Carbon Dioxide Information Center
P.O. Box X
Oak Ridge, TN 37831
The motel is available in two forms: (1) ready to run (executive load module) and (2) uncompiled FORTRAN code. Users who wish to obtain the Fortran code must have access to their own compiler to modify and/or run the model.
This guide assumes that the user has already installed the model, as delivered from CDIC, on their IBM personal computer. To run the model, the following hardware is necessary:
- IBM PC with > 520K
To run the color graphics output module the user also needs to have access to
- IBM Color Monitor
- Color Graphics Board
Another useful piece of hardware is a printer.
The model was developed for the U.S. Department of Energy, Office of Energy Research, Carbon Dioxide Research Division to assist them in their study of energy and global climate change. This model analyzes the relationship between economic, technological , demographic, and geological factors influencing the long-term production, consumption and trade of energy on a global scale. An additional module computes the emission of CO2 as a function of fossil fuel use.
Time Scale: The model is a long-term forecast. It can be run as far into the future as the year 2100. Benchmark years are, 2000, 2025, 2050, 2075, and 2100.
Geopolitical Scale: The model covers energy production and use for the entire world. The world is divided into nine global regions (Figure 1.1 ) :
- Western Europe and Canada
- Japan, Australia, and New Zealand
- USSR and Eastern Europe
- China and other Asian Centrally Planned Economies
- Latin America
- South and East Asia
Other aggregates that will be referred to in this user's guide are: Aggregate Regions Included OECD (Organization for 1 + 2 + 3 Economic Cooperation and Development) North 1 + 2 + 3 + 4 South 5 + 6 + 7 + 8 + 9 CPE (Centrally Planned 4 + 5 Economies) The Data Base: The data base provided with this code is one which contains median (best guess) values for key variables. This data set was developed as part of a study of uncertainty in future CO2 emissions. The researchers felt comfor table with the resulting global aggregate forecasts generated by this data set and model. No attempt was made to insure that the regionally disaggregated pattern of energy supply was reasonable. As a consequence numerous anomolous regional disaggregates appear. The user is cautioned to take care in the use of regionally disaggregated results particularly in the area of energy supply. Neither the authors nor the Oak Ridge Associated Universities, nor CDIC, nor the U.S. Department of Energy nor t he U.S. Government make any warranty, expressed or implied or assume completeness or usefulness of any information contained in this document or in the model it describes.
The model can be thought of as consisting of four parts: demand, supply, energy balance, and CO2 emissions. The first two modules determine the demand of and supply for each of six major primary energy categories (listed in Table 2.1.) and in each of the nine global regions (discussed in Chapter I). The energy balance module ensures model equilibrium in each global fuel market. (Primary electricity is assumed to be untraded, thus supply and demand balance in each region.) The CO2 emissions module is a post-processor. Five benchmark years were chosen for scenarios--2000, 2025, 2050, 2075 and 2100.TABLE 2.1. PRIMARY FUEL TYPES IN THE IEA/ORAU ASSESSMENT FRAMEWORK
1.1 Conventional oil
1.2 Enhanced recovery, shale oil, and tar sands
2.1 Conventional gas
4.0 Resource-constrained renewables
4.1 Hydro, geothermal
6.0 Solar (ex. Biomass)
6.1 Solar electric (other solar is associated with conservation), wind, and tidal power, ocean thermal energy conversion, fusion and other advanced renewable technologies.
Energy demand for each of the six major fuel types is developed for each of the nine regions separately. Four major exogenous inputs determine energy demand: population, labor productivity, exogenous end-use energy efficiency improvement, and energy pric es. (Note: While prices are exogenous to the Energy Demand Module they are endogenous to the full model.)
The model calculates base GNP directly as a product of labor force and labor 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, mo dified within the model to be consistent with energy-economy interactions. The GNP feedback elasticity is regional, allowing the model to distinguish energy supply dominant regions, such as the Mideast where energy prices and GNP are positively related, from the rest of the world where the relationship is inverse.
The exogenous end-use energy efficiency improvement parameter is a time-dependent index of energy productivity. It measures the annual rate of growth of energy productivity which would go on independent of such other factors as energy prices and real inc omes. In the past, technological progress and other nonprice factors have had an important influence on energy use in the manufacturing sector of advanced economies. The inclusion of an exogenous end-use energy efficiency improvement parameter allows sc enarios to be developed that incorporate either continued improvements or technological stagnation assumptions as an integral part of scenarios.
The final major energy factor influencing demand is 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 nontrading 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: it establishes the demand for energy and its services and it maintains a set of energy flow accounts for each region. Oil and gas are transformed into secondary liquids and gases used either directly in en d-use sectors or indirectly as electricity. The solid primary fuels, coal and biomass, can either be used in their solid forms or may be transformed into secondary liquids and gases or electricity. Hydro, nuclear, and solar electric are accounted direct ly as electricity. Nonelectric solar is included with conservation technologies as a reduction in the demand for marketed fuels.
The four secondary fuels are consumed to produce energy services. In the three Organization for Economic Co-Operation and Development (OECD) regions, energy is consumed by three end-use sectors: residential/commercial, industrial, and transport. In the remaining regions, final energy is consumed by a single aggregate sector.
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 co sts of providing these services using each alternative fuel. The demand for 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 disaggregated into two categories, renewable and non-renewable. This categorization is given in Table 2.2.
TABLE 2.2. RENEWABLE AND NON-RENEWABLE ENERGY SUPPLY TECHNOLOGIES IN THE IEA/ORAU LONG-TERM ENERGY ECONOMIC MODEL
|Unconventional Oil||Solar Electric|
*Note that if the breeder reactor is assumed, this technology becomes a renewable one.
Energy supply from all fossil fuels is related directly to the resource base by grade, the cost of production (both technical and environmental) and to the historical production capacity. The introduction of a graded resource base for fossil fuel (and nu clear) supply allows the model to explicitly test the importance of fossil fuel resource constraints as well as to represent fuels such as shale oil in which only small amounts are likely available at low costs but for which large amounts are potentially available at high cost.
Note here that nuclear 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 m odeled as an essentially unlimited source of fuel available at higher cost.
A rate of technological change is now 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 e nergy system balances. Such a result is unlikely at any 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, brings supply and demand nearer a system-wide b alance. Successive energy price vectors are chosen until energy markets balance within a prespecified bound. Figure 2.1 graphically depicts the process by which the global energy balance is achieved.
Given the solution from the energy balance component of the model, the calculation of CO2 emissions rates is conceptually straightforward. The problem merely requires the application of appropriate carbon coefficients (carbon release per unit of energy) at the points in the energy flow where carbon is released. Carbon release is associated with the consumption of oil, gas, and coal. Significant carbon release is also associated with production of shale oil from carbonate rock. A direct zero carbon release coefficient is implicitly assigned to nuclear, hydro, and solar power and to conservation. Actual calculation of CO2 emissions is made somewhat more complex by the need to appropriately account for flows of carbon that are not oxidized (see Figure2.2).
Considerable literature exists concerning appropriate values for CO2 coefficients. Those in Table 2.3 were calculated at IEA/ ORAU by Gregg Marland and Ralph Rotty. The coefficients are representative of average global fuel of a given type an d are consistent with the model's CO2 accounting conventions as indicated by Figure 2.2.
TABLE 2.3. CO2 COEFFICIENTS IN THE IEA/ ORAU, LONG-TERM ENERGY-CO2 MODE
|Carbonate rock mining||27.9|
The four chapters that follow discuss how to generate assumptions for the model and the different forms of output available. Material covered in those four chapters is summarized in Table 3.1 and Figure 3.1.
TABLE 3.1. SUMMARY OF MATERIAL IN CHAPTERS IV, V, VI, and VIIChapter: IV
Material Covered: Basic steps necessary to determine values for major assumption parameters and to run the model. A single command, RUNMODEL, initiates an interactive program. Summary results of the model run are written to a file called R ESULTS.DAT.
Material Covered: Two commands that generate summary output are discussed:
VIEWRUN: Initiates an interactive video display program. Output can be viewed in either tabular or graphic form.
PRINTRUN: Orders tabular output to printer. All tables available in VIEWRUN are printed. Graphs are not printed.
Material Covered: Documents procedures by which a user can modify any assumption. All assumptions are specified in tabular form in a file called NIEA.DAT. Procedures assume the user has his own text editor program.
Material Covered: Procedures are discussed which allow the user to print detailed output from each run. The procedures require the user to supply their own text editor to modify specific items in the file NIEA.DAT.
The general flow of the model is displayed in Figure 3.1, moving from left to right across the page:
(1) RUNMODEL: a single command initiates the model run. All subsequent procedures are directed by an interactive set of commands.
(2) Read Data Files: the master data file NIEA.DAT is read. Advanced users may modify NIEA.DAT to generate scenarios based on specific assumptions. The general user has access to 39 major assumptions through an interactive data editor.
(3) Internal Data Editor: the model next calls an internal data editor. This interactive program allows a user to inspect and/or change any of 39 different major assumptions. When the user has finished inspecting and/or modifying assumptions, the model continues to compute the energy and CO2 emissions consequences of that set of assumptions.
(4) Print Results: if the model was instructed to print detailed outputs, detailed outputs are sent to the printer, which is the only place they are recorded. Special modifications to the NIEA.DAT data base are necessary to cause detailed output to be re corded. (See Chapter VII.) In all cases summary output is written to a file (either floppy disk or hard disk) under the name RESULTS.DAT. The first program then terminates.
To obtain either a visual display on the video screen or a printout of the summary output, one types either VIEWRUN (to initiate an interactive output display) or PRINTRUN (to print all tables available in VIEWRUN).
Center for International Earth Science Information Network (CIESIN). 1995. Thematic Guide to Integrated Assessment Modeling of Climate Change [online]. University Center, Mich.
CIESIN URL: http://sedac.ciesin.org/mva/iamcc.tg/TGHP.html