IV.4. Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE)

A set of integrated models was used to formulate the SRES scenarios at IIASA (Nakicenovic, et al., 1998). Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) is one of the six models that constitute IIASA's integrated modeling framework (Messner and Strubegger, 1995; Riahi and Roehrl, 2000; Roehrl and Riahi, 2000).

The scenario formulation process starts with exogenous assumptions about population and per capita economic growth by region. Energy demand (defined at the useful energy level) is derived using the Scenario Generator (SG) model, 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 trends to determine future structural change. For each scenario, SG generates future paths of energy use consistent with historical dynamics and with the specific scenario features (e.g., high or moderate economic growth, rapid or more gradual energy intensity improvements).

The economic and energy development profiles serve as inputs for the energy systems engineering model MESSAGE (Messner and Strubegger, 1995; Riahi and Roehrl, 2000; Roehrl and Riahi, 2000) and the macro-economic model MACRO (Manne and Richels, 1992). MESSAGE is a dynamic linear programming model that calculates 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 system structures, including energy demand, supply, and emissions patterns, consistent with the evolution of the energy demand produced by SG. MACRO is a modified version of the Global 2100 model, originally published in 1992 (Manne and Richels, 1992) and subsequently used widely in many energy studies around the world. MACRO maximizes the inter-temporal utility function of a single representative producer-consumer in each world region and estimates the relationships between macro-economic development and energy use. MESSAGE and MACRO are linked and used in tandem to test scenario consistency because they correspond to the two different perspectives from which energy modeling is usually carried out - top-down (MACRO) and bottom-up (MESSAGE).

The impacts of energy price changes on energy demand and gross domestic product (GDP) growth are estimated by iterating shadow prices from MESSAGE and energy demands from the MACRO model. The iteration is repeated until energy intensities and GDP growth rates are consistent with the output of the SG model adopted as exogenous input assumptions at the beginning of the scenario formulation process. The demand reductions caused by increasing energy prices in the B2 marker compared to a hypothetical case with constant energy prices were calculated with MACRO. Compared to this hypothetical case the price-induced energy demand savings in the B2 marker are 8% by 2020, 23% by 2050, and 30% by 2100. Table IV-4 gives the shadow prices for international trade for gas, oil, and coal in the B2 marker. Table IV-5 summarizes the regional ranges for extraction costs of gas, oil, and coal in the B2 marker.

The atmospheric concentrations of GHGs and the resultant warming potentials can be estimated by the Model for the Assessment of Greenhouse Gas-Induced Climate Change (MAGICC), a carbon cycle and climate change model developed by Wigley et al. (1994).

Figure IV-4 illustrates the IIASA integrated modeling framework and shows how the models are linked (Nakicenovic, et al., 1998). Of the six models shown in Figure IV-4, four (SG, MESSAGE, MACRO, and MAGICC) were used for the formulation and analysis of SRES scenarios, including the B2 marker scenario. In addition the MESSAGE model was used to quantify all four scenario groups of the A1 storyline and scenario family and a number of scenarios of the B1 storyline and scenario family. Altogether, the IIASA team formulated nine SRES scenarios, including the B2 marker.

The other two models shown in Figure IV-4, RAINS and BLS, were not used to model the SRES scenarios. RAINS (Alcamo et al., 1990) is a simulation model of sulfur and NOx emissions, their subsequent atmospheric transport, chemical transformations of those emissions, deposition, and ecological impacts. BLS (Fischer et al., 1988, 1994) is a sectorial macro-economic 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 and other SRES scenarios were derived from corresponding quantifications 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 in the quantifications with the MESSAGE model.

More detailed information can be obtained by referring to the web site: http://www.iiasa.ac.at/Research/ECS/.

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