metadata: authors: 0: author: Bacon,Robert W.
docna: 0: docna: Measuring the possibilities of interfuel substitution
docty: Policy Research Working Paper
subtopic: Power & Energy Conversion,Energy Policies & Economics,Energy Demand,Energy and Environment,Energy and Mining,Transport Services
teratopic: Energy
abstracts: cdata!: What are the costs of making consumption
            of production activities use less-polluting fuels?  The
            author reviews how the fuel mix used by different industries
            has changed over time and examines 2 techniques for
            estimating the responsiveness of fuel demand to fuel prices:
            econometric models and the engineering approach. With
            econometric models, the elasticity of substitution between
            energy and other inputs determines the costs of making
            activities less energy-intensive, while the elasticity of
            substitution between sources of energy (interfuel
            substitutability) determines the marginal costs of replacing
            one energy source with another. The engineering approach
            uses more detailed technical information and can draw a more
            complete picture, but with less ability to inform about
            activities with a vast number of different economic agents.
            Among the author's main conclusions: There are
            surprisingly large variations in energy and fuel use over
            time and between countries.  Industrial output increased 62
            percent in OECD countries between 1971 and 1988, for
            example, while energy use stayed unchanged. Also, shares of
            energy sources for industry and electricity vary greatly
            with local availablity, indicating that these sectors have
            some flexibility in choice of energy source. A judgment on
            whether this variability indicates that an economy responds
            cheaply if energy prices are changed selectively depends on
            how one reads the more detailed studies in the econometric
            and engineering literature. Lack of data is the biggest
            problem in estimating fuel and energy substitutability in
            non-OECD countries.  Engineering studies of fuel switching
            in industry are rarely available.  They exist, however, for
            the power industry and could be used to estimate the costs
            of alternative fuel-mixes for particular greenfield sites.
            The technique could not be used for assessment of
            economywide policies.  Econometric studies are useful
            inasmuch as they take a sector- or economywide perspective.
            Econometric techniques are challenging, but often represent
            the state of the art in providing reliable estimates for
            elasticies of substitution - particularly when data are
            scarce and the level of aggregation is high.  The issue of
            whether econometrically estimated structural parameters can
            be transferred across borders has not been thoroughly investigated.
repnme: repnme: Measuring the possibilities of interfuel substitution
display_title: Measuring the possibilities of
            interfuel substitution
listing_relative_url: /research/1992/11/699524/measuring-possibilities-interfuel-substitution
topicv3: Energy and Extractives m1326595 507,Transport m1326661 135,Power Systems m1327093 508,Power and Electricity Sector m1327094 515,Iron m1503514 2931
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Energy
origu: Policy Research Department
no_of_pages: 84
author_keys: 0: author_key: 165834
model_extractions: **Policy Research**

# **Public Economics**

WPS 1031

- - This paper a project of the Public Economics Division, Country Economics Department is part of a larger effort in the department to study pollution control policy in developing countries. The study was funded by the Bank's Research Support Budget under research project "Pollution and the Choice of Policy Instruments in Developing Countries" (RPO 67648). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Peggy Pender, room N 10-067, extension 37851 (November 1992, 76 pages).

What are the costs of making consumption or production activities use less-polluting fuels? Bacon reviews how the fuel mix used by different industries has changed over time and examines two techniques for estimating the responsiveness of fuel demand to fuel prices: econometric models and the engineering approach.

With econometric models, the elasticity of substitution between energy and other inputs determines the costs of making activities less energy-intensive, while the elasticity of substitution between sources of energy (interfuel substitutability) determines the marginal costs of replacing one energy source with another.

The engineering approach uses more detailed technical information and can draw a more complete picture, but with less ability to inform about activities with a vast number of different economic agents.

Among Bacon's main conclusions: There are surprisingly large variations in energy and fuel use over time and between countries. Industrial output increased 62 percent in OECD countries between 1971 and 1988, for example, while energy use stayed unchanged! Also, shares of energy sources for industry and electricity vary greatly with local availability, indicating that these sectors have some flexibility in choice of energy source. A judgment on whether this variability indicates that an economy responds cheaply if energy prices are changed selectively depends on how one reads the more detailed studies in the econometric and engineering literature.

L3 ack of data is the biggest problem in estimating fuel and energy substitutability in non-OECD countries.

* Engineering studies of fuel switching in industry are rarely available. They exist, however, for the power industry and could be used to estimate the costs of altemative fuel-mixes for particular greenfield sites. The technique could not be used for assessment of economywide policies.

* Econometric studies are useful inasmuch as they take a sector- or economywide perspective. Econometric techniques are challenging, but often represent the state of the art in providing reliable estimates for elasticities of substitution

- particularly when data are scarce and the level of aggregation is high.

_e_ The issue of whether econometrically estimated structural parameters can be transferred across borders has not been thoroughly investigated.

The Policy Research Working Paper Series disseminates **the** findings of work **under** way in theBank. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The rtndings, interpretations, and conclusions in **these** papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
<!-- Start of picture text --> HEAUIRO THEN POSB!DILITIHS OF !N!Z-VU3f SUM3TITUTU!O<br>by<br>Robert Bacon<br>Lincoln College<br>Oxford, England<br>.... I I<br><!-- End of picture text -->
**DIEASIIEG** ME **PogSDIZBZLXTIE** OF JO=U-FUE SUDS!TUT!ION

# **Table** of Contents

|CMMPTreIs|XXrRODUCrIOK .....<br>.<br>....................<br>.90*0<br>.................. 01|
|---|---|
|**ChAPTER2S**|**UVIDIUCUonZNTErVumU.**S**DTITUTION**........................<br>. 6|
||FuelUse in**the**<br>**O.E.C.D**<br>.........<br>**.**<br>........<br>6|
||Changes in Fuel Use in the O.E.C.D .................<br>8|
||Total Industry Disaggregation by Countries within<br>th O.E.C.D<br>.................<br>11|
||Fuel 8hares in Various Industrles in O.E.C.D ........ 15|
||Fuel Use Outside the O.R.C.D ......................<br>Is|
||Fuel Use in Industry OuteLde the O.Z.C.D ............ 18|
||Fuel Use in Power GoneratLon Outside the O.E.C.D.o. 20|
|CNAPT3R 3:|NXDTOD8 FOR 3STXZRTIU<br>-E*UUSTZTUTA5ZLITY OF|
||EconometrLc Studies of Fuel Substitutability ........ 28|
||The Engineering Approach ............................ 40|
||Non Site Specific Choice of Power Generation<br>Technology ......99*oe...........**...**<br>o<br>.....................<br>42|
||Site Specific Choice of Power Generation<br>T,echnlologWyo<br>......<br>.***.....<br>.....<br>...................................................<br>*45|
||A Comparison of the Econometric and Engineering<br>Fuel Substitutabllltyo .... .....**.........**<br>o<br>..... ............<br>49|
|CRIMTCR4t|**UVIDECS**<br>**ONFMU**<br>**WITCEI**<br>Z LATSCICIS<br>..............<br>53|
||Elasticities in the Electrical Power Generation<br>Sector.****...**a<br>.... ,....<br>o .<br>.....<br>*53|
||Elasticities of Fuel Switching in Industry .......... 56|
||Problems of Using Estimates of Inter-Fuel<br>Fuel Substitutability .......................................<br>68|
|BIBLIOGuuRPY ..|*.**.........<br>..........<br>..........<br>.....<br>75|
> - li -

# List of Tables

|Table 2.1|The Use of Fuel by Sector in the O.B.C.D.<br>in 1988...................<br>****<br>.. *....<br>o....<br>qq***v***7|
|---|---|
|Table 2.2|Shares of Fuel Used by Industry in MTOB for<br>the O.E.C.D. in 1971 and 1988**..........................**<br>**. .** 8|
|Table 2.3|Shares of Fuel Used for Power Generation in<br>the Public Sector in the O.E.C.D...0000-0<br>...... 09|
|Table 2.4|Cost of Fossil Fuels at Steam Electric<br>Utility Plants in Cents/BTU for the UoSoA...oo...,1.1...ll|
|Table<br>2.5|Shares<br>of<br>Fuel<br>Used by Industry<br>in MTOE<br>for the O.E.C.D.<br>in 1971 and<br>1988<br>...............<br>ooo*oo12|
|Table<br>2.6|Shares<br>of Primary<br>fuelsin Power Generation<br>for O.Z.C.D. Countries in**1971..............**<br>**.**.......<br>-13|
|Table 2.7|Shares of Primary Fuels in Power Generation<br>for O.o.C.D. Countries in 1988.....<br>.......<br>o.oooooo9.ooo14|
|Table 2.8|Fuel Shares in Various Industries in<br>O.E.C.D. Europe in 198S... ...... o.o.o...o.o..o..oooo*z16|
|Table 2.9|Fuel Shares in Various Industries in<br>0.o.C.D.<br>Europein**1970.........**<br>**.....**<br>**17**|
|Table 2.10|Shares of Fuels in the Industrial and Construction<br>Sectors for Certain Non-O.E.C.D. Countries in 1986 ... oo19|
|Table 2.11|Fuel Shares in Thermal Power Generation in<br>Non-O.E.C.D.Countries in**1986..............**<br>**.**.e*##.21|
|Table<br>2.12|Fuel<br>Shares<br>in<br>Thermal<br>Power<br>Generat&on<br>in<br>Non-O.E.C.D.<br>Countries<br>in<br>1976.<br>**.**<br>**..**<br>**...... .**<br>**. .**<br>**.o.24**|
|Table3.1|Comparative<br>Assessment<br>of Generation<br>Costs<br>for Large Power Systems .........<br>eee..................43|
|Table 3.2|Comparative Emissions for Gas Combined Cycle<br>and CoalSteamPlant per**kwh..........**<br>44|
|Table 4.1|Griffin's Estimates of Own Price<br>Elasticities in the ElectricitySector<br>**............**<br>**54**|
|Table 4.2|Griffin's Estimates of Cross-Price<br>Elasticities in the Electricity Sector .....<br>.......<br>55|
|Table 4.3|Pindyck's Partial Own Fuel Price<br>Elasticities for the Industrial Sector.. ............... 57|
|Table 4.4|Pindyck's Cross Price Partial Fuel<br>Ilasticities for the Industrial Sector ............ *.....57|
- iii. -

|Table 4.5|Plndyck'c Total Own Prlce ElastLcLtLes for<br>the Industrlal<br>80ctor...................................58|
|---|---|
|Table 4.6|Plndyck's Total Cros. Prlce Fuel BlastLcLtLes<br>for Industry.,-*...,...<br>........ . ............<br>58|
|Table 4.7|ConuLdLne's Partlal Prlce ElastLcltLes for<br>U.S. Statlonary Fuel CombustLon ln 1970 ........ ,<br>........ 61|
|Table 4.8|ConsLdLne's Partlal Prlce ElastLiLtLes for<br>U.S. Statlonary Fuel CombustLon ln 1985 ..................62|
|Table 4.9|Plndyck's Partlal Prlce ElastLcLtLes for<br>U.S. Industry ...................<br>..<br>.**....<br>63|
|Table 4.10|Mountaln and HlaLo's Partial Cross Price ElasticitLes<br>for IndustrLes in Quebec or 1974 .............<br>*65|
|Table<br>4.11|Costs of Electricity Generatlon by Type of Fuel .......... 74|
**ameUrIMSf** E **s05!ttLt**

DE **OPMR-PnM**

# 1. n as

In studying **the** use of policy instruments for reducing pollution in the most practical fashlon, attentlon has been focussed on the poselbllity of encouraging fuel substltutlon **as** a **feaslble** approach. Power generatLon and several Lndustrial processes (as well an domestLc heatlng) can use alternatLve fuels. **Since the** pollutlon characterLtLces of fuels vary very wldely, an effectLve fuel awLtching program could often make a substantial contribution to the reduction of pollutLon ln a regLon and ln a country **as** a whole. Two lmportant and lnter-related Lisues are lnvolved ln thle process. The fLrat li the purely technical issue of the extent to whlch a process can be switched to a dLfforent fuel, and **the** second li that of the esze of LncentLves/dLsLncentlves required to brlng about such a substltutlon. By looking at exLitLng experLence lt wlll be poseLble to obtaln evldence on both the extent to whlch fuel swltchlng mlght occur and also the degree of prlce Lntervention that would be needed to bring thLs about. Section 2 reviews the avaLlable evldence on fuel mix and lts change over time: such data w11 form a benchmark for assessing the potentiality for chvnge in a given country or industry.

ThG third section reviews econometrle models of fuel substLtution and their analytical propertles. Thli is compared and contrasted with the engLneerLng appreach whlch, through a form of cost/beneflt analysis, soeks to identlfy for a glven plant the prLces at whlch the optimal fuel choice would swltch. The fourth sectlon revliws actual evidence on prlce elasticLties for fuel swLtching and dLicusses how such data mlght be used ln an assessment of the potentlal for fuel switching. The maln ecaclusions of the paper can be suammrized as follows: (L) The central problem of evaluating a potentlal pollutlon reductlon program through price related incentives in countries **outside the** O.Z.C.D. is lack of informatlon. An economy wlde program **reqpires** data on the whole industrial and power **sectors** Ln varLous forms. For econometrLc analyLsi time se-Les of fuel **sha rs** ln induetry (preferably disaggregated) and ln power **are** requlred, while for mLero-based engineorLng studles data on a large number of plants would be requLred.
-2-

- (Li) Given the difficulty of obtaLning such data an attractive alternative would be to use elasticLties or fuel switching prLces estimated from those countriLe where data is more readlly available. However from the lsues surveyed in thLs paper it appearo that the opportunity to "import" reliable informatlon of thic type may be very limlted.

- (LiL) Engineering stuiies on fuel .witchlng in industry are rarely avallable. Teihnology Ls so specific to the process studied that the lack of homogeneity in output makes this option of very little interest for extrapolation.

- (iv) EngineoerLng studies of fuel switchlng in power generatlon do exLit ln large numbers. The simpler type of study compares the total costs of alternative methods of generating a given amount of power on a greenfield aLt.. The availability of standard cost and operatlng data as well as local fuel costs and associated infrastructure costs allow a series of alternatlve scenarios to be evaluated and the price at which (ex ante) fuel switching would just be worthwhlle to be identified. This approach is clearly of value in that lt can be readilly extrapolated to any country or aituatLon by alterlng a few parameters (e.g. fuel prices at the burner tip). Variations in local conditions may imply that there is no general answer, but the implied spread sheet methodology would be easy to use and some of the key parameters (capital costs and technlcal performance of alternative technologies) would be universal. The limitation of this type of analysis is that it is really applicable only in a very special aituation. A program to reduce pollution could well be

- economy wide, which would involve all plants in the power generatLon system. The need to consider the system raises two crucLal points for the power sector. First, much of the capital is already installed so that reducing pollution would involve conversion or scrapping decisions - both of these require information on the system in a particular country. Secondly, the fact that the power industry is in practice operated interdependently because of p;ak loads and uncertain demand, means that in considering whether to alter one plant (or even how to add capacity) the total system requirements need to be taken into account. Such a procedure is well established in power system planning, but the implication is that there is no simple or general guide to the prices at which varLous amounts of fuel substLtution take place - the answers are system speciflc.

- (v) Econometric studies of fuel switching in both the power and industrial sectors have been carried out for the industrialized countries. An attractive feature of the econometric approach is that it is not (typically) plant level, but rather is for the whole economy. It gives an average response which in principle could be used for economy wide policy analysis. There are two key issues which must be resolved satisfactorily before such eatimates can be used:

- (a) are the eatinated values an accurate guide to what is the actual situation in the country of study?

- (b) can estimates from one country or industry be used as reliable guides for another country or lndustry?
3-

- (vi) The problems of the econometric approach are reviewed in some detail. For studies pertalnlng to a given country four issues appear to be important in assesolng the reliabillty of the eotlmatest

- (a) The cholce of functi ial form: thli has been the toplc of greatest concern. DLfferent flexLble cost functions have been estlmated. A common feature is the property of non-constant elastLiLties of substitutLon. If such functions are used then the elasticLtLes vary with the fuel shares at whlch they are evaluated - there is no unlque elasticlty of substitution; simple comparisons between industries, time periods or countrles are not meaningful unless allowance li made for thls factor. Recent work suggests that, at a given fuel share, the elasticitLes are fairly robust wlth respect to the choice of flexible functlonal form, although the work of Conidine, using a completely new functlonal form, does not fully support this view.

- (b) The dynamlcs of the fuel choice response: early studies showed that pure tlme serLes data tended to yield lower prlce elasticities than cross-section data. Thls has been lnterpreted as suggesting that the period of adjustment li lengthy so that static models may well underestimate the total responsiveness of fuel cholce to price changes. Little systematic investigatlon of thLs issue has been undertaken, but recent models which allow for a crude dynamic adjustment through an autoregressive error process do yield values substantially higher than some earlier studies.

- (c) The level of aggregation of the lndustrial sector: the shifts in industrial composltlon over lengthy time periods could imply aggregation bias for elasticitles if the values for indivldual industrieo are substantially different. The recent work on Canadian manufacturing suggests that the valueos are very similar when evaluated at their fuel shares for the same year, so that estimates which were based on the aggregate sector may not be strongly dependent on the estimation perLod for reasons of changLng aggregation.

- (d) The stability of estimates: much of the econometrlc estimation of fuel price elasticities has been based on data which finished in the 1970s or early 1980s. Given the very large oil price rise at the end of this period, and the likelihood that full adjustment is a lengthy process, it seems that were the structural cost parameters to have changed then this would not have been captured by such estimates. There has been apparently no check on structural stability and, only in the most recent studies has an attempt been made to allow for shifts in the cost function induced by technical progress. Even if there is insuffLicent data to test for structural stability, it would be desirable to base eotimation on data including as much of the recent past as possible.
-4-

Within **the** limitations implied by **much** comments it does appear that the econometric approach is well established and capable of glving reliable elasticities of substitutLon at an aggregate level.

- (vii) The issue of whether structural parameters are simLlar for different countries has received rather little attention. Models which have pooled data and provided different elasticities have done so on the baLsi of evaluations at different market shares, rather than on allowlng the cost functions to dlffer. Problems of degrees of freedom have previously made thli an impractlcal task, but with the accumulatLon of larger time series Lt should be possible to attempt tests for equalLty of cost structures, and hence for implied equallty of elasticitile at the same fuel Phares. A major gap in evidence is the lack of such studies applied to countries outside **the** O.R.C.D. At present if a model is to be used it would have to be based on highly industrialized countries.

- (viii) Econometric studies of the power sector are much less common so that there **is** less experience on their performance even in advanced economLes. Here there are difficulties, even at an economy wide leva! caused by existing patterns of fuel uses

- (a) many countries, as documented in sectLon 2, only uoe a subset of the three bauic fuels for power gener'ation. This leads to modelling difficulties thep have largely been ignored ln the use of models which are based on the use of all the fuels. This is of particular importance when the aim might be to induce the choice of a fuel not previously utillsed.

- (b) the use of hydro, nuclear and bLomass for power generation has not been explored in econometric modelling of power generation. Studles so far have confined themselves to shares of gas, coal and oil. The nuclear decision may have had a large political element in industrial countries, although it is irrelevant to most developing countries at this stage. Hydro power is seen as being so attractive that it is automatically used first where available and there is no question of price driven substitution. Biomass Li unimportant in the industrialized world, but plays a role Ln fuel choice in many developing countries. Here the problem may be to identify the price of the fuel itself.

- (lx) It would appear that there is certainly insufficient evidence on the quantitatatlve aspects of fuel switchlng to assess the potentlal for government price based policies wlthout further research. The nature of the research needed depends on the nature of the policy envisaged. A specific power plant oriented policy would be best served by an engineeri:j study where an initial assessment might well be based on exLsting calculations whlch were not system specific. If the initial asseessment appeared to indicate a viable program then a more deta±led on the spot study would be needed. For individual industrial plants it
(x) appear that a detailad study would be needed in any case. For economy wide polLeLes an econometrcL study of the country ln que-tlon could be undertaken if there were sufflcLent data avallable. **ThLe** should be ponsLble for both the power and LndustrLal sectors Ln the **cae-** where the attractlve fuel **was** already in use. **Where** lt Lad not prevLously been used it mlght be posesble to carry out econometrLc LnvestlgatLons for sLmilar countrLes where the fuel was already ln **use.** For small economLes it mlght be posslble to carry out a parallel 4 -vestlgation of fuel choice ln tho power sector using both iconometrLc and engineering fuel prlce swltchlng analyaLs on an ax post basLs. Only wlth such an lnvestlgatLon would it be poseLble to know whether the varlous simplificatLons Lntroduced ln econoeotrLc modelling dlitort the quantlficatlon of the potential for fuel switchLng.
- 6 -

# 2. VXflD= ON KNIM-_UL MMSTIMuZOTI

> In order to **assess the** *conomlc viabillty of pursuing a fuel swLtching program it is necessary to havo some assurance that lt li possible to change the propertlons of different fuels used **as** lnputs to dlfferent processes. Some aggregate evldence can be obtalned by examining country statlitics dLiaggregated by **use** sector and over tLr-w

The most comprehensive energy statletlcs avaLlable are those for tho twenty-flve countrles covered by O.3.C.D. publlcatlons. These exlet on a comparable basis for a lengthy perLod of time (more than twenty yaars) and have the greatest degree of dLeaggregatLon. AccordLngly these are taken as a benchmark for lnvestlgatlon of fuel swltchlng ln a wider context. The O.E.C.D countrLes are the highest lncome and most industrialized natLons and many are rlch ln natural sources of energy so that thelr patterns of energy use are not likely to be typlial of that of the rest of the world. Nevertheless the varLatLons shown both between countrLes and for a given country over tlme will indicate the potentlal at high income standards for variatLons in fuel use. FUad **Use** in **the** O.3.C.D.

The startlng polnt for the analysls i$ the overall breakdown of fuel use by sector for the whole of the O.3.C.D. lr a recent year. Table 2.1 shows the use of varlous forms of fuel as lnputs lnto dlfferent sectors of the economy. Each fuel li converted lnto tons of oll equLvalent (to compare the amount of fuel to provlde the same energy output) uslng standard conversLon factors. These flgures for a z.Acent year, well after the effects of the two oil shocks have had time to take effect ln producing aubstitution away from oil, lndlcate several important features of the overall energy market.

- (i) In transport and agrlculture oll is stlll the domlnant fuel. SubstitutLon between fuels Ln these sectors has not responded much to the enormous price shlfts of the past. Clearly the technological advantage of oll ln these sectors is such that fuel substitution will not respond eaelly to prlce LncentLves, although the growth of the sector as a whole and hence their use of **energy** may respond to fuel prlcee.
-7-

Table 2.1t The Use of Fuel by lector ln thg O.F.C.D. in 1988 (Milllon tons of OiL EquLvalentt MTOE)

||Coal|Other<br>Fuel|OI|Gas|ElectrLcLty|Total|
|---|---|---|---|---|---|---|
|Industry<br>Transport<br>AgrLculture|168.85<br>0.13<br>0.21|69.86<br>0.00<br>0.44|297.73<br>861.13<br>40.72|234.56<br>0.40<br>3.48|195.42<br>6.05<br>3.76|949.38<br>867.71<br>48.61|
|Commerce/Publlc<br>Sector|4.18|0.17|86.24|87.72|121.42|302.06|
|ReusdentLal|17.48|43.56|117.34|198.56|150.49|529.58|

Source Energy Balances of the O.E.C.D. countries, 1987- 9P8, I.E.A. 1989.
- (Li) The residentLal and commercial **sectors are** both of consLderable lmportance and **use** substantial amounts of oll, gas and slectrieLty. These sectors are likely to be relatively loss important ln developing countrie where commerce and home heatlng L typlcally much uss developed.

- (LiL) The Lnis-try sector ls the largest, and overall is the only **sector** to use substantial amounts of coal. Since coal tends to **have the** worst pollution characteristics this sector li of key lmportance for studying the potential for fuel switching.

- (Lv) BlectrLcLty, whlch **ls** a secondary fuel, uslng the other prLmary fuels for its goneratLon, ls very Lmportant when viewed in total. Nearly 500 MTOE of electrLLty is used so that the fuel mlx ln thli single intermediate se*tor is worth studylng.

These figures show that the key sectors for study are Lndustry and power goneratLon, although more evidence ls requlred to show the potentlal for change withLn these sectors. Chanass **i el Use** in **the** O.3.CD.A-

**The** lengthy run of consistent data published by the I.E.A. from the O.E.C.D. countries allows **a** comparison to be made between 1971 and 1988. The earlier year **is** before **the** first oil shock so that changes between the two dates reflect changes due both to growth and to shLfts in the price of fuel to other inputs and in the relatLve price of fuels. Table 2.2 glves data on shares of fuel use by the industrLal sector.

Table 2.2s Shares of Fuel Use by Industry in ITON for the O.E.C.D. in 1971 and 1988

||Coal|Other<br>Solid<br>Fuel|Oll|Gas|ElectricLty|Total<br>(MTOE)|
|---|---|---|---|---|---|---|
|1971|20.2|3.5|39.7|23.1|13.4|946.95|
|1988|17.8|7.4|31.4|24.7|20.6|949.38|

Sources As for Table 2.1.

This table reveals two key poLnts for the study of fuel substltutlon over the perLod:
-9-

- (1) The effectn of the increase in fuel prices relative to other lnput prices have been so strong that, despite the very substantial growth in the industrial sector as a whole of 62%, no more fuel was used as an input in 1988 than in 1971. Without the growth in the sector the use of fuel would have declined substantLally.

- (ii) As between fuels there has bea' a strong move away relatively (and absolutely) from the use of oil in industry as a primary fuel.

# In addition it can be eeon that:

- (iLi) The total use of solid fuel has increased slightly, which has implications for pollution.

- (iv) The use of gas has also increased but only by a small amount, which is interesting given the discoveries of substantial gas resources in several O.B.C.D. countries.

- (v) The main increase in fuel use io from the secondary source of electricity. Without an analysis of the fuel mix for power generatien it is not possible to see whether ln fact there has been substantial switching between primary fuels.

For the public power generation sector in the O.R.C.D. the shares of primary fuels used in 1971 and in 1988 are shown in Table 2.3.

Table 2.3s Shares of Fuel Used for Power Generation in the Public Sector in the O.F.C.D.

||Ot<br>Coal|her Soli<br>Fuel|d<br>Oil|Gas|Nuclear|Hydro|Total<br>MTOE|
|---|---|---|---|---|---|---|---|
|1971<br>|38.6<br>|0.1<br>|19.9|14.1|2.9|24.2|301.0|
|1988|42.4|0.2|7.5|8.3|24.3|17.3|528.6|

source: As for Table 2.1.

In relating this table to the two previous tables it is important to bear in mind two limitations imposed by the nature of the data. Firstly, this **is** for public sector power generation rather than total power generation as in table 2.1. secondly, table 2.3 is for all uses of electricity rather than for tho use by the industrial sector as in table 2.2. However since all end uses of electricity in a glven system can be assumed to use the same fuel mix, the relative shares in table 2.3 can safely be taken as representative of the use by industry. Private sector power generation may have a different pattern of
- 10 -

primary fuel use, but its relative unimportance (about 5% in 1988) allows the figures of table 2.3 to stand for those of the whole power sector.

The data in table 2.3 reflects the increase in the total use of electricity both relatively, as ln industry, and absolutely as Ln other sectors. The shares of primary fuels show the great decline in the share of oil and more surprisLngly declines it} the shares of gas and hydro. At the same time tne share of solid fuel increased slLghtly while the share of nuclear increased enormously. The decline in the shares of gas and hydro are not associated with an absolute reduction in their use - rather they have captured in effect very little of the increase ln the generation of electricity. The major changes in absolute terms have been the fall in the use of oil and the increase in nuclear.

The rise ln nuclear reflects political decisions taken in several O.E.C.D. countries to opt for this form of generation. Often the Lming of these decisions go back many years and with the increased concern over safety it is possible that this pattern will not be repeated in countries industrializing at later dates.

The fall in the share of cil relative to all other fuels does indicate that within the power generation sector there is scope for fuel substitution although, if nuclear ia netted out, most of this was towards solid fuels rather than gas.

The picture for the O.E.C.D. industrial sector as a whole shows a double substitution away from oil both as a primary fuel and as a secondary fuel through its decline in power generation. Solid fuels have shown a substantial increase on both counts, while gas shows a modest increase through its share in the increased use of electricity. Tables 2.2 and 2.3 could be combined to give the total primary fuel use by industry lf an energy lose figure between the input and output of power generation were available for the two dates.

in order to place these quantity shLfts in focus it would be useful to know the behavior of fuel prices for the O.E.C.D. as a whole. There are no published aggregate fuel prices so that a full picture cannot be given. However for the U.S.A. there are prices available for the different fuels as purchased
11 -

-

by steam raising electric utility plants. Table 2.4 gives data on these for the widest span of years available (1973 and 1988) and also for the intermediate year 1981 when the oil prices were at their peak.

Table 2.4: Co.t of Fossil Fuels at Steam Electric Utility Plants in Cents/BTU for the U.S.A.

||Coal|Heavy Fuel<br>Oil|Gas|All Fuels|IndustrLal<br>Good Price.<br>index|
|---|---|---|---|---|---|
|1973<br>|40.5<br>|78.5<br>|33.8|47.6|100.0|
|1981<br>|153.2<br>|533.4<br>|280.5|225.6|241.5|
|1988|46.6|240.5|226.3|164.3|263.9|

Source: (EZIA Monthly Energy Statistic.).

This table shows firstly that fuel priceas rose very sharply relative to other prices in the period 1973 to 1981 but fell back some of the way by 1988. The price of heavy fuel oil relative to coal nearly doubled in the first period but fell to a low level by 1988, while the price of heavy fuel oil relative to gas fell throughout the period (the price of coal relative to gas rose slightly ln the second period). Although the U.S.A. is not representative of all countries in the O.E.C.D. it is by far the largest user of energy so that these figures are suggestive. There was a sharp increase in the relative cost of fuel associated with the two oil 'shocks', but by 1988 much of this had been reversed. However, with investment taking tlme to plan and Lnstall, the reaction to the fuel price decline may not yet be fully felt. There were also large shifts in relative fuel prices. The degree of fuel switching that this induced will again be difficult to assess because of the need to take into account lags in adjustment. This has important implicatlons for econometric modelling. Total industry DLaaaareqgation bv Country withln the O.3.C.D.

The picture so far established ie for the O.E.C.D. as a whole. This grouping of countries certainly includes a considerable degree of heterogeneity.
- 12 -

For induotry as a whole the fuel **sharea** for 1988 are shown for each country in table 2.5.

Table 2.5: Fuel Shares in Industry for O.B.C.D. Countries, 1988

||Coal and<br>Other<br>Solid Fuels|Oil|gas|Electricity|
|---|---|---|---|---|
|Australia|31.7|16.4|29.7|22.2|
|Austria|32.5|16.8|27.1|23.7|
|Belgium|29.5|28.0|22.9|18.2|
|Canada|19.6|21.5|33.1|25.0|
|Denmark|16.8|40.1|14.2|26.6|
|Finland|44.0|19.8|7.5|27.5|
|France|21.0|34.2|23.7|21.0|
|Germany|24.7|30.0|22.4<br>|22.8<br>|
|Greece|27.7|45.5|2.4|24.3|
|Iceland|16.7|25.0|0.0|61.1|
|Ireland|19.5|35.7|30.3|14.5|
|Italy|11.6|34.4|32.2|21.7|
|Japan|29.4|41.3|3.0|26.3|
|Luxembourg|58.8|15.8|11.5|3.9|
|Netherlands|10.8|34.9|41.0|13.2|
|Now Zealand|34.0|7.5|32.7|25.7|
|Norway|17.1|31.5|0.0|51.2|
|Portugal|24.2|58.2|0.0|16.9|
|Spain|19.6|47.8|10.5|22.1|
|Sweden|38.5|24.0|1.4|35.2|
|Switzerland|14.9|31.3|15.8|36.5|
|Turkey|41.0|40.7|1.4|16.9|
|United Kingdom|<br>21.0|32.6|27.6|18.7|
|U.S.A.|26.2|24.8|32.2|16.8|
|Yugoslavia|12.9|27.2|29.6|24.3|
|O.B.C.D.|25.2|31.4|24.7|20.6|

foureat As table 2.1.

This table shows that for industry as a whole there are very substantial variations in fuel shares between the industrialized countries at the same point in time. However these variations cannot be taken literally as defining a range of variations that is available for each and every country. Two major influences on fuel share may be largely country specific, thus placing 'imits on the potential for fuel switchings

- (i) the local availability of primary **sources** of energy. Although oil is freely traded and coal Lo also often traded, the trade in gas in much more limited by the enormous infrastructure costs involved. Countries with thelr own gas reserves (e.g. U.S.A., U.K., Netherlands) or very near to **gas** reserves tend to use much
- 13 -

more gas than those far distant from such reserves (e.g. Iceland or Portugal).

- (Li) the industrial mix plays an important role in determining fuel share as shown below. If different industries tend to favor different fuels then the pattern of industrial specializatlon<sup>will</sup> be important ln explaLnLng the fuel shares in the total industrlal uector. since the LndustrLal pattern responds to many other factors than the relative price of dlfferent fuels, it is unlLkely that lt would be posslble, by changLng the<sup>attractiveness of</sup> dLfferent fuels, to achLeve the range of varlatlons ln fuel use lllustrated across the range of O.B.C.D. countries. It is possible to glve equivalent figures for an earlier date,<sup>but glven</sup> the large changes ln industrial structure that have taken place over the last twenty years in many countrLes this is left to<sup>the</sup> more disaggregated analysi below.

fuel Shares in Electriclty por Individual O.B.C.D. Countries The use of primary fuels for power generation (valued in<sup>NTOE) is</sup> avallable over a long perlod of tLme for all the O.B.C.D. countrles.<sup>Slnce power</sup> generatLon is a sLngle lndustry there ls no problem of changing shares of<sup>output</sup> to contend with as for the total LidustrLal sector. Table 2.6 gives<sup>shares for</sup> 1970 and table 2.7 shares for 1988.

Table 2.6S hares of Primary Fuels in Power Generation for O.E.C.D. Countries ln 1971

|Coal andOther<br>SolLd<br>Fuels|Oil|Gas|Nuclear|Hydro|
|---|---|---|---|---|
|Australia<br>75.60<br>Austria<br>9.56<br>Belgium<br>33.64<br>Canada<br>17.91<br>|4.62<br>6.91<br>52.08<br>3.06<br>|0.86<br>12.21<br>13.29<br>3.04<br>|0.00<br>0.00<br>0.19<br>0.49<br>|18.39<br>70.72<br>0.81<br>75.49<br>|
|Denmark<br>31.21<br>Finland<br>20.20<br>France<br>30.22<br>Germany<br>68.62<br>Greece<br>38.32<br>Iceland<br>0.00<br>Ireland<br>1.24<br>Italy<br>4.8<br>Japan<br>16.72<br>Luxembourg<br>47.21<br>Netherlands<br>19.81<br>New Zealand<br>6.12|68.67<br>27.55<br>21.97<br>15.00<br>34.84<br>2.96<br>53.79<br>48.80<br>58.47<br>11.41<br>32.60<br>3.31|0.00<br>0.00<br>4.50<br>5.53<br>0.00<br>0,00<br>0.00<br>4.85<br>1.25<br>0.09<br>46.69<br>0.06<br>|0.00<br>0.00<br>3.89<br>2.49<br>0.00<br>0.00<br>0.00<br>2.70<br>1.28<br>0.00<br>0.90<br>0.00<br>|0.12<br>42.10<br>38.94<br>7.32<br>26.84<br>97.04<br>13.85<br>37.49<br>22.28<br>41.29<br>0.00<br>90.51<br>|
|Norway<br>0.00<br>Portugal<br>4.90<br>Spain<br>21.65<br> <br>|0.63<br>14.44<br>27.14<br>|0.00<br>0.00<br>0.01<br>|0.00<br>0.00<br>1.63<br>|99.37<br>78.18<br>49.49<br>|
|Sweden<br>0.28<br>|30.92<br>|0.00<br>|0.09<br>|68.49<br>|
|switzerland<br>0.00|5.92|0.00|4.98|89.10|
|Turkey<br>34.55<br>|30.30<br>|0.00<br>|0.00<br>|35.15<br>|
|United Kingdom 68.48|18.48|0.33<br>|10.44<br>|2.27<br>|
|U.S.A.<br>46.39|12.12|24.58|1.44|15.46|
|O.E.C.D.<br>38.70|19.90|14.10|2.90|24.20|

Sggrces A for table 2.1.
- 14 -

Table 2.7s Shares of Primary Fuels in Power Generation for O.B.C.D. Countries in 1988

|Coal and Other<br>Solid Fuels|Oil|Gas|Nucloar|Hydro|
|---|---|---|---|---|
|Australia<br>75.84<br>Austria<br>8.97<br>Belgium<br>24.81<br>Canada<br>18.51<br>Denmark<br>93.30<br>Finland<br>18.67<br>France<br>7.31<br>Germany<br>51.14<br>Greece<br>73.03<br>Iceland<br>0.00<br>Ireland<br>56.90<br>Italy<br>16.70<br>Japan<br>14.94|1.85<br>4.21<br>2.63<br>2.41<br>4.55<br>2.66<br>1.50<br>2.58<br>18.91<br>0.16<br>7.33<br>44.12<br>29.07|10.81<br>10.14<br>3.90<br>1.42<br>0.94<br>4.89<br>0.56<br>6.80<br>0.28<br>0.00<br>26.67<br>15.93<br>19.38|0.00<br>0.00<br>65.96<br>16.43<br>0.00<br>36.37<br>70.30<br>33.65<br>0.00<br>0.00<br>0.00<br>0.00<br>23.70|11.16<br>75.75<br>1.79<br>60.79<br>0.13<br>24.85<br>20.10<br>4.80<br>7.78<br>99.84<br>9.11<br>22.91<br>12.90|
|Luxembourg<br>28.85<br>Netherlands<br>35.71|4.28<br>5.45|1.43<br>52.33|0.00<br>5.28|61.98<br>0.00|
|New Zealand<br>2.08|0.00|17.29|0.00|80.63|
|Norway<br>0.04|0.35|0.00|0.00|99.56|
|Portugal<br>26.82|15.57|0.00|0.00|54.71|
|Spain<br>31.28|5.07|0.80|36.37|26.09|
|Sweden<br>1.29|1.58|0.06|47.37|48.31|
|Switzerland<br>0.05|0.47|0.43|37.55|60.64|
|Turkey<br>25.99|6.88|6.74|0.00|60.39|
|United Kingdom<br>67.05|9.48|0.62|20.59|2.26|
|U.S.A.<br>57.31|5.54|9.42|19.45|8.22|
|O.E.C.D.<br>42.60|7.50|8.30|24.30|17.30|

Both tables show that at a given point ln tlme there are enormous variations between countries ln the fuel mix used for power generation. The most extreme examples of thli are those countries where there are suitable conditions for the use of hydros in Iceland, Norway and New Zealand virtually all power came from hydro even in 1988. Natural resource endowment is certainly a crucial factor in explaining the choice of fuel.

Comparing the two tables shows that in the power sector the principal change in share has been the increase in nuclear generation for certain countries (e.g. BelgLum, France and Sweden). Some countrles have not chosen thls route (even excluding those with abundant hydro power e.g. Australia, Denmark and Italy) and this suggests that the decision to move to nuclear has been largely determined outslde strict cost conditions (nuclear being in effect a highly
- 15 -

traded product with few countries being so abundant in the raw materlal<sup>that</sup> there **ia** a local cost advantage). In countries whlch dld not<sup>opt for nuclear</sup> power durlng this twenty year period there has certainly been substantlal lnter fuel substltution. For example, Australla decreased the<sup>shareof</sup> oll and increased that of gas, whlle Denmark and Greece switched from<sup>**a**heavy reliance</sup> on oil to coal and solid fuel. The picture from O.B.C.D.<sup>countries is that</sup> wlthln the homogeneous power generatlon sector not only<sup>is fuel substitution</sup> posslble, but that it actually took place and that in some countries<sup>there was</sup> an extraordinary shift in the fuel mix used.

Fuel Shares in Various Industries Ln the O.R.C.D.

As argued above the LndustrLal sector is the major user of energy<sup>and</sup> fuel substitution ln thLs area is of great importance for pollcy purposes. However the figures for the aggregate sector are not a complete guide to fuel choice since different industries have very different patterns of fuel choice. Hence shifts in the relatlve importance of an indlvidual<sup>sub-sector (e.g. the</sup> decline in lron and steel ln many LndustrialLzed countries) wlll produce changes ln aggregate fuel mix which are unrelated to the possLbilitLes for<sup>fuel swLtchLng</sup> to produce a glven level of output.

Data is available for a sub-set of the O.B.C.D. on<sup>fuel use by</sup> industrLal sub-sectors. Table 2.8 shows fuel shares for O.E.C.D.<sup>Europe for</sup> thirteen industrles in 1988.

These sectors, which vary considerably ln the technologLes<sup>used, show</sup> conslderable variations in their fuel mix. Excluding<sup>chemLcal feedstock, in</sup> whlch the oil and gas are converted into other products,<sup>there is stillagreat</sup> variation from the very coal intensive iron and steel<sup>industry to the electrlclty</sup> intensive non-ferrous metals lndustry. This table<sup>does not of course show the</sup> possibilitLes of fuel substitutlon, sLnce the technologies<sup>used are often</sup> industry specific; it does however show the dangers of using trends ln aggregate industrlal fuel shares to lndicate substitutLon posesbilities, Changes in industrial mlx could obviously bring about large changes in aggregate fuel mlx without any fuel substitution taklng place at a plant level. Of course factors
- 16 -

Table 2.8s Fuel Shares in Variouu Industries in O.B.C.D. Europe in 1988

|Coal<br>S|and Other<br>olid Fuel|Oil|Gas|Electricity|Energy shares<br>in Total<br>Industry|
|---|---|---|---|---|---|
|Total|22.7|33.4|21.6|22.2|100.0|
|Iron and Steel<br>Chemical|64.7|8.0|12.0|15.3|18.5|
|Feedstocks<br>other Chemicals<br>Non-ferrous|0.0<br>11.9|83.0<br>26.0|17.0<br>32.0|0.0<br>30.0|17.1<br>16.2|
|Metals<br>Non-metallic|6.6|16.8|13.2|63.3|3.8|
|Minerals<br>Transport|28.6|30.8|26.8|13.8|10.7|
|Equipment<br>machinery<br>|4.1<br>4.5|17.0<br>26.2|31.1<br>32.8|47.7<br>36.5|2.1<br>5.2|
|Mining/Quarrying<br>Food/Tobacco<br>Parar/Printing<br>Wood/|6.1<br>8.9<br>29.5|33.2<br>32.6<br>16.6|14.6<br>32.3<br>18.6|45.7<br>26.2<br>35.1|1.0<br>6.5<br>7.2|
|Wood Products<br>|37.0|15.9|5.3|41.5|1.2|
|Construction<br>|20.0|61.9|3.5|14.6|1.6|
|Textiles/Leather|6.1|34.4|23.7|36.0|2.7|

Sources As table 2.1.

which bring about change. in industrial structure will bring about changes in aggregate fuel mix, but such cdianges are not likely to be brought about by a desire to alter the overall pollution characteristics of an economy apart from exceptional eases. The projected decline of certain heavy industries in Eastern Europe is being welcomed as a positive contribution to reducing the very high levels of pollution in certain areas (Hughes (19911).

Comparison of the disaggregated industrial data for 1988 in table 2.8 and that for 1970 shown in table 2.9 does allow an insight into the possibilities of fuel swltching for a specific type of technology. However it must be noted that even for industrial data disaggregated into 13 sectors there will still be some changes due to the particular product composition changing within a given sub-sector.
- 17 -

labla 2.9t Fuel **Shares** in Varlous industrLes ln O.F.C.D. Europe in 1970

|Coa<br>S|l and**Other**<br>olLd Fuel|Oll|Gas|ElectrLclty|Energy<br>Shares<br>inTotal<br>Industry|
|---|---|---|---|---|---|
|Total|27.0|50.7|8.5|13.8|100.0|
|Iron and Steel<br>Chemical|63.7|19.8|7.3|9.2|25.3|
|Feedstocks|0.0|99.7|0.3|0.0|8.2|
|Other ChemLcals<br>Non-ferrous|17.5|40.8|21.3|22.5|14.8|
|Metals<br>|20.9|22.1|8.0|49.0|2.7|
|on-meac||||||
|Minerals|20.7|54.2|15.1|9.9|8.9|
|Transport||||||
|Equipment|13.8|58.0|3.9|24.3|1.3|
|Machlnery|9.5|54.3|22.9|13.2|3.3|
|Mining/Quarrying|1.7|60.5|10.3|27.1|0.9|
|Food/Tobacco|13.4|64.9|8.2|13.5|4.7|
|Paper/Printing<br>|11.5|56.2|5.7|26.7|4.6|
|Wood/||||||
|Wood Products|58.2|33.0|0.3|8.5|1.0|
|Construction|0.0|88.8|0.7|10.5|0.5|
|Textile/Leather|6.1|72.6|6.4|14.9|1.9|

Thio table illustrates the substantLal changes in the relatlve importance of the different industries (particularly **the** rlse in the chemical feedstock industry and the decline ln lron and steel). Wlthin industries there has also been substantial change in the fuel mlx used. In every case the relatlve Lmportance of oll has decllned sharply and the shares of gas and electriclty have lncreased. For coal the picture **li** more variable. In some Lndustries (chemicals, nonferrous metals, transport equipment and wood) the share has decreased, while in others (non-metallic mLnerals and paper) lt has increased (the flgure for the constructlon lndustry in 1970 must be treated wlth cautlon). These tables indicate that there was strong fuel substitutLon wlthln industrLes anl that dLfferent patterns occurred in **the** varlous sub-sectors. This suggests that the potentlal for encouraging fuel substLtution ln other countrles may be very promisLng.

The experLence of **the** O.3.C.D. points to cortaln broad conclusLons which have relevance for other countriLs:
- 18 -

- (i) changes in the price of energy to other goods can produce large changes in energy intensity (ratio of energy to output). Any policy which alters the price of energy as a whole is likely to have important effects on the total amount of energy used and hence on the amount of pollution;

- (Ii) there have also been large swings in tho relatlve prices of different fuels coupled wlth large shifts Ln relative fuel use. The exact effects depend crucially on the lags Lnvolved so that to evaluate the full potential of a fuel switching policy it le necesoary to know the speed of adjustment to the pollcy changeg

- (lii) some of the biggest differences in fuel use between countries are assocLated with the domestic availability of the varlous fuels. Any fuel switching policy must allow fully for the price advantage of domestic as opposed to imported fuels.

# Fuel **lisa Out8ide** the O.3.C.D.

The evidence so far presented relates to the most developed sector of the world economy, for which the statistics are the most adequate. However, the focal point of interest is countries at earlier stages of development. Some O.E.C.D. countrles (e.g. Greece and Portugal) in the early 1970s were at a stage of development not very different from many other countries at the present day, but it would be unwise to draw too strongly on their experience which may have been dominated by special factors.

> There _is_ data on a wide range of countries published by the United Nations in "Energy Balances and Electricity Profiles". This covers 48 non O.E.C.D. countries for industry data and 75 non O-E.C.D. countries for power generation data. The time span of years avaLlabl- ^" much shorter so that the evidence on fuel substitution within countries on a consistent basis is rather limited.

Fuel Us, in Industry Outside th O.ELC.D.

Table 2.10 gives the shares of different fuels used by industry for 1986 where all fuels have been converted into energy equivalent units (tetrajoules). An important difference from O.E.C.D. experience is the use of bia-mass which in many cases is very significant and is shown separately.
- 19 -

> Table 2.10S Shares of Fuels in the Industrial and Const..uction Sectors for Certain Non-O.B.C.D. Countries ln 1986

||Coal|Oil|Gas|Electricity|Biomass|
|---|---|---|---|---|---|
|Argentina|2.9|22.5|46.6|16.4|11.3|
|Bangladesh<br>|6.0<br>|10.4<br>|35.1<br>|14.3<br>|34.3<br>|
|Barbados<br>|0.0<br>|14.2<br>|8.4<br>|17.8<br>|59.6<br>|
|Bolivia<br>|10.8<br>|32.6|12.9|10.7|33.0|
|Brazil<br>|16.3<br>|19.1<br>|5.8<br>|22.7<br>|36.1<br>|
|Chile|20.8|38.2|4.4|23.0|13.6|
|Colombia|41.3|14.7|16.6|12.0|15.2|
|Costa Rica|1.1|0.0|0.0|33.7|65.2|
|Ivory Coast|0.0|69.4|0.0|11.8|18.8|
|Cyprus|14.2|76.8|0.0|9.0|0.0|
|Ecuador|0.0|62.6|0.0|15.2|21.4|
|Egypt<br>|6.0<br>|59.7<br>|15.3<br>|14.0|4.9|
|El Salvador|0.0|32.2|0.0|10.8|56.1|
|Fiji<br>|4.9<br>|3.8<br>|0.0<br>|10.0<br>|81.2<br>|
|Gabon|0.0|64.8|25.3|8.0|1.9|
|Honduras|0.0<br>|32.4|0.0|9.5|58.1|
|Hong Kong|1.9|72.5|0.4|25.2|0.0|
|India<br>|68.5<br>|10.8<br>|2.7<br>|11.1<br>|7.0<br>|
|Indonesia|2.7|48.1|32.1|6.9|10.1|
|Israel<br>|0.0<br>|68.2<br>|2.6<br>|29.1<br>|0.0<br>|
|Jamaica<br>|0.0<br>|76.5|0.0|13.3|10.1|
|Jordan<br>|0.0<br>|87.7<br>|0.0<br>|12.3<br>|0.0<br>|
|Kenya|13.4|25.6|0.0|27.8|32.1|
|<br>South Korea<br>|27.0|52.7|0.0|19.3|0.0|
|Kuwait|0.0|42.4|55.5|2.1|0.0|
|Malawi<br>|12.1|2.9|0.0|12.3|72.7|
|Malaysia|8.2|65.2|10.4|14.8|1.2|
|<br>Morocco|3.5|78.4|6.4|11.7|0.0|
|Nepal<br>|25.9<br>|12.4<br>|0.0|10.5|51.1|
|Nicaragua|0.0|26.5|0.0|7.2|65.4|
|<br>Niger<br>|0.0|42.2|0.0|57.8|0.0|
|Nigeria|1.5|57.8|35.5|4.4|0.7|
|Pakistan|26.5<br>|15.2<br>|35.7<br>|10.2|12.4|
|Papua N.G.|0.0|28.3|0.0|26.9|44.2|
|<br>Peru<br>|2.6<br>|55.2<br>|2.3|24.6|14.0|
|Philippines|17.8|39.7|0.0|16.9|25.0|
|Qatar<br>|0.0<br>|0.0<br>|97.8<br>|2.2<br>|0.0|
|Saudi Arabia|0.0|81.0|14.0|5.0|0.0|
|Singapore|0.0|86.1|0.0|13.9|0.0|
|Solomon Islands<br>|<br>0.0<br>|95.9<br>|0.0<br>|4.1<br>|0.0<br>|
|Sri Lanka|0.2|21.4|0.0|11.7|66.7|
|Thailand|6.1|29.8|1.7|16.3|46.0|
|Trinidad|0.0|68.9|23.2|5.8|2.1|
|Tunisia|5.9|57.8|19.8|16.5|0.0|
|Uruguay|0.1|34.9|0.2|19.1|45.4|
|<br>Venezuela<br>|1.1|17.4|59.8|17.7|4.0|
|Zambia|32.0|16.3|0.0|43.2|7.7|
|Zimbabwe|58.9|3.7|1.5|17.6|18.4|

Sourcet "Energy Balances and Electricity Profiles", 1986, United Nations.
- 20 -

The table shows an even larger variation between countrLes ln fuel mix than ia the case for the O.3.C.D. Thli reflects .n large part the varlatlons Ln the endowment of natural resources. The oll producers not surprLiLngly uae large amounts of oll and gas, whlle those countrLes whlch do not produce oll use relatlvely little. CountrLes where there **li** ample materlal for bLomais, notably ln South AmorLca, use thLs to a great extent. The variatlons due to endowments may exaggerate the extent to whlch fuel substltutLon li economlcally feasLble wi.thLn a country for likely varlatlons in domestlc prLces. There will aido be very substantial variations ln the LndustrLal composltLon between theae countrLes whlch accounts for some of the varLatLons ln fuel mix. Nevertheless, they do lndlcate that at a technleal level lt is possLble to use a wlde range of fuel mix. Thli Ln turn suggests that there is subst&ntLal scope to lnduce such substLtutLon.

The domlnant use of hydro power ln certain countrles and oll in others is, as noted above, related to the domestLc avallabillty of the various fuels. ThLi is Ln effect related to the prLces (or potentlal prices) of the varLous fuels c.l.f. at the burner tip. If hydro capaclty has been Lnstalled then lts prlce can be so low (even allowing for the capltal cost element) that the price of any lmported fuel would be uncompetltive. As well as the general world f.o.b. cos0U- other fuels would need to add on the domestLc transport coat margin and associated capital cost where the infrastructure did not already exist (e.g. the costs of a terminal and pipeline for oil or gas). The effective hydro price could be very much lower than the potential oil cost so that a small change in the price of oil relative to hydro would bring no shift in fuel use. Fuel Usl in POMwr Gner-ation Outside the O.B.C.D. Data are also available for the fuels used ln power generatlon ln the non O.B.C.D. countries. For these countries nuclear is unimportant and hydro is **so** dependent on natural resources that the availability of figures for thermal
- 21 -

# 1bkk211: Pael 3hmrIn Thmi ft Gamuslm Is ma O.3C.D. Cwuas in 1936

||SCod ue|0il|**_Cm_**|Ool|_o<br>fterull<br>poe|
|---|---|---|---|---|---|
|AeIa<br>|0.0<br>|3.0<br>|97.0<br>|0.0|97.9|
|_Argonn_|3.6|42.6|53.8|0.0|40.8|
|Bansldes<br>|0.0|25.0|_7n.0_|0.0|90.6|
|Babado<br>|0.0<br>|100.0<br>|0.0|0.0|100.0|
|Bdia|0.0|100.0|0.0|0.0|100.0|
|Dow<br>|0.0<br>|100.0<br>|0.0<br>|0.0<br>|100.0<br>|
|Biltvl<br>|0.0|_Sf.3_|43.7|0.0|22.9|
|Botwna<br>|100.0|0.0|0.0|0.0|100.0|
|Brazil<br>|34.1|46.9|0.0|19.0|6.4|
|_BnmDel_|0.0|7.1|92.9|0.2|100.0|
|Bwmu<br>|4.8<br>|30.3<br>|64.9<br>|0.0<br>|503<br>|
|BunSil<br>|0.0<br>|1O.<br>|0.0<br>|4.0<br>|100.0<br>|
|CeM AfricS Republic<br>|0.0|100.0|0.0|0.0|17.2|
|Cbhd|0.0|100.0|0.0|0.0|100.0|
|**ChUlb**<br>|54.1<br>|31.4<br>|7.2<br>|7.2|6.8|
|Colombia<br>|30.6<br>|3.9<br>|_65J_<br>|0.0<br>|30.0<br>|
|Cost Ric<br>|0.0<br>|63.7<br>|0.0<br>|36.3|0.2|
|Ivory coat|0.0|100.0|0.0|0.0|22.6|
|<br>CypMSu<br>|0.0<br>|100.0<br>|0.0<br>|0.0|100.0|
|DomniRe2.<br>|0.0<br>|100.0<br>|0.0|0.0|72.9|
|Ecuador<br>|0.0<br>|100.0<br>|0.0<br>|0.0<br>|1S.2<br>|
|BSypt<br>|0.0<br>|7S.4<br>|24.6<br>|0.0|74.4|
|D Savador<br>|0.0|100.0|0.0|0.0|3S|
|thIopa<br>|0.0|100.0|0.0|0.0|14.3|
|iVO<br>|0.0<br>|24.2<br>|0.0<br>|753<br>|_5.7_<br>|
|Frech CuIan<br>|0.0<br>|100.0<br>|0.0<br>|0.0|100.0<br>|
|Gabon<br>|0.0|_54._|_45S5_|0.0|_22.5_|
|oha<br>|0.0<br>|100.0<br>|0.0<br>|0.0<br>|1.7<br>|
|Grnad,<br>|0.0<br>|100.0<br>|0.0|0.0|200.0|
|Ouatma<br>|0.0|100.0|0.0|0.0|56.1|
|Raid<br>|0.0<br>|100.0<br>|0.0|0.0|23.9|
|Hondus|0.0|100.0|0.0|0.0|14.1|
|Hong KOng<br>|72.7<br>|26.3|0.0|0.0|100.0|
|bndia<br>|90.2|_5._|4.0|0.0|68.7|
|Indonesa<br>|14.0<br>|68.4|175|_O_|80.9|
|Irel<br>|58.9<br>|41.1<br>|0.0<br>|0.0<br>|100.0<br>|
|Jamac|0.0|97.0|0.0|3.0|_90.3_|
|Jorun<br>|0.0|IO.|0.0|0.0|100.0|
|Ke1a<br>|0.0<br>|100.0<br>|0.0|0.0|43|
|South K1re<br>|54.2<br>|44.9<br>|0.9<br>|0.0<br>|50.2|
|Kuwait<br>|0.0|16.6|83.4|0.0|100.0|
|_Madagasc_<br>|0.0|100.0|0.0|0.0|30.3|
|Malwi|40.7|59.3|0.0|0.0|0.4|
|MaaysIa<br>|0.0<br>|77.6<br>|22.4<br>|0.0|73.3|
|**MA**|0.0|100.0|0.0|0.0|177|
|Mii|||<br>|<br>|.<br>|
|aur|0.0|100.0|0.0|0.0|_72.9_|
|Mexico|_5.0_|83.0|12.0|0.0|74.0|
|Meorcco<br>|27.1|72.9|0.0|0.0|_90.3_|
|Nepal<br>|0.0<br>|100.0<br>|0.0<br>|0.0|0.7|
|Naue|0.0|88.3|0.0|117|434|
|Niger|**59.2**|<br>40.8|<br>0.0|.<br>0.0|.<br>100.0|
- 22 -

TIableM1I Coa'd: Fel Shame InIbumu Powr OsaKa In.- O.3C.D. Ccuswul Ia 198

||CoaleNd Othe<br>Sold Fuel|Oil|Ga|Other|_Shar_of thrmal<br>in tot publi<br>power|
|---|---|---|---|---|---|
|Nige4w|0.1|19.6|803|0.0|77.6|
|Paklusa|0.6|30.5|68.9|0.0|30.4|
|PapuN.O.<br>|0.0|100.0<br>|0.0|0.0|_273_|
|Pau<br>|0.0|83.9|9.2|6.8|103|
|Phitppins|21.6|78.4<br>|0.0|0.0|45.1|
|Pueto Rico|0.0|100.0|0.0|0.0|98.2|
|Qaar<br>|0.0|6.0|94.0|0.0|100.0|
|Rwands<br>|0.0|100.0|0.0|0.0|3.4|
|S. PIee|0.0|100.0|0.0|0.0|100.0|
|So-diAmbie|0.0|92.8|7.2|0.0|100.0|
|Soopl|0.0|100.0|0.0|0.0|100.0|
|Seychelhe<br>|0.0|100.0|0.0|0.0|100.0|
|S9apor|0.0|100.0|0.0|0.0|100.0|
|SohAfrica|100.0|0.0|0.0|0.0|96.0|
|SkiLanka|0.0|100.0|0.0|0.0|03|
|Sudan|0.0|100.0|0.0|0.0|44.5|
|Thailand|27.6|19.1|_533_|0.0|77.5|
|TrIndad|0.0|23.0|77.0|0.0|100.0|
|TM"|0.0|513|48.7|0.0|98.6|
|Uruguy|0.0|64.7|0.0<br>.|35.3|0.7|
|<br>Ven_sala|0.0|40.9|59.1|0.0|51.6|
|_Zae_|0.0|100.0|0.0|0.0|3.0|
|_Zambia_|66.0<br>|34.0<br>|0.0|0.0|0.2|
|Zimbaw|100.0|0.0|0.0|0.0|46.1|

fgum: As for table 2.10.
> - 23 -

power goneration ia adcquate to indLcate **the** range of fuels used ln the developLng world. Table 2.11 shown the **shares** for 1986.

The table shown that a very large number, especially of **the** smaller countrles, rely entirely on oll for thermal power generation. In addltlon there is a much more lLmLted range of fuel used for power generatLon than for Lndustry (for those countrLes where data is available on both). Thus, even when all primary fuels are used ln industry, often **one** or two **fuels are** not used for power. There lo clearly potentLal for substitutLon. The tendency to rely on one fuel may be due to strong oconomLes of **scale** ln the generatlng industry in some of the smaller countrles so that only wlth substantLal growth could a fuel mix evolve.

The same source glves data on fuel shares into power goneration going back to 1976. Thls date is before the second oil shock but will to a certaln extent not fully reflect the impact of the flrst oll shock, so that the changes, lf any, between 1976 and 1986 indLcate only part of the substLtutabliLty between fuels. Table 2.12 glves fuel shares Ln power generatlon for all the non O.E.C.D. countries covered by this U.N. source.

ThLs data for 1976 does not cover such a wlde range of countrLes as that for 1986 and also does not allow for biomass. Nevertheless the picture is very clear. Wlth the exceptlon of a handful of coal or gas produclng countries there was almost complete reliance on oll for thermal power generation. Where there were other fuels already used (e.g. AlgerLa, ArgentLna, Bangladesh, Brazil, etc.) the share of oll dld drop substantLally over the ten year period. Many of those countries completely dependent on oil in 1976 contlnued to be so a decade later despite the oil shocks whlch spanned thli perlod.

For studylng substLtutLon of fuels this appears to point to some lmportant flndings. In many countries, either because of their size or because of their distance from the production of competlng fuels, there is rather little scope for substLtutLon away from oil in thermal power goneratlon. If the shocks of the 1970. did not produce even a small degree of substitution, then lt is llkely that a large and expensive intervention would be needed to change the fuel
- 24 -

# TA 2121: _Patal bw_ In 7bumI low Gemataos Is weO.3.C.D. _CwmHishb_ 1976

||Cad NW o.m<br>am Pad|on|an|
|---|---|---|---|
|_Algu_<br>|0.0|18.0|82.0|
|Augeutlm|33|73.4|23.1|
|amngadg<br>|0.0<br>|46.4<br>|53.6|
|Embeds|0.0|100.0|0.0|
|Bals|0.0|100.0|0.0|
|BM|0.0|100.0|0.0|
|Boi|0.0|0.0|100.0|
|Bdawam<br>|100.0<br>|0.0<br>|0.0<br>|
|ROM<br>|43.4<br>|56.6<br>|0.0<br>|
|1D|0.0|1.7|98.3|
|RUM|**_7.5_**<br>30.1||42.4|
|DuadI|0.0|200.0|0.0|
|_<br>CwArna<br>Affism Rap.|0.0|100.0|0.0|
|<br>eand<br>|0.0<br>|100.0<br>|0.0<br>|
|Cokombis|26.2|36.9|36.9|
|Coat.Ric|0.0|100.0|0.0|
|Cypata<br> <br>|0.0<br>|100.0<br>|0.0<br>|
|Dominloas<br>tip.<br>|0.0<br>|100.0<br>|0.0<br>|
|Ecuador|0.0|100.0|0.0|
|pop|0.0|100.0|0.0|
|<br>El Salvador|<br>0.0|<br>100.0|0.0|
|Edalola|0.0|100.0|0.0|
|F:ich Ouan|0.0|100.0|0.0|
|Gaboa|0.0|42.6|57.4|
|Ghana|0.0|100.0|0.0|
|Oustemal|0.0|100.0|0.0|
|Haiti|0.0|100.0|0.0|
|Hondum|0.0|100.0<br>|0.0|
|Hong KoMg|s|990.5|0.0|
|<br>InI|87.9|11.0|1.1|
|bIones|0.0|100.0|0.0|
|lad<br>|0.0<br>|100.0<br>|0.0<br>|
|Ivorqy<br>com|0.0|100.0|0.0|
|JmaI|O|100.0|0.0|
|Jordan|0.0|100.0|0.0|
|_Knya_|0.0|100.0|0.0|
|South KOM|9.9|90.1|0.0|
|Kuwait|0.0|15.5|54|
|Macao<br>|0.0<br>|100.<br>|0.0<br>|
|Madquoaw|0.0|100.0|0.0|
|<br>MaawI|14.|U.2|0.0|
|MlayI|0.0|100.0|0.0|
|<br>MUa|0.0|100.0|0.0|
|MaudlIn|0.0|100.0|0.0|
|Medio|0.6|77.3|21.9|
|Morocco|89.1|10.9|0.0|
|Nepd|0.0|100.|0.0|
|<br>_Nklca_|0.0|100.0|0.0|
|Nowb|10.2|79|1s.9|
|Paidia|**4.8**|19|93.3|
|Pan|0.0|100.0|0.0|
|hUppin.|2.0|96.0|0.0|
- 25 -

# Tabe.2J Coa'd.: _Fuel_ SuM In lbeUl Powr G_entl In am O.ILC.D. Coutrie in 1976

||Coal d<br>Nohe<br>SoDdFuel|Oil|as|
|---|---|---|---|
|Puero Rico|0.0|100.0|0.0|
|Rwands<br>|0.0|100.0|0.0|
|St.He<br>|0.0|102.0|0.0|
|Saudi Aabia|0.0|71.0|29.0|
|Senegl|0.0|100.0|0.0|
|Seyhee|0.0|100.0|0.0|
|SIgao|0.0|100.0|0.0|
|SouthAfIca<br>|100.0<br>|0.0<br>|0.0<br>|
|SdLanka|0.0|100.0|0.0|
|Sudan|0.0|100.0|0.0|
|Thaa<br>|7.1|92.9|0.0|
|m1h5a|0.0|46.|_53._|
|U. Rep. of Camuon|0.0|100.0|0.0|
|Uruuy<br>|0.0|100.0|0.0|
|Venmijel|0.0|27.9|72.1|
|Za|0.0|100.0|0.0|

-o=: As for table 2.10.
- 26 -

pattern. Those countries worth studying in detail because of a substantial potentiality for fuel swLtching in power are relatively few in number.

There ha. been relatively llttle systematlc otudy of fuel switching in developing countries for the power and industrial sectors. Thiu is largely because to capture the major changes it would be necessary to have a lengthy time serLes of data and this in general does not exist. The ten year span of the U.N. data used in tables 2.10, 2.11 and 2.12 is long enough to indicate certain trends, but it is inoufficient for more sophisticated statistical work as will be discusoed below. Data for a ten year perlod have been discussed for a series of reports prepared for the AsLan Development Banks Pakietan (Rhoe (1986)), 8. Korea (KLm 119861), Thailand (Khumsoong at al. (1986]), India (Bhatia (1986]), Bangladesh (al-Husainy (1986]), and some comparative material on industrial and developing countries is given in Leach, Jurass et al. 119861. In all of these there is much less detailed data available than for comparable studies of industrial countries.

Despite the lack of detailed industrial disaggregation and the relatively short runs of data available, the figures that are avaLlable suggest a number of important features of the mixture of fuels used in industry and in power generation:

- (i) Aggregate fuel mix in the industry sector is strongly influenced by the compoostion of the sector. O.B.C.D. experience suggests that any analysio of changing trends in fuel use ln the sector would need to take account of shifts in industrial structure, which in many countries has been strongly affected by the oil shocks as well as by the pace of economic growth. Thio suggests that it would be necessary to have disaggregated data for a successful investigation of inter-fuel substitution.

- _(LLi_ The fuel mix tends to be more evenly spread in the industrial sector than in the power sector, which suggests that there are more possibilitiLe for substitution in industry.

- (iii) In countries where a mixture of fuels was used for power generation in the 1970s there is usually evidence of substantial substitution away from oil

- in the following decade. These countries look to offer the best cases for studying fuel switching.

- (Lv) Many countrLes relied entirely on oil for thermal power generatlon in the 19709 and continued to do so despite the oil shocks. The reasons for thie pattern are probably connected
27 -

-

with the accessibility of oil compared to other fuels and with the small scale of the power sector which may not have grown enough to permit the use of a second fuel.
- 28 -

# _3. IMETODS FOR_ ESTIMATING THE sUDSTK XTABLxTT or FUELS

In studying the potential for fuel substitution<sup>there are two distinct</sup> approaches available. The econometric acoroach<sup>is essentially historical and</sup> relates actual experience of fuel switching, usually at a rather<sup>aggregate level,</sup> to the various factors which may have produced these<sup>changes. The enaJngerina</sup> annraach typically is more prospective, in the spirit of<sup>project appraisal, and</sup> seeks to describe for a specific situation the cost conditions under which one fuel or another would be used.

Econooetric 8tudies of Fuel Substitutability Many econometric studies of fuel switching and fuel use have been published and these form a natural starting point<sup>for an analyLsi of methods of</sup> measuring fuel substitutability. Interest in the use of fuels began with a series of studies in the 1970. which investigated the Lapacts of the rise in real energy prLces following the first oil shock.<sup>Acrucial point for such studies</sup> was the extent to which energy was a substitute or complement to the other inputs. The methodology developed ln these **studies** was later extended to disaggregating between the different fuels so that it became possible to estimate the degree to which one fuel was switched for another as well as the degree to which fuels were substituted in total against other factors of production.

All econometric studies have made certain assumptions<sup>about the nature of</sup> the use of energy which are central to the models<sup>developed but which are quite</sup> different in spirit from the approach of engineering studies, and it is important to highlight theme before the more detailed description<sup>of the approach is given.</sup> The central assumption is that there **is**<sup>a continuous degree of</sup> substitutability between the various inputs. Small changes in external<sup>forces,</sup> such as relative prices, will then produce small changes in the pattern of energy use and fuel choice. This assumption of continuous substitutability, although very convenient for modelling, does not correspond<sup>to a description of what</sup> happens at any particular site. In particular cases fuel prices may have to change a great deal before there is any switch to<sup>a different fuel, as will be</sup> discussed below. The principle defence for the econometric<sup>approach is that for</sup>
-29-

an aggregate defined over many plants a small change in price will produce a response at some, but not necessarily all, sites, so that there is always an aggregate response to price changes.

The second diLtinctlve feature of the econometrlc studies is that typically the link between capltal and energy **is** not formally specified. They are usually viewed as two factors which may or may not be complements or substltutes. If one fuel is to be substituted for another the prices of the fuels are used as explanatory factors but the capital costs of the alternative fuel technologies are not specLfically introduced. Instead an aggregate cost of capltal is introduced as a separate explanatory factor. This is in sharp distinction to engineerLng studLes where the capital costs of alternative fuel burning tochnologies are crucial in determlning fuel choice. These two aspects of the econometric approach highlight the fact that the technology is not specified, whLie in engineering studies this is the central point in formulating the optimal choice of fuel.

Early econometric studies on the use of energy as a factor input treated it as a single fuel and concentrated on its relation to capital and labor. Berndt and Wood (19753, Fuss 119771, Magnus [19791 using data for a single country found energy and labor to be subatitutes while energy and capital were complements. Fuse also found some substitutability between oil, coal and gas in Canada but none between those fuels and electricity. Halvorsen 119771 found a larger measure of substitutability between all fuels for the _U.S._ Griffin and Gregory 119763 used cross-section data at five year intervals to capture long run effects and found substitutability between energy and capital.

Griffin 119771 and Pindyck 119791 have provided definitive accounts of the standard econometric approach to mode)ling the shares of fuels in industry or electrical power goneration. Most of the subsequent work refers to the ideas of these studies so a detailed account is given of the approach used by Griffin. The starting point is the oroduction function: the output (Q) is assumed to be a twlce differentiable function of the services of capital (K), labor (L) and energy inputs, as well as of technical change (t). In the case of electricity
- 30 -

these inputs are denoted by the quantities of coal (go), gas (Q,) and oil (Q). For industry analysis the fourth input, energy (Qs), would be added. Studies such au Considine (1989) have suggested grouping fuels according to the induatry under analysis. Materials (M) can also be allowed for, am in Pindyck, by asouming that it is "weakly separable" from the other inputs as a group - this in effect allows materials to be ignored which is necessary at an aggregate level because of lack of data. The differentiability of the production function implies that there is continuous substitutability between the various inputs. The production functlon describes the output which will be obtalned from the various combinatLons of inputs. Corresponding to this is a cost function (C) which describes the cheapest total cost of producing a given output with the given prices of the various factors of production. In studies of the energy market analysis almost invariably focuses on the cost function approach.

The general form of the cost function is written: C **C(PK,PL,PC,PO*PO,t,Q) (1)** where Pi denotes the price of input L. The functional form of the cost function is a key issue for econometric modelling. The cost function must be homogeneous of degree 1 ln prices (if every price is doubled total costs must double) and it must satisfy conditions corresponding to those of a well behaved production function. At the same time it is highly desirable to be able to analyze fuel choice separately from the choice of labor and capital, particularly when data are scarce. To this end it is assumed firstly that the fuels are a separable and homogeneous physical energy aggregate (E) which allows the cost function to be written (see Fuss 119771 for more detail): C - C(PK,PL,H(Pc,Po,Po),t,Q) (2) **-** C(PK,PL,PE#t,Q) (3) PE **-** H(Pc,Po,Po) (4) in which Ps is the price of the energy aggregate. This assumption in fact - postulates that there are two sub- models one in which fuel inputs are determined and a second in which capital, labor and energy are determined. The
- 31.

conditions required for separability are that the cost shares of any two fuels are independent of non-fuel prices. This is a critical assumption in that it implies that capital costs of the fuel burning technology do not affect fuel choice. GrLffLn defended thle assumptLon notlng that, at the tLme, the capltal costs of coal fired plants did not exceed those for a gai fired plant by more than 25%. In the last decade capltal costs have certainly changed and this assumption must be regarded as requirLng LnvestLgatLon before Lt is accepted unconditlonally. The most appropriate place for its applicatlon would be in multi-flring where no change in capltal li requLred to swLtch fuel.

Many studLes, as polnted out, have concentrated on estlmating the - equivalent of (4) which explaLne the use of fuels withln total energy. Here the desire is for a functLonal form of the cost function which places few restrLctions on the range of response. Griffin, followlng Christensen, Jorgenson and Lau 11973], uses the tranalog cost function (thls glves different results from a model starting with a translog production function). The translog functlon is one possible second order approximation to an arbitrary twice differentiable cost function. The baslc form isL H - lna + E a1lnP5 + h R Ra1lnP1lnPj (S) L i j where i and j stand for the fuels C, 0 and G, and a., a1 and a% are parameters to be estimated. Assumlng firms are cost mLnimLzLng and that there li no monopoly power in fuel markets (fuel prices are exogenous to the lndustry) equations for fuel cost shares can be derived: S. **_P.Q./Pz3_** - a. + Ea1lnPj So PoQ/PgE **_-_** a3 + :aEjlnPj (j **_-_** c , 0) The cost shares automatically must sum to unity and this lmplies the following parameter restrictions:
- 32 -

(7) ac0 + a00 **+** aoo -0 **aco** + a0O + aoo - 0 while symmetry in the cost function implies that Restrictions (8) and (9) together imply that the translog function is linearly homogeneous, as is required if it is to represent a cost function. These restrictions can be substituted into the fuel expenditure share equations to give the two Itndendentn equations: _Sc_ - ac + acc (lnPlnPO) + acg (lnPo -lnPO) + Uc (10) _So_ - ao + aG (lnPc - lnPO) + aog (lnPo - lnPO) + UG where Uc and UO are error terms. The third equation, for the share of oil, can be entirely predicted from the other two since the shares must add to unity. These equations, which require data on the prices of the fuels and on the share of expenditure on each fuel in total expenditure on energy, need to be estimated jointly by a full information maximum likelihood procedure. This is to ensure that the parameter common to both equations (ac0) is the same in both cases. In addition, if there is contemporaneous correlation between the error terms so that E(UcUO) **1**<sup>0, improved efficiency can</sup> be obtained by the use of a Zellner type estimator. The other parameters from the system (6) can be recovered by using - the identities (7) and (8) together with the estimated parameters from (10) standard errors can be easily computed since the derived parameters are linear functions of the estimated parameters. The own (es) and cross price (Ce) partial (i.e. holding total energy constant) elasticities can then be derived: eV - (au + SjSJ)/(S1) es - (a + _81-_ Si)/8 (11) (12)
- 33 -

where so is the percentage change in the quantity of fuel i used caused by a 1I change in the price of fuel j. To calculate theme values of the ahares (SI) are required - conventionally the man sample shares are used. Standard errors for these shares are more difficult to obtain. Possible solutions **_are_** discussed by Pindyck [19793 and Berndt 119913. The system of fuel share equations allows only for substitution between fuels but not for the additional effects that a change in the general energy price will have upon the total amount of energy used. Hence to obtain a tgtal price elasticity (eU*) (i.e. holding output constant) an adjustment is required as explained by Pindyck (19793 e=* 8 eV + eEBSJ (13) where el is the own price elasticity of aggregate energy in the total cost function.

Since the trannlog function is an approximation device it is not automatically consistent with all of the theoretical properties that an aggregate cost function would have. It has already been pointed out that the required properties of linear homogeneity and symmetry can easily be imposed on the estimation equations. However two other properties have given more problems for empirical work. It is necessary that the estimated cost function should be monotonically increasing and strictly quasi-concave in input prices. The former requires that tI'9 fitted shares should all be positive at every data point while the latter requires that the matrix of substitution elasticities be negative semi-definite at each observation. The former is checked by using the estimated coefficients in (10) and the actual prices to show that the estimated shares are all positive. The substitution elasticities (Hick-Allen form) are given by au - eg/SJ au - (14) For a three fuel model the negative semi-definite property required for strict quasi-concavity requires that (i) each an 0 at each observations (ii) each submatrix: ag _ag_ (i "J) au _a0_ has a positive determinant at each observations
- 34 -

# (iiL) the matrix

_au_ **ajk** a°k ajk _a°"_ has a zero determinant at every observation (this may be near to zero reflecting rounding error in the computations).

The third condition is purely a check on the correctness of calculations but the violation of either of the other condition. implies that the fitted model is not consistent with a genuine cost function.

It can be seen that estimation of the fuel share equations does not allow - all the parameters of the total translog function for energy (5) to be derived here the parameter a is not estimated. Some of the restrictione on the model required by theory can be tested formally. For example symmetry can be tested by estimating the fuel share equations with or without the imposition of the symmetry conditions. The two likelihood scores can be compared by the standard likelihood ratio tests LR - -2(lnL. - ln LI) (15) where Lo is the constrained and LI the unconstrained value of the likelihood statistic given by the computer output. The LR statistic has a chi square distribution, with degrees of freedom equal to the number of restrictions imposed, if the null hypothesis of symmetry is correct. Although much early work on fuel shares was based on the translog approximation to the cost function there have been recently attempts to find alternative and superior models for estimating fuel substitutability. The original reason for the enthusiasm for the translog was that the elasticitieo of substitution between factors were not over-restricted. The definition of an elasticity of substitution - the percentage change in the ratio of two inputs caused by a one percent change in the price ratio of the inputs show,s that they describe the degree of curvature of the isoquants. Cobb-Douglas type functions have all elasticities equal to unity while the C.B.S. function of Arrow et al.
- 35 -

119613 has all elasticities constant. The translog function inutead has elasticities which vary depending on the fuel shares and which thus allow a more flexible description of the relation between the various inputs. It is important to recognize that in the tranaslog approach the elasticities must vary if the fuel share. vary, an (11) indicates. Constancy is not a testable assumption. Other general approximation systems that have been suggested for similar inter factor substitution are discussed by Berndt. They include the generalized Leontief cost functions C - Q [EE d(P 1Pj) 1 (16) hich yields lnput/output demand equations which are functions of the pric. ratios. The c-oss price elastLcities are given by: eV - du (P 1/Pj)"A/2ai (17) where a5 is the ratio of the quantity of lnput i to total output. Variations on the translog form which include an allowance for technical progress on all factors are also diecussed by Berndt.

Consldine (19891 has pointed to some of the weaknesses of the translog function in proposing a linear logietic alternative model for fuel shares. He shows that for empirical problems where there are limited substitution possibilities, a small cost share for one or more inputs and a high variance in relative prices, the translog performs badly in that concavity is not satisfied at all data points. It would be possible to use generalized cost functions in which global concavity can be imposed as in Diewert and Wales (1987] but these require many more parameters and hence more data. Instead Conesdine suggests a model ln which concavity holds automatically but in which symmetry holds only in a limited region. He suggests that this trade off may be preferable since wrong eigne on own price elasticities and predictions of negative fuel shares are unhelpful for policy analysis. The fuel shares (Se) Lnstead of belng linear as in (6) are postulated to be of the form 8 - exp{f 1 )/E exp{f 1 ) (18) where
- 36 -

f, **-** di + Edt In Pj j (19) This in a linear logit (or logistic) model of cost **shares.** It should be noted that it is not related to logit modele of discrete choice. The **share** elasticities are: M - d1k - Z Sjdk (20) and the own and cross price (partial) price elasticities are _Be_ H + _S_ (21) No - Hu + Si -1 (22) Certain ccaditions have to be imposed in order for these equations to be consistent with theory as is shown by Considine and Mount (1984). **Homogeneity** of the cost function with respect to prices can be imposed by the restrictions: Z du - d all i (23) j where d is an unknown scalar that can be set equal to zero. Symmetry can be iAposed by the constraint du* - dj* a lliLj (24) where du* - du/SI* (25) and _Sj*_ is the mean cost share for fuel i. It can be shown that for two inputs the logit cost share model collapses to a constant elasticity (CES) model. The estimating equations for the three fuel case can be written: ln(S,/8 3<sup>)-</sup> (di - _d4)_ - [S2*d,2 * + **_(8,*_ +** 8*)d,,*J ln(P 1 /Pb) **+** (d12* - dD*IS2* ln (PJP) (26) ln(SdS 3<sup>)</sup><sup>**-**</sup> (cl - d3) - 1S,*d4a* + (8a* + Ss*)dJ*]ln(P2/P$) **+** (d1 2* - d,,*)S* ln (PI/Ps) (27) These equations can be estimated by full information maximum lkellhood using price ratios and expenditure shares. Parameters can be recovered using mean shares. ConsidLne shows that, for a set of fuel shares for U.S. industry
- 37 -

data, a translog model falsm coneavLty for 6 observations whlle **the** logistic does not fail once. More importantly the own price **elastioities** from **the** tranalog vary greatly from observeLion to observation, whle for the logiLtLc they are nearly constant. in a search for other functlonal forms Mountaln and _esLao_ [19891 have made omo further suggestions. For a three input **model** they **firstly** look at **a** Quadratic quasi Cobb-Douglas (QQCD) form in which **the relative** inputs **are** related to prices bys ln(-X/X,) - a2 + an ln(P2/P) + an ln(P,/Pg)

**+** s am=ln(PJ/P,) **+** a2 ln(P2P,)ln(P 3<sup>/Pi)</sup> + I a2(ln(P,/P)12 (28)

ln(X 3 /Xl) - a3 + a2 ln(P2Pl) + e% ln(P3/Pl)

**+** 4 aj(ln(P/P,32

**+** am3 ln(P2/P) ln (P1/P,) **+** h am (lln(P/P)31 (29) As an alternative to thLs form they propose a Fournler based approximation for the expenditure shares:

**3** 8- a, + Z a1 ln(Pj/Pt) - 2{do sLn(1n(PJ/Pj)j

+ db sin(ln(P2PJ)j +e cos(ln(PJPj)J + **e,3** cosln(P2P,)I)

# (30)

For this to be consistent with a cost functlon the following restrictions must hold: du - -d4, ean -e",, an - **an** (31) Both models are applied to fuel choice for indLvidual industries in Canada. Both produce good results in **some cases.** An Lmportant **aspeat** of their
- 38 -

models is the introduction of technical progress. Further investigation of these systems is given by Mountain, 8tipdonk and Warren (19893.

A different approach to estimating elasticities of substitution is to use the production function rather than a cost function. The main iLsues have been described by Burgess 119753. For Cobb-Douglas and C.E.S. models the production and cost functions are self dual uo that the approaches are equivalent. However for the more flexible t-.anslog functions the two forms are not self dual so that the elasticities derived will in general be different and so inferences based on the twso approaches will be different. Xf output can be represented by a function quadratic in the logarithms of inputs (measured in physical units) then, with efficient production and competitive factor markets, the cost share equations are linear in the logarithms of factor quantities (as opposed to factor prices for the log quadratic cost function): 81= -pi + B 'y lnXj (32) with parameter restrictions N - 1lj 'yU - 0 (allj) i i The factor share equations require data on prices and quantities, which is also needed for the cost share approach, so that the data requirements are equivalent in the basic case. Burgess estimates a time series model by both approaches and shows that the estimates of the elasticities of substitution (year by year) are quite different in some cases - indeed they are of different sign on occasion. This result, which is based on different approximations to a 'true' underlying cost function, is used by Burgess to suggest that care must be taken before accepting the values derived from any one approach. A further point to notice is that it is usually assumed (for econometric purposes) that quantities are exogenous in (33) while prices are exogenous in the cost function approach. Depending on the nature of the economy one or other assumption may be more realLstic. In general the prices of traded fuels (oil and perhaps coal) are likely to be exogenous to the sectors being estimated. Finally, if a fuel is not
- 39 -

used then the presence of the log of quantity term rules out the use of includLng that fuel ln the model (as ln a cross sectLon where some firms or countrLes do use the fuel _Ln_ question).

_ThLs_ brief review of the varlous econometric models uoed for analyzlng fuel shares and fuel substitution highllghts several practical issues:

- (i) Although most models appear to fit well on superfLcial criterla (e.g. goodness of fit), more extensive testlng for concavLty and the stabliLty of prlce elasticitLes at varLous data poLnts can lead to unsatisfactory performance.

- (LL) No one model appears to be best Ln all cases. Different models may be needed for different circumstances.

- (liL) Care needs to be taken over the definltlon and measurement of the competing fuels in specific lndustrles. For example, ln power generation the fuel used is heavy fuel oil, so that the price used should not be that of petroleum products as a whole (the price of gasoline can move qulte dlfferently from that of HFO).

- (iv) All the models proposed require estimation by full lnformatlon maximum likelLhood technlques. _This_ can be handled by standard software packages (such as TSP) but the performance of such technLques wLth very small sets may not be very reliable.

- (v) The use of these econometric models appears to be largely limlted to hlghly developed countries (e.g. the U.S., Canada, Australia). In principle they are applicable ln any situatLon but data lLmLtatLons are an important consideration. At an industry level it is necessary to have fuel prices and shares in total energy expendLture for a number of years disaggregated by industry type (in order to avoid the effects of the shlft ln the composltlon of industry).

- (vi) All the models described provlde estimates of own and cross prlce elastLcLties for the varLous fuels. These are very important for pollcy purposes since they indicate the potential response to changes in fuel costs which could be brought about by state interventlon. In the light of this it is important to find a model which gives stable values for these elasticities. Merely checklng the elasticltles at the mean values of hLitorLc energy shares is an insufflcient check - they need to be evaluated at all data points. The work of Considlne is partlcularly helpful ln suggesting an alternative to the translog model which is well known for produclng highly variable elasticities.

- (viL) Very lLttle work has been undertaken on dynamlc models. Since adjustment to fuel price changes may take several years this is an important issue. Cross-section studies are one attempt to pLck up the effects of dlfferent relative prlces wlthout the need to model short term dynamlcs, but these raise problems of homogenelty (particularly for the lndustrial sector). The theoretical problems of bulldlng a fully specLfied dynamic model have been discussed by Epstein (19811 and Nakamura (19861. one major problem would be the requirement for dLsaggregated Lnformation on the capital stock by energy type -whlch is rarely avallable. As an alternative Mountaln and Rsaio suggest using autoregressive
**40** -

-

speoLflcatlons for the fuel **share quatlons** to plck up **some** of the partlal adjustment.

Some general conclusions on the suitablilty of econometric models for measuring the degree of fuel substitutability can be drawn. EconometrLi models **are** designed to use published aggregate data to glve an estimate of the degree of substitutLon betwen fueli that has taken placo Ln response to changes Ln relatlve prlces, wlthout detAiled field work or a case by case approach they provLde a **masure** of such substltutablilty. Thlo is best seen as a fLrat approxLwaa<sup>4</sup> -on fe-- the purposes of pollcy - an LadLcatLon of where substitution has taken placo and hence of the potentLal for further change. The extrapolatLon of results from one study to other **cases appears** at thli stage of knowledge to be limited to suggestLng whlih LndustrLes or clrcumstances are worth more detailed LnvestlgatLon. **As** the study by Mountain and HsaLo indicates there is not yet strong agre ment from dLfferent researchers on prlce elasticLties even when they are all analysLng the sam data (e.g. speciflc industry values ln Canada). Different methodologies yield very dLfferent elastLcitLes and would result ln very dlfferent strengths of pollcy rccommended to achLeve the same degree of fuel switching. Comparing **results** between countrles is even le satLsfactory. **Here** there is substantlal dissatisfaction over model choice, with some models on occasLon producLng counter-intuLtlve results (the wrong sign for own prLce elasticitLes etc.). The extrapolatlon of estimates from such models to countrles wlth completely dlfferent lndustrLal structures and technologLes is not likely to be **rellable.** The **0naiaetiLM Anoroach**

**Thes** nglneerlng approach to fuel substitution has generally been at a plant speciflc level, although **some** recent **studles here** attempted to draw more general **lessons** on **the** preferred technology **(see** Moore and Crouasllat 119911 and Pinto and Besant-Jonew 119891).

The key difference ln englneerlng studles **ls** that a speclflc sltuatlon li analyzed and this Lnvolves **an analysis** of **all** the costs of alternatlve proposals. Such costs Lvolve capltal and lnfrastructure costs as well as fuel
> - 41 -

**costs** and **are** technology speciflc. It is necesary to speclfy fully what change is belng proposed and what are the existing condltions. Three examples illustrate the nature of the dlfference from the scdnometrL* approach.

- (i) A country may possess already a multi-fired power statlon - one capable of burning dlfferent fuels. The issue **is** simply whlch fuel to burn. _Thie_ will depend on fuel costs at the burner tlp and on the thermal efflcioncy of the dlfferent fuels - the varlous forms of solid fuel have a wide range of thermal output per unlt weight. If the power station is already in place there are no dLrect capltal costs of swltching but there may be Lnfrastructure costs to be incurred if a switch in fuel is required. The prlce of the fuel must lnclude all the transport costs of getting it to the power station (simple border prLces for imported fuels would not be correct). This example, which is nearest to the puttyputty approach implLcit in much econometric work, demands the least data. Where all capital costs are already incurred then the relative fuel prices at which substitutLon would occur can be simply calculated. The pollutlon characterLstics can also be easly identLfied, unlike the econometrlc case where there is no data avallable at the level of aggregation used.

- (LL) A country has an existLng coal (say) thermal power unlt and is consLdering conversion to a gas fired combined cycle plant. This ex post substitution includes a crucial asymmetry in that the capital costs for one scheme have already been incurred. Any new -

- scheme would require large capital costs in addition the scrap value of existing plant and its expected llfe before replacement would be crucial lnformation in any cost benefit analysis. In addition the thermal efficiency and pollution characteristics of both plants would play an important part in deciding whether the switch were worthwhie. Often lt turns out to be best to technically upgrade a poor plant than to entirely replace it. Econometric models do not attempt to ask such questions and hence cannot be expected to provlde the appropriate solutions.

- (iLL) In the case where an increase in generating capacity ie being contemplated there is genuine ex ante substitutability between fuels - the issue ie then which fuel and whlch technology would be best. In practice a key issue, particularly when pollution ie a concern, ie the locatlon of the plant. Thie affects total costs depending on the access$bilty of the various fuels and the infrastructure avallable. Again econometric modelling can at best hope to provide only an average pLeture for a very specific problem.

The strength and potential accuracy of the engineering method, which is **discussed** in more detail below, is that it takes full account of the conditions under which a fuel switch may take place. Because this ie typically plant specific it may be that no general lessons can be learned. A fuel tax that is predicted to be effective in leading to fuel swltching at a spepfic site cannot **have lts** impact generalized to other sites without a detailed examination of **every** case.
- 42 -

The simple level of engineering related fuel choice is that which focussed solely on the characteristics of the planc itself, while the more detailed utudy looks at the whole context in which a fuel choice li to be made. Examples of both approaches are discussed in some detail oince they illustrate both how the approach ie used and its strengths and weaknesses.

Non sito specific Choice of Power oeneration echanoloav Many studies of the power market attempt to compare different fuels together with the dlfferent technologles available in order to make predictions about future fuel use. One very recent study by Moore and Crousillat (19911 looks at the prospects for gas-fueled combined cycle power generation with specific reference to its future use in developing countries. The conclusions to the study indicate the attempt to draw general conclusions. The advantages are claimed to bet

- (a) a lower capital cost of about $600/kw compared to steam thermal at $800-1500/kw, depending on whether lt is conventional (coal or oil) steam of coal fluidized bed and on the degree of stack emission control equipment;

- (b) thermal efficiency of about 50O compared to 40% or less for the alternatives;

- (c) better environmental performance;

- (d) shorter construction timel

- (e) shorter startup and loading times.

The disadvantages are:

- (a) the limited fuel-switching capability compared to a conventional steam unit, which can be designed for gas, oil and coal firing;

- (b) poor operating performance so far in developing countries largely associated with inadequate preparation for the transfer of the high technology system.

In addition it was shown that for under sensitivity analysis the gas fueled combined cycle plant was least cost over a wide range of situatlons.

The study takes 1994 as its commissioning date for new plant and coneLders base load systems with gas combined cycle or coal or oil alternatives for both large (5000 MW) and small (1000 MW) installed capacity systems. It does not make allowance for high altltude or high ambLent temperature, both of which
- 43 -

affect gas turbine efficiency adversely. The assumptions **are** worth reporting in - detail since they illustrate how much information is required Table 3.1 gives the comparative values for large power systems: lable 3.L : Comparative Assessment of Generation Costs for Large Power Systems

||Combined<br>Cycle Gas|Coal Fired<br>Steam|Fuel-oil<br>Fired Steam|
|---|---|---|---|
|Capacity (MW)<br>Plant factor (%)<br>Energy Supply (Gwh)<br>Unit Cost ($/kw)<br>Total investment cost<br>|450<br>65<br>2562<br>600<br>|500<br>65<br>2847<br>1000|500<br>65<br>2847<br>900|
|(Million$)<br>|310<br>|575|518|
|0 and M Unit cost (c/kwh)<br>|<br>0.5<br>|0.5|0.3|
|Heat content (BTU/unit)<br>Thermal efficiency (%)<br>|1<br>46<br>|25<br>38|5.9<br>39|
|Fuel price ($/unit)<br>|2.5<br> <br>|40.0<br>|15.0|
|Fuel price rise (%/year)<br>Annual fuel cost<br>|<br>1.0<br>|1.0|1.0|
|(Million$)<br>Implementation<br>period<br>|47.5<br>|40.9|63.3|
|(years)<br>|3|5|5|
|Economic Life (years)<br>|20|25|25|
|Generation cost (¢/kwh)<br>|4.2<br>|4.9|5.5|
|Discount rate (%)|10.0|10.0|10.0|

(No allowance has been made for flue gas desulfurization units for coal burning.) Source: Moore and Crousillat [19911 The assumptions for the relative fuel prices and thermal efficiencies are particularly important. The sensitivity analysis showed that under almost all circumstances the combined cycle plants would be least cost. Oil fired were not economic at prices even as low as $10/barrel for oil ($20 was taken to be the constant oil price in the base case). Coal firing became competitive for gas prices of $3.3/MMBTU (base case $2.5) or coal price. of $20/ton (base case $40/tcn). A rise in capital costs of gas of 10% and gas prices of $3.0 also favored coal. The critical parameter, given the experience of combined cycle in developing countries, is the plant factor. If this were about 15% points lower
- 44 -

than for coal at 50% then gau ceases to be the least cost fuel. A similar analysia was carried out for smaller systems and again showed gas combined cycle to be the least cost option.

The methodology adopted in this study is that of identifying current good practice technologies and their actual operating characterLatics (rather than theoretical optima) and using known cost data to compare the discounted costs per unit of generating a certain amount of energy over the llfe time of the plant. Similar technlcal data can be provlded for the emiLsions of each type of fuel/plant per kwh as is shown in table 3.2 reprinted by Moore and Crousaillat: Table 3.2: Comparative EmiLmions for Gas Combined Cycle and Coal Steam Plant per kwh

||Coal Steam<br>with Scrubber|Gas-fueled<br>Combined Cycle|
|---|---|---|
|C02, grams<br>|830<br>|380<br>|
|CO, mg<br>|75<br>|34<br>|
|S02, mg<br>|600<br>|0<br>|
|N02, orig|600<br>|350<br>|
|UHC,mg|0<br>|18<br>|
|Waste water,<br>grams<br>|15<br>|0<br>|
|Ash, grams|34|0<br>|
|Rejected heat, XJ|4.3|2.6|

Source: Haupt, Joyce and Kuenstle (19903.

The detail of the senaitivity analyLsi and of the potential for a precise evaluation of the pollution characteristics of particular technologies cannot be matched by econometric analysis because of its essentially aggregate nature. The broad implications of studies such as this, which are reflected in other comparative technology studies, give some hope of reachlng sufficiently broad conclusions that national policy can be rested on them.
- 45 -

# Site Slocific Choice of Power Goneration !?echgglc

The previous study is applicable in the general case only when the assumptions hold true at every site. In particular it requires every site to be 'greenfield', that fuel prices are always identical and there are no associated infrastructure costs involved in uslng any of the fuels. More specific analysis will pay attention to these factors, but the results (as opposed to the methodology) cannot then be so easily generalized.

An example of the methodology required to assess fuel substitutability in a specific situation is provided by Pinto and Besant-Jones 119891 for power generation. Although the case they examine is hypothetical it is elaborated in sufficient detail to show how it could be applied in actual cases. The objective is to ascertain the demand curve for gas in three different power systems (i.e. the relation between the price of gas and the amount of gas used and the mix of the generation system). The three systems wave chosen to illustrate realistic situations.

System A is small with a current maximum demaz.4 of 100 MW. The load factor is assumed constant over the 20 year period at 54.5%. The initial power system is entirely thermal with no possibility of future hydro and there is no possibility of importing coal due to the high costs of coal handling plant. The inherited plant mix is five 33 MW steam units fired by residual fuel oil and some diesel units. The steam units are new and not retired over the simulation period, whereas the diesels are retired between 6 and 14 years in the future. The rate of load growth is 7%.

System B has a current maximum demand of 2400 MW with a higher load factor of 69.7%. At present it has a varLed plant mix with hydro/storage, lignite fired steam, gas turbines and diesel. All of the inherited plant is retired over the period. Again load growth is 7% per annum.

System C is a 'greenfield' case with an initial maximum of 800 MW. There is no possible hydro source.

For the thermal plants the following details are used:

- (i) Nameplate rating

- (ii) Sent out rating

- (iLL) Maximum availability

- (lv) Full load heat rate

- (v) Average heat rate
- 46 -

# (vi) Variable operation and maintenance costs

# (vii) Fixed staffing costs

# (viii) Capital coats phased over the construction period

# (ix) Economic life.

An important set of variables are the fuel prices. These are aesum.d to escalate in real terms over the period, but gas prices were assumed constant in real terms. For coal it is assumed that transport costs were 29% of the fob price for the system B case. For distillate the transport cost was 4% of the fob - price. An important set of costs are those for conversion to gas firing allowance is made for the fact that in system A only the oil fired steam units are capable of conversion and that this is expensive because they were not designed with this in mind.

The overall system was optimized in each case to determine the least cost development program. Because of the complexities of the analysia only a limited range of unit sizes of each type of plant was considered (gas turbines, combined cycle and steam units). The optimization of a complete power system ls a much more complex operatlon than the cholce of a least cost plant to generate a certaln amount of energy, as is explained ln book on power system economics by Berrie [1983J. Peak demand iA a particularly important phenomenon as well as unpredictability of demand. The former requires some capacity that can easily be switched in and out but which is not used all the time. The latter requires some reserve over expected demand. A merit ordering of power plants exists at any moment in which extra capaclty ls switched on to meet the temporary increments in demand. The economic characteristics of base load and peak load plants can be quite different. A second issue in optimizing a development plan - is its dynamic nature decisions which are best for today may not be best for five years' time, but once taken are very expensive to undo through converaion etc. A series of alternative development plans is evaluated at six different prices for gas (from $l/MMBTU to $6/MMBTU) and in each case the least cost is identified. This then gives figures for the amount of gas used and the amount
- 47 -

of investment au the price of gas is varied. For example in system A when the gas price is in the range up to $2/MMBTU the least cost program is to use only gas turbine plants by conversion of steam units to gas firing in the early years of the program. As the price increases to $3/MMBTU the improved efficiency of combined cycle becomes important. Above $3.17 conversion of steam plant to gas firing is no longer economic. Above $4/MMBTU there is no demand for gas until 1988 when additional fuel efficient combined cycle plant is commissLoned. The demand curve shows rather strong discontinuities in fuel switching with the location of the switch points determined by all the characteristics of the system. Similar analysis is carried out for systems B and C.

Thls approach is clearly site specific in the cost structure of the power plant. In addition, were this approach to be carried out for specific sites therr would be differences in fuel costs due to the transportation cost element (or quality element, especially for coal), local operating cost differences etc. No two sites are necessarily the same, which reinforces the conclusion that the switch point prices are site specific.

The general conclusions of the report are worth stating in full:

- 0(i) For all power systems there is a price of gas below which demand is relatively inelastic. This value is a function of system specific variables such as the available fuels, the mix of generating plant etc. In the case of a relatively small system ... with no provision for coal burning and no hydro capacity, gas demand is not sensitive to price reductions below $3/MMBTU. For a much larger system ... wlth a mix of generating plant including hydro with storage, coal and lignite burning steam, and gas turbines, the critical price of gas is approximately $2/MMBTU. For an intermediate size system with ... a full range of available fuel types, and no constraint on inherited plant mix, the critical price of gas is approximately $3/MMBTU.

- (ii) As the price of gas varies, the demand for gas does not change at a constant rate, but rather moves through a series of discontinuities. Sharp increases in gas demand result from the commissioning of new gas fired generation plant, and decreases can be the result of changes in merit order of gas fired plant relative to other plant.

- (iii) The discontinuities in the gas price/demand relationships occur at prices which represent switches between investment and operating decisions, for example from gas fired steam units to coal fired steam units. These prices depend upon a number of factors, the most important of which are expected fuel prices
> - 48-

- and the relative costs of different types of new generating plants.

- (iv) The precise shape of a demand curve is dictated both by the critical prices of gas which are important in determining the optimal plant mix (the lnvestment decLsLon) and by changes whlch alter the merlt ozder ranklng of plant. It li thus system speciflc. Where the latter is lmportant the demand curve usually assumes the conventional shape of having a negative slope with respect to the prlce axis."

The general shape of the demand curve is of considerable interest, given that econometric studies are an attempt to approximate it. Whereas the econometrlc model shows a continuous and decllnlng rate of substitution between two fuels, leading to a downward and smooth (partial) demand curve for a single fuel, the engLneering approach shows a downward curve made up of a serles of steps - each step corresponds to a switch to a different technology and its associated fuel. The width of the steps depends on the system characteriatics. Although there are a number of studies which analyze fuel choice in power generation from an engineering/least cost approach there ia not the same intereat in industrial processes. Of course individual flrms carry out such analysLs but little is said in general terms. This is because industries are so product specific that the nature of the energy requirements tends to be very specifLc. However there are virtually no problems of system optimization and the choice is able to be made on the characterLstics of individual energy using plants. The same principles apply as ln the first example for power - it is crucial to know the output required, the capital costs of competlng plants, the costs of competing fuels at the burner tip, the efficiency of various technologies in the specific use, the pollution characteristlce of the varLous choices etc.

A study by Fog and Nadkarni [19833 analyses possibilities of fuel substitution for the cement industry. In particular they consLder the economics of switchLng from the use of fuel oil to coal. Not only are the direct fuel costs compared but also the capital costs associated wlth the conversLon. Not only does the coal have to be specially prepared (ground and dried) prior to loading into the kiln but facilities for homogenLizng the calorific value of the
- 49 -

coal have to be constructed. Finally transport costs for the associated infrastructure (depending on the existing facilities) must be added. Rates of return on oil to coal conversions are calculated for various oil/coal cost differentials and kiln sizes. The detail of this study, which takes into account the specific technology and costs of a particular industrial process confirms that in order to apply the methodology of identifying "switch" points it is necessary to have detailed information on the configurations and costs of all existing plants within an industry.

A Comparison of the Econometric and enainserina Anoroaches to Measuring Fuel Substitutability The purpose of this paper is to evaluate the use of various approaches to measuring the potential for fuel substitution in response to price related market instruments with a view to attainlng a lower level of pollution from the encouraged fuel. It is therefore necessary to be able to assess the degree of fuel substitutability brought about by a given price change and the associated change in pollution.

The econometric approach typlcally takes economy wide data on power, aggregate industry or particular industries and establishes the degree to which aggregate fuel substitution in the past is associated with price changes of the different fuels. The data requirements are several years' information on the prLces and volumes of the different fuels used. For more sophisticated models, in which allowance is made for changes ln the total demand for energy brought about by the impact of indivldual fuel prices on the aggregate price of energy, it is necessary to have information on the price and volume of capital and labor used. For developed countrieo such data are readily available, at least for industry as a whole and for the power sector. For developing countries the data is often likely to be lese comprehensive.

The key assumptions for econometric models are that all fuels are used (or at least all of a subset are always used) and that there is continuous substitut&bility between fuels Ln response to fuel price chcnges. models of fuel shares also ignore capital costs of fuel switching or assume that all
> - 50 -

technologies cost the same. This latter assumption fails to distinguish between - ex ante and ex post substitution the former requires completely new plant whichever fuel le chosen, while the latter may require no new plant (multifirlng) or require expenditure on conversion if the fuel mix is to be changed. The assumption of continuous substitution is particularly restrictive since no one plant is likely to use a mixture of fuels in the way that it uses a mixture of labor, capital and energy. Rather there is a preferred fuel/technology which will be switched at a certain value of the price ratio. Only if there are many plants, each with slLghtly different values for the switch point (caused by differences in local fuel prLces, age of capital etc.), will the aggregate behave as if there were a single plant with continuous fuel substitutability. In order for there to be a decreasing rate of substitution, as implled by the traditional cost function, the distribution of switch points would need to follow an extremely limiting and unlikely pattern. However, if plants have idle capacity, then there ie a possibility of continuous substitution until full capacity is reached as pointed out by Johansen [1972]. For industries with many small plants the econometric model may be a reasonable approximation to the actual possibilitiec but it is very unlikely for it to be close for power systems. In addition the published data for power systems in developing countries suggest that often only one or perhaps two fuels are presently used, while policy may be to encourage the choice of another fuel with better pollution characteriatics. Econometric models for such a country could give no guide as to the price at which this might happen, aince the fuel in question could not be included in historical substitution analysis.

By the nature of the aggregate data used econometric models must use average emission rates for the various fuels - they cannot be technology specific.

Engineoring studies attempt to evaluate specific aituations in which one fuel may be substituted for another and by taking all costs and technical information into account to reach an accurate picture of the aensitivity of fuel choice to the price of that fuel, as well as to other variables. In power
- 51 -

generation this calculation is certainly eystem specific so that the inetalled technology plays an important role in determining fuel choice. Capital costs and technological characteristics are also key variables in the analysis. The specificity of the technologies chosen also allow a precise evaluation of the pollution characteristics of the plant or system.

The data required for engineering analysis at a plant level are those which a firm would take into account - capital costs of alternative technologies, likely fuel prices, technical performance, likely demand growth etc. For the government it is likely that such data would be available economy wide for the power sector, since this is often state controlled. Whatever the form of ownership, the management will optimize over its system as a whole when taking investment/fuel choice decisions.

For industry the information required for government intervention to affect fuel choice is much more diverse and very unlikely to be available for the economy as a whole. It would be necessary to have data on existing technology by capacity and performance as well as plant specific fuel prices for every plant in the economy. For a given industry if it were narrowly enough defined the range of potential technologies and costs could be assembled but where a large number of plants were in operation, the necessity to make a separate evaluation of the price at which fuel switching would take place would be very demanding. in summary it appears that engineering studies are likely to be much more reliable in producing a plant level analysis of the sensitivity of fuel choice to prices. Econometric studies cannot be expected to give accurate answers, but because of the lesser level of demand for data, may be able to provide some guidance to the order of magnitude of response that may be produced. There is an apparent distinction between power systems and industry analysis. The former is homogeneous in terms of the technologies used (all sites face the same choices) and is often owned and optimized by a single authority. The data for an engineering approach is likely to be avallable and certainly should be utilized if possible. Even a plant by plant analysis (ignoring system interrelationshipe) is likely to be a more trustworthy guide than econometric
- 52 -

analysis. Industry analyasl may have to be carrLed out by econometric technlques because of lack of information, unless the study la to be confined to a few large scale plant. which domlnate the sector.
- 53 -

# 4. **V1DENCE** ON FUEL **SWITCHING ELTICXTIES**

Econometric modelling produces estimates of own price and cross price elasticities which are based on country and industry level data. They represent average responses over time and over all the various plants in the aggregate. Values of these elasticities could be used for the countries concerned to simulate the effects of pollution taxes. If it were found that such elastLcities have a general pattern then this may be able to provide some guidelines for the analysis of other countries outside the sample. Although the sector which is best suited to econometric analysis is power generation, because of the product homogeneity, the larger emphasis has been on measuring substitutability ln industry. The level of aggregation for the latter is an important factor in determining the extent to which results can be generalized to other situations. Elasticities in the Electrical Power Generation Segtor Griffin (19771 refers to earlter studies which estimated inter fuel substitution elasticities for the electricity sector. These were for single countries. Griffin's study has the great advantage in that it pooled crosssections and time series data which allowed for differences in elasticities between countries. Data was for twenty O.E.C.D. countries measured at four five year intervals (lv55 to 1969). His basic model hypothesized only inter country variations in relative prices were important. This was on the basis that long standing inter-country differentials in fuel prices accounted for fuel input choices in a long run equilibrium content. Hydro and nuclear sources of electricity were excluded. The model, as explained in section 3, was a translog cost function in which fuel shares are determined independently of the share of energy in total factor input. The estimated model imposes both the equality (linear homogeneity) and symmetry restrictions and the assumption of concavity was checked at every data point and found to be satisfied. The own and cross price elasticities at mean fuel shares for each country are calculated. Table 4.1 reports the own price elasticitles between fuels, ignoring the general substitution between energy and other factor inputs. The key point to notice is that the differences in elasticities do not come from attaching separate
- 54 -

parameters to each country (only a single set are used) but from the evaluation of the elasticities at the different shares. If two countries had the same fuel shares then they would have the same elasticities.

TabI2 4.1t Griffin's Estimates of Own Price ElasticLties in the Electricity Sector

||Coal|Gas|Fuel Oil|
|---|---|---|---|
|Canada|-0.66|-0.95|-2.94|
|U.S.A.|-0.66|-0.90|-3.46|
|Japan|-0.55|-2.40|-1.83|
|Austria|-0.79|-0.79|-3.22|
|Belgium|-0.75|-0.83|-3.08|
|Denmark|-0.51|-|-1.79|
|Finland|-0.86|-0.91|-1.99|
|France|-0.74|-0.80|-3.65|
|West Germany<br>|0.39|-1.14|-10.25|
|Greece|-0.93|-|-0.98|
|Ireland|-1.16|-|-0.78|
|Italy|1.01|-1.17|-1.26|
|Netherlands<br>|0.48<br>|-1.65|-2.37<br>|
|Norway|-3.90|-|-0.40|
|Portugal|-0.89|-1.42|-1.27|
|Spain|-0.44|-2.12|-2.45|
|Sweden<br>|-2.36|-4.12|-0.49|
|Switzerland|-|-|-0.34|
|Turkey|-0.69|-1.23|-1.85|
|U.K.|-0.35|-1.82|-3.74|
|O.E.C.D.|-0.57|-0.94|-3.12|

(Blanks indicate that a given country does not use the fuel for electricity generation.) Griffin noted that the own prlce elastlcitLes are conesderably higher than those based just on tLme serLes data for the U.S. His own earlier work had found -0.3 for coal, -0.0 for gas and -0.2 for fuel oil, while Hudson and Jorgenson (19741 had found -0.45 for coal, -0.10 for gas and -0.88 for fuel oll. He interpreted the lower values as being short run (perhaps an adjustment period of only 2 years) while the cross section results were nearer to long run values with a 20 year adjustment pattern. Table 4.2 provides the correspondlng crossprice elasticities which again differ between countries according to their fuel shares.
- 55 -

Table 4.2: Griffin's Estimates of Crogs-Price Elasticities in the ElectrLcity Sector

||Gas/Fuel Oil|Coal/Fuel Oil|Coal/Gas|
|---|---|---|---|
|Canada<br>|0.66<br>|0.53<br>|0.13|
|U.S.A.<br>|0.53<br>|0.50<br>|0.16|
|Japan<br>|3.67<br>|0.62<br>|-0.07|
|Austria<br>|0.50<br>|0.57|0.21|
|Belgium<br>|0.54|0.56<br>|0.19|
|Denmark<br>|-<br>|0.61|-|
|Finland<br>|0.73<br>|0.74<br>|0.12|
|France<br>|0.49<br>|0.52<br>|0.21|
|Went Germany<br>|0.77|0.30<br>|0.08|
|Greece<br>|-|1.11<br>|-|
|Ireland<br>||1.41|-|
|Italy<br>|1.25<br>|1.05|-0.04|
|Netherlands<br>|1.91|0.51<br>|-0.03|
|Norway<br>|-<br>|5.52|-|
|Portugal<br>|1.69<br>|0.96<br>|-0.07|
|Spain<br>|2.93<br>|0.49<br>|-0.05|
|Sweden<br>|8.22|3.14|-0.79|
|Switzerland<br>|-<br>||-|
|Turkey<br>|1.18<br>|0.68|0.01|
|U.K.<br>|2.18<br>|0.38|-0.03|
|O.E.C.D.|0.66|0.47|0.11|

Although this table also shows substantial variations between countries it does suggest that the degree of substitutability between coal and gas was very low over the period. Griffin attributes this in part to the tendency to use coal for base load and gas for peak load in many countries, and to the empirical fact that few countries have indigenous supplies of both coal and gas (which are much leoss traded than oil). The Hudson/Jorgenson estimatea for the U.S. power sector were for gas/oil at 0.20, coal/oil at 0.43 and coal/gas at -0.20. Again the longer run nature of Griffin's data supports a view of a higher degree of substitutability.

There have been relatively few attempts to measure econometrically the potential for fuel switching in power generation using more recent data. The series of studies published just before and just after Griffin's cross country analysis concentrated largely on problems of disentangling long and short run elasticities. Donelly [19871 estimated values for Australia using slightly later data, but there appears to be no wide rangLng study using data covering a
- 56 -

substantial period after the second oil shock. Since it is widely agreed that the life of capital in the power sector is of the order of twenty years, it would be desirable to re-estimate Griffin's model using data which covered all of the 1980s. There have been a large number of studies looking at the comparative economics of different fuels for power generation and trying to identify the break even price but either these are hypothotical greenfield studies or else they are situation specific. Few general lessons can be drawn from these studies particularly since the conclusions are always very dependent on the costs and performance of the technology investigated and the price path assumed for the competing fuels. The method is perfectly suitable for application wherever there is data but, by its very nature, the quantitative aspects of the results cannot be generalized.

Elasticities of Fuel Switchina in Industry Unlike the power sector there has been a continuous interest in econometric modelling of fuel choice in the industrial sector. This may be because the issue of fuel choice appears simpler. Power raises the difficulties of dealing with hydro and nuclear and the crucial distinction in many countries between base and peak load firing. Industry appears to use all major fuels, if a high enough degree of aggregation is used, and time series data suggests that considerable inter fuel substitution has taken place over time.

The first oil shock produced considerable interest in measuring inter fuel substitution (away from oil) as well as energy substitution. Pindyck [19791 is a major example of such studies using translog functions and allowing for the total energy substitution against other factors. The model pooled data on ten industrLalized countries for the period 1959-1973. The coefficients for the parameter of energy substitution against other factors was allowed to vary between countries, but the parameters for inter fuel substitution were constrained to be equal. As in Griffin's work on power, the evaluation of elasticities of country mean shares for each fuel allows the elasticities of substitution to vary between countries. The results for the aggregate industrial sector are extensive but the key values are the partial fuel price elasticities shown ln tables 4.3. and 4.4.
> - 57 -

Table 4.3L Pindyck's PartLal Own Fuel Price ElasticitLes for the Induotrial Sector

||Coal|Fuel Oil|Gas|Electricity|
|---|---|---|---|---|
|Canada<br>|-1.80|-0.81|-0.33|-0.14|
|France|-1.04|-0.20|-1.49|-0.16|
|Italy|-1.49|-0.29|-1.30|-0.13|
|Japan<br>|-1.32|-0.20|-1.49|-0.12|
|Netherlands<br>|-1.67|-0.11|-1.42|-0.07|
|Norway<br>|-2.08|-0.34|-|-0.08|
|Sweden|-1.26|-0.27|-|-0.12|
|U.K.<br>|-1.12|-0.22|-1.38|-0.15|
|U.S.A.<br>|-2.17|-1.10|-0.52|-0.08|
|West Germany|-1.09|0.03|-2.31|-0.12|

The own prLce elastLclty for electricity Ls uniformly low and that for oL1 is surprLLngly low, but thLi may well reflect the fact that the data does not Lnclude the oLl shock perLods. The model did not impose symmetry so for each pair of fuels there are two elasticitLes of substitutLon. The values are not very dlfferent so just one set is presented.

ZSble 4.4s PLndyck's Cross Price PartLal Fuel ElasticitLes for the Industrial Sector

||C/O|C/G|C/N|o/a|0/3|G/N|
|---|---|---|---|---|---|---|
|Canada<br>|0.90|1.17|-0.28|-0.21|0.61|-0.49|
|France<br>|0.20<br>|0.46|0.39|-0.08|-0.08|-0.51|
|Italy<br>|0.27<br>|0.83|0.39|-0.03|0.11|-0.15|
|Japan<br>|0 a1<br>|0.65<br>|0.45|-0.08|0.02|-0.41|
|Netherlands<br>|0.21<br>|0.98|0.48|-0.10|0.01|-0.22|
|Norway<br>|0.37<br>|1.34|0.37|-0.09|0.30|-|
|Sweden<br>|0.24<br>|0.55<br>|0.46|-0.11|0.12|-|
|U.K.<br>|0.21<br>|0.52<br>|0.39<br>|-0.06|-0.04|-0.33|
|U.S.A.<br>|0.99<br>|1.66<br>|-0.48|-0.72|0.85|0.12|
|WestGermany|0.15|0.43|0.49|-0.18|-0.22|-1.82|

The cross prLce elasticities give a faLrly consistent picture. There ls a low degree of substLtutability of coal for oLl and a low degree also of oLl for electricity except in Canada and the U.S. Coal and gas are strongly substitutable while coal and electricity are moderately substitutable. Gas
- 58 -

appears to be complementary to both oil and electricity in many countries. Pindyck also calculates total price elasticities which allow for the additional impacts of a fuel price change on the substitution between energy and other factors of production. Theme are shown in tables 4.5 and 4.6.

Table 4.5s Pindyck's Total Own Price BlasticitLes for the Industrial Sector

||Coal|Fuel Oil|Gas|Electricity|
|---|---|---|---|---|
|Canada|-1.89|-1.03|-0.41|-0.61|
|France|-1.29|-0.34|-1.54|-0.54|
|Italy|1.63|-0.46|-1.37|-0.59|
|Japan|-1.49|-0.35|-1.54|-0.60|
|Netherlands|-1.78|-0.22|-1.48|-0.63|
|Norway|-2.15|-0.56|-|-0.62|
|Sweden|-1.44|-0.44|-|-0.60|
|U.K.|-1.35|-0.37|-1.44|-0.56|
|U.S.A.|-2.24|-1.17|-0.67|-0.63|
|West Germany|-1.31|-0.06|-2.34|-0.59|

The own price total elasticities show a uniformly moderate value for electricity, while coal and gas tend to have much higher values. Oil has a surprisingly small value, but again this may reflect the period analyzed.

Table 4.6: Pindyck's Total Cross Price Fuel Elasticities for Industry

||C/o|C/G|C/E|O/G|O/E|G/E|
|---|---|---|---|---|---|---|
|Canada|0.69|1.08|-0.75|-0.29|<br>0.14|-0.96|
|France|0.06|0.40|0.00|-0.13|-0.46|-0.89|
|Italy|0.09|0.76|-0.06|-0.10|-0.35|-0.61|
|Japan|0.07|0.60|-0.03|-0.13|-0.46|-0.89|
|Netherlands|0.09|0.92|-0.08|-0.16|-0.54|-0.77|
|Norway|0.15|1.33|-0.16|-0.09|-0.24|-|
|Sweden|0.07|0.54|-0.02|-0.12|-0.37|-|
|U.K.|0.06|0.45|-0.02|-0.12|-0.44|-0.74|
|U.S.A.|0.92|1.50|-1.03|-0.88|0.30|-0.43|
|West Germany|.05|0.41|0.01|-0.20|-0.70|-2.29|
- 59 -

The total price elasticities are very different from the partial values and indicate that it is important to allow for substitution away from energy towards capital and labor. The resulting total picture shown very small substitution between coal and oil, coal and electricity and oil and gas except for Canada and the U.S.A., which over the estimation period were distinct in having domestic production of all three primary fuels. Coal is strongly substituted for gas but both oil and gas appear complementary to electricity.

There have been many subsequent studies of price elasticities for industry but few have attempted to give the range of results of Pindyck. Most have also concentrated on the translog cost function approach. For example, Uri (1979) pools sector and time series data (1960-71) for India using a translog cost function - the subsector coefficients were constrained to be equal. At the mean shares for each sector the own price elasticities for coal, oil and electricity were all negative while the cross price elasticities were all positive. The elasticities did show considerable variations between sectors due to the large differences in fuel shares between the sectors. Since the sectors were highly aggregated and very different (mining, transport, domestic, agriculture and commercial) this result is an immediate consequence of the tranalog form.

A series of studies by Sterner (89, 901 has looked at the economics of fuel switching within a single industry (cement) and a number of lndustries in Mexico. The cement study uses both aggregate time series on the industry and a cross section of sixty kilns in 1976 derived from a survey of fuel use. The cross section data allowed Sterner to relax the assumption of smooth and reversible substitution and use a 'putty-clay, approach as originally suggested by Johansen (1972). Once capital is in place there is a fixed input requirement, while in choosing how much capital to have firms can choose over a range of inputs. Due to differences in technology between plants firms can still substitute ex post between inputs by varying the degree of capacity utLiLzation (when there is spare capacity). Using this approach Sterner derives expressions for the short-run substitution of labor for energy. A similar exercise could be
- 60 -

carrled out as between fuels if the data were available. In **essence** this **is** the same approach as the power system analysie of Plnto and Besant-Jones. In his econometric study of manufacturing lndustry ln Mexico, Sterner uses the translog cost function to a time series of cross sections for various industrles. Not only were the partial price elasticities of substltutlon calculated but also those between energy and other lnputs. Sterner compares his elastlcitles wlth others for developing countries and finds that **his** own prlce elastlcitLes (at mean shares) are similar to those for industrialized countries but show a bigger difference against the values obtained by a varlety of methods for developing countries. The absence of a standardized approach means that few general lessons can be drawn. More research, using Sterner's methodology on other developing countrles is still needed.

Recently, new developments ln functional form specifLcation have appeared and these are especially valuable sinethey offer comparisons between model forms and between studles. Considine 119891 developed a logistic fuel share model, as explained in section 3, whlch has the advantage of imposing global concavity (although at the possible price of abandoning global symmetry). Its other distinctive feature La the stabilLty of the elasticities with respect to fuel price changes. The translog function yields very different elasticities at different fuel shares (which is revealed by Griffin and Pindyck's results where the single model is evaluated at indlvidual country shares). Hence lf fuel prices change strongly then so will the elasticities at those prices. Considine defines a subsector "stationary fuel consumption" which is mainly the industrial sector less those subsectors where fuels are feedstocks (petrochemicals, plastLcs, steel). The data is from the U.S. for the period 1970 to 1985 and thus allows for some of the effects of the two oil shocks on fuel choice. The partial price elasticities for both the translog and logistic functional forms are calculated both at the mean shares for the period and for five year intervals. The effects of regulation are also incorporated in both models. Tables 4.7 and 4.8 give the own and cross price elastlcities for the 2 models for 1970 and 1985 prices.
- 61 -

TblI2 4.2i Considine's Partial Price Elasticities for U.S. Stationary Fuel Combustion in 1970

||Petroleum<br>Products|(i) Tr<br>Gao|Ansloa<br>Coal|Electricity|
|---|---|---|---|---|
|P<br>a<br>C<br>E|-0.01<br>0.05<br>0.37<br>-0.08|0.08<br>-0.58<br>-0.44<br>0.31|0.21<br>-0.15<br>-1.02<br>0.17|-0.29<br>0.67<br>1.08<br>-0.41|
|||(11) Log|iatic||
||P|a|C|X|
|P<br>a<br>c|-0.10<br>0.08<br>0.26|0.14<br>-0.55<br>-0.63|0.10<br>-0.10<br>-0.68|-0.13<br>-0.58<br>1.04|
|E|-0.05|0.32|0.13|-0.40|
- 62 -

Table 4,8s Coneldine's Partial _Prioe_ Elasticities for U.S. Stationary Fuel Combustion in 1985

||Petroleum<br>Products|(i) Tr<br>Gas|ansloi<br>Coal|Electricity|
|---|---|---|---|---|
|P|-0.12|0.13|0.15|-0.16|
|G<br>C<br>E|0.09<br>0.5<br>-0.06|-0.57<br>-0.88<br>0.33|-0.17<br>-0.12<br>0.14|0.66<br>1.44<br>-0.41|
||P|(ii)<br>G|LoaiLtic<br>C|E|
|P|-0.09|0..2|0.07|-0.10|
|G|0.09|-0.57|-0.13|-0.61|
|C|0.27|-0.64|-0.71|1.07|
|E|-0.04|0.31|0.11|-0.38|
- 63 -

_-_ Two features are immediately _apparent_ the models give quite different results for certain fuel pairs e.g. the tranolog shows normal substitution between electricity and gas, while the logistic shows complementarity (interestingly Pindyck's partial elasticity between electricity and gas was also positive). The translog model also gives very variable - elasticities for certain fuel pairs coal and all other fuels are notable here, while the logistic yields very stable values. For neither model has symmetry been imposed. For ease of comparison Pindyck's translog partial elasticities for the U.S. are repeated in table 4.9.

Table 4.9: Pindyck's Partial Price Elasticities for U S. Industry

||P|G|C|B|
|---|---|---|---|---|
|P|-1.10|-0.72|0.97|0.85|
|0|-0.32|-0.52|0.72|0.12|
|C|0.99|1.66|-2.17|-0.48|
|3|0.11|-0.03|-0.06|-0.08|

These are completely different from Considine's results for both 1970 and 1985 and suggest that the effect of pooling on the U.S. values may be very considerable. Considine does show that imposing an allowance for regulatory constraints does reduce the elasticities and this, plus the difference in the definition of the sector, must also be a contributory factor in explaining the variation in reoults.

A quite different approach to modelling energy price elasticities is exhibited in the work of Mountain and Haaio (19893. They disaggregate industry data to a regional level within Canada and by industry type (2 digit level). They also argue that, given the need to find a good approximation to the true cost furntion, it is not necossary to impose tie same functional form on separate industries. They try different flexible functional forms in order to see which fits best in each case. In addition they introduce a wider range of
- 64 -

possibilities for functional form by using both quasi quadratic Cobb Douglas and Fourier specifications. In addition they introduce an element of dynamic adjustment by fitting first order autoregressive error structures. The basic model however is again a two stage cost function which gives fuel shares in total energy and then relates total energy used to other factors of production. The data cover the period 1962 to 1979. The partial cross price elasticities are calculated for 1974 fael shares and are shown in table 4.10. Coal was included with oil because of its minor importance. The model also allows for Hicksian technical progress. The results show that a single functional form cannot always be made to fit adequately (indicated by blanks in the table). Mountain and Hsaio compared their results with previous work on Canada and were able to conclude that in general their elasticities of substitution were larger. It La certainly noticeable that in comparison with the aggregate industry estimates of Pindyck and Considine, the use of disaggregated data plus an allowance for technical progress and for autocorrelation together produces very much higher values of the cross price elasticities. One important finding is that where quite different flexible forms are used the elasticities evaluated at the same fuel shares are close to each other. Also the results for the different industries are surprisingly close bearing in mind that no restriction has bee;i placed to limit the results from the various industries. Mountain, Stipdonk and Warren [19891 using a similar methodology with data spanning 1962 to 1984 for Canada, reach similar conclusions.
- 65 -

Table 4.10: Mountain and Hsaio's Partial Cross Price ElasticitLes for Industries in Quebec for 1974

||Electric|ity/Oil|Electri|city/Gas|**oil/G**|**as**|
|---|---|---|---|---|---|---|
||Q|F|Q|F|Q|F|
|Food and<br>|||||||
|Beverages|-|0.80|-|0.66|-|0.72|
|Tobacco|0.81|0.94|1.06|1.00|0.81|<br>1.00|
|Rubber/<br>|||||||
|Plastics|-|.97|-|0.74|-|0.67|
|Leather<br>|-|0.81|-|1.43|-|1.14|
|Textiles|-|0.78|-|1.02|1.40||
|Knitting<br>|0.79|1.00|1.00|1.00|0.79|1.00|
|Clothing<br>|1.00<br>|0.94|1.00<br>|0.86|1.00|1.56|
|Furniture<br>|0.94<br>|-|0.94|-|1.08|-|
|Paper|0.94|-|1.10|-|0.91|-|
|Metal<br>|||||||
|fabricating|-|0.92|-|0.80|-|1.15|
|Machinery|-|0.82|-|0.95|-|0.63|
|Non-metallic<br>|||||||
|minerals|0.89|0.84|0.89|0.93|0.89|0.97|
|Petroleum|||||||
|products|0.80|0.80|1.02|0.72|0.80|1.25|
|Chemicals<br>|-|0.89|-|0.97|-|1.00|
|Miscellaneous|0.96|1.00|1.00|1.00|0.96|1.00|

(Q = quasi quadratic Cobb Douglas, F - Fourier)
- 66 -

This selection of estimated elasticities of inter fuel substitution shows that there certainly is no agreement in the values obtained. However this result needs careful interpretation. A key feature of all the models used is that (deliberately) tbey move away from the restrictiveness of a constant elasticity of substitution specification to one in which it can vary. Since in most models it varies with the level of fuel shares it will be very sensitive to relative prices. In this view there is no value in comparing elasticities from different studies or for different industries or countries unless they are evaluated at the same fuel shares. Where different functional forms have been fitted to the erame data and evaluated at the same fuel shares, then the results of Mountain and Hsaio suggest that there may be a robustness in the elasticities which could allow the structural parameters of the model to be used to calculate elasticities at other fuel shares and in other situations.

Despite the substantial amount of econometric work that has been undertaken on estimating fuel elasticities certain limitations for their potential use in a wider content are very apparents

- (i) the studies are largely concentrated on the most industrialized countries and even when comparative values have been derived, as by Griffin and Pindyck, this has been on the basis of a single set of parameters evaluated at the fuel shares of the individual countries. No formal testing for differences between countries at the same fuel shares has been attempted;

- (Li) much of the econometric work has used data which does not allow for the post oil shock period. There has apparently been no attempt to test for structural stability of the estimated models between (say) the pre and post 1980 period. Given that major movements in relative fuel prices may have taken place outside the estimation period of many models, it is not ideal to use elasticities based solely on earlier data;

- (iii) rather little is known about the comparative behavior of different industries. However the work of Mountain and Hsaio suggests that, for Canada at least, the elasticities may be rather similar so that working with aggregate industry data may not suffer too greatly from potential bias caused by shifts in the relative importance of the various components.

- (iv) very little attention has been paid to specific modelling of dynamic adjustment. The cross section work of Griffin by constraining all countries to lie on the same cost function can be seen as focussing on those long term differences which distinguish one country from another, and hence the values obtained may be more representative of long term elasticities
- 67 -

than ones obtained from sLngle country studies which exclude any dynamic factors. The problems of dynamic modelling, as explained by Mountain and Hsaio, cannot be easily overcome but this in general reduces the credibility of econometrically derived estimates of inter-fuel substitution.

One important issue that has received little attention in the econometric literature is the treatment of caves where one or more of the fuels is not used at all. As is clear from the international evidence, this is a common orc^irrence and is likely to be particularly important when using plant data (either in a cross section or a panel). The presence of zero observations in the dependent variable of fuel shares has been effectively ignored as a problem in most studies. Although the translog will always predict strictly positive shares if a finite fuel price is used as an explanatory variable, the error may not be too severe. However approaching the problem via the production function would lead to immediate difficulties since the fuel shares are related to the log of quantities (which are undefined for zero quantities). The problem with the translog cost function approach is to specify the price for the fuel where expenditure is zero. The simplest solution would be to use mean prices but this does not capture the reason for the non-use of the fuel. If prices really would be mean values for the plant in question then the fuel would be used, according to the model, to a substantial degree. Clearly the "true price" is so high that the fuel is above the switch point. Short of attempting to calculate the implicit price of the fuel at such sites, one solution would be to attach a high price (relative to the general country experience) and to experiment with values of this to see how closely the predictions of fuel share approach zero. Other methods would be to constrain the fuel shares by a Tobit type analysis - i.e. to hypothesize that there is a price above which the fuel would not be used and to attempt to recover this threshold from the data. The threshold can be made a function of own and competing fuel prices. Such an approach would be considerably more complex in computational terms than the simple translog fuel share equations.
- **68** -

The level of aggregation has Lmportant implications for the evaluation of the econometric models used. In an aggregate time series (for a single industry in a particular country) the data is all likely to be drawn from rather homogenous capital stock i.e. the composition of the capital wll not change rapidly. The parameters of the model are thus likely to be very similar at all data points. As opposed to this the model will typically pick up short run responses to fuel price changes unless some explicit dynamic adjustment is included. In a cross section there is likely to be a wider spread of technology and hence a greater chance that the true **parameters** of the cost function vary between observations. If the observations are for countries (each aggregating over plants and industries) the averaging process may reduce this difference. At the same time differences in response between countries will be associated with persistent and long term differences in relative prices (due to local market conditions) so that the model would be more likely to capture long run substitution. Panel data (as used by Orlffin) will zombin **the** features of **time** series and cross section models. For those **cross** section **studies where** the unit of observation is a plant there can be very sharp **differences in** technology associated with the age of the capital stock. However tlv potentlal rlchness of such a sample might allow the use of a "vintage approach to the production or cost function, which would essentially attempt to allow for the **age** of capital. The issue of separability would be crucial ln determining whether separate cost share functions could be estimated for each vintage. Problims of Usain EstLwates of lnter-Fuel BWbstLtutsbliLt For the policy purposes considered by thli paper the key Lssue is the response of fuel use to a tax or subsidy induced price change. In order to encourage the use of gas as opposed to coal (say) in thermal power generation, a government could impose a tax on coal or give a subsidy to gas. This would be expected to change the fuel mix and hence the pollution characteristice of the power sector.
- 69 -

The analysis of the reosponse to the change in taxes **has** four stagess

- (i) an appreciation of the extent to which the tax would be fully passed on to the purchaser;

- (ii) an estimate of the degree to which a non-taxed fuel would be substituted for the taxed fuel to produce the same outputt

- (iii) an estimate of the extent to which the higher average price of fuel would lead to a lower demand for all fuels;

- (iv) an assessment of whether there would be further feedback effect., either through an induced change in the price of the non-taxed fuel or through macro-economic effects linked to the rise in fuel prices and the increase in government revenue.

Both the engineering approach and much of the econometric literature focus on the second issue of the size of fuel substitution elastieLties at a given level of output. This is certainly likely to be the largest effect but the effects of non-infinite supply elasticities for fuels and of energy substitution elasticities as well as demand elasticities may be non-negligible in certain cases.

The elasticities of supply of the various fuels will depend both on market power and on the cost structure of the supplying industries. Where fuels are imported it is certainly true that any individual country can assume that the world supply curve is infinitely elastic. Where domestlc supply is utilized it may be true that there is in effect a rising supply curve due to variations in cost conditions between different parts of the industry. In such a case shLfts of demand curves along these supply curves wlll lead to changes in the supply price. The fall in the demand for the taxed fuel would tend to lower its price and the rise in demand for the untaxed fuel would tend to raise its price - both effects would tend to dampen the degree of fuel substitution. Similarly if the taxed fuel industry decided to absorb some of the tax rather than passing it all on, in order to protect its market share, then the initlal stimulus to switch fuels would be blunted. The Lnteraction between the demand and supply elasticities and the cross price elasticity measurlng fuel substitutability can only be properly calculated using an articulated model of the supply and demand for the two fuels, in which the demands depend upon both prices.
- 70 -

The effect of the rise in the general energy price as substitution away from energy and towards capital and labor can be allowed for as indicated in section 3.9. This gives a further modification of the demand for both fuels which would interact with the supply equations if the supply elasticities are not infinite.

Other less important effects on the quantities of fuels used (and pollution created) are likely to arise from the macroeconomic feedbacks. The rise in energy prices will feed into inflation and hence into the real economy depending on the policy response of the government. At the same time the change in government revenue from tsxation on the fuel will affect the budget balance and hence provide another link to the real economy. It is important to note that if the product is already taxed an increment in the tax rate can lead to a fall in total tax revenue (depending on the elasticity of demand). The introduction of a emall tax is bound however to increase revenue.

It is clear from this brief discussion that the full effects on the quantities of fuels demanded of a rise in the tax rate on one of them requires a complex model to incorporate all the prlce effects. In order to use the results of such analysis lt would be necessary to estimate not only the own and cross price demand elasticities, but also the supply elasticities for the competing fuels. An example of the analysis of the full Lmpact of a fuel substitutlon tax in a macro-economic framework ia provLded by Hall, Truong and Anh (1990J.

Assuming that the simplest possible model L a good approxlmation to reality i.e.:

- (i) the tax rate ia fully passed on to buyers;

- (LL) the supply of both fuels li lnfinitely elastic over the range of demands experienced;

- (LLL) the lmpacts on the demand for the' output (power or Lndustry) through the impacts of the energy prlce change are negligible;

- (iv) the impact on the demand for the product because of the change in the prlce of the aggregate energy lnput is negligLble;
- 71 -

then the analysis of the impact of a change in the tax rate on one fuel requires, for a first approximation, the own price elasticity for the tax fuel and the cross price elasticities for the competing fuels.

Let the tax rate imposed be such that the price of fuel i at the burner tip rises by t1 percent. For an own price elasticity of en the demand for fuel i will fall by (t1eu) percent, while the demand for substitute fuel j will rise by (t1ep) percent, where ej is the price elasti-ity of fuel j with respect to price i. These valuee van then be applied to the emissions characteristics to obtain the net change in pollution. To allow for energy substitution caused by the rise in the aggregate energy price it is first necessary to compute the - percentage rise in the aggregate energy price for small changes in the shares of different fuels this is a (constant) welghted average of the indlvidual fuel price changes (where the weights are the original shares in total energy) so that if just price i changes then the aggregate energy price will change by (t1s1), where ml is the share of fuel i. With this change in the aggregate energy price and an own price elasticity for energy (versus other inputs) of 3i, the fall ln the demand for energy will be (Eitjs1<sup>). The demand for all fuels will be reduced</sup> by this fraction thus contributing further to the reduction in the output of pollutants.

The use cf this elasticity approach, as well as depending on the lack of feedback through supply responses and macro-economic effects, is limited by the limitations of the assumptions used in econometric modelling. Not only may the incorrect form of cost functions be used, but the lack of dynamic adjustment gives no indication of the timetable of the response. Elasticities drawn from engineering studies are likely to be more accurate for the plants from which the data were derived, but they too omit feedback effects. For example, vLrtually all engineering studies take demand levels and fuel prices as given. Allowing for the repercussions of fuel swltching on the parameters of such models can lead to enormously complicated optimization where whole eystems are involved. At a - plant level indeed the feedbacks will usually be negligible plants are rarely
- 72 -

- big enough to affect total fuel supply on economy wide demand but a program designed to change the fuel mix over the whole of the power sector could produce economy wide effects.

From the policy otandpoint, ln which the costs of a tax based antlpollution program would be cruclal in deciding it. feaslbility, the great weakness of the econometric approach is its lack of specification of the associated capital costs. Not only the direct costs of investment in new capacity or conversion of existing capacity (unless multi-firing is in operation) will be important in the context of developing economies, the capital infrastructure required to transport the alternative fuel can be expensive. This will rarely be adequate before the fuel swltching takes place so that lnevLtably there wlll be associated costs to take lnto account. The econometric approach as at present developed does not allow for directly associated capital costs to be Lncluded. Indeed the models used, as was noted in sectlon 3, rely on the assumption that the fuel share choice ie independent of capital costs. This shortcoming also Lmplies that no distinction Ls made between ex ante (greenfield) choices, where an expansion is requLred, and ex-past choices where conversion ie the issue. For both the estimated elasticity of substitution ie the same - the same change in the price of one fuel relative to the other will produce the same predicted quantitative reaction for both situations. The strength of the engineering approach is that it forces the analyst to question the assumptions on fuel supply. If infrastructure needs to be provided (e.g. gas pipelines, compressor stations, peak storage facLilties) by the investing body then the capital costs will enter the calculation, as will the element of operating costs which would be factored into the price of the fuel at the burner tip.

The preceding remarks could be taken to imply that there ie no useful role for econometric models in designing pollution reductlon strategies. This ie to overemphaeize their lack of detail ln specifying the actual situation. In the past when fuel prlces changed there would have been lnvestment changes associated with whatever changes took place in fuel shares. The model in effect does not ignore the costs of the investment, but rather assumes that these costs
- 73 -

relatlve to other costs were constant during the estLmatLon period. Provided that this constancy contlnues to hold during th& policy implementation period, then the prlce elasticlties estLmated allow for the assocLated capital costs of switching at historLc average prlces. The method will therefore be at its most mLeleadLng when the relative capltal costs assoclated wlth fuel switching ln the future are very different from those in the past, and also when the values durlng the estimatLon perlod themselves were highly variable. If, durlng the estLmation period the capltal costs of fuel switching were variable and no account is taken of this, then the estimated price elasticitLes will be biased because of the standard womLtted varLables' effect of econometric theory. This effect is likely to be compounded ln forecasting if the relation between capital cost to fuel cost movements changes from that of the estimation perlod. Hence a situation in which ox ante fuel substitutLon elastLcLties are estimated from data covering a period of expansion when the relevant capital costs are those for alternative plant on a greenfLeld slte (plus assoclated infrastructure), whlle the future is of low growth in which there is ex past fuel switching through conversion of existing plant, will mean that the sensitlvlty of fuel cholce to price changes is likely to be much lower ln the future than the past. Thus, where all the relevant fuels have been used ln the past and where the future capital cost structure is likely to be fairly simllar to that of the past econometric estimates, econometric models may give a good guide to substitution possibilitLes desplte their lack of detailed cost data.

The relative substltutability of fuels depends both on the unit fuel cost and the unit capital cost. Although these are clearly plant speclfic (dependLng on the size of the capital employed) various estimates have been given. A recent survey by Barnes 119911 compares dlfforent costs for generatlng electrlcity as shown in table 4.11.
- 74 -

**Table** 4.11: Costs of Electricity Generatlon by Type of Fuel

||Overall<br>Efficiency<br>percent|Capit<br>U.S.|al Costs<br>cents/kwh|Tot<br>U.S.|al Coats<br>cents/kwh|
|---|---|---|---|---|---|
|Coal<br>Nuclear|33<br>32|2.0<br>3.8|<br>-<br>4.3<br> <br>-<br>10.0|3.8<br>5.3|<br>-<br>6.9<br> <br>-<br>11.8|
|Fuel Oil|33|2.0|<br>-<br>2.5|4.8|<br>-<br>5.7|
|IGCC|29|3.3|<br>-<br>4.5|7.5|<br>-<br>8.9|
|Gas Turbine|32|0.8|<br>-<br>1.1|3.4|<br>-<br>13.9|
|Gas CC|45|1.4|<br>-<br>1.9|3.1|<br>-<br>3.8|

- (IGCC integrated coal gaeiflcation combined cycle Gas CC - natural gas combined cycle)

This range of figures illustrates that the assumption of equal capital costs reuiLred for the standard econometric approach is rather doubtful for certain technologies. The relative importance of running costs to capital costs is also quite variable so that the choice of fuel wlll depend not simply on the relative fuel prlce but also on the relative capital cost. To the extent that changes in capital costs have been greater for some technologies than others over time then the choice of fuels may have been affected as much by shifte in relative capital costs as by shifts in relative fuel prices.