The SRES Emissions Scenario Database (ESD) was designed to fulfill several objectives:

• To facilitate a thorough review and analysis of the literature.
• To enable a statistical analysis of all scenarios in the database - to generate distribution functions of the main scenario driving forces, calculate mean and median values, percentiles, and other sample statistics (the use of such statistical analyses of the scenarios in the database ensures that the new SRES emissions scenarios generally reflect the range of emissions and input assumptions currently found in the open literature).
• The third objective was to make the database accessible through a website (www-cger.nies.go.jp/cger-e/db/ipcc.html) so that data queries, browsing, data retrieval, and entry of new scenarios would be possible by remote users, and necessitated designing a database that could manage flexibly large amounts of data as well as diverse data types.

The database serves primarily to document the GHG emissions, including CO₂, CH₄, N₂O, CFCs (chlorofluorocarbons), HFCs (hydrofluorocarbons), and other radiatively active gases such as SO₂, CO, and NO_x. In addition, it includes information about the main driving forces of GHG emissions, such as population growth and economic development, usually expressed in terms of gross domestic product (GDP), energy consumption, and land use. Each of these scenario characteristics has subcategories and different values in time and space. The temporal dimension is often in steps of 10 years, but this is not standardized across the scenarios in the database. The spatial dimension refers to the regional disaggregation of the scenarios. Priority was given to covering all accessible quantitative scenarios with global and regional coverage. The main scenario characteristics are documented by the name and aggregation given in the original study. In some cases, regional and national scenarios are also included to improve the coverage of some parts of the world. (Table 2-1 lists the number of scenarios in the database that include a given region, from the global level through to some individual countries.) There is great diversity with respect to regional aggregation of scenarios in the database. Inclusion of long-term emissions scenarios for individual countries, when available, would improve the regional coverage of the database. Sectoral studies in developing countries, such as power system emissions (e.g., Chattopadhyay and Parikh, 1993) or transport system emissions (e.g., Ramanathan and Parikh, 1999), were also considered in this assessment to develop SRES emissions scenarios.

A list of scenario characteristics and their frequency of occurrence across the 416 scenarios is given in Appendix V. Most of these scenarios were created after 1994. Of the 416 scenarios 340 provide data on the global level, and 256 scenarios of these 340 report information on CO₂ emissions.

A large majority (230) of the scenarios report only energy-related CO₂ emissions, while only some report non-energy CO₂ and other GHG emissions. For example, only three models estimate land use-related emissions: the Atmospheric Stabilization Framework (ASF) (Lashof and Tirpak, 1990) model that was used to formulate the IS92 scenarios; the Integrated Model to Assess the Greenhouse Effect 2 (IMAGE 2) model (Alcamo, 1994); and the Asian-Pacific integrated model (AIM; Morita et al., 1994). Only a few scenarios report regional and global SO₂ and sulfur aerosol emissions that are also climatically important because of their cooling effect (negative radiative forcing of climate change). Box 2-1 in Section 2.4.1 summarizes the set of scenarios that report non-energy- related CO₂ emissions.

The information documented in the database about emissions scenarios illustrates both areas that are well covered in the scenario literature and areas with substantial gaps in knowledge. For example, the information in the database strongly confirms the findings of the latest IPCC scenario assessment and evaluation (Alcamo et al., 1995). One of the key findings is that of all GHG emissions, CO₂ emissions are by far the most frequently studied, and that of all the CO₂ emissions sources, fossil fuel is the source most extensively analyzed in the literature. In part, this is because energy-related sources of CO₂ emissions contribute more to the current and potential future climate forcing than any other single GHG released by any other human activity. In part, this is also because of improved data, assessment methods, and models for energy-related activities than for other emissions sources. Another information gap example is the rather diverse regional disaggregation chosen for different scenarios. Even when the regions are similar or equivalent in terms of this assessment, the names are sometimes different, which hampers comparisons. Such gaps in knowledge limit the range and effectiveness of the various policy options that logically follow from the discussion. This creates a level of uncertainty that can only be addressed by concentrated research efforts.

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