PNNL-14337 Prepared for the U.S. Department of Energy Under Contract DE-AC05-76RL01830 Model Documentation for the MiniCAM AL Brenkert SH Kim AJ Smith HM Pitcher July 2003 DISCLAIMER This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor Battelle Memorial Institute, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. 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(9/2003) PNNL-14337 Model Documentation for the MiniCAM AL Brenkert SH Kim AJ Smith HM Pitcher July 2003 Prepared for the United States Environmental Protection Agency under Contracts AGRDW89939464-01 and AGRDW89939645-01 for the U.S. Department of Energy under Contract DE-AC05-76RL01830 Pacific Northwest National Laboratory Richland, Washington 99352 Acknowledgments We want to thank Elizabeth L. Malone for steadfast support. We want to thank Marshall Wise for his review. We want to thank the Environmental Protection Agency’s Clean Air Markets Division (Office of Atmospheric Programs) for making this documentation become reality. 3 Summary The MiniCAM, short for the Mini-Climate Assessment Model, is an integrated assessment model of moderate complexity focused on energy and agriculture sectors. The model produces emissions of greenhouse gases (carbon dioxide, methane and nitrous oxide) and other radiatively important substances such as sulfur dioxide. Through incorporation of the simple climate model MAGICC, the consequences of these emissions for climate change and sea-level rise can be examined. The MiniCAM is designed to be fast and flexible. The MiniCAM is a long-term, partial-equilibrium model designed to examine long-term, large- scale changes in global and regional energy system where the characteristics of existing capital stocks are not the dominant factor in determining the dynamics of the energy system. Markets are defined for oil (conventional and unconventional), gas, coal, biomass, carbon, and agricultural products. The MiniCAM has no markets for labor and capital. It is specifically designed to address issues associated with global change, including (1) projecting baseline carbon dioxide emissions over time for a country or group of countries; (2) projecting various other radiatively important gases (e.g., methane, nitrous oxide, sulfur dioxide, reactive gases); (3) evaluating the energy-system, emissions, and other consequences of various technological options; (4) evaluating some aspects of potential climate change, e.g., temperature change, sea-level rise; (5) providing a measure of the carbon price, in dollars per metric ton for an emissions target; and (6) providing a measure of the overall cost of meeting an emissions target. The MiniCAM model can be conceptualized as consisting of four modules (see Figure below). Human activities Land Use ERB AgLU Atmospheric Regional Composition, Climate Climate & Patterns Sea Level Rise SCENGEN MAGICC These modules can be described as simulating • energy supply and demand using an updated version of the Edmonds-Reilly-Barnes model (ERB) (Edmonds and Reilly 1985; Edmonds, Reilly, Gardner and Brenkert 1986; Edmonds, Wise, Pitcher, Wigley and MacCracken 1997); • agriculture and land-use using the Agriculture and Land Use model (AgLU) (Edmonds et al. 1996; Sands and Leimbach 2001; Gillingham et al. 2002; Sands and Edmonds 2002); • atmospheric composition and global-mean climate changes using the Model for the Assessment of Greenhouse-gas Induced Climate Change (MAGICC) (Hulme and Raper 1993; Wigley 1994a,b; Wigley and Raper 1987, 1992, 1993, 2001); and 4 • regional patterns of climate change using the Regional Climate Change Scenario Generator (SCENGEN) (Hulme, Jiang, and Wigley 1995). The MiniCAM projects economic activity, energy consumption, and emissions in 15-year time steps from 1990 through 2095. The MiniCAM has global coverage in the form of 14 distinct regions (United States, Canada, Western Europe, Japan, Australia & New Zealand, Former Soviet Union, Eastern Europe, Latin America, Africa, Middle East, China [& Asian Reforming Economies], India, South Korea, Rest of South & East Asia). The MiniCAM has a strong focus on energy supply technologies. A wide range of technologies, fuels, and energy carriers can be used to supply end-use energy demands. Transformation losses are accounted for throughout the supply system. Technologies include electricity generation (from coal, oil, gas, biomass, hydro power, fuel cells, nuclear energy, wind energy, solar PV, solar-wind storage, and space solar PV), hydrogen production (from coal, oil, gas, biomass, and electrolysis), synthetic fuel production (synthetic liquids from coal, gas, and biomass; synthetic gas from coal, and biomass), geologic carbon sequestration from fossil fuels (during electricity generation, hydrogen production, and synthetic fuel production). Biomass supply includes “waste” biomass streams and commercial biomass produced regionally by the AgLU module. End-use fuels include those currently in widespread use (coal, oil, gas, biomass, electricity), and future options such as synthetic liquids, synthetic gases and hydrogen. Carbon sequestration is an option for all conversion technologies, particularly electric generation and hydrogen production. The MiniCAM contains a large set of parameters to simulate technical change over time. These parameters include the rate of change in efficiency of inputs to any particular production sector in the model (e.g., primary energy transformation to secondary energy; secondary energy transformation to tertiary energy). These rates of change in input efficiency can be varied at each 15-year time step. Production Sectors Energy primary energy fuel conversion secondary energy energy conversion Demand Sectors Transportation tertiary energy Buildings Agriculture Industry biomass Agricultural demand & supplies The conceptual framework of the ERB and AgLU modules is shown in the figure above. Each production process is represented by a fixed-coefficient (Leontief) production function requiring three parameters: fuel input, efficiency, and non-fuel costs. During the transformation processes 5 from raw fuel to refined fuels and to fuels to be consumed by end-users, fuels compete based on their prices. This competition is simulated by means of a share equation (the logit share equation) which is based on fuel prices and their elasticities. The price of each of the fuels is assumed to fall within a fuel-price range based on the cost of fuel and various costs like transportation costs, taxes, non-fuel costs, and structural factors. This variance of each fuel price is captured in an energy price exponent or elasticity coefficient. Through the logit methodology all fuels that compete can contribute in the generation of the supply of a secondary fuel or an energy service, with shares changing in a smooth manner as prices or policies change with time. The demand for energy is determined by regional population levels, levels of economic activity and the price of energy services. The demand by the end-use sectors for energy services is simulated as a demand for the least costly energy service fuel mode through a combination of the constant elasticity and logit share equations. The constant elasticity equation includes price elasticities and income elasticities. End-use energy services can be supplied by refined fossil-fuels, biomass (traditional and from dedicated biomass farms), electricity, hydrogen, and synthetic fuels. The three end-use sectors are buildings, industry, and transportation. Regional populations determine regional economic activity and end-use demands. Technological change impacts energy demand, while regional taxes and tariffs (and climate policies which influence the price of greenhouse gas emissions) determine regional energy prices, which affect, in turn, regional energy demands. Regional energy resources and prices determine regional energy supplies, which impact global supply and demand, which in turn affect world prices that feed back to the regional energy prices, which then impact regional supply and demands. Through this iterative process, global supply and demand of the primary