Working Group III: Mitigation

Other reports in this collection 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.

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
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 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”).

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.

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