BoARIO: A Python package implementing the ARIO indirect economic cost model

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 (Lange et al., 2020), 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 (Coronese & Luzzati, 2022).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.