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 (SOx), and related gases, (such as carbon
monoxide (CO), nitrogen oxides (NOx), 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
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
- 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.
||Subdivision (and brief description)
(For reference scenario, or specific name of scenario, if one exists)
(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)
(Of scenarios storyline and main assumptions)
(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
(Of each Region ID)
(Of each variable for each source)
(A common variable name is used for the same data item when names vary among
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