Socio-economic drivers - IFs

Note: The documentation of IFs is 'under review' and is not yet 'published'!

Model Documentation - IFs
Model scope and methods - IFs Model concept, solver and details - IFs Temporal dimension - IFs Spatial dimension - IFs Policy - IFs Socio-economic drivers - IFs Population - IFs Economic activity - IFs Macro-economy - IFs Energy - IFs Energy resource endowments - IFs Fossil energy resources - IFs Uranium and other fissile resources - IFs Bioenergy - IFs Non-biomass renewables - IFs Energy conversion - IFs Electricity - IFs Heat - IFs Gaseous fuels - IFs Liquid fuels - IFs Solid fuels - IFs Grid, pipelines and other infrastructure - IFs Energy end-use - IFs Transport - IFs Residential and commercial sectors - IFs Industrial sector - IFs Other end-use - IFs Energy demand - IFs Technological change in energy - IFs Land-use - IFs Agriculture - IFs Emissions - IFs GHGs - IFs Pollutants and non-GHG forcing agents - IFs Carbon dioxide removal (CDR) options - IFs Climate - IFs Non-climate sustainability dimension - IFs Water - IFs Other materials - IFs References - IFs
Corresponding documentation
AIM-Hub BLUES C3IAM DNE21+ GCAM GEM-E3 GRACE IFs IMACLIM IMAGE MESSAGE-GLOBIOM POLES PROMETHEUS REMIND-MAgPIE TIAM-UCL WITCH
Previous versions
Archive of IFs version 7.36
Model information
Model link	https://pardee.du.edu/access-ifs
Institution	Frederick S. Pardee Center for International Futures, University of Denver (Pardee Center), Colorado, USA, https://pardee.du.edu/.
Solution concept
Solution method	Dynamic recursive with annual time steps through 2100.
Anticipation	Myopic

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.