Socio-economic drivers - GCAM

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

Model Documentation - GCAM
Model scope and methods - GCAM Model concept, solver and details - GCAM Temporal dimension - GCAM Spatial dimension - GCAM Policy - GCAM Socio-economic drivers - GCAM Population - GCAM Economic activity - GCAM Macro-economy - GCAM Trade - GCAM Energy - GCAM Energy resource endowments - GCAM Fossil energy resources - GCAM Uranium and other fissile resources - GCAM Bioenergy - GCAM Non-biomass renewables - GCAM Energy conversion - GCAM Electricity - GCAM Heat - GCAM Gaseous fuels - GCAM Liquid fuels - GCAM Solid fuels - GCAM Grid, pipelines and other infrastructure - GCAM Energy end-use - GCAM Transport - GCAM Residential and commercial sectors - GCAM Industrial sector - GCAM Other end-use - GCAM Energy demand - GCAM Technological change in energy - GCAM Land-use - GCAM Agriculture - GCAM Forestry - GCAM Land-use change - GCAM Bioenergy land-use - GCAM Agricultural demand - GCAM Technological change in land-use - GCAM Emissions - GCAM GHGs - GCAM Pollutants and non-GHG forcing agents - GCAM Carbon dioxide removal (CDR) options - GCAM Climate - GCAM Modelling of climate indicators - GCAM Non-climate sustainability dimension - GCAM Air pollution and health - GCAM Water - GCAM Appendices - GCAM Mathematical model description - GCAM Data - GCAM References - GCAM
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
No previous version available
Model information
Model link	http://jgcri.github.io/gcam-doc/toc.html https://github.com/JGCRI/gcam-core
Institution	Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, https://www.pnnl.gov/projects/jgcri.
Solution concept	General equilibrium (closed economy)GCAM solves all energy, water, and land markets simultaneously
Solution method	Recursive dynamic solution method
Anticipation	GCAM is a dynamic recursive model, meaning that decision-makers do not know the future when making a decision today. After it solves each period, the model then uses the resulting state of the world, including the consequences of decisions made in that period - such as resource depletion, capital stock retirements and installations, and changes to the landscape - and then moves to the next time step and performs the same exercise. For long-lived investments, decision-makers may account for future profit streams, but those estimates would be based on current prices. For some parts of the model, economic agents use prior experience to form expectations based on multi-period experiences.

The socioeconomic components of GCAM set the scale of economic activity and associated demands for model simulations. Assumptions about population and per capita GDP growth for each of the 32 geo-political regions together determine the Gross Domestic Product (GDP). GDP and population both can drive the demands for a range of different demands within GCAM. Population and economic activity are used in GCAM through a one-way transfer of information to other GCAM components. For example, neither the price nor quantity of energy nor the quantity of energy services provided to the economy affect the calculation of the principle model output of the GCAM macro-economic system, GDP [1].