Global Models, World Futures, and Public

Policy: A Critique

April 1982

NTIS order #PB82-251299
Library of Congress Catalog Card Number 82-600523

For sale by the Superintendent of Documents,

U.S. Government Printing Office, Washington, D.C. 20402
Foreword

A number of long-range forecasts of international demographic and economic trends

have been published over the last decade, many of them based on the findings of global

models—computerized mathematical simulations of the world’s physical, economic, and

political systems. This report responds to a request by the Technology Assessment Board

for an evaluation of the methodologies, findings, and implications of Global 2000 and

other global modeling studies.

Computerized models are a useful way of handling a large amount of data at once and of

keeping assumptions and calculations self-consistent. In this connection, our study not

only reviewed the findings and recommendations of five major global modeling studies,

but also examined the underlying assumptions and data bases on which those results are

based.

The purpose of this report is neither to confirm nor to disprove the sometimes rosy but

more often dire predictions derived from global modeling studies, but rather to examine

the present use and potential usefulness of this rapidly developing technology as a powerful

tool for long-range strategic analysis and policy development. OTA found significant and

growing use of models by a variety of Federal agencies. In addressing the modeling

capabilit y of the Government, the report focuses not on whether models should be

used—they already are, and have proven themselves valuable over a period of years—but

rather on how to improve this capability and make its projections more useful to analysts,

planners, decisionmakers, and the broader public.

We were greatly aided by the contributions of a number of contractors and by the

guidance and expertise of the many individuals who generously provided information and

review comments. Their assistance is greatly appreciated.

Director

l l
Global Models, World Futures, and Public Policy Project Staff

John Andelin, Assistant Director, OTA

Science, Information, and Natural Resources Division

William Mills, Project Director

Paul B. Phelps, Analyst

Marsha Fenn, Administrative Assistant

contractors

Jennifer Robinson, Fog’s Edge Research

The Futures Group, John Stover, principal investigator

Energy Information Administration of the U.S. Department of Energy

Paul Werbos, principal investigator

Claudia Lorge

OTA Publishing Staff

John C. Holmes, Publishing Officer

John Bergling Kathie S. Boss Debra M. Datcher Joe Henson

v
Acknowledgments

OTA wishes to thank the following people who took time to provide information or review part or all

of this study.

David Barnhizer Dennis Little

World Wildlife Fund Congressional Research Service

Barbara Baum William Long

U.S. Department of State U.S. Department of State

Anne Cheatham Marvin Ott

Confessional Clearinghouse on the Future OTA Director of Congressional and

CherylChristiansen -

U.S. Department of Agriculture

Bill Davis

OTA Materials Program

Georges Fauriol

Center for Strategic and International Studies

Georgetown University

Katherine Gilman

Council on Environmental Quality

Dolores Gregory

U.S. Environmental Protection Agency

Khristine Hall

Environmental Defense Fund

Major Gary Knutson

Command and Control Technical Center

U.S. Department of Defense

Barbara Lausche

OTA Food and Renewable Resources Program

Donald Lesh

U.S. Association for the Club of Rome

and Teaching Laboratory

Institutional Relations

Judith Randal

OTA Health Program

John M. Richardson, Jr.

Quantitative Research

American University

Bruce Ross

OTA Food and Renewable Resources Program

Dick Rowberg

OTA Energy Program

Dan Schottenfelf

General Accounting Office

Julie Sullivan

Foreign Policy Association

Richard Thoreson

OTA Energy Program

Louise Williams

OTA Human Resources Program

Peters Willson

Alan Guttmacher Institute

vi
Contents

Chapter

1$ Overview . . . . . . . . . . . . . . . . . . . . . .

2. Major Global Modeling Studies. . .

3. Findings of the Global Models. . . .

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

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

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

4. Global Models and Government Foresight. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5. Priorities and Strategies for Improving U. S. Government Foresight . . . . . . . . . . . . . .

Appendixes

A. Population Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

B. Agricultural Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Page

3

11

43

53

61

69

81

C. Energy Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..................104
CHAPTER 1

Overview
.

ntFwesi@t ● ****   ** **   ** ** *******eke***** + 4

●   ** **   e ** o********,********** *

.

6

44 *******’**.*.*****.******. *

8 ,

1. Summary . ● e...* , 3
CHAPTER 1

Overview

The last 15 years have witnessed a growing international effort to increase understanding and

broaden public awareness of the conditions, problems, and opportunities that are likely to confront

the world through the end of this century and into

the more distant future. This ongoing “futures

debate” has been stimulated in part by the publication of a series of long-range forecasts of global

trends in population growth, resource availability,

economic development, and environmental conditions. Many of these forecasts have been based on

the findings of “global models’ ’—computerized

mathematical simulations of the world’s physical,

economic, and political systems. As tools of strategic analysis, these models have been used to study

the interactions and future implications of past

events and current trends, As tools of policy formulation, global models have been used to evaluate or promote alternative actions and programs

that might bring about different or more favorable

world futures.

This report surveys the assumptions, findings,

and recommendations of five major global modeling studies (see table 1). It also considers the use of

global models within the U.S. Government, such

as the World Integrated Model (WIM) that is being

used by the U.S. Joint Chiefs of Staff (see pp.

23-24), In addition, the report presents strategies

that have been suggested for improving the quality

and relevance of the Government’s modeling capability. Of great interest in this connection is the

newly created White House “national indicators

system” (see p. 65). The appendixes provide detailed comparative analyses of the models’ projections of population, agriculture, and energy

trends.

Table 1 .—Summary Description of the Five Global Modeling Studies Discussed in This Report

Historical Projection Geographical Alternative

Model Date base period time horizon regions scenarios

World 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1972 1900-1970 2100 1 (global) 11

World Integrated Model (WI M). . . . . . . . . . . . . . . . . . . . . . . . . . 1974 1950-1975 2025 10 (later 14) 17

Latin American World Model (LAWM). . . . . . . . . . . . . . . . . . . . 1976 1960-1970 2060 4 (later 15) 7

United Nations Input-Output World Model (UNIOWM) . . . . . . 1977 1970b

2000 15 (3 blocs) 13

Global 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1980 Not consistent 2000 5 to 28 12

(Not consistent)

SOURCE: Office of Technology Assessment.

Findings of the Global Models

Global modeling studies have varied widely in

their purposes, techniques, findings, and prescriptions. The results of some studies have been

guardedly optimistic, while others have been

highly pessimistic. Their specific quantitative

results have been different because they have

made different assumptions and have focused in

different ways on different parts of the global

system. Nevertheless, they have generally identified the same problems and seem to have arrived

at roughly similar qualitative conclusions about

the present state of the world and its plausible

futures:

● Population and physical capital cannot grow

indefinitely on a finite planet without eventually causing widespread hunger and resource

scarcities. However, there is no physical or

3
4• Global Models, World Futures, and Public Policy

technical reason why basic human needs

could not be supplied to all the world’s people,

now and for the foreseeable future. These

needs are not now being met because of unequal distribution of resources and consumption—not overall physical scarcities. The absence of physical limits, however, does not

necessarily imply the existence of a practical

solution.

The continuation of current trends would result in growing environmental, economic, and

political difficulties. As a result, “business as

usual” is not a palatable future course. Technological progress is expected (and in fact essential), but no set of purely technical changes

tested in the models was sufficient in itself

to bring about a completely satisfactory outcome. The models suggest that social, economic, and political changes will also be

necessary.

Over the next two or three decades, the

world’s socioeconomic system will be in a period of transition to a state that will be significantly different from the present. However,

the shape of this future state is not predetermined—it is a function of decisions and

changes being made now.

Because of the complexity, momentum, and

interdependency inherent in the world’s physical and socioeconomic systems, the full longterm effects of a given action are almost impossible to predict with precision or certainty.

However, actions taken soon are likely to be

more effective and less costly than the same

set of actions taken later, and cooperative

long-term approaches are likely to be more

●

●

beneficial for all parties than competitive

short-term approaches.

Many existing plans and agreements—particularly complex, long-term international development programs—are based on assumptions about the world that are either mutually

inconsistent or inconsistent with physical

reality.

Pollution and resource availability may or

may not be problems on a global ‘scale,” but

there is general agreement that regional problems of global concern—such as food shortages in South Asia and perhaps Sub-Saharan

Africa-are far more likely than a global collapse.

In some cases individual global models have

been used to support more dramatic conclusions

and more specific prescriptions than these, but it

would be a mistake to confuse global modeling us a

method of analysis with any particular prediction or

recommendation. As a tool of analysis, global

modeling is in itself neutral, although like any

complex tool a given model can be designed or

used inappropriately. For instance, most global

models contain little or no representation of geopolitics; it would thus be inappropriate to use

them to predict short-term events that may in fact

be more strongly affected by nonquantifiable political variables. Similarly, the findings that come

out of a model will also depend on the data and

assumptions that go into it, the purposes to which

it is put, and the way it is interpreted. As a result,

global modeling can be a useful technique in longrange analysis, but it should not be—nor is it likely

to become—the sole, or even the principal, basis

for decisionmaking.

Global Modeling and Government Foresight

Global modeling is used by a variety of organizations and is by no means the exclusive preserve of

environmentalists or those who advocate a “new

international economic order. ” A growing number of large domestic and multinational corporations routinely employ the projections of private

economic modeling services in their corporate

planning. Several foreign governments and international organizations support ongoing global

modeling programs, and a variety of models—global and otherwise–are also in use throughout the

U.S. Government in a wide range of forecasting

applications. The Joint Chiefs of Staff, for example, are developing a version of WIM for use in

their joint long-range strategic appraisal, and both

the Department of Agriculture and Bureau of
Ch.1—Overview 5

Mines have used WIM as well as other models.

The Global 2000 Study found that numerous Federal agencies (including the Central Intelligence

Agency and Department of Energy, as well as the

Agency for International Development, Bureau of

the Census, and Environmental Protection Agency) routinely use regional or sectoral models in carrying out their long-range analysis and planning

functions. Similarly, the Members and committees

of Congress have access to long-term econometric

models maintained by the Congressional Budget

Office (as well as the findings of models maintained by the executive agencies) for use in their

oversight, assessment, and legislative functions.

This Government modeling capability exists

because it is necessary, and it has shown itself to

be useful over the years.

The expanded and better coordinated use of global models could offer the U.S. Government an

opportunity to improve its existing foresight capability. “Foresight” relates to the ability to effectively address long-range issues by first anticipating

future developments, and then formulating policies and programs that will minimize potential

problems or exploit potential opportunities. Although global models cannot generate precise, detailed predictions of what will happen in the

future, they can be used to produce conditional

forecasts of what is likely to happen or the probability of different outcomes, given certain specific

assumptions about trends, policies, and events.

They can also be used to test the consistency of

assumptions and predicted outcomes for different

policy options. In addition, the models can generate order-of-magnitude estimates of many demographic, economic, and resource factors at the global, regional, and national levels.

This level of forecast accuracy and detail can be

useful for a wide range of applications in longrange assessment and policy-development activities. Deficiencies do exist in the Government’s

current capability, but if these deficiencies are corrected global models could become a more effective input to policymaking in four specific areas:

●

●

●

●

assessing the future impacts of current trends

and existing policies;

monitoring the national and international

situation to identify early signs of potential

problems or opportunities;

formulating and testing a wide range of alternative policies and courses of action for

achieving national goals, avoiding potential

problems, and exploiting potential opportunities; and

providing a framework to ensure consistency

between short- and long-term analyses and

across agency jurisdictions.

Strengths and Weaknesses of Global Models

Global models offer a number of methodological

advantages over traditional techniques of longrange analysis and policy development:

● Longer time horizon. -Traditional methods are ●

used primarily for annual or short-term forecasts, whereas global models typically have

horizons of 20 years or more. This allows global models to assess long-term effects and

cumulative changes that might not otherwise

be anticipated.

● Comprehensiveness. —The computer can contain far more information about a system or

process than any single mental or verbal model, and it can keep track of far more interrelations and variables at the same time. Global

models can therefore enable the analyst to

utilize substantially more information, and do

so more meaningfully (e.g., with regional disaggregation) than could otherwise be done.

Rigor and accessibility. -Modeling requires explicit, precise, and complete statements of objectives, assumptions, and procedures. These

must be written out before they can be run on

the computer, and this makes it easier for all

sides to examine them and point out omissions and inconsistencies. Open communication about the system and the model can lead

to revisions and refinements even before analysis begins, and it can also contribute to the

dialog through which clear-cut goals are established.
6 ● Global Models, World Futures, and Public Policy

•Q Logic.—The computer can draw logically correct and mathematically error-free conclusions from an extremely complicated set of

assumptions and data. This can lead to insights into unexpected or counterintuitive

system behavior, reveal areas in which further

research is needed, and expose assumptions

and objectives that are inconsistent, contradictory, or physically impossible.

● Flexibility. -It is possible to tailor global

models to fit particular problems or regions.

By changing the magnitude of specific variables and relations, global models can also be

used to test a wide range of assumptions and

policy alternatives. This can make the global

model a valuable tool for policy formulation,

as well as a device with which planners and

policy makers alike can sharpen their analytic

skills and improve their intuitive “feel” for the

probable behavior of global systems.

Global models are, however, subject to a number of limitations that can constrain their accuracy, reliability, and usefulness:

● Methodological constraints. -The essence of

modeling is a simplification that improves

understanding, but this means that a limited

●

●

set of discrete factors and relations are used to

describe the complexity and ambiguity of the

real world. There is little agreement, however,

on the proper level of complexity or integration. Similarly, there are no generally accepted tests of model realism, making quality

control and third-party validation important

considerations.

Theoretical constraints.—Current understanding of some causal relationships is far from

adequate, particularly for environmental and

sociopolitical processes, and this too can lead

to inaccurate or invalid assumptions. As a result, the theoretical biases of the modelers-or

the specific needs and assumptions of model

users—can sometimes lead to oversimplification or distortion.

Data constraints. –In many areas there is a lack

of adequate, reliable, and consistent data.

This, too, can be a source of forecast error, as

well as a constraint on the issues or regions to

which global models can be reliably applied.

Because of these limitations, it is vital to evaluate

the assumptions and uncertainties underlying the

forecasts, if the results are to be understood and

used by policy makers.

Institutional Barriers

Several assessments of the Government’s modeling capability have concluded that the institutional context in which models are currently used

is as much of a constraint on their usefulness as

the above technical limitations. Frequently cited ●

institutional barriers include the following:

● poor communication between modelers and

potential model users, resulting in projections ●

that are unresponsive to the information

needs of policy makers;

. narrow specialization of interests and responsibilities, at the expense of interactions among

sectors and cooperation among agencies, complicated by inadequate mechanisms for transferring data and resolving problems between

agencies;

lack of understanding, confidence, or support

for modeling among top-level policy makers,

resulting in a failure to integrate forecasting

and policymaking activities; and

lack of interest in long-term global issues on

the part of the Federal agencies, Congress,

and the general public.
Ch. I—Overview 7

Strengthening the Government’s Modeling Capability

Proposals for improving the Government’s modeling capabilities usually stress the need for a coordinated strategy involving complementary efforts at all levels. The proposed initiatives generally reflect four fundamental priorities:

● Correct existing deficiencies.—Relevant agencies

might create internal advisory committees to:

1) prepare an inventory of existing models,

their uses, their deficiencies, and any planned

modifications; 2) conduct a survey of current

or potential applications by analysts and policymakers, with particular attention to their

specific information needs; 3) evaluate existing

data bases to determine data needs and possible ways of gathering data that are scarce; and

4) improve communication between policymakers and modelers in order to increase the

relevance and responsiveness of forecasts.

● Coordinate existing capabilities and activities.—Some form of interagency mechanism

might be established in order to: 1) identify

areas of compatibility and sources of inconsistency among models; 2) promote communication and technical cooperation among agencies; 3) develop consistent standards and

protocols for the reliability, validation, and

documentation of both models and data;

4) provide a clearinghouse for easier access, exchange, and integration of other agencies’

data, assumptions, and projections; and 5) resolve problems among agencies through negotiation or arbitration.

● Support technical improvements in the Government’s capability and the state of the art.—An independent or “quasi-public” institute might

be created to promote research on global modeling and futures research. Its specific functions might be to: 1) encourage impartial,

third-party validation and assessment of existing models; 2) support nongovernmental research on global models and establish a “glo-

●

bal modeling forum” (analogous to the Energy

Modeling Forum at Stanford University) at

which modelers could exchange ideas and critique one another’s work; 3) enlist the talents

and participation of the private sector in Government foresight activities; and 4) assess

modeling work done outside the United

States and maintain communication with international organizations such as U.N. agencies and the International Institute of Applied

Systems Analysis.

Link foresight with Policymaking. -To ensure

that long-range global issues are routinely

taken into consideration in the formulation

and implementation of U.S. policy, Congress

may wish to coordinate and upgrade the foresight capabilities of its legislative research

agencies and/or authorize the creation of a

new unit in the Executive Office of the President. The functions of this new unit might be

to: 1) supervise and/or coordinate the strategies outlined above; 2) provide the President

and other top-level decisionmakers with

thorough analyses and a broad range of policy

options on global issues; 3) evaluate the longterm effects of agency goals and budget items

on global trends and U.S. strategic interests,

for consideration by the Office of Management and Budget and Congress in the budgetary process; 4) prepare a “policy statement on

the future” to be delivered by the President;

5) issue periodic reports on specific global

issues; 6) conduct comprehensive, integrated

studies of long-range trends and problems at

regular intervals; and 7) in conjunction with

the Department of State, encourage foreign

national assessments of long-range issues and

support the data-gathering, analytic, and

problem-solving activities of the United Nations, international financial institutions, and

nongovernmental organizations.
8 ● Global Models, World Futures, and Public Policy

1.

2.

3.

Conclusions

Global modeling represents an important analytic tool for exploring alternative world futures

and for testing the feasibility and long-term effects of alternative policy actions.

The current state of the art in global modeling

offers the U.S. Government a significant opportunity to improve its foresight capability, if the

models are used judiciously and in combination

with other techniques and inputs to strategic

analysis and policy development.

If models are to be used properly within their

present limitations, it is critical to: 1) determine

and state explicitly the purposes, assumptions,

and theoretical biases of the model; 2) ascertain

the extent of uncertainty in a particular projection and its sensitivity to changes in the underlying assumptions; and 3) differentiate between

descriptive forecasts and those that are prescriptive or normative.

4. Improvements in socioeconomic theory, modeling methodology, and data-gathering technologies could substantially improve the usefulness

of the projections generated by global models.

5. Existing deficiencies in the Government’s modeling capability are institutional as well as technical in nature, and any effort to correct these

deficiencies will require better coordination

among Federal agencies and increased attention

to the information needs of policymakers and

decisionmakers.
CHAPTER 2

Major Global Modeling Studies
Contents

.

Page

Introduction . . . . . . . . . . . . . . . .. ..... 11

Forecasts and Forecasting. . . . . . . . . . . . . . . . . . . . . . 11

Models and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 11

The Trend Away From Technological

Optimism . . . . . . . . . . . .. .. ... ... .. 13

World 3—The Limits to Growth. . . . . . . . . . . . 15

Origin and Purpose. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Structure and Assumptions. . . . . . . . . . . . . . . . . . . . . 15

Findings of World 3. . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Conclusions of World 3.... . . . . . . . . . . . . . . . . . . . 18

World Integrated Model—Mankind at

the Turning Point . . . . . . . . . . . . . . . . . . . . . 19

Origin and Purpose. . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Structure and Assumptions. . . . . . . . . . . . . . . . . . . . . 19

Findings of WIM.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Conclusions of WIM. . . . . . . . . . . . . . . . . . . . . . . . . . 23

U.S. Government Use of WIM. . . . . . . . . . . . . . . . . . 23

Latin American World Model–

Catastrophe or New Society?. . . . . . . . . . . . . 24

Origin and Purpose. . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Structure and Assumptions . . . . . . . . . . . . . . . . . . . . . 25

Findings of LAWM.. . . . . . . . . . . . . . . . . . . . . . . . . . 25

Conclusions ofLAWM. . . . . . . . . . . . . . . . . . . . . . . . 29

United Nations Input-output World Model–

The Future of the World Economy.. . . . . . . 30

Origin and Purpose. . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Structure and Assurnptions . . . . . . . . . . . . . . . . . . . . . 30

Page

Findings of UNIOWM. . . . . . . . . . . . . . . . . . . . . . . . . 31

Conclusions of UNIOWM. . . . . . . . . . . . . . . . . . . . . 34

Globa1 2000–Entering the 21st Century. . . . . 34

Origin and Purposes. . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Structure and Assurnptions . . . . . . . . . . . . . . . . . . . . . 36

Findings and Conclusions of Global 2000. . . . . . . . . 37

Figures

FigureNo . Page

1.

2.

3.

4.

5.

6.

7.

8.

9.

104

11<

Types of Models. . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Possible Global Behavior Modes . . . . . . . . . . . . . 16

World 3 Standard Run... . . . . . . . . . . . . . . . . . . 17

World 3 Equilibrium Run. . . . . . . . . . . . . . . . . . . 17

World 3 Run With Stabilizing Policies

Introduced in the Year 2000. . . . . . . . . . . . . . . . . 18

Regionalization of the World Integrated Model. 20

Block Diagram of the Basic Elements of the

World Integrated Model. . . . . . . . . . . . . . . . . . . . 21

WIM Historical Scenario. . . . . . . . . . . . . . . . . . . . 22

WIM Isolationist Scenario. . . . . . . . . . . . . . . . . . . 22

Comparison of WIM Low- and Optimal-Price

Oil Scenarios. . . . . . . . . . . . . . . . . . . . . .. .....+. 22

LAWM Standard Run for Developed Nations. . 26

12. LAWM Standard Run for Latin America. . . . . . 27

13. LAWM Standard Run for Asia..... . . . . . . . . . 28

14. Regional Economic Growth Under

Three UNIOWM Scenarios . . . . . . . . . . . . . . . . . 32
CHAPTER 2

Major Global Modeling Studies

Introduction

Forecasts and Forecasting

Formal forecasting, which appeared in the early

20th century, is based on the rigorous application

of empirical inquiry and statistical analysis to the

prediction of socioeconomic change. It insists on

careful monitoring, a firm data base, and the judicious use of trend extrapolation, while rejecting

unfounded optimism and utopianism as “wishful

thinking.” The first use of formal forecasting by

the U.S. Government came in 1929, when President Hoover created a Presidential Research Committee on Social Trends, and its techniques and

findings became linked with comprehensive planning and decisionmaking during the New Deal.

Further theoretical improvements and practical

applications have emerged since World War 11

through developments in econometrics, general

systems theory, cybernetics, operations research,

and input-output analysis. 1

For strategic analysis and policymaking purposes, three general types of forecasts can be

distinguished, based on their approach to foreseeing the future:z

. unconditional forecasts, which determine

that certain events or trends will, in all probability, occur in the future (these forecasts

might properly be called “predictions”);

● conditional or probabilistic forecasts

which determine that certain events or trends

are more or less likely to occur in the future,

given certain limiting assumptions concerning

present and future conditions and policy actions (and that, given a different set of

assumptions, different events or trends are

more or less likely to occur); and

● exploratory forecasts, which examine a wider

range of policies and trends in an open-ended

exploration of possible future developments,

with less emphasis on the plausibility of

assumptions or scenarios.

To these three types of descriptive forecasts, which

attempt to project what will or might happen in

the

●

future, a fourth could be added:

prescriptive or normative forecasts, which

identify events or trends that should (or

should not) happen and determine the policies and conditions that will promote the desired outcome.

Models and Modeling

A model is a simplified or generalized representation of something else—an object, process, or

system. The model need not resemble the original

and can in fact take many forms, depending on

the purpose it is to serve: as an aid to memory, a

small two-dimensional photograph can remind us

of a large three-dimensional person or place we

have seen; as an aid to discocery, a 3-lb model

airplane can be tested in a wind tunnel to predict

the performance of a 30-ton airliner built on the

same design; and as an aid to explanation, a set of

gravitational equations can be used to elucidate

the intricate motion of planets orbiting a sun.

The model need not depict every detail of the

thing it represents. A good model reduces the

complexity of the original by eliminating elements

and relations that are irrelevant to the purpose at

hand, retaining only the characteristics that are

needed for that purpose. Ingeniously simple models may be described as “elegant, ” but in the end

“a model can be made and judged only with respect to

a clear purpose.”3

Models can be divided into three basic types:

mental models, physical models, and symbolic

models (see fig. 1). Mental models are the concep-

~ll>nell;~ H, \ft,dc)u ~, ]t)})n R.IC h,lrJw)n, <in Li ( k’rh:lrt f3ruc km’lnn (c-J~ ~,

Gmpnq in tk [)Mk Tlw F[r,r [k~dc [)/ (;l~)hll kl( )d~,linq (>’ctl }’(irk W’111’\ ,

(c,rr}l~ ,)mlr)u), ~.l> 1~- 3K: cmphail. t hcvr..

11
12 . Global Models, World Futures, and Public Policy

Figure I.—Types of Models

SOURCE: Arthur D. Little, Inc.

tual models people carry about in their heads and

use to think about the world. They are flexible,

adaptable, creative, and contain rich stores of information about such intangible factors as values

and motivations. Some mental models are extremely subtle and elaborate, even elegant. But

they can also be vague, shifting, unverbalized, and

immune to objective criticism, and they are often

based on dubious but strongly held assumptions.

Judgmental and qualitative forecasts (including

many unconditional forecasts) are often based on

mental models.4

Physical models are created from tangible

materials, and the process of embodying the model

can usually make it both more explicit and more

+Ibid., pp. 20-21, 37-38; and Arthur D. Little, Inc., op. cit., p. 11.6.

open to objective criticism. Iconic or schematic

models, such as maps or diagrams, are physically

similar to their originals, although they may not

behave in the same manner. Analog models, such

as wind tunnels, reproduce the behavior of their

originals without necessarily resembling them in

appearances Physical models can be a useful

means of communicating, clarifying, and correcting mental models.

Symbolic models make use of some system or

language of symbols to describe the relevant elements and relations of the object, process, or system they represent. Verbal models, such as Das

Kapital or Wealth and Poverty, take the form of oral

or written language. As a result, they can be more

‘Arthur D. Ltttle, Inc., op. ctt., p. 11.6.
Ch. 2—Major Global Modeling Studies ● 1 3

explicit and precise than mental models, but at the

same time they are potentially diffuse, impressionistic , ambiguous, and rhetoric al.

Mathematical models, on the other hand, can

represent the relevant elements and relations of a

real object, process, or system in mathematical

symbols and equations. This allows them to express complex operations concisely, precisely, and

explicitly in a rigorous and consistent language.

This in turn makes them more open to objective

criticism and correction, but mathematical models

remain susceptible to omissions, distortions, and

misinterpretations like those that afflict mental

and verbal models. They can be no more valid or

reliable than the theoretical understanding on

which they are based and the mathematical form

in which they are expressed.

Computerized models are mathematical models that have been rewritten in a programing

language that can be run on a computer. They can

be used to investigate a process, system, or theory

that is too large or too complicated to model adequately (or manipulate conveniently) in words or a

few simple equations. 6 Such models can contain

more elements (variables), more relations (equations), and far more empirical data than simpler

models. The computer can keep track of all of

these factors simultaneously, manipulate them

very rapidly, and produce results that are free from

computational error. However, human judgment

fi\lc,IJ<)u ~, RI< h’irdwln, (Ind ku~ km,lnn, op \ It , p 2(I

The Trend Away From

Until about 1970, most long-range forecasts

were characterized by generalized optimism about

the benefits of continued economic growth and

confidence in the ability of technology to overcome any barriers. The most influential of the

forecasts was The Year 2000, by Herman Kahn and

Anthony Wiener of the Hudson Institute, which

offered a set of alternative “scenarios” as a

“framework for speculation” on the future. Its central finding was “that economic trends will proceed more or less smoothly through the next thirty

years and beyond,” and that “we are entering a

period of general political and economic stability

is still required to determine what factors to include, hem’ to represent them, what data to use,

and how to interpret the numerical findings. Consequently, the results that come out of the computer are only as reliable as the general assumptions, structural decisions, and data that go into it,

and even the best results are subject to biased or

mistaken interpretations.

Global models are simply computerized mathematical models whose purpose is to investigate

systems, theories, and issues of a global scale and

complexity, usually with a relatively long time

horizon:

Global modeling is distinguishable from other

types of modeling of social systems only by the

questions it asks. Its methods, strengths, and

weaknesses are identical to those of all policyoriented computer models. It draws from the same

base of theory and data. Therefore, if there are any

distinct properties of global modeling, they follow

directly from the characteristics of global

problems. i

The following survey will therefore focus not only

on the modeling techniques that have been used

and the findings that have resulted, but also on

the global problems that have been addressed in

the models and the purposes to which their findings have been put.

-It.ld , p 45

Technological Optimism

at least so far as the frontiers and economies of

most of the old nations are concerned.”8 This

“surprise-free” scenario was based on exponential

extrapolations of postwar demographic and eco--

nomic trends, but it was also influenced by the au--

thors’ underlying assumption of “continuity” in

global affairs, particularly the increased rate of

technological innovation, and by their confidence

that society would be able to find “physically nonharmful methods of over-indulging.”9 Kahn and
14 ● Global Models, World Futures, and Public Policy

Wiener do caution that “increasing discrepancies

between rich and poor” could lead to resentment

and instability, and that the “problems of development constitute a serious economic and moral

concern. ”1

° Nevertheless, according to one critic,

“they simply refuse to be overawed by the

magnitude of the problems posed. ”l 1 Although

sharply criticized in recent years, this view of the

future has remained influential in both Government and corporate policy, as well as public opinion, in the United States. 12

Since the late 1960’s–and particularly since the

1973 oil embargo–a less optimistic view of the

future has gained currency, a view characterized

by increased concern for the feasibility and environmental consequences of unrestrained economic growth and by criticism of the social and

political institutions that have supported such

growth, This new mood, which has been characterized as “neo-Malthusian pessimism, ” was influenced in part by the projections of economist

Joseph Spengler and by the popular success of several books by Anne and Paul Ehrlich, who argued

that the world is already over-populated and overdeveloped in terms of its ecological resources.13 By

far the largest stimulus to public debate over these

issues came from the activities of the Club of Rome

(an international group of businessmen, academics, and civil servants) that was organized in 1968

by Italian management consultant Aurelio Peccei,

The Club of Rome’s “Project on the Predicament of Mankind” focuses on the complex interacting socioeconomic problems that make up the

so-called “world problematique:”14

● poverty in the midst of plenty;

● degradation of the environment;

● loss of faith in institutions;

● unconrtolled urban spread;

● insecurity of employment;

● alienation of youth ;

. rejection of traditional values; and

. inflation and other monetary and economic

disruptions.

The predicament of mankind, according to the

Club, is to be able to perceive this problematique

but to be unable to understand its origins or operation and, therefore, unable to respond to it effectivelv.

The Club’s continuing program, consequently,

has two objectives: 1) to gain a better understanding of the limits of the world system, the interaction of its dominant elements, and the constraints

it puts on human numbers and activities; l 5 and 2)

to encourage appropriate sociopolitical reforms by

bringing the world problematique to the attention

of the general public and (more pointedly) the

world’s leaders and decisionmakers. The Club “hit

on the idea of using a computer to advertise their

cause, ” as one critic puts it, not only because “the

field of Systems Dynamics had created a body

of expertise uniquely suited to the research demands,” but also because the resulting report

might prove to be “a vehicle to move the hearts

and minds of men out of their ingrained habits. ”16

These dual purposes led to the first true global

model, which also remains the best known and the

most controversial.
Ch. 2—Major Global Modeling Studies ● 1 5

World 3—The Limits to Growth

Origin and Purpose

In June 1970, when the Club of Rome was seeking a suitable methodology for their investimation

of the global system, Jay For-rester of the Massachusetts Institute of Technology (MIT) invited the

group to Cambridge for a demonstration of the capabilities of systems dynamics. Within 3 weeks he

designed and documented a simple global model

- World l - as the basis for presentations and discussions at the end of July 1970. (A revised version, World 2, was the subject of Forrester’s subsequent World Dynamics (197l).) Impressed, the

Club obtained a $250,000 grant from the Volkswagen Foundation to fund For-rester’s colleague

Dennis Meadow’s and a team from MIT in

developing a full-scale model—World 3—based on

the systems dynamics approach. Under the

auspices and direction of the Club of Rome, the

MIT team produced both an elaborated model

and a popularized presentation of it in less than 2

years—perhaps too quickly, in the view of one

critic:

The Club only relinquished control then the exercise had produced their desired product, as evidenced by the fact that client pressure drove the

modelers to violate their scientific values by publishing The Limits of Growth beforc the technical

documentation for World 3 was completed.

Structure and Assumptions

The World 3 model describes the global system

in terms of five interacting subsystems—population, natural resources, capital, agriculture, and

pollution-which are averaged on a global basis.

Its most important conceptual contribution is the incorporation of “feedback” relations between these vari-

] 

I.r,r,,tcr R(hIII.,111 III ~lL(IJI\\\ RI Il<lr.!.rli <Ir)d Ihu km<ll~n, p Lit ,

;> +’+

ables; due to these relations, attempts to solve one

problem may unintentionally exacerbate another.

The model also introduced the concept of “carry--

ing capacity ’’—the level of population and production that could be sustained indefinitely by the

prevailing physical, political, and biological systems of the world-and posited four possible “behavior modes” that a growing population could

exhibit with regard to this carrying capacity (see

fig. 2). None of these behavior modes reflects the

potential ability of technology to expand the carrying capacity, primarily because the model assumes

nonsubstitutibility between technology and resources.

The purposes of the model, according to the

authors, were “to determine which of [these] behavior modes . . . is most characteristic of the

globe’s population and material outputs under different conditions and to identify the future policies

that may lead to a stable rather than an unstable

behavior mode,”18 According to one critic, however, given the authors’ “ specific motivating concern with limits, the broad conclusions that

emerged from the model are, perhaps, not surprising’’—they assumed that limits exist and would

eventually be reached; “[the] question was when

and how.”19

Findings of World 3

The standard or “reference run” of World 3,

based on a continuation of the trends that have

characterized the world system since 1900, results

in the model output that has given The Limits of

Growth its reputation for “gloom and doom” (see

fig. 3). In this case the collapse of the system is

caused by rapidly expanding population and in-

———-——— 1 fi[)t.l, r,,< [., Alc:lJ(l\\ s, C’r ‘II !

[),,MI1lL \ ,)/ L ;?, It, [h I* I ,i f l~llr, U’ r),l(l \( ‘<111 )-

hrldgt, hl,I\s \Y’rlgl~r Allc,n, 1 Q;+), II ~

‘g~l~:ldtni i, [{I( h{lrdw~l]. <id f’mu~ km(llll~, ~~p ( 11., ;> [> I
16 . Global Models, World Futures, and Public Policy

Figure 2.— Possible Global Behavior Modes

Organic growth

Oscillation

about stable

carrying

capacity

Time

Time Time

Erosion of

carrying

capacity

Time

dustrial output and a diminishing resource base.

Essentially the same results are achieved in additional runs with increasingly optimistic assumptions about the five system variables:

● doubling the nonrenewable resource base;

● “unlimited” nuclear power and extensive recycling;

. strict and effective pollution control;

● doubling the average agricultural yield; and

● “perfectly effective” but voluntary birth control.

In some of the runs population and industrial production climb to higher levels before collapsing,

but–according to this analysis–no single technological change can avert the final catastrophe, nor

can any combination of them delay the collapse

beyond the year 2100. In some runs the collapse is

caused by a resource crisis, in others by a pollution

crisis or a food crisis; but no matter what the assumptions, say the authors, “The basic behavior

mode of the world system is exponential growth of

population and capital, followed by collapse.”20

In keeping with their second objective, the MIT

team also used the World 3 model to identify conditions and policies that would avoid these problems and lead to a stable behavior mode like one of

those in figure 2. Continuous growth was ruled

out by the basic assumptions of the model; they

were looking for an output that represented a “sustainable” world system that would avoid collapse

and would also be “capable of satisfying the basic

material requirements of all of its people. ”21

By working backward from the desired outcome

to the conditions that would produce it, the authors were able to find a combination of “realistic”

policy changes that, implemented simultaneously
Ch. 2—Major Global Modeling Studies ● 1 7

Figure 3.—World 3 Standard Run

1900 2100

The “standard” world model run assumes no major change

in the physical, economic, or social relationships that have

historically governed the development of the world system.

All variables plotted here follow historical values from 1900

to 1970. Food, industrial output, and population grow exponentially until the rapidly diminishing resource base

forces a slowdown in industrial growth. Because of natural

delays in the system, both population and pollution continue to increase for some time after the peak of industrialization. Population growth is finally halted by a rise in the

death rate due to decreased food and medical services.

SOURCE: Limits to Growth.

in 1975, would lead to an “equilibrium state” (see

fig.

●

●

●

4): 22

restrict population growth by reducing average desired family size to two children and

making “perfect” birth control universally accessible (population stabilizes at about 6 billion in 2050, after a delay inherent in the age

structure of the current population);

restrict capital growth by maintaining average industrial output per capita at the 1975

level and holding the capital investment rate

equal to the depreciation rate (excess capacity

is used to produce consumer goods and services);

reduce resource consumption and pollution generation per unit ‘of industrial and

agricultural output to one-fourth of their 1970

1900 2100

Technological policies and growth-regulating policies produce an equilibrium state sustainable far into the future.

Technological policies include resource recycling, pollution control devices, increased lifetime of all forms of capital, and methods to restore eroded and infertile soil. Value

changes include increased emphasis on food and services

rather than on industrial production. Births are set equal to

deaths and industrial capital investment equal to capital

depreciation. Equilibrium value of industrial output per

capita is three times the 1970 world average.

SOURCE: Limits to Growth,

●

●

●

levels (largely through recycling and advanced

abatement technologies);

divert capital to agricultural production in

order to produce sufficient food for all people,

even if such an investment would be considered “uneconomic;”

prevent soil depletion and erosion by using

some of the agricultural capital for enrichment and preservation (e.g., comporting urban organic wastes and returning them to the

land); and

extend the average lifetime of industrial

capital stock through improved durability

and maintenance, in order to reduce obsolescence and make more capital and resources

available for other sectors.

The authors recognized that different combinations of the above policies might be adopted by different societies, and that “[a] society choosing sta-
18 ● Global Models, World Futures, and Public Policy

bility as a goal certainly must approach that goal

gradually.” 23 However, they hastened to add that

action must be taken soon: if the implementation

of these policies were to be delayed by 25 years, for

example, they would not result in an “equilibrium

state” (see fig. 5); this implicitly suggests that far

more severe measures would be required after that

time.

These findings led the authors to call for a “controlled, orderly transition from growth to global

equilibrium, ” but they were vague about the specific actions and tradeoffs this transition would require, explaining that “much more information is

needed to manage the transition. ”24 Some critics

feel that the model’s “no growth” bias “can be seen

as supporting the interests of the materially well25 However, others Point off” and the rich nations.

out that the equilibrium state necessarily implies a

“world-wide radical egalitarian levelling of incomes and property, ”26 yet the MIT team has

“almost nothing to say about what should or

might happen to poor nations . . . under the

policy of no growth. ”27 Because of “their deliberate

self-restriction to physical properties of the world,”

according to another critic, “they have chosen to

be unconcerned with politics [and] social structure; ”28 The Limits to Growth speaks instead of the

greater demands that will be placed on

“humanity’s moral resources. ”29 Above all,

however, the model’s simplification and global aggregation of imperfectly understood factors make

it unsuited for generating specific, detailed policy

recommendations. This is a limitation shared by

other global models:

The breadth of focus and coherent conceptual

development of the world models ensure their utility for clarifying the nature of long-term global

problems. However, their limitations render them

unsuitable as primary tools of analysis or as tools

for detailed analysis of global problems and their

solutions. 30

“ibid., p. 167.

“11md., p. 180.

J5Keith L. R. Pawtt, “Malthus and C)ther Economists: Some Doomsdavs

Rewslted,” In IMocfek oj Doom A CrltIque of [he Llmm to Grouth, H. S. D. Cole

(eel.) (New York: Unlveme Books, 1973), pp. 154-157.

2sHarvey Simmons, “Svstems Dynamtcs and Technocracy)” [n ,Models of

Doom, pp. 206-207.

27c(11e ,n W’Or/d Futures The Grear Debate, P. 29.

28 Marie Jahoda, “Postscript on Social Change,” In Models of Doom, p. 212.

Z~Meadc)w,s, et al., The Ltmtts to Grouth, P. 179.

~O]ennifer Robinson, “worlds 2 and 3,” [n The Glofra/ 2(XN.I ~eporc [0 lb Presldem (Washington, D. C.: U.S. Council on Environmental Qualltv and Department of State, 1980), Iol. 2, ~, 608,

Figure 5.—World 3 Run With Stabilizing Policies

Introduced in the Year 2000

1900 2100

If all the policies instituted in 1975 in the previous figure are

delayed until the year 2000, the equilibrium state is no

longer sustainable. Population and industrial capital reach

levels high enough to create food and resource shortages

before the year 2100.

SOURCE: Limits to Growth,

Conclusions of World 3

Within these limits, World 3 arrives at three central conclusions that have been influential in the

subsequent “futures debate:”31

10

2.

3.

If present growth trends in global population,

industrialization, resource depletion, pollution, and food production are allowed to continue unchanged, the limits to growth on this

planet will be reached sometime within the

next 100 years, resulting in a catastrophic

decline in both population and industrial capacity.

These growth trends can be altered in such a

way as to establish economic stability at levels

that are both sustainable into the foreseeable

future and capable of satisfying the basic material needs of all the world’s people.

If the people and nations of the world decide

to strive for this equilibrium state, the sooner

they start working to attain it, the greater

their chances for success will be.

1

JILleadous, Rl~hardson, and Bruckmann, op. cit., PP. ~7-68.
Ch. 2—Major Global Modeling Studies • 19

Similarly, the technical limitations that restrict creasingly have been to achieve both greater

the utility of World 3 have proven to be a stimulus specificity and greater relevance to the needs of

for subsequent global models, whose purposes in- policy makers.

World Integrated Model —Mankind at the Turning Point

Origin and Purpose

The World 3 model achieved most of the objectives set for it by the MIT team and by the Club of

Rome, but system dynamics was still viewed with

skepticism in traditional scientific and policy circles. As a result, the popularized report on the

model, The Limits to Growth, was the subject of

considerable debate and controversy because of its

methods—primarily its radical aggregation of

global factors—and because of its vagueness on

policy issues. When the club began planning a followup in 1972, therefore, it sought a modeling approach that would accomplish three goals:32

●

●

●

to represent the world as a system of interdependent regions, rather than a single homogeneous unit, and to represent those regions in

greater sectoral detail;

to develop recommendations that would be of

more direct relevance to policy makers; and

to gain greater acceptance from the scientific

community by incorporating more “hard

data” and, wherever possible, by explicitly

employing state-of-the-art theories and methodologies from the relevant academic disciplines.

The model the club chose to support, again with

funds from the Volkswagen Foundation, was the

World Integrated Model (WIM). This model was

developed in parallel by two teams, one led by

Mihajlo Mesarovic at Case Western Reserve University in Cleveland and the other by Eduard

Pestel (a member of the executive committee of the

Club of Rome) at the Technical University in

Hannover, West Germany.

The authors first presented their model at a conference for high-level policy makers sponsored by

the Woodrow Wilson International Center for

Scholars in Washington, D. C., then at the first

global modeling conference of the International

‘>lhd , pp. ;~-?+.

Institute for Applied Systems Analysis (IIASA) in

Austria, and finally at a series of scientific meetings and congresses throughout the world. Only

after these formal presentations—and the distribution of technical documentation to selected experts—did they release the popular description of

the model in the fall of 1974.

Structure and Assumptions

The WIM methodology is based on Mesarovic’s

“multilevel hierarchical systems theory, ” which

views the world in terms of five interrelated planes

or strata:

●

●

●

●

●

the environmental stratum, which combines geophysical and ecological factors and

corresponds roughly to the natural “carrying

capacity,” although perhaps too superficially

to satisfy some environmentalists;

the technology stratum, which embraces activities whose biological, chemical, or physical

terms involve mass and energy transfer;

the demographic-economic stratum, which

combines the human population and industrial capital of World 3 and, with the environment stratum, makes up most of the model’s

content;

the group stratum, made up of sociopolitical

institutions, policies, and decisions, which are

usually represented as sets of alternative scenarios among which the model user chooses;

and

the individual stratum, reflecting personal

attitudes and values, again represented by alternative scenarios to be selected by the model

user.

According to the theory, these levels ordinarily

operate with a fair degree of independence, although they can become highly interactive under

“crisis” conditions. The authors therefore feel that

their model can help us to understand and predict
20 ● Global Models, World Futures, and Public Policy

the system’s behavior in both present and future

crises. 33

The major improvement in the WIM representation of the world system, however, is its greater

geographic and economic detail (regionalization

and disaggregation). Instead of a single homogeneous world, the model contains 10 regions made up

of similar countries (see fig. 6), although in some

runs they are grouped in three or four blocs. As a

result, WIM can represent varying levels of development and resource endowment, as well as cultural and environmental differences; and it can

therefore be used to investigate potential regional

(as opposed to global) problems and crises. In addition, because these regions are connected by a

trade network, WIM can be used to investigate the

potentially mitigating effects of international trade

(see fig. 7). Within each region, physical and economic sectors are differentiated into numerous

subcategories—85 age groups for population, 19

‘JJennLfer Rolxnson, “Mesaro\w-Pestel World Model,” In The Global 2cXXI

Report to the Presderrt, vol. 2, p. 616 .

categories for industrial capital, five for energy capital, two for agricultural capital, and so on. From

the point of view of an economist, in fact, WIM is

a collection of regional economic models. The

resulting mathematical model is quite large: World

1 contained only.40 equations, and World 3 about

200, while WIM (according to its creators) contains over 100,000.34

Another improvement, one that is more directly

relevant to policy applications, is the model’s interactive design. The model user is allowed to estimate social and political behavior by selecting

among alternative scenarios in the individual and

group strata, thereby manipulating certain variables in such a way as to test a wide range of policy

assumptions about energy prices, food exports,

capital investments, and development aid. In addition, WIM’S various submodels can be used independently to generate and test alternative policies

for specific countries and regions. This capability

~+cole in World Futures: The Great Debate, P . 34

Figure 6.—Regionaiization of the Worid integrated Modei

SOURCE: Mankind at the Turning Point.
Ch. 2—Major Global Modeling Studies • 21

Figure 7.—Block Diagram of the Basic Elements of the World Integrated Model

. . ‘ .

Trade and payments

Agriculture

SOURCE: Command and Control Technical Center.

was in fact one of the stated objectives of the modelers:

We hoped thus to furnish political and economic

decisionmakers in various parts of the world with a

comprehensive global planning tool, which could

help them to act in anticipation of the crises at our

doorstep and of those that loom increasingly large

in the distance, instead of reacting in the spirit of

short-term pragmatism .35

In keeping with this objective, which is shared by

the Club of Rome, the WIM team at Case Western

Reserve has actively marketed their model, using

satellite-telephone patches to make presentations

to prime ministers and other officials in at least 18

different nations.

Findings of WIM

The WIM model has shown its versatility in extensive use for policy testing, both to evaluate alternative scenarios within its own assumptions

and to test the scenarios and assumptions of other

~j~~ihallo D, Mesaroi,lc and Ecjuard Pestel, hfankmd at the T[/mw Pomr (New

York: Dutton, 1974), p. Ix.

modelers and futurists. 36 Because the purpose and

output of the model vary significantly from user to

user and run to run, however, it is difficult to

isolate any particular “results, ” although several

test runs are illustrative. The reference or “historical scenario” run of the model, based on a continuation of present trends, results in the model

output shown in figure 8: a steady increase in the

real cost of food on the world market, which

would also drive up domestic prices in the United

States, and a catastrophic increase in the number

of deaths caused by starvation in South Asia. The

alternative “isolationist scenario” (fig. 9) indicates

that, should the United States act to keep domestic food prices down by restricting exports, starvation in South Asia comes sooner and is even more

widespread. In another pair of policy tests (fig. 10),

the model output suggests that a policy of low,

fixed oil prices leads to a catastrophic economic decline in the developed world when the resource is

exhausted, whereas “optimal” price increases (perMsee for example Barry B, Hughes and Mlha]lo D. Mesarowc, “Testing the

Hudson Institute %enarlos” (~’ashlngton, D. C.: U.S. Assmlatmn for the Club

of Rome, Sept. 1979, mimeograph.
22 . Global Models, World Futures, and Public Policy

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

SOURCE: Systems Research Center, Case Western Reserve University.

Figure 10.–Comparison of WIM

(A)

0

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

SOURCE: Systems Research Center, Case Western Reserve University,

1975 1980 1985 1990 1995 2000 2005 2010 2015 20202025

Year

Cheap energy in the form of oil has been a prime fuel for the

unprecedented growth of the world economy in the 1950’s

and 1960’s. The dramatic increase in oil prices in 1973 was

viewed as a catastrophe. However, computer analysis of

our world system model indicates that the continuation of

what amounts to overexploitation of oil, spurred by an

unreasonably low price, would lead to major dislocations

because of the exhaustion of reserves and the lack of

motivation to develop substitutes in time. Pursuance of

short term objectives would lead to major dislocations in

SOURCE: Mankind at the Turning Point.

Low” and Optimal”Price Oil Scenarios

. . . ‘ 14

0

1975 1980 1985 1990 1995 2000 2005 2010 2015 20202025

Year

the long run (see A). A much more beneficial development

for all concerned results from the “optimal price scenario”

in which the price is gradually increased up to the “optimum” level. Such a policy would bring in the substitutes

in a more regular fashion while prolonging the reserves.

Both exporting and importing regions would fare better

(see B). It is only by taking a global and long term view that

such a course of development, most beneficial to all concerned, can be identified.
Ch. 2—Major Global Modeling Studies ● 2 3

mitting gradual adaptation and substitution) benefits both oil producers and consumers; a third oilprice scenario (not shown) suggests that increases

above the “optimal” level leave all regions worse

off. 3

’

Conclusions of WIM

From these and numerous additional interactions with the model, the authors arrive at several

conclusions about the nature of the world system

and

●

●

●

the management of future development:38

the current crises in agriculture, energy, etc.,

are not transient but persistent, and represent

the first signs of an “oncoming era of

scarcity ;“

the solutions to these crises cannot be found

through isolated, short-term, or narrowly nationalistic strategies, but only through an integrated global context and “in the spirit of

truly global” cooperation . . . guided by a rational master plan for long-term organic

growth;” and

“the time that can be wasted before developing such a global world system is running

out .“

The model indicates that oil, substitutes for oil,

and agricultural land will be the greatest constraints on growth. To address these problems, the

authors recommend a policy of “organic growth,”

based on a recognition that the world system is a

“collect ion of functionally interdependent

parts, ”39 This policy, which would encourage

growth where needed and discourage it where it

“threatens not only that part but the whole as

well, ” include such specific steps as the following:40

●

●

●

●

encourage worldwide diversification of industry to achieve a truly global economic system;

build up the economic base and especially the

export potential of the poorest countries so

they can pay for food imports;

give food aid to the poorest countries, but give

investment aid only in the form of “intermediate” or appropriate technology; and

carry out effective social and institutional reforms, because the required economic transfers are impossible under prevailing international economic arrangements.

The authors suggest that unless such steps are

taken, spreading regional collapse and international tension will, like falling dominoes, eventually reach the developed world. If these and other

steps are taken, on the other hand, “the world

growth rates implied by (WIM’s) computer results

are much closer to those of Kahn and Wiener than

to those of Meadows and Forrester. ”41

U.S. Government Use of WIM

More recently, former members of the modeling

team have designed a specially tailored version of

WIM for the U.S. Department of Defense.42 The

model, which is fully operational, is maintained

and operated by the Command and Control

Technical Center (CCTC) in support of the Plans

and Policy Directorate (J-5) of the Joint Chiefs of

Staff. J-5 is currently creating a new division, specifically devoted to long-range analysis, which will

use the model to develop projections of global systems behavior for use in long-range national

security planning. At present, the model is being

developed to provide data on political, economic,

and demographic conditions under various

subcontingencies of four basic scenarios or “future

worlds” defined by J-5:

●

●

●

●

“A-muted bipolarity,” essentially a reference

run based on current trends and international

relations;

“B-superpower dominance (conflict mode),”

including contingencies representing different

levels of East-West conflict;

“C-superpower dominance (cooperation

mode);” and

“D-devolution of power,” representing a future in which the superpowers must share

world power with other groups of nations,

and including contingencies for potential

North-South conflicts, such as an oil embargo.
24 ● Global Models, World Futures, and Public Policy

These WIM projections will become an input to

the Joint Long-Range Strategic Appraisal beginning with its 1982 revision. CCTC also plans a

complete update of its data base (facilitated by a

new software package) and further refinements in

WIM itself that may make it a more flexible tool

for determining future military requirements. For

instance, CCTC's version of WIM contains 12

geographical regions rather than 10 and will soon

be expanded to 14, with the further capability of

subdividing each new region into five subregions;

it also contains 87 rather than 85 age groups and

(for the United States and Soviet Union) a laborskills submodel that further divides the population

into male or female and urban or rural; and there

have been similar refinements in the agricultural

and materials submodels. These improvements

create data problems, however, since reliable data

are not available for many subregions and sectors.

CCTC is working with the Bureau of Mines to update and expand the data base for the materials

sector; in addition, J-5 has instructed CCTC to

contact other Federal agencies about possible coordination of global modeling and strategic assessment activities. Such coordination might be facilitated in at least two cases by the fact that the Department of Agriculture, as well as the Bureau of

Mines, is already using a version of WIM.

Latin American World Model—Catastrophe

or New Society?

Origin and Purpose

When the Club of Rome presented the preliminary results of World 3 at a 1970 meeting in Rio

de Janeiro, the reaction of the mostly Latin American audience was strongly negative. They felt that

predictions of global crises, based on extrapolation

of present trends and arrangements, reflected a parochial developed-world perspective; for twothirds of the world’s people such crises are already

at hand. The audience refused to accept scenarios

that implicitly curbed development and widened

the income gap, and they felt that policies aimed at

achieving a state of global equilibrium would

merely ensure that the present disparities and inequities in the world system are perpetuated. They

resolved, therefore, to design a model of an egalitarian “ideal society” in which basic human needs

(not profits) would be the basis for resource allocation. The purpose of the model is to demonstrate

the material viability of such a society, and thereby to demonstrate “that the different countries

and regions of the world (particularly the poorest)

could reach the goals we advocate in a reasonable

period of time,” relying primarily on their own

human and economic resources.43

This global modeling effort was carried out at

the Fundacion Bariloche in Argentina, with principal support from the International Development

Research Center in Ottawa, and was first presented at the second IIASA modeling conference

in Berlin in October 1974. An expanded version of

the model, developed for the International Labor

Organization (ILO), was warmly received at the

1976 World Employment Conference in Geneva,

where “basic needs” were formally adopted as a

major target of development.44 The model continues to have considerable impact through United

Nations organizations, including the United Nations Educational, Scientific, and Cultural

Organization (UNESCO) and the United Nations

Industrial Development Organization (UNIDO) as

well as ILO, and it is by far the most popular

global model among scientists and decisionmakers

in the Third World.45

Unlike World 3 and WIM, which provide conditional descriptive forecasts of global trends, the

Latin American World Model (LAWM) is openly

and insistently normative—it is not concerned

with “predicting what will occur if the contemporary tendencies of mankind continue, but

rather with sketching a way of arriving at the final

+JAmllCar 0. Herrera, et al., catu.s[~ophe or ~eu,%et>? A Lutm America n

World ,Model (Ottawa: Internatmnal Development Research Center, 1976), p. 8.

++cole In WOTM Futures The Great Debate, P . 39.

+5 Meadow5, Richar&n, and Bruckmann, op. at., P. 92.
Ch. 2—Major Global Modeling Studies ● 2 5

goals of a world liberated from underdevelopment

and misery. ”4b Its purpose is not to demonstrate

that certain changes might bring the present world

system into equilibrium, but rather “to show the

feasibility of solving the fundamental problems

through deep socio-political changes.”47 T h e

model is also distinguished by its emphasis on the

ideological issues involved in global modeling: the

modeling team was composed of humanistic socialists, 48 and on questions of development and redistribution they “explicitly take up a stance favourable to the Third World in general, and to Latin

America in particular.”49

Structure and Assumptions

The LAWM team’s goals and purposes lead

them to include some rather unusual assumptions

in the structure of their model. For instance, the

model divides the world into four regions (later

15), each of which is treated as an economic unit,

which “presupposes total collaboration between

the countries forming it.”50 The model contains

“simplistic” trade linkages: the contribution of international trade relative to regional gross national product (GNP) is held constant at the 1970

level, and all trade deficits are eliminated by

2000. 51 Instead, each region satisfied basic needs

through “autarchy, ” using almost exclusively local

economic resources. However, these resources are

assumed to be available in unlimited quantities

and at constant cost: after a static analysis of current resource data, the modelers conclude that

“the environment and its natural resources will

not impose barriers of absolute physical limits on

the attainment of [an ideal] society,” at least not

within a “historically significant time-scale. ”52 

AS

a result, they do not include these physical factors

in the computer model, and in this specific their

model reflects a technological optimism akin to

Herman Kahn’s.

+

6 Amllcar 0. Herrera, Procwd[ngs of the Zil IIAS,4 Global l+foddmg Con/ercnce

(Beriln, 1~74), quoted h}, hleaclow$, RIC hardwn, and Bruckmann, op. cit.,

p, 9],

‘THURO t). Sc(,lnlk, et al., “The Barlloche !tfodel,” In Meadow, Richardson,

and Bruc kmann, op. cit., p. 247.

‘S]enntfcr Robinson, “The Latin American V’c,rld Model,” in The G/obul ?000

Report co (h,’ PrLw&n[, \wl. 2, p. 647.

~~~ole ,n ~’or/~ Fu[14rcI Tht, GTCU[ Dc/w[c, P. %.

5 0 Herrera,et al., op. Lit., P. ‘~.

‘lRohlnwn, “The Latin Amcrlcan V’orld klociel,” p. 642

‘

: Herrera, ct al., (Jp. cit., p. 8.

However, the authors do assume a radical

change in the sociopolitical factors that control

patterns of resource use—i.e., an equal distribution

of consumption between regions and a total, egalitarian redistribution of income within regions. In

addition, the model includes no assumptions

about population policies, although it does include

several untested assumptions about the effect of

living conditions on demographic change. LAWM

also appears to assume automatic growth in productivity through technological progress, at no

cost, at rates between 0.5 and 1.5 percent annually

depending on the sector.53

LAWM is essentially an economic model that

operates through optimization procedures; i.e., it

has five production sectors representing basic

needs—food, housing, education, capital goods,

and other goods and services—to which labor and

capital are allocated through optimal control techniques in such a way as to maximize life expectancy at birth, which is taken to be the best indicator of general living conditions. These calculations proceed independently for each region from

1960 to 2060, but all countries are assumed to follow optimal policies after 1980.

Findings of LAWM

The standard or reference run of this optimization model indicates that all regions except Asia

can reach their basic needs targets within 30 years.

Developed nations (including the Communist

world) “can reach high levels of well-being even if

their economic growth rate is reduced drastically

in the future” (fig. 11); in reality, economic growth

is restricted to between 1 and 2 percent—far below

the developed region’s capacity for growth—which

the authors acknowledge “assumes a political decision. ”54

Latin America could fulfill basic needs by the

early 1990’s by maintaining a relatively high investment rate, particularly in housing and education (fig. 12). The output for Africa closely resembles that for Latin America, although basic needs

are not met until 2008 and some shortfalls occur in

the housing sector.

Ch. 2—Major Global Modeling Studies ● 2 7

Figure 12.— LAWM Standard Run for Latin America

1960 1970 1980 1990 2000 2010 2020 2030

Key;

1 (B) Birthrate 7 (C) Total calories

2 (5) Percentage of GNP allocated to sector 5 8 (E) Life expectancy

3 (4) Percentage of GNP allocated to sector 4 9 ($) GNP per capita in

4 (A) Population growth rate 10 (P) Total population

5 (M) Enrollment 11 (U) Urbanization

6 (V) Houses per family

SOURCE Catastrophe or New Society?

2040 2050 2060

1960 dollars

Ch. 2—Major Global Modeling Studies Ž 29

not keep up with population growth; daily food intake peaks at less than 3,000 calories per capita in

2008 and declines steadily thereafter. (These investments would also divert resources from the satisfaction of other basic needs, and they would

probably prevent economic growth if investment

in capital goods were not fixed at 25 percent.)

Only an effective population policy and the use of

nonconventional foodstuffs, both of which the authors advocate, could avoid catastrophe in Asia

under these conditions.

The policy tests conducted with LAWM indicate that capital transfers from the industrialized

countries (in isolation from other measures) would

have little effect on the above outcomes, but they

also reveal that both technological progress and

internal income redistribution are vital to achieving regional goals.

●

●

●

In the “international solidarity” run, the developed nations transfer capital aid to Africa and

Asia at a rate that rises from 0.2 percent of GNP

in 1980 to 2.0 percent in 1990 and thereafter.

The result is higher investment rates and faster

economic growth in the industrialized nations

(in order to compensate for the aid), but very little effect on the time needed to satisfy basic

needs elsewhere and a negligible impact on the

food shortage in Asia.

In the “technological stagnation” run, on the

other hand, growth in economic production

due to technical progress falls to zero between

1980 and 2000 and remains there. The outcome

is disastrous in every region except the developed nations. Latin America requires a longer

period of time to satisfy basic needs, particularly

food and housing, and in Africa and Asia “the

economic system finally collapses” sometime between 1990 and 2020 as population steadily outstrips production.

By far the greatest difference in results, however,

comes from the “historical” run, in which the

assumption of egalitarian intraregional redistribution is replaced by a pattern of consumption

that reflects current income distributions and

socioeconomic structures. To satisfy basic needs

in the same period of time under these conditions would require economic growth rates of 10

to 12 percent in the developing countries, rates

which “are in fact impossible to attain. ” The authors conclude that “at the very best” their

goals would be delayed by two or three generations, and would require 3 to 5 times more resources, under these conditions.55

Conclusions of LAWM

The conclusions the LAWM team draws from

its interactions with the model do not always reflect the above results (apparent discrepancies are

noted in parentheses):56

●

●

●

●

●

“it is possible to control population growth to

the point of equilibrium by raising the general

standard of living” (population stabilizes only

in the developed regions, and is still growing

globally at a rate of 1.1 percent per year in

2040);

“if the policies proposed here are applied, all

of humanity could attain an adequate standard of living within a period little longer than

one generation” (this is true for Asia only

with an effective population policy and considerable development aid);

“this equilibrium could be achieved on a

global scale well before the earth’s capacity to

produce food—the only foreseeable physical

limitation within the time horizon of the model—is fully exploited even if food production

continues to be based on currently available

technology” (the model assumes considerable

technical progress at no cost in agriculture

and all other sectors, and fails to achieve its

goals if technology stagnates);

“[the] obstacles that stand in the way of the

harmonious development of humanity are not

physical or economic in the strict sense, but

are essentially sociopolitical;” and

“[the] goals are therefore [to be] achieved . . .

by a reduction of nonessential consumption;

increased investment; . . . the rational use of

land . . . the egalitarian distribution of basic

goods and services; and . . . the implementation of an active policy to eliminate deficits in

international trade.”

~jlbld., pp. 106-107.

Wbd.t p. 107.

‘ L

.-
30 • Global Models, World Futures, and Public Policy

United Nations Input-Output World Model—

The Future of the World Economy

Origin and Purpose

Like LAWM, the United Nations Input-Output

World Model (UNIOWM) represents a Third

World reaction to the unpalatable conclusions of

The Limits to Growth. However, its central concern

is not the satisfaction of basic needs, but rather the

narrowing of the income gap between the rich and

poor nations. The model was commissioned in late

1972 by the Centre for Development Planning,

Projections, and Policies (CDPPP), a U.N. agency

responsible for long-range integrated planning. Initial financial support came from the Government

of the Netherlands, and subsequent funding was

obtained from the U. N., the Ford Foundation,

and the National Science Foundation. Wassily

Leontief, the project director, outlined the concepts behind the model in his acceptance speech

for the Nobel Prize in economics in 1973; the work

of collecting data and building the model was carried out by Anne Carter, Peter Petri, and others at

Brandeis University. Petri presented the model at

the Fifth IIASA modeling conference in September 1977, shortly after The Future of the World

Economy was released in New York.

The study was conducted under U.N. auspices

and direction. Although its findings did not represent official U.N. recommendations, the model’s

primary purpose was to determine whether physical or environmental limits would pose a significant barrier to the economic growth targets set by

the U.N.’S International Development Strategy,

which had been proposed by the General Assembly in 1970 as the basis for the Second Development Decade. As modified and expanded by various U.N. agencies, these targets include the following:

●

●

●

●

reducing the average income ratio between

the developing and developed countries by

almost 50 percent, from 12:1 to 7:1;

improving internal income distribution to

eradicate mass poverty;

creating 1 billion new jobs in the developing

world by 2000;

satisfying the basic needs of all people;

●

●

increasing food production in developing

countries by at least 4 percent per year;

increasing the developing nations’ share of the

world market in manufacturing to 14.3 percent by 1985 and 25 percent by 2000; and

achieving a new international economic order

(NIEO), including stabilized commodity

prices, increased financial and technology

transfers, open markets for the less developed

countries’ (LDC) exports, and a code of conduct of translational enterprises.

Structure and Assumptions

The authors describe UNIOWM as “basically a

general-purpose economic model and thus applicable to the analysis of the evolution of the world

economy from other points of view, ”5’ notably the

environmental. However, Leontief has cautioned

that:

We cannot predict the future of the world

economy. However, we can rule out of our expectations future scenarios that are internally inconsistent and thus impossible.

To rule out internally inconsistent expectations

we need to construct a model that guarantees internal consistency . . . by visualizing the world as a

system of interdependent process in which each

process . . . generates certain output and absorbs a

specific combination of input.58

The rigorous accounting required by this inputoutput analysis forces the model to balance the

growth of one economic sector against its effect on

other sectors; similarly, imports and exports in one

region must be balanced against the imports and

exports of other regions. This technique also permits “an unusual degree of detail” in representing

particular industries or regions, which is “advantageous” because of its “relatively specific policy

significance. “59 On the other hand, critics have

STW~~SllV LeOnrlef, Anne Carter, and Peter Petri, The Future Of t~ wor~

Econom>: A Uruted Nutlom Stud} (New York: Oxford, 1977), p. 7.

58wa5511y Leontlef, “structure of the V’orld Economy: Outline of a Simple

Input-Output Formulation” (Nobel Memorial Lecture), Amencun Econom~c

Retwu, December 1977, p. 823.

5gLeonttef, Carter, and Petr], op. cit., p. 8.
Ch. 2—Major Global Modeling Studies ● 3 1

questioned whether this level of explicit detail is

worthwhile or justifiable, particularly since it demands an enormous amount of data, much of

which had to be adapted from other information

and data bases. 60 UNIOWM’s population sector,

for example, merely assumes the projections

prepared by the U.N. Population Division in 1973

(see app. A).

The model divides the world into 15 regions

composed of fairly homogeneous economies, although for purposes of interpretation and presentation of results these regions are further aggregated in three categories: developed nations (eight

regions), resource-rich LDCs (three regions), and

resource-poor nations (four regions). Each region’s

economy contains 45 sectors of economic activity,

described by 175 equations with 229 variables.

Prices are calculated in a separate submodel, and

(as in LAWM) the representation of international

trade has been kept “almost artificially simple. ”61

The environmental sector includes eight pollutants and five types of abatement activities, but the

model does not reflect the effect of development

on ecological systems, nor does it contain any

other feedback loops; “it cannot, in any sense, be

viewed as a dynamic model. ”62 The model’s equations are solved simultaneously, usually at 5-year

intervals, in order to provide “snapshots” of the

world in 1980, 1990, and 2000.

The model can be applied to a wide variety of

tasks, but its utility is limited by its large data requirements and by the many controversial assumptions that have been included. This has led

one critic to conclude that:

The huge number of assumptions made in

estimating time trends for input-output matrices

makes for confusion when it comes to considering

the model as a whole. There are so many assumptions that one is hard put to evaluate the reasonableness of the total picture. 63

Nevertheless, the model could be and has been

used for a wide range of policy tests that reflect the

S o sam Cole, G/oba[ LIo&/s and [he Inw-nattonal Economc ~d~ A pQPH ~or t~

L’N’ITAR ProJecr on the Future (New York: Pergamon Press, 19?7), p. 22; and .lennlfer Rc>hlnqc>n, “(J, N. World )wfodel,” In The G1oIxI1 2(XXI Report to the Presdenc,

,01.2, p. 649

61Rc)b1n\On, “IJ,N. World Nlodel,” p. 652 .

‘~ifeadows, RIL hardson, and Bruckmann, op. c[t., p. 167; a slmllar crltlclsm

could he made of ‘W’Ihf.

b~Rohlnson, “~1.N. N’orld Model,” p. 652.

interests of the modelers and the organizations

that commissioned them.

Findings of UNIOWM

The study’s optimistic findings, particularly that

“no insurmountable physical barriers exist within

this century to the accelerated development of the

developing regions” and that “pollution . . . is a

technologically manageable problem,”64 received

widespread attention in the media, where it was

sometimes reported that UNIOWM “discredited”

The Limits to Growth. However, the authors have

cautioned that the model “cannot settle, and was

not designed to settle, the many fundamental

questions raised in The Limits-to-Growth debate.”65

And in fact the optimism of these general statements is not supported by the specific results of

most of UNIOWM scenarios.

Policy tests conducted for the UN have included

a number of different scenarios relating to economic growth rates and per capita income gaps.

●

●

The “old economic order” scenario is based

on historical trends in internal and external

investment and existing international economic arrangements. Income per capita grows

in all three categories of nations but income

gaps increase, despite decelerating growth in

the developed regions after 1990, and some

LDCs would face an absolute decline in living

standards (see fig. 14). This scenario “turns

out to be rather pessimistic, ” according to the

authors, and because of its dim economic

prospects for the developing regions it is

“downplayed in the UN documentation. ”66

The standard run, based on the minimum

growth targets of the International Development Strategy (IDS), also turns out to be relatively pessimistic. Because of their higher rates

of population growth, accelerated economic

growth in the LDCs does not lead to corresponding gains in GNP per capita. The income gap between developed and less developed regions remains at the current 12:1 ratio.

6+ LeOntle(, Carter, and Petri, OP. Cit., P P. 48-49.

“Peter Petri, “An Introductmn to the Structure and Applications of the

~lnlred Nations ~’or[d hfodel,” App/,ed hfa[hm[tcal ,Mode/lng, vol. 1, No. 5,

-June 1977, p. ?62.

66 Leontief, Carter, and Petri, op. cit., p. 73; Robinson, “L1. N. World Model,”

p. 653.
32 . Global /Mode/s, Wor/d Futures, and Public Policy

Fig

7,000

6,000

5,000

4,000

3,000

2,000

1,000

900

800

700

600

500
Ch. 2—Major Global Modeling Studies ● 3 3

● Four additional runs were then conducted by

altering the standard or IDS scenario in such

a way as to reflect more optimistic assumptions about: 1) resource endowments; 2) increased foreign aid from the developed regions; 3) fewer constraints on balance-ofpayment deficits; and 4) faster agricultural investments to achieve food self-sufficiency in

low-income Asia.

None of these scenarios, however, was capable of

producing the desired reduction in per capita income ratios. In two final scenarios, therefore, the

authors preset the model in such a way as to

roughly halve the income gap by 2000 and close it

completely by about 2050, and then solved its

equations to determine the investment and

growth rates that would produce the desired

results:

● Scenario “C,” based on the U.N.’s “low”

population growth projections (see app. A),

requires a 6.9 percent GNP growth rate in the

LDCs to reduce the income ratio to 7.15:1 by

2000.

● Scenario “X, ” based on the U.N.’s “medium”

population projections, requires an even

higher GNP growth rate of 7.2 percent in the

LDCs and reduces the income ratio only to

7.69:1. Scenario “X” also requires a fivefold

increase in overall agricultural output in the

developing regions, including a nearly tenfold

increase in resource-poor Latin America.

In subsequent policy tests, UNIOWM has been

used to examine the economic consequences of

mineral- and energy-conservation strategies. A

study of the future production and consumption

of nonfuel minerals, based on the resource conservation strategies of the Economic Council of Canada, is nearing completion but has yet to be publicly documented.b7 Another study, conducted for

the U.S. Department of Commerce, compares the

“old economic order” scenario with an “energy

conservation” scenario based on the maximum

reasonable reduction in fossil fuel consumption

over the next 20 years through the substitution of

labor and capital for energy. It revealed that energy conservation could reduce the balance-of-payments deficits of both developed regions and

b~kleadows, Richardson, and Bruckmann, op. cit., p. 175; Rohlnson, “U.N.

‘ik’orld ktodel,” p. 653.

resource-poor LDCs and allow increased GNP

growth in the latter, but that the capital requirements for conservation would require a 17- to

23-percent increase in the savings rate.ba

More recently, Leontief has conducted two

studies of the economic implications of the NIEO

and of worldwide military spending, with funding

from the U.N. and from American disarmament

organizations.b9 In the NIEO scenario, the

resource-poor LDCs are allowed to import whatever quantities of goods and services are required

to reduce income ratios by 50 percent by the year

2000, with their balance-of-payments deficits–up

to 75 percent of their imports—to be financed by

“extraordinary credits” from the developed regions and resource-rich LDCs, carrying a nominal

5-percent interest rate. The model output for this

scenario (see fig. 1.4) shows that the developed

regions, which would have t o ‘‘work

overtime . . . to provide [these] huge amounts of

economic aid,” would have a higher GNP but

lower per capita consumption in 2000, at which

time they would be allocating 3.1 percent of their

total GNP to development assistance. Leontief

himself doubts that such a plan could be implemented:

On the whole this projection of the future development of the world economy under the new economic order suggests that the practical possibility

of carrying out such an optimistic program must be

seriously questioned.70

As an alternative, Leontief proposes an “arms

limitation” scenario, noting that the current halftrillion-dollar annual worldwide defense spending

represents “the largest existing economic reserve

that might be utilized to accelerate the growth of

the resource-poor less developed regions. ” Where

the “old economic order” scenario assumed that

all regions would continue to devote the same percentage of their respective GNPs to defense that

they had in 1970, the “arms limitation” scenario

assumes that by 2000 the defense expenditures of

the United States and the Soviet Union would be

reduced by one-third, and that all other regions

6B.Anne P. carter and Alan K. Sin, “An Energy Ccmser~wt[on Scenario for

the V’orlcl kfodel,” prepared for the Bureau of Internatmnal and Econornlc

POIICY, L1.S. Department of Commerce, No~ember 1977, p. 2.

s~L~eadc>ws, Richardson, and Bruckmann, op. clt., P. 175.

~OM’assll}, W’, Leontlef, “The V’orld E~onomv of the Year 2030,” Sclentlfic

Amemxm, jol. 243, No. 3, Septemher 1980, p. 230.
34 ● Global Models, World Futures, and Public Policy

would reduce defense spending 25 percent by 1990

and 40 percent by 2000. The savings realized in

each region would first be used to satisfy its own

civilian needs, but the developed regions would allocate 15 percent of their savings to development

aid by 1990 and 25 percent by 2000. The model

output for this scenario (see fig. 14) indicates that

per capita income and consumption in the resource-poor LDCs would increase far faster than

under the old economic order. Since developedregion defense savings would be given to LDCs in

the form of direct aid, their balance-of-payments

deficits would also be far smaller. Based on a comparison of these scenarios, Leontief concludes

that:

. $ . the reallocation of economic resources arising

from the kind of international arms-limitation

agreement that has been suggested repeatedly, both

formally and informally by individuals and organizations inside and outside the U. N., is by far the

most promising of the three schemes for world economic development .71

Conclusions of UNIOWM

The results of these numerous UNIOWM scenarios suggest, in general, that the economic prospects of the resource-poor LDCs are not very optimistic. The growth rate targets of the U.N.’s Second Development Decade are insufficient to begin

closing the

developing

taken into

7 Ilbld., p. 231.

income gaps between developed and

regions when population increases are

consideration. The limits imposed by

mineral resources, agriculture, and the environment are not insurmountable and could be overcome through appropriate policies and investments; but “the principal limits to sustained economic growth and accelerated development are

political, social and institutional in character.”Tz

To

two

●

●

achieve accelerated development, therefore,

general conditions are considered necessary:

far-reaching internal reforms in the LDCs including often drastic changes in sociopolitical

institutions and economic policies—between

30 and 40 percent of GNP ‘must be used for

capital investment, particularly in the agricultural and export sectors, and both equitable

income redistribution and increased publicsector participation are needed to increase the

effectiveness of these investments; and

significant reforms in the international economic order, aimed at reducing the potentially large balance-of-payments deficits in the

developing regions—stabilizing commodity

markets, stimulating exports of manufactured

goods from the LDCs, and increasing financial transfers from the developed regions and

resource-rich LDCs.

Neither of these conditions, taken separately, is

sufficient to ensure a favorable outcome: 4

’Accelerated development leading to a substantial

reduction of the income gap between the developing and the developed countries can only be

achieved through a combination of both of these

conditions. ”73

TILeontief, Carter, and Petri, op. cit., p. 4~.

TIIbld., p. 11.

Global 2000—Entering the 21st Century

Global 2000 is a global modeling study, rather plus a number of projections based on analytical

than a “global model” in the same sense that techniques other than computerized simulation

World 3, WIM, LAWM, or UNIOWM are. Its models. Despite the very limited degree of interacprojections result not from a single, integrated tion among the sectors and agencies, however, and

model but rather from a collection of sectoral despite the frequent lack of consistency in their

models, independently developed or adopted by various assumption and data bases, these sectoral

various Federal agencies and other organizations, “submodels” collectively provide the U.S. Govern-
Ch. 2—Major Global Modeling Studies ● 3 5

ment with the same type of projections that the

other more integrated global models produce for

their users.74

Origin and Purposes

Global 2000 was carried out by an interagency

task force of the U.S. Government in response to a

directive issued by former President Carter in his

environmental message to Congress on May 23,

1977:

Environmental problems do not stop at national

boundaries. In the past decade, we and other nations have come to recognize the urgency of international efforts to protect our common environment.

As part of this process, I am directing the Council on Environmental Quality and the Department

of State, working in cooperation with . . . other appropriate agencies, to make a one-year study of the

probable changes in the world’s population, natural resources, and environment through the end of

the century. This study will serve as the foundation

of our longer-term planning. ’s

This mandate, as interpreted by the Global 2000

task force, imposed dual objectives on the study:

its purpose was not only to “identify [future] problems to which world attention must be directed,”

but also “to identify and strengthen the Government’s capability for longer-term planning and

analysis. ”7b The resulting report, released in July

1980, addressed both of these goals, although relying on the Government’s existing capability may

have detracted from the accuracy and usefulness of

the resulting projections. According to a Science

editorial:

A reading of portions of the report produced

after 3 years reveals more about the functioning of

the federal government than it conveys new reliable information about the future of the world. ”

‘

+ Gerald 0. Barney, stud} director, The Globul 2000 Report m the Pre.wdetr[:

Enwmg the Tu mt>-fmt Cenrum (V’a~hlngrnn, D. C.: U.S. Council on En-

~vronmental Qualitv and Department of State, 1980, ~’01. ~, PP. ~’-vI.

75Jlmmv Carter, The Pre~ldmrr’~ En{ [mnmental Program 1977 (W’ashlngton,

D. C.: U.S. Government Prlntlng Offlce, 1977), p. hi-l 1.

?SBarnel,, The G/o~a/ ~~] ~ePf)7[ tO [~, pr~s&nt, km!. 1, p. 6.

~~rhdlp H, Abelson, “The Global 2@W Reporr,” Scwnce, vol. Z@9, No. 4458,

Aug. 15, 1980, p. 76].

Various Federal agencies are already conducting

a considerable amount of long-term analysis and

planning, and a number of them have the capability to produce projections based on extensive data

bases and sophisticated sectoral models, many of

them computerized. These existing tools and procedures (and the skilled personnel who use them)

represent the “present foundation” of the Government’s long-range global planning—they embody

the assumptions on which current analysis is

based, and they are actually being used as at least a

partial basis for current planning and decisionmaking. As a result, the study plan chosen by the

task force was “to develop trend projections using,

to the fullest extent possible, the long-term global

data and models routinely employed by the Federal agencies. ”78

However, they found that “each agency has its

own idiosyncratic way of projecting the future, ”

based on its individual planning requirements and

area of responsibility .79 As a result, each agency’s

projections tend to focus on a single factor (such as

population, food, or energy) without adequately

considering the feedback involved in a system

where these factors are interacting variables. Furthermore, although these separate projections

“have generally been used by the Government and

others as though they had been calculated on a

mutually consistent basis, ” the different agencies’

models “were never designed to be used as part of

an integrated, self-consistent system. ”8° This leads

to one of the study’s basic findings:

To put it more simply, the analysis shows that

the executive agencies of the U.S. Government are

not now capable of presenting the President with

internally consistent projections of world trends in

population, resources, and the environment for the

next two decades.81

Despite these deficiencies, Global 2000 presents

the most comprehensive and consistent set of projections yet produced by the U.S. Government,

and it represents the first attempt to make such

7L3T~, ~~oba/ 2000 Rcport to the Pr~~[dent, fw1. 1, p. 6.

Wbid., J,oI. 2, p. 454.

‘OIhld.

8

’Ihld.
36 ● Global Models, World Futures, and Public Policy

projections on a coordinated, integrated basis.

The task force has been disarmingly frank and

forthcoming in their analysis of “th e

Government’s global model,” and their discussion

of its current weaknesses points to a number of

ways in which existing long-range analysis and

planning tools can be improved. Several of the

models have in fact been modified or expanded in

the last 3 years, often in response to problems

identified by the task force, although many problems still remain. 82 The task force cautions, however, that “in the absence of ongoing institutional

incentives to address cross-sectional interactions,

the present form of the government’s global model

is not likely to change significantly in the foreseeable future. ”83

Structure and Assumptions

For the purposes of Global 2000, the study team

imposed a “special limited discipline, ” within the

time and resource constraints of the study, under

which: 1) the assumptions, structures, and projections of the agencies’ sectoral submodels were

made “more mutually consistent” and 2) the output from one sector was used as the input for

another “whenever this was readily feasible. ”84 Despite these efforts, however, the Government’s

model has almost no feedback loops and remains

at best “quasi-integrated,” with the result that, “if

anything, the severity of the effects of these basic

trends may be understated. ”8S In addition, the

submodels employ different patterns of regionalization, varying from as few as five to as many as 28

regions, with a similar variation in the degree of

detail provided.86 This not only makes coordination difficult but also leaves the projections without a consistent geographic reference for policy

analysis.

Furthermore, there are numerous major inconsistencies in the values assigned to the same variable in different sectoral submodels, reflecting mutually contradictory agency assumptions about the

behavior of crucial factors.87 The population sector, for example, assumes that birth rates in LDCs

will decline because of continued moderate socioeconomic development, whereas the agricultural

and economic projections indicate only marginal

increases in global food and GNP per capita, with

real declines in some LDCs—hardly reflecting

moderate socioeconomic development.88 Similarly, the GNP submodel assumes that the real price

of wheat will decrease by 0.6 percent per year and

that the real price of oil will remain constant during the early 1980’s, whereas the food and energy

projections indicate real price increases of 2.1 and

5.0 percent per year, respectively, over roughly the

same period.

Far more serious, however, is the absence of any

consistent accounting of capital or resource allocations in any of the sectoral submodels. This leads

to what the report calls “significant omissions and

double-counting’ –in effect, the model recognizes

no conflicts from competing demands or uses, and

it places no constraints on the amount of capital

and resources available to each sector.89 Under

these conditions, the same acre-foot of water is assumed to be available for both irrigation and

energy development, just as the same barrel of oil

is assumed to be available for transportation, energy generation, and petrochemical feedstock.

Different sectors also contain contrasting or contradictory assumptions about the course of public

policy, despite the report’s frequent repetition of

the caveat that its trend projections are made “under the assumption that present policies and policy

trends continue without major change. “9° The most

significant exception to this rule comes in the population projections, which are based on the assumptions of: 1) continued socioeconomic progress

despite marginal gains in food and GNP per capita

in the LDCs; and 2) adoption of family planning

policies in all countries and major extensions of existing programs, especially in rural areas. The

other sectoral projections, however, are based on

equally significant policy assumptions, including

the following:91

ezlbid., vol. 2, p. 46J) n. 2.

a]lbld., VO[. 2, p. 461.

*’Ibid., vol. 2, p. 457.

e>[hid., J*oI. 2, pp. 456, 481.

s’$Ibid., VOI. 2, pp. 485 n. 1, 478; see also table 14-3, p. 479, and the n-d-mdological maps following p. 442.

eTIbld ~,ol. 2, pp. 46 I -476; see also tahle 14-2 and pp. 470-475 for an extensive .!

discussion of “selected contrasrtng assumptmns,”

ssIbld., vol. 2, pp. 481-482.

aglbld., vol. 2, p. 467.

~OIb,d, ~,oI, 2, p. 3; emphasis theirs.

$IIIM,l \(~I, 2, table 14-2 and ~Ip. 470-475; see also pp. 485-4~.
Ch. 2—Major Global Modeling Studies . 37

●

●

●

●

GNP.—Implementation of “prudent” policies

to maximize export earnings, with GNP

growth in the LDCs largely dependent on

GNP growth in the developed regions;

Food.—Major public and private investments

in land development; a worldwide shift toward more fossil-fuel-intensive agricultural

techniques and inputs; and (implicitly) improved resource management to protect fisheries and to prevent overgrazing, erosion, and

farmland degradation;

Energy.–Widespread deployment of lightwater nuclear electric powerplants; implementation of more effective energy conservation

programs in OECD countries; willingness of

OPEC countries to meet oil demand up to

their maximum production capacity; and major public and private investment in air pollution abatement so that by 1985 all countries’

energy facilities are retrofitted to meet U.S.

new-source performance standards for CO,

S OX

, NOX, and particulate; and

Technology.–Major technological progress

in almost ‘all sectors, with no ‘technological

setbacks or adverse side effects; and extensive

worldwide transfer and deployment of familyplanning, yield-enhancing, nuclear-power,

and pollution-abatement technologies.

In addition, the “no-policy-change” assumption

itself explicitly excludes the possibility of either

planned change or sudden upheaval in the world’s

existing political institutions and economic arrangements:

. . . the Study assumes that there will be no major

disruptions of international trade as a result of war,

disturbance of the international monetary system,

or political disruption. The findings of the Study

do, however, point to increasing potential for international conflict and increasing stress on international financial arrangements. Should wars or a significant disturbance of the international monetary

system occur, the projected trends would be altered

in unpredictable ways.92

Findings and Conclusions

of Global 2000

In the absence of more extensive policy testing,

and because of the presence of contradictory and

often controversial policy assumptions, Global

2000 does not provide an adequate basis for coordinated analysis or detailed policy recommendations. Furthermore, because of the omissions and

inconsistencies outlined above, the study team

concludes “that it is impossible to assign a high

probability to any of the specific numeric projections presented” for its different sectors. 9 3

However, the current weaknesses and deficiencies

of “the Government’s global model” do not necessarily or completely invalidate its overall results,

and the study team concludes that “these basic

findings are qualitatively correct,” since they are

in general agreement with past projections by the

same agencies, are supported collaterally by the

alternative sectoral projections of outside organizations, and correspond “in . . . their most basic

thrusts” with projections generated by “less complex but more highly integrated global models. ”9q

Global 2000’s often-quoted general conclusions

about the future are as follows:

If present trends continue, the world in 2000 will

be more crowded, more polluted, less stable ecologically, and more vulnerable to disruption than the

world we live in now. Serious stresses involving

population, resources, and environment are clearly

visible ahead. Despite greater material output, the

world’s people will be poorer in many ways than

they are today.

For hundreds of millions of the desperately poor,

the outlook for food and other necessities of life

will be no better. For many it will be worse. Barring

revolutionary advances in technology, life for most

people on earth will be more precarious in 2000

than it is now—unless the nations of the world act

decisively to alter current trends.95

The principal sectoral findings on which these

general conclusions are based, briefly outlined, are

as follows:

● Population.—Global population growth rates

will not decline significantly by 2000 and, in

absolute terms, net population growth will be

faster than it is today. The world’s total population will increase by 55 percent, from 4.1

billion in 1975 to 6.35 billion in 2000, with 92

percent of the growth occurring in the LDCs,

particularly in Africa and Latin America.

9’Ihld. , \ [)1. 2, p. +31.

‘+Ihd.

‘51hd. , \ 01, 1, p. 1.
.

38 “ Global Models, World Futures, and Public Policy

● GNP.—Worldwide GNP is projected to increase 145 percent during the 1975-2000 period, with faster annual growth in the LDCs

(4.5 percent) than in the developed nations

(3.3 percent), although growth rates in all

regions will decline after 1985. Due to differential population growth, however, GNP

per capita will grow much more slowly—an

overall increase of only 53 percent worldwide,

with marginal improvements or actual declines in a number of LDCs in Africa and

South Asia. Existing income disparities between the richest and poorest nations will

widen, and “dramatically different rates of

change would be needed to reduce the gap significantly by the end of the century. ”9b

● Food.—Worldwide food production is projected to increase by 2.2 percent annually

from 1970 to 2000, a rate approximating the

record increases of the Green Revolution.

Since most of the good arable land is already

under cultivation, most of this increase will

come from more intensive use of energy-intensive inputs and technologies, resulting in an

increased dependence on oil and gas and at

least a doubling of real food prices by the end

of the century. Since food production grows

more rapidly than population, average per

capita consumption will increase 15 percent

worldwide by 2000, but with significant regional variations—increases of 21 percent in

the developed regions but only 9 percent in

the LDCs, with smaller increases in North

Africa, the Middle East, and South Asia; and

a “calamitous” 19. l-percent decline in Central

Africa, where average caloric intake is already

well below the minimum requirements set by

the U.N. Food and Agriculture Organization.

These projections suggest the need for food

imports and food assistance will continue to

grow in the developing regions, particularly in

the poorest countries.

● Energy .-The energy projections, made in

late 1977, indicate that world energy demand

will increase 58 percent over the 1975-90 period. However, petroleum production capacity

is not increasing as rapidly as demand, and

the rate of reserve additions per unit of ex-

~~[b,~, ~,c)l 1 ~,, 13; ‘c,~l,il~c tl,lth rhe Jlic u+ion ()( L ‘h’IIJ\~’hl, Jk)\ c.

●

●

ploratory effort appears to be declining. As a

result, technical considerations indicate that

petroleum production will peak before the end

of the century, although political and economic decisions by OPEC countries could

cause production to level off even earlier. The

resulting transition away from petroleum dependence is projected to be led by nuclear and

renewable sources (primarily nuclear, but including hydro, solar, and geothermal), which

are forecast to increase 226 percent by 1990;

production of petroleum, natural gas, and

coal is projected to increase by 58, 43, and 13

percent, respectively, over the same period.

The projections also indicate considerable potential for reducing energy consumption per

unit of economic production.

Resource prices.–Global 2000 finds that the

real prices of food, fish, lumber, water, and energy will increase significantly by 2000, with

the steepest increases occurring after 1985.

However, this finding shows how the nonintegrated model can lead to an economic paradox:

If the real prices of these commodities increase as projected, for what corresponding

commodities will real prices decrease? If no

compensating real-price decreases are projected, what do these “real” price increases

mean theoretically—or even semantically?

Unfortunately, even attempting to develop

answers to these difficult questions would

have exceeded the time and resource constraints of the study .97

Environment.–Major strains will be placed

on ecological systems throughout the world,

leading to significant deterioration in terrestrial, aquatic, and atmospheric resources that

would have adverse impacts on agricultural

productivity, human mortality, and economic

development. There are already signs of many

of these effects, which will be felt more strongly, particularly in the LDCs, toward the end

of the century. The projected increase in fossil

fuel combustion could be expected to double

the COZ content of the atmosphere by 2050,

leading to a 2° to 3° C rise in temperatures

and significant alterations in weather and precipitation patterns in the temperate zones,
Ch. 2—Major Global Modeling Studies • 39

●

where most of the world’s food-exporting nations are located.

Species extinctions.—Between 0.5 million

and 2.0 million species of plants and animals

could be extinguished by 2000, mainly

through the loss of wild habitats or through

pollution. This threat is particularly great in

the tropical forests, which are an important

potential source of new foods, pharmaceuticals, and building materials. An equally important threat is posed by the possible loss of

local and wild varieties that are needed to

breed pest-and disease-resistant traits into

high-yield cereal grains.

Updates of these projections, developed on the

basis of subsequent events or improvements in the

forecasting tools, generally support the initial findings of Global 2000 and, if anything, provide even

less

●

●

●

reason for optimism:

Fertility rates have declined more rapidly than

expected in some areas, but world population

in 2000 will be only 3 percent lower than originally projected.

GNP projections are somewhat lower, due to

increased petroleum prices and efforts to control inflation in OECD countries, with a consequent drop in LDC growth rates.

Agricultural projections have also been re-

●

vised downward, due to rapid increases in energy-related production costs and diminishing

returns for other yield-enhancing inputs. In

addition, increased concern with the consequences of intensive cultivation has led (in the

United States and other developed nations) to

pressure for resource-management policies

that would prevent further erosion and soil

deterioration. Some LDC governments are intervening in domestic markets to keep food

prices low, often to the detriment of rural

development and production capacity.

The greatest differences are found in the

energy sector: updated projections, reflecting

the sudden large increase in oil prices in 1979,

show that demand will be lower due to higher

prices and slower economic growth caused by

energy impacts in other sectors. Estimates of

maximum OPEC production levels are lower,

reflecting the cartel’s resource-conservation

policies. Estimates of future OECD nuclear

capacity are also lower, reflecting construction

delays and public concern as well as the U.S.

licensing moratorium, and coal is projected to

provide a larger share of energy supplies.

Higher prices are also expected to encourage

the adoption of alternative sources (including

solar) and conservation measures.
CHAPTER 3

Findings of the Global Models
Contents

Page

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Qualitative Conclusions About the World and Its Future. . . . . . . . . . . . . . . . . . . . . . . . . 43

Population Projections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Agricultural Projections. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Technical Progress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Energy Projections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Table s

Table No. Page

2. Comparison of Short- and Long-Term Population Projections . . . . . . . . . . . . . . . . . . . . . .......45

3. Percentage Increases in Projected Global Food Production, Food Prices, and Food per

Capita, 1970-2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
CHAPTER 3

Findings of the Global Models

Introduction

The five global modeling studies addressed in

this report demonstrate at least three fundamentally different “predictive styles’ ’—World 3 and

Global 2000 examine what might happen if present trends continue, while the Latin American

and United Nations (U. N.) world models examine

the goals that might be achieved through broad

changes in those trends, and the World Integrated

Model (WIM) examines the policies and actions

that might bring those changes about.1 The models also vary significantly in their more specific

purposes, assumptions, and methodologies. In addition, they focus on different parts of the world

system, at different levels of detail, and over different spans of time. These fundamental differISee Stuart 13remer, “Test)ng ~fndel~,” in Donella H. Nfeadows, John Rtchardscln, and Gerhart Bruckmann (edi. ), (%oplng ~n the Dark. The First Decude Of

Global Nfodd[ng (New York: N’llev, forth~(~mlng), pp. 376-~77.

ences, as well as the more specific differences in

patterns of regionalization and degrees of aggregation, make it difficult to compare their projections

in any extensive or sustained manner.

The five models nevertheless display a limited

consensus about the nature of the world system

and the identity of the problems facing it, as well

as some of the steps that might be taken to address

them. The following discussion will examine the

areas of general agreement or disagreement that

emerge from these five studies, first in their

qualitative conclusions about the general problems of the world future, and then in their quantitative findings in three key sectors: population,

food, and energy. Extended technical analysis of

their projections in these three key sectors and of

the structural differences between the models is

provided in the appendixes.

Qualitative Conclusions About the World and Its Future

Despite the many differences between these five

global models, it is possible to draw a number of

common themes from them about the present

state of the world and the possible paths it might

follow in the future. The following statements are

based on a list compiled by Donella Meadows,

John Richardson, and Gerhart Bruckmann for a

forthcoming review’ of the first decade of global

modeling. 2 The statements reflect a number of

qualitative findings with which (according to the

authors) almost all global modelers would agree,

and they are arranged in such a way as to form a

loose logical argument:

● Population and physical (material) capital cannot grow indefinitely on a finite planet.

● There is, however, no reliable or complete information about the planet’s ultimate carrying

—

1hicadcm S, Richardson, and 13ruckmann, op CI[., pp 1S- 19, +9-50; see also

John Nf. Rlchardw)n, jr., “Tmvardi Effcctlie Foresight In the United State<

(jc>,crnment” (prepared (or rhe LI. S. Department of Srsre, June 1~7~), pp 5-6

and app. C.

●

●

capacity. * There is a great deal of partial information, which optimists read optimistically

and pessimists read pessimistically.

Nevertheless, there is no known physical or

technical reason why the basic human needs*

of all the world’s people cannot be supplied

now and into the foreseeable future, These

basic needs are not now being met because of

political, economic, and social factors, not because of overall physical scarcities.

Continuing “business as usual” policies over

the next few decades will not lead to the best

possible outcome, nor to a desirable outcome,

nor even to the satisfaction of basic human

needs. It would result instead in an increasing

gap between the rich and the poor, worsening

economic conditions, growing international

tension, problems of resource availability, and

environmental degradation.

*The terms “carrying capac ltv ” and “haslc human need” are h]ghlv valueladen and therefore ln&capablv lead to dehare.

43
44 ● Global Models, World Futures, and Public Policy

●

●

●

●

●

●

●

Because of these difficulties, the continuation

of current trends is not a likely future course.

Over the next three decades, therefore, the

world’s socioeconomic system will be in a period of transition to some new state that will be

both quantitatively and qualitatively different

from the present.

The exact nature of this future state, and

whether it will be better or worse than the

present, is not predetermined—it is a function

of decisions and changes being made now.

Because of the momentum inherent in the

world’s physical and socioeconomic processes,

policy changes that are made soon are likely

to have more impact with less effort and cost

than the same set of changes made later; and

if the changes are put off for too long, they

may not work at all.

Changes in technology are expected and indeed essential: even the most optimistic scenarios might fail if technological progress is inadequate. However, no set of purely technical

changes tested in any of the models was sufficient in itself to bring about a desirable outcome. The models suggest that restructuring

social, economic, and political systems will

also be necessary and may in fact be more effective.

The interdependencies among peoples and nations, over time and space, are far greater than

commonly imagined: actions taken at one

time in one part of the world have far-

-reaching consequences that are often difficult

to anticipate intuitively and are probably impossible to predict (totally, precisely, perhaps

at all) even with computer models.

Because of these interdependencies, isolated

measures intended to reach narrowly defined

short-term goals are likely to be less effective

than anticipated. Decisions should therefore

be made within the broadest possible context,

across space and time and intellectual disciplines.

As a further consequence of these interdependencies, cooperative, long-term approaches to achieving individual or national

g;oals often turn out to be more beneficial to

all parties than short-term, competitive approaches.

● Many existing plans, programs, and agreements—particularly complex international

ones like the U.N."s International Development Strategy—are based on assumptions

about the world that are either mutually inconsistent or inconsistent with physical reality. Much time and effort have thus been

spent in designing and debating policies that

are in fact simply impossible.

In short, according to the authors, the modelers

generally agree that the world system is going to

change in the near future, and that a continuation

of current trends and policies will lead to a change

for the worse. They also agree that changes for the

better are possible, although they disagree sharply

on what those changes should be and which policies would bring them about. In the authors’

words, the models indicate that “we should do

something, [but] we can’t be sure what we should

doo”

3 Environmentalism and the Club of Rome’s

“world problematique” (see below) seem to have

influenced the earlier models, which stressed the

limits of the present system and called for an “equilibrium state” or “organic growth.” The more recent models, which stress the inequities of the present system and call for internal and/or international redistribution of growth and consumption,

seem to have been influenced more directly by the

issues surrounding the “new international economic order. ”

In spite of these differences in emphasis and prescription, however, general agreement does emerge

about the fundamental issues or “problem nexus”

for which projections must be made and solutions

found. 4 The following sections, therefore, summarize projections made by the five global models

in three of these crucial areas:

●

●

●

population, which is addressed by all of the

models and is the most fundamental driving

variable in most of them;

food supply, the most basic of human needs

and the most promising basis for comparisons

between the models; and

energy, which reflects the more general problems of resource depletion but has a uniquely

important impact on agriculture and economic activity.

‘Meadows, Richardson, and Bruckmann, op. cit., p. 51,

4Bremer, op. cit., p. 375.
Ch. 3—Findings of the Global Models ● 4 5

The purpose of these summaries is not to arrive at

a “consensus projection, ” but rather to illustrate

the similarities and differences among the models.

More detailed information and technical analyses

can be found in the historical survey and appendixes.

Population Projections

Table 2 shows the results of a number of studies

of future population growth, including three that

did not employ global models. Population projections play a key role in any assessment of future

world conditions, since the size of the population

will determine the number of consumers of goods

and services and the number of people available to

produce those goods and services. In some global

models, the future size of the population is pro-

—5

The following dlscussmn N htghly condensed; see app. A for a more detailed

comparative analysis of population projections. For further Information on this

topic, see OTA’s assessment, Wodd Populutlon and Fertd~t? Pkmnmg Techndog[es:

77w ,Nexr 20 Yetm, OTA-HR-157 (Washington, D. C.: U.S. Government Prlnttng Off]ce, February 1982).

jected without regard to changes in other conditions; in others, population growth projections are

affected by other factors such as technology or

economic development. These structural differences, combined with uncertainties about the

present size and future behavior of the world’s

population, lead to variations in the projections

themselves and differences in their reliability and

usefulness to the policy maker. Reliability is also affected by time horizon, which in turn reflects one

of two basic goals:

● to provide an accurate short-term (25 years or

less) forecast of world conditions; or

Table 2.—Comparison of Short- and Long-Term Population Projections

Scenario or Population in 2000

Model or source projection (billions) Longer term projections

World 3 . . . . . . . . . . . . . . . . . . . . . . . . .

World Integrated Model . . . . . . . . . . .

Latin American World Model . . . . . . .

United Nations. . . . . . . . . . . . . . . . . . .

Global 2000 (Census Bureau) . . . . . . .

CFSC . . . . . . . . . . . . . . . . . . . . . . . . . . .

World Bank. . . . . . . . . . . . . . . . . . . . . .

Harvard . . . . . . . . . . . . . . . . . . . . . . . . .

Standard run

Equilibrium run

Standard run

Standard run

Second run (improved

conditions in Asia)

1978 assessment

(provisional)

High, medium, low

High, medium, low

Standard

Standard

6.0

NA

6.4

6.4

NA

5.9 to 6.5

5.8 to 6.5

5.8 to 6.0

6.0

5.9

Population increases to 7.0

billion by 2025, then decreases to

4.0 billion by 2100

Population stabilizes at 6.0

billion by 2050

Population stabilizes at just

under 7.0 billion by 2015. Death

rates due to starvation are high

in South Asia

Population reaches 11.0 billion

by 2040 and is still growing at 1.1

percent/yr. Death rates due to

starvation rising rapidly in Asia

Population reaches almost 11.0

billion by 2060 and is growing at

less than 0.5 percent/yr

Population reaches 8.0 to 12.0

billion by 2050 and stabilizes at

8.0 to 14.0 billion by 2150

NA

Population reaches 7.8 to 8.1

billion by 2050 and is virtually

stationary

Population stabilizes at 9.8

billion by 2175

Population reaches 8.4 billion by

2075 and is virtually stationary

SOURCE: The Futures Group.
46 ● Global Models, World Futures, and Public Policy

● to describe the long-term behavior of the

global system.

Findings

There is relatively little variation (plus or minus

about 5 percent) in the population projections for

2000, which range from a low of 5.8 billion to a

high of 6.5 billion. This reflects the higher degree

of certainty inherent in population projections for

periods under 25 years: there is relatively little uncertainty about the number of reproductive-age females between now and 2000, although there is

more uncertainty about the number of children

each will bear. The global models that aim for this

sort of accurate, short-term forecast (the United

Nations Input-Output World Model (UNIOWM)

and Global 2000) are based primarily on expert

judgments about changes in fertility and mortality. As a result, population is linked to other conditions only to the extent that the experts consider

the rest of the world system when they make these

judgments. This short-term approach strives for

accuracy and usefulness by making separate projections for individual countries, which can be

summed to produce a world total.

The long-term global modeling studies (World 3,

WIM, and LAWM) attempt to describe the genera! behavior of the entire global system over the

next 50 to 125 years, and they consider population

as only one of many factors in dynamic, integrated

system behavior. The level of accuracy that is sufficient for this purpose is quite different from that

sought in the U.N. or Census Bureau projections.

However,, the difficulty with this approach is that

the relationships between fertility, life expectancy,

and the factors that affect them—such as food production, pollution, and economic and social development—are not known, nor is the historical evidence rich enough to allow these relationships to

be estimated with any degree of confidence.

As a result, long-range models are more speculative and their population projections show considerably more variation. Two differences in table 2

are particularly notable. In World 3, population

actually begins to decline due to increasing death

rates after 2025, and presumably would do so for

Asia in WIM and LAWM if they were extended

beyond 2060. All of the other projections show

population growing more slowly until it reaches

some stationary level. However, there are immense

variations in the size of that stationary population,

which ranges from a low of 8 billion to a high of 14

billion.

Lim i tat ions

The reliability of the projections, and their usefulness to the policy maker, are also influenced by a

number of theoretical and data constraints and by

the policy assumptions that have been built into

the global models. As mentioned above, there is

neither theoretical agreement nor sufficient historical evidence about the relationships between population variables and conditions in the rest of the

world system. There is also considerable uncertainty in the base-year data for the initial population figures—estimates of China’s population vary

by as much as 14 percent, for instance, and the

current population of Nigeria has been estimated

at anywhere between 65 million and 85 million.

These differences in base-year estimates tend to

cancel out when they are summed at the global

level, and the recent round of censuses has substantially improved the information available for

many countries, particularly in Africa. However,

there is still uncertainty about present rates and

future changes in fertility and life expectancy,

resulting from at least four factors:

●

●

●

●

uncertainty about how much birth rates have

already declined;

uncertainty about the contribution of existing

family planning efforts and technologies to

past declines in birth rates;

uncertainty about how many countries will

adopt family planning programs, and uncertainty about how strong or effective these efforts will be; and

uncertainty about the relevance of past experience with family planning to those countries that have little experience with such programs, notably in Africa.

Different global models contain different assumptions about the above factors as well as about

other policy decisions, all of which may have some

effect on future changes in population growth.

The short-range models that use exogenous popu-
Ch. 3—Findings of the Global Models ● 4 7

lation figures also seem to assume that current

trends will continue unchanged (at least until

2000), thereby excluding such disasters as international conflict and massive starvation. The longrange models, on the other hand, suggest that regional or even global disasters are increasingly

likely in the longer term. World 3 and LAWM

point out the dangers of inaction and delay and

suggest alternatives to present trends, but neither

model has adequate mechanisms for testing specific policy options. WIM, which was designed as a

policy tool, is both more flexible and more disaggregate. The more detailed stand-alone projections, like those used in UNIOWM and Global

2000, can become a valuable input to further analysis for developing policy options, testing development goals, and planning broad strategies for the

world future.

Agricultural Projections

The world food problem has been a major concern for global modelers. All of the well-known

models have one or more agricultural sectors; all

consider measures of food availability to be major

indices of system performance; and all indicate

that the performance of the global agricultural system over the next 20 to 100 years is a matter of major concern. Table 3 compares the projections for

key agricultural variables in 2000 generated by

four global models and by two large-scale

agricultural models, the Model of International

Relations in Agriculture (MOIRA) and the GrainOilseed-Livestock model (GOL) used for Global

2000.

Findings

In general, the most optimistic food supply projections come from assumptions of rapid economic

growth and technical progress, slow population

growth, and large reserves of easily developed agricultural land. There is far greater variation in regional projections than in global projections; the

most severe problems are foreseen in South Asia

and Sub-Saharan Africa. The results are also highly dependent on time horizon—longer time horizons generally lead to more pessimistic findings.

All of the models except UNIOWM indicate

that there will be problems in supplying food to at

least some of the world’s people over the next 20

years. The reason for this finding is fairly straightforward: all of the models except UNIOWM

assume diminishing marginal returns to agricultural inputs and increasing costs for land development as the amount of undeveloped land decreases; in short, the models show agricultural

problems because they include agricultural limits.

Similarly, WIM’s relatively pessimistic estimates of

potentially arable land, which are 25 to 30 percent

lower than the other models, undoubtedly contribute to its dire predictions of impending famine

in the developing regions.

Price projections (for the models that make

them) vary far more than supply projections, with

Table 3.—Percentage Increases in Projected Global Food Production, Food Prices,

and Food per Capita, 1970-2000

World Latin U.N.

Integrated American Input-Output Global 2000

World 3 Model World Model World Model MOIRA GOL Model
48 . Global Models, World Futures, and Public Policy

real increases ranging from 13 to 422 percent by

2000. MOIRA, which assumes that farmers will

increase production to maximize their profits at a

given price for agricultural inputs, concludes that

measures that drive food prices up will be a successful means of reducing world hunger. However,

profit maximization may be a better approximation of the behavior of rich farmers than that of

poor farmers, who may not be able to borrow

funds to expand production, or who may resist

giving up their traditional agricultural practices.

For this reason, MOIRA may overestimate the response to price incentives in the developing countries (see app. B). World 3, LAWM, and

UNIOWM all lack price mechanisms, and none of

the six models takes the international monetary

system into account.

Technical Progress

World 3 assumes that most increases in agricultural production will come from increases in land

under cultivation; Global 2000 assumes that they

will come from increased yields per acre; and

UNIOWM, the most optimistic of the models, assumes both increased cultivation and increased

yields. All of the models except World 3 also include some form of “disembodied technological

progress’ ’—income growth not attributable to increases in capital, labor, or other inputs—which

amounts to an assumption that agricultural productivity will increase automatically at no cost.

The rate of such progress is 1.0 percent per year in

LAWM but is unreported for the other models,

despite the fact that model results are highly sensitive to its presence and magnitude. When technological progress is eliminated from LAWM, for example, Africa as well as Asia faces land constraints

and economic collapse. In World 3, on the other

hand, sufficiently strong assumptions about technological progress can eliminate the overshootand-collapse mode entirely.

Uncertainties

Population and income growth are calculated

independently from food supply in MOIRA,

UNIOWM, and Global 2000. The effects of pollution on agricultural yields is omitted in all of the

models except World 3 and UNIOWM. These factors have an important influence on model results,

as do assumptions about the availability and price

of inputs such as fertilizer, irrigation, and farm

machinery, but there is little agreement among the

models on the values that should be assigned to

them. Nor does any of the six models account for

several trends that are likely to affect agriculture in

the coming decades:

●

●

●

●

regional or sectoral competition for water supplies;

unusually bad weather, including adverse

long-term climatic changes;

Increased productivity due to advances in

genetic engineering; and

potential shrinkage or destabilization of oil

and gas supplies.

Energy Projections

The future availability and price of energy resources are crucial variables in long-term projections of world economic development. However,

some of the models do not address energy specifically or in detail, and the findings of those t-hat do

are significantly influenced by their assumptions

about the global energy system and future energy

The following discussion is highly condensed; see app. C for a more detailed

comparative analysis of energy projections. For further information on this

topic, see OTA’s technical memorandum, Wodd Petroleum Availability f980-

2000, OTA-TM-E-5 (Washington, D. C.: U.S. Government Printing Oflice.

October 1980), and the OTA assessment, Technology and Soviet Energy Avadabd-

~ty, OTA-ISC-153 (November 1981).

trends. In general, those models that include a

finite resource stock tend to show that depletion

will raise prices, slow industrial production, and

dampen global economic growth. In short, as with

agricultural projections, they predict energy problems because they include energy constraints.

Findings

Collectively, the models indicate that the world

faces a near-term transition away from dependence on conventional sources of petroleum and
Ch. 3—Findings of the Global Models . 49

natural gas, and that the major alternatives among

which future energy choices must be made are

coal, nuclear, and solar power. The models whose

projections extend farthest into the next century

indicate that coal, conservation, and conventional

nuclear power may not be enough to sustain continued economic growth. Breeder reactors, fusion,

and large-scale solar power may therefore be necessary.

In World 3, rising extraction costs are the principal cause of economic collapse. As an increasing

percentage of capital is allocated to obtaining nonrenewable resources, investment and productivity

decline in agriculture and other sectors. New mining technologies and nuclear power can delay but

cannot prevent a global economic collapse.

WIM, on the other hand, finds that collapse due

to costly resources is less likely than a future based

on widespread deployment of breeder reactors, but

it also examines an alternative based on large “solar farms” in the deserts of the Middle East. This

model is perhaps the most flexible in dealing with

energy choices, and it has been used in a variety of

policy tests focusing on energy prices and the behavior of both producers and consumers.

LAWM explicitly excludes the problem of

energy and other resources. The authors assume

that conventional fission and the potential development of fusion power will eliminate the global

energy crisis without significantly raising prices.

UN1OWM, because of its restricted time horizon, foresees “[no] problem of absolute scarcity in

the present century. ” However, the model does

project a 77-percent depletion of conventional oil

reserves by 2000. Its optimism about future energy

supplies is based primarily on abundant coal supplies (it projects a decline in the real price of coal

despite a 400-percent increase in demand) and on

the assumption that nuclear power will generate

an increasingly large share of the world’s electricity.

Global 2000’s energy projections indicate a

supply-constrained oil market before 1990, with

production declining thereafter. As a result, “a

world transition away from petroleum dependence

must take place. ” Global 2000 examines several

potential energy systems, but foresees nuclear

power and coal as the most likely alternatives. It

also foresees considerable potential for “conservation-induced reductions in energy consumption. ”

Limitations

The accuracy and reliability of these projections

are affected by their assumptions about a number

of physical, technical, and economic factors:

●

●

●

●

●

the total reserves of each resource;

the rates of population and GNP growth;

the degree to which energy demand growth

will be moderated by conservation or substitution among sources;

the bottlenecks involved in mobilizing additional or alternative energy sources; and

potential energy breakthroughs such as fusion

and solar power.
CHAPTER 4

Global Models and

Government Foresight
Contents

Page

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

Benefits of Global Models. ......,.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ..,.* * 54

Limitations o fGlobal Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

Institutional Opportunities and Barriers . . . . . . . . . . . . . .: .....,... . . . . . . . . . . . . . . 55
CHAPTER 4

Global Models and Government Foresight

Introduction

The “futures debate” of the last 15 years has

taken place against a background of rapid and

sometimes unexpected changes in national and

world affairs, including:

●

●

●

●

●

a general slowdown in the rapid economic

growth that had characterized the world economy since World War II, leading to stagnation

in the industrialized nations and stalled

development in the less developed countries

(LDCs);

rapid population growth and urbanization in

much of the Third World, leading in some

cases to widespread hunger, social unrest, and

political instability;

structural changes in the world’s political and

economic systems, typified by the emergence

of OPEC and new nuclear powers and by

Third World demands for a “new international economic order;”

growing apprehension about the cost and continued availability of natural resources, best

exemplified by the energy crisis; and

increasing concern for the regional and global —

environmental consequences of continued industrialization.

These and similar developments have shown

that long-term global trends can have serious implications for the economic and national security

interests of the United States. This in turn has led

to proposals in Congress and elsewhere that the

U.S. Government should improve its “foresight

capability ’’—its institutional capacity to project

long-range global trends and their consequences,

and to use these projections as inputs in the process of strategic assessment, policy development,

and decisionmaking. A number of Federal agencies are already using global models and other

computerized models as tools of long-range analysis and planning, but the Government’s present

capability is limited by unevenness of data, inconsistency of assumptions, and lack of proper coordination. * If existing deficiencies are corrected, global models could become a more effective tool in

four specific areas:

●

●

●

●

assessing the potential future impacts of current trends, policies, and decisions;

monitoring the national and international

situation to identify early signs of potential

problems or opportunities;

formulating and evaluating a wide range of

alternative policies and courses of action for

achieving national goals, avoiding potential

problems, and exploiting potential opportunities; and

providing a framework to ensure consistency

between short- and long-term analyses and

across agency jurisdictions.

●

●

●

●

●

●

53
54 ● Global Models, World Futures, and Public Policy

Benefits of Global Models

The essential claim for global models is that they

represent the behavior of the real global system, or

at least some of its components, in ways that are

superior to the less formal mental models and forecasting techniques currently being used by decisionmakers, policy analysts, and the general public.2 Their benefits are those of mathematical models in general and computerized models in particular, the difference being that global models are specifically designed to address problems and issues

of a global scale and importance. This can make

them a valuable tool of analysis and a valuable additional input to policy development and decisionmaking. Specific benefits of global models include

the following.3

● Longer time horizon.—Many current forecasting techniques are used primarily for annual or short-term projections, whereas global

models typically have time horizons of 20

years or more. This allows them to assess longterm effects and cumulative changes, however

critical, that might not otherwise be detected.

● Comprehensiveness.—A computerized model can contain far more information about the

world system than any single mental model. It

can also keep track of many more variables

and interrelations at the same time and, according to some modelers, it is far more sensitive to subtle, remote, or counterintuitive effects and outcomes.

● Rigor.—They impose a logical discipline by

requiring the modelers—and the model users

—to make explicit, precise, and complete

statements of their objectives, assumptions,

and procedures. The system or process being

modeled must be clearly divided into its major

components, and the relations between those

components must be specified. This procedure

may lead modelers and model users to revise

—.——

2L. M. N’arci, et al., “N’orld Modellng: Some Crlttcal Foundations,” Beha~Iioral Scwnce, }ToI, 23, hlo. 3, Mat, 1978, p. 138.

‘See Donella H. Meadows, John Richardson, and Gerhart Bruckmann (eds. ),

Gopmg m the Dmk: The Fwst Decade oj Global )vlodehng (New York: Wdey,

forthcoming), pp. 20-21, 42-43, 203, 3 10-312; and Denms L. Little, et al., LongRange Pkmmng (Washington, DC.: U.S. Library of Congress Congressional

Research %rvlce, 1976), prepared for the Subcommittee on the Enwronment

and the Atmosphere of the House Comrnlttee on .%lence and Technology, pp.

456-457.

●

●

●

●

their mental models—or to refine them by

identifying previously ignored components

and relations—even before computer analysis

begins.

Accessibility.-The assumptions and structure of the model must be written out before

they can be run on the computer. This allows

all sides to examine them, point out omissions

or inconsistencies, and suggest improvements.

Open communication about both the system

and the model can lead in turn to the incorporation of fresh insights and differing viewpoints.

Logic.-If properly designed and programed,

a computerized model will draw logically correct and mathematically error-free conclusions from an extremely complicated set of

assumptions and data. This can lead to novel

insights into unexpected or counterintuitive

system behavior, reveal areas in which further

research is needed, and expose assumptions

that are inconsistent or contradictory.

Flexibility and range.–By making small

changes in the magnitude and relations of variables, it is possible to examine the implications of a wide range of alternate assumptions

and to test the sensitivity of the outcome to

changes in different parameters. The models

can also be tailored to “fit” particular problems, regions, or issues. The model can therefore become a powerful planning tool, and

properly updated runs can be used to monitor

program progress.

Instructiveness.–The flexibility of global

models also makes them a valuable tool for

analysts, planners, and policy makers alike, allowing them to examine a broad range of possible outcomes, responses, and policy options.

This can allow them to reject physically impossible options, clarify the nature of various

risks, and evaluate the adequacy of different

options for minimizing those risks. Thus, even

when the models cannot give precise quantitative answers, they allow the users to sharpen

their analytic skills and improve their intuitive “feel” for the operation of the system.
Ch. 4—Globa/ Models and Government Foresight ● 55

Limitations of Global Models

Despite these benefits, global models remain

subject to a number of limitations that may constrain their accuracy, reliability, and usefulness for

policy making:4

●

●

✎

Theoretical limitations.–The structures of

different global models are based on underlying assumptions adapted from systems analysis and several other disciplines, including ●

engineering, economics, and the natural sciences. There is no general agreement on the

relative validity of their competing explanations of socioeconomic phenomena, nor is

there any evidence that one approach produces results that are consistently more reliable than the others. In addition, theoretical

understanding of a number of important processes (for example, the effect of socioeconomic

conditions on fertility rates) is too weak to

allow adequate modeling, although projec- ●

tions based on alternative assumptions can

still be useful and instructive.

Methodological limitations.–The essence of

modeling is a simplification that improves

understanding, but this means that a limited

-.———

‘See Nfeadows, R]c hardwm, and Bruckmann, op. clt., pp. 22, 43-44, 312.3 13;

Little, et al., op. cit., pp. 458-459; V’arcl, et al., op. cit., and The Global 20(xl Report m the Pmwfcn[, \ ol. 2, ch. 14.

number of discrete factors and relations must

be used to describe the dynamic complexity

and ambiguity of the real world. Theoretical

bias and data constraints often determine

which variables and relations are included or

omitted, but no model could include every

factor without becoming as complicated as the

real world.

Data limitations.—Data vital to proper forecasting are often nonexistent, inaccessible, or

unreliable. This is particularly true for environmental data and for most data on the

LDCs. This situation has improved somewhat

in recent years, but data limitations remain a

serious constraint on reliability and on the

sectoral and regional coverage that models

can achieve. In some cases there are inadequate empirical data on which to base improvements in theoretical understanding.

Practical limitations.–An effective global

modeling effort requires considerable time, an

interdisciplinary team of modelers and technicians, a large and continuously updated data

base, access to computers of sufficient capacity, support services, and money. Skimping on

any of these requirements greatly increases

the risk of error and unreliability in the resulting forecasts.

Institutional Opportunities and Barriers

Since President Theodore Roosevelt created the cies of the

National Conservation Commission in 1908, nu- presenting

merous presidential and congressional commit- ent set of

tees, commissions, task forces, and studies have

recommended in one way or another that the U.S.

Government should improve and/or institutionalize its long-range analysis, planning, and policymaking capability. The most recent study discussed in this report, The Global 2000 Report,

reveals that, individually, the executive agencies

possess an impressive, if uneven, capability for

long-range analysis and forecasting within their

separate areas of responsibility and interest; but it

also reveals “that, collectively, the executive agengovernment are currently incapable of

the President with a mutually consistprojections of world trends in population, resources, and the environment. ”5 Nevertheless, according to the report, “Important decisions—involving billion-dollar federal programs

and even the national security—are partially based

on these projections, ” which “have generally been

used by the government and others as though they

had been calculated on a mutually consistent

basis.”6

5The Global 2(xX) R@m to the President, Ywl. 2, p. 3

‘Itxd,, ~ml. 2, p. 45-I.
56 . Global Models, World Futures, and Public Policy

The interagency followup report, Global Future:

Time to Act, carries these conclusions a step further:

If there is one clear lesson from the exercise of

putting the Global 2000 Report together, it is that

the U.S. government currently lacks the capacity

to anticipate and respond effectively to these global

issues. . . . As of today, the government still does

not adequately: 1) project and evaluate future

trends; 2) take global population, resource, and environmental considerations into account in its programs and decisionmaking; and 3) work with other

countries to solve these problems. T

The deficiencies in the Government’s current

foresight capability appear to be institutional

rather than technical. In a recent analysis of the

potential Government policy applications of computer models, A. D. Little found modeling to be

potentially quite useful:

Current state-of-the-art techniques in long-range

forecasting of population, resources, and the environment present significant opportunities for the

State Department to enhance its capabilities for

analysis of the long-run future socioeconomic and

political consequences of foreign national demographic, resource, and environmental conditions.8

After considering the various limitations of computerized modeling (see above), the A. D. Little

report concludes that “Institutional conditions are

often much more of a constraint to the effective

use of forecasting models than methodological or

data considerations.”9

These findings, although

addressed to the Department of State, would appear to be equally relevant to the modeling activities of other agencies and of the Government as

a whole.

The Global 2000 Report includes similar findings—the discrepancies and lack of integration

among its forecasts arise:

. . . essentially because of the institutional context

in which the elements of the model were developed

and are being used, This context emphasizes sectoral concerns at the expense of interactions among

the sectors and leads to distorted and mutually inconsistent projections. 10

TNlcholas Yost, staff director, Global Future: Time to Act, Report to the president

on Global Resources, En(uronment and Popufmon (Washington, D. C.: U.S. Councd on Enwronmental Quahty and Department of State, January 1981), p. 206.

‘Arthur D. Little, Inc., op. cit., pp. IX-X.

‘Ib]d., p. xii.

l’JThe G/obu/ 2~ Report [O the Pres[derrt, VO1. .2, p. 454.

This design is no institutional accident but is in

conformit y with the bureaucratic division of

responsibility within the executive agencies [and

within the committee structure of the U.S. Congress]. . . . Furthermore, in the absence of ongoing

institutional incentives to address cross-sectional

interactions, the present form of the government’s

global model is not likely to change significantly in

the foreseeable future. 11

* * *

Moreover, it would be naive not to recognize that

projections and the procedures used to produce

them have frequently been criticized by Congressional committees and others as subject to influences not purely analytical in origin. Each agency

has its own responsibilities and interests, its own

constituencies, and its own pet projects. Often, an

agency finds it helpful to use advanced analytic

techniques (and associated projections) as weapons

in the adversary process of initiating, justifying, and

defending its programs. As a result, there have been

many occasions in which the elements (and associated projections) of the government’s global model

have been used in support of (or in opposition to)

highly controversial programs, and the credibility

of the projections has become a subject for debate.

This has been especially true in recent times, as

both the issues and the advanced analytic procedures used for examining the issues have become

increasingl y complex and, in a sense, incomprehensible to many nonexperts. 12

Another analysis of the Government’s current

foresight capability points to these and similar institutional barriers. The following list of major

obstacles presents 10 frequently cited reasons for

the Government’s failure to correct the perceived

deficiencies in its existing foresight capability.13

1<

2.

3.

4.

There is little or no top-level support for foresight.

The “best talent” has never worked on

broad, long-term issues.

Bureaucratic rigidity, compartmentalization,

and specialization have frustrated attempts

to promote cooperation among departments

and to take a broad, long-term view.

Time pressures restrict vision to the short

run.

I IIbld., Vol. 2, p. 461 and footnote.

IZIbid., vol. 2, pp. 478-480.

i 3John M.R1chard50n, Jr., “Tov,ard5 Effective Foresight in the United States

Government” (prepared for the U.S. Department of State, June 1979), pp.

13-18.
Ch. 4—Global Models and Government Foresight ● 57

5.

6.

7.

By the time models or forecasts are devel- 8.

oped, policy-level officials have either moved

on or lost interest.

Policy-level officials lack the knowledge and

experience to properly use models. 9.

The products of modelers’ efforts are incomprehensible or irrelevant [to practical policy 10.

issues], or both.

There is poor communication among those

who contract for models and” forecasts, those

who develop them, and those who are supposed to use them.

Congress doesn’t care about the long-term

future.

The public doesn’t care about the long-term

future.
CHAPTER 5

Priorities and Strategies for

Improving U.S. Government

Foresight
Contents

Page

,*. ... .**. *,40, ... ..4* ee. *.. e.*e e.*.******+*.*. * 61
CHAPTER 5

Priorities and Strategies for Improving

U.S. Government Foresight

Introduction

Numerous proposals for improving Government

foresight have been put forward, both in the past

and in response to the Global 2000 Report. 1 Some

have been modest and limited, others sweeping;

some would require major legislation from Congress, while others could be encouraged through

oversight or carried out through executive order

or agency initiative. The following discussion presents the most frequent and representative proposals, which generally reflect four fundamental

priorities:

ISee especially Denms L. Little, et al., Long-Range Plunrung (Washington,

D. C.: U.S. Library of Congress Congressional Research Service, 1976), prepared for the Subcommittee on the Enwronment and the Atmosphere of the

House Committee on Science and Technology, pp. 384-390; John M. Richardson, jr., “Tow.arcls Effectlte Forestght In the United States Grnernment” (prepared for the L’.S. Department of State, June 1979), pp. 5-6 and app. C; Arthur

D. Ltttlc, Inc., LongRange Fo~eca~[Lng Nfode[s Oj Population, Narmd Resources, and

rhe Ent tmnment Their Use in Foreign Po[[o Aswssments at [kc ,N’utional Let el

(prepared for the US. Department of State, Not. 1979), PP. 11.2-3; and G~ohu~

f%we Tune co Act

ACTION

Identified

●

●

●

●

correct the existing deficiencies in Government models, as identified by Global 2000 and

other assessments;

coordinate the Government’s current predictive capability and activities;

support technical improvements in the current capability and advance the state of the

art; and

link the Government’s foresight capability

with its policymaking and management activities.

These priorities and the various strategies for

carrying them out do not represent “options” in

the normal sense of the term. They are complementary and mutually reinforcing parts of a larger,

integrated effort to make the Government’s foresight capabilities more reliable, more coordinated,

and more useful to both analysts and policymakers.

1. —Correct the Existing Deficiencies

by Global 2000 and Other Assessments

Global 2000’s authors, reviewers, and critics

have identified numerous deficiencies in each of its

component submodels, many of which are noted

in chapter 2 and the appendixes to this report.

The followup report by the interagency Task

Group on Data and Modeling Capability identified the correction of these existing deficiencies as

the first priority in improving the Government’s

ability to analyze and address global problems. z

This action could be taken as a matter of course by

individual agencies, many of which are already

planning or carrying out evaluations and modifi1

(

’ Repc~rt ~)f the Glohal 2CKY2 Task Group on Data and Nfodellng Capahlllty, ”

prepared for the Pres&nt’s Task Force on Global Resources and En~wonment,

Nov. 7, 1980.

cations of their present capabilities. However, such

actions might be given higher priority, higher level

attention, and greater coherence by the agencies if

they were encouraged by a presidential directive

and/or congressional oversight.

The Department of Energy’s (DOE) Energy Information Agency has institutionalized this evaluative function in its Office of Energy and Information Validation, which might provide a model for

other agencies. Another possible strategy for carrying out this priority would be the creation of

high-level advisory committees within each of the

relevant agencies. The functions of these agency

advisory committees might include the following:

61
62 ● Global Models, World Futures, and Public Policy

●

●

●

prepare an inventory of existing models and ●

data bases, including the purposes for which

they were originally developed and the uses to

which they are currently put, with particular ●

attention to their scope, complexity, and assumptions;

identify existing deficiencies and evaluate any

plans to modify existing models or obtain new

models, preferably through independent assessment by outsiders; ●

evaluate current and potential applications of

models and projections by agency analysts,

planners, and policy makers, with particular

attention to the specific information needs of

potential users;

encourage expanded use of models and projections through educational and training programs for agency personnel;

improve communication between those who

use models and those who develop or maintain them, with particular attention to increasing the relevance and responsiveness of

model outputs to the needs of potential users;

and

identify likely future problems and issues

within the agency’s area of responsibility and

interest, with the goal of developing problemoriented and policy-relevant models and data

bases.

ACTION 2.—Coordinate the Government’s Current

Modeling Capabilities and Activities

While the creation of agency advisory committees might be a useful and necessary foundation

for improving the Government’s foresight capability, it fails to address the equally important problem of linking and coordinating these agency capabilities, which currently focus on relatively narrow, mission-oriented sectoral concerns. Problemoriented models would most appropriately be

developed by the agencies that would use them,

but overall analysis and policymaking would require consistent, integrated forecasts that incorporate data and projections from several agencies.

Global 2000 shows that it is in this area that current Government efforts have been most unsuccessful: the delays in completing the study itself

were caused in part by problems involving computer compatibility and tape transfer.

The simplest strategy for carrying out this action

would be a process of interagency negotiation and

arbitration to bring about greater consistency and

compatibility between the separate agencies’ data

bases, assumptions, and projections. Ultimately,

however, the effectiveness of such a process might

require the creation of an interagency task force to

provide a focal point and to resolve conflicts. The

coordinatin g functions of such a task force might

include the following:

●

●

●

●

●

●

prepare an inventory of models and data bases

currentl y maintained by individual agencies

(including their respective strengths and

weaknesses), and identify any areas of compatibility, overlap, or redundancy;

identify gaps, sources of inconsistency, and

points of conflict among existing agency capabilities and suggest possible solutions;

promote greater understanding,. communication, and technical cooperation among agencies (simply getting the Government’s modelers together was one of the Global 2000

study’s major accomplishments);

review and coordinate agency plans to modify

or obtain new models or data bases, in order

to prevent redundancy, ensure greater compatibility, and identify software or hardware

needs;

develop consistent procedures and protocols

for data collection, standards of reliability,

and validation, as well as for model documentation and validation;

establish a central clearinghouse to provide

information on the location of models and

data bases and to permit easier access. exchange, and integration of data, assumptions,

and projections;
Ch. 5—Priorities and Strategies for Improving U.S. Government Foresight ● 63

. identify key future problems and issues, desig- grated Model (WIM) maintained by the Joint

nate lead agencies to gather information and Chiefs of Staff and other agencies, the Grainmonitor trends in each area, and ensure the Oilseed-Livestock model maintained by the

publication of timely projections; and Department of Agriculture, and the IEES/

● link existing models, such as the World Inte- LEAP models maintained by DOE.

ACTION 3.—Support Technical Improvements in the

Current Capability and Advance the State of the Art

Technical coordination among agencies and

between the executive and legislative branches

would eventually require some form of third-party

mediation. Creation of an institutional mechanism for this purpose would also present an opportunity for carrying out research and development

aimed at technical improvements in existing agency capabilities, the Government’s capability as a

whole, and the state of the art in global modeling

generally. To carry out these functions, however,

this institutional mechanism would have to be insulated from the day-to-day concerns of the line

agencies and, thus, able to take a long-term view

and incorporate broader and more diverse perspectives. Because its mission would be only indirectly linked to policy-related concerns, however,

it would seem that such an organization should be

created only in conjunction with other initiatives

that are directly relevant to policy development

and coordination (see action 4). Technical improvements are nevertheless a necessary prelude to

policy applications,

Several strategies have been suggested for carrying out this priority. In the short term, an ad hoc

commission or research advisory panel might be

appointed to identify key technical problems and

establish research priorities. To be effective, however, such an effort would have to be both open

and ongoing. A frequently encountered proposal

for the long term is the creation of a “hybrid” or

“quasi-public” institute devoted to long-range

analysis, global modeling, and futures research.

The primary goal of such an institute would be to

encourage private-sector understanding of, support for, and participation in Government foresight activities. Specific functions might include

the

●

●

●

●

●

●

●

following:

solicit the thoughts and enlist the creative talents of the private sector, particularly the

business and educational communities, to

“cross-fertilize” Government ideas and initiatives;

support research by nongovernmental organizations to create and/or improve global models and other analytic tools, especially those

based on paradigms other than economics;

encourage impartial, third-party validation

and assessment of existing or proposed Government models;

establish a “global modeling forum, ” patterned on the Energy Modeling Forum, at

which modelers could exchange ideas and critique one another’s work;

assess work done outside the Government or

outside the United States and, where appropriate, suggest its incorporation into the Government’s capability;

support data-gathering efforts and the development of needed data-gathering technologies

and systems; and

establish and maintain communication with

similar organizations in other countries

through such organizations as the International Institute of Applied Systems Analysis.
64 ● Global Models, World Futures, and Public Policy

ACTION 4.—Link the Government’s Foresight

Capability With Its Policymaking and

Management Activities

The above-described institute, although it could

help to broaden the dialog on global problems and

advance the state of the art in global modeling,

could not by itself ensure that these concerns

would be translated into coordinated Federal policy. If the U.S. Government is at present giving insufficient attention to long-range global problems,

it could be in part because no single agency has the

mandate or the ability to look at these problems

on an integrated, ongoing basis. A final priority,

therefore, might be to create an institutional focus

that could coordinate the various elements of the

Government’s foresight capability and ensure that

long-range global concerns and national priorities

are routinely taken into consideration in the formulation, selection, and implementation of U.S.

policy at all levels.

In Congress, this would require continuing efforts to ensure that legislative proposals are evaluated in terms of their long-term global impacts and

implications. One rationale for such evaluation

might be provided by House Rule X, which directs

in part that each standing committee (other than

Budget and Appropriations):

, . . shall review and study any conditions or circumstances which may indicate the necessity or desirability of enacting new legislation within the jurisdiction of that committee . . . and shall on a

continuing basis undertake futures research and

forecasting on matters within the jurisdiction of

that committee (2(b)(l)).

The long-range analytic capabilities of the legislative support agencies might also be coordinated

and brought to bear on such issues. In addition,

Congress might also encourage appropriate initiatives in the executive branch through oversight

hearings, personal appeals, or directed research.

For example, several committees have already held

or plan to hold hearings on long-range demographic issues, Global 2000, and Government

foresight. In addition, Sen. Charles McC. Mathias

has written a letter to the President, cosigned by

84 other Members, strongly urging that he give the

Global Future: Time to Act report his thoughtful

consideration and that he “put into motion the

machinery that will translate these recommendations into action. ”3 Another example is the request by the Technology Assessment Board for

this OTA study.

Proposals for creating an institutional focus for

long-range global policymaking in the executive

branch usually suggest that–to ensure support

from and access to high-level decisionmakers—it

should be located in the Executive Office of the

President (EOP). The current administration has
Ch. 5—Priorities and Strategies for Improving U.S. Government Foresight • 65

apparently taken a step in this direction by creating a new “national indicator system” directed by

a special assistant to the President. He describes

the project as “a system for providing social and

demographic information to the policy people in a

systematic and regular way, in advance of policy

debates, “ in order to give high-level policy makers

“a view of [the] changing world” and a sense of

how “everything has its cross-impacts on everything else in society. “

4 The system, which will lead

to twice-monthly briefings for the President, VicePresident, Cabinet, and senior EOP staff, will focus primarily on national trends but will also examine international trends if there is an obvious

connection or a special request from the “longrange policy group” that previews the briefings.

Other proposals for structuring and housing an

EOP foresight capability have included the following

●

●

●

options:

Create a new office in EOP devoted exclusively to long-range global issues.—Such an

entity would give the issues greatest emphasis,

ensure access to the President, avoid competing responsibilities, and facilitate coordination of agency capabilities and activities.

Assign responsibility for these issues to an

existing EOP office.—This would avoid the

need to create a new EOP unit. Several existing offices (e.g., the Office of Management and

Budget, National Security Council, or Council on Environmental Quality (CEQ)) have

analogous or complementary missions and expertise, and CEQ has already begun planning

a followup to the Global 2000 study. Additional staff and resources would have to be

made available, and there is a possibility that

long-range global issues might not receive full

attention because of the unit’s existing functions and responsibilities.

Create an interagency coordinating committee on long-range global issues.—Such a

committee might be chaired by the Vice President (in conjunction with his current duties as

chairman of the Crisis Management Team) or

by the head of an existing EOP unit, but even

4Rlchard Flea], Spec]al Asslwant to the Pres]clent and director of planmng and

evaluatmn; quoted by Phlllp J. Hilts, “U’hlte House Uses %clal Sciences But

Cuts Fundtng for Research,” LY’ushlngton Po~t, June 29, 1981, p. A8.

●

with a staff of its own it would probably be less

efficient and less effective than a dedicated office.

Assign responsibility for policy develop”

ment on long-range global issues to a Special Assistant to the President.—Such an individual, with the help of a small staff, could

have access to the President and could ensure

a somewhat better degree of interagency coordination, but this office would have to depend, in turn, on modeling and analytic expertise from other sources.

The functions and objectives that have been

suggested for this new EOP office include the initiation, supervision, and coordination of all of the

functions outlined for the preceding priorities, plus

the

●

●

●

●

●

●

●

following:

ensure that the President and other top-level

decisionmakers are presented with the best

possible analyses and broadest possible range

of policy options on long-range global issues;

use global models (in combination with other

analytic techniques) to determine the effect of

various agency goals and budget items on

long-range global trends and strategic interests;

encourage an open and vigorous national dialogue on long-range global problems and issues, with the goal of developing a clear definition of U.S. national goals and strategic interests in these areas;

prepare a “policy statement on the future,” to

be presented by the President, as a means of

focusing attention and forcing action on longrange global issues;

issue periodic reports, similar to an executive

agency’s annual report to Congress, on major

global issues and, at longer intervals, conduct

comprehensive, integrated studies of longrange global trends and problems;

issue periodic reports on the state of the art in

global modeling and the state of the Government’s foresight capability; and

in conjunction with the Department of State,

encourage similar assessments of long-range issues by foreign governments and cooperate in

the data-gathering and analytic activities of

the various international organizations.
Appendixes
APPENDIX A

Population Projections

Introduction

Population projections play a key role in any assessment of future world conditions, since the size of the

population determines the number of consumers of

goods and services and the number of people available

to produce those goods and services. The rate and distribution of population growth will also determine the

short-term strains that are put on the world’s socioeconomic system, just as the ultimate size of population will

determine the long-term strains that are put on the

planet’s physical and biological systems. Three of the

global models–World 3, World Integrated Model

(WIM), and Latin American World Model (LAWM)

—try to describe the general long-term behavior of all

components of the global system, including population.

In these models, the determinants of population

change—fertility and life expectancy—are linked to

other sectors through feedback loops that simulate the

effect of changing physical and socioeconomic conditions on the rate of population growth. The other models try to provide a detailed, short-term prediction of

world conditions; in these models the future size of the

population is projected in a more straightforward way,

unrelated to the projection of other conditions.

These differences in purpose and technique, combined with differences in time horizon and the uncertainties surrounding the present size and future behavior of the world’s population, lead to variations in the

projections themselves and in their relative reliability.

The projections are also influenced by policy assumptions and by the models’ degree and pattern of geographical regionalization. All of these factors affect the

usefulness of the models and projects for the policymaker.

Purposes,

World 3

Structures, and Projections

This highly aggregated global model was designed to

display the long-term interactions among major world

systems—population, nonrenewable resources, capital,

agriculture, and pollution—in order to investigate the

consequences of five major trends at the global level: accelerating industrialization, rapid population growth,

widespread malnutrition, depletion of nonrenewable re-

—..—

IThe follow Ing material IS based on an OTA umrklng paper prepared bv j.

Stover of the Futures ~JHNIp,Glastonhurv, Corm. For further Information on

thts sub]ect, see OTA’s assessment, Wrodd Popukxtlon and Ferti(lty-Pfunnmg

Technologies The Next 20 Years, OTA-HR-I 57, Fehruary !982.

sources, and environmental deterioration. In order to

examine the long-term effects of these trends, the model

was designed to simulate interactions over a 200-year

period, from 1900 to 2100. The system dynamics modeling technique is particularly useful for simulating two

important kinds of system behavior—feedback and delays–and is thus a powerful tool for understanding

complex system behavior. This makes it a suitable approach for exploring the general shape of long-term global system behavior, but it is generally less useful than

other techniques for producing accurate short-term

forecasts of individual variables.

The population sector of World 3 is highly aggregated: it has only one geographic region and only four

different age groupings: O to 14, 15 to 44, 45 to 64, and

over 64. Global population is determined solely by the

effects of changes in birth rates and death rates, but the

determination of these rates depends heavily on interaction with the rest of the model. The death rate is calculated from average world life expectancy, which in

turn is determined by four influences that the authors,

based on historical evidence and expert judgment, postulate in the following relationships:

●

●

●

●

Life expectancy ‘increases with increasing food

availability, reaching a maximum when food per

capita is about four times the present world average.

Life expectancy increases with increasing health

services per capita, although with about a 20-year

delay.

Life expectancy initially increases with crowding

because of increased industrialization and urban

services, but beyond a certain level crowding has a

negative influence on life expectancy due to the effect of local pollution and stress-related diseases.

Life expectancy decreases as the amount of persistent pollution increases.

These relationships are intended to capture the general

direction and magnitude of the four influences on life

expectancy. Although exact equations must be entered

into the model, these equations are not meant to be rigorous quantifications of the actual relationships.

The birth rate is calculated from the fertility rate,

which in turn is influenced by two factors: desired fertility

, and fertility-control effectiveness. Fertility-control

effectiveness is expressed as a function of level of development, as measured by industrial output per capita,

and reaches 100 percent at a level of development three

times higher than the current average world level. Desired family size is assumed to be affected by two further

influences-the social norm and the individual response

69
.

70 ● Global Models, Word Futures, and Public Policy

to the social norm—in such a way that desired family

size ranges from a high of 5 children when income is low

to a low of 1.5 children when income is high. As with

life expectancy, however, the specification of these relationships is based on expert judgments aided by the

small amount of available historical evidence.

The entire World 3 model was calibrated by simulating the period from 1900 to 1970, during which period

the model correctly represents the broad dynamics of

world population.

In the standard run of the World 3 model, world population increases to about 6 billion by 2000 and reaches

a maximum level of about 7 billion by 2025, but then

declines rapidly to only 4 billion by 2100 (see fig. A-l).

This decline occurs because resource depletion causes

an increasing fraction of capital to be used in extracting

raw materials, thereby reducing the amount available

for other investments, especially agricultural production. As investment in agriculture lags, food per capita

begins to decline, causing a higher death rate, a decline

Figure A-1 .—World 3 Standard Run

The “standard” world model run assumes no major change

in the physical, economic, or social relationships that have

historically governed the development of the world system.

All variables plotted here follow historical values from 1900

to 1970. Food, industrial output, and population grow exponentially until the rapidly diminishing resource base

forces a slowdown in industrial growth. Because of natural

delays in the system, both population and pollution continue to increase for some time after the peak of industrialization. Population growth is finally halted by a rise in the

death rate due to decreased food and medical services.

SOURCE: Limits to Growth.

in life expectancy, and ultimately a decline in population. This model run is meant to represent the most

likely mode of system behavior if there is no change in

past trends and policies, and the authors argue that it

demonstrates the need for action to ensure that such a

result does not occur.

In a series of sensitivity and policy tests, the authors

show that neither increased resources nor improved

technologies have any significant effect on this basic behavior mode: population invariably peaks before 2050

and declines thereafter, although the maximum population varies from 7 billion to 9 billion and the population

in 2100 varies from 3 billion to 5 billion. The reasons

for the collapse do change, however: for example, when

the resource limits to growth are removed, increases in

pollution become the factor that eventually causes the

death rate to increase.

Only two scenarios were able to avoid the basic pattern of overshoot and collapse. In the first, based on the

highly unrealistic condition of removing all the physical

limits to growth, population continued to grow and

reaches about 14 billion by 2100. The second is the

“global equilibrium” scenario, based on an integrated

set of policies designed to bring about a gradual transition to a stable, nongrowth world (fig. A-2). These policy changes, all of which are implemented beginning in

1975, include the availability of “perfectly effective”

birth control and a worldwide reduction of desired family size to 2 children. Under these conditions, the population grows to only 5 billion by 2000 and stabilizes at

around 6 billion by 2050. If the needed policy changes

are delayed until 2000, however, the equilibrium state is

not sustainable (fig. A-3).

The usefulness of this model to policy makers lies

mainly in pointing out the potential dangers ahead, the

costs of delaying action, and the need for considering

the whole system when thinking about one aspect of

the problem. However, since the model does not contain adequate policy levers (i.e., policymakers cannot

directly control the variables in the model), its usefulness for policy testing and evaluation is limited.

World Integrated Model (WIM)

This global model, the first of several to be built in response to what many saw as the inadequacies of World

3, differs from the latter in one major aspect—disaggregation. WIM represents a world composed of many different subsystems that interact with one another hierarchically on five different planes or strata: individual,

group, demographic-economic, technological, and environmental. It also divides the world into 10 or more

geographic regions; each region is a complete model in

itself, but interacts with the other regions through international trade. The model is not an explicit system
Figure A-2.—World 3 Equilibrium Run

I

/

1900 2100

Technological policies and growth-regulating policies produce an equilibrium state sustainable far into the future.

Technological policies include resource recycling, pollution control devices, increased lifetime of all forms of

capital, and methods to restore eroded and infertile soil.

Value changes include increased emphasis on food and

services rather than on industrial production. Births are set

equal to deaths and industrial capital investment equal to

capital depreciation. Equilibrium value of industrial output

per capita is three times the 1970 world average.

SOURCE: LImIts to Growth.

dynamics model, but it does incorporate some of the

feedback relationships and delays that are contained in

World 3, as well as many new ones.

The population sector of each region divides the population into 85 l-year age cohorts. Fertility is determined by the level of socioeconomic development in

each region, including both the reduction in desired

family size and the increased effectiveness of contraceptive use. Increasing levels of education also lead to declining fertility.

Mortality is also linked to the rest of the model. Life

expectancy increases with increasing level of development and (in some versions of the model) it is also affected by food availability: the model calculates the

amount of calories and protein available in each region,

and when these amounts fall below sufficient levels, additional deaths due to starvation result. All of these relationships are judgmentally determined–as in World

3, they are meant to capture the general shape of the

real-world relationships, without attempting to quantify them rigorously.

Figure A-3.—World 3 Run With Stabilizing Policies

Introduced in the Year 2000

1900 2100

If all the policies instituted in 1975 in the previous figure are

delayed until the year 2000, the equilibrium state is no

longer sustainable. Population and industrial capital reach

levels high enough to create food and resource shortages

before the year 2100.

SOURCE: Limits to Growth.

The standard run of the model shows the result of

continuing historical patterns of development. In this

run, however, the relationships between education, development, and fertility are replaced by the “optimistic

though reasonable” assumption that successful population policies will cause fertility rates in all regions to

drop to the replacement level (i.e., births equal deaths)

within 35 years. The authors did this in order to “avoid

biases that might be introduced by the predominance of

population growth factors. ”2 This standard run shows

world population increasing from 4 billion in 1975 to

about 6.4 billion by 2000 and stabilizing at about 6.8

billion by 2015.

In other runs the authors have examined the effects

on world population of delays in starting a worldwide

policy to achieve replacement fertility rates. Assuming

that it takes 35 years from the start of such an effort until replacement level fertility is reached, they found that

world population would stabilize at about 7 billion if

the effort is started in 1975, 10 billion if it is started in

1985, and 12 billion if it is started in 1995.

Because of the differences in regional detail, the conclusions of the WIM study are different from those of

‘M. D, Mesaro\lc and E. Pestel, ,Lfanklnd At the Turntng PoInr (New York: Dutton, 1974), p. 57.
72 . Global Models, World Futures, and Public Policy

World 3. The authors conclude that worldwide collapse

is not likely to result even if past trends continue, but

that catastrophes could well occur at the regional level

well before 2050. These regional collapses, occurring at

different times and for different reasons, will nevertheless profoundly affect the entire global system. The solution to this problem lies not in stopping all growth but

rather in achieving what the authors call “organic

growth’ ’-i.e., different kinds of growth in different regions, such as continued industrial growth in the LDCs

but service-oriented growth in the developed countries.

Without early action to ensure a transition to this kind

of balanced, differentiated growth, the authors say that

regional and finally global disaster is inevitable.

The model was designed to be employed by policymakers. Extensive work has gone into making the model interactive so that policy makers can experiment with

it and, thereby, increase their understanding of the future effects of current policy decisions. Since the original model is not disaggregated to the country level,

policymakers cannot see the effect of specific policies

that the y might implement in their own countries.

However, subregional and country models based on the

same concepts have been developed by former members

of the modeling team in response to the needs of specific

clients.

Latin American World Model (LAWM)

This model was also constructed in response to limitations seen in the World 3 model, and in particular to

its disturbing implications for the developing world, If

economic growth must stop soon in order to avert

world collapse, what hope do the poor countries have

of ever alleviating their poverty? The Latin American

authors of this study concluded that the proper question to ask is: “What changes in the structure of the

present socioeconomic system would be required in order to bring about a better life for all?”

Since the model attempts to answer a different question, its modeling technique is also different. LAWM is

based on optimization techniques, and the model is

structured in such a way as to determine the allocation

of labor and capital that will ultimately maximize life

expectancy—its measure of the satisfaction of basic human needs. The model divides the world into four regions—the developed countries, Latin America, Africa,

and Asia—each of which functions more or less independently. The model does contain some physical constraints, but its structure reflects a general underlying

assumption that physical limits to growth will not be as

important as social and political limits. As a result, its

projections do not reflect how the world will be, but

rather how it could be if the indicated allocation policies

were followed.

The population sector of LAWM, which is designed

to identify the social and economic factors that influence population growth and life expectancy, is both

more detailed and more complicated than those of

World 3 and WIM. The complex web of variables and

influences (shown in fig. A-4) can, however, be reduced

to a single, fairly straightforward hypothesis: “The only

truly adequate way of controlling population growth is

by improving basic living conditions.”3 Thus, the allocation of capital and labor to any of the basic needs sectors—food, education, housing, and other consumer

goods and services—will result in lower birth rates

and/or higher life expectancy. These influences are in

fact compounded, since lower birth rates lead to higher

life expectancy, which in turn leads to a further decrease in birth rates.

Education and agriculture seem to have been given

particular weight in LAWM, but all of the relations

were estimated through descriptive analysis, using scattered data and rather esoteric mathematical techniques,

and they should not be confused with causal relationships, although the formulation of the model implicitly

assumes them to be causal.4 The correlations between

estimated and actual birth rate and life expectancy for

the test period (1960-70) are very high (+0.90 and

+0.95, respectively), but this technique gives no indication of whether or not the postulated functions are correct. If demographic change in the real world follows

different rules, this optimization procedure will not generate reliable projections.

The results of the LAWM standard run are mildly optimistic for all regions except Asia, where the only basic

need that is satisfied is education. By 2010 all available

land in Asia is being cultivated, which means that the

growth of food production is unable to keep up with

population growth; although progress continues in

housing and income, food availability per capita falls to

dangerous levels, and the delay in achieving basic needs

causes a delay in reducing population growth. The population projections show a total world population of 6.4

billion by 2000, 1,2 billion in the developed world and

5.2 billion in the developing world. By 2040 the world

population reaches 11 billion and is still growing at 1.1

percent per year, due largely to the 1.4 percent growth

rate in Asia.

The usefulness of this model for policymakers lacks

mainly in its ability to display the effects of adopting a

particular broad strategy—that of allocating labor and

capital in a manner that corresponds statistically to

‘A. O. Herrera, et al., Cutu.strophe or Neu Soaerj ? A Latin Amemzm World

Model (Ottawa: International Development Research Center, 1976), p. 8.

4

See J. Robinson, “The Latin American World Model,” In The Global 2000

Report to the Pres&nt (Washington, D. C.: U.S. Councd on Enwronmental

Quahty and Department of State, ]980), vol. 2, pp. 638 and 641; and D. H.

Meadows, J. Richardson, and G. Bruckmann (eds.), Gro#ung m the Dark. The

F[rst Demde of Globul ,Modelmg (New York: Wiley, forthcornlng), p, 97.
App. A —Population Projections ● 7 3

Figure A-4.— Basic Structure of LAWM

(

Food

production )

I Health t Population

I

I

●

Expectancy

of life at birth

m

Capital Labor
74 Ž Global Models, world Futures, and Public Policy

maximized life expectancy. LAWM does not contain

policy levers that might be exercised by an individual

planner, but the model has nevertheless contributed to

the debate on broad strategy by describing an alternative to the present world order.

The United Nations Input-Output

World Model (UNIOWM)

The particular goal of this study was to assess the impact of various economic issues and policies on the International Development Strategy for the Second

United Nations Development Decade. Specifically, the

model was to address the question of whether the existing policies and development targets were consistent

with the availability of world resources. Because it uses

an input-output approach, the model can be used either

to investigate the rates of economic growth achievable

given certain constraints (resources, balance of payments, etc. ) or to investigate investment and consumption levels that would be consistent with given rates of

economic growth.

Unlike the other global models, however, population

growth is entirely exogenous in UNIOWM. Although

the size and composition of the population affect the

rest of the model, there is no feedback from the rest of

the model to population. In fact, the model has no population sector as such: it simply uses the population

projections prepared by the United Nations Population

Division for the 1973 assessment of world population,

(This assessment was updated for all countries in 1978,

and the discussion below deals with the more recent figures.) These projections, include four variants-high,

medium, low, and constant-the constant case is merely a reference projection assuming no change in a country’s fertility rate. The medium variant is designed to

represent the most likely future demographic trends,

based on past and present demographic trends, expected social and economic progress, ongoing government policies, and prevailing public attitudes toward

population issues. The high and low variants are intended to reflect plausible variations on these factors.

The fertiltiy assumptions underlying the projections

are based on past and present fertility trends in each

country, adjusted judgmentally by experts in the U.N.

regional population offices around the world and reviewed in the Population Division at U.N. headquarters in New York. These judgments were guided by

three general principals:5

● Fertility rates will decline as economic and social

development takes place.

● Existing or anticipated government policies and

programs, as well as nongovernmental activities

aimed at such a goal, will expedite the process of

fertility decline.

● Once initiated, fertility decline will begin slowly,

gain momentum, and then slow down again. For

those countries with fertility levels near or below

the replacement level, it is assumed that fertility

will begin to converge on replacement level before

the end of this century.

Judgmental estimates of life expectancy were also prepared for each country, following the general rule that

life expectancy would increase by 2.5 years every 5 years

until it reaches 55, after which the rate of increase

would be less. (For some countries where development

has been slow, this rule was changed to allow slower increases. ) The projections also take into account assumptions about internal migration (urbanization) in each

country.

In the projections prepared by the U.N. in 1978, the

population of the world increases from 4 billion in 1975

to 5.9 billion, 6.2 billion, and 6.5 billion by 2000 for the

low, medium, and high variants, respectively. Most of

this growth occurs in the developing world: the population of the more developed countries increases by only

13 to 21 percent from 1975 to 2000, while the increase

for the less developed world is 57 percent in the low variant to 77 percent in the high projections.

The most rapid rate of growth is in Africa, where

population increases by 114 percent from 1975 to 2000,

and in Central America and the Caribbean, where it

increases by 115 percent. China’s population is projected to increase by 26 to 37 percent; this is only half

the average rate for all developing countries, although it

means an additional 235 million to 335 million people

by 2000. India will add even more people to its population, an additional 360 million to 480 million people.

These population projections are used by UNIOWM

to explore the consequences of the goals adopted for the

Second Development Decade. This model has great

utility in displaying the results that would occur if these

goals were achieved, and as such it has been useful to

those planning the broad strategy of the development

decade. Unfortunately, the goals and prescriptions of

the development decade do not always have a significant influence on actions taken by individual countries.

Global 2000

This study, conducted by an interagency task force

that drew on the expertise and models of the entire

U.S. Government, is one of the most comprehensive

models in terms of the number of variables it examines.

However, the separate submodels comprising “the Government’s global model” are not linked to each other,

and as a result there is no feedback or interaction between sectors. As a result, the population figures do not
App. A—Population Projections ● 7 5

reflect the model’s pessimistic projections of GNP or

food production.

The population projections that were used in most

sectors of Global 2000 were prepared by the U.S. Bureau of the Census in 1977. Like the U.N. projections,

they are presented with high, medium, and low variants

for all regions and selected countries. Underlying all

three variants are the assumptions that: 1) fertility will

decline more or less continuously throughout the period; 2) all countries will have adopted some kind of family planning program by 2000; and 3) the effectiveness

and coverage of such programs will increase. The different fertility rates used in the high, medium, and low

variants are based on the judgment of experts and reflect the range of uncertainty about current fertility, future development patterns, and government family

planning policies. Specific individual judgments are not

reported, however.

The final Census Bureau projections extend to the

year 2000 and are substantially the same as the U.N.

projections: global population grows to 5.8 billion in

the low variant, 6.2 billion in the medium variant, and

6.5 billion in the high variant. The population of the

developed regions grows to between 1.3 billion and 1.4

billion by 2000, while the less developed regions grow to

between 4.5 billion and 5.1 billion.

An alternative set of projections, prepared by the

Community and Family Study Center (CFSC) at the

University of Chicago under the sponsorship of the

Agency for International Development (AID), is also

presented in the Global 2000 Report but is not used in

other sectors. These projections, too, include high, medium, and low variants, but in this case the projected

fertility rates are based on a model of fertility decline

that relates the rate of decline to the current level of fertility and the strength of family planning efforts within

each country. The variants therefore reflect more explicit assumptions about family planning efforts: the

high variant assumes that each country maintains its

present level of family planning; the medium variant assumes that nations will implement strong family-planning programs by 2000; and the low variant assumes

that strong programs will be in place by 1995 and that

the effectiveness of these efforts will increase. These assumptions were developed through regression analysis

of data for 1968-75, which showed that the strength of

family-planning efforts had as much effect on fertility

rates as all the measures of socioeconomic development

combined.

Because they assume policy changes that would increase the efficacy of family-planning services, t he

CFSC projections are generally lower than those of the

U.N. and Census Bureau: world population reaches 5.8

billion, 5.9 billion, and 6.0 billion by 2000 in the low,

medium, and high variants, with the developed regions

growing to between 1.2 billion and 1.3 billion and the

less developed regions to between 4.5 billion and 4.7 billion. These projections have also been extended to

2050, at which date they show a global population of

between 7.8 billion and 8.1 billion people.

The Census Bureau projections, those prepared by

CFSC for AID, and all other “stand-alone” projections

are useful to the policy maker mainly because they provide an input describing the population conditions

under which policy must operate. Such projections are

of little direct use to policy makers, but they often

become key elements in the analyses that are prepared

during the development of policy options.

Other Population Projections

Projections of world population have also been prepared, without the use of a global model, by the World

Bank and Harvard University. The World Bank projections, last revised in 1979, were prepared by estimating,

for each country, the year in which fertility will reach

replacement Ievel. b (As defined by the World Bank, “replacement-level fertility” refers to the level at which the

number of births equals the number of deaths at the

prevailing level of mortality. For countries with high

mortality rates the replacement fertility level is considerably higher than 2 children per couple; as mortality

declines, fertility must also continue to decline. Thus, a

stationary population might not be achieved for more

than 100 years after the achievement of replacementlevel fertility.) The World Bank projections assume that

fertility decline toward the replacement level had

started by 1975 in all regions except Sub-Saharan Africa, where the decline was expected to start between

1980 and 1985. These projections show population increasing to 6.0 billion by 2000, with 1.3 billion in the

developed world and 4.7 billion in the developing

world; the population of the entire world becomes stationary around the year 2175 at a level of 9.8 billion.

In 1977, the Center for Population Studies at Harvard University prepared a single population projection

for all countries and regions of the world to the year

2075.7 In these projections, fertility is assumed to decline to replacement levels by 1990-95 for the developed

countries and by 2000-05 for the developing countries;

they show the population of the world increasing to 5,9

billion by 2000 and 8.4 billion by 2075.

‘K. C. Zachartah and My Thl \’u, Popukmon Pro]ecrton~, J 975-1000 and LongTerm (Stutmruq Pop&tmn) (V’ashlngton, D. C.: Nrorld Bank, 1979).

TG. Llttman and N. Keyfitz, The .Nexc Hundred Years (Cambrtdge, Mass.: Harvard Umverslty, 1977).
76 ● Global Mode/s, World Futures, and Public Policy

Strengths and Weaknesses of

Population Projection Techniques

Among the studies discussed here there are at least

two different goals:

● to describe the long-term behavior of the global system; or

● to provide an accurate, short-term (25 years or less)

picture of the world situation.

The long-term global modeling studies (World 3, WIM,

LAWM) are clearly concerned with the first goal only.

They examine the behavior of the entire global system

over the next 50 to 125 years, but they make no attempt

to provide accurate forecasts of world population (or

any other variable) for 2000. All three studies take great

pains to point out that accuracy of individual numbers

is not a concern. The studies that limit themselves to

projecting population only (World Bank, Harvard) are

generally trying to satisfy both goals; they present country-by-country forecasts of population in 2000, but they

also present longer views of population growth. The

two modeling studies that limit themselves to 2000

(UNIOWM and Global 2000) are clearl y interested primarily in the second goal.

These two different goals require the use of two fundamentally different modeling techniques. The first approach (long-term system behavior) considers population as only one of the many elements involved in the

integrated behavior of the system: it affects the other

sectors and, in turn, is affected by them. With this approach the most important aspects of the population

sector are the effects of the rest of the model on population growth. The second technique (accurate shortterm forecasts) considers population alone, without integrating it into the world system, It strives for accuracy

by making separate projections of the individual countries, which are useful in themselves and can also be

summed to produce a world total.

Long-Term Integrated Projections

The first technique has obvious advantages for projecting long-term system behavior. Population reall y is

affected by the rest of the global model—by food production, pollution, and economic and social development—and it is essential to take these factors into account in projecting population growth over a long time

period. The difficult y with this approach, however, is

that the relationships between fertility, life expectancy,

and other variables are not known precisely, nor is the

historical evidence rich enough to allow these relationships to be estimated with any degree of confidence. In

order to include the relationships in their model, therefore, the modelers are often forced to rely on rough estimates and informed guesses. The sensitivity of the model’s behavior to these judgments can be tested, of

course, but the reliance on judgment does make the

model speculative.

Within this first general approach the models discussed here have adopted three variations. World 3 aggregates everything to the level of the world, with no

geographical divisions. This was certainly appropriate

for the preliminary versions of the model, which were

meant to be simple, easily explained outlines of the

eventual model. The large effort that went into constructing and documenting World 3 might have been

better served if some regional disaggregation had been

employed, but this would have at least doubled the

amount of work involved in developing, testing, and

documenting the model.

The development gap between the developed and less

developed world is so large, however, that the two regions cannot properly be considered as one. WIM divides the world into 10 or more regions, and it therefore

supplies a much more detailed representation of realworld behavior. In most of the initial WIM runs, however, the link between fertility and the rest of the model

was broken and an optimistic fertility assumption imposed, although mortality rates did respond to the rest

of the model.

LAWM takes the same general approach to the population sector as the other two models, with one major

difference: the model is not designed to simulate actual

world conditions, but rather to simulate what would

take place if resources were allocated optimally in order

to maximize life expectancy. Since this is not the way

the world actually operates, LAWM’s usefulness is in

describing the world that would evolve, given a particular new mode of behavior, and not in describing what is

likel y to happen.

Short-Term Stand-Alone Projections

The second method of projecting population is used

by each of the other studies. In this approach the population projections are linked to the rest of the world

only insofar as the rest of the world is considered by the

experts when they make their judgments about fertility

and mortalit y decline. When the U.N. experts made

their estimates, for example, they had in mind some

view of the future world which influenced their judgments. Their worldview obviously excluded such disasters as nuclear war and massive famine, or their life expectancy assumptions would not have shown steady increases; but it is impossible to describe just what that

worldview was. Indeed, since many different people

were involved in this exercise, many different (and possibly conflicting) views of the world were used. It is

impossible to say whether these worldviews were internally or mutually consistent, or to what extent any of
App. A—Population Projections ● 7 7

them reflect the feedback effects between population

and development. The most that can be said is that the

experts generally assumed that the forces at work in the

past would continue to operate, with the future largely

growing out of a continuation of past trends.

 Is this assumption of a “surprise-free” future reasonable? All of the global studies discussed here (including

the long-term studies as well as Global 2000 and the

UNIOWM) apparently consider this assumption reasonable in the short term. None of the studies show

large-scale effects on population due to famine or resource shortages before 2000, although WIM shows regional starvation in Asia. In the longer term, however,

the situation is different. The three long-term modeling

studies show a high likelihood of significant changes

beyond 2000: World 3 shows a collapse of population

after 2030, WIM shows rapidly increasing deaths due to

starvation in South Asia before 2025, and LAWM

shows food availability in Asia dropping to starvation

levels by 2040 in its standard run. Other studies not

based on mathematical models, such as Herman Kahn’s

The Next 200 Years,a do not foresee such problems, but — —

‘H. Kuhn and A. Vrlener, The Year 2(W A Frameu ork ~c~~ Specdutmn on the

?Y’ext ThmwThree Ye~rs (New York. Morrow, 1967).

the assumption of a long-term continuation of past

trends is certainly questionable. The likelihood of such

a future actually developing can only be addressed

through a comprehensive, integrated study of all factors

affecting population growth.

Factors Affecting the Reliability and

Accuracy of Population Projections

Differences in purpose and time horizon also affect

the reliability and accuracy of the resulting population

projections. World 3, for example, attempts to describe

the general behavior of the global system to the year

2100. The precision sufficient for such a model is quite

different from that desired in the U.N. or Census Bureau population projections, which are used to predict

the populations of individual countries in 2000.

Table A-1 shows the results of the studies that have

made long-term population projections. There are two

notable differences. Due to increasing death rates, population actually begins to decline in the World 3 model

after 2025 and presumably would do so for Asia in

WIM and LAWM if they had continued beyond 2060;

the other projections all show steadily growing populaTable A-l.—Comparison of Long. Range Population Projections

Projection source Projection Results

World 3 . . . . . . . . . . . . . . . . . . . . . .

World Integrated Model . . . . . . . .

Latin American World Model . . . .

United Nations. . . . . . . . . . . . . . . .

CFSC . . . . . . . . . . . . . . . . . . . . . . . .

World Bank. . . . . . . . . . . . . . . . . . .

Harvard . . . . . . . . . . . . . . . . . . . . . .

Standard run

Stationary world

Standard run

Standard run

Second run—improved

conditions in Asia

1978 population

assessment

Medium and low

project ions

Standard

Standard

Population increases to 7 billion

by 2025 then decreases to 4

billion by 2100

Population stabilizes at 6 billion

by 2050

Population stabilizes at just

under 7 billion by 2015. Death

rates due to starvation are high

in Southeast Asia

Population reaches 11 billion by

2040 and is still growing at 1.1

percent/yr. Death rates are rising rapidly in Asia

Population reaches almost 11

billion by 2060 and is growing at

less than 0.5 percent/yr.

‘Population reaches 8 to 12

billion by 2050 and stabilizes at

8 to 14 billion by 2150

Population reaches 7.8 to 8.1

billion by 2050 and is virtually

stationary

Population stabilizes at 9.8

billion by the year 2175

Population reaches 8.4 billion by

2075 and is virtually stationary -

aThe~e are unpublished, provisloflal estimates that are not official.

SOURCE: The Futures Group.
78 ● Global Models, World Futures, and Public Policy

tion until some stationary level is reached. The second

major difference is in the size of that stationary world

population, which ranges from a low 8 billion (U.N.

low, CFSC, Harvard) to a high of 14 billion (U.N.

high). (The lower figures of World 3 and WIM are based

on exceptional population programs begun in 1975,

and are thus not comparable.)

There is much less variation in the population projections for 2000, which are compared in table A-2 and figure A-5. They vary from a low of 5.8 billion (Census

Bureau, CFSC) to a high of 6.5 billion (U.N. and Census Bureau). This much smaller variation indicates the

much higher degree of certainty inherent in population

projection for periods under 25 years: there is relatively

little uncertainty about the number of reproductive-age

females between now and 2000, although there is much

greater uncertainty with respect to the number of children that each woman will have.

The reasons for the differences among these different

projections and their relative accuracy and reliabilit y

depend on several factors, among which are:

Ž projection technique;

Ž data base for initial population figures; and

Ž estimates of future fertility and life expectancy.

Table A.2.—Comparison of Population Projections

for 2000

Population

in 2000

Projection source Projection (billions)

World 3 . . . . . . . . . . . . . . . . . . . . . . . . . Standard run About 6

World Integrated Model . . . . . . . . . . . Standard run 6.4

Latin American World Model . . . . . . . Standard run 6.4

United Nations. . . . . . . . . . . . . . . . . . . High 6.5

Medium 6.2

Low 5.9

U.S. Bureau of the Census . . . . . . . . . High 6.5

Medium 6.2

Low 5.8

CFSC. . . . . . . . . . . . . . . . . . . . . ... , . . High 6.0

Medium 5.9

Low 5.8

World Bank. . . . . . . . . . . . . . . . . . . . . . Standard 6.0

Harvard ., . . . . . . . . . . . . . . . . . . . . . . . Standard 5.9

SOURCE: The Futures Group.

Projection Technique

The integrated models include the effects on population from other parts of the global system, and this results in the projection of increasing death rates due to

food shortages in at least some parts of the world. The

Figure A-5.-Aiternative Projections of World Population in 2000

Billions

7.0

6.0

5.0

4.0

3.0

2.0

1960 1970 1980 1990

Year

2000 
App. A—Population Projections ● 7 9

projections that did not employ global models assumed

that such disasters would be prevented by technical

change, but when forecasters are forced to specify their

assumptions mathematically in a model, the y were all

drawn to similar conclusions: it is unlikely that technological advances alone will allow the world sufficient

time to solve the problem of rapidly growing population. The technology assumptions or, indeed, the very

structure of the models could well be wrong; but those

who develop long-term population projections without

the use of models have certainly side-stepped this issue.

The problems identified by the integrated global models

might be solved through improved technology or different social responses, but none of the projections prepared without models explicitly addresses these questions.

Data Base for Initial Population Figures

All of the recent projections use essentially the same

data base for base-year population. Information from

national sources is collected by the U. N., the World

Bank, and the U.S. Census Bureau, and for most countries all three organizations report the same population

totals. However, there is very little available information for some countries; it is therefore necessar y t o

prepare estimates based on incomplete data, and these

estimates may vary.

Uncertainty about base-year data is not expressed in

the ranges reported by the U. N., and the Census Bureau projections incorporate different base-year data

only for China. The recent round of censuses has substantially improved the information available for many

countries, particularly in Africa, but for many countries

the variation in estimates can still be quite large. China,

for example, has not had a census since 1953, and there

is a 14-percent difference between the U.N. low and

Census Bureau high estimates for China in 1980, a difference about equal to the population of the United

States. Similarly, the current population of Nigeria has

been estimated at anywhere between 65 million and 85

million, and the most recent census in India found 12

million more people than expected. Even though these

differences may be quite large for individual countries,

however, they are fairly small once they are summed to

the global level: the difference between the highest

estimate for world population in 1975 (Census Bureau

high series of 4,134 million) and the lowest estimate

(World Bank estimate of 4,014 million) is only 3 percent.

Estimates of Future Fertility and

Life Expectancy

It has generally been true that projection of populations for countries and regions is relatively accurate in

the short term (15 to 20 years) and fairly inaccurate in

the long term (over 25 years). The reason for this lies in

the nature of population change. Population size

changes through three mechanisms: births, deaths, and

migration. Except for certain special cases, migration is

usually not a major factor in population growth, although movements from rural to urban areas can have

significant social, economic, and political consequences. Uncertainty in birth and death rates, on the

other hand, can be quite large.

Changes in the birth rate have occurred quite rapidly

in the past, and since the reasons for changes in the

birth rate are poorly understood, projections of birth

rates have tended to err on the side of assuming less

change than actually takes place. However, even if the

projection of the birth rate misses the mark by a significant amount, the short-term projection of the size of

total population may still be reasonably accurate.

After 15 or 20 years, however, errors in projecting

birth rates begin to have a much larger effect. By that

time children born during the first years of the projections would begin to have children of their own, and

any overestimation or underestimation of births will

become compounded, leading to exponentially increasing errors in the projection. Since changing death rates

affect the mortality of children much more than older

age groups, this delayed compounding effect also operates on errors in death-rate projections. For this reason

the record of population projections beyond 20 years

has not been especially good.

Another crucial factor is the accuracy of the base-year

data for birth and death rates. If they are not known

with much accuracy, then the projection will have a

built-in error and, even if the future change in these

rates is correctly forecast, the total error of the projection can be substantial. These factors most seriously affect projections for developing countries, where population data are often scant and unreliable.

The Accuracy of Past and

Present Projections

The 1950’s and 1960’s were a period of rising population growth rates, and U.N. projections made during

this period generally underestimated future population.

Past U.N. projections of world population for 1980 illustrate this point: the latest assessment in 1978 projected the 1980 population at 4.41 billion; in 1957 it was

projected at 4.22 billion (4.3 percent less than the latest

estimate); in 1963 at 4.33 billion (1.8 percent less); and

in 1973 at 4.37 billion (0.9 percent less) (see fig. A-6).

The major factor affecting these estimates was inaccurate base-year data on death rates, which were 10 or

12 percent lower than estimated. Birth rates, which

were estimated more accurately for the base year, have

declined more rapidly than predicted. The current esti-
80 ● Global Models, World Futures, and Public Policy

Figure A-6.—United Nations Population Projections

for 1960 Prepared Over the Past 25 Years

Billions Billions

4.6

4.4

4 . 2

4 . 0

3.8

3.6

4.6

4.4

4.2

4.0

3.8

3.6

1957 1963 1973 1978

Year in which assessment was made

SOURCES: 1957 and 1963: World Population Prospects as Assessed in 1963

(New York: United Nations, 1966), 1973: Se/ecfed Wor/d

Demographic Indicators by Countries, 1950-2000 (New York:

United Nations, 1975). 1978: Wor/d Popu/atlon Trends and Prospects by Country, 1950-2000, Summary of the 1978 Assessment

(New York: United Nations, 1979),

mates of population size, birth rates, and death rates are

much improved over what they were in the 1950’s and

1960’s, but large uncertainties still remain, particularly

with the LDCs.

The uncertainty over present birth rates also contributes to uncertainties about future birth rates. In the

1980-2000 period birth rates will probably decline for

almost all ‘countries of the world, but projecting the

amount of that decline is made difficult by at least four

factors:

●

●

●

●

uncertainty about how much birth rates have already declined up to the present;

uncertainty about how many countries will adopt

serious family planning policies and how strong

those efforts will be;

uncertainty about how much family planning efforts have contributed to past declines in birth

rates; and

uncertainty about how relevant the experience of

family planning effectiveness in countries that have

already adopted such efforts will be in those countries which have not yet done so.

Most of the uncertainty is about birth rates in the developing countries, notably in Africa where there is almost no experience with strong family-planning programs. It is simply impossible to know whether the family-planning experience of countries in Asia and Latin

America will be repeated in Africa.

Michael Stoto of Harvard University has calculated

the average error in individual country projections

made by the U.N. in 1957, based on the difference between predicted and actual growth rates for 40 countries for four time periods: 1955-60, 1955-65, 1955-70,

and 1955-75. He found that the absolute average error

in annual growth rates for all four time periods was

about 0.46 percent, or about 25 percent of the absolute

average annual growth rate for the world (about 1.9

percent). 9 The Futures Group has repeated these calculations for the period 1970-75, using projections made

by the U.N. in 1973 and actual growth rates taken from

its 1978 assessment, Using the same 40 countries, but

eliminating those for which no new data are available

since 1972, they found an absolute average error of 0.27

percent. Most of the improvement is probably due to

the improved quality of base-year data available for the

1973 forecasts, which were used in UNIOWM, If these

errors are representative of the kind of errors we can expect in the future, projections of population size 20

years into the future should have an uncertaint y range

of 10 to 20 percent.

In the longer term the accuracy is much lower. This is

due to the greater uncertainty about fertility and mortality trends, the compounding of errors made in the

short-term forecasts, and uncertainty about the effects

of the rest of the world system (food availability,

economic development, pollution) on population

growth. The range of projections shown in the studies

discussed here–from a projection of steadily growing

population reaching a level of 8 to 14 billion, to a projection of population growth and collapse—is representative of the kind of uncertainty that exists in trying to

project 50 to 100 years into the future, even if we are

willing to exclude such disasters as nuclear war (see fig,

A-7).

.

‘J. Stover, op. cit.

Figure A.7.—lncreasing Range of Uncertainty in

Longer Range Projections of World Population

1980 1980 2000 2020 2040 2080 2080 2100

NOTE: For illustrative purposes only; see table A-1 and text for explanation.

SOURCE: Office of Technology Assessment.
APPENDIX B

Agricultural Projections

Introduction

World food supply has been a major concern for global modelers. All of the well-known global models have

one or more agricultural sectors; all of them consider

measures of food availability as major indices of system

performance; and all indicate that performance of the

global agricultural system over the next 20 to 100 years

is a matter of concern. The apparent discrepancies in

their findings result from differences in time horizons,

model structure, assumed rates of population and economic growth, the pace of technological progress, and

assumptions about the quantity of agricultural land

available.

In general, the most optimistic findings come from

assumptions of high income growth, low population

growth, continued rapid technological progress, and

large reserves of agricultural land. Longer time horizons, however, lead to more pessimistic results; and

those models that include diminishing returns to land

and agricultural inputs show the situation in the global

agricultural system getting tighter as time progresses,

with South Asia and Sub-Saharan Africa the most severely affected regions.

Purposes, Structures, and Findings

World 3

The World 3 model was intended to examine the interactions between a set of global trends and to identify

the long-term impact of their interdependent evolution.

as a result, the World 3 model assumes that agriculture

must compete with the industrial and service sectors for

investment capital and natural resources, and it contains a mechanism by which capital and resources flow

to sectors that show signs of supply shortfalls. The

model also assumes diminishing returns for investments

in land development and agricultural inputs, such as

fertilizer and farm machinery; but it excludes both the

price mechanism and “disembodied technological progress” (see below). The model does assume, however,

that soil degradation and pollution will have negative

effects on yields.

In the standard run (see fig. B-1), all indices of

agricultural performance improve until the second

decade of the next century. Yields and land under

IThe followlng material IS based on an OTA working paper prepared by, Jenmfer Rolmnson, a fellow of the International Institute of Applled Systems Analvsls, For further Information on this sub]ect, see OTA’s forthcoming assessment, The Impact Oj Technolog> on [he PmducrN [tv of the Lun d

cultivation both make considerable gains between 1980

and 2000, and food per capita increases 10 percent

despite rapid population growth. Around 2015, however, at about the same time that the limits of arable

land are reached, industrial growth so depletes the

resource base that investment must be shifted away

from agriculture in order to compensate for the increasing costs of resource extraction (see fig. B-2). Industrial

output declines, as does the use of agricultural inputs,

causing both yields and total production to decline

more rapidly in the 21st century than they had expanded in the 20th century (see fig. B-3). Since population continues to increase, the result is widespread

hunger, mass starvation, and a delayed but catastrophic

decline in global population.

World 3 produces different projections when plausible changes are made in its assumptions, but although

the timing of events may be changed by a few decades

the net result is the same. For example, given more optimistic assumptions about industrial resources and/or

more pessimistic assumptions about agricultural resources, agricultural decline causes investments and

resources to be drawn away from industry rather than

vice versa; but decline feeds on decline and mass starvation ensues. However, one group of critics reports that

they have been able to move the physical limits to agricultural production beyond the time horizon of the

model by assuming continuing technical progress in

both land-development techniques and high-yield plant

varieties, as well as a more rational use of agricultural

resources.

2 The structure of World 3 is not sufficiently

detailed or flexible, however, to determine what specific

poli cy actions this might entail. Its sensitivity to resource depletion in other policy tests also suggests that

the world agricultural crisis, though moved beyond

2100, might still occur.

World Integrated Model (WIM)

WIM’S projections have greater relevance for food

policy analysis because its structure allows a region-byregion investigation of the interaction between population, agriculture, and industrialization. WIM goes into

more detail than World 3 and also includes both the

price mechanism and a simulation of food and other

trade between regions. Environmental effects have been

excluded, but the shortened time horizon—2025—is still

long enough to encompass the crises foreseen by World

3. WIM also includes numerous “policy levers,” making

‘H. S, D. Cole (cd.), Models OJ Doom (New York: Universe Books, 1973), p. 64.

81
82 ● Global Models, World Futures, and Public Policy

II

0000 0

0000 0

Simulation of World

1900 1950 2000 2050 2100

NOTE: Around 2015 the rapid decline of nonrenewable resources forces the industrial sector to shift investments away from industrial and agricultural production to compensate for increasing

resource extraction costs. This causes declines in industrial output and agricultural production.

The latter, in turn, causes massive starvation and decline of global population by nearly 3 biillon

over the period 2030-2100.

SOURCE: Dynamics of Growth in a Firtite Wor/d, pp. 501-s03.

it a flexible tool for testing different combinations of actions that could be taken to address potential food supply problems.

The WIM standard or “historical” scenario is based

on a continuation of present trends, but it nevertheless

makes some rather optimistic assumptions about agricultural progress in the developing regions. (The report

focuses on South and Southeast Asia because of this

region’s existing food problems and the number of people involved, but the authors assert that their conclusions “are applicable to Tropical Africa and to any

other needy region. “ 3

) It assumes, for instance, that all

available arable land is quickly brought under cultivation and that all technological inputs, such as irrigation

systems and farm machinery, will be available as

needed. It also assumes “quite optimistically” that the

average use of fertilizer per hectare in the region will

surpass the present North American level by 2025, at

which time South Asia alone will be consuming more

fertilizer per year than the entire world consumed in

‘M. D. Mesarovlc and E, Pestel, IMunkind U[ the Turning Pmnt (New York: Dutton, 19 T-1), p. 121.

1960. These factors increase yields by about 1,000 kg

per hectare, approximately the same increase achieved

by the Green Revolution on the best land before fertilizer prices began to soar. Finally, the standard run

assumes that other regions make enough food available

to cover any production shortfall in South Asia.4

Despite these assumptions, however, and despite the

assumption that population will stabilize by 2025, the

food supply projections for South Asia are grim (see fig.

B-4). The region’s annual protein production increases

by two-thirds, but the population almost triples and the

annual protein deficit grows from 12 million to 50 million tons–an amount equal to the region’s own production. Deficits amounting to half the region’s protein

needs “could never be closed by imports, ” according to

the authors: paying for them would require one-third of

South Asia’s total economic output and three times its

export earnings, and “the physical problems of handling those quantities of food would be incredible.”5

Even if the needed imports were available, the annual

‘Ibd., p. 121.

51bd., p. 122.
(

1

I

<

1

B-2.–Output From Standard Simulation of World 3

output allocated to

●

/

● — ●

output

Agricultural

input per

hectare

1900 1950 2000 2050 2100

Around 2015 the rapid decline of nonrenewable resources forces the industrial sector to shift investments away from industrial and agricultural production to compensate for increasing

resource extraction costs. This causes declines in industrial output and agricultural production.

The latter, in turn, causes massive starvation and decline of global population by nearly 3 billion

over the period 2030-2100.

SOURCE: Dynamics of Growth in a F)rwle World, pp. 501-503.

number of child deaths caused by malnutrition will

double by 2005 (see fig. B-5).

Policy tests conducted with WIM indicate that only a

combination of food aid, population policies, and balanced development can avert tragedy in South Asia. In

the “isolationist” or “tragic” scenario, in which food imports are not available because of balance-of-payments

constraints, annual child mortality is twice as high as in

the standard run; it rises sharply after 1985, peaks in

2010, and declines thereafter only because of the delayed impact of earlier deaths on the later number of

fertile women (see fig. B-5). A third scenario, designed

to investigate policies aimed at food self-sufficiency for

South Asia, assumes that virtually all regional investment is shifted from industrial development to agriculture; but the results indicate that yields per hectare,

after initially rising faster than in the standard run,

would peak around 2000 and decline thereafter. This

decline occurs because the agricultural sector would not

be able to maintain its growth without the industrial

base that must supply it with fertilizer and machinery.

By 2025, gross regional product is only half what it was

in the standard run, and the region is left with even

fewer means of paying for food imports.

In further policy tests, WIM shows that population

policies aimed at achieving an equilibrium fertility rate

could have a significant effect on food deficits and child

mortality, even in the absence of imports, if they are implemented quickly enough. Such policies, if initiated in

1995, would not reduce the number of child deaths in

the “isolationist” scenario; if initiated in 1990, however,

the same policies-might save more than 150 million lives

(see fig. B-5). If initiated in 1975, these policies could

avoid more than 500 million child deaths. b The need

for food imports would be significantly reduced and

would come later in the period, but the cost would still

be prohibitive. In a final scenario, therefore, it is assumed that the developed regions provide South Asia

with sufficient investment aid to develop “its own

exportable and competitive industrial specialization, ”

whose exports could pay for most of its food imports.7

‘Ihlcl., p. 124 and fig. %9.

Vtd., p. ] ~T.
84 ● Global Models, World Futures, and Public Policy

i

II

0000 0

0000 0 1900 1950 2000 2050 2100

NOTE: Around 2015 the rapid decline of nonrenewable resources forces the industrial sector to shift investments away from industrial and agricultural production to compensate for increasing

resource extraction costs. This causes declines in industrial output and agricultural production.

The latter, in turn, causes massive starvation and decline of global population by nearly 3 billion

over the period 2030-2100.

SOURCE: Dynamics of Growth in a Finite Wor/d, pp. 501-503.

Latin American World Model (LAWM)

LAWM was developed to show that, given optimal

resource allocation and the universal objective of satisfying basic human needs, the global system need not be

troubled by physical limits. Because development proceeds “autarchically” in each of its four regions, international food trade is unimportant and is largely excluded

from the model. Environmental effects are also omitted,

as are food prices; and although the model assumes

diminishing returns on land development and yieldenhancing inputs, it also assumes “disembodied technological progress” (see below) in the form of an automatic

I. O-percent annual increase in the productivity of the

food and agriculture sector.

The food and agriculture sector is by far the most

complicated in LAWM, containing three subsectors

—agriculture, livestock, and fisheries–among which

capital and labor are allocated in patterns that shift

over simulated time as the return on investment diminishes in each. Each of these subsectors contains at least

one optimistic assumption. The agriculture subsector,

for instance, assumes that fertilizer will be available in

unlimited quantities and at constant prices throughout

the simulation period, and that processing losses in the

developing regions will decrease automatically each

year until they reach the levels currently found in the

developed region. Similarly, the livestock subsector

assumes that agricultural wastes and excess agricultural

products will be used for animal fodder once human

needs are met, thereby transforming food wastes into a

measure of meat consumption. The fisheries subsector

assumes a maximum sustainable catch of 120 million

metric tons per year, considerably higher than the level

indicated by more recent reports.

The model assumes no policies to limit population

growth, other than the general improvement of living

conditions. However, it does assume a radical, egalitarian redistribution of income and consumption within

regions, which greatly increases the effective demand

and relative benefit for the lowest socioeconomic strata.

Given these optimistic assumptions, the standard run

of this optimization model indicates that all regions except Asia will be able to satisfy their own food needs
App. B—Agricultural Projections ● 8 5

Figure B-4.—World Integrated Model Standard Run for South and Southeast Asia

South Asia

L /

Annual protein needs

Population

protein production

Annual protein

deficit

I I I I I I J

Year

f

within 30 years. The agriculturally relevant variable in

the simulation outputs is the total daily caloric intake

per capita: in the developed nations, it rises to 3,200 calories by 1980 and equilibrates at that level thereafter

(fig. B-6); in Latin America it rises to 3,000 calories by

1990 and stabilizes at that level, which is lower due to

differences in climate and diet (fig. B-7); Africa achieves

and stabilizes at a similar level around 2008. In Asia,

however, the only need that is met is education; food

per capita peaks at less than 3,000 calories per day in

2010 and declines steadily thereafter (fig. B-8). This agricultural collapse is similar to the catastrophe foreseen

by WIM in both its standard and self-sufficiency runs:

The problem in Asia arises in the food sector. By 2010,

all available land is being cultivated. Thereafter, economic effort in the sector is devoted to increasing livestock and fisheries. This, however, is not enough to feed

the growing population adequately, and consumption

drops rapidly to below the minimum needed for survival.

The rapid increase in the cost of producing food, due to

the development of new land for agriculture, takes resources from the rest of the economy, causing backward-
86 ● Global Models, World Futures, and Public Policy

Figure B-5.—Child Mortality in Four Scenarios of the World Integrated Model

Scenario 1 is the standard scenario. Scenario 2 shows the consequences for Scenario 1 if imports are not available to cover the protein deficiency gap. Scenario 3 shows the reduction in

child mortality achieved over Scenario 2 by a population policy instituted in 1990. Scenario 4

shows the consequences of implementing the same population policy 5 years later. Composite of Figures 9-2 and 9-4, Mankind at the Turning Point, pp. 122 and 128. The projection of

Scenarios 2 and 4 are essentially identical in the original.

SOURCE: Mankind at the Turning Point,

ness and also hindering the satisfaction of the other basic

needs. In summary, the delay in reaching adequate levels

of well-being leads to a sustained high population growth

rate, and a vicious circle develops: increased population

and the increased cost of producing food make it more

and more difficult to satisfy basic needs.8

Rather than show the full details of this catastrophe,

the modelers have truncated the Asia run at 2040, 20

years before simulations for other regions are terminated. The authors advocate effective population

policies and the use of nonconventional foodstuffs to

avoid mass starvation in Africa, but then present neither specific details nor policy tests to support these recommendations.

The policy tests that were conducted by the LAWM

team indicate that capital transfers from the developed

region would have a negligible impact on the food

shortage in Asia. They also show that technological

stagnation after 1980 would lead to a similar collapse in

8A. 0. Herrera, et al,, Cutustrophe m .\Teu SO C ie ty 7 A Lar[n Amerlcun Workf

Model (Ottawa: international Development Research Center, 1976), p. ,?9.

Africa as well as Asia and that, in the absence of

regional income redistribution, the satisfaction of basic

needs (though possible) would require three to five

times more resources and two to three more generations

of human suffering.

United Nations Input-Output World Model

(UNIOWM)

UNIOWM is the only model that does not indicate

potential problems in supplying food to all the world’s

people over the next two decades. Its optimistic findings, however, result in part from its purpose and structure: its projections do not show what is likely to happen in the agricultural sector, but rather what trends

would be required in order to achieve the goals of the

U.N.’s Second Development Decade. The input-output

approach is well suited for consistent accounting of intersectoral flows, but it is not particularl y well adapted

for agricultural analysis because it is totally linear.

Many biological processes, on the other hand, are
App. B—Agricultural Projections • 87

Figure B-6.—Time Period and Conditions Required for Developed Countries to

Satisfy Basic Needs to Given Levels

I I I I 1 1 I 1 i 1 I

1960 1970 1980 1990 2000 2010 2020

1 (5) Percent GNP allocated to sector 5

7 (v)

2 (B) Birthrate 8 (C)

3 (4) Percent GNP to other goods and services 9 (E)

4 (U) Urbanization 10 ($)

5 (A) Population growth rate 11 (P)

6 (M) Enrollment

SOURCE: Catastrophe or New Societyq

2030 2040 2050 2060

Key:

Houses per family

Total calories per capita

Life expectancy

GNP per capita

Total population
88 ● Global Models, World Futures, and Public Policy

Figure B-7.—Latin American World Model Simulation for Latin America

1 (B) Birthrate 7 (C) Total calories

2 (5) Percentage of GNP allocated to sector 5 8 (E) Life expectancy

3 (4) Percentage of GNP allocated to sector 4 9 ($) GNP per capita in 1960 dollars

4 (A) Population growth rate 10 (P) Total population

5 (M) Enrollment 11 (U) Urbanization

6 (V) Houses per family
App. B—Agricultural Projections Ž 89

Figure B-8.— Latin American World Model Simulation for Asia

1960 1970 1980 1990 2000 2010 2020

Key:

1 (A) Population growth rate 7 (M)

2 (V) Houses per-family 8 (U)

3 (5) Percentage of GNP allocated to sector 5 9 ($)

4 (B) Birthrate 10 (E)

5 (4) Percentage of GNP to other goods and services 11 (P)

6 (C) Total calories per capita

SOURCE. Catastrophe or New Soc/ety? p. 92.

Enrollment

Urbanization

GNP per capita in

Life expectancy

Total population

1960 dollars
90 “ Global Models, World Futures, and Public Policy

highly nonlinear, and many critical agricultural flows

(such as the externalities associated with overgrazing

and deforestation, or the changing probabilities of pest

damage under different cropping systems) are difficult

to include in an input-output framework. In addition,

UNIOWM shows linear returns on investments in agricultural inputs and must depend on off-line analysis to

determine the amount of land under cultivation. These

features produce odd results in some places, such as a

169-percent increase in the productivity of Japanese

farmlands, which are already intensively cultivated,

and a 387-percent increase in the Middle East.

In general, the projections show rapid increases for

almost all agricultural variables in almost all regions,

with the most dramatic gains being made in the developing countries. Over the 30 years of the simulation

(1970-2000), global grain production almost triples and

global production of animal products more than triples

(table B-1). Developing regions achieve astounding increases in both land productivity and total agricultural

production (table B-2), and by 2000 all regions have

reached an average daily per capita consumption of

over 2,400 calories and 66 grams of protein (table B-3).

These results, however, do not seem to be accompanied

by any symptoms of economic or financial stress. Agricultural prices, relative to general price levels, increase

only 14 percent over 30 years. In no region does investment in irrigation or land development grow by more

than a few percent per year, and in many region s

agricultural investments actuall y decline. If anything,

the pressure on the agricultural system appears to be

easing in 2000: rates of agricultural demand growth

decrease slightl y in the last decade of the simulation,

largely because incomes have risen to a point where

consumers spend less of each additional dollar of income on food.

It should be repeated that these projections are intended only to prove the technical and physical feasibility

Table B- I.—Global Agricultural Output in UNIOWM Standard Scenario

1970 1980 1990 2000
App. B—Agricultural Projections ● 9 1

Table B-2.–Land Requirements and Yields in 2000

in UNIOWM Standard Scenario

(Index, 1970 = 100 percent)

Agricultural

Region output

Arable Land

land productivity

Developed market:

North America . . . . . . . . . . . 196

Western Europe

(high-income) . . . . . . . . . . 130

Japan . . . . . . . . . . . . . . . . . . . 176

Oceania. . . . . . . . . . . . . . . . . 192

Centrally planned:

Soviet Union . . . . . . . . . . . . . 164

Eastern Europe . . . . . . . . . . . 143

Asia (centrally planned) . . . . 468

Developing market:

Latin America

(medium-income) . . . . . . . 495

Latin America

(low-income) . . . . . . . . . . . 532

Middle East . . . . . . . . . . . . . . 950

Asia (low-income) . . . . . . . . . 506

Africa (arid) . . . . . . . . . . . . . . 409

Africa (tropical). . . . . . . . . . . 438

SOURCE: The Future of the Wor/d Economy, p. 40.

111

100

100

183

100

100

120

166

140

126

113

131

152

194

162

269

162

215

186

2 7 8

311

328

487

331

282

324

Table B-3.—Regional Daily per Capita Food

Consumption in 1970 and 2000, UNIOWM Standard

Scenario

Kilo-calories Proteins

(thousands) (grams)

Region 1970 2000 1970 2000

Developed market:

North America. . . . . . . . . . . . . . . . . . . . 3.2 3.2 96 100

Western Europe (high-income) . . . . . . 3.0 3.2 91 105

Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 3.2 71 117

Centrally planned:

Soviet Union . . . . . . . . . . . . . . . . . . . . 3.2 3.2 92 108

Eastern Europe . . . . . . . . . . . . . . . . . . . 3.1 3.2 93 108

Asia (centrally planned) . . . . . . . . . . . . 2.1 2.5 59 79

Developing market:

Latin America (medium-income) . . . . . 2.4 3.0 60 86

Latin America (low-income) . . . . . . . . . 2.2 2.9 50 74

Middle East . . . . . . . . . . . . . . . . . . . . . . 2.0 2.9 53 92

Asia (low-income) . . . . . . . . . . . . . . . . . 2.0 2.4 52 66

Africa (arid) . . . . . . . . . . . . . . . . . . . . 2.5 2.5 72 78

Africa (tropical) . . . . . . . . . . . . . . . . . . . 2.2 2.8 62 87

SOURCE: The Fufure of the World Economy, p. 39.

of certain U.N. development goals The modelers involved in the UNIOWM have tended to merely state

their findings and allow readers to draw their own

policy conclusions. Like WIM and LAWM, however,

their model points to the need for increased food selfsufficiency and export earnings in the LDCs. Findings

relevant to food and trade policy include the following:

The most pressing problem of feeding the rapidly increasing population of the developing regions can be

solved by bringing under cultivation large areas of currently unexploited arable land and by doubling and trebling land productivity. Both tasks are technically feasible

but are contingent on drastic measures of public policy favorable to such development and on social and institutional changes in the developing countries.9

Self-sufficiency in food is a promising kind of “import

substitution” for reducing balance of payments deficits in

developing countries.10

A relatively stable increase in the prices of minerals and

agricultural goods exported by the developing countries,

as compared to the prices of manufactured goods, is one

way of increasing the export earnings of these countries

and closing their balance of payments deficit . . . . For

developing regions which are not large net exporters of

minerals or agricultural goods, the main way to reduce

the potential trade imbalance is to significantly decrease

their import dependence on manufactured products . . .

while . , . increasing their share of world exports of some

manufactured products, particularly those emanating

from light industry. . . . Increase in aid; measures to

create a more favorable climate for a better mix of capital

investment flows to these regions; [and] . . . reduction in

the financial burden arising from foreign investment are

important, but . . . secondary . . . compared to . . .

changes in the commodity markets and trade in manufactured products.

To ensure accelerated development two general conditions are necessary: first, far reaching internal changes of

a social, political and institutional character in the developing countries, and second, significant changes in the

world economic order.l 1

Global 2000

The agricultural projections in Global 2000 were generated by the grain-oilseed-livestock (GOL) model that

was developed in 1974 by the U.S. Department of Agriculture (USDA) to assist in the formulation and execution of U.S. agricultural and trade policy. Maintained

by the Foreign Demand and Competition Division of

USDA’s Economics, Statistics, and Cooperative Service, GOL is a computer-based static equilibrium econometric model that was specifically designed to capture

the interaction between the largely cereal-oriented food

economies of the developing regions and the livestockoriented food economies of the industrialized regions.

For the purposes of Global 2000, GOL was supplemented with three independently developed submodels

that project the availability of ‘arable land, the total

food supply (including fisheries and other miscellaneous

sources), and the use of fertilizer in each region. GOL

has been used to analyze the potential impact of U.S.

parity pricing policies on international food trade and

to analyze the potential impact of alternative assistance

programs for the U.S. Agency for International

Development.

‘AI’. Leontlef, et al., The Future oj the Wodd Econom? (New, York: Oxford,

1977), p. 21.

*OIbld., p. 22.

1 IIbld., p, ,?3.
92 ● Global Models, Worlds Futures, and Public Policy

The greatest advantage of the GOL is its scope and

detail: it consists of 28 interactive regional submodels

containing equations for the supply, demand, trade,

and prices of 16 different food commodities. USDA

analysts claim that the model represents 70 to 80 percent of world production and consumption and an

even larger percentage of food trade. Its greatest

weakness is that, as a static equilibrium model, it is incapable of representing market behavior that is in disequilibrium. In addition, dynamic factors such as population and income growth must be calculated exogenously in advance and thus are not affected by the

model’s operation. These factors were adjusted to be

more consistent with other sectors of Global 2000, but

a number of minor discrepancies exist between the

GNP and population projections and the corresponding GOL assumptions. Other critical assumptions incorporated into GOL include the following:

●

●

●

no major wars, changes in the international economic order, or natural disasters such as climatic

change or large-scale land degradation;

no increase in world grain reserves to keep pace

with population growth, no change in Western Europe’s somewhat protectionist agricultural and

trade policies, and no major increase in U.S. food

trade with the Soviet Union, Eastern Europe, or

the People’s Republic of China; and

continued technological progress (measured in

yields) comparable to: the rapid growth of the last

20 years, with the industrialized nations and (to a

lesser extent) the LDCs taking advantage of technology according to the incentives provided by

changes in the prices of production factors and

food commodities.

GOL generated four alternative sets of agricultural

projections for Global 2000, using different assumptions

about population, income, weather, and energy prices:

Alternative 1, the standard or “baseline” projection, assumes medium rates of population and income growth, constant weather, and constant real

energy prices at 1974-76 levels.

Alternative IA, a variant of the standard run, assumes a doubling of real energy prices by 2000.

Alternative II, the optimistic upper-bound projection, also assumes constant real energy prices, but

assumes lower population growth and higher per

capita income growth, as well as more favorable

weather conditions than over the last 25 years.

Alternative 111, the pessimistic lower bound projection, assumes a doubling of real energy prices,

higher population growth, lower income growth,

and less favorable weather conditions.

Detailed regional projections for 1985 and 2000 of

total and per capita grain and food production, consumption, and trade are presented in tables B-4 and

B-5. “Other African LDCs” are included in order to

show the model’s most problematic region, and South

Asia is included to allow comparison with the results of

other global models. World grain production and regional per capita consumption figures are compared in

figures B-9, and B-10, which illustrate the range of uncertainty that results from different exogenous assumptions. In case of energy variables, according to the

report, “[the] range reflects not so much uncertainty

about petroleum price increases as uncertainty about

. . . the ability of farmers to maintain or expand production while shifting awa y from energy-intensive inputs.”

l2

Within this range, the results indicate a near doubling of global food supply between 1970 and 2000.

Roughly speaking, this comes from a 50-percent increase in the developed regions and a 150-percent increase in the LDCs. In both cases the increase comes

from fertilizer use rather than land development: global

cultivated land increases less than 5 percent by 2000,

whereas the application of fertilizer per hectare increases 160 percent, doubling in the developed regions

and quadrupling in the LDCs. However, because population growth is more rapid in the LDCs than in the

developed regions, LDC consumption generally increases more rapidly than production. As a result, international food trade will expand ,briskly, with the

United States and Argentina benefiting most from the

larger markets (see table B-6). Gains in per capita consumption are small and unevenly distributed in the

LDCs: Tropical Africa shows net declines in per capita

food consumption even in the most optimistic scenario,

and gains in South Asia are less than 10 percent at best;

on the other hand, per capita increases of 10 to 30 percent are projected for Latin America and East Asia.

The real price of food on the world market is projected

to increase by between 30 and 115 percent, depending

on the scenario; under the higher figure, the poorest

LDC importers could find themselves priced out of the

market as they were in 1973-75. 13

These findings lead to several conclusions relevant to

food policy. The world has the physical and economic

capacity to meet substantially increased food demand

through 2000, but to do so it must maintain the nearrecord growth rates of the 1960’s and 1970’s. Significant

increases in food trade will be needed to balance excess

demand in food-deficit Western Europe, Japan, and the

centrally planned economies, as well as parts of developing Africa and Asia. Variations in supply will become more important as the world’s productive capacity is used at higher levels, particularly if there is no increase in world grain reserves. This suggests, according

lq~ G&d 2~ Re@rt co che Pres&nr (Washington, D. C.: U.S. Council on

Enwronmental Quality and Department of State, 1980), vol. 2, p. 85.

I

] Ibid., vol. 2, p. 556.
App. B—Agricultural Projections ● 9 3

Table B-4.–Grain and Total Food Production, Consumption, and Trade (Alternatives 1, II, Ill)

568.1

515.7

+ 52.4

536.2

455.9

+ 60.3

126.6- 118.1

121.0 -116.6

127.3 118.2

127. ? 114.6

730.0 683.3

848.4- 610.8 687.6 590.2

+ 93.1

157.0- 143.7

155.8- 147.7

157.1 143.5

165.7 143.6

297.5

229.5

+ 68.0

309.7

194.4

+ 115.3

137.8 -134.9

119.6 -114.0

135.1 140.2

129.2 111.2

416.0- 402.0 409.8 414.0

290.2- 272.4 +325.0 +256.8

+ 126.0- + 129.6 + 84.6 + 157.2

184.3 -178.5

160.3 -151.3

181.8 183.5

178.3 143.2

589.5

597.5

– 6.0

485.3

529.7

-44.4

534.0

578.5

– 44.5

138.2

143.3

174.0

179.9

179.5 166.1

179.2 173.2

470.5

506.3

-35.8

154.4 -161.4

163.4 -162.8

244.5- 247.7

247.8 -242.6

266.2 246.4

261.2 249.0

163.6 168.4

169.2 159.2

279.5-284.4

269.7 -265.3

298.4 288.1

275.4 257.0

104.3

103.7

+ .6

107,6

97.2

+ 10.4

158.7 -174.8

162.7 -160.7

57.3

60.9

-23.6

48.6

51.5

– 2.9

53.0

79.9

-26.9

146.3- 148.1

167,4 -165.1

149.6 136.9

168.1 165.8

92.2- 89.0 94.5 88.1

127.5- 123.7 125.5 132.5

-35.3- -29.7 -31.0 -44.4

252.5 -257.8

276.1 -267.3

259.3 240.4

271.4 267.7

45.5

48.5

– 3.0

150.7 -160.2

161.2- 160.0

155.6 145.5

160.0 150.5

61.3- 63.7 63.1 61.5

63.3- 63.0 60.7 62.0

-2.0- +. 7 + 2.4 – .5

197.1 -204.9

196.4 -196.4

203.0 197.8

189.1 193.2

190.0

200.0

-10.0

178.6

186.3

– 7.7

154.0-155.5

158.7- 158.2

158.9 149.3

159.0 148,0

265.0- 259.0 269.0 271.0

284.3- 275.7 290.7 293.9

-19.3- -16.7 -21.7 -22.9

221,6-216.8

226.2 -219.4

225.2 226.9

231.3 233.9

38.6

29.9

+ 8.7

39.6

30.7

+ 8.9

43.2

61.3

– 18.1

179.1 -194.3

168.0- 168.0

180.6 185.5

164.5 169.1

62.0- 65.0 62.6 64.1

47.9- 47.0 48.0 49.9

+ 14.1- + 18.0 + 16.6 + 14.2

71.9- 73.0 72.4 72.2

98.0- 97.0 95.0 100.5

-26.1- -24.0 -22.6 -26.3

295.3 -310.0

268.8 -263.6

298.3 305.6

257.8 280.4

46.5

63.7

-17.2

155.8- 154.7

173.1 -172.3

161.4 149.7

172.9 166.2

251.4 -255.3

267.7 -264.9

253.1 252.4

259.4 274.6

1,642.9

1,642.9

141.5 -140.5

141.5 -140.5

144.5 137.0

144.5 137.0

2,196,7 -2,141.7 2,233.0 2,119.6

2,196 .7-2,141.7 2,233.0 2,119.6

194.0 -191.0

194.0 -191.0

198.0 191.5

198.0 191.5

1,540.7

1,540.7

NOTE: In trade figures, + indicates export; minus sign indicates import.

SOURCE: The Global 2000 Report to the President, vol. 2, pp. 91-92.

to the report, that “[the] agricultural and trade policies

of a small number of importers and exporters will play

an increasingly dominant role in determining the quantities and prices of food traded on the world market.”

The United States is projected to play an increasingly

dominant role in balancing world supply and demand

by expanding or contracting production in order to

moderate price fluctuations. *4

The Global 2000 environmental projections related

to agriculture suggest that food production could fall

significantly below the quantities projected by GOL

due to soil deterioration, pest- or pathogen-control

problems, water shortages caused by deforestation, and

unstable supplies of energy-related inputs. U.S. and foreign government policies will play a large role in

deciding whether or not environmental problems seri14[t)l~, , \X>l. 2, pp. 77-W.

ously erode global agricultural production potential.

These concerns will probably seem more important to

the industrialized nations than to the LDCs, which are

more likely to face the pressing problem of expanding

production to meet rapidly expanding food needs, often

regardless of long-term environmental costs. Modelcomparison exercises similarly suggest that if GOL were

more integrated—i.e., if it gave greater attention to the

interactions between agriculture and other economic

sectors or environmental conditions—its results would

probably be less optimistic:

The rising food prices and regional food shortages projected in the agricultural model would be intensified by

the fact that agriculture is not the only sector wanting

capital to cope with increasing population demands and

diminishing returns. Land degradation caused by intense

pressure on the land and by pollution would tend to

151bd. , voi. ~ , p, 89.
94 ● Global Models, World Futures, and Public Policy

Table B-5.–Per Capita Grain and Total Food Production, Consumption and Trade (Alternatives 1, II, Ill)

As projected by the GOL Model for Global 2000

1985 2000

Grain Food Grain Food

(kilograms) (1989-71 = 100) (kilograms) (1989-71 = 100)

I II Ill I II

115.2

115.2

Ill

105.0

102.1

I

838.5- 769.8

735.0- 692.4

+ 103.5- + 77.4

II Ill

847.5 716.9

798.3 619.2

+ 49.2 + 97.7

1,719.1 1,479.5

1,383.3 917.7

+ 355.8 + 581.8

I II Ill

Industrializated countries:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . .

United States:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

Centrally planned countries:

Production. . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

Less developed countries:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . .

Latin America:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

North Africa/Middle East:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

OtherAfrican LDCs:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

South Asia:

Production . . . . . . . . . . . . . . .

Consumption . . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . .

SoutheastAsia:

Production . . . . . . . . . . . . . . .

Consumption . . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

East Asia:

Production . . . . . . . . . . . . . . .

Consumption . . . . . . . . . . . . .

Trade . . . . . . . . . . . . . . . . . . .

World:

Production . . . . . . . . . . . . . . .

Consumption. . . . . . . . . . . . .

716.9- 883.8

613.7- 587.3

+105.1- +76.5

719.2 889.7

858.9 569.4

+62.3 +100.3

112.9-104.5

108.8-104.9

128.8-118.4

127.7-121.2

131.8 108.8

139.1 110.0

1,331.2- 1,301.0

923.5- 874.9

+407.7-+428.1

1,324.6 1,322.1

1,021.9 629.9

+302.7 +492.2

124.8-122.2

108.5-103.4

124.2

118.9

124.0

98.7

1,697.4- 1,840.3

1,183.3- 1,111.5

+514.1-+528.8

158.0-151.1

135.9-128.3

157.8 137.4

154.8 107.9

411.5

432.5

-21.0

452.5 369.6

458.5 400.4

-6. 0 -30.8

116.7

122.4

127.6

125.0

107.2

115.9

451.1

473.9

–22.8

489.2 375.3

495.1 398.5

-5. 9 -21.2

210.2 176.6

219.4 189.5

-9. 2 -12.9

129.6

135.6

135.6 112.8

138.4 119.0

182.0- 189.4

203.0- 201.6

-21.0- -12.2

190.4 178.3

207.8 191.8

-17.4 -13.5

101.7-106.5

107.7-106.7

108.7

110.6

99.1

101.8

195.6- 197.1

210.2- 205.5

-14.6- –8. 4

109.5-110.8

111.0-108.6

119.5 99.1

116.7 99.9

247.7- 247.4

244.0- 240.8

+3.7- +33.6

201.8- 203.9

289.4- 285.8

–87.6- –81.9

264.3 259.6

262.8 234.5

+1.5 +25.1

209.0 188.4

295.1 264.1

-88.1 -95.6

108.2-118.9

110.9-109.6

114.9

118.7

113.0

108.9

305.9- 311.4

282.8- 278.1

+23.1- +33.3

218.3- 222.5

301.8- 292.8

-83.6- -70.3

348.6 288.0

308.0 243.8

+40.6 +42.2

239.9 188.6

318.6 283.7

-78.7 -95.0

131.5-133.7

127.1-125.1

147.6 123.6

138.7 110.8

87.2- 88.3

101.8-100.3

91.0

104.2

80.1

99.7

95.9- 98.2

105.9-102.2

107.4 80.3

112.9 98.4

130.7- 138.7

144.0- 142.9

–13.3- -4. 2

138.2 125.5

144.4 133.7

-8.1 -6. 3

98.1-104.3

105.0-104.2

102.3

105.3

94.0

97.3

109.0- 113.2

112.5- 112.0

-3.6- +1. 2

123.8 108.0

119.1 108.8

+4.7 –0.8

81.2- 84.5

81.3- 80.9

92.7 80.5

88.3 78.5

170.0- 171.7

184.3- 183.7

-14.3- –12.0

177.1 160.7

188.4 167.7

–9.3 -7. 0

104.6-105.6

107.8-107.4

108.9

109.0

98.8

98.0

174.0- 170.0

186.7- 181.0

–12.7- –11.0

176.1 152.1

192.4 184.9

-14.3 -12.8

322.7 282.5

237.1 219.9

+85.6 +62.6

107.0-104.6

109.2-105.8

109.6 93.5

112.5 98.4

273.6- 295.8

217.9- 217.9

+55.7- +77.9

282.0 276.1

218.5 215.6

+63.6 +62.5

116.3-128.4

108.9-108.9

120.1

109.2

118.4

107.6

301.9- 316.5

233.2- 228.5

+68.7- +87.5

129.2-135.9

117.1-114.6

138.7 120.4

119.2 110.0

139.9- 138.9

198.8- 197.8

-58.9- -58. 9

148.4 131.9

203.3 187.1

–54.9 -55.2

104.6-104.9

116.2-115.6

111.2

118.9

98.5

109.2

161.1- 163.5

219.5- 217.3

–58.5- –53.8

188.7 140.4

221.3 195.5

-52.6 -55.1

121.1-122.6

128.7-127.3

126.9 105.0

129.7 114.2

337.7- 332.6

337.7- 332.6

354.4 315.4

354.4 315.4

109.5- 108.5

109.5-108.5

114.0

114.0

103.0

103.0

352.2- 343.2

352.0- 343.2

373.0 302.0

373.0 302.0

117.0- 114.5

117.0 -114.5

126.0 104.0

128.0 104.0

NOTE: In trade figures, + indicates export; minus sign Indicates import.

SOURCE: The Global 2000 Report to the President, VOl

make the projection of agricultural

gloomy. 16

Other Agricultural Projections

One of the most disaggregated and

2, pp. 93-94.

outpu t more global food situation in terms of its underlying causal

factors; and 2) to evaluate policy measures, particularly

international ones, that might redirect future developments towards improvements in the world food situation. The latter purpose focuses on the growth of food

deficits in poor countries and the effect of agricultural

and trade policies in the rich countries on the development of food production and consumption in the Third

World.

Structurally, MOIRA is similar to GOL in that both

of them address only the agricultural sector, leaving

critical factors such as population, GNP, and energy inputs as exogenous variables. Both of them rely heavily

on traditional economic theories and techniques to represent a world food system controlled by market supply,

demand, and prices. They differ, however, in what they

disaggregate: whereas GOL disaggregates crops but considers only one class of consumers in each of its 28

mathematically

elegant models of the world food system is the Model of

International Relations in Agriculture (MOIRA), commissioned by the Club of Rome in 1973 and carried out

by Dutch economists and agronomists with the support

of the Government of the Netherlands. 17 It was originally constructed to investigate the consequences for

the food system of a doubling of the world’s population,

and it is explicitly concerned with the problem of world

hunger. Its specific purposes are: 1) to describe the

lblbld., vol. 2, p. 672.

17H. Linnemann et al., MO[~—MO&/ oj Internuttord Rekmons m Agncukure

(Amsterdam: North-Holland Pubhshlng Co., 1979).
App. B—Agricultural Projections ● 9 5

Figure B.9.–World Grain Yields, Actual 1960-1976 and Projections to 2000 Under Alternatives 1, Ii, and Ill of

GOL for Global 2000

3.25

1960 1965 Actual 1970 1975

SOURCE: The Global 2000 Report to the President, vol. 2, p. 76

regions, MOIRA considers only one agricultural output—consumable protein—but disaggregates consumers

into 12 different income-and~ccupation classes in each

of 106 individual nations, which are linked through an

equilibrium model of international food trade. This

structure allows the model to simulate conflicts of interest between producing and consuming nations or

between agricultural and nonagricultural sectors within

nations, which in some ways makes it a better tool for

food policy analysis.

The model is solved by yearly increments over a

50-year simulation (1960-2010), with the world market

assumed to clear each year and the world market price

(along with factor costs and technical considerations)

assumed to affect the next year’s production decisions.

Each nation’s producers are assumed to operate in such

a way as to maximize expected sectoral—not individual—income. National markets buffer themselves from

international markets by tariffs or price subsidies that

affect the motivations of producers, but a “seepage” effect tends to drive domestic prices toward world prices.

1985 Projected 2000

Time

In its normal mode of operation, MOIRA’s structure

shows demand being steadily increased by population

growth and income growth, which leads to higher

prices, which in turn stimulate additional supply. Due

to the costs of expanding production, however, supply

increases more slowly than demand, and low-income

consumers who cannot purchase food at the going market price “demand” less food than they actually need to

avoid malnutrition. In short, only high prices can increase production, but high food prices relative to consumers’ incomes will result in people going hungry. This

outcome reveals how, in one critic’s view, “this model

structure emphasizes the fact that, in today’s world

—and in the foreseeable future-it is poverty, much

more than supply constraints, that is the cause of world

hunger.” 18

18D, H, Mea&u,~,J. R1chard~on, andG,Bruckmann(eds. ), Groping [n [h

Dark The FWS[ Decade o/ Global Nfodellng (?Qeu York: Wiley, forthcoming), pp.

113-115.
96 ● Global Models, World Futures, and Public Policy

Figure B-10.- Projected Regional Per Capita Food Consumption for South Asia

and Sub-Saharan Africa in 1985 and 2000 Under Alternatives i, ii, and iii of GOL

for Global 2000

(Index 1969-71 = 100)

120

110

100

90

80

1970 1985 2000

Table B-6.–Projected Net Exporters of Wheat Under Alternative I-A (Medium Growth, Rising Energy Prices)

of GOL for Global 2000a

(Million metric tons) Average annual

1970 1985 2000 growth percent

Exports Percent Exports Percent Exports Percent 1970-85 1985-2000 share share share

United States . . . . . . . . . . . . . . . 17,881 39 48,838 58 58,228 57

Australia-New Zealand . . . . . . . 8,300 18 12,165 15 16,084 16 3 2

Argentina, . . . . . . . . . . . . . . . . . . 1,640 4 6,410 8 13,974 14 10 5

Canada . . . . . . . . . . . . . . . . . . . . 11,750 26 15,288 18 7,311 7 2 – 1

South Africa . . . . . . . . . . . . . . . . 60 – 839 1 4,108 4 19 11

U.S.S.R. . . . . . . . . . . . . . . . . . . . . 4,799 11 127 — 1,995 2 – 2 2 20

India . . . . . . . . . . . . . . . . . . . . . . . — — — — 186 –

Euro Six . . . . . . . . . . . . . . . . . . . .

— —

1,170 3 — — — — — —

Total. ., . . . . . . . . . . . . . . . . . . 45,600 101b

83,887 100 101,864 100 4 1
App. B—Agricultural Projections ● 9 7

The standard run of MOIRA, based on a continuation of present trends, assumes moderate population

growth, “relatively high” growth in nonagricultural

GNP, and no new policy interventions. The results of

this scenario shows steady increases in agricultural production (fig. B-1 1) and per capita protein consumption

(fig. B-12), along with higher but unstable world market

prices (fig. B-13). Nevertheless, there is also a large increase in the number of people below the minimum

food standard (fig. B-14). Nutritional gains are greater

in the developed regions than in the LDCs, and significantly lower in South Asia than in the LDCs as a

whole. The LDCs show a decrease in food self-sufficiency, and North America becomes even more dominant

as the leading food exporter. Sensitivity tests conducted

to assess the effect of exogenous variables on these results produced the following results:

●

●

lower growth rates in nonagricultural GNP (3.5

rather than 7 percent in the LDCs) reduces demand, prices, and output, resulting in a 35-percent

increase in world hunger;

lower population growth rates (about half the rate

of the standard run) also results in lower prices and

output, but does produce a 30-percent reduction in

world hunger; and

internal income redistribution outside each country’s agricultural sector (gradually reducing present

inequities by half their magnitude over the 1975-

2010 period) leads to significantly higher effective

demand and to price increases 50 percent greater

than in the standard run, and, although it increases the food imports of the LDCs, it also reduces world hunger by about 50 percent.

The authors conclude from this last test that “the

hunger problem is, to a large extent, a problem of income distribution, ” but they are quick to point out the

limitations of their model. The general trends it projects

are more significant than its precise numerical results,

but these sensitivity tests all point to a similar outcome:

. , . all simulation runs with alternative assumptions regarding exogenous variables have one thing in common:

if policies remain unchanged, the number of people who

cannot obtain sufficient food will increase. 19

As a consequence, the authors have used MOIRA extensivel y for policy testing, with the objective of discovFigure B-n .—Projections of Population and Food Production in MOIRA, Standard

300

260 -

220

180

140 -

100 ~ . .

1 I I I I i’ ! t

.

I 1 1’ 1 i’ I

’75 ’80 ’85 ’90 ’95 2000 ’05 ‘lo ’75 ’80 ’85 ’90 ’95 2000 ’05 ‘lo

’75 ’80 ’85 ’90 ’95 2000 ’05 ‘'10

— Populatio n

--- Food production

Run

NOTE: In all regions production increases faster than population, although in Southern Asia gains are quite modest,

SOURCE: Extracted from MOiRA
98 . Global Models, World Futures, and Public Policy

Figure B-12.– Projection of Annual Per Capita

Protein Consumption From MOIRA, Standard Run

’75 ’60 ’85 ’90 ’95 2000 ’05 ‘1O

Developed

countries

Centrally

planned

countries

Developing

countries

Southern

Asia

NOTE: Greatest gains occur in the developed countries. There is a steady gain

in developing regions, but Southern Asia shows very little gain.

SOURCE: Extracted from MOIRA, p. 29.

Figure B-13.— Food Prices on World Market in

MOIRA, Standard Run

+ 600

+ 400

+ 200

+ 100

0

-100

’75 ’80 '85 '90 '95 2000 2005 201 0

SOURCE: Extracted from MOIRA, p. 291.

ering what actions might be taken by the rich nations

to contribute effectively to the goal of reducing world

hunger. The four basic policy tests involve two measures intended to achieve a redistribution of available

food and two measures intended to stimulate food production in developing regions:

Reduction of food- consumption in the rich

countries, which might be achieved by shifting

consumption patterns (i.e., fewer animal products),

results in a low world market price that weakens

the incentives to production and thereby leads to

increases in total world hunger.

Food aid, if it is purchased by the rich countries at

the prevailing world market price and distributed

to those under the food norm in such a way that it

does not disturb local markets, is capable of eliminating world hunger almost completely. This optimistic scenario, which requires the developed nations to devote about 0.5 percent of their GNP to

Figure B-14.–Total World Hunger (WHUNG) a

Hunger in the Agricultural Sector (AWHUNG)

MOIRA, Standard Run

cons

10” Kg Million

umable protein people

nd

in

’75 ’80 ’85 ’90 ’95 200020052010

NOTE: Right axis shows number of people with a consumption level below the

minimum food standard. Left shows total food deficit in 10 Kg consumable protein.

SOURCE: Extracted from MOIRA, p. 291.

food aid, differs from the first in that it raises world

market prices, particularly between 1980 and 1990,

and thereby stimulates additional production.

Regulating international food trade, in order to

stabilize world market prices at a relatively high level, also proves to be an effective way to stimulate

production and improve the food situation in the

LDCs, particularly under the assumption of low

economic growth outside the agricultural sector.

Such a policy might be difficult to implement, however, because it would require the rich nations to

create buffer stock and to regulate their imports

and exports in order to keep prices at desired levels;

under some conditions this might require North

America to give up its leading position as a food exporter.

Liberalizing international food trade, which

would require rich countries to cease protecting

their domestic markets from world food prices,

causes lower food prices and (over the long term)

considerably more hunger. Because this policy

lowers’ production in the LDCs, it also increases

the developed regions’ share of food exports, with

the greatest increases coming from North America;

in short, it benefits the rich at the expense of those

less fortunate.

The polic y package the authors find most effective

combines price stabilization and food aid. They conclude that “[as] long as the rich nations are able and

willing to provide the funds” there is little potential for

conflict between these apparentl y contradictor y objectives. However, they also found that deliberate changes

in income distribution in the poorest countries would
App. B—Agricultural Projections ● 9 9

“remarkably strengthen the positive effect of food aid

and world market regulation. ”20 This “global food supply policy” would nevertheless require a concerted effort on the part of the rich nations to adapt their domestic production to the international demand and

supply situation.

Strengths and Weaknesses of

Projection Techniques

Anyone putting an agricultural sector into a global

model must arrive at formulations for the determinants

of agricultural production (supply) and consumption

(demand). What he includes and how he includes it determines the results his model will produce, although

his biases may strongly influence the way he interprets

these results. For example, all of the models except

UNIOWM indicate there will be problems supplying

food to at least some of the world’s people over the next

20 years. The reason for this finding is fairly straightforward: all of the models except UNIOWM show increasing costs for land development as the amount of undeveloped land decreases, and all except UNIOWM show

diminishing returns to agricultural inputs. In short, the

models show agricultural problems because they include agricultural limits.

However, their other major similarity–advocacy of

drastic social and political change—appears to be something modelers interject into their models rather than

something they learn directly from the models. The

conclusions drawn from global models often suggest

that the only way to avert major catastrophe is to alter

some difficult-to-change trend such as fertility, investment rate, or income distribution; but the models

themselves are insufficiently detailed to tell what these

changes would mean in practice.

Table B-7 compares how the six models treat some of

the basic factors influencing agricultural systems, with

factors affecting demand on the left, factors affecting

supply on the right, and factors affecting both supply

and demand in the center. It shows that models are

more alike in what they leave out (make exogenous)

than in what the y include, but even when a variable is

endogenous there are differences in how models treat it.

All of these differences affect results in some way, although it is sometimes difficult to establish how. For example:

• Differences in aggregation cause large differences in

the form of model output, but they frequently have

no effect on model behavior other than making results more or less detailed, or more or less difficult

to interpret. Experimentation with different levels

201 bLd., p. 329.

of aggregation has led many modelers to prefer

more aggregated structures, which give nearly identical results at a much lower cost. On the other

hand, sometimes disaggregation does matter: if one

were to aggregate grains and livestock in GOL,

UNIOWM, or any other model that differentiates

between agricultural products, one would probably

observe a change in the way the model responds to

high prices and/or poor harvests.

Structural differences that have no effect under one

set of circumstances may be all-important under

another. For example, the soil degradation mechanism in World 3 has almost no effect on the

model’s standard run, but when nonrenewable resource constraints are removed the resulting pollution causes agricultural yields to plummet, and the

system must begin devoting a large part of its industrial output to rescuing the agricultural sector.

Seemingly different structures can behave similarly,

while seemingly similar structures behave very differently. For example, World 3, UNIOWM, and

LAWM all lack price mechanisms; however, both

the LAWM and World 3 will cause investment to

flow in the direction of a sector that shows signs of

supply shortfalls, while UNIOWM contains no

such mechanism.

Important structural features may be buried in accounting matrices. For example, a zero entry in an

input/output matrix, or an assumption of non-substitutability between two classes of agricultural

commodities, could greatly affect intersectoral

flows in any of the multicommodity models (GOL,

UNIOWM, and WIM).

In short, the way a model’s structures affects its

results can be complicated, so much so that modelers

themselves are often at a loss to explain system behavior. There are, however, some situations in which model behavior is easily traced to model structure, and there

are many other situations where this influence is at least

a plausible explanation. LAWM’s pronounced tendency to rapid urbanization, for instance, results from the

fact that its optimization routines are based on statistical research that showed a high correlation between living in cities and life expectancy; LAWM’s behavior

with regard to housing and education can be explained

in the same way. Similarly, the relatively pessimistic

projections of GOL and World 3 with respect to food

per capita certainly result in part from the fact that

neither model includes labor in its agricultural production functions; the relatively optimistic findings of

LAWM and MOIRA, on the other hand, probably owe

something to their inclusion of labor.

MOIRA assumes that farmers produce the quantity

of outputs that will maximize their profits at a given

price level for agricultural inputs, which explains why
100 ● Global Models, World Futures, and Public Policy

Table B-7.—Comparison of Structural Assumptions in Six Global Models

Demand Supply and demand Supply

Income Intersectoral

Model Population growth interaction Prices Trade Constraints Technology Environment

, J . , L. — ——.—. Agriculture, industry, compete for investment. If one

sector fails it

brings others

down.

Multisectoral,

Input-output with

detail on energy

and machinery,

energy shortage

reduces production, agricultural

development needs

industrial growth.

40 sector inputoutput, no intersec.

toral competition,

just accounting of

intersectoral flows.

Omitted, im- Omitted.

plicit in links

between supply

Diminishing returns

for land investment

and. agricultural inputs, depletable

mineral resources.

Diminishing returns

on land investment

and agricultural inputs, depletable

energy resources.

 , worm 

WIM

Births and endogenous,

deaths driven by

endogenous. supply of

4 cohorts, 1 capital and

region. inputs.

embodied in capital

stock, properties may be

changed by policy, no

automatic progress in

disembodied technology

soil delerloration and pollution affect

and demand. yields.

Births and Endogenous,

deaths semi- driven by

endogenous, supply of

10+ capital and

regions, 85 inputs.

cohorts.

Explicit,

endogenous,

by sector.

Food and other imports constrained by

balance of payments.

Embodied in capital

stocks, investment

shifted by price and profit criteria, no automatic

progress in disembodied

technology.

Omitted.

UNIOWM

MOIRA

Exogenous, Exogenous,

15 regions. fast enough

to meet UN

targets.

Exogenous, by

sector for all

sectors, based

on input cost

projections.

All sectors, Import

substitution exogenously determined.

None, linear returns

to inputs.

Exogenous updates of

input-output coefficients

changes production efficiencies, i.e., automatic

disembodied technological progress.

Emissions of

pollutants

calculated for

each sector,

no feedback

to yields.

Exogenous, Exogenous.

106 nations,

6 rural and

5 urban

income

groups,

urban-rural

migration

endogenous.

Nonagricultural

growth affects income, hence

agricultural demand. Relative income levels in

agriculture and

nonagriculture affect mlgratlon.

5 sectors compete

for capital and

labor; allocation

based on sectoral

contribution to

basic needs fulfillment. Agriculture

needs capital sector inputs.

Sensitive to energy

prices, little other

interaction; within

agriculture, high

level of interaction

between grain and

livestock.

Domestic food

prices endogenous, policy

controlled,

world food

price balances

supply and

demand.

Food only, detailed

representation of

trade policies, assure.

ing policy tries to

keep agricultural incomes in line with

nonagricultural

incomes.

Diminishing returns

on land Investment

and agricultural

inputs.

Embodied shifts through

producers investing to

maximize profits. Disembodied progress

automatic.

Omitted (to

be included

in MOIRA2)

LAWM Births and Endogenous,

deaths en- driven by

dogenous, 4 supply of

regions, capital and

maximizes labor.

life expectancy.

Omitted, Unimportant, mostly

omitted.

Diminishing returns

on land investment

and yields.

Disembodied progress

automatic, in agriculture

Omitted.

1 percent per year gain

in efficiency. I

G2000 Exogenous, Exogenous.

28 regions.

Agriculture, Multicommodity trade Apparently linear

returns on inputs,

no limits.

Disembodied progress

automatic, following

trends observed in the

recent past.

Omitted in

GOL, analyzed verbally.

endogenous by in agricultural prodcommodity; ucts, some policy

energy, representation parexogenous. ticularly for United

States.

SOURCE: Office of Technology Assessment.

measures that drive up agricultural prices are successful Factors Affecting the Reliabil

. . ity and

as a means of reducing hunger. However, profit maximization may be a better approximation of the behavior of rich farmers than of poor farmers (who may not

be able to borrow funds for expanding production, and

who may resist giving up their traditional agricultural

practices), and for this reason MOIRA probably overestimates the response to price incentives in the developing countries, thereby underestimating hunger in its

rapid-economic-growth scenarios. Finally, as discussed

below, the structural decision to make important parts

of the system exogenous often results in a model whose

reliabilit y and accuracy are heavil y dependent on the

projections used to drive the model.

Accuracy of Agricultural Projections

Model Structure and Assumptions

Most global modelers enjoin readers against taking

their numerical forecasts as precise estimates, Nonetheless, comparison of numerical results, in conjunction

with comparison of structures and assumptions, is a

useful means of determining why various global models

make the projections the y do. Table B-8 compares the

projections made by six models for key agricultural variables in 2000, including food production, prices, expansion of cultivated land, and yields. To eliminate differ-
App. B—Agricultural Projections Ž 101

Table B-8.—Comparison of Projected Values of Critical Agricultural Variables in Different Global Models

Food Price Global

production increase Cultivated land Yield food/capita Regional food/capital

World 3 280 Not relevant 130 120 109 Not included

UNIOWM 285 114 Region Arable Land 158 Calories Protein

grain Developed market: land productivity grain South Asia . . . . . . . . . . . . . 120 127

274 North America . . . . . . . . . . . . 111 194 155 Africa (tropical) . . . . . . .

livestock

127 140

Western Europe (high-income) . . . 100 162 animal Latin America (low income) ., 125 143

Japan. . . . . . . . . . . . . . . . . ., . . 100 269 products Latin America (high income) 132 148

Oceania. . . . . . . . . . . . . . . . 183 162

Centrally planned:

Soviet Union ... . . . . . . . . . . . . 100 215

Eastern Europe, . . . . . 100 186

Asia (centrally planned). . . . . . . 120 278

Developing market:

Latin America (medium-income) 166 311

Latin America (low-income) . . . . . . 140 328

Middle East. . . . . . . . . . . . . . . . . 126 487

Asia (low-income), . . . . . . . . . 113 331

Africa (arid) ... . . . . 131 282

Africa (tropical). . . . . . . . . . . . . . 152 324

MOIRA 244 522 Not documented Not documented 140 High Low

high growth high growth high growth South Asia ., . . . . . . . . . . . 125 75

185 113 108 Africa (tropical) . . . . .

low growth

164 100

low growth low growth Latin America ... . . . . . . . . . 168 117

Global 191 to 201 130 104 160 126 High Low

2000 grain optimistic to optimistic South Asi a 113 96

191 to 198 to 190 104 Other African LDCs. ... . . . . . 86 78

food 215 pessimistic Latin America, . . . . . . . . . . 137 111

pessimistic

LAWM Not documented Africa . . . . . . . . . . . . . . . . . -13 0

Asia, . . . . . , . . . . . . . . . . . -15 0

Latin America . . . . . . . . . -12 0

WIM Not documented South Asia . . . . . . . . . . . . . . 68

NOTE: Scaled such that 1970 = 100. (For illustrative purposes only, Some of these numbers have been read off of imprecise plotted output and may err by as much as 5

or 10 percent).

SOURCE: Office of Technology Assessment.

ences in measurement, values for 2000 have been indexed to their values in 1970. Generally, world aggregates are used because the models use very different regional aggregations. It has not been possible to include

values for all models, because not all models calculate

all variables and model documentation often fails to

present needed data. Indeed, lack of information has

made it impossible to include either the WIM or the

LAWM in most calculations.

Projected food production in 2000 ranges from 185 to

285 percent of 1970 figures, an increase equivalent to

average annual compound growth rates varying from

2.0 to 3.5 percent. UNIOWM and World 3 give the

highest figures; the MOIRA low-growth scenario and

the Global 2000/GOL projections give lowest. Price

projections vary far more than supply projections: the

MOIRA’s high-growth scenario, at one extreme, shows

a price increase of over 400 percent (most of which, incidentally, occurs before 1985); on the low side, the

MOIRA low-growth scenario and UNIOWM project

price increases of below 15 percent. Columns 3 and 4

show how much of the increase in output is attributable

to expanded cultivation and how much to increased

yields; the figures show that World 3 anticipates land

expansion, GOL yield increases, and UNIOWM both.

Finally, columns 5 and 6 show global and selected regional figures for per capita food intake. From the differences between columns 5 and 1, one can infer that

global population growth is faster in World 3 than in

other models and slower in Global 2000. The regional

figures, however, show that there is greater variation in

regional projections than global projections, with the

most severe problems in South Asia and Tropical Africa. These figures also hint that WIM may be considerably more pessimistic than other global models. It

should also be noted, however, that model results are

highly dependent on time horizon. If one truncates the

World 3 standard run at 2000 its projections look op

timistic, and similar results arise from truncating the

LAWM standard run for Asia at 2000.

Population, economic growth, and disembodied technological progress are exogenous variables in at least

three of the six models (see table B-7). All of these variables have important influence on model results, as do

their assumptions about the amount of potentially arable land, the cost of developing that land, and the cost

of other inputs such as fertilizer, irrigation, and farm

machinery. Vital though these factors are to accurac y

and reliability, however, there is little agreement among

the models on what values they should be assigned. In
102 ● Global Models, World Futures, and Public Policy

some cases this disagreement cannot be resolved because of a lack of statistical evidence or theoretical understanding.

Disembodied Technological Progress

It has become conventional for economists to identify

that fraction of income growth (or input cost reduction)

that cannot be statisticall y ascribed to increases in

labor, capital, or other inputs as “disembodied technological progress,” and to make projections of economic

growth under the assumption that rates of technological progress observed in the recent past will continue in

the future. This amounts to an assumption that productivity will increase automatically. In most such formulations, the productive contribution of technological

progress is very significant: rates above 1 percent per

year are common, and it is not uncommon for improved technology to appear to contribute more to economic growth than either labor or capital.

Formulations of this type have been employed in the

agricultural sectors of LAWM, UNIOWM, GOL, and

MOIRA, but not in World 3 or in some versions o f

WIM. The specific rates of technological progress assumed in various global models are generall y not reported in model documentation, however: LAWM assumes a 1.O-percent annual growth in agricultural productivity, 1.5 percent for capital goods, and rates of either 0.5 or 1.0 percent for other sectors; but analogous

figures for UNIOWM, GOL, and MOIRA are not

available, Nevertheless, testing shows that model results are quite sensitive to both the presence and the

rates of disembodied technological progress. When

technological progress is omitted from LAWM, for example, Africa joins Asia in being forced against its land

constraints and facing economic collapse. On the other

hand, if sufficiently strong assumptions about technological progress are introduced into World 3, the overshoot-and-collapse mode can be eliminated from the

model altogether. z’ It should be added, however, that

nobody has a very good understanding of technological

change; its exclusion or inclusion in global models

therefore stems ultimatel y from hunches, beliefs, and

values—not from scientific understanding.

Population

Population growth is exogenous in UNIOWM, GOL,

and MOIRA. UNIOWM assumes somewhat higher

population growth rates than the two other models, so

its optimistic projections of food per capita cannot be

ascribed to low population growth rates. MOIRA, on

the other hand, assumes rapidly declining rates of population growth, especially for countries whose 1970

growth rates were high, and this may contribute to

MOIRA’S tendency to become more stable in the last

decades of simulation than it is in the first decades.

GOL assumes significantly lower population growth

rates in the industrialized countries than those projected by the Census Bureau (see app. A). Better linkage

in Global 2000 might thus have resulted in less optimistic food trade projections.

Economic Growth

The right side of table B-7 shows the rates of economic growth projected as exogenous drives for the

UNIOWM, GOL, and MOIRA. In all three models

these projections are used (in combination with the

demographic projections) to project income per capita,

which in turn is used in projecting demand for agricultural products. The standard runs of UNIOWM and

MOIRA assume rapid economic growth in the developing countries (7.2 percent and 7.6 percent per year,

respectively). This leads to rapid increases in agricultural demand, due to the fact that demand for food in

poor countries is quite income elastic. In UNIOWM, increased demand causes increased production directly,

while in MOIRA it causes price increases, which in turn

stimulate increased production. When lower rates of

economic growth are assumed in MOIRA, prices and

production stay low and the food situation improves

less rapidly (it even deteriorates, in South Asia). The

Global 2000 income growth projections are generally

lower, particularly for non-OPEC developing countries,

which undoubtedly contributes to the fact that GOL

projections of food availability in the LDCs are much

more pessimistic than analagous projections in the

UNIOWM and MOIRA,

Potential Agricultural Land

MOIRA, World 3, and WIM all use formulations in

which the costs of further increases in land development are dependent on the amount of land already under cultivation. A prodigious amount of work went into

estimating, these relationships for MOIRA, including

derivations of absolute maximum dry-matter production potential based on FAO soil maps and plant physiology models. 22 The figures used in World 3 derive from

the President’s Science Advisory Committee (PSAC)

report, The World Food Problem ( 1967), and are likewise

based on detailed studies of soil maps and climatological

data. The derivation of the figures used in WIM has not

been documented.

21H. s. D. Cole, op. cit., p. ~. 22 LLnnemann, et al., 0p. cit., PP. 19”74
App. B—Agricultural Projections ● 103

A comparison of the values used in these three models shows that differences between MOIRA and the

PSAC/World 3 are relatively minor–PSAC gives

South America somewhat more agricultural land,

North America slightly less, and Europe quite a bit less,

The WIM estimates, however, proved to be markedly

lower for all regions except Western Europe, and WIM’s

estimate of the world’s total potentiall y arable land are

only about 70 percent of the MOIRA figures and about

76 percent of those used in World 3. WIM’s relativel y

pessimistic assumptions about land availability, particularly in the developing regions, undoubtedly contributes to its dire predictions of impending famine. However, as shown in sensitivity tests of the World 3 agriculture sector, an increase in potentially available land of

30 percent or more does not change the net outcome of

the model—it merely postpones by a few years the date

at which scarcity becomes acute. 23

Other Factors

and Accuracy

Affecting Reliability

Global agricultural data are often poor, and all models necessarily contain a lot of guesswork. For example,

no one has very many or very good economic data on

China, nor are there reliable data on the soils (and

hence the agricultural potential and costs of agricultural

development) in Amazonia. In addition, agriculture will

undoubtedly be affected in the coming decades by several trends for which no global model accounts. None

of these six models accounts for potential competition

between the agriculture and energy sectors for water or

land. None takes the global monetary system into account. None looks at the effects of climatic change or

increased levels of atmospheric carbon dioxide. Nor

does any of them examine the agricultural consequences of unusally bad weather or a serious destabiliza--

tion of oil and gas supplies.

“J. Ran&rs and E. K. O. Zahn, “The Agricultural Sector,” In D>namlcs of

Grouth m a Flnitc W’O,M, D. Lfeadcrws, et al. (eds. ) (Camhndge, Mass.. Y1.I.T.

Press, 19?4), pp. J39-N8.
APPENDIX C

Energy Projections

Introduction

The future availability and price of natural resources

are crucial variables in long-range projections of world

economic development. Energy supply and demand in

particular affect not only industrial production but also

agriculture, transportation, and general living conditions. As a result, the findings and conclusions of these

five global modeling studies are significantly influenced

by their treatment of the global energy system and their

assumptions about future energy trends. Some of the

models do not address energy specifically, or in detail,

and others merely assume that energy will not be a constraint within their restricted time horizons.

In general, those models that include a finite resource

stock tend to show that depletion will raise prices, slow

production, and dampen global economic growth; in

short, resource constraints make them susceptible to

economic collapse. Even the most optimistic findings,

however, suggest that the world faces a difficult transition away from dependence on oil. Coal, nuclear, and

solar power are offered as the principal alternatives for

the future energy system. The accurac y and reliability

of these projections are also influenced by their assumptions about population and economic growth, potential

technological progress in extraction and conservation,

the potential for substitution and alternative sources,

and the future political and economic behavior of both

producers and consumers.

Purposes, Structures, and Findings

World 3

Although The Limits to Growth is sometimes said to

have “predicted” the energy crisis of the 1970’s, the

World 3 model itself does not specifically address future

trends in energy supply and demand. The purpose of

the model is to describe the world as a general system

rather than to predict its parts in detail, so energy

resources—petroleum, natural gas, and coal—are

lumped together with 16 other raw materials (primarily

metals) in a single category called “nonrenewable resources. ” The authors cite U.S. Government estimates

showing total reserves of individual resources ranging

from 7 to 5,100 years, but they assume that,on average,

there are about 250 years worth of nonrenewable reIThe followlng material IS based In part on the draft working paper, “The

Role of Energy in the Global System)” by Paul Werbos, Office of Energy Information Vahdarlon, Energy Information Admln[strat[on, U.S. Department of

Energy.

sources at 1970 consumption levels. They also assume,

however, that the quantity of resources consumed per

capita is a fixed function of average income per capita,

and that continued population and economic growth

will lead to a 4-percent growth rate in total world

resource consumption. Consequently, this 250-year

reserve would be completely used up by about 2040 if

growth continues unabated. In addition, the model

assumes that the cost of obtaining resources will rise

dramaticall y once 50 percent of the reserves have been

depleted, due to declining resource quality and increasing transportation costs.

The standard run of World 3 demonstrates the consequences of this combination of assumptions for the behavior of the nonrenewable resource sector and for the

entire world system (fig. C-1). Rising population, combined with rising industrial production per capita, results in the rapid depletion of resources and an equally

rapid increase in the costs of obtaining the resources.

As more and more capital is diverted from agricultural

and industrial production to the obtaining of raw materials, per capita food and industrial output alike begin

to decline. This leads eventually to mass starvation and

the collapse of the world economic system,

Sensitivity tests conducted by the authors of World 3

indicate that the general behavior of the nonrenewable

resource sector and of the integrated world system are

not particularly sensitive to their assumptions about resource reserves, consumption rates, or extraction costs.

●

●

●

●

If reserves are set at twice their initial value, the collapse is delayed by only 15 years (fig. C-2).

A

- tenfold increase in initial reserves will eliminate

resources as a constraint to growth—at least before

2 100—but the general behavior of the world system

remains unchanged. In this case, persistent pollution causes a decline in production followed by

starvation and a decline in population (fig. C-3).

Improved extraction technologies, by reducing the

short-term cost of obtaining virgin materials,

would eliminate the economic incentives for conservation and substitution. These technologies

could delay the collapse a few years, but they would

cause a faster depletion of resources and a sharper

eventual decline in industrial output (fig. C-4).

Improved conservation technologies, sufficient to

reduce per capita consumption by a factor of four,

allow much higher industrial output and postpones

its decline for about 40 years, but they cannot prevent eventual collapse (fig. C-5).

The only model run that succeeds in postponing the

collapse beyond 2100 is based on the combination of

104
App. C—Energy Projections ● 105

Impact of Resource Depletion on the Nonrenewable

World 3 Standard Run

/

/

1950 2050 2100

Run 5-1: standard run for the nonrenewable resource

sector.

Resource Sector and Integrated Model,

II

0 0 0

0 0 0

1950 2000 2050 2100

Run 7-6A: World 3 reference run.

This is the World 3 reference run, to be compared with the

sensitivity and policy tests that follow. Both population

POP and industrial output per capita IOPC grow beyond

sustainable levels and subsequently decline. The cause of

their decline is traceable to the depletion of nonrenewable

resources.

Figure C-2.— Behavior of the Nonrenewable Resource Sector and Integrated World 3 Model

With Doubled Reserves

/

/

m

.

1900

Run 5-2:

1950 2000 2050

behavior of the sector with double

value of nonrenewable resources.

the initial

.

1900 1950 2000 2050 21C

Run 7-7: sensitivity of the initial value of nonrenewable

resources to a doubling of NRI.

To test the sensitivity of the reference run to an error in the

estimate of initial nonrenewable resources, NRI is doubled.

As a result, industrialization continues for an additional 15

years until growth is again halted by the effects of resource

depletion.
106 ● Global Models, World Futures, and Public Policy

Figure C-3.—Behavior of the World 3 Model When Initial Resource Reserves

000 0

000 0

increased Tenfold

1900 1950 2000 2050 2100

Are

Run 7-8: sensitivity of the initial value of nonrenewable resources to a tenfold increase in

NRI.

The initial value of nonrenewable resources NRI is increased by a factor of 10, to a value well

outside its most likely range. Under this optimistic assumption, the effects of nonrenewable

resource depletion are no longer a constraint to growth. Note that there is no dynamic difference in this run between setting resources at 10 times their reference value or assuming an infinite value of resources. However, population and capital continue to grow until constrained

by the rising level of pollution.

SOURCE: Dynamics of Growth in a Finite World.

cost-reducing and resource-conserving technologies

with zero population growth after 1975 (fig. C-6). Even

this run, however, shows the beginning of the characteristic rise in the amount of capital that must be allocated to obtain resources—the collapse of the economic

system, although delayed beyond the model’s time horizon, will undoubtedly still occur in the 22d century. No

test was performed to measure the model’s sensitivity to

the assumption that industrial output per capita will

continue to increase exponentiall y as it has in the past.

World Integrated Model (WIM)

WIM is both more detailed and more flexible in its

treatment of energy resources than World 3, and it has

been used extensivel y for testing alternative energy futures. WIM specifically represents both the supply and

the demand for five different energy sources—oil, gas,

coal, hydroelectric, and nuclear—within each of its 10

or more geographical regions. 2 The model also represents energy trade between regions, a significant advantage in modeling a world system where some 110 nations import over two-thirds of their energy needs and

90 percent of all oil supplies move through the international trade network. J

WIM’s structural equations reflect the assumption

that rising oil prices will lead to both conservation and

the development of alternative sources of energy. Investment in energy development is partially controlled

by price, which in turn is determined by supply and demand. The model also assumes that oil will cover a declining portion of total world energy demand after

‘The fifth category, “nuclear energy, ” also includes solar energy and other

capital-intensive alternatives. Source: Command and Control Technical Center (CCTC), Wodd Integrated Model Muhtkvel Hterarchumi Theoretic Concepts,

CCTC Technical Memorandum TM 197-79 (Washington, D. C.: U.S. Defense

Communications Agency, June 15, 1979), pp. 2-17.

‘M. D. Mesarovic and E. Pestel, Mankind at the Tummg Potnt (New York: Dutton, 1974), p. 180.
App. C—Energy Projections . 107

Figure C-4.– lmpact of Cost-Reducing Extraction Technologies on the

Integrated World 3-Model

Run 5-3: the effects of cost. reducing technologies on the

behavior of the nonrenewable resource sector

SOURCE: Dynamics of Growth in a Finite World.

Figure C-5.–impact of

0.

Oooa

II

II

000 0

000 0

.

\ 1

.

1950 2000 2050 210(

Resource. Conserving Technologies on the

Integrated World 3 Model

Run 7-12: improved resource exploration and extraction

technologies.

The implementation of improved resource exploration and

extraction technologies in 1975 is modeled by lowering the

capital cost of obtaining resources for Industrial production. This policy allows industrial production to continue growing for a few more years than in the reference

run, but it is ineffective in avoiding the effects of resource

depletion.

1 .

1950 2050 2100

the effects of resource-conserving technologies

on the behavior of the nonrenewable resource

sector.

a!kax

x

n.

a

z

000 0

000 0

Resource Sector and

Resource Sector and

1950 2054 21OQ

Run 7-13: recycling technologies.

The advances in resource exploration and extraction

technologies of Run 7-12 are supplemented by an improvement in recycling technologies that reduces per capita

resource usage by a factor of eight in 1975. That policy

removes the constraining effects of resource depletion and

allows population and capital growth to continue until

checked by persistent pollution

SOURCE: Dynamics of Growth in a Finite World.
108 ● Global Models, World Futures, and Public Policy

Figure C-6.—Combined Impacts of Zero Population Growth, Resource Conservation, and Improved Extraction Technologies on the Behavior of the World 3

Nonrenewable Resource Sector

K

z

000 0

1900 1950 2000 2050 2100

Run 5-5: the effects of zero population growth and advanced technological policies on the

behavior of the nonrenewable resource sector.

SOURCE: Dynamics of Growth in a Finitefe World.

1990, and that substitution between energy sources will

be based largely on cross-price elasticities, Consequently, although oil and oil substitutes are the most binding

resources in WIM, energy supply appears to be constrained less by absolute scarcity than by the speed with

which substitutes can be developed—energy is an economic and engineering problem, rather than a physical

one .

4

For use as a policymaking tool, the model provides

“scenario variables” or “policy levers” with which users

can test the consequences of nonmarket pricing behavior by producers or shifting patterns of substitution by

consumers. 

s In general, the model runs suggest that cooperation would benefit both producers and consumers

and would also ease the transition away from oil, and

that continued long-term economic growth may be

possible under either of two alternatives: the rapid and

widespread deployment of breeder reactors; or the construction of vast “solar farms” and hydrogen plants in

the deserts of the Middle East.

4P. VanderWerf, “Energy, ” in The Global 2000 Report to the Pres/denr (Washington, D. C.: Council on Enwronmental Qual[ty and Department of State,

1980), VOI, 2, p. 618.

5CCTC, Op. cit., pp. 2-17.

The world oil crisis is a major focus of the many scenarios reported in Mankind at the Turning Point. In the

first pair of computer runs, the model was used to demonstrate the long-term economic benefits of an “optimal” oil pricing policy (fig. C-7). Continuation of low

oil prices would encourage overexploitation and rapid

depletion, discourage the development of substitutes,

and lead to major dislocations in the developed regions

when reserves are exhausted. Both exporters and importers would fare better under an “optimal price scenario, “ in which the real price of oil rises 3 percent annually until it reaches an optimal level (about 50 percent above the initial price of $13.50/barrel, as determined in a separate analysis), at which level it stabilizes

thereafter. Optimal oil pricing is assumed in a second

set of computer runs that indicates that both exporters

and the developed regions benefit under scenarios in

which the flow of oil from the Middle East is unimpeded and international energy trade is “governed solely by the economic forces without undue interference

from the political level” (fig. C-8).6 All of these runs,

however, project a transient world oil deficit between

‘Mesarowc and Pestel, op. cit., p. 112.
App. C—Energy Projections ● 109

Figure C-7.—Comparison of Long-Term World Development for World integrated Model “Fixed Low Oil Price”

Scenario and World integrated Model “Optimal Oil Price” Scenario

(A)

I

Developed world

(Regions 1-4) I

1975 1980 1985 1990 1995 2000 2005 2010 2015 20202025

Year

Cheap energy in the form of oil has been a prime fuel for the

unprecedented growth of the world economy in the 1950’s

and 1960’s. The dramatic increase in oil prices in 1973 was

viewed as a catastrophe. However, computer analysis of

our world system model indicates that the continuation of

what amounts to overexploitation of oil, spurred by an

unreasonably low price, would lead to major dislocations

because of the exhaustion of reserves and the lack of

motivation to develop substitutes in time. Pursuance of

short term objectives would lead to major dislocations in

SOURCE: Mankind at the Turning Point.

1997 and 2002 and a severe, persistent deficit beginning

around 2020; substitutes or alternative sources would

be necessary after that date.

One possible energy future, based on nuclear power,

has been proposed by some technological optimists.

Tests using the WIM “fast-nuclear scenario” raise questions about the short-term feasibility and long-term

consequences of this alternative. After testing shortterm scenarios based on Herman Kahn’s The Next TW O

Hundred Years, the authors conclude that:

It is impossible to design any energy program in Western Europe or Japan which could, over a ten-year period,

reduce energy demand and increase production of energy

from non-petroleum sources sufficiently to compensate

for the loss of the Persian Gulf by 1987. The Hudson

report statement regarding the ease of adjustment to a

quick disappearance of oil reserves is therefore erroneous. ’

Other WIM tests indicate that the longer term feasibility of the nuclear option is just as questionable. The

U.S. Atomic Energy Commission estimated in the early

1970’s that nuclear energy would provide 30 percent of

TB. B. Hughes and M.D. Mesarowc, “Testing the Hudson Institute Scenarios”

(Washington, D. C.: US Assoclatlon for the Club of Rome, September 1979),

mimeograph, p. 22.

(B)

1975 1980 1985 1990 1995 2000 2005 2010 2015 20202025

Year

the long run (see A). A much more beneficial development

for all concerned results from the “optimal price scenario”

in which the price is gradually increased up to the “optimum” level. Such a policy would bring in the substitutes

in a more regular fashion while prolonging the reserves.

Both exporting and importing regions would fare better

(see B). it is only by taking a global and long term view that

such a course of development, most beneficial to all concerned, can be identified.

the developed world’s energy needs by 2000, based on

higher demand growth than is now expected. By extrapolating from this figure, the authors found that by

2025 nuclear power would have to provide 60 percent

and by 2075 almost 100 percent of all energy needs (see

fig. C-9). The social, economic, and security impacts of

such a course would be enormous: sole reliance on fission nuclear power would require 24,000 fast-breeder

reactors worldwide by 2075, which in turn would require the construction of 4 reactors per week for a century and the eventual construction of about 2 reactors

per day just to replace reactors that have reached the

end of their 30-year lifespans, at a cost of $2 trillion per

year for replacements alone (see fig. C-lO).S This scenario would also require the energy sector to process

and transport 33 million pounds of plutonium each

year; only 10 pounds of this element are needed to construct a nuclear bomb.

Mankind at the Turning Point concludes that an energy

future based on nuclear power would be a “Faustian

bargain,” but this conclusion involves several important assumptions. For one thing, fusion as well as fission could provide the growing nuclear share of energy

sMesarovic and Pestel, op. cit., pp. 132-1 M
110 ● Global Models, World Futures, and Public Policy

Figure C-8.—Comparison of Middle East Oii Production, World Oil Deficits, Developed=Worid Gross

Regional Product. and Middle East Accumulated

‘Wealth Under Two World integrated Model

Scenarios

.25

,20

,15

,10

.05

Solid line: Middle East withholds oil, Imposes ultimate ceilino of 14 billion

bbllyr.

Dotted line: Production and trade governed solely by economic factors.

SOURCE: Mankind at tha Turning Point,

supply, although fusion power on such a scale would involve similar financial and engineering problems and

(at present) even greater technical problems. Another

significant factor is the assumed rate of growth for energy demand, which seems unrealistically high in view of

subsequent events; in updated projections, higher

energy prices and slower demand growth would make

the nuclear share—whether fission or fusion-substantially smaller. The authors prefer an energy future that

places mid-term reliance on coal and coal-derived synfuels, combined with long-term development of huge

“solar energy farms” in the present oil-producing

regions. This alternative has not been tested with the

model, but it too would involve massive capital and

engineering requirements, even with slower energy demand growth. Because of its technical uncertainties (see

below), and because it is not based on explicit analysis

with WIM, this solar alternative remains speculative.

Latin American World Model (LAWM)

LAWM assumes the nuclear-energy future that WIM

rejects, but unlike the other global models it contains

no representation of energy or any other resource. The

structure of LAWM is based on the assumption that,

“for the foreseeable future, the environment and its natural resources will not impose barriers of absolute physical limits” on the satisfaction of basic human needs.9

The authors base this assumption on two studies conducted independentl y of the model itself: a surve y o f

currently known reserves of fossil fuels, which found

enough oil and gas to last 100 years, and enough coal to

last 400 years, at present consumption levels; to and an

analysis of future production costs for energy, which

found that:

. . . the so-called energy crisis . . . is of a conjunctural

character, such as others of similar importance that occurred in the past. And it may be perceived that the main

reactions of the system will be to establish a new

equilibrium, which, generally speaking, in the long term

will not differ from the previously observed trends. ’ 11

The latter conclusion appears inconsistent with the

authors’ own reserve estimates, which represent slightly

more oil and gas but considerably less coal than those

used in World 3 and WIM, However, the authors assert

that “the most important fuels for the future are nuclear

fuels.” The y cite predictions that nuclear power will

generate 50 percent of the world’s electricity by 2000,

and, although their own estimate of uranium oxide reserves reflects only 33 years supply even at 1970 levels,

they suggest that “a small increase in the price or an advance in technology” will make it economical to extract

vast amounts of uranium from granite or seawater. 12

The model itself, however, does not reflect the capital

costs or potential technical bottlenecks involved in this

nuclear scenario.

United Nations Input-Output World Model

(UN IOWM)

The central concern of UNIOWM is to reduce the income gap between the rich and poor nations before the

year 2000. In order to determine whether U.N. development targets are consistent with the availability of nonrenewable resources, the model projects the levels of

production and world trade in six metals and three en-

‘A. 0. Herrera, et al., Catastrophe or NeuI Soctety? A .Latm American Wodd

Model (Ottawa: [nternatlonal Development Research Centre, 1976), p. 8.

IOIbid., pp. 32-33.

“lbId., p. 34.

121 bid., p. 33.
App. C—Energy Projections ● 111

Figure C:9.- Projected U.S. Energy Supply Distribution for World Integrated

Model “Fast-Nuclear” scenario

8

“Imports and Oil Shortages”

#

(fission and

/ / ’ fusion)

& B “Demand” / / /

laska oil

omestic gas

t Hydro

Geothermal

1

Lower 48 States oil —

I I 1 1 I I

SOURCE: Mankind at the Turning Point.

ergy sources (oil, gas, and coal) that would be required

to support its relatively high rates of population and

economic growth. Its major conclusion:

The problem of the supply of mineral resources for accelerated development is not a problem of absolute scarcity in the present century but, at worst, a problem of exploiting less productive and more costly deposits . . . and

of intensive exploration for new deposits . . . .13

Reserves and prices are determined independently of

the model itself, and the amount of each resource required for expansion in each economic sector is a djusted for assumptions about increased efficiency due to

●

1 ~w. ~ontlef, A. Caner, and P, Petrt, Tk Future of [k WOdd Ecmmv: A

Umted F+’atIons Study (New York: Oxford, 1977), p. 11; emphasis added.

technology. Most of these technical assumptions are

not reported, although the model does assume 55-

percent recycling of all materials, worldwide, by 2000.14

The model also assumes that all nations will rapidly develop domestic reserves, but that extraction costs will

rise as high-grade deposits are exhausted.

The authors project that 77 percent of the world’s

known petroleum reserves will be depleted by 2000, but

their confidence about future energy supplies rests on

the world’s plentiful reserves of coal, which they “conservatively” estimate at 9 trillion metric tons (roughly

the same as World 3). The model assumes “autonomous

—,

“lbld., pp. 5, 45.
112 . Global Models, World Futures, and Public Policy

Figure C-10.– Long-Term Consequences of U.S.

Energy Self-Sufficiency Under World integrated

Model “Fast-Nuclear’S Scenario

----- Cars of coal produced, United States

— Days between starts of new

nuclear plants, United States

— — World price of oil

~ GNP of United States

— World oil production

1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Year

SOURCE: Systems Research Center, Case Western HeSe~e university.

substitution” between energy sources, but it does not

rely on cross-price elasticities nor does it compute prices

endogenously. For example, it assumes that shale oil

and gasified coal will replace petroleum and natural gas

in North America before 2000, but it also assumes that

the price of coal will decline despite a sevenfold increase

in the price of natural gas. 15 In addition, UNIOWM assumes a growing substitution of nuclear for conventional fuels in the utility sector, although this assumption is not reported in the documentation.l6

In all of its many scenarios, UNIOWM projects a tremendous increase in world consumption of minerals

and energy between 1970 and 2000. The global demand

for petroleum is projected to increase 5.2 times, natural

gas 4.5 times, and coal 5.0 times 1970 levels. Rapid industrialization in the developing regions causes them to

more than double their share of world energy consumption. The Middle East remains the major net exporter

of petroleum, with output projected to rise almost eightfold—a projection that now appears unrealistic in view

of subsequent OPEC production decisions, Non-OPEC

developing regions, along with Western Europe and

‘Ybld., p. 65.

16A. Carter, private commumcatlon, April 1981.

Japan, become increasingly dependent on imported petroleum; but the U.S. oil deficit disappears after 1990,

apparently due to the development of shale oil and a

sudden doubling of domestic coal consumption in the

1990’s. Despite anticipated advances in mining technology and industrial efficiency, the percentage of capital

stock used in resource extraction increases steadily in all

regions, just as it did in World 3.

In the standard scenario, based on the U.N.’S International Development Strategy, the real price of natural gas increases by 656 percent and that of petroleum

by 225 percent over the 1970-2000 period; but the price

of coal—the most plentiful energy resource—actuall y

declines by 14 percent in constant dollars.17 In a second

scenario based on “more generous” resource endowments, production and consumption levels change very

little and extraction costs, although they rise later, are

just as high by 2000. The most significant impact of

higher reserve estimates is on trade deficits: this scenario reduces the balance-of-payments deficit of the developed regions by 50 percent, but the non-OPEC developing regions, after being better off in 1990, have accumulated greater debts by 2000 than they do in the

standard, “conservative” scenario.

In a later policy test conducted for the U.S. Department of Commerce, UNIOWM indicates that aggressive fossil-fuel conservation in developing regions could

reduce LDC trade deficits and thereby remove the main

economic constraint to Third World development .18

Such a course would also increase LDC capital requirements and (implicitly) would require an even greater

nuclear share to provide adequate energy for continued

industrial expansion. In general, the authors find

energy to be an economic rather than a physical problem, at least until 2000: internal reform in the LDCS

and the creation of a “new international economic

order” are the necessary conditions for accelerated

development in this century, although the model’s restricted time horizon prevents it from examining the

sustainability of such growth in the next century.

Global 2000

The energy projections in the Global 2000 Report include a variety of short-term and midterm forecasts,

based on different methodologies and assumptions,

whose purpose is “to define a range of credible futures

against which alternative policy options can be

tested. ”19 The report’s estimates of total world reserves

IT~ontlcf, Carter, and petri, op. cit., p. 65, table 61; these projections, like

those for production, appear unrealistic in view of subsequent events.

ISA. p. Cafier and A. K. Sire, “An EnerW Conservation Scenario for the

World Model,” prepared for the Bureau of International and Economic Policy,

U.S. Department of Commerce, November 1977, p. 2.

1~J. Pearson, et al., “Energy Projections,” In ThE Global 2(XM Report to the President (Washington, D. C.: U.S. Council on Environmental Quality and Department of State, 1980), vol. 2, p. 173.
App. C—Energy Projections . 113

of fuel minerals come from figures prepared by the

World Energy Conference, the Congressional Research

Service, and the U.S. Geological Survey; but these

reserve estimates are not used as inputs to the production or consumption forecasts, The short-term projections (1975-90) were prepared by the Department of

Energy’s Energy Information Administration (EIA)

soon after its creation in late 1977, using two similar

computer models: the Project Independence Evaluation

System (PIES) for U.S. figures, and the International

Energy Evaluation System (IEES) for global figures. The

forecasts made for the Global 2000 study use the study’s

low, medium, and high growth-rate assumptions for

population and GNP, but the energy projections were

not used as inputs for other sectors; in essence, “GNP is

implicitly treated as independent of the energy

market .“2

°

Both PIES and IEES are equilibrium market simulations, in which producers compete to satisfy short-term

world demand and the entire global energy system is assumed to act in such a way as to minimize total costs

without regard for the future value of the resources. A s

a result, both models assume that unlimited world oil

supplies will be available at prices (determined outside

the models) that rise from $13/barrel in 1978 to

$23/barrel in 1990. The models contain detailed representations of the OECD countries and the major fuels,

but they contain no representation of resource depletion or political factors and only simplified demand

equations for the growing LDC market. The models

assume that coal and nuclear will substitute for oil in

response to price elasticity, but they impose no limit on

the creation of new generating capacity in the utility

sector. (The number of new generating plants is determined by expert judgment rather than price, a procedure that may be reasonable for forecasts through 1990:

the lead time for developing new productive capacity is

so long that current investment plans are a good guide

to what will happen in the next 10 years.)

EIA was unwilling to extend its IEES forecasts beyond 1990, and Global 2000’s midrange energy projections (1985-2000) are based instead on four different

studies representing diverse philosophical and methodological approaches, as well as different assumptions

about future demand growth and fuel substitution (see

fig. C-1 1):

● the Workshop on Alternative Energy Strategies

study, based on estimates from independent national experts, predicts global supply-demand

“gaps” of 15 million to 20 million barrels of oil per

day by 2000 and examines the consequences of

choosing either nuclear or coal as the major replacement;

lov~nder~’erf, op. cit., VOI. 2, p. 571.

●

●

●

✎

the World Energy Conference study, after examining different assumptions about world oil reserves

and recovery rates, predicts that global production

will peak at a ceiling of 82 million to 104 million

barrels per day around 1990, and emphasizes coal

and hydropower as replacements when oil supply

falls short of demand;

the Stanford Research Institute (SRI) model of the

U.S. energy market through 2020 (see below),

which foresees a relatively plentiful energy supply

based on coal but assumes perfect consumer foresight about future shortages and prices; and

a Brookhaven National Laboratory/Dale Jorgenson Associates study of the U.S. energy market,

which emphasizes conservation and coal as well as

the development of nuclear and renewable sources.

in general, to the degree that these diverse projections

can be compared, Global 2000 suggests that a rapid increase in the supply of energy will be needed through

the end of the century, even with a declining rate of

economic growth. Demand growth will be moderated

only by price increases, and significantly higher oil

prices will be needed to encourage substitution. Because

petroleum production capacity is increasing more slowly than demand, a supply-constrained market is likely

before 1990. Furthermore, because the rate of petroleum reserve additions is falling, world production is

likely to peak between 1990 and 2010 and gradually decline thereafter. As a result, “a world transition away

from petroleum dependence must take place, but there

is still much uncertainty as to how this transition will

occur. ”21

The findings suggest a considerable potential for coal

and natural gas beyond 2000, but the short-term projections indicate that nuclear power will expand far faster

than any other source, particularly if oil prices continue

to increase. The potential contribution of solar and

other renewable sources is rather limited at best. However, there does appear to be a substantial long-term

potential for “aggressive, conservation-induced reductions in energy consumption. ”22 The alternative energy

systems examined in the different studies indicate that

options do exist, however limited, and that current

decisions about the future fuel mix will have increasingly significant impacts after 2000.

Other Energy Projections

Concurrently with its PIES/IEES short-term global

projections, EIA also produced a set of long-term (1978-

2020) energy projections for the United States using the

Long-term Energy Analysis Program (LEAP), an upzlThe Global 2000 Report to the Pres;dent, vol. 1, P . 27.

22pear~On, et al., Op. cit. I p. 161.
114 ● Global Models, World Futures, and Public Policy

Figure C-Il.–Comparisons of Global 2000 Projections of World and U.S. Energy Consumption and Supply

Mix, 1975-2000

6 0 0

500

400

a

; 300

F

200

100

0

150

135

120

105

90

2

? 75

b

60

45

30

15

0

(A) World projections Nuclear

Coal

Gas

Oii

tar

Low Medium High Low Medium High WEC case H-5 WAES cases D-7, C-2

1975 1965 1990 2000 2000

(B) U.S. projections

Low Medium High Low Medium High WAES WAES BNL/ SRI

Other

Nuclear

Coal

Gas

oil

case case DJA base

D-7 C-1

1975 1965 1990 2000

SOURCE: The Global 2000 Report to the President.
App. C—Energy Projections 115

dated version of the SRI -energy model (see above) .23

This forecast, which was not reported in the Globa l

2000 Report, describes the energy requirements for continued economic growth in the United States during a

transition from oil to coal and nuclear power. It

assumes the rapid development of shale oil and synfuels

to replace oil imports, as well as an ambitious level of

energy conservation: a 48-percent reduction in the energy-consumption-to-GNP ratio, based on improvements considered possible on the basis of known technologies, with almost no increase in residential consumption and most of the demand growth coming in

the industrial sector. It also assumes that the rate of real

GNP growth will decline to 2.4 percent by 1995. Despite

these assumptions, the SRI model’s projections would

require U.S. coal production to triple by 2000 and

reach 5 times present levels by 2020; nuclear power

would increase eightfold by 2000 and reach 16 times

present levels by 2020. This vast expansion is required

partly to satisfy growing industrial demand, but primarily because coal and nuclear must grow from 23 percent

of primary energy to 72 percent in order to replace

depleted U.S. oil and gas. (This energy supply mix corresponds roughly to the “fast-nuclear scenario” that

was tested with WIM; see figs. C-9 and C-10, above).

EIA’s Office of Energy Information Validation has

conducted an evaluation of this 1978 forecast, and their

report cites a number of factors that might modify these

projections :24

. statistical studies of U.S. oil reserves suggest that

there may be only half as much domestic oil

available as previously estimated by the U.S.

Geological Survey and assumed in the forecast;

. recent studies show that the actual costs for shale

oil and synfuels may be two to four times greater

than earlier engineering estimates, and that there

are severe physical constraints on the development

of a massive synfuels industry;

 the forecast assumes that extraction costs for coal

and uranium will not increase significantly over

time due to depletion or scale, and that environmental consideration will not impede the development of these industries;

 the forecast does not address the capabilities of the

relevant industries and therefore fails to consider

potential bottlenecks and constraints, including

the need to build up the U.S. railroad system, a potential shortage of engineers and deep-seam miners,

the risk aversion and capital constraints of potential consumers, and the potential efforts of State

2~Energy Inforrnatlon Admlnlstratlon, Anntud Re/mt [O Confless 1978 (Washington, D. C.: Department of Energy, 1979), vol. 3.

Z+ C) fflce of Energy Information Valldatlon, Amd?so @l[t> RePorr: 1978 LongTer-rrr Forecasts and Methodolov (Washlrrgton, D. C.: Energy Information Admlmstratlon, 1981).

●

governments to prevent rapid expansion in the

West; and

more recent data, and more realistic technical assumptions, suggest that electric cars can capture 50

percent of the U.S. personal-transportation market

by 2000—despite optimistic oil prices and moderate

consumer prejudice—if automobile companies can

acquire enough capital to keep up with the potential market.

These and other problems are discussed in more recent forecasts by EIA, which continues to refine, validate, and expand its energy modeling capability; but despite a number of structural changes in the models, the

1980 forecasts are based on many of the same assumptions as the Global 2000 projections.25 Midterm global

energy projections (1975-95) now come from an improved version of the IEES model that incorporates the

annual oil production capacity forecasts provided by

DOE’s Office of International Affairs and the oil price

forecasts generated by the Oil Market Simulation

(OMS) Model. OMS assumes that OPEC will raise oil

prices only when world demand requires them to use almost all of their annual production capacity, and it also

reflects lower rates of economic growth. Oil prices are

projected to reach $50/barrel in constant mid-1979 dollars by 1995, with almost no increase in oil consumption between now and then. This projection assumes,

however, that all OECD nations will reach their official

conservation targets and that higher prices will lead to

further substitution away from oil. Coal is projected to

provide a slightly larger share of total energy demand;

nuclear projections are lower due to lower estimates of

the speed with which new reactors will be built.

Long-term U.S. energy projections (1975-2030) now

come from LEAP, a descendent of the SRI model that

EIA used in 1978. LEAP still assumes an unlimited supply of oil imports, at the OMS/IEES world price, but it

predicts that the United States will require far fewer imports over the next 40 years. This is due in part to conservation and in part to massive deployment of shale oil

and synfuels: U.S. oil consumption is cut in half between 1978 and 2020; and of the remaining demand for

liquid fuels, coal-based synthetics provide 50 percent,

shale oil 27 percent, and conventional oil only 23 percent. This assumes that the goals of the Synthetic Fuels

Corp. (1.5 million barrels per day by 1990 and 3.0 million by 1995) will be met or exceeded, and that, there

will be virtually no constraints on the expansion of the

synfuels industry after 1995. This projection also accepts with little modification the current engineering estimates of synfuel costs; sensitivity tests, using capital

costs twice as high as these estimates, lead to an inIJEnergy Information Acfmlnlstratlon, 1980 Annwd Re@rt [O congress (washmgton, D, C.: Department of Energy, 1981 ), 1,01, 3.
116 ● Global Models, World Futures, and Public Policy

crease in U.S. oil imports through 2020. LEAP also assumes an upper limit to the net contribution of renewable energy sources (hydro, wind, biomass, small-scale

solar, etc.) of about 6 percent of U.S. demand in 2020.26

Strengths and Weakness of Energy

Projection Techniques

All of these models are generalized analytic tools that

can accommodate a variety of assumptions and serve a

variety of applications. Some of them have features that

limit their usefulness in examining the future of the global energy system, but these features were usually appropriate to the models’ original purposes. World 3, for

instance, was intended to give a generalized description

of the long-term behavior of the entire global system except agricultural land; as a result, it treats energy only as

part of a highly aggregated “nonrenewable resources”

sector and makes no specific projections of energy supplies or prices. LAWM explicitly excludes all resource

constraints except agricultural land; it assumes that energy and other resources will be available and concentrates instead on how they should be allocated in order

to satisfy basic human needs. UNIOWM and Global

2000, because of their shorter time horizons and the

absence of resource depletion in their structures, are illsuited for examining the long-term effects of resource

depletion. In addition, UNIOWM concerns itself primarily with development targets and trade balances—although it treats the energy sector in detail, it

does little more than tabulate resources as they are consumed in meeting those goals. Global 2000’s IEES projections represents major producers and consumers of

oil in detail, but they contain considerably less detail for

other fuels or for the rapidly growing Third World market. Furthermore, Global 2000’s energy projections do

not interact with other sectors and therefore do not reflect competing demands for energy resources. WIM

strikes a balance between detail and generality, and its

policy levers provide more flexibility than the other

models for testing alternative energy futures and different producer and consumer behavior. Because of its

complexity and lack of documentation, however, the

~sFor further assessment of the technologies and assumptions involved in

these projections, see the following OTA reports and technical memoranda:

Nuclear Pmhferarim mrd Safeguards, OTA-E-48 (June 1977); Gas Po[entud From

Devoruan ShaIes o~rhe APp&chum Basin, OTA-E-57 (November 1977); Enhanced

Od Recotq Potenttal m [he United States, OTA-E-59 (January 1978); A Technolo~ Assessment o] Coal Slu~ Ptpelmes, OTA-E-60 (March 1978); APp/imtion of

%km Technolo~ m Today’s Ener~ Requirements, OTA-E-66 (June 1978); The

Dwect Use of (20u1: Prospects and Probkrns OJ Production and Combu.men, OTAE-86 (April 1979); Gasafui A Techmczd Memorandum, OTA-TM-E-1 (September

1979); The Future o/ Lu@ied Natural Cm Imports, OTA-E-110 (July 1980);

Energy From Biologmd Processes, OTA-E-124 (July 1980); Wodd Petroleum Atai/-

ahhty 1984) -2(3(XI: A Techmczd Memorandum, OTA-TM-E-5 (October 1980); Nuclear Pouerpkmt .Randard[zation: Light Water Reactors, OTA-E-1 34 (April 1981).

value of WIM’s conclusions may not have been fully

tested or understood.

Factors Affecting the Accuracy and

Reliability of Energy Projections

These differences in purpose and technique have an

influence on the projections generated by the different

models, but the accuracy and reliability of these projections are also affected by a number of factors and uncertainties on which there is presently little general agreement or understanding. Among these factors are the

following:

 total resource reserves and future prices;

● population and GNP growth rates;

● conservation;

● Third World energy choices;

 development bottlenecks; and

● future energy breakthroughs.

Total Resource Reserves and Future Prices

Table C-1 shows the estimates of total world reserves

of conventional energy resources on which the different

models are based. There is little agreement among the

models on the size of these reserves or, how they should

be measured, and even less agreement on the costs of

extracting them. In general, however, those models that

consider prices show that lower grades and unconventional sources will become available as prices rise. Extraction cost will be higher for these low-grade deposits,

however, and recovery rates will be significantly lower.

Consequently, a steadily larger percentage of capital

will have to be allocated to obtaining resources, leaving

less capital for investment in other sectors. Improvements in extraction and processing technologies might

modify this trend, but recent studies have shown that

capital costs for shale oil and coal synfuels are likely to

be higher rather than lower than previously estimated.

Several of the models predict an energy future based

on abundant reserves of coal, but estimated total reserves of coal have increased little since 1913, when

they were estimated at 8,000 billion metric tons.27 Of

the 9,000 billion metric tons now generally agreed

upon, 50 percent or less is recoverable with current

techniques, and two-thirds are reserves claimed by the

U.S.S.R. that some experts treat with skepticism. Until

recently, however, there has been little incentive for

further exploration; new deposits may soon be discovered due to renewed interest in coal.

Z?H. S. D. Co]e (eCl. ), Mo&/s of Doom: A Crmque oj the Limus to Grwth (NeW

York: Universe Books, 1973), p. 98; on the other hand, these reserves have

seemed so large relative to expected demand that there has been Ilttle incentive

for increased exploration.
App. C—Energy Projections ● 117

Table C-l .—Energy Resource Reserves as Described by Five GLobaL Modeling Studies

Petroleum Natural gas Coal Uranium oxide

(billions (trillions of ft) (billions (millions

Global model Year of barrels) of metric tons) of metric tons)

World 3 . . . . . . . . . . . . . . 1972 630 identa

1,000 ident 8,600 ident N/A

1,200 hyp/specb10,000 hyp/spec 6,600 hyp/spec

WIM . . . . . . . . . . . . . . . . . 1974 667 proven 285 proven 4,200 recoverable Implicitly

2,300 ultimatec

8,400 totaldunlimited

LAWM . . . . . . . . . . . . . . . 1976 1,800 total 103 total 9,640 total 0.76 @ $10/lb; practically unlimited @

$20/lb

UNIOWM . . . . . . . . . . . . . 1977 1,555 total N/A 9,080 total NIA

Global 2000 ......, . . . . 1978 646 proven 2,520 proven 786 proven 1.661 @ $10-$35/lb

2,100 total 5,984 hyp/spec 12,682 total 2.794 additional at

higher prices

N/A = not available.

aldentified resemes include both proven and inferred reserves, including deposits that are currentlY su~conomic.

bHypothet~cal resources include undiscovered deposits in known districts; speculative resources include undiscovered deposits in districts nOt PreSOntlY known to

contain deposits.

cljltimately recoverable reserves; total reserves greater.

dAssuming that so percent of total is recoverable.

SOURCE: Office of Technology Assessment.

The rate at which new petroleum reserves are being

discovered, on the other hand, appears to be falling. In

addition, many geologists are pessimistic about the

prospects for discovering vast new reserves of natural

gas at greater depths than have been explored thus far.

There is as yet no indication of diminishing returns in

uranium exploration. Current reserves (including speculative reserves at much higher prices) represent less

than 50 years of total world energy supplies at current

consumption levels (assuming the use of conventional

nuclear reactors), although with breeder reactors these

reserves would last far longer. Technologies for extracting huge amounts of uranium from granite or seawater

remain speculative, and DOE has at times taken the position that uranium is scarce enough to justify the deployment of the breeder reactor.28

Finally, none of the models deals with potential

reserves of lithium. Despite the LAWM team’s confidence that fusion power will solve the world’s longterm energy problems, successful development and

widespread deployment of this technology remains

speculative (see below).

Population and GNP Growth Rates

Appendix A shows that there is general agreement

among the projections of world population in 2000,

however much the projections differ in the longer term.

There is far less agreement among the models on future

rates of economic growth. Both factors influence the

ZW, S, Atomic Energy Commlsslon (USAEC), Proposed FiruI[ Emwonmend

Statement Llqwd Metal Fast Breeder Program (Washlngton,D.C.: National Techmcal Information Service, 1974).

energy projections, although

tions is somewhat greater.

The standard run of World

the effect of GNP projec3 assumes that population

will continue to grow exponentially. It also assumes

that the recent rate of world economic growth, 1.7 percent per year measured in real GNP per capita, will also

continue through 2000. These two factors quickly force

the model against its resource limits, and the global economic system collapses sometime after 2010 due to rising extraction costs. Sensitivity tests indicate that policies designed to slow population growth or limit industrial expansion could delay (but not prevent) this

collapse. Critics have claimed, however, that World 3

underestimates the ability of the free-market system to

anticipate and thereby prevent a possible catastrophe,

although they have not explained how it would be possible to prevent such a catastrophe in the absence of

specific new energy sources .29

UNIOWM assumes a slightly faster population

growth and a much higher growth rate for gross product per capita—3.O percent per year even in its most pessimistic “business as usual” scenario, and as high as 6.0

percent per year for the LDCs in other scenarios. The

model does not examine the long-term sustainability of

these growth rates, however, and the principal reason

why UNIOWM does not predict a catastrophe is that it

stops at 2000.

LAWM’s population projections are higher still, particularly in the longer term, and its optimization procedures impose high investment rates that lead to economic growth rates of 4.0 percent per year for the devel2QCole, op. cit., p. 66
118 ● Global Models, World Futures, and public policy

oped regions and up to 6.0 percent for the LDCs

through 2000, gradually declining thereafter to 2.0 and

3.0 percent, respectively. These growth rates do not

lead to an energy-related catastrophe because LAWM

contains no representation of resource availability.

WIM assumes relatively ambitious population control

in many of its scenarios. Even so, it shows that population will not stabilize before 2050 under the best of conditions. Economic growth varies considerably among

scenarios, but in at least one policy test the economic

growth rate (supported by investment aid from developed nations) remains at 7.0 percent in Latin America

and 8.2 percent in South Asia through 2025.

Global 2000’s short-term projections test three different sets of population and GNP growth assumptions,

in order to illustrate a range of possible futures. Economic growth rates are highest for OPEC and mediumincome LDCs, somewhat lower for the developed nations, and lowest for the low-income LDCs and Communist bloc. In all cases, economic growth slows

significantly after 1985.

Conservation

Total demand for energy can be represented as the

product of three variables: population and GNP per

capita (see above), and the ratio of energy consumption

to GNP. Conservation can reduce this latter ratio (and

thus the total demand for oil and energy at any given

level of population and GNP per capita) in either of two

ways: 1) by improving the efficiency with which energy

is used (e.g., through residential insulation or improved

industrial machinery); or 2) by improving the way in

which the overall energy system matches energy sources

with particular end uses (e.g., a large-scale coal or nuclear generator is more appropriate to the energy needs

of a major industrial city than to those of a rural village,

which might well be better served by a small-scale wind,

hydro, or solar source). Biomass, dispersed solar, and

other small-scale alternatives can contribute to the second form of conservation, but conservation of conventional fuels will depend primarily on the response of

large-scale industrial and utility consumers.

Many economists believe that higher prices are an efficient mechanism for inducing this kind of conservation, and economic models generally assume that demand will fall in response to future price increases to

the same degree that it has in the past. For example,

pre-embargo studies of energy demand in the United

States generally showed little responsiveness to price:

when prices were low, other variables (such as the price

of automobiles or new capital equipment) are more important to consumers than energy costs. As prices first

began to rise rapidly, there was a large initial demand

response due to “housekeeping” conservation and

other simple measures, but the long-term response was

expected to be slower due to the slower conversion to

more efficient automobiles and capital equipment.

However, the response to the 1979 oil price hike suggests that long-term elasticity will be greater than many

people had expected. Conservation has been greater—

and demand growth slower—than was previously foreseen, even when the effects of economic slowdown are

eliminated. In addition, some economists claim that

economic models based on the United States do not reflect the full global potential for long-term conservation

through more efficient capital equipment. JO Other

studies, however, have shown that technological progress in some critical industrial sectors requires an increasing use of energy per unit of output .31

The World 3 standard run assumes that resource consumption per capita will continue to be a fixed function

of GNP per capita. This assumption implicitly rejects

any significant potential for conservation and—

although reasonable when the results were published in

1972–it gives the standard run a pessimistic bias that

has been contradicted by subsequent events. However,

World 3’s “recycling” run (fig. C-5) does show that conservation, in combination with improved exploration

and extraction technologies, can have a significant impact on resource depletion.

WIM, published after the 1973 embargo and price

hikes, does assume some conservation in response to

higher prices. In the case of oil, a l. O-percent increase in

price is converted into a direct decrease of 0.225 percent

in consumption, plus additional decreases due to substitution. The authors conclude, however, that even optimistic assumptions about conservation will not prevent

a substantial increase in total energy demand—the

world will need to develop alternative sources, either

nuclear or solar.32

LAWM assumes that the global energy system will

make more efficient use of different energy sources in

the future, and that technological progress will increase

the productivit y of the capital goods sector by 1.5 percent per year (a rate that would double the output-toinput ratio in 47 years). For the most part, LAWM’s optimism about the availability of energy is based not on

conservation but rather on unlimited supplies of nuclear power, including the deployment of fusion technology within 50 years.

UNIOWM also assumes that technological progress

will change the energy requirements of every sector.

The documentation does not reveal, however, the pre-

~OR. S. Pindyck, “The Characteristics of Demand for Energy,” in J. c. Sawhill

(cd.), Ener~ Cmsemmon and Pub//c Pohcy (Englewood Cliffs, N.J.: Prentice-Hall,

1979), pp. 38, 39.

IID,W. Jorgenson and B.M. Fraumeni, “Relative Prices and Technical

Change,” prepared for AEA Annual Meeting in Denver, Colo., Sept. 5, 1980.

IZMe~arC,vic and Pestel, OP. Cit., P. 136.
App. C—Energy Projections . 119

cise value of these assumptions or how sensitive the results are to them.

Global 2000’s short-term projections assume energy

conservation in response to price ($23/barrel in 1978

dollars by 1990), above and beyond the official conservation targets of various national governments. The

midterm projections extrapolate from engineering and

economic forecasts for 1995 and result in a final level of

conservation—48 percent reduction in the energy-toGNP ratio by 2020–that indicates the technological

limits of what can be achieved. More recent DOE forecasts of conservation are based on higher prices

($50/barrel in 1979 dollars by 1995) and on detailed

analyses of specific technological possibilities in every

end-use sector, many of them derived from engineering

studies conducted by the Oak Ridge National Laboratory. These and other studies, combined with the demand response to the 1979 oil price hikes, suggest that

long-term conservation in the 50-percent range is in fact

a reasonable expectation.

Third World Energy Choices

None of the models except Global 2000 deal with the

firewood crisis, which may have severe social and envi--

ronmental repercussions in many Third World countries. Industrialization and economic development in

these countries will also require electricity, however,

and all of the models assume, at least implicitly, that

nuclear power will be widely deployed in the future.

This common assumption is of political as well as economic interest, particularly in the case of LAWM and

UNIOWM. Both of these models represent Third

World attempts to chart a future in which the gap between the rich and poor nations is narrowed through

local and international efforts to accelerate development and increase industrial output. Nuclear power offers developing nations an alternative to their current

dependence on fossil fuels, whether to preserve their domestic resources or to reduce their energy-related trade

deficits. There is evidence that, whether or not the

United States and other OECD nations finally accept

the hazards of nuclear power, many Third World nations are likely to accept them on a very large scale, and

very soon, for lack of a credible large-scale alternative.

Business commentators already speak of “the nuclear

power boom getting under way in the Third World,” including such nations as Mexico, Egypt, Korea, Taiwan,

and the People’s Republic of China. 33 Conventiona l

enriched-uranium reactors in such countries represent a

potentially lucrative market for the U.S. and European

nuclear industries. Many Third World governments are

also investigating natural-uranium technologies that

‘]H. Rowen, “Nuclear Reactors: Fear of Losing Export Race,” Wa.slungton

Post, May 17, 1981, p. K1.

would allow them to exploit domestic uranium rather

than depending on Europe or the United States for expensive enriched fuel. Argentina, Pakistan, India, and

South Korea already have operational reactors based

on a Canadian natural-uranium, heavy-water system.

Mexico’s recently published National Energy Program

calls for the construction of as many as 16 such reactors

to meet a tripling of demand for electricity by 2000, despite that nation’s abundant oil and gas reserves .34

The WIM “fast-nuclear” scenario, however, foresees

an energy future based not on conventional fission but

rather on the more efficient “breeder” reactor, which effectively produces more nuclear fuel than it consumes.

As a result, breeder reactors might produce 60 to 100

times as much energy from a pound of uranium as do

conventional reactors. Both past U.S. Government

studies and current expert opinion suggest that, given

the limited size of world uranium reserves (even including speculative reserves), nuclear fission may not be

able to provide a large-scale, long-term contribution to

the world’s energy supply without the widespread use of

breeder reactors. 35 Because breeders would also produce

large quantities of weapons-grade plutonium, however,

their deployment throughout the Third World could

also pose a serious threat to domestic and international

security.

Development Bottlenecks

Some economists would argue that the world’s major

energy problem is not finding adequate resources, but

rather overcoming the bottlenecks in getting those resources to market. Three of the models considered here

—World 3, LAWM, and UNIOWM—implicitly assume

that there will be no major bottlenecks in the exploitation of energy resources. Although WIM’s authors

point out the numbers and speed with which reactors

would have to be built for the “fast-nuclear” scenario,

they do not report any further analysis of development

bottlenecks. DOE’s models for Global 2000 involve a

serious attempt to address problems of timing where

they involve liquid fuels and the transition from oil to

coal and nuclear power. However, DOE’s own evaluation of the 1978 long-term forecasts points out that the

models describe the requiremenrs for U.S. energy independence rather than the capabilities of the relevent

industries, as well as making questionable assumptions

about investor foresight, extraction costs, and environment constraints (see above). Further consideration of

these and other factors suggest both that considerable

cooperation between Government and industry may be

needed to reduce the impact of these potential bot-

‘+C. D]ckey, “Scenic Mexican Reactor Site Entangles Indians, Umons, Natlonalwts,” W’a.shlngton Post, May 18, 1981, p. A12.

J5see, for ~xample, USAEC, oP cit.
120 ● Global Models, World Futures, and Public Policy

tlenecks, and that other energy sources and mixes

should be examined more carefully.

Future Energy Breakthroughs

Finally, the accuracy and reliability of energy projections will also be affected by the assumptions they make

about the successful development and deployment of

entirely new energy sources. Some such systems are implicitly assumed by one or more of the models, but

others are not foreseen by any of them.

The widespread deployment of fusion technology, for

instance, could possibly invalidate the pessimistic assumptions of Limits to Growth if the supply of lithium

were large enough or if deuterium-deuterium fusion

were developed. The LAWM study bases its exclusion

of resource problems in part on the expectation that fusion power will in fact be deployed within 20 to 50

years. Commercial-scale fusion power remains purely

speculative at present, however. Although fusion researchers hope that a commercial fusion reactor design

might be possible by 1990, until then all claims about

engineering problems, capital costs, and net energy production will also remain speculative. In addition, none

of the models discusses the potential world reserves of

lithium. Other sources, however, have estimated that

minable U.S. lithium reserves alone are worth over

160,000 quadrillion Btu prior to conversion losses, or

about 640 years total world energy supply at 1976 consumption levels.3b

The WIM solar scenario envisions a long-term energy

future based on centralized solar power in the form of

huge “solar farms” in the deserts of the Middle East, to

be financed by OPEC oil money .37 With higher energy

prices due to scarcity, the capital costs of such facilities—which the authors estimate at $20 trillion to $50

trillion in 1974 dollars—might become bearable; solar

might even prove cheaper than nuclear. Theoretically it

would be possible to supply all of the present U.S. demand for electricity from a “farm” of solar cells the size

of Massachusetts, but DOE studies claim that there are

not enough economically feasible sites in the United

States for centralized solar to make more than a marginal contribution to U.S. energy supply in the foreseeable future.38 On the vast scale foreseen by Mankind a t

the Turning Point— 1 percent of the world’s land surface–such facilities would involve scientific, engineering, and planning problems that are beyond the current

state of the art. Furthermore, some critics question

MS. s. penner (e&), ~uc~~r Errergy and Ener~ Politics (Reading, Mass.: Addison-Wesley, 1976), p. 562.

3TMesarovic and Pestel, op. cit., pp. 139-141.

J8DoW~Clc po~im Re~lew, O/ SOIUT Ener~ (Washington, D. C.: Department of

Energy, February 1979), table 8; DOE’s long-term forecasts therefore assume an

a priori hmit of less than 1.0 Quad central solar by 2020.

whether the energy produced by these solar farms

would be greater than the total amount of energy involved in building them, maintaining them, and distributing the hydrogen and electricity they would produce, Since this scenario was not subjected to rigorous

testing with the model, it remains speculative.

A number of other groups have suggested another alternative: solar-power satellites orbiting in space and

beaming power down to earth in the form of microwaves or lasers, The National Aeronautics and Space

Administration (NASA) has produced detailed designs

for such a system, for which it envisions commercial operation by 2000, or about 20 years before fusion is predicted to become commercial. NASA studies have indicated that marginal costs and net energy production

will be comparable to present energy sources, although

these findings are highly controversial. DOE claims

that potential receiving sites are probabl y more than

adequate to supply several times the present level of

U.S. electricity demand, since more energy can be received per acre than with central solar and without necessarily prohibiting agricultural uses of the sites, although the effects of low-level microwave radiation are

problematic. Research and development costs for the

NASA design, however, would be over $100 billion,

and the cost per power station would be somewhat

higher than that of conventional fission reactors regardless of the scale of production. An alternative design developed at Princeton Universit y involves less

R&D and employs small modular processing units

whose cost and performance could be tested prior to

any commitment to large-scale deployment. The Princeton design is somewhat riskier than NASA’s, but it

might be possible to deploy it sooner and to reduce energy costs substantiall y as the scale increases. This

might in turn make it possible to sell energy to the

LDCs at prices much lower than would be possible with

breeder reactors.39

J~Further discussion and analysis of this technology can & found m the following sources: .!iatehte Power System Concept Development and Evahatlon Program Reference System Report, DOE/ER-0323 (Washington, D. C.: U.S. Department of Energy and National Aeronautics and Space Administration, October

1978); The .Schr Power Satelhte Concept: The Past Decade and the Next 13ecde,

NASA/JCS-14898 (Washington, D. C.: National Aeronautics and Space Administration, July 1979); R.A. Herendeen, T. Kary, and J. Rebltzer, “Energy

Analysis of the Solar Power Satellite,” hence, vol. 205 No. 4405, Aug. 3, 1979,

pp. 451-454; Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, Space Systems Laboratory, Extraterresticd Processing and

ManuJcsctunng of Large S@ce Systems, NASA contract CR-161293 (Washington,

D. C.: National Aeronautics and Space Administration, September 1979), vol.

3, pp. 47-49; Of%ce of Energy Research, Solar Power Satellite Project Division,

Program Assessment Report, DOE/ER-0085 (Washington, D. C.: U.S. Department of Energy, November 1980); and Office of Technology Assessment, U.S.

Congress, solar Pou,er Satellites, OTA-E-144, (Washington, D. C.: U.S. Government Printing Office, August 1981).