Successful recovery from extreme weather events is key to avoid long-term poverty implications. Yet, in disaster prone regions, there may not always be enough time to recover between events. There is a common narrative that the resulting incomplete recoveries aggravate adverse impacts, but approaches allowing for a systematic quantitative assessment are missing. Here, we extend an agent-based model to study welfare effects in the Philippines depending on household exposure and income. We find that incomplete recoveries increase cumulative consumption and well-being losses across the study period 2000-2018 by 40%. While low-income households suffer the highest well-being losses, the effect of incomplete recoveries is most relevant for middle-income households. Consequently, losses can be critically underestimated when drawing conclusions about the impacts of recurrent events based on the impacts of individual events. Accounting for incomplete recoveries may help to better prepare for an intensification of extreme events under climate change.

The authors describe a tropical cyclone risk model for the Philippines using open-source methods that can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally forced tropical cyclone hazard model, are combined with data representing exposed value and vulnera-bility to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is dis-tributed across a country following population density and night-lights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, with free parameters chosen by a sensitivity analysis in which simulated and histori-cal reported damages are compared for different parameter values and further constrained by information from household surveys about regional building characteristics. Use of different vulnerability parameters for the region around Manila, Philippines, yields much better agreement between simulated and actually reported losses than does a single set of parame-ters for the entire country. Despite the improvements from regionally refined vulnerability, the model predicts no losses for a substantial number of destructive historical storms, a difference the authors hypothesize is due to the use of wind speed as the sole metric of tropical cyclone hazard, omitting explicit representation of storm surge and/or rainfall. Bearing these limitations in mind, this model can be used to estimate return levels for tropical cyclone-caused wind hazards and as-set losses for regions across the Philippines, relevant to some disaster risk reduction and management tasks; this model also provides a platform for further development of open-source tropical cyclone risk modeling. SIGNIFICANCE STATEMENT: Landfalling tropical cyclones are devastating disasters for which the Philippines is particularly at risk. Here we develop a model for tropical cyclone risk, quantified as property losses, over the Philip-pines and demonstrate its effectiveness by comparing to historical damages. We find that capturing the difference in vulnerability between the largest city in the Philippines (Manila) and more rural areas is important to accurately repre-sent this risk. Using this model, we can more accurately constrain the risk of very extreme tropical cyclone events in the Philippines. The model can also be straightforwardly adapted for emergency planning in other countries and for climate change scenarios using openly available information.

Natural disaster risk assessments typically consider environmental hazard and physical damage, neglecting to quantify how asset losses affect households’ well-being. However, for a given asset loss, a wealthy household might easily recover, while a poor household might suffer from major, long-lasting impacts. Ignoring such differential impacts can lead to inequitable interventions and exacerbate the impact of disasters on vulnerable populations. This research proposes a methodology for assessing socioeconomic effects of disasters that integrates the three pillars of sustainability: (1) environmental, i.e. environmental hazard and asset damage modeling; (2) economic, i.e. macro-economic modeling to quantify changes in sectors’ production and employment; and (3) social, i.e. micro-simulations of disaster recovery at the household level. The model innovates by assessing the impact of disasters on people’s consumption, considering asset losses and changes in income among other factors. We apply the model to quantify the effect of a hypothetical earthquake in the San Francisco Bay Area, considering the differential impact of consumption loss on poorer and richer households. The analysis reveals that poorer households suffer only 19% of the overall asset losses, but experience 41% of the well-being losses. The well-being losses extend over a larger region than that of severe asset losses, requiring design of policies to help people recover, in addition to reducing asset losses. Furthermore, we demonstrate that the effectiveness of specific policies varies across cities, depending on their built environment and social and economic profiles.

Conventional risk assessments underestimate the human and macroeconomic costs of disasters, leading to inefficient risk management strategies. This happens because conventional assessments focus on asset losses, neglecting important relationships between vulnerability and development. When affected by a hazard, poor households take longer to recover from disasters and are more likely to face long-term consequences. Forced to manage trade-offs between essential consumption and reconstruction, these households are more likely to face persistent health or education costs. This chapter proposes a review of existing research into the natural disaster-poverty-inequality nexus and the various metrics that can be used to measure disaster impacts, such as recovery times, economic (income or consumption) losses, poverty incidence, inequality, and welfare or well-being losses. Each of these metrics provides a different perspective on disaster costs and suggest different spatial and sectoral priorities for action. Focusing on the concepts of well-being losses and socioeconomic resilience, this chapter shows how more comprehensive accounting of disaster impacts can better inform disaster risk management and climate change adaptation strategies and support their integration into development and poverty-reduction policies.

The COVID-19 pandemic has caused a massive economic shock across the world due to business interruptions and shutdowns from social-distancing measures. To evaluate the socio-economic impact of COVID-19 on individuals, a micro-economic model is developed to estimate the direct impact of distancing on household income, savings, consumption, and poverty. The model assumes two periods: a crisis period during which some individuals experience a drop in income and can use their savings to maintain consumption; and a recovery period, when households save to replenish their depleted savings to pre-crisis level. The San Francisco Bay Area is used as a case study, and the impacts of a lockdown are quantified, accounting for the effects of unemployment insurance (UI) and the CARES Act federal stimulus. Assuming a shelter-in-place period of three months, the poverty rate would temporarily increase from 17.1% to 25.9% in the Bay Area in the absence of social protection, and the lowest income earners would suffer the most in relative terms. If fully implemented, the combination of UI and CARES could keep the increase in poverty close to zero, and reduce the average recovery time, for individuals who suffer an income loss, from 11.8 to 6.7 months. However, the severity of the economic impact is spatially heterogeneous, and certain communities are more affected than the average and could take more than a year to recover. Overall, this model is a first step in quantifying the household-level impacts of COVID-19 at a regional scale. This study can be extended to explore the impact of indirect macroeconomic effects, the role of uncertainty in households' decision-making and the potential effect of simultaneous exogenous shocks (e.g., natural disasters).

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. Here, an expanded risk assessment is proposed based on a framework that adds “socioeconomic resilience” — that is, the ability of affected households to cope with and recover from disaster asset losses — and uses “wellbeing losses” as its main measure of disaster severity. Using a new agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in the Philippines. Its first conclusion is the close link between natural disasters and poverty. On average, estimates suggest that almost half a million Filipinos per year face transient consumption poverty due to natural disasters. Nationally, the bottom income quintile suffers only 9% of the total asset losses, but 31% of the total wellbeing losses. As a result of the disproportionate impact on poor people, the average annual wellbeing losses due to disasters in the Philippines is estimated at US$3.9 billion per year, more than double the asset losses of US$1.4 billion. The second conclusion is the fact that the regions identified as priorities for risk-management interventions differ depending on which risk metric is used. While cost-benefit analyses based on asset losses direct risk reduction investments toward the richest regions and areas, a focus on poverty or wellbeing rebalances the analysis and generates a different set of regional priorities. Finally, measuring disaster impacts through poverty and wellbeing impacts allows the quantification of the benefits from interventions like rapid post-disaster support and adaptive social protection. While these measures do not reduce asset losses, they efficiently reduce their wellbeing consequences by making the population more resilient.

Thousands of scenarios are used to provide updated estimates for the impacts of climate change on extreme poverty in 2030. The range of the number of people falling into poverty due to climate change is between 32 million and 132 million in most scenarios. These results are commensurate with available estimates for the global poverty increase due to COVID-19. Socioeconomic drivers play a major role: optimistic baseline scenarios (rapid and inclusive growth with universal access to basic services in 2030) halve poverty impacts compared with the pessimistic baselines. Health impacts (malaria, diarrhea, and stunting) and the effect of food prices are responsible for most of the impact. The effect of food prices is the most important factor in Sub-Saharan Africa, while health effects, natural disasters, and food prices are all important in South Asia. These results suggest that accelerated action to boost resilience is urgent, and the COVID-19 recovery packages offer opportunities to do so.

Natural disaster risk assessments typically consider environmental hazard and physical damage, neglecting to quantify how asset losses affect households’ well-being. However, for a given asset loss, a wealthy household might quickly recover, while a poor household might suffer major, long-lasting impacts. This research proposes a methodology to quantify disaster impacts more equitably by integrating the three pillars of sustainability: environmental (hazard and asset damage), economic (macro-economic changes in production and employment) and social (disaster recovery at the household level). The model innovates by assessing the impacts of disasters on people’s consumption, considering asset losses and changes in income, among other factors. We apply the model to a hypothetical earthquake in the San Francisco Bay Area, considering the differential impact of consumption loss on households of varying wealth. The analysis reveals that poorer households suffer 19% of the asset losses but 41% of the well-being losses. Furthermore, we demonstrate that the effectiveness of specific policies varies across cities (depending on their built environment and social and economic profiles) and income groups. Natural disaster risk assessments neglect impacts on households’ well-being. A model to quantify disaster impacts more equitably shows that, in a hypothetical earthquake in the San Francisco Bay Area, poorer households suffer 19% of the asset losses but 41% of the well-being losses.

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. This paper proposes an expanded risk assessment based on a framework that adds socioeconomic resilience and uses wellbeing losses as the main measure of disaster severity. Using an agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in Sri Lanka. The analysis indicates that regular flooding events can move tens of thousands of Sri Lankans into transient poverty at once, hindering the country's recent progress on poverty eradication and shared prosperity. As metrics of disaster impacts, poverty incidence and well-being losses facilitate quantification of the benefits of interventions like rapid post-disaster support and adaptive social protection systems. Such investments efficiently reduce wellbeing losses by making exposed and vulnerable populations more resilient. Nationally and on average, the bottom income quintile suffers only 7 percent of the total asset losses but 32 percent of the total wellbeing losses. Average annual wellbeing losses due to fluvial flooding in Sri Lanka are estimated at US$119 million per year, more than double the asset losses of US$78 million. Asset losses are reported to be highly concentrated in Colombo district, and wellbeing losses are more widely distributed throughout the country. Finally, the paper applies the socioeconomic resilience framework to a cost-benefit analysis of prospective adaptive social protection systems, based on enrollment in Samurdhi, the main social support system in Sri Lanka.

T he 2017 Unbreakable report made the case that disaster losses disproportionately affect poor people.The report showed that they have limited ability to cope with disasters, and estimated that the impact on well-being is equivalent to consumption losses of about $520 billion a year around the world-outstripping previous estimates of pure asset losses by as much as 60 percent.The Caribbean Hurricane season of 2017 was a tragic illustration of this.Two Category 5 hurricanes wreaked destruction on numerous small islands, causing severe damage in places like Barbuda, Dominica, and Saint Martin.The human cost of these disasters was immense, and the impact of this devastation was felt most strongly by poorer communities in the path of the storms.And yet, amidst the destruction it is essential to look forward and to build back better.In this report we follow up on the Unbreakable report and explore how countries can strengthen their resilience to natural shocks through stronger, faster, and more inclusive post-disaster reconstruction.It shows that reconstruction needs to be strong, so that assets and livelihoods become less vulnerable to future shocks; fast, so that people can get back to their normal life earlier; and inclusive, so that nobody is left behind in the recovery process.This report shows how the benefits of building back better could be greatest among the communities and countries that are hit by disasters most intensely and frequently.For a selection of small island states, this report shows that stronger, faster, and more inclusive recovery would lead to an average reduction in disaster-related well-being losses of 59 percent.For Antigua & Barbuda, the reduction is as large as 74 percent.