Skip to main content

GitHub Actions Workflow Status Code Style - Black Contribution - Welcome Licence - GPLv3 PyPI - Version PyPI - Python Version Joss Status

BoARIO : The Adaptative Regional Input Output model in python.

Disclaimer

Indirect impact modeling is tied to a lot of uncertainties and complex dynamics. Any results produced with BoARIO should be interpreted with great care. Do not hesitate to contact the main author when using the model!

Furthermore, this project is currently developed on the free time of its main contributor, without any financial supports. If you are interested in its further development and have funding capacity, please reach out!

What is BoARIO ?

BoARIO, is a python implementation project of the Adaptative Regional Input Output (ARIO) model [Hal13].

Its objectives are to give an accessible and inter-operable implementation of ARIO, as well as tools to visualize and analyze simulation outputs and to evaluate the effects of many parameters of the model.

This implementation would not have been possible without the Pymrio module and amazing work of [Sta21].

It is still an ongoing project (in parallel with a PhD project).

You can find most academic literature using ARIO or related models here

What is ARIO ?

ARIO stands for Adaptive Regional Input-Output. It is an hybrid input-output / agent-based economic model, designed to compute indirect costs from economic shocks. Its first version dates back to 2008 and has originally been developed to assess the indirect costs of natural disasters [Hal08].

In ARIO, the economy is modelled as a set of economic sectors and a set of regions. Each economic sector produces its generic product and draws inputs from an inventory. Each sector answers to a total demand consisting of a final demand (household consumption, public spending and private investments) of all regions (local demand and exports) and intermediate demand (through inputs inventory resupply). An initial equilibrium state of the economy is built based on multi-regional input-output tables (MRIOTs).

For a more detailed description, please refer to the Mathematical documentation of the model.

Multi-Regional Input-Output tables

Multi-Regional Input-Output tables (MRIOTs) are comprehensive economic data sets that capture inter-regional trade flows, production activities, and consumption patterns across different regions or countries. These tables provide a detailed breakdown of the flows of goods and services between industries within each region and between regions themselves. MRIOTs are constructed through a combination of national or regional input-output tables, international trade data, and other relevant economic statistics. By integrating data from multiple regions, MRIOTs enable the analysis of global supply chains, international trade dependencies, and the estimation of economic impacts across regions. However, they also come with limitations, such as data inconsistencies across regions, assumptions about trade patterns and production technologies, and the challenge of ensuring coherence and accuracy in the aggregation of data from various sources.

Where to get BoARIO ?

You can install BoARIO from pip with:

pip install boario

Or from conda-forge using conda (or mamba):

conda install -c conda-forge boario

The full source code is also available on Github at: https://github.com/spjuhel/BoARIO

More info in the installation page of the documentation.

How does BoARIO work?

In a nutshell, BoARIO takes the following inputs :

  • a (possibly Environmentally Extended) Multi-Regional IO table (such as EXIOBASE 3 or EORA26) in the form of an pymrio.IOSystem object, using the Pymrio python package. Please reference the Pymrio documentation for details on methods available to pymrio objects.

  • multiple parameters which govern the simulation,

  • event(s) description(s), which are used as the perturbation to analyse during the simulation

And produces the following outputs:

  • the step by step, sector by sector, region by region evolution of most of the variables involved in the simulation (production, demand, stocks, …)

  • aggregated indicators for the whole simulation (shortages duration, aggregated impacts, …)

Example of use

See Boario quickstart.

Credits

Associated PhD project

This model was developed during project founder’s PhD on the indirect impact of extreme events (Thesis available here). This PhD was supported by the French Environment and Energy Management Agency (ADEME).

ADEME Logo

Development

Contributions

All contributions to the project are welcome !

Acknowledgements

I would like to thank Vincent Viguie, Fabio D’Andrea my PhD supervisors as well as Célian Colon, Alessio Ciulo and Adrien Delahais for their inputs during the model implementation.

Metadata

Release files for boario 0.7.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for boario 0.7.1
File Size Uploaded
boario-0.7.1.tar.gz 2.0 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for boario 0.7.1
File Interpreter ABI Platform
boario-0.7.1-py3-none-any.whl Python 3 none any Details

Total release size: 2.0 MB

Release files / boario-0.7.1.tar.gz

Download URL boario-0.7.1.tar.gz
Size 2.0 MB
Tags Source
SHA-256 checksum
How to use checksums
ac6326f938dcb50f9ba92401291a80a5653e64d12edaddef61bb92b02c3a5add
BLAKE2b-256 checksum
How to use checksums
fae68798517d564bfa011a7a014e50bee4eeb7f5d61ca0f74717e2232523ee1e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/2.1.3 CPython/3.12.3 Linux/6.8.0-134-generic

Release files / boario-0.7.1-py3-none-any.whl

Download URL boario-0.7.1-py3-none-any.whl
Size 58.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
adfcb47580425257e88fd520483e568fcf15d71bc396bcc1d10a4cf11bd20f49
BLAKE2b-256 checksum
How to use checksums
1727a6c3f7a8e63abeb60699d98dd93cb1e2fbc3f9e12d9d8c4131fca335fade
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via poetry/2.1.3 CPython/3.12.3 Linux/6.8.0-134-generic
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page