Population - GCAM
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|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.|
GCAM’s inputs include information on population for each of GCAM’s energy-economic regions. GCAM requires globally consistent data sets for each of its historical model periods, currently 1990, 2005, 2010 and 2015, to initialize the model. Each scenario requires assumptions about population for future time periods. See the socioeconomic inputs and outputs section for more information.
- Population: The number of people living in each GCAM region in the benchmark and projection years
Historical population and observed GDP are used to calibrate a GCAM simulation using data from 1990, 2005, 2010, and 2015. Prognostic values for population and GDP per capita growth rates are provided by the user, though a default set is provided in the GCAM data base. Alternative assumptions associated with the SSPs are also implemented in the GCAM implementation of the Shared-Socioeconomic Pathways.