Enhancing the policy relevance of scenarios through a dynamic analytical approach

We present a new dynamic analytical approach for studying scenarios produced by an integrated assessment (IA) model. Our approach involves the analysis of a large number of scenarios, which can better address three principal shortcomings of how uncertainty is traditionally handled in IA scenario studies. The shortcomings are all a result of the prevailing practice of investigating a small number of scenarios and include (1) the ad hoc nature of exploring vast socioeconomic uncertainties with only a small number of scenarios; (2) the conventional representation of alternative scenario typologies as "parallel universes" or "diverging universes", which provide little insight on possible socioeconomic conditions that could lead to bifurcations or trend reversals. These shortcomings may inhibit the policy relevance of IA scenario studies. As an analytical approach that may improve the situation, we describe and demonstrate a dynamic method for analysing large numbers of scenarios and provide an example application using the framework for Shared Socioeconomic Pathways (SSPs), which are new socioeconomic scenarios being developed for future climate change research. We systematically tested alternative assumptions for an IA model parameters to generate a database of scenarios and classified them as consistent with each of the five SSP typologies. We then develop a visualization of their evolution through the SSP typology space dynamically over the years 2001-2090. We found that the dynamic analytical approach can reveal the influence of socioeconomic conditions that change the scenario typology classification of individual scenarios over time. Explanations for such scenario behaviour could be highly policy relevant.