Please note that the data and information posted under the "Open Process" are for historical information only. They were preliminary information released in 1998-1999 as part of the SRES open process and for use in analysis to be contained in the IPCC Third Assessment Report (TAR). For final data (version 1.1) please go back to home page and follow the link.
The AIM Model
The Asian-Pacific Integrated Model (AIM) is a large-scale model for scenario analyses of greenhouse gas (GHG) emissions and the impacts of global warming in the Asian-Pacific region. This model is being developed mainly to examine global warming response measures in the Asian-Pacific region, but it is linked to a world model so that it is possible to make global estimates. The AIM comprises three main models - the GHG emission model (AIM/emission), the global climate change model (AIM/climate) and the climate change impact model (AIM/impact).
The AIM-based quantificationwas conducted as an Asian collaborative project using a new linked version of the AIM/emission model which couples bottom-up models and top-down models (see Figure 1).
Figure 1 Outline of AIM/Emission-Linkage.
The bottom-up models were prepared using the original AIM bottom-up components which can reproduce detailed processes of energy consumption, industrial productions, land use changes and waste management as well as technology development and social demand changes. On the other hand, two kinds of top-down models were prepared for this quantification. These are: the revised ERB Model, which can estimate interactions between energy sectors and economic sectors; and, an original land use model which can reproduce interactions between land use changes and economic sectors. The original AIM bottom-up components were integrated with these two top-down models through a newly developed linkage module. This new structure maximizes the ability to simulate a variety of inputs at a variety of levels, and to calculate future Greenhouse Gas (GHG) emissions in a relatively full range analysis.
The AIM modelhas nine regions: USA, Western Europe OECD and Canada, Pacific OECD, Eastern Europe and Former Soviet Union, China and Central Planned Asia, South and East Asia, Middle East, Africa, Middle and South America. Its time horizon is from 1990 to 2100. Before 2030, the period step is 5 year, but then jump to 2050, 2075 and 2100. The GHGs and related gases include CO2, CH4, N2O, CO, NOx and SO2 emissions from energy production/use processes, CO2 from deforestation, CH4 and N2O from agricultural productions, SO2 from biomass combustion, and CO2, CH4, N2O, NOx, CO and SO2 emissions from industrial process, waste management and land use changes.
More detailed information can be obtained by referring to the World Wide Web site: http://www-cger.nies.go.jp/ipcc/aim/
The Atmospheric Stabilization Framework (ASF) Model
The current version of the ASF includes energy, agricultural, deforestation, GHG emissions and atmospheric models and provides emission estimates for nine of the world’s regions (Table 1).
Table 1: ASF regions.
All African countries
Centrally Planned Asia (CPASIA)
China, Laos, Mongolia, North Korea, Vietnam
Eastern Europe and NIS (EENIS)
Albania, Bulgaria, Czech Republic, Hungary, Poland, Romania, former USSR, former Yugoslavia
Latin America (LAMER)
All Latin American countries (including Mexico, Central and South America)
Middle East (MEAST)
All Middle Eastern countries including Iran, Iraq, Kuwait, Qatar, Saudi Arabia, and U.A.E.
OECD East (OECDA)
Australia, Japan, New Zealand
OECD West (OECDW)
Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom
South East Asia and Oceania (SEASIA)
Afghanistan, Bangladesh, Bhutan, India, Indonesia, Malaysia, South Korea, Burma, Pakistan, Philippines, Singapore, Thailand and other countries of the region
USA, Puerto Rico, and other US territories
The ASF energy model consists of four end-use sectors (residential, commercial, industrial, and transportation). These sectors consume liquid fuels, solid fuels, gaseous fuels, and electricity. An electricity generation sector converts liquid fuels, solid fuels, gaseous fuels, nuclear energy, hydro energy, and solar energy to electricity. A synfuels sector converts coal and/or biomass to either a liquid or gaseous fuel. There is no direct consumption of solar energy or biomass by the end-use sectors. In the model, coal, crude oil, and natural gas prices are solved for and reflect demand, resources, and supply. Oil and gas prices can be modified by adjusting the resource curves (resource available at different prices), rates of technological change, and variables defining the ability to expand/reduce production rates over time.
The agricultural ASF model estimates production of major agricultural products, such as meat, milk, and grain, that are driven by population and GNP growth. This model is linked with the ASF deforestation model, which estimates the area of land deforested annually as a function of population growth and demand for agricultural products.
The ASF GHG emissions model uses outputs of the energy, agricultural, and deforestation models to estimate GHG emissions in each ASF region. These emissions are estimated by mapping GHG emission sources to corresponding emission drivers and changing them according to changes in these drivers. For example, methane emissions from landfills are mapped to population, while carbon dioxide emissions from cement production are mapped to GNP.
Finally, the ASF atmospheric model uses GHG emission estimates to calculate GHG concentrations, and corresponding radiative forcing and temperature effects. A detailed description of the ASF is provided in the ASF 1990 Report to Congress (EPA, 1990).
EPA. (1990). Policy Options for Stabilizing Global Climate. Report to Congress. Washington, DC, USA: United States Environmental Protection Agency.
The IMAGE 2 Model
The IMAGE 2 model consists of three fully linked systems of models:
• The Energy-Industry System (EIS);
• The Terrestrial Environment System (TES); and
• The Atmosphere-Ocean System (AOS).
The Energy-Industry System (EIS) computes the emissions of greenhouse gases in 13 world regions. The energy-related emissions are based on the Targets Image Energy Regional (TIMER) simulation model. TIMER is a systems dynamics model with investment decisions into energy efficiency, electricity generation and energy supply based on anticipated demand, relative costs or prices and institutional and informational delays. The model uses 13 world regions and 5 economic sectors. Technological change and fuel prices dynamics influences energy-intensity, fuel substitution and penetration of non-fossil option such as solar electricity and biomass-based fuels.
The objective of the Terrestrial Environment System (TES) is to simulate global land-use and land-cover changes and their effect on emissions of greenhouse gases and ozone precursors, and on carbon fluxes between the biosphere and the atmosphere. This sub-system can be used to evaluate the effectiveness of land use policies for controlling the build-up of greenhouse gases, to assess the land consequences of large-scale use of biofuels, to evaluate the impact of climate change on global ecosystems and agriculture, and to investigate the effects of population, economic, and technological trends on changing global land cover.
Figure 2:The structure of the Terrestrial Environment System (TES) of IMAGE 2 (including links to other modules).
IMAGE 2.1 Regions:
Alcamo, J. (Ed.) (1994). IMAGE 2.0: Integrated Modelling of Global Change. Kluwer Academic Publishers, Dordrecht.
Alcamo, J., R. Leemans and E.Kreileman (Eds.) (1998). Global Change Scenarios of the 21st Century. Elsevier Science (forthcoming).
Rotmans, J. and H. J. M. de Vries (Eds.) (1997). Perspectives on global futures: the TARGETS approach. Cambridge, Cambridge University Press.
The MESSAGE Model
A set of integrated models was used to formulate the IPCC SRES scenarios at IIASA. MESSAGE (Messner and Strubegger, 1995) is one of the five models that constitute the integrated modeling framework at IIASA.
The scenario formulation process starts with exogenous assumptions about population and per capita economic growth by region. Energy demand is derived using the Scenario Generator (SG) model (Gritsevski and Gruebler, 1998). SG is a dynamic model of future economic and energy development. It combines extensive historical data about economic development and energy systems with empirically estimated equations of past economic and energy developments to determine future structural change. For each scenario, the SG generates future paths of energy use consistent with historical dynamics and with the specific scenario features, for example, high or moderate economic growth, rapid or more gradual energy intensity improvements.
Figure 1 IIASA integrated modeling framework (Nakicenovic, et al., 1998)
The economic and energy development profiles serve as inputs for the energy systems engineering modeled MESSAGE (Messner and Strubegger, 1995) and macroeconomic modeled MACRO (Manne and Richels, 1992). MESSAGE is a dynamic linear programming model, calculating cost-minimal supply structures under the constraints of resource availability, the menu of given technologies, and the demand for useful energy. It estimates detailed energy systems structures, including energy demand, supply and emissions patterns, that are consistent with the evolution of primary and final energy consumption produced by the SG. MACRO is a modified version of Global 2100 model, originally published in 1992 (Manne and Richels, 1992) and subsequently used widely in many energy studies around the world. It estimates the relationships between macroeconomic development and energy use. MESSAGE and MACRO are linked and used in tandem for testing scenario consistency because they correspond to the two different perspectives from which energy modeling is usually done: the top-down (MACRO) and bottom-up (MESSAGE). The atmospheric concentrations of greenhouse gas emissions and the resulting warming potentials are estimated by the MAGICC model, a carbon cycle and climate change model developed by Wigley et al. (1994).
Figure 1 illustrates the IIASA integrated modeling framework and shows how the models are linked (Nakicenovic, 1998). Four of the six models shown in Figure 1 were used for the formulation and analysis of SRES scenarios including the B2 marker scenario. In addition the MESSAGE model was also used to quantify all four variants of the A1 storyline and scenario family and one variant of the B1 storyline and scenario family.
The other two models shown in Figure 1, RAINS and BLS were not required for the modeling of the SRES scenarios. RAINS (Alcamo, 1990 and Amann, 1995) is a simulation model of sulfur and nitrogen oxides emissions, their subsequent atmospheric transport, chemical transformations of those emissions, deposition, and ecological impacts. BLS (Fischer et al. (1993 and 1994) is a sectoral macroeconomic model that accounts for all major inputs (such as land, fertilizer, capital, and labor) required for the production of 11 agricultural commodities.
The IIASA model set covers energy sector and industrial emission sources only. Agricultural and land-use related emissions for the B2 marker scenario were derived from a quantification of the B2 storyline by the AIM model. They are consistent with the energy-related emissions because they are based on assumptions about the main driving forces that are in line with those described below for theB2 quantification with MESSAGE model.
A. Gritsevskii, A. Gruebler, The Scenario Generator: A Tool for Scenario Formulation and Model Linkages, IIASA ECS, 1998.
S. Messner, M. Strubegger, User's Guide for MESSAGE III, WP-95-69, International Institute for Applied Systems Analysis, Laxenburg, Austria, 1995.
Wigley, T.M.L., Solomon, M. and Raper, S.C.B., Model for the Assessment of Greenhouse-gas Induced Climate Change, Version 1.2, Climate Research Unit, Unverstity of East Anglia, UK, 1994.
N. Nakicenovic, Gruebler, A. and Mcdonald, A., (Eds.) ., Global Energy Perspectives, Cambridge University Press, , Cambridge, 1998.
(see also http://www.iiasa.ac.at/cgi-bin/ecs/book_dyn/bookcnt.py)
Manne, A.S., and Richels, R, Buying Greenhouse Insurance, The Economic Costs of CO2 Emissions Limits, MIT Press, Cambridge, MA, USA.
Alcamo, J., Shaw, R., and Hordijk, L., The RAINS model of acidification: Science and Strategies in Europe, Kluwer Academic Publishers, Dordrecht, Netherlands.
Amann et al., Cost-effective Control of Acidification and Ground Level Ozone. First Interim Report to the European Commission, DGXI. Oct. 1996, IIASA, Laxenburg, Austria.
Fischer, G. Frohberg, K., Keyzer, M.A., and Parikh, K.S., Linked National Models: A Tool for International Policy Analysis, Kluwer Academic Publishers, Dordrecht, Netherlands, 1988.
The MARIA Model
MARIA - Multiregional Approach for Resource and Industry Allocation is a compact integrated assessment model to assess the interrelationships among economy, energy, resources, land use and global climate change. The origin of the model is the DICE model, developed by W. Nordhaus. Involving energy flows and dividing the world into regions, MARIA has been developed to assess the technology and policy options to address global warming. Like GLOBAL 2100 developed by A. Manne, MARIA is currently an intertemporal non-linear optimization model dealing with the international trading among seven regions, the USA, Japan, Other OECD countries, China, ASEAN countries, EEFSU, and ROW. It also encompasses the energy flows and simplified food production and land use changes to show the potential contribution of biomass. Carbon sequestration technologies are also taken into account. The Negishi weight technique is employed to evaluate the international trade prices on carbon products, energy resources, and carbon emission permits, if needed.
Global Warming Subsystem
MARIA uses Wigley's five-time constant model for the emission-concentration mechanism and the level thermal reservoir model, which follows the DICE model. Only global carbon emission is currently assessed.
CES production functions with capital, labor, electricity and non-electric energy are set for the above seven world regions. Industry sectors are aggregated into one. The Negishi weight technique has been employed.
MARIA involves three fossil primary energy resources, i.e., coal, natural gas and oil, biomass, nuclear power and renewable energy technologies, e.g., hydraulic power, solar, wind, and geothermal. Energy demand consists of industry, transportation, and other public uses. Carbon sequestration is also involved. MARIA basically generates the profile where gas is mainly used in the first half of the next century, and then carbon-free sources (e.g., solar, nuclear, and biomass) as well as coal share the main roles in the second half of the century.
Food and Land Use
To assess the potential contributions of biomass. A simplified food demand and land-use subsystem was included. Nutrition, calorie, and protein demand is a function of per capita income. Either directly or via meat, crop and pasture supply these demands. Forest is a source of biomass and wood products, but also their function as a carbon sink is evaluated.
Since MARIA is designed for the macro level evaluation of various options consistently, detailed information such as grid SOX emission, industry structure change, and urbanization issues are not generated. However, MARIA can provide long-term profiles on fuel mix changes and possible trade premiums.
MiniCAM—The Mini Climate Assessment Model
MiniCAM is a small rapidly running Integrated Assessment Model that estimates global greenhouse emissions with the Edmonds, Reilly and Barns (ERB) (Edmonds et al 1994, 1996a) model and the agriculture, forestry and land use model (ALM)(Edmonds et al 1996b). 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, Richels and Mendelsohn (1995) damage functions to examine the impacts of climate change. 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 all the Kyoto gases.
Regions: At present the model consists of 11 regions: USA, Canada, Western Europe, Japan, Australia, Eastern Europe and the Former Soviet Union, Centrally Planned Asia, the Mid-East, Africa, Latin America, and South and East Asia, which provide complete world coverage. A fourteen-region version is nearing completion.
Macro Economic Activity Level: The MiniCAM uses a straightforward population times labor productivity process to estimate aggregate labor productivity levels. The resulting estimate of GNP is corrected for the impact of changes in energy prices using a GNP/energy elasticity. For the scenario exercise, we developed an extended economic activity level process to allow a clearer understanding of the potential impacts of the new population scenarios. First, we included a detailed age breakdown so working age populations could be computed. Second, we added a labor force participation rate to estimate the labor force, and third we created an external process for estimating the long-term evolution of the rate of labor productivity increase.
Energy Sector: The ERB is a partial equilibrium model that uses prices to balance the supply and demand for the seven major primary energy categories (coal, oil, gas, nuclear, hydro, solar, and biomass) in the eleven regions in the model.
The energy demand module initially estimates demands for three categories of energy services (residential/commercial, industrial, and transportation) as a function of price and income. Energy services are provided by four secondary fuels: solids, liquids, gases, and electricity. Demand for the secondary fuels depends on their relative costs and the evolution of the end use technologies, represents by the improvement in end-use energy efficiency. Demand for primary fuels is determined by the relative costs of transforming them into the secondary fuels. Nuclear, solar, and hydro are directly consumed by the electricity sector, while coal and biomass can be transformed into gas and liquids if the fossil oil and gas become to expensive or run out. 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 by the three final demand sectors.
The energy supply sector provides both renewable (hydro, solar, and biomass) and non-renewable (coal, oil, gas, and nuclear) resources. The cost of the fossil resources relates to the resource base by grade, the cost of production (both technical and environmental) and to historical production capacity. The introduction of a graded resource base for fossil fuel allows the model to explicitly test the importance of fossil fuel resource constraints as well as to represent unconventional fuels such as shale oil, and methane hydrates, in which only small amounts are available at low costs, but large amounts are potentially available at high cost, or after extensive technology development. Fuel specific rates of technical change are available for primary fuel production and conversion, as are technical change coefficients for each category of electricity production.
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 ALM 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.
Emissions: Once the model has reached equilibrium for a period, emissions of greenhouse gases are computed. For energy, emissions of CO2, CH4, and N2O reflect fossil fuel use by type of fuel, while for agriculture emissions of these gases reflect land use change, the use of fertilizer, and the amount and type of livestock produced. The high GWP gases (CFC’s, HCFC’s, HFC’s and PFC’s) 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 whose effectiveness is determined by a Kuznets curve relating control levels to per capita income.
Climate Change and Impacts: The emissions estimates are aggregated to a global level and then used as inputs to MAGICC to produce estimates of greenhouse gas concentrations, changes in radiative forcing, and consequent changes in global mean temperature. The global mean temperature change is then used to drive SCENGEN derived changes in climate patterns, 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.
Edmonds, J., M. Wise, and C. MacCracken. 1994. Advanced Energy Technologies and Climate Change: An Analysis Using the Global Change Assessment Model (GCAM), PNL-9798, UC-402. Pacific Northwest Laboratory, Richland, WA 99352.
Edmonds, J., Wise, M., Pitcher, H., Richels, R., Wigley, T., and MacCracken, C. 1996a. "An Integrated Assessment of Climate Change and the Accelerated Introduction of Advanced Energy Technologies: An Application of MiniCAM 1.0," Mitigation and Adaptation Strategies for Global Change, 1(4):311-339.
Edmonds, J., M. Wise, R. Sands, R. Brown, and H. Kheshgi. 1996b. Agriculture, Land-Use, and Commercial Biomass Energy: A Preliminary Integrated Analysis of the Potential Role of Biomass Energy for Reducing Future Greenhouse Related Emissions. PNNL-11155. Pacific Northwest National Laboratories, Washington, DC.
Hulme, M., T. Jiang, and T. Wigley. 1995. SCENGEN: A Climate Change SCENario GENerator: Software User Manual, Version 1.0. Climate Change Research Unit, School of Environmental Sciences, University of East Anglia, Norwich, United Kingdom, 38pp.
Manne, A.S., R. Mendelsohn, and R. Richels. 1995. "MERGE -- A Model for Evaluating Regional and Global Effects of GHG Reduction Policies." Energy Policy, 23(1):17-34.
Wigley, T.M.L. and S.C.B. Raper. 1993. "Future Changes in Global-Mean Temperatue and Sea Level," in Climate and Sea Level Change: Observations, Projections and Implications, R.A. Warrick, E. Barrow, and T.M.L. Wigley (eds.), pp.111-133. Cambridge University Press, Cambridge, 424pp.