Emissions Scenarios - IPCC

232 An Overview of Scenarios
be found in the literature in which per capita emissions in
industrialized and developing countries reached similar levels.
Thus the absence of such convergence in the SRES scenarios
reflects the current literature, and resuhs from the nature of the
SRES scenarios as "no climate policy" scenarios.
SRES scenarios do follow the recommendation to explore
possible pathways of closing the income gap between the
industrial and now developing regions (Alcamo et a!., 1995).
For reasons of plausibility and foundation in the reviewed noclimate-policy
literature, the SRES scenarios do not achieve
full income convergence in the scenario period analyzed.
However, income levels in developing countries do reach the
1990 levels of the industrial countries in the second half of the
next century in three out of four scenario families.
4.6. A Roadmap to the SRES Scenarios
In the preceding sections the characteristics of the SRES
scenarios are summaiized in terms of scenario driving forces
such as population, economic development, resources,
technology, land-use changes, and other factors. The scenarios
were designed in such a way as to deliberately span a wide
range, reflecting uncertainties of the future, but not cover the
very extremes from the scenario literature conceming driving
forces. A distinguishing feature of the SRES scenarios is that
various driving-force variables are not combined numerically
(or arbitrarily), but instead try to reflect current understanding
of the interrelationships between important scenario driving
forces. For instance, according to the literature review of
Chapter 3 it would be rather inconsistent to develop scenarios
of rapid technological change in a macro-economic and social
context of low labor productivity and stagnant income per
capita. Scenario storylines were the method developed within
SRES to help guide the scenario quantifications and to assure
scenario consistency in terms of the main relationships
between scenario driving forces.
The different quantifications discussed in the previous sections
demonstrate that even if scenarios share important main input
assumptions in terms of population and GDP growth.
Box 4-10: Spatial Distributions of Economic Activities Based on Nighttime Satellite Imagery Data.
Spatially explicit data on socio-economic activity is sparse. The reason aiises mainly in that Systems of National Accounts and
similar socio-economic statistics are available only at high levels of spatial aggregations defined by administrative boundaries
(countries, provinces, or regions). As a result, gridded emission inventories largely rely on estimations of current population
density distributions (e.g. Olivier et al., 1996) and modeling approaches to date have also relied exclusively on rescaling/wíMre
socio-economic activities (economic output, energy use, etc.) based on current or future population density distribution pattems
(see e.g., S0rensen and Meibom, 1998; S0rensen et al., 1999). Population density is, however, not necessarily a good indicator
for spatial pattems of socio-economic activities (Sutton et ai, 1997). At higher levels of spatial aggregation, it is estimated that
about two billion people remain outside the formal economy, most of them in rural areas of developing countries (UNDP, 1997).
At lower levels of spatial aggregations, locations such as airports, industrial zones, and commercial centers have low resident
population densities, but high levels of economic activity. Also, with increasing urbanization future population distribution will
be markedly different from present ones (HABITAT, 1996).
Night satelUte hnagery from the US Air Force Defense Meteorological Satellite Program (DMSP) Operational Lmescan System
(OLS) offers an interesting altemative based on direct observations. Early nighttune lights data were analyzed from analog film
strips (Croft, 1978, 1979; Foster, 1983; Sullivan, 1989). Digital DMSP-OLS data have recentiy become available with global
coverage (Elvidge etal., 1997a, 1997b, 1999). Nocturnal lighting can be regarded as one of the defining features of concentrated
human activity, such as flaring of nahiral gas m ou fields (Croft, 1973), fishing fleets, or urban settiements (Tobler, 1969; Lo
and Welch, 1977; Foster, 1983; Gallo et al., 1995; Elvidge et al, 1997c). Consequentiy, extent and brightness of nocturnal
lighting correlate highly with mdicators of city size and socio-economic activities such as GDP, and energy and electricity use
(Welch, 1980; Gallo et ai; 1995; Elvidge et al., 1997a).
Figure 4-13 (bottom panel) shows a 1995/1996 night-luminosity map of the world developed by National Oceanic and
Atmospheric Administi-ation's National Geophysical Data Center. The map was derived from composites of cloud-free visible
band observations made by the DMSP-OLS (see Elvidge et al., 1997b; Imhoff et al., 1997). The DMSP-OLS is an oscillating
scan radiometer that generates images with a swath width of 3000 km. The DMSP-OLS is uiüque in its capability to perform
low-light imaging of the entire earth on a nighfly basis. With 14 orbits per day, the polar orbiting DMSP-OLS is able to generate
global daytime and nighttime coverage of the Earth every 24 hours. The "visible" bandpass straddles the visible and nearinfrared
(VNIR) portion of the spectram. The thermal infrared channel has a bandpass that covers lO^i^ jj^g spectrum.
SateUite altitude is stabilized using four gyroscopes (three-axis stabilization), a starmapper, Earth limb sensor, and a solar
detector. Image time series analysis is used to distinguish lights produced by cities, towns, and industrial facifities from sensor
noise and ephemeral lights that arise from frres and lightning. The time series approach is required to ensure that each land area
is covered with sufficient cloud-free observations to determine the presence or absence of VNIR emission sources.