Emissions Scenarios - IPCC

An Overview of Scenarios 225
4.4.8.7. B2 Scenarios
The B2 (MESSAGE) marker scenario first follows a trend
toward increasing shares of gas, followed by renewables, and
finally - as oil and gas start to become scarce - increasingly
returns to coal. By 2100, the B2 scenario ends up somewhere
in the middle of the triangle in Figure 4-11 (i.e., it relies on a
broad, diversified mixture of different primaiy energy sources).
This global diversification results from heterogeneous trends at
the regional level and is largely a function of the more modest
assumpfions concerning technology improvements and oil and
gas resource availability (compared to other scenario families,
in particular Al and Bl) that are characteristic of the B2
scenario family. By 2100, the main primary energy carriers are
biomass (23%), coal (22%), oil and gas (29%), and other
renewables and nuclear (26%). Countries with low income and
high resource availability continue to rely on fossil fuels up to
the end of the 2P* century, such as China (mainly coal), Fonner
Soviet Union (mainly gas), and Middle East (first oil and later
gas). Regions with low resource availability, such as Africa and
South America, rely on renewables and nuclear. The decreasing
share of coal and oil in the primary energy structure of OECD
countries is substituted by the growing share of renewables,
gas, and nuclear. A major characteristic of the B2 scenario is
the increasing importance of synthetic liquid fuels in the
second half of the 2P' century, because of a continuous phase
out of conventional oil in all regions.
4.4.8.8. Harmonized and Other B2 Scenarios
Altemative B2 scenarios show a great diversity in changes in
energy systems structures compared to the B2 marker.
Common to all scenarios is their gradual transition away from
conventional oil and gas, which are assumed to be
comparatively scarce in the B2 scenario storyline. However
alternative scenarios depict very different trends for this
structural change, ranging from increased reliance on coal and
coal-derived synfuels (B2-ASF, B2High-MiniCAM) to more
biomass- and nuclear-intensive scenarios (B2-AIM, B2-
MARIA, and B2-IMAGE). Generally, this reflects the
considerable uncertainty as to direction and pace of
technological change in the technologically more fragmented
worid described in the B2 scenario storyline. B2-MiniCAM
anticipates a strong reliance on oil and natural gas as
transitional fuels, with a share in primary energy of about 50%
over the next 100 years to 2100 (i.e., gas shares in B2-
MiniCAM are as high as in the AIG scenario group). In tum,
B2-ASF suggests an increasing reliance on coal and coalderived
synfuels. A third group of scenarios tends to follow
similar directions of structural change as those of the B2-
marker - a gradual introduction of post-fossil alternatives (with
different weights for nuclear and renewables as a function of
technological progress), along with gas (or in some scenarios
coal-derived synfuels) as transitional technology options.
Structural changes in energy systems of the various B2
scenarios largely follow the main directions of the marker
scenario developed with a particular model. That is, differences
in alternative B2 scenarios appear to relate strongly to
differences in model parametrizations derived from the
respective marker scenario rans, most notably in the domains
of resource availability and technology (see Sections 4.4.6 and
4.4.7). Thus, B2-ASF depicts structural changes in energy
technologies and systems akin to the trends of the A2 marker
scenario, whereas B2-AIM and B2-IMAGE largely follow the
pattems of change of their marker scenarios (Al and Bl,
respectively). Altemative pattems of change are illustrated by
the B2-MiniCAM and B2-MARIA scenarios, which have also
explored scenario sensitivities by developing altemative B2
scenario quantifications (B2C-MARIA, B2High-MiniCAM)
that show a higher reliance on coal (and hence higher GHG
emissions).
4.4.9. Land-Use Changes
The main driving forces for land-use changes are related to
increasing demands for food because of a growing population
and changing diets. In addition, numerous other social,
economic, and institutional factors govern land-use changes,
such as deforestation, expansion of cropland areas, or their reconversion
back to forest cover (see Chapter 3). Global food
production can be increased, either thi-ough intensification
(e.g., using multi-cropping, raising cropping intensity,
applying fertilizers, new seeds, improved farming technology)
or through land expansion (e.g., cultivating land, converting
forests). Especially in less developed countries, many
examples show the potentials for intensification of food
production in a more or less ecological way that may not lead
to higher GHG emissions (e.g., multi-cropping; agro-forestry).
Different assumptions on these processes translate into
altemative scenarios of future land-use changes and GHG
emissions, most notably CO^, methane (CH^), and nitrous
oxide (N2O). A distinguishing characteristic of several models
(e.g., AIM, IMAGE, MARIA, and MiniCAM) used in SRES is
the explicit modeling of land-use changes from expanding
biomass uses, and hence exploration of possible land-use
conflicts between the energy and agricultural sectors. The
con"e.sponding scenarios of land-use changes are summarized
in Table 4-17 and Figure 4-12 for the four SRES marker
scenarios and all SRES scenarios. The distinction in scenario
groups is related to the energy system and is thus not relevant
in this section.
As discussed further in Chapter 5, model treatment of land-use
change and base-year parameterization differ substantially.
Therefore, comparisons between different models can yield
substantial differences. Land-use change assumptions for each
of the marker scenarios are described below. More detailed
inter-model comparisons of land-use change and emissions
models, as well as a deeper analysis of potentials and rates of
change of main driving force variables, such as agricultural
productivity growth and dietary changes, remain an important
area for future research.