paper.md: integrates C. Vernon remarks

paper.md

@@ -8,7 +8,6 @@ tags:
 authors:
   - name: Samuel Juhel
     orcid: 0000-0001-8801-3890
-    equal-contrib: true
     affiliation: "1, 2" # (Multiple affiliations must be quoted)
 affiliations:
  - name: CIRED, France
@@ -35,15 +34,15 @@ 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 [@coronese-2022-econom-impac].
 However, the assumptions and hypotheses underlying these economic mechanisms
-vary a lot, and sometime lack transparency, making it difficult to properly
+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.
 
 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
-[@hallegatte-2008-adapt-region]. ARIO is now a well-established and pivotal
+[@hallegatte-2008-adapt-region]. 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 [@wu-2011-region-indir; @ranger-2010-asses-poten;
 @henriet-2012-firm-networ; @hallegatte-2013-model-role;
@@ -52,14 +51,14 @@ extensions or adaptations [@wu-2011-region-indir; @ranger-2010-asses-poten;
 @wang-2020-econom-footp; @wang-2018-quant-spatial].
 
 In ARIO, the economy is modelled as a set of economic sectors and regions, and
-we call a specific (region,sector) couple an *industry*. Each industry produces
+we call a specific (region, sector) couple an *industry*. 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 economic, as depicted, responds to a shock (or multiple ones).
+how the economy, as depicted, responds to a shock (or multiple ones).
 
 `BoARIO` is an open-source Python package implementing the ARIO model. Its core
 purpose is to help support better accessibility, transparency, replicability and
@@ -68,16 +67,16 @@ comparability in the field of indirect economic impacts modeling.
 # Statement of need
 
 Although the ARIO model has been used in multiple studies, and several extensions
-exists, only a few implementation of the model or similar ones are openly available.
+exists, only a few implementations of the model or similar ones are openly available.
 We found the following existing implementations:
 
-   - A python implementation of MRIA [@koks-2016-multir-impac], is available on the [personal GitHub repository of E. Koks](https://github.com/ElcoK/MRIA).
-   - [C. Colon repository](https://github.com/ccolon/disrupt-supply-chain-model/) holds a python implementation of Disrupt Supply Chain [@colon-2020-critic-analy].
-   - A C++ implementation of the Acclimate model [@otto-2017-model-loss], is available [here](https://github.com/acclimate/acclimate).
-   - A Matlab implementation of C. Shughrue's model [@shughrue-2020-global-spread], is available on [his repository](https://github.com/chrisshughrue/GlobalUrbanCycloneImpactSimulation).
-   - The ARIO models version used in [@wang-2020-econom-footp, @guan-2020-global-suppl] are both available on [D. Wang personal repository](https://github.com/DaopingW/)
+   - A Python implementation of MRIA [@koks-2016-multir-impac].
+   - A Python implementation of Disrupt Supply Chain [@colon-2020-critic-analy].
+   - A C++ implementation of the Acclimate model [@otto-2017-model-loss].
+   - A Matlab implementation of C. Shughrue's model [@shughrue-2020-global-spread].
+   - The ARIO models version used in [@wang-2020-econom-footp, @guan-2020-global-suppl].
 
-We found that none of these implementation offers a comprehensive documentation, and are generally
+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 `BoARIO` package is to offer
 a generic, documented, easy to use, easy to extend, and replicability-oriented model for indirect impact assessment.
 
@@ -91,24 +90,23 @@ simple steps:
 The ARIO model relies on Multi-Regional Input-Output Tables (MRIOTs) to define
 the initial state of the economy. `BoARIO` was designed to be entirely agnostic
 of the MRIOT used, thanks to the `pymrio` package [@stadler2021_Pymrio]. This
-aspect notably allows to fully benefit from the increasing number of such tables
-are becoming available [@stadler18-exiob; @oecd-2021-oecd-inter;
+aspect notably permits full benefit from the increasing availability of such tables [@stadler18-exiob; @oecd-2021-oecd-inter;
 @thissen-2018-eureg; @lenzen-2012-mappin-struc].
 
 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-study can be conducted (at different scope, for
+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 of from the impact, etc. but also from the default
-modeling choices common in the corresponding literature. The rationale for detailed
+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".
 
 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 `pandas DataFrames` objects, allowing in depth
+step and for each industry, in `pandas DataFrame` 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 `Snakemake` package from @koester-2012-snakem-scalab[^1].
@@ -126,44 +124,30 @@ documentation](https://spjuhel.github.io/BoARIO/) (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 inter-operable with other modeling software: for instance the `CLIMADA`
+at making it interoperable with other modeling software: for instance the `CLIMADA`
 platform [@gabriela-aznar-siguan-2023-8383171] to which `BoARIO` is in the
 process of being integrated.
 
-`BoARIO` is at the core of its author's PhD thesis, and was notably used in
-[@juhel-2023-robus], in review process. Other notable ongoing projects,
-are:
-- an evaluation of the indirect costs of future floods at the global scope and
-comparing its results to similar studies using the Acclimate and MRIA models
-[@willner-2018-global-econom; @koks-2019-macroec-impac].
-- a study on the compounding effect of indirect impacts from multiple events,
-using time-series of tropical cyclones generated with `CLIMADA`, and comparing
-the effect of considering events as isolated or consecutive.
-
 # Status
 
 `BoARIO` 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).
 
-`BoARIO` can be installed from pip or conda using:
+`BoARIO` can be installed from PyPi or Conda-Forge using:
 
     pip install boario
 
     conda install -c conda-forge boario
 
-Integration tests can be run using `pytest`.
-
-Further improvements, notably the implementation of additional economic mechanisms or variations of existing ones are already planned.
-
 # Acknowledgements
 
 I wish to acknowledge Vincent Viguié and Fabio D'Andrea for their support in the
 development of `BoARIO` 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 its interest in the package, its valuable suggestions and
+Alessio Ciullo, for their interest and valuable suggestions as well as
 the work done to integrate `BoARIO` to `CLIMADA`.
 
 # References