can help identify suitable regions for pro-poor adaptation measures, as well as other policies that require accurate information on the location of poor households ( _e.g._ cash transfers). The third, and most important, concern relates to their conceptualization and quantification of welfare impacts. H2016 only model a single channel through which shocks affect welfare, which is damage to assets resulting in income and consumption losses, assuming that income and consumption are suitable metrics to contrast the disproportionate welfare impacts.

A growing body of literature has emphasized that income and consumption are imperfect metrics of poverty. For example, the global Multidimensional Poverty Index (MDP), developed by the Oxford Poverty and Human Development Initiative (OPHI), captures the acute deprivations in health, education and living standards household face. Such multidimensional poverty measures better capture a households’ ability to achieve worthwhile goals ((Stiglitz et al., 2009; Alkire and Foster, 2011), and are therefore aligned with Amartya Sen’s idea of capabilities (Sen, 1989) and directly related to the UN’s Sustainable Development Goals. The data required for such a framework are also generally available at a more granular level allowing more detailed analyses of the affected households’ characteristics. Hence, specifying a modeling framework that can capture alternative channels of disasters impacting household welfare (in this case MDP) can help complement existing disaster risk modeling studies. Moreover, it allows decision-makers to evaluate the wider benefits of adaptation options in terms of asset risk reduction, reduce the time that people face additional deprivations that can negatively affect wellbeing, and prevent people from falling temporarily into poverty.

In this study, we extend the H2016 model and aim to construct a new spatial risk framework to quantify the welfare implications of present-day and future climate extremes to households. The methodology is applied to a case study in the coastal zone of Bangladesh, an area inhabited by around 43 million people and considered to be among the most climate vulnerable regions globally. Using a recently developed synthetic household survey dataset that contains the location and socio-economic characteristics of all households, and linking each household to the critical infrastructures they depend on, we map the baseline level of multidimensional poverty (MDP) in coastal Bangladesh. The MDP index is a combined metric made up of nine indicators based on the global MDP (developed by OPHI). We quantify how cyclone-induced flooding at-present and in the future (2050) can temporarily increase MDP in this region. Moreover, we evaluate the benefits of adaptation, in particular the wider economic benefits of hard infrastructure interventions by upgrading coastal embankments.

# **Research questions**

Several research questions are set to achieve this aim:

- What is the spatial distribution of MDP in Bangladesh’s coastal zone, and how does it compare to other poverty metrics?

- What is the cyclone-induced risk in terms of asset damages, population exposed and infrastructure services provision, and how will this change in the future?

- Are poor households disproportionally exposed to cyclone-induced flooding?

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