Future changes in population size, composition, and spatial distribution are key factors in the
analysis of climate change, and their future evolution is highly uncertain. In climate change
analyses, population uncertainty has traditionally been accounted for by using alternative
scenarios spanning a range of outcomes. This paper illustrates how conditional probabilistic projections
offer a means of combining probabilistic approaches with the scenario-based approach typically
employed in the development of greenhouse gas emissions projections. The illustration
combines a set of emissions scenarios developed by the Intergovernmental Panel on Climate
Change (IPCC) with existing probabilistic population projections from IIASA.
Results demonstrate that conditional probabilistic
projections have the potential to account more fully for uncertainty in emissions within
conditional storylines about future development patterns, to provide a context for judging the
consistency of individual scenarios with a given storyline, and to provide insight into relative
likelihoods across storylines, at least from a demographic perspective. They may also serve as a
step toward more comprehensive quantification of uncertainty in emissions projections.