From c16b519811fab17e2cae2cee62612fc9f419dfeb Mon Sep 17 00:00:00 2001
From: The Open Journals editorial robot
 <89919391+editorialbot@users.noreply.github.com>
Date: Tue, 11 Jun 2024 20:01:52 +0100
Subject: [PATCH 1/3] Creating 10.21105.joss.06547.pdf

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From: The Open Journals editorial robot
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Date: Tue, 11 Jun 2024 20:01:52 +0100
Subject: [PATCH 2/3] Creating 10.21105.joss.06547.crossref.xml

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+disaster analysis</article_title>
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+            <journal_title>Economic Systems Research</journal_title>
+            <issue>4</issue>
+            <volume>28</volume>
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+            <author>Colon</author>
+            <journal_title>Nature Sustainability</journal_title>
+            <issue>3</issue>
+            <volume>4</volume>
+            <doi>10.1038/s41893-020-00649-4</doi>
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+Control</journal_title>
+            <volume>83</volume>
+            <doi>10.1016/j.jedc.2017.08.001</doi>
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+      </journal_article>
+    </journal>
+  </body>
+</doi_batch>

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+<?xml version="1.0" encoding="utf-8" ?>
+<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.2 20190208//EN"
+                  "JATS-publishing1.dtd">
+<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="1.2" article-type="other">
+<front>
+<journal-meta>
+<journal-id></journal-id>
+<journal-title-group>
+<journal-title>Journal of Open Source Software</journal-title>
+<abbrev-journal-title>JOSS</abbrev-journal-title>
+</journal-title-group>
+<issn publication-format="electronic">2475-9066</issn>
+<publisher>
+<publisher-name>Open Journals</publisher-name>
+</publisher>
+</journal-meta>
+<article-meta>
+<article-id pub-id-type="publisher-id">6547</article-id>
+<article-id pub-id-type="doi">10.21105/joss.06547</article-id>
+<title-group>
+<article-title>BoARIO: A Python package implementing the ARIO indirect
+economic cost model</article-title>
+</title-group>
+<contrib-group>
+<contrib contrib-type="author">
+<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-8801-3890</contrib-id>
+<name>
+<surname>Juhel</surname>
+<given-names>Samuel</given-names>
+</name>
+<xref ref-type="aff" rid="aff-1"/>
+<xref ref-type="aff" rid="aff-2"/>
+</contrib>
+<aff id="aff-1">
+<institution-wrap>
+<institution>CIRED, France</institution>
+</institution-wrap>
+</aff>
+<aff id="aff-2">
+<institution-wrap>
+<institution>LMD, France</institution>
+</institution-wrap>
+</aff>
+</contrib-group>
+<pub-date date-type="pub" publication-format="electronic" iso-8601-date="2017-08-13">
+<day>13</day>
+<month>8</month>
+<year>2017</year>
+</pub-date>
+<volume>9</volume>
+<issue>98</issue>
+<fpage>6547</fpage>
+<permissions>
+<copyright-statement>Authors of papers retain copyright and release the
+work under a Creative Commons Attribution 4.0 International License (CC
+BY 4.0)</copyright-statement>
+<copyright-year>2022</copyright-year>
+<copyright-holder>The article authors</copyright-holder>
+<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
+<license-p>Authors of papers retain copyright and release the work under
+a Creative Commons Attribution 4.0 International License (CC BY
+4.0)</license-p>
+</license>
+</permissions>
+<kwd-group kwd-group-type="author">
+<kwd>Python</kwd>
+<kwd>economy</kwd>
+<kwd>indirect impacts</kwd>
+<kwd>input-output modeling</kwd>
+</kwd-group>
+</article-meta>
+</front>
+<body>
+<sec id="summary">
+  <title>Summary</title>
+  <p>The impacts of economic shocks (caused by natural or technological
+  disasters for instance) often extend far beyond the cost of their
+  local, direct consequences. Part of these indirect consequences are
+  caused by the propagation of the economic perturbations along supply
+  chains. Understanding the additional impacts and costs stemming from
+  this propagation is key to design efficient risk management policies.
+  The interest is rising for the evaluation of these “indirect risks” in
+  the context of climate change–which leads to an increase in the
+  average risk of weather extremes
+  (<xref alt="Lange et al., 2020" rid="ref-lange-2020-projec-expos" ref-type="bibr">Lange
+  et al., 2020</xref>), and globalized-just-in-time production
+  processes. Such evaluations rely on dynamic economic models that
+  represent the interactions between multiple regions and sectors.
+  Recent research in the field argues in favor of using more Agent-Based
+  oriented model, associated with an increase in the complexity of the
+  mechanisms represented
+  (<xref alt="Coronese &amp; Luzzati, 2022" rid="ref-coronese-2022-econom-impac" ref-type="bibr">Coronese
+  &amp; Luzzati, 2022</xref>). However, the assumptions and hypotheses
+  underlying these economic mechanisms vary a lot, and sometimes lack
+  transparency, making it difficult to properly interpret and compare
+  results across models, even more so when the code used is not
+  published or undocumented.</p>
+  <p>The Adaptive Regional Input-Output model (or ARIO) is an hybrid
+  input-output / agent-based economic model, designed to compute
+  indirect costs consequent to economic shocks. Its first version dates
+  back to 2008 and was originally developed to assess the indirect costs
+  of natural disasters
+  (<xref alt="Hallegatte, 2008" rid="ref-hallegatte-2008-adapt-region" ref-type="bibr">Hallegatte,
+  2008</xref>). ARIO is now a well-established and a pivotal model in
+  its field, has been used in multiple studies, and has seen several
+  extensions or adaptations
+  (<xref alt="Guan et al., 2020" rid="ref-guan-2020-global-suppl" ref-type="bibr">Guan
+  et al., 2020</xref>;
+  <xref alt="Hallegatte, 2008" rid="ref-hallegatte-2008-adapt-region" ref-type="bibr">Hallegatte,
+  2008</xref>,
+  <xref alt="2013" rid="ref-hallegatte-2013-model-role" ref-type="bibr">2013</xref>;
+  <xref alt="Hallegatte et al., 2010" rid="ref-hallegatte-2010-asses-climat" ref-type="bibr">Hallegatte
+  et al., 2010</xref>;
+  <xref alt="Henriet et al., 2012" rid="ref-henriet-2012-firm-networ" ref-type="bibr">Henriet
+  et al., 2012</xref>;
+  <xref alt="Jenkins, 2013" rid="ref-jenkins-2013-indir-econom" ref-type="bibr">Jenkins,
+  2013</xref>;
+  <xref alt="E. E. Koks et al., 2015" rid="ref-koks-2015-integ-direc" ref-type="bibr">E.
+  E. Koks et al., 2015</xref>;
+  <xref alt="Ranger et al., 2010" rid="ref-ranger-2010-asses-poten" ref-type="bibr">Ranger
+  et al., 2010</xref>;
+  <xref alt="C. Wang et al., 2018" rid="ref-wang-2018-quant-spatial" ref-type="bibr">C.
+  Wang et al., 2018</xref>;
+  <xref alt="D. Wang et al., 2020" rid="ref-wang-2020-econom-footp" ref-type="bibr">D.
+  Wang et al., 2020</xref>;
+  <xref alt="Wu et al., 2011" rid="ref-wu-2011-region-indir" ref-type="bibr">Wu
+  et al., 2011</xref>).</p>
+  <p>In ARIO, the economy is modelled as a set of economic sectors and
+  regions, and we call a specific (region, sector) couple an
+  <italic>industry</italic>. Each industry produces a unique product
+  which is assumed to be the same for all industries of the same sector.
+  Each industry keeps an inventory of inputs it requires for production.
+  Each industry answers a total demand consisting of the final demand
+  (from households, public spendings and private investments) and of the
+  intermediate demand (from other industries). An initial equilibrium
+  state for the economy is built based on a multi-regional input-output
+  table. The model can then describe how the economy, as depicted,
+  responds to a shock (or multiple ones).</p>
+  <p><monospace>BoARIO</monospace> is an open-source Python package
+  implementing the ARIO model. Its core purpose is to help support
+  better accessibility, transparency, replicability and comparability in
+  the field of indirect economic impacts modeling.</p>
+</sec>
+<sec id="statement-of-need">
+  <title>Statement of need</title>
+  <p>Although the ARIO model has been used in multiple studies, and
+  several extensions exists, only a few implementations of the model or
+  similar ones are openly available. We found the following existing
+  implementations:</p>
+  <list list-type="bullet">
+    <list-item>
+      <p>A Python implementation of MRIA
+      (<xref alt="Elco E. Koks &amp; Thissen, 2016" rid="ref-koks-2016-multir-impac" ref-type="bibr">Elco
+      E. Koks &amp; Thissen, 2016</xref>).</p>
+    </list-item>
+    <list-item>
+      <p>A Python implementation of Disrupt Supply Chain
+      (<xref alt="Colon et al., 2020" rid="ref-colon-2020-critic-analy" ref-type="bibr">Colon
+      et al., 2020</xref>).</p>
+    </list-item>
+    <list-item>
+      <p>A C++ implementation of the Acclimate model
+      (<xref alt="Otto et al., 2017" rid="ref-otto-2017-model-loss" ref-type="bibr">Otto
+      et al., 2017</xref>).</p>
+    </list-item>
+    <list-item>
+      <p>A Matlab implementation of C. Shughrue’s model
+      (<xref alt="Shughrue et al., 2020" rid="ref-shughrue-2020-global-spread" ref-type="bibr">Shughrue
+      et al., 2020</xref>).</p>
+    </list-item>
+    <list-item>
+      <p>The ARIO models version used in Guan et al.
+      (<xref alt="2020" rid="ref-guan-2020-global-suppl" ref-type="bibr">2020</xref>).</p>
+    </list-item>
+  </list>
+  <p>We found that none of these implementations offer a comprehensive
+  documentation, and are generally specific to the case study they were
+  used for. The purpose of the <monospace>BoARIO</monospace> package is
+  to offer a generic, documented, easy to use, easy to extend, and
+  replicability-oriented model for indirect impact assessment.</p>
+  <p>The <monospace>BoARIO</monospace> package allows to easily run
+  simulations with the ARIO model, via simple steps:</p>
+  <list list-type="bullet">
+    <list-item>
+      <p>Instantiating a model</p>
+    </list-item>
+    <list-item>
+      <p>Defining one or multiple events</p>
+    </list-item>
+    <list-item>
+      <p>Creating a simulation instance that will wrap the model and
+      events, allow to run the simulation, and explore the results.</p>
+    </list-item>
+  </list>
+  <p>The ARIO model relies on Multi-Regional Input-Output Tables
+  (MRIOTs) to define the initial state of the economy.
+  <monospace>BoARIO</monospace> was designed to be entirely agnostic of
+  the MRIOT used, thanks to the <monospace>pymrio</monospace> package
+  (<xref alt="Stadler, 2021" rid="ref-stadler2021_Pymrio" ref-type="bibr">Stadler,
+  2021</xref>). This aspect notably permits full benefit from the
+  increasing availability of such tables
+  (<xref alt="Lenzen et al., 2012" rid="ref-lenzen-2012-mappin-struc" ref-type="bibr">Lenzen
+  et al., 2012</xref>;
+  <xref alt="OECD, 2021" rid="ref-oecd-2021-oecd-inter" ref-type="bibr">OECD,
+  2021</xref>;
+  <xref alt="Stadler et al., 2018" rid="ref-stadler18-exiob" ref-type="bibr">Stadler
+  et al., 2018</xref>;
+  <xref alt="Thissen et al., 2018" rid="ref-thissen-2018-eureg" ref-type="bibr">Thissen
+  et al., 2018</xref>).</p>
+  <p>The package allows for different shocking events to be defined
+  (currently, shocks on production or shocks on both production and
+  demand, by including a demand stemming from the reconstruction effort,
+  the inclusion of shocks on demand only and other types of shock will
+  be added in future versions). As such, different types of case studies
+  can be conducted (at different scope, for multiple or singular
+  events). Users benefit from a precise control on aspects such as the
+  distribution of the impact towards the different sectors and regions,
+  the recovery from the impact, and also from the default modeling
+  choices common in the corresponding literature. The rationale for the
+  detailed configuration of the model is “allowing for, but not
+  require”.</p>
+  <p>Simulations log the evolution of each variable of interest
+  (production, production capacity, intermediate demand, reconstruction
+  demand, etc.) at each step and for each industry, in
+  <monospace>pandas DataFrame</monospace> objects, allowing in depth
+  descriptions and understanding of the economic responses. The package
+  can be used “live”, e.g. in a Jupyter Notebook, as well as in large
+  simulation pipelines, for instance using the
+  <monospace>Snakemake</monospace> package from Köster &amp; Rahmann
+  (<xref alt="2012" rid="ref-koester-2012-snakem-scalab" ref-type="bibr">2012</xref>)<xref ref-type="fn" rid="fn1">1</xref>.</p>
+  <p>As such, <monospace>BoARIO</monospace> is designed to be used by
+  researchers in economics and risk analysis and analysts, and possibly
+  students, either as a theoretical tool to better understand the
+  dynamics associated with the propagation of economic impacts, for more
+  applied-oriented case studies in risk management, or simply as a
+  pedagogical tool to introduce the indirect impact modeling field.</p>
+  <p>The Python implementation, accompanied by the
+  <ext-link ext-link-type="uri" xlink:href="https://spjuhel.github.io/BoARIO/">online
+  documentation</ext-link> (where a more in depth description is
+  available), offers an accessible interface for researchers with
+  limited programming knowledge. It also aims to be modular and
+  extensible to include additional economic mechanisms in future
+  versions. Finally, its API aims at making it interoperable with other
+  modeling software: for instance the <monospace>CLIMADA</monospace>
+  platform
+  (<xref alt="Siguan et al., 2023" rid="ref-gabriela-aznar-siguan-2023-8383171" ref-type="bibr">Siguan
+  et al., 2023</xref>) to which <monospace>BoARIO</monospace> is in the
+  process of being integrated.</p>
+</sec>
+<sec id="status">
+  <title>Status</title>
+  <p><monospace>BoARIO</monospace> is released under the open-source
+  GPL-3.0 license and is currently developed by Samuel Juhel. The core
+  of its development was made over the course of a PhD at CIRED and LMD,
+  under the supervision of Vincent Viguié and Fabio D’Andrea, and funded
+  by ADEME (the French agency for transition).</p>
+  <p><monospace>BoARIO</monospace> can be installed from PyPi or
+  Conda-Forge using:</p>
+  <preformat>pip install boario
+
+conda install -c conda-forge boario</preformat>
+</sec>
+<sec id="acknowledgements">
+  <title>Acknowledgements</title>
+  <p>I wish to acknowledge Vincent Viguié and Fabio D’Andrea for their
+  support in the development of <monospace>BoARIO</monospace> during his
+  PhD, as well as Adrien Delahais for his feedbacks on the model use. I
+  also want to thank David N. Bresch for indirectly inspiring me to
+  develop a package for more than just my personal use, and Alessio
+  Ciullo, for their interest and valuable suggestions as well as the
+  work done to integrate <monospace>BoARIO</monospace> to
+  <monospace>CLIMADA</monospace>.</p>
+</sec>
+</body>
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+<fn-group>
+  <fn id="fn1">
+    <label>1</label><p>Both these uses have already been extensively
+    employed in ongoing studies.</p>
+  </fn>
+</fn-group>
+</back>
+</article>