Directory UMM :Journals:Journal Of Policy Modeling:Vol22.Issue6.2000:

A CGE Model for Lithuania:

The Future of Nuclear Energy

Arvydas Galinis,Lithuanian Energy Institute

Marko J. van Leeuwen, SEO, University of Amsterdam

Awareness in Central and Eastern European countries of environmental problems is growing rapidly. Yet the social, economic, and political barriers to implementing drastic policy measures are still formidable. The acceptability of policy measures can be improved when the environmental and economic implications of alternative measures are shown in conjunction. Therefore, a prototype Computable General Equilibrium (CGE) model is constructed for Lithuania. A key question in Lithuania concerns the future use of nuclear-generated power. Using the CGE model, medium-term scenarios describing combinations of high or low economic activity, fuel prices, and nuclear potential are run for the year 2000. 2000 Society for Policy Modeling. Published by Elsevier Science Inc.

Key Words: Equilibrium modeling; Energy policy; Pollution; Sustainability, Lithuania.

1. INTRODUCTION

In this article we describe a “Computable General Equilibrium model for Lithuania” (CGE-LI model), constructed at the Lithua-nian Energy Institute (LEI) and the Foundation for Economic Research (SEO) of the University of Amersterdam. This multisec-tor macroeconomic model can be used to study the (medium and long-term) effects of policy measures on economic and environ-mental. The model consists of four major blocks, i.e., production, consumption, foreign trade, and environment. The energy sectors Address correspondence to M.J. van Leeuwen, University of Amsterdam, SEO, Roeters-staat 11, 1018 WB Amsterdam, The Netherlands.

Arvydas Galinis is Senior Research Associate at the Lithuanian Energy Institute (LEI), Kaunas, Lithuania, and Marko van Leeuwen is Senior Research Fellow at the SEO, Foundation for Economic Research of the University of Amsterdam, The Netherlands. The project is sponsored by the Phare/ACE program, project No. P95-2049-R. The support is gratefully acknowledged.

Received July 1997; final draft accepted February 1998. Journal of Policy Modeling22(6):691–718 (2000)

2000 Society for Policy Modeling 0161-8938/00/$–see front matter Published by Elsevier Science Inc. PII S0161-8938(98)00028-3

and the production, consumption, and trade of energy products are intertwined with all the blocks of the model. The environmen-tal block distinguishes between three types of emission gases (CO2, SO2, and NOx), of which a maximum output level can be imposed and abatement costs can be calculated. Furthermore, a basic con-cept of tradable emission permits is introduced.

Lithuania is the biggest of the three Baltic states sharing a border with Latvia, Belarus, Poland, and with the Kaliningrad region of the Russian Federation, as well having a 99-km coast line. Lithuania is contrary to Western perception, quite different from the other Baltic States in language, culture, and history. Its area is 65.3 thousand km2_{, with a population of 3.7 million people.} In the last 2 centuries Lithuania was made a part of its Eastern neighbor. For almost half a century Lithuania was fully integrated into the Former Soviet Union (FSU) and had to live under its centrally planned system.

Lithuania has been in transition to a free market economy since the first days of regained independence in 1990, has become a democratic state, and has started to implement comprehensive reforms. One of the main foreign policy goals is to join the Euro-pean Union (EU). In moving in this direction it became an associ-ate member of the EU in June 1995.

Lithuania has followed a Polish-like shock transition path with tight monetary stability and with reducing the rate of inflation as its main goals, following exhortations of the IMF and the World Bank. The first stage of the transition process showed successful privatization, and has invited foreign investors to participate in several large-scaled infrastructural and rehabilitation projects. However, up to 1995 the inflow of foreign capital was disappoint-ingly low: less than $200 million of foreign direct investments (FDI), which is merely 1.7 percent of GDP. The economy recov-ered strongly in 1996 and the first half of 1997 after the banking crisis of late 1995: growth accelerated, inflation kept falling, and unemployment decreased.

1A. Status of the Lithuanian Economy

The real Gross Domestic Product (GDP) grew steadily until 1989. Yearly GDP growth rates since 1985 are shown in Figure 1. In 1990, the GDP level was estimated, by World Bank experts and the Lithuanian Ministry of Economic Affair, at 2,133 to 2,370 ECU per capita. At the end of 1993, real GDP dropped to 40

A CGE MODEL FOR LITHUANIA 693

Figure 1. Real GDP growth in Lithuania, 1985–96.

percent of the 1990 value, and industrial output in 1993 has halved and during 1994—it decreased further by 32 percent. The decline lasted until 1994, when GDP finally increased again by 1 percent. The decline of the Lithuanian economic activity in the beginning of the transition period is huge, and more severe than in the other East European countries, mainly due to the tight interlinkage with the economies of the republics of FSU.1

The national currency, the Litas, was introduced in the middle of 1993. To forestall a general crisis of confidence in the economy and to curb inflation the Litas was pegged to the dollar (4 Litas5

$1). The fixed exchange rate was maintained through a tight mone-tary policy. To reach long-term fiscal sustainability VAT exemp-tion were eliminated in 1997, and a 18 percent rate was introduced for all goods and most services. Also, excise duties on alcohol and tobacco were sharply increased. On the expenditure side, the pension system was reformed by raising the retirement age and restricting indexation, and except for the low-income households, energy subsidies will be eliminated. Further measures will be needed, though, to achieve a financial viable government sector. In 1990 about 95 percent of Lithuanian trade was with the FSU; now this share is drastically reduced and represents no more than 1_{The rapid and large decrease of the GDP during the 1991–93 period seems to be} overstated due to the growth of the shadow economy. Many economists have their doubts about the real magnitude of this decrease in industrial output. They think the output figures are too low as producers try to hide a part of their activities to avoid taxes, some production gets foreign labels, etc.

45 percent. A rapid increase of energy and raw material prices in 1992 resulted in a loss of former export markets for manufacturing goods as prices for energy and raw materials in the FSU were still much lower, keeping production cost low. To fill up the gap in demand, Lithuanian producers and exporters are trying to redirect their export efforts towards neighboring countries in the west and the EU. The move to market prices and hard currency trading has deteriorated the terms of trade, which hampers the opening of new export markets. New important trading partners outside the FSU are Germany (share in total exports of 14.4% in 1995), The Netherlands (4.9%) and Poland (3.9%).

Inflation in 1992 reached 1,163 percent, but already 1 year later it decreased to 189 percent, and dropped further during the following years, respectively, to 45.1 and 35.7 percent. In 1997, a further stabilization of prices is expected.

Unemployment is steadily increasing from 4.4 percent in 1993 to 7.4 percent in May 1995, reflecting first of all a fall in employ-ment. In 1996, after the level of employment increased again, unemployment rates continued to rise, driven by an increase in supply of labor. Due to the privatization, the share of public sector employment fell to just over 30 percent in 1995.

The structure of the Lithuanian economy has changed signifi-cantly during the last 5 years; the share of agriculture has decreased sharply and the share of trade and services have grown consider-ably. Where the share of agriculture in GDP in 1990 was 27.5 percent, in 1993 it dropped to merely 11.2 percent. At the same time, share of trade in GDP almost quadrupled, from 4.9 to 17.1 percent. Also, within the industry the structure has changed dra-matically during the last 3 years. The most unstable situation is in those industries most integrated into the former Soviet econ-omy, for example, machinery and light industry; i.e., those sectors that were importing raw materials from the FSU and were ex-porting final products back to it.

Many branches of industry suffer from insufficient local raw materials, import constraints, obsolete equipment, and old-fash-ioned production processes. The pulp and paper industry is in a critical state because equipment at the factory is old and worn out. Significantly reduced cement production better reflects its real demand than previously. Earlier, the production levels were high, producing about 1 ton of cement per capita, which is obvi-ously by far too much for internal needs only. At the same time, the technology used in cement production was old and very energy

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Table 1: Main Economic Indicators of Lithuania 1991–96, Million Litas (Except as Indicated)

1991 1992 1993 1994 1995 1996

Inflation (%) 383% 1163% 199% 45.1% 35.7% 13.1%

GDP (% change, constant prices) 213.1 234.0 230.0% 1.0% 3.0% 3.6% Industrial production (% change) 24.9% 251.6% 234.7% 229.8% 0.9% 23.5 Population (31000) 3,742 3,741 3,730 3,721 3,714 3,712 Active population (31000) 2,128 2,120 2,108 2,102 2,111

Active population (% of total) 56.9% 56.7% 56.5% 56.5% 56.8%

Employed persons (31000) 1,898 1,855 1,778 1,675 1,643 1,659 Registered unemployment (%) 3.0% 1.3% 4.4% 3.8% 6.1% 7.1% Government revenues 12694a ₈₅₉₆₉b _{2,739 4,042 5,758 6,720}

Government expenditures 11142a ₈₁₁₃₂b _{2,646 4,355 6,199 7,510}

Budget deficit 1552a ₄₈₃₇b _{93 2313 2438,8 2790,0}

Gross external dept ($ million) — — 894 1,100 821

Exports 12300a ₁₀₇₇₅₄b _{8,707 8,077 10,820 13,420}

Imports 8729a ₇₇₁₄₃b _{9,798 9,355 14,594 18,235}

Trade balance 3571a ₃₀₆₁₁b _{21,091 21,278 23,774 24,815}

Current account ($ million) — — 284 2160 250 2400

Long-term interest rate (average) — — 47.7% 32.5% 18.3% 12.3% Exchange rate (Litas/$, ultimo) — 3.79 3.90 4.00 4.00 4.00

Exchange rate (Lt/DEM) — — — 2.46 2.79

Gross foreign debt ($ bn) — — — 0.5 0.8 1.2

Source: Lithuanian Department of Statistics (1996a, 1996b, 1996c), European Union (1996a, 1996b, 1997), IMF (1996), OECD (1996).

Figure 2. Share of costs of fuels and other energy inputs in total production costs.

intensive; therefore, cement produced in Lithuania could not and still cannot compete on international markets, resulting in a poor export performance and overcapacity.

The two largest sectors of the national economy are industry (29% of GDP in 1995) and trade (23% of GDP in 1995). Within the industrial sector the main branches are food processing, ma-chinery, and light industry. More details on the structure of the Lithuanian economy can be found in the input/output table shown in the appendix.

A significant and probably determining role in the reduction of industrial output and restructuring of the economy was played by fuel and energy prices, which increased rapidly in 1992. The share of fuel and energy in total production costs has increased significantly, as is shown in Figure 2 for 1990 and 1993.

Many industrial enterprises were operated below full or even normal capacity, resulting in a strong under utilization of their equipment. Because during the last 3 years the decrease of indus-trial output was higher than that of indusindus-trial energy demand (including the demand for electricity), industrial energy intensity has grown further, and is significantly higher than in Western countries. On average, in Lithuania in 1990, two to three times more energy was needed to produce one unit output compared to Western standards. More details on energy use are given in the next section.

A CGE MODEL FOR LITHUANIA 697

Figure 3. Structure of final energy consumption in Lithuania, 1990–95.

1B. Energy Demand

Analyzing final consumption of different energy carriers (elec-tricity, heat, natural gas, and fuels) one notices that the final electricity consumption decreased from 12 TWh in 1990 to 7 TWh in 1995. District heat consumption almost halved, and was 13.6 TWh in 1995, final consumption of fuels decreased from 247 PJ in 1990 to 130 PJ in 1995. Analysis of the final energy demand by sectors shows a sharp decrease in the shares of agriculture, construction, and industry (Figure 3). The shares of final energy consumption in these sectors dropped in 1995, correspondingly to 23 and 36 percent of the 1990 value. The shares of trade and services and transport increased slightly. Energy demand in house-holds fell much less to 88 percent of the 1990 value. Therefore, its share in total energy demand increased sharply—from 21 per-cent in 1990 to 34 perper-cent in 1995.

Structural changes in energy consumption in the sectors of the economy mentioned above have caused a growth of energy inten-sity, i.e., the final energy consumed per unit of GDP in Lithuania increased during the transition period. One can see a positive point though: since 1993, the energy efficiency has also increased slightly.

In 1990, primary energy use per capita in Lithuania mounted to 190 GJ, which is only slightly lower than in most other West European countries. However, primary energy consumption per unit of GDP was 80 GJ/ECU, indicating a high energy intensity in Lithuania measured in terms of economic development. One

of the worst points in the former socioeconomic system was the huge waste of energy. The excessive use of energy resources was caused by: (a) very low energy prices; (b) inadequate or nonexis-tent metering and control of energy use; (c) lack of incentives for energy efficiency; and (d) poor buildings insulation.

To improve the unsustainable situation the Lithuanian govern-ment has prepared the National Energy Strategy and the National Efficiency Improvement Program, where main directions of en-ergy sector reconstruction are foreseen.

Although Lithuania has very limited domestic energy resources, with indigenous energy supplying only about 7 percent of the total domestic energy demand, it is in a vulnerable position. The supply security is low, and the dependence on exogenous shock in world market prices is high. The country possesses only modest oil re-sources. Renewables like hydro, wind, biogas, geothermal, and solar energy are likely to have only limited future potential for Lithuania.

Peat is probably the indigenous resource with the largest theo-retical potential. Total geological reserves of peat in Lithuania are estimated to be 9,200 PJ, of which 500 PJ can currently econom-ically and feasibly be explored. Furthermore, there are 1.8 million hectares of forest in Lithuania. It is estimated that it would be possible to produce about 3.5 million m3_{of wood for fuel, which} corresponds to 20 PJ, if all reserves of the forests are used in the future. Natural gas has been used in Lithuania since 1961, when a gas pipeline from Ukraine was built. At present, less than 40 percent of the dwellings in Lithuania are connected to the gas network. A major part of natural gas in the household is used for cooking. In 1990, the share of natural gas reached 27 percent in the primary energy balance.

Since 1984, when the first unit at the Ignalina Nuclear Power Plant (INPP) was commissioned, the role of nuclear energy in-creased sharply. The production in PJ declined strongly during the first years of the transition period, mainly as a consequence of the sharp decline in the overall energy demand. Its share in the primary energy balance steadily increased, and from 23 percent (163.7 PJ) in 1990 it reached 31 percent (113.6 PJ) in 1995 (see Table 2).

1C. Environment and Safety

As is well known, the global environmental situation has been giving rise to some concern. Environmental problems like the

A CGE MODEL FOR LITHUANIA 699

Table 2: Primary Energy Balance, PJ 1985–95

1985 1990 1991 1992 1993 1994 1995 Total primary energy 672.3 708.7 738.2 472.3 373.7 328.5 363.1 Oil and oil products 375.1 303.7 335.8 181.2 157.9 149.1 132.1 Natural gas 151.3 195.8 202.6 115.8 62.6 72.4 85.0

Coal 35.0 28.6 19.7 16.7 16.2 13.6 13.2

Wood and peat 18.5 15.5 15.4 16.8 17.7 17.7 17.9

Hydro 1.4 1.5 1.2 1.1 1.4 1.6 1.3

Nuclear 91.1 163.7 163.4 140.7 117.8 74.1 113.6

Source: Lithuanian Energy Institute (1996), own calculations.

greenhouse gas effect, depletion of the ozone layer, and acidifica-tion could be a burden on both the current and the future genera-tions. The huge emissions of greenhouse gases and other sub-stances, for example, CO2, CO, NH3, NOx, SO2and VOCs, seem to be causing considerable environmental damage. For example, a doubling of carbon emissions in the atmosphere is expected to lead to a temperature rise of anywhere between 1.5 and 4.58C. In turn, this is expected to influence the world climate, especially creating regional changes influencing agricultural output and caus-ing a sea level rise. In the case of Lithuania, we can add to these problems related with the use of nuclear power. More than 80 percent of electricity in Lithuania is generated using nuclear fuel. At present, most governments realize that some form of sustain-able development has ultimately to be achieved. This idea implies implementation of policy measures to reduce the current and future environmental threats. There is a continuous flow of inter-national conferences, scientific research projects, policy proposals, protocols, and laws—on a national and international level—with the main issue of how to realize a sustainable development. What fuel mix and which economic structure should be persuade to achieve a sustainable development of the Lithuanian economy?

Environmental emissions in Lithuania are not as severe as in other countries that are in transition to a free market system (compare, e.g., the situation in Poland). However, the levels of emissions are not low enough to conform to current EU standards, mainly due to the high energy intensity and low energy efficiency. Due to the existence of the Ignalina Nuclear Power Plant (INPP), overall CO2and SO2emission levels are low compared with other countries in transition. The total level of CO2emissions

Table 3: National Annual Total Emissions (Kton), 1985–95

1985 1990 1991 1992 1993 1994 1995

CO2,31000 45 42 45 29 25 25 25

SO2 304 222 234 139 125 117 107

NOx 166 158 166 98 78 77 60

VOC 124 116 122 76 62 62 68

Particulates 70 60 50 28 19 17 14

Source: Lithuanian Ministry of Enviornment Protection (1996).

was 42 Mton in 1990, and about 25 Mton in 1995. Sulphur dioxide emissions in 1990 were 222 Kton, corresponding to 59 kg per capita per year, still quite high if compared the range of 20–45 kg per capita across EU countries. During the transition period SO2emissions decreased with almost 50 percent. One can notice the especially large reduction of SO2 in electricity production (power sector), because the share of nuclear generation increased from 60 percent in 1990 to 87 percent in 1995. Nitrogen oxides emissions in 1990 were 158 Kton. The highest proportion (51%) was due to activity in the transport sector. In connection with changes in the economy and reduction of activities in industrial branches, the transport sector emissions of NOxhalved during the transition period. With the increase in economic activity and the shift towards trade and services, it is expected that NOxemissions will rise again unless additional policies are implemented. The dynamics of overall emissions and present environmental situation in Lithuania are shown in Table 3.

As activity in all sectors of the economy have been increasing since 1994, levels of emissions are likely to increase if not con-trolled. Lithuania is aiming at reaching the EU environmental standards by the beginning of the next century. From this point of view it is very important to rehabilitate or to remove plants from service at the end of the transition period. Lithuania will follow the new emission standards that have been adopted by the Government and is valid from 1996 (Table 4). All existing plants will either be rehabilitated to cater for the new standards or will be removed from service. New investments could be necessary to implement the best available technologies.

Additional modification of the Lithuanian power plants is re-lated to use of orimulsion. The uncontrolled emissions of SO2and NOxwhen burning orimulsion are higher than in the case of heavy

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Table 4: Emission Targets, 1993–96

Maximum permissible concentrations, mg/m3

SO2 NOx CO2 Particulates

Boiler thermal

Type of fuel capacity MW 1993 1996 1993 1996 1993 1996 1993 1996

Gas 1–50 — — 400 350 400 400 20 20

natural gas 51–300 — — 450 350 300 300 20 20

LPG .300 — — 500 350 200 200 20 20

Liquid fuel 1–50 3400 2700 580 450 500 500 150 110

light fuel oil 51–300 3400 2700 680 450 400 400 140 100

heavy fuel oil .300 3400 2700 780 450 300 300 130 90

Solid fuel 1–50 2400 1600 500 400 1200 1000 1600 700

coal, coke, oil-shale 21–50 2400 1200 650 400 1000 800 1300 500 peat, straw, wood .50 2400 800 650 400 900 700 1000 300

fuel oil. This shows the orimulsion burning at the Marbach power plant in Germany test data (Stoubler and Maier, 1993). Therefore, additional measures for reduction of emissions are required.

The INPP plays a crucial role for the future of the Lithuanian power sector. But safe operation of the INPP remains an important issue. The State Nuclear Safety Inspectorate (VATESI) in 1993 approved the plants’ Safety Improvement Programme. To support implementation of this program 13 Western European countries, through the EBRD, allocated 33 million ECU for this purpose. The number of unplanned reactor shutdowns in 1990–95 was 28, and number of nuclear events in IAEA scale was 8, of which 7 were according to scale 1 and 1 according to scale 2.

There was the National Environment Protection Strategy pre-pared and approved in 1995. Lithuania also signed several interna-tional conventions: Geneva Convention on long-range Trans-boundary Air Pollution, Vienna Convention, on Liability for Nuclear Damage Nonproliferation Treaty, Safeguards Agree-ment, Joint Protocol, Law on Liability, Convention on Protection of Nuclear Material, etc.

2. GENERAL EQUILIBRIUM MODELING

The starting point for the CGE-LI was a “Computable Equilib-rium Model for Poland” (see Hille, 1993, Van Leeuwen, 1997) and the “Bergman Computable General Equilibrium Model” (see Bergman, 1990).

The CGE-LI is a static computable general equilibrium model of an open economy. In some respects it differs from the standard CGE modeling. First, it includes emissions and emission control activities, as well as markets, and market prices, for tradable emis-sion permits. Second, some of the tradable-producing sectors are taken as price takers on international markets in the standard Heckler-Ohlin fashion,2 _{while others to some extent are price} makers in the accordance with the so-called Armington assump-tion.3

The sector classification is adjusted for the Lithuanian situation, and for the analytical purpose of the model. For the same reason 2_{In Heckler-Ohlin models it is assumed that products are homogeneous across countries.} 3_{The Armington assumption (Armington, 1969) treats similar products produced in} different countries as different goods. This formulation also makes it possible that a country is both importing and exporting the same goods.

A CGE MODEL FOR LITHUANIA 703 a division is made between government and family consumption. In Figure 4 an overview is given of the model. The different parts of the model are explained in more detail in the next section.

3. SPECIFICATION OF THE LITHUANIAN MODEL

In this section we present a general overview of the CGE model for Lithuania. Only the most essential elements of the model are presented and explained. Equations are only given when they have a positive explanation value.4

The model consists of four major blocks: production, consump-tion, foreign trade, and environment. The energy sectors and the production and consumption and trade of energy products are intertwined with all the blocks of the model. The main inputs and outputs of the model are an aggregated input–output table (i/o table). The model uses the information from the i/o table of 1994 (see the Appendix) as starting point and generates, based on the accounted equilibrium situation, an i/o table for the projection year and detailed energy, and environmental indicators.

3A. Sector Classification

The sector classification of the Lithuanian CGE model was made by aggregation of different economy branches into 15 groups. Ten sectors were specified for the production sector of the Lithuanian economy and five sectors for energy. The sector classification used in the Lithuanian CGE model is presented in Table 5.

3B. Production

The production block forms the core of the model, and is related to all the other blocks. The production block is fed with informa-tion from the input–output table, and with informainforma-tion per sector and the costs of emission abatement. The technology of production is represented by a nested constant elasticity of substitution (CES) and Leontief production function in each production sector. The structure of the production function is the same for all sectors, but the elasticities of substitution between various inputs (interme-diate inputs, labor, capital, energy) may differ across sectors. This 4_{For a detailed description of the equations and structure of the model, see Bergman} (1990), Hille et al. (1993), and Van Leeuwen (1997).

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Table 5: The Sector Classification of the Lithuanian CGE Model

Model code Economy branches Share in GDP

T1 Tobacco industry 0.5%

T2 Chemical industry 2.0%

M1 Agriculture and forestry, other branches 13.0%

M2 Food industry 12.0%

M3 Metallurgy, plastics, equipment, paper industry, furniture, clothing, etc. 18.3%

M4 Building materials, construction 6.6%

M5 Trade, restaurants 15.9%

M6 Commercial services 15.9%

M7 Transport and communication 14.6%

N Public services 1.2%

Total nonenergy branches 100%

E1 Electroenergetics (conventional), Heat 15.0%

E2 Gas industry 11.8%

E3 Coal industry 1.5%

E4 Refinery 49.2%

E5 Electroenergetics (nuclear) 22.5%

Total energy branches 100%

part of the Lithuanian CGE model was significantly changed with the Polish version. A change related to new energy carriers was also introduced in the Lithuanian model. Because significant over-capacity in the Lithuanian power system is one of the peculiarities of the Lithuanian economy—domestic electricity demand can be covered either from nuclear or fossil fuel—electricity generation in the model is split into conventional and nuclear electricity. Furthermore, natural gas is introduced. Because of those changes the mixed CES-Leontief production structure was extended.

Obviously, electricity generated from fossil fuel or from nuclear fuel is for consumers with exactly the same product, only the prices are different. An approach of weighted price calculation is used to determine the price of composite from both electricity types:

Pe5 t1P11 t2P2, (1)

wherePeis the price of composite from electricity generated from

fossil fuel and nuclear fuel;

P1is the price of electricity generated from fossil fuel;

P2is the price of electricity generated from nuclear fuel; and

t1and t2are the share of electricity produced from fossil and nuclear fuel.

In fact, the price of electricity in the model is calculated using a general specification of the implicit price equation:

Pfx,j5(djPf112sj1(12 dj)P1f22sj) 1

12sj, (2) wherejis the index of production sectors;

Pfxjis the implicit factor price (composite of factor 1 and factor

2) of sectorj;

Pfi is the price of production factorPi;

sjis the price elasticity of substitution between factor 1 and 2

of sectorj; and

djis the distribution parameter of sectorj.

Price elasticity of substitution between electricity generated from fossil fuel and nuclear fuel in this case is set equal to zero. In other words,sj50 converts Equation 2 to Equation 1. From

the weighted electricity price and the price of coal the implicit price of composite electricity–coal is determined. Next, the price of fuel is added and the implicit price of electricity–coal–fuel is calculated. The total price of energy is determined by adding the price of gas that takes a significant share in the Lithuanian energy

A CGE MODEL FOR LITHUANIA 707 balance. Then, the price of capital and later the price of labor are added to calculate the composite energy–capital–labor price. In a final step, the composite factor input is combined with intermedi-ate sector input, using fixed Leontief coefficients based on the 1994 input–output table.

A sector uses both capital, energy (electricity, gas, coal, fuel, nuclear), and intermediate demand to produce output. The total supply of these production factors is predetermined, and is called the factor endowment. The proportion in which the different pro-duction factors are used is based on the relative prices of produc-tion factors and the technical substituproduc-tion possibilities. Each sector has its own substitution elasticity. The substitution elasticities are adapted to the Lithuanian situation based on expert opinions.

3C. Consumption

Consumers are assumed to maximize their utility, subject to the budget constraint. Total utility is a function of the utility derived from the different consumer goods. In this model it is assumed that the consumption demand of both households and the govern-ment can be described by a Linear Expenditure System (LES) in which the constants are equal to 25 percent (households) and 75 percent (government) of the base-year (1990) consumption levels, and the marginal expenditure shares are equal to the base-year average expenditure shares. The LES uses disposable income (INC), output prices, import prices, and factor prices (Px), the

marginal expenditure shares (bx) and exogenous demand (Dx) to

calculate domestic final demand (Dx) The parameters of the LES

(bxandDx) are derived from the i/o table. The general specification

of the final demand of the goods and services of sectorjreads: Dj5Dj1

Bj

Pj

(INC2

_o

i

DiPi),

o

j

Bj51, (3)

wherei, jare the index of production and energy sectors;

Djis the final demand for sectorj; Pjis the producer price of sector j;

Djis the minimum required (constant) quantity of good j; Bjis the marginal expenditure share in the base year; and INC is the total income.

3D. Foreign Trade

Foreign trade is subdivided into imports and exports. Imports results from the LES and are, in turn, input for both the factor

supply (energy) and the supply per sector. Exports for the produc-tion sectors depend on domestic and world market prices, and are input for the demand per sector and production. Exports per energy sector in the prototype model is set at the base-year level. This approach was not suitable for Lithuania, especially for elec-tricity. Before the transition period, Lithuania exported a lot of electricity to its neighboring countries. Exports in the base year of the model (1994) was rather small because of economy crisis in the region. Now, electricity exports are increasing again. It is also expected that exports will continue to grow, because the existing nuclear power plant can compete on the international energy market. Thus, it was necessary to introduce in the model some changes in the estimation of the future electricity export. The level of the future electricity export in the Lithuanian version of the CGE model was defined exogenously using a parameter of early exports growth.

Zt5Zo(11

a 100)

t2T_{, (4)}

whereZtis the electricity exports level in year t; Zo is the exports level in the base year;

a is the percentage electricity exports growth per year;

T is the base year; and

t is the year of the defined time horizon.

This approach does not reflect the real situation in the electricity market because it does not deal with local electricity price and electricity price in the electricity market of neighboring counties. The actual situation is more complex, because two types of electric-ity are distinguished in the model. The proposed principle of export evaluation is only correct if the electricity exports growth rate is confirmed with information from additional studies. Elec-tricity export level from the Ignalina NPP is about 6 TWh. Thus, electricity export growth rate is calculated from this amount of exported electricity. In future versions of the model it is necessary to include a more precise description of electricity trade.

3E. Environment

As mentioned in the Introduction, the effects of economic growth on environmental issues is an important topic in the politi-cal and scientific debate. Our model distinguishes three types of emission gases (CO2, NOx, and SO2), and calculates their emission

A CGE MODEL FOR LITHUANIA 709 for each sector. The sectoral level of emissions resulting from combustion is proportional to the amount of energy used in the sector. For each sector, the use of coal, gas, and fuel is multiplied by an emission coefficient to calculate total emissions for each pollutant. Emission coefficients in the CGE model are expressed in physical terms per monetary unit of energy consumed. The actual emissions in economy sectors, actual fuel consumption per economy sectors and branches, as well as its monetary equivalent were used for calculation of emission coefficients (see Table A2 in the Appendix).

A maximum emission level for each pollutant can be imposed in the model. Environmental policies can be simulated by lowering these levels. To meet more restrictive environmental goals, each sector has the possibility to either implement abatement technolo-gies or to switch to a cleaner input mix. In practice, a government can control total emissions by setting regulations and standards. In the model, however, the concept of tradable emission permits is used. This means that the maximum amount of emissions set by the government is distributed between the producers by means of permits. The total number of permits a producer owns deter-mines the maximum level of emissions the producer is allowed to release into the atmosphere. Emission permits can be traded among producers. In this way a market of emission permits is established, and a price of permits is determined. Ultimately, the price of a permit becomes equal to the marginal cost of abatement in each sector. Mathematically, the concept of tradable emission permits can be described as follows:

PEMem(TOTEMPem2CLEANem2EMLIMem)50 (5)

whereemis SO2, NOx, CO2;

TOTEMem is the total emission of pollutantem;

CLEANemis the total abatement of pollutant em;

EMLIMemis the exogenous emission limit for pollutantem; and

PEMemis the price of an emission permit for pollutantem.

4. SCENARIO RESULTS

The CGE model for Lithuania is tested by running a European scenario “Forum.” This scenario was modeled in accordance with the scenario described by the European Union. Within the pre-viously mentioned Phare-ACE project, a translation of the sce-nario into model inputs was given for the three Central European

countries—Poland, the Czech Republic, and Lithuania. A descrip-tion of the “Forum” scenario, its assumpdescrip-tions, and output is given in Van Leeuwen (1997, pp. 219–224). Furthermore, two policy scenarios have been developed especially for Lithuania. These scenarios aim at measuring the economic and environmental im-pact of limiting the growth of nuclear fuel-generated electricity. Below we introduce the two scenarios and briefly the assumptions and results.

The Lithuanian government has started restructuring the energy sector, and has privatized a number of big companies and factories. The next step will be the restructuring of the district heating sector, which currently is under the ward of the Joint Stock Company “Lietuvos energija.” This company also owns all the power plants—except INPP—and the electricity network. The local dis-trict heating systems will be brought under municipal ownership. Later, the restructuring of the power company will be taken at hand. Electricity generation should be separated from transmis-sion, distribution, and supply, and each power station should ulti-mately be independently owned and managed. Transmission, dis-tribution, and supply of electricity should be kept together in the short and medium term, but there should be an objective of establishing separate regional distribution supply entities. All the components of the industry should remain in stage ownership in the medium term, and there will be a long-term objective of introducing private-sector capital.

Furthermore, the medium- and long-term development of elec-tricity generation in Lithuania depends on short-term decisions about the future of nuclear power generation. The INPP can either be stopped because of safety reasons, as preferred by the EU, or because reaching the technological end of the channels in the reactors. Once decided, the first unit of this power plant can be decommissioned before 2000. Thus, it is expedient to analyze the effects on the Lithuanian economy within this time horizon. In the first policy scenario we assume continuation of the INPP as historically planned. In that case, the capacity of nuclear power could increase with 14.2 percent per year up to 2000. If, on the other hand, the first unit is decommissioned, nuclear power capac-ity could only grow, with 1.7 percent, per year until 2000. This regime is simulated in scenario 2. We have run the two scenarios leaving all other model input unchanged (see Table 6).

In both scenarios conventional electricity generation—starting from a very low level in the base year 1994—may grow fast (41.6%

A CGE MODEL FOR LITHUANIA 711

Table 6: Assumptions of the Two Policy Scenarios for 2000

Scenario values Variables and parameters Case 1 Case 2 Annual growth rate of:

price of fuel 0.0% 0.0%

coal endowment 0.0% 0.0%

gas endowment 0.0% 0.0%

conventional electricity endowment 41.6% 41.6%

nuclear electricity endowment 14.2% 1.7%

capital endowment 5.0% 5.0%

labor endowment 0.3% 0.3%

total productivity per sector 7.0% 7.0%

energy efficiency per fuel type per production sector 0.5% 0.5% energy efficiency per fuel type per energy sector 0.5% 0.5%

export demand 0.2–0.5% 0.2–0.5%

Export demand elasticities

baseind 21.7 21.7

bulkind 20.6 20.6

agricul, foodind, constr, trade, transp 20.1 20.1

exptrad 20.4 20.4

servic, public 20.5 20.5

World market prices production sectors

baseind, agricul, foodind 1.00 1.00

bulkind, exptrad, trade, servic, transp 1.05 1.05

constr, public 1.01 1.01

Substitution elasticities fixed fixed

Emissions limits for SO2, NOx, and CO2 free free

Source: Lithuanian Energy Institute/SEO.

per year), while energy efficiency growth (0.5% per year) and investments (capital growth: 5% per year) are stimulated by the opening the economy to foreign investors and foreign aid. Given the technical potential in factories and infrastructural develop-ments, the overall productivity growth rate in Lithuania up to 2000 is expected to be 7 percent per annum. In Table 7 the results of both scenarios are presented and compared with the base year situation.

In scenario 1 the increase in nuclear capacity stimulates eco-nomic activity, especially in the export goods sector, agriculture, and the base and bulk goods industry. Energy demand remains directed towards nuclear electricity, while economic growth boosts fuel consumption. The factors of production capital and labor become relatively scarce, resulting in high equilibrium prices.

Table 7: Main Indicators of Two Policy Scenarios (Million Litas or 199451) Base year value Case 1 Case 2 Variable million Litas 199451 199451

Gross domestic product 16,217 2.324 1.486

Sectoral output

baseind 133 1.410 1.607

bulkind 543 1.458 1.580

agricul 3,550 1.316 1.301

foodind 3,289 1.258 1.203

exptrad 5,009 1.409 1.512

constr 1,800 1.236 1.164

trade 4,349 1.189 1.045

servic 4,349 1.185 1.029

transp 4,000 1.149 1.024

public 337 1.132 0.997

Energy demand

electricity/heat 976 0.328 1.058

natural gas 771 0.740 1.024

coal 96 1.236 0.877

fuel 3,200 1.089 0.954

nuclear 1,464 1.857 1.106

Final demand

households 12,033 1.039 1.016

government 3,094 1.039 1.016

Foreign trade

total exports 9,360 1.393 1.478

total imports 10,378 1.147 1.010

Marginal costs (5price) index

baseind 1 0.970 0.890

bulkind 1 0.870 0.730

agricul 1 0.930 0.810

foodind 1 0.920 0.730

exptrad 1 0.920 0.720

constr 1 0.910 0.710

trade 1 0.930 0.740

servic 1 0.940 0.790

transp 1 0.890 0.660

public 1 0.920 0.730

Prices index

capital 1 2.230 0.240

labor 1 1.310 0.670

coal 1 1.000 1.000

natural gas 1 1.000 1.000

nuclear 1 1.650 0.570

fuel 1 1.000 1.000

electricity/heat 1 1.070 0.930

Emissions Ktons

SO2 107 1.215 1.047

NOx 60 1.167 0.900

CO2 25,000 0.871 0.871

A CGE MODEL FOR LITHUANIA 713 These results are combined with a relative strong increase in greenhouse gas emissions, and at the same time the nuclear dan-gers remain unsolved.

Scenario 2 shows the vulnerability of the Lithuanian economy for limiting the nuclear power potential. Economic growth is rela-tively low, especially in the sectors trade (commercial and public), services, and transport. Due to low factor prices, marginal costs are low and exports are stimulated; the trade balance improves. The moderate economic growth limits the increase in energy use and emissions decrease (CO2and NOx) or increase only slightly (SO2). Comparing the results of the two cases one notices that low nuclear potential in Lithuania results in a combination of low economic development, low fuel prices, a strong increase in the use of gas, a decrease in fuel consumption, relatively low SO2and NOx emissions, and structural change in the production sectors. If, on the other hand, the share of nuclear power is maintained at its current level, economic activity and production costs are higher, the price of capital increases fast and labor costs increase. Due to higher economic development emissions are higher.

The two examples visualize the possible economic and environ-mental effects (dilemmas) of current policy choices. Because of the lack of information and economic data and the assumptions made during the study, the results presented are preliminary, and are merely meant as illustrations of the working of the model. The modeling effort is a first step on a long road towards better understanding of future options for Lithuania. Although many ques-tions remain unsolved, and still much has to be done, the CGE model proves to be a useful tool in the decision-making process.

REFERENCES

Armington, P.S. (1969) A Theory of Demand of Products Distinguished by Place of Production.IMF Staff Papers16:159–176.

Bergman, L., Jorgenson, D.W., and Zalai, E., Eds. (1991)General Equilibrium Modeling and Economic Policy Analysis.

Bergman, L. (1990) Energy and Environmental Constraints on Growth: A CGE Modeling Approach.Journal of Policy Modeling12:671–691.

Hille, E., van Leeuwen, M.J., van der Linden, N.H., Umer, A., Velthuy¨sen, J.W. (1993) Task Force on Integrated Energy and Environmental Planning. Volume II: Integrated Economy–Energy–Environment Policy in Poland; A Computable General Equilib-rium Approach. The Netherlands Energy Research Foundation (ECN) Petten. Matthes, F.C., and Mez, L., Eds. (1996)10 Years After The Chernobyl Disaster. Electricity

in Eastern Europe. Berlin: Heinrich-Bu¨ll-Stiftung/Oeko-Institut FFU Forschungs-stelle u¨r Umweltpolitik.

Shoven, J.B., and Whalley, J. (1984) Applied General Equilibrium Models of Taxation and International Trade: An Introduction and survey.Journal of Economic Litera-tureXXII.

Stoubler, K., and Maier, H. (1993) Der Einsatz von “Orimulsion.” EVS-Kraftwerk Mar-bach.-Energiewirtschaftliche Tagesfragen. Heft 10.

Van Leeuwen, M.J.. Laroui, F., and Visee, H. (1995) An Economy Energy Environment Computable General Equilibrium Model for The Netherlands. InTop-Down or Bottom-up Modelling? An application to CO2abatement. F. Laroui and M.J. van Leeuwen, Eds.). SEO Report 356. Amsterdam.

Van Leeuwen, M.J. Ed. (1997) Energy. Environment and the Economy in a CGE Model Concept: final report. SEO-report no. 419. Amsterdam.

DATA SOURCES

Department of Statistics (1995) Statistical Yearbook of Lithuania 1994–95. Vilnius. Department of Statistics (1996a) Macroeconomic indicators of Lithuania. Vilnius. Department of Statistics (1996b) Economic and social development in Lithuania. Monthly

Bulletin July 1996. Vilnius.

Department of Statistics (1996c) Review of the Lithuanian economy. May 1996. Vilnius. European Commission (1996a) European Economy—Supplement A: Economic Trends.

No. 2 February. Brussels.

European Commission (1996b) European Economy—Supplement C: Economic Reform Monitor. No. 2 July, pp. 10–11. Brussels.

European Commission (1997) European Economy—Supplement C: Economic Reform Monitor. No. 2 June, p. 11–12. Brussels.

International Monetary Fund (1996) International Financial Statistics. July 1996. Washing-ton, DC.

Lithuanian Ministry of Environmental Protection (1996) Various Statistics 1995. Vilnius. OECD (1996) Short-Term Economic Indicators—Transition Economies. No. 1995/4.

APPENDIX: The Input–Output Table of Lithuania 1994

One of the biggest problem for modeling of the Lithuanian economy using the CGE model is that there is no official i/o table in Lithuania. The last officially available i/o table is for 1985. Later, this table was revised and adopted to the conditions of 1990. This table was not created from real statistical data. Its background was the table for 1985 and experience of experts. This table could not be used in the model because of very drastic changes in the national economy after the regained independence. Therefore, it was decided to create a new i/o table based on the most recent available information. This new i/o table of Lithuania cannot be considered as an i/o table fully representing the real economy and the economic structure as it is, but merely as the first step of representation of the country’s economy.

As a first step the i/o table coefficient of The Netherlands were used and multiplied by Lithuanian data on gross production. After

A CGE MODEL FOR LITHUANIA 715 that, additional available data was introduced into this table: labor, depreciation, profits in almost all economic branches, as well as data characterizing total energy consumption by type of energy carrier, export quantities, and stock changes.

The energy (electricity, coal, fuel, etc.) consumed in each sector was calculated from real fuel and energy consumption in each sector and actual prices. This calculation was made using a detailed energy balance. A subdivision of inputs into fossil electricity and a nuclear electricity sector from other branches was also made according to the quantity of electricity generated from those types of fuels.

For balancing row and column totals for each sector a special optimization mechanism was created and used. The objective func-tion of this optimizafunc-tion mechanism was minimizafunc-tion of the sum of statistical differences in rows and columns together. Details on the optimization process can be found in Van Leeuwen (1997). Table A1 shows the i/o table of Lithuania in 1994 as it is used in the CGE model.

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Table A1: Input/output Table of Lithuania in 1994, Million Litas Bulk

Base goods Agri- Food Export Commercial Public

industry industry culture industry oriented Construction Trade services Transport services

1994 T1 T2 M1 M2 M3 M4 M5 M6 M7 n

T1 Base industry 1.2 0.9 1.0 1.3 9.1 3.0 0.5 0.2 0.1 0.2 T2 Bulk good industry 0.2 13.8 0.6 8.2 7.3 5.1 8.3 8.3 2.3 1.6 M1 Agriculture 0.3 1.7 336.9 1,220.6 5.4 2.6 2.0 7.6 10.2 3.8 M2 Food industry 0.5 1.4 321.9 274.3 6.3 0.6 2.4 51.3 6.4 10.4 M3 Export oriented industry 2.7 10.3 37.0 25.1 479.6 107.1 21.8 22.8 41.5 20.1 M4 Construction 0.7 2.4 10.1 7.2 10.8 259.4 6.1 33.3 23.9 13.9 M5 Trade 18.5 73.0 219.1 104.3 299.4 149.9 913.0 253.2 286.9 56.1 M6 Commercial services 18.5 73.0 219.1 104.3 299.4 149.9 913.0 253.2 286.9 56.1 M7 Transport 2.4 17.1 16.2 17.0 76.1 37.5 97.4 41.5 978.7 29.4 N Public services 0.2 0.9 1.8 1.5 4.3 0.8 1.7 3.2 3.0 5.2

E1 Electricity 1.7 2.3 7.6 6.8 10.9 2.4 7.6 4.1 8.3 3.6

E2 Natural gas 3.4 3.1 32.9 12.2 9.5 4.2 3.6 4.5 2.3 3.7

E3 Coal 0.1 0.0 0.0 0.01 15.9 0.4 0.4 0.3 1.0 0.1

R4 Fuels 13.3 2.9 18.0 7.5 25.3 15.5 5.9 6.7 100.4 7.9

E5 Nuclear electricity 10.5 14.3 46.0 41.6 66.2 14.9 46.4 25.1 50.6 22.2 M Imports 56.1 234.4 106.7 695.9 1,274.8 288.9 178.8 123.8 414.3 46.2

T Taxes 0.1 2.5 42.1 30.9 56.8 11.5 24.0 17.2 57.0 0.6

L Labor 1.1 52.2 516.1 646.4 1,502.8 504.0 955.9 1,024.5 1,156.5 36.5 K Depreciation 0.0 7.9 19.8 45.5 51.9 7.7 35.0 58.3 97.3 3.3 P Profits 1.7 29.5 1,597.8 38.0 797.2 234.4 1,125.7 2,410.1 472.5 16.1

O Other inputs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Statistical difference 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Gross production 133.2 543.5 3,550.7 3,288.9 5,008.9 1,799.7 4,349.3 4,349.3 4,000.0 337.1 Source: Lithuanian Central Statistical Office, Lithuanian Energy Institute.

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MODEL

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LITHUANIA

717

Table A1: Continued

Domestic demand

Natural Nuclear

Electricity gas Coal Fuels electricity House- Govern- Invest- Stock Total

1994 E1 E2 E3 E4 E5 holds ment ments Exports changes deliveries

T1 Base industry 0.1 0.1 0.0 0.1 0.6 1.4 0.4 1.5 111.6 0.0 133.2 T2 Bulk goods industry 0.0 0.1 0.0 0.2 0.2 48.7 12.5 10.2 416.1 20.2 543.5 M1 Agriculture 0.3 1.4 0.1 0.3 3.9 165.4 42.5 2.7 1,745.1 22.2 3,550.7 M2 Food industry 0.0 0.0 0.0 0.0 0.0 1,125.9 289.6 1.3 1,197.4 21.0 3,288.8 M3 Export oriented industry 0.7 8.9 0.9 5.6 8.4 179.2 46.1 409.0 3,584.9 22.8 5,008.9 M4 Construction 0.1 1.1 0.1 1.1 0.7 50.8 13.1 845.0 521.6 21.2 1,799.7 M5 Trade 17.1 22.3 2.3 45.1 27.6 1,640.6 422.0 366.8 0.0 2568.1 4,349.3 M6 Commercial services 17.1 22.3 2.3 45.1 27.6 1,640.6 422.0 366.8 0.0 2568.1 4,349.3 M7 Transport 0.1 48.4 5.1 23.8 1.3 2,030.7 522.3 91.7 0.0 236.6 4,000.0 N Public services 0.2 0.3 0.0 0.7 1.7 242.6 62.4 2.9 4.1 20.3 337.1 E1 Electricity 141.4 0.5 0.0 1.3 0.0 65.4 16.8 60.7 0.0 0.0 341.6 E2 Natural gas 302.1 0.0 0.0 0.0 0.0 197.6 50.8 8.2 0.0 0.0 638.0

E3 Coal 0.0 0.0 0.0 0.0 0.0 31.4 8.1 0.0 0.0 0.0 57.7

E4 Fuels 308.1 1.4 0.0 26.7 0.0 468.0 120.4 6.7 950.0 20.6 2,084.2 E5 Nuclear electricity 0.0 3.2 0.3 159.1 268.3 398.2 102.4 0.0 829.2 0.0 2,098.4 M Imports 87.9 444.4 46.4 1,625.3 286.5 2,666.9 686.0 1,110.0 0.0 5.2 10,378.4 T Taxes 7.8 5.2 0.0 19.0 21.9 1,079.9 277.8 0.0 0.0 0.0 1,654.2 L Labor 464.8 57.2 0.0 72.0 105.6 0.0 0.0 0.0 0.0 0.0 7,095.7 K Depreciation 54.2 4.6 0.0 20.2 571.8 0.0 0.0 0.0 0.0 0.0 977.6 P Profits 2426.0 16.5 0.0 38.6 138.0 0.0 0.0 0.0 0.0 0.0 6,490.1 O Other inputs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Statistical difference 0.0 0.0 0.0 0.0 0.0 0.0 20.8 0.0 0.0 0.0 20.8 Gross production 976.0 638.0 57.7 2,084.2 1,464.0 12,033.3 3,094.4 3,283.5 9,360.0 21,176.0 59,175.6

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Table A2: Emission Coefficients, Kton/Million Litas

SO2 NOx CO2

Coal Fuel Gas Coal Fuel Gas Coal Fuel Gas

Base industry T1 0 5 0 0 11 0 0 2,237 0

Bulk goods industry T2 217 262 0 38 26 23 10,589 7,379 5,138

Agriculture M1 24 4 0 22 8 15 39,649 9,961 16,956

Food industry M2 228 128 0 39 19 23 10,431 5,144 5,138

Export oriented industry M3 203 193 0 36 24 23 10,905 7,079 5,138

Construction M4 149 36 0 35 14 23 11,589 3,381 5,138

Trade M5 239 10 0 39 12 23 10,277 2,706 5,138

Commercial services M6 257 27 0 32 11 16 11,204 43 4,696

Transport M7 42 10 0 145 54 85 15,613 6 7,913

Public services N 257 27 0 32 11 16 11,204 43 4,696

Electricity E1 26 43 0 8 7 6 10,689 7,883 4,847

Gas E2 0 0 0 0 3 6 0 2,132 4,847

Coal E3 22 2 0 6 3 0 11,391 3,200 0

Fuels E4 37 20 0 10 7 0 9,341 7,397 0

Nuclear E5 0 0 0 0 0 0 0 0 0

A CGE MODEL FOR LITHUANIA 713

These results are combined with a relative strong increase in greenhouse gas emissions, and at the same time the nuclear dan-gers remain unsolved.

Scenario 2 shows the vulnerability of the Lithuanian economy for limiting the nuclear power potential. Economic growth is rela-tively low, especially in the sectors trade (commercial and public), services, and transport. Due to low factor prices, marginal costs are low and exports are stimulated; the trade balance improves. The moderate economic growth limits the increase in energy use and emissions decrease (CO2and NOx) or increase only slightly (SO2). Comparing the results of the two cases one notices that low nuclear potential in Lithuania results in a combination of low economic development, low fuel prices, a strong increase in the use of gas, a decrease in fuel consumption, relatively low SO2and NOx emissions, and structural change in the production sectors. If, on the other hand, the share of nuclear power is maintained at its current level, economic activity and production costs are higher, the price of capital increases fast and labor costs increase. Due to higher economic development emissions are higher.

The two examples visualize the possible economic and environ-mental effects (dilemmas) of current policy choices. Because of the lack of information and economic data and the assumptions made during the study, the results presented are preliminary, and are merely meant as illustrations of the working of the model. The modeling effort is a first step on a long road towards better understanding of future options for Lithuania. Although many ques-tions remain unsolved, and still much has to be done, the CGE model proves to be a useful tool in the decision-making process. REFERENCES

Armington, P.S. (1969) A Theory of Demand of Products Distinguished by Place of Production.IMF Staff Papers16:159–176.

Bergman, L., Jorgenson, D.W., and Zalai, E., Eds. (1991)General Equilibrium Modeling and Economic Policy Analysis.

Bergman, L. (1990) Energy and Environmental Constraints on Growth: A CGE Modeling Approach.Journal of Policy Modeling12:671–691.

Hille, E., van Leeuwen, M.J., van der Linden, N.H., Umer, A., Velthuy¨sen, J.W. (1993) Task Force on Integrated Energy and Environmental Planning. Volume II: Integrated Economy–Energy–Environment Policy in Poland; A Computable General Equilib-rium Approach. The Netherlands Energy Research Foundation (ECN) Petten. Matthes, F.C., and Mez, L., Eds. (1996)10 Years After The Chernobyl Disaster. Electricity

in Eastern Europe. Berlin: Heinrich-Bu¨ll-Stiftung/Oeko-Institut FFU Forschungs-stelle u¨r Umweltpolitik.

714 A. Galinis and M. J. van Leeuwen

Shoven, J.B., and Whalley, J. (1984) Applied General Equilibrium Models of Taxation and International Trade: An Introduction and survey.Journal of Economic Litera-tureXXII.

Stoubler, K., and Maier, H. (1993) Der Einsatz von “Orimulsion.” EVS-Kraftwerk Mar-bach.-Energiewirtschaftliche Tagesfragen. Heft 10.

Van Leeuwen, M.J.. Laroui, F., and Visee, H. (1995) An Economy Energy Environment Computable General Equilibrium Model for The Netherlands. InTop-Down or Bottom-up Modelling? An application to CO2abatement. F. Laroui and M.J. van Leeuwen, Eds.). SEO Report 356. Amsterdam.

Van Leeuwen, M.J. Ed. (1997) Energy. Environment and the Economy in a CGE Model Concept: final report. SEO-report no. 419. Amsterdam.

DATA SOURCES

Department of Statistics (1995) Statistical Yearbook of Lithuania 1994–95. Vilnius. Department of Statistics (1996a) Macroeconomic indicators of Lithuania. Vilnius. Department of Statistics (1996b) Economic and social development in Lithuania. Monthly

Bulletin July 1996. Vilnius.

Department of Statistics (1996c) Review of the Lithuanian economy. May 1996. Vilnius. European Commission (1996a) European Economy—Supplement A: Economic Trends.

No. 2 February. Brussels.

European Commission (1996b) European Economy—Supplement C: Economic Reform Monitor. No. 2 July, pp. 10–11. Brussels.

European Commission (1997) European Economy—Supplement C: Economic Reform Monitor. No. 2 June, p. 11–12. Brussels.

International Monetary Fund (1996) International Financial Statistics. July 1996. Washing-ton, DC.

Lithuanian Ministry of Environmental Protection (1996) Various Statistics 1995. Vilnius. OECD (1996) Short-Term Economic Indicators—Transition Economies. No. 1995/4. APPENDIX: The Input–Output Table of Lithuania 1994

One of the biggest problem for modeling of the Lithuanian economy using the CGE model is that there is no official i/o table in Lithuania. The last officially available i/o table is for 1985. Later, this table was revised and adopted to the conditions of 1990. This table was not created from real statistical data. Its background was the table for 1985 and experience of experts. This table could not be used in the model because of very drastic changes in the national economy after the regained independence. Therefore, it was decided to create a new i/o table based on the most recent available information. This new i/o table of Lithuania cannot be considered as an i/o table fully representing the real economy and the economic structure as it is, but merely as the first step of representation of the country’s economy.

As a first step the i/o table coefficient of The Netherlands were used and multiplied by Lithuanian data on gross production. After

A CGE MODEL FOR LITHUANIA 715

that, additional available data was introduced into this table: labor, depreciation, profits in almost all economic branches, as well as data characterizing total energy consumption by type of energy carrier, export quantities, and stock changes.

The energy (electricity, coal, fuel, etc.) consumed in each sector was calculated from real fuel and energy consumption in each sector and actual prices. This calculation was made using a detailed energy balance. A subdivision of inputs into fossil electricity and a nuclear electricity sector from other branches was also made according to the quantity of electricity generated from those types of fuels.

For balancing row and column totals for each sector a special optimization mechanism was created and used. The objective func-tion of this optimizafunc-tion mechanism was minimizafunc-tion of the sum of statistical differences in rows and columns together. Details on the optimization process can be found in Van Leeuwen (1997). Table A1 shows the i/o table of Lithuania in 1994 as it is used in the CGE model.

716

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Table A1: Input/output Table of Lithuania in 1994, Million Litas Bulk

Base goods Agri- Food Export Commercial Public industry industry culture industry oriented Construction Trade services Transport services

1994 T1 T2 M1 M2 M3 M4 M5 M6 M7 n

T1 Base industry 1.2 0.9 1.0 1.3 9.1 3.0 0.5 0.2 0.1 0.2

T2 Bulk good industry 0.2 13.8 0.6 8.2 7.3 5.1 8.3 8.3 2.3 1.6

M1 Agriculture 0.3 1.7 336.9 1,220.6 5.4 2.6 2.0 7.6 10.2 3.8

M2 Food industry 0.5 1.4 321.9 274.3 6.3 0.6 2.4 51.3 6.4 10.4

M3 Export oriented industry 2.7 10.3 37.0 25.1 479.6 107.1 21.8 22.8 41.5 20.1

M4 Construction 0.7 2.4 10.1 7.2 10.8 259.4 6.1 33.3 23.9 13.9

M5 Trade 18.5 73.0 219.1 104.3 299.4 149.9 913.0 253.2 286.9 56.1

M6 Commercial services 18.5 73.0 219.1 104.3 299.4 149.9 913.0 253.2 286.9 56.1

M7 Transport 2.4 17.1 16.2 17.0 76.1 37.5 97.4 41.5 978.7 29.4

N Public services 0.2 0.9 1.8 1.5 4.3 0.8 1.7 3.2 3.0 5.2

E1 Electricity 1.7 2.3 7.6 6.8 10.9 2.4 7.6 4.1 8.3 3.6

E2 Natural gas 3.4 3.1 32.9 12.2 9.5 4.2 3.6 4.5 2.3 3.7

E3 Coal 0.1 0.0 0.0 0.01 15.9 0.4 0.4 0.3 1.0 0.1

R4 Fuels 13.3 2.9 18.0 7.5 25.3 15.5 5.9 6.7 100.4 7.9

E5 Nuclear electricity 10.5 14.3 46.0 41.6 66.2 14.9 46.4 25.1 50.6 22.2

M Imports 56.1 234.4 106.7 695.9 1,274.8 288.9 178.8 123.8 414.3 46.2

T Taxes 0.1 2.5 42.1 30.9 56.8 11.5 24.0 17.2 57.0 0.6

L Labor 1.1 52.2 516.1 646.4 1,502.8 504.0 955.9 1,024.5 1,156.5 36.5

K Depreciation 0.0 7.9 19.8 45.5 51.9 7.7 35.0 58.3 97.3 3.3

P Profits 1.7 29.5 1,597.8 38.0 797.2 234.4 1,125.7 2,410.1 472.5 16.1

O Other inputs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Statistical difference 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Gross production 133.2 543.5 3,550.7 3,288.9 5,008.9 1,799.7 4,349.3 4,349.3 4,000.0 337.1

A

CGE

MODEL

FOR

LITHUANIA

717

Table A1: Continued

Domestic demand Natural Nuclear

Electricity gas Coal Fuels electricity House- Govern- Invest- Stock Total 1994 E1 E2 E3 E4 E5 holds ment ments Exports changes deliveries

T1 Base industry 0.1 0.1 0.0 0.1 0.6 1.4 0.4 1.5 111.6 0.0 133.2

T2 Bulk goods industry 0.0 0.1 0.0 0.2 0.2 48.7 12.5 10.2 416.1 20.2 543.5

M1 Agriculture 0.3 1.4 0.1 0.3 3.9 165.4 42.5 2.7 1,745.1 22.2 3,550.7

M2 Food industry 0.0 0.0 0.0 0.0 0.0 1,125.9 289.6 1.3 1,197.4 21.0 3,288.8

M3 Export oriented industry 0.7 8.9 0.9 5.6 8.4 179.2 46.1 409.0 3,584.9 22.8 5,008.9

M4 Construction 0.1 1.1 0.1 1.1 0.7 50.8 13.1 845.0 521.6 21.2 1,799.7

M5 Trade 17.1 22.3 2.3 45.1 27.6 1,640.6 422.0 366.8 0.0 2568.1 4,349.3

M6 Commercial services 17.1 22.3 2.3 45.1 27.6 1,640.6 422.0 366.8 0.0 2568.1 4,349.3

M7 Transport 0.1 48.4 5.1 23.8 1.3 2,030.7 522.3 91.7 0.0 236.6 4,000.0

N Public services 0.2 0.3 0.0 0.7 1.7 242.6 62.4 2.9 4.1 20.3 337.1

E1 Electricity 141.4 0.5 0.0 1.3 0.0 65.4 16.8 60.7 0.0 0.0 341.6

E2 Natural gas 302.1 0.0 0.0 0.0 0.0 197.6 50.8 8.2 0.0 0.0 638.0

E3 Coal 0.0 0.0 0.0 0.0 0.0 31.4 8.1 0.0 0.0 0.0 57.7

E4 Fuels 308.1 1.4 0.0 26.7 0.0 468.0 120.4 6.7 950.0 20.6 2,084.2

E5 Nuclear electricity 0.0 3.2 0.3 159.1 268.3 398.2 102.4 0.0 829.2 0.0 2,098.4

M Imports 87.9 444.4 46.4 1,625.3 286.5 2,666.9 686.0 1,110.0 0.0 5.2 10,378.4

T Taxes 7.8 5.2 0.0 19.0 21.9 1,079.9 277.8 0.0 0.0 0.0 1,654.2

L Labor 464.8 57.2 0.0 72.0 105.6 0.0 0.0 0.0 0.0 0.0 7,095.7

K Depreciation 54.2 4.6 0.0 20.2 571.8 0.0 0.0 0.0 0.0 0.0 977.6

P Profits 2426.0 16.5 0.0 38.6 138.0 0.0 0.0 0.0 0.0 0.0 6,490.1

O Other inputs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Statistical difference 0.0 0.0 0.0 0.0 0.0 0.0 20.8 0.0 0.0 0.0 20.8

718

A.

Galinis

and

M.

J.

van

Leeuwen

Table A2: Emission Coefficients, Kton/Million Litas

SO2 NOx CO2

Coal Fuel Gas Coal Fuel Gas Coal Fuel Gas

Base industry T1 0 5 0 0 11 0 0 2,237 0

Bulk goods industry T2 217 262 0 38 26 23 10,589 7,379 5,138

Agriculture M1 24 4 0 22 8 15 39,649 9,961 16,956

Food industry M2 228 128 0 39 19 23 10,431 5,144 5,138

Export oriented industry M3 203 193 0 36 24 23 10,905 7,079 5,138

Construction M4 149 36 0 35 14 23 11,589 3,381 5,138

Trade M5 239 10 0 39 12 23 10,277 2,706 5,138

Commercial services M6 257 27 0 32 11 16 11,204 43 4,696

Transport M7 42 10 0 145 54 85 15,613 6 7,913

Public services N 257 27 0 32 11 16 11,204 43 4,696

Electricity E1 26 43 0 8 7 6 10,689 7,883 4,847

Gas E2 0 0 0 0 3 6 0 2,132 4,847

Coal E3 22 2 0 6 3 0 11,391 3,200 0

Fuels E4 37 20 0 10 7 0 9,341 7,397 0

Nuclear E5 0 0 0 0 0 0 0 0 0