Environmental Engineering Reference
In-Depth Information
storylines that depict divergent futures and describe the
factors controlling emissions and their evolution over
time in response to demographic, social, economic, tech-
nological and environmental change.
The four storylines can be summarized as:
causal structures. However, they also suggest eight major
limitations of the technique: (1) the models produced
may be overly complex, affecting their ability to com-
municate ideas simply; (2) the models may aggregate
processes to an unacceptably coarse level; (3) the crossing
of disciplinary boundaries usually causes suspicion from
all sides[!]; (4) the models generally deal with uncertainty
poorly; (5) these models tend not to account for stochastic
behaviour; (6) they are difficult to verify and validate (in
the traditional sense: see discussion in Chapter 2); (7) as
with most models, our knowledge and methodology are
generally limited (but remember that model building can
provide a means of assessing this); and (8) there are 'signif-
icant pitfalls [in] that policymakers and researchers may
treat integrated assessment models as ''truth machines'',
rather than as heuristic tools for developing a better
understanding of the issues' (Rotmans and Dowlatabadi,
1998: 302).
One of the IAMs used to generate some of the 40
SRES scenarios was the Integrated Model to Assess the
Greenhouse Effect (IMAGE 2.1, Alcamo
et al
., 1998).
IMAGE 2.1 represents processes of global change from
1970 to 2100 at a variety of spatial resolutions (from 0
.
5
◦
to
an aggregated globe) organizing these processes into three
submodels; Energy-Industry, Terrestrial Environment,
and Atmosphere-Ocean. The Energy-Industry submodel
represents the effect of economic and population story-
lines (for example from SRES) on industrial production
and energy consumption which is then used to generate
GHG emissions. In this submodel, technological change
and fuel price dynamics influence energy-intensity, fuel
substitution and adoption of nonfossil options such as
solar electricity and biomass-based fuels. The Terres-
trial Environment submodel simulates global land-use
and land-cover changes as a result of population, eco-
nomic and technological trends and land-use policies
(from storylines) and fuel, timber and biofuel demands
(from the Energy-Industry submodel) and their subse-
quent effects on terrestrial carbon balances and GHG
emissions (Figure 18.1). Both these submodels influ-
ence the Atmosphere-Ocean submodel, which represents
changes in climate in a far simpler manner than GCMs
(and so is vastly more computationally economic). For
example, IMAGE 2.1 parameterizes many processes that
are explicitly represented in GCMs (for example, zonal
heat circulation) and represents many processes at a
very coarse spatial resolution (for instance, continen-
tal regions). In turn, climate-change estimates from the
atmosphere-ocean submodel feed back into the terrestrial
A1: Rapid economic growth, global population peak-
ing mid-century and then declining thereafter, rapid
introduction of new efficient energy technologies, con-
vergence among regions, capacity building and reduced
region to region differences in per capita income. This
scenario is further subdivided into a fossil fuel-intensive
future (A1F1), an alternative energy future (A1T) and
a balance across all sources (A1B).
•
A2: This scenario describes a very heterogeneous world
of self-reliance and preservation of local identities. Fer-
tility patterns between regions converge very slowly,
which leads to a continuously increasing global popu-
lation. Per capita economic growth and technological
change are fragmented and slower than other scenarios.
•
B1: This scenario describes a convergent world with the
same population trends as A1 but with rapid changes
towards a service and information-based economy
with the introduction of clean and resource efficient
technologies. The emphasis is on global solutions and
increased equity.
•
B2: A world of continuously increasing global popula-
tion (at a rate lower than A2) where the emphasis is on
local solutions to economic, social and environmental
sustainability. It is a world of intermediate levels of eco-
nomic development and of less rapid and more diverse
technological change than B1 and A1.
•
Six integrated assessment models (IAMs) - each with
their own assumptions about the different drivers of
future GHG emissions - were used to produce the final
40 SRES scenarios. These scenarios were believed to cover
the range of uncertainty in GHG emissions and deemed
equally valid with no probability of occurrence attached
to them. IAMs take a synoptic, and often systemic, per-
spective to quantify the cause-effect relationships of events
and the interactions between phenomena (Janssen, 1998).
Rotmans and Dowlatabadi (1998) provide a thorough
overview of integrated assessment and suggest that IAMs
have great utility because of the ability to explore such
interactions and feedbacks with them; the availability
of tools that can quickly and flexibly produce results;
the production of counter-intuitive results (allowing
us to evaluate our preconceptions); and the provision
of tools for effective communication of even complex
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