Long-Term GHG Emissions Outlook for Greece Results of a Detailed Bottom-up Energy System Simulation

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1 Long-Term GHG Emissions Outlook for Greece Results of a Detailed Bottom-up Energy System Simulation Sebastian Mirasgedis, Guenter Conzelmann, Elena Georgopoulou, Vladimir Koritarov, and Yannis Sarafidis 1 This paper presents a baseline projection until 2020 of the future energy supply and demand situation in Greece, the associated trajectories of greenhouse gas (GHG) emissions, and the effects of an environmental tax equivalent to $40/t of carbon dioxide (CO 2 ). The latter scenario represents a rough estimation of the GHG emissions abatement potential in the country s various energy sectors. The energy market and emissions forecasts were developed using the ENergy and Power Evaluation Program (ENPEP), a bottom-up, integrated supply and demand simulation framework. Driven by a growing population, an increasing per-capita income, and a 3.3% growth in the overall economy, Greece s final energy consumption is projected to rise from 19.3 to 29.0 million tons of oil equivalent (Mtoe) over the next 20 years. While the country s primary energy production is expected to be relatively stable, net imports are shown to surge substantially, thus increasing Greece s energy import dependency. The forecasts predict substantial changes for Greece s power system with the generation mix shifting away from oil and lignite to natural gas. Energy consumption patterns are also forecast to change, driven by underlying structural changes in the economy. Notable changes are projected for the services and transportation sectors. Projected fuel shifts in final consumption are induced by expected price developments and government policies. As a result of the projected growth and shifts in energy production and consumption and fuel mix, baseline GHG emissions in 2010 are forecast to exceed 1990 levels by 47.2%. By 2020, emissions are projected to exceed 1990 levels by 70.3%. The environmental tax scenario shows significantly lower GHG emissions, primarily a result of emissions reductions in the power sector, as well as the industrial and residential sectors. Keywords: Greece, energy projection, GHG mitigation, CO2 tax, bottom-up modeling 1. INTRODUCTION The latest reports of the Intergovernmental Panel for Climate Change continue to make a strong case to link observed changes in the global climate with anthropogenic emissions of GHGs. The introduction of the Kyoto Protocol (KP) in 1997 represents an important attempt to implement an effective international mechanism for the reduction of emissions of the six major greenhouse gases (CO 2, methane, nitrous oxide, halogenated fluorocarbons, perfluorocarbons, and sulfur hexafluoride). According to the targets agreed upon within the framework of the KP, total GHG emissions in developed countries during the first commitment period ( ) must be reduced by at least 5% below 1990 levels. The European Union (EU) and its Member States as a whole are committed to an 8% reduction, while Greece, within the burden-sharing agreement among the EU Member States, has agreed to limit the growth of its GHG emissions during this period to +25%. Between 1970 and 2002, Greece s final energy demand was marked by a sharp increase, rising at an average annual rate of 3.7% from 6.6 Mtoe to 21.1 Mtoe. Along with this growth in energy consumption, Greece saw its CO 2 emissions increase from 22 million tons (Mt) in 1970 to 84.4 Mt in 1990 and Mt in Several studies estimate that 92.7% of the country s CO 2 emissions derive from energy production and consumption (MEPPPW and NTUA 1995; MEPPPW and NOA 2004). Consequently, a national action plan to reduce GHG emissions and meet the Kyoto Protocol commitments will have to rely heavily on the implementation of energy sector-related options and policies. 1 Sebastian Mirasgedis (seba@meteo.noa.gr), Elena Georgopoulou, and Yannis Sarafidis are with the National Observatory of Athens, Lofos Nymfon Thission, Athens, Greece. Guenter Conzelmann (guenter@anl.gov) and Vladimir Koritarov are with Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL, 60439, U.S.A. Work supported by the Greek Ministry for Environment. September th IAEE European Conference on Modeling in Energy Economics and Policy 1

2 This paper presents an attempt to develop a baseline future energy demand projection as well as the associated GHG emissions trajectories for the various sectors of economic activity in Greece. The analysis takes into account current policies and trends except for those specifically aimed at reducing GHG emissions to reach the KP targets. Particularly, the analysis includes: The dynamics of technology progress with improvements in energy efficiencies, The restructuring and liberalization of electricity and gas markets, and The observed sectoral patterns of economic growth. Furthermore, the paper investigates the changes in baseline energy consumption and GHG emissions induced by the implementation of an environmental tax that is equivalent to $40/t CO 2 (ETS40). Given that this level of environmental tax is more or less similar to the excess emissions penalty adopted within the context of the EU Directive on Emissions Trading (2003/87 EC) for the 1st trading period ( ), the results of this scenario show a rough approximation of the GHG emissions abatement potential in the various sectors of the Greek energy system. The analysis of the above scenarios was performed using the ENPEP model, which traces the flow of energy throughout the entire energy system, employing a market-based simulation approach to project future energy supply/demand balances. The model projects energy demand levels in each of the economic sectors (agriculture, industry, residential, commercial, and transport) and estimates the penetration rates of various alternative technologies in the different energy sectors. The remainder of this paper is organized as follows: Section 2 briefly presents the modeling methodology and framework. Section 3 focuses on the main assumptions of the study. Sections 4 and 5 discuss the quantitative effects of the two scenarios on energy consumption and GHG emissions. Finally, the paper summarizes the main findings of the study in Section MODELING FRAMEWORK The analysis of national energy systems is reaching unprecedented degrees of complexity. In addition to uncertainty of future energy demand and technology performance and costs, decision-makers are confronted with issues such as environmental protection, sustainable development, deregulation, and market liberalization. An integrated energy analysis that tries to take into account most, if not all, of these issues therefore requires advanced modeling tools and detailed simulations of the reference energy system under consideration. ENPEP was developed by Argonne National Laboratory (2001) and contains a set of analytical tools for use in integrated energy/electricity system planning and the quantification of environmental burdens. Its basic module, BALANCE, is used to trace the flow of energy throughout the entire energy system from resource extraction, via processing and conversion, to demands for useful energy (e.g. heating, transportation, electrical appliances) and employs a market-based simulation approach to project future energy supply/demand balances. ENPEP uses a non-linear, equilibrium approach to determine the energy supply demand balance. It is based on the concept that the energy sector consists of autonomous energy producers and consumers that carry out production and consumption activities independently, each optimizing individual objectives. For its simulation, the model uses an energy network that is designed to trace the flow of energy from primary resources through to final or useful energy (Figure 1). A fundamental assumption of the model is that producers and consumers both respond to changes in price. Furthermore, energy demand is sensitive to the prices of alternatives, as supply price is sensitive to the quantity demanded. ENPEP seeks to find the intersection of the supply and demand curves for all energy forms and uses included in the model. The equilibrium is reached when the software finds a set of Figure 1: Greece Generalized ENPEP Network September th IAEE European Conference on Modeling in Energy Economics and Policy 2

3 prices and quantities that satisfy all relevant equations and constraints. As market shares of energy are dependent on energy prices and energy prices are dependent on the quantity of fuel demands, ENPEP uses an iterative process to bring network prices and quantities into equilibrium. The energy network used represents all energy production, conversion, transport, distribution, and utilization activities in a country or region, as well as the flows of energy and fuels among those activities. The data that are necessary to calibrate the model for a base year as well as to project the future energy needs can be divided in the following categories: Macro-economic data that correspond to demographic national accounts, sectoral activity, and income variables; Structure of energy consumption in the base year and structure of activity variables (production, dwellings, passenger-kilometers, etc.); and Technical-economic performance data for technologies (e.g. capital cost, unit efficiency, variable costs, lifetime, etc.). The Greek energy system is represented in the ENPEP model by sub-systems and sectors that cover the main economic and energy activities. More specifically, the network developed comprises the three subsystems described below. Energy Supply. Energy supply is disaggregated into solid fuels (lignite and imported coal), imported liquid fuels (crude oil, diesel, gasoline, heavy fuel oil, LPG, jet fuel, naphtha and other liquid fuels), domestic liquid fuels (crude oil), natural gas, renewable energy sources (wind energy, solar energy, biomass, hydro and geothermal energy), and imports of electricity. Energy Conversion. Energy conversion is disaggregated into refineries (based on the total installed capacity of the four Greek refineries) and the power generation sector that is further disaggregated into the interconnected system in the mainland and the autonomous island systems (Figure 2). Liberalization of the electricity market is Tertiary consumer Residential consumer Other consumers Electricity exports Industrial consumer Allocation Allocation Distribution Renewables Independent Tertiary Company Transmission Renewables Independent Industrial Company Industrial Auto-producers Electricity imports INTERCONNECTED SYSTEM Dispatch Allocation AUTONOMOUS SYSTEMS Hydro Utility Company Other renewables Utility Company Renewables Figure 2: Modeling the Greek Power Generation Sector in ENPEP September th IAEE European Conference on Modeling in Energy Economics and Policy 3

4 considered and is simulated through the definition of four main categories of producers which are differentiated according to their economic characteristics and the needs they cover: (1) large electric utilities, (2) industrial auto-producers, (3) independent producers in the industrial sector covering their heat and electricity consumption through co-generation, and (4) independent producers in the tertiary sector that use cogeneration to satisfy their heat and electricity needs. Final Demand. Final demand includes five main sectors (agriculture, industry, transport, tertiary and residential), which are further decomposed into sub-sectors and then into specific energy end-uses (e.g. space heating, air conditioning, steam production, etc.). The network includes more than 70 energy uses and 300 alternative technologies. Technologies consume final energy forms or fuels and convert them to useful energy to provide a specific energy service, such as hot water or space heating. The strength of this approach is that it allows for a comprehensive assessment of the various interactions between the different sectors of the energy system. The market-based equilibrium, together with the detailed technical description of the energy sectors and uses, leads to a more realistic representation of the energy system and improved modeling of various policy instruments. However, the solution obtained is closely related to the level of detail of the developed energy network. Also, analysts have to exogenously set market simulation parameters. 3. MAIN MODELING ASSUMPTIONS The level of emissions estimated in any scenario depends on assumptions regarding parameters, such as growth of population, gross domestic product (GDP) forecasts, and underlying energy prices. Results also depend on the specific reduction policies incorporated into the scenario. The main assumptions made for the projection of energy consumption and associated GHG emissions in the baseline scenario are presented in Table 1. Additional assumptions are described below. Climate. Greece s future climate was assumed to remain the same as in the last five years. Assuming climate conditions to be closer to the historical average would ignore the fact that the average annual temperature has already increased noticeably in the last decade. Consequently, the use of the lower historical average temperature would lead to a sudden, counter-intuitive increase in space heating requirements after the year Sectoral Value Added. The tertiary sector, excluding public services, shows the highest annual rate of growth (4.5%), while its share in GDP in 2010 and 2020 is estimated at 52% and 58%, respectively (42% in 1990). The public sector is also projected to develop at an average annual rate of 2.3% during the period , with the growth rate falling to 1.8% after Industry growth is approximately 2.7% per year during the period and 1.8% during the period , while its contribution to the total GDP decreases from 20.0% in 1990 to 16.4% in 2010 and 14.6% in Finally, the annual rate of growth in the primary sector is 2.0% between 2000 and 2010, although this rate is expected to drop to 0.7% for the subsequent decade. The Table 1: Main Assumptions in the Baseline Scenario Historic data Projections Population (millions) % 0.4% 0.3% 0.2% Household size (persons) % -0.8% -0.8% -0.8% GDP (billion Euro 1995) % 3.4% 3.0% 2.9% International fuel prices Coal (1990 $/t) Oil (1990 $/bbl) % 0.0% 1.8% 1.7% Natural gas (1990 $/toe) % 0.0% 1.8% 1.7% Transport activity Passengers (billion p-km) % 3.0% 2.6% 2.3% Goods (billion t-km) % 2.5% 2.2% 2.0% September th IAEE European Conference on Modeling in Energy Economics and Policy 4

5 above analysis utilizes information from the National Statistical Service of Greece and the National Bank of Greece. Discount Rates. The discount rates used for the various energy technologies are differentiated based on the specific characteristics of the energy actors involved. More specifically, residential consumers usually prefer investments with a short payback period reflected by an assumed discount rate of 14%. On the other hand, industrial consumers, utilities, and refineries usually plan their investment policy on a long-term basis, and therefore a 6% discount rate seems more appropriate. A discount rate of 9% was adopted for the tertiary sector. The baseline scenario defines the future development of the energy system under a combination of current policies and consumer behavior and emerging trends. Specifically, this scenario addresses: The liberalization of the local electricity market; The agreement between the EU and car auto manufacturers (ACEA, KAMA, JAMA) regarding the decrease of fuel consumption in new cars, with the aim to achieve an average CO 2 emission factor of 140 g/km by 2008 (with an intermediate target of 170 g/km by 2003); The continuation of present economic instruments for the promotion of renewable energy sources (RES), cogeneration, natural gas, and energy conservation; The integration of the expected GHG emissions reductions from RES and energy conservation projects, which have been approved and/or already implemented within the Operational Program of Competitiveness; and Council Directive 2002/91/EC of 16 December 2002 on the energy performance of buildings. With the exception of fuel prices, which are influenced by the implementation of the CO 2 tax, the environmental tax scenario adopts similar assumptions as the baseline case. 4. BASELINE SCENARIO RESULTS Gross inland consumption increases continuously at (ktoe) an average rate of 2.2% per year from 23.9 Mtoe in to 34.7 Mtoe in 2010 and to 41.5 Mtoe in (Figure 3). Liquid fuels still account for the major part of gross inland consumption, but their contribution decreases from 59% in 1995 to 54% in 2010 and 53% in Consumption of solid fuels shows an increase of about 12% during , while their share 5000 falls from 35% in 1995 (8.4 Mtoe) to 23% in (9.5 Mtoe). Also, natural gas is projected to be a significant part of the gross inland consumption, Solid fuels Liquid fuels Renew ables Natural gas Electricity 15.2% in 2010 and 20.1% in This decreases the relative contribution of solid and liquid fuels in the Figure 3: Baseline Gross Inland Consumption consumption mix. The share of RES, including large hydro, in gross inland consumption for the entire study period declines from 4.8% in 1995 (1.15 Mtoe) to 4.4% in 2020 (1.79 Mtoe). In absolute values though, their utilization increases by 55% between 1995 and Electricity consumption is expected to grow at an average rate of 3% per year during , dropping to 2.5% per year in subsequent years. As a result, total installed power generation capacity in Greece increases by some 9.5 GW in the period (Table 2). The use of traditional lignite and oil power does not change significantly during the study period. The increased capacity needs are mainly met by the installation of natural gas combined cycle power. Their capacity increases by almost 5 times over the period to reach 6 GW, or about 32% of the total installed capacity by At the same time, the installed capacity of large hydro units remains nearly the same during the study period. Wind farms with a capacity of about 1.6 GW are expected to be installed until 2020 as a result of the strong wind potential in Greece and the support policies implemented by the Greek government. September th IAEE European Conference on Modeling in Energy Economics and Policy 5

6 Table 2: Baseline Results for Greek Power Generation Sector Energy Source Net electricity generation (ktoe) Lignite 2,251 2,596 2,584 2,611 2,657 2,714 Oil Natural gas ,105 1,676 2,145 2,728 Hydro Wind Other RES Electricity imports Total 3,356 4,375 5,035 5,765 6,471 7,277 Generation capacity (MW) Lignite 4,533 4,900 5,210 5,210 5,210 5,210 Oil 2,287 2,069 2,183 2,414 2,564 2,734 Natural gas 1,159 2,410 3,884 4,835 6,059 Hydro 2,524 2,959 3,261 3,261 3,261 3,261 Wind ,264 1,590 Other RES Total 9,371 11,261 13,674 15,686 17,133 18,854 Final energy consumption increases continuously from 16.1 Mtoe in 1995 to 24.8 Mtoe in 2010 and 29.7 Mtoe in 2020 (Figure 4). This reflects an average annual growth rate of 2.5%. Liquid fuels have the highest share in final energy consumption, though their contribution slightly declines from 70% in 1995 to 65% in 2010 and 63% in Electricity s share of final energy consumption increases from 19% in 1995 (3.1 Mtoe) to 20.9% in 2010 (5.2 Mtoe) and 22.3% in 2020 (6.6 Mtoe). Natural gas represents approximately 6.4% of final energy consumption by 2010 (1.6 Mtoe), growing to 7.9% by 2020 (2.4 Mtoe). The share of RES decreases from 5.1% in 1995 to 3.4% in 2010 and 3.1% in 2020, while in absolute values their utilization increases by 13.1% in The drop in RES share in final energy consumption is a result of the shift away from biomass consumption in the residential sector and occurs despite a significant penetration of solar systems. The structure of final energy consumption by sector is in line with the expected economic development. The share of industry in final energy consumption declines from 32.2% in 1995 to 29.9% in 2010 and to 28.3% in The agricultural share remains essentially the same during the entire study period (between 5% and 6.3%). The contribution of the tertiary and transport sectors increases by 4.6% and 2.4%, respectively. Finally, the share of the residential sector drops off slightly during the forecast period ktoe Distribution per sector ktoe Distribution per fuel agriculture industry residential tertiary transport solid fuels liguid fuels electricity thermal energy renew ables natural gas Figure 4: Baseline Final Energy Consumption GHG emissions from the energy sector under the baseline scenario, expressed in CO 2eq, show an increase of 47.2% between 1990 and 2010 and 70.3% between 1990 and 2020 (Table 3). Emissions increased annually by 2.3% in the period, though the rate is projected to drop to 1.6% for and 1.5% for The emissions growth is slowed mainly because of the penetration of natural gas and of various renewable energy sources, especially in the power generation sector. It is not surprising that, in the period to 2020, the sectors with the fastest increase in emissions are those where energy demand is expected to grow fastest, namely the tertiary and transport sectors. While emissions from residential sources grew at an average rate of 4.9% during , this growth is projected to decline to 0.8% per year during the forecast period. September th IAEE European Conference on Modeling in Energy Economics and Policy 6

7 In terms of absolute contribution to total GHG emissions from the Greek energy sector, the electric sector clearly dominates with more than 50% of the total emissions between 1990 and CO 2 accounts for more than 95% of total GHG emissions. Finally, it should be noted that GHG emissions grow at less than the rate of economic growth because of the projected improvements in energy efficiency and the penetration of natural gas. Table 3: Baseline Results for GHG Emissions from the Greek Energy Sector (kt CO 2eq ) Sector Electric 42,911 44,568 53,791 56,536 60,848 65,109 70,034 Industry including refineries 12,484 12,575 14,459 15,051 15,755 16,126 16,775 Transport 15,657 17,389 19,839 23,511 27,104 30,481 33,939 Agriculture 3,149 2,908 2,972 3,134 3,304 3,479 3,653 Residential 5,084 5,241 8,185 8,754 9,084 9,377 9,588 Tertiary ,122 1,435 1,722 1,990 Total 79,852 83, , , , , , CARBON TAX SCENARIO RESULTS As explained in Section 2, a fundamental assumption of the model is that energy producers and consumers both respond to changes in prices. Imposing a carbon environmental tax to the whole energy system changes the relative energy prices. These changes reflect the carbon content of each fuel and provide incentives to the economic agents to reduce their consumption of carbon. More specifically, the imposition of an environmental tax is expected to affect both the future total energy demand of a given energy system and the share of energy carriers/technologies used in order to satisfy this demand. Consequently, the different penetration rates of alternative energy sources and technologies affect also the level of energy-related GHG emissions. However, this effect is constrained by (1) the inertia of consumption patterns and (2) behavioral characteristics that determine technical change and the evolution of lifestyles. The former factor is related to the rate of renewal of existing end-use equipment and the length of time required for turnover of the energy supply system, while the latter refers to social determinants of individual consumption behavior that are hard to change quickly. Results for the environmental tax scenario (ETS40) show a drop in the consumption of solid fuels as well as electricity, increased utilization of RES, and a stimulation of a rational and efficient use of energy (Figure 5). Specifically, solid fuel consumption decreases from about 9.1 Mtoe to 7.3 Mtoe by 2010, electricity consumption drops from 5.2 Mtoe to 4.9 Mtoe by 2010, and RES will reach 1,884 ktoe in 2010, accounting for 5.8% of gross inland consumption. In other words, the implementation of ETS40 leads to an increase of RES contribution to gross inland consumption by 32% in 2010 and 35% in 2020, as compared to the baseline scenario. At the level of final energy demand, the application of ETS40 induces a significant decrease in energy consumption, primarily in the tertiary and residential sectors and secondarily in industry and transport. Changes in Gross Inland Consumption Changes in final energy consumption 30% % % 10% 0% -1% -2% 0% -3% -10% -20% -4% -5% -6% -30% -7% Solid fuels Liquid fuels Renew ables Natural gas Total Agriculture Industry Residential Tertiary Transport Figure 5: Changes in Energy Consumption Induced by ETS40 (compared to the Baseline Scenario) September th IAEE European Conference on Modeling in Energy Economics and Policy 7

8 With respect to electricity generation, the implementation of ETS40 leads to significant modifications in the national power system (Table 4). At first, a large-scale exploitation of wind energy is favored, generating 379 ktoe of electricity (total capacity of 1,738 MW) in In addition, there is a significant exploitation of hydro potential, mainly through the construction of small, with an additional total capacity of 400 MW. The total contribution of RES in electricity generation reaches 16.7% of the total electricity produced by 2010, considerably higher than the 11.7% estimated under the baseline scenario, yet significantly lower than the 20.1% foreseen in the EU Directive for RES. In addition, the higher CO 2 emission factor for lignite, in combination with the lower electricity demand driven by new energy conservation measures, leads to a lower usage of lignite power. Finally, despite the fact that the total installed capacity of natural gas power declines compared to the baseline as a result of the lower electricity demand, ETS40 leads to a rather constant penetration of natural gas as a result of the increased load factor of the existing gas-fired power stations. It is clear that the implementation of ETS40 will induce substantial GHG emissions reductions in the energy sector as shown in Table 5, amounting to 9.5% in 2010 and 10.3% in 2020 compared to the baseline (11.2 and 14 Mt CO 2eq, respectively). Nonetheless, emissions are still projected to increase 33.2% over 1990 levels by This is roughly the amount of increase over 1990 levels set by the 2nd National Program for Climate Change (MEPPPW/NOA 2002), which projects an increase of GHG emissions of 35.4% by 2010 over the base year (1990). The electric sector presents the largest GHG emissions abatement potential, mainly due to the considerable decrease of electricity demand, as well as from the use of cleaner and, particularly, renewable energy forms in electricity production. The industrial and residential sectors show also considerable emissions abatement potential, with the largest part of emissions reductions achieved by the use of more energy-efficient technologies and equipment. In the transport sector, significant changes do not occur, since substantial technological developments were already incorporated in the baseline scenario. Finally, the GHG emissions from the tertiary and agricultural sectors are projected to slightly increase, mainly due to a substitution of electricity with other conventional fuels (e.g. diesel) in various energy uses. Table 4: Results for the Power Generation Sector under the Baseline and ETS40 Scenarios Electricity generation (ktoe) Installed capacity (MW) Type of unit Baseline ETS40 Baseline ETS40 Baseline ETS40 Baseline ETS40 Lignite 2,611 2,072 2,714 2,064 5,210 5,210 5,210 5,210 Oil ,414 2,405 2,734 2,707 Natural gas 1,676 1,652 2,728 2,625 3,884 3,159 6,059 5,052 Hydro ,261 3,661 3,261 3,661 Wind ,738 1,590 2,554 Electricity imports Total 5,765 5,410 7,277 6,708 15,686 16,174 18,854 19,184 Table 5: GHG Emissions Reductions Induced by ETS Sector Emissions (kt) Changes compared to baseline Emissions (kt) Changes compared to baseline Electric 51, % 58, % Industry including refineries 14, % 15, % Transport 26, % 33, % Agriculture 3, % 3, % Residential 8, % 9, % Tertiary 1, % 2, % Total 106, % 122, % 6. CONCLUSIONS The study developed baseline projections for Greece s energy demand and associated trajectories of GHG emissions. Additional model runs investigated the changes in the energy system caused by the implementation of an environmental tax that is equivalent to $40/t CO 2. The analysis was performed with the ENPEP model, September th IAEE European Conference on Modeling in Energy Economics and Policy 8

9 which traces the flow of energy throughout the entire energy system, employing a market-based simulation approach to project future energy supply/demand balances. The entire Greek energy system was modeled in ENPEP, with sufficient detail covering all economic sectors. According to the baseline scenario, final energy consumption in Greece is expected to reach 24.8 Mtoe by 2010 and 29.7 Mtoe by 2020, equivalent to an annual average growth rate of about 2.5%. Similarly, energy-related GHG emissions are to increase by 47.2% in 2010, and 70.3% in 2020, compared to 1990 levels. The implementation of ETS40 imposes significant changes in the structure of the energy system, including a drop in the consumption of solid fuels and electricity, an increase in RES utilization, and a stimulation of conservation measures in the end-use sectors. These changes lead to considerable reductions in GHG emissions as compared to the baseline. If compared to 1990 levels, emissions under ETS40 are 33.2% (2010) and 52.8% (2020) above base year levels. For Greece to meet KP commitments implies substantial reductions from baseline emissions. According to the 2nd National Program on Climate Change, a number of activities should be undertaken that are aimed at an efficient use of energy and a diverse mix of energy supply. These actions include: (1) energy efficiency improvements in the electricity sector and promotion of RES, (2) interventions in the industrial sector through the use of more energy efficient production procedures, application of best available techniques and technologies, and increased use of RES, (3) interventions in the transportation sector through a shift towards less polluting forms of transport, improvements of vehicle efficiencies, and improvements in road networks, and (4) interventions in the residential/services sector through the use of more efficient electric appliances, improvements in building insulation, etc. Economic instruments (i.e. taxation, subsidies, etc.) may be very useful tools for supporting the interventions described above. The results of the present analysis clearly reveal that the implementation of an environmental tax will reduce GHG emissions primarily from the power generation sector and secondarily from industrial and residential sectors. On the other hand, the transport sector is characterized by a relatively small GHG emissions abatement potential, while GHG emissions from tertiary and agricultural sectors are projected to slightly increase because of the higher penetration of conventional fuels instead of electricity. REFERENCES Argonne National Laboratory, 2001, Greenhouse Gas Mitigation Analysis Using ENPEP: A Modeling Guide, International Atomic Energy Agency. Ministry for the Environment, Physical Planning and Public Works (MEPPPW)/National Observatory of Athens (NOA), 2004, National Inventory of Greenhouse and Other Gases Emissions for the Years , Athens. Ministry for the Environment, Physical Planning and Public Works (MEPPPW)/National Technical University of Athens (NTUA), 1995, Climatic Change the Greek Program for Carbon Dioxide and Greenhouse Gases Emissions Abatement, Athens. Ministry for the Environment, Physical Planning and Public Works (MEPPPW)/National Observatory of Athens (NOA), 2002, 2nd National Program for Climate Change: , Athens. The submitted manuscript has been created by the University of Chicago as Operator of Argonne National Laboratory ( Argonne ) under Contract No. W ENG-38 with the U.S. Department of Energy. The U.S. Government retains for itself, and others acting on its behalf, a paid-up, nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. Argonne National Laboratory's work was supported under U.S. Department of Energy contract W Eng-38. September th IAEE European Conference on Modeling in Energy Economics and Policy 9