Emissions Scenarios - IPCC

Scenario Driving Forces 113
recent and mostly carried out in a qualitative way. Alcamo et
al. (1995) reviews the literature available up to 1994.
Nakicenovic et al. (1998a) report a long-term scenario study in
which timing and extent of economic catch-up in developing
countries were found to be tied to timing and pace of their
demographic transition. Pattems of the range of per capita
GDP growth rates for developing countries available in the
literature (and given in Figure 3-10) also appear to reflect this
relationship. Growth trends for the period 2020-2050 are
generally higher than those for earlier or later periods; it is in
this period that, according to demographic projections, the
fastest change in demographic variables (especially fertility)
will take place.
Figure 3-7: TFRs in 1995 versus GDP per capita in US
dollars at 1995 prices for most of the world's countries. Data
source: World Bank, 1997b.
from higher fertility to lower fertility. This move is further
prompted by the rising relative costs of childcare, which
include preferences that increase a child's quality of life, such
as better schooling and extracurricular activities.
This income effect is primarily interpreted with respect to
fertility changes in currently developing countries. In currently
industrial countries, any change of fertility in response to
increasing wealth is likely to be different from and probably
even opposite to that of developing countries' feitility. Indeed,
there is evidence of this in Eastem Europe, Sweden, Russia, and
(recently) the United States (UN, 1997a), and it is linked to the
question of long-term fertility rates in industrial countries (see
Section 3.2.3.2.). However, it is evident that future world
population levels will be dominated by growth in developing
countries. Thus, if it is accepted that fertility is lower with
greater affluence, then in emissions scenarios lower populations
will still tend to cortelate with higher per capita incomes.
Another countervailing factor is that mortality rates should also
decline with wealth; as an isolated effect, this obviously results
in higher population levels. However, the combined impact of
both fertility and mortality reduction on population size is a net
reduction in population levels (Lutz, 1996).
From a demographic point of view, the primary effect seen in
Figure 3-7 is interpreted as infant and child mortality decline
with increasing affluence. Families, in a sense, have to use
birth control to achieve their desired number, which has always
been lower than fertility rates. Increased affluence results in
increased knowledge of, access to, and use of birth control, and
accordingly families shift their reproductive behavior to lower
fertility rates. Accompanying this basic premise is a host of
complex social changes, including increased opportunities for
education, employment, and non-maternal roles for women.
Incorporation of the inverse relationship between economic
and population growth in long-term emission scenarios is
3.2.6. Conclusions
From the available population projections, only those from the
UN and IIASA fulfill the characteristics needed for use in
long-term emission scenarios. First, the UN and IIASA data
are published and available in the public domain, and second
(more importantly) the scenarios consider uncertainty by
developing more than just one, central demographic
projection.
We use the medium UN projections in the SRES emissions
scenarios because they have greater recognition internationally,
and garner considerable attention as evident from the press
focus devoted to the 1996 Revision (mentioned above). In
addition, the UN assumption of replacement-level fertility in
the long term, in contrast to the IIASA below-replacement
assumption, is an important normative approach widely used
heretofore in projections.
The rapid and slow demographic transition variants from
IIASA projections remain attractive as the "high" and "low"
population variants to be considered for the new IPCC
emissions scenarios. The incorporation of a correlation
between mortality rates and fertility (Lutz, 1996) is a logical
first-order relationship not used in previous population variants
and, in particular, not a feature of the UN variants. The two
IIASA variants also represent well the uncertainty range as
spanned by the probabilistic projections of Lutz et al. (1997),
which represent an important methodological advance in the
field. As shown in Figure 3-2, the resultant IIASA population
range falls within the range of the UN projections.
Based on the above recommendations, Figure 3-8 compares the
older IS92 population range with the population range
described in this section. The population projections in IS92
scenarios comprise the UN 1992 medium-high and
medium-low variants for the high and low ranges with the
World Bank 1991 projection as the central case. As seen in
Figure 3-8, the new range for SRES is somewhat narrower and
lower than the IS92 range. The cause is partly the positive
correlation between mortality rates and fertility rates within the
IIASA variants, which mildly offset each other in terms of
future population size. Another reason is the recent downward