Emissions Scenarios - IPCC

An Overview of Scenarios 189
• High economic growth in all regions, with significant
catch-up in tlie 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 scenaiios of В1 were developed using the AIM (Bl-
AIM), ASF (Bl-ASF), MARIA (Bl-MARIA), MESSAGE (57-
MESSAGE), and MiniCAM (BI-MiniCAM) models. Some of
these scenarios explore altemative technological developments
(akin to the Al scenario, e.g. BIT-MESSAGE) or alternative
interpretations on rates and potentials of future
dematerialization and energy-intensity improvements (e.g.,
BlHigh-MESSAGE and BlHigh-MiniCAM explore scenario
sensitivities of higher energy demand compared to the Bl
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 (Al and Bl), 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
pattem 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)^^ IMAGE (B2-IMAGE),'0 ¡yjARlA (B2-
MARIA; Mori, 2000), and MiniCAM (B2-MiniCAM)i3
models. Again, more than one B2 scenario inteipretation was
generated. Some models (e.g., B2-MAR1A 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 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 changes,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 littie theoretic or empii'ic 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
'2 The B2-ASF shares global population and GDP assumptions with
the B2 marker by 2100, but explores different dynamics of growth in
the intervening time period.
'3 The scenario deviates only slightiy frotn the global population and
GDP assumptions of other "harmonized" scenarios within this
scenario family.
The
dynamic profiles of land-use changes mean that scenario
comparisons for any given year, such as 2100, are somewhat
misleading. Hence, cumulative carbon emissions that result from
land-use changes over the 1990 to 2100 period are used as a proxy
indicator in Figure 4-4 (see Chapter 5 for a more detailed discussion).