“…We ultimately determined that the optimum policy can change depending on stakeholder's priorities toward different risk types. This finding, which mirrors the conclusions of similar studies in different contexts (e.g., Cremen & Galasso, 2021), underlines the critical importance of a collaborative risk assessment process that integrates stakeholder participation, capacity and feedback (Galasso et al., 2021). For the specific case study examined, it was found that a “soft policy” of providing post‐disaster financial assistance for city inhabitants is the best option if stakeholders are most interested in minimizing population displacement, whereas a “hard policy” of replacing low‐income housing and facilities with code‐compliant buildings is the optimal solution for stakeholders who are particularly motivated to reduce the relative burden of financial loss on the city's poorest.…”
Section: Discussionsupporting
confidence: 82%
“…Here we use the word ’environment’ to indicate the potential for iterative engagement with stakeholders to evolve optimized low‐risk solutions within externally imposed constraints. Hence, the proposed framework is more than just a risk model or computational tool but provides an environment to support risk‐sensitive planning decisions, incorporating a participatory approach to risk understanding and quantification that can account for diverse stakeholder priorities toward different dimensions of risk (see Galasso et al., 2021). The stakeholder steps into the process and is encouraged to engage with its functionality, potentially modifying its construction and many of its assumptions.…”
Numerous approaches to earthquake risk modeling and quantification have already been proposed in the literature and/or are well established in practice. However, most of these procedures are designed to focus on risk in the context of current static exposure and vulnerability, and are therefore limited in their ability to support decisions related to the future, as yet partially unbuilt, urban landscape. We propose an end‐to‐end risk modeling framework that explicitly addresses this specific challenge. The framework is designed to consider the earthquake (ground‐shaking) risks of tomorrow's urban environment, using a simulation‐based approach to rigorously capture the uncertainties inherent in future projections of exposure as well as physical and social vulnerability. The framework also advances the state‐of‐practice in future disaster risk modeling by additionally: (a) providing a harmonized methodology for integrating physical and social impacts of disasters that facilitates flexible characterization of risk metrics beyond physical damage/asset losses; and (b) incorporating a participatory, people‐centered approach to risk‐informed decision making. The framework is showcased using the physical and social environment of an expanding synthetic city. This example application demonstrates how the framework may be used to make policy decisions related to future urban areas, based on multiple, uncertain risk drivers.
“…We ultimately determined that the optimum policy can change depending on stakeholder's priorities toward different risk types. This finding, which mirrors the conclusions of similar studies in different contexts (e.g., Cremen & Galasso, 2021), underlines the critical importance of a collaborative risk assessment process that integrates stakeholder participation, capacity and feedback (Galasso et al., 2021). For the specific case study examined, it was found that a “soft policy” of providing post‐disaster financial assistance for city inhabitants is the best option if stakeholders are most interested in minimizing population displacement, whereas a “hard policy” of replacing low‐income housing and facilities with code‐compliant buildings is the optimal solution for stakeholders who are particularly motivated to reduce the relative burden of financial loss on the city's poorest.…”
Section: Discussionsupporting
confidence: 82%
“…Here we use the word ’environment’ to indicate the potential for iterative engagement with stakeholders to evolve optimized low‐risk solutions within externally imposed constraints. Hence, the proposed framework is more than just a risk model or computational tool but provides an environment to support risk‐sensitive planning decisions, incorporating a participatory approach to risk understanding and quantification that can account for diverse stakeholder priorities toward different dimensions of risk (see Galasso et al., 2021). The stakeholder steps into the process and is encouraged to engage with its functionality, potentially modifying its construction and many of its assumptions.…”
Numerous approaches to earthquake risk modeling and quantification have already been proposed in the literature and/or are well established in practice. However, most of these procedures are designed to focus on risk in the context of current static exposure and vulnerability, and are therefore limited in their ability to support decisions related to the future, as yet partially unbuilt, urban landscape. We propose an end‐to‐end risk modeling framework that explicitly addresses this specific challenge. The framework is designed to consider the earthquake (ground‐shaking) risks of tomorrow's urban environment, using a simulation‐based approach to rigorously capture the uncertainties inherent in future projections of exposure as well as physical and social vulnerability. The framework also advances the state‐of‐practice in future disaster risk modeling by additionally: (a) providing a harmonized methodology for integrating physical and social impacts of disasters that facilitates flexible characterization of risk metrics beyond physical damage/asset losses; and (b) incorporating a participatory, people‐centered approach to risk‐informed decision making. The framework is showcased using the physical and social environment of an expanding synthetic city. This example application demonstrates how the framework may be used to make policy decisions related to future urban areas, based on multiple, uncertain risk drivers.
“…Journal Pre-proof of tomorrow"s natural-hazard risks, by leveraging existing tools (e.g., related to multi-criteria decision-making and multi-hazard theories) that have been successfully adopted for risk quantification in other contexts. The Tomorrow"s Cities Hub of the United Kingdom Research and Innovation (UKRI) Global Challenge Research Fund (GCRF), which is aiming to develop a comprehensive methodology for facilitating the risk-sensitive design of future cities that is co-produced with local stakeholders, accounts for multiple hazards, and incorporates both social and physical vulnerabilities (Galasso et al, 2021), represents a promising step in the right direction towards addressing some of the current challenges. 9…”
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“…Armijos et al 2017; Barclay et al 2019;Phillips et al 2019) as well as films and exhibitions designed to increase community engagement with volcanic risk. Beyond these programs, the importance of better quantifying vulnerability to volcanic eruptions has started to be recognised outside of academia and in global organisations that can have direct input to governmental policy (Loughlin et al 2015;Galasso et al 2021). For example, the 2015 United Nations…”
Section: Volcanic Impact and Risk Assessmentmentioning
confidence: 99%
“…This means using present day exposure and vulnerability to make damage estimates fails to account for future changes in landuse or changes typical construction practices. Such changes can and increasingly are being modelled in disaster risk studies (Mestav Sarica et al 2020;Galasso et al 2021) and the methods employed there could be transferred to volcanic risk and impact. Many factors that control tephra fall vulnerability such as building age and condition, roof cladding, framing and pitch, are typically not captured in exposure databases, even in the insurance industry (Blong et al 2017b).…”
From her brutally honest career advice, to the stream of research projects sparked over casual tea chats, to the unrivalled organisation and strength she has displayed as a leader during this challenging pandemic.To my Thesis Advisory Committee, I thank Profs Benoit Taisne and David Lallemant for teaching me Matlab and R -an unenviable task -and for their tireless support and enthusiasm. You and Susanna have shown me academics can be successful without sacrificing kindness and generosity.
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