The Scenario Generator (SG) is a key element in ECS’s modeling framework, generating consistent energy demand inputs for MESSAGE, ERIS and macroeconomic models. ECS is currently developing a revised SG branching off of the original SG which was developed in 1995.

The SG is used as a simulation model to help formulate scenarios of economic and energy development for eleven world regions analyzed by MESSAGE. Its main objective is to allow fast scenario formulation and documentation of key scenario assumptions, and to provide common, consistent input data for MESSAGE and MACRO. The SG includes historical economic and energy time series, and a set of regression equations that represent key relationships between economic and energy development that can be used selectively in formulating scenarios. Inputs to the SG are future population trajectories for eleven world regions used by ECS’s energy and economic models plus key parameters determining regional per capita GDP growth. The SG calculates final energy trajectories for each region by combining the population and per capita GDP growth trajectories with energy intensity profiles based on the SG's set of empirically derived equations. To allow adjustments for different storylines and variants, all important variables are formulated so that a user can overwrite the values suggested by the equations of the SG. The historical data and relationships in the SG provide the user with some guidance for the likely range of key economic and social variables, and the potential impact of policies on energy demand, improving the plausibility of possible scenarios and estimates of policy impacts.

The revised SG will greatly improve the modeling of key relationships between energy use and economic, social and environmental trends, based on more sophisticated causal chains. It is anticipated that the new SG will include many of these features of the current SG, and will also employ a number of alternative approaches for estimating future sectoral final energy demand, based on both monetary and physical characteristics of the global economy, as well as incorporating updated historical data.

References:
Nakicenovic, N., Grübler, A., and McDonald, A., (ed.), 1998: Global Energy Perspectives, Cambridge University Press, Cambridge, UK.

MESSAGE is a systems engineering optimization model used for medium- to long-term energy system planning, energy policy analysis, and scenario development (Messner and Strubegger, 1995). The model provides a framework for representing an energy system with all its interdependencies from resource extraction, imports and exports, conversion, transport, and distribution, to the provision of energy end-use services such as light, space conditioning, industrial production processes, and transportation (across 11 macro-regions). Scenarios are developed by MESSAGE through minimizing the total systems costs under the constraints imposed on the energy system. Given this information and other scenario features such as the demand for energy services, the model configures the evolution of the energy system from the base year to the end of the time horizon (in ten year steps). It provides the installed capacities of technologies, energy outputs and inputs, energy requirements at various stages of the energy systems, costs, emissions, etc.

The degree of technological detail in the representation of an energy system is flexible and depends on the geographical and temporal scope of the problem being analyzed. A typical model application is constructed by specifying performance characteristics of a set of technologies and defining a Reference Energy System (RES) to be included in a given study/analysis that includes all the possible energy chains that the model can make use of. In the course of a model run, MESSAGE then determines how much of the available technologies and resources are actually used to satisfy a particular end-use demand, subject to various constraints, while minimizing total discounted energy system costs.

References:
Messner, S. and Strubegger, M., 1995: User's Guide for MESSAGE III, WP-95-69, International Institute for Applied Systems Analysis, Laxenburg, Austria. (revised version)

Nakicenovic, N., (ed.), 2000: Global greenhouse gas emissions scenarios: Integrated modeling approaches, Technological Forecasting and Social Change, 63(2-3).

Riahi, K., and R.A. Roehrl, 2000a: Energy technology strategies for carbon dioxide mitigation and sustainable development, Environmental Economics and Policy Studies, 3(2), 89-124.

Riahi, K., and R.A. Roehrl, 2000b: Greenhouse gas emissions in a dynamics-as-usual scenarios of economic and energy development, Technological Forecasting & Social Change, 63, 175-206.

MACRO corresponds to the macroeconomic module of the top-down macroeconomic model MERGE (Manne and Richels, 1992). Some modifications and extensions were made at IIASA-ECS, where, MACRO is now mainly used in connection with MESSAGE.

MACRO defines an inter-temporal utility function of a single representative producer-consumer in each of the model’s world regions, which is maximized. The main variables of this module are the production factors capital stock, available labor, and energy inputs, which together determine the total output of an economy according to a nested CES (constant elasticity of substitution) production function. The optimal quantities of the production factors are determined by their relative prices.

Energy demand curves are given in two categories, electric and non-electric energy, as quadratic functions of energy prices. These functions are defined for the two categories and for all time periods. Actual demands are determined by MACRO in a way consistent with projected GDP. MACRO also disaggregates total production into macroeconomic investment, overall consumption, and energy costs.

MESSAGE and MACRO are linked to include the impact of policies on energy costs, GDP and on energy demand. The link is established by using MESSAGE results on total and marginal costs of energy supply to derive the quadratic demand functions for MACRO. The linked model is iterated until MACRO’s resulting energy demands do not deviate from MESSAGE’s by more than a given fraction.

An elaborate description of the link between the linked models is presented in Messner and Schrattenholzer (2000).

ECS is currently developing a new macroeconomic model that will feature additional energy demand categories, tighter calibration with scenario parameters, and an alternative utility function. This model will initially be linked with the ERIS.

References:
Manne, A. and R. Richels, 1992: Buying Greenhouse Insurance: The Economic Costs of CO2 Emissions Limits, The MIT Press, Cambridge, MA, USA.

Messner, S., and L. Schrattenholzer, 2000: MESSAGE-MACRO: Linking an Energy Supply Model with a Macroeconomic Model and Solving It Interactively, Energy, 25, 267-282.

The ERIS (Energy Research and Investment Strategy) model was originally created within the EC-sponsored TEEM project to examine the effects of different representations of technological change in energy optimization models, focusing primarily on the electricity generation sector. Since that time the ERIS model has been substantially expanded to cover the entire energy system, with additional technology detail in the transport system. ERIS has also been supplemented with more detailed abatement options and technologies. The strength of the ERIS model is its flexibility and adaptability, which allow the agile implementation of different modeling approaches, and the quick assessment of a wide range of policies instruments. The model has been developed as a joint effort between ECS-IIASA, the Paul Scherrer Institute (PSI) in Switzerland and the National Technical University of Athens (NTUA).

ECS uses a number of formulations of ERIS. These include a standard linear programming (LP) version with exogenous specification of cost trends, and a non-linear, non-convex formulation that incorporates experience curves. A third version of ERIS incorporates a linearized formulation of learning curves applying mixed integer grogramming (MIP) techniques. The model has also been adapted to incorporate so-called two-factor learning curves, allowing the endogenization of R&D expenditures as a second learning channel complementary to the market experience one. More recent developments include modeling R&D and demonstration and deployment (D&D) policy ‘shocks’, and their interaction with climate change policies for the EC MINIMA-SUD and SAPIENTIA projects.

References:
Barreto, L., Kypreos, S., 2004: Endogenizing R&D and market experience in the "bottom-up" energy-systems ERIS model. Technovation 24(8):615-629.

Miketa, A., Schrattenholzer, L., 2004: Experiments with a methodology to model the role of R&D expenditures in energy technology learning processes. Energy Policy 32/15:1679-1692 (link).

Turton, H., Barreto, L., 2004: The extended energy-systems ERIS model: An overview. IIASA Interim Report, IR-04-010, Laxenburg, Austria.

ECS disseminates CO2DB free of charge so that it can be useful to researchers in their individual studies. In return, for our own studies we would appreciate receiving from users the data they enter into the database. To order a copy, please send an e-mail to Angela Dowds specifying whether you wish to receive a self-extracting file sent by e-mail or a CD-ROM and including your e-mail address and postal address.

References:
Strubegger, M.: 2003: CO2DB Software: Carbon Dioxide (Technology) Database. Version 3.0, International Institute for Applied Systems Analysis, Laxenburg, Austria.

Strubegger, M., McDonald, A., Gritsevskii, A., Schrattenholzer, L., 1999, CO2DB Manuel, Version 2.0, IIASA, Laxenburg, Austria, 17 pp.

Messner, S., Strubegger, M., 1995, Part A: User's Guide to CO2DB: The IIASA CO2 Technology Data Bank - Version 1.0. Working Paper, WP-91-31, IIASA, Laxenburg, Austria.

Messner, S., Strubegger, M., 1995, User's Guide to CO2DB: The IIASA CO2 Technology Data Bank - Working Paper, WP-91-31a, IIASA, Laxenburg, Austria.