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The risk of extreme climatic conditions leading to unusually low global agricultural production is exacerbated if more than one global 'breadbasket' is subject to climatic extremes at the same time. Such shocks can pose a risk to the global food system amplifying threats to global food security 1,2 and have the potential to trigger other systemic risks 3,4. So far, while the possibility of climatic extremes hitting more than one breadbasket has been postulated 5,6 little is known about the actual risk. Here we present quantitative risk estimates of simultaneous breadbasket failures due to climatic extremes and show how risk has changed over time. We combine region-specific data on agricultural production with spatial statistics of climatic extremes to quantify the changing risk of low production for the major food producing regions ('breadbaskets') in the world. We find evidence that there is increasing risk of simultaneous failure of wheat, maize and soybean crops, across the breadbaskets analyzed. For rice, risks of simultaneous adverse climate conditions have decreased in the breadbaskets analyzed in this study in the recent past mostly owing to solar radiation changes favoring rice growth. Depending on the correlation structure between the breadbaskets, spatial dependence between climatic extremes globally can mitigate or aggravate the risks for the global food production. Our analysis can provide the basis for more efficient allocation of resources to contingency plans and/or strategic crop reserves that would enhance the resilience of the global food system. Climate variability explains at least 30% of year-to-year fluctuations in agricultural yield 7. Under 'normal' climatic circumstances the global food system can compensate local crop losses through grain storage and trade 8. However, it is doubtful whether the global food system is resilient to more extreme climatic conditions 9 , when export restrictions 10 and diminished grain stocks may undermine liquidity in agricultural commodity markets, resulting in higher price volatility. The food price crisis in 2007/08 has shown that climatic shocks to agricultural production contribute to food price spikes 1 and famine 2 , with the potential to trigger other systemic risks including political unrest 3 and migration 4. Climatic teleconnections between global phenomena such as El Niño Southern Oscillation (ENSO) and regional climate extremes such as Indian heatwaves 11 or flood risks around the globe 12 could lead to simultaneous crop failure in different regions, therefore posing a risk to the global food system 8,10 , and amplifying threats to global food security. While the possibility of a climatic extreme hitting more than one breadbasket has been a growing cause for concern 5,6 , only few studies have investigated the probability of simultaneous production shocks 13 or estimated the joint likelihoods of adverse climate conditions 14. Here we present, to our knowledge for the first time, quantitative risk estimates of simultaneous breadbasket failures due...

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Major natural disasters in recent years have had high human and economic costs, and triggered record high postdisaster relief from governments and international donors. Given the current economic situation worldwide, selecting the most effective disaster risk reduction (DRR) measures is critical. This is especially the case for low-and middle-income countries, which have suffered disproportionally more economic and human losses from disasters. This article discusses a methodology that makes use of advanced probabilistic catastrophe models to estimate benefits of DRR measures. We apply such newly developed models to generate estimates for hurricane risk on residential structures on the island of St. Lucia, and earthquake risk on residential structures in Istanbul, Turkey, as two illustrative case studies. The costs and economic benefits for selected risk reduction measures are estimated taking account of hazard, exposure, and vulnerability. We conclude by emphasizing the advantages and challenges of catastrophe model-based cost-benefit analyses for DRR in developing countries. Keywords Catastrophe modeling, cost-benefit analysis, disaster risk reduction Disciplines Emergency and Disaster Management | Other International and Area Studies | Physical and Environmental Geography Established in 1984, the Wharton Risk Management and Decision Processes Center develops and promotes effective corporate and public policies for low-probability events with potentially catastrophic consequences through the integration of risk assessment, and risk perception with risk management strategies. Natural disasters, technological hazards, and national and international security issues (e.g., terrorism risk insurance markets, protection of critical infrastructure, global security) are among the extreme events that are the focus of the Center's research. The Risk Center's neutrality allows it to undertake large-scale projects in conjunction with other researchers and organizations in the public and private sectors. Building on the disciplines of economics, decision sciences, finance, insurance, marketing and psychology, the Center supports and undertakes field and experimental studies of risk and uncertainty to better understand how individuals and organizations make choices under conditions of risk and uncertainty. Risk Center research also investigates the effectiveness of strategies such as risk communication, information sharing, incentive systems, insurance, regulation and public-private collaborations at a national and international scale. From these findings, the Wharton Risk Center's research team-over 50 faculty, fellows and doctoral students-is able to design new approaches to enable individuals and organizations to make better decisions regarding risk under various regulatory and market conditions. The Center is also concerned with training leading decision makers. It actively engages multiple viewpoints, including top-level representatives from industry, government, international organizations, interest groups and academics...

Limited studies have shown that disaster risk management (DRM) can be cost-efficient in a development context. Cost-benefit analysis (CBA) is an evaluation tool to analyse economic efficiency. This research introduces quantitative, stochastic CBA frameworks and applies them in case studies of flood and drought risk reduction in India and Pakistan, while also incorporating projected climate change impacts. DRM interventions are shown to be economically efficient, with integrated approaches more cost-effective and robust than singular interventions. The paper highlights that CBA can be a useful tool if certain issues are considered properly, including: complexities in estimating risk; data dependency of results; negative effects of interventions; and distributional aspects. The design and process of CBA must take into account specific objectives, available information, resources, and the perceptions and needs of stakeholders as transparently as possible. Intervention design and uncertainties should be qualified through dialogue, indicating that process is as important as numerical results.

Background:Temperature-related mortality risks have mostly been studied in urban areas, with limited evidence for urban–rural differences in the temperature impacts on health outcomes.Objectives:We investigated whether temperature–mortality relationships vary between urban and rural counties in China.Methods:We collected daily data on 1 km gridded temperature and mortality in 89 counties of Zhejiang Province, China, for 2009 and 2015. We first performed a two-stage analysis to estimate the temperature effects on mortality in urban and rural counties. Second, we performed meta-regression to investigate the modifying effect of the urbanization level. Stratified analyses were performed by all-cause, nonaccidental (stratified by age and sex), cardiopulmonary, cardiovascular, and respiratory mortality. We also calculated the fraction of mortality and number of deaths attributable to nonoptimum temperatures associated with both cold and heat components. The potential sources of the urban–rural differences were explored using meta-regression with county-level characteristics.Results:Increased mortality risks were associated with low and high temperatures in both rural and urban areas, but rural counties had higher relative risks (RRs), attributable fractions of mortality, and attributable death counts than urban counties. The urban–rural disparity was apparent for cold (first percentile relative to minimum mortality temperature), with an RR of 1.47 [95% confidence interval (CI): 1.32, 1.62] associated with all-cause mortality for urban counties, and 1.98 (95% CI: 1.87, 2.10) for rural counties. Among the potential sources of the urban–rural disparity are age structure, education, GDP, health care services, air conditioners, and occupation types.Conclusions:Rural residents are more sensitive to both cold and hot temperatures than urban residents in Zhejiang Province, China, particularly the elderly. The findings suggest past studies using exposure–response functions derived from urban areas may underestimate the mortality burden for the population as a whole. The public health agencies aimed at controlling temperature-related mortality should develop area-specific strategies, such as to reduce the urban–rural gaps in access to health care and awareness of risk prevention. Future projections on climate health impacts should consider the urban–rural disparity in mortality risks. https://doi.org/10.1289/EHP3556

As climate change continues, it is expected to have increasingly adverse impacts on child nutrition outcomes, and these impacts will be moderated by a variety of governmental, economic, infrastructural, and environmental factors. To date, attempts to map the vulnerability of food systems to climate change and drought have focused on mapping these factors but have not incorporated observations of historic climate shocks and nutrition outcomes. We significantly improve on these approaches by using over 580,000 observations of children from 53 countries to examine how precipitation extremes since 1990 have affected nutrition outcomes. We show that precipitation extremes and drought in particular are associated with worse child nutrition. We further show that the effects of drought on child undernutrition are mitigated or amplified by a variety of factors that affect both the adaptive capacity and sensitivity of local food systems with respect to shocks. Finally, we estimate a model drawing on historical observations of drought, geographic conditions, and nutrition outcomes to make a global map of where child stunting would be expected to increase under drought based on current conditions. As climate change makes drought more commonplace and more severe, these results will aid policymakers by highlighting which areas are most vulnerable as well as which factors contribute the most to creating resilient food systems.