Data - 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 COFFEE-TEA DNE21+ GCAM GRACE POLES PROMETHEUS 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 GCAM Data System combines and reconciles a wide range of different data sets, and systematically incorporates a range of future assumptions. The output of the data system is an XML dataset with historical and base-year data for calibrating the model along with assumptions about future trajectories such as GDP, population, and technology. It includes the necessary information for representing energy, water, land, and the economic system. The GCAM Data System is largely constructed in R, but accommodates inputs in a range of different formats. Creating new scenarios does not require the use of the GCAM data system. New, “add on” xml files can be created to overwrite key future scenario assumptions such as population, economic activity, and technology cost and performance, among others. See Overview of GCAM Computational Components and the gcamdata GitHub repository for more details.