Data - GCAM

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Model Documentation - GCAM

Corresponding documentation
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Model information
Model link
Institution Pacific Northwest National Laboratory, Joint Global Change Research Institute (PNNL, JGCRI), USA, http://www.globalchange.umd.edu.
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.