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Modelling the Implications of the Kyoto Protocol for a Developing Country A. Cadena1 and A. Haurie2

Abstract: This

paper deals with the simulation of the implications for the economy of Colombia of the Kyoto Protocol on reduction of greenhouse gas emissions. The Protocol proposes three flexibility instruments for reaching at a lower global cost the reduction targets: the Clean Development Mechanism and Joint Implementation that would permit developing and developed countries to co-operate on a project by project basis, as well as the International Emissions Trading among developed countries. An important question is to identify precisely the benefits accruing to both parties in such agreements or trade schemes. Taking, as an hypothetical example, a co-operation between Switzerland and Colombia, and using a family of techno-economic models related to MARKAL, one presents a set of simulations of the response of the energy system of Colombia to abatement measures proposed in the Kyoto Protocol. The paper shows the necessity to go through this type of modelling approach if the policies recommended in the Kyoto protocol have to be successfully implemented. Keywords: Kyoto Protocol, Clean Development Mechanism, International Emissions Trading, Energy modelling, CO2 abatement, technology assessment

1

Professor, School of Electrical Engineering, University of Los Andes, Bogot‡, Colombia. e-mail: [email protected] 2 Professor of Operations Research, HEC, University of Geneva, Geneva, Switzerland. e-mail: [email protected]

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Modelling the Implications of the Kyoto Protocol for a Developing Country 1. Introduction The aim of this paper is to present the results of a comprehensive modelling task that has been undertaken to assess the implications for Colombia of the Kyoto Protocol on Greenhouse Gases (GHG) reductions. Global climate change due to human (anthropogenic) intervention in the carbon cycle has become a relevant concern in recent times. Global warming is a phenomenon due to the atmospheric concentration of GHG. The global nature of this phenomenon points to the need for an internationally co-ordinated effort to prevent and control the concentration of anthropogenic GHG in the atmosphere. Recently, on the basis of imperfect information but discernible evidence of human influence on climate change, a Ôprecautionary approachÕ has been adopted by the United Nations Framework Convention on Climate Change (UN FCCC), after the Rio Conference in June 1992. The Kyoto Protocol to the UN FCCC agreed on December 1997 [39], strengthened the Rio commitments by defining legally binding emission targets for industrialised countries and creating mechanisms to enrol developing countries in the GHG reduction effort. The Protocol sets out targets for curbing emissions of a ÔbasketÕ of six GHG in industrialised countries and countries in transition towards market economies (Annex I countries). Total emissions reduction shall reach 5.2% below 1990 levels by 2012. Targets are differentiated by country according to their national socio-economic situation and prospects. Developing countries (nonAnnex I countries) are not compelled to cut their emissions but to Çenable economic development to proceed in a sustainable mannerÈ. To accomplish this goal, the Protocol conceives different ways to reduce emissionsindividual actions, collective targets or bubblesand flexible mechanismJoint Implementation (JI) projects and International Emissions Trading (IET) between Annex I countries and a Clean Development Mechanism (CDM) between Annex I and non-Annex I countriesto facilitate the achievement of the reduction targets in most cost effective manner. Identification of international co-operative projects and evaluation of their cost, benefits and impacts drive us to linking different national models under compatible platforms. In this paper we use a systems analytic method to assess the opportunities and implications of the Kyoto Protocol for Colombia, a non-Annex I country. The paper is organised as follows: In the next two sections we dimension the Kyoto permits market and briefly sketch the Colombian economy and energy system. Then, in section 4, we describe the modelling approach, which is based on the MARKAL-family of models. MARKALMARKet ALlocation, is a dynamic process model that allows one to quantitatively assess national and multinational GHG emissions control strategies. A family of MARKAL models have been developed to represent different aspects of the long rang interactions between economy, energy and the environment in a given region1. The Colombian models include a complete refinery representation and take explicitly into consideration the uncertainty on water availability for hydro power generation and on international coal prices.

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Section 5 discusses the simulation results obtained from the numerical computation of economic equilibria. We use the common MARKAL based systems analytic approach to evaluate the proposed Kyoto international co-operation strategies and perform a series of studies for identifying cost-efficient ways to curb CO2 emissions between an Annex-I country and one non-Annex I country. We show that the different models of the MARKAL-family are useful tools to evaluate national environmentally sound energy policies within a global world. We end the paper, with some concluding remarks, policy recommendations and suggestion for further research.

2. The Emissions Market from the Kyoto Protocol Table 1 shows the emission baselines for Annex I (USA, Japan JPN, European Union EEC, Other OECD countries OOE, Eastern European countries EET, former Soviet Union FSU) and non-Annex I countries as well as the reduction targets in 2010, as calculated by the MIT Group using the EPPA model2. Table 1: GHG emission baselines and reduction targets - Kyoto Protocol (million tonnes of CO2) USA 4994 6730 4646

JPN 1093 1555 1027

Baseline 1990 Baseline 2010 Kyoto target 2010 Fuente: Ellerman et al. [12], pp. 2-3.

EEC 3014 3901 2772

OOE 1166 1731 1104

EET 975 1448 1001

FSU 3267 2798 3201

Non-Annex I 7414 15187 15187

Only five of the six Annex I EPPA regions will actually need to take action to reduce emissions. The reduction effort corresponds to about one third of the expected baseline levels in 2010. The FSU regionÕs emissions will fall far below the Kyoto targets, originating what is called Ôhot air'. According to these previsions, this legal right to emit and thus to sell permits amounts to 8.46% of the whole reduction market. Hot air would undermine the emission trade regime, or as others say, even the whole Protocol regime [16], and it is not fair to non-Annex I countries. Which portion of this market (4400-4800 Mtonnes of CO2, depending on the decisions about the hot air issue) may be captured by a non-Annex country like Colombia ? It is difficult to give a precise answer at this very moment. It would depend on the final terms and conditions of the Kyoto Protocol: stronger targets for Russia and Ukraine, ceilings on the use of the flexibility mechanisms, inclusion of sinks in the Clean Development Mechanism (CDM), crediting of permits, compensation measures for energy exporting countries, new and additional financial resources, and organisational framework, etc. To assess the implications of different economic policy instruments to control climate change on the energy system and economy of Colombia, as well as the Colombian competitiveness in the emissions market under different scenarios of reduction policies, a set of models have been developed. Since the emission abatement might be obtained via a change of technologies in the energy system, it is important to represent faithfully the production structures, the technology choices, the possible introduction of new technologies and new energy forms, with the costs

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involved over the life duration of the equipments. The interdependency of the different energy production subsystems is best represented by the many ways electricity can be generated (from fossil fuel thermal units to hydro, nuclear plants or fuel cells, É). The models to be used to represent these technology choices have to be dynamic with a long enough time horizon, so as to represent the investment and life cycle processes, and comprehensive in order to take into consideration the whole energy systems and all the possible production organisations. If the technologies are correctly described in technical and economic terms, the energy model will provide a good representation of the Ôsupply functionÕ for the different energy forms. As a consequence, the marginal cost for the energy carriers and the marginal cost for the emission reductions will be correctly evaluated. As it is well known, the evaluation of marginal costs is essential in the design of effective market based instruments. We have thus decided to develop a family of MARKAL models3. MARKAL [14] is an integrated multi-period linear programming model designed to assess national and regional energy systems. It evaluates the optimal contribution of different mixes of energy carriers and technologies to fulfil an objective of a given country or region. The model has been continuously evolving4, most recently enhanced to allow for the evaluation of national and multinational GHG emissions control strategies. The cumulative polluting effect for each environmental indicator (e.g., CO2, SO2, NOx, ...) can be computed for each time period and the marginal abatement costs for the emission targets per period are available from the dual program [22]. An implicit cost abatement function for the energy system can thus be derived. 3. The Colombian Economic Situation Before we go through the modelling schemes and the numerical simulations, we briefly describe the Colombian economy and energy system in the next section. Colombia is one of the developing countries that may be greatly influenced by global GHG emission reduction measures. In the first place, its exports still depend primarily on raw materials. Exports of oil and coal play an important role in the country's balance of payments. Oil is the first export product whilst coal is the third one. In 1995, the contribution of the mining sector to this balance was about 40%. Next, the country is endowed with a diversity of naturalenergy and forestryresources. It has relatively large oil and natural gas reserves. Excellent quality coal is an abundant resource. The country's hydropower potential was estimated to 93 GW, of which only 10% has been tapped. However, recent environmental regulations may reduce this potential by one third. The electricity is basically produced by hydroelectric plants. Historically, thermal installed capacity and generation have not been more than 30% of the national installed capacity and annual electricity production. This heavy dependence on hydropower yields to a vulnerable system. During 1992, the country underwent a prolonged period of electric power rationing caused largely by the El Ni–o phenomenon. Therefore, an increase in the system's firm energy capacity by means of thermal generation is expected. Final energy demand was growing at a 4% annual average rate while Gross Domestic Product (GDP) was growing at a 3.7% annual average rate in the period 1980-1995. Both trajectories have changed since 1995; however if the economic adjustment program and the peace

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negotiations are successful, we may expect the previous economic growth path to recover in two or three years. Even if a reduction in the energy intensity is foreseen, the economic development goals will necessitate increasing energy to be accomplished. Finally, Colombia has a huge cultural and biological diversity which is not extensively known. Concerns about bio-diversity could affect energy expansion decisions. New oil reserves, as well as some of the prospective hydrological resources, are located in the zones of high biodiversity. But, from the other side, afforestation, reforestation and conservation of natural parks, forestry and land management are attractive options to capture carbon and produce emissions credits when sink projects are included in the CDM [39] . Up to now, Colombia's economic activity has not resulted in a significant contribution to the global warming problem. By recent estimations [2], based on energy consumption data and traditional deforestation figures, CO2 emitted in Colombia is about 5.3 tonnes per capita. Landuse changes and deforestation have been responsible for about 70% of the CO2 emitted, and the energy activities are responsible for the rest. This figure might have temporarily changed as a consequence of the recent economic recession and the sharpening of the social and political conflict that force the abandonment of the agricultural activity, resulting most likely in a natural growing of forest vegetation. Obviously, the country's economic growth could result in a greater participation in the global emissions if a responsible development path is not assumed.

4. The Colombian MARKAL-family of models In this section we discuss the main modelling steps followed to set up the MARKAL-family of models for Colombia (MARKAL, MARKAL-MACRO and MARKAL-ED) as well as the new features included in these models. MARKAL was developed in the Energy Technology System Analysis Programme (ETSAP) Annex IV, a co-operative project of national experts under the aegis of the International Energy Agency (IEA). The MARKAL family models are being used in more than twenty developed countries since more than two decades and in a few developing countries like India, China, Colombia, Turkey, Mexico and Brazil. The Colombian family of MARKAL models include a complete representation of oil refining that allows for optimal mixes of crude oils and for changes in the configuration of processes to produce oil derivatives that could be induced by carbon emissions constraints. Modelling extensions permit the analysis of uncertainty on water availability for hydro power generation and on international coal prices. The base year is 1990, that corresponds to the reference year for the Kyoto Protocol targets. The planning horizon is 35 years and not 45 years as usual in MARKAL models, since forecasting of useful energy demands and indigenous energy reserves is quite cumbersome for a developing country like Colombia with the actual economic and social problems. 4.1.

The Colombian Reference Energy System (RES)

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The development of a MARKAL model is based on network representation of the energy flows through different groups of production and demand technologies that is called the Reference Energy System (RES). In the RES one draws the energy flows from the sources to the end-uses through the technologies that are used to extract, transform distribute energy carriers and provide energy services. The RES for the basic Colombian MARKAL model [8, 9] includes the different demand sectors (DM)residential, industrial, transport, commercial and public, building, agricultural and non-energy uses with a large number of end-use technologies (DMD); transformation technologiesprocesses (PRC) and conversion (CON)5; and energy sources (SRC), as described in more detail as follows. 4.1.1. Energy Demands The demand categories were defined according to those included in the Colombian Energy and Mining Information System (SIMEC) (See www.upme.gov.co/SIMEC). We modelled sectors and end-uses as follows: • Residential sector: A distinction between urban and rural energy demands was made. Their consumption patterns are different. In the first category, we considered end-uses, such as: lighting, cooking, refrigeration, water heating, air conditioning, and other electric and electronic appliances. In the second category, we included lighting, cooking, refrigeration, motive power and other uses. • Industrial sector: The industrial sector was divided in eight sub-sectors corresponding to the ISIC6Food, Beverages and Tobacco, Textile and Confections, Pulp and Printing, Chemical, Cement, Stone, Glass and Ceramics, Iron and Steel and Other Industries. For each sector we considered the classical end-uses: steam, direct heat, motive power, and other uses. • Transport sector: Trying to fit consumer needs and preferences, mobility demands were modelled in physical units. Considering the different transport ways, we initially defined eleven demand categories. However, given that mobility demands do not strictly respond to economic rationale, we have differentiated passenger public transport demands and goods transport demands even more so as to consider different technologies (types of buses and trucks). Finally, we used the following sixteen demand categories: urban and interurban passenger, urban publiccar, light bus and bus, interurban publiccar and bus, urban load, interurban loadlight, medium and heavy trucks, rail passengers, rail load, fluvial passengers and load, maritime passengers and load, air passenger and air load. Fluvial and maritime passengers and load demands were modelled in energy units. Modelling this sector is a complex and a challenging task. Transport is the first energy consuming sector in Colombia and there are vast prospects for energy conservation measures. • Commercial and Public sectors: In this demand sector we grouped such activities as, commercial activities, restaurants and hotels, services and public lighting. The considered end-uses are: lighting, cooking, refrigeration, water heating, air conditioning and other uses. • Building sector: The building activity is always considered as a strategic sector in the Colombian economy. So it is reported separately in all the official statistics. Specific energy uses are difficult to determine in this sector. We followed the same approach as in the SIMEC [34] which includes aggregated series of electricity and other fuels consumption. • Agricultural sector: We modelled activities related with crops and processing of agricultural

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products and thus end-uses such as processing, traction, dry processes, motive power and some others. • Non-energy uses: This demand category includes final demand of asphalt, aromatics, lubes and greases and coke. Energy demands for the planning horizon were estimated for each category as described it in the sub-section 4.3. 4.1.2. Energy Sources We defined extraction and mining possibilities (technologies) for every primary energy source and international trade for primary and secondary energy sources in the following way: • Extraction of heavy and light oil, and natural gas and mining of thermal coalbig mining and medium and small mining, and metallurgical coal. • Production of wood and bio-massagricultural pellets and waste pulp. • Imports of natural gas from Venezuela, oil refined products and electricity. • Exports of light oil, natural gas, big mining coal, oil refined products, electricity and coke. Nuclear energy was not considered as a possible energy source for the planning horizon. Conservation options different from efficiency fuel-technology improvements were not considered either. 4.1.3 Transformation Processes Transformation processes that convert a primary or secondary energy carrier in a secondary carrier are grouped in MARKAL in two different classes. Electricity and low-temperature (centralised) heat production are grouped under the conversion class (CON). The rest of transformation processes are grouped under the so-called process class (PRC). It includes the refinery process, which was specially modelled for Colombian case and is to be presented in sub-section 4.2. Technologies are modelled by three activity levels: investments, capacity and production or activity. In the standard version of the Colombian model, the CON class consider existing technologies such as hydro plants of different capacities and regulation levels, steam natural gas and coal plants, natural gas turbines, diesel oil plants, and co-generation plants; and new technologies which go from a simple efficiency improvements to clean coal technologies, photo-voltaic and wind plants, and zero emission bio-mass plants. The PRC class includes, in addition to the refinery processes, the high-temperature heat production for the industrial sector and the production of coke and brickets. Each technology is described by technical parameters such as efficiency, useful life, residual capacity, energy and power availability factors, capacity and production bounds, and by economic information such as investment cost, fixed and variable operation and maintenance costs, fuel delivery costs, and expansion or investments bounds.

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4.1.4 Demand Technologies For every demand and end-use sector considered in the demand set, we include different enduse technologies, existing and new ones, grouped in most cases by the dominant fuel. Demand technologies are modelled in MARKAL via two activity level variables representing investment and capacity respectively; production is then assumed to be equal to the capacity. These technologies are described by technical parameters such as efficiency, useful life, residual capacity, average utilisation factor, capacity bounds, and economic information, such as investment cost, fixed operation and maintenance costs, fuel delivery costs, and investments bounds. Modelling this set is one of the real challenges of this task. Information about individual enduse devices and their use patterns do not always exist in Colombia. We should resort to specific national conservation and demand side management studies and to general and particular international information. 4.2.

Modelling Refinery Choices for Colombia

Given the importance of the oil industry for the energy system and economy of Colombia, we undertook a complete modelling of the Colombian refinery processes based on the Canadian version of the MARKAL refinery model [6]. In the standard MARKAL, all flow variables are expressed in energy units, thus every input and output of a technology process is conceived just in terms of energy. This is not the case of oil refinery system, where final products must meet strict quality constraints, and flow variables are more easily handled if they are expressed in volumetric units or units or mass, like barrels or tonnes of oil. The refinery facility sub-model is written in GAMS and allows for blending of inputs by weight, volume or energy content to meet several product specifications such as octane number, sulphur content and cetane index [13]. The Colombian model features the following: • Two different types of crudes, CMA gravity of 20.4 API, specific density of 0.9315 Kg/lt and 1.26% of sulphur content per weight unit and CMB gravity of 30.1 API, specific density of 0.8756 Kg/lt and 0.62% of sulphur content per weight unit. • Refined products as Liquid Petroleum Gas (LPG), Extra and Regular Gasoline, Jet-Fuel, Kerosene, Diesel-Oil, Fuel-Oil, Aromatics, Asphalt and Lubes and Greases obtained according to specified quality standards. shadow prices are available from the model solution. • Different processes and technologies including new options as well as classical and special existing ones such as Atmospheric Distillation, Vacuum Distillation, Demex, Unibon, Visbreaking, Catalytic Craking, Naptha Catalytic Hydrotreating (80 RON, 90 RON and 100 RON), Alkilation and Aromatics Recovery. • Blending options and quality requirements for the final products according to international standards and the specifications included in the Colombian refinery operation model [11].

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4.3.

The MARKAL-Colombia Useful Energy Demands

MARKAL is a demand-driven model in which a solution must satisfy the exogenously specified set of end-use demands for all periods of time. These demands express socioeconomic needs. Useful energy demands for each one of the categories already defined are estimated for the chosen planning horizon, together with the other exogenous parameters relative to resource availability and import and export prices. Estimating the useful energy demands for the planning horizon requires, on one hand, to know the base year useful consumption and, on the other hand, to obtain detailed information about the expected evolution of socio-economic scenario variablestypically population and Gross Domestic Product (GDP), as well as of other related indicatorslike construction area, mobility needs, etc. To picture the energy useful consumption, not available from energy statistics in Colombia, we resorted to final energy consumption data and to available information about efficiency of enduse devices at national and international levels. Next, we validated the obtained figures with available results from particular demand studies in Colombia. If energy indicators evolution can be built, forecasting useful energy demands is then a relatively straightforward task. In the Colombian case, we had however to resort to different procedures according to the category of use, that is7: • Econometric models for captive electric uses in the residential and commercial sectors. Different models are available at the Energy and Mining Planning Office (UPME), see for example [33, 36]. • Forecasted penetration rates for natural gas in the residential, commercial and industrial sectors [35, 37]. • Sound sectoral GDP growth rates for the industrial, commercial, building and agricultural sectors, validated by the National Planning Department. • Car sales forecasts with estimate of an average distance travelled and of the number of passengers or tonnes of freight per vehicle. Resulting forecasts in physical units were validated with estimates of total and final energy consumption in the transport sector by fuel8. For the reference case, we used an annual average growth rate of 1.7% for the population and of 4% for the GDP, during the period from 1995 to 2020. A low demand scenario defined by the annual GDP growth rate of 2.5% was also considered. Discount rate in both cases was assumed to be 7%. 4.4. Risk hedging in the MARKAL-Colombia model There are many sources of risk in the energy system. In addition to the uncertain state of nature, like e.g. water availability, uncertainty prevails in world prices of fossil fuel, as well as for predicting the severity of the required pollution abatement, estimating the true investment cost and date of introduction of a new technology (for example, backstop technology) and, more generally, the availability of equipment [19]. A stochastic version of MARKAL is

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already available9. In MARKAL-Colombia, risk management is introduced to cope with (i) water availability uncertainties as a result of climate variability typically due to the El Ni–oSouthern Oscillation (ENSO) phenomenon10 and, (ii) the evolution of fuel exports, namely oil and coal trade prices, under the Kyoto measures. 4.4.1. Modelling uncertainty in water availability To take into account the inter-annual climatic and hydrologic variability in the MARKAL framework, we made use of the reservoir management features included in the Canadian version of the model [5]. The temporal uncertainties of water availability was modelled through the annual reservoir availability factor (araf) and the seasonal reservoir availability factor (sraf) parameters. That is, random behaviour was introduced in the technical coefficients. A two-stage stochastic was generated for these availability factors11. The stochastic program applies a probability of occurrence to each scenario and finds a solution that is feasible for all the realisations and which minimises the expected cost. In other words, the MARKAL model determines a hedging investment strategy that allows the energy system to adapt the production capacity, in this case the electricity generation, according to the possible evolution of the scenarios, in this case the hydrologic inflows. A much more diversified portfolio of technologies and fuel mix for electricity generation is thus obtained. In the first two planning periods, the stochastic model installs and dispatches a wider gamut of thermal capacity. It goes from classical steam plants up to more efficient technologies, passing by natural gas and waste pulp co-generation systems. However the two-stage single recourse model is still limited in scope. Ideally an additional occurrence of the ENSO event should be considered for evaluating its impact on the possible hydroelectric response to CO2 abatement requirements. The stochastic MARKAL-Colombia is a first step in the direction of implementing robust investment planning tools in the energy/environment domain. 4.4.2. Stochastic Fuel Prices in the MARKAL Model The potential effects of the Kyoto GHG emission reduction targets on the Colombian oil and coal exports are not straightforward. First of all, even though exports of oil and coal are crucial for the Colombian economy, Colombia is a small player on the oil market and a price taker on the coal market. Global models hardly consider Colombia as a separate country in the analyses. Second, the definitive impacts of the control measures depend on how the Kyoto Protocol will be finally designed and implemented, as we said before. Third, and independently from the Kyoto Protocol, the dynamics of the natural gas market might considerably affect the future of coal exports. Not all the potential reduction of coal consumption should be attributed to GHG reduction concerns. Availability of natural gas in Europe and America make darker the future of the coal market if cost-efficient and cleaner coal technologies are not developed. To deal with the incidence of different price scenarios for the international fossil fuels markets, we have also introduced stochasticity in price exports and cost imports in the MARKAL model. That is, we introduce random behaviour in the cost coefficients of the objective function. Thus, the stochastic MARKAL applies a probability of occurrence for the realisation of the export prices and calculates the expected value for the objective function under all

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possible price scenarios. This allows us to model stochastic cost for mining and renewable resources as well. 4.5. The Colombian MARKAL-MACRO and MARKAL-ED Models The MARKAL least cost solution corresponds to a price system for the energy forms which equilibrates demand and supply of energy carriers. If we assume, as usual in a standard MARKAL model, that the useful demands are price-inelastic, then a competitive partial equilibrium in the energy market can be obtained through a single run cost minimisation linear programme. However severe or sustained GHG reduction measures may produce a change in energy prices large enough to trigger a reallocation of resources in the economy affecting capital formation and economic growth. The energy price rise will thus modify the demands for energy services, i.e. what we called the useful demands. Thus, one should take into account the to price and income effects on useful demands. Three different approaches have been envisioned to include price responsive demands. •

•

•

MARKAL-MACRO12 [18] is a coupling of a MARKAL energy supply model with a macroeconomic model. Useful demands are endogenously determined in MARKALMACRO by macroeconomic growth and by autonomous and price-driven conservation. The implementation of a MARKAL-MACRO model requires additional data to estimate or calibrate the parameters of the macroeconomic equations. These data are : the base year GDP, potential GDP growth rates, depreciation rate, initial capital-GDP ratio (KGDP), capital's value share (KPVS) and elasticity of substitution between the pair capital-labour and useful energy demands (ESUB). This last parameter is the hardest to estimate because no statistical information about useful energy by sector and prices of energy services is currently available. MARKAL-MICRO is an extension of the MARKAL model in which the demands for energy services are elastic to their own prices. It allows the modeller to take into account direct price effects on useful energy demands. Demands are adjusted in reaction to price changes by using specified demand functions with constant elasticities. The model falls short of computing a general equilibrium. It computes a partial equilibrium considering no feedback from the rest of the economy. Fortunately income effects are not so important, since income elasticities of energy services are usually very low. Recently, to facilitate the evaluation of different measures of GHG emission reduction, R. Loulou and D. Lavigne [26] carried out a development to incorporate the price effects over the useful energy demands, while remaining in the realm of a linear programming environment. The resulting model is known as MARKAL-ED. Building this partial equilibrium model, requires defining elasticities for the different categories of energy services and for different time periods. This information is not easily available since no statistical series are available for useful energy and prices by sector. In the Colombian case, we have introduced non-zero elasticities for the residential, industrial and commercial sectors. In the case of captive uses, we take the price elasticities of demand for energy carriers from the econometric models [34, 36]. In other cases, we ÔguessedÕ global price elasticities of sectoral or subsectoral energy demands.

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5. Simulation results 5.1. Supply of CERUs from the Colombian Energy System The simulations performed with MARKAL-Colombia permit us to figure out the technically feasible supply on the Certified Emission Reduction Units (CERUs) market coming from the Colombian energy system. Table 2 shows the CERU's production (in millions tonnes of CO2), production value (in millions dollars of 1990 discounted at 7%) and marginal reduction cost in 2020 (in dollars of 1990 per tonne of CO2) for 5 reduction scenarios. Reduction scenarios correspond to a different reduction percentages below 2020 baseline level starting in 2000. They were calculated by using the MARKAL standard and stochastic (El Ni–o) models [9]. Table 2: CERU's, production value and marginal reduction cost obtained from MARKAL simulations

Scenario

Standard MARKAL Total CERUs

(Mt. of CO2)

1 2 3 4 5

334 535 735 929 1126

Total Value (MUS$)

2333 5814 10697 17063 24907

Mg. Cost (US$/t)

85.4 103.5 119.3 196.2 207.9

Stochastic MARKAL Total CERUs

(Mt. of CO2)

324 497 676 845 1046

Total Value (MUS$)

911 2978 6572 11268 17354

Mg. Cost (US$/t)

80.6 114.4 118.8 176.9 196.2

At first glance, the total and marginal costs are lower when taking into account uncertainty in water availability. When El Ni–o is modelled, the thermal participation is widened the first periods and CO2 emission baseline is higher then. This results in an easier and cheaper emission reduction possibilities. This shows that it is not a straightforward task to compare reduction costs when base cases are not the same. In the stochastic model, we relax a cap in hydropower electricity production representing the thermal increase policy. In the case sinks were included in the CDM scheme, the Colombian supply would move up. Despite of this, it not obvious that Colombia will become an important producer on the CERU's market and that the income of this activity will be an important item of the balance of payments. Nevertheless, CERU's may offer some business opportunities for private industries and have important positive impacts for development. 5.2. The Kyoto flexibility mechanisms To evaluate the international co-operation strategies considered in the Kyoto Protocol for reducing GHG emissions, we have used the whole family MARKAL of models. We used the standard national MARKAL models to estimate CO2 emission baselines. Then, to assess the potential of CDM-JI and eventually of IET schemes for Colombia, we used a two-country MARKAL and MARKAL-ED model with a global coupled constraint on CO2 emissions. In the international emissions trading analysis, a global constraint to balance the trade of goods is

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also formulated. The resulting models minimise the total energy and emission control cost of the two countries to reach global CO2 emissions reduction target. They enable a preliminary identification of projects in the host country energy system and illustrate the benefits to be gained through international co-operation to reduce CO2 emissions in both countries. The work was done in collaboration with a team of Paul Scherrer Institute (PSI) and has been reported in [3] and [24]. The simulations show that MARKAL or MARKAL-(MICRO/)ED (or MARKAL-MACRO) are informative tools that provide a first good evaluation of the alternatives considered in the Kyoto Protocol to jointly curb GHG emissions. 5.2.1 CDM Scheme Starting from the national MARKAL models of the investor (Annex I) country and of the host (non-Annex when we are evaluating the CDM or Annex I if we are identifying JI projects) country, a larger joint MARKAL model is designed by linking the two models by a global constraint on CO2 emissions. This joint model enable us to identify CDM (or JI) projects in a non-Annex I (or Annex I) country energy system based on a comparison of marginal costs, and to estimate the profits to be gained from the CDM (JI) scheme. The linear programming formulation of the joint model can be sketched as follows13: Minimise

r  {investor, host}

c Tr x r

s. t. r  {investor, host}

Er x r ≤ e

S r x r ≤ sr , r  {investor, host},

where for each country r ∈ {investor, host}: cr is the cost vector of the energy system activities of r, whose level vector is xr; Er is the emission coefficients matrix of CO2 by the activities of r; e is the vector of overall targets for CO2 emissions (see below); Sr is the constraints matrix for the national MARKAL model r and sr its associated right-hand-side. The objective function of the model corresponds to minimising the total system costs for the two countries. The first constraint is the ÔcoupledÕ constraint that links together the emissions of the two countries and ensures that the total CO2 emissions are reduced down to the level e . This level e corresponds to the emission reduction targets of the Annex I country ( investor ) plus the emissions of the non-Annex I country in the baseline caseno reductionas determined by the corresponding national MARKAL model: e = e investor + E host x *host , where

{

}

x host minimise c host x host : S host x host ≤ shost . *

T

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Finally, the second set of constraints describes the two national MARKAL models without emission constraints. To fulfil the optimally differentiated national reduction targets, the joint model gives the optimal set of technologies to be installed in each country during the planning horizon, as well as the optimal share of energy fuels for each national energy system. The potential CDM (JI) projects correspond then to the additional technologies the host country needs to implement. The simulations for Switzerland and Colombia performed in collaboration with the PSI show as the distribution of emission reductions, what we report on Table 3.

Table 3: CO2 emission paths (million tonnes) and sharing of emission reduction efforts (percentage)

Scenario

Country

2000

2010

2020

Kyoto

Switzerland

43.2

6%

47.0

18%

49.8

15%

Colombia

84.8

93%

107.7

82%

143.4

85%

Fuente: A. Cadena [9], pp. 120-123. Comparing with the initial targets of Switzerland and the baseline emissions of Colombia, we can notice that Colombia would be in charge of a large fraction (between 85% and 93%) of the reduction efforts. This distribution is done to take full advantage of the low-cost abatement options in Colombia. Consequently, marginal reduction costs under the CDM scheme are significantly reduced, being between $ 28 in 2000 and $ 42 in 2020 undiscounted USD per tonne of CO2. Detailed results and preliminary CDM technologies are reported in O.Bahn et al. [3] and A. Cadena [9]. They clearly indicate the attractiveness of the Kyoto CDM's for developed countries, which can expect substantial savings for complying with the commitment to reduce CO2 emissions. Developing countries may benefit from an improved energy system and/or environment. To perform the final selection of the projects to be implemented, cost-benefit analyses should be used to take into account all direct and indirect costs and benefits associated with the actions finally selected (e.g., monitoring and verification costs, indirect environmental benefits and costs, leakage effects). 5.2.2 IET market In another simulation case we have evaluated the potential offered by the International Emissions Trading (IET) schemes by calculating, for the same two countries, the pattern of trade and the market and the social prices of the CO2 emission permits. We used multi-regional MARKAL or MARKAL-ED models when the possible effects of the rising of fossil fuels prices on the energy services demand in the investor country had to be taken into account. This work generalises the previous joint model proposal into a multi-regional model with trade of CO2 emissions and fossil fuels. This development builds up on previous works realised under the IEA/ETSAP project14.

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Table 4 summarises the trading specification (initial endowments of emissions rights and the Swiss reduction target ) as well as the trade equilibrium solution as calculated by the joint MARKAL-ED in one of the simulations performed with the PSI. Table 4: CO2 emission paths (million tonnes) and trade specification and equilibrium (million tonnes)

Trade Specification & Solution Initial Endowments

Country Switzerland Colombia

(Swiss) Emission Reduction Emissions Trade Domestic Emissions Switzerland Colombia Fuente: A. Cadena [9], pp. 123-125.

2000

2010

2020

42.9 92.7 2.0 3.3 44.8 89.5

40.0 116.7 10.3 9.8 49.8 106.9

37.1 150.3 22.2 15.0 52.1 135.3

The marginal cost of the emission permits is close to zero in 2000, $ 8.5 in 2010 and $ 41.1 undiscounted USD per tonne of CO2. We can confirm the advantages of the trade of emission permits for fulfilling the Kyoto Protocol goal, as reported in S. Kypreos and A. Cadena [24] and A. Cadena [9]. Buyer countries could reach their reduction targets at considerably less costs. Seller countries could have a new source of resources to foster economic growth and sustainable development. However, developed and developing countries should ensure that the economic advantages of this flexibility mechanism would not be offset by the reduction of its innovation capacity and technological learning processes.

6. Final Remarks Colombian technological answers to CO2 emissions limitation, in addition to no-regret or best practice measures, will finally rely on hydropower increase, renewable wood and waste pulp management and internal-use coal elimination. Domestic measures taken by Annex I countries to fulfil their Kyoto commitments will affect Colombian exports of fossil fuels, mainly coal. To properly evaluate these options and impacts, we introduce, in addition to the Refinery Model, some additional features to the MARKAL-Colombia family of models. For Colombia, the critical point of the Kyoto Protocol, at least in the short and medium term is the potential reduction in the world prices for fossil fuels, in particular for coal. A decrease by one dollar per metric tonne traded in the international coal market will entail a reduction in income from 30 to 60 millions dollars. Thus, the decrease by 5 dollars per metric tonne foreseen by ABARE [1] will result in an income reduction from 150 to 300 millions dollars per annum, that is between 2.5% and 5% of the corresponding steady state mining exports in 2010. The Ôdark' panorama for the coal industry, shows us on one hand that a better modelling effort to represent the global market of fossil fuels and emissions trading must be undertaken. The

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international coal market is unique in size and diversity [40]. The world's largest coal producers (China, the USA and India) are not necessarily the largest exporters (Australia, the USA and South Africa). ``For some countries, such as Australia and Colombia, coal exports are of much greater importance to the national economy than the domestic consumption of their coal. On the other hand, the major producers also import coal for reasons of quality and logistics''. The international coal market can be classified into two different types: an oligopsonic market, the Asia-Pacific market where Japan is the biggest buyer and defines a benchmarking price and a quasi-competitive market, the Western European market, where prices are determined by the supply-demand equilibrium. Different approaches have been proposed to model the fossil fuel market: A non-cooperative equilibrium consisting in a co-ordination scheme among a group of agents or countries that compete on an oligopolistic market to achieve a global environmental target has been proposed by A. Haurie [20], A. Haurie and G. Zaccour [21], and D. Carlson and A. Haurie [10]. The coordination schema is reached by ``an ad nominem tax scheme which distributes the burden of satisfying the common constraint''. The distribution of the burden is done by means of a weighting vector that indicates the relative importance of each country in achieving the global environmental target. A. Haurie [20] uses the concept of normalised equilibrium of Rosen for computing the implicit noncooperative equilibrium for each possible weighting vectors. This proposal should be explored in a future work. A second possibility is to use global general equilibrium models of the world economy which facilitate a good representation of the international oil, coal and natural gas markets. Australians (ABARE) [1] and the MIT group [12], have been working on improving the global market representation of fossil fuels. In any case, including trade flows of fossil fuels must be complemented with trade flows of energy intensive goods and emissions permits. Climate change control measures will affect trade patterns of production and consumption. The strongest criticism against the IET mechanism is the possible slow down in technology development. Technological change can be seen as a social learning process [32]. It does not take place in an autonomous way, but evolves as an endogenous process from within the social system. It occurs as a result of knowledge accumulation and a complex mixture of luck, money and perseverance [17]. Usually, it takes a long time for a technology to be conceived, developed and then brought to the market. In particular, the energy sector exhibits a considerable inertia. Thus, the flexibility mechanism in the Kyoto Protocol could contribute to increase efficiency and cost effectiveness but could fail in triggering off or motivating sufficient technological progress and shift to cleaner technologies and fuels [28]. These aspects should be evaluated with the help of a global model including endogenous technical change15. A second important agenda item for the energy exporting negotiators, including Colombia, must be to obtain that removal by sinks in the CDM's be accepted as a compensation measure. GHG emissions produced by the combustion of imported fossil fuel in Annex I countries will be offset in the resource origin. In such a way, fossil fuel coming from developing countries will be economically and environmentally competitive with cleaner fuels. And none additional mechanisms or distortingexceptional and exempting compensations measures should be considered. If energy exporting countries succeed in obtaining this, the implementation of an

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efficient and effective Green Coal Market (GCM) must be a priority for our country. Modelling sinks in the MARKAL framework is then another challenge. Finally, the UN FCCC should consider the feasibility of extending the IET mechanism to include non-Annex I countries, concentrating the CDM on those projectsreduction with new technologies and mainly removal if approvedthat generate long term and/or complex externalities and require special attention and follow up. In the case IET would be enlarged, special attention should be given to the initial endowments of emission rights. An appropriate definition of property rights is crucial for guaranteeing an efficient and equitable solution to the climate change problem. As an overall conclusion we want to emphasise that the work reported here show the important potential contribution of comprehensive OR-based energy/environment models in the assessment and implementation of market based instruments implying co-operation between developed and developing countries in an international trade context.

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ABARE. ÇThe Kyoto Protocol and Developing Countries: Impacts and Implications for Mechanism DesignÈ. Jun (2000). Academia Colombiana de Ciencias Exactas F’sicas y NaturalesÑACCEFÑ. ÇPreliminary Results of Greenhouse Gas Inventory Source, Sinks and Reservoirs ProjectÈ. Bogot‡ (1996) Bahn O, Cadena A, Kypreos S. ÇJoint Implementation of CO2 emission reduction measures between Switzerland and ColombiaÈ International Journal of Environment and Pollution, Vol. 12, Nos. 2/3. (1999). Barreto L and Kypreos S. ÇTechnological Learning in Energy Models: Experience and Scenario Analysis with MARKAL and ERIS Model PrototypeÈ. PSI Beritch 99-08, Paul Scherrer Institute, Switzerland (1999). Berger C, Dubois R, Haurie A, Lessard E, Loulou R, Waaub J.-P. ÇCanadian MARKAL: An advanced Linear Programming System for Energy and Environmental ModellingÈ. Les Cahiers du GERAD, G-90-53, Montreal (1991). Berger C, Loulou R. ÇExtended MARKAL: A Brief User ManualÈ. Les Cahiers du GERAD, Montreal (1993). Bueler B and Kypreos S. ÇA Multiregional MARKAL-MACRO Model to Study an International Market of CO2 Emission PermitsÈ. PSI Beritch No. 97-09, Switzerland (1997). Cadena A, Navas F, Ram’rez R, Rojas C. ÇDesarrollo e implementaci—n de herramientas de modelizaci—n para gesti—n energŽtica en ColombiaÑEl Proyecto MARKAL ColombiaÈ. Revista EnergŽtica, Vol. 11, Medell’n, Colombia (1994). Cadena A. ÇModels to assess the implication of the Kyoto Protocol on the energy system and economy of ColombiaÈ. Thesis, HEC, University of Geneva (2000). Carlson D and Haurie A. ÇA Turnpike Theory for Infinite-Horizon Open-Loop Competitive ProcessesÈ. SIAM Journal of Control and Optimization, Vol. 34, No. 4, July. (1996). ECOPETROL. ÇPrograma de Operaci—n de la Refineria de Barrancabermeja, 19942008È. Process Industry Modelling System, Version 5.00. (1993). Ellerman A, Jacoby H and Decaux A. ÇThe Effects on Developing Countries of the Kyoto Protocol and CO2 Emissions TradingÈ, Joint Program on the Science and Policy of Global Change, Report No. 36, Massachusetts Institute of Technology (1998). ETSAP-GERAD. ÇModelling the Oil Refineries in GAMS-MARKALÈ. May (1997). Fishbone L.G. and Abilock H. ÇMARKAL, a linear-programming model for energy systems analysis: technical description of the BNL versionÈ International Journal of Energy Research. (1981). Fragnieri E and Haurie A. ÇA Stochastic Programming Model for Energy/Environment Choices under UncertaintyÈ. International Journal of Environment and Pollution, Vol. 6, No. 4-6 (1997). Grubb M with Vrolijk C and Brack D. ÇThe Kyoto Protocol, A Guide and

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AssessmentÈ. The Royal Institute of International Affairs, Earthscan (1999). Gruebler A. ÇTechnology and Global ChangeÈ. Cambridge University Press (1998). Hamilton L, Goldstein G, Lee J, Manne A, Marcuse W, Morris S and Wene C.-O. ÇMARKAL-MACRO: An OverviewÈ, Brookhaven National Laboratory, BNL48377 (1992). Haurie A, Loulou R. ÇModeling Equilibria and Risk under Global Environmental Constraints in Energy Models. Les Cahiers du GERAD, G-93-08. Montreal (1993). Haurie A. ÇEnvironmental Coordination in Dynamic Oligopolistic MarketsÈ. Group Decision and Negotiation, Vol. 4 (1995). Haurie A and Zaccour G. ÇDifferential Game Models of Global Environmental ManagementÈ. In: Carraro C and Filar J (ed). Control and Game-Theoretic Models of the Environment, Annals of the International Society of Dynamic Games (1995). Kram T. ÇNational Energy Options for Reducing CO2 Emissions, Vol. 1: The International ConnectionÈ. Report of ETSAP/Annex IV, ECN (1993). Kypreos S. ÇThe MARKAL-MACRO Model and the Climate ChangeÈ. PSI Bericht No. 96-14. Switzerland (1996). Kypreos S and Cadena A. ÇPartial and General Equilibrium Versions of Markal Models with Multi-Regional Trade: Model Specifications and ApplicationsÈ. Join IEAALEP/IEA-ETSAP Annex VI, 6th Workshop, Antalya Turkey, October (1998). Kypreos S and Barreto L. ÇExperience Curves in MARKAL and Links to Macroeconomic ModelsÈ. Workshop on Modeling Technological Learning, IIASA, Laxenburg, March (1998). Loulou R and Lavigne D. ÇMARKAL Model with Elastic Demand: Application to Greenhouse Gas Emission ControlÈ. In: Carraro C and Haurie A (ed). Operation Research and Environmental Management, Kluwer Academic Publisher, (1996). Loulou R, Shukla P.R, and Kanudia A. ÇEnergy and Environment Policies for a Sustainable Future: Issues, Models and Analysis for IndiaÈ. Allied Publishers, New Delhi, India (1997). MacDonald A. ÇClimate Change and World EnergyÈ. IIASA Interim Report, IR-00006, February (2000). Mesa O, Poveda G, Carvajal L, Salazar J. ÇReservoir Reliability Design Under Interannual Climatic and Hydrological VariabilityÈ. Managing Water: Coping with Scarcity and Abundance, ASCE (1997). Ministerio de Minas y Energ’a. ÇEstudio de la demanda de energ’a en el sector transporteÈ, Bogot‡, (1987). Ministerio de Transporte, Organizaci—n de Estados Americanos, ÇEstudio del sector Transporte en ColombiaÈ. Informe Final, Bogot‡ (1982). Nakicenovic N. ÇTechnological Change as a Learning ProcessÈ. IIASA Induced Technology Workshop, IIASA, June (1997). Unidad de Planeaci—n Minero EnergŽtica. ÇEstudio de la Demanda de Energ’a en Colombia 1994-2010È, Bogot‡ (1994). Unidad de Planeaci—n Minero EnergŽtica. ÇEstad’sticas Minero-EnergŽticas de Colombia 1972-1995È. Bogot‡ (1996).

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[35] Unidad de Planeaci—n Minero EnergŽtica, Instituto de Econom’a EnergŽtica. ÇEstudio de la demanda de gas natural en ColombiaÈ. Bogota (1998). [36] Unidad de Planeaci—n Minero EnergŽtica. ÇModelos de Demanda de Energ’a ElŽctrica para ColombiaÈ. Bogot‡ (1998). [37] Unidad de Planeci—n Minero EnergŽtica. ÇLa cadena de gas natural en ColombiaÈ. Bogot‡ (1998). [38] Ministerio del Medio Ambiente. ÇEstrategia Nacional para el Mecanismo de Desarrollo LimpioÈ, Documento Final Preliminar. Oficina de Asuntos Econ—micos, Bogot‡ (2000). [39] United Nations. ÇKyoto Protocol to the United Nations Framework Convention on Climate ChangeÈ. Conference of the Parties, FCCC/CP/1997/L.7/Add.1 (1997). [40] World Coal Institute. ÇCoal - Power for the ProgressÈ. Fourth Edition, Home Page (1999).

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1

MARKAL models have been designed for a region of the size of a city, for states, for countries and for groups of countries. 2

12 regions are defined in the model: the United States of America (USA), Japan (JPN), the European Union (EEC), other OECD countries (OOE), Eastern Europe (EET), the Former Soviet Union (FSU), Energy Exporting Countries (EEX), China (CHN), India (IND), Dynamic Asian Economies (DAE), Brazil (BRA) and the Rest of the World (ROW). In this table non-Annex I countries are grouped.

3

There is a "family" of MARKAL models since different versions of the same techno-economic modelling scheme are adapted to different representations of uncertainty, risk, and energy demand. 4

Initially the development of MARKAL models has been motivated by the oil crisis and the perceived need to find substitutes to fossil fuels. Later on MARKAL models have proved to be useful tools to assess the energy pollution abatement policies. 5

Process technologies produce storable energy carriers (typically oil refineries) whereas Conversion technologies produce non-storable energy forms (like electricity and low temperature heat) that have to be distributed according to a load duration curve. 6

ISIC (International Standard Industrial Classification) is an industry classification system which bunches up establishments by their primary type of activity. 7

An interesting procedure for estimating useful demands in a developing country by using market share factors, has been proposed for the Indian MARKAL model [27]. 8

We used several studies, most of them quite old: [30] for series of cars and the [31] for estimations of average distances and specific consumptions. 9

The initial proposal to model risk under global environmental constraints in the context of the MARKAL model can be found in A. Haurie and R. Loulou [19]. Following these ideas E. Fragnieri and A. Haurie [15] of the University of Geneva applied stochastic programming to analyse different potentials of demand side management in order to achieve different CO2 abatement targets in the energy sector in the canton of Geneva.

10

With some regional differences in timing and amplitude, dry periods associated with the warm phase of ENSO (El Ni–o) and humid periods associated with the cold phase of this oscillation (La Ni–a) have been observed since 1525. The average recurrence of ENSO is of about four years, varing from two to ten years [29]. Colombia experienced one of the driest period of the century during 1991-1992. It caused a long period of electricity rationing which led to economic losses estimated about 1000 millions US$ [29]. In 1997-1998, the country scarcely avoided a repetition of the same impacts of this event thanks to the new installed capacity of gas plants and the system operation management lessons learned in 1992. By contrast, 1998-1999 was a very humid period that seriously reduced the electricity generation from coal plants, worsening the social problems in the interior mining regions. 11

The annual and the seasonal reservoir management constraints were included in the GAMS version and then a two-stage stochastic was generated for the annual and seasonal availability factors with the help of Gary Goldstein. 12

MARKAL-MACRO is a model of two-way linkage---energy demand and energy cost---between the energy sector and the balance of the economy. MACRO is a small neo-classical economic growth model. It takes an aggregate view of a closed economy. The basic input factors of production are capital, labour and useful energy. The model incorporates the possibility of energy conservation through substitution by non-energy production factors. In addition, there is the possibility of representing autonomous improvements in energy efficiency, due to non-price variables [18]. 13

Taken from O. Bahn, A. Cadena and S. Kypreos [3], pp. 311-312.

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14

First, on the formulation and implementation of a partial equilibrium MARKAL model [26], and second on the formulation of a multiregional MARKAL with trade (See S. Kypreos [23] for the formulation of a Global MARKAL and B. Bueler [7] for a formulation an implementation of a multiregional MARKAL-MACRO model to study an international market of CO2 emission permits). 15

The International Institute for Applied Systems Analysis (IIASA) has led the conceptualisation and analysis of the dynamics of technological change and its incidence on energy systems (See www.iiasa.ac.at/Research). At the same time, the ETSAP community and among them Paul Scherrer Institute [4, 25], have undertaken the inclusion of learning curves in the MARKAL framework. They have provided the MARKAL model `with a mechanism to represent path-dependence and self-reinforcing phenomena intervening in shaping the technological trajectories of the system' [4]. The preliminary evaluations show that the results are significantly different from those obtained with the classical version of MARKAL: ``New innovative technologies hardly considered by the standards models, are introduced to the solution when endogenous learning is present. Up-front investments in initially expensive, but promising technologies allow the necessary accumulation of experience to render them costeffective.''

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