This is documentation for users of the G-Cubed model implemented in Python.

It has been tailored, in places, to the teaching version of G-Cubed, with 2 regions and 2 sectors. It also includes an overview of a full multi-region/multi-sector version of the G-Cubed model that is suited for analysis of a variety of economic and environmental issues.

A roadmap for new users

If you are first-time user of the the G-Cubed Python implementation, start by ensuring you have correctly set up the Python environment.

After properly setting up the environment, you can have a quick taste of how to run the model and what the results look like by following the steps in Quickstart guide. If you want to try out the graphics system designed specifically for the gcubed model results, please follow the steps in G-Cubed charts.

Next, learn about the teaching version of the G-Cubed model and how to configure your own experiments by reading G-Cubed model setup and Model data files.

Then, you may want to have a deeper understanding of how the model is defined using the SYM model definition language. With an understanding of SYM, the model definition language, the documentation for the teaching model including the variable and equation details will be straightforward to understand.

The documentation of the teaching model also explains how to configure the model, how to set up the information needed to calibrate the model parameters, and how to use a database of past data on the model variables to do projections. The processes for doing so generalise to G-Cubed model versions with many regions and many sectors.

If you want to learn more about how G-Cubed can be customised, the Python API documentation will be relevant.