Emissions Scenarios - IPCC

An Overview of the Scenario Literature 83
• The third objective was to malee 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, N,0, CFCs (chlorofluorocarbons), HFCs
(hydrofluorocarbons), and other radiatively active gases such as
SO,, CO, and N0^. 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 CO^ emissions.
A large majority (230) of the scenarios report only energyrelated
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 SOj 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 nonenergy-related
CO2 emissions.
The information documented in the database about emissions
scenarios illustrates both areas that are well covered in the
scenario fiterature 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, COj emissions are
by far the most frequentiy 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 CO, emissions contribute more to the cun-ent 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.
2.4. Analysis of Literature
Individual scenarios are considered independent entities in the
database. Clearly, in practice, individual scenarios are often
related to each other and are not always developed
independenfly. Some are simply variants of others generated
for a particular purpose. Many "new" scenarios are designed to
track existing benchmark scenarios. A good example is the set
of IS92 scenarios, especially the "central" IS92a scenario,
which was often used as a reference from which to develop
other scenarios. A further consideration is that not all scenarios
are created in an equal fashion. Some are the result of elaborate
effort, which includes extensive reviews and revisions; others
are simply the outcome of input assumptions without much
significant reflection. Some are based on extensive formal
models, while others are generated using simple spreadsheets
or even without any fomial tools at all.
Numerous factors influence future emissions paths in the
scenarios. Clearly, demographic and economic developments
play a crucial role in determining emissions. However, many
other factors are involved also, from human resources, such as
education, institutional frameworks, and lifestyles, to natural
resource endowments, technologic change, and intemational
trade. Many of these important factors are not documented in
the database, and sometimes not even in the respective scenario
reports and publications. Some are neither quantified in the
scenarios nor explicitly assumed in a narrative form.
For this analysis, a simple scheme is used to decompose the
main driving foices of GHG emissions. This scheme is based
on the Kaya identity (Kaya, 1990; Yamaji et al, 1991), which
gives the main emissions driving forces as multiplicative