Appendix V: Database Description

The SRES Emission Scenario Database (ESD) was developed to manage and access a large number of data sets and emissions scenarios documented in the literature. The SRES Terms of Reference call for the assessment of emissions scenarios in the literature (see Appendix I). The database was developed for SRES by the National Institute for Environmental Studies (NIES) of Japan and can be accessed via the ftp site www-cger.nies.go.jp/cger-e/db/ipcc.html. This section summarizes the database structure and the data collection for the database. Chapter 2 gives further detail about the quantitative assessment of the scenarios in the database. At the time of writing the database included 416 scenarios from 171 sources.

V.1. Database Structure

The main purpose behind the development of the new database is to make it easier to manage and utilize the vast amounts of data related to emission scenarios of greenhouse gases (GHGs), which include carbon dioxide (CO₂), nitrous oxide (N₂O), methane (CH₄), sulfur oxides (SO_x), and related gases, (such as carbon monoxide (CO), nitrogen oxides (NO_x), and hydrofluorocarbons (HFCs). The need for such a database is a function of both the increasing number of emission scenarios (because of increasing political and research interests in this topic) and the necessity to identify the strengths and weaknesses of current scenarios (to allow research to be focused on the most crucial or under-investigated areas).

These emission scenarios have been quantified mainly using computer simulation models, which in turn utilize many assumptions on factors such as population growth, gross domestic product (GDP) growth, technology efficiency improvements, land-use changes, and the energy resource base. The assumptions used in incorporating these factors often differ between simulations, as do the actual factors represented in the simulations. As a result, the database was designed to organize and store the input assumptions behind the scenarios as well as GHG emissions and other output.

Given the diversity of data types that must be accommodated, the database was designed with a relational database structure (using MS Access '97). The data represent large samples, and it is important that they be stored according to a structure that also allows the relationships between different data types to be represented and stored. A detailed description of the database structure is given in Morita and Lee (1998).

Each individual data entry is stored in the DATAMOM. Using the relational structure, it is possible to call data from within any of four main fields (Source ID, Scenario ID, Region ID, or Variable) using a number of subcategories specific to the individual fields. For example, the Source ID data entry field has the following subcategories:

Source ID (an abbreviated model or organization name with multiple data sets distinguished by the year of publication).
Authors (individual name or organization name).
Reference (publication in which the data are found).
Model (main simulation models).
Category (of simulation model, such as bottom-up or top-down, dynamic optimization of general equilibrium, etc.).
Update date (of the most recent publication).
Notes (if any).

Table V-1 briefly summarizes the subdivisions in the other key fields.

Table V- 1: Overview of Subdivisions in Key Fields in the Scenario Database. The main data fields for the SRES ESD are shown for the field names that identify the scenario by reference and name of world region and variable type.

Field Name	Subdivision (and brief description)

Scenario	Scenario ID (For reference scenario, or specific name of scenario, if one exists)	Category (Of scenario: non- intervention, intervention, or uncertain( ty). Blank if scenario not identifiable. Non- intervention means a scenario with no reduction policies for carbon emission, but which might include policies on other GHGs)	Description: (Of scenarios storyline and main assumptions)

Region	Region ID (Many scenarios use different names for the same region. These are converted into one unique Region ID. The full country name is used for national studies)		Definition (Of each Region ID)

Variable	Description (Of each variable for each source)	Varable (A common variable name is used for the same data item when names vary among sources)	Unit

The database has the primary function of acting as a data storage tool, and as an interface that will allow the user easy access to the data sets contained therein. Thus, it only provides data, and analyses are conducted using other tools such as spreadsheets. However, the relational structure of the database makes it possible to call up comparable data sets across the key fields, giving maximum flexibility in manipulation, extraction, and presentation of all the data in the database. Similarly, there is great flexibility in importing new data, or making a data set from the database using combinations of specific sources, specific scenarios (or categories), specific regions, and specific variables. The extraction screen in Figure V-1, for example, shows the settings used to extract all information on all scenarios that are generated with the AIM Japan source model and to examine global sea level rise.

The writing team recommends that this database or a new revised one should also be maintained by some institution in the future to facilitate comparisons and assessments of emissions scenarios. However, this would require additional resources.

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