Other materials - 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 COFFEE-TEA DNE21+ GRACE IFs IMACLIM PROMETHEUS
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

Education

The model forecasts gender- and country-specific access, participation and progression rates at levels of formal education starting from elementary through lower and upper secondary to tertiary. The model also forecasts costs and public spending by level of education. Dropout, completion and transition to the next level of schooling are all mapped onto corresponding age cohorts thus allowing the model to project educational attainment for the entire population at any point in time within the analysis horizon.

From simple accounting of the grade progressions to complex budget balancing and budget impact algorithm, the model draws upon the extant understanding and standards (e.g., UNESCO's ISCED classification) about national systems of education around the world. One difference between other attempts at projecting educational participation and attainment and that of IFs is the embedding of education within an integrated model in which demographic and economic variables interact with education, in both directions, as the model runs.

Figure 1 displays the major variables and components that directly determine education demand, supply, and flows in the IFs system. We emphasize again the inter-connectedness of the components and their relationship to the broader human development system. For example, during each year of simulation, the IFs cohort-specific demographic model provides the school age population to the education model. In turn, the education model feeds its calculations of education attainment to the population model’s determination of women’s fertility. Similarly, the broader economic and socio-political systems provide funding for education, and levels of educational attainment affect economic productivity and growth, and therefore also education spending.