2.5.1.3 A Short Description of the SRES Scenarios
Figure 2.11: Schematic illustration of SRES scenarios. The four
scenario families are shown, very simplistically, for illustrative
purposes, as branches of a two-dimensional tree. The two dimensions shown
indicate global and regional scenario orientation, and development and
environmental orientation, respectively. In reality, the four scenarios
share a space of a much higher dimensionality given the numerous driving
forces and other assumptions needed to define any given scenario in a
particular modelling approach. The schematic diagram illustrates that
the scenarios build on the main driving forces of GHG emissions. Each
scenario family is based on a common specification of some of the main
driving forces.
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Since there is no agreement on how the future will unfold, the SRES tried to
sharpen the view of alternatives by assuming that individual scenarios have
diverging tendencies one emphasizes stronger economic values, the other
stronger environmental values; one assumes increasing globalization, the other
increasing regionalization. Combining these choices yielded four different scenario
families (Figure 2.11). This two-dimensional representation
of the main SRES scenario characteristics is an oversimplification. It is shown
just as an illustration. In fact, to be accurate, the space would need to be
multi-dimensional, listing other scenario developments in many different social,
economic, technological, environmental, and policy dimensions.
The titles of the four scenario storylines and families have been kept simple:
A1, A2, B1, and B2. There is no particular order among the storylines; they
are listed in alphabetical and numerical order:
- The A1 storyline and scenario family describes a future world of very rapid
economic growth, global population that peaks in mid-century and declines
thereafter, and the rapid introduction of new and more efficient technologies.
Major underlying themes are convergence among regions, capacity building,
and increased cultural and social interactions, with a substantial reduction
in regional differences in per capita income. The A1 scenario family develops
into three groups that describe alternative directions of technological change
in the energy system. The three A1 groups are distinguished by their technological
emphasis: fossil intensive (A1FI), non-fossil energy sources (A1T), or a balance
across all sources (A1B).12
- The A2 storyline and scenario family describes a very heterogeneous world.
The underlying theme is self-reliance and preservation of local identities.
Fertility patterns across regions converge very slowly, which results in continuously
increasing global population. Economic development is primarily regionally
oriented and per capita economic growth and technological change are more
fragmented and slower than in other storylines.
- The B1 storyline and scenario family describes a convergent world with
the same global population that peaks in mid-century and declines thereafter,
as in the A1 storyline, but with rapid changes in economic structures towards
a service and information economy, with reductions in material intensity,
and the introduction of clean and resource-efficient technologies. The emphasis
is on global solutions to economic, social, and environmental sustainability,
including improved equity, but without additional climate initiatives.
- The B2 storyline and scenario family describes a world in which the emphasis
is on local solutions to economic, social, and environmental sustainability.
It is a world with a continuously increasing global population at a rate lower
than in A2, intermediate levels of economic development, and less rapid and
more diverse technological change than in the B1 and A1 storylines. While
the scenario is also oriented towards environmental protection and social
equity, it focuses on local and regional levels.
In all, six models were used to generate the 40 scenarios that comprise the
four scenario families. They are listed in Table 2.5. These
six models are representative of emissions scenario modelling approaches and
different integrated assessment frameworks in the literature, and include so-called
top-down and bottom-up models.
Table 2.5: Models used to generate the
SRES scenarios |
|
Model |
Source
|
Reference
|
|
Asian Pacific Integrated Model (AIM) |
National Institute of Environmental Studies in Japan |
Morita et al., 1994
Kainuma et al., 1998, 1999a, 1999b |
Atmospheric Stabilization Framework
Model (ASF) |
ICF Consulting in the USA |
EPA 1990; Pepper et al., 1992 |
Integrated Model to Assess the Greenhouse
Effect (IMAGE), used in connection with the WorldScan model |
IMAGE: RIVM and WorldScan: CPB (Central Planning Bureau),
The Netherlands |
IMAGE: Alcamo 1994; Alcamo et al.,1998; de Vries et al., 1999
WorldScan: CPB Netherlands, 1999 |
Multiregional Approach for Resource
and Industry Allocation (MARIA) |
Science University of Tokyo in Japan |
Mori and Takahashi, 1998 |
Model for Energy Supply Strategy Alternatives
and their General Environmental Impact (MESSAGE) |
IIASA in Austria |
Messner et al., 1996; Riahi and Roehrl, 2000 |
The Mini Climate Assessment Model
(MiniCAM) |
PNNL in the USA |
Edmonds et al., 1996 |
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2.5.1.4 Emissions and Other Results of the SRES Scenarios
Figure 2.12: Global CO2 emissions from energy and industry,
historical development from 1900 to 1990 and in 40 SRES scenarios from
1990 to 2100, shown as an index (1990 = 1). The range is large in the
base year 1990, as indicated by an error bar, but is excluded
from the indexed future emissions paths. The dashed time-paths depict
individual SRES scenarios and the blue shaded area the range of scenarios
from the literature (as documented in the SRES database). The median (50th),
5th, and 95th percentiles of the frequency distribution are shown. The
statistics associated with the distribution of scenarios do not imply
probability of occurrence (e.g., the frequency distribution of the scenarios
in the literature may be influenced by the use of IS92a as a reference
for many subsequent studies). The 40 SRES scenarios are classified into
six groups. Jointly the scenarios span most of the range of the scenarios
in the literature. The emissions profiles are dynamic, ranging from continuous
increases to those that curve through a maximum and then decline. The
coloured vertical bars indicate the range of the four SRES scenario families
in 2100. Also shown as vertical bars on the right are the ranges of emissions
in 2100 of IS92 scenarios, and of scenarios from the literature that apparently
include additional climate initiatives (designated as intervention
scenarios emissions range), those that do not (non-intervention),
and those that cannot be assigned to either of these two categories (non-classified).
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Figure 2.12 illustrates the range of global energy-related
and industrial CO2 emissions for the 40 SRES scenarios against the
background of all the 400 emissions scenarios from the literature documented
in the SRES scenario database. The six scenario groups are represented by the
six illustrative scenarios. Figure 2.12 also shows a range
of emissions of the six scenario groups next to each of the six illustrative
scenarios.
Figure 2.12 shows that the four marker and two illustrative
scenarios by themselves cover a large portion of the overall scenario distribution.
This is one of the reasons that the SRES Writing Team recommended the use of
all four marker and two illustrative scenarios in future assessments. Together,
they cover most of the uncertainty of future emissions, both with respect to
the scenarios in the literature and the full SRES scenario set. Figure
2.12 also shows that they are not necessarily close to the median of the
scenario family because of the nature of the selection process. For example,
A2 and B1 are at the upper and lower bounds of their scenario families, respectively.
The range of global energy-related and industrial CO2 emissions for
the six illustrative SRES scenarios is generally somewhat lower than the range
of the IPCC IS92 scenarios (Leggett et al., 1992; Pepper et al., 1992). Adding
the other 36 SRES scenarios increases the covered emissions range. Jointly,
the SRES scenarios cover the relevant range of global emissions, from the 95th
percentile at the high end of the distribution all the way down to very low
emissions just above the 5th percentile of the distribution. Thus, they only
exclude the most extreme emissions scenarios found in the literature
those situated out in the tails of the distribution. What is perhaps more important
is that each of the four scenario families covers a sizable part of this distribution,
implying that a similar quantification of driving forces can lead to a wide
range of future emissions. More specifically, a given combination of the main
driving forces is not sufficient to uniquely determine a future emission path.
There are too many uncertainties. The fact that each of the scenario families
covers a substantial part of the literature range also leads to an overlap in
the emissions ranges of the four families. This implies that a given level of
future emissions can arise from very different combinations of driving forces.
This result is of fundamental importance for assessments of climate change impacts
and possible mitigation and adaptation strategies.
An important feature of the SRES scenarios obtained using the SAR methodology
is that their overall radiative forcing is higher than the IS92 range despite
comparatively lower GHG emissions (Wigley and Raper, 1992; Wigley et al., 1994;
Houghton et al., 1996; Wigley, 1999; Smith et al., 2000; IPCC, 2001). This results
from the loss of sulphur-induced cooling during the second half of the 21st
century. On one hand, the reduction in global sulphur emissions reduces the
role of sulphate aerosols in determining future climate, and therefore reduces
one aspect of uncertainty about future climate change (because the precise forcing
effect of sulphate aerosols is highly uncertain). On the other hand, uncertainty
increases because of the diversity in spatial patterns of SO2 emissions
in the scenarios. Future assessments of possible climate change need to account
for these different spatial and temporal dynamics of GHG and sulphur emissions,
and they need to cover the whole range of radiative forcing associated with
the scenarios.
In summary, the SRES scenarios lead to the following findings:
- Alternative combinations of driving-force variables can lead to similar
levels and structure of energy use and land-use patterns, as illustrated by
the various scenario groups and scenarios. Hence, even for a given scenario
outcome, for example, in terms of GHG emissions, there are alternative combinations
and alternative pathways that could lead to that outcome. For instance, significant
global changes could result from a scenario of high population growth, even
if per capita incomes would rise only modestly, as well as from a scenario
in which a rapid demographic transition (low population levels) coincides
with high rates of income growth and affluence.
- Important possibilities for further bifurcations in future development
trends exist within one scenario family, even when adopting certain values
for important scenario driving force variables to illustrate a particular
possible development path.
- Emissions profiles are dynamic across the range of SRES scenarios. They
portray trend reversals and indicate possible emissions crossover among different
scenarios. They do not represent mere extensions of a continuous increase
of GHGs and sulphur emissions into the future. This more complex pattern of
future emissions across the range of SRES scenarios reflects the recent scenario
literature.
- Describing potential future developments involves inherent ambiguities
and uncertainties. One and only one possible development path (as alluded
to for instance in concepts such as business-as-usual scenario)
simply does not exist. And even for each alternative development path described
by any given scenario, there are numerous combinations of driving forces and
numerical values that can be consistent with a particular scenario description.
This particularly applies to the A2 and B2 scenarios that imply a variety
of regional development patterns that are wider than in the A1 and B1 scenarios.
The numerical precision of any model result should not distract from the basic
fact that uncertainty abounds. However, in the opinion of the SRES writing
team, the multi-model approach increases the value of the SRES scenario set,
since uncertainties in the choice of model input assumptions can be more explicitly
separated from the specific model behaviour and related modelling uncertainties.
- Any scenario has subjective elements and is open to various interpretations.
While the SRES writing team as a whole has no preference for any of the scenarios,
and has no judgement about the probability or desirability of the scenarios,
the open process and reactions to SRES scenarios have shown that individuals
and interest groups do have such judgements. This will stimulate an open discussion
in the political arena about potential futures and choices that can be made
in the context of climate change response. For the scientific community, the
SRES scenario exercise has led to the identification of a number of recommendations
for future research that can further increase understanding about potential
development of socio-economic driving forces and their interactions, and associated
GHG emissions.