Different types of adaptation options, including hard and soft options, are often hard to compare to one another in a quantitative manner, in particular softer solutions that do not reduce asset risk. Here, we have quantified the wider benefits of hard infrastructure solutions, while our framework also allows complementary interventions such as flood-proofing critical infrastructure and improved disaster recovery efforts. Although not done in this work, putting a dollar value on experienced service disruptions, or temporary MDP incidence, one could include such wider welfare impacts in the CBA, allowing one to compare and contrast different interventions in a more comprehensive way. As such, the framework presented provides the basis for evaluating how portfolios of interventions can reduce both asset risk as well as benefit people in terms of welfare, underlining that a holistic portfolio including hard and certain soft interventions (only those affecting service disruptions, not those related to consumption such as direct payments), is required to achieve both. In this work, we have only evaluated a limited number of interventions, and did not run combinations of interventions. In particular, evaluating the benefits of complementary portfolios of interventions allows looking at the synergies and trade-offs between interventions and is a promising avenue of future research.

The methodological advances presented in this working paper are driven by the recent developments in terms of constructing synthetic population datasets of entire countries, as done for Bangladesh here, including realistic, but synthetic, locations. The entire methodology (except the flood hazard maps) relies on publicly available data sources, making it (in theory) possible to replicate such analysis in other regions globally. However, extending the risk analysis from asset damages alone to the welfare implications of individual households increases the complexity, data requirements, and computational resources considerably. For instance, constructing the synthetic household data relies on detailed Census data, which is not available everywhere, as well as being very computationally intensive for large countries. As such, the quality of the synthetic household output, including which variables can be considered, is dependent on the level of detail of input data, in particular the socioeconomic survey data. We present a method, using a Small Area Estimation technique, to add an additional variable that is not originally in the Census data to the synthetic household data. However, this might not be feasible for other variables or using different survey data. As such, moving forward, less complex versions of the methodology presented could be developed and tested, such that it can be more universally applied.

In future work, the methodology could be enhanced by 1) incorporating socioeconomic projections and associated changes in exposure and vulnerability to enable a more comprehensive future scenario analysis, 2) employing a stress-testing approach to better capture uncertain hazard inputs and cross-correlations and spatial dependence between hazards and infrastructure networks, and (3) test portfolios of interventions to evaluate their synergies and trade-offs, (4) more directly incorporating the health status of households, and (5) incorporating employment and household income as additional household characteristics that could be affected after disaster impact.

Overall, our work demonstrates the added value in terms of improving quantitative estimates of hazard-induced welfare impacts. First, the synthetic household survey data enables an 35