This paper argues that there has

been insufficient analysis of long-

term forecasting methods for

resource demand at national level,

compared with the attention

devoted to supply factors. It

suggests several important issues

which need to be understood about

the nature of demand projection

and its pitfalls. Some significant

and widely used resource demand

models are summarized, all of

which are particularly vulnerable to

the uncertainty of macroeconomic

modelling.

The author is with the Department of

Geography, University College

London, Gower Street, London,

WC1 E 6BT. UK.

The author thanks his supervisor, Mr Gerald Manners, for patient assistance, in general and during the writing of this article. Part of the research for the paper has been supported by the Social Science Research Council.

’ L.L. Fischman, ‘Forecasting the demand for minerals’, in Proceedings of the Council of Economics of the American Institute of Mining, Metallurgical and Petroleum Engineers, AIME, New York, 1968, pp 1-l 3. 2 Resources for Freedom: A Report to the President by the President’s Materials Policy Commission, US Government Printing Office, Washington, 1952. 3 R.N. Cooper, ‘Resource needs revisited’, Brookings Papers on Economic Activity, No 1, 1975, pp 238-245.

Long-term resource demand

Models for projection

Stephen Wright

Most recent discussions on resources and the world economy have been concerned with supply - the physical, economic and political availability of particular materials. Of the references to demand, many are in the context of conservation and recycling. In general, it has simply been assumed that demand will expand at rates comparable to those of the recent past, fuelled by the expected rise in per capita incomes. However, while an expansion may not be in serious doubt, future rates of growth cannot be accurately estimated merely by reflection on past trends.

The preoccupation with supply analyses is surprising for two reasons. First, demand forecasting is rather important - both to wide issues like the scarcity-of-resources thesis, and to less ambitious matters such as calculations of required investment by individual mining companies. Second, demand forecasting might be thought easier and more comprehensible to social scientists than speculation about supply, which bears as much on geostatistics, Lasky rules and the like as on more conventional social questions.

There are several methods of projecting consumption of materials. This article reviews the most common of the quantitative approaches, and so is mainly about forecasting techniques - described somewhat lightheartedly by Fischman as ‘the cloaking of judgement in cabalistic formulations of a numerical variety’.’

Many of the projections take their style from the US Paley Commission Report of the early 195Os,* which was the first thorough assessment on a national scale of likely demand trends and supply availabilities in resources. Paley forecast too small a growth in income and too large a growth in resource demand, Drimarily because of a failure to Glow-for sufficient fall in ratios- of maierial input economic output. However, the report has been influential, methodology if not in prescriptions for policy.3

Demand forecasting for resources

to in

Virtually all resource consumption forecasts derive in some fashion a coefficient for resource use related to income. A number of issues,

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Long-term resource demand: Models for projection

however, complicate the calculation of what might otherwise seem a relatively straightforward coefficient.

There is considerable ambiguity over the meaning of ‘demand’. It does not represent ‘needs’ in an absolute sense nor even ‘demand’ in the usual economic interpretation (desire and ability to pay). The observed past is an amalgam of supply and demand forces, and this constitutes what is forecast as well. The assumption for the future is usually that consumption will reach certain levels given a degree of supply stringency comparable to that of the past. Demand projections are often used to test for likely supply difficulties. If any do arise in such an exercise, the demand forecasts will almost certainly be systematically wrong.

Forecasting is usually predicated on an assumption of i-0 major changes in the resource market environment -it is ‘surprise-free’. The rationale for this is that resource markets operate in a particular political, institutional and technological setting which provides the data base for a quantitative model. Any major change in this setting would invalidate trends derived from it. This implies that it is particularly difficult to forecast from a period of dislocation in mineral markets.

A difficulty in applying projections to precise future years arises through the coefficients used, which almost always refer to trends rather than year-by-year outcomes. Characteristically, they will yield results for years of average demand pressure - which is especially relevant when it is recognized that resource consumption often fluctuates more violently than economic activity in general. This leads to difficulties in the estimation of a model, compounded by the fact that data are not usually very good (including, for example, the data series for stocks or for prices).

One consideration occasionally treated quite brusquely in forecasting is that of the exogenous forecasts which drive the projection model. Highly sophisticated consumption forecasts are sometimes built on rather naive assumptions as to economic growth - often straight-line extrapolations of previous trend. Income forecasts are as important as the coefficients used for a resource model, and often more so for those formulations in which resource consumption depends highly on income. It can be very difficult to untangle the effects of income growth from those of substitution between materials, technical progress and taste change (which may additionally be major determinants of income growth). Most models also are fitted to post Second World War data, a period during which iricomes have consistently risen quite fast - so we have no experience of falling or slowly rising income to estimate the effects on material demand.

Another important point is the attitude adopted to uncertainty and risk. In a formal interpretation, some of the variation could be examined by probability analysis, but such an approach is rarely taken in long-term forecasting, mainly because uncertainty dominates actuarial-type risk. Any purely mechanical forecast is unlikely to be acceptable - some degree of judgment is necessitated by lack of knowledge as to which of the feasible trends will occur. If there were no stability in social relationships, only persona1 judgment would be useful, but some rules are seen to apply. This points to the utility of a balance of theory and judgment, or, for material demand forecasting, to econometrics and guesses. The differing balance chosen for any

262 RESOURCES POLICY December 1977

4 Usually, the terms ‘forecast’ and ‘projection’ are used interchangeably, but there is formally a difference. A forecast is essentially a calculation of what is expected to occur given certain conditions (and an unconditional forecast would be a ‘prediction’). A projection is the working-out of the implications of certain logical assumptions, without any belief that these assumptions are the ones that will necessarily hold in the future. 5H.H. Landsberg. L.L. Fischman, and J.L. Fisher, Resources in America’s Future: Patterns of Requirements and Availabilities 1960-2000, Johns Hopkins Press for Resources for the Future, Baltimore, MD, 1963. 6The model first derives the major economic aggregates. Population change is exogenous, enabling the ‘plugging-in’ of various rates of demographic change. With participation and labour productivity rates, Gross National Product can be determined (‘supply’). This calculation is checked against projections of expenditure on personal consumption, investment and government activity (‘demand I’). and economic sector projections of construction, services and goods (‘demand II’).

The economic aggregates are then used to produce projections of intermediate goods - food, clothing, construction, transportation, durables, containers, paper, defence and heat and power. Each of these is related to an economic aggregate either by stable or by trending coefficients (often obtained by ‘judgment’). The intermediate goods provide the base for calculations of demand for key materials - crops, timber, water, mineral fuels, metals and chemicals. The calculation usually takes the form of an output-input coefficient, mainly extrapolated by trend from historical data, with modification on the judgment of experts.

’ L.L. Fischman, ‘Long range minerals modelling: An eclectic approach’, in Mineral Materials Modelling: A State-of- the-Art Review, ed W.A. Vogely. RFF Working Paper EN-5. Johns Hopkins Press for Resources for the Future, Baltimore, MD, 1975.

Long-term resource demand: Models for projection

particular model also, however, has implications for the method used to incorporate potential variations. A single ‘best estimate’ is probably unsatisfactory. A sensitivity analysis of the results of different assumptions is a step in the right direction, though not integrated sufficiently with the analysis. Arguably, the only real solution is to incorporate ranges. The high/medium/low forecast pattern allows a ‘best guess’, plus reasonable variation - as supposed by the forecaster -in either direction.

The criteria for judging the usefulness of a forecasting model include ‘precision’, ‘accuracy’ and - for want of a better term - ‘success’. Precision involves the level of detail (at the limit, the number of decimal points) at which projected magnitudes are delivered. Accuracy can only relate to the objectives of the particular projection, and will depend crucially on how well the chosen method picks up trends and tendencies. Success can only be judged in the outturn, and will depend on the unknowables which have intruded during the forecast term. In general, forecasts should eliminate precision to the extent that this does not unduly compromise accuracy. The latter is the most important aspect of any examination of the virtues of projection methodologies.4

Some significant models

Comprehensive appraisal ‘Comprehensive appraisal’ is the name dubbed on a model built and maintained over a number of years (from the early 1960s to the present) at Resources for the Future, Washington DC.5 The study is orientated entirely towards the USA, and is probably the largest single mineral demand forecasting model. Although this is a highly quantitative model, or agglomeration of models, no single methodology or technique is adopted for projection. It is a rather eclectic exercise designed to interrelate economic parameters at all levels of magnitude.6

The original 1963 model has been updated in recent years, with some alterations such as deletion of ranges and elimination of certain elements found not to be crucial.’ However, it still retains its original nature - a wealth of ad hocery.

There are several key features about the model which require emphasis:

It is cross-sectional or static - ie magnitudes in year t do not depend on t-l, t-2 etc. Values are simply set to estimate conditions in the desired projection year and the output magnitudes derived. It is linear - any non-linearities are subsumed in exogenous alterations of parameters over time. It is sequential, not matrix, and can enjoy a flexibility .of classification not given to, say, an input-output model. It is highly integrated, in that the model strives for consistency among variables by interrelationships. The model explicitly attempts to pick up the many ramifications of substitution between materials, to avoid the errors of looking at resources individually.

Some of the virtues of this sort of model are implicit in the above description of what the model tries to do. Given that a small number

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Long-term resource demand: Models for projection

a R. Ridker, ed, Population, Resources and the Environment: A Report to the US Commission on Population Growth and the American Future, US Government Printing Office, Washington, 1972. ‘The central core of the projection is a dynamic input&output model of 185 economic sectors developed at the University of Maryland by Clopper Almon and his associates. (See C. Almon, The American Economy to 1975. Harper and Row, New York, 1966.) All outputs and frnal demands are forecast in constant 1967 dollars. The model is linked to time in a number of ways: first, it is recursive, requiring some prior-year projections to obtain future-year projections: second, technical change is handled explicitly by allowing the input-output coefficients to change over time; third, the equations that predict personal consumption expenditures as a function of several independent variables each contain ‘shift parameters’, which alter the relative importance of these variables over time as a consequence of demographic changes.

To this core model are attached resource coefficients. These are usually stable, created by taking the total US demand for each mineral in 1970 (in million tons) and dividing this by the dollar output figure (plus imports) of the corresponding Almon sector, with some exploration of a policy assumption of increased recycling. Some of the coefficients, however, have a time trend. The resource coefficients are used with the corresponding Almon sector for any future year to provide projections. (M. Harrell, ‘The US demand for mineral resources, methodology for the Year 2000 forecasts’, Resources for the Future, Washington, December 197 1.) lo P.C. Roberts and V.E. Outram, ‘A method of projecting energy demand in the UK’, Department of the Environment, London, 1974. ” The method relies on a forecast of GNP and of distribution of personal income, which provides estimates of future household income categories. Current household expenditure patterns are then used with the household income projections to provide forecasts of future household consumption patterns. The primary energy content per Cl of each class of goods is found by inversion of input+utput matrices. (See D.J. Wright, ‘The natural resource requirements of commodities’, Applied Economics, Vol 7. 1975, pp 31-39. and D.J. Wright, ‘Goods and services: An input-output analysis’, Energy Policy, Vol 2, No 4, December 1974, pp 307-315.) Taking the future consumption patterns and these energy contents together yields an estimate of future total household energy demand. The model assumes constant proportions in energy terms of government expenditure and capital formation. l2 Committee on Economic Studies,

continued on p 265

264

of capable researchers is prepared to devote large amounts of time to building such a system, it is liable to be successful. However, this points to the difficulty of doing it on a larger scale. For more than one economy, the enterprise would become increasingly complex and more prone to errors in construction and interpretation.

Input-output analysis

Taking into account the impact of input-output on empirical economic modelling over the last 20 years, it is not surprising that a number of attempts has been made to employ the methodology for mineral forecasting. The most ambitious attempt has been in the USA, again by Resources for the Future, with the development of a model of the US economy, its resource requirements and pollution loadings, for the US Commission on Population Growth and the American Future.8l9

Economists at the UK Department of the Environment (DOE) have made another attempt to use input-output for resource forecasting.” Their work employs published input-output tables and was originally intended for energy projection, though it can in principle be used for any material.”

These two input-output projections are interesting examples of the application of a powerful and consistent methodology to resource forecasting. However, there is a number of Aaws, some of which could be removed, but some of which are inherent in the nature of inputoutput. In both cases, a great deal of effort is expended on goods consumption projection by family or household categories. In the DOE exercise it is acknowledged that this does not lead to satisfactory long-term projections, because the sample size in high income groups is too small for reliable forecasting. In any event, cross-section data are treated as analogous to time-series data. In both models the resource coefficients are in general not derived in as sophisticated a fashion as the goods consumption forecast, and are mainly assumed stable. Since technological progress and substitution effects are prominent in materials, this seems unsatisfactory.

Intensity of use analysis

Intensity of use analysis differs from input-output or a comprehensive appraisal in attempting to estimate coefficients of resource use at the national economy scale, rather than associated with particular consumption goods. ‘Intensity of use’ is the amount of resource input (tons) per unit of economic output (GDP or GNP measured across countries in a common currency - usually the US dollar). While it often appears that resource use correlates strongly and positively with per capita income, the intensity of use coefficient in fact has no unidirectional correlation with income.

The first application of this method was apparently by the International Iron and Steel Institute in Brussels.‘* Essentially, demand for a material is viewed as derived from two components - the level of overall economic activity and the intensity of use of the material at that level of activity. A forecast therefore involves a projection of GDP, of population, and of intensity of use at the resultant level of per capita income.i3

Another example of intensity of use analysis is the study by Malenbaum for the US National Commission on Materials Policy.i4 This examines a number of materials - steel, copper, aluminium, zinc,

RESOURCES POLICY December 1977

continued from p 264

Projection 85: World Steel Demand, International Iron and Steel Institute, Brussels, March 1972. I3 The main methodological problem is to plot satisfactorily the course of intensity of use as income changes. The IISI study uses time-series information to calculate rates of change of steel use intensity for succeeding periods over the span

1955-l 968 for each country in the OECD. This sample of rates of change of intensity is then used to derive an ‘average’ curve representing the experience of the set of countries. (A better description of the calculation is in OECD, .Forecasting Steel Consumption:

Cross-Section and Time-Series Approaches, Paris, 1974, which also contains more conventional attempts to find a statistical relationship between steel consumption and economic variables.) Each nation relates to the average curve by a ‘constant country differential’. The average curve extends up to the highest income achieved by the wealthiest country (the USA in 1968). Forecasting is achieved by projecting per capita income growth for any individual country. reading pff the average intensity for that income level, and adding the constant country differential. The average intensity curve is extrapolated for certain countries (especially. for example, the USA). The method builds the average curve on time-series information, but forecasts for each country by a

cross-section analogy. The development of each country’s intensity of use is assumed to follow the pattern of the average curve and thus the pattern of countries with a greater per capita income. However, it will preserve a differential from the average experience and from that of other individual countries. The differential expresses the nature of the particular economic structure of the country maintained in part by the long life of capital stock. (This exercise is not a pooling of cross-section and time-series data, but explicitly separates them.) The final shape of the curve shows a sharp rise at low incomes, tailing off and eventually falling above a per capita income of about $2000. l4 w. Malenbaum, IVlaterials Requirements in the United States and Abroad in the year 2000: Report Prepared for the National Commission on Materials Policy, National Technical Information Service, Springfield, VA, 1973. l5 D.B. Brooks and B.W. Andrews, ‘Mineral resources, economic growth and world population’, in Materials: Renewable and Nonrenewable Resources, ed P.H. Abelson and A.L. Hammond, American Association for the Advancement of Science, Washington, 1976. l6 H.H. Landsberg, ‘Materials: Some recent trends and issues’, in Materials:

continuedonp 266

Long-term resource demand: Models for projection

fluorspar, sulphur and total energy - for ten regions worldwide. Malenbaum notes that there are several forces changing the resource use intensity of any country. First is the alteration through demand changes of the composition of GDP towards services, with a lower materials use per unit, as income increases. A second force is technological progress, which will mainly lower intensities, though in certain cases technology can sharply increase material demands. Finally, there is displacement of one material by another and substitution of synthetics for naturals. Malenbaum’s intensity curves seem to have a similar shape for many materials to those of the IISI model - a rising then falling intensity as per capita income increases. However, for some materials intensity carries on increasing. This is particularly the case for aluminium, presumably because of the widespread substitution of this material for copper and tin plate. Findings similar to these in a number of respects have also been demonstrated by Brooks and Andrews.15

Intensity of use analysis is comparatively new. The studies attempted so far have picked up an interesting possibility - that resource use intensity may systematically decline at high income levels. The method is also rather convenient to apply, needing only the calculation of a relatively straightforward coefficient of resource use. Projections of resource demand which split the economy into sectors require a correspondingly greater number of coefficients of resource consumption. It is generally accepted that the higher the level of aggregation, the more stable the economic value from random fluctuations. Consequently, an intensity of use coefficient can be expected to be more predictable than sectoral resource coefficients. Another advantage is the relative ‘transparency’ of the technique - it is obvious what is happening in the model. Larger, economic sector models tend to operate to some degree as ‘black boxes’, making it difficult to check the realism of the relationships built into them.

However, there are reservations about intensity of use analysis. First, the precise shape of the intensity curve can be explained only partly by a shift in the economy towards services, with low materials use. The rest of the explanation must concern technological change and substitution, which have ambiguous effects on intensity, and cannot be expected to correlate in any obvious way with income. As Landsberg has commented, we are not sure of the reasons for the shape of the intensity curve, l6 though some work on the steel curve has been done by IISI. I7 Second, the justification for making the country differential a constant is not entirely clear - in some instances it may change fairly regularly. Finally, and most important, where the average intensity curve is extrapolated, the reliability of the curve must be in doubt, since there is no experience on which to rely.

Contingency forecasting Comprehensive appraisal, input-output and intensity of use analysis have been distinguished by the nature of the connection they seek to draw between economic aggregates and resource use. ‘Contingency’ or ‘technological’ forecasting (the latter referring to the first applications of the method, which were specifically to analyse technological developments) is notable mainly for the type of output it produces and the situations it envisages.

Contingency forecasting is used by the US Bureau of Mines to project and simulate alternative futures based on contingencies

RESOURCES POLICY December 1977 265

Long-term resource demand: Models for projection

continuedfromp 265

Renewable and Nonrenewable Resources, op cit. Ref 15. ” Committee on Economic Studies, Steel intensity and GNP Structure, International Iron and Steel Institute, Brussels, May 1974. ” US Bureau of Mines, Mineral Facts and Problems, US Government Printing Office, Washington, 1970. l9 W.E. Morrison, ‘Projecting and forecasting methods’, in Economics ofthe Mineral Industries. ed W.A. Vogely, Al M E, New York, 1976.

*’ Forecasts produced by the Bureau of Mines are very widely used in forecasting discussions. (See J.D. Morgan, ‘Future use of minerals: The question of “demand” ‘, in The Mineral Position of the United States, 1975-2000, ed E.N. Cameron, University of Wisconsin Press for Society of Economic Geologists, Madison, Wisconsin, 1973. ” W.E. Morrison and CL. Readling, ‘An energy model for the United States, featuring energy balances for the Years 1947 to 1965 and projections and forecasts to the Years 1980 and 2000’. US Bureau of Mines Information Circular 8384, US Government Printing Office, Washington, 1968.

22 Obviously, some of these factors are unquantifiable. Effectively, the first stage of the model calculation is a conventional extrapolation of energy balances - ie a least squares historical trend for

1947-l 965, of the quantitative structure of total energy demand E and its components by major forms, consuming sectors and sources. The trends are related in this calculation to projections of several relevant economic indicators for which a high degree of historical correlation can be determined (GNP. industrial output, etc). This yields a ‘base case’ projection to 1980. To this, the Bureau’s forecasters apply contingency analyses, The first main contingency envisages a continuation beyond 1980 to the year 2000 of conventional energy systems, assuming normal improvements in efficiency and continuation of present trends of interfuel competition. The second major contingency is for a hydrocarbon-air fuel-cell energy system with on-site generation of power and heat recovery, and no purchased utility

electricity. The third contingency

examines an all-electric economy supplied from centralized power stations.

These numerical scenarios enable estimates to be made of the quantitative impact of ‘revolutionary technologies’ and ‘energy policy’ etc on the magnitude and structure of final energy demand - in this case, from a fuel-cell economy to a largely nuclear electric economy. The high and low contingencies for end uses are assumed to be maximum and minimum points out of a range of possible

occurrences, and a normal curve is

constructed to reflect 95% of the continued on p 267

assumed for technological, economic, social, environmental and other relevant influences.18 The contingencies and their assumptions are identified, quantified and analysed through ‘scenarios’. A number of techniques is used as appropriate, and there is considerable opportunity for the analyst to contribute judgment, experience and intuition to the forecasting procedure. The method attempts to avoid the rigidities of forecasting by trend extrapolation or trend correlation - though these and other techniques may be applied if they seem usefu1.19~20

The only one of the Bureau’s models published in detail is that for energy, *’ although a very wide selection of materials is examined. The energy model is described as a logarithmic linear or exponential function:22

E=(Xr,Xf,...,XM 13

where

E

a, h XI x2

x3

x4

x5

x6

Xl

x8

x9

Xl0

Xl 1 x12

x13

= total energy demand , m = parameters of the independent variables

= economic activity (GNP) = population = industrial production = real cost or price of energy resources = domestic supply = foreign trade = environmental restrictions = evolutionary technology = revolutionary technology = regional factors = energy policy = political considerations and trade-offs = other variables

It is not clear from published literature how precisely the Bureau applies this analysis to other materials, but presumably it is on the basis of greater or lesser conceivable substitution between different materials in various end uses.

The Bureau comments that scenario forecasting depends on correct projection of the determining variables. For the conditional forecast to 1980 and the continuation to 2000, only GNP (Xi), population (X,) and industrial production (X3) are quantitatively projected. Assumptions for the other variables are mainly qualitative. Where quantified, this is either by simple extrapolations of past trends, or by judgment and intuition. Other factors are generally kept constant and simple two-variable regression used wherever this is possible.

Contingency forecasting must be seen in its proper context. The idea is primarily to illustrate the feasible range to which present decisions could eventually commit the future, so enabling implications of all sorts of policies and developments to be tested. However, the method of analysis can be criticized on a variety of grounds. First, the determining variables and their relationships to resource use are employed very simply - there could easily be shifts in historical relativities, which the Bureau does not explore. Second, many of the smaller aggregates cannot be reliably projected - they are a function of particular industrial technologies and not of major economic indicators as such. Third, the projections do not show a true-time

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Long-term resource demand: Models for projection

path (eg an S-shaped curve of introduction of an innovation), but rather energy balances for a complete displacement with a new technology at three points in time - 1966, 1980, and 2000. Finally, the model does not produce a particularly satisfactory forecast of the ‘most likely’ outcome. It generates instead very wide ranges, with the outside estimates being simply different paths for key variables, rather than extremes derived probabilistically as being less likely. The statistical method of narrowing the ranges does not seem, therefore, to be particularly sound.

coniinued from p 266

possibilities, by adjusting the normal curve to two standard deviations about the mean. This gives a probabilistic range of demand centred around a medium estimate. 23 I. Rajaraman. ‘Non-renewable resources: A review of long-term projections’, Futures, Vol 8, No 3, June 1976, pp 228-242.

Summary and conclusions

There are four mineral demand forecasting models currently in use, apart from simple trend extrapolation. These are comprehensive economic appraisal, input-output analysis, material intensity of use, and resource contingency analysis.

Evaluation of any one of these models depends critically on the purpose for which it is built, or the way in which the output is used. None of them as constructed can deliver a comprehensive view of resource futures, since they cannot take into account supply or institutional factors endogenously.23

A central difficulty with all the models is their heavy dependency on relatively simple economic aggregate projections. More sophisticated projections are rarely available. Input-output models are perhaps an exception, though there is little consensus on their reliability for projection over a number of years, since adjustment of the input-output technical coefficients is essentially rather arbitrary. It is also only possible to produce a recent input-output framework for those few economies with an elaborate statistical service, able to make available the vast amounts of data required.

Both input-output and comprehensive appraisal models are highly complex, and the disaggregated nature of the information within them creates instability for projection. On the other hand, a system such as an intensity of use model aggregates across economic categories, but in doing so disguises the impact of forces other than those related to income.

After rates of economic growth, the single most important variable in each of the models is the resource coefficient. Yet these variables are often treated as stable. Only intensity of use analysis explicitly attempts to define a regular change in the resource coefficient. The comprehensive appraisal model employs changing coefficients, but these are usually simple linear trends or adjustments through individual judgment, so their reliability is necessarily suspect.

All the projection methods show increasing rates of material consumption with rising income. There is considerable evidence that any attempt to reduce material throughput in an advanced economy will need to accomplish significantly greater changes in economic structure than any that have been observed to date. Resource demand modelling confirms that increasing resource consumption is, and is likely to remain, an essential feature of high income countries.

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