) and the Futures Group have developed several HIV/AIDS software modeling packages: Estimation and

Projections Package (EPP), the Workbook Method, and SPECTRUM. The EPP and Workbook Method both fall into the category of epidemic models. They make use of current and past HIV prevalence to find the best fitting curve that shows trend in adult HIV prevalence over time. Limitations of these models include: no explicit consideration of gender, no movement between risk groups, does not incorporate Anti-Retroviral Therapy (ART) usage or prevention of mother-to-child transmission (PMTCT), and cannot estimate high and low future scenarios based on parameters fit to input. UNAIDS itself has stated “current version of EPP…is still only a curve fitter.”6 Further, UNAIDS cautions that

analyses should cover a maximum time horizon of five years into the future.

The SPECTRUM program is actually a suite of policy models including:

DemProj: Demography, FamPlan: Family Planning, AIM: AIDS Impact Model,

RAPID: Resources for the Awareness of Population Impacts on Development,

PMTCT: Prevention of Maternal-to-Child Transmission, NewGen: Reproductive health for adolescents, BenCost: Financial benefits and costs of family planning

programs, and CR: Condom Requirements. Although it delivers many key

demographic and epidemiologic output variables, one major limitation is that it

usually receives a portion of its’ required input from either the EPP or the

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Workbook method, which have their own limitations. Thus the input requires an a priori knowledge of future HIV prevalence, which is speculative at best.

The Asian Epidemic Model (AEM) is a process model that falls into the behavioral/demographic class. Input parameters are varied by the user until the model bears a reasonable resemblance to the current situation. It has been effective in replicating recent developments for the concentrated epidemics in

Asia. But, the AEM does not consider persons of age less than fifteen years. In addition, a more complex set of inputs also prevents its’ use in a number of places with insufficient data on epidemiologic indicators and behavioural patterns.

The main shortcoming of models in the demographic/behavioral category is the enormous amount of data required and the large number of assumptions that inherently must be made.

5.2.2 Relation of Current Model Structure to Prior HIV/AIDS Models

The HIV/AIDS models developed in this project are a cross between the early models by Bulatao and the more recent Asian Epidemic Model (AEM).

Demographic indicators such as population, sex ratio, fertility, mortality, etc. and epidemiologic data such as disease prevalence rates, HIV infection rates, AIDS rates, mother-to-child transmission rates etc. are incorporated in combination with the human user, forming a system to model disease progress and generate meaningful scenarios and conclusions. Similar to the AEM, the 3rd Level

HIV/AIDS Model developed uses a base set of known and assumed parameters which are fine tuned to simulate the current state of the epidemic. This process

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tailors specific parameters to the correct values for epidemic evolution in the area

of investigation. This parameter tuning provides unique values for different

regions and virus populations that are consistent with the variability of progression due to regional norms, cultural values, access to prevention and treatment, etc.

Advantages of these models over previous techniques are described in

Sections 5.3, 5.4, and 5.5. The hierarchy of HIV/AIDS models developed in this

project is the most extensive of its kind in the literature.

5.3 The 3rd Level HIV/AIDS Model and BaU Projections to 2050

5.3.1 Model Formulation

Unlike the population hierarchy constructed in Chapter 3, the HIV/AIDS

hierarchy is defined initially on the third level and then linked to the second and first levels thereafter. This is due to the age-specific features of the virus and the

type of data and statistics that are available regarding the disease and transmission. For example, prevalence rates for countries are the number of people aged 15 to 49 that are infected with the virus divided by the number of people in the 15 to 49 age cohort. The statistic is defined in this way to make it more comparable across different populations such that the severity of the epidemic in the age group most afflicted is not skewed by substantially large or small elderly or child populations.

The cornerstone of the Third Level HIV/AIDS Model is the Third Level

Population Model developed in Chapter 3. Epidemiologic and population

categorization features necessary to represent the HIV/AIDS epidemic are

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incorporated with the Third Level Population methodology. The result is a model

with variable length time horizon, gender cohorts, age-specific sexual behavior,

multiple transmission methods (heterosexual, homosexual, intravenous drug use,

blood transfusions, and perinatal), tracking of healthy individuals, new infections, disease-age specific HIV and AIDS patient cohorts, age-specific population requiring ART, and, natural and disease related deaths.

The progression of the population through various stages of the virus is illustrated in Figure 5.2.

Uninfected HIV+ AIDS Deaths

Male Female

Dashed lines indicate births entering the population

Figure 5.2: Diagram of HIV/AIDS Progression

The population is divided into male and female categories. The top of the male

and female categories with the dotted lines entering represent newborn males

and females respectively. To illustrate the progression, an example is used. If a

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woman is uninfected and contracts HIV she moves from the uninfected block to

the HIV+ block along the solid black arrow. If while in the HIV+ block she has a

baby, then the baby is either infected with HIV and follows the dashed line to the

appropriate male or female HIV+ block or the baby is HIV- and follows the

dashed line all the way to the uninfected block. Transition from HIV to AIDS is

duration dependent with the average time in sub-Saharan Africa being about 9.1 years without ART. When the transition from HIV+ to AIDS occurs, the woman moves to the AIDS block along the solid black arrow. Length of time in the AIDS block is generally 1 to 2 years. Transition to death from the AIDS category is also duration dependent. When the woman dies, she moves along the solid black arrow to the Deaths block. The process is similar for male progression with the elimination of births.

The first relation presented here from the model will focus on births. The population must be split in to three distinct groups: uninfected, HIV+, and full- blown AIDS, as depicted in Figure 5.2. This categorization is essential to represent accurately the possible states an individual can assume. Let the uninfected, HIV+, and AIDS populations for region r in a given age cohort j at time t be represented respectively by:

popc__ negrjt,,, popc hiv rjt ,, and popc _ aids rjt ,,

The variables for uninfected births and age-specific fertility from cohort j and the

male-to-female ratio for women of child-bearing age for region r at time t are

specified correspondingly by:

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brtc__ negrjt,,, frtc rjt ,, and mfratio frtrt,

Equation 5.1 defines the number of uninfected births from uninfected mothers in region r age cohort j at time t equal to the number of uninfected women in the cohort in the previous year times the age-specific fertility rate for region r age cohort j at time t. frtc mfratio_ frtrt, rjt,, brtc__ neg=×× popc neg (5.1) rjt,, rjt,,− 1 100 1000

The total number of births from uninfected mothers is then the summation of the births in each cohort for women of child-bearing age, 15 through 49, as given by the equation:

49 brt__ neg= ∑ brtc neg (5.2) rt, j=15 rjt,,

To determine the number of HIV infected births from HIV+ mothers, the mother-to-child transmission rate, mtchivrrt, , for region r at time t is used. The number of HIV+ births, brtc_ hivrjt,,, and the population with HIV, popc_ hivrjt,,, for region r, cohort j at time t are also necessary for this calculation. Then, the number of infected births for region r, cohort j at time t equals the HIV+ female population per cohort from the previous year times the mother-to-child transmission rate times the age-specific fertility rate, as given in the following equation.

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mfratio_ frt frtc brtc__ hiv=××× popc hiv mtchivr rt, rjt,, rjt,, rjt ,, rt, 100 1000 (5.3) Therefore the total number of HIV+ births, brt_ hivrt, , is given by the relation:

49 brt__ hiv= ∑ brtc hiv (5.4) rt, j=15 rjt,,

Consequently, the number of uninfected births from infected mothers for region r cohort j at time t, brtc_ neg_ hivrjt,,, is given by the equation:

mfratio_ frt frtc ⎛⎞ rt, rjt,, brtc__ neg hiv=×−×× popc _ hiv⎜⎟ 1 mtchivr rjt,, rjt,,− 1 ⎝⎠rt, 100 1000 (5.5) And so, the total number of uninfected births from HIV+ mothers,

brt_ neg_ hivrt, , is the summation of these births in each cohort, written:

49 brt__ neg hiv= ∑ brtc __ neg hiv (5.6) rt, j=15 rjt,,

Thus the total number of births for women of age j, brtcrjt,,, is the sum of the uninfected births from uninfected mothers of age j plus the uninfected and infected births from infected mothers of age j, given by the equation:

brtcrjt,,=+ brtc_ neg rjt ,, brtc__ neg hiv rjt ,, + brtc _ hiv rjt ,, (5.7) So the total number of births is then the summation of the births in every age cohort of reproductive capacity, ages 15 thru 49, as given by the equation:

49 brt= ∑ brtc (5.8) rt, j=15 rjt,,

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The next set of calculations involves deaths due to age-specific mortality

for both the uninfected and infected populations. Age-specific mortality is the

same for uninfected and HIV infected individuals that have not yet progressed to

AIDS. A separate age-specific mortality is applied to the AIDS populations as their mortality levels are far greater than that of the non-AIDS infected groups.

The mortality for an individual not infected by AIDS in region r of age j at time t, mrtcrjt,,, is applied to the populations in the uninfected and HIV+ but

asymptomatic categories. The number of deaths in a year for the uninfected

population of age j, dthc_ negrjt,,, is the product of the previous years’ uninfected cohort population times the age-specific mortality rate, given by the equation:

mrtcrjt,, dthc_=_ neg popc neg × (5.9) rjt,, rjt,,− 1 100

Subsequently, the total number of deaths in a year for uninfected people is the summation of their deaths in every age group from newborns through 100 and

over, as given in the equation:

100 dth__ neg= ∑ dthc neg (5.10) rt, j=0 rjt,,

The number of deaths for individuals of age j who have HIV but are asymptomatic for AIDS, dthc_ hivrjt,,, is then:

mrtcrjt,, dthc_=_ hiv popc hiv × (5.11) rjt,, rjt,,− 1 100

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As a result, the total number of deaths for the HIV+ population that has not yet

progressed to AIDS, dth_ hivrt, , is:

100 dth__ hiv= ∑ dthc hiv (5.12) rt, j=0 rjt,,

The population with AIDS is subject to a much higher level of mortality,

mrtc_ aidsrjt,,, as individuals without ART survive, in general, less than a

year or two. The number of deaths in a year for AIDS infected individuals of age

j, dthc_ aidsrjt,,, is the product of the population with AIDS from the previous year and the age-specific mortality rate for AIDS, written:

mrtc_ aidsrjt,, dthc_=_ aids popc aids × (5.13) rjt,, rjt,,− 1 100

Then the total number of deaths due to AIDS is the summation of the deaths in

every age cohort from newborn through 100 and over, given by the equation:

100 dth__ aids= ∑ dthc aids (5.14) rt, j=0 rjt,,

Now that the deaths in each category have been calculated, the total

number of deaths for a region in cohort j in a given year is the sum of the deaths

in the uninfected, infected but non-AIDS, and the AIDS populations in the cohort,

as given by the equation:

dthcrjt,,=++ dthc_ neg rjt ,, dthc__ hiv rjt ,, dthc aids rjt ,, (5.15)

It follows that the total number of deaths is then the summation of all the deaths in every age group, written as:

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100+ dth= ∑ dthc (5.16) rt, j=0 rjt,,

The next section of calculations determines the transition from the uninfected to the HIV+ category. Factors affecting this transition include the number of sexual contacts an individual has per year, transmission probability per sexual contact, and use of contraceptives, vaginal microbicides, or other preventative techniques.

The number of new HIV cases for region r, age cohort j, in year t is the product of the number of uninfected people of age j-1 in year t-1, the average sexual contact for age j-1 in year t-1, the HIV transmission rate per sexual contact, and the percentage of the population that is infected and capable of transmitting the disease in year t-1, as given by the equation:

pop__ hiv per popc__ newhiv=××× popc neg contact hivinr rt,1− rjt,, rj,1,1−− t rj ,-1, t rt, 100 (5.17)

Consequently, the total number of new HIV infections is the summation of the infections in each age, given by:

100 pop__ newhiv= ∑ popc newhiv (5.18) rt, j=0 rjt,,

Knowing the number of new HIV infections from the previous year, it is possible to compute the uninfected population in the subsequent year. The population of age j that is uninfected by the epidemic in year t is equal to the population of age j-1 in the previous year minus the number of those aged j-1

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that died last year minus the number of individuals of that age that move to the

HIV infected status, as given by the equation: popc__ neg=−− spopc neg dthc _ neg popc _ newhiv rjt,,rj,1,1−− t rj ,1,1 −− t rjt ,, (5.19)

Therefore, the total uninfected population equals the summation of the negative populations in all cohorts, as given by the equation:

100 pop__ neg= ∑ popc neg (5.20) rt, j=0 rjt,,

The next consideration in calculating cohort populations is the transition from HIV+ status to that of developed AIDS. The AIDS transition rate for region r at time t, aidsinrr,t , determines the number of people that transition from HIV+ to AIDS. The total number of individuals of age j in year t that contract the AIDS virus is equal to the HIV+ population of age j-1 in the prior year times the AIDS transition rate.

popc_ newaids= spopc_ hiv× aidsinr (5.21) rjt,, rj,1,1−− t rt,

Therefore, the total new AIDS population in year t is given by the equation:

100 pop__ newaids= ∑ popc newaids (5.22) rt, j=0 rjt,,

The HIV+ population of age j in year t is then equal to the HIV+ population of age j-1 in year t-1 minus the number of them who die, plus the new HIV infections in the cohort minus the number in the cohort that develop AIDS, as given by the equation:

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popc__ hiv=−+− spopc hiv dthc _ hiv popc _ newhiv popc _ newaids rjt,,rj,1,1−− t rj ,1, − t rjt ,, rjt ,, (5.23) The total population infected with HIV is the summation of the infected persons of

every age, given by the equation:

100 pop__ hiv= ∑ popc hiv (5.24) rt, j=0 rjt,,

Next, the population with AIDS in cohort j at time t is equal to the

population of age j-1 in the previous year minus the number of those of age j-1 in the previous year that died from AIDS plus the individuals of age j-1 in year t-1

that develop AIDS by year t, as given by the following equation:

popc__ aids=−+ popc aids dthc _ aids popc _ newaids rjt,,rj,1,1−− t rj ,1, − t rjt ,, (5.25) As a consequence, the total population with AIDS is the summation of the AIDS

populations in each age cohort, as given by:

100 pop__ aids= ∑ popc aids (5.26) rt, j=0 rjt,,

The total population for region r, cohort j, in year t is the sum of the

uninfected, HIV+, and AIDS populations in each cohort, as given by:

popcrjt,,=++ popc_ neg rjt ,, popc__ hiv rjt ,, popc aids rjt ,, (5.27)

It then follows that the total population for region r in year t is the summation of

the populations of every age, given by the relation:

100+ poprt, = ∑ popc (5.28) j=0 rjt,,

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The populations in each cohort, disease status and aggregation groups

are further divided into male and female categories. This feature enables the

production of demographic representations such as population pyramids and

helps to demonstrate the disproportionate effects that the epidemic has on the

different sexes.

Among the critical outputs of the model are the HIV prevalence rates for

specific age aggregates. Dimensions are incorporated within the model to

address prevalence within the 15-24, 15-49, 0-14, and 51 and over age

aggregates. Prevalence within the 15 through 24 year old group is considered a good proxy for incidence as a majority of new infections are contracted within this demographic. The 15 through 49 age grouping is a standard for comparison across different regions to enable a picture of the infection that is not biased by regional variances in population structure outside the age groups mainly affected.

The HIV prevalence rate for ages 15 through 49 is equal to the total number of people infected with the virus between the ages of 15 and 49 divided by the total population aged 15 through 49, as shown in the equation:

49 ∑ ( popc__ hivrjt,,+ popc aids rjt ,,) hiv_100 prevr =×j=15 rt,15...49, 49 ∑ ()popcrjt,, j=15 (5.29)

Similarly, the HIV prevalence within the youth ages 15 through 24 is given by:

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24 ∑ ( popc__ hivrjt,,+ popc aids rjt ,,) j=15 hiv_100 prevr =× rt,15...24, 24 ∑ ()popcrjt,, j=15 (5.30)

Among other outputs of the third level model are all the required inputs for

the second and first level abstractions of this representation, described in

sections 5.4 and 5.5 respectively.

Outputs for a second level approach include crude birth and death rates.

The crude birth rate for region r in year t equals the total number of births per

one-thousand people in the population in year t, as given by the equation:

brtrt, crbrtrt, =×1000 (5.31) poprt, The constant factor of 1000 is for proper scale since poprt, is given in units of thousands of people. Similarly, the crude death rate is the total number of deaths divided by the total population in region r in year t, as given by:

dthrt, crdthrt, =×1000 (5.32) poprt,

The first level parameter for population growth rate is the population for

region r in year t divided by the population in the previous year, minus one, all

times 100, since the growth rate is given as a percentage.

⎛⎞poprt, rpop =100×− 1 (5.33) rt, ⎜⎟ ⎝⎠poprt,1−

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The required inputs and the outputs for the third level approach can be represented by the input/output diagram shown in Figure 5.3. The illustration shows the required inputs to replicate a BaU scenario for the HIV/AIDS epidemic.

Figure 5.4 shows a detailed block diagram for the third level representation. The block diagram can be used to visualize the feedback loops present and to aid understanding of progression and its’ mechanisms via a pictorial representation.

rpopr frtcr, j popcr, j popr mfratio_frtr popc_hivr, j pop_hivr

mfratior 3rd Level popc_aidsr, j pop_aidsr HIV/AIDS mtchivrr popc_noaidsr, j pop_noaidsr Model crbrt r hivinr r

crdthr aidsinrr brtcr, j brt r mrtcr, j brtc_hiv r, j brt_hivr mrtc_aidsr, j dthcr, j dthr

ipopar, k dthc_aidsr, j dth_aidsr

ipopc_hivr, j popc_maler, j pop_maler

ipopc_aidsr, j popc_femaler, j pop_femaler

hiv_prevrr, Age15to49 contactr, j

hiv_prevrr, Age15to24 = Data, BaU Inputs

Figure 5.3: 3rd Level HIV/AIDS Input/Output Diagram

The detailed block diagram includes model variables used in

GLOBESIGHT for scenario analysis. They are highlighted in pink and contain

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the string “_m” as a suffix. Scenario variables are used to perform scenario analysis by varying BaU parameters from established values to values which represent assumed changes within the parameters for specific investigation for a chosen scenario. This chapter provides projections for the BaU scenario,

Chapter 7 then examines optimistic futures achieved via success of the MDGs, and Chapter 8 investigates worst-case possibilities resultant from ODA cuts due to global oil deficits and consequential economic losses.

= Scenario Variable rd = Data, BaU Inputs 3 Level HIV/AIDS Model Block Diagram = Feedbacks; One Year Delay mtchivr_b [r] mfratio [r] popc_male [r, j], j=0…100 ● mtchivr [r] ● mtchivr_m [r] 49 100 brtc_neg_hiv [r, j], j=15…49 ∑ pop_male [r] ∑ j=0 ● j=15 - frtc_b [r, j], j=15…49 brtc_hiv [r, j], j=15…49 100 ∑ pop_female [r] 49 j=0 frtc [r, j], j=15…49 ∑ brt_neg_hiv [r] frtc_m [r] j=15 ● popc_female [r, j], j=0…100 spopc_hiv [r, j], j=0…100 D brt_hiv [r] mfratio_frt [r] + brt [r] crbrt [r] 49 ÷ ● brtc_neg [r, j], j=15…49 ∑ brt_neg [r] aidsinr_b [r] j=15 popc [r, j], j=0…100 spop [r] aidsinr [r] D D ● ● popc_neg [r, j], j=0…100 100 + ∑ pop [r] spopc_neg [r, j], j=0…100 j=0 ÷ aidsinr_m [r] popc_hiv [r, j], j=0…100 +

mrtc_m [r] ● mrtc [r, j], j=0…100 ● popc_aids [r, j], j=0…100 rpop [r] + D mrtc_b [r, j], j=0…100 ÷ ● dthc_neg [r, j], j=0…100 + spopc_aids [r, j], j=0…100 mrtc_m_aids [r] crdth [r] 100 dthc_hiv [r, j], j=0…100 dthc [r, j], j=0…100 ∑ dth [r] + j=0 ● mrtc_aids [r, j], j=0…100 spopc_aids_loss [r, j-1], j=1…100 D ● mrtc_b_aids [r, j], j=0…100 dthc_aids [r, j], j=0…100 + popc_aids_loss [r, j], j=0…100 49 popc_newhiv [r, j], j=0…100 ∑ hivinr_b [r] hivinr [r] j=15 ÷ hiv_prevr [r, j], j=15…49 ● ● hivprevr [r, j], j=15…49 hivinr_m [r] contact [r, j] D popc_newaids [r, j], j=0…100

Figure 5.4: 3rd Level HIV/AIDS Block Diagram

5.3.2 BaU Projections for Sub-Saharan Africa

The base year for all model calculations is 2000. This provides a five year start-up period to provide model validation and perform parameter estimation and

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evaluation. Data and projections for the BaU scenario are drawn from various accepted sources including:

• UNAIDS - Joint United Nations Programme on HIV/AIDS

o UNHCR – United Nations High Commissioner for Refugees

o UNICEF – United Nations Children’s Fund

o WFP – World Food Programme

o UNDP – United Nations Development Program

o UNFPA – United Nations Population Fund

o UNODC – United Nations Office on Drugs and Crime

o ILO – International Labour Organization

o UNESCO – United Nations Educational and Scientific and Cultural

Organization

o WHO – World Health Organization

o World Bank

• U.S. Census Bureau – HIV/AIDS Surveillance Database

• UN World Population Prospects Database

• UNAIDS’ Estimations and Projections Package (EPP)

• Spectrum Modeling Package by The Futures Group

The model can easily incorporate available data for relevant transmission drivers which vary between localities and concentrated versus generalized epidemics; the focus and subsequent structure here being the generalized epidemic ravaging sub-Saharan Africa and Botswana. All required demographic and epidemiologic input data is located in Appendix 5 and includes:

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population projection aggregates by age group, age-specific fertility rates, male-

to-female ratios for overall and reproductive ages, non-AIDS age-specific mortality, AIDS age-specific mortality, age-specific exposure risk from sexual contact, HIV infection rate from exposure, initial HIV and AIDS age distribution, age-specific, duration dependent, AIDS transition rates, and a profile for the number of years with cumulative percentage dying from AIDS, without ART, for both adults and children.

General epidemiologic data used are briefly described here with a detailed listing of the required inputs given in Appendix 5. The probability of infection from sexual contact without protection (such as condoms, microbicides, etc.) is

higher for women than it is for men. This is due to the physical anatomical

differences between the two sexes; differences which prove the female sex

nearly three times more prone to initial infection. Men average about a 3 percent

and women about a 9 percent chance of infection from unprotected contact with an infected partner.7 The HIV infection rate in the model incorporates this feature

of the disease and uses an average HIV infection rate of 6 percent as the

generalized form of the virus spreads mainly via heterosexual contact which

yields an average rate of 6 percent.

Once infected, the number of years to progress from HIV to AIDS

averages about 9 years with roughly 80 percent of adults and 99 percent of

children dying within thirteen years of infection as shown in the following figure.

This is the default disease progression profile for sub-Saharan Africa used by the

Futures Group in their Spectrum HIV/AIDS modeling module. This parameter is

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represented in the model by the HIV to AIDS transition rate which determines the number of new AIDS cases per cohort per year.

Cumulative % Dying from AIDS w/o ART # Years Male Female Children 1 0 0 34 2 3 1 49 3 7 3 55 4 12 7 59 5 19 12 61 6 27 19 65 7 36 27 71 8 45 36 77 9 54 46 84 10 62 56 90 11 69 65 95 12 76 73 98 13 82 81 99 14 86 86 100 15 90 91 100 16 93 94 100 17 95 96 100 18 97 98 100 19 98 99 100 20 99 99 100 Figure 5.5: Number of Years with Cumulative Percent Dying from AIDS [Source: UNAIDS, 2005] After progressing from HIV into the status of AIDS, the AIDS mortality rates determine the number of deaths per age cohort per year. Age-specific mortality for individuals infected with AIDS is given in Figure 5.6. The figure illustrates that young children and the elderly suffer the highest mortality rate once transitioning to AIDS with 89.1 percent mortality within one year of infection.

The adult rate for ages 15-49 is assumed to equal 56 percent yearly which tapers up to the elderly rate from age 50 to 60.

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Age 0-10 11 12 13 14

AIDS 89.1% 83.0% 76.0% 69.0% 62.0% Mortality

Age 15-49 50 51 52 53 54 55 56 57 58 59 60+

AIDS 56.0% 59.0% 62.0% 65.0% 68.0% 72.0% 75.0% 78.0% 81.0% 84.0% 87.0% 89.1% Mortality

Figure 5.6: Age-specific AIDS Mortality Rates [Source: Ljung, 2002]

The current number of individuals infected in sub-Saharan Africa as of the

end of 2005 is estimated by UNAIDS in the 2006 Global AIDS Report at 24.5

million people. The adult prevalence in 2005 for ages 15-49 is estimated at 6.1

percent with the trend in sub-regional epidemics stabilizing in some countries

while still gaining momentum in overall number of infections. Deaths total

approximately 2 million with new infections weighing in at 2.7 million in 2005.

Mother-to-child transmission rates at 30 percent cause nearly 500 thousand

infected births in the model base year 2000. Current indicators and the

epidemiologic and demographic data and projections are combined to project the

BaU scenario from year 2000 through 2050.

The table shown in Figure 5.7 lists several important epidemic indicators

for sub-Saharan Africa for the UN/WHO BaU Scenario. The indicators include:

size of population infected with HIV/AIDS, HIV prevalence for ages 15-49, HIV

prevalence for ages 15-24 (proxy for incidence), number of HIV infected births,

number of new HIV infections, number of people receiving ART, number of deaths from AIDS, and total population. The table lists the number of individuals on ART rather than a dollar amount cost figure for the infected since the prices

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for anti-retroviral drugs are variable with negotiations, production scales and distribution channels all influencing current and future costs; thus the magnitude of the population on ART is reported and can be multiplied by whatever the current or projected ARV drugs cost per person to determine a total cost for ARV drugs.

Over the course of the scenario time horizon, the number infected continues to increase while prevalence declines slightly. HIV infected births continue to take their toll and ART is received by less than 1 in 6 in need.

Despite the epidemic, population growth continues and more than doubles over the fifty year period from 2000 through 2050.

Sub-Saharan Africa: 2005, 2025 & 2050 BaU Model Results Indicator 2005 2025 2050 HIV/AIDS 24.8 37.2 54.2 Population (mil.) HIV Prevalence % 6.1 5.7 5.5 Ages 15-49 HIV Prevalence % 3.93 3.79 3.64 Ages 15-24 HIV+ Births 498 570 566 (thousands) New HIV 3.0 4.2 5.8 Infections (mil.) ART Coverage 815 1215 1770 (pop., thousands) Deaths from AIDS 2.1 3.5 5.0 (millions) Total Population 751 1137 1686 (millions) Figure 5.7: BaU Model Results for Sub-Saharan Africa 2005, 2025 & 2050

Figure 5.8 shows an HIV population pyramid for sub-Saharan Africa. It shows the UN/WHO BaU Scenario population infected with HIV and AIDS in

2005 of 24.8 million, grouped by five-year age-cohorts. This type of pyramid is

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useful for determining the absolute scale of the problem in terms of infected

population size, without consideration for the total population. It illustrates the

magnitude of the problem in per person terms whereas prevalence rates

compare the proportion of those infected to the total population or age aggregate

of interest.

The overall population pyramid for sub-Saharan Africa, Figure 5.9, gives a

visual snapshot of the population age structure and distribution of the epidemic

within the various cohorts. The pyramid shows the HIV-, HIV+ and AIDS populations. Thus, it can be seen that although prevalence may sound low at 6.1 percent for sub-Saharan Africa relative to say 24.1 percent in Botswana, the sub-

Saharan rate represents an enormous infected population totaling over 24 million individuals by 2005.

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24.8 Million HIV/AIDS Cases 2.7 Million New Infections 2 Million Deaths 6.1% HIV Prevalence Total Population 751 Million

Figure 5.8: UN BaU HIV Pyramid for Sub-Saharan Africa in 2005

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Figure 5.9: UN BaU HIV/AIDS Pyramid for Sub-Saharan Africa in 2005

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Figures 5.10 and 5.11 show the projected HIV age distribution of the virus

for the years 2025 and 2050 respectively. Although the general shape of the

pyramid is similar, the horizontal population scale pushes out from 2.5 million in

2005, to 4 million in 2025, finally reaching over 5 million by 2050. Thus, even

though prevalence rates are declining, overall infections continue to grow due to

population growth within the region and the fact that new infections outpace deaths from AIDS.

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37.2 Million HIV/AIDS Cases 4.2 Million New Infections 3.5 Million Deaths 5.7% HIV Prevalence Total Population 1.14 Billion

Figure 5.10: UN BaU HIV Pyramid for Sub-Saharan Africa in 2025

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54.2 Million HIV/AIDS Cases 5.8 Million New Infections 5.0 Million Deaths 5.5% HIV Prevalence Total Population 1.69 Billion

Figure 5.11: UN BaU HIV Pyramid for Sub-Saharan Africa in 2050

In order to demonstrate the substantial impact that the virus has on the demographic structure of a population, the model includes variables which

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calculate all demographic indicators as if a cure for HIV/AIDS had been

introduced and the virus did not exist after the year 2000. Figure 5.12 shows the

projected population cohort structure for year 2050 for the BaU AIDS and AIDS

Cure scenarios. The red area represents the population under effects of the virus while the blue extensions represent population levels without the virus or

“what could have been” if the virus were eradicated. Population totals fall from about 1.9 to 1.7 billion, differing by about 260 million people, as illustrated in

Figure 5.13. Quite clearly, the epidemic is taking millions of lives and exerts a disproportionate amount of its’ devastation on those in the prime and primary years of life.

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Figure 5.12: UN BaU Population Pyramid with and w/o Effects of AIDS: Sub-Saharan Africa in 2050

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Figure 5.13: UN BaU Population with and w/o Effects of AIDS: Sub-Saharan Africa 2000-2050 Figure 5.14 shows the population in the BaU scenario in 2050 in red with the blue extensions representing the number of people in the cohorts that died from AIDS. Thus, the AIDS deaths move or age along with the population to demonstrate just how many individuals relative to the age group examined have died from AIDS (i.e. if a 20 year old dies from AIDS in 2010, the individual will show in the 30 year age cohort in 2020, since age 30 = age 20 + 2020 - 2010).

While the blue area may not seem relatively large, the magnitude of sub-Saharan

Africa must be recalled to realize that tens of millions are infected and already deceased.

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Figure 5.14: UN BaU Population Pyramid with AIDS Losses from Cohort: Sub-Saharan Africa in 2050

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5.3.3 BaU Projections for Botswana

Since Botswana is one of the most severely affected countries within sub-

Saharan Africa, it is modeled to demonstrate the change in scales that is

possible using the methodology. Rather than examining sub-Saharan Africa as a

region, the sub regions can be modeled and aggregated to generate the profile

for sub-Saharan Africa as a consequential output which is the average of the individual results. The aggregate method is outside the scope of this investigation but merits study to determine benefits and associated costs in terms of required data, additional assumptions necessary, etc.

Figure 5.15 lists important epidemic indicators for Botswana for the

UN/WHO BaU Scenario.

Botswana: 2005, 2025 & 2050 Model Results Indicator 2005 2025 2050 HIV/AIDS 264 267 282 Population (thous.) HIV Prevalence % 24.5 24.9 26.8 Ages 15-49 HIV Prevalence % 15.0 15.8 16.8 Ages 15-24 HIV+ Births 3.1 2.5 2.3 (thousands) New HIV 25.6 24.3 22.8 Infections (thous.) ART Coverage 208 210 222 (pop., thousands) Deaths from AIDS 18.9 20.1 21.5 (thousands) Total Population 1754 1656 1658 (millions) Figure 5.15: BaU Scenario Results for Botswana 2005, 2025 & 2050

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Figure 5.16 shows the HIV population pyramid for Botswana. It shows the

UN/WHO BaU scenario population infected with HIV/AIDS in 2005 of 264 thousand, grouped by five-year age-cohorts.

264 Thousand HIV/AIDS Cases 25.6 Thousand New Infections 18.9 Thousand Deaths 24.5% HIV Prevalence Total Population 1.77 Million

Figure 5.16: UN BaU HIV Pyramid for Botswana in 2005

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The overall population pyramid for Botswana is shown in Figure 5.17. The extremely severe level of the epidemic shows cohorts in which nearly half are infected, specifically the 30-34 year age aggregate.

Figure 5.17: UN BaU HIV/AIDS Pyramid for Botswana in 2005

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Figures 5.18 and 5.19 show the projected HIV age distribution of the virus for the years 2025 and 2050 respectively.

267 Thousand HIV/AIDS Cases 24.3 Thousand New Infections 20.1 Thousand Deaths 24.9% HIV Prevalence

Figure 5.18: UN BaU HIV Pyramid for Botswana in 2025

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282 Thousand HIV/AIDS Cases 22.8 Thousand New Infections 21.5 Thousand Deaths 26.8% HIV Prevalence Total Population 1.66 Million

Figure 5.19: UN BaU HIV Pyramid for Botswana in 2050

The enormous force that the virus has on demographic composition is illustrated in Figure 5.20 which shows the projected population cohort structure for year 2050 for the BaU AIDS and AIDS Cure scenarios. The red area represents the population under effects of the virus while the blue extensions

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represent population levels without the virus. Population totals fall from about 2.8 to 1.6 million, differing by about 1.2 million lives, as illustrated in Figure 5.21.

Figure 5.20: UN BaU Population Pyramid with and w/o Effects of AIDS: Botswana in 2050

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Figure 5.21: UN BaU Population with and w/o Effects of AIDS: Botswana 2000-2050 Figure 5.22 shows the population in the BaU scenario in 2050 in red with the blue extensions representing the number of people in the cohorts that died from AIDS. The blue area representing cohort deaths due to AIDS is quite prominent as Botswana pays an extremely high toll in terms of premature human mortality due to the virus.

Other outputs from the third level model include crude birth, crude death, and population growth rates. These outputs will be used on the 2nd and 1st levels as inputs in addition to overall population HIV and AIDS infection rates, as described in Sections 5.4 and 5.5.

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Figure 5.22: UN BaU Population Pyramid with AIDS Losses from Cohort: Botswana in 2050

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5.4 The 2nd Level HIV/AIDS Model

The next level up the hierarchy is the 2nd Level HIV/AIDS Model. It

requires far less data but provides much less detail, for example there is no

representation of age on the 2nd level. Required inputs include initial population, crude birth and death rates, the percentage of the population that has HIV, and

the percentage that has advanced to AIDS. Population for region r at time t,

popsrt, , on the second level is equal to the population from time t-1 times the

quantity 1 plus the quantity crude birth minus crude death divided by 1000 as

given in Equation 5.34:

⎛⎞crbrtsrt,,- crdths rt pops=+ pops ×⎜⎟1 (5.34) rt, rt,1− ⎜⎟1000 ⎝⎠

Notice that the growth factor in Equation 5.34 is simply the population growth rate

which is a required input for the 1st level approach. The population growth rate

on the 2nd level can also be calculated as equal to the quantity of the population in year t divided by the population in year t-1 minus 1 times 100 to convert to a percentage as shown in the equation:

⎛⎞popsrt, rpops =100×− 1 (5.35) rt, ⎜⎟ ⎝⎠popsrt,1−

The percentage of the population that has HIV and AIDS are not explicitly

given in most data or reports but can be easily calculated. This is since the

standard for country comparisons thus far has been the prevalence rate in the 15

through 49 age range. Although, the UNAIDS 2006 GAR just this year lists the

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total number of Adults infected with HIV/AIDS since the 50+ population has shown significant positives in their ranks. The number of people with HIV in region r at time t, pops_ hivrt, , is equal to the total population times the percentage infected, as shown in the following equation.

pops_ hiv_ perrt, pops_ hiv=× pops (5.36) rt,, rt 100

Similarly for AIDS, the number of AIDS cases is equal to the total population times the percentage with AIDS, as given in the equation:

pops_ aids_ perrt, pops_ aids=× pops (5.37) rt,, rt 100

The number of births is equal to the crude birth rate times the population given by:

crbrtsrt, brts=× pops (5.38) rt,, rt 1000

The number of births with HIV can be calculated by the equation:

brts_ hivrt,,,= brts__ hiv per rt× brts rt (5.39)

Which relates the number of births with HIV as equal to the percentage of births with HIV times the total number of births.

Deaths equal the crude death rate times the population, written as:

crdthsrt, dths=× pops (5.40) rt,, rt 1000

The number of deaths due to AIDS can then be calculated as equal to the percentage of deaths from AIDS times the total number of deaths, as given by:

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dths_ aidsrt,,,=_ dth aids _ per rt× dths rt (5.41)

The HIV growth rate is equal to the number infected with HIV at time t

divided by the number infected at t-1 minus 1 times 100 to yield a percentage as

shown in the equation:

⎛⎞pops_ hivrt, rpops_ hiv =100×− 1 (5.42) rt, ⎜⎟ ⎝⎠pops_ hivrt,1−

Similarly, the growth rate of AIDS is given by the equation:

⎛⎞pops_ aidsrt, rpops_ aids =100×− 1 (5.43) rt, ⎜⎟ ⎝⎠pops_ aidsrt,1−

The input/output diagram for this representation is given in Figure 5.23. A detailed block diagram for the model structure is shown in Figure 5.24 and all 2nd

Level HIV/AIDS Model Equations are located in Appendix 1.

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rpopsr,t crbrtsr,t

rpops_hivr,t 2nd Level crdths rpops_aids r,t HIV/AIDS r,t Model crbrtsr,t

ipopsr,t crdthsr,t

popsr,t

pops_hiv_perr,t pops_negr,t

pops_hivr,t pops_aids_perr,t

pops_aidsr,t

brtsr,t

dthsr,t = Data, BaU Inputs

Figure 5.23: 2nd Level HIV/AIDS Input/Output Diagram

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= Scenario Variable = Data, BaU Inputs D spops_hiv [r] = Feedbacks; One Year Delay

pops_hiv_per_b [r] ● pops_hiv_per [r] ÷ rpops_hiv [r] pops_hiv_per_m [r] ● pops_hiv [r] crbrts_b [r] ● crbrts [r] ● pops [r] - pops_neg [r] crbrts_m [r] + rpops [r] ● pops_aids [r] crdths_b [r] crdths [r] ● spops [r] D ÷ rpops_aids [r] crdths_m [r] D spops_aids [r] ● brts [r] pops_aids_per_b [r] ● dths [r] pops_aids_per_m [r] ● pops_aids_per [r]

Figure 5.24: 2nd Level HIV/AIDS Block Diagram

5.5 The 1st Level HIV/AIDS Model

Moving to the top of the hierarchy, the 1st Level, only the major aspects of

the system are visible. The only information available from one time step to the next is the level of population including the number infected with HIV and AIDS.

The equation for total population on the 1st Level is the same as developed in

Chapter 3, which is given by the equation:

⎛⎞rpopfrt, popf=×+ popf 1 (5.44) rt, rt,1− ⎜⎟ ⎝⎠100

The HIV population equals the total population times the HIV percentage of that population, written as:

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popf_ hiv_ perrt, popf_ hiv=× popf (5.45) rt,, rt 100

The HIV growth rate is then equal to the quantity of the HIV infected population at

time t divided by the HIV infected population at time t-1, minus 1, times 100 to

convert to a percentage as given by the equation:

⎛⎞popf_ hiv ⎜⎟rt, rpopf_ hivrt, =100×−⎜⎟ 1 (5.46) ⎜⎟popf_ hiv ⎝⎠rt,1−

Similarly, the AIDS population equals the total population times the

percentage infected with AIDS, given by:

popf_ aids_ perrt, popf_ aids=× popf (5.47) rt,, rt 100

The AIDS growth rate is then equal to the quantity of the AIDS infected

population at time t divided by the AIDS infected population at time t-1, minus 1,

times 100 to convert to a percentage as given by:

⎛⎞popf_ aids ⎜⎟rt, rpopf_100 aidsrt, =×⎜⎟ (5.48) ⎜⎟popf_ aids ⎝⎠rt,1−

The input/output diagram for the 1st Level HIV/AIDS model is shown in

Figure 5.25. It requires the least amount of data but yields fewer detailed population indicators. Figure 5.26 is a detailed block diagram illustrating the relations within the model representation.

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rpopfr,t rpopfr,t st 1 Level rpopf_hivr,t HIV/AIDS

ipopfr,t0 Model rpopf_aidsr,t

popfr,t popf_hiv_perr,t

popf_negr,t popf_aids_per r,t popf_hivr,t

popf_aidsr,t

= Data, BaU Inputs

Figure 5.25: 1st Level HIV/AIDS Input/Output Diagram

= Scenario Variable = Data, BaU Inputs D spopf_hiv [r] = Feedbacks; One Year Delay

popf_hiv_per_b [r] ● popf_hiv_per [r] ÷ rpopf_hiv [r] popf_hiv_per_m [r] ● popf_hiv [r] rpopf_b [r] ● rpopf [r] ● popf [r] - popf_neg [r] rpopf_m [r] ● popf_aids [r]

spopf D ÷ rpopf_aids [r]

D spopf_aids [r] popf_aids_per_b [r] ● popf_aids_per [r]

popf_aids_per_m [r]

Figure 5.26: 1st Level HIV/AIDS Block Diagram

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5.6 Advantages of Multilevel Design

The 1st, 2nd, and 3rd Level HIV/AIDS Models each have specific

advantages and disadvantages. The correct model for use depends on the

question(s) to be answered. A hierarchy of models provides flexibility in finding

the right modeling tool for the job.

As described, the 1st Level approach requires the least amount of data but

provides fewer population descriptors. This approach is appropriate for simple trend extrapolations and provides estimates for overall HIV/AIDS populations.

The 2nd Level approach provides more information at the nominal cost of

knowledge of crude birth and death rates, percentage of HIV births, and

percentage of AIDS deaths. If this information is available, births and deaths in

the population as well as HIV infected births and AIDS deaths can be obtained.

Required data for 3rd Level analysis is extensive but so are the results. A true

third level approach which evolves independently in time from one year to the

next, ungoverned by a priori assumptions about prevalence provides a wealth of

demographic and epidemiologic indicators useful for assessing the current state

of affairs and in determining the direction of future developments.

5.7 Conclusions

The BaU Scenario presented reveals growing numbers of HIV infected

individuals and increasing numbers of deaths due to the virus. Continuation of

current levels of funding and prevention and treatment levels will result in a subsequent continuation of the epidemic. The Cure Scenario presented

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demonstrates the millions of lives that are already lost and projected numbers

that could follow if the epidemic persists.

Next, Chapter 6 examines the effects of HIV/AIDS on the economy and on

the working-age populations of sub-Saharan Africa and Botswana for the BaU

Scenario.

Works Cited:

1 Bulatao, R A. “The Bulatao Approach: Projecting the Demographic Impact of the HIV Epidemic Using Standard Parameters.” The AIDS Epidemic and Its Demographic Consequences (New York: UN/WHO, 1991), 90-104.

2 Bongaarts, J. “A model of the spread of HIV infection and the demographic impact of AIDS.” Statistics in Medicine, 1990, 8.1, 103-20.

3 Palloni, A. & Lamas, L. “The Palloni Approach: A duration-dependent model of the spread of HIV/AIDS in Africa.” The AIDS Epidemic and Its Demographic Consequences (New York: UN/WHO, 1991), 109-118.

4 Stanley, E.A., Seitz, S.T., Way, P.O., Johnson, P.D. & Curry, T.F. “The United States Interagency Working Group Approach: the IWG model for the heterosexual spread of HIV and the demographic impact of the AIDS epidemic.” The AIDS Epidemic and Its Demographic Consequences (New York: UN/WHO, 1991), 119-136.

5 Ljung, Tracy. “Systems Approach to Assessing the Effect of AIDS on Sub- Saharan Africa.” Case Western Reserve University, 2002.

6 Ghys P.D., Brown T., Grassly N.C., et al. “The UNAIDS Estimation and Projection Package: a software package to estimate and project national HIV epidemics.” Sexually Transmitted Infections 2004; 80 (Suppl I) :i5–9.

7 “Systems Approach to Assessing the Effect of AIDS on Sub-Saharan Africa.”

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Chapter 6: Socio-Economic Impact of HIV/AIDS

6.1 Introduction

While the number of infected individuals and direct number of lives lost due to HIV/AIDS continues to grow, economic ramifications of the virus become more severe. This is due in large part to the number of working age individuals afflicted with the disease. Since the virus disproportionately affects those in the prime years of their lives with respect to working and family care responsibilities, economic losses and breakdown in family structure are products left in the wake of the epidemic. The loss of premium age workers directly affects the “bottom- line” for economic growth. In addition, breakdown in family structure often results in children leaving school in order to care for the afflicted and/or sibling orphans left behind, which has a net result of lesser education and insufficient ability to obtain future employment.

A First Level Economic Model is developed to examine a few of the potential effects of the virus on the economy. Growth of Gross National Income

(GNI), GNI levels per capita, and potential workforce population are the indicators examined. However, the projections generated should be used to gain an understanding of how the system responds to given assumptions and not for strict numerical accuracy or prediction.

6.1.1 Chapter Organization

The First Level Economic Model relationships are developed in Section

6.2. The model is then propagated with requisite economic development data 196

taken from The World Bank Group’s online database; results are presented in

Section 6.3. Section 6.3.1 presents model results for sub-Saharan Africa and

Section 6.3.2 presents results for Botswana.

6.2 The Economic Model

The economic model developed is a basic first level representation in

order to provide transparency and acknowledge the uncertainties involved in

future economic activity. Little is gained from the use of complex modeling and predictive techniques if they are based on uncertain or poorly understood assumptions. The driver for economic growth is the growth rate of GNI per capita. The GNI per capita for a given region at time t is equal to the GNI per capita at time t-1 times the GNI growth factor, as shown in Equation 6.1.

⎛⎞rgni_ pcrt, gni__1 pc=×+ gni pc ⎜⎟ (6.1) rt, rt,1− ⎜⎟100 ⎝⎠

GNI is then the GNI per capita times the population of the given region, as given

in Equation 6.2.

gni_ pcrt,,× pop rt gni = (6.2) rt, 1000

The growth rate for GNI is calculated as GNI for region r at time t divided by GNI

at time t-1, subtract 1, and multiply by 100 to yield a percentage as given by

equation 6.3.

⎛⎞gni_ pc ⎜⎟rt, rgni =100×−⎜⎟ 1 (6.3) rt, ⎜⎟gni_ pc ⎝⎠rt,1−

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The number of uninfected people of working age is the sum of all

uninfected individuals from age 18 through ages 65. Although all of these people

do not work, the uninfected number available to work is of interest since they

represent the workforce that can be retained more reliably with lower health care

costs over time and may mature into future corporate leaders and instrumental

contributors, in part due to experience that comes with time. The uninfected

population of workforce age is given by equation 6.4.

65 wrk__ pop= ∑ popc neg (6.4) rt, j=18 rjt,,

The percentage of uninfected working age people is the uninfected population of

working age divided by the total population times 100 to yield a percentage, as

given by Equation 6.5.

wrk_ poprt, wrk__ pop perrt, =× 100 (6.5) poprt,

6.3 BaU Scenario Economic Projections

Economic data required for the model includes initial GNI per capita and

GNI per capita growth rate. Data is obtained from The World Bank Group Online

Database1 and is available up to the year 2005. The other variables in the model

come from the Third Level HIV/AIDS Model from the BaU Scenario. Results for

sub-Saharan Africa and Botswana are summarized in Sections 6.3.1 and 6.3.2 respectively.

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6.3.1 Economic Model Results for Sub-Saharan Africa

The amount of GNI per capita in sub-Saharan Africa in year 2000 was

$1,589 International Dollars. Growth in GNI per capita for sub-Saharan Africa is

estimated at 1 percent over the projection period and is consistent with recent

trends in growth. Results for the BaU HIV/AIDS Economic Scenario are shown

in table form in Figure 6.1. Indicators are shown for years 2005, 2025 and 2050.

Sub-Saharan Africa: BaU Economic Model Results Indicator 2005 2025 2050 Gross National 1.4 2.6 4.9 Income (tril. Intl.$) GNI Growth Rate 3.2 % 2.9 % 2.3 % (percent) GNI per Capita 1863 2273 2915 (Intl.$) Uninfected Working Age Percentage of 51.2 % 54.5 % 59.8 % Population Figure 6.1: BaU Scenario Economic Results for Sub-Saharan Africa 2005, 2025 and 2050 The tabled results of the BaU Economic Scenario demonstrate that while overall GNI and GNI per capita increase, growth in GNI slows from 3.2 percent in

2005 to 2.3 percent by 2050. Also, there is an increase in uninfected working

age persons as the prevalence of the virus declines slightly over the time horizon

from 2005 through 2050. Model results comparing the MDG and ODA Cut

Economic Scenarios to the BaU Economic Scenario are presented in Chapter 8

and illustrate the comparative advantages and disadvantages between the

different possible futures.

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6.3.2 Economic Model Results for Botswana

The amount of GNI per capita in Botswana in year 2000 was $7,187

International Dollars which is considerably higher than the average for sub-

Saharan Africa. This is due in large part to the diamond mining industries which prosper from indigenous wealth. Growth in GNI per capita for Botswana is estimated at 4 percent over the projection period and is consistent with recent trends in growth. Results for the BaU HIV/AIDS Economic Scenario are shown in table form in Figure 6.2. Indicators are shown for years 2005, 2025 and 2050.

Botswana: BaU Economic Model Results Indicator 2005 2025 2050 Gross National 17.6 36.2 96.7 Income (bil. Intl.$) GNI Growth Rate 4.1 % 3.8 % 4.2 % (percent) GNI per Capita 9982 21872 58306 (Intl.$) Uninfected Working Age Percentage of 46.3 % 47.2 % 48.2 % Population Figure 6.2: BaU Scenario Economic Results for Botswana 2005, 2025, 2050

Results for Botswana indicate steady growth in GNI and robust growth in average per capita income. The actual number of uninfected workers remains fairly constant over the time horizon since total population in Botswana actually declines slightly from year 2005 through 2050. The disparities between economically developed regions like Botswana and the undeveloped regions in sub-Saharan Africa are set to diverge greatly under these assumptions. Thus the gap between the rich and poor within the framework of the sub-Saharan continent could increase far beyond present levels seen today.

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6.4 Conclusions

The results presented provide greater insight when compared to other scenarios to determine the relative benefits or advantages under varying underlying assumptions. The BaU results developed in this chapter are compared and contrasted with the results generated from analysis of the MDG and ODA Cut Scenarios developed in Chapters 7 and 8 respectively. Optimistic visions for the future of the HIV/AIDS epidemic are developed next, in Chapter 7, and outline the possible benefits of successful implementation of the UN’s

Millennium Development Goals.

Works Cited:

1 World Bank Group. World Bank Online Development Database, http://devdata.worldbank.org/query/default.htm (accessed: June 11, 2006).

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Chapter 7: Optimistic Vision- Achieving the UN Millennium

Development Goal for HIV/AIDS

7.1. Introduction

Any discussion of combating the extreme poverty and HIV/AIDS epidemic in sub-Saharan Africa must consider external sources of funding and aid. The reasons for this are quite numerous and include: severity of the HIV/AIDS epidemic, lack of local funding resources, inadequate health systems and related infrastructure, need for medical professionals and long-term loan indebtedness to name a few.

Sustainability and development in sub-Saharan Africa will be abysmal

without a concerted aid effort on the part of the worlds more developed and subsequently more affluent countries. Globally, there are a myriad of organizations, international development banks, and UN agencies which provide funding for critical human services in sub-Saharan Africa. A key problem with the number of different donors, governments, organizations, agencies, etc. has been coordination of programs. This lack of coordination tears at the fabric of program targets by creating competition for required personnel, early initiative cancellations due to lack of funds which are being spent on starting similar efforts which may not be as advanced, inability to enact national programs due to government or donor restrictions, etc. “In some countries, the number of international entities- donor agencies and their contractors, UN agencies and international development banks, the Global Fund, international nongovernmental organizations and foundations- involved in funding,

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implementing, or overseeing AIDS programs or advising national governments can reach into the dozens. Organizations, initiatives, and the relationships among them form a bewildering tangle, as illustrated in considerably simplified form for Tanzania” in Figure 7.1.1 The OECD DAC position within this web shows its’ multiplicity of connections to both the UN agencies and international development banks.

Figure 7.1: Regional HIV/AIDS Network for Tanzania [Source: UN Millennium Project, 2005]

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As the case of Tanzania shows, developing a coherent response to AIDS at the

country level requires coordinating the activities of many players (note: Lines

represent important institutional links.).2

In response to this dilemma and the global state of poverty, disease,

famine, and other human inequities, world leaders have drafted a set of goals,

known as the Millennium Development Goals (MDGs), to guide policy making

and create interventions aimed at combating poverty and improving the quality of life for hundreds of millions of people. Funding for the MDGs is expected to come from the OECD countries through the DAC by increasing the percentage of

GNI for ODA to seven-tenths of one percent. Thus it must be emphasized; realization of the MDGs is dependent upon the generosity of the world’s wealthy countries which may not last in the face of a world-wide economic recession due to an energy crisis from the oil deficit in the post-peak era.

7.2 The Millennium Development Goals

7.2.1 The Goals

The MDGs were inspired from the Millennium Declaration espoused by world leaders at the UN Millennium Summit in September of 2000. The Millennium

Declaration addresses such issues as: freedom, equality, solidarity, tolerance, respect for nature, shared responsibility, peace, security, disarmament, development and poverty eradication, protecting our common environment, human rights, democracy, good governance, protecting the vulnerable, meeting the special needs of Africa, and strengthening the UN in combination with

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recommendations and resolutions.3 The declaration was ratified by hundreds of

Heads of State who met for this special summit at the UN. The potential benefits

of realizing these goals are immense. According to a UN Development Group

commissioned press release, “If the world achieves the Millennium Development

Goals, more than 500 million people will be lifted out of poverty. A further 250

million will no longer suffer from hunger. 30 million children and two million mothers who might otherwise have been expected to die will be saved.”4

“The Millennium Development Goals (MDGs) are the world's time-bound

and quantified targets for addressing extreme poverty in its many dimensions-

income poverty, hunger, disease, lack of adequate shelter, and exclusion-while

promoting gender equality, education, and environmental sustainability. They are

also basic human rights-the rights of each person on the planet to health,

education, shelter, and security.”5 The eight goals are to:

Millennium Development Goals

1. ERADICATE EXTREME POVERTY AND HUNGER

2. ACHIEVE UNIVERSAL PRIMARY EDUCATION

3. PROMOTE GENDER EQUALITY AND EMPOWER WOMEN

4. REDUCE CHILD MORTALITY

5. IMPROVE MATERNAL HEALTH

6. COMBAT HIV/AIDS, MALARIA AND OTHER DISEASES

7. ENSURE ENVIRONMENTAL SUSTAINABILITY

8. DEVELOP A GLOBAL PARTNERSHIP FOR DEVELOPMENT

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Achievement of these goals will require immediate action. Thus, in 2002

the UN Secretary General and the United Nations Development Program

(UNDP) formed the UN Millennium Project.

7.2.2 The Millennium Project

“The Millennium Project is an independent advisory body commissioned

by UN Secretary-General Kofi Annan to recommend a global plan for achieving

the Millennium Development Goals (MDGs) by 2015. The Project is directed by

Prof. Jeffrey Sachs of Columbia University and based at the headquarters of the

United Nations Development Program (UNDP) in New York.”6 In order to

achieve the various MDGs, the Millennium Project subsequently formed several

task forces, each responsible for their own role in achieving the overall goals.

7.2.3 The Millennium Project Task Force on HIV/AIDS

“The Millennium Project’s Task Force on HIV/AIDS, Malaria, TB, and

Access to Essential Medicines, concerned with Millennium Development Goal 6

on combating HIV/AIDS, malaria, and other diseases, consists of four

operationally independent working groups focusing on HIV/AIDS, malaria,

tuberculosis, and access to essential medicines.”7 The group working on

HIV/AIDS is known as the Working Group on HIV/AIDS. This group seeks to

determine effective interventions and methods for successful implementation to

realize their goal. Since this work group was formed to provide immediate action

plans to create real improvements by 2015, the Working Group on HIV/AIDS first step was to focus on the stated MDG target for HIV/AIDS.

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7.3 Targets

Within the MDGs, there are 18 specific targets. The target for our focus

with regard to goal 6, combating HIV/AIDS, is Target 7. Target 7 is to “have

halted by 2015 and begun to reverse the spread of HIV/AIDS.”8 Clearly this is a

bold initiative which will require substantial funding, coordination, and innovation

with regard to creating and implementing effective strategies to mitigate the spread of HIV/AIDS. This target differs itself from many other program targets as it is quantifiable (i.e. future prevalence levels can be compared with past data).

Many indicators for program success are often qualitative or intuitive in nature.

As Target 7 alone offers little guidance on how to reach it, the Working

Group on HIV/AIDS defines more specific targets. These targets are an extension until 2015 of the United Nations General Assembly Special Session

(UNGASS) targets for 2005, and coverage targets for key prevention or treatment interventions endorsed by UNGASS as essential elements of a comprehensive response.9 The quantifiable targets of the Working Group on

HIV/AIDS include: 50% reduction in prevalence rates for concentrated or low-

level epidemics (i.e. prevalence rates less than 1% in the general population);

prevalence rate reduction to 5% for severely afflicted countries (i.e. prevalence

rates greater than or equal to 5% in the general population) and for young people

ages 15-24 and by 50% elsewhere; antiretroviral treatment coverage for 75% of

those in need; and 80% coverage to prevent mother-to-child transmission.

These targets provide explicit benchmarks for evaluating policies, interventions,

and measuring progress. The targets are tabled for reference in Figure 7.2.

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Indicator Target HIV Prevalence Rate 50% Reduction Low-Level or Concentrated Epidemic HIV Prevalence Rate 5% Severe Epidemic HIV Prevalence Rate, Youth Ages 15-24 50% Reduction Low-Level or Concentrated Epidemic HIV Prevalence Rate, Youth Ages 15-24 5% Severe Epidemic Antiretroviral Therapy Treatment Coverage 75% Access to Mother-to-Child Transmission 80% Preventions Coverage Figure 7.2: Targets for Millennium Project’s Working Group on HIV/AIDS [Source: UN Millennium Project, 2005] The official MDG indicators and Millennium Project Working Group on

HIV/AIDS proposed prevention and treatment targets for HIV/AIDS are shown in

Figure 7.3.

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Figure 7.3: Official MDG Indicators and Millennium Project Working Group on HIV/AIDS Prevention/Treatment Targets [Source: UN Statistics10, 2003] 7.3.1 Targets for Prevention

The Millennium Project Task Force on HIV/AIDS has identified five main prevention intervention coverage targets. They are:

• Ensure that by 2015 affordable HIV testing and appropriate counseling are

offered at all sexually transmitted infection, tuberculosis, and antenatal

clinics globally, and at all medical facilities in high-prevalence countries.

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• Ensure that 100 percent of patients receiving HIV treatment and care have

access to effective “prevention for positives” by 2015.

• Ensure that 80 percent of injecting drug users have access to harm

reduction services by 2015.

• Ensure that 80 percent of pregnant women have access to services for

preventing mother-to-child transmission by 2015

• Ensure that 100 percent of young people have access to reliable

information about the epidemic and how to protect themselves by 2015.

Note: “prevention for positives” refers to an integration of prevention and treatment for HIV positive individuals.11

7.3.2 Targets for Treatment

There are several methods being employed to increase and promote treatment. One of the most notable campaigns already underway is the “3 by 5” target set by WHO. The initiative aims to have 3 million people receiving antiretroviral therapy by 2005. The plan was announced in April of 2002 and subsequently adopted by UNAIDS in November of 2003. Although there is no measure of exactly how many people the initiative has reached, it has served to begin the massive mobilization of sponsors, donors, required personnel, pharmaceutical production expansion, etc. required in order to achieve the goal of massive expansion of ART to those most in need.

In terms of the Millennium Project, they have stated an overall target for

ART coverage by the year 2015. The target is:

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• Ensure equitable and sustainable access to antiretroviral therapy to at

least 75 percent of those in need by 2015.12

The two key elements of this target are equitable and sustainable access. The goal will be to provide ARV therapy free so that affordability is not a factor.

Further, factors of discrimination and lack of representation, especially for women and children, must be addressed in order to accomplish this task. In April of

2005, former President Clinton said. “One in every six AIDS deaths each year is a child, yet children represent less than one of every thirty persons getting treatment in developing countries today. These children need hope, and we know what must be done. The global community has the means to save many lives, and we must meet that responsibility as quickly as we can,” he urged.13

7.4 Impact of Achieving Targets

Reaching the MDGs will have a profound impact on the lives of millions of

those most in need. Estimated benefits from attaining the stated goals are

numerous. Simple simulations from UNAIDS and WHO indicate that globally,

between 9.5 million and 16.7 million adults will require ART in 2015, compared

with approximately 5 million to 6 million in need today.14 At 75 percent coverage

for ART in sub-Saharan Africa, this represents between 6.7 million and 11.7

million people on ARV in sub-Saharan Africa alone by 2015. The Millennium

Project Working Group on HIV/AIDS further asserts that if treatment is scaled up

without prevention then 9.2 million people will need ART in 2020, whereas only

4.2 million people will require ART if the anticipated synergies of the Millennium

Project Working Group on HIV/AIDS’ integration of prevention and treatment

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materialize.15 Globally, the integration of prevention and treatment is expected to

avert 29 million new infections by 2020, 10 times more than treatment alone.16

Thus for sub-Saharan Africa, this represents approximately 20 million new infections prevented by implementation of the MDGs.

Benefits in sub-Saharan Africa for achieving the MDGs, including MDG 6

Task 7, “to have halted by 2015 and begun to reverse the spread of HIV/AIDS” are listed in Figure 7.4. These are the official estimates by UNAIDS, UNDP,

WHO, and UNICEF regarding projected values for 2015 in two cases; continuation of current trends versus adoption of the MDGs and the expanded response, enabled through the Millennium Project, it will require.

2005 Current trend MDG Scenario Indicator estimate extrapolated to 2015 for 2015 Poverty Headcount (millions) 345 431 198 GDP per-capita (2003 US$) 520 509 712 Individuals suffering from 228 255 155 undernourishment (millions) Child Mortality 4.7 4.7 1.9 (millions of lives lost) Number of individuals receiving 1.7 2.2 6.7 – 11.7 Antiretroviral Therapy (millions) 2005 Current trend MDG Scenario Indicator estimate extrapolated to 2010 for 2010 New HIV infections 2002-2010 3.1 21.0 8.8 (millions) Figure 7.4: Benefits of Achieving MDGs for Sub-Saharan Africa [Source: UN Millennium Project, 2005] 7.5 Cost of the Millennium Project for HIV/AIDS

The costs associated with enacting essential programs and interventions

to achieve the MDGs are quite high. But, in terms of overall percentage of GNI

required from more developed countries, it is less than 0.7 percent. Figure 7.517

shows OECD DAC members’ 2003 and 2004 ODA in billions of US$ and as a

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percentage of GNI. Successful implementation and realization of the MDGs will

require increased investment in light of the current 0.42 percent average shown

in the following figure. Although all UN member countries have agreed to the 0.7

percent target, a similar 0.7 percent target was set back in the 1970’s and never

came to fruition. It could be argued that the economic effects of oil supply

disruptions over the 1970’s period contributed to the failing of meeting the 0.7

percent pledge in that decade.

DAC member’s Official DeveloDevelopmentpment Assistance in 2003 and 2004

Norway Denmark Luxembourg Sweden Netherlands Portugal Belgium Switzerland France Ireland United Kingdom ODA (US$billion) current Finland 2004 Germany Canada 2003 Australia Average UN Spain country target, Greece effort, 0.70% ODA/GNI (%) Austria 2004 New Zealand 0.42% Japan 2003 United States Italy

20 18 16 14 12 10 8 6 4 2 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Source: Organisation for Economic Co-operation and Development (2005). 10.13

Figure 7.5: DAC Member’s ODA in 2003 and 2004 [Source: OECD, 2005]

Total ODA committed to HIV in 2004 by OECD DAC member countries is

illustrated in Figure 7.6. The portion of ODA needed to combat HIV/AIDS in sub-

Saharan Africa is of primary concern for this study in terms of meeting Goal 6 of the MDGs on fighting HIV.

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DAC members’ Official Development Assistance committed to HIV in 2004

Canada 202 208 Sweden 60 172 Netherlands 96 167 Luxembourg 4 126 Denmark 26 108 Norway 26 103 United States 1160 100 Belgium 31 86 United Kingdom 157 72 Finland 8 43 Germany 105 38 Ireland 6 37 Australia 7 12 Switzerland 4 10 New Zealand 1 8 Spain 8 7 France 11 5 Italy 7 4 Austria 1 4 Greece 1 4 Portugal 0.1 1 Japan 3 1 1300120011001000 900 800 700 600 500 400 300 200 100 0 50 100 150 200 250 300 Aid for HIV (US$ million) Aid for HIV per million GNI

Source: UNAIDS, based on data from DAC members’ reports to OECD.

Figure 7.6: DAC Member’s ODA in 2003 and 2004 [Source: OECD, 2005]

7.5.1 Costs for Sub-Saharan Africa

While regional governments within sub-Saharan Africa are expected and often do contribute to fighting the virus, local support, especially in low-income regions, needs considerable additional funding to meet required levels to achieve the MDGs. Figure 7.7 shows the trend in domestic per capita expenditures on

HIV/AIDS by country income level. Clearly the trend for sub-Saharan Africa has shown improvement in recent years over the average of other low income countries, but remains far below the standards achieved in more affluent areas.

The trend for sub-Saharan Africa, detailed in Figure 7.8, reflects a domestic per

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capita expenditure of US $0.65 for year 2005 which is more than double the amount spent in 2001.

Per capita HIV and AIDS expenditures by country income level*

2.5

2.0 Low income

US$ 1.5 Lower middle per capita 1.0 Higher middle

0.5 Low income SSA

0.0 2000 2001 2002 2003 2004 2005

• Trends based on a sample of 25 countries from sub-Saharan Africa and 57 countries from other regions

Figure 7.7: Per Capita HIV Expenditures by Country Income Level 2000-05

[Source: UNAIDS, 2006]

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Trends in HIV and AIDS per-capita expenditures in current US$, selected sub-Saharan countries

1.0 0.9 0.8 0.7 0.65 HIV 0.6 per capita 0.5 spending 0.49 (US$) 0.4 0.31 0.3 0.2 0.22 0.1 0.15 0.0 2001 2002 2003 2004 2005

Sources: Countries reporting on UNGASS on domestic public expenditure; UNAIDS estimates 11.3

Figure 7.8: HIV/AIDS per Capita Expenditures Trend from 2001-2005

[Source: UNAIDS, 2006]

The estimated cost of the MDGs for 2006 is US $70 to $80 per-capita, per year rising to $120 to $160 per-capita, per year by 2015.18 This figure is inclusive of funding for all the MDGs not just funding for HIV/AIDS. The difference between local resource pools and requisite funds must come from external sources for low-income countries. Even with an initiation of local resource mobilization, low-income countries are expected to require US $40 to

$50 per capita in external financing in 2006, increasing to $70 to $100 by 2015.19

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7.5.2 Costs for Botswana

As a middle-income country, Botswana will require fewer subsidies than

other less affluent sub-Saharan regions. The concerted effort and national

policies enacted to curb the virus in Botswana have met with some success and

is possible, in part, due to their economy. The UNAIDS GAR 2006 indicates that

85 percent of HIV infected women and men in Botswana are receiving ART.20

This type of treatment alone is more expensive than many low-income countries can afford. Thus relative to other less affluent sub-Saharan countries, Botswana has a head start in terms of mobilizing the response and reaching ART coverage targets.

7.6 Analysis of the Millennium Project Scenario using GLOBESIGHT

Scenario analysis requires a significant amount of data and research including population demographics, epidemiologic indicators, modes and methods for HIV/AIDS transmission and progression, understanding of cultural and political values and motivations, etc. to provide meaningful results. The potential effects of the Millennium Project targets will be applied to the UN/WHO

BaU Scenario developed in Chapter 3 to generate a Millennium Development

Goal Scenario. The BaU Scenario provides a reference case to be used as a benchmark against which the MDG Scenario impacts will be compared. This method enables quantifiable analysis often unavailable in program evaluation.

This is a unique and important contribution of the dissertation unavailable in other analyses to date.

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7.6.1 Feasibility of Reaching the Goal on HIV/AIDS by 2015

In order to assess feasibility of the MDG on HIV/AIDS, several aspects

must be considered simultaneously. These aspects include both current and

projected available resources, per capita requirements, prevention interventions,

and treatment interventions. Model parameters representing these criteria are

used to project the impact of the proposed targets and determine if they are

consistent with the proposed benefits. This demonstrates, for example, how many children will be saved by 2015 by providing mother-to-child transmission prevention to 80 percent of pregnant women in need versus continuing provision at BaU levels far below the MDG’s Working Group on HIV/AIDS targets. Results are contrasted against the intentions of the action or intervention and the costs required and benefits achieved.

7.6.2 Resources: International Aid and the 0.7 Percent GNI Target

Regarding the 0.7 percent GNI target, the MDG Scenario assumes that sufficient funding will be available to achieve the goals and reach the coverage targets outlined by the Millennium Project’s Working Group on HIV/AIDS, summarized in Figure 7.2. Current sources for funding, assuming no new commitments, for 2005 through 2007 are displayed in Figure 7.921. UNAIDS estimates US $14.9 billion is required by low and middle-income countries for the

AIDS response in 2006, $18.1 billion in 2007, and $22.1 billion by 2008; current commitment of $8.9 and $10 billion in resources for 2006 and 2007 respectively, fall short of requisite monies needed to meet the MDGs.22 Approximately 43

percent of the required resource funding for HIV would be needed in sub-

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Saharan Africa, according to UNAIDS.23 The 43 percent cut for sub-Saharan

Africa, however, seems slightly low since it contains roughly 64 percent of the infected population globally, but perhaps economies of scale and values of local currencies adjust for this difference.

Sources of the estimated and projected funding for the AIDS response from 2005 to 2007*

12 US$ 10 billion Private Sector 8 Multilateral 6 Bilateral 4 Domestic 2

0 2005 2006 2007

* Assuming there are no new commitments

Figure 7.9: Funding Sources for Expanded AIDS Response in Low and Middle-Income Countries from 2005-2007 [Source: UNAIDS, 2005]

7.6.3 Modeling Prevention Interventions

7.6.3.1 Mother-to-Child Transmission 80 Percent Coverage Target

Currently in developing countries lacking intervention for pregnant women with HIV, mother-to-child transmission rates are as high as 15-35 percent. While,

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“In the rich world, transmission rates as low as 1-2 percent are achieved with a

combination of triple antiretroviral therapy, sound obstetrical management, and

substitution of formula for breastfeeding.”24

The Millennium Project’s Working Group on HIV/AIDS endeavor to attain an increase in access to these services as this type of transmission is almost completely preventable. Current access to this type of intervention in less

developed countries is estimated at 8 percent or less.25 The Millennium Project’s

Working Group on HIV/AIDS target is for 80 percent access. This represents an

increase of services to those in need of twenty fold. This may sound unrealistic

but if you consider the current accepted MTCT rate of 30 percent, and then

divided by 20, it yields a result of 1.5 percent. This is exactly in line with

estimates for more affluent countries, thus it is an attainable goal with access to

proper intervention services.

7.6.3.2 Integrated Prevention and Treatment

Integration of prevention and treatment effects are accounted for in the

scenario within the estimated changes for coverage targets. Although, the

synergy enabled by linking these two interventions may provide further

prevalence reduction through increased detection, provision of care in earlier

stages of infection, access to microbicides for safer sex, etc.

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7.6.4 Modeling Treatment Interventions

7.6.4.1 Antiretroviral Therapy 75 Percent Coverage Target

Antiretroviral therapy reaches only 4 percent of those currently in need.

The Millennium Project’s Working Group on HIV/AIDS target is for 75 percent

coverage. This represents an increase of 9-10 times the current level. This

translates into an increase in the length of time it takes to progress from HIV to

AIDS, an overall decrease in the mortality rate of AIDS infected individuals, and a

reduction in sexual transmission rates per contact.

It currently takes approximately 9 years or less to progress from HIV to

AIDS in Africa. The Millennium Project’s Working Group on HIV/AIDS target will be assumed to translate into 16 years on average which is a substantial improvement and is in line with previously achieved results.

Current adult AIDS mortality rates are estimated at 56 percent yearly. The

Millennium Project’s Working Group on HIV/AIDS target will be assumed to lower this rate to 20 percent which is consistent with current parameters used in models employed by UNAIDS and WHO.

7.6.4.2 HIV Infectivity Reduction

HIV transmission infectivity per sexual contact is currently estimated at 6 percent per sexual contact on average, this incorporates both male and female average transmissions, which are 3 percent and 9 percent per sexual contact on average respectively. The Millennium Project’s Working Group on HIV/AIDS target will lower this rate by decreasing viral load in infected populations which

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results in decreased transmission ability. The potential decrease will be estimated at 3 percent or half of the current value.

7.6.4.3 Other Treatment and Prevention Interventions

In terms of model parameters, all other treatment and prevention interventions are assumed to be included in the reduction of HIV infectivity from 6 percent to 3 percent. This includes: educational programs, condom campaigns, etc.

7.6.5 Demographic Impact of the Millennium Project

Simulations from the MDG Scenario suggest overwhelmingly that expansion of treatment and prevention interventions, in addition to education and awareness will result in decreased prevalence, increased longevity, and overall reductions in the negative impacts of the epidemic.

7.6.5.1 Demographic Impact: Sub-Saharan Africa

The following table, Figure 7.10, lists several important epidemic indicators for sub-Saharan Africa for comparison between the UN/WHO BaU

Scenario, developed in Chapter 4, and the MDG Scenario. The indicators include: size of population infected with HIV/AIDS, HIV prevalence for ages 15-

49, HIV prevalence for ages 15-24 (proxy for incidence), number of HIV infected births, number of new HIV infections, number of people receiving ART, number of deaths from AIDS, and total population.

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Sub-Saharan Africa: 2005 & 2015 Model Results 2005 BaU Scenario MDG Scenario Indicator Estimate 2015 2015 HIV/AIDS 24.8 30.9 21.7 Population (mil.) HIV Prevalence % 6.1 5.9 4.2 Ages 15-49 HIV Prevalence % 3.9 4.0 1.9 Ages 15-24 HIV+ Births 498 547 20.5 (thousands) New HIV 3.0 3.6 1.2 Infections (mil.) ART Coverage .81 1.0 15.2 (pop., millions) Deaths from AIDS 2.1 2.9 1.4 (millions) Total Population 751 933 942 (millions) Figure 7.10: BaU versus MDG Scenario for Sub-Saharan Africa 2005 & 2015

The next two figures show the HIV pyramid for year 2015 for the BaU

Scenario versus the expanded response of the MDG Scenario. The BaU

Scenario yields nearly 30.9 million HIV/AIDS infections compared with 21.7 million infections in the MDG Scenario for year 2015, which is of course the year set to reach the MDG targets.

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Figure 7.11: BaU Scenario HIV Pyramid for Sub-Saharan Africa: 2015

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Figure 7.12: MDG Scenario HIV Pyramid for Sub-Saharan Africa: 2015

Figure 7.13 shows the HIV pyramids for the BaU and MDG Scenarios placed side-by-side for comparison. The blue bars represent the BaU Scenario and the red signify the MDG Scenario. The figure illustrates the significant reduction in new infections and the increased longevity of those already infected.

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Figure 7.13: BaU vs. MDG Scenario HIV Pyramid, Sub-Saharan Africa: 2015

A direct comparison of overall HIV/AIDS infections is shown in Figure

7.14. This figure demonstrates the direct benefit of not being infected with

HIV/AIDS for nearly 10 million people.

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Figure 7.14: Total HIV/AIDS Population, BaU vs. MDG Scenario Sub-Saharan Africa: 2000-2015 A comparison of HIV prevalence rates for ages 15-49 years between the

UN/WHO BaU Scenario and the MDG Scenario is shown in Figure 7.15. An increased reduction in prevalence is achieved in the MDG Scenario compared with the UN/WHO BaU Scenario. The almost 2 percent relative decrease in total

HIV prevalence for ages 15-49 roughly translates into the nearly 10 million infections averted by the expanded response of the MDGs.

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Figure 7.15: HIV Prevalence Ages 15-49, BaU vs. MDG Scenario Sub-Saharan Africa: 2000-2015 The UN/WHO BaU Scenario shows a result of apparent prevalence stabilization by maintaining current levels of aid relief. The MDG scenario meets the Millennium Project Working Group on HIV/AIDS’ goal of 5 percent prevalence or less, with a prevalence of 4.2 percent. The MDG Scenario’s greater reduction in the prevalence rate is of primary importance in consideration of reducing possible future infections. Overall prevalence is reduced by almost 2 percent in just 10 years in the MDG Scenario.

The next graph, Figure 7.16, depicts the HIV prevalence rates for ages 15-

24 years for both scenarios. This indicator is considered a good proxy for incidence, and thus merits individual analysis as preventing new infections is one key to eliminating the disease. The relative improvement between scenarios in prevalence reduction is slightly over 2 percent. This reduction precisely meets

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the Working Group on HIV/AIDS’ goal of cutting the prevalence rate for ages 15-

24 by 50 percent.

Figure 7.16: HIV Prevalence Age 15-24, UN/WHO BaU vs. MDG Scenario, Sub-Saharan Africa: 2000 - 2015 The next indicator of interest is HIV infected births. The UN/WHO BaU

Scenario projects 548 thousand mother-to-child transmissions (MTCT) of HIV in comparison with 20 thousand MTCT in the MDG Scenario. The number of yearly

MTCT infections up to the year 2015 is shown in Figure 7.17.

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Figure 7.17: HIV+ Births, BaU vs. MDG Scenario, SS. Africa: 2000-2015

7.6.5.2 Demographic Impact: Botswana

The table in Figure 7.18 lists several comparisons of epidemic indicators for Botswana between the UN/WHO BaU Scenario, developed in Chapter 4, and the MDG Scenario.

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Botswana: 2005 & 2015 Model Results 2005 BaU Scenario MDG Scenario Indicator Estimate 2015 2015 HIV/AIDS 264 261 201 Population (thous.) HIV Prevalence % 24.5 24.2 18.1 Ages 15-49 HIV Prevalence % 15.0 15.5 7.9 Ages 15-24 HIV+ Births 3.1 2.7 0.11 (thousands) New HIV 25.6 25.0 9.6 Infections (thous.) ART Coverage 208 205 162 (pop., thousands) Deaths from AIDS 18.9 20.4 10.4 (thousands) Total Population 1754 1690 1747 (thousands) Figure 7.18: BaU versus MDG Scenario for Botswana 2005 & 2015

The UN/WHO BaU Scenario produces the HIV+/AIDS population pyramid

shown in blue on the left side of Figure 7.19 for the year 2015. This pyramid

shows the demographic impact of the infection after another decade of living

without the improvements suggested by the Millennium Project Working Group on HIV/AIDS.

In contrast to the UN/WHO BaU Scenario, the MDG Scenarios’ HIV population pyramid shown in red on the right half of Figure 7.19 reveals a dramatically lower level of the virus due to the interventions implemented.

Clearly if the services can be provided, reduction in the severity of the epidemic can be achieved.

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Figure 7.19: BaU versus MDG Scenario HIV Pyramid for Botswana: 2015

A comparison of total population by cohort also demonstrates the massive positive effects that the MDG Scenario can produce in just ten years. Figures

7.20 and 7.21 show the population pyramids for the BaU and MDG scenarios for year 2015. Notice that prevalence in the 0-10 age group has been all but

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eliminated. Further, the HIV and AIDS infected population (i.e. the blue area) in the MDG Scenario is significantly less than in the UN/WHO BaU projection.

Figure 7.20: BaU Scenario Population Pyramid for Botswana in 2015

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Figure 7.21: MDG Scenario Population Pyramid for Botswana in 2015

A comparison between the BaU and MDG Scenario in Botswana for the number of HIV/AIDS infections up to year 2015 is shown in Figure 7.22. The figure reflects 60 thousand individuals spared infection by enactment of the policies to achieve the MDGs.

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Figure 7.22: Total HIV/AIDS Population, BaU vs. MDG Scenario Botswana: 2000-2015 A comparison of HIV prevalence rates for ages 15-49 years between the

UN/WHO BaU Scenario and the MDG Scenario for Botswana is shown in Figure

7.23. An increased reduction in prevalence is achieved in the MDG Scenario compared with the UN/WHO BaU Scenario.

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Figure 7.23: HIV Prevalence Age 15-49, UN/WHO BaU vs. MDG Scenario, Sub-Saharan Africa: 2000-2015 The UN/WHO BaU Scenario shows a result of apparent prevalence stabilization by maintaining current levels of aid relief. The MDG scenario does not meet the Millennium Project Working Group on HIV/AIDS’ goal of 5 percent prevalence for severe epidemics, with prevalence rates at 24 percent in the BaU

Scenario and 17.5 percent in the MDG Scenario. The MDG Scenario’s reduction in the prevalence rate is of crucial importance in consideration of reducing possible future infections. Overall prevalence is reduced by almost 6.5 percent in just 10 years in the MDG Scenario.

The next graph, Figure 7.24, depicts the HIV prevalence rates for ages 15-

24 years for both scenarios. The relative improvement between scenarios in prevalence reduction is 7.7 percent by year 2015. This reduction does not meet the 5 percent target of the Millennium Projects’ HIV/AIDS Working Group, but is a

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significant drop in the variable considered as a proxy for incidence, which falls

from 15.5 percent in the BaU Scenario versus just under 8 percent in the MDG

Scenario.

Figure 7.24: HIV Prevalence Age 15-24, BaU vs. MDG, Botswana: 2000-2015

The next indicator of interest is HIV infected births. The UN/WHO BaU

Scenario projects just over 2,700 mother-to-child transmissions (MTCT) of HIV in comparison with 100 MTCT in the MDG Scenario. The number of yearly MTCT infections up to the year 2015 is shown in Figure 7.25 for both scenarios.

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Figure 7.25: HIV+ Births, BaU vs. MDG Scenario, Botswana: 2000–2015

7.6.6 Socio-Economic Impact of the Millennium Project

Socio-economic impacts of the MDGs on sub-Saharan Africa and

Botswana, per the modeling methodology derived in Chapter 6, are detailed in the tables in Figures 7.26 and 7.27. Economic data from the BaU Scenario are held constant so the demographic effect of the HIV/AIDS epidemic can be assessed.

7.6.6.1 Socio-Economic Impact: Sub-Saharan Africa

Economic indicators for sub-Saharan Africa are tabulated in Figure 7.26.

As expected in the case of increased funding and an expanded response for prevention and treatment of HIV, overall economic activity shows substantial improvement over the BaU Scenario economic indicators developed in Chapter 6 and displayed in Figure 6.1.

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Sub-Saharan Africa: MDG Economic Model Results Indicator 2005 2025 2050 Gross National 1.4 2.7 5.4 Income (tril. Intl.$) GNI Growth Rate 3.2 % 3.1 % 2.6 % (percent) GNI per Capita 1863 2273 2915 (Intl.$) Uninfected Working Age Percentage of 51.2 % 56.0 % 62.8 % Population

Figure 7.26: MDG Scenario Economic Indicators for Sub-Saharan Africa

7.6.6.2 Socio-Economic Impact: Botswana

Economic indicators for the MDG Scenario for Botswana are tabulated in

Figure 7.27. Similar improvements are achieved in the MDG Scenario in

Botswana relative to the BaU Scenario indicators displayed in Figure 6.2.

Botswana: MDG Scenario Economic Model Results Indicator 2005 2025 2050 Gross National 17.6 40.4 136.1 Income (bil. Intl.$) GNI Growth Rate 4.1 % 4.6 % 5.3 % (percent) GNI per Capita 9982 21872 58306 (Intl.$) Uninfected Working Age Percentage of 46.3 % 54.1 % 62.2 % Population

Figure 7.27: MDG Scenario Economic Indicators for Botswana

Economic impacts of the virus create a downward spiral since as countries lose

premium age workers to HIV and AIDS they also lose GNI and as economic

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growth slows funds for battling HIV and AIDS are diminished. Thus,

improvement in the health condition promotes subsequent improvement in the

number of workers available and capacity for economic growth.

7.7 Impact of Millennium Project Continuation through 2050

In light of the beneficial effects provided by enacting programs to reach

the MDGs, program extension to the year 2050 is evaluated. Progress of the

MDG Scenario will be compared and contrasted with the BaU Scenario for years

2025 and 2050.

7.7.1 Impact of Continuation through 2050: Sub-Saharan Africa

The scenario results for year 2025 are tabulated in Figure 7.28.

Sub-Saharan Africa: 2005 & 2025 Model Results 2005 BaU Scenario MDG Scenario Indicator Estimate 2025 2025 HIV/AIDS 24.8 37.2 19.1 Population (mil.) HIV Prevalence % 6.1 5.7 2.8 Ages 15-49 HIV Prevalence % 3.9 3.8 1.2 Ages 15-24 HIV+ Births 498 570 13.5 (thousands) New HIV 3.0 4.2 1.0 Infections (mil.) ART Coverage 0.81 1.3 14.3 (pop., millions) Deaths from AIDS 2.1 3.4 1.2 (millions) Total Population 751 1137 1172 (millions) Figure 7.28: BaU versus MDG Scenario for Sub-Saharan Africa 2005 & 2025

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The demographic and epidemiologic progress indicators listed in Figure 7.28 reveal the benefits of continuing the expanded response requisite for the MDGs until the year 2025.

Continuation of the MDG Scenario until the year 2050 shows that the virus can be drastically reduced if prevention and treatment coverage targets set by the MDGs’ Millennium Project Working Group on HIV/AIDS are achieved. Figure

7.29 shows the progress indicators for the BaU versus the MDG Scenario for

2005 and 2050.

Sub-Saharan Africa: 2005 & 2050 Model Results 2005 BaU Scenario MDG Scenario Indicator Estimate 2050 2050 HIV/AIDS 24.8 54.2 11.1 Population (mil.) HIV Prevalence % 6.1 5.5 0.9 Ages 15-49 HIV Prevalence % 3.9 3.6 0.4 Ages 15-24 HIV+ Births 498 566 4.6 (thousands) New HIV 3.0 5.8 0.5 Infections (mil.) ART Coverage .81 1.9 8.3 (pop., millions) Deaths from AIDS 2.1 5.0 0.8 (millions) Total Population 751 1686 1845 (millions) Figure 7.29: BaU versus MDG Scenario for Sub-Saharan Africa 2005 & 2050

The UN/WHO BaU Scenario produces the population pyramid shown in

blue on the left side of Figure 7.30 and the red area on the right represents the

HIV infected population in the MDG Scenario for the year 2025. The BaU

Scenario pyramid shows the demographic impact of the infection after two

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decades of living without the improvements suggested by the MDGs’ Millennium

Project Working Group on HIV/AIDS. Whereas the MDG Scenario demonstrates

the substantial positive effects that the MDG scenario can produce in twenty years. Notice that prevalence in the 0-15 age group has been almost completely eliminated. Further, the HIV and AIDS infected population (i.e. the red area) in the MDG scenario is significantly less than in the UN/WHO BaU projection shown in blue.

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Figure 7.30: BaU vs. MDG Scenario HIV Pyramid, Sub-Saharan Africa: 2025

The HIV population pyramid for year 2050, shown in Figure 7.31 confirms that continued efforts to combat the disease will result in lowered prevalence, decreases in new infections and AIDS related deaths.

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Figure 7.31: BaU vs. MDG Scenario HIV Pyramid, Sub-Saharan Africa: 2050

Total HIV infected population from 2000-2050 for the two scenarios is shown in Figure 7.32. The red line (upper) represents the BaU Scenario and the blue line (lower) shows the MDG Scenario.

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Figure 7.32: Total HIV/AIDS Population, BaU vs. MDG Scenario Sub-Saharan Africa: 2000-2050

Prevalence rates for ages 15-49 and 15-24 are presented in Figure 7.33 and 7.34, respectively, for years 2000 through 2050 for the BaU and MDG scenarios. The upper, red line shows the BaU Scenario and the lower, blue line shows the MDG Scenario.

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Figure 7.33: HIV Prevalence Age 15-49, BaU vs. MDG Scenario Sub-Saharan Africa: 2000-2050

Figure 7.34: HIV Prevalence Ages 15-24, BaU vs. MDG Scenario Sub-Saharan Africa: 2000-2050

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The number of new HIV infections from 2000 to 2050 for the two scenarios

for sub-Saharan Africa is depicted in Figure 7.35. It shows the enormous

reduction in incidence which is a key for controlling and reducing the epidemic.

Figure 7.35: New HIV Infections, BaU vs. MDG Scenario, SS. Africa: 2000-50

The next figure, Figure 7.36 shows the number of HIV infected births over the time period from 2000 to 2050 for the BaU and MDG scenarios. This graph clearly illustrates the need for short-course anti-retrovirals to prevent MTCT.

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Figure 7.36: HIV Infected Births, BaU vs. MDG Scenario, SS. Africa: 2000-50

Figure 7.37 compares the number of deaths due to AIDS over the time frame from 2000 to 2050 for both the BaU and MDG scenarios. The figure confirms the fact that millions of lives can literally be saved by an expanded response as provided by the MDGs.

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Figure 7.37: AIDS Deaths, BaU vs. MDG Scenario, SS. Africa: 2000-2050

7.7.2 Impacts of Continuation through 2050: Botswana

Zooming in on the sub-region of Botswana, progress indicators are tabulated in Figures 7.38 and 7.39 for BaU vs. MDG continuation through the year 2025 and 2050, respectively. By 2025, the MDG Scenario relative to the

BaU Scenario reflects approximately 100 thousand less people with HIV, half the prevalence in the 15-49 age group, one-third the prevalence in the 15-24 age range, an almost complete elimination of MTCT, one-third of the amount of new infections, 70 thousand less individuals receiving ART, 12 thousand less deaths due to AIDS, and an overall population gain of 190 thousand people.

Differences in scenario projections are even more dramatic by the year

2050. The MDG Scenario benefits over the BaU Scenario include almost one- third the number infected with HIV/AIDS, one-fifth the prevalence for ages 15-49,

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Botswana: 2005 & 2025 Model Results 2005 BaU Scenario MDG Scenario Indicator Estimate 2025 2025 HIV/AIDS 264 267 162 Population (thous.) HIV Prevalence % 24.5 24.9 13.0 Ages 15-49 HIV Prevalence % 15.0 15.8 5.6 Ages 15-24 HIV+ Births 3.1 2.5 0.07 (thousands) New HIV 25.6 24.3 7.5 Infections (thous.) ART Coverage 208 210 138 (pop., thousands) Deaths from AIDS 18.9 20.1 8.0 (thousands) Total Population 1754 1656 1846 (thousands) Figure 7.38: BaU versus MDG Scenario for Botswana 2005 & 2025

Botswana: 2005 & 2050 Model Results 2005 BaU Scenario MDG Scenario Indicator Estimate 2050 2050 HIV/AIDS 264 282 100 Population (thous.) HIV Prevalence % 24.5 26.8 5.7 Ages 15-49 HIV Prevalence % 15.0 16.8 2.3 Ages 15-24 HIV+ Births 3.1 2.3 0.03 (thousands) New HIV 25.6 22.8 3.9 Infections (thous.) ART Coverage 208 222 85 (pop., thousands) Deaths from AIDS 18.9 21.5 5.5 (thousands) Total Population 1754 1658 2334 (thousands) Figure 7.39: BaU versus MDG Scenario for Botswana 2005 & 2050

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one-seventh the prevalence for ages 15-24, near eradication of HIV+ births, almost one-sixth the number of new infections, 16 thousand fewer AIDS deaths, and a total population difference of a little over 670 thousand people – which is an increase of 40 percent over the BaU population.

The UN/WHO BaU Scenario produces the population pyramid shown in blue on the left side of Figure 7.40 and the red area on the right represents the

HIV infected population in the MDG Scenario for the year 2025.

Results for the HIV population pyramid for the year 2050, shown in Figure

7.41, shows that continued funding and efforts to fight the epidemic will result in lowered prevalence, decreases in new infections and AIDS related deaths.

Pyramids for the BaU and MDG scenarios for the total population, with the

HIV/AIDS infected proportion shown in blue, are shown in Figures 7.42 and 7.43, respectively. Differences in the number of infected and total population are quite striking between the BaU and MDG scenarios, as the figures display.

Graphical representation for the other progress indicators tabled in Figure

7.39 is delayed until the next chapter which presents the BaU, MDG, and pessimistic or ODA Cut projections, developed in Chapter 8, together.

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Figure 7.40: BaU vs. MDG Scenario HIV Pyramid, Botswana: 2025

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Figure 7.41: BaU vs. MDG Scenario HIV Pyramid, Botswana: 2050

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Figure 7.42: BaU Scenario Population Pyramid for Botswana in 2050

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Figure 7.43: MDG Scenario Population Pyramid for Botswana in 2050

7.8 Conclusions

Although the projected results are not intended for numerical accuracy, the dominant relations revealed by increasing funding for preventative and treatment methods for HIV imply that substantial improvement in the condition of

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millions of individuals lives can be achieved with an expanded response for

combating the HIV/AIDS epidemic as provided by the MDGs.

Next, Chapter 8 will examine the effect of ODA funding cuts on the

progression of the virus. Then, the results for all three scenarios will be

compared, contrasted, and linked to the impending oil deficit in the post-peak oil

era.

Works Cited:

1 Combating AIDS in the Developing World, 138.

2 Molin, A. “Harmonization, Alignment, and HIV/AIDS: A Case Presentation at the 5th meeting of the DAC Working Party on Aid Effectiveness.” Paris: July 6-7 2004.

3 United Nations General Assembly. “United Nations Millennium Declaration.” United Nations 8th Plenary Meeting, New York: September 8, 2000, http://www.un.org/millennium/declaration/ares552e.htm (accessed: January 13, 2004).

4 United Nations Millennium Project. “What are the Millennium Development Goals?” http://www.unmillenniumproject.org/press/qa2_e.htm (accessed: March 3, 2006).

5 United Nations Millennium Project. “About the Goals: What they are.” http://www.unmillenniumproject.org/goals/ (accessed: March 2, 2006).

6 United Nations Millennium Project. “What is the Millennium Project?” http://www.unmillenniumproject.org/press/qa1_e.htm (accessed: March 3, 2006).

7 Combating AIDS in the Developing World, xi.

8 Combating AIDS in the Developing World, xvi.

9 Combating AIDS in the Developing World, 25.

10 United Nations Statistics Division. Millennium Indicators Online Database, http://millenniumindicators.un.org/unsd/mi/mi_goals.asp (accessed: December 16, 2003.)

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11 Combating AIDS in the Developing World, 45.

12 Combating AIDS in the Developing World, 80.

13 Gara, Ron. “Clinton Pledges $10 Million to Help Kids and Rural Africans Fight AIDS.” Health News, April 11, 2005. http://health.dailynewscentral.com/content/view/618/63 (accessed: April 13, 2005).

14 Combating AIDS in the Developing World, 83.

15 Combating AIDS in the Developing World, 83.

16 Combating AIDS in the Developing World, 84.

17 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition, Figure 10.13.

18 United Nations Millennium Project. Investing in Development: A Practical Plan to Achieve the Millennium Development Goals (New York: UNAIDS, 2005), 45.

19 Investing in Development: A Practical Plan to Achieve the Millennium Development Goals, 45.

20 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition, 320.

21 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition, Fig 10.10.

22 2006 Report on the Global AIDS Epidemic: A UNAIDS 10th Anniversary Special Edition, 19.

23 Combating AIDS in the Developing World, 129.

24 Combating AIDS in the Developing World, 37.

25 Combating AIDS in the Developing World, 23.

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Chapter 8: Impact of the Post-Peak Oil Era on the Sub-Saharan

HIV/AIDS Epidemic

8.1 Introduction

The current state of the HIV/AIDS epidemic and its’ future trajectory are largely influenced by the amount of preventive care and treatment interventions available. These services, the professionals and infrastructure needed to deliver them, and required medicines must have a source of funding. This chapter will examine the effects of a 10 year cut in ODA and other funding assistance due to a global economic recession caused by the impending oil shortages. These results are compared and contrasted with the BaU and MDG Scenario results and used to project the potential number of increased deaths due to AIDS per barrel of oil deficit. The additional AIDS deaths per barrel of oil deficit projections are a unique contribution of the dissertation and demonstrate the complex global relationship that exists between resource use and global health.

8.1.1 Chapter Organization

Section 8.2 presents a scenario formulation for the potential reductions and temporary elimination of funding assistance from ODA contributors due to economic crisis experienced during the post-peak oil era; ramifications on the demographic, epidemiologic, and economic indicators for sub-Saharan Africa are developed. Section 8.3 investigates the relationship between the post-peak oil deficit and the HIV/AIDS BaU, optimistic (i.e. MDG), and pessimistic (i.e. ODA

Cut) scenarios. Summarizations of the integrated scenarios are offered in

Section 8.4.

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8.2 Impacts of the Oil Deficit on ODA and the HIV/AIDS Epidemic

The scenarios for oil resource use developed in Chapter 4 indicate that a global maximum supply capacity will be reached by 2015 assuming BaU growth in global demand between years 2005 and 2015. The scenario developed will assume that as the peak approaches, oil prices rise and economic activity slows down. Historically, this has proved to be the case as the OECD GDP versus oil consumption relationship illustrated in Chapter 4 and the periods of “stagflation”, recession and “What now?” displays in Figure 8.1

Figure 8.1: World Price of Oil 1977-20051 [Source: Davis & Diegel, 2004]

The ODA Cut Scenario assumes that the amount of funding for HIV/AIDS will continue at BaU levels until 2010 at which point it will gradually decline until eliminated completely in 2015. The next decade, from 2015 until 2025, is

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assumed to be a period of extreme economic hardship as the world pays

increasing amounts for less and less oil while having to simultaneously develop

and implement alternative energy sources to replace the deficit left in the wake of oil depletion. The energy deficit and related economic conditions are assumed to find solution and betterment over the hardship decade from 2015 to 2025 at which point ODA is gradually re-instated and back on track at BaU levels by 2030 and continues at that level for the remainder of the time horizon from 2030 through 2050.

Consequences of losing essential funding for prevention services, condom programs, media campaigns, education, testing sites, antenatal clinics, microbicides, short-course drugs preventing MTCT, and treatment facilities are multifold. First, an increase in the MTCT rate is assumed. In addition to the

MTCT rate increase from the reasons mentioned, a potential lack of adequate food supplies may result in infants nursing from infected mothers for a longer duration than if alternate sustenance were readily available. The rate in sub-

Saharan Africa is expected to increase linearly from 30 percent in 2010 to 40

percent by 2015. It is expected to stay at this level during the period of ODA

elimination from 2015 to 2025 and returns linearly to the BaU level of 30 percent

by 2030, continuing at the BaU level until 2050.

The percentage of people receiving ART is assumed to decrease from the

BaU level in 2010 to zero in 2015 where it remains until 2025, increasing

gradually to the BaU level by 2030 where it remains until 2050. The cut in ART

and the degradation of the overall sub-Saharan medical system due to funding

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losses is assumed to result in an increase in transition time from HIV to AIDS.

The BaU average of approximately 9 years is cut to 6.25 years to reflect this

reduction. Consequentially, mortality due to AIDS in the 12 through 56 year age

range is temporarily higher, reflecting the increase in transition time.

Lastly, the ODA Scenario assumes an increase in per contact

transmission of the virus for several reasons. First, a lack of testing will ensure

that asymptomatic infected individuals do not change behavior, increasing their

transmissions to partners. The ART received by individuals is expected to lower

the total viral load of the disease in the blood of an infected individual thus

lowering their potential to infect others (this is the reason the MDGs call for all

HIV infected to receive ART). Condom programs, educational awareness, and

other forms of prevention services are assumed to dissipate due to the lack of

resources during the crisis period. In total, these changes are assumed to

increase the per contact sexual transmission of the virus from an average of 6 percent to 12 percent. This increase is gradually incorporated from 2010 to

2015, lasts until 2025, and returns to BaU levels by 2030 where it remains for the remainder until 2050.

Results of the ODA Scenario for years 2025 and 2050 are presented in

Figure 8.2. Projections for economic indicators are tabulated in Figure 8.3. In order to assess the relative differences between the BaU, optimistic MDG, and pessimistic ODA scenarios, demographic/epidemiologic and economic progress indicators for all three scenarios for the year 2050 are given in Figure 8.4.

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Sub-Saharan Africa: ODA Scenario Model Results Indicator 2005 2025 2050 HIV/AIDS 24.8 87.2 120.6 Population (mil.) HIV Prevalence % 6.1 14.9 15.0 Ages 15-49 HIV Prevalence % 3.93 13.3 9.8 Ages 15-24 HIV+ Births 498 1798 1294 (thousands) New HIV 3.0 17.4 12349 Infections (mil.) ART Coverage 810 0 7839 (pop., thousands) Deaths from AIDS 2.1 10.9 11.4 (millions) Total Population 751 1074 1376 (millions)

Figure 8.2: ODA Cut Scenario Results, Sub-Saharan Africa 2005, 2025, 2050

Sub-Saharan Africa: ODA Scenario Economic Model Results Indicator 2005 2025 2050 Gross National 1.4 2.4 4.0 Income (tril. Intl.$) GNI Growth Rate 3.2 % 2.0 % 1.8 % (percent) GNI per Capita 1863 2273 2915 (Intl.$) Uninfected Working Age Percentage of 51.2 % 49.3 % 52.7 % Population Figure 8.3: ODA Cut Scenario Economic Results Sub-Saharan Africa 2005, 2025, 2050

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Sub-Saharan Africa: 2050 Model Results Indicator BaU Scenario MDG Scenario ODA Scenario HIV/AIDS 54.2 11.1 120.6 Population (mil.) HIV Prevalence % 5.5 0.9 15.0 Ages 15-49 HIV Prevalence % 3.6 0.4 9.8 Ages 15-24 HIV+ Births 566 4.6 1294 (thousands) New HIV Infections 5.8 0.5 12349 (mil.) ART Coverage 1.9 8.3 7839 (pop., thousands) Deaths from AIDS 5.0 0.8 11.4 (millions) Total Population 1686 1845 1376 (millions) Gross National 4.9 5.4 4.0 Income (tril. Intl.$) GNI Growth Rate 2.3 % 2.6 % 1.8 % (percent) Uninfected Working Age Percentage of 59.8 % 62.8 % 52.7 % Population

Figure 8.4: BaU, MDG, and ODA Scenario Results, Sub-Saharan Africa 2050

The total HIV/AIDS population for all three scenarios is displayed in Figure

8.5. Figures 8.6 and 8.7 show the prevalence rates for ages 15-49 and 15-24 respectively for the scenarios. HIV infected births are shown graphically in

Figure 8.8. Deaths due to AIDS for the three scenarios are shown in Figure 8.9 and total population is compared in Figure 8.10.

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ODA Cut Scenario

BaU Scenario

MDG Scenario

Figure 8.5: BaU, MDG, and ODA Scenario HIV/AIDS Population Sub-Saharan Africa 2000-2050

ODA Cut Scenario

BaU Scenario

MDG Scenario

Figure 8.6: BaU, MDG, and ODA Scenario HIV Prevalence Ages 15-49 Sub-Saharan Africa 2000-2050

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ODA Cut Scenario

BaU Scenario

MDG Scenario

Figure 8.7: BaU, MDG, and ODA Scenario HIV Prevalence Ages 15-24 Sub-Saharan Africa 2000-2050

ODA Cut Scenario

BaU Scenario

MDG Scenario

Figure 8.8: BaU, MDG, and ODA Scenario HIV Infected Births Sub-Saharan Africa 2000-2050

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ODA Cut Scenario

BaU Scenario

MDG Scenario

Figure 8.9: BaU, MDG, and ODA Scenario AIDS Deaths Sub-Saharan Africa 2000-2050

BaU Scenario

ODA Cut Scenario

MDG Scenario

Figure 8.10: BaU, MDG, and ODA Scenario Total Population Sub-Saharan Africa 2000-2050

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8.3 Additional AIDS Deaths per Barrel of OECD Oil Deficit

The relative difference in demographic and epidemic indicators between the BaU, MDG, and ODA scenarios are linked to the BaU Demand oil scenario developed in Chapter 4 with peak production occurring in year 2015. The oil price increases which begin affecting ODA contributions begin in 2010 and continue through 2015 at which point the ODA was eliminated. The oil deficit is expected to persist for a period of 10 years until 2025 at which point alternative energies and/or other solutions have been implemented. The oil deficit experienced during the period from 2015 to 2025 is the difference between the

BaU demand curve extended through 2025 and the 2015 peak year supply curve. The “deficit” between 2010 and 2015 is defined to be the amount of current supply minus the peak year supply reached in 2015, thus the value is negative. The additional number of AIDS deaths in the ODA Scenario is considered relative to the number of AIDS deaths in the BaU Scenario. Plotting the oil deficit on the x-axis and the additional number of AIDS deaths in the ODA

Scenario relative to the BaU Scenario on the y-axis yields the series plot in

Figure 8.11. The figure indicates the period of oil price increases, position of peak year, physical oil supply deficit, and additional number of AIDS deaths.

267

Sub-Saharan Africa: Additional AIDS Deaths per Billion Barrels of OECD Oil Deficit 8

Additional 3

AIDS Deaths (millions) 2

0 -5 -3 -1Oil 1 Deficit (billions 3 of barrels) 5 7 9 11

Figure 8.11: Additional AIDS Deaths per Billion Barrels of OECD Oil Deficit

If the proper funding for prevention and treatment is provided as in the

MDG Scenario, presented in Chapter 7, it is assumed that the projections for the

MDG Scenario are accurate for the same period as covered by the oil deficit.

Thus, the number of AIDS deaths that are averted due to implementation of the

MDGs can be plotted on the y-axis of Figure 8.11 against the oil deficit values on the x-axis, as shown in Figure 8.12, to show the number of deaths averted due to an expanded prevention and treatment response provided via sufficient funding.

The additional AIDS deaths due to the oil deficit are shown as the blue series of diamonds in the figure and the red circular series represents the number of lives that can be saved by appropriate funding for programs, personnel, and infrastructure necessary to combat the epidemic. This amount of funding is more likely and is assumed to be provided by the anticipatory oil peak policy developed

268

as the Peak Shift Scenario in Chapter 4 in which the maximum sustainable production capacity is pushed from year 2015 to 2025 in order to gain time in implementing alternative energy solutions to avoid or at least minimize the transition. Thus the MDG Scenario is tied to the oil production peak year shift scenario, Peak Shift Scenario, which assumes international cooperation and coordination, which is cornerstone to MDG success and the official international

0.7 percent pledge of GNI for ODA target.

Sub-Saharan Africa: Additional AIDS Deaths per Billion Barrels of OECD Oil Deficit 8

Additional 0 -5 0 5 10

AIDS Deaths (millions) Deaths AIDS -2

-4 Oil Deficit (billions of barrels)

Figure 8.12: Additional AIDS Deaths per Billion Barrels of OECD Oil Deficit and AIDS Deaths Averted Due to Increased Funding The results presented in Figure 8.12 for OECD oil deficit versus additional

AIDS deaths in sub-Saharan Africa is modified to display global oil deficit versus additional AIDS deaths for the region in Figure 8.13. Both figures for additional

AIDS deaths confirm the complex interrelationship that exists between regional and global resource use and health.

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Sub-Saharan Africa: Additional AIDS Deaths per Billion Barrels of World Oil Deficit 8

Additional Additional 0 -10-50 5 101520253035 -2 AIDS Deaths (millions)

-4 Oil Deficit (billions of barrels)

Figure 8.13: Additional AIDS Deaths per Billion Barrels of World Oil Deficit and AIDS Deaths Averted Due to Increased Funding

8.4 Integrated Scenarios through Year 2050

In an effort to clearly illustrate the scenarios and their differences, the change in overall number of AIDS deaths for the MDG and ODA Scenarios relative to the BaU Scenario are graphed on the y-axis and the year is plotted on the x-axis. AIDS deaths in the BaU Scenario are represented in Figure 8.14 as the x-axis. The oil scenario which provides the BaU number of AIDS deaths is assumed to follow BaU oil demand growth through the peak year in 2025 at which point alternative energies seamlessly pick-up the deficit left by reduced oil supplies. This enables ODA funding to continue at BaU levels otherwise a spike in HIV/AIDS infections will occur after year 2025.

Additional AIDS deaths in the ODA Cut Scenario are represented by the upper series plot in blue in Figure 8.14. The figure shows that as oil prices

270

increase from 2010 to 2015, AIDS deaths increase over the BaU Scenario due to

a reduction in ODA which falls from its’ BaU value in 2010 down to zero by 2015.

The increase in deaths from 2015 to 2025 represents a period in which ODA funding is eliminated due to global oil shortages and consequent losses in economic growth experienced by reaching a peak in oil production by year 2015.

MDG vs. ODA Cut Scenario - Added AIDS Deaths 9

-12010 2015 2020 2025 2030 2035 2040 2045 2050

-3 Additional AIDS Deaths

-5 Additional AIDS Deaths ODA (millions) Additional AIDSYear Deaths MDG (millions)

Figure 8.14: MDG vs. ODA Cut Scenario - Additional AIDS Deaths from Oil Crisis and Deaths Averted due to Adequate Funding of MDGs Deaths begin to decline from year 2025 to 2030 as ODA is gradually resumed and back at BaU levels by 2030. The return of ODA is assumed to become

possible by the development of alternative energies to fill the gap left by declining

oil supplies. BaU progression of the virus after year 2030 in the ODA Cut

Scenario is assumed since global markets and economies are assumed to have

found and implemented a solution to the energy deficit by that time.

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The MDG Scenario shown as the lower series in Figure 8.14 in red

assumes that the Peak Shift Scenario developed in Chapter 4 provides adequate

funding and cooperation between the countries of the world to fund the MDGs at

0.7 percent of OECD GNI. The Peak Shift Scenario assumes that oil

consumption is reduced to peak in 2025 at the same level it would have peaked

at in 2015 if BaU consumption had continued. The extra time and international

cooperation required in order to achieve the Peak Shift Scenario results in saving

millions of lives and improvement of overall global health and economy as

presented in Figure 8.4.

Works Cited:

1 Davis, S. and Diegel, S. Transportation Energy Data Book: Edition 24 (Oak Ridge National Laboratory, Publication No. ORNL-6973, December 2004), http://cta.ornl.gov/data/index.shtml (accessed: February 10, 2005).

272

Chapter 9: Conclusions & Recommendations

9.1 Conclusions and Recommendations

A number of questions have been answered over the course of this

investigation. In addition, several emergent properties and dominant relations governing the interrelationship between oil use, population, the HIV/AIDS epidemic and economic growth have been revealed. Three possible futures for the integrated relationship between the various disciplines demonstrate the need

for and benefits of either a reduction in oil consumption or development of an

alternative fuel, increases in OECD ODA contributions to the 0.7 percent target,

and a comprehensive global effort to mitigate the devastating demographic and

economic consequences of the HIV/AIDS pandemic.

One question of primary interest answered in this dissertation is “Can

peak oil fuel the sub-Saharan AIDS epidemic?”. The answer is definitely yes. If

funding for relief aid for HIV/AIDS is negatively impacted by the coming peak in

oil production and subsequent oil crisis, preventive and treatment programs for

the epidemic will be diminished resulting in higher prevalence and mortality due

to the virus, as shown by the ODA Cut Scenario. A lack of alternative energies

capable of replacing oil could result in global economic recession as historic

supply constrictions have always resulted in economic losses and increased oil

prices. The Army Corp of Engineers states the unique importance and value of

oil saying:

“Historically, no other energy source equals oil’s intrinsic qualities of extractability, transportability, versatility, and cost. The qualities that

273

enabled oil to take over from coal as the front-line energy source for the industrialized world in the middle of the 20th century are as relevant today as they were then. Oil’s many advantages provide 1.3 to 2.45 times more economic value per MBtu than coal (Gever, Kaufman et al. 1991). Currently, there is no viable substitute for petroleum.”1

Clearly the need for investment and research into alternative fuels has

implications for global security and sustainability beyond those presented here

and must be addressed for these reasons as well.

Strategic planning and cooperative efforts between countries of the world

to reduce oil consumption and shift the potential peak year further into the future could provide tremendous benefits for global health and economic growth especially in sub-Saharan Africa. The MDG Scenario developed shows the many benefits of funding the Goals in terms of lives saved, infections averted, reductions in prevalence, growth in economy, and increase in number of uninfected available workers. In addition to the millions of lives saved by the

MDGs, an estimated 500 million people will be lifted out of poverty by 2015.2

Although the costs of the MDGs may seem high at 135 billion in 2006, climbing to

195 billion by 2015; the total price for achieving the MDGs pales in comparison to the 2005 world military budget of over 900 billion.3

Several possible outcomes for the evolution of oil resource use,

population, the HIV/AIDS epidemic, and economic growth have been analyzed

and integrated to produce the conclusions reached in this dissertation. It is of utmost importance to keep in mind that the projections here are based on given assumptions which may or may not occur due to future uncertainties involved in the complex integrated assessment and the various sub-models developed.

274

Regardless, this study presents the dominant relationships involved in the integrated and sub-model structures and provides an envelope within which the actual future may reside.

9.2 Future Research

There are numerous areas and topics within this investigation worthy of further exploration. First, detailed oil models broken down by region and sector would be useful for projecting possible demand patterns in light of regional economic growth and increases in vehicle ownership. Next, HIV/AIDS scenarios for India and other areas in danger of increasing prevalence should be assessed to determine the best methods and costs for mitigating the disease. In addition, a more robust economic model could be developed to answer various questions of interest regarding economic growth and its’ relationship to demography and resource use. Finally, the integrated assessment could be used to create a regionalized HIV/AIDS scenario which is linked to a regionalized oil supply and demand scenario in which the union of the regions is the world in order to examine regional effects outside of sub-Saharan Africa.

Works Cited:

1 Energy Trends and Their Implications for U.S. Army Installations, 5.

2 Investing in Development: A Practical Plan to Achieve the Millennium Development Goals, 60.

3 Investing in Development: A Practical Plan to Achieve the Millennium Development Goals, 64.

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Appendix 1: Mathematical Model Equations

Variable Naming Convention for Model Equations

Note: a complete description of variables, dimensions, etc. is given in Appendix 2

Subscript r represents region Subscript j represents individual age j where 0 ≤ j ≤ 100 and j is integer Subscript t represents time or year

Prefix r indicates growth rate Prefix wrd_ indicates world as region for variable

Suffix _b basic variable, uses data value Suffix _m multiplier variable, set to 1 for Business-as-Usual Scenario Suffix _male indicates male portion of variable Suffix _female indicates female portion of variable Suffix _dm indicates demand of variable Suffix _sp indicates supply of variable Suffix _df indicates deficit of variable Suffix _cml indicates cumulative value of variable Suffix _ult indicates ultimate quantity of variable Suffix _peak indicates peak value of variable Suffix _pc indicates per capita value of variable Suffix _per indicates variable portion as percentage of total Suffix _hiv indicates HIV portion of variable Suffix _aids indicates AIDS portion of variable Suffix _neg indicates uninfected or HIV negative portion of variable Suffix _newhiv indicates new HIV cases for variable Suffix _newaids indicates new AIDS cases for variable popf represents 1st level population ( f appended to a variable indicates first level ) pops represents 2nd level population ( s appended to a variable indicates second level ) pop represents 3rd level population ( third level variables not appended for level ) popc represents 3rd level population per cohort ( c appended to a variable indicates age cohorts )

aidsinr AIDS infection rate ART antiretroviral therapy brt births brtc_neg_hiv uninfected births per cohort from infected mothers

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contact age-specific average number of unprotected sexual contacts with infected partner per year crbrt crude birth rate crdth crude death rate dth deaths frtc age-specific fertility per cohort gni gross national income hivinr HIV infection rate mfratio male-to-female population ratio mfratio_frt male-to-female population ratio ages 15 to 49 (i.e. split for fertile ages) mrtc age-specific mortality per cohort mtchivr mother-to-child HIV transmission rate oda official development assistance oil oil

1st Level Population Equations

1) rpopfrt,,,=× rpopf_ b rt rpopf_ m rt { Population Growth Rate }

⎛⎞rpopfrt, 2) popf=×+ popf 1 rt, rt,1− ⎜⎟ ⎝⎠100 { Regional Population }

2nd Level Population Equations

1) crbrtsrt,,,= crbrts_ b rt× crbrts_ m rt { Crude Birth Rate }

2) crdthsrt,,,=× crdths_ b rt crdths_ m rt { Crude Death Rate }

⎛⎞crbrtsrt,,- crdths rt 3) pops=+ pops ×⎜⎟1 rt, rt,1− ⎜⎟1000 ⎝⎠ {Regional Population}

277

popsrt, 4) brts=× crbrts rt,, rt 1000 { Total Number of Births }

popsrt, 5) dths=× crdths rt,, rt 1000 { Total Number of Deaths }

⎛⎞popsrt, 6) rpops =×100 − 1 rt, ⎜⎟ ⎝⎠popsrt,1− { Regional Population Rate }

3rd Level Population Equations

1) frtcrjt,,=_ frtc b rjt ,,× frtc _ m rjt ,, { Fertility / Cohort }

frtc rjt,, mfratio_ frtrt, 2) brtc=× popc × rjt,, rjt,,− 1 1000 100 { Births / Cohort }

49 3) brtrt, = ∑ brtc j=15 rjt,, { Total Births }

4) mrtcrjt,,=_ mrtc b rjt ,,× mrtc _ mrt, { Mortality / Cohort }

mrtcrjt,, 5) dthc= popc × rjt,, rjt,,− 1 100 { Deaths / Cohort }

100+ 6) dthrt, = ∑ dthc j=0 rjt,,

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{ Total Deaths }

7) popc= brt rt,0, rt, { Population of Newborns }

8) popc= popc− dthc rjt,, rj,1,1− t−− rj ,1, t { Population / Cohort }

mfratiort, 9) popc_ malerjt,,=× popc rjt ,, mfratiort, +100 { Male Pop. / Cohort }

10) popc_ femalerjt,,= popc rjt ,,− popc_ male rjt ,, { Female Population / Cohort }

100+ 11) poprt, = ∑ popc j=0 rjt,, { Total Population }

100+ 12) pop__ malert, = ∑ popc male j=0 rjt,, { Total Male Population }

100+ 13) pop__ femalert, = ∑ popc female j=0 rjt,, { Total Female Population }

brtrt, 14) crbrtrt, =×1000 poprt, { Crude Birth Rate }

dthrt, 15) crdthrt, =×1000 poprt, { Crude Death Rate }

279

16) popinc=− pop pop rt,, rt rt,1− { Population Increase / Year}

⎛⎞poprt, 17) rpop =×100 − 1 rt, ⎜⎟ ⎝⎠poprt,1− { Population Growth Rate }

1st Level Oil Equations

1) rwrd_ oil______dmtt= rwrd oil dm b× rwrd oil dm m t { World Oil Demand Growth Rate }

⎛⎞rwrd__ oil dm 2) wrd__ oil dm=×+ wrd __ oil dm ⎜⎟ 1 t t t−1 ⎜⎟100 ⎝⎠ { World Oil Demand }

3) wrd_ oil___ sptt= wrd oil dm { Pre-Peak Year World Oil Supply }

t wrd___ oil sp cml= wrd __ oil sp 4) tx∑ x=2000 { World Oil Supply Cumulative }

5) wrd____ oil sp ult asymtt=× 2( wrd ___ oil sp ult − wrd ___ oil sp cml ) {Hubbert Ult. Recov. Asymptote}

6) wrd_ oil__ sp peak= wrd _ oil _ sp t peakyear { Peak Year World Oil Supply }

wrd_ oil__ sp peak 7) b =×4 wrd____ oil sp ult asym t peakyear { Hubbert Constant }

280

wrd_ oil__ sp peak 8) wrd__ oil sp =× 2 t ⎛⎞⎛⎞ 1cosh+×−⎜⎟btt⎜⎟ ⎝⎠⎝⎠peakyear {Hubbert Post-Peak World Oil Supply}

9) wrd_ oil_____ dfttt=− wrd oil dm wrd oil sp { World Oil Deficit }

2nd Level Oil with Economic Feedback Equations

1) rgnirt,,,=× rgni_ b rt rgni_ m rt { GNI Growth Rate }

⎛⎞rgnirt, 2) gni=×+ gni ⎜⎟1 rt, rt,1− ⎜⎟100 ⎝⎠ { Gross National Income }

gnirt, 3) gni_ pcrt, =× 1000 popfrt, { GNI per Capita }

r −1

4) gni_ w = ∑ gni r=0 r { World GNI }

gni_ wt 5) gni__ pc wt = popf_ wt { World GNI per Capita }

6) oil_ usert,,,=× oil__ use b rt oil __ use m rt { Regional Consumption Share of World Supply }

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r −1

7) ∑ oil_ use=≤ 100, t peakyear r=0 rt, { Total Oil Use Equal to 100% of World Supply }

oil_ usert, 8) oil_ dm=× wrd__ oil dm, if switch =0 rt, t 100 { Oil Demand: Growth Rate as Driver }

9) oil_ dm______gnirt,,,=× oil dm gni b rt oil dm gni m rt { Oil Demand Intensity for GNI }

10) oil_ dmrt,,=× gni rt oil__ dm gnirt, , if switch = 1 {Oil Demand: GNI as Demand Driver}

oil_ usert, 11) oil___ sp=× wrd oil sp rt, t 100 { Oil Supply/Consumption }

⎛⎞oil_ sprt, 12) roil_ sp =×100⎜⎟ − 1 rt, ⎜⎟oil_ sp ⎝⎠rt,1− { Oil Supply Growth Rate }

13) oil_ dfrt,,,= oil__ dm rt− oil sp rt { Oil Deficit }

14) gni_ odart,,,=× gni__ oda b rt gni __ oda m rt { Percent of GNI for ODA }

gni_ odart, 15) oda=× gni rt,, rt 100 { Official Development Assistance }

16) oda_ ssafrt,,,=× oda__ ssaf per rt oda rt { ODA to Sub-Saharan Africa }

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Dimensions Variables Region r∈{ OECD, non− OECD} Year t∈{2000,2001,2002,...,2100}

1st Level HIV/AIDS Population Equations

1) rpopfrt,,,=× rpopf_ b rt rpopf_ m rt { Population Growth Rate }

⎛⎞rpopfrt, 2) popf=×+ popf 1 rt, rt,1− ⎜⎟ ⎝⎠100 { Regional Population }

3) popf __hiv perrt,,= popf __hiv per _ b rt× popf __hiv per _ m rt , { HIV+ Percentage of Population }

popf_ hiv_ perrt, 4) popf_ hiv=× popf rt,, rt 100 { HIV+ Population }

⎛⎞popf_ hiv ⎜⎟rt, 5) rpopf_ hivrt, =×100⎜⎟ − 1 ⎜⎟popf_ hiv ⎝⎠rt,1− { HIV+ Population Growth Rate }

6) popf __aids perrt,,,=× popf ___aids per b rt popf ___aids per m rt {AIDS Percentage of Population}

popf_ aids_ perrt, 7) popf_ aids=× popf rt,, rt 100 { AIDS Population }

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⎛⎞popf_ aids ⎜⎟rt, 8) rpopf_100 aidsrt, =×⎜⎟ ⎜⎟popf_ aids ⎝⎠rt,1− { AIDS Population Growth Rate }

9) popf_ negrt,,=− popf rt popf__ hiv rt , − popf aids rt , { HIV- Population }

2nd Level HIV/AIDS Population Equations

1) crbrtsrt,,,= crbrts_ b rt× crbrts_ m rt { Crude Birth Rate }

2) crdthsrt,,,=× crdths_ b rt crdths_ m rt { Crude Death Rate }

⎛⎞crbrtsrt,,- crdths rt 3) pops=+ pops ×⎜⎟1 rt, rt,1− ⎜⎟1000 ⎝⎠ { Population }

⎛⎞popsrt, 4) rpops =×100 − 1 rt, ⎜⎟ ⎝⎠popsrt,1− { Population Growth Rate }

5) pops__ hiv perrt,,,=× pops __ hiv per _ b rt pops __ hiv per _ m rt { HIV+ Percentage of Population }

pops_ hiv_ perrt, 6) pops_ hiv=× pops rt,, rt 100 { HIV+ Population }

7) pops__ aids perrt,,,=× pops ___ aids per b rt pops ___ aids per m rt { AIDS Percentage of Population }

284

pops_ aids_ perrt, 8) pops_ aids=× pops rt,, rt 100 { AIDS Population }

9) pops_ negrt,,=− pops rt pops__ hiv rt , − pops aids rt , { HIV- Population }

popsrt, 10) brts=× crbrts rt,, rt 1000 { Births }

crbrtsrt, 11) brts___ hiv=×× brts hiv per pops rt,, rt rt,1− 1000 { HIV+ Births }

popsrt, 12) dths=× crdths rt,, rt 1000 { Deaths }

13) dths_ aidsrt,,,=_ dth aids _ per rt× dths rt { AIDS Deaths }

⎛⎞pops_ hivrt, 14) rpops_ hiv =×100 − 1 rt, ⎜⎟ ⎝⎠pops_ hivrt,1− { HIV+ Population Growth Rate }

⎛⎞pops_ aidsrt, 15) rpops_ aids =×100 − 1 rt, ⎜⎟ ⎝⎠pops_ aidsrt,1− { AIDS Population Growth Rate }

3rd Level HIV/AIDS Population Equations

1) frtcrjt,,=× frtc_ b rjt ,, frtc_ mrt, { Fertility }

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frtc mfratio_ frtrt, rjt,, 2) brtc__ neg=×× popc neg rjt,, rjt,,− 1 100 1000 { HIV- Births / Cohort }

3) mtchivrrt,,,=× mtchivr_ b rt mtchivr_ m rt { Mother-to-Child Trans. Rate }

mfratio_ frt frtc 4) ⎛⎞ rt, rjt,, brtc__ neg hiv=×−×× popc _ hiv⎜⎟ 1 mtchivr rjt,, rjt,,− 1 ⎝⎠rt, 100 1000 { HIV+ → Neg Births / Cohort}

frtc mfratio_ frtrt, rjt,, 5) brtc__ hiv=××× popc hiv mtchivr rjt,, rjt,,− 1 rt, 100 1000 { HIV+ Births / Cohort }

6) brtcrjt,,=+ brtc_ neg rjt ,, brtc__ neg hiv rjt ,, + brtc _ hiv rjt ,, { Total Births / Cohort }

49 7) brt__ neg= ∑ brtc neg rt, j=15 rjt,, { Total HIV- Births }

49 8) brt__ neg hiv= ∑ brtc __ neg hiv rt, j=15 rjt,, { Total HIV→Neg Births }

49 9) brt__ hiv= ∑ brtc hiv rt, j=15 rjt,, { Total HIV+ Births }

49 10) brt= ∑ brtc rt, j=15 rjt,, { Total Births }

11) mrtcrjt,,=_ mrtc b rjt ,,× mrtc _ mrt,

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{ Non-AIDS Mortality }

12) mrtc_ aidsrjt,,=__ mrtc b aids rjt ,,× mrtc __ m aidsrt, { AIDS Mortality }

mrtcrjt,, 13) dthc_=_ neg popc neg × rjt,, rjt,,− 1 100 { HIV- Deaths / Cohort }

mrtcrjt,, 14) dthc_=_ hiv popc hiv × rjt,, rjt,,− 1 100 { HIV+ Deaths / Cohort }

mrtc_ aidsrjt,, 15) dthc_=_ aids popc aids × rjt,, rjt,,− 1 100 { AIDS Deaths / Cohort }

16) popc__ aids loss= popc __ aids loss+ dthc _ aids rjt,, r,1,1j− tr−− ,1,j t { AIDS Population Loss / Cohort }

17) dthcrjt,,=++ dthc_ neg rjt ,, dthc__ hiv rjt ,, dthc aids rjt ,, { Total Deaths / Cohort }

100 18) dth__ neg= ∑ dthc neg rt, j=0 rjt,, { Total HIV- Deaths }

100 19) dth__ hiv= ∑ dthc hiv rt, j=0 rjt,, { Total HIV+ Deaths }

100 20) dth__ aids= ∑ dthc aids rt, j=0 rjt,, { Total AIDS Deaths }

100+ 21) dth= ∑ dthc rt, j=0 rjt,,

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{ Total Deaths }

22) popc_ neg=+ brt___ neg brt neg hiv rt,0, rt,, rt { Population of HIV- Newborns }

23) popc_ hiv= brt_ hiv rt,0, rt, { Population of HIV+ Newborns }

24) popc_ newhiv= popc_ hiv rt,0, rt ,0, { New HIV Cases in Cohort j=0 }

25) popc= brt rt,0, rt, { Total Population of Newborns }

26) hivinrrt,,,= hivinr_ b rt× hivinr_ m rt { HIV Infection Rate }

pop__ hiv per 27) popc__ newhiv=××× popc neg contact hivinr rt,1− rjt,,rj,1,1−− t rjt ,, rt, 100 {New HIV Cases/Cohort j=15-100}

100 28) pop__ newhiv= ∑ popc newhiv rt, j=0 rjt,, {Total Population New HIV Cases}

29) popc__ neg=−− spopc neg dthc _ neg popc _ newhiv rjt,,rj,1,1−− t rj ,1,1 −− t rjt ,, { HIV- Pop. / Cohort }

100 30) pop__ neg= ∑ popc neg rt, j=0 rjt,, { Total HIV- Population }

31) aidsinrr,t = aidsinr_b r,t ×aidsinr_m r,t { AIDS Infection Rate }

32) popc_newaids = spopc_hiv ×aidsinr r,j,t r,j-1,t-1 r,t { New AIDS Cases / Cohort }

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100 33) pop__ newaids= ∑ popc newaids rt, j=0 rjt,, { Total Population New AIDS Cases }

34) popc_ hiv=−+− spopc____ hiv dthc hiv popc newhiv popc newaids rjt,,rj,1,1−− t rj ,1, − t rjt ,, rjt ,, { HIV+ Population / Cohort j>0 }

100 35) pop__ hiv= ∑ popc hiv rt, j=0 rjt,, { Total HIV+ Population }

36) popc__ aids=−+ popc aids dthc _ aids popc _ newaids rjt,,rj,1,1−− t rj ,1, − t rjt ,, { AIDS Population / Cohort )

100 37) pop__ aids= ∑ popc aids rt, j=0 rjt,, { Total AIDS Population }

38) popcrjt,,=++ popc_ neg rjt ,, popc__ hiv rjt ,, popc aids rjt ,, { Total Population / Cohort }

mfratiort, 39) popc__ neg malerjt,,=× popc _ neg rjt ,, mfratiort, +100 { HIV- Male Population / Cohort }

mfratiort, 40) popc__ hiv malerjt,,=× popc _ hiv rjt ,, mfratiort, +100 { HIV+ Male Population / Cohort }

mfratiort, 41) popc__ aids malerjt,,=× popc _ aids rjt ,, mfratiort, +100 { AIDS Male Population / Cohort }

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mfratiort, 42) popc_ malerjt,,=× popc rjt ,, mfratiort, +100 { Total Male Population / Cohort }

43) popc_ neg____ femalerjt,,= popc neg rjt ,,− popc neg male rjt ,, { HIV- Female Population/ Cohort}

44) popc_ hiv____ femalerjt,,= popc hiv rjt ,,− popc hiv male rjt ,, { HIV+ Female Population / Cohort}

45) popc_ aids____ femalerjt,,= popc aids rjt ,,− popc aids male rjt ,, { AIDS Female Pop. / Cohort}

46) popc_ femalerjt,,= popc rjt ,,− popc_ male rjt ,, { Total Female Population / Cohort}

100+ 47) poprt, = ∑ popc j=0 rjt,, { Total Population }

brtrt, 48) crbrtrt, =×1000 poprt, { Crude Birth Rate }

dthrt, 49) crdthrt, =×1000 poprt, { Crude Death Rate }

⎛⎞poprt, 50) rpop =×100 − 1 rt, ⎜⎟ ⎝⎠poprt,1− { Population Growth Rate }

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49 ∑ ( popc__ hivrjt,,+ popc aids rjt ,,) j=15 51) hiv_ prevr =× 100 rt,15...49, 49 ∑ ()popcrjt,, j=15 { HIV Prevalence Rate, Ages 15-49}

24 ∑ ( popc__ hivrjt,,+ popc aids rjt ,,) j=15 52) hiv_ prevr =× 100 rt,15...24, 49 ∑ ()popcrjt,, j=15 { HIV Prevalence Rate, Ages 15-24}

14 ∑ ( popc__ hivrjt,,+ popc aids rjt ,,) j=0 53) hiv_100 prevr =× rt,0...14, 49 ∑ ()popcrjt,, j=15 { HIV Prevalence Rate, Ages 0-14 }

100 ∑ ( popc__ hivrjt,,+ popc aids rjt ,,) j=50 54) hiv_100 prevr =× rt,50...100, 49 ∑ ()popcrjt,, j=15 {HIV Prevalence Rate, Age 50-100}

ART_ perrt, 100 55) pop___ ART=× popc hiv + popc aids rt, 100 ∑ ( rjt,, rjt ,,) j=0 { Population Receiving ART }

56) ART_ costrt,,,=× ART__ cost pc rt pop _ ART rt { Cost for Anti-Retroviral Therapy }

291

1st Level Economic Equations

1) rgni_ pcrt,,,=× rgni__ pc b rt rgni __ pc m rt { Gross National Income Growth Rate per capita }

⎛⎞rgni_ pc ⎜⎟rt, 2) gni__1 pc=×+ gni pc ⎜⎟ rt, rt,1− ⎜⎟100 ⎝⎠ { GNI per capita }

gni_ pcrt,,× pop rt 3) gni = rt, 1000 { GNI }

⎛⎞gni_ pc ⎜⎟rt, 4) rgni =×100⎜⎟ − 1 rt, ⎜⎟gni_ pc ⎝⎠rt,1− { GNI Growth Rate }

65 5) wrk__ pop= ∑ popc neg rt, j=18 rjt,, { Uninfected Workforce Age Population }

wrk_ poprt, 6) wrk__ pop perrt, =× 100 poprt, {Percentage of Uninfected Workforce Age Population}

292

Appendix 2: GLOBESIGHT Variable Declarations (XML)

Population Project

Multi-Level Population Models Year 293

294

295

296

LongName="Population, Millions, First Level" Dim1="regions" Type="Output" />

297

ShortName="crdths" LongName="Crude Death Rate, Deaths/Thousand, Second Level" Dim1="regions" Type="Output" />

298

299

LongName="Mortality of Population Cohort, Basic, Third Level" Dim1="regions" Dim2="ncohorts" Type="Parameter" />

300

LongName="Male Population Aggregate, Millions, Third Level" Dim1="regions" Dim2="nag" Type="Output" />

Oil Transition Project

301

Oil Transition Project First Level Oil Model - Demand with Growth Rate as Driver Second Level Oil Model - Demand with Gross National Income as Driver

Year

302

ShortName="wrd_oil_sp" LongName="World Oil Supply, Billions of Barrels, 1st Level" Type="Output" />

303

ShortName="iwrd_oil_dm" LongName="Initial Oil Demand, Billions of Barrels, 1st Level" Type="Initial Condition" />

304

305

306

ShortName="rgni_df2" LongName="GNI Growth Rate with Oil Deficit2, Percent, Second Level" Dim1="regions" Type="Output" />

307

Dim1="regions" Type="Output" />

308

Type="Output" />

309

310

ShortName="oil_df1" LongName="Oil Deficit1, Billions of Barrels, Second Level" Dim1="regions" Type="Output" />

311

LongName="ODA to Sub-Saharan Africa1, Billions, Second Level" Dim1="regions" Type="Output" />

HIV/AIDS Population & Economic Project

Three Level AIDS Population Model Year

312

313

314

315

Dim1="regions" Type="Output" />

316

Dim1="regions" Type="Output" />

317

Dim1="regions" Type="Output" />

318

ShortName="crdths_b" LongName="Crude Death Rate, Basic, Deaths/Thousand, Second Level" Dim1="regions" Type="Parameter" />

319

ShortName="pops_aids_per" LongName="AIDS Population, Percent, Second Level" Dim1="regions" Type="Output" />

320

LongName="HIV+ plus AIDS Population1, Thousands, Third Level" Dim1="regions" Type="Output" />

321

322

ShortName="dth_noaids" LongName="Death Total without AIDS, Thousands, Third Level" Dim1="regions" Type="Output" />

323

324

Dim1="regions" Type="Input" />

325

326

ShortName="popc_hiv_male" LongName="HIV+ Male Population of Cohort, Thousands, Third Level" Dim1="regions" Dim2="ncohorts" Type="Output" />

327

328

ShortName="popag_noaids" LongName="Population Aggregate 2 without AIDS, Thousands, Third Level" Dim1="regions" Dim2="nag2" Type="Output" />

329

LongName="Population Sex Ratio, Males per 100 Females, Third Level" Dim1="regions" Type="Parameter" />

330

331

LongName="Gross National Product1, Third Level" Dim1="regions" Type="Output" />

332

333

334

Appendix 3: GLOBESIGHT Model Code (Java Language)

1st Level Population Model

//*************************************************************** // // First Level Population Model // By Craig Atzberger 10/9/2003 // Update 11/19/2003, 02/28/2005, 03/23/2005 // //*************************************************************** public class FirstLevel extends GsBaseUserModel { private float [] spopf = new float[regions]; public void runModel(int currentYear, int firstYear, int lastYear) {

/* Compute Regional Migration */

for (int r=0; r

popmig[r] = popmig_b[r] * popmig_m[r]; }

/* Set Initial Conditions */

if (currentYear == firstYear) {

for (int r=0; r

popf[r] = ipopf[r]; rpopf[r] = rpopf_b[r] * rpopf_m[r] * rpopf_s; } }

/* Compute Population Using Growth Rates */

if (currentYear > firstYear) {

for (int r=0; r

rpopf[r] = rpopf_b[r] * rpopf_m[r] * rpopf_s; popf[r] = (spopf[r] * (F1 + rpopf[r] / F100)) + popmig[r]; } }

/* Backup Population Variables */

for (int r=0; r

spopf[r] = popf[r]; 335

} } }

2nd Level Population Model

//************************************************************************************ // // Second Level Population Model // By Craig Atzberger 10/20/03, 02/28/2005, 03/23/2005, 09/12/2005 // //************************************************************************************ public class SecondLevel extends GsBaseUserModel { private float [] spops = new float[regions] ; public void runModel(int currentYear, int firstYear, int lastYear) {

/* Compute Regional Migration */

for (int r=0; r

popmig[r] = popmig_b[r] * popmig_m[r]; }

/* Set Initial Population */

if (currentYear == firstYear) {

for (int r=0 ; r < regions ; r++) {

pops[r] = ipops[r]; spops[r] = ipops[r]; crbrts[r] = crbrts_b[r] * crbrts_m[r] * crbrts_s; crdths[r] = crdths_b[r] * crdths_m[r] * crdths_s; brts[r] = crbrts[r] * pops[r] / F1000; dths[r] = crdths[r] * pops[r] / F1000; } }

/* Compute Population Indicators */

if (currentYear > firstYear) {

for (int r=0; r < regions; r++) {

crbrts[r] = crbrts_b[r] * crbrts_m[r] * crbrts_s; crdths[r] = crdths_b[r] * crdths_m[r] * crdths_s; pops[r] = spops[r] + ((crbrts[r] - crdths[r]) * spops[r] / F1000) + popmig[r]; rpops[r] = (pops[r] - spops[r]) * F100 / spops[r]; brts[r] = crbrts[r] * pops[r] / F1000; dths[r] = crdths[r] * pops[r] / F1000;

336

/* Backup Variables */

spops[r] = pops[r]; } } } }

3rd Level Population Model

//******************************************************************************* // // Third Level Population Model // Age-Specific Fertility Driver // Age-Specific Mortality Driver // By Craig Atzberger 01/27/04, 02/04/04, 03/03/04, 02/28/2005 // //******************************************************************************* public class ThirdLevel extends GsBaseUserModel {

/* Backup Variable Declaration */ private float [] spop = new float[regions]; private float [] [] spopc = new float [regions][ncohorts];

/* Model Code */ public void runModel(int currentYear, int firstYear, int lastYear) {

/* Initialize Variables */

if (currentYear == firstYear) {

for (int r=0; r

spopc[r][j] = ipopc[r][j]; popc[r][j] = ipopc[r][j]; popc_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc[r][j]; popc_female[r][j] = popc[r][j] - popc_male[r][j]; }} }

/* Compute Cohort Fertility */

for (int r=0; r

frtc[r][j] = kfrtc[r] * frtc_b[r][j] * frtc_m[r];

337

}}

/* Compute Births per Cohort */

for (int r=0; r

brtc[r][j] = (frtc[r][j] / F1000) * spopc[r][j] * (mfratio_frt[r] / F100); }}

/* Compute Total Births */

for (int r=0; r

brt[r] = F0;

for (int j=0; j

brt[r] += brtc[r][j]; } }

/* Compute Cohort Mortality */

for (int r=0; r

mrtc[r][j] = kmrtc[r] * mrtc_b[r][j] * mrtc_m[r]; }}

/* Compute Deaths per Cohort */

for (int r=0; r

dthc[r][j] = (mrtc[r][j] / F100) * spopc[r][j]; }}

/* Compute Total Deaths */

for (int r=0; r

dth[r] = F0;

for (int j=0; j

dth[r] += dthc[r][j]; } }

if (currentYear > firstYear) {

/* Compute Population of Cohort 0 - Newborns */

for (int r=0; r

338

popc[r][0] = brt[r]; popc_male[r][0] = (mfratio[r] / (mfratio[r] + F100)) * popc[r][0]; popc_female[r][0] = popc[r][0] - popc_male[r][0]; }

/* Compute Cohort Population */

for (int r=0; r

popc[r][j] = spopc[r][j-1] - dthc[r][j-1]; popc_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc[r][j]; popc_female[r][j] = popc[r][j] - popc_male[r][j]; }} }

/* Compute aggregate variables (up to age 99) */

for (int r=0; r

popa[r][k] = F0;

for (int j=k*5; j<((k+1)*5); j++) {

popa[r][k] = popa[r][k] + popc[r][j]; } popa_male[r][k] = (mfratio[r] / (mfratio[r] + F100)) * popa[r][k]; popa_female[r][k] = popa[r][k] - popa_male[r][k]; }}

/* Compute aggregate variables (age 100+) */

for (int r=0; r

popa[r][nag-1] = F0; popa[r][nag-1] = popa[r][nag-1] + popc[r][ncohorts-1]; popa_male[r][nag-1] = (mfratio[r] / (mfratio[r] + F100)) * popa[r][nag-1]; popa_female[r][nag-1] = popa[r][nag-1] - popa_male[r][nag-1]; }

/* Compute aggregate variables (0-20) */

for (int r=0; r

popag[r][0] = F0;

for (int j=0; j<21; j++) {

popag[r][0] = popag[r][0] + popc[r][j]; } popag_male[r][0] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][0]; popag_female[r][0] = popag[r][0] - popag_male[r][0]; }

/* Compute aggregate variables (21-59) */

339

for (int r=0; r

popag[r][1] = F0;

for (int j=21; j<60; j++) {

popag[r][1] = popag[r][1] + popc[r][j]; } popag_male[r][1] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][1]; popag_female[r][1] = popag[r][1] - popag_male[r][1]; }

/* Compute aggregate variables (60+) */

for (int r=0; r

for (int j=60; j

popag[r][2] = popag[r][2]+popc[r][j]; } popag_male[r][2] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][2]; popag_female[r][2] = popag[r][2] - popag_male[r][2]; }

/* Compute aggregate population (65+) */

for (int r=0; r

pop65p[r] = pop[r] - (popag[r][0] + popag[r][1] + popa[r][Age60to64]); }

/* Compute College going population (18-21) */

for (int r=0; r

popcol[r] = popc[r][18] + popc[r][19] + popc[r][20] + popc[r][21]; }

/* Compute aggregate percentages */

for (int r=0; r

popag_per[r][j] = F100 * popag[r][j] / pop[r]; }}

if (currentYear > firstYear) {

/* Compute Population Increase Annual */

for (int r=0; r

popinc[r] = pop[r] - spop[r];

340

} }

/* Compute Population Rate */

for (int r=0; r

rpop[r] = (pop[r] - spop[r])/ spop[r] * F100; }

/* Backup Population variables */

for (int r=0; r

pop[r] = F0;

for (int j=0; j

spopc[r][j] = popc[r][j]; pop[r] += popc[r][j]; } spop[r] = pop[r]; }

/* Compute Crude Birth/Death Rate */

for (int r=0; r

crbrt[r] = (brt[r] / pop[r]) * F1000; crdth[r] = (dth[r] / pop[r]) * F1000; } } }

1st Level Oil Model

//************************************************************* // // Oil Supply & Demand Model // Created: May 19, 2003 by Craig Atzberger // Updated: Aug 1, 2005 Aug 15, 2005 // //************************************************************* public class FirstLevelOil extends GsBaseUserModel { private float swrd_oil_sp_ult_asym = F0; private float swrd_oil_sp_peak = F0; private float swrd_oil_sp_cml = F0; private float swrd_oil_dm = F0; private float swrd_oil_sp = F0; private float swrd_supinc_cml = F0;

341

public void runModel(int currentYear, int firstYear, int lastYear) {

/* Initialize First Year Values */

if (currentYear == firstYear) {

wrd_supinc_cml = F0; wrd_supinc_yr = F0;

/* World Oil Demand */

wrd_oil_dm = iwrd_oil_dm; wrd_oil_dm1 = wrd_oil_dm; wrd_oil_dm2 = wrd_oil_dm; wrd_oil_dm3 = wrd_oil_dm; swrd_oil_sp_cml = 800; }

/* World Oil Demand Growth Rate */

rwrd_oil_dm = rwrd_oil_dm_b * rwrd_oil_dm_m; rwrd_oil_dm1 = rwrd_oil_dm;

if (currentYear > firstYear) {

wrd_oil_dm = swrd_oil_dm * (1 + rwrd_oil_dm/F100); wrd_oil_dm1 = wrd_oil_dm; wrd_oil_dm2 = wrd_oil_dm; wrd_oil_dm3 = wrd_oil_dm; }

/* World Oil Supply */

if (currentYear <= peakyear) {

wrd_oil_sp = wrd_oil_dm; wrd_oil_sp1 = wrd_oil_sp; wrd_oil_sp2 = wrd_oil_sp; wrd_oil_sp3 = wrd_oil_sp;

if ((wrd_oil_sp + swrd_oil_sp_cml) > wrd_oil_sp_ult) {

wrd_oil_sp = wrd_oil_sp_ult - swrd_oil_sp_cml; wrd_oil_sp1 = wrd_oil_sp; wrd_oil_sp2 = wrd_oil_sp; wrd_oil_sp3 = wrd_oil_sp; } }

/* Cumulative World Oil Supply */

if (currentYear <= peakyear) {

wrd_oil_sp_cml = swrd_oil_sp_cml + wrd_oil_sp; }

342

/* Hubbert Curve Calculations */

if (currentYear == peakyear) {

wrd_oil_sp_ult_asym = 2*(wrd_oil_sp_ult - wrd_oil_sp_cml); wrd_oil_sp_peak = wrd_oil_sp; }

if (currentYear > peakyear) {

wrd_oil_sp_ult_asym = swrd_oil_sp_ult_asym; wrd_oil_sp_peak = swrd_oil_sp_peak;

if ((wrd_oil_sp_ult - wrd_oil_sp_cml) > F0) {

b = 4*wrd_oil_sp_peak/wrd_oil_sp_ult_asym; wrd_oil_sp = 2*wrd_oil_sp_peak/(1+ ((float)((Math.exp(b*(currentYear- peakyear))) + (Math.exp(-b*(currentYear-peakyear))))/2)); wrd_oil_sp1 = wrd_oil_sp; wrd_oil_sp2 = wrd_oil_sp; wrd_oil_sp3 = wrd_oil_sp; wrd_oil_sp_cml = swrd_oil_sp_cml + wrd_oil_sp; } else { wrd_oil_sp = F0; wrd_oil_sp1 = wrd_oil_sp; wrd_oil_sp2 = wrd_oil_sp; wrd_oil_sp3 = wrd_oil_sp; wrd_oil_sp_cml = swrd_oil_sp_cml + wrd_oil_sp; } }

/* World Oil Deficit */

wrd_oil_df = wrd_oil_dm - wrd_oil_sp; wrd_oil_df1 = wrd_oil_df; wrd_oil_df2 = wrd_oil_df; wrd_oil_df3 = wrd_oil_df;

if (currentYear > firstYear) {

/* World Oil Supply Increase per Year */

wrd_supinc_yr = wrd_oil_sp - swrd_oil_sp;

/* World Oil Supply Increase from 2000 */

wrd_supinc_cml = swrd_supinc_cml + wrd_supinc_yr; wrd_supinc_cml1 = wrd_supinc_cml; wrd_supinc_cml2 = wrd_supinc_cml; wrd_supinc_cml3 = wrd_supinc_cml; }

/* Backup Variables */

343

swrd_oil_dm = wrd_oil_dm; swrd_oil_sp_cml = wrd_oil_sp_cml; swrd_oil_sp_ult_asym = wrd_oil_sp_ult_asym; swrd_oil_sp_peak = wrd_oil_sp_peak; swrd_oil_sp = wrd_oil_sp; swrd_oil_sp_cml = wrd_oil_sp_cml; swrd_supinc_cml = wrd_supinc_cml; } }

2nd Level Oil Model

//********************************************************** // // Second Level Oil Transition Model // Oil Demand is Growth Rate or GNI Driven // By Craig Atzberger 02/25/2006, 03/28/2006 // //********************************************************** public class SecondLevelOilGNI extends GsBaseUserModel { private float [] spopf = new float[regions]; private float [] sgni = new float[regions]; private float [] sgni_df = new float[regions]; private float [] soil_sp = new float[regions]; private float [] srgni_loss_cml = new float[regions]; private float [] sgni_lost_cml = new float[regions]; public void runModel(int currentYear, int firstYear, int lastYear) { /* Initialize Variables */

if (currentYear == firstYear) {

for (int r=0; r

popf[r] = ipopf[r]; gni[r] = igni[r]; gni_df[r] = igni[r]; gni_df1[r] = gni_df[r]; gni_df2[r] = gni_df[r]; gni_df3[r] = gni_df[r]; rgni_df[r] = rgni[r]; rgni_df1[r] = rgni_df[r]; rgni_df2[r] = rgni_df[r]; rgni_df3[r] = rgni_df[r]; rgni_loss_cml[r] = F0; } }

/* Compute Population Growth Rate */

344

for (int r=0; r

rpopf[r] = rpopf_b[r] * rpopf_m[r]; }

if (currentYear > firstYear) {

/* Compute Population */

for (int r=0; r

popf[r] = spopf[r] * ( F1 + rpopf[r] / F100 ); } }

/* Compute World Population */

popf_w = F0;

for (int r=0; r

popf_w += popf[r]; }

/* Compute Regional Oil Share Percentages */

for (int r=0; r

oil_use[r] = oil_use_b[r] * oil_use_m[r]; }

for (int r=0; r

/* Compute Regional Oil Demand from Growth Rate */

oil_dm[r] = ( oil_use[r] / F100 ) * wrd_oil_dm; oil_dm1[r] = oil_dm[r]; oil_dm2[r] = oil_dm[r]; oil_dm3[r] = oil_dm[r]; } else { /* Compute Regional Oil Demand from GNI */

oil_dm_gni[r] = oil_dm_gni_b[r] * oil_dm_gni_m[r]; oil_dm[r] = gni[r]/F100 * oil_dm_gni[r]/F100 * ( oil_use[r] / F100 ); oil_dm1[r] = oil_dm[r]; oil_dm2[r] = oil_dm[r]; oil_dm3[r] = oil_dm[r]; }}

/* Compute Regional Oil Consumption */

345

for (int r=0; r

oil_sp[r] = ( oil_use[r] / F100 ) * wrd_oil_sp; oil_sp1[r] = oil_sp[r]; oil_sp2[r] = oil_sp[r]; oil_sp3[r] = oil_sp[r]; }

/* Compute Oil Supply Growth Rate */

for (int r=0; r

roil_sp[r] = ( oil_sp[r] / soil_sp[r] - F1 ) * F100;

if (currentYear == firstYear) { roil_sp[r] = rwrd_oil_dm;} roil_sp1[r] = roil_sp[r]; roil_sp2[r] = roil_sp[r]; roil_sp3[r] = roil_sp[r];

}

/* Compute Regional Oil Deficit */

for (int r=0; r

oil_df[r] = ( oil_use[r] / F100 ) * wrd_oil_df; oil_df1[r] = oil_df[r]; oil_df2[r] = oil_df[r]; oil_df3[r] = oil_df[r]; }

/* Compute GNI Growth Rate */

for (int r=0; r

rgni[r] = rgni_b[r] * rgni_m[r]; rgni1[r] = rgni[r]; rgni2[r] = rgni[r]; rgni3[r] = rgni[r]; }

/* Compute GNI Growth Loss */

if (currentYear <= peakyear) {

for (int r=0; r

rgni_loss[r] = F0; rgni_loss1[r] = rgni_loss[r]; rgni_loss2[r] = rgni_loss[r]; rgni_loss3[r] = rgni_loss[r]; } }

if (currentYear > peakyear) {

346

for (int r=0; r

rgni_loss[r] = F0; rgni_loss1[r] = rgni_loss[r]; rgni_loss2[r] = rgni_loss[r]; rgni_loss3[r] = rgni_loss[r];

if ( oil_sp[r]>F0 ) {

rgni_loss[r] = F100 * (( oil_sp[r] / soil_sp[r] - F1 )/2) ;

if ((currentYear-peakyear)>5) {

rgni_loss[r] = F100 * (( oil_sp[r] / soil_sp[r] - F1 )/2) / (currentYear-peakyear);

}

rgni_loss1[r] = rgni_loss[r]; rgni_loss2[r] = rgni_loss[r]; rgni_loss3[r] = rgni_loss[r];

rgni_loss_cml[r] = srgni_loss_cml[r] + rgni_loss[r]; } else { sgni_df[r] = F0; } } }

if (currentYear > firstYear) {

/* Compute GNI */

for (int r=0; r

gni[r] = sgni[r] * ( F1 + rgni[r] / F100 );

if(currentYear>peakyear){rgni[r]=F0;}

gni_df[r] = sgni_df[r] * ( F1 + rgni[r] / F100 + rgni_loss[r] / F100 );

if (gni_df[r] < 0) {

gni_df[r] = F1; }

gni_df1[r] = gni_df[r]; gni_df2[r] = gni_df[r]; gni_df3[r] = gni_df[r]; }

347

/* Compute GNI Lost to Oil Deficit */

for (int r=0; r

gni_lost[r] = gni[r] - gni_df[r]; gni_lost1[r] = gni_lost[r]; gni_lost2[r] = gni_lost[r]; gni_lost3[r] = gni_lost[r]; gni_lost_cml[r] = sgni_lost_cml[r] + gni_lost[r]; gni_lost_cml1[r] = gni_lost_cml[r]; gni_lost_cml2[r] = gni_lost_cml[r]; gni_lost_cml3[r] = gni_lost_cml[r]; }

/* Compute GNI Growth Rate with Oil Deficit */

for (int r=0; r

if (sgni_df[r]>F0) {

rgni_df[r] = ( gni_df[r] / sgni_df[r] - F1 ) * F100; rgni_df1[r] = rgni_df[r]; rgni_df2[r] = rgni_df[r]; rgni_df3[r] = rgni_df[r]; } else { rgni_df[r] = F0; rgni_df1[r] = rgni_df[r]; rgni_df2[r] = rgni_df[r]; rgni_df3[r] = rgni_df[r]; } } }

/* Compute GNI per Capita without Oil Deficit */

for (int r=0; r

gni_pc[r] = gni[r] / popf[r] * F1000; gni_pc1[r] = gni_pc[r]; gni_pc2[r] = gni_pc[r]; gni_pc3[r] = gni_pc[r]; }

/* Compute GNI per Capita with Oil Deficit */

for (int r=0; r

gni_df_pc[r] = gni[r] / popf[r] * F1000; gni_df_pc1[r] = gni_df_pc[r]; gni_df_pc2[r] = gni_df_pc[r]; gni_df_pc3[r] = gni_df_pc[r]; }

/* Compute World GNI */

348

gni_w = F0;

for (int r=0; r

gni_w += gni[r]; }

/* Compute World GNI per Capita */

gni_pc_w = gni_w / popf_w;

/* Compute ODA percent from GNI */

for (int r=0; r

gni_oda[r] = gni_oda_b[r] * gni_oda_m[r]; }

/* Compute Official Development Assistance */

for (int r=0; r

oda[r] = (( gni_oda[r] ) / F100 ) * gni[r] * ( F1 + kloss[r] * rgni_loss_cml[r] ); oda[r] = Math.max(0,oda[r]); oda1[r] = oda[r]; oda2[r] = oda[r]; oda3[r] = oda[r]; }

/* Compute ODA for Sub-Saharan Africa */

for (int r=0; r

oda_ssaf[r] = oda_ssaf_per / F100 * oda[r]; oda_ssaf1[r] = oda_ssaf[r]; oda_ssaf2[r] = oda_ssaf[r]; oda_ssaf3[r] = oda_ssaf[r]; }

/* Backup variables */

for (int r=0; r

spopf[r] = popf[r]; sgni_df[r] = gni_df[r]; sgni[r] = gni[r]; soil_sp[r] = oil_sp[r]; srgni_loss_cml[r] = rgni_loss_cml[r]; sgni_lost_cml[r] = gni_lost_cml[r]; }

} }

349

1st Level HIV/AIDS Model

//*************************************************************** // // HIV/AIDS First Level Population Model // By Craig Atzberger 02/07/2005 // //*************************************************************** public class FirstLevelAIDS extends GsBaseUserModel { private float [] spopf_neg = new float[regions]; private float [] spopf_hiv = new float[regions]; private float [] spopf_aids = new float[regions]; private float [] spopf = new float[regions];

public void runModel(int currentYear, int firstYear, int lastYear) {

/* Set Initial Conditions */

if (currentYear == firstYear) { for (int r=0; r

/* Initial HIV+ and AIDS Population Percentages */

popf_hiv_per[r] = popf_hiv_per_b[r] * popf_hiv_per_m[r]; popf_aids_per[r] = popf_aids_per_b[r] * popf_aids_per_m[r];

/* Compute HIV-, HIV+, AIDS Population */

popf_hiv[r] = popf_hiv_per[r] / F100 * ipopf[r]; popf_aids[r] = popf_aids_per[r] /F100* ipopf[r]; popf_neg[r] = (F1-((popf_hiv_per[r] + popf_aids_per[r]) / F100)) * ipopf[r]; popf[r] = popf_neg[r] + popf_hiv[r] + popf_aids[r]; } }

/* Compute Population Using Growth & Infection Rates */

if (currentYear > firstYear) { for (int r=0; r

/* Compute Population Rate */

rpopf[r] = rpopf_b[r] * rpopf_m[r];

/* Compute Population Infection Percentages */

popf_hiv_per[r] = popf_hiv_per_b[r] * popf_hiv_per_m[r]; popf_aids_per[r] = popf_aids_per_b[r] * popf_aids_per_m[r];

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/* Compute HIV-, HIV+, AIDS Population */

popf[r] = spopf[r] * ( F1 + rpopf[r] / F100 ); popf_hiv[r] = popf_hiv_per[r] / F100 * popf[r]; popf_aids[r] = popf_aids_per[r] / F100 * popf[r]; popf_neg[r] = popf[r] - popf_hiv[r] - popf_aids[r];

/* Compute HIV+ and AIDS Population Rates */

rpopf_hiv[r] = ( popf_hiv[r] / spopf_hiv[r] - F1 ) * F100; rpopf_aids[r] = ( popf_aids[r] / spopf_aids[r] - F1 ) * F100; } }

/* Backup Population Variables */

for (int r=0; r

spopf_neg[r] = popf_neg[r]; spopf_hiv[r] = popf_hiv[r]; spopf_aids[r] = popf_aids[r]; spopf[r] = popf[r]; } } }

2nd Level HIV/AIDS Model

//*************************************************************************** // // HIV/AIDS Second Level Population Model // By Craig Atzberger 02/08/05 // //*************************************************************************** public class SecondLevelAIDS extends GsBaseUserModel { private float [] spops_neg = new float[regions]; private float [] spops_hiv = new float[regions]; private float [] spops_aids = new float[regions]; private float [] spops = new float[regions]; public void runModel(int currentYear, int firstYear, int lastYear) { if (currentYear == firstYear) {

for (int r=0 ; r < regions ; r++) {

/* Initial HIV+ and AIDS Population Percentages */

pops_hiv_per[r] = pops_hiv_per_b[r] * pops_hiv_per_m[r]; pops_aids_per[r] = pops_aids_per_b[r] * pops_aids_per_m[r];

/* Initialize Variables */

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pops_hiv[r] = pops_hiv_per[r] / F100 * ipops[r]; pops_aids[r] = pops_aids_per[r] /F100 * ipops[r]; pops_neg[r] = ( F1 - ((pops_hiv_per[r] + pops_aids_per[r]) / F100 )) * ipops[r]; pops[r] = pops_neg[r] + pops_hiv[r] + pops_aids[r]; spops[r] = ipops[r]; } } if (currentYear > firstYear) {

for (int r=0 ; r < regions ; r++) {

/* Compute Crude Birth/Death Rates */

crbrts[r] = crbrts_b[r] * crbrts_m[r]; crdths[r] = crdths_b[r] * crdths_m[r];

/* Compute Population Infection Percentages */

pops_hiv_per[r] = pops_hiv_per_b[r] * pops_hiv_per_m[r]; pops_aids_per[r] = pops_aids_per_b[r] * pops_aids_per_m[r];

/* Compute HIV-, HIV+, AIDS Population */

pops[r] = spops[r] + ((crbrts[r] - crdths[r]) / F1000 * spops[r] ); pops_hiv[r] = pops_hiv_per[r] / F100 * pops[r]; pops_aids[r] = pops_aids_per[r] / F100 * pops[r]; pops_neg[r] = pops[r] - pops_hiv[r] - pops_aids[r];

/* Compute Births and Deaths */

brts[r] = crbrts[r] / F1000 * pops[r]; dths[r] = crdths[r] /F1000 * pops[r];

/* Compute HIV+, AIDS, and Total Population Rates */

rpops[r] = ( pops[r] - spops[r] ) / spops[r] * F100; rpops_hiv[r] = ( pops_hiv[r] / spops_hiv[r] - F1 ) * F100; rpops_aids[r] = ( pops_aids[r] / spops_aids[r] - F1 ) * F100; } } /* Backup Variables */

for (int r=0 ; r < regions ; r++) {

spops_neg[r] = pops_neg[r]; spops_hiv[r] = pops_hiv[r]; spops_aids[r] = pops_aids[r]; spops[r] = pops[r]; } } }

352

3rd Level HIV/AIDS Model

//******************************************************************* // // HIV/AIDS Third Level Population Model // Age-Specific Fertility Driver // Age-Specific Mortality Driver // By Craig Atzberger 01/18/2005 // Updates: 01/21/2005, 01/30/2005, 02/09/2005, 02/01/2006 // //******************************************************************* public class ThirdLevelAIDS extends GsBaseUserModel {

/* Backup Variable Declaration */ private float [] spop = new float[regions]; private float [] spop_noaids = new float[regions]; private float [] spop_neg = new float[regions]; private float [] spop_hiv = new float[regions]; private float [] spop_hiv_per = new float[regions]; private float [] spop_aids = new float[regions]; private float [] sdth_aids_cml = new float[regions]; private float [] sbrt_hiv_cml = new float[regions]; private float [] spop_newhiv_cml = new float[regions]; private float [] spop_newaids_cml = new float[regions]; private float [] [] spopc = new float [regions][ncohorts]; private float [] [] spopc_noaids = new float [regions][ncohorts]; private float [] [] spopc_neg = new float [regions][ncohorts]; private float [] [] spopc_hiv = new float [regions][ncohorts]; private float [] [] spopc_aids = new float [regions][ncohorts]; private float [] [] spopc_aids_loss = new float [regions][ncohorts];

/* Model Code */ public void runModel(int currentYear, int firstYear, int lastYear) {

/* Initialize Variables */

if (currentYear == firstYear) {

for (int r=0; r

spopc[r][j] = ipopc[r][j]; spopc_noaids[r][j] = ipopc[r][j]; spopc_hiv[r][j] = ipopc_hiv[r][j] / F100 * ipopc[r][j]; spopc_aids[r][j] = ipopc_aids[r][j] / F100 * ipopc[r][j]; spopc_neg[r][j] = ipopc[r][j]-ipopc_hiv[r][j] / F100 * ipopc[r][j]- ipopc_aids[r][j] / F100 * ipopc[r][j]; popc[r][j] = ipopc[r][j]; popc_noaids[r][j] = ipopc[r][j];

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popc_hiv[r][j] = ipopc_hiv[r][j] / F100 * ipopc[r][j]; popc_aids[r][j] = ipopc_aids[r][j] / F100 * ipopc[r][j]; popc_neg[r][j] = ipopc[r][j]-ipopc_hiv[r][j] / F100 * ipopc[r][j]- ipopc_aids[r][j] / F100 * ipopc[r][j]; popc_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc[r][j]; popc_female[r][j] = popc[r][j] - popc_male[r][j]; } brt_hiv_cml[r] = F0; pop_newhiv_cml[r] = F0; pop_newaids_cml[r] = F0; sdth_aids_cml[r] = idth_aids_cml[r]; } }

/* Compute HIV & AIDS Infection and Mother-to-Child Transmission Rates */

for (int r=0; r

mtchivr[r] = mtchivr_b[r] * mtchivr_m[r]; hivinr[r] = hivinr_b[r] * hivinr_m[r]; aidsinr[r] = aidsinr_b[r] * aidsinr_m[r]; }

/* Compute Cohort Fertility */

for (int r=0; r

frtc[r][j] = kfrtc[r] * frtc_b[r][j] * frtc_m[r]; }}

/* Compute Births per Cohort */

for (int r=0; r

brtc_neg[r][j] = (frtc[r][j] / F1000) * spopc_neg[r][j] * (mfratio_frt[r] / F100); brtc_neg_hiv[r][j] = (frtc[r][j] / F1000) * spopc_hiv[r][j] * ((F100 - mtchivr[r]) / F100) * (mfratio_frt[r] / F100); brtc_hiv[r][j] = (frtc[r][j] / F1000) * spopc_hiv[r][j] * (mtchivr[r] / F100)* (mfratio_frt[r] / F100); brtc[r][j] = brtc_neg[r][j] + brtc_neg_hiv[r][j] + brtc_hiv[r][j]; brtc_noaids[r][j] = (frtc[r][j] / F1000) * spopc_noaids[r][j] * (mfratio_frt[r] / F100); }}

/* Compute Total Births */

for (int r=0; r

brt[r] = F0; brt_neg[r] = F0; brt_neg_hiv[r] = F0; brt_hiv[r] = F0; brt_noaids[r] = F0;

354

for (int j=15; j<50; j++) {

brt[r] += brtc[r][j]; brt_neg[r] += brtc_neg[r][j]; brt_neg_hiv[r] += brtc_neg_hiv[r][j]; brt_hiv[r] += brtc_hiv[r][j]; brt_noaids[r] += brtc_noaids[r][j]; } brt_hiv1[r] = brt_hiv[r]; brt_hiv_cml[r] = sbrt_hiv_cml[r] + brt_hiv[r]; brt_hiv_cml1[r] = brt_hiv_cml[r];

if (currentYear == firstYear) {

brt[r] = ibrt[r]; } }

/* Compute Cohort Mortality */

for (int r=0; r

mrtc[r][j] = kmrtc[r] * mrtc_b[r][j] * mrtc_m[r]; mrtc_aids[r][j] = kmrtc_aids[r] * mrtc_b_aids[r][j] * mrtc_m_aids[r];

if (mrtc[r][j]>100) { mrtc[r][j] = F100;} if (mrtc_aids[r][j]>100) { mrtc_aids[r][j] = F100;} }}

/* Compute Deaths per Cohort */

for (int r=0; r

dthc[r][j] = F0; dthc_neg[r][j] = F0; dthc_hiv[r][j] = F0; dthc_aids[r][j] = F0; dthc_noaids[r][j] = F0; dthc_noaids[r][j] = (mrtc[r][j] / F100) * spopc_noaids[r][j]; dthc_neg[r][j] = (mrtc[r][j] / F100) * spopc_neg[r][j]; dthc_hiv[r][j] = (mrtc[r][j] / F100) * spopc_hiv[r][j]; dthc_aids[r][j] = (mrtc_aids[r][j] / F100) * spopc_aids[r][j];

if (j==0) { popc_aids_loss[r][j] = dthc_aids[r][j]; } if (j>0) { popc_aids_loss[r][j] = spopc_aids_loss[r][j-1] + dthc_aids[r][j]; }

dthc[r][j] = dthc_neg[r][j] + dthc_hiv[r][j] + dthc_aids[r][j]; }}

/* Compute Total Deaths */

355

for (int r=0; r

dth[r] = F0; dth_neg[r] = F0; dth_hiv[r] = F0; dth_aids[r] = F0; dth_noaids[r] = F0;

for (int j=0; j

dth[r] += dthc[r][j]; dth_neg[r] += dthc_neg[r][j]; dth_hiv[r] += dthc_hiv[r][j]; dth_aids[r] += dthc_aids[r][j]; dth_noaids[r] += dthc_noaids[r][j]; } dth_aids_cml[r] = dth_aids[r] + sdth_aids_cml[r]; dth_aids_cml1[r] = dth_aids_cml[r]; dth_aids1[r] = dth_aids[r]; }

if (currentYear > firstYear) {

/* Compute Population of Cohort 0 - Newborns */

for (int r=0; r

popc[r][0] = brt[r]; popc_neg[r][0] = brt_neg[r] + brt_neg_hiv[r]; popc_hiv[r][0] = brt_hiv[r]; popc_male[r][0] = (mfratio[r] / (mfratio[r] + F100)) * popc[r][0]; popc_female[r][0] = popc[r][0] - popc_male[r][0]; popc_noaids[r][0] = brt_noaids[r]; }

/* Compute Cohort Population */

for (int r=0; r

popc_newhiv[r][j] = spopc_neg[r][j-1] * contact[r][j-1] * spop_hiv_per[r] / F100 * hivinr[r] * khivinr[r]; popc_neg[r][j] = spopc_neg[r][j-1] - dthc_neg[r][j-1] - popc_newhiv[r][j]; popc_newaids[r][j] = spopc_hiv[r][j-1] * aidsinr[r] * kaidsinr[r]; popc_hiv[r][j] = spopc_hiv[r][j-1] - dthc_hiv[r][j-1] + popc_newhiv[r][j] - popc_newaids[r][j]; popc_aids[r][j] = spopc_aids[r][j-1] - dthc_aids[r][j-1] + popc_newaids[r][j]; popc[r][j] = popc_neg[r][j] + popc_hiv[r][j] + popc_aids[r][j]; popc_neg_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc_neg[r][j]; popc_hiv_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc_hiv[r][j]; popc_aids_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc_aids[r][j]; popc_male[r][j] = (mfratio[r] / (mfratio[r] + F100)) * popc[r][j]; popc_neg_female[r][j] = popc_neg[r][j] - popc_neg_male[r][j]; popc_hiv_female[r][j] = popc_hiv[r][j] - popc_hiv_male[r][j]; popc_aids_female[r][j] = popc_aids[r][j] - popc_aids_male[r][j];

356

popc_female[r][j] = popc[r][j] - popc_male[r][j]; popc_noaids[r][j] = spopc_noaids[r][j-1] - dthc_noaids[r][j-1]; }} }

/* Compute aggregate variables (up to age 99) */

for (int r=0; r

popa[r][k] = F0; popa_neg[r][k] = F0; popa_hiv[r][k] = F0; popa_aids[r][k] = F0; popa_neg_hiv[r][k] = F0; popa_loss[r][k] = F0; popa_noaids[r][k] = F0;

for (int j=k*5; j<((k+1)*5); j++) {

popa[r][k] = popa[r][k] + popc[r][j]; popa_neg[r][k] = popa_neg[r][k] + popc_neg[r][j]; popa_hiv[r][k] = popa_hiv[r][k] + popc_hiv[r][j]; popa_aids[r][k] = popa_aids[r][k] + popc_aids[r][j]; popa_noaids[r][k] = popa_noaids[r][k] + popc_noaids[r][j]; popa_loss[r][k] = popa_loss[r][k] + popc[r][j] + popc_aids_loss[r][j]; } popa_male[r][k] = (mfratio[r] / (mfratio[r] + F100)) * popa[r][k]; popa_female[r][k] = popa[r][k] - popa_male[r][k]; popa_neg_hiv[r][k] = popa_neg[r][k] + popa_hiv[r][k]; popa_hiv_aids[r][k] = popa_hiv[r][k] + popa_aids[r][k]; popa_hiv_aids1[r][k] = popa_hiv_aids[r][k]; }}

/* Compute aggregate variables (age 100+) */

for (int r=0; r

popa[r][nag-1] = F0; popa_noaids[r][nag-1] = F0; popa[r][nag-1] = popa[r][nag-1] + popc[r][ncohorts-1]; popa_noaids[r][nag-1] = popa_noaids[r][nag-1] + popc_noaids[r][ncohorts-1]; popa_male[r][nag-1] = (mfratio[r] / (mfratio[r] + F100)) * popa[r][nag-1]; popa_female[r][nag-1] = popa[r][nag-1] - popa_male[r][nag-1]; }

/* Compute aggregate variables (0-14) */

for (int r=0; r

popag[r][Age0to14] = F0; popag_neg[r][Age0to14] = F0; popag_hiv[r][Age0to14] = F0; popag_aids[r][Age0to14] = F0; popag_noaids[r][Age0to14] = F0;

357

for (int j=0; j<15; j++) {

popag[r][Age0to14] = popag[r][Age0to14] + popc[r][j]; popag_neg[r][Age0to14] = popag_neg[r][Age0to14] + popc_neg[r][j]; popag_hiv[r][Age0to14] = popag_hiv[r][Age0to14] + popc_hiv[r][j]; popag_aids[r][Age0to14] = popag_aids[r][Age0to14] + popc_aids[r][j]; popag_noaids[r][Age0to14] = popag_noaids[r][Age0to14] + popc_noaids[r][j]; }

neg_prevr[r][Age0to14] = F100 * popag_neg[r][Age0to14] / popag[r][Age0to14]; hiv_prevr[r][Age0to14] = F100 * popag_hiv[r][Age0to14] / popag[r][Age0to14]; hiv_prevr1[r][Age0to14] = hiv_prevr[r][Age0to14]; aids_prevr[r][Age0to14] = F100 * popag_aids[r][Age0to14] / popag[r][Age0to14]; popag_male[r][Age0to14] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][Age0to14]; popag_female[r][Age0to14] = popag[r][Age0to14] - popag_male[r][Age0to14]; }

/* Compute aggregate variables (15-24) */

for (int r=0; r

for (int j=15; j<25; j++) {

popag[r][Age15to24] = popag[r][Age15to24] + popc[r][j]; popag_neg[r][Age15to24] = popag_neg[r][Age15to24] + popc_neg[r][j]; popag_hiv[r][Age15to24] = popag_hiv[r][Age15to24] + popc_hiv[r][j]; popag_aids[r][Age15to24] = popag_aids[r][Age15to24] + popc_aids[r][j]; popag_noaids[r][Age15to24] = popag_noaids[r][Age15to24] + popc_noaids[r][j]; } neg_prevr[r][Age15to24] = F100 * popag_neg[r][Age15to24] / popag[r][Age15to24]; hiv_prevr[r][Age15to24] = F100 * popag_hiv[r][Age15to24] / popag[r][Age15to24] + F100 * popag_aids[r][Age15to24] / popag[r][Age15to24]; hiv_prevr1[r][Age15to24] = hiv_prevr[r][Age15to24]; aids_prevr[r][Age15to24] = F100 * popag_aids[r][Age15to24] / popag[r][Age15to24]; popag_male[r][Age15to24] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][Age15to24]; popag_female[r][Age15to24] = popag[r][Age15to24] - popag_male[r][Age15to24]; }

/* Compute aggregate variables (15-49) */

for (int r=0; r

358

for (int j=15; j<50; j++) {

popag[r][Age15to49] = popag[r][Age15to49] + popc[r][j]; popag_neg[r][Age15to49] = popag_neg[r][Age15to49] + popc_neg[r][j]; popag_hiv[r][Age15to49] = popag_hiv[r][Age15to49] + popc_hiv[r][j]; popag_aids[r][Age15to49] = popag_aids[r][Age15to49] + popc_aids[r][j]; popag_noaids[r][Age15to49] = popag_noaids[r][Age15to49] + popc_noaids[r][j]; } neg_prevr[r][Age15to49] = F100 * popag_neg[r][Age15to49] / popag[r][Age15to49]; hiv_prevr[r][Age15to49] = F100 * popag_hiv[r][Age15to49] / popag[r][Age15to49] + F100 * popag_aids[r][Age15to49] / popag[r][Age15to49]; hiv_prevr1[r][Age15to49] = hiv_prevr[r][Age15to49]; aids_prevr[r][Age15to49] = F100 * popag_aids[r][Age15to49] / popag[r][Age15to49]; popag_male[r][Age15to49] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][Age15to49]; popag_female[r][Age15to49] = popag[r][Age15to49] - popag_male[r][Age15to49]; }

/* Compute aggregate variables (50+) */

for (int r=0; r

for (int j=50; j

popag[r][Age50p] = popag[r][Age50p]+popc[r][j]; popag_neg[r][Age50p] = popag_neg[r][Age50p] + popc_neg[r][j]; popag_hiv[r][Age50p] = popag_hiv[r][Age50p] + popc_hiv[r][j]; popag_aids[r][Age50p] = popag_aids[r][Age50p] + popc_aids[r][j]; popag_noaids[r][Age50p] = popag_noaids[r][Age50p] + popc_noaids[r][j]; }

neg_prevr[r][Age50p] = F100 * popag_neg[r][Age50p] / popag[r][Age50p]; hiv_prevr[r][Age50p] = F100 * popag_hiv[r][Age50p] / popag[r][Age50p]; hiv_prevr1[r][Age50p] = hiv_prevr[r][Age50p]; aids_prevr[r][Age50p] = F100 * popag_aids[r][Age50p] / popag[r][Age50p]; popag_male[r][Age50p] = (mfratio[r] / (mfratio[r] + F100)) * popag[r][Age50p]; popag_female[r][Age50p] = popag[r][Age50p] - popag_male[r][Age50p]; }

/* Compute aggregate percentages */

for (int r=0; r

popag_per[r][j] = F100 * popag[r][j] / pop[r]; }}

359

/* Compute Population variables */

for (int r=0; r

for (int j=0; j

pop_newhiv[r] += popc_newhiv[r][j]; pop_neg[r] += popc_neg[r][j]; pop_newaids[r] += popc_newaids[r][j]; pop_hiv[r] += popc_hiv[r][j]; pop_aids[r] += popc_aids[r][j]; pop[r] += popc[r][j]; pop_noaids[r] += popc_noaids[r][j]; } pop_neg_per[r] = F100 * pop_neg[r] / pop[r]; pop_hiv_per[r] = F100 * pop_hiv[r] / pop[r]; pop_aids_per[r] = F100 * pop_aids[r] / pop[r]; pop_hiv_aids[r] = pop_hiv[r] + pop_aids[r] - dth_aids[r]; pop_hiv_aids1[r] = pop_hiv_aids[r]; pop_ART[r] = ART_per[r] / F100 * pop_hiv_aids[r]; pop_ART1[r] = pop_ART[r]; pop_newhiv1[r] = pop_newhiv[r]; pop_newaids1[r] = pop_newaids[r]; pop_newhiv_cml[r] = spop_newhiv_cml[r] + pop_newhiv[r]; pop_newhiv_cml1[r] = pop_newhiv_cml[r]; pop_newaids_cml[r] = spop_newaids_cml[r] + pop_newaids[r]; pop_newaids_cml1[r] = pop_newaids_cml[r]; pop1[r] = pop[r]; }

/* Compute Population Rate */

for (int r=0; r

rpop[r] = (pop[r] - spop[r])/ spop[r] * F100; rpop_noaids[r] = (pop_noaids[r] - spop_noaids[r])/ spop_noaids[r] * F100; }

/* Compute Crude Birth/Death Rate */

for (int r=0; r

crbrt[r] = brt[r] / pop[r] * F1000; crdth[r] = dth[r] / pop[r] * F1000; }

/* Backup Variables */

for (int r=0; r

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for (int j=0; j

spopc[r][j] = popc[r][j]; spopc_neg[r][j] = popc_neg[r][j]; spopc_hiv[r][j] = popc_hiv[r][j]; spopc_aids[r][j] = popc_aids[r][j]; spopc_aids_loss[r][j] = popc_aids_loss[r][j]; spopc_noaids[r][j] = popc_noaids[r][j]; } spop[r] = pop[r]; spop_hiv_per[r] = pop_hiv_per[r]; sdth_aids_cml[r] = dth_aids_cml[r]; spop_noaids[r] = pop_noaids[r]; sbrt_hiv_cml[r] = brt_hiv_cml[r]; spop_newhiv_cml[r] = pop_newhiv_cml[r]; spop_newaids_cml[r] = pop_newaids_cml[r]; sbrt_hiv_cml[r] = brt_hiv_cml[r]; } } }

1st Level Economic Model

//*************************************************************** // // GNI, GNI per capita, GNI Growth and Workforce Population // By Craig Atzberger 02/02/2005, 07/31/2006 // //*************************************************************** public class FirstLevelEcon extends GsBaseUserModel { private float [] sgni_pc = new float[regions]; private float [] sgni = new float[regions];

public void runModel(int currentYear, int firstYear, int lastYear) {

if (currentYear == firstYear) {

for (int r=0; r

gni_pc[r] = igni_pc[r]; } }

/* Compute gni, gni_pc, Workforce Population, Population Productivity */

for (int r=0; r

wrk_pop[r] = F0;

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rgni_pc[r] = rgni_pc_b[r] * rgni_pc_m[r];

if (currentYear > firstYear) {

gni_pc[r] = sgni_pc[r] * ( F1 + rgni_pc[r] / F100); }

gni[r] = gni_pc[r] * pop[r] / F1000 /F1000;

if (currentYear > firstYear) {

rgnp[r] = ( gni[r] / sgni[r] - F1 ) * F100; }

for (int j=15; j<66; j++) {

wrk_pop[r] += popc_neg[r][j]; }

prod_pop[r] = wrk_pop[r] / pop[r] * F100; }

/* Backup Variables */

for (int r=0; r

sgni_pc[r] = gni_pc[r]; sgni[r] = gni[r]; } } }

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Appendix 4: Model Data

Data Source : World Population Prospects : The 2004 Revision Population Database World Wide Web Address http://esa.un.org/unpp World 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Age (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. 0 613840 617149 632987 647844 647342 635905 624428 619965 621236 616551 604373 1-4 5-9 607066 600596 604673 621557 637732 638575 628376 618018 614493 616597 612670 10-14 606996 603299 596751 600892 618076 634769 636103 626354 616373 613160 615530 15-19 560166 603908 600530 594213 598530 615903 632798 634364 624845 615074 612046 20-24 510560 554967 599042 596093 590193 594745 612293 629387 631209 621962 612458 25-29 501166 503445 547701 591880 589439 583984 588852 606699 624130 626389 617635 30-34 473435 492699 494597 538477 582537 580547 575645 580988 599266 617189 620090 35-39 425102 464766 483327 485107 528408 572223 570781 566507 572385 591138 609597 40-44 369864 416318 455447 473846 475599 518415 562119 561244 557601 563981 583156 45-49 331077 360710 406679 445668 464042 465986 508516 552110 551823 548801 555630 50-54 266200 320515 349729 395306 434141 452598 454953 497140 540524 540858 538484 55-59 210859 253992 306723 335515 380487 418986 437590 440437 482091 525036 526080 60-64 187891 196667 238058 288478 316577 360452 398253 416921 420372 461124 503215 65-69 152784 168235 177555 216374 263400 290323 332305 368720 387233 391383 430578 70-74 119287 128935 143154 152724 187745 229798 254828 293646 327576 345518 350409 75-79 79026 91901 100557 112863 122106 151811 187086 209175 243082 273061 289727 80-84 41621 53212 62669 69610 79266 87294 110173 136829 154660 181681 205929 85-89 20400 22751 29930 35857 40625 47127 53152 68394 85634 98200 116980 90-94 6678 8491 9826 13398 16421 19073 22646 26312 34616 43568 50770 95-99 1375 1929 2596 3165 4489 5666 6798 8278 9942 13377 16806 100+ 180 265 392 560 733 1058 1408 1775 2226 2768 3739 Total 6085573 6464750 6842923 7219427 7577888 7905238 8199103 8463263 8701317 8907416 9075902

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World Age-specific fertility rates Medium variant 2000-2050 Age 15- Age 20- Age 25- Age 30- Age 35- Age 40- Age 45- Period 19 24 29 34 39 44 49 2000-2005 58.52 159.18 154.23 93.24 45.67 17.15 4.74 2005-2010 52.9 156.54 152.56 92.02 42.57 14.43 3.82 2010-2015 48.81 154.2 150.51 90.38 41.17 12.62 3.01 2015-2020 44.67 151.14 148.14 87.07 39.71 11.52 2.48 2020-2025 40.68 146.98 145.59 84.61 37.31 10.5 2.07 2025-2030 36.64 142.5 142.94 82.97 35.61 9.37 1.75 2030-2035 33.36 139.12 140.66 81.29 34.46 8.54 1.45 2035-2040 31.15 136.41 139.47 80.19 33.66 8.07 1.27 2040-2045 29.53 133.52 137.96 78.69 32.83 7.67 1.12 2045-2050 28 130.52 136.23 77.25 31.77 7.27 0.99

World Population sex ratio (males per 100 females) Year mfratio 2000 101.2 2005 101 2010 100.9 2015 100.8 2020 100.6 2025 100.4 2030 100.2 2035 99.9 2040 99.7 2045 99.5 2050 99.4

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World Births per year, both sexes combined (thousands) Medium variant 2000-2050 Births per year Period both sexes combined 2000-2005 132508 2005-2010 135104 2010-2015 137412 2015-2020 136558 2020-2025 133507 2025-2030 130510 2030-2035 129007 2035-2040 128732 2040-2045 127289 2045-2050 124373

World Women Aged 15-49 Medium Variant Year mfratio_frt % 2000 51.6 2005 51.9 2010 51.8 2015 50.9 2020 49.7 2025 48.9 2030 48.5 2035 47.8 2040 46.8 2045 45.9 2050 45.3

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Data Source : World Population Prospects : The 2004 Revision Population Database, http://esa.un.org/unpp Sub-Saharan Africa 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Age (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. 0 113932 124959 135909 145584 152723 158576 163843 168285 171059 171881 171330 1-4 5-9 97383 106761 117574 128829 139122 147001 153566 159497 164538 167882 169230 10-14 85507 94995 104049 114773 126139 136795 145022 151885 158063 163317 166849 15-19 73926 83970 93394 102443 113116 124526 135270 143614 150596 156889 162254 20-24 61443 71797 81818 91258 100305 110962 122375 133161 141600 148703 155128 25-29 49889 57843 68039 77994 87446 96533 107193 118652 129542 138184 145540 30-34 40029 45477 53021 62928 72698 82110 91243 101938 113491 124564 133520 35-39 32942 36165 41075 48331 57805 67338 76661 85809 96548 108210 119476 40-44 27389 29949 32770 37498 44458 53542 62865 72036 81176 91938 103668 45-49 22732 25117 27376 30118 34701 41412 50202 59289 68305 77410 88146 50-54 18350 20901 23053 25232 27912 32356 38849 47331 56167 65004 74013 55-59 14891 16743 19074 21126 23235 25847 30140 36373 44521 53065 61673 60-64 11677 13262 14958 17128 19073 21104 23624 27707 33613 41351 49514 65-69 8738 9902 11306 12851 14817 16622 18530 20887 24661 30099 37251 70-74 5910 6799 7771 8966 10301 12001 13599 15302 17400 20716 25479 75-79 3422 4004 4666 5413 6336 7389 8732 10022 11411 13113 15770 80-84 1572 1861 2215 2633 3114 3715 4417 5316 6197 7153 8319 85-89 476 622 752 917 1118 1353 1654 2013 2477 2938 3443 90-94 96 126 169 210 263 329 410 514 641 808 975 95-99 12 16 21 29 38 48 63 80 104 132 171 100+ 1 1 2 2 3 4 6 7 10 13 17 Total 670317 751270 839012 934263 1034723 1139563 1248264 1359718 1472120 1583370 1691766

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Sub- Saharan popa HIV+ AIDS HIV% AIDS% ipopc_hiv ipopc_aids Africa 2000 Age 0- 4 113932 1184 249 1.039% 0.219% 0.820% 0.219% 5- 9 97383 454 101 0.466% 0.104% 0.363% 0.104% 10-14 85507 16 13 0.019% 0.016% 0.003% 0.016% 15-19 73926 1759 54 2.380% 0.072% 2.307% 0.072% 20-24 61443 2975 285 4.843% 0.464% 4.378% 0.464% 25-29 49889 4110 428 8.239% 0.858% 7.381% 0.858% 30-34 40029 4597 553 11.484% 1.381% 10.103% 1.381% 35-39 32942 2659 446 8.072% 1.354% 6.719% 1.354% 40-44 27389 1694 303 6.187% 1.107% 5.079% 1.107% 45-49 22732 1013 178 4.458% 0.785% 3.673% 0.785% 50-54 18350 478 89 2.607% 0.486% 2.121% 0.486% 55-59 14891 292 54 1.960% 0.359% 1.601% 0.359% 60-64 11677 105 36 0.903% 0.305% 0.597% 0.305% 65-69 8738 41 18 0.464% 0.204% 0.260% 0.204% 70-74 5910 18 18 0.305% 0.302% 0.003% 0.302% 75-79 3422 0 0 0.000% 0.000% 0.000% 0.000% 80-84 1572 0 0 0.000% 0.000% 0.000% 0.000% 85-89 476 0 0 0.000%0.000% 0.000% 0.000% 90-94 96 0 0 0.000%0.000% 0.000% 0.000% 95-99 12 0 0 0.000%0.000% 0.000% 0.000% 100+ 1 0 0 0.000%0.000% 0.000% 0.000% Total 670317 21397 2825

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Sub-Saharan Africa Age-specific fertility rates Medium variant 2000-2050 Age 15- Age 20- Age 25- Age 30- Age 35- Age 40- Age 45- Period 19 24 29 34 39 44 49 2000-2005 131.35 246.25 256.63 214.39 147.85 72.3 24.92 2005-2010 117.75 238.2 253.51 202.5 133.68 62.91 21.26 2010-2015 103.27 227.21 246.38 186.6 118.1 53.47 17.49 2015-2020 89.22 214.2 235.92 169.31 101.78 44.33 14.19 2020-2025 76.68 202.06 224.9 153.62 87.21 35.93 11.35 2025-2030 65.53 190.95 214.24 139.93 75.08 28.98 8.86 2030-2035 55.71 180.32 203.89 127.9 65.11 23.41 6.82 2035-2040 47.46 169.62 193.6 117.36 57 19.04 5.22 2040-2045 40.82 159.41 183.45 108.31 50.49 15.71 4.04 2045-2050 35.56 150.41 174.14 100.72 45.26 13.17 3.16

Sub-Saharan Africa Births per year, both sexes combined (thousands) Medium variant (thousands) 2000-2050 Births per year Period both sexes combined 2000-2005 28760 2005-2010 30993 2010-2015 32803 2015-2020 34021 2020-2025 34944 2025-2030 35738 2030-2035 36357 2035-2040 36637 2040-2045 36527 2045-2050 36157

Sub-Saharan Africa Women Aged 15-49 Medium Variant Year mfratio_frt % 2000 46 2005 46.5 2010 47.1 2015 47.8 2020 48.9 2025 50.2 2030 51.4 2035 52.3 2040 52.9 2045 53.3 2050 53.5

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Population sex ratio (males per 100 females)

Year mfratio 2000 99.1 2005 99.6 2010 100.2 2015 100.6 2020 101 2025 101.3 2030 101.5 2035 101.6 2040 101.6 2045 101.6 2050 101.4

Sub-Saharan Africa: Average Number of Unprotected Sexual Contacts per Person per Year Age contact <15 0 15-19 2.25 20-24 2.25 25-29 2.25 30-34 2.1 35-39 1 40-44 0.92 45-49 0.7 50-60 0.3 >60 0

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Sub-Saharan Africa 2000 AIDS Mortality Non-AIDS Mortality

Age 0- 4 89.1% 4.614% 5- 9 89.1% 0.779% 10-14 75.8% 0.463% 15-19 56.0% 0.485% 20-24 56.0% 0.520% 25-29 56.0% 0.573% 30-34 56.0% 0.587% 35-39 56.0% 0.758% 40-44 56.0% 0.905% 45-49 56.0% 1.117% 50-54 65.2% 1.411% 55-59 81.0% 1.957% 60-64 89.1% 2.749% 65-69 89.1% 4.059% 70-74 89.1% 5.854% 75-79 89.1% 8.699% 80-84 89.1% 22.954% 85-89 89.1% 22.54% 90-94 89.1% 29.56% 95-99 89.1% 39.16% 100+ 89.1% 100%

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Data Source : World Population Prospects : The 2004 Revision Population Database, http://esa.un.org/unpp Botswana 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Age (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) (1000s) Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. Pop. 0 233 218 204 199 193 184 174 166 162 158 153 1-4 5-9 227 222 204 195 195 191 183 173 166 162 158 10-14 226 224 213 192 187 192 190 182 172 166 161 15-19 217 225 222 210 189 185 190 189 181 172 165 20-24 185 206 217 215 204 185 181 186 185 178 169 25-29 146 148 175 189 192 184 168 166 172 173 169 30-34 114 104 102 129 146 152 150 139 140 149 154 35-39 96 83 67 65 90 106 114 116 110 114 126 40-44 82 75 57 43 44 64 78 88 91 89 95 45-49 67 68 56 39 30 32 49 61 71 76 75 50-54 48 58 55 43 30 23 25 40 52 61 66 55-59 36 43 50 46 36 25 19 22 35 46 54 60-64 29 32 38 43 40 31 22 17 19 31 41 65-69 20 25 28 33 37 34 27 19 15 17 28 70-74 13 16 20 23 27 31 29 23 17 13 15 75-79 10 10 12 15 17 20 24 22 18 13 10 80-84 4 6 6 7 9 11 13 15 15 12 9 85-89 1 2 2 3 3 4 5 6 8 8 6 90-94 0 0 0 1 1 1 1 2 2 3 3 95-99 0 0 0 0 0 0 0 0 0 0 1 100+ 0 0 0 0 0 0 0 0 0 0 0 Total 1754 1765 1728 1690 1670 1655 1642 1632 1631 1641 1658

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Botswana Births per year, both sexes combined (thousands) Medium variant (thousands) 2000-2050 Births per year Period both sexes combined 2000-2005 47 2005-2010 44 2010-2015 42 2015-2020 40 2020-2025 38 2025-2030 35 2030-2035 34 2035-2040 33 2040-2045 32 2045-2050 31

Botswana Age-specific fertility rates Medium variant 2000-2050 Age 15- Age 20- Age 25- Age 30- Age 35- Age 40- Age 45- Period 19 24 29 34 39 44 49 2000-2005 78.82 149.76 153.86 121.43 80.42 39.67 16.85 2005-2010 71.41 139.32 145.68 110.32 69.95 33.08 13.65 2010-2015 65.8 131.71 139.92 101.86 61.7 27.75 11.07 2015-2020 61.33 125.9 135.85 95.13 54.86 23.29 8.84 2020-2025 57.67 121.3 132.85 89.63 49.13 19.46 6.96 2025-2030 54.57 117.61 130.48 84.98 44.14 16.13 5.3 2030-2035 51.89 114.56 128.72 80.98 39.73 13.11 3.81 2035-2040 49.58 112.06 127.47 77.56 35.75 10.38 2.41 2040-2045 47.49 109.84 126.41 74.45 32.15 7.91 1.16 2045-2050 45.61 107.93 125.58 71.65 28.85 5.59 0

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Botswana Women Aged 15-49 Medium Variant Year mfratio_frt % 2000 51.7 2005 51.1 2010 50.6 2015 50.7 2020 51.3 2025 52.5 2030 54.7 2035 56.8 2040 58.2 2045 58.7 2050 58.6 Population sex ratio (males per 100 females)

Year mfratio 2000 96 2005 96.6 2010 99.1 2015 102.2 2020 105 2025 107.6 2030 109.5 2035 110.6 2040 111.2 2045 111.4 2050 111.2

Cumulative % Dying from AIDS w/o ART # Years Male Female Children 1 0 0 34 2 3 1 49 3 7 3 55 4 12 7 59 5 19 12 61 6 27 19 65 7 36 27 71 8 45 36 77

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Cumulative % Dying from AIDS w/o ART # Years Male Female Children 9 54 46 84 10 62 56 90 11 69 65 95 12 76 73 98 13 82 81 99 14 86 86 100 15 90 91 100 16 93 94 100 17 95 96 100 18 97 98 100 19 98 99 100 20 99 99 100

Botswana: Average Number of Unprotected Sexual Contacts per Person per Year

Age contact <15 0 15-19 2.25 20-24 2.25 25-29 2.25 30-34 2.1 35-39 1 40-44 0.92 45-49 0.7 50-60 0.3 >60 0

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Botswana HIV+ AIDS popa HIV% AIDS% ipopc_hiv ipopc_aids 2000 Adjusted Adjusted

Age 0- 4 233 10.9 3.4 4.657% 1.459% 3.198% 1.459% 5- 9 227 3.0 0.8 1.302% 0.339% 0.963% 0.339% 10-14 226 0.0 0.0 0.007% 0.013% -0.007% 0.013% 15-19 217 18.8 0.5 8.666% 0.241% 8.424% 0.241% 20-24 185 36.2 3.0 19.584% 1.641% 17.943% 1.641% 25-29 146 46.4 3.9 31.810% 2.671% 29.139% 2.671% 30-34 114 50.1 5.1 43.933% 4.493% 39.441% 4.493% 35-39 96 37.1 4.8 38.648%4.961% 33.687% 4.961% 40-44 82 19.1 2.6 23.342%3.123% 20.219% 3.123% 45-49 67 11.8 1.5 17.667%2.302% 15.365% 2.302% 50-54 48 8.8 1.3 18.280%2.742% 15.538% 2.742% 55-59 36 5.0 0.8 13.938%2.091% 11.847% 2.091% 60-64 29 3.7 0.6 12.677%1.902% 10.776% 1.902% 65-69 20 2.5 0.4 12.591%1.889% 10.702% 1.889% 70-74 13 1.6 0.2 12.359%1.854% 10.505% 1.854% 75-79 10 1.0 0.2 10.076%1.511% 8.565% 1.511% 80-84 4 0 0 0 0 0 0 85-89 1 0 0 0 0 0 0 90-94 0 0 0 0 0 0 0 95-99 0 0 0 0 0 0 0 100+ 0 0 0 0 0 0 0 Total 1754 256.1 29.0 0 0 0 0

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Botswana 2000 Non-AIDS Mortality AIDS Mortality

Age 0- 4 1.908% 89.1% 5- 9 0.262% 89.1% 10-14 0.121% 75.8% 15-19 0.204% 56.0% 20-24 1.059% 56.0% 25-29 1.734% 56.0% 30-34 2.939% 56.0% 35-39 3.685% 56.0% 40-44 2.696% 56.0% 45-49 2.349% 56.0% 50-54 1.702% 65.2% 55-59 1.494% 81.0% 60-64 1.949% 89.1% 65-69 3.022% 89.1% 70-74 5.400% 89.1% 75-79 8.726% 89.1% 80-84 12.94% 89.1% 85-89 19.73% 89.1% 90-94 n/a 89.1% 95-99 n/a 89.1% 100+ 100% 89.1% Total

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Appendix 5: Instructions for 3rd Level Population Model

How to Build Datasets for Regions in the 3rd Level Population Model This is a GLOBESIGHT users guide for obtaining, entering and balancing datasets for Regions in the Third Level Population Model.

Part 1: Getting the Data

1. All data is available at the United Nations World Populations Prospects web page located at: http://esa.un.org/unpp/

Go to this address and then proceed to the next step

2. Highlight the region you are analyzing from the ‘Select Country/Region’ window

3. Highlight ‘Population’ in the ‘Select Variables’ window

4. To set the years, ‘Select Start Year’ highlight ‘2000’ and ‘Select End Year’ highlight ‘2050’

5. Leave ‘Variant’ at default level of ‘Medium variant ’

6. Click download as .CSV file

7. Save this data in a new folder for this project {suggest pop.csv for filename}

8. Click on ‘Panel 2 Detailed data’ on the left of the screen

9. Using the ctrl key to highlight multiple variables, select variables:

a. Population by five-year age group and sex {popa} b. Population sex ratio {mfratio} c. Births {brt} d. Age specific fertility rates {frtc} e. Women aged 15-49 {mfratio_frt}

10. Click download and save in project folder. You now have all the data you need!

Part 2: Spreadsheet Data Entry

1. All data obtained above will be transferred in to the supplied Excel file, ‘Third Level Data Template’. Open this file and proceed to step 2.

2. Click on the ‘popa’ worksheet. Fill-in entries with data from 9a) Population by five-year age group and sex.

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3. Click on the ‘avg brt’ worksheet and fill-in data from 9c) Births.

4. Click on ‘Fertility’ worksheet and fill-in data from 9d) Age specific fertility rates.

5. Click on ‘mfratios’ worksheet and fill-in data from 9e) Women aged 15-49 for top chart and 9b) Population sex ratio for bottom chart.

6. Click on ‘pop’ worksheet and enter population data in column B cells 10-16 only! Columns C,D,E,F,G will auto fill.

7. Save this file as ‘Third Level Data Region’. {ex. ‘Third Level Data China’}

Part 3: Entering the Data in Globesight

1. Double-click the ‘rungs.bat’ file located in the bin folder of your Globesight directory. This will execute Globesight.

2. Double-click on the ‘Population’ project in the projects box.

3. Click on the ‘Cross-Scenario Views’ tab and click the ‘Add’ button.

4. Select variable ibrt and change the region to the appropriate setting.

5. Click on the ‘modify’ tab and enter the value from the ‘avg brt’ worksheet cell B8. Click the ‘Save as’ button, save view as ‘ibrt – your region’.{ex. ‘ibrt- China’} Close this window.

6. On the ‘Cross-Scenario Views’ tab, click the ‘Add’ button.

7. Right-click on the frtc_b variable and choose ‘select all Ncohorts’. Delete variables for ages 0-14 and 50-Over 99. Change region to appropriate setting.

8. Click on the ‘modify’ tab. Enter data for 2000-2005 in to cells for years 2000- 2005. Use data in 2005-2010 for years 2006-2010. Continue through 2050. Click the ‘Save as’ button, save view as ‘frtc_b – your region’. Close this window.

9. On the ‘Cross-Scenario Views’ tab, click the ‘Add’ button.

10. Click the check to select variable mfratio. Change region accordingly.

11. Click on the modify tab and click the ‘Interpolate All’ button. For ‘First year’ choose 2000, ‘Last year’ 2005, ‘First Value’ from ‘mfratios’ worksheet cell B5 and ‘Last Value’ cell B6. Now click ‘Apply’.

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12. Now change the ‘First year’ to 2005 and ‘Last year’ to 2010 and enter ‘First Value’ from ‘mfratios’ worksheet cell B6 and ‘Last Value’ from cell B7. Now click ‘Apply’. Continue in this manner through 2050. Click the ‘Save as’ button, save view as ‘mfratio – your region’. Close this window.

13. On the ‘Cross-Scenario Views’ tab, click the ‘Add’ button.

14. Click the check to select variable mfratio_frt. Change region accordingly.

15. Click on the modify tab and click the ‘Interpolate All’ button. For ‘First year’ choose 2000, ‘Last year’ 2005, ‘First Value’ from ‘mfratios’ worksheet cell B20 and ‘Last Value’ from cell B21. Now click ‘Apply’.

16. Now change the ‘First year’ to 2005 and ‘Last year’ to 2010 and enter ‘First Value’ from ‘mfratios’ worksheet cell B21 and ‘Last Value’ from cell B22. Now click ‘Apply’. Continue in this manner through 2050. Click the ‘Save as’ button, save view as ‘mfratio_frt – your region’. Close this window.

17. On the ‘Cross-Scenario Views’ tab, click the ‘Add’ button.

18. Right-click on the ipopa variable and choose ‘Select all Nag’. Change region accordingly.

19. Click on the modify tab and enter values from worksheet ‘popa’ cells B7 through B28 in year 2000.

20. Enter the values for year 2005 from worksheet ‘popa’ cells C7 through C28 in year 2001.

21. Enter the values for year 2010 from worksheet ‘popa’ cells D7 through D28 in year 2005. Continue in this manner entering year (x) in year (x-5). For example, E7 through E28, year 2015 will be entered in year 2010. Continue through 2050. Click the ‘Save as’ button, save view as ‘ipopa – your region’. Close this window.

Part 4: Balancing and Running the Third Level Model

1. From the ‘Models’ tab, check run box to select ‘ipopcInterpolator’ and check run box to select scenario. {ex. Test Scenario} Set ‘First year’ as 2000 and ‘Last year’ as 2003. Click the ‘run’ button and wait for the model to completely run.

2. From the ‘Models’ tab, check run box to select ‘mrtc_bPropagator’ and check run box to select scenario. {ex. Test Scenario} Set ‘First year’ as 2000 and ‘Last year’ as 2050. Click the ‘run’ button and wait for the model to completely run.

3. On the ‘Cross-Scenario Views’ tab, click the ‘Add’ button.

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4. Select variables kfrtc, frtc_m, kmrtc, mrtc_m and change region accordingly. Click on the ‘Modify’ tab. Click the ‘Set All’ button. Enter ‘Value’ as ‘1.0’ and click the ‘Apply’ button. Click the ‘Save as’ button, save view as ‘kfrtc,frtc_m,kmrtc,mrtc_m – your region’. Close this window.

5. From the ‘Models’ tab, check run box to select ‘ThirdLevel’ and check run box to select scenario. {ex. Test Scenario} Set ‘First year’ as 2000 and ‘Last year’ as 2050. Click the ‘run’ button and wait for the model to completely run.

6. On the ‘Cross-Scenario Views’ tab, click the ‘Add’ button.

7. Select variables brt, kfrtc, kmrtc, pop and change region accordingly. Click the ‘Save as’ button, save view as ‘Tuner your region’. Click on the ‘Modify’ tab.

8. Print out worksheet ‘brt&pop’ from ‘Third Level Data Region.xls’. This worksheet will automatically fill-in with the correct values from your dataset. It will be used to “Balance the Model”.

9. Looking at the data on the modify tab, year 2000 displays the correct value for pop. Note the number of births for 2001 in Globesight{i.e. brt}. Compare this to the value on your balance data sheet. If it is higher than the balance value- reduce kfrtc slightly, if it is lower- increase kfrtc slightly, close this window, and re-run the ‘ThirdLevel’ model over time frame 2000 through two years past year being balanced {i.e. 2000-2003 for the first run}.

10. On the ‘Cross-Scenario Views’ tab, double click the view you just created for model Tuning {ex Tuner China}. Click on the ‘Modify’ tab and repeat steps 9 and 10 Until the 2001 Globesight value for ‘brt’ is the same as your value from the balance data sheet for births in year 2001.

11. After the value for ‘brt’ is correct, note the value for pop in 2001. If pop is higher than the value from the balance data sheet- increase kmrtc slightly, if it is lower- decrease kmrtc slightly, close this window, and re-run the ‘ThirdLevel’ model over time frame 2000 through two years past year being balanced.

12. On the ‘Cross-Scenario Views’ tab, double click the view you created for model Tuning . Click on the ‘Modify’ tab and repeat steps 11 and 12 Until the 2001 Globesight value for ‘pop’ is the same as your value from the balance data sheet for population in year 2001. Year 2001 is now balanced.

13. Continue this process for years 2002 through 2050 balancing one year at a time exactly as indicated. When you finish with year 2050, your model is BALANCED! Validate your model by insuring data accuracy. You are now ready to begin Scenario Analysis.

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