Pollutants and non-GHG forcing agents - GCAM

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Alert-warning.png Note: The documentation of GCAM is 'under review' and is not yet 'published'!

Model Documentation - GCAM

Corresponding documentation
Previous versions
No previous version available
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.

Air Pollutant Emissions

Air pollutant emissions (E) such as sulfur dioxide (SOs) and nitrogen oxides (NOx) are modeled as

where A is activity level, EF is emissions factor, and EmCtrl is a function that represents decreasing emissions intensity as per-capita income increases:

where pcGDP stands for the per-capita GDP, and steepness is an exogenous constant, specific to each technology and pollutant species, that governs the degree to which changes in per-capita GDP will be translated to emissions controls. The purpose here is to capture the general global trend of increasing pollutant controls over time, but does not capture regional and technological heterogeneity. See the documentation's section on air pollution.