Emissions Scenarios - IPCC

An Overview of Scenarios 215
production. Cost decreases down to USCents2.5^Wh are
anticipated once non-fossil options penetrate on a large scale.
The costs of gaseous biofuels in the major producing regions
(Latin America, Africa, NIS) are assumed to be in the order of
US$3 to 5 per GJ from 2020 to 2030 onward. Liquid biofuels
are produced in small amounts in almost all regions at costs in
the order of US$3 to 6 per GJ. In all regions a gradual transition
occurs from fossil fuels to non-fossil options in electric-power
generation, because of rising fuel prices and declining specific
investment costs for fossil alternatives. Learning rates were
assumed, conservatively, to yield 2 to 6% cost reductions for
every doubling of cumulative production. The shift would start
in resource-poor industrialized regions such as Japan and
Western Europe, but is somewhat tempered by rising
conversion efficiencies of fossil-fueled power plants. One of
the factors that constrains the use of natural gas in the scenario
is the assumption that only a limited part of the transport
market is open to competition from non-liquid fuels (between
50% around 2050 to 80% around 2100). Also, the market share
of coal in industry is fixed exogenously at 10 to 15% in some
regions, to reflect the decreasing environmental and social
attractiveness of the more "dirty" coal.
4.4.7.5. B2 Scenarios
The approach that underlies the B2 scenario storyline translates
into important future improvements of technologies, albeit at
more conservative rates than in scenarios Al or Bl, but with
higher rates than in scenario A2. Compared to AI and В1, cost
improvements are more modest, because of the regionally
fragmented technology policies assumed to characterize a B2
world. Hence, technology-spillover effects and benefits from
shared development expenditures are more limited in the
scenario. The high emphasis of environmental protection at the
local and regional levels is reflected in faster development and
diffusion of energy technologies with lower emissions,
including advanced coal technologies, nuclear, and renewables.
For instance, solar and wind electricity-generating costs are
assumed to decline to USCents3/kWh, that is, a similar level as
assumed for the long-term costs of advanced, clean coal
technologies (such as IGCCs). As conventional oil supplies
dwindle, initially high-cost synfuels from coal and also
biofuels are introduced as substitutes. With increasing
production volume, costs are assumed to decline from initial
levels of some US$7/GJ to US$2.6/GJ. Conventional coal
technologies undergo the lowest aggregate rates of
improvement in the scenario and are also subject to increasing
controls of social and envnonmental extemalities (mining
safety, particulates, and sulfur emissions). Increasingly,
therefore, only advanced coal technologies are deployed.
Nonetheless, extraction and conversion costs increase,
especially in regions with a large share of deep-mined coal and
in high population density agglomerations. In regions with
abundant surface minable coal reserves (e.g.. North America
and Australia), coal extraction costs remain relatively low.
4.4.7.6. Harmonized and Other Scenarios
As a consequence of the "multi-model approach" used in
SRES, detailed improvement assumptions and scenario
implementations for individual technologies vary greatiy from
one model to another, although the same storyline
characteristics were used as guiding principles and many
scenarios share similar assumptions on improvement potentials
for different technologies. Detailed quantitative comparisons
are difficult because of different time profiles of technology
improvements assumed in the different models, different
representations of regional technology, and the modeling of the
intemational diffusion of technology. For instance, many
models assume aggregate regional rates of technological
change (e.g., MARIA, MiniCAM, ASF), whereas others
attempt to represent spatial and temporal diffusion pattems
more explicitly (e.g., MESSAGE, AIM).
It is difficult to quantify the influence of varying technologyspecific
scenario assumptions on scenario outcomes, because
in most model simulations the technology assumptions were
varied in conjunction with other salient scenario
characteristics, such as economic growth and resource
availability (e.g. in the MiniCAM simulations). Therefore, the
impact of ahemative assumptions with respect to technological
change can be best quantified within a particular scenario
family and with "fully harmonized" scenario quantifications
(i.e. with comparable energy demand), as discussed for the Al
scenario groups above. In some scenarios within other scenario
families, technology-specific sensitivity analyses were
performed, such as in the B2C-MARIA scenario vaiiant of the
B2-MARIA quantification. The main differences between the
two scenarios are the respective costs of coal and nuclear
power. In B2C-MARIA, the price of coal was assumed to be
US$1.4/GJ, while that in B2-MARIA is US$1.8/GJ. In
contrast, the capital costs of nuclear power stations are
US$1400/kW in B2-MARIA, while those in B2C-MARIA are
assumed to remain at US$1800/kW. Thus, even comparatively
small variations in relative technology characteristics such as
costs and efficiencies can lead to wide differences in scenario
outcomes. As discussed in Chapter 5, for instance, changing
the relative economics between coal and nuclear in the two
MARIA scenarios results in a difference of more than 200 GtC
cumulative emissions^^ over the 2P' century
An illustration of inter-scenario variability in technology costs
and diffusion is given in Box 4-9 for the MESSAGE model
simulations for one representative scenario of each scenario
family and scenario group. As stated above, differences in
technology diffusion across scenarios are influenced by many
more factors than just altemative technology characteristics
and cost assumptions. Growth of energy demand, resource
availability and costs, and local circumstances (local airquality
regulations that require desulfurization of fuels or stack
Cumulative carbon emissions (all sources) are 1359 GtC for B2-
MARIA and 1573 GtC for B2C-MARIA (see Chapter 5).