Socio-economic drivers - IFs
|Institution||Frederick S. Pardee Center for International Futures, University of Denver https://pardee.du.edu/ (Pardee Center), Colorado, USA, .|
|Solution method||Dynamic recursive with annual time steps through 2100.|
In forecasting long-term change, it is useful to distinguish distal and proximate drivers. The former consist of driving variables that help account for long-term structural change. They may operate at some causal distance from the variable we are forecasting, but we know them to be deeply structurally related to that variable, often via multiple paths. On the other hand, proximate variables are those that create shorter-term variation, often in part as intermediate variables between the deeper or distal drivers and the target variable, but often also as levers that policy or other short-term factors may influence somewhat independently of the deeper drivers. Key formulations across the models of IFs generally involve a combination of distal and proximate drivers.
The common approach across other IAMs represented in this ADVANCE project-based Wiki is to use this section to discuss the representation of population and economic activity (exogenously or endogenously) as drivers for the biophysical systems that other Wiki sections then elaborate. However, the IFs system contains a variety of models (see Model scope and methods/Model concept, solver and details) that require some elaboration here. These models are not only drivers, but driven subsystems of IFs, responding to each other and to change in biophysical systems. In keeping with the structure of this Wiki, we will save discussion of the energy model for another top-level topic and similarly elaborate the economic model in its own topic. But in addition to population and an introductory survey of economy activity, we will also provide subtopics here on the IFs models of education, health, governance, infrastructure, interstate politics, and socio-political variables.