The SRES Emissions Scenario Database (ESD) was designed to fulfill several objectives:
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 NOx. 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.
Table 2-1: Number of regional and global GHG emissions scenarios in the SRES ESD. The database (from 3 April 1998) included a total of 416 regional and global scenarios from 171 sources. The individual number of scenarios per region or country exceeds the global total because some scenarios include both global and regional data. There are also more scenarios at the regional level than at the global level. In addition to the original sources of individual emissions scenarios, the database utilized the large number of scenarios compiled in the following assessments: International Energy Workshop Poll (Manne and Schrattenholzer, 1995, 1996, 1997); Energy Modeling Forum (EMF-14; see, e.g., Weyant, 1993) data; and the previous database compiled for the IPCC (Alcamo et al., 1995). | ||||
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Region ID |
Number of Matching Scenarios
|
Region ID |
Number of Matching Scenarios
|
|
|
||||
World |
340
|
Europe |
12
|
|
OECD |
164
|
OECD West f |
13
|
|
Non-OECD |
158
|
Middle East/North Africa |
12
|
|
China |
153
|
East Asia |
12
|
|
USA |
136
|
Extra g |
12
|
|
FSU |
121
|
West Europe |
11
|
|
EEC |
85
|
DC |
7
|
|
Japan |
69
|
OSEAsia h |
7
|
|
FSU+EEU a |
61
|
SubSAfrica |
6
|
|
Annex 1 2 |
46
|
Annex 2 2 |
6
|
|
Non-Annex 1 2 |
46
|
Opacific |
6
|
|
Latin America |
42
|
Poland |
5
|
|
India |
36
|
OPEC |
4
|
|
Africa |
34
|
United Kingdom |
4
|
|
CPAsia b |
32
|
LDC i |
4
|
|
East Europe c |
31
|
Non-OPEC DC j |
3
|
|
ALM d |
30
|
Hungary |
3
|
|
CANZ |
29
|
Switzerland |
3
|
|
Mexico and OPEC |
29
|
INDUS k |
3
|
|
Non-OECD Annex 1 |
29
|
Asia Pacific |
2
|
|
Middle East |
27
|
Austria |
2
|
|
Oceania |
25
|
Brazil |
2
|
|
Canada |
24
|
Germany |
2
|
|
OECD Pacific e |
23
|
Korea |
2
|
|
SouthAsia (incl. India) |
23
|
Netherlands |
2
|
|
OECD Europe |
22
|
Sweden |
2
|
|
SEAsia (South and East Asia) |
16
|
Nigeria |
2
|
|
North America |
15
|
Other regions |
26
|
|
|
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Abbreviations: ALM, Africa, Latin America, and Middle East; CANZ, Canada, Australia, New Zealand; DC, Developing Countries; EEC, European Economic Community; EEU, Eastern Europe; FSU, Former Soviet Union; OECD, Organization for Economic Development and Cooperation; Opacific, Other Pacific Asia; OPEC, Organization of Petroleum Exporting Countries; SubSAfrica , Sub Saharan Africa; USA, United States of America a. Economies under transition, Former Soviet Union and Eastern Europe. |
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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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