Emissions Scenarios - IPCC

An Overview of Scenarios 183
capita grows at an intermediate rate to reach about US$12,000
by 2050. By 2100 the global economy might expand to reach
some US$250 trillion. International income differences
decrease, although not as rapidly as in storylines of higher
global convergence. Local inequity is reduced considerably
through the development of stronger community-support
networks.
Generally, high educational levels promote both development
and environmental protection. Indeed, environmental
protection is one of the few truly intemational common
priorities that remain iit B2. However, strategies to address
global environmental challenges are not of a central priority
and are thus less successful compared to local and regional
environmental response strategies. The govemments have
difficulty designing and implementing agreements that
combine global environmental protection, even when this
could be associated with mutual economic benefits.
The B2 storyline presents a particularly favorable climate for
community initiative and social innovation, especially in view
of the high educational levels. Technological frontiers are
pushed less than they are in Al and Bl, and innovations are
also regionally more heterogeneous. Globally, investment in
energy R&D continues its curtent declining trend (EIA, 1997,
1999), and mechanisms for international diffusion of
technology and know-how remain weaker than in scenarios Al
and Bl (but higher than in A2). Some regions with rapid
economic development and limited natural resources place
particular emphasis on technology development and bilateral
cooperation. Technical change is therefore uneven. The energy
intensity of GDP declines at about 1% per year, in line with the
average historical experience since 1800.
Land-use management becomes better integrated at the local
level in the B2 world. Urban and transport infrastmcture is a
particular focus of community innovation, and contributes to a
low level of car dependence and less urban sprawl. An
emphasis on food self-reliance contributes to a shift in dietary
pattems toward local products, with relatively low meat
consumption in countries with high population densities.
Energy systems differ from region to region, depending on the
availability of natural resources. The need to use energy and
other resources more efficiently spurs the development of less
carbon-intensive technology in some regions. Environment
policy cooperation at the regional level leads to success in the
management of some transboundary environmental problems,
such as acidification caused by sulfur dioxide (SOj), especially
to sustain regional self-reliance in agricultural production.
Regional cooperation also results in lower emissions of
nitrogen oxides (N0^^) and volatile organic compounds
(VOCs), which reduce the incidence of elevated tropospheric
ozone levels. Although globally the energy system remains
predominantly hydrocarbon-based to 2100, a gradual transition
occurs away from the current share of fossil resources in world
energy supply, with a corresponding reduction in carbon
uitensity.
4.4. Scenario Quantiñcation and Overview
4.4.1. Scenario Terminology
In this section representative quantifications of the four
scenario storylines described in Section 4.3 are summarized,
and the evolution of the main scenario driving forces and
associated quantitative scenario characteristics are described.
Their resultant GHG and other emissions are discussed in more
detail in Chapter 5.
To elucidate differences in uncertainties that stem both from
adopting alternative (exogenous) scenario driving-force
assumptions and from the uncertainties that arise from
different model representations, alternative scenario
quantifications are differentiated into harmonized and
unharmonized scenarios (see Section 4.2, Tables 4-1 and 4-2,
and Box 1-1 for terminology description).
To achieve harmonization across six different modeling
approaches is not a tiivial task. For example, most of the
models have different regional disaggregations, so that
harmonization at the level of the four SRES regions required
some "inverse" solutions, often achieved through iterative
model mns and adjustments of input assumptions. Also, in
some modeling frameworks the harmonized "input"
parameters are actually outputs of components of the modeling
framework (e.g., GDP as an output of economic general
equilibrium models, or final energy as an output variable after
considering endogenous energy prices and exogenously prespecified
energy-intensity improvement rates). Therefore,
harmonization of important scenario driving-force inputs was
neither possible for all scenarios and for all participating
modeling teams, and nor was it judged desirable, as the
adoption of any harmonization criterion somewhat artificially
compresses uncertainty. This is also why simpler
harmonization criteria were adopted (see Section 4.2. above)
that focused on global population and GDP growth profiles.
These are referred to as "globally harmonized" scenarios in
the subsequent Subsections.
From the 40 SRES scenarios, 26 are classified as "globally
harmonized" scenarios and 14 are classified as "other"
scenarios. (The latter category includes three scenarios that
only deviate slightly from the harmonization criteria.)
Harmonized scenarios are thus comparable in that they
describe similar global development pattems with respect to
demographics and economic growth. In the subsequent
discussion of scenario driving forces a three-tiered structure is
adopted. First, for each scenario family (and where applicable
for each scenario group in the Al scenario family), the
discussion starts with a presentation of the respective marker
and "fully harmonized" scenarios. Subsequentiy, "globally
harmonized" scenarios and "other" scenarios are discussed.
"Globally harmonized" scenarios shed additional light into
uncertainties that stem from adopting different regional
assumptions (see above). Finally, "other" scenarios are
presented that offer a different quantitative interpretation of a