Elasticities for OECD aggregate final energy demand

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Lakis Vouyoukas

ABSTRACT

The IEA model is used primarily for the construction of global long-term energy scenarios that serve as the basis for IEA's Energy Outlook. Its global orientation imposes a fairly aggregate treatment of energy demand. In terms of final demand, there are ten regions, four fuels and three consuming sectors. Most of the demand parameters are econometrically estimated over the period 1960-90, with great emphasis being placed on the use of end use prices rather than primary fuel prices. The results indicate that energy price elasticities are generally low and not always well determined. It is suggested that a major explanation of this could be the assumed exogeneity of the economic structure and the technology incorporated in the energy-using capital stock.

INTRODUCTION

The purpose of this chapter is to present the aggregate activity and price elasticities of OECD final energy demand used in the present version of the IEA energy model. The first section of the chapter gives an overview of the model, as it is currently being developed, and puts the results, aggregation and methodology in the context of the objectives of the model. This is most important since if pure estimation was the only objective a much cleaner and more disaggregated set of data could and should have been used. The second section of the chapter presents the results on elasticities with a very brief evaluation. No effort has been made in this chapter to discuss in detail the key driving forces and historical developments of the major energy-using sectors of OECD economies. The chapter concludes with some comments on the implications for primary energy markets and policy.

THE FRAMEWORK OF ESTIMATION AND DATA

The primary objective of the IEA long-term energy model is to serve as the basic analytical tool for constructing the scenarios that comprise the long-term global energy outlook of the IEA. The essential requirements in terms of model output are regional energy balances so that the underlying trends in energy demand, supply, trade and security of supply can be analysed. Secondary objectives include the analysis of the environmental impact of energy use, and sensitivity analysis of policy action, technological change and resource availability.

The structure and design of the model have, to a large extent, been determined by these objectives in conjunction with available resources. In the context of the present chapter, it should be emphasized that only a small part of the model, that which is concerned with final energy demand in the OECD, will be presented. Most other parts of the demand side of the model as well as most of the supply side and price determination modules are much less reliant on time-series econometric estimates. The models of final demand for nonOECD regions rely mostly on cross-sectional parameters owing to the lack of sufficiently long and high quality time series of end use prices in these regions.

Given the modelling objectives and resource limitations, the econometric results presented here are based on fairly aggregate data in terms of regions, fuels and sectors, as can be seen from Table 6.1. In terms of regions, four groupings have been identified, three of which, North America, Europe (excluding the eastern Lander of unified Germany) and Japan, have been econometrically estimated as reported. The modelling of Australia/New Zealand is based on a different methodology and not reported here. Clearly, for more accurate and better determined results, estimation of individual country parameters would be preferable. In terms of energy data, North America and Europe are rather large aggregates with very large intra-regional differences. However, modelling particular sub-regions would increase the size of the model manyfold given that many of the variables that appear to be exogenous in this chapter are endogenous within the overall model.

The sectoral disaggregation adopted reflects the materiality of the sector, its economic coherence/ homogeneity and the ready availability of relevant data. For example, in the case of transportation, given its significance for energy as a whole and its paramount importance for oil demand, three types of inland transportation (road passenger, road freight and air transport) have been distinguished. Bunker fuel demand has also been modelled separately. The

Table 6.1 Aggregation scheme for final energy demand in the OECD

Regions

Sectors

Fuels

North America

Transportation

Oil

Europe

Industry

Gas

Japan

Building

Solids

Australia/New Zealand

Non-energy

Electricity

other two major sectors, industry and the building sector, are far too aggregate and current work aims at further disaggregation. Some of the aggregation issues involved are discussed in the next section. Non-energy use has been modelled through a mixture of econometric and other techniques and the results for this sector are not reported here.

Finally, in terms of fuel aggregation four major aggregates have been adopted. While gas and electricity are fairly homogeneous, oil and solids consist of a very large number of quite different products used for very different purposes. Again given the importance of transportation, three oil products have been separately modelled, namely gasoline, diesel and aviation fuel. In general, where possible, the end use price of the dominant product of each fuel was used in each sector. For example, the price of oil was proxied by the price of residual fuel oil in the industrial sector and by the price of heating oil in the building sector while for transportation the gasoline and diesel prices were directly used. In total, ten energy end use prices are used for each region.

Another very important limitation imposed by the objectives of the model was the selection of explanatory variables. Given that the model is primarily a tool for long-term projections, explanatory variables themselves need to be projected. For example, for each region, activity variables like industrial production, consumer expenditure etc. as well as the end use energy prices used in the model need to be modelled. Given resource considerations this clearly limits the number of explanatory variables that could be used. Furthermore, since primary data are usually generated at country level, the use of regional aggregates necessitated a large number of approximations. In the case of Europe, activity and end use price series and their constituent components (taxes, wholesale prices etc.) for the region have been approximated by the use of the relevant series for only the four or five biggest countries. This should not be a poor approximation given that these countries are diverse and account for the bulk of European energy demand.

Almost all estimation reported here is based on single-equation techniques. No cross-equation restrictions have been attempted because of the presence of very complex and diverse dynamic patterns often running to fifteen years. These arise partly because of the longevity of the energy-using capital stock (e.g. more than forty years for buildings and up to thirty years for some types of industrial plant) and partly because of the potential flexibility in the choice of fuel. Thus, two or three cycles of response to a price change can be detected, namely the instantaneous fuel switching from dual-fired plant, the medium-term retrofitting and the longer-term replacement of the capital stock.

In modelling industrial and residential/commercial energy demand a choice needs to be made between (a) modelling the total energy demand of each sector and then the shares of the fuels or (b) modelling the individual fuels independently. The first approach, on which the results presented here are based, emphasizes the substitutability among different forms of energy while the second approach emphasizes the individuality of each fuel. It is arguable that for most non-transportation energy consumption consumers are simply interested in heating, steam raising etc. and not in the form of the fuel that they use. However, in many applications a specific form of fuel may well be necessary, like coking coal for traditional steel production and specific forms of hydrocarbons for petrochemical feedstocks. These special cases can in principle be isolated and the present version of the model already subtracts feedstock use in Europe and Japan from the energy use in industry. The potentially more serious criticism of the approach adopted is the frequent lack of substitutes for electricity use which is often based on quite specific technological and other characteristics of electricity. For example, the use of electricity for lighting in the building sector or for electrolysis and arc furnaces in many energy-intensive industries is not really subject to competition. Thus, the optimal separation of the problem could well be an initial nesting of electric versus non-electric energy and then a second nesting of non-electric energy into the appropriate fossil fuels.

All energy demand data used for the estimation of the elasticities presented in this chapter are based on the IEA's 'Energy Balances' databases. These data are converted from original units to million tons of oil equivalent. Data on energy prices and taxes since 1978 come from IEA's 'Energy Prices and Taxes' databases. For the period before 1978 a variety of sources has been used and a reasonably consistent set of data has been created back to 1960. All data are annual. The most common estimation period for the results reported here is 1965-90. In equations with severe parameter instability, the results from the latter part of the sample were usually used.

ESTIMATED ELASTICITIES1 6.2.1

Transportation sector

The transportation sector is the best documented, in terms of data, and the most heavily researched area in energy economics. This to a large extent is due to its importance not only for the energy industry but also for the automotive industry and the economy as a whole. In the case of the demand for gasoline, almost every conceivable modelling approach has been attempted and elasticity results vary according to region and the methodology and data used (for a recent survey see Dahl and Sterner 1991). In the context of the present IEA model, and given its long-term nature, it was considered essential to disaggregate the transportation sector fuel demand into road passenger, road freight and air travel. Both forms of road transport use gasoline and diesel. For the most important sectors, in terms of fuel demand, the preferred approach has been to impose a minimum structure on the problem and model the underlying driving forces of demand rather than model the fuel demand directly.

Road passenger transportation

In the case of the United States and Europe equations for the per capita distance travelled by passenger cars have been estimated. The resulting projections of travel are then combined with estimates of efficiency improvements and car turnover rate and diesel/gasoline penetration assumptions in order to arrive at projections of fuel demand. In the case of Japan, owing to the much smaller demand for passenger fuel demand, a gasoline demand equation has been estimated directly. It is beyond the scope of the present version of the model to endogenize the choice of mode of passenger transport between bus, car and rail or the purpose of travel (recreation, work, shopping etc.). This is the domain of specialist transportation models or of much larger energy models. Also, no information on the number of cars has been utilized. It is not clear what the effect of this variable would be on gasoline demand. It could be argued, for example, that, as the number of cars per household increases, consumers could reduce fuel consumption by selecting the most efficient of their cars for a large portion of their driving needs.

The principal driving forces of road passenger transportation would be expected to be income, cost of travel, the cost of alternate means of transport and population. The influence of population is taken care of through defining travel and income in per capita terms and income has been approximated with consumer expenditure. The cost of potential substitutes has not been incorporated owing to lack of readily available price data, but their effect is likely to be very modest as a large proportion of driving is non-discretionary. Estimating the cost of travel presents a major problem since, at least in the long term, it should include the lifetime cost of owning and operating a car. Even the short-run cost of travel is not equivalent to the cost of gasoline or diesel but should take into account the efficiency of the car and other variable costs (Greene 1992). Unfortunately, the data requirements for a proper derivation of the cost of travel are prohibitive in this context. However, in the case of the United States where a reasonable series of the average car efficiency is available, the price series used is the effective fuel cost per mile driven.

As can be seen from Table 6.2, the income effect is generally quite high and varies from 0.8 in the United States to 0.9 in Europe and Japan. The somewhat lower US elasticity could be due both to the higher per capita GDP there and to road transportation being more of a necessity. One surprising result of Table 6.2 is the large price elasticity of European travel where the cost of travel has been approximated by the real price of gasoline. If the same variable had been used for the US equation the estimated elasticity would be even smaller than the reported -0.26. This is because changes in the price of gasoline tend to overstate changes in the fuel cost of travel since consumers can reduce the impact of gasoline price changes by driving more or less efficient cars. Thus, in the United States, while the price of gasoline has risen marginally between

Table 6.2 Long-term3 transportation demand elasticities

United States

Europe

Japan

Income

Price

Income

Price

Income

Price

Distance travelled

0.76

-0.26

0.92

-0.75

Gasoline demand

0.86

-0.80

Freight ton miles/km

0.95

-0.23

1.11

Truck share in freight

Truck ton km

-0.10

-0.20

0.87

-0.21

Air miles

1.45

-0.60

Aviation fuel demand

1.39

-0.07

1.22

-0.32

Note: aIt is important to note that the size of the estimated long-term elasticities in this table and elsewhere in the chapter should be seen in the context of the length of the estimation period. Since this is usually around twenty-five years, the reported values may well underestimate the 'true' long-term elasticities which will reflect changes in lifestyles, long-term technological innovations and changes in infrastructure, such as roads and railways.

Note: aIt is important to note that the size of the estimated long-term elasticities in this table and elsewhere in the chapter should be seen in the context of the length of the estimation period. Since this is usually around twenty-five years, the reported values may well underestimate the 'true' long-term elasticities which will reflect changes in lifestyles, long-term technological innovations and changes in infrastructure, such as roads and railways.

1970 and 1990, the fuel cost per mile has actually fallen as a result of efficiency improvements. One reason for which European car travel might be more price elastic than that of the United States is that it faces potentially much greater competition from the railways and the urban mass transit systems which tend to be much less developed in the States. However, it must be stressed that while the income elasticities of distance travelled are consistently close to unity, the price elasticity estimates are much more unstable and highly sensitive to the time period of estimation. It should also be mentioned that the short-run price effects of car travel are extremely small.

The price elasticity of Japan is not directly comparable with that of the United States and Europe as it refers to gasoline demand rather than distance travelled. Consequently, it includes the second round effects of changes in the price of gasoline on the efficiency of new cars and the average size of new cars selected by consumers. Preliminary work on the effect of price on car efficiency, using US data, indicates very significant price effects with some evidence of asymmetry. However, these results are very unstable and price elasticity estimates vary from -0.2 to -2! There are many potential explanations for this instability including poor data on efficiency, regulatory changes on efficiency standards etc. (Greene (1990) examines the comparative impact of price and regulation on changes in the efficiency of cars in the United States.) The selected value, for projection purposes, for the price elasticity of the efficiency of new cars is -0.4. In conjunction with the figure reported in Table 6.2, the overall long-term price elasticity of gasoline demand becomes -0.66 for the United States, -1.25 for Europe and -0.8 for Japan.

Freight transportation

The dependent variable for freight transportation is ton miles/kilometres. For the United States and Europe, total freight equations are first estimated and then an equation for the share of freight carried out by truck (as opposed to railways etc.) is estimated. The real price of diesel oil has been used as a proxy for the relative price of truck freight and GDP has been used as a proxy for economic activity. As expected, the income elasticity is around unity for all three regions. The overall long-term price effect is around -0.2. The cost of fuel is almost a negligible proportion of the cost of freight with labour and capital costs being much more significant.

Air transportation

Lack of readily available data for air distance and cost of travel in Europe and Japan precluded a more 'structural' approach and equations of total aviation fuel were directly estimated. For the United States passenger air miles was the dependent variable and a series was constructed for the cost of air travel rather than just the cost of fuel.2 The income effect for all three regions is quite high and between 1.2 for Japan and 1.5 for the United States. This is likely to be due to the luxury nature of air travel. The seemingly very different price elasticities of air travel in Table 6.2 provide a good illustration of the importance of the price series used for interpreting elasticities. In terms of the reaction of fuel demand to the crude oil price all three regions have much more similar elasticities than would appear from Table 6.2. The -0.6 US price elasticity relates to the cost of air travel, only a small proportion of which is accounted for by the fuel cost (the 1990 value used in the model is 25 per cent for the United States). The European -0.07 elasticity on the other hand is with respect to the price of crude oil which is only part of the cost of aviation fuel, let alone the cost of air travel.

6.2.2 Industrial sector

The industrial sector is a very diverse sector requiring energy for a variety of purposes, including motive power, steam raising and process heat. It also includes the large petrochemicals and iron and steel industries where the use of energy products takes place not just for energy purposes but also for their transformation into non-energy products.

In some of these uses, only a unique form of energy is possible (and, consequently only activity effects would be expected), while in others, like steam raising, almost any form of energy could be used and we would expect a very high price elasticity, at least in the long term. This diversity makes aggregate modelling and the interpretation of parameters exceedingly difficult. However, there is little choice in the absence of a full set of data on the end use of energy. With the exception of North America, petrochemical feedstocks have been modelled separately.

A second problem with modelling industry energy demand is the treatment of own electricity generation. Given that a large part of the inefficiency of electricity takes place in its production stage rather than its use, own generation rather than purchasing of electricity can lead to apparent inefficiency of industrial energy use. What makes this problem worse is that there has been a gradual shift towards electricity, for reasons not always due to the competitiveness of electricity (e.g. mini mills, controllability, technology etc.), which is likely to lead to a trend of apparent improvement in efficiency. One potential solution to this problem would be to consider measuring industrial energy demand in primary energy requirement terms.

Another problem with industrial energy demand is the great scope for relocation of industry. Modelling this would involve modelling individual industries on a global scale. However, it is not clear that the cost of energy is the only or, in some cases, even the major reason for relocation, making modelling of the process quite difficult. Again the phenomenon of relocation and the absence of suitably disaggregated output data can lead to the appearance of great improvements in energy efficiency as industrial countries may import the energy embodied in the products. Finally, the econometric modelling of industrial energy demand is most unlikely to capture the phenomenon of large-scale switching between fuels that depends on the specific configuration of the price vector and on the availability of dual-firing capital stock. This is because this stock has been changing rapidly over the past fifteen years with industry putting more emphasis on flexibility. Within the model, judgemental adjustments are likely to be used to capture this.

Total industrial energy demand

In estimating total industrial energy demand the explanatory variables used were (a) an energy weighted industrial production index for Japan and Europe and the ordinary US industrial production index for North America and (b) the real weighted average of industrial fuel prices for each region. In the case of Europe, industrial energy demand, the dependent variable, excludes petrochemical feedstocks for the reasons given above. The results given in Table 6.3 indicate that the impact of industrial activity on energy demand varies from 0.6 in Europe to 0.8 in North America and 0.9 in Japan. Activity elasticities significantly less than unity indicate the well-established trend for energy efficiency improvements in all OECD regions.

For price elasticities, significant asymmetry effects were found for North America and Europe.3 The short-term reaction to a price increase is almost identical, at just over 0.1, in all three regions. A price fall seems to have a significant short-term impact only in Japan. In the long term, there is a large difference in all regions between the effect of price increases, between -0.35

Table 6.3 Long-term industrial energy demand elasticities

Activity

Energy price

North America

0.78

-0.40 (+ ve dp)

-0.28 (-ve dp)

Europe

0.61

-0.35 (+vedp)

-0.21 (-ve dp)

Japan

0.89

-0.50 (+ve dp)

-0.13 (-ve dp)

and -0.5, and the effect of price decreases, which vary between -0.13 and -0.28. Over the estimation period Japan, with no significant indigenous energy resources, appears to have become impressively energy efficient, and to the extent that much of this improvement may have been due to the relocation of heavy industry there must be a question mark on whether this high elasticity may not moderate in future. In all three regions significant price effects were found over a period of at least nine years. This is not surprising given the long average lifespan of much industrial equipment.

Industrial fuel shares

In modelling fuel shares the major explanatory variable was the price of the appropriate fuel relative to either the total industrial energy price or the price of the major competing fuel, or both. Exceptionally, for the North American and European share of solid fuels the ratio of the industrial production in the iron and steel sector to the total industrial production was also used as a relative activity indicator. The reason for this is the especially large weight of this sector for solids demand and the substantial structural changes that have taken place in it over the past twenty years.

In general, relatively little uniformity would be expected in terms of the elasticities of the fuel share equations across regions owing to the large differences in the industrial structures and comparative economics. One of the few uniform results is the high relative price elasticity of oil which is probably due to the fact that oil has often been the residual fuel in industry and is often used by dual-firing equipment. However, as residual fuel oil has been gradually backed out of industry, and as an increasing proportion of oil goes to premium uses like motive power and feedstocks, it is possible that these elasticities overestimate the potential future flexibility of oil usage. Most other relative price effects tend to be smaller and, almost always, when a short-run/long-term distinction is possible the short-run effects tend to be extremely small. This, of course, is due to the relative inflexibility of the energy-using capital stock in its choice of fuel. No asymmetric effects were investigated in the share equations even though they would be expected to be

Table 6.4 Long-term price elasticities of industrial fuel shares

Activity Own price relative to total industrial price Own price relative to competing fuel

Table 6.4 Long-term price elasticities of industrial fuel shares

Activity Own price relative to total industrial price Own price relative to competing fuel

North America

Electricity Gas

-0.44

Gas: Oil:

-0.64 -0.33

Oil

-1.36

Gas:

-0.37

Solids

0.64

Gas:

Electricity

Gas

-0.38

-0.55 Oil:

-0.06

Oil

-1.07

Solids

0.27

-1.04

Gas:

Electricity

Gas

-0.34 -1.30

Oil

-0.96

Solids

-0.52

quite significant. For example, there is a long established trend for electricity to increase its share in industrial energy demand and this is almost certainly only partly due to the corresponding trend of declining relative electricity prices. Many other considerations increasingly favour electricity as an industrial fuel, including many technological advances, the greater controllability of electricity and its environmental benefits at the point of use. Consequently, it is possible that the elasticity estimates in Table 6.4 may be too high, especially for a rising relative price of electricity.

elasticities for oecd aggregate final energy demand 133 6.2.3

The building sector

This sector consists primarily of the residential and commercial sectors as well as the small components of the agricultural and 'non-specified' sectors. While the incorporation of these is likely to affect the quality of the data somewhat, the effect is likely to be very small as they only account for about 9 per cent of the energy in this overall sector. The bulk of energy use in this sector is linked to the building stock.

Key influences on the energy-using capital stock in this sector include the longevity of buildings, which leads to an extremely gradual incorporation of new technologies and materials that might arise as a result of higher energy prices, and the five to ten year lifespan of boilers and appliances, with the boilers being a key influence on the extent of inter-fuel competition. Unlike the industrial sector, very little dual firing is available in the residential and commercial sectors. Other key aspects of the sector include the importance of the severity of the winter and, in the case of North America, the heat intensity of the summer (this is likely to become much more important in future for some areas of Europe and Japan as, with rising incomes, more households are likely to install air-conditioning).

Total building energy demand

The major explanatory variables for the overall per capita energy demand equations were per capita consumer expenditure, as a proxy for household income, the weighted average of end use fuel prices and a series that estimates the degree of the abnormality of weather. In the case of Europe the change in consumer expenditure was also found to be very strongly significant although it is difficult to give an intuitive interpretation to this variable.

As can be seen from Table 6.5, the activity elasticities for aggregate demand in Europe and North America tend to be quite close and, at around 0.5, modest in size, probably reflecting saturation effects given that heating, household appliance use and lighting are necessities and unlikely to be much affected by rising income. The income elasticity for Japan is nearly twice this level, probably because of the scarcity of space in this country which makes the size of homes highly income sensitive. Cross-section work by Hiroshi Sakamaki on the link between residential energy use and income cohort, based on household expenditure surveys, found that the income elasticity in Japan is around 0.3 and in the United States around 0.2. The higher time-series elasticities reported here may be due to the inclusion of the commercial sector, which would be expected to be more sensitive to income effects, and to the inclusion of price effects.

The price elasticities for Europe and Japan tend to be very small in the short term and around 0.3 in the long term. Given that the average life of buildings can be very long, it is quite possible that the length of the data series used, up to thirty years, is not sufficient to capture the long-term price effect which might be somewhat higher. In the short run, of course, only a negligible fraction of the building sector can react to price changes. In the case of North

Table 6.5 Elasticities for per capita total energy building demand

Per capita consumer expenditure

Energy price

North America

0.51

-0.43 (+ve dp)

-0.15 (-ve dp)

Europe

0.45

-0.26

Japan

0.87

-0.29

America the price effects are much stronger as well as asymmetric. This could well be due to the much more common use of air-conditioning which tends to be somewhat more discretionary. As with the industrial sector, the length of lags of price terms is very long.

Building fuel shares

The fuel share results given in Table 6.6 include activity variables for North America and Japan in the form of the degree of penetration by a specific fuel as the major fuel in housing. Apart from the relative prices of competing fuels, many equations also include time trends. The fuel penetration is currently exogenous to the model as it depends on the available infrastructure which is to a large extent determined by policy, resource availability and price. The long-term price elasticities are likely to underestimate the price effect somewhat as they ignore the impact of price on the selection of fuel for new buildings, Unsurprisingly, almost all cross-price elasticities refer to electricity, as the choice is often between installing a second fuel for heating purposes apart from electricity. The solids category is relevant only for Europe but even here nearly half of solids consumption is in the form of wood rather the traditional coal which is in the process of being backed out of the building sector by cleaner fuels.

Since coal is already being backed out to a large extent, the most common competing fuel in all regions is oil which is the fuel most under threat now in the building sector. No asymmetry effects have been investigated in the fuel share equations even though they would be expected to be significant. For

Table 6.6 Long-term elasticities of building fuel shares

Penetration of fuel

Own price

relative to competing fuel

Own price

relative to competing fuel

North America

Electricity

0.37

Oil:

-0.13

Gas:

-0.44

Gas

Oil:

-0.26

Oil

1.74

Electricity:

-0.44

Europe

Electricity

Oil:

-0.33

Gas

Oil:

-0.58

Oil

Electricity:

-0.46

Gas:

-0.07

Solids

Oil:

-1.07

Japan

Electricity

Gas:

-0.22

Oil:

-0.39

Gas

0.37

Oil:

-0.53

Oil

Electricity:

-0.22

example, the cost of installing a gas network in an all-electric residential area can be prohibitive if it is not done during the initial construction phase.

CONCLUSIONS

The major conclusion to emerge from the results presented here is that, in general, long-term energy price elasticities tend to be small, at least compared with the corresponding income or activity elasticities. This is despite the fact that, in almost all cases, end use prices rather than primary energy prices have been used. This is an important distinction since the link between primary and end use prices is often extremely weak, especially in the transportation and residential sectors. For example, the proportion of the price of gasoline accounted for by the cost of crude oil varies from 61 per cent in the United States to 20 per cent in Europe while only around 10 per cent of the cost of residential electricity is due to the cost of primary fossil fuel energy.

There are two important implications of this combination of relatively low energy demand price elasticities with limited feedback from primary to end use prices. First, primary energy markets would be expected to be rather volatile as moderate supply shocks would lead to large price changes. Second, small additional energy taxes would be unlikely to have a major impact on energy demand. Thus, in the context of the current debate on the effectiveness of carbon taxes to control energy-related CO2 emissions, the results presented in this chapter would suggest that rather large carbon taxes would be necessary in the medium term and in a world where, in the absence of additional taxes, energy demand would be expected to continue to increase (see IEA 1993).

NOTES

Hiroshi Sakamaki and Teresa Malyshev contributed to the estimation of the domestic and industrial sectors respectively. I am also grateful to Keith Welham for comments on a previous draft. The views expressed here are those of the author and do not necessarily reflect those of the IEA.

1 The results presented here should be seen as work in progress and subject to change as the model is constantly being refined and the data revised and extended. The version of the model described in this chapter was completed in January 1993 and the resulting projections were published in the IEA's 1993 World Energy Outlook; see later issues of the Outlook for updates of the model and its estimates. Given the coverage of energy sectors, fuels and regions in this chapter the listing of individual equations and the details on data sources are not included. Readers interested in further details should contact the author.

2 This was only possible for the period 1980-90 and the results from this equation must be treated with special caution.

3 For the methodology used to capture asymmetry see Dargay (1992).

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