Conventional Worlds:
Technical Description of
Bending the Curve Scenarios
Charles Heaps, Eric Kemp-Benedict and Paul Raskin

PoleStar Series Report no. 9, 1998

GlobalScenarioGroupë L E S T A R
The P O

Publication SeriesThis publication series is produced by the Stockholm Environment Institute’s PoleStar Project.
Named after the star that guided voyagers through uncharted waters, the multi-year PoleStar
Project addresses critical aspects of the transition to sustainability. Scenario analysis illuminates
long-range problems and possibilities at global, regional, national and local levels. Capacity
building strengthens professional capabilities for a new era of development. Policy studies
fashion strategies and actions. The PoleStar System© provides a user-friendly tool for organizing
pertinent data, formulating scenarios, and evaluating strategies for sustainable development. For
more information, visit http://www.seib.org/polestar.html on the Internet. The Global Scenario
Group was established to carry forward the global aspects of this work.
The PoleStar publication series includes:
1. The Sustainability Transition: Beyond Conventional Development (Raskin, Chadwick,Jackson and Leach)
2. PoleStar System Manual (Raskin, Heaps and Sieber)
3. Global Energy in the 21st Century: Patterns, Projections and Problems (Raskin andMargolis)
4. Water and Sustainability: A Global Outlook (Raskin, Hansen and Margolis)
5. Global Land and Food in the 21st Century: Trends and Issues for Sustainability (Leach)
6. Accounting for Toxic Emissions from the Global Economy: The Case of Cadmium (Jackson
and MacGillivray)
7. Branch Points: Global Scenarios and Human Choice (Gallopin, Hammond, Raskin andSwart)
8. Bending the Curve: Toward Global Sustainability (Raskin, Gallopin, Gutman, Hammond andSwart)
9. Conventional Worlds: Technical Description of "Bending the Curve" Scenarios (Heaps,
Kemp-Benedict and Raskin)

Global Scenario GroupThe Global Scenario Group engages a diverse group of development professionals in a long-term
commitment to examining the requirements for sustainability. The GSG is an independent,
international and inter-disciplinary body, representing a variety of geographic and professional
experiences. Its work program includes global and regional scenario development, policy
analysis and public education. The diversity and continuity of the GSG offer a unique resource
for the research and policy communities. The GSG pursues its objectives through research,
publication and collaboration with regional sustainable development projects. This report is acompanion to Bending the Curve: Toward Global Sustainability (PoleStar #8), which reviews thebroad trends and policy implications of two Conventional World scenarios. This report provides
additional detail on the data sources, methods and results for those scenarios. For more
information, visit http://www.gsg.org on the Internet.

Conventional Worlds:
Technical Description of Bending the Curve Scenarios.
Charles Heaps, Eric Kemp-Benedict, Paul Raskin

ii

Heaps, Kemp-Benedict and Raskin

Stockholm Environment Institute
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No part of this report may be reproduced in any form by photostat, microfilm, or any othermeans, without the written permission of the publisher.
Front cover photo: View of Africa and Saudi Arabia from Apollo 17. NASA/NSSDC.

ISSN:
ISBN:

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91 88714 62

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

iii

TABLE OF CONTENTSACKNOWLEDGMENTS ________________________________________________________________ IX
1. INTRODUCTION_______________________________________________________________________1
1.1 CONVENTIONAL WORLDS SCENARIOS ______________________________________________________1
1.2 GLOBAL REGIONS _____________________________________________________________________4
1.3 THE STRUCTURE OF THIS REPORT _________________________________________________________6
2. DEMOGRAPHICS, ECONOMICS, EQUITY AND POVERTY_________________________________8
2.1 DEMOGRAPHICS AND GDP ______________________________________________________________8
2.2 SECTORAL VALUE ADDED ______________________________________________________________10
2.3 INCOME DISTRIBUTION AND POVERTY ____________________________________________________11
3. ENERGY _____________________________________________________________________________26
3.1 CLIMATE CHANGE ____________________________________________________________________26
3.2 ENERGY CONSUMPTION _______________________________________________________________31
3.3 ENERGY CONVERSION _________________________________________________________________85
3.4 ENERGY SUPPLY _____________________________________________________________________94
3.5 ENERGY RESOURCES __________________________________________________________________99
3.6 A COMPARISON WITH SELECTED GLOBAL ENERGY SCENARIOS ________________________________105
4. AGRICULTURE, LAND AND FOOD ____________________________________________________108
4.1 LAND AND FOOD ____________________________________________________________________108
4.2 DEMAND FOR FOOD__________________________________________________________________110
4.3 LIVESTOCK ________________________________________________________________________118
4.4 OTHER USES FOR CROPS AND LOSSES ____________________________________________________128
4.5 CROPS ____________________________________________________________________________129
4.6 LAND RESOURCES AND LAND USE ______________________________________________________146
5. WATER _____________________________________________________________________________159
5.1 RESOURCES ________________________________________________________________________159
5.2 WITHDRAWALS AND WATER STRESS_____________________________________________________160
6. LOCAL AND REGIONAL POLLUTION _________________________________________________179
6.1 TRADITIONAL BIOMASS FUELS AND INDOOR AIR POLLUTION __________________________________179
6.2 ACIDIFICATION _____________________________________________________________________180
6.3 TOXIC WASTE _____________________________________________________________________184
REFERENCES _________________________________________________________________________187
ANNEX
THE CONVERGENCE ALGORITHM ___________________________________________________________A-1

iv

Heaps, Kemp-Benedict and Raskin

TABLE OF FIGURESFIGURE 1-1. SELECTED SCENARIO INDICATORS ____________________________________________________4
FIGURE 2-1. UN POPULATION PROJECTIONS TO 2050________________________________________________9
FIGURE 2-2. GDP PER CAPITA IN THE SCENARIOS _________________________________________________10
FIGURE 2-3. VALUE ADDED SHARES BY MACRO-REGION ____________________________________________11
FIGURE 2-4. THE LORENZ CURVE AND QUINTILES _________________________________________________12
FIGURE 2-5. QUINTILES VS. GINI COEFFICIENTS: NATIONAL VALUES AND LOGNORMAL DISTRIBUTION ________14
FIGURE 2-6. MEASURED VS. ESTIMATED NATIONAL LOW/HIGH RATIOS _________________________________15
FIGURE 2-7. HUNGER LINES VS. MEAN INCOME: MEASURED AND ASSUMED IN THE STUDY __________________18
FIGURE 2-8. PERFORMANCE OF THE HUNGER METHODOLOGY APPLIED TO HISTORICAL DATA ________________21
FIGURE 2-9. HUNGER IN THE FAO REGIONS (MILLIONS OF PEOPLE)____________________________________24
FIGURE 3-1: CUMULATIVE GLOBAL CO2 EMISSIONS AND ATMOSPHERIC CONCENTRATIONS__________________28
FIGURE 3-2. ANNUAL PER CAPITA CO2 EMISSIONS ________________________________________________29
FIGURE 3-3. ANNUAL CO2 EMISSIONS BY MACRO-REGION __________________________________________29
FIGURE 3-4. ANNUAL GLOBAL CO2 EMISSIONS BY SOURCE __________________________________________30
FIGURE 3-5: GLOBAL FINAL ENERGY CONSUMPTION SHARES - 1995 ___________________________________32
FIGURE 3-6. GLOBAL FINAL ENERGY DEMAND BY MACRO-REGION ____________________________________34
FIGURE 3-7. HOUSEHOLD ENERGY INTENSITIES - 1995 _____________________________________________35
FIGURE 3-8. HOUSEHOLD ENERGY SHARES - 1995_________________________________________________36
FIGURE 3-9. GLOBAL HOUSEHOLD ENERGY DEMAND BY FUEL ________________________________________40
FIGURE 3-10. GLOBAL HOUSEHOLD ENERGY DEMAND BY MACRO-REGION ______________________________40
FIGURE 3-11. REGIONAL HOUSEHOLD ENERGY DEMAND ____________________________________________42
FIGURE 3-12. SERVICE SECTOR ENERGY CONSUMPTION PER CAPITA - 1995______________________________44
FIGURE 3-13. SERVICE SECTOR ENERGY INTENSITIES - 1995 (MJ/$) ___________________________________45
FIGURE 3-14. SERVICE SECTOR FUEL SHARES - 1995 _______________________________________________45
FIGURE 3-15. SERVICE SECTOR ENERGY INTENSITIES_______________________________________________47
FIGURE 3-16. GLOBAL SERVICE SECTOR ENERGY DEMAND BY FUEL ___________________________________47
FIGURE 3-17. GLOBAL SERVICE SECTOR ENERGY DEMAND BY MACRO-REGION ___________________________48
FIGURE 3-18. REGIONAL SERVICE SECTOR ENERGY DEMAND_________________________________________49
FIGURE 3-19. AGRICULTURE SECTOR FUEL SHARES - 1995 __________________________________________51
FIGURE 3-20. AGRICULTURE SECTOR ENERGY INTENSITIES - 1995 (MJ/$)_______________________________52
FIGURE 3-21. AGRICULTURAL SECTOR ENERGY INTENSITIES _________________________________________53
FIGURE 3-22. GLOBAL AGRICULTURAL ENERGY DEMAND BY FUEL ____________________________________54
FIGURE 3-23. GLOBAL AGRICULTURAL ENERGY DEMAND BY MACRO-REGION ____________________________54
FIGURE 3-24. REGIONAL AGRICULTURAL ENERGY DEMAND _________________________________________56
FIGURE 3-25. INDUSTRIAL SECTOR ENERGY USE BY SECTOR - 1995____________________________________58
FIGURE 3-26. INDUSTRIAL SECTOR ENERGY USE BY FUEL - 1995______________________________________59
FIGURE 3-27. INDUSTRIAL ENERGY INTENSITIES - 1995 _____________________________________________59
FIGURE 3-28. INDUSTRIAL ENERGY INTENSITIES BY SUBSECTOR - 1995_________________________________60
FIGURE 3-29. GLOBAL INDUSTRIAL ENERGY DEMAND BY MACRO-REGION ______________________________65
FIGURE 3-30. GLOBAL INDUSTRIAL SECTOR FUEL SHARES ___________________________________________65
FIGURE 3-31. GLOBAL INDUSTRIAL ENERGY DEMAND BY SUBSECTOR __________________________________66
FIGURE 3-32. REGIONAL INDUSTRIAL ENERGY DEMAND ____________________________________________67
FIGURE 3-33. TRANSPORT ENERGY DEMAND PER CAPITA BY REGION - 1995 _____________________________70
FIGURE 3-34. AUTOMOBILE TRAVEL PER CAPITA - 1995 ____________________________________________70
FIGURE 3-35. AUTOMOBILE FUEL ECONOMY - 1995 _______________________________________________71
FIGURE 3-36. PASSENGER TRANSPORT ENERGY USE: MODAL SHARES - 1995 ____________________________72
FIGURE 3-37. FREIGHT TRANSPORT ENERGY USE: MODAL SHARES - 1995 _______________________________72
FIGURE 3-38. TRANSPORT ENERGY INTENSITIES BY MODE - 1995 _____________________________________73
FIGURE 3-39. AVERAGE INCOME VERSUS PER CAPITA PASSENGER TRANSPORT (ALL MODES) ________________74
FIGURE 3-40. AVERAGE INCOME AND ROAD TRANSPORT IN PRIVATE VEHICLES ___________________________75

Conventional Worlds: A Technical Description of Bending the Curve Scenariosv

FIGURE 3-41. AVERAGE INCOME AND AIR TRANSPORT _____________________________________________75
FIGURE 3-42. PRIVATE VEHICLE SHARES OF ROAD TRANSPORT _______________________________________77
FIGURE 3-43. GLOBAL PASSENGER TRANSPORT REQUIREMENTS BY MACRO-REGION _______________________78
FIGURE 3-44. GLOBAL PASSENGER TRANSPORT REQUIREMENTS BY MODE ______________________________78
FIGURE 3-45. AVERAGE INCOME VERSUS FREIGHT INTENSITY ________________________________________81
FIGURE 3-46. GLOBAL TRANSPORT ENERGY DEMAND BY FUEL _______________________________________83
FIGURE 3-47. GLOBAL TRANSPORT ENERGY DEMAND BY MACRO-REGION_______________________________83
FIGURE 3-48. GLOBAL TRANSPORT ENERGY DEMAND MODE SHARES __________________________________84
FIGURE 3-49. ELECTRICITY GENERATION BY REGION (1995) _________________________________________86
FIGURE 3-50. GLOBAL ELECTRICITY REQUIREMENTS _______________________________________________87
FIGURE 3-51. REGIONAL ELECTRICITY GENERATION SHARES _________________________________________90
FIGURE 3-52. ELECTRICITY GENERATION BY MACRO-REGION ________________________________________91
FIGURE 3-53. AVERAGE ELECTRIC POWER PLANT EFFICIENCIES _______________________________________93
FIGURE 3-54. GLOBAL PRIMARY ENERGY REQUIREMENTS BY REGION AND FUEL - 1995 ____________________95
FIGURE 3-55. PRIMARY ENERGY SUPPLY SHARES BY REGION - 1995 ___________________________________95
FIGURE 3-56. NET PRIMARY ENERGY EXPORTS BY REGION __________________________________________96
FIGURE 3-57. GLOBAL PRIMARY ENERGY REQUIREMENTS BY MACRO-REGION ___________________________98
FIGURE 3-58. GLOBAL FOSSIL AND URANIUM RESERVES COMPARED TO REQUIREMENTS ___________________101
FIGURE 3-59. UNRECOVERED RESERVES OF FOSSIL FUELS - 1995 ____________________________________102
FIGURE 3-60. INDICATORS FOR SELECTED SCENARIOS IN 2050 ______________________________________106
FIGURE 3-61. PRIMARY ENERGY REQUIREMENTS IN 2050 FOR SELECTED SCENARIOS _____________________107
FIGURE 4-1. PRINCIPAL CONNECTIONS BETWEEN LAND USE AND FOOD CONSUMPTION ____________________109
FIGURE 4-2. CALORIC CONSUMPTION PER CAPITA PER DAY _________________________________________111
FIGURE 4-3. CROP AND ANIMAL COMMODITIES __________________________________________________111
FIGURE 4-4. CROP COMMODITY SHARES IN DIETS ________________________________________________112
FIGURE 4-5. ANIMAL COMMODITY SHARES IN DIETS ______________________________________________113
FIGURE 4-6. DAILY PER CAPITA CALORIC CONSUMPTION; HISTORICAL PATTERNS AND SCENARIOS ___________114
FIGURE 4-7. CONTRIBUTION OF ANIMAL PRODUCTS TO DIETS _______________________________________116
FIGURE 4-8. INDEX OF RAINFED AREA IN DEVELOPING REGIONS, 1961-1994 ____________________________132
FIGURE 4-9. INDEX OF RAINFED AREA IN OECD AND TRANSITIONAL REGIONS, 1961-1994 _________________133
FIGURE 4-10. INDEX OF IRRIGATED AREA IN DEVELOPING REGIONS, 1961-1994 _________________________133
FIGURE 4-11. INDEX OF IRRIGATED AREA IN OECD AND TRANSITIONAL REGIONS, 1961-1994 ______________134
FIGURE 4-12. HISTORICAL ANNUAL CEREAL YIELDS IN DEVELOPING REGIONS ___________________________137
FIGURE 4-13. HISTORICAL ANNUAL CEREAL YIELDS IN TRANSITIONAL AND OECD REGIONS ________________138
FIGURE 4-14. MANUFACTURED FERTILIZER APPLICATION VERSUS AVERAGE YIELD _______________________141
FIGURE 4-15. NET CEREAL IMPORTS OF DEVELOPING REGIONS ______________________________________144
FIGURE 4-16. NET CEREAL EXPORTS OF OECD AND TRANSITIONAL REGIONS ___________________________145
FIGURE 4-17. BUILT ENVIRONMENT PER CAPITA IN THE SCENARIOS___________________________________149
FIGURE 5-1. HOUSEHOLD WITHDRAWALS VS. INCOME _____________________________________________163
FIGURE 5-2. RELATIVE VALUES FOR MANUFACTURING INTENSITIES___________________________________164
FIGURE 5-3. DOMESTIC WATER INTENSITIES IN THE SCENARIOS ______________________________________169
FIGURE 5-4. DOMESTIC WITHDRAWALS BY REGION IN THE SCENARIOS ________________________________170
FIGURE 5-5. AGGREGATE MANUFACTURING WATER INTENSITIES _____________________________________172
FIGURE 5-6. MANUFACTURING WITHDRAWALS IN THE SCENARIOS ___________________________________172
FIGURE 5-7. THERMOELECTRIC GENERATION INTENSITIES IN THE SCENARIOS ___________________________174
FIGURE 5-8. WATER WITHDRAWALS FOR ENERGY CONVERSION IN THE SCENARIOS _______________________174
FIGURE 5-9. YIELD RESPONSE TO WATER AND NON-WATER INPUTS ___________________________________176
FIGURE 5-10. IRRIGATION WATER INTENSITIES IN THE SCENARIOS ____________________________________176
FIGURE 5-11. IRRIGATION WATER WITHDRAWALS IN THE SCENARIOS _________________________________177
FIGURE 6-12. SULFUR EMISSIONS BY MACRO-REGION _____________________________________________182
FIGURE 6-13. CURRENT ACCOUNTS TOXIC WASTE EMISSIONS ______________________________________185
FIGURE 6-14. LOWER BOUND TOXIC EMISSIONS BY EMISSION MEDIUM (1995) _________________________185
FIGURE 6-15. LOWER BOUND TOXIC EMISSIONS SCENARIOS _______________________________________186

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Heaps, Kemp-Benedict and Raskin

TABLE OF TABLESTABLE 1-1. SOCIAL INDICATORS AND TARGETS ____________________________________________________2
TABLE 1-2. ENVIRONMENTAL INDICATORS AND TARGETS ____________________________________________3
TABLE 1-1: REGIONAL STRUCTURE _____________________________________________________________5
TABLE 2-1. REGIONAL POPULATION REPRESENTED ________________________________________________20
TABLE 3-2. GLOBAL ENERGY CONSUMPTION BY FUEL - 1995 (PJ) ____________________________________32
TABLE 3-3. GLOBAL ENERGY CONSUMPTION BY SECTOR - 1995 (PJ) __________________________________33
TABLE 3-1. TRENDS IN HOUSEHOLD ENERGY INTENSITIES (GJ/CAPITA) ________________________________38
TABLE 3-2. TRENDS IN HOUSEHOLD BIOMASS FUEL SHARES ________________________________________39
TABLE 3-3. INDUSTRIAL VALUE ADDED PER CAPITA IN THE REFERENCE SCENARIO ($/CAPITA) _______________61
TABLE 3-4. INDUSTRIAL VALUE ADDED PER CAPITA IN THE POLICY REFORM SCENARIO ($/CAPITA)____________61
TABLE 3-5. TOTAL INDUSTRIAL VALUE ADDED IN THE REFERENCE SCENARIO ($) _________________________62
TABLE 3-6. TOTAL INDUSTRIAL VALUE ADDED IN THE POLICY REFORM SCENARIO ($) _____________________62
TABLE 3-7. INDUSTRIAL ENERGY INTENSITIES (GJ/$ VALUE ADDED)___________________________________64
TABLE 3-8. PASSENGER TRANSPORT REQUIREMENTS (PASS-KM/CAPITA)________________________________77
TABLE 3-9. PASSENGER AND FREIGHT TRANSPORT INTENSITIES IN OECD REGIONS _______________________79
TABLE 3-10. PASSENGER TRANSPORT ENERGY INTENSITIES (MJ/PASS-KM) ______________________________79
TABLE 3-11. FREIGHT TRANSPORT (BILLIONS OF TONNE-KM) ________________________________________81
TABLE 3-12. TRANSPORT FREIGHT INTENSITIES (MJ/TONNE-KM) _____________________________________82
TABLE 3-13. 1995 GLOBAL ENERGY BALANCE (EJ) _______________________________________________85
TABLE 3-14. NET EXPORTS AS PERCENT OF PRIMARY ENERGY SUPPLY _________________________________96
TABLE 3-15. PROVEN AND ADDITIONAL RESERVES (EJ) ____________________________________________97
TABLE 3-16. DATE AT WHICH CUMULATIVE OIL CONSUMPTION EQUALS HALF ULTIMATE RESOURCE ___________99
TABLE 3-17. CHARACTERISTICS OF SELECTED ENERGY SCENARIOS ___________________________________105
TABLE 4-1. FOOD REQUIREMENTS FOR ANIMAL PRODUCTS IN THE SCENARIOS __________________________117
TABLE 4-2. FOOD REQUIREMENTS FOR CROP PRODUCTS IN THE SCENARIOS ____________________________118
TABLE 4-3. REQUIREMENTS FOR ANIMAL PRODUCTS AS FEED _______________________________________119
TABLE 4-4. EXPORTS OF ANIMAL PRODUCTS ____________________________________________________120
TABLE 4-5. SSRS FOR ANIMAL PRODUCTS IN THE SCENARIOS _______________________________________120
TABLE 4-6. FEED FROM CROPS IN 1994 ________________________________________________________122
TABLE 4-7. ESTIMATED BASE- YEAR FEEDLOT FRACTIONS _________________________________________123
TABLE 4-8. MEAT AND MILK PRODUCTION ON GRAZING LAND _______________________________________123
TABLE 4-9. FEEDLOT FRACTIONS IN THE SCENARIOS ______________________________________________124
TABLE 4-10. REQUIREMENTS FOR CROP PRODUCTS AS FEED ________________________________________125
TABLE 4-11. IMPROVEMENT FACTORS IN THE REFERENCE SCENARIO _________________________________126
TABLE 4-12. GRAZING LAND AREA IN THE SCENARIOS_____________________________________________127
TABLE 4-13. CROPLAND AREA_______________________________________________________________130
TABLE 4-14. POTENTIALLY CULTIVABLE LAND IN DEVELOPING REGIONS_______________________________131
TABLE 4-15. CROPLAND AREA IN THE SCENARIOS ________________________________________________135
TABLE 4-16. YIELDS AND CROP INTENSITIES FOR CEREALS _________________________________________136
TABLE 4-17. YIELDS AND CROP INTENSITIES FOR NON-CEREAL CROPS ________________________________137
TABLE 4-18. ANNUAL CEREAL YIELDS IN THE SCENARIOS __________________________________________139
TABLE 4-19. ANNUAL YIELDS FOR NON-CEREAL CROPS IN THE SCENARIOS _____________________________139
TABLE 4-20. CEREAL EXPORTS AND SELF-SUFFICIENCY RATIOS IN 1994 _______________________________142
TABLE 4-21. EXPORTS AND SELF-SUFFICIENCY RATIOS OF “OTHER CROPS” IN 1994 ______________________142
TABLE 4-22. SSRS FOR CEREAL CROPS IN THE SCENARIOS _________________________________________144
TABLE 4-23. SELF-SUFFICIENCY RATIOS FOR “OTHER CROPS” IN THE SCENARIOS ________________________145
TABLE 4-24. CURRENT ACCOUNTS BUILT ENVIRONMENT PER CAPITA _________________________________147
TABLE 4-25. CLEARING FOR CROPLAND (MILLION HECTARES PER YEAR)_______________________________151
TABLE 4-26. LAND DEGRADATION FROM AGRICULTURE: HISTORICAL AND REFERENCE SCENARIO RATES ______152
TABLE 4-27. FOREST AREA IN THE SCENARIOS___________________________________________________155
TABLE 4-28. PROTECTED LAND ______________________________________________________________155
TABLE 4-29. “OTHER” LAND ________________________________________________________________156
TABLE 4-30. AREA OF “OTHER” LAND IN THE SCENARIOS __________________________________________157

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

vii

TABLE 5-1. ANNUAL RENEWABLE WATER RESOURCES ____________________________________________159
TABLE 5-2. DESALINIZED WATER AND RECYCLED WASTEWATER IN THE SCENARIOS_____________________160
TABLE 5-3. WITHDRAWALS BY SECTOR AND REGION IN 1995 _______________________________________162
TABLE 5-4. DOMESTIC WITHDRAWAL INTENSITIES________________________________________________163
TABLE 5-5. MANUFACTURING WATER INTENSITIES _______________________________________________164
TABLE 5-6. THERMOELECTRIC COOLING INTENSITIES IN OECD COUNTRIES ____________________________165
TABLE 5-7. THERMOELECTRIC WATER INTENSITIES _______________________________________________166
TABLE 5-8. IRRIGATION WATER INTENSITIES ____________________________________________________166
TABLE 5-9. POPULATION IN WATER STRESS _____________________________________________________167
TABLE 5-10. STANDARD MANUFACTURING INTENSITIES ___________________________________________173
TABLE 5-11. IRRIGATION EFFICIENCY IMPROVEMENT OVER BASE-YEAR PRACTICES ______________________178
TABLE 6-1. GLOBAL EMISSIONS OF SULFUR OXIDES BY SECTOR IN 1995 ______________________________182

TABLE OF BOXES
BOX 4-1. GRAIN PRODUCTION IN CHINA _______________________________________________________146
BOX 4-2: HISTORICAL CROPLAND TRENDS _____________________________________________________151

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Heaps, Kemp-Benedict and Raskin

Conventional Worlds: A Technical Description of Bending the Curve Scenariosix

Acknowledgements
The authors are grateful to the members of the Global Scenario Group for their advice andguidance. Special thanks go to Gilberto Gallopin, Pablo Gutman, Al Hammond and Rob
Swart. Gil Pontius and David Calef provided significant contributions to the preparation of
the analysis. We are grateful to Amy DiBiasio and Faye Camardo for their editorialassistance.

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Heaps, Kemp-Benedict and Raskin

1. Introduction
The Global Scenario Group is developing a range of global scenarios, intended to inform
global, regional and national strategies for sustainable development. The scenarios are
based on a two-tier hierarchy introduced in Branch Points: Global Scenarios and Human
Choice (Gallopin et al., 1997). The two tiers are classes, distinguished by fundamentallydifferent social visions, and variants, reflecting a range of possible outcomes within eachclass. Three broad classes have been depicted: Conventional Worlds, Barbarization, andGreat Transitions. These are characterized by, respectively, essential continuity withcurrent patterns, fundamental but undesirable social change and fundamental andfavorable social transformation. Conventional Worlds envision the global system of the
21st Century evolving without major surprises, sharp discontinuities, or fundamental
transformations in the basis for human civilization. The future is shaped by the continued
evolution, expansion and globalization of the dominant values and socioeconomic
relationships of industrial society. By contrast, the Barbarization and Great Transition
scenario classes relax the notion of the long term continuity of dominant values andinstitutional arrangements.
Bending the Curve: Toward Global Sustainability (Raskin et al., 1998), which we refer toin this report as BTC, reviews the broad trends and policy implications of two
Conventional Worlds scenario variants: Reference1 and Policy Reform. The present
report is a companion to BTC, providing detail on data sources, methods and results. An
overview of the scenarios can be found in the BTC Scenario Highlights.
The current accounts and scenarios described in this report have been developed with thehelp of the PoleStar system, a software tool for mounting economic, resource andenvironmental information, and for examining alternative development scenarios. A
copy of the PoleStar system can be downloaded for evaluation purposes from the SEIBoston web site (http://www.seib.org/polestar.html).

1.1 Conventional Worlds Scenarios
BTC provides a detailed description of the basis for two archetypal Conventional Worlds
scenarios: Reference and Policy Reform. Here, we briefly summarize the text from that
document — describing the basis for the two scenarios and reviewing the main targets set
in the Policy Reform scenario.
1.1.1 The Reference Scenario
Within Conventional Worlds, the Reference scenario is a story of a market-driven worldin the 2lst Century in which demographic, economic, environmental and technological
1 Our analysis of current patterns and the Reference scenario are based, to a large extent, on three earlier PoleStar studies thatdeveloped Conventional Development scenarios for the energy sector (Raskin and Margolis, 1995), for agriculture, land and food(Leach, 1995) and for water (Raskin et al., 1995; Raskin et al., 1997). In addition, our Reference scenario also updates the Toxic
Waste scenario described in Raskin et al. (1996). Our analysis uses a later year base year (1995 instead of 1990), newly updated
sources of data (described in this document), and updated methodologies for making projections of certain key variables.

2

Introduction

trends unfold without major surprise. Continuity, globalization and convergence are key
characteristics of world development — institutions gradually adjust without major
ruptures, international economic integration proceeds apace and the socioeconomic
patterns of poor regions converge slowly toward the model of the rich regions. Despite
economic growth, extreme income disparity between rich and poor countries, andbetween the rich and poor within countries, remains a critical social trend.
Environmental transformation and degradation is a progressively more significant factor
in global affairs.
1.1.2 The Policy Reform scenario
In contrast to the Reference scenario, the Policy Reform scenario assumes the emergenceof a popular consensus and strong political will for taking action to ensure a successfultransition to a sustainable future.
Rather than a projection into the future, the normative Policy Reform scenario isconstructed as a backcast from the future (Robinson, 1990). We begin with a vision of adesired future state defined by a set of sustainability goals, and seek to identify plausible
development pathways for getting there. In the Policy Reform scenario, we examine therequirements for simultaneously achieving social and environmental sustainability goals.
BTC sets out target values for a common set of core global indicators. Social indicatorsand targets are summarized in Table 1-1, environmental indicators and targets are
summarized in Table 1-2. For detailed information on how target values were developed,
see BTC.
Table 1-1. Social indicators and targets
Indicator
HungerUnsafe Water

Illiteracy

Life Expectancy

millions of people

1995
900

Target in
2025
445

Target in
2050
220% of 1995 value

-

50%

25%

% of population

16%

6%

2%

millions of people

1,360

680

340

% of 1995 value

-

50%

25%

% of population

24%

9%

4%

millions of people

1,380

690

345

% of 1995 value

-

50%

25%

% of population

24%

9%

4%

Years

Source: BTC, which discusses the basis for target values.

66> 70 in all countries

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

3

Table 1-2. Environmental indicators and targets
RegionIndicator

1995

Target in
2025

Target in
2050

ClimateOECD

CO2 concentration
Warming rate
CO2 emissions
CO2 emissions ratevarious and rising

non-OECD

CO2 emissions ratevarious and risingWorld

360 ppmv

stabilize at < 450 ppmv by 2100
average 0.1°/decade, 1990-2100< 700 GtC cumulative, 1990-2100< 65% of 1990<35% of 1990(< 90% of 1990 by2010)
increases slowing,reach OECD per
capita rates
energy efficiency risingby 2075

Resource Use
OECD

Eco-efficiency$100 GDP/300 kg

non-OECD

Materials use/capita
Eco-efficiency
Materials use/capita

80 tonnes
various but low
various but low

Releases of persistentorganic pollutants &heavy metals
Releases of persistentorganic pollutants &heavy metals

various but high

< 50% of 1995

< 10% of 1995

various and rising

increases slowing

Converge to OECD
per capita values

Use-to-Resource ratio

various and risingreaches peak values

Population in water
stress

1.9 billion(34%)

less than 3 billion(<40%)

0.2-0.4 maximum(in countries>.4 in
1995, less than1995 values)
less than 3.5 billion,
begins decreasing(<40%)

various but highvarious but highfish stocks declining

no further deforestation
no further degradation
over-fishing stopped

4-fold increase
10-fold increase($100 GDP/30 kg)($100 GDP/75 kg)< 60 tonnes< 30 tonnes
converge toward OECD practices
converge toward OECD per capita values

ToxicsOECD
non-OECD

FreshwaterWorld

Ecosystem Pressure
World

Deforestation
Land degradation
Marine over-fishing

net reforestation
net restoration
Healthy fish stocks

Source: BTC, which discusses the basis for target values.

Figure 1-1 summarizes the values in 2050 of a range of indicators from the Reference andPolicy Reform scenarios relative to their 1995 values. The chart shows both the results of
the Reference scenario, and the constraints governing the evolution of the Policy Reform
scenario. Notice that both scenarios share very similar assumptions about populationlevels, average per capita incomes and total global economic output. However, thescenarios clearly differ in terms of their attainment of social and environmental goals.

4Introduction

Figure 1-1. Selected scenario indicators
52050 Reference
2050-Policy Reform

2050 value/1995 value

4
3
2
1

SulfurForest Area

CO2 Emissions

Water

EnergyHungerFoodNational Equity

International
EquityWorld GDP

GDP per
CapitaPopulation

0

1.2 Global Regions
As with other global assessments, we have adopted multi-country regions as the basic
structure of analysis. Specifically, we have grouped countries into ten global regions,based on the comparability of socioeconomic development and geopoliticalconsiderations. The regional groupings used in this analysis and the countries includedin each region are displayed in Table 1-1.
In some cases, we further combine the ten regions into three macro-regions: Transitional(FSU and Eastern Europe), OECD (North America, Western Europe, and OECD Pacific)
and Developing (Africa, Latin America, Middle East, China+ and South and Southeast
Asia).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

Table 1-1: Regional structure
North America
The Bahamas
CanadaPuerto Rico
United States
WesternEuropeAustria
Belgium
Bosnia & Herzegovina
Croatia
Denmark
FinlandFranceGermany
GreeceIceland
Ireland
Italy
Luxembourg
Macedonia, FYR
Malta
Netherlands
NorwayPortugalSlovenia, Rep
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
Yugoslavia, FR(Serbia/Montenegro)
Pacific OECD
Australia
Fiji
Japan
Kiribati
Marshall Islands
Micronesia, Fed. Sts.
New Zealand
Solomon Islands
Tonga
VanuatuWestern Samoa
Eastern Europe
Albania
Bulgaria
Czech RepublicHungary
PolandRomania
Slovak Republic

AfricaAlgeria
AngolaBenin
Botswana
Burkina Faso
BurundiCameroon
Cape VerdeCentral African Republic
Chad
Comoros
Congo
Cote d'Ivoire
Djibouti
Egypt, Arab Rep.
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
GuineaGuinea-Bissau
Kenya
LesothoLiberia
Libya
Madagascar
MalawiMali
Mauritania
Mauritius
MoroccoMozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Seychelles
Sierra Leone
SomaliaSouth Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
UgandaZaire
ZambiaZimbabwe

Latin America
Antigua and Barbuda
Argentina
Aruba
Barbados
BelizeBolivia
BrazilChile
Colombia
Costa Rica
Cuba
Dominica
Dominican Republic
EcuadorEl Salvador
GrenadaGuatemala
GuyanaHaiti
Honduras
Jamaica
MexicoNicaragua
PanamaParaguay
Peru
St. Kitts and Nevis
St. Lucia
St. Vincent and the
Grenadines
Suriname
Trinidad & Tobago
Uruguay
Venezuela
Former Soviet
Union (FSU)
Armenia
Azerbaijan
BelarusEstonia
Georgia
Kazakstan
Kyrgyz Republic
LatviaLithuania
Moldova
Russian Federation
Tajikistan
Turkmenistan
UkraineUzbekistan

Middle East
Afghanistan
Bahrain
CyprusIran, Islamic Rep.
Iraq
IsraelJordanKuwaitLebanonOman
Qatar
Saudi Arabia
Syrian Arab Republic
United Arab Emirates
West Bank & Gaza
Yemen, Rep.
China+
China (including
Hong Kong)
Korea, Dem. Rep.
Lao PDR
Macao
Mongolia
VietnamSouth and Southeast
Asia
Bangladesh
BhutanBrunei
Cambodia
India
Indonesia
Korea, Rep.
Malaysia
Maldives
Myanmar
Nepal
PakistanPapua New Guinea
Philippines
Singapore
Sri Lanka
TaiwanThailand5

6

Introduction

1.3 The Structure of this Report
Each chapter of this report deals with a major topic or sector. We first introduce the data
and assumptions used to construct the current accounts (base year) data for the topic. Thebase year is 1995, the most recent year for which data are generally available across awide range of topics. Next, we introduce the data, methodologies and assumptions used
to construct the Reference and Policy Reform scenarios for each topic, and review thedetailed results of the scenario projections.
•

Chapter 2 summarizes the methodology used for developing demographics,
economics, equity and poverty patterns in the scenarios.

•

Chapter 3 examines energy sector and climate change issues. Climate goals aretranslated into carbon emissions budgets, which constrain energy sector production
and consumption patterns in the Policy Reform scenario. Consumption is analyzed
for five sectors: industry, transport, households, services and agriculture.

•

Chapter 4 discusses agriculture, land and food. It considers the demands for foodand other agricultural products that result from the population and income
assumptions of the two scenarios. Also it considers the agricultural production
systems that satisfy those demands and the impact on land resources.

•

Chapter 5 considers final water requirements and the sufficiency of water resources.
It examines trends in water use and water scarcity in the scenarios, and describes how,in the Policy Reform scenario, measures are introduced to mitigate water scarcity.

•

Chapter 6 reviews local and regional environmental issues, focusing specifically on
acidification, toxic wastes and indoor air pollution associated with the use of
traditional biomass fuels.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

78

Demographics, Economics, Equity and Poverty

2. Demographics, Economics, Equity and Poverty
BTC describes the major regional trends in demographics, economics, equity and poverty
in the two scenarios, including information about sources of data, methods and major
assumptions.
In this chapter, we provide additional details. Section 2.1 reviews
demographic and economic patterns in the scenarios. Section 2.2 examines the sectoral
distribution of GDP (sectoral value added). Section 2.3 examines income distribution
and poverty and describes the hunger-projection methodology.
Demographic and economic assumptions play a fundamental role in driving patterns ofresource consumption and pollutant emission in the scenarios. They are also important in
the poverty analysis, where in combination with assumptions about income distribution,
they determine the incidence of poverty.
•

In the Reference scenario, poverty levels are a consequence of the assumed trends in
population, average income and income distribution, each of which is developed toreflect the overall business-as-usual perspective of the scenario.

•

In the Policy Reform scenario, achieving the normative social targets constrains theset of assumptions. The economic growth rates and national income distributions arechosen to be consistent with these goals.

2.1 Demographics and GDP
Population
In the Reference scenario, populations are assumed to follow the mid-range UN (1997)“medium” projections, in which global population increases from 5.7 to 9.4 billion people
between 1995 and 2050. The projections are shown in Figure 2-1. For comparison, the“low” and “high” UN population projections are also shown in the figure.
In the Policy Reform scenario, as described in BTC, the population growth assumptions
for the OECD regions are those of the Reference scenario, while those for the non-OECD
regions are slightly smaller: by 2% in 2025 and by 5% in 2050.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

9

Figure 2-1. UN population projections to 2050
12

Billions of people

108Developing

6

Transitional
OECD

4
2
1995

2025

2050

Medium

2025

2050

Low

2025

2050

High

Source: UN (1997)
GDP
Reference scenario economic trends are based on the mid-range assumptions of the IS92
IPCC scenarios (IPCC, 1992). In the IPCC studies, economic growth rates were applied
to GNP per capita at market exchange rates (MER), or GNPMER per capita. In Raskin etal. (1996), the rates were adapted for the ten global regions used in the study, and applied
to regional GDPMER per capita. However, as discussed in BTC, for this analysis GDP
adjusted for purchasing power parity (PPP) is used, rather than GDP at market exchange
rates. The PPP conversion factors give the amount, in national currencies, that would
buy the same amount of goods and services that one dollar would buy in the U.S. (WRI,1998). To construct growth rates for GDPPPP per capita that correspond to the MER
growth rates of Raskin et al. (1996), we project values for the ratio GDPPPP/GDPMER, or
exchange rate deviation index (ERD). Typically, the ERD is higher than 1 for developing
countries, and close to 1 for most OECD countries.
We examined cross-sectional data for different years to look for quantitative patterns. The
historical data, taken from WRI (1996b), suggest that the ERD is close to 1 for countries
whose GDPPPP per capita is higher than about one-half the U.S. value. In the scenarios it
is assumed that regional ERDs vary with income in different ways depending on whether
the income is greater than or less than one-half the North American value. Specifically,
the ERD for non-OECD regions is set to 1 when GDPPPP per capita is above one-half theNorth American value. Otherwise the ERD varies along a curve that includes the baseyear value and the point defined by (income = one-half the U.S. value, ERD = 1). The
OECD regions were treated differently from the non-OECD regions. For Pacific OECD,
where the ERD was unusually low in 1995, the ERD was set close to 1 in 2025 and 2050,
while the ERDs for the other two OECD regions were kept at their base year values.

10

Demographics, Economics, Equity and Poverty

In the normative Policy Reform scenario, regional patterns of average income growth andincome distribution are set to meet the hunger reduction target (see Section 2.3).
Regional and global scenario values for GDPPPP per capita are shown in Figure 2-2.
Average GDPPPP per capita for the world is roughly the same in both scenarios, but theregional distribution is quite different. In the Policy Reform scenario incomes are higher
in the non-OECD regions and lower in the OECD regions than in the Reference scenario.
(See Sheet E-3 in BTC for growth rates.)Figure 2-2. GDP per capita in the scenarios
80,000

US$ PPP/capita

1995
2050-Ref

60,0002050-PR
40,00020,000World

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0

2.2 Sectoral Value Added
Total GDP is reported in national accounts as contributions of value added from different
sectors of the economy. Value added is used in this study as a measure of sectoral
activity. Figure 2-3 shows how the structure of economic output in the three macroregions evolve over time in the scenarios. It changes as consumption patterns continue
along historical trends toward greater emphasis on services and light manufacturing, andaway from agriculture and industry.
Regional GDPPPP is first decomposed into three major economic sectors: Services,
Industry, and Agriculture. The current breakdowns by sector (World Bank, 1997) areadjusted over time based on the following approach.2
1. In the OECD regions, agricultural GDPPPP per capita is assumed to remain constant at
1995 values. This assumption is broadly supported by trends in agricultural GDPPPP
per capita since 1970 in a number of OECD countries. Since total GDPPPP is growing
2 The approach used here is based on that of the Conventional Development scenario (Raskin et al., 1996). The text in thissubsection draws on that report.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

11

faster than population in these regions, the effect is that the agricultural share of value
added continues to decrease over time. In non-OECD regions future agricultural
GDPPPP per capita values approach the base year OECD average values as their GDPPPP
per capita approaches the base year OECD GDPPPP per capita.3
2. For certain countries in the OECD regions, the share of GDP in industry decreased
about 3-4% per decade between 1970 and 1990 (WRI, 1992b). This trend is assumed
to continue at a slower rate in the scenarios, leading to an additional 5% decrease by
2050. In the non-OECD regions the a convergence algorithm (see Annex) is used todetermine the share of GDP in industry.
3. The share of GDP in services is computed as the remainder.
Figure 2-3. Value added shares by macro-region
100%

Agriculture
Industry80%

Services
60%
40%
20%
0%

1995

2050- Reference

2050- Policy Reform

2.3 Income Distribution and Poverty
In order to see how the scenarios address both social and environmental goals, poverty istreated explicitly. The analytic focus here is on hunger as a representative indicator ofmany possible measures of absolute deprivation. In the Policy Reform scenario, thehunger reduction targets constrain the range of possible income growth rates. However,average income alone is not sufficient to characterize poverty, so the distribution ofincome within countries is also taken into account.

This is a departure from the standard convergence algorithm used elsewhere in the analysis (see Annex). The modified
convergence algorithm applied to agricultural production reflects a convergence to current patterns, rather than the technologicalleapfrogging embodied in the standard algorithm.
3

12

Demographics, Economics, Equity and Poverty

2.3.1 Methodology
Representing Income Distribution
The distribution of national income can be presented in more than one way. One
convenient form is the Lorenz curve, a plot of the fraction of total income held by a given
fraction of the population, with the lowest-income portion of the population counted first.
A representative Lorenz curve is shown in Figure 2-4. Also shown in the figure are thequintiles, the fraction of total income held by the lowest-earning 20%, 40%, 60% and
80% of the population. They are often reported in tabulations of income distribution data,such as the ones used for this study (Deininger and Squire, 1996; USBC, 1997;
Tabatabai, 1996; World Bank, 1998).4 Another summary measure of income distributionthat is often reported is the Gini coefficient, given by the ratio of the areas A and ( A + B )in Figure 2-4. The coefficient can take values from zero to one, with zero representing
complete equality. Throughout this report, Gini coefficients and ratios of the average
income of the lowest-earning 20% of the population to the highest-earning 20% (the“low/high” ratio) are used as measures of inequality. The low/high ratio is given in termsof the quintiles as (Quintile 1)/(1 - Quintile 4).
Figure 2-4. The Lorenz curve and quintiles
1
0.9

0.7

Quintile 4

0.6
0.5
0.4

A

Quintile 3
0.3

B

Cumulative income fraction

0.8

0.2

Quintile 2

0.1

Quintile 1

1

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

0

Cumulative population fraction

4 The definition of the quintiles may vary from source to source. For example, the second quintile is sometimes given as thedifference between Quintile 2 and Quintile 1, as they are defined in Figure 2-4.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

13

The Lorenz curve contains information about how income is distributed throughout apopulation, but has no information about the scale of the national income or the size ofthe population. This information, which is needed for analytically addressing poverty, is
included in the income distribution function, which gives the number of people who
receive a certain income, per unit income. That is, if the income distribution function isrepresented by f ( y ) , where y is the income, then f ( y ) ⋅ ∆y is the number of people withincomes between y and y + ∆y .
The Lorenz curve and the income distribution function are related in the following way.
The cumulative population fraction with income below some cutoff value y c is given byy

1 c
Cumulative population fraction =dy f ( y ) ,
P ∫0

(2-1)

while the cumulative income fraction below the same cutoff y c is given byy

1 c
Cumulative income fraction =dy yf ( y ) .
Py ∫0

(2-2)

As the cutoff value y c ranges over the incomes represented in the country, from thelowest to the highest, the cumulative population and income fractions take different
values. Plotting those values against each other gives the Lorenz curve.
Approximating National Distributions: The Lognormal Distribution
In principle, many variables might be required to characterize a country’s income
distribution, in addition to population and average income. However, by examining
pooled national data, we found that they can be satisfactorily represented by a function
with one additional parameter. We took the following approach: for many of thecountries for which Gini coefficients are available, quintile values have also been
computed. If one parameter is sufficient to characterize income inequality, then any
summary measure of inequality (such as the Gini coefficient, low/high ratio or one of thequintiles values) should depend only on that parameter. In this case, if we pool national
data we should observe a strong correlation between Gini coefficients and quintiles. A
plot of measured quintiles against Gini coefficients shows that they are strongly
correlated (Figure 2-5).

14

Demographics, Economics, Equity and Poverty

Figure 2-5. Quintiles vs. Gini Coefficients: National Values and Lognormal Distribution
0.8
0.7

Quintile 4

0.6
0.5

Quintile 3
0.4
0.3

Quintile 2
0.2
0.1

Quintile 1

0.0
0.00

0.20

0.40

0.60

0.80

Gini Coefficient

The solid lines shown in Figure 2-5 were calculated using a particular income distributionfunction, the lognormal. Some of the properties of the lognormal function are given
below. In particular, it depends on three parameters: population, average income and ameasure of inequality. From Figure 2-5 it appears that the lognormal curve fits themeasured data well, suggesting that national income distributions can be reasonably
represented by lognormal distribution functions.
We further tested this conclusion by performing a regression analysis. National low/highratios were computed using the measured quintile data shown in Figure 2-5. These werethen compared to low/high ratios calculated from the Gini coefficient assuming alognormal income distribution. The results are shown in Figure 2-6. Also shown in the
figure is a linear least-squares fit to the data. If the national income distributions were
exactly lognormal, then the estimated and measured low/high ratios would be identical.
In that case, the data would lie on a straight line with a slope of one and an intercept ofzero. Since we do not expect the relationship to hold exactly, and expect that there will
be errors in the measurements, we ask instead that the slope and intercept of theregression line be consistent with values of one and zero, respectively. In fact, theestimated value for the slope is 0.978±0.026, where 0.026 is the standard error, and theintercept is 0.003±0.004. The lognormal distribution function passes the test: a slope of
one is within one standard error of the estimated slope, and an intercept of zero is within
one standard error of the estimated intercept.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

15

Figure 2-6. Measured vs. estimated national low/high ratios
0.35

Calculated from measured quintile datameasured = 0.003 + 0.978 x (lognormal approximation)

0.3

2

R = 0.944

0.25
0.2
0.15
0.1
0.05
0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Calculated from Gini coefficient assuming lognormal distribution

Based on these results, for this study national income distributions are approximated bylognormal distributions. As mentioned above, the lognormal distribution depends on
three parameters: the total population P; the mean income y ; and a measure of income
inequality, denoted by σ . It has the functional formf ( y) =2
 122)  .
exp −
2 (ln( y y ) + σ
2πσy
 2σ


P

(2-3)

The Gini coefficient and σ are related by

Gini =1σ∫ dy exp( − y 4) = 2 N (σ
2

π 0

)

2 −1,

(2-4)

where N ( x ) is the cumulative normal distribution,N ( x) =x∫ dy exp( − y 2) .
2π
1

2

−∞

(2-5)

16

Demographics, Economics, Equity and Poverty

Using Equations (2-1) and (2-2), the cumulative population and income distribution
fractions below a cutoff income y c are given by[

Cumulative population fraction = N (1 σ ) ln( y c y ) + σ 2

]

(2-6)

and

[

]

Cumulative income fraction = N (1 σ ) ln( y c y ) − σ 2 .

(2-7)

Connecting Income Distribution to Poverty
Using the lognormal approximation for national income distributions, we can address
poverty issues in the scenario analysis. As mentioned above, in this study hunger is used
as a primary indicator of poverty. We define a “hunger line,” a threshold income below
which an individual is unable to obtain the calories required to sustain a normal level ofactivity. The hunger line, the number of people hungry and the income distribution f ( y )are related by# hungry = ∫hunger line0

dy f ( y ) .

(2-8)

Note that the hunger line represents more than the cost of food, since clothing, shelter andfuel must also be obtained even by those just able to meet their basic nutritional needs.
Also, the hunger “line” is of course an approximation: some people with incomes above
the line will suffer from hunger and some people below the line will be adequately fed.
Finally, it should be noted that while GDPPPP per capita is used in this study as a proxyfor income, they are not, in fact, identical.5
The estimation of income inequality and future hunger levels is performed at the national
level, and the results aggregated to give regional totals. Gini data are not available for1995 for most countries, so the most recent values available from 1985 to 1995 are used
(Deininger and Squire, 1996; USBC, 1997; Tabatabai, 1996; World Bank, 1998). Where
data on income distribution are lacking, values are estimated from available data, as
described below.6
Statistics on the incidence of chronic undernutrition are collected by the FAO for
developing countries (FAO, 1996e; 1997a). The most recent survey was for 1990-92.
5 As a consequence, hunger lines estimated using Equation (2-8) from base-year data do not represent actual income levels.

However, since the difference between GDPPPP per capita and income is “built into” the estimated base-year hunger lines,
projections from the base year will also reflect this difference.
6 The only exception is Ethiopia, for which no recent national survey data were available and the estimation procedure based onother data gave an unrealistic result. The Gini coefficient used here is from a 1981 survey that covered 80% of the population.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

17(The values are three-year averages.) The hunger figures were brought forward to the1995 baseline taking into account changes between a previous FAO study for 1969-1971
and the one for 1990-1992 (FAO, 1997a).
For OECD and transitional countries comparable hunger estimates are not collected. For
this study, hunger in the United States is given by the prevalence of “food insecurity” asdefined and measured by the U.S. Department of Agriculture (USDA) (reported in Roseet al., 1995).7 For countries with no data on hunger, but with income distribution data,
hunger levels are estimated, as explained below.
For countries where both hunger and income distribution data are available, it is possible
to calculate base year hunger lines. The estimated values for developing countries and for
the U.S. are plotted against income in Figure 2-7. For the developing countries, hunger
data from 1990-92 are taken from the FAO (1997a), and GDPPPP/capita for 1991 (inconstant 1985 international dollars8) from WRI (1996b). The 1985 currency values areconverted to 1995 currencies using a deflator derived from historical data (WRI, 1996b).
For the U.S., the hunger figure from Rose et al. (1995) and the 1995 income from the CIA
World Factbook are used (CIA, 1997). For all countries the Gini coefficients are from theset compiled for this study.
The hunger lines in Figure 2-7 tend to increase with income. Possible reasons for thisbehavior are given in BTC (page 41). In the scenarios, national hunger lines are assumed
to increase from their base-year values toward $3,670, the current inferred value for theUnited States, as mean income approaches $21,880. This is the income at which theregression line in Figure 2-7 intersects the constant line at $3,670. Above an income of$21,880, hunger lines remain constant.9 Note that this approach builds in someconvergence toward U.S. economic patterns. Also, as noted above, GDPPPP per capita is
not identical to income; this approach implicitly assumes that the ratio of GDPPPP per
capita to income converges toward the U.S. value in 1995.

7 Provisional estimates of the prevalence of undernutrition using a method consistent with the one the FAO applies to developing

countries were prepared by the USDA’s Economic Research Service (ERS), with the Center for Nutrition Policy and Promotion(CNPP) (Rose, 1997). The USDA researchers were critical of the applicability of the FAO method to the U.S., so the estimates were
not used for this study. However, it is interesting to note that after ERS and CNPP adjusted for some factors that tended to inflate theresult, the estimated incidence of hunger was close to the value used in this study.
8 PPP-adjusted GDP figures, which differ from GDP converted to U.S. dollars using market exchange rates, are reported in“international dollars” (WRI, 1998).
9 The base-year data for four of the countries — Brazil, Hong Kong, Mauritius and Turkey — were atypical in some way.

countries slightly different approaches were adopted from the general one described here.

For these18

Demographics, Economics, Equity and Poverty

Figure 2-7. Hunger lines vs. mean income: measured and assumed in the study
4,000

Hong Kong
Mauritius

Hunger Line $/capita, PPP

3,500

US

3,000
2,500
2,000
Hunger Line = 0.15 x GDP/Cap + $478/Cap

1,500

2

R = 0.59
1,000
500
0
0

5,000

10,00015,00020,00025,00030,00035,00040,00045,000Mean Income $ GDP/capita, PPP

Note: Mauritius, Hong Kong and the U.S. are excluded from the regression. See the text
for sources and procedures.
In constructing the current accounts, the dashed line in Figure 2-7 is used to estimate
either the incidence of hunger or the Gini coefficient for countries where one of thesevalues is lacking, but the other is available. The relationship given in Equation (2-8)
between the hunger line, number of people hungry and income distribution is used tocreate the estimated hunger levels or Gini coefficients.10
In the scenarios, national levels of income inequality are assumed to converge to anarrower range than is seen today. As discussed in BTC (page 39), in the United States
inequality has been increasing for the last 30 years. In the Reference scenario, the Gini
coefficient for the United States is assumed to increase at half the historic rate, while theother countries converge toward the U.S. pattern. In the Policy Reform scenario, allcountries converge toward a common pattern; this pattern, and the income growth
assumptions, are set so that the hunger reduction targets are met in the scenario.
Combining the assumptions about Gini coefficients with the population and income
growth assumptions, levels of hunger in the scenarios are computed using Equation (2-8)
on page 16.

10 In some cases the estimation procedure gives an unrealistically high or low Gini coefficient, or no Gini coefficient is found thatsolves Equation (2-8). These countries are excluded when estimating regional hunger levels.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

19

2.3.2 Sensitivity and Comparative Studies
As discussed in BTC, in the Reference scenario the world’s hungry population grows
from about 900 million in 1995 to 950 million in 2050—a 6% increase. This increase inhunger follows from the model described above, as well as scenario assumptions about
key variables as discussed in BTC. In this section, we explore the sensitivity of theReference scenario results to some of the model parameters and assumptions. In addition,other topics are addressed: how the model performs when applied to historical data;alternative ways of meeting the hunger targets; and a comparison of the scenario results to
those of an FAO study (Alexandratos, 1995; FAO, 1996c).
Sensitivity to Base Year Gini Coefficients
Data on income distribution are relatively scarce. In order to get the fullest coverage
possible for each region, we expanded the set of Gini coefficients using three procedures.
First, as explained in the previous section, we used the most recent results of surveys
carried out between 1985 and 1995. Second, we used Gini coefficients estimated fromboth income and expenditure surveys (although wherever possible we used the results ofincome surveys). Third, where hunger data were available but income distribution data
was not, we estimated Gini coefficients from the available data on hunger and average
income, using the relationship shown in Figure 2-7 between the hunger line and average
income. The scenario hunger estimates could potentially be sensitive to any of theseprocedures. Data limitations prevented us from testing the sensitivity to the firstprocedure — the use of data from multiple years. Below, we present the results ofsensitivity studies of the second and third procedures.
According to Deininger and Squire (1996), expenditure survey Gini coefficients tend tobe lower than ones estimated from income surveys by about 6.6 percentage points a
difference which could affect the estimated hunger levels. To test the sensitivity of theReference scenario result to the inclusion of Gini coefficients from different types ofsurveys, all expenditure survey Gini coefficients were increased by 6.6 percentage points.
This produces slightly lower global average values for national equity (populationweighted average low/high ratios) in 1995, 0.13 rather than 0.15. Under Reference
scenario assumptions, hunger increases 13% rather than 6% between 1995 and 2050. The
basic conclusion of persistent poverty in the Reference scenario remains unchanged.

20

Demographics, Economics, Equity and Poverty

The populations represented in each region when Table 2-1. Regional Population
countries with estimated Gini coefficients are Represented
included or excluded from the data set are shown
Estimated Ginis
in Table 2-1. The largest differences are in
Excluded Included70
90
Africa and the Middle East, where the incidence Africa
Latin
America85
100
of hunger is high. As a result, when countries
45
98
with estimated Gini coefficients are excluded Middle East
98
98
from the calculation, the incidence of hunger in China+
S+SE Asia
94
95
the base year drops by 4%, from 898 million to
E Europe
97
97
11
860 million. It is therefore important to include
FSU
100
100
these countries in the base-year hunger estimates. N America
100100This procedure would be problematic if the W Europe
92
92
estimated levels of inequality depended in a Pacific OECD
99
99
sensitive way on the estimated Gini coefficients.
However, excluding countries with estimated Gini coefficients has little effect on the
average measure of income distribution or the rate of increase of hunger in the scenarios.
The global average of national equity estimates in the Reference scenario remains
unchanged, while hunger increases by 5% rather than 6% from 1995 to 2050. In Africa,
base-year national equity changes from 0.13 to 0.12, while in the Middle East it changes
from 0.10 to 0.11.
Dependence of the Hunger Line on Average Income
A key element in determining hunger levels in the scenarios is the hunger line, as shownin Equation (2-8). Different assumptions about the way hunger lines change in the
scenarios could have a significant effect on the estimated hunger levels. The
reasonableness of the method for projecting hunger lines used for this study is tested here:by studying the sensitivity of the Reference scenario result to different values for the
regression parameters in Figure 2-7, and by applying the methodology to historical data.
The parameters in the regression line shown in Figure 2-7 have standard errors associatedwith them, reflecting the degree of scatter around the regression line. The standard error
for the slope is 16% of its value, or 0.02. Adding this to the slope, hunger increases 20%
rather than 6% in the Reference scenario between 1995 and 2050. Subtracting thestandard error from the slope, hunger decreases by 7% over the course of the scenario.
The quantitative value for the hunger level is therefore somewhat sensitive to changes inthis parameter, in that a given percent change in the slope leads to roughly the same
percent change in the level of hunger in 2050. However, the qualitative picture ofwidespread poverty persisting into the next half-century remains the same, when the slope
is varied within its standard error. The sensitivity to the intercept is much less than thesensitivity to the slope. Adding the standard error of $66 for the intercept to its value of$480 leads to an increase in hunger of 7%, rather than the 6% increase seen between 1995
11 In each case the hungry population is estimated by multiplying the percentage of the population that is hungry by the total regional

population.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

21

and 2050 in the Reference scenario. Subtracting the error from the intercept leads to anincrease of 5% between 1995 and 2050.
Comparison to Historic Data
For a few countries, historical data are available for hunger, income distribution and GDP
per capita adjusted for purchasing power parity: FAO estimates of the incidence of hunger
are available for 1969-71 and 1990-92 (FAO, 1997a); the income distribution data set of
Deininger and Squire (1996) contains values for multiple years for some countries, andfor some countries Gini coefficients estimated from the same type of survey are available
for years close to 1970 and 1991; finally, historical values for GDPPPP per capita are
available for many countries (WRI, 1996b). Using these data it is possible to apply themethodology described in the previous section to a 1970 base year, to construct estimatedhunger levels for 1991. We performed this calculation for 18 countries. The difference
between the estimated and measured incidence of hunger in 1991, as a percentage of the
population, are shown in Figure 2-8. Generally, the methodology performed well. For 10of the 18 countries the estimated value is within 5 percentage points of the measured
value (and for 7 of the 10 it is within 2 percentage points). For all but two of thecountries the estimated value is within 10 percentage points of the measured value. For
two of the countries (Bangladesh and the Philippines) the approach used for this study did
not do well, with the difference between the estimated and measured values exceeding 10
percentage points.12 In the case of Bangladesh the methodology led to an underestimateof the actual hunger level, while for the Philippines it led to an overestimate.

Number of countries in sample .
binned by absolute difference

Figure 2-8. Performance of the hunger methodology applied to historical data
10
9
8
7
6
5
4
3
2
1
0
5

10

15

20

25

Absolute difference between measured and estimated incidenceof hunger (percentage points)

12 Note that large discrepancies between the estimated and measured incidence of hunger could result from several possible

causes: failure of the model, errors in the data, or historical policy interventions (for example, a targeted food aid program introduced
between 1970 and 1991).

22

Demographics, Economics, Equity and Poverty

Changing U.S. Gini Coefficient Trends
In the Reference scenario, the Gini coefficient in the United States rises at half its
historical rate of increase, while Gini coefficients for the other countries move gradually
toward the U.S. pattern. The assumption of global convergence is fundamental to theConventional Worlds story, but certainly other assumptions could have been made about
the course the U.S. Gini coefficient follows in the scenario. This would be problematic if
global hunger levels in the Reference scenario are sensitive to small changes in the rate ofincrease of the U.S. Gini coefficient. However, they are not very sensitive: increasing or
decreasing the U.S. rate by 10% leads to an increase or decrease of about 5% in global
hunger levels in 2050. The basic conclusion of the Reference scenario, that hunger isunlikely to decrease significantly over the next 50 years, is unaffected by the changes.
Alternative Ways of Meeting the Hunger Target
In the Policy Reform scenario the hunger reduction targets constrain the income growthand income distribution assumptions. As discussed in BTC, a balanced approach is taken:
both the relative average income levels between non-OECD and OECD regions(international equity) and national income distributions (national equity) are more equalin the Policy Reform scenario than in the Reference scenario. Here, the boundaries of the
problem are explored by considering how the hunger reduction targets might be met
under different demographic, economic and income distribution assumptions.
First, consider the world in 1995. What level of income redistribution within countries,
all else being equal, would be required to meet the hunger targets? Leaving populationand national GDPPPP at 1995 levels, halving the number of hungry would require
improving national equity levels from 0.15 to 0.24. This is similar to the value for
Eastern Europe or the FSU in 1995 (see Sheet S-1 of the BTC Scenario Highlights for asummary of national equity levels in the scenarios). Reducing hunger to one-quarter ofcurrent levels would imply increasing the global average low/high ratio from 0.15 toabout 0.34, a value approached by only one country (Belarus) in the base-year data set.
These relatively high figures suggest that income growth is a crucial factor in reducing
poverty in developing nations.
Second, consider variations away from the Reference scenario assumptions. In the Policy
Reform scenario, both national equity and international equity are higher than in the
Reference scenario. What level of effort is required if only one or the other is changed
relative to the Reference scenario?
•

Changing only national equity: Under Reference scenario assumptions for
population growth and international equity, to meet hunger targets national equity
must increase from 0.15 in 1995 to 0.17 in 2050. In contrast, both historical trends
and Reference scenario assumptions suggest that national equity will decrease in the
future. While not impossible, this outcome cannot be considered plausible.

•

Changing only international equity: To meet hunger targets under Reference
scenario assumptions for population growth and national equity would requireConventional Worlds: A Technical Description of Bending the Curve Scenarios

23

economic growth rates in developing regions of about 6.5% per year, considerably
higher than the 3.4% growth rate assumed in the Reference scenario. The
corresponding GDPPPP per capita growth rate is about 5.4% per year. This presents a
picture in which the developing world as a whole experiences economic growth forfifty years similar to that of the Asian tigers over the past few decades. This is notplausible, and has not been achieved historically. Therefore, some moderation innational equity trends will need to play a role in meeting social goals.
Comparison to FAO Projections
Hunger projection in the scenarios can be compared to an earlier FAO study, which
projected hunger levels between 1991 and 2010 (Alexandratos, 1995; FAO, 1996c).13
The results of the FAO study for each of five regions are displayed in Figure 2-9alongside the hunger levels in the two Conventional Worlds scenarios, which have beenrecast to match the five regions used in the FAO study.

13 Note that the FAO methodology, rather than considering income distribution, examines the distribution of caloric intake. As with

the income distributions in this study, national distributions of caloric intake are represented by lognormal functions.

24

Demographics, Economics, Equity and Poverty

Figure 2-9. Hunger in the FAO regions (millions of people)
East Asia

Latin America & the Caribbean

500
80

450
70

1000s of people

400
350

60

300

50

250
40

200
30

150
20

100

10

50
0
1970

0

1980

1990

2000

2010

2020

2030

2040

2050

1970

1980

1990

Near East and North Africa

2000

2010

2020

2030

2040

2050

2030

2040

2050

South Asia

100
90

350

80
300

70
250

60
200

50
40

150

30
100

20
50

10
0
1970

0

1980

1990

2000

2010

2020

2030

2040

2050

1970

1980

1990

2000

2010

2020

Sub-Saharan Africa
400

350

300

250

Historical
FAO
Reference
Policy Reform

200

150

100

50

0
1970

2060

1980

1990

2000

2010

2020

2030

2040

2050

Source: Historical trends and FAO projections from FAO (1996c).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

25

In general, the hunger levels seen in the two Conventional Worlds scenarios straddle
those of the FAO study.
The only major deviation is for Latin America and the
Caribbean, where the FAO study projects sharp decreases in hunger, in contrast to both of
the Conventional Worlds scenarios and to recent historical trends.
The FAO study relies in part on expert opinion in refining estimates of food demand,
supply and other variables (Alexandratos, 1995). For this reason, it is difficult tocompare the underlying assumptions of the FAO study to those in the Conventional
Worlds scenarios. In regions where the two studies diverge, the reasons appear to be due
primarily to differences in assumptions about future levels of equity, although these areleft implicit in the FAO study and so cannot be directly compared. In general, the FAO
study appears to assume lower levels of inequality than exist today or are likely in the
future under a typical business-as-usual scenario.

26

Energy3. Energy
In this chapter, we describe the data, methods and assumptions for energy and climatechange in the scenario. The Reference scenario analysis is, to a large extent, an update ofan earlier report (Raskin and Margolis, 1995), incorporating a later year base year (1995
instead of 1990), additional sources of data, and updated methodologies. Some of thetext in this chapter is adapted from the earlier report.
In the Reference scenario, both energy and greenhouse gas emissions are driven by the
demographic, economic and technical trends of the scenario. In the normative Policy
Reform scenario energy sector trends are constrained by the climate change mitigation
targets imposed on the scenario. For this reason, we start the discussion of the energy
sector by reviewing the analysis of climate assumptions and the constraints imposed on
greenhouse gas emissions in the Policy Reform scenario. Then, we review regional
patterns of energy consumption, energy conversion, and primary energy supply in each of
the two scenarios.

3.1 Climate Change
The climate change mitigation goals of the Policy Reform scenario are described in BTC,
which also describes how a criterion in terms of temperature change can be translated
first into approximate targets for atmospheric CO2 concentrations, and then to a global
cumulative emissions budget. For reference, the criterion selected is that warming should
occur no faster than 0.1oC/decade on average between 1990 and 2100, or a cumulativechange of about 1.1° between 1990 and 2100. This translates into a target value for theequilibrium concentration of CO2 in 2100 of about 450 ppmv, which corresponds to a
cumulative carbon emissions budget between 1990 and 2100 in the 640-800 GtC range.
Energy-related emissions are computed by applying standard fuel-specific CO2 emission
factors (IPCC, 1995b) to energy use. In addition, land-use change emission factors (SEI,
1993) are applied to land-use change patterns in each scenario. In the Reference scenario,
emissions are projected as a consequence of the energy and land-use change projections.
In the Policy Reform scenario, targets for reducing deforestation (described in BTC) leadto lower land-use change emissions than in the Reference scenario.14 Emissions from theenergy and industrial sectors in the Policy Reform scenario are constrained so that total
emissions fall within the target carbon emissions budget for the scenario. Energy andindustrial sector emissions are allocated between regions in a manner reflecting thegeneral spirit of improved equity in the Policy Reform scenario as follows:•

Each OECD region is required to reduce total annual emissions by 35% in 2025
compared to their 1990 values. This translates into reductions in each region of
between 40% and 50% in 2025 compared to the Reference scenario.

14 After 2025, net reforestation acts as a net carbon sink.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

•
•

•

27

Each transitional region is required to reduce total annual emissions by 30% in 2025
relative to the Reference scenario.
Each developing region is required to reduce emissions intensities (tonnes per dollar
GDPPPP) in 2025 by 15% relative to the Reference scenario. This translates into
absolute levels of emissions between 92% and 102% of those in the Reference
scenario in all regions except Africa, where due to the much higher economic growthin the Policy Reform scenario, absolute emissions in 2025 are actually 40% higher
than in the Reference scenario.
After 2025, emissions from all regions are assumed to converge toward a common
annual per capita allowance of 0.6 tC per capita. This is achieved by 2075, with equal
per capita emissions thereafter. In 2100, annual global emissions are constrained tobe 3 GtC per year.

Figure 3-1 summarizes cumulative global emissions pathways of the Reference scenario
and Policy Reform scenarios, with the detailed results of the two scenarios to the period
2050 projected forward to the year 2100. The figure includes estimates of total industrial
emissions since 1850 from combustion of fossil fuels and cement production plus
estimates of net emissions from land-use change. The Reference scenario, which closely
tracks the IPCC mid range (IS92a) scenario to the period 2050, is assumed to continue tofollow the IS92a path after 2050. It is clear that the Reference scenario does not meet thetarget. Cumulative carbon emissions between 1995 and 2100 would be about 1500 GtC.
This is about double the emissions from the Policy Reform scenario (750 GtC).

28

EnergyFigure 3-1: Cumulative global CO2 emissions and atmospheric concentrations
1,800

700 ppm v
in 2100

Cumulative Emissions since 1850 (GT C)

1,600

Reference

1,400
1,200

Policy Reform

1,000

450 ppm v
in 2100

800
600

IPCC 350 ppm v Profile
350 ppm v
in 2100

400
200
1995

2025

2050

2075

2100

Notes:• In addition to the Reference and Policy Reform scenario, a third profile is shown leading to stabilization
at 350 ppmv, following the pathways defined in IPCC (1995a). For reference, current CO2
concentrations stand at about 360 ppmv, while concentrations prior to the industrial revolution (pre1850) stood at 280 ppmv. Achieving this concentration would require cumulative emissions ofapproximately 220 GtC between 1995 and 2100.

Figure 3-2 compares annual per capita CO2 emissions for the three macro-regions of thestudy in the Reference scenario and Policy Reform scenario. In the Reference scenario,
per capita emissions from all three macro-regions initially continue to grow as incomesincrease. After about 2030, per capita emissions from the transitional regions level off
due to the large potential for energy efficiency improvements, and for fuel switchingaway from coal and towards less carbon intensive fuels such as natural gas, hydropower
and renewables. The Policy Reform scenario shows dramatically different trajectories. In
the developing regions, emissions per capita start from a much lower level than in theindustrialized or transitional ones. They grow steadily until 2025, without ever exceeding
the industrial or transitional rates, then drop gradually to reach the 2075 convergencetarget of 6 tonnes per capita. Per capita emissions from all regions converge on thisfigure in 2075 and decline thereafter to 3 tonnes C per capita.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

29

Figure 3-2. Annual Per capita CO2 emissions

tonnes C/capita

5

Reference

Policy Reform
5

4

4

3

3

2

2

1

1

0

1995

2025

2050

2075

2100

OECD
Developing
Transitional

0
1995

2025

2050

2075

2100

Notes: Industrial emissions (not including net land-use change emissions) divided by population in each region.

Figure 3-3 shows annual emissions by macro-region. In 1995, emissions are dominatedby the OECD, which accounts for about 48% of the total. By 2050 in the Reference
scenario, this position has altered, so that the developing regions account for 64% of totalindustrial emissions. By 2100 the developing regions’ share rises to about 72% of global
emissions. In the Policy Reform scenario, the developing regions share of the total
increases even faster. By 2050, they account for 78% of total global emissions, and thisshare increases to 86% by 2100.
Figure 3-3. Annual CO2 emissions by Macro-region

Annual Emissions (GtC)

15

Policy Reform

Reference

Developing
OECD
Transitional

10

5

0
1995

2025

2050

1995

2025

2050

2075

2100

Notes: Industrial emissions (not including net land-use change emissions) divided by population in each region.

Figure 3-4 summarizes annual emissions by major source category for the years 1995,
2025 and 2050 (including land-use change emissions). In the Reference scenario
industrial emissions continue to grow from about 6.1 GtC in 1995 to 14.8 GtC in 2050
and to about 21 GtC in 2100. In the Policy Reform scenario, total industrial emissions
increase to 2025, reaching 8.6 GtC annually. Thereafter they decline, reaching 7.6 GtC in
2050 and 3 GtC in 2100.

30EnergyFigure 3-4. Annual Global CO2 emissions by source
16

Land-Use Change
Agriculture
Services
Households
Transport
Industry
Energy Conversion

Emissions (Gt C)

12

8

4

0
1995

2025
2050
Reference

2025
Policy Reform

2050

Note: “Industry” includes all emissions from fossil fuel combustion in the industrial sector.
(primarily from cement manufacture are accounted for separately.

Process emissions

Annual global net CO2 emissions from forest loss, estimated at about 1.3 GtC in 1995,
decrease in the Reference scenario, reaching 0.78 GtC/year in 2050. Following theassumptions of the IS92a scenario, they are then expected to decline further, reaching
0.15 GtC/year in 2075 and -0.1 GtC/year in 2100 — reflecting net afforestation. In the
Policy Reform scenario, net deforestation rates are reduced more quickly, with carbon
emissions declining to 0.4 GtC/year in 2025. By 2050, net afforestation at an annual rateof 0.1GtC/year is occurring. This rate of afforestation thereafter is assumed to remain
constant.
In the Policy Reform scenario, the cumulative emissions from fossil fuel combustion,
cement production, other industrial CO2 emissions, and net emissions from changes inforest area total about 750 GtC, which achieves the climate change mitigation target forthe scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

31

3.2 Energy Consumption
Energy consumption refers to fuel and electricity requirements at the point ofconsumption for the various production and consumption activities of society.
Consumption is analyzed in each region for each of the five major final energy-using
sectors: industry, transport, households, services and agriculture. In each sector, theanalysis is based on further disaggregation, subject to the availability of data. For
example, the industrial sector is divided into five major energy consuming subsectors
plus a sixth subsector dealing with all other energy consumption. Similarly, in thetransport sector, freight and passenger transport are considered separately and each is
divided by mode (road, rail, air, water).
In section 3.2.1, we summarize current total final consumption patterns and the aggregate
consumption patterns in the two scenarios. In sections 3.2.2 - 3.2.6, we then examine
how these patterns are built up from a detailed analysis of the five energy-consuming
sectors.
3.2.1 Total Final Consumption
Current Accounts
Global total final energy consumption in 1995 was about 271 EJ. As shown in Figure 35, final fuel composition is currently dominated by fossil fuels, which account for 68% of
the global total. Other significant final energy forms are electricity, biomass and district
heat. The direct use of non-biomass renewable energy forms (e.g., direct use of solar forwater heating) currently accounts for less than one tenth of one percent of final energy
consumption.
Figure 3-5 shows the breakdown of total final demand by sector. Global consumption isled by the industrial sector (38%), followed by households (27%), transport (25%),
services (7%), and agriculture (3%). Turning to regional patterns, global energy
consumption is dominated by the OECD regions, which account for 47% of global energy
consumption. The developing regions account for 40% of global energy consumption andthe transitional regions account for the remaining 13%.

32

EnergyFigure 3-5: Global final energy consumption shares - 1995
E lectricity
14%

Heat

S ervices4%

7%

T ran s p ort
25%

B iomass

Natu ral gas

14%

15%

Renewables
0%

A g ricu ltu re
3%

Coal
12%

In d u s try
38%

O il

Households

41%

27%

By fuelBy sector
S + S E A sia
12%

P acific O E C D
6%

W E u rope17%

A f rica
5%

N A m erica
24%

C h ina+14%

M iddle E ast
3%

Latin America

FSU

6%

11%

E E u rope
2%

By region

The breakdown by fuel is presented for each region in Table 3-2. Petroleum products
account for more than 50% of final demand in 5 regions (North America, Western
Europe, Pacific OECD, Middle East, and Latin America), while coal accounts for almost
50% of final demand in China+. Energy from biomass is significant in Africa, Latin
America, China+ and South and Southeast Asia.
Table 3-2. Global energy consumption by fuel - 1995 (PJ)
AfricaChina+
Latin America
Middle East
S+SE Asia
E Europe
FSU
Pacific OECDW Europe
N. America
World
OECD
Developing
Transitional

Coal
665
18,148
628
49
3,341
1,500
3,278
1,846
2,881
1,582
33,9186,309
22,831
4,778

Oil Natural Gas
3,182
396
5,581
561
9,069
2,044
5,878
1,706
12,402
1,593
1,503
1,474
6,100
8,064
9,426
1,262
23,267
9,009
33,247
14,963
109,655
41,072
65,94025,234
36,112
6,299
7,603
9,538

Electricity
1,013
2,880
2,234
1,048
3,033
883
3,210
3,741
7,977
12,53338,552
24,251
10,208
4,093

Heat
812
793
8,280
15
910
316
11,127
1,241
812
9,074

Biomass Renewables
7,847
8,729
3,542
37
19
12,966
124
467
327
42
1,510
27
1,540
37,08989
3,377
6933,122
19
590
-

Total
13,10436,71017,517
8,738
33,334
6,277
29,39916,66045,58264,181
271,501
126,422
109,40335,676

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

33

The sectoral composition of final demand across regions is presented in Table 3-3. The
variation of relative sectoral shares is related to a number of factors specific to eachregion. First, the structure of economic activity evolves with the level of industrial
development and the particular economic growth strategy that has been adopted. For
example, service sector output is a rising share of total national output in OECD regions;heavy industry is important in Eastern Europe, FSU, and China+; and natural resource
exploitation is relatively important in the economies of many developing countries. The
household sector remains dominant in Africa, where subsistence lifestyles retain theirimportance and modern forms of economic activity remain relatively undeveloped.
Table 3-3. Global energy consumption by sector - 1995 (PJ)
AfricaChina+
Latin America
Middle East
S+SE Asia
E Europe
FSU
Pacific OECDW Europe
N. America
World
OECD
Developing
Transititional

Industry
4,118
19,145
6,778
3,447
11,351
3,249
15,802
6,627
14,904
18,383103,805
39,91444,84019,051

Households
6,768
12,203
3,228
1,595
13,976
1,692
8,142
2,416
10,637
11,616
72,272
24,67037,7689,834Transport
1,874
2,866
6,075
3,165
6,665
753
2,651
5,176
14,57825,10968,911
44,862
20,645
3,404

Services
151
1,389
720
294
626
240
653
1,898
4,316
8,257
18,543
14,471
3,179
893

Agriculture
194
1,107
717
237
716
343
2,151
542
1,147
816
7,971
2,505
2,971
2,494

Total
13,10436,71017,517
8,738
33,334
6,277
29,39916,66045,58264,181
271,501
126,422
109,40335,676

Second, the energy efficiency of end-use equipment influences the sectoral pattern. For
example, electric devices, which tend to grow in importance with rising incomes, arerelatively efficient at the end-use level while traditional wood stoves are quite inefficient,
thereby contributing to the anomalously high share for household energy consumption in
Africa. Third, infrastructure and life-style variations influence the sectoral composition.
For example, in North America, where there is heavy reliance on the private automobile
and a highly mobile population, the share of energy use in transportation is large.
Scenarios
Figure 3-6 summarizes total final demand in the Reference scenario and Policy Reform
scenario. In the Reference scenario, total final demand more than doubles from 271.5 EJ
in 1995 to 649 EJ in 2050. In the Policy Reform scenario this growth is substantiallyreduced, reaching 476 EJ in 2050. In the Reference scenario, the developing regions
account for 79% of the growth in demand between 1995 and 2050, although demand also
increases in both the OECD and transitional regions as well.

34

EnergyFigure 3-6. Global final energy demand by macro-region
700

EJ

Developing
600

Transitional

500

OECD

400
300
200
100
1995

2025
Reference

2050

2025

2050

Policy ReformIn the Policy Reform scenario, the effects of slower economic growth and intensive policy
efforts to improve energy efficiency lead to declines in energy demand in the OECD andtransitional regions, where total final demand in 2050 drops to 67% and 81% of its 1995
values respectively. All of the net increase in total final demand is therefore accounted
for by the developing regions where, in spite of energy efficiency improvements beyond
those in the Reference scenario, energy demand still increases at a rate of 2.2% per year,
only slightly less than the 2.4% per year growth seen in the Reference scenario. This
does not reflect inaction, but rather the increased economic growth in the Policy Reform
scenario compared to the Reference scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

35

3.2.2 Households
Current Accounts
Energy is used in households for a wide variety of end-uses: cooking, lighting, spaceheating and cooling, water heating, refrigeration, etc. The character of household
consumption varies between and within regions as a function of income, culture, lifestyle, climate and access to energy forms, such as electricity.
Ideally, a satisfactory understanding of household energy use requires information broken
down by end-use, and when examining developing countries, broken down by urban andrural households. Unfortunately, such data are available in no more than a few countries,
and when analyzing global and regional patterns we have to rely upon aggregate fuelconsumption data (IEA, 1997a, b). Even this data presents serious analytical problems.
For example, estimates of traditional biomass consumption in developing country
households (firewood, charcoal, crop and animal residues) remain poorly known.
Current regional household sector energy consumption patterns are reported in Table 3-3.
Figure 3-7 shows current household energy consumption per capita by region andcompared to the global average, underscoring the disparity between industrialized anddeveloping regions. Household per capita energy use in North America, for example, isfour times the level in Asia. Moreover, the efficiency of converting fuel into final
services (that is, cooking fuel into boiling water, boiler fuel into warm spaces) is
generally higher in advanced industrial countries, implying that final energy use figures
for a region such as Africa are misleadingly high since a considerable quantity of fuel iswasted in inefficient fuelwood use.

40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
-

N.
America

W. Europe

Pacific
OECD

FSU

E. Europe

S+SE Asia

MiddleEast

Latin
America

China +

Global Average

Africa

GJ per Capita

Figure 3-7. Household energy intensities - 1995

A great variation in household fuel use patterns is suggested by the fuel share breakdowns
presented in Figure 3-8. Noteworthy are the large share for biomass in the householdenergy mix in developing regions, the importance of coal in China+ and Eastern Europe,and the increasing reliance on more convenient energy forms (electricity, natural gas andoil) as household incomes rise to OECD levels. The large share for “heat” in the fuel mix

36

Energyof the FSU and Eastern Europe (44% and 28% respectively) is due to the continuing
widespread use of centralized district heating systems in these areas.15
Figure 3-8. Household energy shares - 1995
100%

Renewables
Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass

80%
60%
40%
20%

World

N. America

W. Europe

Pacific OECD

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0%

Scenarios
The household sector scenario analysis relies on three elements: a measure of activity,
energy intensity and final fuel mix. The activity measure used is the population in eachregion. In principle, further disaggregation into household income groups would bedesirable in order to illuminate energy trends as a function of changing income
distribution, but there is insufficient data for this. The energy intensity is expressed as thehousehold final energy demand per capita. Household scenarios are developed in four
steps:
1. First, energy intensities (GJ/capita) are projected for the three OECD regions. Since
1973, in industrialized countries these have remained almost constant or in somecases have declined, in spite of increasing household floor space and increasing
penetration of electric appliances. In the United States, for example, final energy use
per capita in households declined by 11% between 1972/3 and 1988 (Schipper andMeyers, 1992). Part of the decline reflects the increased use of electricity (which is
intrinsically more efficient than combusted fossil fuels), but there also appears to havebeen significant declines in energy intensities for heating and appliances (IEA,
1997c). Action to reduce energy use in the built environment was in part spurred by
the oil crises of the 1970s. The numerous initiatives of governments, utilities, tradeassociations and other institutions led to new regulations mandating efficiency
standards and new products such as compact fluorescent lights, more efficient
refrigerators and other appliances, passive solar heating, wider adoptions of doubleglazed windows and spectrally selective window glazing. These improvements canbe expected to continue, and this effect is represented in the Reference scenario.
Nevertheless, the adoption of cost-effective measures to reduce building energy use
15 It is conventional to define final demand as the fuel reaching the end-user. In the case of the household sector, for example, coalused to fire an on-site boiler for space heating would be counted as final fuel use. However, coal or other fuels used to produce heatcentrally is accounted for as an input to the district heating energy conversion process.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

37

remains far below its economic potential. Large market barriers remain, preventing atransition to a more energy-efficient future for the household sector. Issues such as
imperfect information, high transaction costs, inadequate internalization of pollution
costs, and the lack of choice for consumers (who are often faced with the choice ofpurchasing an energy-efficient appliance or another less efficient model with a wider
range of desired features) all contribute to the problem. The policies and programs
considered for the Policy Reform scenario assume concerted action to accelerate thedecline in household energy intensities, including: improved appliance and building
energy performance standards, business and consumer information programs, targeted
government procurement strategies, measures to promote market transformation andovercome market barriers, increased funding of research and development, andinitiatives to encourage voluntary action and utility energy management programs.
Scenario energy intensity assumptions for OECD regions are informed by the recent
U.S. study Energy Innovations (1997). The Energy Innovations study, based on enduse engineering and econometric analysis, examined changes in household energy
intensities in light of expected trends in prices, end-use saturations and householdincome levels, and by making various assumptions about changes in technology. It
modeled the sector using suitably constrained behavioral models that determine theleast-cost technology available to meet various service demands. The study created
two scenarios, called the Present Path and Innovation Path, which are broadly
compatible with the viewpoints of the Reference and Policy Reform scenarios
respectively. Scenario assumptions in this study are guided by more modest
assumptions about rates of decline in energy intensities than the Energy Innovations
study.
In the absence of new policy actions, the Reference scenario nevertheless assumes
significant reductions in energy intensities, which decline at a rate of 0.7% per year inOECD regions. The improvements assume the introduction of a variety ofincreasingly efficient appliances and building shells, including improved heating andair conditioning systems, and more efficient lighting and cooking systems. The
Policy Reform scenario assumes both more rapid and more widespread penetration ofefficient technologies, with OECD intensities declining at a rate of 0.9% per year. In
later periods, the scenario assumes the introduction of advanced technologies andbuilding designs such as passive solar heating, efficient heat pumps and greater use ofrenewable energy.
2. Second, intensities for non-OECD regions are introduced. These converge towards
the average values of the OECD region as average incomes increase, using atechnology convergence algorithm (see Annex). Table 3-1 summarizes the evolutionof energy intensities by region in each scenario.
In the Reference scenario,
developing country and Eastern European energy intensities approach a higher
average intensity (15.6 GJ/capita), while in the Policy Reform scenario, they approacha lower average intensity (13.8 GJ/capita) as set by OECD patterns. At the sametime, there is greater overall convergence in the Policy Reform scenario than in the
Reference scenario, due to the assumption of improving equity and more rapid

38

Energyeconomic growth in the developing countries in the former scenario. These two
effects largely counteract one another, resulting in broadly similar trends indeveloping country energy intensities in the two scenarios. The exception is the FSU,
where household energy intensities start at levels well above the OECD average, andthus the effects of the convergence algorithm and trends in OECD intensities
reinforce one another.
Table 3-1. Trends in Household Energy Intensities (GJ/capita)

North America
W. Europe
Pacifc OECD
FSU
E. Europe
AfricaLatin America
Middle East
China+
S+SE Asia
World

1995
39.2
22.8
16.2
27.8
17.1
9.4
6.8
8.9
9.2
8.3
3.3

Reference
2025
2050
31.7
26.6
18.4
15.5
13.1
11.0
25.9
23.4
17.6
16.6
9.9
10.5
10.7
14.0
11.1
12.8
11.6
13.3
10.1
11.9
4.3
6.2

GrowthGrowthRate Policy Reform
95-50
2025
2050
-0.7%
29.9
23.8
-0.7%
17.3
13.8
-0.7%
12.3
9.8
-0.3%
24.1
17.4
-0.1%
17.2
14.9
0.2%
10.6
11.4
1.3%
11.1
13.7
0.7%
11.5
12.8
0.7%
11.8
13.1
0.6%
10.6
12.1
1.2%
3.7
4.9

Rate
95-50
-0.9%
-0.9%
-0.9%
-0.8%
-0.3%
0.4%
1.3%
0.7%
0.6%
0.7%
0.8%

3. In the third step, biomass fuel shares are projected for each region. Since traditional
biomass fuels currently dominate household energy use in most developing countries,
assumptions on modern versus traditional fuels is a key issue in the Reference andPolicy Reform scenarios. Household fuel use tends to climb an "energy ladder" withrising income. As incomes increase, wood and dung are replaced by charcoal andkerosene and then by LPG and electricity. Each succeeding rung in the ladder ischaracterized by greater efficiency of use, but also by higher equipment prices.
Consequently, differences in income and its distribution can imply very different
requirements for energy.
To shed light on this question, regression analysis was used to explore the relationshipbetween three variables, compiled from a cross-section of national statistics: the shareof biomass in the total household energy budget, average income levels and a measureof income distribution. The regression yielded low R2 values, suggesting that
additional variables are important for determining biomass consumption, which is
expected since the availability of biomass varies widely across countries.
Nevertheless, both the income and income distribution parameters were found to bestatistically significant at the 5% level (t greater than about 2) in explaining thevariation in household biomass shares, and are consistent with the energy ladder
hypothesis, i.e., that biomass fuel consumption decreases as average income
increases. The regression analysis guided future household biomass shares in eachregion and in both scenarios. Specifically, the regression was used to calculatebiomass fuel shares in all regions for 2025 and 2050. In the Policy Reform scenario,
the regression-based estimates of biomass fuel shares in 2050 for Africa, China andConventional Worlds: A Technical Description of Bending the Curve Scenarios

39

Latin America were adjusted to reflect a less dramatic decline in biomass fuel use, on
the assumption that in these regions, the current dominance of biomass is likely tospur modern energy-efficient uses for biomass in households and hence slow its
decline relative to other fuels.
The resulting trends in the biomass shares are summarized in Table 3-2 for regions
where biomass was a significant household fuel in 1995.
Table 3-2. Trends in Household Biomass Fuel Shares
4. Finally, the shares of eachnon-biomass
fuel
are
Reference
Policy Reform
introduced. In the Reference
1995
2025
2050
2025
2050
2%
1%
1%
1%
0%
scenario these are guided by FSU
Africa
89%
73%
52%
37%
15%
household fuel consumption
43%
19%
9%
9%
5%
trends in the IPCC mid-range Latin America
1%
0%
0%
0%
0%
scenario(IPCC,
1992). Middle East
China+
68%
31%
16%
19%
10%
These reflect the effects of S+SE Asia
78%
47%
29%
18%
8%
changes
inpricesasresources are depleted andtechnical change occurs.16 Taking the Reference scenario as a starting point, thePolicy Reform scenario assumes an accelerated transition away from the more carbon
intensive fuels (coal and oil) towards lower carbon and more convenient fuels(electricity, natural gas, and some renewables). This reflects a pattern of higher andmore evenly distributed income levels in the developing countries (leading to higher
consumption of the more convenient fuels), and the effects of policies to reducegreenhouse gas emissions.

Household energy demands are shown by fuel in Figure 3-9. In both scenarios, total
household energy consumption increases by almost 60%, from 73 EJ in 1995 to 123 EJ in
2050 in the Reference scenario and to 116 EJ in 2050 in the Policy Reform scenario.

16 In the IPCC IS92a scenario, it is assumed that the world oil price increases to $55 per bbl in 2025 and $70 per bbl in 2100, while

the cost of non-fossil energy supplies are assumed to fall (e.g., the cost of solar electric energy is assumed to decline to $0.075 per
kWh by 2050). Similar price trends are implied in the Reference Scenario.

40

EnergyFigure 3-9. Global household energy demand by fuel

EJ

140

Petroleum

120

Natural Gas

100

Heat

80

Electricity

60

Coal
Biomass/Renewables

40
20
0
1995

2025
Reference

2050

2025
2050
Policy Reform

Household energy demands are shown by macro-region in Figure 3-9 and by region in
Figure 3-11. In the Reference scenario, the stable or decreasing household energy
demands in the OECD and transitional regions are the result of low population growth,high appliance saturations, and efficiency improvements. The developing regions
experience dramatic increases in household energy demand, largely due to population
growth and the gradual transition to modern patterns of household energy use. Biomassenergy use continues to dominate household demand in Africa and, to a lesser extent, inSouth and Southeast Asia, while coal continues to dominate in China+.
Figure 3-10. Global household energy demand by macro-region
140

Developing
Transitional
OECD

120
100
EJ

80
60
40
20
0
1995

2025

2050
Reference

2025
Policy Reform

2050

Consumption of biomass fuels by households decreases over time in most regions, as
increasing incomes allow people to move up the “energy ladder” to more convenient
forms of household energy. However, this trend is counterbalanced by the continuing
expansion of biomass use by households in Africa, in which rapid population growth andcontinuing poverty combine so that biomass fuels continue to dominate in the household
sector until well past 2025. The overall effect is a slight decline in global householdbiomass use from 28 EJ in 1995 to 26 EJ in 2050. In fact, the plausibility of this scenario

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

41

for Africa is questionable, since the future levels of consumption in the scenario may not
be consistent with sustainable rates of extraction of biomass resources.
In the Policy Reform scenario, energy consumption is slightly lower than in the Reference
scenario and is distributed differently between fuels. In particular, the scenario sees amore rapid decline in biomass consumption and a more rapid transition towards lesscarbon-intensive fuels and renewable energy than the Reference scenario. The regional
distribution of consumption is also different, as illustrated in Figure 3-11. In the OECD
regions, additional energy intensity improvements produce further reductions in
consumption compared to the Reference scenario. In most non-OECD regions, energy
consumption patterns are slightly lower due to two counteracting effects. First, higher
levels of economic growth lead to greater convergence of energy intensities towards
OECD values. However, these OECD intensities are lower in the Policy Reform scenario
than in the Reference scenario. The exception is the FSU, where consumption declines
are much greater — a result of the high initial energy intensities in the region.

42

EnergyFigure 3-11. Regional household energy demand
Africa

25.0

20.0

20.0

15.0

15.0

EJ

EJ

25.0

10.0

5.0

5.0

0.0
2025 2050

Latin America

40.0

Heat
Coal
Biomass2025 2050

South & Southeast Asia

35.0
30.0
EJ

8.0
EJ

Natural Gas

1995 2025 2050

10.0

6.0

25.0
20.0
15.0

4.0

10.0

2.0

5.0

0.0

0.0
1995 2025 2050

2025 2050

1995 2025 2050

Eastern Europe

9.02025 2050

FSU

8.0
7.0
6.0
EJ

EJ

Petroleum

0.01995 2025 2050

1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

Renewables

Electricity

10.0

12.0

China+

5.0
4.0
3.0
2.0
1.0

1995 2025 2050

2025 2050

Reference

Policy Reform

0.0
1995 2025 2050

2025 2050

Reference

Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

43

Figure 3-11: Regional household energy demand (continued)
14.0

North America

12.0

Petroleum
Natural Gas

8.0

8.0

EJ

EJ

Renewables

10.0

10.0

Heat

6.0

Electricity

4.0

4.0

Coal

2.0

2.0

6.0

0.0

3.0

Biomass

0.0
1995 2025 2050

2025 2050

1995

Pacific OECD

2.5

6.0

2.0

5.0

1.5

2025

2050

2025

2050

Middle East

7.0

EJ

EJ

Western Europe

12.0

4.0
3.0

1.0

2.0

0.5

1.0

0.0

0.0
1995 2025 2050

Reference2025 2050

Policy Reform

1995

2025 2050
Reference2025 2050
Policy Reform44

Energy3.2.3 Services
The service sector is broadly defined to include such economic activities as offices, retail,education, hospital and other non-industrial enterprises. Service sector energy use islargely related to building end-uses: lighting, HVAC (heating, ventilation, and air
conditioning) and equipment. The assessment would benefit from subsectoral and enduse disaggregation, but the absence of reliable survey and statistical data makes thisinfeasible.
Current Accounts
Regional service sector energy consumption is reported in Table 3-3 (page 33). There isa striking difference between service energy use in industrialized and other regions.
North America’s per capita demand for energy in the service sector is more than twenty
times that of the developing regions, reflecting both higher incomes, and a greater shareof the service sector in overall economic activity as income increases (see Figure 3-12).
Figure 3-12. Service sector energy consumption per capita - 1995
30.0

GJ per capita

25.0
20.0
15.0

Global Average

10.0
5.0
AfricaChina+

Latin
America

Middle
East

S+SE
Asia

E.
EuropeFSU

N.
America

Pacific
OECD

W.
EuropeFigure 3-13 shows service sector energy consumption per dollar of service sector value
added. Here there is much less variation between regions, although the OECD andtransitional regions do reflect the more capital-intensive nature of services in thosecountries compared to the more labor-intensive service sector in developing countries.
Amongst the developing economies, China’s energy intensity appears anomalously high.
This may reflect particular inefficiencies in energy use in China+ (which as Figure 3-14
shows, still relies heavily on coal), statistical inaccuracies, or problems in using aggregate
value-added figures as the basis for energy intensities.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

45

MJ/US$ Service Sector Value Added (PPP)

Figure 3-13. Service sector energy intensities - 1995 (MJ/$)
1.60

Global Average

1.40
1.20
1.00
0.80
0.60
0.40
0.20
AfricaChina+

Latin
America

Middle
East

S+SE
Asia

E.
EuropeFSU

N.
America

Pacific
OECD

W.
EuropeCurrent account fuel shares are shown in Figure 3-14. Electricity, petroleum, and natural
gas are the dominant fuels accounting for more than 90% of global service sector energy
consumption. Coal remains important in China+ — accounting, at least in part, for that
region’s high service sector energy intensity. Biomass remains important in the sector
only in Africa. However, it is likely that the heavily biomass-reliant informal sector, thesmall and often unregistered enterprises in many developing regions, are not fullyreflected in service sector energy statistics (O’Keefe et al., 1984). In some cases, thisconsumption may be included within household statistics. District heat is significant inEastern Europe and the FSU countries and to a lesser extent in Western Europe. It
remains largely undeveloped in North America and the rest of the world.
Figure 3-14. Service sector fuel shares - 1995
100%

Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass

80%

60%

40%

20%

W. Europe

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin
America

China +

Africa0%

The current determinants of service sector energy use, and the variables which willcondition future patterns, include the scale of service sector activity, the mix of activities
at the subsectoral level, end-use energy efficiency, building and architectural standards,
trends in office automation, and fuel availability and price. Detailed consideration ofthese factors is difficult because of an unusually sparse data base.

46

EnergyScenarios
The service sector scenario analysis relies on three elements: measures of activity, energy
intensity and final fuel mix. The activity measure is defined as service sector value
added, the product of regional GDP and the sectoral share (Sheets E-1 and E-2 of the BTC
Scenario Highlights). The energy intensity is expressed as the service sector final energy
demand divided by value added for the sector. Service sector scenarios are developed infour steps:
1. First, energy intensity trends (GJ/$) are developed for the three OECD regions.
Historically, service sector energy intensities have been declining in industrialized
nations since 1973. In nine OECD countries, intensities declined by 27% over theperiod 1973 and 1988 (Schipper and Meyers, 1992). Declines in the energy intensityof space heating appears to be the major reason for these overall declines, as businessswitched from less efficient fuels and devices (e.g., fuel-oil systems) to natural gas
and electricity. Additions of new buildings with lower heating requirements, retrofit
improvements to existing building envelopes and improved energy management also
contributed to the decline. Structural shifts between service sectors appear to have
had relatively little effect (Schipper and Meyers, 1992). These improvements are
expected to continue in the future even without new policy interventions, and this isreflected in the Reference scenario. Nevertheless, the adoption of cost-effective
measures to reduce service sector energy use remains far below its economic
potential. The Policy Reform scenario reflects concerted action to accelerate thedecline in service sector energy intensities. Many of the policies and programs
required to achieve this are common with those in the household sector, and aredescribed in Section 3.2.2.
Scenario intensities are guided by the findings of the U.S. Energy Innovations study(Energy Innovations, 1997). In the Reference scenario, OECD service sector energy
intensities decline at a rate of 1.3% per year from a starting value of 1.2 GJ/$ in 1995
to 1.6 GJ/$ in 2050. In the Policy Reform scenario, they decline at a rate of 1.8% peryear to 0.44 GJ/$ in 2050. Both scenarios require increasingly energy-efficientbuilding shells, as well as improvements in areas such as space heating and cooling,water heating and commercial appliances. The Policy Reform scenario requires bothmore rapid and more widespread penetration of efficient technologies and, in later
periods, the introduction of advanced technologies and building designs.
2. Second, intensities for non-OECD regions are introduced. In each scenario, theseconverge towards the average values of the OECD region as average incomesincrease, using a convergence algorithm (see Annex).
The overall pattern ofconvergence in the two scenarios is shown in Figure 3-15. In the Policy Reform
scenario there is greater overall convergence to a lower average intensity, in
accordance with the exogenous assumptions of improving equity and more rapid
economic growth in the developing countries compared with the Reference scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

47

Figure 3-15. Service sector energy intensities
Policy Reform

GJ/$

Reference
1.6

1.6

1.4

1.4

1.2

1.2

1.0

1.0

FSU

0.8

0.8

E Europe

0.6

0.6

Africa0.4

0.4

Lat Amer

0.2

0.2

Mid East-

N AmerW Europe
Pac OECD

China+

1995

2025

2050

1995

2025

2050

S+SE Asi

3. Third, fuel shares are introduced. In the Reference scenario, these are guided by thetrends in the IPCC mid-range scenario (IPCC, 1992). Taking the Reference scenario
trends as a starting point, the Policy Reform scenario assumes an accelerated
transition towards lower-carbon fuels. Given the stringent CO2 reduction targets of
the Policy Reform scenario, by 2050 the service sector is dominated by electricity and,to a lesser extent, natural gas.
Multiplying service sector value added and the intensity figures we derive service sector
energy demand. Service sector energy demand by fuel is shown in Figure 3-16. In bothscenarios, the size of the global service sector (measured in PPP dollars of value added)
increases by a factor of more than 5 from 1995 to 2050, increasing its share of total global
GDP from 56% in 1995 to 67% in 2050.
Figure 3-16. Global service sector energy demand by fuel70

Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass

60
50
EJ

40
30
20
10
0
1995

2025
2050
Reference

2025
2050
Policy Reform48

EnergyService sector energy demands are shown by macro-region in Figure 3-17. Energy
requirements for the service sector grow rapidly in the Reference scenario, increasing
globally by a factor of three over the scenario time-frame, and by factors of 8 to 14 indeveloping regions. The OECD region grows by a factor of 1.8 and the transitional
region by a factor of 2.5 over the same time period. On a global basis, services rise from
7% of final energy demand to 9% by 2050. Increased energy efficiency results in lower
growth in service sector energy demand in the Policy Reform scenario, which increases by
a factor of 2.4 from 1995 to 2050. This growth is concentrated in the developing regions,where growth factors range from 6 to 16 over the period 1995 to 2050. OECD service
sector consumption in 2050 declines to 80% of its 1995 value, while the transitional
regions increase by a factor of 2.1.
Figure 3-17. Global service sector energy demand by macro-region
70

Developing
Transitional
OECD

60
50
EJ

40
30
20
10
0
1995

2025

2050
Reference

2025
2050
Policy Reform

Service sector energy demands are shown by region in Figure 3-18. In most non-OECD
regions, service sector energy consumption in 2050 is lower in the Policy Reform scenario
than in the Reference scenario, as energy intensity improvements outweigh the effect ofincreased growth of the sector. The exceptions are Africa and South and Southeast Asia,
where initially low energy intensities in the base year increase over time in the Policy
Reform scenario, in spite of the fact that they are converging to more efficient OECDtarget values. This effect, when combined with rapid economic growth in those regions
produces higher total energy consumption than in the Reference scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

Figure 3-18. Regional service sector energy demand
4.0

Africa

12.0

3.5

EJ

EJ

2.0
1.5
1.0
0.0

Heat

6.0

Electricity

4.0

Coal
Biomass

0.0
1995 2025 2050

2025 2050

1995 2025 2050

Latin America

12.0

5.0

10.0

4.0

8.0
EJ

EJ

Natural Gas

2.0

0.5

3.0

4.0

1.0

2.0

0.0

2025 2050

South & Southeast Asia

6.0

2.0

0.0
1995 2025 2050

2025 2050

1995 2025 2050

Eastern Europe

2025 2050

FSU

1.8
1.6

0.6

1.4

0.5

1.2

0.4

EJ

EJ

Petroleum

8.0

2.5

0.7

Renewables

10.0

3.0

6.0

China+

1.0

0.3

0.8

0.2

0.6
0.4

0.1

0.2

0.0
1995 2025 2050

2025 2050

Reference

Policy Reform

0.0
1995 2025 2050
Reference2025 2050
Policy Reform

49

50

Energy

18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

North America

Petroleum

6.0

Natural Gas

5.0

Heat

4.0

Electricity

3.0

Coal

2.0

Biomass

0.02025 2050

1995

Pacific OECD

2025

2050

2025

2050

2025

2050

Middle East

3.0

3.0

2.5

2.5

2.0

2.0

EJ

EJ

Renewables

7.0

1.0
1995 2025 2050

3.5

Western Europe

8.0

EJ

EJ

Figure 3-18. Regional service sector energy demand (continued)

1.5

1.5
1.0

1.0

0.5

0.5
0.0

0.0
1995 2025 2050

Reference2025 2050

Policy Reform

1995

2025

2050

Reference

Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

51

3.2.4 Agriculture
The agriculture sector includes activities in farming, livestock and forestry systems.
Agricultural energy consumption reflects the use of final fuels (especially, fossil fuels andelectricity) consumed at the end-use, e.g., for field operations, irrigation, drying and soon. With this definition, energy used in the agricultural sector accounts for about 3% ofworld final energy consumption.
Current Accounts
Regional agricultural energy consumption is reported in Table 3-3 (page 33). Energy use
in the agricultural sector is dominated by petroleum products, which account for 60% of
global agricultural energy consumption (Figure 3-19). In fact, in six of the regions
petroleum products account for more than 50% of agricultural energy demand. Energy
use per dollar value added is reported in Figure 3-20. In general, energy intensities aremuch higher in the OECD and transitional countries where agriculture is far moremechanized. Other significant variations across regions may be related to underlying
differences in agricultural practices such as irrigation levels and geophysical factors.
However, variations may also be related to uncertainty in agriculture energy statistics andin accounting procedures. For example, it remains unclear (even with recent revisions to
IEA databases) whether FSU agricultural statistics include energy consumed for foodprocessing and transportation of agricultural products, or assign these to industry andtransportation sectors, respectively, as is the practice in OECD regions. Failure to follow
this practice may help to explain the unusually high intensities for agricultural energy use
in the FSU. (Note, however, that FSU industrial energy intensities are also very high.)
Figure 3-19. Agriculture sector fuel shares - 1995
100%

Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass

80%
60%
40%
20%

World

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0%

52EnergyMJ per US$ agriculture sector value added (PPP)

Figure 3-20. Agriculture sector energy intensities - 1995 (MJ/$)
16.0
14.0
12.0
10.0
8.0

Global Average

6.0
4.0
2.0
AfricaChina +

Latin
America

Middle
East

S+SE
Asia

E.
EuropeFSU

N.
America

Pacific
OECD

W.
EuropeScenarios
The Agricultural sector energy scenario is based on a three step-analysis of activity levels,
energy intensities, and final fuel mix. The activity measure is defined as agricultural
sector value added — the product of regional GDP and the sectoral share (see sheets E-1
and E-2 of the BTC Scenario Highlights. Energy intensity is expressed as the agricultural
sector final energy demand divided by value added for the sector.
1. First, energy intensity trends (GJ/$) are developed for the three OECD regions. In theabsence of more detailed studies of the trends in the agriculture sector, improvements
in agricultural energy intensities are guided by our assumptions for the industrial
sector (see section 3.2.5), with more modest rates of improvement assumed. In the
Reference scenario, OECD agricultural sector energy intensities decline at a modest
0.5% per year from a starting value of 2.4 GJ/$ in 1995 to 1.9 GJ/$ in 2050. This isconsistent with trends in OECD countries over the last few decades where the limited
available data (e.g., WRI, 1992b, IEA, 1997b) suggest that agriculture energy
intensities have been declining only very slowly. In the Policy Reform scenario,
agricultural sector energy intensities decline at a more rapid 0.9% per year.
2. Second, for the non-OECD regions we apply the convergence algorithm (see Annex),
assuming that agricultural practices and energy intensities approach OECD average
figures as each region’s GDP per capita approaches the OECD average GDP per
capita in 1995. Agricultural sector energy intensities are summarized in Figure 3-21.
Just as in the service and industrial sectors, the interpretation of intensity trends would
be more transparent if they were expressed in physical units, e.g., energy consumption
per unit of agricultural product. Then, in the transition to modern high-input
agriculture, energy intensities would be driven by the interplay of two
counterbalancing processes, increasing energy use per unit area and increasing
product per unit area. But data inadequacies prevent conducting such an energy
analysis. In relying on agriculture sector GDP, an additional variable and additional
uncertainty is introduced — the relationship between physical product and the value
added in the sector.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

53

Figure 3-21. Agricultural sector energy intensities
Policy Reform

GJ/$

Reference

N Amer

16

16

14

14

12

12

10

10

8

8

6

6

4

Lat Amer

4

2

Mid East

2

China+

1995

2025

2050

W Europe
Pac OECDFSU
E Europe
Africa-S+SE Asi
1995

2025

2050

3. Examining fuel share patterns over the time period 1970 to 1990 (IEA, 1997a, b)
shows year-to-year fluctuation in the reported agriculture energy statistics, but nodiscernible regional trends. Thus for the Reference scenario, agricultural fuel sharesare held constant. In the Policy Reform scenario, we again consider a transition toless carbon-intensive fuels, principally through greater penetration of electricity andnatural gas for irrigation and other end-uses, as well as limited penetration of biomass
and renewable energy for use in such applications as space heating and drying ofagricultural commodities. Nevertheless, oil remains the dominant fuel in all regionsin the Policy Reform scenario, due to the importance of motive power in the
agricultural sector.
Agriculture energy demand by fuel is shown in Figure 3-24. In the Reference scenario,
total demand increases from 8 EJ in 1995 to 14.2 EJ by 2050. This growth is slowed
overall in the Policy Reform scenario, reaching 12.1 EJ in 2050. Demand remains
dominated by petroleum fuels in both scenarios, reflecting the importance of motive
power and the internal combustion engine in meeting the sector’s energy demands.
While a small part of the global total, the importance of coal in the sector actually
increases in both scenarios, due to the large growth of agriculture in developing regions,and the importance of coal use in China (see Figure 3-24), not to a general pattern ofswitching to coal in other regions.

54

EnergyFigure 3-22. Global agricultural energy demand by fuel15

Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass/Renewables

EJ

10

5

0
1995

2025

2050
Reference

2025
2050
Policy Reform

Agricultural sector energy demands are shown by macro-region in Figure 3-23.
Compared to the Reference scenario, an increased proportion of the growth in the Policy
Reform scenario comes from the developing regions. In the OECD regions, demand
declines in both scenarios from 2.5 EJ in 1995 to 2.1 EJ in 2050 in the Reference scenario
and to 1.7 EJ in the Policy Reform scenario. In the transitional regions, demand remains
almost constant in the Reference scenario at 2.5 EJ, but declines dramatically in the
Policy Reform scenario to 1.3 EJ in 2050, reflecting the large differences in convergence
between the two scenarios and the relatively high initial energy intensities in the FSU. In
the developing regions, demand more than triples in both scenarios from 3.0 EJ in 1995
to 9.5 EJ in 2050 in the Reference scenario, and to 9.1 EJ in 2050 in the Policy Reform
scenario.
Figure 3-23. Global agricultural energy demand by macro-region
15

Developing
Transitional
OECD

EJ

10

5

0
1995

2025
Reference

2050

2025
2050
Policy Reform

Agricultural sector energy demands are shown by region in Figure 3-24. In most nonOECD regions, agricultural energy consumption in 2050 is lower in the Policy Reform
scenario than in the Reference scenario, as energy intensity improvements outweigh theeffect of increased growth in the sector. The exception is Africa where energy intensities

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

55

do not change dramatically in the Policy Reform scenario, but rapid growth in agriculturein the region produces much higher total energy consumption than in the Reference
scenario. Also notable is the FSU, where roughly energy consumption declines
dramatically in the Policy Reform scenario, but remains roughly constant in the Reference
scenario. These trends reflect the high base year energy intensities in the region, and theincreased level of convergence in the Policy Reform scenario compared to the Reference
scenario.

56

EnergyFigure 3-24. Regional agricultural energy demand
Africa

1.6

3.0

1.4

EJ

EJ

0.8
0.6
0.4
0.0

Heat

1.5

Electricity

1.0

Coal
Biomass

0.0
1995 2025 2050

2025 2050

1995 2025 2050

Latin America

3.52025 2050

South & Southeast Asia

3.0
2.5
EJ

EJ

Natural Gas

0.5

0.2

2.0
1.5
1.0
0.5
0.0

1995 2025 2050

2025 2050

1995 2025 2050

Eastern Europe

2.5

0.4

2025 2050

FSU

2.0

0.3
0.3

EJ

EJ

Petroleum

2.0

1.0

0.4

Renewables

2.5

1.2

2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

China+

0.2

1.5
1.0

0.2
0.1

0.5

0.1
0.0
1995 2025 2050
Reference2025 2050
Policy Reform

0.0
1995 2025 2050
Reference2025 2050
Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0

North America

Petroleum

1.0

Natural Gas

0.8

Heat
Electricity
Coal

0.4

Biomass0.2
0.02025 2050

1995

Pacific OECD

0.5
EJ

0.4
EJ

Renewables

1.2

0.6

1995 2025 2050

0.6

Western Europe

1.4

EJ

EJ

Figure 3-24. Regional agricultural energy demand (continued)

0.3
0.2
0.1
0.0
1995 2025 2050

Reference2025 2050

Policy Reform

2025

2050

2025

2050

2025

2050

Middle East

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
1995

2025

2050

Reference

Policy Reform

57

58

Energy3.2.5 Industry
Current Accounts
Regional industrial energy consumption is reported in Table 3-3 (page 33). The industrial
sector accounts for almost 40% of global final energy consumption17. The sector isdominated by five subsectors: chemicals; paper and pulp; stone, glass and clay; nonferrous metals; and iron and steel. The global and regional breakdown of consumption by
sector is provided in Figure 3-25. The five highly energy-intensive industries account for
about 50% of global industrial energy use and from 15% to 71% across regions. Futureindustrial requirements will be strongly influenced by projected output and energy
intensities in these subsectors although the relative importance of smaller sectors(included in the “other” category) is expected to increase due to saturation in the
consumption of the types of raw materials produced by heavy industries.
Figure 3-25. Industrial sector energy use by sector - 1995
100%

Chemicals
Iron + steel
Non-Ferrous Metals
Paper and Pulp
Stone,glass,clay
Other

80%
60%
40%
20%

World

W. Europe

Pacific
OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin
America

China +

Africa0%Turning to the fuel composition of industrial demand, global industrial energy demand isdominated by fossil fuels (70%) and electricity (17%), with significant variation acrossregions in fossil fuel choice (Figure 3-26). Industries tend to rely on domesticallyavailable and relatively inexpensive fuels. For example, in China+, coal is the dominantindustrial fuel, accounting for more than 70% of industrial fuel use, while in the Middle
East, oil and natural gas together account for 85% of industrial fuel use. Industrial energy
use is driven largely by the requirements for process heat (although electricity for motors
is also substantial).

17 Total world industrial fuel consumption (103.8 EJ in 1995) includes approximately 12.2 EJ of mainly petroleum products consumed

as feedstocks in chemical processes and a further 4.2 EJ also of mainly petroleum products consumed for non-energy purposes(e.g., oil for lubricants).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

59

Figure 3-26. Industrial sector energy use by fuel - 1995
100%

Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass/Renewables

80%
60%
40%
20%

World

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0%

Industrial energy intensities, defined as industrial energy use divided by value added in
the industrial sector are shown in Figure 3-27. These provide only a crude measure of theefficiency of converting a unit of energy input to a unit of economic output. Ideally, it
would be desirable to examine industrial energy intensities in physical units of output(e.g., tonnes of steel) rather than in monetary terms since this more closely reflects theactual activities that consume energy. However, lack of data on physical outputs for the
myriad products comprising an industrial sector rules this out for comprehensive
representation of current energy demands. It should also be noted that additional
uncertainty is introduced in cross-regional comparisons where exchange rates (even thoseadjusted for purchasing power parity) are needed to express national economic statistics
in common units.

MJ per US$ industrial value added (PPP)

Figure 3-27. Industrial energy intensities - 1995
35
30
25
20

Global Average

15
105
AfricaChina +

Latin
America

Middle
East

S+SE
Asia

E.
EuropeFSU

N.
America

Pacific
OECD

W.
EuropeThese problems notwithstanding, the high values for FSU and Eastern Europe are worth
noting, though difficult to interpret. In principle, gross industrial energy intensities

60

Energydepend on three major factors: type of process technology, energy management and themix of industrial activities. To shed some light on the latter, we display intensities foreach major subsector in Figure 3-28, which shows that the variation in intensity for agiven industrial subsector varies across regions in roughly the same manner as does
industry as a whole. Consequently, it appears that process technology and management
are the primary determinants of regional industrial energy intensities today. Moreover,
the analysis suggests that policies to steer regions toward state-of-the-art practices could
significantly decrease the energy needed per unit of industrial activity.
Figure 3-28. Industrial energy intensities by subsector - 1995
MJ/US$ value added (1995 PPP)70

Chemicals
Iron + steel
Non-Ferrous Metals
Paper and Pulp
Stone,glass,clay
Other

60
50
40
30
20
10
World (Avg)

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa-Scenarios
As with the other sectors, industrial sector scenario energy patterns are defined by future
activity levels, trends in energy intensities and fuel shares. Activity levels are expressedin terms of overall value added in industry and further by the shares among the industrial
subsectors.
1. Value added as a whole for industry is based on GDP and industrial shareassumptions (see Sheets E-1 and E-2 of the BTC Scenario Highlights). Because of
the different energy intensities of each subsector, we further disaggregate the analysis
into five energy-intensive subsectors: iron and steel, non-ferrous metals, non-metallic
minerals, paper and pulp, and chemicals. Base year shares are calculated from 3 digit
ISIC subsector value added statistics (UNIDO, 1997). There are indications that inOECD countries, historical growth rates of material consumption have leveled off
over recent decades (Williams et al., 1987; Bernardini and Galli, 1993). In bothscenarios, we capture this saturation effect in characterizing the structure of industrial
activity in our scenarios. Continuing recent patterns, we assume that value added per
capita for iron and steel, and non-metallic minerals decrease by ten percent over the
1995-2050 period, and that value added per capita for non-ferrous metals, paper andpulp, and chemicals remain constant. Since industrial activity increases sector-wide
more rapidly than population, these assumptions imply that expansion of industrial

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

61

sector activity in OECD regions is predominantly in the less material and energyintensive “other” category.
2. Second, for the non-OECD regions, we apply the convergence algorithm (see Annex)
to develop industrial activity assumptions for each energy-intensive subsector.
Specifically, the value added per capita in a given subsector approaches the projectedOECD average value added per capita as a region’s total GDP per capita approaches
the 1995 OECD average GDP per capita. The value added per capita in the “other”
industrial subsector is calculated as a remainder. The results are displayed in Table 33 and Table 3-4 for dollars value added per capita, and in Table 3-5 and Table 3-6 fortotal dollars value added.
Table 3-3. Industrial value added per capita in the Reference scenario ($/capita)
Reference
N. America
Pacific OECDW Europe
E Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia

Chemical
1995
2050
1,160
1,160
1,232
1,232
712
712
282
721
173
479
63
203
178
643
396
846
267
630
153
515

Iron +Steel
Non-Ferr Metals
1995
2050
1995
2050
173
157
110
110
367
333
97
97
177
161
56
56
120
164
31
64
138
158
127
110
32
56
14
25
145
170
30
61
98
168
38
73
250
217
57
70
51
112
15
45

Paper + Pulp Stone, Glass, Clay
1995
2050
1995
2050
819
819
185
168
664
664
332
301
428
428
233
212
73
422
120
179
38
261
114
152
39
129
36
63
38
380
106
168
105
509
131
193
62
350
200
205
42
297
42
118

Other
1995
2050
4,738 13,387
5,031 15,867
2,787
8,688
1,626
2,870
959
2,228
323
896
845
2,968
1,153
2,647
1,131
2,527
577
2,031

Table 3-4. Industrial value added per capita in the Policy Reform scenario ($/capita)
PolicyChemicalIron +Steel
Non-Ferr Metals
Paper + Pulp Stone, Glass, Clay
Reform1995
2050
1995 2050
1995 2050
1995 2050
1995 2050
E Europe
282
834
120
174
31
73
73
512
120
193
FSU
173
607
138
166
127
102
38
355
114
168
Africa
63
392
32
89
14
39
39
249
36
100
China+
178
753
145
175
30
68
38
460
106
182
Latin America
396
939
98
182
38
81
105
594
131
205
Middle East
267
743
250
206
57
75
62
440
200
207
S+SE Asia
153
633
51
132
15
55
42
381
42
142
Note: OECD countries not shown since values are the same in both scenarios.

Other
1995
2050
1,626
2,895
959
2,557
323
1,565
845
3,057
1,153
2,737
1,131
2,728
577
2,352

62

EnergyTable 3-5. Total Industrial value added in the Reference scenario ($)
Billion $PPP
N America
Pac OECDW Europe
E Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia
OECD
Transitional
Developing
World

Chemical1995 2050-Ref
344
444
184
180
332
331
28
66
50
139
46
416
237
1,101
188
688
48
314
257
1,507
860
955
78
205
776
4,025
1,714
5,186

Iron + steel
Non-ferrous metals
1995 2050-Ref
1995 2050-Ref
51
61
32
43
54
48
15
13
82
77
27
24
12
15
3
6
40
46
37
32
23
115
10
51
193
292
39
105
47
137
18
61
45
108
10
36
86
329
25
137
187
186
74
80
52
61
40
38
393
981
103
390
632
1,228
217
507

Paper + Pulp
Stone, Glass, Clay
1995 2050-Ref
1995 2050-Ref
243
316
56
67
99
97
49
43
199
201
109
101
7
38
12
16
11
76
33
44
28
264
26
129
50
644
141
285
50
414
62
158
11
176
35
102
69
867
71
347
541
615
214
211
19
115
45
61
209
2,366
335
1,021
769
3,095
594
1,293

Other
1995 2050-Ref
1,407
5,149
749
2,311
1,300
4,063
161
263
281
650
233
1,836
1,124
5,063
549
2,143
202
1,263
967
5,944
3,456 11,523
442
913
3,075 16,248
6,973 28,684

Total
1995 2050-Ref
2,132
6,079
1,151
2,693
2,049
4,796
223
404
454
988
366
2,811
1,784
7,489
915
3,602
351
1,999
1,475
9,131
5,331 13,569
677
1,393
4,891 25,031
10,899 39,993

Table 3-6. Total Industrial value added in the Policy Reform scenario ($)
Billion $PPP

Chemical
2050-PR
N America
344
447
Pac OECD
184
179
W Europe
332
332
E Europe
28
73
FSU
50
168
Africa46
762
China+
237
1,217
Latin America 188
721
Middle East
48
353
S+SE Asia
257
1,756
OECD
860
959
Transitional78
241
Developing
776
4,808
World
1,714
6,008

Iron + steel
Non-ferrous metals
2050-PR
2050-PR
51
61
32
43
54
48
15
14
82
76
27
27
12
15
3
7
40
46
37
28
23
175
10
76
193
281
39
114
47
138
18
62
45
98
10
36
86
370
25
154
187
186
74
83
52
61
40
35
393
1,063
103
441
632
1,309
217
560

Paper + Pulp
Stone, Glass, Clay
2050-PR
2050-PR
243
315
56
64
99
96
49
43
199
199
109
100
7
44
12
17
11
98
33
46
28
483
26
194
50
746
141
297
50
455
62
157
11
209
35
98
69
1,058
71
390
541
611
214
207
19
143
45
63
209
2,950
335
1,136
769
3,703
594
1,405

Other
2050-PR
1,407
2,648
749
1,340
1,300
2,590
161
252
281
706
233
3,042
1,124
4,952
549
2,105
202
1,295
967
6,530
3,456
6,578
442
959
3,075 17,924
6,973 25,461

Total
2050-PR
2,132
3,579
1,151
1,721
2,049
3,325
223
408
454
1,093
366
4,730
1,784
7,607
915
3,637
351
2,089
1,475
10,257
5,331
8,624
677
1,501
4,891
28,321
10,89938,447

3. Next, OECD industrial energy intensities are developed, expressed in GJ/$ value
added at the industrial subsectoral level. Future energy intensities for each industrial
subsector depend on changes in process efficiencies and the product mix.
Historically, energy intensities have been declining steadily in the industrial sectors ofOECD countries. Aggregate manufacturing intensities (measured across all industrial
subsectors) decreased by an average of 40% between 1973 and 1988 across eight
OECD countries. Approximately three quarters of this decline was due toimprovements in the energy efficiency of industrial processes, while the remaining
quarter was due to structural shifts to less energy-intensive subsectors and to the
production of less energy-intensive products (Schipper and Meyers, 1992). This latter
effect reflects a process of dematerialization, in which less physical material isrequired to deliver a given function, an example of which is the increasing
miniaturization of semiconductors and related products (Ross et al., 1993) Declining
intensities in industrialized nations were spurred in part by the high fuel prices seen
during the oil crises of the 1970s. Increased competition between national industries
also appears to have spurred intensity improvements as a means of reducing costs(IEA, 1997c).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

63

These improvements are expected to continue in the future even without new policy
interventions (although at a slower rate than in the past). In the Reference scenario,
subsectoral energy intensity assumptions are again guided by the findings of theEnergy Innovations study (Energy Innovations, 1997). They decrease by an averagerate of 0.8% per year over the scenario period. The overall effect is that, by 2025,
OECD intensities have dropped to 80% of their 1995 values, while by 2050, they
have dropped to 64% of their 1995 values.
Beyond these declines, the adoption of cost-effective measures to reduce industrial
energy use remains far below its economic potential. Estimating the magnitude ofpotential savings is difficult, because of the heterogeneity of technologies, processes
and products, even within a particular subsector. Nevertheless, it is clear thatsignificant cost-effective savings are available even in modern, efficiently-run
industries (Elliot, 1994). Some generic areas in which energy can be saved include
the increased adoption of efficient combined heat and power systems, retrofitting of
efficient lighting and motors, improved maintenance of boilers, the “right-sizing” ofcompressed air and process heat delivery systems, and increased use of recycled
feedstocks. In the longer term, capital turnover is capable of bringing even more
dramatic reductions in energy intensities, as older, more energy-intensive processes
are replaced completely. For example, in the iron and steel sector, blast furnaces firedwith coke could be replaced with electric arc furnaces.
Overcoming market barriers will be key to achieving large reductions in industrial
energy intensities. In part this will require changing the structure of energy prices tobetter reflect their full social and environmental costs. It will also require a broad
package of policies such as incentives for investment in new energy efficientequipment; increased funding of research, development, demonstration and education
programs; changing tax structures to eliminate subsidies for the use of virginmaterials; and changing environmental regulations and electric market structures to
expedite the adoption of industrial combined heat and power.
The Policy Reform scenario reflects this type of concerted action. With assumptions
again guided by the findings of the Energy Innovations study (Energy Innovations,1997), energy intensities decrease by an average annual rate of 1.7% per year,
reaching 60% of their 1995 values by 2025 and 40% by 2050.
4. In the non-OECD regions, we apply the convergence algorithm (see Annex),
assuming subsectoral energy intensities approach the projected OECD average energy
intensity as GDP per capita approaches the current OECD average GDP per capita.
This treatment captures technological leapfrogging to some extent since developing
regions, rather than recapitulating OECD patterns, approach intensities which reflect
the better practices assumed for OECD regions in the future. Scenario intensity
assumptions are collected in Table 3-7.

64

EnergyTable 3-7. Industrial energy intensities (GJ/$ value added)
Chemical1995 2050-Ref
N America
4.2
2.7
W Europe
5.9
3.8
Pacific OECD
2.7
1.7
FSU
25.4
16.7
E Europe
18.8
8.6
Africa1.7
1.9
Latin America
4.2
3.2
Middle East
9.5
6.1
China+
13.6
7.3
S+SE Asia
5.8
4.6

Iron + Steel
2050-PR
1995 2050-Ref
1.6
20.2
13.0
2.3
30.4
19.5
1.0
33.7
21.7
7.2
61.2
45.0
3.8
60.1
33.5
1.8
14.5
15.3
1.9
22.5
19.3
4.3
0.8
10.3
5.0
21.6
20.0
3.5
17.3
18.0

Paper + Pulp
1995 2050-Ref
N America
6.3
4.0
W Europe
5.9
3.8
Pacific OECD
4.9
3.1
FSU
36.8
24.1
E Europe
14.2
7.6
Africa0.2
0.8
Latin America
5.8
4.3
Middle East
0.1
2.2
China+
11.7
7.1
S+SE Asia
2.8
3.4

Stone, Glass + Clay
2050-PR
1995 2050-Ref
2.4
8.5
5.5
2.3
13.4
8.6
1.9
11.8
7.6
10.3
9.7
9.1
3.9
27.7
14.9
1.1
2.5
3.4
2.5
7.3
7.6
1.8
0.6
4.4
4.9
25.2
14.9
2.7
16.0
12.3

Non-Ferr Metals
2050-PR
1995 2050-Ref
7.9
17.0
10.9
11.8
17.8
11.4
13.1
29.1
18.7
23.0
20.9
18.1
17.5
18.1
15.0
13.8
5.5
6.7
11.4
15.6
13.5
8.5
5.7
9.7
14.4
12.6
13.0
14.2
0.7
6.4

2050-PR
6.6
6.9
11.3
11.1
9.6
6.7
8.0
7.6
9.5
5.4

Other
2050-PR
3.3
5.2
4.6
6.0
7.6
3.5
4.7
3.6
10.3
9.5

1995 2050-Ref
6.4
4.1
3.2
2.1
1.7
1.1
32.1
20.7
6.8
4.3
13.1
11.5
5.0
3.3
12.3
7.4
4.8
3.7
5.5
4.4

2050-PR
2.5
1.2
0.7
8.7
2.4
9.0
1.8
5.1
2.6
3.4

5. Finally, we complete the characterization of industrial energy use by adjusting thecurrent mix of fuel shares. In the Reference scenario, the current mix is adjusted overtime, again guided by the aggregate regional demand analysis in the IPCC (1992)
mid-range scenario. In most regions, fuel shares are assumed to remain largely
unchanged. In the Policy Reform scenario, we again consider a transition to lesscarbon-intensive fuels, principally through greater penetration of electricity andnatural gas at the expense of coal and petroleum products. Increased use ofrenewables and modern biomass fuels is also assumed, particularly in the paper andpulp sector.
Global industrial sector energy consumption patterns are summarized by macro-region in
Figure 3-30. Global industrial energy demand increases from approximately 104 EJ in
1995 to 250 EJ by 2050 in the Reference scenario, and to 185 EJ in 2050 in the Policy
Reform scenario. Energy intensity improvements reduce overall growth in industrial
energy demand between the Reference and Policy Reform scenarios. As in other sectors,
the developing regions account for most of the growth in demand in the Reference
scenario due to their faster rates of growth and their low average base year intensities
compared to the OECD regions. In the Policy Reform scenario, industrial energy
consumption in OECD and transitional regions decreases between 1995 and 2050 asenergy intensity improvements outweigh the effect of growth in the sector.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

65

Figure 3-29. Global industrial energy demand by macro-region
250

Developing
Transitional
OECD

200

EJ

150
100
50
0
1995

2025

2050

2025
2050
Policy Reform

Reference

Industrial fuel shares are summarized in Figure 3-30. In the Reference scenario there is
gradual fuel switching away from coal and biomass, and toward electricity and naturalgas, reflecting ongoing trends in the OECD regions and a gradual convergence to OECD.
More dramatic fuel switching is seen in the Policy Reform scenario with increased sharesfor electricity and a large drop in the share for coal, both of which reflect the need toreduce the carbon emissions associated with industrial energy consumption.
Figure 3-30. Global industrial sector fuel shares
100%

Petroleum
Natural Gas

80%

Heat
60%

Electricity
Coal

40%

Biomass/Renewables

20%
0%
1995

2025
Reference

2050

2025
2050
Policy Reform

Subsector shares are summarized in Figure 3-30. With similar energy intensity
improvements assumed across all sectors, both scenarios show broadly similarsubsectoral shares. In both scenarios, the growth in importance of the less energy
intensive “other” sector reflects the gradual dematerialization seen in both scenarios.

66

EnergyFigure 3-31. Global industrial energy demand by subsector
100%

Chemical/Feedstocks
Chemical/Energy

80%

Iron + steel
60%

Non-ferr. metals
Paper + pulp

40%

Stone,glass,clay
Other/Non-Energy

20%

Other/Energy

0%
1995

2025
2050
Reference

2025

2050
Policy Reform

Note: Industrial figures include fuel used for non-energy purposes. This consists of fuels used as feedstocks in thechemical industry (primarily oil and natural gas) as well as oil used for non-energy purposes (e.g., lubricants),
accounted in the “other” sector. These non-energy use categories do not involve combustion of fossil fuels, but areincluded here because they do contribute towards total primary fossil fuel requirements.

Industrial sector energy demands are shown by region in Figure 3-32. In the Reference
scenario, energy consumption grows in most regions between 1995 and 2050. The
exceptions are Eastern and Western Europe and the Pacific OECD, where the effects ofincreases in industrial sector activity are outweighed by the effects of gradually
decreasing energy intensities. In most regions in the Policy Reform scenario, energy
consumption in 2050 is lower in the Policy Reform scenario than in the Reference
scenario. The only exception is Africa, where rapid growth in industry in the region
produces much higher total energy consumption than in the Reference scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

67

Figure 3-32. Regional industrial energy demand
60.0

Heat
Electricity

20.0

Coal
Biomass

0.0
1995 2025 2050

Latin America

60.02025 2050

South & Southeast Asia

50.0
40.0
EJ

EJ

Natural Gas

30.02025 2050

15.0
10.0

30.0
20.0

5.0

10.0

0.0

0.0
1995 2025 2050

2025 2050

1995 2025 2050

Eastern Europe

2025 2050

FSU

25.0
20.0
EJ

EJ

Petroleum

10.0

20.0

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

Renewables

40.0

1995 2025 2050

25.0

China+

50.0
EJ

EJ

Africa
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0

15.0
10.0
5.0

1995 2025 2050

2025 2050

Reference

Policy Reform

0.0
1995 2025 2050
Reference2025 2050
Policy Reform68

EnergyFigure 3-32. Regional industrial energy demand (continued)
35.0

North America

30.0
20.0

EJ

EJ

25.0
15.0
10.0
5.0
0.0
1995 2025 2050

7.02025 2050

6.0

EJ

EJ

5.0
3.0
2.0
1.0
0.0
1995 2025 2050
Reference2025 2050
Policy Reform

Renewables
Petroleum
Natural Gas
Heat
Electricity
Coal
Biomass

1995

Pacific OECD

4.0

Western Europe

18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

18.0
16.0
14.0
12.0
10.0
8.0
6.0
4.0
2.0
0.0

2025

2050

2025

2050

Middle East

1995 2025 2050
Reference2025 2050
Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

69

3.2.6 Transportation
Current Accounts
Regional transportation energy consumption is reported in Table 3-3 (page 33). Current
accounts are based on standard statistical compilations of transport activity levels andenergy use. Energy use is taken from IEA (1997a, b). Activity levels are synthesizedfrom a variety of sources including the International Road Federation (1996), Europa
Publications (1997), International Union of Railways (1994), International Civil Aviation
Organization (1995), Economic Commission for Europe (1995, 1996).
The analysis considers passenger and freight transport separately, since they are
determined by very different factors. Within each category, transport modes are
considered: road, rail, air and water (freight only) 18. To focus on the important case of
the private automobile, passenger road transport is further divided into private (i.e., cars
and taxis, light trucks, motorcycles) and public (i.e., buses) sub-modes of transportation.
Passenger and freight transportation energy demand is shown for each region on a percapita basis in Figure 3-33. North America, the highest-consuming region, consumes
more than 30 times more energy per capita for transportation than Africa and China+.
Furthermore, per capita transport energy consumption in North America is more than twotimes that in the other two OECD regions. Insight into this disparity can be gleaned fromexamining automobile travel more closely. Here we find both average distance traveled(Figure 3-34) and average fuel intensities (Figure 3-35), which are the inverse of
efficiency, to be significantly higher in North America than in other OECD regions. The
disparities can be attributed to a number of factors including annual distance traveled per
capita, the weight of vehicles, their load factor (the average number of vehicle occupants),
and the long-term decline in the U.S. of more energy-efficient mass transit systems.
These factors are in turn affected to varying degrees by variables such as average income
levels, gasoline prices and transport policies. For a deeper analysis of the issues affecting
transport energy use in OECD countries, see IEA (1997c).

18 The IEA lists energy use in pipeline as a consumption category.

In our analysis, energy use in pipelines (which is primarily used
for transporting oil and natural gas products) is not included as a freight transport mode. Instead it is accounted for in our supplyside analysis of the transmission and distribution of fuels.

70

EnergyFigure 3-33. Transport energy demand per capita by region - 1995
90

Freight
Passenger

GJ per capita .

80
70
60
50

Global Average

40
30
20
10

W. Europe

FSU
E.
EuropePacific OECD

E. Europe
S+SE
Asia

N. America

S+SE Asia
MiddleEast

Middle East

Latin America

China +

Africa-Figure -34
16,000

Average Annual Transport
Pass-km/Cap

14,00012,00010,0008,000

Global Average

6,000
4,000
2,000
AfricaChina +

Latin
America

FSU

N.
America

Pacific
OECD

W.
EuropeConventional Worlds: A Technical Description of Bending the Curve Scenarios

71

Figure 3-35. Automobile fuel economy - 1995

Average Fuel Economy
Liters/Veh-km

0.16

Global Average

0.14
0.10
0.08
0.04
0.02
AfricaChina +

MiddleAmerica

S+SE
Asia

FSU
EuropePacific
America

W.
EuropePatterns of transport energy use are more difficult to interpret in the developing andtransitional regions, due to the lack of any but the most basic information. In particular,
there are few reliable data describing vehicle fuel economy and vehicle load factors indeveloping countries. In most developing countries, poor road infrastructure and vehicle
maintenance practices, older vehicle designs and older vehicle vintages combine to force
up vehicle fuel use. However, smaller vehicle sizes, which are thought to be coupledwith higher average load factors, likely bring down energy use per pass-km. We assume
load factors of between 2 and 2.5 for developing countries compared to load factors of 1.5
for the USA), and 1.75 for W. Europe, Pacific OECD, Eastern Europe and FSU.19
The breakdown of energy use by mode is shown for passenger and freight transportationin Figure 3-36 and Figure 3-37, respectively. Road transport is clearly the dominantenergy-consuming mode for both passenger and freight transportation. It accounts formore than 60% of total passenger transportation energy consumption in all regions except
the FSU, Africa and China+. For freight transport, roads dominate in all regions except
FSU and South and Southeast Asia.

19 Load factor data based on country level data in International Road Federation Statistics (IRF, 1992) for USA, Western Europe andPacific OECD.

72

EnergyFigure 3-36. Passenger transport energy use: modal shares - 1995

% share of energy consumption

100%

Air
Rail
Road

80%
60%
40%
20%

World

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0%

Figure 3-37. Freight transport energy use: modal shares - 1995
% share of energy consumption

100%

Water
Air
Rail
Road

80%
60%
40%
20%

World

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0%

The figures for water transport include consumption for both inland water transportation,
which is small in all regions except China+, as well as estimates of energy consumption
for international transport of goods. Estimates of energy consumption in this latter
category are based on sales of “bunker fuel” in each country. Because water transport has
a higher efficiency on an energy/tonne-km basis than road and other modes, it accounts
for a higher share of physical transport than its share of energy consumed.
Figure 3-38 displays passenger and freight energy intensities by region. For passenger
transport, the charts show the energy-efficiency benefits of public mass transport modes(bus and rail) over private cars, and similarly for freight transport, they show the energyefficiency benefits of rail and water over road transport. Perhaps more surprisingly, air

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

73

transport intensities, while higher than for any other mode, are quite close to those forroad transport. This is due to the high load factors and long transportation distances that
apply in the air transport sector.
Figure 3-38. Transport energy intensities by mode - 1995
Passenger Intensities
6

Car
Bus
Rail
Air

MJ/Pass-KM

5
4
3
2
1
W. Europe

Pacific OECD

N. America

FSU

E. Europe

Freight Intensities

8
MJ/Tonne-KM

S+SE Asia

Middle East

Latin America

China +

Africa

0

Road
Rail
Water
Air

6
4
2

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0

The transportation sector is almost entirely dependent on petroleum products, which
account for 98% of global transportation fuel demand. The remainder is primarily due toelectricity and coal for rail transport (although coal use for railways is now very limited
and is important only in China+), and to alcohol fuels for road vehicles, especially in
Brazil.

74

EnergyPassenger Transport Scenarios
Passenger transport energy patterns are defined by future activity levels, trends in energy
intensities and fuel shares. For each region, we first specify the distance traveled per
capita. Then we disaggregate by applying the share of travel for each mode (road, rail
and air). Road transport is further split into two types of vehicles: private and public. To
these modal activity levels, we then apply energy intensities (MJ per passengerkilometer). Finally, we multiply by total population and specify fuel shares to calculateenergy consumption for passenger transport.
We start by exploring passenger transport requirements across all modes (road, rail andair). To shed light on average distance traveled per capita, we begin with national figuresand explore the relationship to average income. Available country level data aredisplayed in Figure 3-39, which plots total passenger-kilometers20 against GDP per
capita. The regression shown on the figure is used to project overall transport
requirements in both the Reference and Policy Reform scenarios.
Figure 3-39. Average income versus per capita passenger transport (all modes)
20,000
Passenger-km/capita

18,0001.016

y = 0.57x

16,0002

R = 0.85

14,00012,00010,000
8,000
6,000
4,000
2,000
-

5,000

10,00015,00020,00025,00030,000

Average Income ($PPP/capita)

While this simple cross-sectional analysis is sufficient for drafting broad sustainability
scenarios, it is worth questioning whether it will tend to overstate total passenger
transport requirements, especially over the longer term in OECD countries as incomesrise dramatically. With an income elasticity of 1.016, the projections foresee per capita
passenger transport requirements continuing to increase with average income growth. No
adjustments are made for saturation effects, since there is no evidence for impending
saturation in per capita transport requirements, even in the richest economies. We do notinclude the effects of factors such as better land-use planning and telecommuting, whichmight allow the same level of consumer benefits to be provided with reduced overall
20 The activity unit ofpassenger-kilometers is employed in distinction to vehicle-kilometers which refers to the average distancetraveled per vehicle. For automobile travel, the connection between these variables is the relationship: passenger-kilometers equals
vehicle-kilometers times load factor (average number of passengers per vehicle).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

75

transport requirements. The effect of these measures on per capita passenger transport
requirements is controversial and poorly understood.
Modal share patterns across countries also suggest a correlation with income. This is
illustrated for country-level data in Figure 3-40, which shows the relationship between theshare of total road passenger-kilometers traveled by private vehicles and GDP per capita,
and Figure 3-41, which shows the relationship between air travel and GDP per capita.21
As incomes grow, not only does total per capita travel grow, but so too do the shares oftravel by private car and by air. The regressions shown in Figure 3-40 and Figure 3-41
are used to project air and private road transport requirements in both the Reference andPolicy Reform scenarios.

Private Share of Road Transport
Pass-kmFigure 3-40. Average income and road transport in private vehicles
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%

y = 0.34Ln(x) - 2.49
2

R = 0.52-

5,000

10,00015,00020,00025,00030,00025,00030,000

Average Income ($PPP/capita)

Air Transport (pass-km/capita)

Figure 3-41. Average income and air transport
4,000
3,500
3,000

1.323

y = 0.0026x

2,500

2

R = 0.6835

2,000
1,500
1,000
500
-

5,000

10,00015,00020,000

Average Income ($PPP/capita)

21 Air travel is well documented, based on airline receipts.

Travel data by private and public vehicles are more spotty. In someregions, public transportation travel is only roughly monitored. Private transportation estimates are often calculated from fuel sales
data and an assumed fuel efficiency.

76

EnergyIn the Reference scenario, rail travel per capita is assumed to remain constant, which iscompatible with available historic data, and air travel per capita is assumed to vary with
GDP per capita based on the regression curve shown in Figure 3-41. Road transport
requirements are solved as the remainder. Finally, within road transport, the split
between private vehicles and public vehicles is projected using the regression analysis
shown in Figure 3-4022. In absolute terms, the Reference scenario requires a large
expansion in transport infrastructure, with total pass-km increasing by a factor of 4
globally from 18,900 billion pass-km in 1995 to 72,400 billion pass-km in 2050. In thedeveloping regions, transport requirements increase by a factor of 6.6 over the same time
period. Absolute increases in road transport requirements are even more dramatic. In one
region, China+, road transport requirements increase from 285 billion pass-km in 1995 to
3,270 billion pass-km in 2050, a factor of 11.5. The assumed stagnation in railtransportation leads to only small increases in rail transport from 1,800 billion pass-km in
1995 to 2,200 billion pass-km in 2050. On the other hand, passenger air transport is
expected to mushroom from 2,400 billion pass-km in 1995 to 13,400 billion pass-km in
2050.
The Policy Reform scenario makes alternative assumptions, compatible with the notion ofcreating a less resource-intensive and more environmentally acceptable transport system.
Specifically, the declining importance of public transport systems in the Reference
scenario is reversed or slowed in all regions to take advantage of the energy-efficiency
benefits of public transport compared to private vehicles. In the OECD regions, railways
increase their share of passenger transportation with future shares based on the current
maximum rail share in the OECD regions (23% in the Pacific OECD) and the current
share in each region. In the developing and transitional regions, railways are assumed tomaintain their existing shares, with the exception of China+ and the FSU where thecurrent very high share for railways (52% and 45%, respectively) declines, although at a
slower rate than in the Reference scenario. Similarly, a less precipitous decline in the
public vehicle share of passenger road transport is assumed. For example, in South andSoutheast Asia, the public vehicle share of road transport declines from 35% in 1995 to
15% in 2050 in the Policy Reform scenario, compared to a value of 5% in 2050 in the
Reference scenario (Figure 3-42).

22 Projections of private vehicle shares are assumed to reach a maximum at 95% of total road passenger-km, except in North

America, where the share of private vehicles is already 99% in the base year.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

77

Figure 3-42. Private vehicle shares of road transport
Private Vehicle
Share of Road Transport .

100%

1995
2050-Ref
2050-PR

75%
50%
25%

S+SE Asia

Middle East

Latin America

China+

AfricaFSU

E. Europe

Pacific OECD

W. Europe

N. America0%

The resulting passenger transport requirements are shown in Table 3-8 and Figure 3-43.
In absolute terms, Policy Reform scenario foresees similar global levels of passenger
transport requirements (69,900 billion pass-km in 2050 compared to 72,000 billion passkm in the Reference scenario in 2050). The developing regions share of this total
increases from 48% in the Reference scenario to 60% in the Policy Reform scenario, andtheir overall transport requirements increase by a factor of 7.9 between 1995 and 2050. In
spite of policies to promote rail transport, absolute increases in road transport
requirements are still dramatic. In China+, road transport requirements still increase by afactor of 8.3 between 1995 and 2050, while in Africa road transport requirements increaseby a factor of 11.5 over the same period. The assumed revival of rail transport in the
Policy Reform scenario leads to dramatic increase in total rail transport requirements —by a factor of 4.7 globally. Air transport requirements increase by a factor of 5 — similar
to the Reference scenario, although with more growth witnessed in the developing
regions.
Table 3-8. Passenger transport requirements (pass-km/capita)
N America
W Europe
Pacific OECD
E Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia

Road
Rail
Air
1995 2050-Ref 2050-PR 1995 2050-Ref 2050-PR 1995 2050-Ref 2050-PR
15,184 29,992 22,286
73
73
1,472 3,062 11,465
5,682
9,463 24,987 16,930
678
678
2,694 1,152
4,568
2,807
8,449 25,014 13,678 2,898
2,898
4,914 1,474
5,674
3,131
1,570
4,771
3,827
494
494
2,076
102
354
599
1,129
4,546
4,049 1,229
1,229
3,098
343
1,166
1,571
344
1,005
1,521
67
67
357
91
356
812
215
1,919
1,462
285
285
1,129
53
395
494
2,282
6,837
7,305
192
192
643
227
931
1,104
2,738
6,679
7,878
304
1,016
1,272
1,169
4,749
5,212
74
74
729
157
1,008
1,349

78

EnergyFigure 3-43. Global passenger transport requirements by macro-region

Billion Pass-km .

80,000Developing
TransitionalOECD

60,00040,00020,000
0
1995

2025

2050
Reference

2025
2050
Policy Reform

Figure 3-44. Global passenger transport requirements by mode

Billion Pass-km

80,000

Air
Rail
Road

60,00040,00020,000
0
1995

2025

2050
Reference

2025
2050
Policy Reform

Transport energy intensity assumptions for OECD countries are summarized in Table 3-9,
again guided by the recent Energy Innovations study (Energy Innovations, 1997), as wellas earlier studies such as Schipper and Meyers (1992). Improvements in the Policy
Reform scenarios assume the introduction of progressively stronger vehicle emissions
standards, pricing reforms, investments in research and development and a range ofmarket-based incentives to promote more efficient vehicles. In the longer term, theintensity projections assume the introduction of advanced vehicles and fuels, graduallyreplacing the petroleum-based internal combustion engine with technologies such as fuelcells and electric powered vehicles.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

79

Table 3-9. Passenger and freight transport intensities in OECD regions
Reference% Changeper year

2050/1995
Intensity Ratio

Policy Reform% Changeper year

2050/1995
Intensity Ratio

0.60%
0.45%
1.20%

0.72
0.78
0.51

1.80%
1.40%
1.80%

0.37
0.46
0.37

0.35%
0.45%
1.20%

0.82
0.78
0.51

1.00%
1.40%
1.80%

0.58
0.46
0.37

Passenger Transport
Road
Rail
Air
Freight Transport
Road
Rail/Water
Air

Notes: Annual % improvement and ratio of 2050 to 1995 values.

Passenger transport energy intensities for non-OECD regions are assumed to converge
towards the average values of the OECD region as average incomes increase, using theconvergence algorithm (see Annex). The overall pattern of convergence in the two
scenarios is shown in Table 3-10. In the Policy Reform scenario there is greater overall
convergence to a lower average intensity, in accordance with the exogenous assumptionsof improving equity and more rapid economic growth in the developing countries
compared with the Reference scenario.
Table 3-10. Passenger transport energy intensities (MJ/pass-km)
N. America
W. Europe
Pacific OECD
E. Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia

1995
2.8
1.7
1.8
1.6
2.5
2.0
1.6
2.7
2.7
1.6

Road
2050-Ref
2.0
1.2
1.3
1.6
2.1
1.9
1.6
1.8
2.1
1.6

2050-PR
1.0
0.6
0.7
1.0
1.2
1.6
1.1
0.9
1.4
1.2

1995
7.2
5.6
4.7
8.1
9.6
6.8
7.3
7.1
9.7
4.6

Air
2050-Ref
4.3
2.9
2.4
5.1
7.2
6.3
5.0
4.0
6.4
4.1

2050-PR
2.6
2.1
1.7
3.5
4.1
5.3
3.7
2.5
4.8
3.4

1995
3.2
3.2
1.7
2.1
2.9
2.3
1.6
3.1
2.3
2.3

Rail
2050-Ref
1.3
2.5
1.4
1.9
3.4
2.2
1.7
2.1
2.0
2.1

2050-PR
0.8
1.5
0.8
1.3
2.0
1.9
1.2
1.1
1.5
1.6

Finally, we introduce fuel shares for each mode of transport. Given the current prototype
status of alternative fueled vehicles it is hard to make predictions of future transport fuelshares. In the scenarios we assume a mix of non-conventional fuels and technologies are
introduced alongside conventional petroleum-based technologies. Scenario assumptions
are guided by a range of studies, including Energy Innovations (1997), EC (1996) and
Lazarus et al. (1993), which indicate the broad potential for non-conventional fuels to
replace conventional petroleum-based transport.
For road transport, the range of possible technologies include conventional internal
combustion engine vehicles based on alcohol fuels and natural gas, hybrid vehicles using
more than one drive train, electric vehicles, and advanced vehicles powered by fuel cells

80

Energyand using hydrogen. In the Reference scenario, we assume only very modest penetrationof alcohol and electric vehicles in the OECD countries (5% biofuels, 5% electricity, 5%natural gas by 2050). Hydrogen-based vehicles do not gain a significant market share
before 2050. The Policy Reform scenario sees more dramatic shifts in road transport fuelshares away from petroleum-based fuels towards lower carbon fuels (electricity, natural
gas and biomass-based ethanol). In the OECD regions, the fuel shares of natural gas,alcohol, hydrogen and electricity reach 10%, 25%, 10% and 10% respectively by 2050,
with the remaining 45% still met from petroleum fuels. In the Reference scenario, fuelshares in non-OECD regions converge slowly towards OECD patterns using theconvergence algorithm (see Annex). In the Policy Reform scenario, more growth of nonconventional transport fuels is assumed in non-OECD regions, although levels ofadoption of non-conventional fuels stay below the levels of adoption in OECD regions.
For rail transport the historical trend toward electrification persists in the Reference
scenario. This leads to an increase in electrification of the rail system of approximately
0.5% per year. More rapid electrification is assumed in the Policy Reform scenario, inline with the general spirit of the scenario, in which policies are assumed that encourage
fuel switching away from fossil fuels and towards electricity.
Air transport in the Reference scenario is assumed to remain 100% petroleum-based. In
the Policy Reform scenario, biofuels are assumed to gain a 25% market share by 2050.
Air transport is assumed to be an essentially global industry, with similar fuel shares inOECD and non-OECD regions.
Freight Transport Scenarios
Regional freight activity is the product of GDP and freight activity intensity (tonne-km
per dollar). This is disaggregated by applying mode shares for road, rail, air and water.
The resulting activities by mode are then multiplied by energy intensities (MJ per tonnekm). Finally, specifying fuel shares completes the energy picture for freight transport.
In both scenarios, freight activity values freight transport requirements per dollar are
assumed to decrease as income levels increase. This is weakly supported by the
regression shown in Figure 3-45, which plots average income against freight intensity
across all modes (road, rail, air and water). With an elasticity of -0.26, freight transport
requirements per dollar tend to decrease as income levels increase, probably reflecting theincreasing share of economic value added which derives from those services that do not
require transportation.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

81

Freight Transport (tonne-km/$GDP
PPP)

Figure 3-45. Average income versus freight intensity
0.80
0.70

-0.259y = 2.448x

0.60

2

R = 0.06

0.50
0.40
0.30
0.20
0.10
-

5,000

10,00015,00020,00025,00030,000

Average Income ($PPP/capita)

Total global freight transport requirements continue to increase as the global economy
expands in both scenarios, as shown in Table 3-11. In the Reference scenario, therequirements for road, rail and air transport increase by a factor of 2.2 from 11,400 billion
tonne-km in 1995 to 25,300 billion tonne-km in 2050. In the Policy Reform scenario,
they increase by a factor of 2.7, reaching 30,300 billion tonne-km in 2050. Water freighttransport projections and energy requirements are included in the transport scenario
analysis, but are not shown in Table 3-11 due to low confidence in water freight transport
data. There is significant uncertainty in the absolute levels shown in the table, primarily
for the developing and transitional regions. However, it is important to note that the
scenario analyses depend only on the assumed changes in driving values and intensities,
not their absolute values; so broad results are robust against uncertainties in current
values.
Table 3-11. Freight transport (billions of tonne-km)
N. America
W. Europe
Pacific OECD
E. Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia
World

Road
1995 2050-Ref 2050-PR
1,712
5,730
3,134
1,318
2,936
2,042
368
782
550
140
343
275
103
532
420
207
1,284
1,902
298
2,121
1,537
413
1,740
1,609
225
1,242
1,339
401
2,048
2,216
5,185
18,75715,023

Rail
1995 2050-Ref 2050-PR
2,320
2,320
4,246
267
256
1,301
88
87
131
133
133
262
1,612
1,613
2,646
21
21
195
1,319
1,315
4,535
267
266
1,040
113
115
623
6,139
6,125
14,978

Air
1995 2050-Ref 2050-PR
26
93
47
31
68
52
27
57
41
0
1
1
4
17
14
2
14
21
3
10
11
4
20
14
4
20
21
20
98
110
121
397
331

Turning to modal shares, during the past 20 years the OECD regions have experienced ashift in freight activity from rail to trucks (Schipper and Meyers, 1992). These trends
have been even more marked in several developing regions. The Reference scenario
assumes a continuation of these trends, so that the share of road freight continues to82

Energyincrease, while that of rail freight continues to decline. Specifically, it is assumed that
total rail tonne-km (not share) in each region remains constant in the future, whileabsolute growth in freight activity is allocated to the other modes in proportion to theircurrent shares. The Policy Reform scenario reverses this assumption, assuming a rangeof policies to encourage rail over road freight transport. These policies assist in reducing
the overall energy intensity and carbon emissions of the Policy Reform scenario comparedto the Reference scenario. Specifically, rail transport is assumed to maintain its current
modal share in all countries except China+ and FSU, where the rail share continues todecline, although at a much slower rate than in the Reference scenario. Other modal
shares are allocated as in the Reference scenario.
Energy intensity assumptions are similar to those described for the passenger transport
analysis. The overall pattern of convergence in the two scenarios is shown in Table 3-12.
Table 3-12. Transport freight intensities (MJ/tonne-km)
N. America
W. Europe
Pacific OECD
E. Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia

N. America
W. Europe
Pacific OECD
E. Europe
FSU
AfricaChina+
Latin America
Middle East
S+SE Asia

Road
1995 2050-Ref
4.4
3.6
3.0
2.4
4.8
3.9
3.3
3.3
5.0
4.3
4.3
4.2
4.5
3.7
5.3
3.6
5.2
4.1
4.5
3.9
Air
1995 2050-Ref
3.9
2.0
2.8
1.4
3.2
1.7
6.7
3.6
8.0
5.6
4.8
4.3
5.1
3.1
4.9
2.4
6.8
4.2
3.3
2.6

2050-PR
2.5
1.7
2.8
2.5
2.9
3.6
2.9
2.3
3.2
3.2
2050-PR
1.4
1.0
1.2
2.3
2.8
3.5
2.3
1.4
3.1
2.1

Rail
1995 2050-Ref
0.2
0.2
0.4
0.3
0.2
0.2
0.4
0.3
0.5
0.4
0.4
0.4
0.3
0.3
0.6
0.3
0.4
0.3
0.4
0.3
Water
1995 2050-Ref
0.3
0.2
0.3
0.2
0.3
0.2
0.4
0.3
0.4
0.3
0.4
0.4
0.3
0.3
0.4
0.3
0.4
0.3
0.4
0.3

2050-PR
0.1
0.2
0.1
0.2
0.2
0.3
0.2
0.2
0.2
0.3
2050-PR
0.1
0.1
0.1
0.2
0.2
0.3
0.2
0.1
0.2
0.3

Finally, we introduce fuel shares for each mode of freight transport. We assume the same
penetration of alternative fuels (natural gas, biomass-based alcohol, hydrogen, electricity)
as in the passenger transport analysis.
Energy demand for transportation in the scenarios is presented in Figure 3-46. In the
Reference scenario, Global demand increases from about 69 EJ in 1995 to about 204 EJ
in 2050. The fastest growth is in developing regions, which account for 51% of the total

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

83

in 2050. In the Policy Reform scenario, total demand is reduced dramatically to 121 EJ in
2050, and the developing regions share of this increases to 68%. In the Reference
scenario, petroleum products remain the dominant transportation fuel, accounting for
93% of total transportation energy in 2050, down from 98% in 1995. The Policy Reform
scenario sees a marked shift towards alternative fuels with the share of petroleum
products reduced to 61% by 2050.
Figure 3-46. Global transport energy demand by Fuel
Other
Petroleum

200

EJ

150

100

50

0
1995

2025
2050
Reference

2025
2050
Policy Reform

Notes: “Other” fuels include current usage of electricity and coal (mainly in trains), plus current use of alcohol fuels(principally in Brazil). In the future, the “other” category includes alternative, non-petroleum based energy sources
including hydrogen, natural gas, biomass-based alcohol and direct use of electricity.

Figure 3-47. Global transport energy demand by macro-region
Developing
Transitional
OECD

200

EJ

150

100

50

1995

2025

2050

Reference

2025

2050

Policy Reform84

EnergyFigure 3-48. Global transport energy demand mode shares
100%

Passenger-Road
Passenger-Rail
Passenger-Air
Freight-Water*
Freight-Road
Freight-RailFrieght-Air

80%
60%
40%
20%
0%
1995

2025

2050

Reference

2025

2050

Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

85

3.3 Energy Conversion
Energy conversion refers to the transformation of primary sources of energy into products
suitable for final use. Conversion accounts also include the transmission and distributionof final energy forms to end-uses. Major energy conversion sectors include electric
power generation, petroleum refining, alcohol production and district heating.
Current Accounts
Table 3-13 summarizes the global energy balance for 1995.23 Global final demand forenergy is approximately 272 EJ. In addition, 113 EJ of energy are consumed in the
production and distribution of final energy forms. These “conversion” losses are
dominated by electric power generation (73 EJ), and by transportation and distribution
losses and own-use (29 EJ).
Table 3-13. 1995 Global energy balance (EJ)PJ
Coal
PRODUCTION
95
NET IMPORTS
0
TPES
95
ELECTR GEN-51
DIST HEAT-6OIL REFINING
0
ALCOHOL PROD
0LOSSES & OWN USE
-4
TFC
34
HOUSEHOLDS
5SERVICES
1
TRANSPORTATION0INDUSTRY
27
AGRICULTURE
1

Biomass
41
0
41
-3
0
0
-1
0
37
28
0
0
8
0

Crude
141
0
141
0
0
-141
0
0
0
0
0
0
0
0

Oil N. Gas
0
73
0
0
0
73
-14
-19
-3
-5
134
0
0
0
-7
-8
110
41
10
13
4
5
68
0
23
22
5
0

Uranium
25
0
25
-25
0
0
0
0
0
0
0
0
0
0

Hydro Renew.
9
0
0
0
9
0
-9
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0

Elec
0
0
0
48
0
0
0
-9
39
11
8
0
18
1

Heat
0
0
0
0
13
0
0
-2
11
5
0
0
6
0

Note: numbers may not sum due to rounding. 0 indicates value less than 0.5 EJ.

23 Our current accounts are broadly consistent with those of the IEA (1997a, b).

However, a few important differences are worth
noting. First, unlike the IEA’s energy balance, our figure for global total final consumption (271.5 EJ) includes sales of bunker fuels(5.4 EJ) but excludes energy use in pipelines (1.6 EJ), which we have accounted for as usage under the transmission anddistribution category. Second, the IEA’s published figures for industrial consumption of coal and oil include fuels used by autoproducers of electricity and district heat. By including this consumption as an energy demand, the IEA’s energy balance tends tooverestimate the efficiencies of thermal electric generation plants and district heat plants. For example, the IEA data suggests
average oil-fired plant efficiencies of 40% worldwide, with values as high as 50% for the U.S. and 70% for Pacific OECD. In our
analysis, we have adjusted total industrial oil and coal consumption and used lower fossil electric generation efficiencies (using
efficiency assumptions drawn from Lazarus et al., 1993 and other sources). Third, we have simplified our analysis in comparison to
the IEA statistics. In particular, there is no representation of the sectors labeled Liquefaction, Gas Works or Non-specified (which
are subsumed into losses and own-use). Similarly, we have not attempted to represent stock changes, transfers or statisticaldifferences (which in the IEA data serve to reduce primary energy supply requirements by approximately. 2.9 EJ). In our analysis,
these are subsumed into overall production values. In scenarios, it is reasonable to assume that these average out to zero over the
long-term. Finally, we use a slightly different accounting approach for electricity generation. In a similar fashion to the IEA statistics,
we express hydro power as its electric equivalent (using an efficiency of 100%) and nuclear power as its thermal equivalent (using
an efficiency of 33%). However, unlike the IEA, we combine geothermal, solar and other non-biomass renewables into a single
category and express their primary energy supply as an electric equivalent (using an efficiency of 100%). In contrast, IEA uses a
10% efficiency for geothermal.
Overall, our calculated primary energy supply of 384 EJ is very close to the IEA figure of 380 EJ (9.1 billion TOE), the difference
being primarily due to reclassification of bunker fuels (5.4 EJ) and the elimination of statistical differences. Note though that when
examining IEA statistics, world totals currently exclude biomass consumption in developing regions, due to the unreliability of thatfigure (33 EJ).

TOTAL
384
0
384
-73
-2
-7
-1
-30
272
72
19
69
104
8

86

EnergyRegional breakdowns of electricity generation by feedstock fuel are shown in Figure 349. Coal, oil, hydro, nuclear and, to an increasing extent, natural gas-fired power plants
dominate the picture. Recent decades have seen a virtual halt in the expansion of nuclear
power in OECD countries — a technology that grew rapidly in the decades of the 1950s
to 1970s. This has been due to concerns over siting, waste disposal, cost and the wider
issue of nuclear proliferation. Nevertheless, it seems likely that nuclear power will form
an important part of the plans of a number of developing regions to expand theirgeneration of electricity.
It now appears that the era of centralized, public planning of electricity supply in OECD
regions is giving way to a far more deregulated environment. The impact that thesechanges will have on the electric mix is not yet clear, although the evidence from early
adoptees of this approach, such as the UK, is that plants with low capital costs, such as
natural gas, will receive a significant boost. Generation from renewable energy forms
such as wind, geothermal, biomass, municipal solid waste, and solar technologies remains
a small fraction of total generation.
Figure 3-49. Electricity generation by region (1995)
Renewables
Hydro
NuclearNatural Gas
Petroleum
Coal

4,000

TWh

3,000

2,000

1,000

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa-In addition to electricity generation, the current accounts data also include otherconversions: the conversion of crude oil to petroleum products in oil refineries, thecombustion of coal, oil and natural gas in district heating plants, the conversion ofsugarcane to ethanol in alcohol plants, and losses and own-use consumption of various
fuels during the conversion and transportation of fuels.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

87

Scenarios
Global electricity requirements are shown in Figure 3-50. For each time period, these arecalculated by adding total electricity demands across all final demand sectors to an
estimate of electricity losses and own-use. Losses and own-use of electricity and otherfuels are expected to converge towards current OECD practices in the Reference scenario.
The Policy Reform scenario assumes concerted action to reduce losses in all regions,particularly in the developing and transitional regions.
Figure 3-50. Global electricity requirements
50,000Developing
Transitional

40,000OECD
TWh30,00020,00010,000
1995

2025
2050
Reference

2025

2050

Policy Reform

Globally, in the Reference scenario, annual electricity demand expands rapidly from 13.2
PWh (47.6 EJ) in 1995 to 41.7 PWh (150.2 EJ) in 2050. Global demand in the Policy
Reform scenario is higher, reaching 45.1 TWh (162.5 EJ) in 2050. In future years in the
Policy Reform scenario, the combined effect of changing economic activities, demandside management, and adjustments in the final fuel mix, lead to modest reductions inelectricity requirements in the OECD and transitional regions compared to the Reference
scenario. However, the demand for electricity increases rapidly in the developing
regions, where rising personal incomes and industrialization are associated withincreasing use of electricity in both scenarios. The growth in demand for electricity indeveloping country regions is particularly pronounced in the Policy Reform scenario. In
spite of aggressive efficiency improvements, larger GDP growth relative to the Reference
scenario and energy policies that guide final consumption away from the use of the
carbon-intensive fuels, and towards electricity and natural gas, increase electricity
demands.
Electricity Generation Fuel Shares in the Reference scenario.
As an indigenous and renewable energy resource, hydropower offers the benefits ofreducing fuel import dependency and air pollution relative to fossil fuel-fired generating
options. These benefits are conditional, however. The renewability of hydropower is

88Energyonly approximate, since reservoir siltification gradually erodes the resource potential, andthe contribution of hydropower to self-reliant development is compromised insofar as thefinancing and construction of these capital-intensive facilities depend on external hardcurrency loans and foreign contractors. Beyond issues of engineering feasibility andcost-competitiveness, the construction of large-scale hydroelectric facilities cansignificantly disrupt local communities and ecosystems. The environmental and social
impacts of hydroelectric plants include disturbance of the spawning grounds of migratory
fish; flooding of natural habitats, farmlands, and communities; displacement of
populations; alteration of local hydrological patterns; and risks of catastrophic flooding
from dam failure. In recent years, sensitivity to the environmental and social
repercussions of large-area flooding for reservoirs has increased. Political and social
opposition has challenged new construction, while international donors have begun totake a tougher look at the full costs of hydropower development (Lenssen, 1992). With
many of the best sites already exploited, it seems likely that opposition will increase as
facilities are proposed that carry increased costs — economic, environmental and social.
Meanwhile, the expansion of settled areas and farmland as populations and economies
grow will encroach on potential hydroelectric sites and raise the stakes and possibility for
conflict. These frictions will delimit the expansion of hydroelectric resources to well
below the ultimate potential. In this regard, smaller scale hydro facilities may be an
attractive alternative to large-scale projects although there is some indication thatenvironmental impact per unit of generation does not diminish with decreasing size
(Gleick, 1992).
Hydropower development in the Reference scenario is based on national estimates of
economically exploitable hydropower capability (WEC Survey of Energy Resources,
1995). Following the approach of Lazarus et al. (1993), a reduction factor of 35% isapplied to these figures to reflect growing public opposition and at least partial avoidanceof the more environmentally and socially detrimental hydro projects. Achieving theselevels of development will likely require greater sensitivity by development authorities toenvironmental and social costs in project evaluation, and more active involvement byaffected communities in the siting process.
For other types of electricity generation, Reference scenario generation shares are basedon a review of a variety of scenarios including IPCC (1992), European Commission(1997), and WEC/IIASA (1995). For North America, shares are based on two sources:
for the near term, Annual Energy Outlook (DOE/EIA, 1996) and for the longer term,
Energy Innovations (1997).
Electricity Generation Fuel Shares in the Policy Reform scenario.
In the Policy Reform scenario, future electricity generation shares for 2025 and 2050 aregoverned by a number of assumptions and constraining factors, and the overallrequirement to substantially reduce greenhouse gas emissions relative to the Reference
scenario:Conventional Worlds: A Technical Description of Bending the Curve Scenarios

89a) First, hydropower development is constrained to be no higher than in the Reference

scenario (for the same reasons as discussed above). This tends to increase the future
share of hydro in the Policy Reform scenario in OECD and transitional regions, but todecrease its share in most of the developing countries where overall electricity
demand substantially increases.
b) Second, nuclear power is assumed to be completely phased out by the year 2050 in all

regions, for reasons discussed in BTC related to cost, safety, radioactive waste
disposal and security issues. Reference scenario trends are assumed to continue atfirst, with new nuclear plants being added until the year 2010. Thereafter, no new
nuclear plants are assumed to be added. For a more detailed review of issues related
to the development of nuclear power, see Raskin and Margolis (1995).
c) Third, the market penetration of biomass and other non-hydro renewable energy

forms (solar, wind, municipal solid waste, geothermal, wave and tidal) is assumed toincrease dramatically. By 2050, their share of global electricity generation reaches
35%, compared to 16% in the same year in the Reference scenario, and up from thecurrent fraction of about 1.4%. Assumptions are guided by a review of a number ofglobal and regional scenarios, including Energy Innovations (1997), European
Commission (1997), WEC/IIASA (1995), Lazarus et al. (1993) and Johansson et al.
(1993). In all regions, the penetration of intermittent (non-biomass) renewables in
2050 is kept below 35%, which is well within what is considered feasible, even
without considering the power storage or exchange with other systems (Grubb, 1991)d) Finally, electric generation shares for the three major fossil fuels (coal, oil and naturalgas) are developed. Future fuel shares reflect the increasing scarcity of oil over the
study period; the availability of fossil resources in each region; a continuation of thecurrent trend away from coal-fired and oil-fired electricity generation and towards
natural gas-fired thermal plants; and the overall requirement to constrain CO2emissions in each region to within the target regional carbon budgets discussed in
Section 1.1.2.
Figure 3-51 shows the resulting regional electric generation fuel shares for each scenario
for 1995 and 2050. Figure 3-52 shows the breakdown of electricity generation in each of
the three macro-regions. In the Reference scenario, generation expansion is expected for
all feedstock fuels. The most rapid expansion is expected for renewable and natural gas
plants, for which generation expands at an annual average growth rate of 2.8% and 6.8%
respectively (the latter from a very low base in 1995). The generation from oil-fired
plants almost doubles, even though the share of total electricity generated from oil
declines from 6% in 1995 to 2% in 2050.

W Europe
World

World

0%

W Europe

20%

Pacific
OECD

40%

Pacific
OECD

60%

N America

80%

N America

2050-Policy Reform
FSU

100%

FSU

E Europe

S+SE Asia

Middle East

Latin
America

China+

AfricaWorld

W Europe

Pacific
OECD

N America

FSU

E Europe

S+SE Asia

Middle East

Latin
America

China+

Africa100%

E Europe

S+SE Asia

Middle East

Latin
America

China+

Africa90
EnergyFigure 3-51. Regional electricity generation shares
1995

80%

60%

40%

Renewable
Hydro
NuclearNatural Gas
Petroleum
Coal

20%

0%

100%

2050-Reference

80%

60%

40%

20%

0%

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

91

Figure 3-52. Electricity generation by macro-regionOECD
14,000

Renewables
Hydropower
NuclearNatural Gas
Oil
Coal

12,000TWh

10,000
8,000
6,000
4,000
2,000
0
1995

2025

2050

2025

Reference

2050

Policy Reform

Transitional
3,500
3,000

TWh

2,500
2,000
1,500
1,000
500
0
1995

2025

2050

2025

Reference

2050
Policy Reform

Developing
40,000TWh

30,00020,00010,000
0
1995

2025

Reference

2050

2025

2050

Policy ReformIn the Policy Reform scenario, nuclear energy is phased out completely by 2050, while oiland coal both lose market share. By 2050, the global electric generation system comes tobe dominated by advanced natural gas, biomass, hydropower, and renewable energy
technologies (solar, wind, geothermal, municipal solid waste, etc.) In absolute terms,
electric generation from coal declines slowly at a rate of 0.1%/year while oil-fired plants
decline more rapidly at a rate of 0.5%/year. This places the burden of growth inelectricity generation onto natural gas, biomass and renewables. Natural gas generation

92

Energyincreases at an annual average growth rate of 4.1%, while for renewables a sustained
growth rate of 8.5%/year is required. Although very high, it should be noted that thislatter figure is based upon a very low starting point.24
In 2050, the total annual requirements for non-biomass renewable electricity reaches
15,872 TWh. This figure, which most likely would be met by a combination of solar,
wind, geothermal and other renewable energy forms, is still far less than a recent estimateof the global potential for wind power of 53,000 TWh (Grubb and Meyer, 1993).
Trends in power plant efficiencies are summarized in Figure 3-53. In the Reference
scenario, the average efficiency of plants in all regions reaches values typical of current
prototype plants.25 In the OECD regions, these efficiencies are reached by 2025 asexisting plants are retired and new plants phased in. In other regions, which are lagging
in these technologies, it is assumed that these efficiencies are reached by 2050. The
Policy Reform scenario assumes a concerted drive to develop and deploy more advanced
electric generation technologies, with average electric efficiencies reaching between 55
and 60% in all regions by 2050. Existing technologies, such as combined cycle thermal
plants and integrated coal and biomass gasification are capable of meeting the goals set
for the period to 2025 in the Policy Reform scenario, while in the longer term, fuel cell
technologies can provide even greater improvements by overcoming the inherent
thermodynamic limitations of thermal power generation. A similar effect would arise
from the greatly expanded use of conventional technologies implemented as combined
heat and power systems. Such systems are already capable of combined electric and heatefficiencies in excess of 60%. However, this assumes that district heating is capable ofbeing developed much more widely than its current status, a possibility not explicitly
modeled in this study.

24 It may also be worth noting that in California, during the first ‘boom’ period for wind power between 1982 and 1986, annual

generation from wind power was increased from 6 GWh to 1200 GWh — an annual average growth rate of 276% (Golob and Brus,
1993).
25 In the Reference scenario, we use target efficiencies of 42% for coal, 45% for oil, 48% for natural gas, and 33% for biomass.

In
the Policy Reform scenario we use target efficiencies of 60% for coal, oil and natural gas and 55% for biomass, based on a range ofsources including Johansson et al. (1993), and Lazarus et al. (1993).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

93

Figure 3-53. Average electric power plant efficiencies
60%

1995
2050 Reference
2050 Policy Reform

50%
40%
30%
20%
10%
0%
Biomass

Coal

Natural Gas

Petroleum

For district heating, the Reference scenario assumes the continued dominance of fossil
fuels, with little change in overall fuel mixes and efficiencies. The Policy Reform
scenario assumes efficiency improvements and gradual fuel switching away from coaland oil towards natural gas and biomass, in accordance with the overall decarbonization
trends of the scenario.

94

Energy3.4 Energy Supply
Primary supplies include the production of natural resources in a given region (fossil
fuels, uranium, hydropower, biomass, geothermal, wind and solar), the trade in energy
commodities and net changes in stocks. Primary supply in a given region is equal to
primary production plus net imports (defined as imports minus exports) plus stockchanges. As such, it is a measure of the total primary energy forms needed for activities ineach region (and imports of final energy forms such as electricity). The energy systems ofeach region are linked through trade in fuel commodities and the interconnection of
distribution systems.
Primary production is derived from both non-renewable natural resources (e.g., oil andgas extraction, coal mining, uranium mining) and renewable or potentially renewable
resources (e.g., direct solar, wind, hydropower, biomass). Summing over regions, net
imports in principle add to zero (since each unit of export from one region corresponds toan import in another). 26
Current Accounts
Current global primary energy use is about 384 EJ per year. The breakdown by type ofenergy source and region is summarized in Figure 3-54. The energy mix is dominated byfossil fuels which account for 80% of global primary energy supplies. Fossil fuels include
crude oil (36%), coal (25%) and natural gas (19%), which along with uranium (7%) andhydropower (2%) comprise the major modern energy sources.27 The other major primaryenergy source is biomass, which contributes 11% of global energy requirements. Biomasssources are dominated by the so-called traditional energy sources used in developing
countries, such as wood for cooking, wood for charcoal kilns, dung and agricultural
waste. Biomass statistics are quite uncertain, since the trade in these sources lies largely
outside formal markets, where more reliable transaction statistics are available.

26 In practice, they do not precisely match due to statistical and reporting errors.

Care should be taken in interpreting primary energy supply figures since different statistical sources often use different
conventions for reporting the primary energy content of nuclear and renewable sources of energy. The conventions for efficiency
adopted in this study are: uranium at 33%, geothermal at 10%, and hydro, wind, solar and other renewables at 100%. Other reports
use the so-called “substitution” method, which express hydro resources in terms of an equivalent fossil fuel feedstock requirement.
27

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

95

Figure 3-54. Global primary energy requirements by region and fuel - 1995

Natural Gas
19%

Hydropower
2%

Uranium
7%

Biomass /Renewables
11%

Coal
25%
Crude Oil
36%

There are substantial differences across regions in the mix of primary energy sources, as
shown in Figure 3-55. For example, coal provides the largest share of primary energy inEastern Europe and China+, while crude oil is dominant in Latin America and the Middle
East. It is also worth underscoring the significant role played by biomass in Africa, Latin
America, South and Southeast Asia and China+, accounting for between 18% and 46% of
total primary energy.
Figure 3-55. Primary energy supply shares by region - 1995
100%

Renewables
Biomass
Uranium
Hydropower
Natural Gas
Crude Oil
Coal

80%

60%

40%

20%

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China +

Africa0%

Notes: excludes electricity imports and exports

Since energy supplies are derived from either indigenous resources or from imports fromother regions, the natural resource endowment is a critical factor defining a region’senergy circumstance. Energy-dependent countries must devote foreign exchange earnings
to cover essential energy imports for development and are vulnerable to the price96

Energyfluctuations and interruptions in international energy markets. By contrast, those areas
with substantial energy resources, such as the Middle East, command considerable
geopolitical influence, but as exporters are also vulnerable to price fluctuations in the
global market for oil.
Figure 3-56 shows net exports by region for the three major types of fossil fuels: oil, coaland natural gas. To gauge the degree of energy independence, we review the share of
regional primary energy requirements currently derived from imports. The statistics aregathered in Table 3-14 for total fossil supplies and for each fuel expressed as the ratio of
net exports (or imports) to primary energy supply of each fuel. A value greater than zero
implies that the region is self-sufficient. One notes the high dependence of the OECD
region on oil imports.
Figure 3-56. Net primary energy exports by region
Coal
Nat GasCrude OilW. Europe
Pacific OECD
N. America
FSU
E. Europe
S+SE Asia
Middle East
Latin America
China +
Africa

-20

-10

0

10

20

EJ
Note: imports shown as negative values.

Table 3-14. Net exports as percent of primary energy supply
AfricaChina +
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

Coal
52%
2%
13%
-80%
-19%
15%
-1%
14%
9%
-39%

Crude Oil
144%
9%
50%
244%
-51%
-93%
33%
-42%
-97%
-53%

Nat Gas
101%
-2%
-2%
6%
30%
-59%
21%
1%
-56%
-33%

All
Fossil Fuels
57%
2%
27%
176%
-18%
-25%
18%
-14%
-50%
-36%

30

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

97

Trade patterns correspond directly to the availability of exploitable reserves. Mineral
statistics are generally organized into the categories of resources, which are estimates ofall physical deposits, and reserves, that proportion of resources which is considered
economically exploitable. The latter is in turn composed of proven reserves, which canbe commercially exploited under today’s technological and economic conditions, andestimated additional reserves recoverable, which are estimated to be recoverable (withreasonable certainty) given current geological and engineering information. Fossil fueland nuclear reserves, including both proven and additional, are gathered in Table 3-15.
Table 3-15. Proven and additional reserves (EJ)
Crude

NaturalUranium @

Coal
Oil
Gas
< $130/kg
Africa
1,854
491
512
384
China+
7,958
1,606
142
2
Latin America
609
2,371
705
59
Middle East
8
3,899
2,793
South+South East Asia
4,543
140
442
19
E Europe
2,765
12
61
7
FSU
7,230
336
3,173
North America
7,475
264
726
268
Pacific OECD
15,7481757
311
W Europe
3,147
143
289
53
World
51,336
9,279
8,900
1,102
Developing
14,971
8,507
4,593
463
Transitional
9,995
348
3,234
7
OECD
26,370
424
1,072
632
Source: Survey of Energy Resources (WEC, 1995 and WEC, 1992). Data for
uranium are converted from mass to energy units assuming 1 tonne of uranium =336 TJ, a figure appropriate for conventional open-cycle reactors. If fast-breeder
reactors are assumed, the resource estimate increases by a factor of about 60.

A crude measure of the sufficiency of global energy reserves is obtained by dividing
world reserves by the current annual consumption — about 141 EJ for crude oil, 73 EJ
for natural gas, 91 EJ for coal and 25 EJ for uranium. If current patterns were tocontinue, the reserves reported in Table 3-15 would satisfy many decades of requirementsfor oil, natural gas and uranium, and several centuries of coal use. However, growing
energy demands, environmental pressures and geopolitical concerns would constrain theabundance of these resources substantially, as we shall discuss in the next section.
Scenarios
Global primary energy requirements by macro-region are shown in Figure 3-57. They arealso summarized by region and by source in sheets En1-1 and En-2 of the BTC Scenario
Highlights, respectively. In the Reference scenario they grow at an annual average rate of
1.6% per year from 384 EJ in 1995 to over 900 EJ in 2050. A particularly rapid expansion
is seen in the developing regions, where primary energy use quadruples from 150 EJ in
1995 to 591 EJ in 2050 — reaching more than half of the global total. By 2050, China+
overtakes the U.S. to become the single largest user of primary energy. Fossil fuelscontinue to dominate, accounting for 82% of global primary energy supply in 2050

98

Energycompared to 80% in 1990. Nevertheless, over the same period these is significant
absolute expansion in all fuels, particularly biomass and renewable energy forms.
Figure 3-57. Global primary energy requirements by macro-region
By Macro-region
1,000

Developing
Transitional

800

OECD
EJ

600
400
200
0
1995

2025
Reference

2050

2025
2050
Policy Reform

The annual average rate of growth in global primary energy requirements is halved to
0.8% per year in the Policy Reform scenario, with global primary energy requirements
reaching 599 EJ in 2050. Fossil fuels continue to dominate, although their share falls to
72% of global primary energy supply in 2050. Primary energy requirements fall in theOECD and transitional regions to 58% and 62% respectively of their 1995 values, while
demand in the developing regions triples, reaching 460 EJ — 77% of the total.
By
2050, the primary energy use of the South and Southeast Asia region alone is more than
the total of all OECD and transitional region countries. The changing mix of primaryenergy requirements is also noteworthy. Total requirements for coal decrease by about
one third — reflecting the need to find less carbon-intensive fuels to help meetgreenhouse gas mitigation targets. Because of its dominance in the transport sector,
reducing oil consumption proves more difficult, so that in spite of initiatives to reduce its
use in other sectors, overall oil consumption actually increases slightly, growing at anannual average growth rate of 0.1% per year. Natural gas and renewable energy forms
grow rapidly; however, because of the lower total primary requirements, absolute natural
gas requirements in 2050 are only 6% higher in the Policy Reform scenario than in the
Reference scenario (215 EJ compared to 203 EJ respectively).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

99

3.5 Energy Resources
BTC reviews the long-term adequacy of fossil fuel resources in the two scenarios. Thissection expands upon that discussion.
While additions to proven reserves of crude oil continue to keep pace with increases indemand, the long-term outlook for conventional crude oil resources is less comforting.
Estimates of ultimately recoverable reserves — the total of previously extracted, provenand undiscovered reserves recoverable — have not increased greatly since the 1960s(Masters et al., 1990). Rather, estimated additional reserves have been gradually
reclassified as proven reserves.
Remaining proven and estimated undiscovered oil reserves are approximately 1600
billion barrels (WEC, 1995; Masters et al., 1994), of which 1000 billion barrels is proven.
As the demand for oil grows in both scenarios, these reserves would be depleted around2035 (Figure 3-58). However, according to standard theories of oil exploitation,
production decreases may occur much sooner, perhaps as early as 2010 when half of the
global resource will have been extracted. These theories were first exploited in 1956 by
M. King Hubbert to correctly predict that oil production from the lower 48 American
states would peak around 1969 (Hubbert, 1956). According to the theory, adding theoutput of fields in a large region usually produces a bell-shaped curve that predicts crude
oil production over time. While this simple production-based model does not takeeconomic or political factors affecting consumption into account, its empirical approach
does embody long-run technical improvements in extraction technologies (Mackenzie,
1996). Table 3-16 shows a range of estimates of ultimate crude oil resources and, using asimple linear interpolation of scenario crude oil consumption patterns, calculates theapproximate date at which half the ultimate resource base will have been consumed in thetwo scenarios.
Table 3-16. Date at which cumulative oil consumption equals half ultimate resource
Cumulative Production Utlimate
to date - 1995 Resource
GBO
GBO
Reference Reserves
770
2,363
Low Reserves
770
1,770
High Reserves
770
2,890

Aproximate date at which cumulative
consumption = half of resource
Reference
Policy Reform
2009
2011
1999
1999
2019
2021

Sources: Cumulative production based on Masters et al. (1994). Reference reserves from WEC (1995) and Masters etal. (1994), Low reserves from Campell and Laherre (1998), High reserves from WEC/IIASA (1995).

The results are remarkably insensitive to the differences in consumption between the two
scenarios. Even with the most optimistic combination of the highest reserve estimate andthe lower consumption levels seen in the Policy Reform scenario, the half-way point(beyond which rates of production could begin to fall) is reached by about 2021. It is
worth noting that in the Reference scenario, annual crude oil requirements continue togrow enormously over the whole study period — more than doubling by 2050. Even in

100

Energythe Policy Reform scenario, annual requirements continue to grow at a rate of almost 1%
per year until 2025 before declining thereafter. Even so, the rate of consumption in 2050
remains almost 10% higher than in 1995 — an outcome that does not appear plausible if
the Hubbert theory proves correct, unless massive levels of unconventional oil arebrought into production (see note 2 in Figure 3-58).
In practice, the gap between oil supply and demand is likely to be closed in a number ofways: supply might be increased by new discoveries of conventional sources, though thisis not likely, or by unconventional options such as tar sands and oil shale (see notes to
Figure 3-58). However, exploitation of these unconventional resources is likely to posesevere environmental problems and to be much more costly than conventional crude. On
the demand side, the pressure on oil resources could be curtailed through fuel switchingand efficiency improvements — options taken up in the Policy Reform scenario.
Similarly, renewable and biomass-based substitutes for oil (e.g., hydrogen and ethanol)
may gain acceptance more quickly than is assumed in either the Reference or Policy
Reform scenario. The evolution of the transport sector will be especially important in thisregard. Overall though, in the Reference scenario, the heightened risk of economic
vulnerability and international conflict challenges the continuity and growth assumptionsof the scenario.
To a lesser extent, natural gas requirements in the scenario also push against conventional
resource constraints, with the Policy Reform scenario actually requiring higher
consumption than the Reference scenario. Global proven and undiscovered natural gas
reserves are estimated at about 10-15 EJ (WEC, 1995; Masters et al. 1994) — roughly
300-400 trillion cubic meters. In both scenarios, proven global reserves are depleted
around 2035, while remaining undiscovered resources are between 60% and 80%
exhausted by 2050 in the Reference and Policy Reform scenarios, respectively. Coal
reserves remain abundant despite the increasing demands in the Reference scenario.
Environmental concerns further restrict the use of coal in the Policy Reform scenario.
Beyond global supply and demand relationships, the geopolitics of oil seems set tobecome a resurgent theme in the Reference scenario as industrialized regions increasingly
depend on imports from the Middle East and Latin America (Swart, 1996). As shown in
Figure 3-59, remaining oil reserves are heavily concentrated in the Middle East (42%),
Latin America (26%) and China+ (17%).28 The rest of the world, including OECD
regions, accounts for only 15% of proven and additional oil reserves. Natural gas
reserves are heavily concentrated in the FSU (36%), the Middle East (31%), Latin
America (8%) and North America (8%).

28 Again, these figures reflect both proven and undiscovered reserves. Considering proven reserves alone, the Middle East sharerises to 64%.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

101

Figure 3-58. Global fossil and uranium reserves compared to requirements
Conventional Crude Oil

10^12 cubic meters

300

2,000
billion barrels

ProvenConventional Natural Gas

2,500

1,500
1,000
500

Proven +Undiscovered

200
Policy Reform
Cumulative
Consum ption
Reference
Cumulative
Consum ption

100

-

0
1995

2025

2050

1995

Uranium

2025

2050

Coal

7,000

2,000

5,000

billion tonnes

thousand tonnes

6,000

4,000
3,000
2,000

1,500
1,000
500

1,000
0

0
1995

2025

2050

1995

2025

2050

Notes:
1. Our estimate of crude oil reserves are based on WEC (1995) and Masters et al. (1994). We estimate provenreserves at 1,010 GBO with an additional 580 GBO of undiscovered reserves. A recent critique of industry
figures (Campbell and Laherre, 1998) suggests that total unrecovered reserves may be much lower, due primarily
to inflated estimates of current reserves based on P90 (90% probability) assessments, which by definition are 90%
likely to be exceeded. That study asserts that much of the apparent recent increase in global oil reserves is
actually due to reclassification of existing oil reserves, as early P90 estimates are upwardly revised.
They
estimate that remaining global proven reserves at the end of 1996 are as low as 850 GBO, and that undiscovered
reserves account for only an additional 150 GBO. Other studies have used higher estimates of total unrecovered
reserves (e.g., WEC/IIASA, 1995 uses an estimate of about 2,120 GBO), but these are based on P05% estimates,
which by definition are only 5% likely to be met or exceeded. Advances in crude oil recovery technologies willprobably help extend crude oil reserves, although Campbell and Laherre (1998) assert that oil companies routinely
count on technological progress when computing reserve estimates
2.

Estimates of conventional fossil reserves do not include non-conventional sources of oil and natural gas. Reservesof these fuels are poorly known but are likely to be enormous. They include, for example, an estimated 1,200
GBOE of heavy oil in the Orinoco oil belt in Venezuela, and 300 GBO of tar sands and oil shale mainly in Canada
and the FSU. Exploitation of these resources, which will require large-scale strip-mining, is likely to pose severe
environmental problems and to be much more costly than the exploitation of conventional crude oil. Natural gas
hydrates (icy substances composed of methane and water found in sediments below the oceans and in cold regions
such as Alaska, Canada and Siberia) are also a potentially huge source of fossil energy. One estimate(WEC/IIASA, 1995) places reserves of hydrates at 13,500 GBO. Again however, exploitation of these resources
would be costly and pose environmental problems.

3.

Uranium estimates assume conventional fission reactors. A full shift by 2025 to breeder reactors, which generatefissionable materials as a by-product and use uranium 60 times more efficiently than open-cycle reactors, would
address the resource constraint in the scenario. However, in addition to the proliferation risks discussed
elsewhere, breeder reactors pose the risks of higher cost and increased complexity relative to conventional nuclear
designs. The only functioning breeder reactor, the Superphenix in France, has had severe operating difficulties(Sanger, 1994). The expansion of breeder reactors to meet the nuclear power requirements of the Reference
scenario cannot be considered likely.

102

EnergyFigure 3-59. Unrecovered reserves of fossil fuels - 1995

Notes: Includes regional shares of proven plus undiscovered reserves. Shares based on physical units (tonnesof coal, barrels of oil, m3 of natural gas) rather than energy content.

As a consequence of increasing oil demands and the regional variation in resource
endowments, areas relatively poor in resources become increasingly dependent on
imported oil. In the Reference scenario, by the year 2025, North America, Western andEastern Europe, the Pacific OECD, South and Southeast Asia, and the FSU have become
almost completely dependent on imports. The Middle East, China+ and Latin America
are net exporters, while Africa meets more than half of its own requirements. These
patterns have profound ramifications for international politics, global economic stability(the oil shocks of the 1970s suggest the potential vulnerabilities to oil pricemanipulations), and international security (the 1990 Gulf War is a recent case where oilpolitics contributed to international conflict). The Policy Reform scenario helps extend
the timeframe over which the OECD and transitional regions can adapt their energy
policies but, nevertheless, by 2050 all of the OECD and transitional regions are
essentially completely dependent on imported oil. Developing countries, because of theConventional Worlds: A Technical Description of Bending the Curve Scenarios

103

increased levels of consumption in the Policy Reform scenario, become even more
dependent on oil. Thus, both of these Conventional Worlds scenarios progressively
intensify the role of the politics of oil in world affairs, with the attendant risks of
economic vulnerability and heightened international tensions.
International trade is more constrained for natural gas because transport is more costly
and technically complex than for oil. Most current trade occurs between contiguous
regions using natural gas pipelines. For example, natural gas is piped in substantial
quantities from the FSU to Europe, and from Canada to the United States. In addition, asignificant amount of liquefied natural gas (LNG) is shipped from South and Southeast
Asia to Japan, and from Northern Africa to Western Europe. However, the end-use costsof LNG are 30-80% higher than for natural gas, due to increased processing and shipping
costs (BP, 1993).
As with oil, increasing natural gas demands and regional variations in resource
endowments cause regions with relatively small resources to become increasingly
dependent on imported natural gas. By the year 2025, in the Reference scenario, North
America, Europe and the Pacific OECD all become more than 50% dependent on
imported natural gas. The main exporters are Middle East, Latin America, FSU and, to alesser extent, Africa. Between 2025 and 2050 all other regions become increasingly
dependent on exports from these four regions. Similar patterns are seen in the Policy
Reform scenario, except that the general level of dependency on imports in the OECD andtransitional regions is reduced and that Africa becomes a net importer before 2025. These
patterns of supply dependence have similar implications as those discussed above for oil.
Hydropower
Annual hydroelectric power production increases from about 2,530 TWh in 1995 to 5,560
TWh in 2050, in both scenarios. This level of output is well below both the technically
exploitable potential, which is estimated to be 13,500 TWh/year (WEC, 1995), as well as
the economically exploitable potential, which is estimated to be up to 8,830 TWh/year(WEC, 1995). In both scenarios, hydropower development has been explicitly limited toreflect at least partial avoidance of more environmentally and socially detrimental hydroprojects (see Section 3.3). This stipulation severely limits hydropower development in
the OECD, where hydropower generation increases by only 2% over the period 1995 to2025. In the transitional regions, hydropower generation is able to expand by a factor of2, while in the developing countries it almost quadruples.
Traditional Biomass Fuels
The debate about the “woodfuel crisis” has progressed since the early days in the early
1980s when woodfuel consumption was thought to be leading to large-scale deforestationin Africa and other regions. Since then, most experts have come to agree that traditional
woodfuel consumption is not the major cause of deforestation in most parts of the world.
Land clearing for agriculture and logging are now seen as the most common culprits ofdeforestation (see, for example, O’Keefe et al., 1984; Leach and Mearns, 1988; Munslow
et al., 1988; Heaps et al., 1993). Nevertheless, issues of resource sufficiency, rural

104

Energyeconomic development and environmental stress remain important when considering
traditional fuel scenarios.
While a lack of good data on consumption, collection, and resources makes the issue hardto quantify, there are known to be many areas of woodfuel shortage today. Indicators ofshortage are increasing wood collection distances by rural householders (generally
women and children), the substitution of animal dung and agricultural waste forwoodfuel, and actions by rural people to either use wood more efficiently or plant trees.
In the Reference scenario, there is a gradual transition away from traditional biomass
fuels as incomes improve and people move up the “energy ladder” to cleaner and moreconvenient fuels. However, this effect is counteracted by the high rate of population
growth so that actual levels of household biomass consumption decline only slightly,
from 28.5 EJ in 1995 to 25.8 EJ in 2050. In the Policy Reform scenario, strong actions to
reduce poverty also tend to dramatically reduce traditional household biomass
consumption, so that the total falls to 10.1 EJ in 2050. These declines in traditional
biomass consumption are more than offset by the large increases in biomass consumption
and production for “modern” uses such as electricity generation and production oftransportation fuels.
These “modern” forms of biomass are assumed to be grownsustainably and, while they do contribute to pressures on agricultural lands, they are
assumed not to compete with existing stocks of wood used to meet traditional biomassenergy requirements.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

105

3.6 A Comparison with Selected Global Energy Scenarios
The two Conventional Worlds scenarios may be compared to the A1, B and C1 scenarios
created for the report WEC Global Energy Perspectives to 2050 and Beyond(WEC/IIASA, 1995), the IPCC IS92a scenario (IPCC, 1992) and variants of the Low
CO2-Emitting Energy Supply System (LESS) (Williams, 1995). Where appropriate, wehave interpolated the published results of those scenarios or converted them to a common
basis to make them comparable to those from the two Conventional World scenarios
presented in this report. The basic features of these scenarios are listed in Table 3-17.
Table 3-17. Characteristics of selected energy scenarios
Author
IPCC

Scenario
IS92aWilliams

LESS (BI)LESS (NI)LESS (NGI)WEC/IIASA

A1 - “High
Growth”

B - “Middle
Course”
C1 - “Ecologically
Driven”

Characteristics
The IPCC’s reference greenhouse gas emissions scenario.
Assumes carbon intensive supplies, moderate efficiency gains,
moderate deforestation, and modest emissions control
technologies. Additional variants (IS92b-f) reflect uncertainty ina range of assumptions for population and economic growth
rates, energy supplies, restrictions on CFCs and other gases, etc.
(not reviewed here).
Biomass-intensive variant of a scenario examining supply-side
options for climate change mitigation.
Nuclear-intensive variant.
Natural gas-intensive variant. Assumes high recoverable
reserves and modest decarbonization of natural gas.
Additional coal-intensive and high demand variants not
examined here.
High economic growth, high fossil resource availability,
moderate energy intensity improvements, and no CO2 emissions
constraints. Variant A2 assumes lower oil and gas availability
and extensive switching to coal (not reviewed here).
Medium economic growth, medium fossil resource availability,
low energy intensity improvements, and no CO2 emissions
constraints.
Medium economic growth, low fossil resource availability, highenergy intensity improvements, and CO2 emissions constraints.
Variant C2 is a nuclear-intensive variant (not reviewed here).

Figure 3-60 shows selected scenario indicators in 2050 for each of the scenarios relative
to the equivalent value in the Reference scenario. All six of the scenarios show similarlevels of population growth. However, the scenarios diverge on their assumptions
concerning world economic growth. In particular, the LESS scenarios assume a world
economy that is approximately 40% larger in 2050 than the IPCC or Conventional
Worlds, while the WEC scenarios assume a world economy that is between 8% and 27%
smaller than the Conventional Worlds scenarios.

106

EnergyFigure 3-60. Indicators for selected scenarios in 2050
1.4

Reference
Policy Reform
2050 value/Reference 2050 value

IPCC IS92AWEC-A1
WEC-B

1.0

WEC-C1LESS (BI)LESS (NGI)
LESS (NI)

0.6

0.2

Population

GDP (PPP)

Primary Energy
Requirements

Annual CO2Emissions

Carbon Intensity Primary Energy(C/$)
Intensity

Note: GDP and primary energy requirements adjusted to conventions used in this study.

Primary energy use varies widely. The IPCC IS92a and WEC-A1 scenarios show similarlevels of primary energy use to the Reference scenario, reflecting their overall ‘businessas-usual’ perspective. Given their different perspectives, it is not surprising that thevarious scenarios embody different emissions trajectories. In particular, the three LESS
scenarios and the WEC-C1 scenario all depict emissions trajectories that are consistent
with stabilization of CO2 concentrations at below the 450 ppmv assumed in the Policy
Reform scenario.
The consequences of these policies can be seen in the primary energy requirements in
2050 for each the scenarios, shown in Figure 3-61. The three ‘business-as-usual’scenarios (Reference, IPCC IS92a and WEC A-1) show a continuing dominance of fossil
fuels and relatively modest penetration of renewables. The remaining three scenarios
show varying degrees of fuel-switching, away from coal and oil and towards natural gas
and renewable energy forms, and varying perspectives on the future role of nuclear
energy. Note the broad similarities across the Policy Reform scenario and the WEC-C1(“ecologically driven”) scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

107

Figure 3-61. Primary energy requirements in 2050 for selected scenarios
100%

Renewables
Nuclear80%

Hydropower
Natural Gas

60%

Crude Oil
Coal

40%

20%

0%
Reference

PolicyReform

IPCC
IS92A

WEC-A1

WEC-B

WEC-C1LESS (BI) LESS (NI)

LESS
(NGI)

LESS (CI)

Notes:•
Primary energy is expressed using IEA conventions for primary energy content (as used in the rest of this study).
Hydropower and non-biomass renewables are expressed as their electrical energy equivalent.
•

The WEC scenarios do not explicitly separate hydropower, biomass and renewables.
hydropower is subsumed into the renewables category.

For these scenarios,

108

Agriculture, Land and Food

4. Agriculture, Land and Food
4.1 Land and Food
Agriculture and land are treated together because of the important role that agriculture
plays in determining how land is used, through requirements for cropland and grazing
land. These agricultural demands compete with land requirements for human settlements,
parks and protected reserves, issues which are explored in Section 4.6. Agriculture also
places pressure on freshwater supplies, for irrigation, watering of livestock andaquaculture. Of these, irrigation is by far the most important, and will be treated withother water requirements in Section 5. Finally, demand for seafood places pressures on
fisheries, which are already stressed today (FAO, 1997c). This topic will be addressed invarious subsections.
The Reference scenario analysis is an update and elaboration of an earlier study ofagricultural production and land use (Leach, 1995). The analysis uses a later base year
(1995 versus 1990), newly updated sources of data, and updated methodologies formaking projections of certain key variables. Some of the text in this chapter is adaptedfrom the report of the earlier study.
In one sense the agriculture scenarios can be seen as translating human dietary andindustrial requirements for agricultural products into a land-use “footprint” (fisheries
aside). The linkages are illustrated in Figure 4-1. In the analysis, agricultural products
are separated into crops and animal products. Both crops and animal products are
consumed for food and feed; in addition, some crops are used as fuel or as industrial
feedstocks. As illustrated in the figure, crops can be grown on either rainfed or irrigatedland. Livestock can be raised on feedlots, on grazing land, or through some combinationof these two sources. In addition, livestock can be fed crop residues or wastes. The foodproduction capacity in a given region depends on the amount of land and water available,
the yields that can be achieved, and the mix of crops, livestock and production practices
that are adopted.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

109

Figure 4-1. Principal connections between land use and food consumption

Note: Some livestock products (e.g., milk) are consumed by livestock as feed, a link not shown here.

In the land-use analysis, requirements for livestock and crop production are balanced
against other demands. As populations and average incomes increase, the built
environment (land for human settlements) expands into cropland, grasslands, forest andother areas. In many countries land is set aside for preservation, recreation and scientificstudy, and expansion into these protected areas is limited in the scenarios. Increasing
demands for crops, livestock and settlements lead to a loss of forest land. In this study,
the difference between the total land area in each region and the sum of cropland, grazing
land, the built environment, forest, woodland and protected areas is a category called“Other Land.” It can include wasteland, parks (other than those included in protectedareas or built environment), and any other land not included in the other categories.
The principal source for data on food and land for this analysis is the FAOSTAT database(FAO, 1996b).29 The nominal base year for the analysis is 1995. However, not all
statistics were available for 1995, so the current accounts are based on the 1994 values.
Also, the land-use categories in the FAOSTAT database differ from the ones used in thisstudy; they have been supplemented with information from other sources, as explained in
Section 4.6.
Requirements for agricultural products as food are presented first, in Section 4.2.
Livestock production and demand for feed is treated in Section 4.3. Crop requirements

29 Some adjustments have been made to the FAO statistics to construct the dataset used in the analysis.

For example, the barleyused in beer is added to total grain consumption, applying an assumed ratio of tonnes of barley per tonne of beer to the FAOSTAT
figure for beer production. Several adjustments are necessary for the FSU, where there are gaps in the available data.

110

Agriculture, Land and Foodother than for food and feed are treated in Section 4.4. Crop production and trade isdiscussed in Section 4.5. The land-use scenario is presented in Section 4.6.

4.2 Demand for Food
Current Accounts
The composition of human diets varies widely from region to region, based on theavailability of different foods and local preferences. At the coarsest level, the analysis
starts with the calories per capita of food available for human consumption in eachregion.30 The total is then separated into consumption from different commodity
groupings. Note that total calories per capita is larger than the amount of food actuallyconsumed (FAO, 1996b). The remainder is lost to spoilage and plate waste (W. H.
Bender, 1994; 1997).
Of the total calories consumed, some come from crop products and some from animal
products (including fish). Total caloric consumption, with the contribution from these
two broad sources, is shown in Figure 4-2. Consumption in the OECD and transitional
regions is higher than in the developing regions, and more of the calories come fromanimal products. The lowest level is in Africa, about 2320 Calories per capita. The
highest is in North America, where average consumption is 60% higher than thephysiological food requirements estimated by Bender (1997) for high-income countries,
indicating high levels of waste (W. H. Bender, 1994; 1997) or overeating. Note that the
average does not represent the food availability for each individual; unequal distributionof food can lead to chronic hunger in part of a population, even if the average
consumption is sufficient.31 The approach to estimating chronic hunger in the scenarios
is discussed in Section 2.

30 In food consumption analysis, the unit “Calorie” (= 1,000 standard calories) is often used, a convention we employ here in

reporting quantitative results.
31 Assuming a minimum plausible spread for the distribution of calories within a country, the FAO (1996e) estimated that 2700-2860

Calories per capita are required for food adequacy in developing countries.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

111

Figure 4-2. Caloric consumption per capita per day
4000
3500

Calories / cap / day

3000
2500
From Animal
Products

2000

From Crops

1500
1000
500

W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China+

Africa0

Diets are broken down further into contributions from individual crop commodities andanimal products. This detailed disaggregation structure is shown in Figure 4-3. Crops
used primarily as food and feed are grouped into the categories “Wheat and Coarse
Grains,” “Rice” and “Other Crops.” This last category incorporates all non-cereal crops,
such as vegetables, fruits, tubers and sugar crops. Sugarcane used for energy is put in a
separate category, “Energy-Sugar.” Today, only Latin America has substantial sugarcane
production for energy. The animal products are separated into “Meat,” “Milk,” and“Fish,” which includes all seafood.
Figure 4-3. Crop and animal commodities
HUMAN DIETSANDOTHER USES

Crops

Animals

Wheat & Coarse
GrainsRice
Other Crops
Energy-Sugar

Meat
Milk
Fish

Feed

Rainfed

Irrigated

Grainfed

Grazed112

Agriculture, Land and Food

The shares of individual crop commodities in total crop product calories in diets varies
considerably between regions, as shown in Figure 4-4. Cereals (wheat, coarse grains andrice) provide more than half the calories from crops in seven of the ten regions, theexceptions being Latin America (where fruit and starchy roots form a large part of diets),and the affluent regions of North America and Western Europe. The smaller fraction of
cereals in the OECD is consistent with a general trend of a decreasing contribution of
starchy foods to diets as incomes grow (W. H. Bender, 1994; 1997). Rice makes arelatively small contribution to the diets in most regions, with the exception of the Asian
regions — China+, Pacific OECD and South and Southeast Asia — where the climate isfavorable for rice production, and rice has long been a staple crop. However, because of
the large populations in these regions, rice production worldwide (by weight) slightly
exceeded that of wheat in 1994, and was only slightly less than that of maize. The three
together amounted to 84% of all cereal production.
Figure 4-4. Crop commodity shares in diets
100%
90%
80%
70%

Other Crops

60%

Rice

50%

Wheat & C.G.

40%
30%
20%
10%
W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China+

Africa0%

The shares of different animal products in total animal product calories in diets are shownin Figure 4-5. The relative contributions of milk and meat products to diets vary over abroad range from region to region. Meat dominates the caloric intake of animal products
in China+ and Pacific OECD, while milk contributes the least. Fish is relatively
unimportant as a source of calories for regions overall, with the exception of PacificOECD. However, they can be important sources of protein and fats (Leach, 1995).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

113

Figure 4-5. Animal commodity shares in diets
100%
90%
80%
70%

Fish

60%
50%

Milk

40%

Meat

30%
20%
10%
W. Europe

Pacific OECD

N. America

FSU

E. Europe

S+SE Asia

Middle East

Latin America

China+

Africa0%

Putting all of these factors together—calories per capita, the fraction of calories fromanimal or crop products (crop or animal share), and the share of total animal or cropderived calories from individual commodities (commodity shares)—and multiplying by
the regional population, gives requirements for different foods within each region, incaloric terms. For the analysis, a further conversion is needed. In trade balances,production and net exports of agricultural products are conventionally expressed in massterms, rather than energy terms, and this convention is followed here. The necessary
conversion factor, the energy content of the agricultural commodities, can be calculatedusing FAOSTAT data, as the calories of food produced per unit mass of commodity
leaving the farm. Combining all of these elements, the human food requirements, intonnes, for a particular commodity are given by
Requirements = Population × Caloric Intake × Crop or Animal Share ×

Commodity Share
Commodity Energy Content

.

(4-1)
Scenarios
In the scenarios, requirements for food are determined once each of the factors inEquation (4-1) is given a value. The populations in the Reference scenario are the UN
medium projections, while those in the Policy Reform scenario are slightly smaller, as
explained in Section 2. The caloric intake and shares give the composition of averageregional diets; the scenario assumptions for these factors are described below. Theenergy contents are left at their base year values, which carries an implicit assumptionthat the composition within the different groupings stays roughly the same throughout the
scenario.
The Reference scenario projections are based on those of Leach (1995). They takehistorical trends into account, as well as anticipated departures in the future from the114

Agriculture, Land and Foodhistorical trends. In North America and Western Europe, where caloric intake per capita
is currently high (Bender, 1997), it is expected that intake levels off, or declines, due tosaturation effects and health concerns, e.g., with respect to high-fat diets. In the otherregions, as they become more urbanized and their average incomes grow, diets tend
towards the pattern typical of Europe today. Historical trends and scenario projections
are shown in Figure 4-6.
Figure 4-6. Daily per capita caloric consumption; historical patterns and scenarios
Reference
3600
3400
3200

Calories / cap. / day

3000
2800

N AmerW Europe

2600

Pac OECD
2400

FSU

2200

E EuropeA f r ica

2000

Lat Amer

1800

Mid East
China+

1600
1400
1960

S+SE A s i

1970

1980

1990

2000

2010

2020

2030

2040

2050

2030

2040

2050

Policy Reform
3600
3400

CALORIES/(PERSON*DAY)

3200
3000
2800
2600
2400
2200
2000
1800
1600
1400
1960

1970

1980

1990

2000

2010

2020

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

115

In the Policy Reform scenario, the relatively higher incomes in developing andtransitional regions lead to higher average caloric intake.32 In most regions thedifferences from the Reference scenario are small. The largest difference in caloric intake
in 2050 between the scenarios is in Africa; consistent with this, the difference in hunger
levels in 2050 between the two scenarios is also largest in Africa.
Generally, the variations in the contribution of animal products to total calories acrossregions is expected to converge in the scenarios to a narrower range than is seen today,
varying in the Reference case in 2050 from about 13% in Africa to about 30% in theOECD and transitional regions, and in the Policy Reform case from about 17% to 30%.
In contrast, today the figures range from about 7% to 31%. The recent trend in North
America of decreasing animal calories continues in both scenarios, but at a slower rate,
while in most other regions the contribution of animal products in diets grows, primarily
at the expense of grains, as incomes and average caloric intake increase.
The historical patterns and scenario values are shown in Figure 4-7, and total
consumption of animal products is shown in Table 4-1. The combination of population
growth, increasing caloric intake and an increasing share of animal products in diets leads
to large increases in requirements in some regions. In the Reference scenario, in Africa
and the Middle East, requirements increase by over five times between 1995 and 2050,
while in China+ they more than double. In the OECD and transitional regions changes
are much less extreme, the largest being a 31% increase in FSU. In all regions except
Africa, requirements in the Policy Reform scenario are close to those in the Reference
scenario, as relatively higher consumption per capita in the non-OECD regions is offset
by the smaller populations assumed.

32 In both scenarios, caloric intake per capita in non-OECD regions is assumed to follow an “s”-shaped (logistic) curve withincreasing income, asymptotically approaching 3400 Calories/capita. The other two parameters in the curve are fixed for eachregion by the base year intake and the intake in 2050 assumed in the Reference scenario. (In Eastern Europe, where caloric
consumption is assumed to reach exactly 3400 Calories/capita in 2050, the value in 2025 is a linear interpolation.)

116

Agriculture, Land and Food

Figure 4-7. Contribution of animal products to diets
Reference% OF CALORIES FROM ANIMAL PRODUCTS .

40%

35%

30%
N Amer25%

W Europe
Pac OECD

20%FSU
E Europe

15%

AfricaLat Amer

10%

Mid East
China+

5%

S+SE Asi

0%
1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Policy Reform% OF CALORIES FROM ANIMAL PRODUCTS

40%

35%

30%
N Amer25%

W Europe
Pac OECD

20%FSU
E Europe

15%

AfricaLat Amer

10%

Mid East
China+

5%

S+SE Asi

0%
1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

117

Table 4-1. Food requirements for animal products in the scenarios
Million tonnes
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

1994
43
101
81
16
118
27
67
124
35
160
772

Reference
2025
123
180
137
47
260
29
82
149
44
169
1221

2050
234
220
183
84
412
29
88
154
46
162
1613

Policy Reform
2025
162
190
141
49
295
29
84
149
44
169
1314

2050
302
222
175
85
419
28
85
154
46
162
1679

In most developing regions, grains decline as a fraction of total diet. This is in keepingwith historical patterns, in which higher-valued foods are substituted for grains as
incomes increase (Conway, 1997; Leach, 1995). In Africa, grain consumption per capita
increases as a substitute for other starchy foods, such as roots and tubers, which areincluded in the non-cereal “Other Crops” category. In South and Southeast Asia, grain
consumption per capita declines slightly in preference for higher-valued crops, such as
vegetables and fruit, as incomes rise. In the Asian regions, where rice consumption ishigh, wheat and coarse grains gradually substitute for rice in diets, in keeping with recent
trends (Leach, 1995). Growing pressures on fish stocks lead to increasing use of
aquaculture in both scenarios. However, the resulting increases in the price of fish are
expected to constrain the growth of fish consumption in the scenarios.
Total food requirements for crop products are shown in Table 4-2. In the developing andtransitional regions, total requirements are generally smaller in the Policy Reform
scenario than in the Reference scenario, due to a relatively smaller share of crop products
in diets and smaller populations. In most regions in both scenarios, the increases inrequirements for crop products are not as dramatic as for animal products, a result of thedeclining share of crop products in diets. The largest increase is a factor of 3 in Africa
over the course of the scenario.

118

Agriculture, Land and Food

Table 4-2. Food requirements for crop products in the scenarios
Million tonnes
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

1994
393
663
385
118
932
70
202
233
89
357
3441

Reference
2025
817
899
592
251
1580
72
234
291
105
379
5221

2050
1167
958
711
340
2019
70
233
300
101
366
6265

Policy Reform
2025
844
876
577
247
1549
71
231
291
105
379
5172

2050
1141
900
685
325
1942
66
228
300
101
366
6055

4.3 Livestock
The nutritional requirements of livestock place significant pressures on land resources.
They can do so directly, when the animals are grazed, or indirectly, when they are fed
grain or other crops grown on cropland. The increasing consumption of animal productsthat occurs in the scenarios (Table 4-1) leads to large increases in feed demand.
4.3.1 Animal Products as Feed, Other Uses and Losses
Animal products are used for purposes other than just food. For example, cows’ milk isused as feed for calves, and casein, a product of milk, is used in making paint and otherproducts. The FAOSTAT database records consumption of animal products as feed andfor “other uses,” including industrial uses. These, combined with food requirements, givethe total demand for animal products. In addition, there are inevitably some losses incollecting and transporting the final product: these are also recorded in the FAOSTAT
statistics.
In the analysis, consumption of animal products for other uses is combined with losses.
Together, in the base year they amount to less than 10% of total requirements in every
region. In the scenarios, consumption for other uses and losses as a fraction of total
production are kept at their base-year value.
Animal products used as feed are treated in a similar fashion to other uses and losses.
Feed products are treated as a by-product of livestock production systems, rather than atarget for production, which is reflected in the analysis by keeping the fraction of total
production of animal products used for feed at the base year value in each region
throughout the scenario. The resulting feed requirements are shown in Table 4-3.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

119

Table 4-3. Requirements for animal products as feed
Million tonnes
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

1994
2.2
6.1
8.0
2.3
14.8
3.9
26.6
4.9
6.3
32.4
107.6

Reference
2025
5.3
12.1
13.5
7.4
28.5
4.1
30.3
6.6
7.9
33.1
148.8

2050
9.2
15.6
16.9
11.9
41.8
4.3
33.6
6.8
7.3
33.1
180.6

Policy Reform
2025
6.1
12.8
13.7
7.5
30.7
4.1
31.4
6.6
7.9
33.6
154.6

2050
10.5
15.8
16.2
11.9
41.7
4.2
33.0
6.9
7.4
33.7
181.3

4.3.2 Trade
The sum of food, feed and other uses in each region is the domestic demand for
agricultural products. Demand can be met through production within the region or
through trade.
Current Accounts
According to FAOSTAT figures, in 1994 some 30 million tonnes of fish and 20 million
tonnes of other kinds of meat were shipped across national borders. Regional flows,
computed by aggregating from the national to the regional level, are smaller than nationalflows, since some national exchanges are internal to regions.
Net exports are reported here both in absolute terms and in terms of a self-sufficiency
ratio (SSR). The SSR is defined as the amount of food (in tonnes) available fromdomestic sources, divided by requirements. In the FAOSTAT statistics, domestic
production and changes in stocks are recorded for each country. In the current accounts,
both of these are considered as domestic sources when computing the SSR, while in the
scenarios, stock changes are set to zero. The SSR is given by:
SSR=

Production + Stock Drawdowns
Requirements= 1+

Net Exports
Requirements

.

(4-2)

Trade in animal products is shown in Table 4-4. Note that global net exports are notzero; this is a reflection of statistical errors in the reported data. The SSRs for mostregions are quite close to one. The smallest ratio is that of the Middle East, with a valueof 0.79. The small variation in the SSRs indicates that there is little trade compared todomestic production.

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Agriculture, Land and Food

Table 4-4. Exports of animal products

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
Total

Net Exports(million tonnes)
(6)
(4)
5
(4)
(7)
2
9
(1)
5
8
7

SSR
0.88
0.96
1.05
0.79
0.95
1.05
1.09
1.00
1.11
1.04
1.01

Scenarios
In the scenarios, patterns of trade in animal products are based on those for the base year.
The SSRs (Table 4-5) change little between 1995 and 2050. With the exception ofChina+, SSRs increase for regions that were net exporters in the base year and decrease
for base-year importers. China+ moves gradually from a position of near self-sufficiency
in the base year to complete self-sufficiency in 2050. The largest change is in the Middle
East, where the SSR decreases in the Reference scenario from 0.79 in 1995 to 0.64 in
2050. This helps to reduce the pressures placed by livestock on land resources. SSRs in
the Policy Reform scenario are very close to those of the Reference scenario, with slightadjustments to ensure that global net exports are zero.
Table 4-5. SSRs for animal products in the scenarios

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
0.88
0.96
1.05
0.79
0.95
1.05
1.09
1.00
1.11
1.04

Reference
2025
0.83
0.98
1.09
0.66
0.95
1.07
1.09
1.09
1.11
1.06

2050
0.84
1.00
1.08
0.64
0.96
1.11
1.13
1.13
1.19
1.12

Policy Reform
2025
2050
0.84
0.85
0.98
1.00
1.10
1.09
0.66
0.64
0.95
0.96
1.09
1.13
1.10
1.15
1.10
1.15
1.13
1.21
1.07
1.13

4.3.3 Production
In the FAOSTAT database, the amount of each crop and animal product that is consumed
as feed in the base year is recorded (in ktonnes), as is total meat and milk production (inboth Calories and ktonnes). For this analysis, it is necessary to make some additional
assumptions, to allocate feed resources to the production of different animal products. In
this study three feed sources are considered explicitly: non-fodder crops, animal products

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

121

and grazing land.33 Other possible sources, which are not treated explicitly, are fodder
crops, crop residues and wastes. Crop residues and wastes are important feed sources indeveloping regions. All three sources are expected to increase in importance in the Policy
Reform scenario as part of combined crop-livestock systems. They are included
implicitly in the analysis as a contribution to the productivity of grazing land.
Livestock production in the scenario is governed by the practice employed (grazing
versus feedlot) and the productivity of each practice (the land-to-production ratio or feedto-production ratio). Key considerations guiding scenario assumptions on productivity
include:•Animal characteristics: Energy requirements and the production of meat, milk andother products vary both between and within species. Animal characteristics havebeen improved in the past in all regions through breeding. The result of this historicaleffort is a variety of animals specially adapted for specific tasks, such as milk or meatproduction, or draft. In the scenarios, breeding programs are assumed to continue. In
the future, improvements may also be gained through genetic engineering, a more
controlled but perhaps more risky approach to producing animals with desirable
characteristics (Conway, 1997; Krimsky and Wrubel, 1996).

•

Feed characteristics: The quality and quantity of available feeds and forages varyfrom region to region. In the Reference scenario, in some regions continuing
rangeland degradation is expected to lead to the replacement of grasses with lesspalatable plants. This trend will be reversed in the Policy Reform scenario. In bothscenarios, production on grazing land may be improved by introducing new forage
species.

•

Disease: One-third of Africa is affected by trypanosomiasis (sleeping sickness),which limits cattle densities to as little as 30% of their potential in humid areas, andto 63% of their potential in sub-humid areas (FAO, 1998). Where it is not fatal, it
reduces animals’ weight, fertility, milk production and work performance. The
disease has also seriously impeded growth in crop production, as it limits the number
and productivity of draft animals in affected areas. In both scenarios the impact oftrypanosomiasis and other diseases is expected to decline as a result of focusedprograms. Also, in the case of trypanosomiasis, the decrease will occur as a sideeffect of clearing land (Crosson and Anderson, 1992).

•

Competition for feed resources: Livestock are raised for many purposes other thanthe production of food; for example, they may be raised for draft, manure, prestige or
financial security (Galaty and Johnson, 1990; Conway, 1997; Hudson, 1992; Durning

33 “Grazing Land” is land suitable for grazing livestock (see, e.g., FGTC, 1991).

Here the term is applied to the FAOSTAT land-use
category “Permanent Meadows and Pastures.” The composition of this category can vary from region to region, since often it isdifficult to establish whether land suitable for grazing animals is actually used for that purpose (FAOSTAT Inquiries, 1998). As aresult, this category is best thought of as a mixture of land use (land where livestock is grazed) and land cover (land suitable forgrazing livestock). Note that this explains some variation in the reported totals. For example, in the United States, the USDA gives
an area of 51 Mha for pastureland and 212 Mha for rangeland in 1992 (USDA, 1994), while the FAOSTAT total for the same year is239 Mha. In contrast, Maheshwari (1995) reports that in India 4% of total land area is pastures (which agrees with the FAOSTAT
figure), while 40% of the total area is available under rangelands.

122

Agriculture, Land and Foodand Brough, 1991). Animals raised for these purposes compete for feed with animals
raised for food, increasing the ratio of input to food production. On grazing land,
domesticated animals may also compete with wildlife for forage. In developing
regions competition is expected to decline in both scenarios, as mechanization
replaces draft power, chemical fertilizers replace manure, the social importance of
livestock is reduced in the process of globalization and increasing wealth in all
regions makes animals less important as “savings accounts.” With the expansion ofcommercial ranching, wildlife densities are expected to decrease on rangelands.
•

Husbandry practices: Many factors affect the overall productivity of a herd, suchas the size of the breeding herd relative to the number of productive animals and therate at which animals are culled and slaughtered . In the scenarios, practices may varyfrom region to region depending on local conditions, but to the extent possible, they
are expected to approach those of the OECD and transitional regions, where theemphasis is on commercial food production.

•

Infrastructure:. The availability of markets, veterinary services, wells, dip tanks andother resources affects overall productivity. In areas where these are scarce, a hardy
but low-yielding animal is preferable to a higher-yielding but susceptible one(Homewood and Rogers, 1991). In the scenarios, the supporting infrastructure is
expected to expand in developing regions.

Current Accounts
Feedlots
Total feed requirements for crop products areshown in Table 4-6. The highest demand for feedis in North America, where meat consumption ishigh.
Western Europe and China+ havecomparable levels of feed consumption, despite
the much larger population in China+, due todifferent dietary preferences and degree of feedlot
use.

Table 4-6. Feed from crops in 1994
Million tonnes

AfricaLatin America
Middle East
China+
S+SE Asia
E Europe
FSU
N America
W Europe
Pacific OECD

Cereals
15
57
18
69
23
40
94
189
108
22

Other
Crops
8
35
1
86
12
17
26
6
40
1

The amount of meat and milk production on
feedlots in the current accounts is estimated using
a two-step procedure. First, the mass of feed
consumed is converted to a caloric equivalentusing the energy contents of crops estimated from FAOSTAT food statistics. Second, theestimated calories of feed are divided by a caloric feed-to-production ratio to obtain an
estimate of the amount of production attributable to feedlots. For simplicity, the feed-toproduction ratios in this study are taken to be the same for each region, with separate
values for meat and milk.34
34 The values used in the analysis are:2 calories of feed per calorie of food produced from milk products, and 7 calories of feed per
calorie of food produced from meat products. These values are based on the ratio of the mass of feed to mass of product used inConventional Worlds: A Technical Description of Bending the Curve Scenarios

123

Using the assumed values for the feed-to-production ratios, and the FAOSTAT data on
feed consumption and animal production, the feedlot fraction (the fraction of production
attributed to feed from crops) can be estimated. For simplicity, in the analysis we assume
that the feedlot fractions are the same for milk and meat production. The resulting
estimates are shown in Table 4-7.
Feed requirements for fish are not considered explicitly inthe analysis. Most of the feed demand for grain today isfrom cattle, pigs, sheep and poultry, with only about 5%
being fed to fish.35
Grazing LandAll livestock production that does not take place infeedlots is attributed to grazing land, with the total
allocated to meat and milk production in proportion to thecalories of meat and milk produced. The resulting
estimates of the calories of meat and milk produced per
hectare of grazing land are shown in Table 4-8. The
ratios vary over a wide range, consistent with the generalcomments above about the variation in productivity andpractices, and the uncertainty in the utilization of land.

Table 4-7. Estimated baseyear feedlot fractions

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

0.28
0.18
0.40
0.75
0.18
0.71
0.73
0.70
0.31
0.35

Table 4-8. Meat and milk production on grazing land

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

Million Calories
per hectare
0.03
0.31
0.09
0.02
2.56
0.75
0.07
0.15
0.06
1.65
0.17

the study by Tyers and Anderson (1992). Note that they are smaller than the feed-to-production estimates given in, e.g., Balch andReid (1976), because the energy contents used to convert the feed from mass units to energy units are calculated from FAOSTAT
data on food for human consumption, while (ruminant) animals can make use of more of the plant matter than humans can. Aftercorrecting for this factor, the feed-to-production ratios used in this study were found to be comparable to reported estimates.
35 Based on figures from Brown et al. (1998), aquaculture production was 23.1 million tonnes in 1996, of which 85% was from non-carnivorous species. These require about 2 tonnes of grain per tonne of fish produced, which implies feed requirements of about 40
million tonnes, compared to 865 million tonnes of crops used for feed in 1994.

124

Agriculture, Land and Food

As discussed in the introduction to this section, the estimates in Table 4-8 of production
from grazing land implicitly include crop residues, wastes and fodder crops (to the extent
that the feed-to-production ratios are accurate). For this reason, the very high value of 2.6
million Calories of meat produced per hectare of grazing land in South and Southeast
Asia may reflect a substantial amount of livestock production from sources other thanfeedlots or grazing land (Crotty, 1980), or it may be a result of different ways of defining“Permanent Meadows and Pastures” (see Footnote 33).
Scenarios
Feedlots
In the developing regions in both scenarios, demand for animal products increases more
rapidly than population, with greater increases in the Policy Reform scenario than in the
Reference scenario. As requirements for meat increase, it is expected that economic
shifts and growing pressures on grazing land will lead to increased use of feedlots (BTC;
Leach, 1995). In both scenarios the caloric feed-to-production ratios are kept at theirbase-year values, as are the energy content of the feed and its composition. Requirements
for feed are then determined by the required livestock production (given by the regional
demand, less net imports) and the amount of production that takes place on feedlots. In
both the Reference and Policy Reform scenarios feedlot fractions increase steadily from1995 to 2050. Base year estimates and scenario assumptions for feedlot fractions arepresented in Table 4-9. The feedlot fractions increase faster in the Policy Reform
scenario than in the Reference scenario, substantially faster in Africa, where the increase
in meat consumption relative to the Reference scenario is greater than in the otherregions. In part, this is due to the higher value placed on forests and productive croplandin the Policy Reform scenario relative to the Reference scenario, which constrains the
expansion of grazing land.
Table 4-9. Feedlot fractions in the scenarios

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
0.28
0.18
0.40
0.75
0.18
0.71
0.73
0.70
0.31
0.35

Reference
2025
2050
0.32
0.33
0.19
0.20
0.41
0.41
0.85
0.89
0.21
0.23
0.75
0.76
0.80
0.81
0.76
0.79
0.45
0.52
0.42
0.46

Policy Reform
2025
2050
0.45
0.58
0.19
0.24
0.42
0.50
0.87
0.92
0.30
0.35
0.75
0.78
0.80
0.81
0.78
0.82
0.50
0.55
0.43
0.47

Combined with the rapid increase in meat demand, the increase in feedlot fractions leads
to very high rates of growth in feed requirements. Over the course of the Policy Reform
scenario, feed requirements for wheat and coarse grains as a fraction of total requirements

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

125

increase from 14% to 49% in Africa, from 14% to 53% in South and Southeast Asia andfrom 39% to 61% in the Middle East.
Requirements for crops as feed are shown in Table 4-10. Between 1995 and 2050, global
feed demand for crops increases by over two times in the Reference scenario and byalmost three times in the Policy Reform scenario. The increases are much higher in somedeveloping regions: by a factor of over 4 in Africa, and over 5 in the Middle East.
Table 4-10. Requirements for crop products as feed
Million tonnes
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

Reference
1994
2025
23
85
155
256
92
174
19
62
35
123
57
70
119
196
194
255
23
47
148
192
865
1460

Policy Reform
2050
2025
178
169
322
268
241
193
115
68
240
210
77
72
232
206
286
262
66
54
216
198
1974
1699

2050
424
381
292
122
385
76
229
303
71
218
2502

Grazing LandIn the scenarios, assumptions are made of the change in productivity of grazing lands,
expressed as the calories of animal products produced per hectare. In the Reference
scenario, the transitional and OECD regions maintain the same caloric production perunit area as in the base year. In the developing regions production per unit area increases
steadily throughout the scenario.
The scenario results suggest that in Africa and South and Southeast Asia, the area ofgrazing land must increase to accommodate the increased demand for animal products inthe scenarios. In Africa, grazing land area increases by 36% over the course of the
scenario, while in South and Southeast Asia it increases by 54% (albeit from a relatively
small base). This is in contrast to historical trends in the FAOSTAT data, which show
slightly decreasing, but almost constant grazing land areas in these two regions since1961. However, the qualitative trend of increasing grazing land in these regions is inagreement with the results of a study by RIVM and UNEP (1997). The increase takes
place despite large increases in feed consumption and robust assumptions about
improvements in land-to-production ratios.
Changes in a wide range of factors can lead to increased productivity. Some contributing
factors were discussed in the introduction to this section. Furthermore, due to theambiguity in the definition of “Permanent Meadows and Pastures” in the FAOSTAT
database it is possible that some of the reported land area is underutilized; expanding into
this land would increase production per hectare. A further factor affecting production of

126

Agriculture, Land and Foodgrazing land in this analysis is the implicit contribution of crop residues to increases in
the productivity of grazing land. In both scenarios, crop production increases, and with it,
crop residue production. However, in general, the amount of crop residues available forfeed can be expected to increase at a slower rate than crop production, and may evendecrease. In part, this is because an important strategy for increasing yields in plant
breeding programs is to breed crops with a greater “economic” portion of the crop (e.g.,
the grain) relative to the rest of the total biomass; since the rest of the above-groundbiomass is the residue, this is expected to lead to a declining ratio of residue production tocrop production. Furthermore, if developing regions follow the pattern of OECD regionsof separating livestock and crop production, then it may not be economical to transport
the residues to the animals. Finally, in developing countries today, use of crop residues as
feed is diverting nutrients from the soil (Bumb and Baanante, 1996); if more residues are
retained in the future to improve the soil, it will reduce the supply available for animals.
However, with total crop production almost tripling in Africa, and doubling in South andSoutheast Asia in the scenarios, it can be expected that in some areas more crop residues
will be available as feed. Moreover, in the Policy Reform scenario, especially, increased
recycling of crop residues and wastes is expected as part of combined crop-livestock
systems, in which a portion of the nutrients removed from the field with the residue
returns in the form of manure (Conway, 1997; J. Bender, 1994).
In the Reference scenario no distinction is madebetween the different effects listed above; they are
all combined into one improvement factor. The
improvement factors are shown in Table 4-11.
Even with the assumed increases in productivity
from grazing land and a shift to feedlots, grazing
land area expands in several regions, especially
Africa and South and Southeast Asia. Thechanges are shown in Table 4-12. The small
relative changes in the Middle East arenevertheless significant, since over one-third of
the land area is designated as “Permanent
Meadows and Pastures” in FAOSTAT. In
comparison, only 7% of the land area was undercrops in 1994.

Table 4-11. Improvement factors
in the Reference scenario

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

2025
2.3
1.7
1.8
1.6
1.8
1.0
1.0
1.0
1.0
1.0

2050
4.0
1.9
2.3
2.1
2.7
1.0
1.0
1.0
1.0
1.0

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

127

Table 4-12. Grazing land area in the scenarios
Million hectares
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
884
518
590
225
36
14
354
267
429
78

Reference
2025
1082
531
618
228
48
14
364
265
448
76

2050
1202
552
646
229
56
14
379
251
462
71

Policy Reform
2025
1136
517
599
217
51
14
371
264
448
74

2050
1102
510
577
212
48
12
358
250
464
72

In the Policy Reform scenario, it is assumed that actions are taken to meet terrestrial
ecosystem preservation goals by reducing grazing land requirements. In most developing
regions, increasing diets and animal fractions lead to more meat consumption than in the
Reference scenario, despite slightly smaller populations. Using Reference scenario
practices, this would lead to an expansion in grazing land requirements. Changes inpractices and improvements in herds, including changes brought about throughbiotechnology, lead to improvements over the Reference scenario. For the developing
regions there is an assumed 2.5% improvement over Reference practices in 2025 and a
5% improvement in 2050. In addition, there is a greater use of feedlots. The ratio of thearea of grazing land to the area of cropland for feed required to support the same
production of animal products is taken to be 1.8 for all regions.36 With this combinationof practice changes, improvements, and shifts to feedlots, the increase in grazing landarea in the Policy Reform scenario is less than in the Reference case in developing
regions, as shown in Table 4-12.
4.3.4 Fisheries and Aquaculture
Production of seafood from fisheries and aquaculture is summarized on Sheet P-9 in the
Scenario Highlights of BTC. In the base year, demand for seafood is about 100 million
tonnes, while the current yield from fisheries is between 85 million and 90 million tonnes(FAO, 1997c). The remaining demand is met from aquaculture. However, the fisheryyield today is not optimal. Production can be increased in some areas, while in others
stocks are being over-fished. In many areas there is potential for increasing yields by
reducing discarded by-catch.
In the Reference scenario the demand for fish and other seafood increases between 1995
and 2050 from about 100 million tonnes per year to 170 million tonnes. The production
from fisheries is assumed to stay at base-year levels, while aquaculture production
increases from about 10-15 million tonnes to 80-85 million tonnes.
36 The factor of 1.8 was arrived at by examining characteristic production parameters for grazed livestock in the U.S. and comparing

the estimated productivity to the productivity from cropland in North America, using the feed-to-production ratios adopted for thisanalysis.

128

Agriculture, Land and FoodIn the Policy Reform scenario the demand for seafood in 2050 is slightly less than that in
the Reference scenario, about 160 million tonnes. The main reason for the difference isthe slightly smaller populations assumed in the Policy Reform scenario. Production fromfisheries is assumed to increase over the course of the scenario to a level of about 100
million tonnes, through improved planning and reduction of discards, as explained in
BTC. As a result of the higher production from fisheries and lower demand, production
from aquaculture in 2050 (60 million tonnes) is about 25% lower than in the Reference
scenario.

4.4 Other Uses for Crops and Losses
Current Accounts
Agricultural products may meet with a variety of fates in addition to being consumed asfood or feed, although these are by far the most important. Crop and animal products arealso used as industrial feedstocks of various kinds. Also, unless seeds must be purchased,
as for some disease-resistant hybrids (Conway, 1997; Krishnamoorthy, 1983), some ofthe harvest from crops must be retained as seed for the next season’s planting. Added toall of the requirements are the inevitable losses between the farm and market. In the baseyear, industrial uses of crops are less than 10% of total requirements in every region,
while losses range from about 5% to 15%, using FAOSTAT figures. Seed requirements
are less than 10% of crop production.
Scenarios
In future years in both scenarios, with the important exception of sugarcane for alcohol in
Latin America, industrial consumption for crops is kept in the same proportion to the total
food and feed requirements as in the base year. Similarly, the fraction of the harvest
retained as seed is kept at its base year value in each region. Losses as a fraction of total
production are kept at the base year value.
Requirements for sugarcane for fuel are derived from the demand for fuel alcohol
calculated as part of the energy scenarios (see Section 3). In the base year, Latin America
is the only region with a significant use of sugarcane as an energy crop, and this is
assumed to be true in the scenarios as well. Other energy crops that may become
important in the future are perennial grasses and tree crops. These are not included in the
scenarios but, because of their potential impact on land use, a rough estimate of theamount of land that might be required for energy crops is given below.
Tree Crops for Energy
One potentially important type of energy crop is fast-growing tree crops. In the base year,
such crops make a negligible contribution to either land cover or energy. It is anticipatedthat targeted policies will be required if they are to become an important energy source,
so they will not make a significant impact in the Reference scenario. For this calculation,in the Policy Reform scenario it is assumed that all biomass requirements above those in
the base year are supplied by tree crops. This assumption allows us to make an order-of-Conventional Worlds: A Technical Description of Bending the Curve Scenarios

129

magnitude estimate of the amount of land required.37 Taking losses into account, Hall etal. (1993) estimate that hybrid poplar can provide 224 GJ of energy per hectare, using
current technology. Applying this to the difference in biomass energy requirements in
2050 and those in the base year, gives an estimated 200 Mha of land required. This is
11% of cropland area in 2050, 5% of forest area and 6% of grazing land area; energy
crops could potentially compete for land with any of these other uses. However, indeveloping regions, biomass plantations are likely to be sited on deforested or otherwise
degraded land (Hall et al., 1993). These lands contribute to “other” land in this analysis,so the establishment of biomass plantations does not necessarily conflict with the
expansion of cropland that occurs in the scenarios, or with the sustainability goal for
deforestation. The estimated area required for biomass is 8% of “other” land area in
2050.

4.5 Crops
In the preceding sections regional crop requirements for food, feed and other uses weredeveloped. The requirements are met either through production within regions or throughtrade. The potential to expand production in each region is determined by the area of land
suitable for crop production and the potential for increasing the productivity of cropland.
In this study, expansion of cropland and potential increases in yields are set based on
historical trends, following Leach (1995). Regional scenario assumptions are constrainedby the assumption of continuity in trade patterns: i.e., SSRs are consistent with historical
trends.
Crop production over the past 30-40 years was characterized by the advances of theGreen Revolution (Conway, 1997). World production of cereals more than doubled
between 1961 and 1994, while cropland increased by only 8% over the same period. 38
The considerable expansion in production, combined with only modest increases incropland area, were made possible by increasing the yield for each harvest and thenumber of harvests each year on a given piece of land, through the introduction of
improved crop varieties, widespread use of fertilizer and pesticides, expanded irrigationand mechanization. The rapidly increasing yields over this period were driven in largepart through the efforts of international agricultural research centers, such as those of
CGIAR (Conway, 1997). They developed and disseminated new varieties of plants andencouraged the use of fertilizers and pesticides to increase production.
These efforts undoubtedly enabled the world to avoid severe food shortages in developing
countries: around 1970, 35% of the population in developing regions was hungry. By
1991 the percentage had dropped to 20%, despite an almost 60% increase in population indeveloping regions (FAO, 1996e). The absolute number of hungry in developing regions
also fell slightly over the same period, by 8%, from 918 million to 841 million (FAO,
1996e). However, despite these successes, extreme hunger still persists, as discussed in
37 The “biomass” category in the energy scenarios (Section 5) covers a diverse set of fuels, including wood and wood residues,charcoal, dung, agricultural residues and municipal solid waste. The provisional estimate made here can give an idea of thepotential land-use requirements, but is not intended to replace a detailed analysis.
38 Cropland is the sum of the FAO categories “arable land” and “land under permanent crops.”

130

Agriculture, Land and Food

Section 2. Also, the general improvement masks considerable differences between
regions. In the effort to increase global production, agricultural research initially focusedon improving yields on the best land (Conway, 1997). As a result, gains wereconcentrated in the “Green Revolution Lands” of South and East Asia, West Asia, North
Africa and Latin America. People in less well-endowed countries and poorer people inbetter-endowed countries working poor and marginal lands, experienced much smaller
productivity increases. The greatest decrease in hunger occurred in East and Southeast
Asia, where many of the benefits of the Green Revolution technologies wereconcentrated, while in Sub-Saharan Africa the hungry population more than doubled
between 1970 and 1991, from 103 million to 215 million (FAO, 1996e). Furthermore,
recently growth in production has shown signs of slowing for some crops and someregions (Conway, 1997; Brown et al., 1997; Pinstrup-Anderson et al., 1997). Faced with
a 60% increase in world population over the next half-century, this raises the crucial
question of whether crop production will be sufficient to feed the world.
Today, the focus is shifting. While research in the 1960s and 1970s was aimed towards
breeding improved varieties of the world’s main grain crops, rice, wheat and maize, more
recently research programs have expanded to include other crops, such as potatoes,sorghum and millet. There are calls for improving production on marginal land andfinding solutions that have local, rather than global, applicability (Conway, 1997;
Hudson, 1992).
4.5.1 Cultivated Land
Current Accounts
Globally, cropland accounts for 11% of total land area (Table 4-13). About 18% of
cropland is irrigated, with the proportion differing considerably from region to region. In
China+, for example, about half of the total area is irrigated, much of it riceland, while in
Africa only 7% of the land is irrigated.
Table 4-13. Cropland area

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. EuropeWorld

Area in Mha
Rainfed
Irrigated
173
12
54
53
126
18
29
15
205
84
35
5
209
19
211
22
50
5
108
16
1200
250

Cropland as %of total land area
6
9
7
7
34
44
10
13
7
27
11

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

131

Potentially Cultivable Land
Increases in cropland area in the scenarios are constrained by the suitability for crop
production of land currently being used for other purposes. For rainfed crops, several
factors limit potential productivity (Alexandratos, 1995). The supply of rainfall mustsatisfy the water requirements of the crops, which are affected by environmental factors.
The ability to grow crops is also affected by the fertility, slope and other properties of thesoil. Note that these factors are not constant: the fertility of the soil can be enhanced ordegraded by agricultural practices, pollution, or other causes.
Estimates of potential productivity of rainfed land for 91 developing countries wereprepared by the FAO and the International Institute for Applied Systems Analysis in
Vienna (Alexandratos, 1995; Fischer, 1993). The extent of potentially cultivable land
and its distribution in different land-use categories was calculated based on theseestimates. The results are shown in Table 4-14. In the Reference scenario, the land underbuilt environment or in protected areas is considered unavailable for cultivation, so
cropland can expand only into the potentially available land currently under forests andgrasslands. (In the Policy Reform scenario, in the OECD regions some land currently
protected or under the built environment is converted to cropland, as described in Section
4.6.) In Africa and Latin America, potentially available arable land accounts for about
80% of the total cultivable land, indicating considerable scope for rainfed croplandexpansion in these regions. In the Middle East and South and Southeast Asia, only about
30% of the potentially cultivable land was undeveloped in 1989.39
Table 4-14. Potentially cultivable land in developing regions
Area circa 1989 (millions of hectares)
Total
Under Forest and Grazing Under Built Environment
RainfedLand
and Protected Land
Africa973
793
101
China+
190
83
16
Latin America
944
777
110
Middle East20
101South and Southeast Asia
316
102
56
Source: Based on Fischer (1993).

In areas where rainfall is insufficient to support crops, or where limited rainfall constrains
the length of the growing season, cropland can be irrigated to make it productive.
However, estimates of irrigation potential are beset with difficulties: in particular, they
are influenced by economic and political considerations, especially when the water mustbe transported across country boundaries (Alexandratos, 1995).
39 A peculiarity of the data in Table 4-14 is that the reported total area of potentially cultivable rainfed land in the Middle East issmaller than the extent of rainfed land actually cultivated in 1989, an area of some 26 million hectares. However, as noted in
Alexandratos (1995), land classified as “not suitable” based on potential yield may still be used for rainfed agriculture in somecircumstances. They note that in North Africa and the Near East a large amount of potentially productive land is located in
mountainous areas, much of which is judged unsuitable because of the steep slope of the land. Nevertheless, such land may beterraced to make it productive, overcoming one of the criteria used in the assessment (Alexandratos, 1995). In this study it isassumed that the economic and social conditions that make otherwise marginal land attractive for cultivation in the Middle East
continue in the future, so the total increase in cultivated area in the scenarios exceeds the 10 million hectares reported in the table.

132

Agriculture, Land and Food

Bearing the uncertainties in mind, various sources suggest that the potential for expansionof irrigated agriculture is generally much less than that of rainfed agriculture(Alexandratos, 1995; FAO, 1995; Leach, 1995; Seckler, 1996). In the scenarios, theexpansion of irrigated land is based on Leach (1995). The increase in irrigated area ismodest, consistent with the general consensus.
Scenarios
Changes in cropland area in the scenarios are based on historical trends. As seen in
Figure 4-8 and Figure 4-9, rainfed cropland area either remained steady or declined inmost of the regions between 1961 and 1994. In only three regions — Africa, Latin
America and Pacific OECD — did rainfed area grow over the period. However, thegrowth in all three regions is showing signs of slowing. In the Reference scenario,
rainfed cropland decreases in China+ and increases in Africa, Latin America and PacificOECD, in keeping with the historical trends. However, with the exception of Africa, therates of change are slower than in the past. For Africa, which has a large area of potential
cultivable land, the rapidly growing food demand that occurs in both scenarios is met inlarge part by expanding rainfed cropland.
Figure 4-8. Index of rainfed area in developing regions, 1961-1994
150

Africa140

Latin America

130

Middle East

120

China+

110

South &Southeast Asia

100
90
80
70
60
50
1960

1965

1970

1975

1980

1985

1990

1995

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

133

Figure 4-9. Index of rainfed area in OECD and transitional regions, 1961-1994
150

North America
Western Europe

140

Pacific OECD
130

FSU

120

Eastern Europe

110
100
90
80
70
60
50
1960

1965

1970

1975

1980

1985

1990

1995

In contrast to the historical trends in rainfed cropland area, irrigated area increased in all
10 regions between 1961 and 1994, as shown in Figure 4-10 and Figure 4-11. The recent
decrease in irrigated area in the FSU is assumed to reverse in the scenarios. In the
Reference scenario, irrigated land area in every region is assumed to either increase or
remain steady.
Figure 4-10. Index of irrigated area in developing regions, 1961-1994
350

AfricaLatin America

300
Middle East
China+

250

South &Southeast Asia
200

150

100

50
1960

1965

1970

1975

1980

1985

1990

1995

134

Agriculture, Land and Food

Figure 4-11. Index of irrigated area in OECD and transitional regions, 1961-1994
350

North America
Western Europe
Pacific OECD

300

FSU
Eastern Europe
250

200

150

100

50
1960

1965

1970

1975

1980

1985

1990

1995

The cropland assumptions in the scenarios are shown in Table 4-15. In the Middle East,in the Policy Reform scenario water-conserving policies increase the cost of water, so
irrigation water is increasingly diverted to higher-valued uses, leading to a decrease in theirrigated area. At the same time, the efficiency of application of irrigation water improves(see Section 5). In other regions, irrigated areas are the same in both scenarios. Higher
demand for food and feed in the Policy Reform scenario leads to almost 10% more
cropland area than in the Reference scenario in 2050.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

135

Table 4-15. Cropland area in the scenarios
Area in Mha

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
World

1994
Rainfed
Irrigated
173
12
54
53
126
18
29
15
205
84
35
5
209
19
211
22
50
5
108
16
1200
250

Reference2025
Rainfed
Irrigated
242
14
54
56
142
20
29
17
199
95
32
5
198
21
202
23
57
6
105
17
1260
274
Policy Reform
2025
Rainfed
Irrigated
261
14
58
56
151
20
39
14
188
95
35
5
214
21
229
23
57
6
115
17
1347
271

2050
Rainfed
Irrigated
263
14
47
58
153
22
29
17
210
99
32
5
204
23
213
24
59
6
108
19
1318
288
2050
Rainfed
Irrigated
273
14
51
58
174
22
44
13
159
99
36
5
229
23
307
24
54
6
135
19
1461
283

In the Reference scenario most of the increase in land area occurs in Africa, Latin
America and Pacific OECD. In Africa the increase of 49% over the course of the
scenario occurs due to pressure on agricultural production systems for food and grain as
populations increase, and as diets improve and include more meat. In the Policy Reform
scenario, the largest increase is also in Africa, where cropland expands by 55% between
1995 and 2050. Land areas also increase significantly in Latin America, North America
and Western Europe, in part from increases in exports to meet the demand from theincreasingly affluent developing regions. (Exports also increase substantially in Eastern
Europe and FSU, but the increased production is met primarily through yield increases.)
Cropland area also expands in the Middle East, despite substantial imports, by almost
30%, much more than the 5% increase in the Reference scenario. In part, the relatively
greater area is made possible because of the greatly reduced rate of land degradation
assumed in the Policy Reform scenario (see Section 4.6.2). Sustainable production on thefragile rainfed land in this region will be challenging, especially if slower landdegradation is to be achieved.

136

Agriculture, Land and Food

4.5.2 Crop Yields and Cropping Intensity
Current Accounts
The FAOSTAT database provides data on the production and area harvested for eachcrop. The ratio of these is the yield per harvest. Also provided in the database is total
cropland area. The ratio of total harvested area to total cropland area is an aggregate
measure of cropping intensity, the number of crops planted each year on the same pieceof land. The cropping intensity can be less than one, if land is left fallow or planted toforage crops in some years, and can be greater than one if conditions are suitable forhaving two or three crop cycles in one year.
Separate yields (by crop) and aggregate cropping intensities on rainfed and irrigated land
were available for four regions: Africa, Latin America, the Middle East and South andSoutheast Asia (Fischer, 1993). These regions are primarily tropical, while the others areprimarily temperate. Estimates of separate yields and cropping intensities on rainfed andirrigated land were made for the temperate regions, but due to the uncertainty in theseestimates, for these regions only average yields and cropping intensities will bereported.40 Base year estimates for cereal crops are shown in Table 4-16, and for noncereal crops in Table 4-17. Also shown are the annual yields, the product of the yield per
harvest and the cropping intensity.41
Table 4-16. Yields and crop intensities for cereals

AfricaLatin America
Middle East
S+SE Asia
China+
E. Europe
FSU
N. America
Pacific OECD
W. Europe

Harvest Yield (t/ha)
Rainfed
Irrigated
1.0
3.8
2.0
4.5
0.9
2.4
1.7
3.2
4.4
3.0
1.5
4.9
2.1
4.1

Crop Intensity
Rainfed
Irrigated
0.8
1.2
0.8
1.1
0.5
1.1
0.9
1.2
1.4
0.8
0.6
0.6
0.4
0.7

Annual Yield (t/ha/yr)
Rainfed
Irrigated
0.8
4.6
1.5
4.7
0.4
2.7
1.5
3.7
6.3
2.4
0.8
2.7
0.8
2.9

Note: For the four developing regions listed separately, separate data on rainfed andirrigated production were available from Fischer (1993). For the other six regions
estimates were constructed for yields and cropping intensities on rainfed and irrigatedland to project water and fertilizer requirements, but are not reported here.

40 The data from Fischer (1993) for the tropical regions are from around 1990; to assign values for the 1995 baseline, the ratios of

the irrigated and rainfed yields were kept the same as in the original data, but the absolute values were adjusted to recover the
average yields in the FAOSTAT database. For temperate regions the crop yields were derived from FAOSTAT data, and then split
into separate values for irrigated and rainfed land using the average irrigated : rainfed ratios for the tropical regions.
41 Note that it is likely that the actual yields in China+ are lower than the reported ones by as much as 15-20%, because the area ofcropland is regularly underreported (Paarlberg, 1997). Taking this into account, the actual yields achieved may be closer to 5
tonnes/ha·yr for cereals and 10 tonnes/ha·yr for non-cereal crops. These values are still large compared to other regions because ofextensive multiple cropping.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

137

Table 4-17. Yields and crop intensities for non-cereal crops

AfricaLatin America
Middle East
S+SE Asia
China+
E. Europe
FSU
N. America
Pacific OECD
W. Europe

Harvest Yield (t/ha)
Rainfed
Irrigated
3.5
19.6
6.6
34.6
2.9
10.1
3.2
17.7
8.6
8.9
5.5
5.4
11.3
10.4

Crop Intensity
Rainfed
Irrigated
0.8
1.2
0.8
1.0
0.4
0.9
1.0
1.2
1.4
0.8
0.5
0.6
0.4
0.7

Annual Yield (t/ha/yr)
Rainfed
Irrigated
2.9
22.9
5.2
35.9
1.1
9.1
3.1
20.9
12.3
7.1
3.0
3.0
4.3
7.4

Scenarios
The yield and cropping intensity projections for both scenarios are based on those of
Leach (1995). In that study, no attempt was made to separate the various biophysical andsocial effects. Instead, the yield projections were based on historical trends and available
data on maximum yields and potential improvements that had already been identified. As
shown in Figure 4-12 and Figure 4-13, the productivity of cropland increased in all
regions between 1961 and 1995, and yields continue to increase in the scenarios. Note,however, the wide disparities in historical yield increases; e.g., between China+ and
Africa, or Western Europe and the FSU.
Figure 4-12. Historical annual cereal yields in developing regions
5

AfricaLatin America

43China+

2

South &Southeast Asia

1

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

0
1961

tonnes/ha

Middle East

138

Agriculture, Land and Food

Figure 4-13. Historical annual cereal yields in transitional and OECD regions
5

North America
Western Europe

4OECD Pacifictonnes/ha

Eastern Europe
FSU

3
2
1

1995

1993

1991

1989

1987

1985

1983

1981

1979

1977

1975

1973

1971

1969

1967

1965

1963

1961

0

As discussed above, the base year yield and cropping intensity values are calculated from1994 data in the FAOSTAT database and other sources. Because yields can fluctuatesignificantly from year to year due to weather and economic conditions, the different
base-year values in some cases led to trends from 1994 to 2025 that were different fromthose assumed in Leach (1995). Nevertheless, the same arguments that led to theparticular values for the yields and intensities are assumed to hold in the Reference
scenario.42 The yields and cropping intensities in the Policy Reform scenario are assumed
to be the same as in the Reference scenario. The social, economic and policy
environments in the two scenarios are quite different; however, some of the differences
would lead to higher yields and some to lower yields when comparing one scenario to theother. Different influences are discussed below.
The annual yields assumed in the two scenarios are shown in Table 4-18 and Table 419.43 These include the combined effect of changes in harvest yields and cropping
intensities. The differences between the two scenarios are due to different mixes ofcrops, in the case of cereals, and, for the temperate regions, different mixes of production
on irrigated and rainfed land.

42 Briefly, the assumptions were that for most crop groups and most regions, yields and cropping intensities increase steadily

throughout the scenario, but at slower rates than in the past. The yields in 2050 were kept below the maximum national yields
achieved as of 1989. In Africa, the Middle East and the transitional regions of the FSU and Eastern Europe, in contrast to thegeneral trend, yield increases are assumed to be faster than in the past. Also, yields for certain crops, which in this study are placedin the “Other Crops” category, were assumed to increase faster than historical yields because of recent scientific advances orexisting opportunities to increase productivity.
43 In all regions except China+, the yields assumed for 2050 are well below the constraint-free yields estimated by Luyten (1995) in a

global study. The “crop” used for the estimates has characteristics typical of a modern high-yielding cereal crop. If the constraintfree yield for China can be taken as a guide, then considering the likely inflation of the base year yields for China (Paarlberg, 1997)
and the recent progress toward developing a high-yielding rice plant suitable for irrigated rice production (IRRI, 1998) (the mostimportant in China), the yields for China+ in 2050 appear challenging, but achievable.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

139

Table 4-18. Annual cereal yields in the scenarios
tonnes / ha / yr

AfricaLatin America
Middle East
S+SE Asia
China+
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
Rainfed
Irrigated
0.8
4.6
1.5
4.7
0.4
2.7
1.5
3.7
6.3
2.4
0.8
2.7
0.8
2.9

Reference2025
Rainfed
Irrigated
1.6
7.1
2.1
6.2
0.7
4.5
2.7
6.2
9.0
4.1
2.1
3.3
1.2
4.5

2050
Rainfed
Irrigated
2.2
8.4
2.4
7.4
1.2
5.7
2.9
7.6
10.1
4.4
2.3
3.6
1.3
4.6

Policy Reform
2025
Rainfed
Irrigated
1.6
7.1
2.1
6.2
0.7
4.5
2.9
6.2
8.5
4.1
2.0
3.2
1.2
4.4

2050
Rainfed
Irrigated
2.2
8.4
2.4
7.4
1.2
5.8
3.3
7.6
9.5
4.4
2.2
3.4
1.3
4.4

Table 4-19. Annual yields for non-cereal crops in the scenarios
tonnes / ha / yr

AfricaLatin America
Middle East
S+SE Asia
China+
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
Rainfed
Irrigated
2.9
22.9
5.2
35.9
1.1
9.1
3.1
20.9
12.3
7.1
3.0
3.0
4.3
7.4

Reference2025
Rainfed
Irrigated
3.8
35.7
7.4
50.7
2.5
16.8
4.3
30.4
15.0
8.8
4.8
4.4
4.1
8.6

2050
Rainfed
Irrigated
5.9
51.4
8.9
58.7
3.4
21.3
5.9
37.4
18.7
11.0
5.9
4.7
4.7
9.3

Policy Reform
2025
Rainfed
Irrigated
3.6
34.3
6.8
49.2
2.4
16.1
4.1
29.2
17.4
9.4
5.3
4.2
4.5
8.2

2050
Rainfed
Irrigated
5.7
49.5
9.9
57.2
3.2
20.5
5.7
35.9
21.0
11.1
6.3
4.5
4.9
8.8

One factor that could lead to increased yields in the Policy Reform scenario relative to theReference scenario is wider use of small-scale soil and water-conserving technology.
Increasing the efficiency of use of available rainwater can effectively extend the growing
season, so that more than one crop can be harvested. This can be accomplished in several
ways: surface runoff can be captured behind earthen bunds; conservation tillage can be
introduced; and in areas with a monsoon season, water storage and mulching can makewater available for crops during the dry season (Hudson, 1992; Conway, 1997;
Krishnamoorthy, 1983). These efforts and others can reduce the rate of croplanddegradation, in addition to raising yields.
The effects of degradation are present in the historical yield trends. Since the Reference
scenario projections are based in part on the historical trends, to some degree the effects
of future land degradation are incorporated in the scenario (Leach, 1995). In the Policy
Reform scenario, land degradation is assumed to slow and then reverse, as damaged land
is recovered through careful management. Land degradation has had a profound effect on
the world’s agricultural land. In a study commissioned by UNEP, it was estimated that
almost 2 billion hectares of cropland, grazing land and forest has been degraded to somedegree as a result of human action since the second world war (Oldeman, 1991). Of this,about 30% was due to agricultural practices. In some cases the damage led to reduced
yields, through nutrient loss, salinization or other reasons. In other cases, however, theeffects were severe enough that working the land became infeasible. The scenario
assumptions for land degradation of this severity are discussed in Section 4.6.2. The
effects of land degradation on yields have been found to be significant in the few cases

140

Agriculture, Land and Food

where they have been estimated (Scherr and Yadav, 1996). In Africa, for example, as aconsequence of erosion, yields may have been between 2% and 40% lower than theywould have otherwise. The effect in the Policy Reform scenario of reduced landdegradation could have a significant impact on yields.
One factor that can lead to lower yields in the Policy Reform scenario relative to theReference scenario is the replacement of high-input farming practices with low-input
approaches. For example, as discussed in BTC, application of manufactured fertilizer isassumed to decrease in the Policy Reform scenario relative to Reference scenario
practices. The reduction is accompanied by increases in natural inputs, such as green
fertilizer, manure and crop resides. As discussed in the next section, in the analysis it isassumed that manufactured inputs can be cut in half without a significant decrease inyields or cropping intensities. However, note that this would most likely not be true if
inputs were removed completely, as soil nutrients might then be a limiting factor on
production, leading to a reduction in yield (Penning de Vries et al., 1996). Also, thenecessity of introducing nitrogen-fixing crops into a crop rotation sequence can reduce
the production of food crops.
4.5.3 Fertilizer
Plant growth can be restricted if any of several crucial chemical elements are in short
supply. Furthermore, unless nutrients are replenished, agriculture will gradually deplete
the soil, as nutrients are carried away from the field with the harvest, or washed awaywith the soil exposed by land clearing.
Mineral shortages can be corrected, and nutrient levels maintained, by applying
fertilizers. Fertilizers play an important role in producing and sustaining the yield
increases assumed in the scenarios. However, they also pose both social andenvironmental problems. They are associated with pollution and health problems and, inthe case of manufactured fertilizers, their manufacture consumes nonrenewable resources.
However, there are alternatives to manufactured fertilizers. Knowledge-intensive andmanagement-intensive farming methods can reduce external inputs while greatly
enhancing crop yields (Leach, 1995). Some approaches are discussed in BTC.
In this study we construct a Reference scenario for manufactured fertilizer use, and thenexplore the potential for reducing manufactured inputs in agriculture in the Policy Reform
scenario relative to the Reference scenario. In the analysis, only manufactured fertilizers
are considered. The amount of manufactured fertilizer applied per hectare (the intensity)
is calculated in terms of yields, so that the trends in fertilizer use are consistent with theyield assumptions described in Section 4.5.2.
In the Policy Reform scenario, it is assumed that alternatives to manufactured fertilizer
are introduced in varying combinations in different regions. Combined withimprovements in the efficiency of fertilizer application relative to the Reference scenario,
it is assumed that use of manufactured fertilizers can be cut in half without affecting
yields or cropping intensities.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

141

Current Accounts
The FAOSTAT database gives total national consumption of different kinds of fertilizer.
Regional consumption of manufactured fertilizer is plotted against average yield for all
crops in Figure 4-14. The curve in the figure was used to allocate fertilizer use todifferent crops in the base year. Using this approach, fertilizer application is greater on
irrigated land than on rainfed land, consistent with the greater benefits that can beachieved from fertilization on irrigated land.
Figure 4-14. Manufactured fertilizer application versus average yield
250

Fertilizer (kg/ha)

200

kg fert./ha = 206 x ln(yield/2.3 t ha-1)
R2 = 0.85

150100
Latin America

50

0
0

2

4

6

8

Yield (tonne/ha)

Note: Latin America is excluded from the regression.
Scenarios
In the Reference scenario, fertilizer use is estimated as a function of yields, based on thesame curve used for the current accounts (Figure 4-14). In the Policy Reform scenario, an
initial estimate for fertilizer application is computed based on yields, as in the Reference
scenario, but is then reduced by 25% in 2025 and 50% in 2050. The yield assumptions forboth scenarios are given in Section 4.5.2.
In the Reference scenario, fertilizer consumption in the developing regions increases from71 million tonnes in 1995 to 132 million tonnes in 2025 and 174 million tonnes in 2050.
In the transitional and OECD regions it increases from 52 million tonnes in the base year
to 74 million tonnes and then 78 million tonnes in 2025 and 2050.
4.5.4 Trade
The sum of food, feed and other uses in each region is the domestic demand for crop
products. Demand can be met through production within the region or through trade.

142

Agriculture, Land and Food

Current Accounts
According to FAOSTAT figures, in 1994
some 240 million tonnes of cereals and 80
million tonnes of fruit were shipped acrossnational borders. Most of the exchanges arecommercial transactions: cereal food aid
shipments were about 9.5 million tonnes in
1994/95 (FAO, 1997d), or about 4% of total
cereal exports. As for trade in livestock
products, discussed in Section 4.3.2, net
exports are reported both in absolute terms
and as self-sufficiency ratios.

Table 4-20. Cereal exports and selfsufficiency ratios in 1994

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe
Total

Net Exports(million tonnes)
(30)
(3)
(21)
(21)
(20)
(1)
(51)
98
(13)
25
(37)

SSR
0.79
0.99
0.84
0.63
0.95
0.99
0.74
1.35
0.74
1.12
0.98

For cereals as a whole (Table 4-20), in 1994
eight of the ten regions were net importers,with North America and Western Europe theonly net exporters, although Eastern Europe and China+ were very close to being selfsufficient. However, for rice, China+ and South and Southeast Asia were net exporters,
as well as North America and the FSU. The arid Middle East had the lowest SSR, 0.63.
Note that global net exports are not zero; this is a reflection of statistical errors in thereported data. In the developing regions as a whole, cereal production fell short of needs
by about 95 million tonnes.
The pattern of trade for the “Other Crops”
category (Table 4-21) is the reverse of that for
cereals, with the developing regions being net
exporters, while each of the OECD regions is a
net importer. The range of self-sufficiency
ratios is not as large as that for cereals, although
the value for the Middle East, 0.65, is close tothat for cereals.
The exports from thedeveloping regions are entirely from Latin
America and South and Southeast Asia. A
variety of crops are exported from Asia, while
fruit dominates exports from Latin America.

Table 4-21. Exports and selfsufficiency ratios of “other crops” in
1994
Net Exports(million tonnes)
Africa(22)
China+(12)
Latin America
117
Middle East
(35)
S+SE Asia
20
E. Europe
(1)
FSU
(46)
N. America(0)Pacific OECD
(8)
W. Europe
(34)
(20)
Total

SSR
0.94
0.98
1.29
0.65
1.03
0.99
0.78
1.00
0.90
0.92
0.99

Total world crop production was close to 5
billion tonnes, or just under one tonne perperson. Production per capita in the OECD andtransitional regions (1.4 tonnes) was almost twice as great as in the developing regions(0.76 tonnes).

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

143

Scenarios
In the Reference scenario, assumptions are made for the demand and production of crop
products. In the analysis, trade — the difference between the two — emerges principally
as a residual factor. As discussed in Sections 4.5.1 and 4.5.2, crop production in the
scenarios is set by specifying the future productivity of cropland, as well as future growthin cropland area. The areas under rainfed cropland, yields for non-cereal crops and selfsufficiency ratios are initially set to values based on those of Leach (1995), and thenadjusted so that, globally, net exports are zero. Generally, the rainfed areas and yields
were only allowed to vary by a few percent; the self-sufficiency ratios were the mostimportant adjustment parameter in balancing net trade.
In the Policy Reform scenario, it is assumed that regions will rely to a greater extent on
trade than in the Reference scenario. Regional production levels are constrained by thesustainability targets given in Section 1. The target of halting, and then reversing,
deforestation, and the requirement to minimize the growth in water stress, strongly
restrict the expansion of cropland and irrigation in some regions.
The resulting scenario values for the self-sufficiency ratios for cereals are shown in Table
4-22. All of the developing regions are cereal importers in the base year, and they
continue to be net importers in both scenarios (Figure 4-15). For the developing regions
as a whole, imports are greater in the Policy Reform scenario than in the Reference
scenario, both as a response to greater demand for agricultural products and as a strategy
for reducing pressure on land and water resources. In the Reference scenario the regional
pattern and total imports for developing regions in 2025 are comparable to the projections
to 2020 by the International Food Policy Research Institute (IFPRI) (Pinstrup-Andersen,
et al., 1997). Turning to the OECD and transitional regions, in the base year North
America and Western Europe are the only net cereal exporters (Figure 4-16). In the
scenarios, Pacific OECD continues to be a net importer, while the transitional regions ofEastern Europe and the FSU become significant exporters, as they recover from a recent
drop in production and as yields in the FSU, which today are well below those of Western
Europe and North America, increase. The Reference scenario values in 2025 are again
broadly comparable to those of IFPRI. In particular, the transitional regions become net
exporters in the IFPRI scenario.

144

Agriculture, Land and Food

Table 4-22. SSRs for cereal crops in the scenarios

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
0.79
0.99
0.84
0.63
0.95
0.99
0.74
1.35
0.74
1.12

Reference
2025
0.80
0.91
0.84
0.44
0.93
1.39
1.15
1.41
0.62
1.31

2050
0.70
0.93
0.92
0.39
0.92
1.46
1.21
1.57
0.61
1.40

Policy Reform
2025
2050
0.69
0.52
0.95
0.96
0.89
0.89
0.40
0.35
0.82
0.78
1.38
1.57
1.19
1.30
1.57
2.05
0.56
0.55
1.41
1.61

Figure 4-15. Net cereal imports of developing regions
800

S+SE Asia
Middle East
Latin America
China+
AfricaMillion tonnes

600

400

200

0
1994

2025

2050

Reference

2025

2050

Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

145

Figure 4-16. Net cereal exports of OECD and transitional regions
W. Europe
Pacific OECD
N. America
FSU
E. Europe

Million tonnes

700

500

300

100

-100
1994

2025
2050
Reference

2025

2050

Policy Reform

The self-sufficiency ratios for non-cereal crops are shown in Table 4-23. In the Reference
scenario, the OECD and transitional regions maintain self-sufficiency ratios close to one;for the FSU this represents an increase in self-sufficiency relative to the base year. Latin
America remains the dominant exporter, although exports decline as a fraction of
consumption over the course of the scenario. South and Southeast Asia, also, maintains
its position as an exporter. In the Policy Reform scenario the pattern is substantially thesame. However, Latin America exports more than in the Reference scenario, while Southand Southeast Asia becomes a significant importer by the end of the scenario, as policies
aimed at reducing and then reversing forest loss (see Section 4.6.4) constrain the
expansion of rainfed cropland.
Table 4-23. Self-sufficiency ratios for “other crops” in the scenarios

AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

Reference
1994
2025
0.94
0.83
0.98
0.99
1.29
1.27
0.65
0.67
1.03
1.01
0.99
0.96
0.78
1.02
1.00
1.04
0.90
0.99
0.92
0.99

Policy Reform
2050
2025
0.93
0.84
1.04
0.99
1.15
1.28
0.65
0.64
1.02
1.01
1.05
1.10
1.02
1.02
0.97
1.04
1.01
0.99
0.97
0.99

2050
0.90
1.08
1.20
0.63
0.90
1.25
1.33
1.07
1.02
1.07

146

Agriculture, Land and FoodBox 4-1. Grain production in China
Recently, the future of cereal imports in China has received close attention, spurred by the claimof Brown (1995) that China will have difficulty feeding its population from its own resources, asa result of increasing food consumption, land degradation, conversion of land for urban
expansion and industry, increasing contribution of meat in diets and declining potential forincreasing yields. Brown’s story has some similarities to the one presented here, with similarconsequences: to meet the growing demand China must increase imports. However, the twoanalyses differ in the degree to which imports will increase. The results obtained here do not
paint as dire a picture as that presented by Brown, principally because this study is more
optimistic about the potential for China to continue raising cereal yields (discussed in Section
4.5.2). As a result, although in the Reference scenario cropland in China+ is lost to expanding
human settlements and degradation at a rate of about one million hectares per year between 1995
and 2025, net cereal imports in 2025 are 50 million tonnes per year, compared to Brown’s
anticipated range of 207-369 million tonnes per year by 2030. In the Policy Reform scenario,
China+ has smaller net imports in 2025 than in the Reference scenario, about 27 million tonnes.
These values are similar to those presented in a recent article by Rozelle and Rosegrant (1997),
which summarized the conclusions of the papers collected in a special issue of Food Policy
devoted to a discussion of China’s agricultural potential. Rozelle and Rosegrant constructed
several scenarios for agricultural production in China in 2020, differing in the economic growthrate assumptions and the degree of land degradation. For two of the scenarios, the income
growth in China is similar to the income growth assumed here for the China+ region, about 4.5%
per year. The imports in both the Reference and Policy Reform scenarios fall between the
projected imports for these two scenarios: one in which resource degradation is assumed tocontinue at historical rates, in which case their imports are 25 million tonnes; and one in which
degradation proceeds at twice historical rates, in which case imports are 70 million tonnes.

4.6 Land Resources and Land Use
The requirements for cropland and grazing land place considerable pressure on existingland resources. These are exacerbated by land degradation, since severely degraded land
must be replaced by land elsewhere to maintain production. These agricultural demands
compete with other demands, which are incorporated in the scenarios, as discussed
below. Forests are valued for their products, as carbon sinks and for other reasons. Also,
land is required for human settlements, recreation and nature. For this study land isseparated into six land-use types: built environment, cropland, grazing land, forest,
protected land and a residual “other” land category. In this section each land-use type isdiscussed, with the procedure for estimating the base-year values, and the rates of
conversion of each land-use type to and from the others.
For the analysis, net conversion between different land-use types is determined in a
stepwise, hierarchical manner, and this section is arranged in the same way. First, land
required for the built environment is determined, as well as the conversion to the built
environment from other types of land—forest, cropland, grazing land and “other” land.
Next, cropland is considered: the total area is determined by agricultural production; thearea of cropland converted to the built environment and the amount of cultivated land lostConventional Worlds: A Technical Description of Bending the Curve Scenarios

147

to degradation is subtracted from the total; and any additional cropland area required isconverted from the remaining suitable land uses. Grazing land is considered next, thenforest and finally “other” land. (With the exception of North America in the Policy
Reform scenario, protected land is kept at its base year value throughout the scenario.)
The overall pattern of land-use changes in the scenarios are described in BTC. Most of
the changes seen for the world are dominated by changes in the developing regions,where the built environment, cropland and grazing land expand at the expense of forestsand “other” land.
4.6.1 Built Environment
Current Accounts
People settle land for many purposes other than agriculture. In both urban and rural
settings, land is cleared and altered for businesses, residences, roads, parking lots, parks,
landfills, burial grounds and other uses. The area of this built environment is not wellknown (Crosson and Anderson, 1992). For this study a combination of different sourcesand estimates was used to arrive at regional estimates for built-up land. The area of thebuilt environment per capita is shown by region in Table 4-24, along with the sources on
which the estimates are based. The highest value by far is that for North America, where
at 0.13 ha/capita, people on average require twice as much space as in Africa, with thesecond highest land requirements. (Note that the North American figure agrees with theestimate of urban density in the U.S. given in Crosson and Anderson, 1992).
Table 4-24. Current accounts built environment per capita

AfricaLatin America
Middle East
China+
S+SE Asia
E Europe
FSU
N America
W Europe
Pacific OECDha/capita
0.07
0.05
0.06
0.03
0.03
0.04
0.04
0.13
0.06
0.06

Source
1
1
1
1
1
2
4
3
2
4

Sources: (1) based on Fischer (1993); (2) based on WRI (1996a); (3) estimate given by
the area of “built-up land” (USDA, 1994) less the area of state parks (USBC, 1992;
1995); (4) own estimates based on other regional values.
Scenarios
In the Reference scenario, the growth in built environment was guided by the assumptionthat the amount of built-up land each person requires is determined primarily by theamount of land available and secondarily by cultural preferences and income. Support forthis view can be found in Table 4-24: regions with relatively low incomes and highpopulation density, such as China+ and South and Southeast Asia, have low built

148

Agriculture, Land and Foodenvironment per capita; regions with higher income and high population density, such as
Europe, have built environment per capita higher than in the Asian regions, but smaller
than in North America; finally, in Africa and Latin America, where land is plentiful but
incomes are relatively small, built environment per capita is comparable to that ofWestern Europe.
In the OECD regions built environment in the Reference scenario increases at a fixed rate
based on historical trends in the United States, consistent with the similar economic
growth in those regions. In the U.S., between 1982 and 1992 developed land per capita
increased at a rate of about 0.6% per year on average. It is assumed that built environment
per capita increases in all OECD regions at half this rate, 0.3% per year, throughout the
scenario. For the other regions the convergence algorithm was applied (see Annex), butwith different convergence targets for different regions, consistent with the comments
above: Africa and Latin America converge towards the OECD average, while China+,
South and Southeast Asia, the Middle East, Eastern Europe and FSU all converge
towards the more compact value for Western Europe and OECD Pacific.44 The resulting
values are shown in Figure 4-17.
In the Policy Reform scenario, as discussed in BTC, policies to protect forest and croplandfrom encroaching human settlements are expected to restrain the growth in the built
environment in all regions compared to the Reference scenario, although this tendency is
counteracted to some extent by relatively higher income growth in developing regions.
Specifically, it is assumed that all regions approach the Western European average, while
in Western Europe itself human settlements become slightly more compact throughout
the scenario. The developing regions approach the Western European pattern as theirincomes approach the base year OECD values, using the convergence algorithm. The
scenario values are shown in Figure 4-17.

44 In the Middle East land is plentiful, but water is not.

The convergence towards the Western European value rather than towards
the OECD average is based on an assumption that the costs of expanding water delivery systems will limit the growth of the built
environment. The convergence pattern for the FSU region, where land is also plentiful, is based on the assumption that FSU willtend more towards European patterns than towards North American ones. In the case of FSU, alternative assumptions would havelittle effect on land pressure.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

149

hectares per capita

Figure 4-17. Built environment per capita in the scenarios
Reference
Policy Reform
0.16

0.14

0.14

0.12

AfricaChina+

0.12

0.1

E Europe

0.1

FSU
0.08

Lat Amer

0.08

Mid East

0.06

N Amer0.06
0.04

S+SE Asi
0.02

0.02
0
1995

Pac OECD

0.04

W Europe

0
2025

2050

1995

2025

2050

For developing regions, in the Reference scenario it is assumed that built environment
expands into cropland, pasture land, forest and “other” land in the same proportion inboth scenario years, 2025 and 2050. The distribution in each region was based on datafrom Fischer (1993). For the OECD and transitional regions it is assumed that built
environment expands into pasture land, cropland and forest in equal proportions (30% toeach), and the remainder (10%) into other land. This assumption is based on observed
land-use changes in the U.S. between 1982 and 1992 (USDA, 1994).
For the developing regions, in the Policy Reform scenario, consistent with thesustainability targets given in Section 1, conversion of forests to the built environment
slows. Conversion of other land-use types is assumed to continue in the same proportions
as in the Reference scenario. In the OECD and transitional regions the area under built
environment declines between 2025 and 2050, the result of tighter settlement patterns
and, in some regions, declining populations.

4.6.2 Land-Use Changes from Cropland Expansion
Current Accounts
The current distribution of cropland is described in Section 4.5.1. The percentage of total
cropland area varies considerably from region to region, ranging from 6% in Africa to
44% in Eastern Europe.
Scenarios
Requirements for cropland in the scenarios and its distribution are discussed in Section
4.5.1. Future demands are driven by dietary and other requirements for crops, moderated
by assumed increases in cropland productivity.
In the Reference scenario, cropland in the developing regions expands into pasture land
and forests in proportion to the amount of potentially cultivable land currently under150

Agriculture, Land and Food

those uses.45 For the OECD and transitional regions it is assumed that cropland expands
into forest and grazing land in equal proportions. Historical rates for these processes areshown in Box 4-2.
Even as requirements for cropland increase in the scenario, some existing cropland
becomes degraded to the point where it is no longer productive. Historical rates ofcropland degradation are discussed in Box 4-2. In the analysis this is represented as aconversion from cropland to “other” land. Degradation processes add to the total area offorest and grazing land that must be brought under cultivation in the scenario. Globally,
cropland is assumed to be lost to severe degradation in the Reference scenario at a rate of
3 million hectares per year. 46 The regional distribution is discussed below.
On average, crop area increases by about 3 million hectares per year throughout theReference scenario (Section 4.5.1). Added to the 3 million hectares lost each year tosevere degradation, this gives roughly 6 million hectares of land that must be converted
from existing forest or grazing land, close to the total of 5.7 million hectares per year
shown in Box 4-2.

45 Ideally, the estimated rates would take into account the suitability of the land for different crops, its availability for exploitation, or

the productivity of the additional land compared with land already under cultivation. However, data were not available to this level ofdetail.
46 This value is higher than the historical rate of 2.15 million hectares per year, shown in Table 4-26, but lower than some estimatesof current rates of degradation. Kendall and Pimentel (1994) report that cropland is lost to urbanization and degradation at a rate ofover 10 million hectares per year. In the Reference scenario, cropland is converted to the built environment at about 1.5 million
hectares per year on average: added to degradation rates, the total loss of 4.5 million hectares per year is about half their estimate.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios151

Box 4-2. Historical Cropland Trends
Clearing for Cropland
Historical rates of land use change were estimated by Houghton and Hackler (1995) for eight regions, six ofwhich are very similar in composition to six of the ten regions used in this study. Coverage in time, andcoverage of ecosystems, was limited by the available data: for South and Southeast Asia they present
conversions from six forest types and grassland to cropland, while for tropical Africa they present only total
clearing rates for two types of forest. The rates of conversion of all forest types and grassland to croplandfor each of their regions are shown in Table 4-25.
Table 4-25. Clearing for cropland (million hectares per year)
Mha / year
N. America
Pacific Dev.
EuropeFSU
Tropical Africa
N. Africa+Middle East
China
S+Central America
S+SE Asia

Forest + Woodland
Clearing
Shifting Cult.
0.00
0.08
-0.60
0.02
Forest clearing: 6 Mha/year
0.15
0.07
1.13
16.25
2.46
10.68

Grass + Scrub
Clearing
Shifting Cult.
0.00
0.12
-0.20
0.02
1.32
0.59
0.52
0.00

0.00

Year of
Estimate
1980
1980
1980
1985
1990
1980
1980
1990
1990

Source: Houghton and Hackler, (1995).
Based on these estimates, about 3.3 million hectares of forest and 2.4 million hectares of grassland arecleared annually for crops. A further 27 million hectares of forest land is cleared each year for shifting
cultivation. This temporary clearing is generally part of a long-term cycle, in which previously cleared landis allowed to grow back under native vegetation (Greenland and Okigbo, 1983). However, within the past
decade, at least, the rate of clearing appears to be exceeding the rate of regrowth (FRA, 1996).
Cropland Degradation
The major effect of cropland degradation is to reduce yields, but some degradation is severe enough that theland must be pulled from production. Rates for such processes are difficult to estimate, and strongly
debated. A recent study, the Global Assessment of Soil Degradation (GLASOD) was conceived in responseto this debate (Oldeman et al., 1991), although these rates have also been disputed (Alexandratos, 1995).
The goal of the project was to provide an estimate of the extent of global soil degradation, using asystematic set of criteria for all parts of the world. The study covers land degradation that has occurredroughly in the period following the end of the second world war. Four degrees of degradation weredistinguished: “light,” “moderate,” “strong” and “extreme.” The last two represent land so degraded it mustbe pulled from production, and are differentiated from one another by the effort required to return them touse. Moderately degraded land is considerably reduced in quality, but can still support some level ofproduction. Historical values of land degradation from agricultural practices are shown in the text below, in
Table 4-26. The rates shown in the table were estimated by dividing the total area by 45 years, roughly theperiod covered by the GLASOD study. The rate of severe degradation is much higher in the developing
regions than in the transitional or OECD regions, with the highest rate occurring in Africa. The pattern formoderate degradation is more varied between the macro-regions. The highest rate occurs in North America,
mostly as a result of topsoil loss from wind and water action, while the second highest occurs in Africa.

Historical rates of cropland degradation are shown in Table 4-26. In the future, some of
the land that is “moderately” degraded today is likely to become severely degraded, and in
this way serves as an indicator of possible future trends. However, note that in all but oneof the transitional and OECD regions rates of moderate degradation are considerably152

Agriculture, Land and Food

higher than rates of severe degradation, indicating that some effort has been expended tokeep cropland from becoming unproductive. After examining the historical rates ofmoderate and severe degradation, and bearing in mind this pattern in the OECD andtransitional regions, the rates of cropland loss due to severe degradation shown in Table
4-26 were adopted for the Reference scenario. In all regions except the Middle East therate of cropland degradation in the scenario is between the rates of “moderately” and“severely” degraded cropland in the past. (In the Middle East there is very little
potentially cultivable land left, and it is assumed that even in the Reference scenario someeffort will be made to slow the rate of degradation.)
Table 4-26. Land degradation from agriculture: historical and Reference scenario rates
Average rate (Mha / year)
Light
Moderate
Severe 1995-2050
Africa
0.59
1.16
1.03
1.08
China+
1.01
0.99
0.38
0.68
Latin America 0.73
1.09
0.25
0.66
Middle East 0.04
0.21
0.30
0.15
S+SE Asia
0.22
0.40
0.10
0.25
E. Europe
0.12
0.17
0.02
0.05
FSU
0.56
0.25
0.02
0.07
N. America
0.16
1.27
0.00
0.02
Pacific OECD 0.09
0.03
0.05
0.04
W. Europe
0.49
0.22
0.01
0.01
World
4.00
5.78
2.15
3.00
Notes: “Severely” degraded land is the sum of “strongly” and “extremely” degraded land in Oldeman et al. (1991). The
estimates in the table were developed using the electronic map constructed by GRID/UNEP from the GLASOD map(GRID/UNEP, 1991). Regional areas were estimated by summing up degraded areas in polygons whose centroid lay
within the regional boundaries; at the regional level the differences between the areas estimated using this approachand the actual areas are small.

In the Policy Reform scenario, in keeping with the sustainability goals described in
Section 1, land degradation is assumed to proceed at less than the historical rate. For
lightly and moderately degraded land this contributes to the maintenance of yields relative
to the Reference scenario, as discussed in Section 4.5.2. It is also reflected in a lower rateof cropland loss to severe degradation. Between 1995 and 2025 cropland is lost at half theReference rate (1.5 Mha/year). Between 2025 and 2050 the trend reverses as severely
degraded land is returned to production, so that there is no net loss of cropland over theperiod 1995 to 2050.
In addition to reduced land degradation, in the Policy Reform scenario conversion offorests to cropland slows. As a result, most of the new cropland in the scenario isconverted from grazing land. Also, in North America in the Policy Reform scenario somefarmland currently withheld from production (and included with “other” land here) isreturned to cultivation in response to increasing global demand for agricultural products.
This land-use conversion is considered below, in the discussion of “other” land.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

153

4.6.3 Pasture and Rangeland
Current Accounts
The available data on pasture and rangeland were described in Section 4.4, as well as thecurrent distribution of grazing land among the ten scenario regions. As discussed in that
section, there is little quantitative information on the dynamics of grazing land use.
However, some qualitative trends have been described. For example, in the High Plainsof the United States around the turn of this century, and today in the Sahel, grasslands
used for grazing have been converted to cropland by sedentary farmers (Moran, 1982;
Durning and Brough, 1991), while in Latin America, grazing lands have expanded at theexpense of forests.47
Scenarios
The changes in grazing land area in the scenarios, driven by meat and milk production
requirements, are discussed in Section 4.4. In previous subsections, conversion ofgrazing land to the built environment and cropland was described. To make up the totalrequirement for grazing land in the scenario, it expands at the expense of forest and“other” land. There are few data on the composition of “other” land, in each region, andlittle information about the relative rates of conversion for these two land use types. For
simplicity, in the Reference scenario it is assumed that in every region half of the land
converted to grazing land is from forest and half from “other” land. In the Policy Reform
scenario, most of the conversion is from “other” land, with the portion from forest
gradually dropping to zero in the course of the scenario.

4.6.4 Forests
Current Accounts
From 1980 to 1995, the average rate of forest loss worldwide was about 12 million
hectares each year (FAO, 1997b). In the developing world there was a net loss of some
200 million hectares over this period, while net reforestation and afforestation in theindustrialized countries added about 20 million hectares. This reduction of forest areameans the loss of many valued services (FAO, 1997b): forests are believed to containtwo-thirds of the world’s species; they are home to forest-dwelling people; they provide a
potentially renewable supply of a variety of products for human needs; they are a sourceof income for the poor; and they are a potentially important component in mitigating
global climate change. In both scenarios, deforestation continues as a result of expanding
human settlements and agricultural land.
The term “forest” is applied to a wide range of land covers. For this study, the FAOSTAT
category “forest and woodland” is used.48 Within this category are included natural

47 Although it is not treated explicitly in the scenarios, land has also been degraded in the past due to grazing practices. As withcropland, the main effect is reduced productivity, but a certain amount of the degradation is severe enough to make the land
unusable for livestock production. Also as with cropland, rates and the degree of severity are subject to debate.
48 For six of the regions — the OECD and transitional regions and China+ — protected forest area was removed from the total. For
these regions data on total protected area and protected forest area were taken from WCMC (1998a; 1998b). For the remaining

154

Agriculture, Land and Foodforests and plantations, and such diverse habitats as closed forests and sparse woodlands.
In a recent assessment of forest cover change in tropical countries, it was found thatbetween 1980 and 1990, 92 million hectares of closed forest were lost in the survey area,
while only 4 million hectares were added (FRA, 1996). Of the total lost, about 60% wasconverted to land types with some woody cover, but with significantly reduced biomass,
such as open or fragmented forest; much of this change would result in no net loss of“forest land” as the term is used here. Also, the forest land category does not take into
account the quality of the forests — their health, genetic diversity and age profile (WRI,1998). Finally, the total area does not capture the extent of fragmentation of forests,
which affects their resilience, as fragmented forests cannot provide secure habitats fornative plants and animals (WRI, 1998).
As a result of the diverse set of land covers included within the forest land category, thedeforestation rates reported here are consistent with a range of scenarios for biomass andbiodiversity loss. However, some indications can be gained from detailed studies of pastland-use changes. From the study mentioned above of forest cover changes in tropicalregions, between 1980 and 1990 the most stable land-cover classes were forest
plantations and non-forested land. Thus, afforestation and reforestation were notdominant trends. For both plantations and non-forested land about 99% of the land areathat was in the category in 1980 was still in that category in 1990. The least stable
category was fallow land associated with shifting cultivation (“long fallow”), with 86%
remaining in the same category, while 8% was converted to short fallow or non-forested
land. Note that even in the case of shifting cultivation, where in traditional systems forest
cover is allowed to regenerate, the dominant change is to land with lower biomass
content. The percentages of closed and open forest in 1980 that remained in 1990 weresimilar to each other, about 93%, but because of the much larger area of closed forest, theabsolute changes were larger. Overall, conversion of forest land was dominated by theloss of closed forest, and nearly all of the changes were to land with smaller biomass
content.
Scenarios
In both scenarios, deforestation rates are determined as a result of all of the land-use
conversions described above: to the built environment, cropland and rangeland. The
resulting land areas are shown in Table 4-3. In the Reference scenario there is a steadyloss of forest land, with the total area decreasing at close to historical rates. On average,
about 12.5 Mha of forest land is lost each year between 1995 and 2050. Most of thedecrease is in the developing regions, where forest area decreases at about 11.5 Mha peryear over the course of the scenario. In the Policy Reform scenario, as a result of policies
aimed at protecting forests, there are fewer conversions of forest to other land-use types,
and less deforestation between 1995 and 2025 than in the Reference scenario. The
sustainability target of halting, and then reversing, deforestation given in Section 1
constrains the expansion of cultivated land in developing regions, and drives the
agricultural trade assumptions in the Policy Reform scenario (discussed in Section 4.5.4).
regions, data on protected area were taken from Fischer (1993), to maintain consistency with the estimates of the distribution ofpotential cultivable land in these regions reported in Section 4.5.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

155

Between 2025 and 2050 there is net regrowth of forests in all developing regions, while
forest areas in transitional and OECD regions remain steady. However, the regrowth isnot sufficient for forest areas to return to base year levels.
Table 4-27. Forest area in the scenarios
Millions of hectares
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

1994
721
174
922
18
341
24
795
725
163
141

Reference
2025
523
142
862
9
290
24
794
722
146
139

2050
403
109
805
2
225
22
778
715
137
137

Policy Reform
2025
602
163
896
13
310
23
786
718
155
138

2050
622
172
899
16
315
23
786
718
155
138

4.6.5 Protected Land
Current Accounts
In many countries land has been set aside for environmental Table 4-28. Protected
protection, scientific research, education, maintenance of land
traditional cultures and other reasons. The extent of this % of total land area
protected land is generally well recorded. For the base year
accounts, two different sources were used for two groups of Africa
5.5
regions. For Africa, Latin America, the Middle East and Latin America
7.6South and Southeast Asia and China+, data from Fischer Middle East
3.2
(1993) were used.49 For the other regions, data on total China+
0.1
9.1
protected areas and protected forest land from the World S+SE Asia
E
Europe8.1
Conservation Monitoring Centre were used (WCMC, 1998a;
3.2
1998b). The amount of land protected in each region is FSU
NAmerica
10.2
shown in Table 4-28 as a percentage of total land area.
9.8
Globally, about 6% of all land is listed as protected for some W Europe
12.4
purpose or another. In the OECD regions, Eastern Europe Pacific OECD
World
6.3
and South and Southeast Asia close to 10% is protected,
while in other regions the amount is smaller. Only about 0.1% of land area in China+ isreported as being protected.

49 The data from Fischer (1993) were used to maintain consistency with the estimates of the distribution of potential cultivable land inthese regions reported in Section 4.5.

156

Agriculture, Land and Food

Scenarios
In the Reference scenario it is assumed that there is no net change in protected areas,
which are kept at the base year value in every region. In the Policy Reform scenario some
protected land is removed and some added, as a result of changing land-use policies. In
the U.S. circa 1995, about 14 million hectares of land were held in the Conservation
Reserve Program (USDA, 1994; FSA, 1998). This represents land that has been pulledfrom agricultural production because it is highly erodible. It has been estimated that onethird can be safely returned to production (Conway, 1997). In the Policy Reform scenario
it is assumed that this area of about 5 million hectares is returned to production,
represented in the land accounts as a shift from protected land to cropland. In the Policy
Reform scenario, as discussed in BTC, it is also assumed that protected forest land
increases, as one of several policy measures to reduce the rate of deforestation.50
4.6.6 Other Land
Current Accounts“Other” land contains any land that is not included in any of the previous categories. It
might include wetlands, deserts and barren land, semi-desert areas with vegetation too
sparse to support livestock or ice. It might also include rangeland that is suitable forgrazing but not used for the purpose.
The “other” land category used for this study differs fromthat in the FAOSTAT database, because some protected
land and the built environment have been subtracted fromthe FAO totals. The land areas computed this way are
shown in Table 4-29. The amount of total land areaclassified as “other” varies widely between different
regions. In the Middle East nearly half of the total land area
is in this category, while in Eastern Europe only 1% is.

Table 4-29. “Other” land
% of total land area
AfricaLatin America
Middle East
China+
S+SE Asia
E Europe
FSU
N America
W Europe
Pacific OECD
World

32.5
9.1
47.5
27.4
6.0
0.8
33.5
21.2
9.6
8.5
23.4

50 However, this is not recorded in the land-use balances, as it would give an apparent reduction in forest area where none exists.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

157

Scenarios
In the scenarios, there are no requirements for “other” land. Instead, the area of “other”
land is determined by its conversion to other types of land use, as described above, in amanner similar to the evaluation of changes in forest area. In the scenarios, land in thiscategory is converted to the built environment and grazing land, and land is added to it
through cropland degradation. The land areas in both scenarios are show in Table 4-30.
Table 4-30. Area of “other” land in the scenarios
Millions of hectares
AfricaChina+
Latin America
Middle East
S+SE Asia
E. Europe
FSU
N. America
Pacific OECD
W. Europe

Reference
1994
2025
964
844
317
308
183
176
287
280
51
44
1
2
736
734
389
388
71
60
44
44

Policy Reform
2050
2025
776
698
297
300
164
160
274
280
35
33
3
1
726
719
390
381
53
53
45
42

2050
667
283
146
271
16
0
715
319
39
25

In both scenarios “other” land tends to decrease in all regions. The decrease is muchgreater in the Policy Reform scenario than in the Reference scenario, as a result of lower
cropland degradation rates and a slower rate of expansion of grazing land into forest.

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Conventional Worlds: A Technical Description of Bending the Curve Scenarios

159

5. WaterIn this section the current accounts and scenarios for water use are described. Because of
the strong local nature of water supply problems, the analysis is performed at a national
level, with the results presented for the ten global regions.51
Section 5.1 discusses freshwater resources. Section 5.2 discusses withdrawals and water
stress. Section 5.2.1 covers the current accounts, and Section 5.2.2 describes the
scenarios.

5.1 Resources
Data on national renewable freshwater resources use for this study are drawn primarily
from compilations conducted for the UN Comprehensive Freshwater Assessment (Najlis,
1996; Shiklomanov, 1997), and supplemented by data from Raskin et al. (1995). For thenational-level analysis, river inflows from adjacent countries are included in the totals,
which may lead to double counting of freshwater supplies in some cases (Raskin et al.,1995). Nonrenewable sources — such as “fossil” aquifers that are either not being
recharged or are being recharged very slowly — are not included in total resources, since
their use cannot be sustainable over the long term.
Total annual water resources are shown for each of the ten regions in Table 5-1. For theregional totals, national cross-border flows have been excluded. Resources per person
vary from 1,500 cubic meters per year in the Middle East to 22,200 cubic meters per year
in Latin America — a fifteen-fold difference. Variation within regions is hidden by theregional totals, but is captured to some degree in the estimates of national water stresslevels, as described below.
In the scenarios it is assumed that
renewable resources remain at base-yearlevels. This assumption would beviolated if the global climate changes
significantly, as current models suggest(Raskin et al., 1997). Also, in many
places, water pollution is reducing theusable freshwater resources (Postel,1992). It is also possible to add to theuseful resources, by capturing water thatis currently lost as runoff using smallscale technology (Conway, 1997;
Hudson, 1992; Postel, 1992). In addition
to these changes in useable renewable
resources,
many
countries
today

Table 5-1. Annual renewable water resources
Total *Per Person
3

AfricaLatin America
Middle East
China+
S+SE Asia
E Europe
FSU
N America
W Europe
Pacific OECD
World

3

(1000 km ) (1000 m /capita)
4.0
5.5
10.6
22.2
0.3
1.5
3.5
2.7
9.3
5.5
0.2
2.1
4.9
16.7
5.3
17.9
2.0
4.4
1.2
8.4
41.3
7.3

* Totals exclude cross-border river flows

51 Ideally it would be performed at a river basin level, but data relating to current and potential demands, grouped by river basin, are

not widely available. The use of a national-level analysis is a compromise consistent with the availability of the data.

160

Water

effectively increase their resources by recycling wastewater and desalinating brackish
water and sea water. In some cases, these sources contribute significantly to total watersupplies (FAO, 1996a; Postel, 1992). In the analysis, these supplies are included
explicitly, as discussed below.
Desalinated Water and Wastewater
In the base year, several countries in the Middle East and North Africa supplied some of
their water needs from desalinization and wastewater treatment plants. In the context ofthe high water stress found in the scenarios, these countries may be forced to rely moreheavily on this expensive option, with negative but uncertain implications on economic
growth. The scenario assumptions for supplies from these sources are shown in Table 52. In the Reference scenario, supplies roughly double in the Middle East between 1995
and 2050, while in North Africa they more than double. In the Policy Reform scenario, aspart of the effort to meet the sustainability goals outlined in Section 1, supplies fromdesalinization and wastewater treatment plants increase more rapidly than in the
Reference scenario. The increase is greatest in the Middle East, where production fromthese sources increases by 9 times over the course of the scenario. However, as a fractionof withdrawals, the supply from desalinization and wastewater treatment plants in the
Middle East in 2050 is only 6% of the withdrawals in that year. This is close to the value
for Jordan in the base year, but well below that of Bahrain, the United Arab Emirates,
Qatar and Kuwait, where between 16% and 60% of water withdrawals were supplied bytreated water.
Table 5-2. Desalinized Water and Recycled Wastewater in the Scenarios
Million cubic meters
Middle East
North Africa

1995
1,403
493

Reference
2025
2050
2,121
2,649
752
1,096

Policy Reform
2025
2050
7,176
12,671
6,681
12,510Sources: 1995 values based on FAO (1996a) and Gleick (1993)

5.2 Withdrawals and Water Stress
In the analysis, the estimation of water stress is determined at the national level, sinceregional aggregation can mask local problems. However, water withdrawals aredeveloped using a disaggregated approach at the sectoral level. For this study, thesectoral analysis was performed at the regional level, so in the scenarios withdrawals are
determined at the regional level. The analytical approach is outlined below.
Withdrawals Analysis
Water withdrawals are disaggregated at the sectoral and subsectoral level. Ideally,
withdrawals would be disaggregated to the level of end-uses; for example, toilets inhouseholds, or particular processes and technologies in industries. However, data
limitations do not permit this level of detail in this study. Instead, withdrawals are
assigned to the agriculture, domestic, industry and energy sectors, further broken down by
manufacturing subsector and energy conversion process (thermal electric generation andConventional Worlds: A Technical Description of Bending the Curve Scenarios

161

oil refining). However, even at this level data are rarely available, so in constructing thecurrent accounts, for several regions sectoral water withdrawals are estimated, based on
available data.
In the analysis, sectoral withdrawals are expressed as the product of an activity level —such as population in the household sector or subsectoral value added in the industrial
sector — and an intensity. In the scenarios, the activity levels are determined byassumptions made outside of the water analysis: population and income growth, changing
sectoral composition of GDP, and irrigated harvested area. The emphasis in the wateruse analysis is therefore on the changing intensities. The relationship between sectoral
withdrawals, activity levels and intensities can be expressed as:
Withdrawal = Activity Level × Intensity .

(5-1)

Water Stress Analysis
As described above, water withdrawals in both scenarios are computed at the regional
level. However, because water supply issues are very local in nature, the analysis of thedegree of water stress is performed at the national level.
Several measures of “water stress” have been introduced (Raskin et al., 1997).
Depending on the measure, they are intended to provide rough indicators focusing on
different aspects of water stress: the pressures placed on water resources, the reliability ofwater supplies, or the ability for society to cope with limited renewable freshwatersupplies. The measure used in this analysis is the use-to-resource ratio, the ratio of total
freshwater withdrawals to renewable freshwater resources. The ratio is a measure of thepressure placed on water resources. In some countries, part of the withdrawal demand is
supplied by desalinization and wastewater treatment plants. These withdrawals are
excluded when computing the use-to-resource ratio, since they do not put pressure on
renewable water resources. Expressed as a formula, the water-stress indicator used in thisstudy is given by:
Use - to - Resource Ratio =Withdrawals - (desalinization & wastewater)
Renewable resources

.

(5-2)

A population in a given area is considered to be in water stress if the use-to-resource ratio
for that area exceeds certain critical values. In Raskin et al. (1997), the following criteria
were used: a use-to-resource ratio of less than 0.1 indicates no stress, between 0.1 and
0.2 low stress, between 0.2 and 0.4 stress, and greater than 0.4, high stress. At use-toresource ratios higher than 0.2, water stress can begin to be a limiting factor on economic
development (Raskin et al., 1997). Using an approach adapted from that of Raskin et al.
(1997), the population within a country in areas of water stress, expressed as a proportionof the total population in the country, is assumed to increase smoothly as the national useto-resource ratio rises from 0.1 to 0.4. When the national use-to-resource ratio is 0.1,
none of the population in the country is in water stressed areas, and when the use-toresource ratio is 0.4, all of the national population is experiencing water stress.

162

Water

5.2.1 Current Accounts
In global compilations of water use, withdrawals are usually reported for three sectors:
domestic, industrial and agricultural. The sources for this study provide withdrawals inthese categories (Najlis, 1996; Shiklomanov, 1996; Raskin, et al., 1995). Withdrawals
for these sectors are shown by region in Table 5-3.
Table 5-3. Withdrawals by sector and region in 1995
km

3

AfricaLatin America
Middle East
China+
S+SE Asia
E Europe
FSU
N America
W Europe
Pacific OECD
World

Domestic
16
15
9
87
107
6
47
72
42
21
423

Industrial
12
20
2
31
48
34
170
255
125
40
738

Agricultural
139
222
188
436
986
22
164
212
107
61
2,538

Total
167
257
200
553
1,142
63
381
540
275
121
3,699

The domestic sector includes water use in households and in the service sector. The
industrial sector includes energy sector uses (thermoelectric generation and petroleum
refining), in addition to standard industrial activities (manufacturing, mining, quarrying,
and construction). Water is used in agriculture for irrigation, watering livestock andaquaculture. However, nearly all of the water withdrawn for agriculture is used for
irrigation (Raskin et al., 1995; Luyten, 1995), and in this study, all of the reported
agricultural withdrawals are assumed to be for irrigating crops. As mentioned in Section
5.2.1, in this study withdrawals are assigned to domestic uses, manufacturing subsectors,
energy conversion processes and agriculture. The allocation is based on the available
data, as described below.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

Domestic
Water is used in households for human consumption,
toilets, dish washing, bathing, cleaning and outdooruse (e.g., lawn watering, car washing and decorativeuses). The service sector includes such waterintensive establishments as restaurants, cleaners,
hotels and hospitals. Data limitations require that
we aggregate over these diverse activities.

163

Table 5-4. Domestic
withdrawal intensities
3

m /capita
1995
22
32
53
65
64
66
160
244
91
138

AfricaLatin America
Middle East
China+
S+SE Asia
E Europe
FSU
N America
W Europe
Pacific OECD

Water requirements in each sector are calculated as
the product of two factors: an activity level and awater intensity. For domestic withdrawals, theactivity level is population and the water intensity iswater use per person. Domestic water intensities ina given region reflect many factors, for example,
income levels, water infrastructure, technology andwater availability. As illustrated in Figure 5-1, current domestic water intensities vary
widely between countries and regions, and generally increase with income. Regional
annual per capita withdrawals are shown in Table 5-4. Values range from as little as 22
cubic meters per person each year in Africa to over 240 cubic meters in North America.
Figure 5-1. Household withdrawals vs. income
300

Canadacubic meters per capita

250

U.S.

200
FSU

150

OECD
Transitional

100

Developing
50
Netherlands

0
0

5,000

10,00015,00020,000$GDP per capita

25,00030,000

Manufacturing
Water withdrawals for manufacturing are disaggregated by the six subsectors introducedin the energy analysis in Section 3.2.4: iron and steel; non-ferrous metals; non-metallic
minerals; paper and pulp; chemicals; and a miscellaneous “other” category. The fivesubsectors that are treated explicitly are relatively more water-intensive than the ones

164

Water

grouped into the “other” category (Raskin et al., 1995). The activity level for this sector
is subsectoral value added. The water intensity is defined as withdrawals per unit value
added.
Withdrawals for manufacturing contribute to total Table 5-5. Manufacturingindustrial withdrawals, the conventional category in water intensities
international compilations of water use. Withdrawals for m3/$1000 VA
energy conversion, described in the next subsection, also
1995
contribute to the total. For the non-OECD regions, the Africa
9general approach taken in constructing the current Latin America
103accounts data set is to start with estimated intensities for Middle East
5
manufacturing and energy conversion based on available China+
S+SE
Asia
5
data, and then to adjust them to reproduce the reported
E
Europe28
industrial withdrawals.
Withdrawal data by
89
manufacturing subsector are scarce, so in every region, FSU
N America
16
manufacturing intensities are based on data for the
W Europe
19
United States in the mid-1980s (Raskin et al., 1995). Pacific OECD34
The relative intensities in each subsector, shown in
Figure 5-2, are the same for every region. The absolute intensities are set so that when
they are multiplied by the base-year subsectoral value added, they reproduce the total
regional manufacturing withdrawals, which are adjusted in combination with withdrawals
for energy conversion.
Figure 5-2. Relative values for manufacturing intensities
Iron & steel
ChemicalNon-ferr. metals
Paper & pulp
Stone, glass & clay
Other
Water withdrawals per unit value added

The aggregate intensities are shown in Table 5-5. The values vary over a wide range.
Generally, the intensities are lowest in the developing regions, and highest in thetransitional regions. The highest value for any region is that for the FSU, at 89 cubic

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

165

meters of water per $1,000 of value added. As shown in the next subsection, theestimated intensity for thermoelectric generation is also high in the FSU.
Energy Conversion
As for the other sectors, water withdrawals for energy conversion processes—petroleum
refining and thermal electric generation—are expressed as the product of an activity level
and an intensity. For this sector, energy production is used as an activity measure.
Petroleum and electricity production are discussed in Section 3.3.
Water is used in thermoelectric generation principally for cooling purposes. A variety ofcooling technologies is available. These include once-through systems, which have highwithdrawal rates, but low consumption rates (Gleick, 1993); cooling towers, which have
low withdrawal rates but relatively high consumption rates (Gleick, 1993); and closed
systems, which have both low withdrawal and low consumption rates (Willenbrock andThomas, 1980). Closed systems are usually introduced when little or no water supply isavailable, and are not widely used.
Table 5-6. Thermoelectric
Data on water withdrawals for energy conversion are cooling intensities in OECD
relatively scarce. The OECD collects data on water countries
use for thermoelectric generation from its members m3/GJ(OECD, 1996). In addition, data on water use for oil
1995
37
refining are available for the United States. The Canada
16estimated thermoelectric intensities for the OECD United States
Austria
13
countries, computed using the withdrawal statistics
Finland2
from the OECD and energy production data from IEA
15(1997) are shown in Table 5-6. The values vary over a France
Germany
13
wide range, due to differences in the mix of generation
Italy
10
and cooling technology, as discussed above, and in the
Netherlands
16
fraction of total water requirements that are supplied Portugal
28
from freshwater sources. For example, Japan meets Spain
11much of its requirements for thermoelectric cooling Sweden
0.1
using salt-water (Raskin et al., 1995).
Turkey24
United Kingdom
Japan

4
0.3

166

Water

Using the available data for the OECD regions and Table 5-7. Thermoelectric
estimated intensities for the non-OECD regions as water intensities
developed for the Conventional Development scenario m3/GJ(Raskin et al., 1995), regional base-year intensity
1995
5.8
estimates are developed (see Table 5-7). For the Africa
3.2
OECD regions, the thermoelectric intensities are Latin America
MiddleEast
0.3
computed directly, extrapolating from the figures in
China+
5.5
Table 5-6. Refining intensities for the OECD regions
10.0
are set based on the value for the United States. For the S+SE Asia
E Europe
22.3
non-OECD regions, the intensities of Raskin et al.
FSU
28.4(1995) are applied initially, and then adjusted in
N America
14.7
combination with the manufacturing intensities to W Europe
9.0
reproducethereportedbase-year
industrial Pacific OECD
0.2
withdrawals.
The thermoelectric and refining
intensities are adjusted by the same factor, while ensuring that the adjusted thermoelectric
intensities were kept within the range of values seen in the OECD regions (Table 5-6).
Agriculture
Nearly 70% of current global fresh water withdrawals are for agricultural applications, asseen in Table 5-3. In the developing regions, agriculture claims the dominant share of
freshwater withdrawals. In the OECD and transitional regions, agricultural withdrawals
are also important, roughly equaling the total requirements from industry.
For this analysis, all agricultural withdrawals are Table 5-8. Irrigation water
assigned to irrigation. In the agriculture sector, the intensities
activity level is the harvested irrigated area, and the 1000 m3/ha
intensity is water withdrawal per harvested area. The
1995
9
distribution of irrigated land is discussed in Section 4.5. Africa
12
Roughly 17% of the world’s cultivated land is currently Latin America
12
under irrigation, with yields typically much higher than Middle East
5
for rain-fed agriculture. For example, in the United China+
S+SE
Asia
10
States, average irrigated farming yields are about four4times those of rain-fed farms (Bajwa et al., 1987). E Europe
FSU
11
Irrigation water intensities depend on a number of
N America
12
factors, including the local climate and soils, crop mix, W Europe
7
practices, efficiency of water use and economic Pacific OECD
22
conditions. As a result of these interacting factors,
aggregate irrigation intensities, shown in Table 5-8, show considerable variation between
regions.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

167

Water Stress
Table 5-9. Population in water stress
The total regional water withdrawals 1995
Millions of % of Regional
given above in Table 5-3 are
PeoplePopulation
constructed based on withdrawal data N. America
88
30
29
19
at the national level. National levels of Pacific OECD182
39
water stress are determined by the W. Europe
E.
Europe17
17national use-to-resource ratio, or thewithdrawals as a fraction of total FSU
Africa155
22
renewable resources, as discussed in
338
25
Section 5.2.2. The results, aggregated China +Latin America
74
16
to the regional level, are shown in
Middle East
164
92
Table 5-9.52 The local nature of water- S+SE Asia
816
49
supply problems is evident from the Developing
1,549
35
patterns seen in the table, in that there Transitional17
4
is no sharp distinction between the OECD
298
33
OECD and developing regions. This is World
1,863
33
in contrast to many of the other issues
explored in the Conventional Worlds scenarios. The incidence of water stress in thetransitional regions is low because of relatively abundant water resources. At a global
level, about one-third of the world’s population experiences some degree of water stress.
5.2.2 Scenarios
In this section the water scenarios are presented. The Reference scenario analysis is, to alarge extent, an update and elaboration of two earlier studies (Raskin et al., 1995; Raskin
et al., 1997). The analysis uses a later year base year (1995 instead of 1990), newly
updated sources of data, and updated methodologies for making projections of certain key
variables. Some of the text in this chapter is adapted from the reports of those studies.
As discussed in Section 5.2.1, withdrawals are determined at the sectoral level. Sectoral
intensities are then multiplied by sectoral activity levels to determine withdrawals. The
assumptions for each sector are introduced below, after first discussing the sustainability
goals.
The sustainability of the Reference scenario is determined by comparing the national useto-resource ratios in the scenario to the sustainability goals introduced in Section 1. The
Policy Reform scenario is constructed to satisfy the goals. Two indicators are used todefine the goals: the use-to-resource ratio and the population in water stress. The
scenario goals are summarized here.
To meet the goals, the use-to-resource ratio must peak by 2025, and then decrease. In
2050, for countries that were not experiencing high water stress in the base year (i.e., a
use-to-resource ratio greater than 0.4), the use-to-resource ratio in 2050 should be less
52 For countries in the Middle East and North Africa, the contribution from desalinization and wastewater is first subtracted from thewithdrawals, as shown in Equation (5-2). The contribution from these sources for each country is discussed in the next section.

168

Water

than some value in the range 0.2-0.4, with the particular value depending on the level ofeffort required to meet the goal in the particular country. For countries in high stress inthe base year, the use-to-resource ratio must be below the base-year level. Considering
the high population and income growth in developing regions in the Policy Reform
scenario, these targets present considerable challenges for countries with limited water
resources.
In the specification of the sustainability goals, there is some flexibility in that there is no
general target for the use-to-resource ratio in 2050. The particular target varies fromcountry to country depending on the level of effort required. The required level of effort
is determined for the Policy Reform scenario based on the withdrawals that would arise ineach year assuming Reference scenario intensities, but Policy Reform scenario activity
levels. These withdrawal levels are estimated by scaling Reference scenario withdrawals
by a GDP-dependent factor.53
In terms of the population in water stress, the target is to restrain the size of thispopulation to less than 40% of the total world population. This is an increase over 1995
levels, when 34% of the world was experiencing water stress. After 2050, the population
experiencing water stress is targeted to decrease.
In constructing the Policy Reform scenario, the following approach is taken:
1. Maximum Withdrawals: Maximum withdrawals from renewable freshwater sources
are set at the country level, consistent with the sustainability goals. Added to these
are contributions from desalinated water and recycled wastewater. The total fromthese two sources gives the maximum water available for withdrawal, within theconstraints placed by the scenario goals.
2. Standard Intensities: Regional withdrawals in the scenarios, which are determinedfrom a sectoral analysis, are kept below the maximum levels. This is achieved byhaving sectoral intensities move away from Reference level intensities toward“standard” values (which they do not necessarily reach) over the course of the Policy
Reform scenario. The standard intensities are discussed for each sector below.
Within a particular region, the degree to which the region approaches the standards is
set to be the same for each sector.
Domestic
Reference
Domestic water intensities in the Reference scenario are based on trends in the OECD
regions. Historically, in the United States, the intensity of domestic water withdrawals
increased between 1960 and 1980, after which it leveled off. The earlier rise in water use
was due in part to the growth in single-family suburban housing, where large lots required
53 The scaled withdrawals are intended as rough guides, in the absence of a detailed sectoral analysis at the country level.

Changein GDP is used as a proxy for changes in industrial value added and energy use. The other drivers — population and harvested
irrigated area — are very similar in the two scenarios.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

169

more water, especially for outdoor use (Raskin et al., 1995). In the future, domestic water
withdrawals per capita are expected to decline in the United States, based on recent
policies and available technologies.
In North America, domestic water intensities are assumed to decrease by 20% over thecourse of the Reference scenario, from nearly 250 to a little under 200 cubic meters per
capita per year. In Western Europe and Pacific OECD, current domestic intensities aremuch less than those in North America, about 90 and 140 cubic meters per capita peryear, respectively. In the scenario, domestic water intensities in these regions are
assumed to remain constant. In the developing regions, intensities converge toward
average OECD patterns following the convergence algorithm (see Annex). The resulting
intensities are shown in Figure 5-3.
Figure 5-3. Domestic water intensities in the scenarios
Reference
Policy Reform
250

250

200

200

N America

cubic meters per capita .

W Europe
Pacific OECDFSU
E Europe
Africa150

150

Latin America
Middle East

100

100

50

50

0
1995

2025

2050

0
1995

China+
S+SE Asia

2025

2050

Domestic water withdrawals are computed by multiplying these intensities by the
population assumptions in the scenario. The results are shown in Figure 5-4. Water use
in the OECD and transitional regions changes little, due to low population growth andrelatively steady water intensities; however, the developing regions show large increases.
Withdrawals for the developing regions as a whole nearly double over the course of the
scenario. This increase dominates the global trend, in which domestic withdrawals reach
653 km3 in 2025 and 882 km3 in 2050, compared to 423 km3 currently.

170

Water

Figure 5-4. Domestic withdrawals by region in the scenarios
1000

Developing
TransitionalOECD

cubic kilometers

800

600

400

200

0

1995

2025

2050

Reference

2025

2050

Policy Reform

Policy Reform
As discussed in the introduction to this section, in the Policy Reform scenario, standard
intensities are set in each sector. Over the course of the scenario, regional intensities
move toward the standard value relative to the Reference value. For the domestic sector,
several factors were considered when setting the standard: current patterns, basic needs,
the potential for efficiency improvements, and the global convergence that is part of theConventional Worlds story.
Current domestic withdrawals per capita are given in Table 5-4. There is a more than 10fold difference between the value for Africa, 22 cubic meters per capita per year, andNorth America, with 244 cubic meters per year. The lowest intensity in the OECD is inWestern Europe, at about 90 cubic meters per capita. The average values in every region
are well above the minimal requirements for metabolic, hygienic and domestic
requirements as estimated by the World Health Organization, a value of about 7 cubic
meters per person annually (World Bank, 1998). However, the value for Africa is only
slightly higher than the estimated minimum requirement reported in Seckler et al. (1998)
of 20 cubic meters per person for basic domestic needs each year. As discussed in BTC,
in OECD regions, domestic intensities can be reduced through expanded use of waterefficient appliances and tighter plumbing codes. In addition, water use for landscaping
and lawns can be reduced through better practices, and in arid regions native plants can beused for landscaping, rather than more water-intensive ones (Postel, 1992; Gleick et al.,1995). In non-OECD regions, increased use of water-consuming appliances is expected
to increase in the process of convergence, leading to increasing withdrawals.
For the scenario, for every region except Africa, the standard value is set to about 80% of
the current value in Western Europe, or 75 cubic meters per capita per year. In Africa a

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

171

smaller value is used.54 The domestic water intensities in the scenario are shown in
Figure 5-3.
Domestic withdrawals are computed by multiplying the intensities by the populations
assumed in the scenarios (discussed in Section 2). The results are shown in Figure 5-4.
There are decreases in the OECD and transitional regions, compared to the almost
constant level of withdrawals in the Reference scenario. The developing regions stillshow significant increases, but the withdrawals for the developing regions as a whole areabout 20% less than those in the Reference scenario in 2050.
Manufacturing
Reference
Subsectoral water intensities in the scenario are based on historical and anticipated trends
in the United States, where disaggregated time-series data are available. The water
withdrawal intensity in manufacturing decreased by about 50% between 1960 and 1990.
The drop was due both to the decreasing share of total manufacturing output from waterintensive subsectors and the penetration of more efficient water-use practices (Raskin etal., 1995). The two effects that caused a decrease in the U.S. in the past play a role in the
scenario as well. In the OECD regions, the rising share of the less water-intensive
manufacturing subsectors in itself lowers aggregate manufacturing water intensity (Table
5-6). This is traced to the stable per capita output of traditional heavy industries such asiron and steel, non-ferrous metals, paper and pulp and chemicals, as discussed in Section
3.2.5. Consequently, the mix of manufacturing output shifts toward less water-intensive
subsectors, thereby lowering the aggregate manufacturing water intensity. Increasing
efficiency and the changing mix of industrial activities are reflected in the aggregate
manufacturing intensities shown in Figure 5-5. Manufacturing intensities in the nonOECD regions are assumed to converge toward the average for the OECD regions as
incomes rise (see Annex). The changing subsectoral intensities, combined with thesubsectoral composition of industrial value added, leads to the aggregate manufacturing
intensities shown in Figure 5-5.

54 In Africa, the standard domestic intensity of 75 cubic meters per capita is higher than the Reference scenario intensities, while thestandard agricultural intensity (discussed below) is lower than the intensities in the Reference scenario. For increasing degrees of
convergence toward the standard intensities across sectors, agricultural withdrawals decrease, but domestic withdrawals increase,resulting in no solution to the sustainability constraint. Unlike the domestic standard intensity, the agricultural and industrial
standards are set based on technical potential, so the domestic standard was adjusted to meet the sustainability goal. For Africa, astandard intensity of 40 cubic meters per capita is assumed.

172

Water

Figure 5-5. Aggregate manufacturing water intensities
Reference
Policy Reform
90.0

90.0

80.0

80.0

70.0

70.0

60.0

60.0

50.0

50.0

40.0

40.0

30.0

30.0

20.0

20.0

10.0

10.0

N America
W Europe

cubic meters per 1000$PPP

Pacific OECDFSU
E Europe
AfricaLatin America
Middle East

0.0
1995

2025

2050

0.0
1995

China+
S+SE Asia

2025

2050

Manufacturing water withdrawals in the scenario are given by the product of thesubsectoral intensities and subsectoral value added. Results are shown for the threemacro-regions in Figure 5-6. Globally, annual water withdrawals in the sector increaseby 2050 to over 2.5 times the 1995 value. Regional variations are due to the interplay ofregion-specific assumptions about industrial scale, structure and water intensity. The
dramatic growth in developing regions is particularly striking: mainly as a result of
economic growth, withdrawals for the developing regions as a whole in 2050 are greaterthan the world total in 1995.
Figure 5-6. Manufacturing withdrawals in the scenarios
500

Developing
Tra n s itionalOECD

cubic kilometers

400

300

200

100

0

1995

2025

2050

Refe rence

2025

2050

Policy Re form

Policy Reform
The standard value for manufacturing intensities is based on industrial water use in Israel.
As a result of low water pricing policies, and efficiency standards, industrial water use inConventional Worlds: A Technical Description of Bending the Curve Scenarios

173

Israel today is very efficient compared to other countries (Lonergan and Brooks, 1994).
The intensity standard used for all regions (Table 5-10) is set to 80% of the estimatedsubsectoral value for Israel, assuming the same relative subsectoral intensities in Israel as
for the United States in 1990.55,56 Methods for achieving efficiency improvements are
given in BTC. The manufacturing intensities in the scenario are shown in Figure 5-5.
Table 5-10. Standard manufacturing intensities3

m /$1000
Iron & Steel
ChemicalNon-ferr. metals
Paper & pulp
Stone, glass & clay
Other

51
14
10
9
3
2

Total manufacturing withdrawals are given by the product of subsectoral value added andthe intensities. Scenarios for value added in manufacturing subsectors are described in thecontext of the energy analysis in Section 3.2.5. The resulting water withdrawals areshown in Figure 5-6. For the OECD regions, slower economic growth in the Policy
Reform scenario compared to the Reference scenario, combined with sharp reductions in
manufacturing intensities, lead to withdrawals in 2050 roughly half those of the Reference
scenario. In the developing regions the gains are not as great, due to higher economic
growth. However, despite the higher growth, as a result of the lower intensities,
manufacturing withdrawals in 2050 in the Policy Reform scenario are 27% lower thanthose in the Reference scenario.
Energy Conversion
Reference
Trends in petroleum refining intensities are assumed to be the same as in the
manufacturing sector. Intensities in the OECD regions decrease by 10% in 2025 and 20%
in 2050, due to assumed efficiency improvements, while in the non-OECD regions,
intensities converge toward the OECD average following the convergence algorithm (seeAnnex).
For thermoelectric generation, the scenarios are based on historical data for the UnitedStates. Between 1960 and 1990, average freshwater intensity in the U.S. decreased by
60%, as cooling systems became less water-intensive and power plant efficiencies
55 Since electric generating plants in Israel use salt water for cooling (Lonergan and Brooks, 1994), and oil refining generally makes

a small contribution to industrial withdrawals, all of the reported industrial water withdrawals may be assigned to manufacturing.
56 For some regions, including the Middle East, the base-year values were below the standard.

This may indicate inconsistencies inthe base-year data, or the limitation of using value added, rather than physical production, as a measure of activity. In view of theconsiderable water-supply constraints in the Middle East, the manufacturing intensities for the region were kept at their base-year
values in the scenario.

174

Water

improved (Raskin et al., 1995). This trend toward lower water intensities is assumed tocontinue in the future, though at a slower rate. In the Reference scenario, it is assumed
that in all regions thermoelectric generation water withdrawal intensities decrease by 20%
between 1995 and 2025 and by 30% between 1995 and 2050. The resulting intensities
are shown in Figure 5-7.
Figure 5-7. Thermoelectric generation intensities in the scenarios
Reference
Policy Reform
35.0

35.0

30.0

30.0

25.0

25.0

20.0

20.0

15.0

15.0

10.0

10.0

5.0

5.0

N America
W Europe
Pacific OECD

cubic meters per GJ

FSU
E Europe
AfricaLatin America
Middle East
China+
S+SE Asia

0.0
1995

2050

2025

0.0
1995

2025

2050

Multiplying the intensities with the scenario assumptions for thermal electric generationand petroleum production (Section 3.3) gives the withdrawals shown in Figure 5-8.
Withdrawals for the OECD and transitional regions stay fairly steady, while those of thedeveloping regions increase sharply, a result of increases in energy consumption.
Figure 5-8. Water withdrawals for energy conversion in the scenarios
1000

D e ve l o p i n g
Tra n s itio n a lOECDcubic kilometers

800

600

400

200

0

1995

2025

2050

R e fe re n c e

2025

2050

P o l i c y R e form

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

175

Policy Reform
A wide range of technologies is available for cooling thermoelectric plants. The water
withdrawal requirements range from over 30 cubic meters per GJ to zero, indicating
considerable scope for reducing withdrawals. However, the technologies that result inessentially zero withdrawals are either expensive and not thoroughly tested on a large
scale (closed systems) or are relatively expensive and require special siting (once-throughcooling with sea water). Consequently, it is not expected that regions will reach theselevels. However, in the spirit of the “standard” intensities, it is expected that regions will
move toward using technologies that require no withdrawals. So, for the Policy Reform
scenario, a value of zero withdrawals is used as the standard in every region. Information
on potential efficiency improvements for refining withdrawals are scarce. Given the lackof information and the small contribution of refining withdrawals to total waterrequirements, an intensity of zero was also chosen as a standard value for this use.
The water intensities for thermoelectric plants in the Policy Reform scenario are shown in
Figure 5-7. Note that the rate of decrease in North America is similar to the historicalrate of decrease in the United States between 1960 and 1990.
Combining the thermoelectric and petroleum refining intensities with the values forelectricity and oil production results in the withdrawals shown in Figure 5-8. The
withdrawals in each of the macro-regions is much lower in the Policy Reform scenario
than in the Reference scenario. This results both from the lower intensities shown in
Figure 5-7 and from the greater use of renewable fuels in electricity production.
Agriculture
Reference
Irrigation water intensities depend on a number of interacting factors. These include crop
mix, land quality, weather conditions, irrigation methods, management practices, nonwater inputs, relative prices of water and crops, and yield response to irrigation. Figure 59 illustrates the relationship between yield response and water intensity for irrigatedagriculture. Different curves are shown for different levels of non-water inputs. The crop
yields in the Reference scenario are assumed to increase for two reasons: 1) increases inwater intensity; and 2) improvements in non-water agricultural inputs, such as
management practices, chemical applications, and improved crop varieties. Following
Raskin et al. (1997), for this study the increase in yield associated with an increase inwater intensity is estimated based on empirical yield-response curves (Hexem and Heady1978). The resulting intensities are shown in Figure 5-10. They increase gradually inevery region, consistent with the increasing yields.

176

Water

YIELD
(mass of crop / harvested area)

Figure 5-9. Yield response to water and non-water inputs

Year = 2025very high non-water inputs
higher non-water inputs
scenario pathcurrent practicelower non-water inputs

Year = 1995

WATER INPUTS(volume of water / harvested area)
Note: Figure from Raskin et al. (1997).

Figure 5-10. Irrigation water intensities in the scenarios
Reference
Policy Reform

31000 m per harvested ha

30

25
N America

25

W Europe

20

Pacific OECD

20

FSU

15

E Europe

15

Africa10

Latin America

10Middle East
5

5

0

0

China+
S+SE Asia

1995

2025

2050

1995

2025

2050

Irrigation withdrawals in the scenario are determined by multiplying the intensities by theirrigated harvested area (Section 4.5.1). The result is shown in Figure 5-11. The relative
increase in withdrawals is highest in the transitional regions, which experience a 70%
increase in total withdrawals. However, the absolute increase is much greater in thedeveloping regions, where the increase in water requirements is greater than the total
withdrawals of the OECD and transitional regions in the base year.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

177

Figure 5-11. Irrigation water withdrawals in the scenarios
4000

Developing
TransitionalOECD

cubic kilometers

3000

2000

1000

0

1995

2025

2050

Reference

2025

2050

Policy Reform

Policy Reform
In part, the irrigation intensities for the Policy Reform scenario are developed in the same
way as for the Reference scenario, with crop water requirements assumed to increase
consistent with the increases in yields. However, moderating the increase in waterrequirements are improvements in irrigation efficiency over base-year practices. For the
agriculture sector, rather than a standard intensity value, regions move toward a standard
irrigation efficiency improvement relative to the base year.
Separate potential
improvements were developed for each region, based on a study by Seckler et al. (1998).
In that study, potential efficiency improvements were estimated by country, covering allof the regions used in this study except the FSU. Current irrigation efficiencies were
estimated by comparing reported water withdrawals to the water requirements of areference crop, taking into account the water supplied by rainfall, and allowing for water
storage between planting seasons. The current efficiencies were then compared to apotential efficiency of 70%. For this study the potential efficiency for the Middle East
was assumed to be 85%, anticipating that water shortages will make more expensive but
water-conserving alternatives more cost-effective than in the other regions. For
comparison, the basin-wide efficiency of the Nile basin has been estimated at 91% (Keller
and Keller, 1996), while at the farm level, the efficiency of drip irrigation systems canreach 95% (Lonergan and Brooks, 1994). The potential improvement in the FSU was set
close to the value for Western Europe.

178

Water

Regions approach the standard efficiency
improvements over the course of the
Policy Reform scenario. The resulting
improvements over base-year practices
are shown in Table 5-11, along with thestandard efficiency improvement in eachregion. Improvements in the developing
regions range from 12% to 19% in 2025
and from 24% to 30% in 2050. The
corresponding intensities are shown in
Figure 5-10.

Table
5-11.
Irrigation
efficiency
improvement over base-year practices% Improvement
N America
W Europe
Pacific OECDFSU
E Europe
AfricaLatin America
Middle East
China+
S+SE Asia

2025
9
11
18
10
3
19
15
12
19
14

2050
15
17
29
16
5
24
25
29
30
28

Standard
24
26
45
25
8
27
39
30
48
32

Multiplying the intensities by the irrigatedharvested area (Section 4.5.1) gives theirrigation water withdrawals shown in Figure 5-11. Irrigation withdrawals are almost
constant as a result of the assumed irrigation efficiency improvements and, in the Middle
East, a decline in the harvested irrigated area over the course of the scenario.
Water stress.
The incidence of water stress in the scenarios is determined at the national level.
National water withdrawals within regions are set in proportion to their base-year
withdrawals, such that the total regional withdrawals equal the total determined by thesectoral analysis. The use-to-resource ratios are then computed as in Equation (5-2). The
contribution from desalinization and wastewater treatment plants (Table 5-2) is first
subtracted from the total withdrawals, and the result compared to the renewable
resources. The incidence of water stress is then estimated from the use-to-resource ratios
as explained in Section 5.2.2. The changing levels of water stress in the two scenarios aredescribed in BTC, and the results for the 10 regions are presented in Sheet P-3. The
results are summarized here.
In the Policy Reform scenario, as a result of the measures taken in each of the sectors,
although the total population experiencing some stress increases steadily throughout the
scenario, the growth is much less than in the Reference scenario: an 82% increase rather
than the 158% increase under Reference scenario assumptions. As a percentage of the
population, the incidence of water stress stays almost constant between 2025 and 2050,
below the 40% required by the sustainability goals. Also, the total population in severe
stress stays almost constant over the same period, in contrast to the increase of about 30%
in the Reference scenario.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

179

6. Local and Regional Pollution
Conventional Worlds scenarios are implicated in numerous regional and local
environmental impacts. Problems such as oil spills, indoor air pollution, urban air
pollution, regional acidification and exposure to toxic and hazardous wastes will beaffected in different ways by the Reference and Policy Reform scenarios.
While some impacts may be controlled in an evolutionary way, in other cases, thedevelopment patterns of the Reference scenario present a huge challenge for the future.
The environmental sustainability goals of the Policy Reform scenario (e.g. climate change
mitigation and toxic waste reduction) generally reduce various other local and regional
environmental impacts. These co-control benefits amplify the social and economic
benefits of the Policy Reform scenario as compared to the Reference scenario. Moreover,in some policy-making contexts — particularly in the developing regions — the regional
and local pollution control benefits of the Policy Reform scenario are likely to be of more
importance than the climate change mitigation benefits of the scenario. We focus here on
three examples of impacts at the local and regional level: indoor air pollution from theuse of traditional biomass fuels, acidification from the deposition of regional sulfur andnitrogen oxide pollutants, and levels of production of toxic wastes.

6.1 Traditional Biomass Fuels and Indoor Air Pollution
Traditional biomass fuels — wood, charcoal, animal dung and agricultural waste arewidely used in developing countries, particularly for domestic cooking. Rural households
generally rely on fuelwood and, as this resource becomes more scarce, on dung andwastes. In some countries, many urban households use charcoal, a compact andtransportable fuel with cost advantages over direct use of fuelwood in certain areas where
markets are far from forest and other wood resources.
In addition to pressure on wood resources, the production and use of biomass fuels raiseother environmental issues, most notably the human health impacts of indoor air
pollution. Smith (1987) identifies four major groups of indoor air pollutants contained in
the smoke from biomass fuel combustion: carbon monoxide, particulates, polycyclicorganic matter, and formaldehyde. These pollutants are associated with a range of healthimpacts including: respiratory infections in young children; adverse pregnancy outcomes(including low birth weights of babies) for women exposed during pregnancy; chroniclung diseases and associated heart diseases; and cancer.
There is increasing evidence of a correlation between high exposure to pollution and illhealth. Smith (1993) describes a number of studies that have found strong relationships
between exposure to indoor air pollution and adverse health impacts. However, quantitative
assessment of the issue is complicated by the lack of reliable health data, the overlapping
effect of other pollutants such as tobacco smoke and outdoor air pollution, and the lack ofproper monitoring of exposure levels. In addition, some studies have failed to find such

180

Local and Regional Pollution

connections. A study of health effects in low-income urban women in Lusaka, Zambia(Ellegard and Egneus, 1992) failed to find evidence that the health of biomass-fuel users
was significantly poorer than those using cleaner fuels (electricity or kerosene). This
negative result does not disprove the link between biomass use, indoor pollution andhealth, but does point to the necessity for local studies (which can correct for possible
compounding factors such as smoking and socioeconomic status) and monitoring of
exposure. In the particular example of Lusaka, the measured exposure levels were foundto be much lower than those in other parts of the world, probably due to site-specificfactors such as stove location, ventilation, mode of food preparation and type of housing.
Historically, industrializing countries have undergone an energy transition fromtraditional fuels to modern fuels and electricity as incomes rise. This dynamic is also
seen in the Reference scenario and in more dramatic form in the Policy Reform scenario.
While we do not explicitly link the scenarios to their associated indoor air pollution
human health impacts, it is worth noting what effect each scenario has on levels of
traditional biomass use in the developing regions, and thus on expected human healthimpacts.
In the Reference scenario, as incomes increase, the use of biomass fuels gradually
declines as households switch to electricity and modern fossil fuels. For example, inAfrica the share of biomass fuels in average household energy budgets declines from
89% in 1995 to 52% in 2050. Similar declines are seen in all of the developing regions.
This occurs as populations become more urbanized and average incomes increase. Per
capita biomass fuel use declines in all developing regions over the period 1995-2050 byamounts ranging from a 35% decline in Africa to a 66% decline in China+. Indoor air
pollution can be expected to decline as the overall level of traditional biomass fuel use
decreases. Far more dramatic declines are seen in the Policy Reform scenario. For
example, in Africa, the share of biomass fuels in average household energy budgets
declines from 89% in 1995 to only 15% in 2050, reflecting the increased economic
development in the region in the Policy Reform scenario. Similar patterns occur in theother developing regions. Per capita traditional biomass fuel use also declines
dramatically in the Policy Reform scenario in the developing regions over the period 1995
to 2050: by over 80% in all developing regions. This rapid decline is likely to be
associated with dramatic decreases in the human health impacts associated with indoor air
pollution

6.2 Acidification
Small amounts of sulfur are naturally present in the environment through volcanic
eruptions, from sea bacteria and other sources (RIVM/UNEP, 1997). When deposited
onto soils and lakes, the naturally occurring quantities of sulfur are normally smallenough to be beneficial to plants, without causing acidification to soil and water.57
Ultimately, most sulfur passes into the oceans.

57 A notable exception is the case of volcanic activity, which can cause high levels of sulfur deposition.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

181

Human economic activity, leading to high emissions of sulfur and nitrogen oxides canlead to levels of deposition of pollutants so large that they cannot be fully attenuated by
the natural geochemical cycle. Acidification can cause a range of environmental impacts
including effects on human health, corrosion of materials, reductions in crop yields, lossof fish stocks, and possible forest damage. Until recently, these impacts were largely
confined to Europe and North America. It now appears that regional air pollution is aserious and growing problem in many parts of the World, particularly in certain
developing countries (SEI-York, 1998).
In terms of ecological impacts, the problem of acidification arises when high levels ofacid deposition occur in regions where soils, forests and aquatic ecosystems are
particularly sensitive to acidification.
Acidification is related to deposition of both sulfur and nitrogen oxides. While emissionsof nitrogen oxides, which arise primarily from the combustion of fuels in the energy
conversion and transport sectors, are not covered by this analysis, they are thought to be
an important contributor to the acidifying load in a number of regions (see for example,
Brodin and Kuylenstierna, 1992).
6.2.1 Current Accounts
Estimates of current emissions of sulfur are shown below in Table 6-1. The estimates
include emissions from the combustion and conversion of fossil fuels, which in 1995
amounted to approximately 68 million tonnes of sulfur (equivalent to 132 million tonnes
of SO2). The combustion of coal for electricity generation and other purposes accounts
for approximately 70% of this figure, the remainder arising primarily from the
combustion and refining of oil products. The estimates also include non-energy related
emissions from industrial processes, which arise primarily from the smelting of nonferrous metals (zinc, copper, and lead) plus smaller amounts from the pulp and paper
sector.
Current sulfur emissions are based on a review of various sulfur emissions inventories for1990 by Posch et al. (1996) and Kuylenstierna (1998). The 1995 estimates account forchanges in fuel consumption in each region and changes in economic output in the nonferrous metals subsector in each region between 1990 and 1995.

182

Local and Regional Pollution

Table 6-1. Global Emissions of Sulfur Oxides by Sector in 1995
Emissions (million tonnes S)
AfricaChina+Lat Amer
Mid East
S+SE Asi
E Europe
FSU
N AmerPac OECDW Europe
Developing
Transitional
OECD
World

EnergySectorIndustry
combustion

0.9
4.4
0.8
0.7
2.3
1.2
3.5
5.7
1.6
2.7
9.0
4.7
10.0
23.7

0.2
4.9
0.6
0.3
1.1
0.3
0.7
0.6
0.6
0.8
7.0
1.1
2.0
10.1

Industry
Other
process combustion
1.4
2.1
1.4
1.0
2.6
0.5
5.6
4.8
2.3
4.0
8.5
6.1
11.1
25.6

0.3
2.1
0.6
0.4
1.3
0.2
0.7
1.2
0.5
1.2
4.8
0.9
2.9
8.6

Total
2.8
13.5
3.4
2.4
7.3
2.2
10.5
12.3
4.9
8.8
29.4
12.7
25.9
68.0

6.2.2 Scenarios
Figure 6-12. Sulfur Emissions by macro-region

Emissions (Million Tonnes SOx)

160

Developing
T ransitional

120

OECD

80

40

0
1995

2025

2050
Reference

2025

2050
Policy ReformIn the Reference scenario, emissions increase over time from 68 million tonnes of sulfur
in 1995 to 133 million tonnes sulfur in 2050. Over this period, emissions from the OECD
regions decrease slightly reflecting small amounts of growth in the use of coal (a 13%
increase in primary coal requirements over the period 1995-2050), and at the same time,
the widespread adoption of more efficient coal-fired power plants (average efficiencies
increase from about 39% to about 42%) using sulfur emission controls that cut the rate ofemissions from approximately 0.32 kg/GJ to approximately 0.09 kg/GJ of coal consumed.
The decrease in emissions in the OECD regions also reflects the slow rate of growth inConventional Worlds: A Technical Description of Bending the Curve Scenarios

183

the non-ferrous metals sector, where total value added increases by less than 9% over theperiod 1995-2050.58
In the developing and transitional regions, Reference scenario sulfur emissions more thantriple. This reflects the dramatic increases in coal consumption expected in the scenario(primary coal consumption increases by a factor of 3.88 over the period 1995 to 2050)
and the assumption of a lower rate of adoption of more efficient power plants using sulfur
controls. The scenario also reflects the high levels of growth expected in the industrial
sectors of the developing regions. Total value added in the non-ferrous metals sector in
the developing regions increases by a factor of 3.8 over the period 1995-2050. Coupledwith the assumption of constant emission factors, this leads to dramatic increases inindustrial sector process emissions of sulfur.
The Policy Reform scenario achieves dramatic reductions in sulfur emissions with total
emissions reduced from 68 million tonnes sulfur in 1995 to 50 million tonnes sulfur in
2050.
In part, the scenario reflects the co-control benefits of the greenhouse gas
mitigation policies that guide the scenario. Those policies require a dramatic shift awayfrom the combustion of coal towards other less carbon-intensive, and at the same timeless sulfurous fuels (biomass, renewables and natural gas). At the same time, the scenario
reflects substantial decreases in emission factors in the non-ferrous metals sector, with all
regions reaching the current best regional average by 2050.
Sulfur emissions in OECD regions fall dramatically from 26 million tonnes in 1995 to 6
million tonnes in 2050. Fuel-switching policies, when combined with a greater emphasis
on demand-side and electricity generation efficiency, dramatically reduce the primary
requirements for coal in the OECD regions from 35 EJ in 1995 to only 3 EJ in 2050
(compared to a slight increase to 40 EJ in 2050 in the Reference scenario).
Emissions of sulfur from the developing regions increase from 29 million tonnes in 1995
to 41 million tonnes in 2050 in spite of fuel-switching policies and substantial efforts to
reduce emission factors (for example reflected in the rapid introduction of sulfur control
technologies in the electricity generation sector). In these regions, absolute increases aredue, not to a lack action, but rather to the dramatic increase in economic activity and thecontinued reliance of a number of the developing regions on coal. This is reflected in the
primary consumption of coal in the developing regions, which continues to grow from 44
EJ in 1995 to 54 EJ in 2050.
Linkage between Acidification and Climate Change
It is worth noting two important linkages between acidification and climate change.
Firstly, changes in weather patterns stimulated by climate change may change theintensity and distribution of acid deposition; and secondly, emissions of sulfur dioxide,
which lead to the accumulation of particles in the upper atmosphere, are thought to partly
mask the global warming trends caused by other greenhouse gases. One recent analysis
58 In the Reference scenario, we assume that emission factors for industrial process emissions and all non-electric sector fuelconsumption remain constant over time in each region.

184

Local and Regional Pollution

using energy assumptions similar to our Reference scenario, estimated a cumulative
masking effect of approximately 0.25ºC by 2050 compared to a similar scenario in which
concentrations of pollutants are assumed to remain constant (RIVM/UNEP, 1997).

6.3 Toxic Waste
A wide variety of toxic and hazardous substances is used in the manufacture of industrial
products. Nearly 100,000 industrial chemicals are now in commercial use worldwide,
and this figure is increasing by 500 to 1000 each year. Some of these chemicals may notpresent significant threats to human health or to the environment, while others are known
to represent specific toxicological and ecotoxicological impacts. However, there isinsufficient scientific information even for a partial health assessment for about 90% ofthem (NAS, 1984). Toxic wastes pose particular threats in the environment on account of
their toxicity, persistence, and tendency to "bioaccumulate" in living organisms, andthrough the food chain (Jackson and Taylor, 1992; Dethlefson et al., 1993).
The material and chemical flows underlying the modern industrial economy are
immensely complicated and are not well-tracked and understood. Nevertheless, it isimportant to develop estimates of existing levels of emission of toxic and hazardous
substances into the environment, to project trends and to explore alternative scenarios forkeeping emissions within acceptable risk levels.
With this aim in mind, the World Bank has devised an "Industrial Pollution Projection
System" based on U.S. data, which provides lower bound toxics "emission factors" formany industries expressed as emissions per unit of value added in each industrial
subsector (Hettige et al., 1994). These factors have been updated and adapted for use
with the value added data used in this analysis (expressed in purchasing power parity
units).
The Reference scenario analysis of toxic wastes is an update and expansion of an earlier
report (Raskin et al., 1996). Some of the text in this chapter is adapted from that report.
6.3.1 Current Accounts
Applying the emission factors to industrial sector value added current accounts yields
total emissions of approximately 22 million tonnes in 1995. Figure 6-14 shows how thisis broken down by region and industrial subsector. The chemicals industry has the largest
sectoral share (37%) with metals industries also significant. The regional picture isdominated by the OECD regions, which account for 46% of all toxic releases.

Conventional Worlds: A Technical Description of Bending the Curve Scenarios

185

Figure 6-13. Current Accounts Toxic Waste Emissions
Total Emissions = 22 million tonnes
Pacific OECD
10%

S+SE Asia
14%

Other
28%
W Europe Non-ferr. metals
12%18%

N America
18%

Africa3%

Middle East
4%
Latin America
9%

FSU
5%

China+
17%
Easten Europe
2%Paper + pulp
6%
Stone,glass,clay
1%

Iron + steel
16%

Chemical37%

The fate of toxic emissions by environmental
Figure 6-14. Lower Bound Toxic
Emissions by Emission Medium (1995)
media is presented in Figure 6-14: 60% ofAiremissions are to land, about 36% to the36%
atmosphere, with aquatic environments
receiving only 4% of the total burden. Note
Water
that these proportions refer only to the initial
4%
partition of emissions into specificenvironmental media. The "ultimate" fate ofemissions is determined by environmental
transport and transfer mechanisms.
In
particular, atmospheric emissions will be
subject to subsequent deposition both on land
and into waters. Moreover, chemicals
deposited on land may leach into watersupplies. However, it is infeasible to computeLand
the ultimate burden on specific environmental
60%
media which depends not only on transportand deposition, but also on metabolization
rates, sedimentation patterns, plant uptake, and so on.
These estimates should be regarded as illustrative only, and a number of limitations
concerning the quality of the data, and the applicability of the accounting methodology,
need to be highlighted. In the first place, the data have been drawn from a single country(the U.S.) and the degree of validity of extrapolation on a global basis is not clear,particularly since the factors are based on monetary measures of industrial activity rather
than a more accurate physical measure (e.g., tonnes of output by industrial subsector).
Although the pattern of technological development in other OECD regions may not bevastly dissimilar to the U.S. example, the extension of these emission factors to lessdeveloped economies may introduce significant inaccuracies. In general, the effect will
be to underestimate actual emissions in less developed regions, because technological

186

Local and Regional Pollution

efficiencies and environmental controls are likely to be inferior (Jackson and
MacGillivray, 1995).
6.3.2 Scenarios
The Reference and Policy Reform scenarios examine how toxic waste emissions mightevolve over time. In the Reference scenario, emission factors per unit of value added areassumed to remain constant, while in the Policy Reform scenario, emissions are projected
in two phases. Firstly, total emissions in OECD regions are assumed to decrease to 50%
of 1995 levels by 2025 and further to 10% of 1995 levels by 2050. Secondly, theemission factors implied by this level of decrease are applied also to non-OECD regions,and the results used to calculate overall levels of emissions. This is an ambitious target,
but one in line with the general eco-efficiency assumptions of the scenario as described in
BTC.
The results for each scenario are shown in Figure 6-15. In the Reference scenario,
emissions increase dramatically especially in the developing regions, to a level that is
likely to be unacceptable.
The sectoral composition of this burden is similar to thecurrent allocation. The chemicals sector is still important, as are the metals sectors.
However, a greater proportion of the burden now comes from "other" sectors — an effectconsistent with the reduction in material intensity of conventional materials, such as ironand steel, which is assumed in the scenario. In the Policy Reform scenario, dramatic
reductions are achieved as a result of the stringent emissions targets set in the scenario.
Figure 6-15. Lower Bound Toxic Emissions Scenarios
80

Developing
Transitional
OECD

Million Tonnes

60
40
20
1995

2025

2050

Reference

2025

2050

Policy ReformConventional Worlds: A Technical Description of Bending the Curve Scenarios

187

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Annex: The Convergence Algorithm
A central feature of the Conventional Worlds scenarios is the gradual convergence ofregional consumption patterns. To aid in the development of the scenarios, in several
instances we used a convergence algorithm, which reflects the basic features of
convergence.
The algorithm is based on the idea that, in the course of economic development, thestructure of economies, certain activity levels (such as travel patterns) and intensities in
non-OECD regions converge toward those of the highly industrialized societies withincreasing GDP/capita. However, the non-OECD regions are not expected to recapitulate
the history of the OECD regions; rather, they are expected to move more quickly along
the development path, through technological “leapfrogging,” as they take advantage ofimproved technologies and materials use that were not available earlier.
In mathematical terms, the algorithm used in the scenarios to reflect this behavior can be
expressed as:
 ir ,2025 − ir ,1995 Vr ,2025 = Vr ,1995 + (VOECD ,2025 − Vr ,1995 ) × 
 iOECD ,1995 − i r ,1995 and i r ,2050 − i r ,2025 .
Vr ,2050 = Vr ,2025 + (VOECD ,2050 − Vr ,2025 ) × 
 iOECD ,1995 − ir ,2025 
where Vr , y is the value of the value added share, activity level or intensity in region r inyear y; VOECD, y is the average OECD value in year y; i r , y is the income (GDP/capita) inregion r in year y; and iOECD, y is the corresponding average OECD value.
The formulas produce a basic pattern of convergence, in that the regional value in the
scenario year (2025 or 2050) approaches that of the OECD in the scenario year as theregional income increases; the linear dependence on income is chosen for simplicity.
Moreover, the formulas reproduce the basic pattern of technological leapfrogging,
because the regional value converges to the OECD value as the regional GDP/capita
approaches that of the OECD in the base year, rather than the scenario year, arriving at
the OECD level at a lower income than in the OECD. Table A-3 shows the convergencefactors calculated in the scenarios

A-2

Annexes

Table A-1: Scenario Convergence Factors
Reference Scenario
Non-OECD Region
2025
2050
Africa0.06
0.10
China+
0.26
0.46
Latin America
0.34
0.69
Middle East
0.23
0.39
S & SE Asia
0.18
0.33
E Europe
0.34
0.47
FSU
0.20
0.23

Policy Reform Scenario
2025
2050
0.15
0.25
0.33
0.61
0.41
0.94
0.30
0.55
0.25
0.46
0.41
0.68
0.28
0.38

Notes:
1.
2.

 ir , futureyear − ir ,1995 

i

−
i
OECD
,
1995
r
,
1995



In any given column, the convergence factor is the following ratio 

In the Policy Reform scenario, the convergence factors for the FSU shown in the above table were
relaxed in some parts of the energy analysis. Specifically, the low levels of convergence in the FSU
were increased to produce additional energy efficiency improvements beyond what might be expectedfrom the income effect of the convergence algorithm.

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