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close this bookThe Global Greenhouse Regime: Who Pays? (UNU, 1993, 382 pages)
close this folderPart III National greenhouse gas reduction cost curves
close this folder12 Carbon abatement in Central and Eastern Europe and the Commonwealth of Independent States
View the document(introductory text...)
View the documentEnergy-environment nexus
View the documentScenarios for the future
View the documentCountry results
View the documentPolicy implications
View the documentConclusion
View the documentReferences

Scenarios for the future

Three scenarios were created for future energy supply and demand in Central and Eastern Europe by using the EPA energy end-use model. A base case scenario (BCS) considers the future in the absence of special incentives to save energy, although it incorporates structural changes that may occur (and indeed, are already occurring) in these countries. An energy efficiency scenario (EES) was simulated to show the effects of cost-effective energy efficiency measures on energy supply and demand. And finally, an inter-fuel substitution scenario (IFS) was used to estimate the effects on carbon dioxide emissions of reducing reliance on coal as the predominant fuel, and increasing the use of natural gas and non-fossil energy sources. Data were drawn from a series of detailed energy studies for the FSU and Eastern Europe.

Methodology

The EPA energy end-use model is a parametric model which estimates future energy demand as a function of economic growth, energy prices, price-, income-, and price cross-elasticities of demand, technical improvements in energy efficiency, and structural change in the economy. The user specifies initial base year demand for oil, natural gas, coal, and electricity for six major industrial categories by two-digit standard industrial classification (SIC) for the residential and commercial sectors, and for transportation. The user also provides initial base year activity levels for each sector. Using these data, the model calculates initial sectoral energy intensity coefficients and carbon dioxide emission levels.

The model projects future energy demand to the year 2030 in five-year increments, giving results for the major fuel types and electricity as well as future aggregate industrial energy intensity. It estimates energy demand on the basis of economic growth, structural change, price response, and technical energy efficiency improvements not attributable to price response. Carbon dioxide emissions are calculated for each sector and for each of the six industrial subsectors according to carbon coefficients for each fuel and the composite coefficient for electricity.

Structural change is characterized as the ratio of the growth of the five industrial (two-digit SIC) sectors to growth in overall GNP, plus an additional sector for general manufacturing. Structural change assumptions affect the energy intensity of the overall economy because shifting levels of energyintensive activities (for example steel output as a share of GNP) change the energy required per unit of economic output. This rate should not be confused with energy-efficiency changes. Both GNP growth and sectoral growth rate ratios are exogenous assumption provided by the user.

The model projects future energy demand on the basis of three other important factors. First, it incorporates energy price response, which the user exogenously specifies by selecting rates of price change for oil, natural gas, coal, electricity, and the price elasticities of demand for each consuming sector. Second, the model modifies future energy demand estimates with a socalled technical factor, which is essentially a rate of change in energy intensity per unit of industrial activity - over and above the price response. This factor is justified empirically, and on the basis of case studies and is exogenous. Third, the model permits the user to specify a price cross-elasticity of demand for electricity which determines the rate of change in electricity demand as a function of the difference between fuel price and electricity price changes.

Marginal cost estimates for carbon dioxide emissions reduction were made using a simple spreadsheet model that calculates levelized cost for each category of energy efficiency measures. These selected energy efficiency measures are based on a Czechoslovak report prepared by a government agency in Prague, and applied to other countries of East Central Europe, except to Poland where separate estimates were developed by Polish experts.

Modelling energy demand scenarios

The base case and energy efficiency scenarios were generated with the assumption that these economies will grow at an annual rate of 2.5 per cent. Population growth also varies from country to country, with Hungary having negative population growth and Poland the fastest growing. These inputs were specified for each country, though a common set of basic assumptions was used for selected, less country-specific variables. These include energy prices, and price and income elasticities of demand, as follows:

• Energy prices: real oil and natural gas prices grow by an average 2.5 per cent per year and coal and electricity prices by 1 per cent per year.

• Price elasticities of energy demand: the model uses -0.25 as the basic price elasticity of energy demand in all economic sectors. Most analyses of future global energy demand use price elasticities that range from -0.25 to 0.75. The more conservative estimate was used because the EPA model does not generate cross-elasticities among different energy carriers, except from primary fuels to electricity.

• Income elasticities of demand: most income elasticities of demand are specified exogenously based on case studies developed by country experts. However, income elasticities of demand for transportation reflect the belief that transportation demand is driven by certain common factors. These include:

- truck travel - driven by average elasticities of service sector and heavy industry activities;
- growth in number of heavy trucks - a function of GNP growth and average growth in heavy industry, chemicals production, and service sector activity;
- bus travel - a function of population growth;
- air travel - a function of GNP growth;
- rail passenger travel - a function of population growth;
- rail freight travel - a function of GNP growth.