Publications Published in Technological Forecasting and Social Change
Regional, national, and spatially explicit scenarios of demographic and economic change based on SRES. Grubler, A.; O'Neill, B.; Riahi, K.; Chirkov, V.; Goujon, A.; Kolp, P.; Prommer, I.; Scherbov, S.; Slentoe, E..
Technological Forecasting and Social Change:
We report here spatially explicit scenario interpretations for population and economic activity (GDP) for the time period 1990 to 2100 based on three scenarios (A2, B1, and B2) from the IPCC Special Report on Emissions Scenarios (SRES). At the highest degree of spatial detail, the scenario indicators are calculated at a 0.5 by 0.5 degree resolution. All three scenarios follow the qualitative scenario characteristics, as outlined in the original SRES scenarios. Two scenarios (B1 and B2) also follow (with minor adjustments due to scenario improvements) the original SRES quantifications at the level of 4 and 11 world regions respectively. The quantification of the original SRES A2 scenario has been revised to reflect recent changing perceptions on the demographic outlook of world population growth. In this revised “high population growth” scenario, A2r world population reaches some 12 billion by 2100 (as opposed to some 15 billion in the original SRES A2 scenario) and is characterized by a “delayed fertility transition” that is also mirrored in a delayed (economic) development catch-up, resulting in an initially stagnating and subsequently only very slow reduction in income disparities. The spatially explicit scenario interpretation proceeds via two steps. Through a combination of decomposition and optimization methods, world regional scenario results are first disaggregated to the level of 185 countries. In a subsequent second step, national results are further disaggregated to a grid cell level, taking urbanization and regional (rural–urban) income disparities explicitly into account. A distinguishing feature of the spatially explicit scenario results reported here is that both methodologies, as well as numerical assumptions underlying the “downscaling” exercise, are