Introduction to. Energy Environment Models

Transcription

1 Introduction to Energy Environment Models 1

2 Contents 1. Overview and Context Energy Environment models (EEMs) Top Down models Second Generation Model (SGM) Edmonds-Reilly-Barns (ERB) Model Bottom Up models Asia-Pacific Integrated Model- Enduse (AIM-Enduse) MARKAL Model Integrated modeling system Asia Pacific Integrated Model Structure of the AIM Model Mini Climate Assessment Model India energy environment IAM Bottom-Up Model Integration Top-Down Models and Linkages Other models Applications of models SGM application AIM integrated modeling application India IA model application A regional analysis application- South Asia Bibliography

3 1. Overview and Context Due to the complex and dynamic interactions between economy, energy and the environment, the study of integrated macro-economic, energy and environmental issues offers varied challenges and opportunities for contribution to policy research. Growth and structural transformations in the economy and alterations in production, consumption, investment, market relations, resources, technologies and institutional structure affect energy and environment trajectories. Policy issues explore the manner in which long term energy and environment trajectories are influenced by macro-economic effects such as energy price feedback, revenue recycling, economic growth, trade and evaluation of various policy measures like carbon or energy taxes. Developing countries like India can leapfrog the conventional development paths through policy decisions on infrastructure like rails and communications, renewables and energy efficient technologies, managing the urbanization pattern, location planning to promote lower logistics and educating consumers to influence the consumption behaviour. The long-term and global character of the energy system and the multiplicity of resources, technologies and uses tend to expose the energy decisions to uncertainties from energy prices, technological innovations, consumption behaviour, structural shifts in the economy and environmental impacts. Uncertainties arise due to the long-term nature of energy investments, global character of resources and environmental interface of energy use with inherently uncertain phenomenon like climate change. Investment decisions and policies need to incorporate these uncertainties to derive hedging strategy that minimizes risk and for developing preparedness plan to deal with extreme events. In the energy-environment systems, diversity exists at various levels such asregional energy resource availability and consumption, sectoral, temporal, emission-type, technology, and future energy paths. Diversity of emissions includes greenhouse gases (GHGs) like Carbon dioxide (CO2), Methane (CH4), Nitrous oxide (N2O), Chloro-fluoro carbons (CFCs), Per-fluoro carbons (PFCs) and Sulfur hexafluoride (SF6) and local pollutants such as Nitrogen Oxides (NOX), Sulfur dioxide (SO2), Suspended particulate matter (SPM) and Carbon mono-oxide (CO). Energy systems are characterized by the predominance of one or the other form of fuel like coal, oil, gas, nuclear or hydroelectric. 3

4 This fuel predominance is dictated by the available reserves or proximity to such reserves. Most carbon emissions in the world arise from use of coal in electric power and industry sectors. Carbon-di-oxide is a major contributor towards global warming. Rising particulate and SO2 emissions at local levels due to coal combustion is also a concern among policy makers. Integrated macro-economic, energy and environment paths need to address issues related to investment requirements and their availability, energy supply (indigenous availability vis-à-vis imports), technology R&D and transfer issues, local and global environmental implications, institutional requirements and capacity building measures, especially in developing countries. For instance, some specific policy concerns for India are rising demand supply gap in the power sector retarding economic growth, rising petroleum oil imports, low energy efficiency of industry as compared to international levels resulting in reduced competitiveness, and rising pollution levels in various urban centers and their adverse impact on human health. The synergy between economic policies, energy and environmental policies is therefore vital for sustainable national development that addresses the above concerns. Technology assessment and its representation in an economy poses a challenge and forms an integral part of the any model based analysis for addressing economic and energy policy issues. The complexity in technology representation arises from the diverse array of technological stock across sectors with widely varying characteristics. For electricity generation, say, the technologies have widely varying cost and performance parameters differing in type of input fuel, conversion efficiencies, capital investment requirements, scale economies, suitability to meet peaking loads, and pollutant emission levels etc. The dynamism in technological change is related to reduction in costs and performance improvements through learning, which affects the relative competitiveness of technologies over a period of time. Complexity also arises from the fact that most of the decisions have long-term implications (30 to 40 years) due to the inherent nature of the technologies on the energy supply side, especially for electricity generation. Energy prices are a critical factor affecting technology choices. Location specific factors like 4

5 land and labor prices also affect their relative costs. These diversities create non-linearity in technology grades (Kanudia,1998). The criteria for evaluation of technologies include economic and social considerations (average and marginal costs, capital and operating costs, opportunity costs, implications for development, effects on trade, interest and inflation rate, equity considerations); environmental consideration (local and global pollution abatement); administrative, institutional and political considerations (Dowlatabadi, 1995). System discount rates reflecting variations in the cost of capital affect the relative competitiveness of the technologies. Technology assessment needs to consider interrelated technical, economic and market deployment potential of the technologies. Complexities also arise in diversity representation of energy resource endowments. Different energy sources appear to be competitive at different geographical locations and times mainly depending upon the availability of the resource, extraction costs and transportation costs. This results in competition among multiple energy sources that results in their partial penetration. Coal is the mainstay of Indian energy system. However other energy sources also become competitive at different geographical locations and times due to interplay of fuel quality (mainly heat value, sulfur and ash contents), delivered fuel costs (extraction and transportation) and relative costs of conversion technologies. Therefore near Maharashtra and Gujarat coasts, imported natural gas may become competitive to high ash coal mined and transported from eastern India. The partial penetration of multiple energy sources may be represented by incorporating the energy source prices as probability distributions rather than the deterministic average values (Kainuma et al, 1997). Scenario analysis forms an integral part of the assessment of energy and environment policies. Each scenario is one alternative image of how the future can unfold and represent the interplay of certain economic, social, technological, environmental and policy paradigms. Scenarios are viewed as a linking tool, which integrate stories of the future, quantitative formulations to enhance understanding of how the system works and to capture systems behaviour and evolution. The complexities in 5

6 assessment of energy and environment policies lend itself to complex model-based analysis using diverse modeling approaches. 2. Energy Environment models (EEMs) Models can be used as 'forecasting tools as well as 'heuristic tools' but energy policy models tend to favour the latter view. A heuristic approach relies less on the actual model results and more on the story behind it. The qualitative judgments are termed as insights provided by the models for the policy makers. EEMs are powerful tools for simulating the present and future scenarios incorporating the likely impacts of GHG and aerosol emissions, macro-economic parameters, geographic and demographic characteristics of the region, energy usage patterns, technological mapping of various sectors, and socio-economic development patterns of the regions etc. The regions may cover global, regional, national, state or any other smaller level. The sub-systems/sectors may be terrestrial & aquatic ecological systems, human health, and socio-economic systems (e.g. agriculture, forestry, fisheries and water resources), economic sectors (e.g. energy, industry & infrastructure etc.). Alternate modeling approaches can be adopted for addressing the above energy and environment policy requirements- a single monolith model or an integrated framework employing diverse models, each with its inherent characteristics. A monolith model will have to establish complex inter-linkages across diverse economic, technological, social, environmental and energy sector variables. This hard linking, i.e. integrating model codes and runs is a very complex process. It corresponds with state-ofthe-art energy and emissions modeling practice. The hard linked bottom-up and top-down models are not available. MARKAL-MACRO model (Fishbone and Abilock, 1981) has such a linkage, however the top-down component in this model remains very weak. The literature on Integrated Assessment Models suggests enormous time and resource requirements for setting up such models. They are also not suitable for detailed analysis of individual components. An integrated modeling framework utilizes the inherent strengths of existing individual models that are best suited to address specific energy and environment issues. The models are soft-linked through consistent and similar 6

7 assumptions for all the models, a shared database and talking between the models. Multiple feedbacks are required among the models to ensure consistency of results and policy analysis. However a high level of skill is needed in integrating various models and it necessitates being conversant with the nuances of all the models for consistent integration. IPCC emission scenarios (Nakicenovic et al, 2000) used six models to project future emission scenarios viz. Asian Pacific Integrated Model (AIM), Atmospheric Stabilization Framework Model (ASF), Integrated Model to Assess the Greenhouse Effect (IMAGE), Multi-regional Approach for Resource and Industry Allocation (MARIA), Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) and Mini Climate Assessment Model (MiniCAM). These six models are representative of different approaches to modeling emissions scenarios and different integrated assessment (IA) frameworks. These models have projected global emission scenarios upto The present set of models can be classified under two paradigms (Figure 1), topdown and bottom-up models, depending on how the model captures the interactions between energy, environment & economy. Top-down models are aggregated and are general equilibrium models whereas Bottom-up models are detailed with representation of energy sources, conversion technologies and end use demands. These models are generally partial equilibrium models. Energy Models Bottom-Up Top-Down Technology Assessment Macro- Optimisation & Simulation Equilibrium Single-Country Multi-Region Figure 1: Classification of Energy Sector Models 7

8 2.1 Top Down models The top-down modelling takes a macro view of the modeled region and intends to reproduce macro level interactions between economic sectors. They have higher sectoral aggregation, but better characterisation of impacts on economic growth, price feedbacks, and trade (Hourcade, 1993). These models represent the macro-economic interlinkages between the aggregate production sectors of an economy, consumers, and the government. They are weak in capturing the technology details. GLOBAL 2100 model constructed by Manne and Richels in 1990, Dynamic General Equilibrium Model (Jorgenson and Wilcoxen, 1990), Second Generation Model (SGM), a computable general equilibrium (CGE) model (Edmonds et. al, 1993), and Edmonds-Reilly-Barns: ERB model (Edmonds and Reilly, 1983; Reilly et al., 1987; Edmonds and Barns, 1992) are some examples of top-down models. These models stress consistency of variables and parameters across sectors, however, they make exogenous assumptions relating to efficiency and social changes. A typical top-down model will help the policy maker in assessing the macro-economic impacts (overall change in GDP, consumption, investments, imports and foreign exchange) of a particular market instrument employed for GHG and aerosol emission mitigating (e.g. Carbon tax). Some of the Top-Down models like SGM and ERB are discussed below Second Generation Model (SGM) The Second Generation Model (SGM) is a computable general equilibrium (CGE) model in the top-down group of models. It is designed to provide estimates of future time paths of environmentally important emissions associated with economic activity, and provide estimates of the economic cost of actions to reduce greenhouse gas emissions (Edmonds et al 1993). The SGM is actually a collection of CGE models for fourteen world regions that can be run individually, or as a group that trades carbon emissions rights. The emphasis is on energy transformation and consumption, economic activity, and greenhouse gas emissions. The SGM projects economic activity, energy consumption and greenhouse gas emissions for each region in five-year time steps from 1990 through A representative SGM 2000 Model Flow Chart is depicted in Figure 2. 8

9 Figure 2: SGM 2000 Model Flow Chart. The SGM contains a large set of parameters to simulate technical change over time for any given production process. These parameters, individualized to production processes, influence the rate of change in efficiency of the inputs to production sectors in the model. The structure of the model resolves five issues: regional detail, energy sector detail, scope of human activities, temporal resolution, and theoretical description. Regional detail includes both individual countries and regional groupings. Many of the regional models, including Japan, China, India, South Korea and Brazil, were developed in collaboration with local institutions using local data. This approach has extended the value of the SGM for all users. 9

10 The SGM was designed to run either as an individual national (or regional) model, or as a set of models with trade links. The model uses 23 producing sectors, 25 consuming sectors, energy production detail, 12 regions, and a suite of anthropogenic greenhouse gases, all shown in Table 1. A sector may consist of several sub-sectors, each using a different set of technologies and fuel grades. Each sub-sector within a sector produces a homogenous good. The sectors and sub-sectors include Agriculture (primary agriculture, food processing); services; energy production (oil, natural gas, coal); energy transformation (coke, electricity, refined petroleum, gas distribution); energy-intensive industries (paper and pulp, chemicals, cement, iron and steel, non-ferrous metals, other industry); transportation (passenger transport, freight transport). The production relations are represented by constant elasticity of substitution (CES) functions or Leontief production function. Technological change is assumed to be "Hicks Neutral" and is exogenously introduced as change in total factor productivity. Economic growth occurs through enhanced factor supply and improved productivity, i.e. technological progress. Technological progress also results from the selection of new technologies. Investment in a sector (or sub-sector) in each period depends on the savings in the economy and expected profit in the sector. Investment allocation in the SGM is determined by a logit function. Capital assumes that once the investment occurs the technology cannot be changed. The model couples the vintaged capital stock with a "putty-semi-putty" representation and investments operate for life or till they cover operating expenses.. The capital stock is fixed once an investment is made, but the elasticity of substitution among other inputs may be reduced. The model was developed with recognition that energy production and use is the most important set of human activities associated with greenhouse gas emissions. The SGM employs a finite resource model with production out of reserves. Both reserves and resources are graded. The models that link with the SGM to produce a full set of impacts analysis are shown in Table 2. The data required for the SGM model include input-output tables, past capital investment pattern, labour supply, energy flows in the economy at sub-sector and technology level, reserves of resources, land supply, and current emissions. Carbon tax is modelled as an additive tax per ton of carbon 10

11 content of fossil fuels. Revenue from carbon tax is recycled to households by adding to income. It endogenously generates the macroeconomic information such as energy prices and loss of sectoral GDP and consumption. Table 1: Second Generation Model Input table. SGM Model Inputs Producing Sectors 1. Other Agriculture 9. Oil Refining 18. Freight Transport 2. Everything Else 10. Gas Distribution 18.1 Truck 3. Oil Production 11. Paper and Pulp 18.2, Railway 4. Gas Production 12. Chemicals 18.3 Sea 5. Coal Production 13. Cement 18.4 Air 6. Coal Products 14. Primary Metals 19. Residential Building Energy Services 7. Biomass 15. Food Processing 20. Commercial Building Energy Services 8. Electricity Production 16. Other Industry 21. Grains and Oil Crops 8.1 Oil-Fired 17. Passenger Transport 22. Animal Products 8.2 Gas-Fired 17.1 Automobile 23. Forestry 8.3 Coal-Fired 17.2 Railway 8.4 Nuclear 17.3 Sea 8.5 Hydro 17.4 Air 8.6 Biomass 8.7 Solar & Wind Consuming Sectors A household sector, a government sector, and the 23 producing sectors noted above Regions United States, Canada, Western Europe, Japan, Australia, Former Soviet Union, Eastern Europe, China, India, Mexico, South Korea, and Rest of World. Greenhouse Gases CO 2, CO, CH 4, NO x, and N 2 O Source: Pacific National Laboratory website, 11

12 Table 2: SGM model as part of PGCAM model in its Modules. PGCAM Modules Model Institutional Affiliation SGM PNNL The SGM is a computable general equilibrium model of energy, economic activities, and greenhouse emissions. EPIC Texas A&M Simulates crop growth and yield, runoff, erosion, and water quality HUMUS Texas A&M Simulates hydrologic cycle on scales varying from major river basins to local watersheds. BIOME-3 University of Lund, Sweden Description Inputs & Outputs References Simulates vegetation coverage and productivity of unmanaged ecosystems. Inputs: factor productivity growth rates by sector (9 in SGM 98) and region; capital stocks by vintage, demographic determinants (endogenous demographics), fossil and non-fossil fuel resources; Outputs: Energy supplies and demands by fuel (6 primary, 4 final) and region (12), greenhouse gas emissions, and economic activity. Temporal Resolution: 5-year time step. Spatial Resolution: 12 regions Inputs: Temperature, precipitation, solar radiation, humidity, soil type, and topography. Outputs: Crop yields, erosion Temporal Resolution: Daily Inputs: Same as EPIC (see above) plus current land use. Outputs: evaporation, runof Temporal Resolution: Daily Inputs: Temperature, precipitation, soil type. Outputs: Vegetation coverage, crop productivity Temporal Resolution: NA (steady-state) Spatial Resolution: 0.5o x 0.5o Source: Pacific National Laboratory website: Edmonds, Pitcher, Barnes, Baron, and Wise (1995); Fisher- Vanden et al. (1993); Sands et al. (1998); Sands et al. (2002). Williams (1995) Arnold et al., 1999; Srinivasan et al. (1993) Haxeltine and Prentice (1996) The output from the SGM is often summarized as a set of marginal abatement cost curves that show the relationship between a carbon price and emissions reduction relative to a baseline (Figure 3). Some SGM applications including the cost curves in Figure 3 is discussed further in this write-up in the Model applications section. 12

13 carbon price (1990 US$ per tc) reduction in US carbon emissions (million tc) Figure 3: Marginal abatement cost curves for carbon emissions from the U.S. energy system using PNNL SGM. Source: Sands (2002)-foragforum.rti.org/documents/Sands-paper.doc Edmonds-Reilly-Barns (ERB) Model ERB Model is a well documented, frequently used, behavioral, long-term model of global energy and fossil fuel greenhouse gas emissions. The model consists of four modules: supply, demand, energy balance, and GHG emissions. The first two modules determine the supply of and demand for each of the six major primary energy categories in each of the nine global regions of US, OECD West, Europe, Africa, Russia, Middle East, Japan-Australia-New Zealand, China and Asia (excluding India), and India. The six primary energy categories are coal, oil, gas, biomass, hydro and others. The energy balance module ensures model equilibrium in each global fuel market. Primary energy is assumed to be untraded; thus supply and demand balance in each region. The GHG emissions module is a set of three post-processors which calculate the energy-related emissions of CO2, CH4 and N2O. The original version of the model is documented in Edmonds and Reilly (1985), while major revisions are discussed in Edmonds et al and An ERB model flow chart is represented in Figure 4. Energy demand for each 13

14 of the six major fuel types is developed for each of the nine regions. Five major exogenous inputs determine energy demand: population; labour productivity; exogenous energy end-use intensity; energy prices; and energy taxes, subsidies and tariffs. The model calculates base gross national product (GNP) directly as a product of labour force and labour productivity. An estimate of base GNP for each region is used both as a proxy for the overall level of economic activity and as an index of income. The base GNP is, in turn, modified within the model to be consistent with energy-economy interactions. Each region has its own GNP feedback elasticity, allowing the model to distinguish energy supply dominant regions where energy prices and GNP are positively related, from the rest of the world where the relationship is inverse. For example, a substantial rise in world oil prices would tend to enhance the GNP in the Mideast, while reducing GNP in other regions. Regional Population Regional Labour Forces Regional Labour Productivities Regional GNP Technical Change Regional taxes and tariffs Regional Energy Demands Regional Prices World Prices Global Supplies and Demands CO 2 Regional resource constraints Technology and Cost Char. Regional Energy Supplies Figure 4: ERB model flow chart (Edmonds and Reilly, 1983) The exogenous end-use energy-intensity improvement parameter is a timedependent index of energy productivity. It measures the annual rate of growth of energy 14

15 productivity which would continue independent of such other factors as energy prices and real income changes. In the past, technological progress and other non-price factors have had an important influence on energy use in the manufacturing sector of advanced economies. By including an exogenous end-use energy-intensity improvement parameter, scenarios can be developed that incorporate either continued improvements or technological stagnation assumption as an integral part of scenarios. The final major energy factor influencing demand is the energy prices. Each region has a unique set of energy prices derived from world prices (determined in the energy balance component of the model) and region specific taxes and tariffs. The model can be modified to accommodate non-trading regions for any fuel or set of fuels. It is assumed that no trade is carried on between regions in solar, nuclear, or hydroelectric power, but all regions trade fossil fuels. The energy demand module performs two functions. One, it establishes the demand for energy and its services, and two, it maintains a set of energy flow accounts of each region. Oil and gas are transformed into secondary liquids and gases used either directly in end-use sectors or indirectly as electricity. Hydro, nuclear, and solar electric or fusion are accounted for directly as electricity. Non-electric solar energy is included with conservation technologies as a reduction in the demand for marketed fuels. The four secondary fuels i.e. liquid, solid gases and electricity are consumed to produce energy services. For all regions, energy demand is disaggregated into residential/commercial, industrial and transport sectors. The demand for energy services in each region s end-use sector(s) is determined by the cost of providing these services and by the levels of income and population. The mix of secondary fuels used to provide these services is determined by the relative costs of providing these services using each alternative fuel. The demand of fuels to provide electric power is then determined by the relative costs of production, as is the share of oil and gas transformed from coal and biomass. Energy supply is disintegrated into two categories, renewable and non-renewable. Energy supply from all fossil fuels is related directly to the resource base by grade, to the cost of production (both technical and environmental), and the historical production capacity. The introduction of a graded resource base for fossil fuel (and nuclear) supply 15

16 allows the model to explicitly test the importance of fossil fuel resource constraints as well as to represent fuels such as shale oil, where only small amounts are likely to be available at low cost, but for which large amounts are potentially available at high cost. Nuclear energy is treated in the same category as fossil fuels. Nuclear power is constrained by a resource base as long as light-water reactors are the dominant producers of power. Breeder reactors, by producing more fuel than they consume, are modeled as an essentially unlimited source of fuel that is available at higher cost. Modern biomass is treated as if its carbon absorption occurred in the year of release. This approximation can either under- or over-estimte actual net annual fluxes depending upon whether the underlying stock of biomass is either expanding or contracting. A rate of technological change is also introduced on the supply side. This rate varies by fuel and is expected to be both higher and less certain for emerging technologies. The supply and demand modules each generate energy supply and demand estimates based on exogenous input assumptions and energy prices. If energy supply and demand match when summed across all trading regions in each group for each fuel, then the global energy system balances. Such a result is unlikely at an arbitrary set of energy prices. The energy balance component of the model is a set of rules for choosing energy prices which, on successive attempts, bring supply and demand nearer to system-wide balance. Successive energy price vectors are chosen until energy markets balance within a prespecified bound. Given the solution of the energy balance component of the model, greenhouse gas emissions for CO2, CH4 and N2O are calculated by applying emission coefficients to the different fuels. The ERB model was initially calibrated to start in 1975, and uses 25-year intervals through to Later, the periodicity was changed to a 15-year interval and the parameters were suitably modified to conform to the specified energy consumption data for 1990 based on actual OECD energy balances. In the recalibration, the number of enduse consumption sectors for developing countries was expanded from one to three. Primary energy prices to clear 1990 markets were production-weighted averages of prices for the previous 15 years. 16

17 2.2 Bottom Up models Bottom-up models follow the optimistic engineering paradigm in that they presume the existence of an efficiency gap. They are more appropriate for policy analysis involving assessment of the impacts on technology and fuel mix within a sector involving detailed representation of fuel flow and technological linkages. They examine technology options in energy supply side and end-use sectors in terms of costs, fuel inputs and emission characteristics, with detailed representation of technologies. They also enable capture of geographical diversities in energy use and emission patterns within countries and address local, regional and global environmental concerns. These models can represent the diversity of macro-economic sectors and their consistent representation with macro-economic parameters and future demand projections (Shukla et al 2003b and 2003 e). A few Bottom-Up models like MARKAL and AIM (End-use) models are discussed below Asia-Pacific Integrated Model- Enduse (AIM-Enduse) AIM/ENDUSE is a bottom-up, dynamic end-use sector model. It considers demands for energy services, economic growth, sectoral structure, and then calculates the period-wise technology mix that minimizes the discounted sector-level cost. The cost includes capital, energy and material costs. AIM/ENDUSE model is based on the sectoral reference system which links energy service demands, service devices (technologies), and energy resources for the sector (Kainuma et al, 1997). It focuses on the end-use technology selection in energy consumption as well as energy production. It calculates future demand of energy services for several sectors, determines the optimal set of technologies for the demand by total cost minimization based on exogenous energy price, and then, it estimates future energy consumptions as well as future emissions of GHGs. AIM/Enduse was recently evolved to AIM/Local model which is linked to local emission inventories in order to simulate multi-gas emissions from large point source. AIM/Enduse was developed also for more comprehensive bottom-up model (AIM/Bottom-up) by adding industrial process module and lifestyle change module to the energy enduse model. 17

18 2.2.2 MARKAL Model MARKAL is a multi-period energy systems model suitable for national or regional analysis. MARKAL provides technology, fuel mix and investment decisions at detailed end-use level while maintaining the consistency with system constraints such as energy supply, demand, investment, emissions etc. (Loulou et. al, 1997; Manne and Wene, 1992). It is a dynamic model which consistently links the decisions over time. The model is driven by a set of demands for energy services. An important feature of MARKAL is the separate representation of peak and off-peak demands for electricity. Energy demand technology representation is fairly detailed. ECONOMY MINING IMPORT COLLECTION RENEWABLE EXPORT COAL N. GAS OIL BIOMASS NUCLEAR RENEWABLE TECHNOLOGY ELECTRICITY PRODUCTION COAL GAS HYDRO NUCLEAR SOLAR ENERGY FUEL PROCESSING PETROLEUM REFINERY GAS PROCESSING CAPITAL ENDUSE DEVICES PUMP TRACTOR FURNACE MOTOR LIGHT BULB COOLER BUS TRAIN STOVE FAN AGRICULTURE INDUSTRY COMMERCIAL TRANSPORT RESIDENTIAL EMISSIONS ENVIRONMENT Figure 5: MARKAL Model Flow Chart (Shukla et al, 2003c) The supply technologies in MARKAL represent different fuel mix and cost structure. The technology and fuel costs vary across nations, especially large nations like U.S. or India due to variable natural and location specific causes like resource endowment, logistics costs and factor costs. The cost for a resource or a technology is 18

19 therefore not a single number but a non-linear distribution which can be captured by the MARKAL model as a step-wise linear approximation through multiple grades for a technology or a fuel. This allows realistic competition among technologies and fuels. The model makes decisions, which minimize the discounted cost of energy system. MARKAL thus computes a partial economic equilibrium of the energy system, i.e. a set of quantities and prices of all energy forms and materials, such that supply equals demand at each time period (Loulou et al, 1997). A MARKAL model flow chart is represented in Figure Integrated modeling system No single model, for the time being, is fully global (i.e. represent all major world regions), and at the same time fully local (i.e. provide sufficiently detailed representation of each and every national system for detailed decision making). Various models try to approach such an ideal representation at the expense of some loss of detail and realism for individual country representation. Some of the details which a model based policy analysis has to address include investment requirements and their availability, energy supply (indigenous availability vis-à-vis imports) and their prices, technology R&D and technology transfer issues, local and global environmental implications, institutional requirements and capacity building measures, role of international collaborations, and socio-political implications. An integrated assessment (IA) of economic, energy and environment policies is thus imperative for progressing on a path towards a sustainable energy and environment future. Energy stocks have long life as once a technology becomes acceptable in the market, a lot of investment takes place around it in creation of institutional, technical and market infrastructure. This is expected to prevent overnight changeover to new energy technologies in a major way. Penetration of new and exotic technologies in the second half of 21st century, like nuclear fusion or hydrogen for power generation, are very longterm issues and are expected to offer very different solutions to climate change concerns in the long run and require in-depth analysis. The diversity of macro-economic sectors and their consistent representation with respect to macro-economic parameters and future demand projections also pose a modeling challenge. Similarly macro and micro level 19

20 policy issues have to be integrated. These include economic, technological and environmental issues. This underlines the importance of an integrated assessment of all the involved issues. Though the first trial of model development for IA is observed in the works of Meadows and Mesarovic in the beginning of 1970s, formal IA models (IAMs) emerged after the late 1970s. Nordhaus (1979), Haefele et al. (1981) and Edmonds et al. (1985) developed energy-economy integrated models for climate change assessment, and Alcamo et al. (1990) developed the first IAM to extend fully from emission to impact for acid rain assessment. In 1990s, IAM studies for climate change assessment have rapidly expanded, and there now exists more than twenty IA models for climate change alone. A selection of seven representative models with their characteristics is presented in Table 3. All of the seven models have the components of GHG emissions, climate change, and impacts caused by climate change, which are integrated for the purpose of future scenario assessments as well as climate policy assessments. IAM studies continue to be developed and the direction of these developments can be classified into the following three. First, modeling targets and phenomena are becoming wider and more detailed than before in order to respond to widened audiences, new policy needs, and new scientific knowledge. Very detailed climate change scenarios were prepared for IAMs (Hulme et al. 2002), special dynamic models of land use and land degradation are trying to be integrated with emission and impact models (Hootsman et al. 2001; Groeneveld et al. 2000), technology factors are trying to be introduced as endogenous variables in IAMs (Weyant and Olavson 1999), and pluralism in value system are trying to be operated and reflected to future projection in IAMs (Janssen et al. 1998; Asselt et al. 2002). Furthermore, institutional factors such as governmental regulations, international regime and cultural systems have been proposed to be incorporated with IAMs. 20

21 Table 3. Seven representative IAMs Model name AIM (Asia-Pacific Integrated Model DICE (Dynamic Integrated Climate & Economy model) IIASA (International Institute for Applied System Analysis IMAGE2.0 (Integrated Model to Assess the Greenhouse Effect) MERGE (Model for Evaluating Regional & Global Effects of GHG Reduction Policies) MiniCAM (Mini Global Change Assessment Model) TARGETS (Tool to Assess Regional &Global Environmental & Health Targets for Sustainability) Model type Large-scale policy assessment model Policy optimization model Large-scale policy assessment model Large-scale policy assessment model Policy optimization model Large-scale policy assessment model Strategic policy assessment model Model components emission climate impact Reference complex complex complex --- simple simple simple complex simple complex complex complex complex complex simple simple complex simple simple simple simple complex Noldhaus (1994) WEC/IIA SA (1995) Alcamo (1994) Manne et al. (1993) Edmonds et al. (1994) Rotmans et al. (1995) Source: Garg et al (2001) The second direction is to apply IAMs to participatory IA process where stakeholders including policy makers and scientists communicate with each other to recognize the priority of information and decisions. The typical examples of the participatory IAM application are ULYSSES (Urban Lifestyles, Sustainability and Integrated Environmental Assessment) project (Asselt et al. 2001), VISIONS (Integrated Visions for a Sustainable Europe) project (Rotmans et al. 2001), and COOL (Climate Options for the Long Term) project (Berk et al. 1999). These projects supported European scenario development process of long term environmental change and policy introductions with policy makers and citizens. The third direction of IAM development is to apply IAMs to regional and local assessment rather than global scale assessment. Lorenzoni et al. (2000) downscaled the IPCC global emission scenarios (Nakicenovic, 2000) into British impact assessment by 21

22 means of their IAM, Green et al. (2000) estimated co-benefit of air pollution abatement policies and global warming mitigation policies for several countries, Amann et al. (2001) tries to extend an IAM on acid rain (RAINS) to multi-pollution and multi-effect model including air pollutants of SO2, NOx, VOC, NH3 and to integrate it with country models (NIAM: National Integrated Assessment Model) to assess environmental impact in a national level (Johansson et al. 2001). Such new developments of IAMs increase the audience and users, and IAMs are been adopting in many important processes of environmental policy making as core tool to link science frontier to their processes. We now proceed to discuss some more detailed features of a few IA models like AIM, MiniCAM and the India IA model Asia Pacific Integrated Model The Asia-Pacific Integrated Model (AIM) is a set of computer simulation models for assessing policy options on sustainable development particularly in the Asia-Pacific region (Kainuma et al, 1997 and 2002; Matsuoka et al, 1995; Morita et al, 1994 and 1996). It started as a tool to evaluate policy options to mitigate climate change and its impacts, and extended its function to analyze other environmental issues such as air pollution control, water resources management, land use management, and environmental industry encouragement. More than 20 models have been developed so far, and they are classified into emission models, climate models and impact models from the viewpoint of climate policy assessment. The distinguished feature of AIM is that it involves Asian country teams from China, India, Korea, Indonesia, Thailand, Malaysia, Vietnam, Japan and so on, has very detailed description on technologies, and uses information from a detailed geographic information system to evaluate and present the distribution of impacts at the local and global levels. Besides preparing country models for detailed evaluation at the state and national level, AIM also helps develop global models to analyze international economic relationships and climate impacts for evaluating policy options from global viewpoint. AIM has several unique structures of model integration in addition to the emissionclimate-impact integration which is commonly observed in representative IAM models. 22

23 Integration between economic modules and environmental modules in order to analyze tradeoffs between Asian rapid economic growth and its environmental conservation, and to assess sustainable development policies in Asia; Linkage between top-down modules and bottom-up modules for comprehensive and consistent assessment of various policy options including top-down macro-economic policies and bottom-up technological options, which are both essential to solve the Asian complicated environmental issues; Linkage between short/middle-term simulation modules and long-tem simulation modules in order to integrate urgent Asian policies against current pollution and nature destruction issues with long-term policies for climate change mitigation; Linkage between country/local modules and world modules in order to design effective policies for regional collaboration in Asia, that include technology transfer and assistance in environmental investment desired by most of developing countries in the region. These structures were prepared to respond to actual requests from policy making processes. This has led to AIM s use in actual policy making process in the Asian region. Several Asian countries have adopted this model as an in-house model for policy assessment, and Environmental Ministers Congress for Asia and Pacific (Eco Asia) adopted AIM as core tool for policy design. AIM also contributed to other international activities: AIM was selected as reference model in the Special Report on Emission Scenarios (SRES) and in Third Assessment Report (TAR) both of Intergovernmental Panel on Climate Change (IPCC) and also in the Global Environment Outlook (GEO) of United Nations Environmental Program (UNEP). AIM simulation results were used by many other international organizations including OECD, ESCAP, ADB, UNU, and WWF. Recently, the AIM modeling team started to prepare new set of models for Millennium Ecosystem Assessment (MA) and Asia-Pacific Environmental Innovation Strategy Project (APEIS) Structure of the AIM Model The roadmap and the correspondence between the AIM models is shown in Figure 6. The original AIM is an integrated 'top-down and bottom-up' model and 23

24 comprises three main models - the greenhouse gas emission model (AIM/Emission), the global climate change model (AIM/Climate) and the climate change impact model (AIM/Impact) (Morita et al. 1993; Matsuoka et al. 2000). The several emission models including both top-down and bottom-up models developed to estimate greenhouse gas emissions and other related gases include: AIM/Emission AIM/Enduse (Chap. 5-10) AIM/Emission-Linkage AIM/Bottom-up AIM/Energy-Economics AIM/Land-Equilibrium (Chap. 2) AIM/CGE (Energy) (Chap. 4) AIM/Trend (Chap. 13) AIM Family AIM/Local Inventory activity AIM/Country AIM/Global AIM/CGE-Linkage AIM/Ecosystem AIM/Database (Chap. 14) AIM/Material (Chap. 11) AIM/Impact AIM/Land AIM/Water AIM/Agriculture AIM/Vegetation AIM/Health etc. (Chap. 3, 12) AIM/Climate Atmospheric model UD Ocean model Radiative forcing model GCM,RegCM interface Figure 6: Roadmap of AIM family Source: Kainuma et al (2002) AIM/Enduse model is a bottom-up energy model focussing on the end-use technology selection in energy consumption as well as energy production; AIM/Enduse was developed also for more comprehensive bottom-up model (AIM/Bottom-up) by adding industrial process module and lifestyle change module to the energy enduse model. AIM/Emission is a result of combination of three top-down models which are AIM/Energy-Economics, AIM/CGE (Energy), and AIM/Land-Equilibrium. AIM/Energy-Economics is a partial equilibrium model to analyze long-term energy demand and supply. It was mainly used for GHG emission forecast over next one century. AIM/CGE (Energy) is a general equilibrium model focused on energy sectors to analyze the relationship between emissions and international trade. It was 24

25 frequently used for the assessment of Kyoto Mechanism such as emission trade and Clean Development Mechanism. The third top-down model, AIM/Land-Equilibrium is a general equilibrium model focused on agriculture and forestry sector in order to analyze land use change based on international agricultural market. This model was applied to quantification of GHG emissions caused by land use change. AIM/Emission-Linkage: For the purpose of quantifying comprehensive emission scenarios for IPCC, a specific linkage model, AIM/Emission-Linkage was developed. Three models were combined in this model, those were, AIM/Bottom-up, AIM/Energy-Economics, and AIM/Land-Equilibrium, and then, comprehensive GHGs emissions as well as related gas emissions such as SO2, NOx were simulated based on the model. AIM/Climate: The estimated GHG emissions and related gas emissions are input into the AIM/Climate. Except for CO2, GHGs emitted into the atmosphere are gradually transformed by chemical reactions, which are calculated within the AIM/Climate. For short-life chemicals such as ozone and OH radicals, pseudoequilibrium state is assumed, and the oxidation and photochemical reactions of CH4 and other molecules are represented by simple kinetic equations. The absorption of CO2 and heat to ocean is calculated using an upwelling-diffusion (UD) model with the oceans divided into a surface mixed layer and an intermediate layer, which extends down to about the 1000 meters. Carbon cycle between atmosphere and terrestrial ecosystem is also reproduced by simple model which was validated by dynamic vegetation simulations. Global averaged surface temperature changes are calculated by a simple radiative forcing model which is linked to an energy balance/upwelling-diffusion ocean model. Regional distribution of climate parameters is estimated based on the experiments of GCMs and regional climate models. AIM/Climate includes specific interface between simple module of AIM/Climate and GCMs and regional climate models in order to interpolate regional climate distribution. AIM/Impact treats mainly the impact on water supply, vegetation change, primary production industries such as agriculture and forest products, and on human health 25

26 such as malaria spread. It can also be used to assess higher-order impacts on the regional economy. For these impact assessments, environmental and socio-economic data of the region have been gathered and filed in a geographical information system (GIS). The resolution of the data is from half degree to five minutes in latitude. AIM/Database for AIM/Emission as well as AIM impact has been developed to support the development and utilization of AIM models. Other models of AIM Family: Over the years of developing AIM models, a variety of new models, which are interrelated and interconnected, have been added to the AIM family. These new models are AIM/Trend, AIM/Material, AIM/CGE-Linkage, AIM/Ecosystems, AIM/Country, and AIM/Global. AIM/Trend is developed to project the basic trend of economy, energy and environment in Asia-Pacific region. It covers as wide a range of countries in Asia-Pacific region (42 countries), and it estimates environmental conditions through It uses simple method (econometric) and develops several scenarios for capacity building. AIM/Material intends to estimate economic and environmental effects of environmental investment. It assesses the effects of policy integration for comprehensive environmental problems including solid, water and air pollution. It achieves consistency of material flow along with economic activities. AIM/CGE-Linkage integrates original global CGE model with Material, Energy-economics and Land-equilibrium modules to assess global economic development taking into account the international markets. AIM/Ecosystem is an extended version of AIM/Impact and intends to evaluate not only the impact of climate change, but also other important environmental issues especially ecosystem assessment. It has several modules to estimate surface runoff, river discharge, potential crops productivity, vegetation classification, and health impacts. AIM/Country and AIM/Global models integrate various modules at country and global levels respectively in order to assess the country and global sustainable development. 26

27 2.3.2 Mini Climate Assessment Model The Mini Climate Assessment Model (MiniCAM) is a small rapidly running longterm, partial-equilibrium model (Figure 7) of the energy, agriculture, and climate system. It is an Integrated Assessment Model that estimates global GHG emissions with the ERB model (Edmonds et al., 1994, 1996b) and the agriculture, forestry and land-use model (Edmonds et al., 1996a). MiniCAM uses the Wigley and Raper MAGICC (Wigley and Raper, 1993) model to estimate climate changes, the Hulme et al. (1995) SCENGEN tool to estimate regional climate changes, and the Manne et al. (1995) damage functions to examine the impacts of climate change (Table 4). MiniCAM, developed by the Global Change Group at Pacific Northwest Laboratory, undergoes regular enhancements. Recent changes include the addition of an agriculture land-use module and the capability to estimate emissions of the full range of Kyoto greenhouse gases. ATMOSPHERIC COMPOSITION MAGICC Atmospheric Chemistry CLIMATE & SEA LEVEL MAGICC Climate MAGICC Ocean Carbon Cycle MAGICC--Ocean temperature sea level HUMAN ACTIVITIES ECOSYSTEMS ERB Energy System ERB Other Human Systems MAGICC Terrestrial Carbon Cyc. Un-managed Eco-system & Animals ALU Ag., L'stock & Forestry (none) Coastal System ALU Crops & Forestry ALU Hydrology Figure 7: MiniCAM model structure Regions: Presently the model consists of 14 regions: United States, US, Canada, W. Europe, Australia & New Zealand, Japan, Eastern Europe, The Former Soviet Union, China, Mid-East, Africa, Latin America, Korea, Southeast Asia, and India that provide 27

28 complete world coverage. In addition, three others are under development: Mexico, Argentina, and Brazil. It considers the major new alternative technologies that are pertinent to questions about the future structure of energy supply. The MiniCAM is used for modeling over long time scales where the characteristics of existing capital stocks are not the dominant factor in determining the dynamics of the energy system. Macro Economic Activity Level: MiniCAM uses a straightforward population times labor productivity process to estimate aggregate labor productivity levels. The resultant estimate of GNP is corrected for the impact of changes in energy prices using GNP/energy elasticity. For the scenario exercise, an extended economic activity level process was developed to allow a clearer understanding of the potential impacts of the new population scenarios. First, a detailed age breakdown was included so working age populations could be computed. Second, a labor force participation rate was added to estimate the labor force, and third an external process was created to estimate the longterm evolution of the rate of labor productivity increase. Energy Sector: The ERB is a partial equilibrium model that uses prices to balance energy supply and demand for the seven major primary energy categories (coal, oil, gas, nuclear, hydro, solar, and biomass) in all the fourteen regions in the model. This model has been discussed earlier in this note under the discussion on Top-Down models. Hydrogen has recently been added to the model, and it, like refined gas and oil, can be used to generate electricity or as a secondary fuel for the three final demand sectors. Agriculture Sector: Biomass is supplied by the agriculture sector, and provides the link between the agriculture, forestry, and land-use module and the energy module. The former module estimates the allocation of land to one of five activities (crops, pasture, forestry, modern biomass, and other) in each region. This allocation reflects the relative profitability of each of these uses. Profitability is determined by the prices for crops, livestock, forest products, and biomass, which reflect regional demand and supply functions for each product. There are separate technical change coefficients for crops, livestock/pasture, forestry, and modern biomass production. 28

29 Table 4: Component models of the MiniCAM system model Model & Institutional Affiliation Description Inputs & Outputs References ERB by PNNL The Edmonds- Reilly-Barnes (ERB) model is a market equilibrium model of the energy and economic systems. Inputs: labor productivity growth; population, fossil and non-fossil fuel resources, energy technologies (69) and productivity growth rates; Outputs: Energy supplies and demands by fuel (9 primary, 5 final) and region (14), greenhouse gas emissions (CO 2, CH 4,N 2 O, SO 2 ), and economic activity. Temporal Resolution: 15-year time step. Edmonds and Reilly (1985); Edmonds, Reilly, Gardner & Brenkert (1986);Edmonds, Wise, Pitcher, Wigley & Mac- Cracken (1997); AgLU by PNNL The Agriculture- Land-use Model (AgLU) is a market equilibrium model of the land use. Inputs: Income, population, regional climate, initial land-use allocation, productivity growth rates, and biomass energy price. Outputs: Agriculture and forestry production; greenhouse emissions; land-use and land-use emissions, agricultural prices and land rental rates. Edmonds, Wise, Sands, Brown, and Kheshgi (1996a), Sands and Leimbach (2002). Temporal Resolution: 15-year time step. MAGICC by NCAR MAGICC is an integrated model of the carbon cycle, atmospheric chemistry, radiative forcing, sea level, and global mean climate change. Inputs: Emissions of greenhouse gases; historic atmospheric composition, climate feedback parameters, ocean inertia; Outputs: Concentration of greenhouse gases; radiative forcing; global mean temperature; sea level. Temporal Resolution: 1-year time step. Hulme and Raper (1993); Wigley (1994a,b); Wigley and Raper (1987, 1992, 1993, 2001). SCENGEN by NCAR SCENGEN models the regional pattern of climate change. Inputs: Global mean temperature; sulfur-dioxide emissions; Outputs: Geographic patterns of temperature and precipitation change. Hulme, Jiang, and Wigley (1995) Temporal Resolution: NA (steady-state) Global Runoff by U.CO Global hydrologic model. Inputs: Monthly precipitation and average temperature Soil type, and Holdridge biome type Outputs: Monthly runoff in either mm or cubic kilometers for each 0.5o x 0.5o grid cell, aggregated either to river basin (about 180) or MiniCAM region. Temporal Resolution: NA (steady-state) Source: Bandy (1998) 29

30 Emissions: Once the model has reached equilibrium for a period, emissions of GHGs are computed. For energy, emissions of CO2, CH4 and N2O reflect fossil fuel use by type of fuel, while agriculture emissions of these gases reflect land-use change, the use of fertilizer, and the amount and type of livestock produced. The high global warming gases (chlorofluoro-carbons, hydrochlorofluorocarbons, hydrofluorocarbons, and perfluoro-carbons) are estimated only for each category and not by their individual components. Sulfur emissions are estimated as a function of fossil fuel use and reflect sulfur controls, the effectiveness of which is determined by a Kuznets curve that relates control levels to per capita income. Climate Change and Impacts: The emissions estimates are aggregated to a global level and used as inputs to MAGICC to produce estimates of GHG concentrations, changes in radiative forcing, and consequent changes in global mean temperature. The global mean temperature change is used to drive SCENGEN-derived changes in climate patterns and to produce estimates of regional change in temperature, precipitation, and cloud cover. Finally, the regional changes in temperature are used to estimate market and non-market based damages. Developing region damage functions produce higher damages than those for developed regions, reflecting the higher vulnerability of regions with low per capita income India energy environment IAM An integrated modeling framework used for energy, economy and emissions mitigation analysis in India is shown in figure 8. This framework has three modules; the top-down models, the bottom-up models and other models. These three modules are softlinked through various parameters. For example, the top-down models provide GDP and energy price projections that are used as inputs to the bottom-up models. The bottom-up models, on their part, provide future energy balance that is used for tuning the top-down models. Such multiple feedbacks ensure that the results from both the modules are in congruence. Similarly the bottom-up models provide detailed technology and sector level emission projections that are used for health impact assessment. These projections along with future scenario assumptions also provide inputs to the GIS based energy and 30

31 emissions mapping for the country. The other models provide health costs to the economy and these help in analyzing local pollution control policies through the bottomup models. The objectives, outputs and policy issues addressed by each model are indicated in table 5. Each module consists of multiple individual models. The top-down module uses two models namely Second-Generation Model (SGM) and Edmonds-Reily-Barnes model (ERB). In the instant case, the long-term analysis of India s energy and emissions profiles is done using the ERB model. Linkage between the models is soft, in the sense that the consistency is attempted by reconciling the scenario inputs for each model. For instance, for a given scenario, the end-use demands that are used as inputs for bottom-up models are projected using the sectoral GDP projections from SGM which in turn drive the Demand projection model. The models are however not hard-linked. The bottom-up module integrates five individual models: MARKAL - an energy systems optimization model (Berger, 1987; Garg, 2000a; Rana 2001; Shukla 1996a, 1996b and 2002a) which is used for overall energy system analysis, AIM/ENDUSE model (Alcamo,1994; Kainuma et al, 1997; Morita et al, 1994, Matsuaka et al, 1995) which is a sectoral optimization model used to model fourteen end-use sectors, A demand model which projects demands for each of the thirty seven end-use services, Stochastic MARKAL model for uncertainty analysis, Power sector linear programming (LP) model for regional analysis. Each of these models address specific questions and complement one another for a comprehensive analysis of energy and environmental concerns. The demand projection model, for example, provides end-use demands to the MARKAL and AIM/ENDUSE models that are demand driven. The integration of demand and supply within a bottom-up framework is achieved through a soft linkage of MARKAL with the AIM/ENDUSE model whereby the output of each end-use modeling exercise using AIM/ENDUSE is exogenously passed to the MARKAL model as an input. The power sector LP model is 31

32 used for regional analysis of the power sector in India as well as for the regional energy system analysis for South Asia. TOP DOWN MODELS SGM Productivity Global Energy Prices ERB Model GDP Prices BOTTOM-UP MODELS Energy Balance MARKAL Scenarios Stochastic MARKAL Technology Details End-use Demand Technology Share Power Sector LP Model Demand Projection End-use Demand AIM/ENDUSE Technology Specifications Health Costs OTHER MODELS Inventory Estimation Model Emissions GIS based Energy & Emissions Model Health Impact Model Figure 8: Soft-linked Integrated Modeling Framework for India (Garg, 2000a) 32

33 Table 5: Characteristics of models used in Integrated Modeling Framework Model Objective Output Policy Analysis Bottom-Up Models End-Use Demand Projection End-use Sector Demand Trajectory AIM/ENDUSE MARKAL Stochastic- MARKAL Top-Down Models SGM Demand Projections consistent with macroeconomic scenario Minimize discounted sectoral cost Minimize discounted Energy system cost Minimize expected value of discounted system cost Determine market clearing prices for economic sector outputs ERB Determine Global / Regional Energy Prices and Energy Use Other Models Inventory Estimation Model GIS Based Energy and Emission Model Power Sector LP Model Health Model Impact Estimate national emission inventory for various gases Determine regional spread of energy and emissions Minimize discounted Power sector cost Estimate local pollutant emission impacts on human health Sectoral energy, and technology mix, investments and emissions National energy and technology mix, energy system investments, and emissions Energy and technology mix under uncertain future, Value of information GDP and consumption trajectories;, prices of sectoral outputs and energy; sectoral investment patterns Long-term global and regional energy prices, energy mix and emissions National emission inventory Regional maps Power plant capacity and generation mix, emissions profile, total costs Impact of individual plants, per capita and total national human health impacts, sensitivity analysis Sectoral investment, technology and infrastructure policies Sectoral technology, energy, investment and emissions control policies Energy sector policies like energy taxes and subsidies; energy efficiency; emissions taxes and targets Hedging strategies for energy system investments; identify information needs Macro-economic impacts of policy interventions such as energy tax / subsidies; emissions limitations Implications of very longterm global energy resource, tech. expectations Regional and sectoral emission variability, benchmarking, emission hot-spot assessment Linking energy and environment policies across time and space Power sector technology, energy, investment, emissions control policies Plant location and stack height policies, emission norm analysis, enforcement policy assessment 33

34 This integrated model based analysis, which spans four decades, examines a reference scenario and several other policy scenarios like carbon mitigation, local pollution control, grid integration, regional co-operation etc. This provides insights like implications of mitigation commitments on energy and technology mix, energy costs, mitigation costs and competitiveness of Indian industries. The ensuing sections describe how the integration was brought about in the India IA model Bottom-Up Model Integration Whereas figure 8 showed the integration between bottom-up and other model modules, figure 9 expands the soft-links among the components of the bottom-up module. Though the time horizon of this model is forty years, from 1995 to 2035 (Garg et al, 2000a, 2001 and 2003a; Shukla, 2002b and 2003c) however, ANSWER version of MARKAL permits extending it further up to 100 years (Nair, 2003). For each period, the MARKAL model decides the energy and technology for forty years while minimizing the discounted capital and energy cost. End-use sectors are modeled separately for two reasons. First, the technology selection in each end-use sector is determined by the sector-wide objective. Second, separate modeling of each end-use sector allows detailed representation of technologies within the sector. The AIM/ENDUSE model is adapted for modeling of end-use sectors. AIM/ENDUSE selects the technology mix within each enduse sector while minimizing the discounted costs of capital, energy and materials over a forty years horizon. 34

35 Energy System Optimization Model (MARKAL) GMARKAL ANSWER Stochastic MARKAL Long-Term Demand Projection Model End-use Demands Sectoral Technology Shares Power Sector LP Model Industry Steel Cement Sugar Transport Agriculture Residential Commercial Road Rail Urban Rural Ship Air Textiles Fertilizer Aluminum Chlor-Alkali Brick Paper Others End-use Sub-Sector Models (AIM/ENDUSE) Figure 9: Soft-linked Bottom-up module (Kapshe, 2002) This technology mix for each end-use sector is provided as an input to MARKAL together with exogenous bounds on technology penetration. For each end-use sector, the technology mix thus gets selected via an end-use sector model that is soft-linked to MARKAL. Such an integration of bottom-up models facilitates consistent and detailed assessment of technology policies. The long-term end-use demand projections are exogenous inputs to energy system model and end-use sector models. These are projected using logistic regression (Kanudia, 1996) in a manner, which ensures macroeconomic consistency. The AIM/ENDUSE model considers demands for energy services, economic growth, sectoral structure, and then calculates the period-wise technology mix that minimizes the discounted sector-level cost. The cost includes capital, energy and material costs. AIM/ENDUSE model is based on the sectoral reference system which links energy service demands, service devices (technologies), and energy resources for the sector (Kainuma et al, 1997). Indian AIM/ENDUSE model is developed for fourteen end-use sectors, including ten industries, transport, agriculture, urban and rural households, and 35

36 services sectors. The choice of these sectors is based on their importance in the national energy consumption. Each sector is modeled with considerable technological details about consumption of different energy forms, emission of various gases, cost components, and technological shares. AIM/ENDUSE model provides a solution where an end-use sub-sector optimizes its cost with an exogenously given state of the supply side and other demand sectors. Hence, the output from the Indian AIM/ENDUSE models are used to introduce exogenous technology penetration bounds in the Indian MARKAL model. This approach allows scope for response of an end-use sector to the changes in the rest of the economy, and helps in achieving partial equilibrium of the energy system. The Indian MARKAL (Kanudia, 1996; Garg, 2001; Shukla, 1996a, 1996b, 2003a and 2003b) is set up for the forty-year period spanning years It has been updated to represent 235 present and future technologies (Table 6). Demand technologies are identical to those in the AIM end-use sector model. Supply technologies fall under two categories: electricity generation technologies and oil refineries. There are 22 different types of electricity generating technologies (pulverized coal, gas turbine, large hydro, nuclear fission etc) and one refinery technology representing different fuel mix and cost structure. The future technological progress is assumed to consist of three factors: autonomous energy efficiency improvement, improvements in present technologies and investments in new technologies. Bounds on penetration of demand technologies are set around the optimal technology mix trajectories obtained from the AIM/ENDUSE model. Table 6: Technology specification in the models Sectors Sub-Sectors Technologies Grades Industrial Residential Agricultural Transport Commercial Power Sector Oil Refinery Total Source: Shukla (2003c, 2003e) 36

37 Stochastic MARKAL model is used to handle the uncertainties facing the energy policy decisions arising out of the long-term and global nature of the energy system and the multiplicity of resources, technologies and uses. Some of the uncertainties include i) the future prices of energy, ii) rates of technological change, iii) consumption behavior, iv) changes in the structure of the economy, and v) nature of environmental impacts. These uncertainties often have greater consequences on energy system decisions in developing countries due to their rapid growth rates, institutional weaknesses and scarcity of capital. The Indian Stochastic MARKAL model (Shukla, 1996a and 1997b) uses a multi-stage recourse framework. The stochastic MARKAL model minimizes the discounted expected cost of the energy system. The uncertainties are modeled for three parameters - economic growth, price of natural gas and limitations on carbon emissions from India. The analysis suggests strategies for energy sector investments. Stochastic MARKAL also computes value of information, i.e. the value of resolving uncertainties, which is an important policy input Top-Down Models and Linkages The top-down models are appropriate tools to endogenously derive the macroeconomic indicators for a reference future. These models endogenously specify the economic variables such as the future investments and prices. Besides, these models can also compute the changes in macro-economic indicators over the reference scenario resulting from alternative scenario specifications. This information is essential to recompute some input parameters for the bottom-up models so as to make the results of top-down and bottom-up exercises constant. The Indian IA model uses two top-down models for energy and carbon mitigation analysis - i) Second Generation Model (SGM), and ii) Edmonds-Reilly-Barns (ERB) model. The SGM is used for national level analysis. ERB is a global energy systems partial equilibrium model with nine regions, where India is specified as a separate region. The ERB analysis provides a long-term energy and emissions trends (105 years horizon from 1990 to 2095) for India under different scenarios that are specified globally. A detailed discussion on ERB and SGM has been dealt within the discussion on Top-Down models earlier in this write-up. 37

38 Other models Other models used in the Indian IA model include: Inventory Estimation Model: The emissions inventories are traditionally reported on a cumulative national basis or for geographical grid dimensions. Although national level emissions for India would provide general guidelines for assessing mitigation alternatives, they fail to capture the regional and sectoral variability in Indian emissions. Districts reasonably capture this variability to a fine grid since 80% of these districts are smaller than 1ox1o resolution with 60% being smaller than even 1/2ox1/2o. Moreover districts in India have well established administrative and institutional mechanisms that would be useful for implementing and monitoring mitigation measures. It should however be noted here that the mitigation policies are presently formulated at the national level and the implementation can be monitored at the district level using the existing institutional frameworks innovatively. Therefore district level emission estimates offer finer regional scale inventory covering the combined interests of the scientific community and policy makers. Geographical Information System (GIS) Based Energy and Emissions Model: GIS is a computer-assisted system for the acquisition, storage, analysis and display of geographic data. GIS is a useful policy tool for regional analysis of energy use and emission patterns. The district level data has been linked to the district topology for India available in GIS package, converted into per unit area for each of the 466 districts and plotted (Garg, 2000b and 2000c). The analysis of regional and sector specific gas inventories contributes to effectiveness of emissions mitigation by indicating the hotspot locations and sectors where controls can lead to maximum benefits. GIS also assists in identifying energy and environment zones in India that would offer a mechanism to monitor these hotspots in future. These zones may be formed on the basis of dominant energy and emission forms, energy consumption per capita, total annual energy consumption and emissions, average annual concentrations, growth rates of emissions, mitigation possibilities etc (Garg, 2000b). Power Sector Linear Programming (LP) Model: The scenario analysis for grid integration and regional co-operation in power sector uses a LP model that determines 38

39 the optimal combination of new plants needed to meet given levels of power demand (Shukla, 1996b). The modeling framework uses a detailed, bottom-up representation of technologies (Figure 10). It allows constraints on fuel availability, emissions, investments, and technology improvements that mimic policy measures and set limits over which values can be obtained. The objective function of this least-cost optimization model minimizes the entire system cost including power generation costs, coal cleaning and transportation costs, electricity transmission costs, and pollution control internal and external costs. Representation of the separate regions captures the variation in energy availability, demand patterns, and fuel cost. Simulation begins with a base year (1995) and then determines the amount of new capacity from each type of power plant needed to meet demand over 5-year intervals till User Inputs Exogenous Power Plant Characteristics (cost, performance, emission control) Fuel Characteristics (cost, heat value, composition) Transmission Grid Characteristics (cost, geometry, performance) Levelized Cost Calculations Least-Cost Optimization of New Power Plants Power Demand Fuel Availability (coal, gas, oil) Emission Caps or Limitations Renewable Energy Availability (hydro, wind, bio) Environmental Damage (Optional) (emission externalities) Existing Power System (capacity, generation, emissions, plants under construction) OUTPUT: Power Plant Capacity Mix, Emissions Profile, Total Costs Equipment Manufacturing and Import Limitations Figure 10: The Power Sector Linear Programming Model (Garg, 2000a) 39

40 Health Impact Model: The impacts of energy sector activities on human health are linked to the emissions of local air, water and solid pollutants. These emissions alter the existing ambient levels of local pollutants. Human populations, living species and even buildings exposed to these altered concentrations are effected in varying degrees. Human health impacts of local air pollutants, namely Suspended Particulate Matter (SPM), SO2 and NOX is maximum out of all kinds of air pollutants (WB, 1995 and 1997). These health impacts, also called the Burden of Disease (BOD), are estimated in terms of mortality (premature deaths) and morbidity (sickness and lower activity levels). These are modeled for representative new plants in power, cement, steel, aluminium, sugar and brick industries. These sectors are chosen since they have the highest emission levels among various sectors. These impacts are then extrapolated to national level under suitable assumptions. This model estimates the emission loads from a new plant, converts them into changes in ambient concentration levels using a dispersion model, estimate the exposed human population, use dose-response functions to estimate incidences of mortality and morbidity, and then project health impact values based on their specific health cost estimations for various incidences of disease. In reality each of these are very complex processes. For example, the first two steps involve interaction between multiple factors like meteorological parameters (wind directions and speed, temperature, humidity, cloud cover, time of the day etc), stack height, existing ambient concentration levels etc. The modeling requirements are also therefore very site specific and stringent. A combination of widely accepted Gaussian Dispersion and Rectangular Box models is developed to take advantage of their inherent strengths as well as to keep the analytical framework simple Garg, 2000a). Gaussian Dispersion model is used to first determine the mass distribution profile of emissions as one move away from the emission source. Box model then converts these into average increase in ambient concentration levels (micro gm/mt3) in various concentric circles up to 50 km away from the source. This two-step methodology of estimating emission load distribution profile and change in particulate concentration levels accounts for the effects of varying wind circulation patterns. 40

41 The methodological aspects and specific characteristics of the models used in the Indian integrated modeling system are summarized in Table 7. Table 7: Computational details of the models Model Scaling: Time, Space and Sector Computation Method Bottom-Up Models End-Use Demand Projection AIM/ENDUSE MARKAL Stochastic-MARKAL Top-Down Models SGM ERB Other Models Inventory Estimation Model GIS Based Energy and Emission Model Power Sector LP Model Health Impact Model Horizon: 40 years Space: National Sector: End-Use Horizon: 40 years Space: National Sector: End-Use Horizon: 40 years Space: National Sector: Energy Horizon: 40 years Space: National Sector: Energy Horizon: 60 years Space: National Sector: Economy Horizon: 105 years Space: Global Sector : Energy Horizon: Annual Space: National Sector : All Horizon: Annual Space: National Sector : All Horizon: 20 years Space: National Sector : Power Horizon: Annual Space: Plant level Sector : All Logistic regression Linear programming (with step-wise linearization of non-linear functions) Linear programming (with step-wise linearization of non-linear functions) Stochastic-Linear Programming Computable General Equilibrium Non-linear Energy System Partial Equilibrium Linear summation Software packages Linear programming Combination of Gaussian Dispersion and Cylindrical Box models 41

42 3. Applications of models 3.1 SGM application As indicated earlier in this write-up, the output from the SGM is often summarized as a set of marginal abatement cost curves that show the relationship between a carbon price and emissions reduction relative to a baseline. Figure 3 displays several marginal abatement cost curves from the U.S. component of SGM (SGM-USA) where a carbon price was applied in 2010 and held constant thereafter 1 (Sands et al, 2002). Reductions in carbon emissions relative to a baseline case are plotted against the carbon price for years 2010 through Even with a constant carbon price, the marginal abatement curves shift outward, or to the right, over time for two reasons: it takes time for capital stocks to turn over in the model, and carbon emissions are steadily increasing in the baseline case. Several factors influence the slope of the marginal abatement cost curves, especially elasticities of technical substitution in production, and consumer income and price elasticities of demand. Another factor is the share of coal in the baseline energy system. The area under a marginal abatement cost curve is often used to approximate the cost of a carbon mitigation policy. The baseline case in SGM-USA is constructed to generally follow projections from the U.S. Energy Information Administration (USEIA, 2002) until 2020, and then continue with the same rates of change in energy and labor productivity through The SGM model, along with many other leading econometric models, participates in the Stanford-based Energy-Modeling Forum (EMF). The EMF s often compilation of the results of these models shows the SGM in the middle of the pack of estimates of the costs of Kyoto, when standardizing on the policy experiment under consideration. The SGM s estimates of costs in 2010 are near the median, in the experiment with no international trading of emission credits or in the case with Annex I trading. With full global emissions trading, SGM cost estimates are a bit below the median, but far from the lowest of the models (Frankel, 2000). 1 One-half of the final carbon price was applied in 2005 to approximate a gradual introduction of a carbon policy. The 2005 time step in SGM can be thought of as covering years

43 Frankel further reports that on running SGM with international emissions trading, the important quantitative findings which support the contention that cost of meeting Kyoto commitments for the United States of America would be modest, are as follows: Full and successful implementation of Annex I trading would reduce costs by onehalf, relative to a situation where each country had to satisfy its commitment domestically. Full and successful implementation of global trading (including developing countries) would reduce costs by 80-87%, again relative to the no-trading case. This illustrates the importance of developing country participation in any future emissions trading regime. Global emissions trading would reduce resource costs to an estimated $7-$12 b/yr in 2010, which is 0.1 % GDP in The effect on the price of carbon is estimated at $14-$23/ton. The effect on the energy bill of the average U.S. household is estimated at $70- $ AIM integrated modeling application Results from the application of AIM model integrated assessment by Kainuma et al (2002) are presented below: Findings from global assessment: 1. Without the Kyoto target, the global temperature would increase by more than 2 C in In such a case, severe impacts can be predicted on water resource supply, agriculture productions, vegetation and human health. 2. Their impacts would be significant and with serious negative damages in the low latitude regions, especially developing countries in tropical and sub-tropical zones, while climate change could cause positive effects in the high latitude regions. 3. Future development of the Asia-Pacific region has significant influence on global emission scenarios. The Developing Asia-Pacific becomes dominant in climate change issue. 4. Different development paths require different technology/policy measures and 43

44 show different costs of mitigation to stabilize atmospheric CO 2 concentrations at the same level. No single type of measure will be sufficient for the timely development, adoption and diffusion of mitigation options for CO 2 stabilization. 5. It is predicted that ratification of the Kyoto Protocol may cause economic impacts. However there are several ways including technological development and international cooperation to mitigate the economic impacts and a possibility of promoting the growth of economies. Findings from regional and country assessment 1. It could be possible for the Developing Asia-Pacific to continue high economic growth while maintaining GHG emissions at a low level. Technological progress and technology transfer should be emphasized to maintain low GHG emissions in the Developing Asia-Pacific's economic development. The market mechanism is an efficient way to achieve the diffusion of advanced technologies. 2. It is important for the Developing Asia-Pacific to introduce sophisticated measures to control GHG emissions before Robust policy options should be designed to respond to very wide range of alternative development path. In China, sophisticated policies should be designed at the sectoral level, especially in transport, commerce and the chemical industry. 3. New environmental policies are required that are designed for the early stages of development in China and India in order to integrate strategies for both the global environment and local environment including pollution control and waste management. Although under a SO2 mitigation policy regime, the SO2 and CO2 trajectories get decoupled in India, extents of SO2 mitigation and CO2 mitigation are strongly correlated under a CO2 mitigation policy regime. 4. Long-term adaptation investment for climate change could create co-benefit in other policy fields. Chinese current flood damage could be drastically reduced by the adaptation investment in flood prevention infrastructure to mitigate climate change impact. 5. The LPS would continue to be responsible for considerable part of the carbon 44

45 emissions. Power sector is the predominant emission source for CO2 and SO2. Operational improvements (like heat rate reduction, excess air control etc.), better maintenance, reducing transmission and distribution losses in the power sector would go a long way in emissions mitigation in India and China. 6. Energy savings or low CO2 emitting devices could be difficult to introduce into the market in every sector by 2020 without any climate policy measures in Korea. The marginally higher cost of new low CO2 emitting devices is too large for them to penetrate the Korean market. 7. Japanese cost to reach Kyoto target depends on Japan s future development pattern and climate policy design. The development toward Recycle-based Society (B1) could reduce cost and lead economic growth. Japanese cost could also be reduced by increased environmental investment, environmental industry encouragement, technology improvement, integration with waste management policy, shift to green consumption, and introduction of Kyoto mechanism. 8. International collaboration for capacity building and knowledge transfer on IA and IAM is essential for global participation in climate change mitigation. 3.3 India IA model application The integrated modeling application in India provides insights to the energy and environmental policy concerns. It integrates ten models in a consistent framework and demonstrates the methodological soundness of the analysis. The results of the models and scenario analysis generate information required for strategic policy analysis. The topdown results in the model are consistent with the bottom-up model results. Garg (2000a, 2001 and 2003a), Ghosh (2001), Kanudia (1996 and 1998), Shukla (1996a, 1996b, 1997a, 2001a, 2002b, 2003c and 2003d) have used this model for useful policy insights into energy planning and emission mitigation. A typical output from the demand projection model is shown in figure 11 below. It projects the end-use demand for cement sector up to Such projections are input to AIM/ENDUSE and MARKAL models. The AIM/ENDUSE model optimizes the 45

46 technology fuel mix for individual sectors. Figure 12 is a typical output indicating the technology selection for the Indian cement industry. These outputs are used to provide initial exogenous bounds in the MARKAL model as explained earlier. The technology selection under the carbon mitigation scenario is a MARKAL result and indicates higher penetration of less carbon intensive technologies when a carbon tax is employed. This figure also demonstrates the usefulness of integrated modeling system for policy analysis Reference High Growth Low Growth Demand(mt) Year Figure 11: Cement sector demand projections 46

47 Reference scenario Moderate carbon mitigation scenario Share Year Wet kiln Dry kiln with Precalcinator Advanced Dry Kiln Year Dry kiln Figure 12: Technology selection dynamics in cement industry under different carbon mitigation scenarios The MARKAL model mainly provides energy system optimization results. Some important results include future projections for the Indian energy system indicating a three times increase in the energy demand in the reference scenario over a period of forty years. Coal continues to dominate the Indian energy sector, although its share in the commercial energy consumption reduces from above 60 percent in 1995 to 53 percent in However coal use becomes more efficient and cleaner due to higher penetration of clean coal technologies in future and it partly offsets reduction in coal share. The decline in coal share is mainly due to coal to gas substitution, mainly in the power sector, with the natural gas share rising from the present 7 percent to 12 percent in forty years. The sectoral fuel consumption (Table 8) indicates continued dominance of power sector in coal use and transport in petroleum products with each having 70 percent share in Power sector share in natural gas consumption increases to more than half from the present one fourth, caused by increasing competitiveness of Combined Cycle Gas Turbine technologies (CCGT) for electricity generation. Absolute gas consumption also rises in other industries like fertilizers and petro-chemicals. While the share of gas in 47

48 primary energy still remains low, the trends suggest a rising penetration of natural gas that emerges as the main hedging option to coal in future. These are results from the MARKAL model. Table 8: Sectoral fuel consumption shares (%) (MARKAL model results) Fuel (PJ) Sector Coal products Power Steel Cement Brick Paper Textile Transport Residential Petroleum products Transport Industry Natural gas Power Fertilizer Non-energy The emission inventory projections for emissions basket of GHGs and local pollutants are given in table 9. The carbon and local pollutant emissions are MARKAL outputs while those for methane and N2O are Inventory Estimation Model outputs since these two are mainly due to non-energy activities, mainly in the agriculture sector, that are not covered by MARKAL. While the carbon emissions grow by about 3.5 times over , those for local pollutants in general grow less than twice. Methane emissions grow along agriculture sector rate while N 2 O trajectories follow those of nitrogen fertilizer use. The emissions growth rates follow a decreasing trend for almost all the gases and are in fact negative for some local pollutants in later years. The national 1995 emissions from the above models are calibrated with the district and sector level inventory estimates from the Inventory Estimates Model. The GIS interface of the integrated modeling system provides regional distribution maps of emissions. Figure 13 is a typical output and gives the distribution for Carbon dioxide emissions across the country. This analysis also suggests that pollution control is not as horrendous as it appears. Concentrate on 70-point sources to tackle 50% of sulfur and carbon emissions. These include 50 power plants, 5 steel plants and 15 cement plants. 48

49 Table 9: Emission inventory projections for India Emissions (MT) CAGR* Carbon Methane N 2 O CO 2 equivalent GHG SO NO X Particulate CO Compounded Annual Growth Rate over (%) The analysis of regional and sector specific gas inventories contributes to effectiveness of emissions mitigation by indicating the hotspot locations and sectors where controls can lead to maximum benefits (Garg, 2000c). However measures like stricter enforcement of ESP norms at cement and power plants, sulfur reduction in petroleum oil products (especially diesel and fuel oil) through out the country and gradual replacement of older vehicles with at least Euro-II complaint stocks should continue simultaneously to speed up local air cleaning. These policies also constitute the priorities for reducing the adverse health impacts of local air pollution. Such comprehensive policy analysis is possible since we employ diverse models to probe the individual policy issues keeping a consistent integration across the models. 49

50 Figure 13: Regional spread of CO 2 emissions for India in 1995 The health impact model indicates that if the health impacts of various industries are compared based on per unit of energy used in production by each plant, brick and cement sector impacts are most pronounced. This indicates the relative damage potential to human health from these sectors. The policy implications are to have stricter monitoring of these industries for local pollutant emissions especially particulate. We may even consider placing these industries far away (more than 25 km) from human populations. 50