Suchergebnisse

This article examines two possible strategies for financing post‐disaster infrastructure rehabilitation in developing and transition countries: relying on ex ante financing instruments (including insurance, catastrophe bonds, and other risk‐transfer instruments) and ex post borrowing or credit. Insurance and other ex ante instruments will increase a country's stability, especially if the government authorities have a difficult time borrowing or otherwise raising funds after a major disaster; however, these instruments have an opportunity cost and can reduce the country's economic growth potential. The cost‐benefit tradeoff is therefore one between economic growth through infrastructure investment and added solvency and stability for the economy. This article develops a model to illustrate this tradeoff. The model, which views the infrastructure of a developing or transition country as a nondiversifiable portfolio that generates returns, can provide a basis for evaluating alternative financing options depending on the country's objectives in terms of growth, solvency, and stability.

Ongoing global changes pose fundamentally new scientific problems requiring new concepts and tools. A key issue concerns a vast variety of practically irreducible uncertainties, which challenge traditional models and require new concepts and analytical tools. Uncertainty can dominate, as in the climate change debates. Increasing the resolution of models does not always yield sufficient certainty. This book presents much-needed new tools for modeling and management of uncertainty.

AbstractMany risks we face today will very likely not stay the same over time. For example, it is expected that climate change will alter future risks of natural disaster events considerably and, as a consequence, current risk management and governance strategies may not be effective anymore. Large ambiguities arise if future climate change impacts should be taken into account for analyzing risk management options today. Risk insurance, while albeit only one of many risk management actions possible, plays an important role in current societies for dealing with extremes. A natural starting point for our analysis is therefore the question of how ambiguity may be incorporated in a world with changing risks. To shed light on this question, we study how ambiguity can affect the uptake of insurance and risk mitigation within a risk‐layer approach where each layer is quantified using distortion risk measures that should reflect the risk aversion of a decisionmaker toward extreme losses. Importantly, we obtain a closed‐form solution for such a problem statement which allows an efficient numerical implementation. We apply this model to a case study of drought risk for Austrian farmers and address the question how ambiguity will affect the risk layers of different types of farmers and how subsidies may help to deal with current and future risks. We found that especially for small‐scale farmers the consequences of increasing risk and model ambiguity are pronounced and subsidies are especially needed in this case to cover the high‐risk layer.

AbstractAs recent events have shown, simultaneous crop losses in different parts of the world can cause serious risks to global food security. However, to date, little is known about the spatial dependency of lower than expected crop yields from global breadbaskets. This especially applies in the case of extreme events, i.e., where one or more breadbaskets are experiencing far below average yields. Without such information, risk management approaches cannot be applied and vulnerability to extremes may remain high or even increase in the future around the world. We tackle both issues from an empirical perspective focusing on wheat yield. Interdependencies between historically observed wheat yield deviations in five breadbaskets (United States, Argentina, India, China, and Australia) are estimated via copula approaches that can incorporate increasing tail dependencies. In doing so, we are able to attach probabilities to interregional as well as global yield losses. To address the robustness of our results, we apply three different methods for constructing multivariate copulas: vine copulas, ordered coupling using a minimax approach, and hierarchical structuring. We found interdependencies between states within breadbaskets that led us to the conclusion that risk pooling for extremes is less favorable on the regional level. However, notwithstanding evidence of global climatic teleconnections that may influence crop production, we also demonstrate empirically that wheat production losses are independent between global breadbaskets, which strengthens the case for interregional risk pooling strategies. We argue that through interregional risk pooling, postdisaster liabilities of governments and international donors could be decreased.

Losses due to natural hazard events can be extraordinarily high and difficult to cope with. Therefore, there is considerable interest to estimate the potential impact of current and future extreme events at all scales in as much detail as possible. As hazards typically spread over wider areas, risk assessment must take into account interrelations between regions. Neglecting such interdependencies can lead to a severe underestimation of potential losses, especially for extreme events. This underestimation of extreme risk can lead to the failure of riskmanagement strategies when they are most needed, namely, in times of unprecedented events. In this article, we suggest a methodology to incorporate such interdependencies in risk via the use of copulas. We demonstrate that by coupling losses, dependencies can be incorporated in risk analysis, avoiding the underestimation of risk. Based on maximum discharge data of river basins and stream networks, we present and discuss different ways to couple loss distributions of basins while explicitly incorporating tail dependencies. We distinguish between coupling methods that require river structure data for the analysis and those that do not. For the later approach we propose a minimax algorithm to choose coupled basin pairs so that the underestimation of risk is avoided and the use of river structure data is not needed. The proposed methodology is especially useful for large‐scale analysis and we motivate and apply our method using the case of Romania. The approach can be easily extended to other countries and natural hazards.

Abstract. Drought is a major natural hazard in the Bolivian
Altiplano that causes large agricultural losses. However, the drought effect
on agriculture varies largely on a local scale due to diverse factors such
as climatological and hydrological conditions, sensitivity of crop yield to
water stress, and crop phenological stage among others. To improve the
knowledge of drought impact on agriculture, this study aims to classify
drought severity using vegetation and land surface temperature data, analyse
the relationship between drought and climate anomalies, and examine the
spatio-temporal variability of drought using vegetation and climate data.
Empirical data for drought assessment purposes in this area are scarce and
spatially unevenly distributed. Due to these limitations we used vegetation,
land surface temperature (LST), precipitation derived from satellite
imagery, and gridded air temperature data products. Initially, we tested the
performance of satellite precipitation and gridded air temperature data on a
local level. Then, the normalized difference vegetation index (NDVI) and LST
were used to classify drought events associated with past El
Niño–Southern Oscillation (ENSO) phases. It was found that the most
severe drought events generally occur during a positive ENSO phase (El
Niño years). In addition, we found that a decrease in vegetation is
mainly driven by low precipitation and high temperature, and we identified
areas where agricultural losses will be most pronounced under such
conditions. The results show that droughts can be monitored using satellite
imagery data when ground data are scarce or of poor data quality. The
results can be especially beneficial for emergency response operations and
for enabling a proactive approach to disaster risk management against
droughts.

Implementation of agriculturally related early warning systems is fundamental for the management of droughts. Additionally, risk-based approaches are superior in tackling future drought hazards. Due to data-scarcity in many regions, high resolution satellite imagery data are becoming widely used. Focusing on ENSO warm and cold phases, we employ a risk-based approach for drought assessment in the Bolivian Altiplano using satellite imagery data and application of an early warning system. We use a newly established high resolution satellite dataset and test its accuracy as well as performance to similar (but with less resolution) datasets available for the Bolivian Altiplano. It is shown that during the El Niño years (warm ENSO phase), the result is great difference in risk and crop yield. Furthermore, the Normalized Difference Vegetation Index (NDVI) can be used to target specific hot spots on a very local scale. As a consequence, ENSO early warning forecasts as well as possible magnitudes of crop deficits could be established by the government, including an identification of possible hotspots during the growing season. Our approach therefore should not only help in determining the magnitude of assistance needed for farmers on the local scale but also enable a pro-active approach to disaster risk management against droughts that can include economic-related instruments such as insurance as well as risk reduction instruments such as irrigation.

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.