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Ecology","sub_category":"Input Output Model","monthly_downloads":1014,"total_dependent_repos":0,"total_dependent_packages":0,"readme":".. role:: pythoncode(code)\n   :language: python\n\n#######\nBoARIO\n#######\n|build-status| |black| |contribute| |licence| |pypi| |pythonv| |joss|\n\n.. |build-status| image:: https://img.shields.io/github/actions/workflow/status/spjuhel/boario/CI.yml\n   :target: https://github.com/spjuhel/BoARIO/actions/workflows/CI.yml\n   :alt: GitHub Actions Workflow Status\n.. |black| image:: https://img.shields.io/badge/code%20style-black-000000\n   :target: https://github.com/psf/black\n   :alt: Code Style - Black\n.. |contribute| image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat\n   :target: https://github.com/spjuhel/BoARIO/issues\n   :alt: Contribution - Welcome\n.. |licence| image:: https://img.shields.io/badge/License-GPLv3-blue\n   :target: https://www.gnu.org/licenses/gpl-3.0\n   :alt: Licence - GPLv3\n.. |pypi| image:: https://img.shields.io/pypi/v/boario\n   :target: https://pypi.org/project/boario/\n   :alt: PyPI - Version\n.. |pythonv| image:: https://img.shields.io/pypi/pyversions/boario\n   :target: https://pypi.org/project/boario/\n   :alt: PyPI - Python Version\n.. |joss| image:: https://joss.theoj.org/papers/71386aa01a292ecff8bafe273b077701/status.svg\n   :target: https://joss.theoj.org/papers/71386aa01a292ecff8bafe273b077701\n   :alt: Joss Status\n\n`BoARIO` : The Adaptative Regional Input Output model in python.\n\n.. _`Documentation Website`: https://spjuhel.github.io/BoARIO/boario-what-is.html\n\nDisclaimer\n===========\n\nIndirect impact modeling is tied to a lot of uncertainties and complex dynamics.\nAny results produced with `BoARIO` should be interpreted with great care. Do not\nhesitate to contact the main author when using the model!\n\nFurthermore, this project is currently developed on the free time of its main contributor,\nwithout any financial supports. If you are interested in its further development and have funding\ncapacity, please reach out!\n\nWhat is BoARIO ?\n=================\n\nBoARIO, is a python implementation project of the Adaptative Regional Input Output (ARIO) model [`Hal13`_].\n\nIts objectives are to give an accessible and inter-operable implementation of ARIO, as well as tools to visualize and analyze simulation outputs and to\nevaluate the effects of many parameters of the model.\n\nThis implementation would not have been possible without the `Pymrio`_ module and amazing work of [`Sta21`_].\n\nIt is still an ongoing project (in parallel with a PhD project).\n\n.. _`Sta21`: https://openresearchsoftware.metajnl.com/articles/10.5334/jors.251/\n.. _`Hal13`: https://doi.org/10.1111/j.1539-6924.2008.01046.x\n.. _`Pymrio`: https://pymrio.readthedocs.io/en/latest/intro.html\n\nYou can find most academic literature using ARIO or related models `here \u003chttps://spjuhel.github.io/BoARIO/boario-references.html\u003e`_\n\n\nWhat is ARIO ?\n===============\n\nARIO stands for Adaptive Regional Input-Output. It is an hybrid input-output / agent-based economic model,\ndesigned to compute indirect costs from economic shocks. Its first version dates back to 2008 and has originally\nbeen developed to assess the indirect costs of natural disasters [`Hal08`_].\n\nIn ARIO, the economy is modelled as a set of economic sectors and a set of regions.\nEach economic sector produces its generic product and draws inputs from an inventory.\nEach sector answers to a total demand consisting of a final demand (household consumption,\npublic spending and private investments) of all regions (local demand and exports) and\nintermediate demand (through inputs inventory resupply). An initial equilibrium state of\nthe economy is built based on multi-regional input-output tables (MRIOTs).\n\nFor a more detailed description, please refer to the `Mathematical documentation`_ of the model.\n\nMulti-Regional Input-Output tables\n-------------------------------------\n\nMulti-Regional Input-Output tables (MRIOTs) are comprehensive economic data sets\nthat capture inter-regional trade flows, production activities, and consumption\npatterns across different regions or countries. These tables provide a detailed\nbreakdown of the flows of goods and services between industries within each\nregion and between regions themselves. MRIOTs are constructed through a\ncombination of national or regional input-output tables, international trade\ndata, and other relevant economic statistics. By integrating data from multiple\nregions, MRIOTs enable the analysis of global supply chains, international trade\ndependencies, and the estimation of economic impacts across regions. However,\nthey also come with limitations, such as data inconsistencies across regions,\nassumptions about trade patterns and production technologies, and the challenge\nof ensuring coherence and accuracy in the aggregation of data from various\nsources.\n\n.. _`Mathematical documentation`: https://spjuhel.github.io/BoARIO/boario-math.html\n\n.. _`Hal08`: https://doi.org/10.1111/risa.12090\n\nWhere to get BoARIO ?\n==========================\n\nYou can install BoARIO from ``pip`` with:\n\n.. code:: console\n\n   pip install boario\n\nOr from ``conda-forge`` using conda (or mamba):\n\n.. code:: console\n\n   conda install -c conda-forge boario\n\n\nThe full source code is also available on Github at: https://github.com/spjuhel/BoARIO\n\nMore info in the `installation \u003chttps://spjuhel.github.io/BoARIO/boario-installation.html\u003e`_ page of the documentation.\n\nHow does BoARIO work?\n=========================\n\nIn a nutshell, BoARIO takes the following inputs :\n\n- 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 \u003chttps://github.com/IndEcol/pymrio\u003e`_ for details on methods available to pymrio objects.\n\n- multiple parameters which govern the simulation,\n\n- event(s) description(s), which are used as the perturbation to analyse during the simulation\n\nAnd produces the following outputs:\n\n- the step by step, sector by sector, region by region evolution of most of the variables involved in the simulation (`production`, `demand`, `stocks`, ...)\n\n- aggregated indicators for the whole simulation (`shortages duration`, `aggregated impacts`, ...)\n\n.. _`EXIOBASE 3`: https://www.exiobase.eu/\n.. _`EORA26`: https://worldmrio.com/eora26/\n\nExample of use\n=================\n\nSee `Boario quickstart \u003chttps://spjuhel.github.io/BoARIO/boario-tutorials.html\u003e`_.\n\nCredits\n========\n\nAssociated PhD project\n------------------------\n\nThis model was developed during project founder's PhD on the indirect impact of extreme events (`Thesis available here`_).\nThis PhD was supported by the French Environment and Energy Management Agency (`ADEME`_).\n\n.. image:: https://raw.githubusercontent.com/spjuhel/BoARIO/master/imgs/Logo_ADEME.svg?sanitize=true\n           :width: 400\n           :alt: ADEME Logo\n\n.. _`ADEME`: https://www.ademe.fr/\n.. _`Thesis available here`: https://theses.hal.science/tel-05045805\n\nDevelopment\n------------\n\n - Samuel Juhel (pro@sjuhel.org)\n\nContributions\n---------------\n\nAll `contributions \u003chttps://spjuhel.github.io/BoARIO/development.html\u003e`_ to the project are welcome !\n\nAcknowledgements\n------------------\n\nI would like to thank Vincent Viguie, Fabio D'Andrea my PhD supervisors as well as Célian Colon, Alessio Ciulo and Adrien Delahais\nfor their inputs during the model implementation.\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1111/j.1539-6924.2008.01046.x","https://doi.org/10.1111/risa.12090"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["archiving","measur","transforms","generic","optimize","compose","observation","conversion","projection","animals"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/328280","html_url":"https://ost.ecosyste.ms/projects/328280"}