4.4.2.1. A1 Scenarios

The A1 marker scenario (Jiang et al., 2000) was created with the AIM model, an integrated assessment model developed by NIES, Japan (see Appendix IV). The A1 scenario family is characterized by:

• An affluent world, with a rapid demographic transition (declining mortality and fertility rates) and an increasing degree of international development equity.
• Very high productivity and economic growth in all regions, with a considerable catch-up of developing countries.
• Comparatively high energy and materials demands, moderated however by continuous structural change and the diffusion of more efficient technologies, consistent with the high productivity growth and capital turnover rates of the scenario.

The first group of A1 scenarios, which includes the A1B marker, assumes "balanced"⁶ progress across all resources and technologies from energy supply to end use, as well as "balanced" land-use changes. Three other groups of A1 scenarios were identified which describe three alternative pathways according to different resource and technology development assumptions:

• A1C: "clean coal" technologies that are generally environmentally friendly with the exception of GHG emissions.
• A1G: an "oil- and gas-rich" future, with a swift transition from conventional resources to abundant unconventional resources including methane clathrates.
• A1T: a "non-fossil" future, with rapid development of solar and nuclear technologies on the supply side and mini-turbines and fuel cells used in energy end-use applications.

The divergence between the various scenario groups (in terms of resource availability and the direction of technological change) results in a wide range of GHG emissions. The two fossil-fuel dominated alternatives, A1C and A1G (combined into the fossil-intensive A1FI scenario group in the SPM, see Footnote 1), have higher, and the A1T alternatives have lower, GHG emissions than the A1 marker scenario (see Chapter 5).

"Balanced" A1 scenarios quantifications were also calculated by the models⁷ A1B-ASF, A1B-IMAGE, A1B-MARIA, A1B-MESSAGE, and A1B-MiniCAM. Additional scenarios representing A1 scenario groups were developed using the AIM (A1C-AIM, A1G-AIM, A1T-AIM ⁸ ), MARIA (A1T-MARIA), MESSAGE (A1C-MESSAGE, A1G-MESSAGE, A1T-MESSAGE), and MiniCAM (A1C-MiniCAM, A1G-MiniCAM) models. The MiniCAM modeling team also evaluated alternative interpretations of the A1 scenario storyline with different demographic, economic, and energy development patterns (A1v1-MiniCAM and A1v2-MiniCAM) on top of the alternative technology-resource developments examined in the other A1 scenarios.

4.4.2.2. A2 Scenarios

The A2 marker scenario (A2-ASF) was developed using ASF (see Appendix IV), an integrated set of modeling tools that was also used to generate the first and the second sets of IPCC emission scenarios (SA90 and IS92). Overall, the A2-ASF quantification is based on the following assumptions (Sankovski et al., 2000):

• Relatively slow demographic transition and relatively slow convergence in regional fertility patterns.
• Relatively slow convergence in inter-regional GDP per capita differences.
• Relatively slow end-use and supply-side energy efficiency improvements (compared to other storylines).
• Delayed development of renewable energy.
• No barriers to the use of nuclear energy.

Additional scenario quantifications of A2 were developed using the AIM (A2-AIM)⁹ , IMAGE (A2-IMAGE)¹⁰, MESSAGE (A2-MESSAGE), and MiniCAM (A2-MiniCAM)¹¹ models. An alternative interpretation of the A2 scenario storyline in the form of a "delayed development" or "transitional" scenario between the A2 and A1 scenario families was developed by the MiniCAM modeling team (A2- A1-MiniCAM).

4.4.2.3. B1 Scenarios

The B1 marker scenario (de Vries et al., 2000) was developed using the IMAGE 2.1 model (see Appendix IV). Earlier versions of the model were used in the first IPCC scenario development effort (SA90). B1 illustrates the possible emissions implications of a scenario in which the world chooses consistently and effectively a development path that favors efficiency of resource use and "dematerialization" of economic activities. The scenario entails in particular:

• Rapid demographic transition driven by rapid social development, including education.
• High economic growth in all regions, with significant catch-up in the presently less-developed regions that leads to a substantial reduction in present income disparities.
• Comparatively small increase in energy demand because of dematerialization of economic activities, saturation of material- and energy-intensive activities (e.g., car ownership), and effective innovation and implementation of measures to improve energy efficiency.
• Timely and effective development of non-fossil energy supply options in response to the desire for a clean local and regional environment and to the gradual depletion of conventional oil and gas supplies.

Additional scenarios of B1 were developed using the AIM (B1- AIM), ASF (B1-ASF), MARIA (B1-MARIA), MESSAGE (B1- MESSAGE), and MiniCAM (B1-MiniCAM) models. Some of these scenarios explore alternative technological developments (akin to the A1 scenario, e.g. B1T-MESSAGE) or alternative interpretations on rates and potentials of future dematerialization and energy-intensity improvements (e.g., B1High-MESSAGE and B1High-MiniCAM explore scenario sensitivities of higher energy demand compared to the B1 marker).

4.4.2.4. B2 Scenarios

The B2 marker scenario (Riahi and Roehrl, 2000) was developed using the MESSAGE model (see Appendix IV), an integrated set of energy-sector simulation and optimization models used to generate the IIASA-WEC long-term energy and emission scenarios (IIASA-WEC, 1995; Nakicenovic et al., 1998). Compared to the other storylines (A1 and B1), the B2 future unfolds with more gradual changes and less extreme developments in all respects, including geopolitics, demographics, productivity growth, technological dynamics, and other salient scenario characteristics. A more fragmented pattern of future development (not that different from present trends) precludes any particularly strong convergence tendencies in the scenario quantification:

• Model parameter values for projections to 2100 were derived typically from long-term historical data series where applicable (Marchetti and Nakicenovic, 1979; Nakicenovic, 1987; Grübler, 1990; Nakicenovic et al., 1996; Grübler, 1998a; Nakicenovic et al., 1998), or adopted from the medians of the analysis of the scenario literature (see Chapter 2).
• The scenario quantification assumes effective policies in solving local and regional problems such as traffic congestion, local air pollution, and acid rain impacts.

Additional B2 scenarios were developed using the AIM (B2- AIM), ASF (B2-ASF)¹² , IMAGE (B2-IMAGE)¹⁰, MARIA (B2- MARIA; Mori, 2000), and MiniCAM (B2-MiniCAM)¹³ models. Again, more than one B2 scenario interpretation was generated. Some models (e.g., B2-MARIA or B2High-MiniCAM) offered additional perspectives of both inter- and intra-model variability in the interpretation of the B2 storyline, particularly with respect to resource availability and technology development assumptions (see Section 4.4.7) and their resultant impact on GHG emissions (see Chapter 5).

Figure 4-4: Global cumulative CO 2 emissions in the scenarios and their main driving forces. The minimum, maximum, and median (50 th percentile) values shown on the six axes of each hexagon, for the cumulative energy and land-use CO2 emissions from 1990 to 2100 and 2100 values for the four driving forces, are based on the distribution of scenarios in the literature (see Chapter 2). The four hexagons show the ranges across the four scenario families (A1, A2, B1, and B2), cumulative CO2 emissions in GtC, population (POP) in billions, gross world product (GDP) in trillion US dollars (T$) at 1990 prices, final energy intensity of the gross world product (FE/GDP) in MJ per US dollar at 1990 prices (MJ/$), and CO2 emissions intensity of primary energy (PE) (tC/TJ).

Figure 4-4 summarizes the main global scenario indicators of the four SRES marker scenarios by 2100, including population and global GDP levels, final energy intensities, final energy use, corresponding carbon intensities, land-use changes¹⁴, and energy-related CO₂ emissions. It illustrates that the range of the most important scenario characteristics spanned by the four SRES marker scenarios and the entire SRES scenario set covers the uncertainty range well, as reflected in the scenario literature. The scenario space defined by the lines "SRES-max" and "SRES-min" lies well within the range spanned by the scenario literature contained in the SRES scenario database and analyzed in Chapter 2. The two exceptions are:

• The low range of future CO₂ emissions from the literature is not reflected in the SRES scenarios, consistent with the SRES Terms of Reference to consider only scenarios that assume no "additional climate policy initiatives" (see Appendix I).
• The low end of the range of global GDP and energy use from the literature is equally not reflected in the SRES scenarios. Very low global GDP values arise from a combination of rapid demographic transition with low per capita productivity growth, a combination for which there is little theoretic or empiric support in the available literature on demographic and economic growth reviewed in Chapter 3. Low GDP scenarios can also reflect a combination of average population growth and low economic growth; this type of future usually depicts low-income, inequitable, and possibly unstable worlds that are not analyzed in this report (see Box 4-2).

Equally, while the SRES scenarios cover the range from the literature, the four marker scenarios cannot and do not replicate the frequency distributions of individual scenario variables as discussed in Chapter 2. Nor can their quantitative characteristics segment the relevant distributions in approximately equal intervals. Two distinguishing features characterize the SRES scenarios. First, probabilities or likelihood are not assigned to any quantitative scenario characteristics (inputs or outputs). Thus, that two of the SRES marker scenarios deploy the same (low) demographic projection does not imply that such a scenario is considered more likely. It only indicates that such a demographic scenario was judged by the SRES writing team to be consistent with two of the four SRES storylines, as opposed to arbitrarily assigning different population projections to other "high" or "low" scenario characteristics. Second, the SRES scenarios incorporate current understanding of important interrelations between various scenario-driving forces (see Chapter 3). Thus, a "free," or "modeler's choice," numeric combination of scenario indicators is simply not possible. For instance, intermediary levels of global GDP or energy use could result both from a medium population projection combined with intermediate per capita GDP or energy use growth, or alternatively from low or high population projections combined with high or low GDP and energy per capita values, respectively. The fact that for some quantitative scenario characteristics a number of SRES marker scenarios cluster more toward the upper or lower range spanned by the scenario literature merely indicates the existence of important relationships between scenario characteristics. It also indicates that a limited number of scenarios (four markers) cannot replicate the distribution of individual scenario values arising out of an analysis of more than 400 scenarios published in the literature¹⁵. Hence, it is important to consider always the entire range across all 40 SRES scenarios when analyzing uncertainties in all driving-force variables and the resultant emission categories.

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