Sufficiency production network

This code was developed to run a two-region open economy production network model, and to estimate the mitigation potential of sufficiency consumption changes. The calibration relies on the GLORIA input-output database.

Installation

Step 1: Git clone the folder in your computer.

git clone https://github.com/celiaescribe/production_networks.git

Step 2: Create a conda environment from the environment.yml file:

• The environment.yml file is in the production_networks folder. The only packages we rely on to run the code are numpy, pandas, matplotlib, seaborn.
• Use the terminal and go to the production_networks folder stored on your computer.
• Type the following command:

conda env create -f environment.yml

Step 3: Activate the conda environment:

conda activate networks

Getting started

Step 1: Calibrating the model

This calibration can be done in three steps. First, preprocessing is done by running the script gloria_preprocessing.py. This requires specifying the two-region aggregation chosen, and the year for the input-output tables. Currently, available implementation includes a two-region economy based on a single country (e.g., the United States) and a Rest of the World region, or the European Union and a Rest of the World region. GLORIA database can be accessed at the following url: https://ielab.info/resources/gloria/supportingdocs

python gloria_preprocessing.py --country eu --year 2018

Some postprocessing is then required, by running the script

python gloria_postprocessing.py --country eu --year 2018

Finally, calibration can be done by running the script

python calibrate.py --country eu --year 2018

This saves a configuration file in the folder production_networks/outputs.

Step 2: Running the model

The standard way to run the model is to launch the script main.py. This requires providing the configuration which you want to run. The configuration file specifies different options, such as the calibration file to use, the reference elasticity parameters, and the specification of the shocks, among others. Examples of configuration can be found in production_networks/inputs/configs.

The script is then run as follows:

python main.py --config config_eu.json

Step 3: Explore outputs

Output files are stored in production_networks/outputs.