metadata: authors: 0: author: Mechler, Reinhard
1: author: Hochrainer, Stefan
2: author: Pflug, Georg
3: author: Lotsch, Alexander
4: author: Williges, Keith
count: World
docna: 0: docna: Assessing the financial vulnerability to
            climate-related natural hazards
docty: Policy Research Working Paper
subtopic: Adaptation to Climate Change,Science of Climate Change,Climate Change and Health,Climate Change and Environment,Hazard Risk Management,Social Risk Management,Natural Disasters,Disaster Management
teratopic: Macroeconomics and Economic Growth,Urban Development,Finance and Financial Sector Development
abstracts: cdata!: National governments are key actors in
            managing the impacts of extreme weather events, yet many
            highly exposed developing countries -- faced with exhausted
            tax bases, high levels of indebtedness, and limited donor
            assistance -- have been unable to raise sufficient and
            timely capital to replace or repair damaged infrastructure
            and restore livelihoods after major disasters. Such
            financial vulnerability hampers development and exacerbates
            poverty. Based on the record of the past 30 years, this
            paper finds many developing countries, in particular small
            island states, to be highly financially vulnerable, and
            experiencing a resource gap (net disaster losses exceed all
            available financing sources) for events that occur with a
            probability of 2 percent or higher. This has three main
            implications. First, efforts to reduce risk need to be
            ramped-up to lessen the serious human and financial burdens.
            Second, contrary to the well-known Arrow-Lind theorem, there
            is a case for country risk aversion implying that disaster
            risks faced by some governments cannot be absorbed without
            major difficulty. Risk aversion entails the ex ante
            financing of losses and relief expenditure through calamity
            funds, regional insurance pools, or contingent credit
            arrangements. Third, financially vulnerable (and generally
            poor) countries are unlikely to be able to implement
            pre-disaster risk financing instruments themselves, and thus
            require technical and financial assistance from the donor
            community.  The cost estimates of financial vulnerability --
            based on today's climate --  inform the design of
            "climate insurance funds" to absorb high levels of
            sovereign risk and are found to be in the lower billions of
            dollars annually, which represents a baseline for the
            incremental costs arising from future climate change.
repnme: repnme: Assessing the financial vulnerability to
            climate-related natural hazards
display_title: Assessing the financial
            vulnerability to climate-related natural hazards
listing_relative_url: /research/2010/03/11890346/assessing-financial-vulnerability-climate-related-natural-hazards
topicv3: Disaster Risk Management m1326682 820,Climate Change m1326829 567,Disaster Risk Financing and Insurance
            m1327142 613,Climate Change Adaptation m1327767 574,Natural Disaster m1503325 3138
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Urban Development,Finance and Financial Sector Development
no_of_pages: 35
model_extractions: WPS5232 Policy Research Working Paper 5232 Background Paper to the 2010 World Development Report Assessing the Financial Vulnerability to Climate-Related Natural Hazards _Reinhard Mechler Stefan Hochrainer Georg Pflug Alexander Lotsch Keith Williges_ The World Bank Development Economics Office of the Senior Vice President and Chief Economist March 2010
Policy Research Working Paper 5232

# **Abstract**

National governments are key actors in managing the impacts of extreme weather events, yet many highly exposed developing countries—faced with exhausted tax bases, high levels of indebtedness, and limited donor assistance—have been unable to raise sufficient and timely capital to replace or repair damaged infrastructure and restore livelihoods after major disasters. Such financial vulnerability hampers development and exacerbates poverty. Based on the record of the past 30 years, this paper finds many developing countries, in particular small island states, to be highly financially vulnerable, and experiencing a resource gap (net disaster losses exceed all available financing sources) for events that occur with a probability of 2 percent or higher. This has three main implications. First, efforts to reduce risk need to be ramped-up to lessen the serious human and financial burdens. Second, contrary to the wellknown Arrow-Lind theorem, there is a case for country risk aversion implying that disaster risks faced by some governments cannot be absorbed without major difficulty. Risk aversion entails the ex ante financing of losses and relief expenditure through calamity funds, regional insurance pools, or contingent credit arrangements. Third, financially vulnerable (and generally poor) countries are unlikely to be able to implement pre-disaster risk financing instruments themselves, and thus require technical and financial assistance from the donor community. The cost estimates of financial vulnerability—based on today’s climate—inform the design of “climate insurance funds” to absorb high levels of sovereign risk and are found to be in the lower billions of dollars annually, which represents a baseline for the incremental costs arising from future climate change.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2010: Development in a Changing Climate_ —is a product of the Development Economics Vice Presidency. The views expressed in this paper are those of the authors and do not reflect the views of the World Bank or its affiliated organizations. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at alotsch@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Assessing the Financial Vulnerability to Climate-Related Natural Hazards**

_Background paper for the World Development Report 2010 “Development and Climate Change”_

## **Reinhard Mechler**<sup>*****</sup> **, Stefan Hochrainer**<sup>*****</sup> **, Georg Pflug**<sup>*****</sup> **, Alexander Lotsch**<sup>**#**</sup> **with Keith Williges**<sup>*****</sup>

> ***** International Institute for Applied Systems Analysis, Laxenburg, Austria

> **#** The World Bank Group, Development Economics, Washington DC
# **1 Introduction**

Given increasing empirical evidence on climate change altering intensities and frequencies of natural hazards, the management of extreme event risk has been receiving increasing attention in international climate change policy. In Bali at the 13<sup>th</sup> Conference of the parties to the UNFCCC, agreement was found regarding the creation of a _Climate Change Adaptation Fund,_ which is to sponsor concrete adaptation projects in vulnerable countries. We suggest that one key impediment for finally releasing support from this fund has been a lack of operational methodologies for assessing vulnerability to natural hazards and climate change, which are integrated with the cost implications of supporting efforts to reduce vulnerability. A host of estimates for the adaptation costs, often put together using “hand-waving“ methodologies, have been suggested, and the robustness and underlying basis of such estimates has often been difficult to verify.

For weather extremes, which form a subset of the adaptation challenge and are supposed to increase in intensity and frequency with a changing climate, we conduct an assessment of the costs of managing and financing today’s public sector risks. National governments have become key actors in managing climate variability and change. Yet, many highly exposed developing countries are faced with exhausted tax bases and high levels of indebtedness and cannot raise sufficient and timely capital to replace or repair damaged assets and restore livelihoods following major disasters, leading to an exacerbation of disaster impacts on poverty and development (Mechler, 2004; Bayer et al., 2005; Hochrainer, 2006; Cummins and Mahul, 2007). Exposed countries often have to rely on donors to “bail” them out after devastating events. However, the evidence regarding ex–post assistance shows that only partial relief and reconstruction funding is usually being made available; furthermore, this support is often associated with substantial time lags (of at a minimum one year); on top of this, such post disaster aid has often not reached those in need effectively.

Over the last few years, there has been a paradigm shift in national and international responses to this problem towards more proactive efforts and upgrading the role of pre disaster risk management (Linnerooth-Bayer, Mechler and Pflug, 2005). Countries, donors and the international community have been working together to devise and implement risk management systems for reducing, pooling and sharing risk. One focus has been on risk financing mechanisms for transferring private and public sector risks from local and national levels to a global scale. Important precedents have been implemented over the last few years as privatepublic partnerships with donors and governments providing technical assistance, subsidizing or paying premiums, such as the Caribbean Catastrophe Risk Insurance Facility (CCRIF).

This paper focuses on countries’ ability to absorb risks within its own limits, or vice versa the need for transferring risks more globally by implementing novel risk sharing mechanisms. Based on an estimate of country-wide risk for the 70+ countries most exposed to weather extremes, we assess countries’ current financial vulnerability to climate extremes, which we operationalize as the public sector’s ability to pay for relief to the affected population and support the reconstruction of affected public sector assets such as infrastructure. We find a number of countries already highly financially vulnerable for smaller to medium sized events, and suggest that efforts to reduce risk need to be seriously stepped up. In such cases of obvious risk aversion, where disaster risks faced by governments cannot be absorbed without major difficulty, there is also a case for considering to prefinance risks, as the benefits of financing risks can often 2
outweigh their costs. Almost without exception, financially vulnerable countries are highly unlikely to be able to implement pre-disaster risk financing instruments themselves. Thus, there is a need for technical and financial assistance from the donor community. Our estimates may inform decisions pertaining to a “climate insurance fund” absorbing extreme event country risk, which exceeds the ability of any given country to absorb the losses in a given event. We find the costs of such a high risk layer backup fund to be in the lower billions of dollars annually. Our assessment relates to today’s climate, yet we suggest that estimates of today’s climate variability and related risks, although also associated with substantial uncertainty, can be interpreted as a baseline for vey uncertain future projections.

The report proceeds as follows. In section 2, we discuss the developmental challenges imposed by disaster risk and the case for prefinancing disaster risk. In section 3, we present our methodology for assessing country-wide financial vulnerability to extreme events based on the CATSIM model. Section 4 finally presents salient findings and a short discussion of key implications.

# **2 Disasters and development: The financial dimension**

Climate-related disaster losses<sup>1</sup> have escalated in recent decades. Although largely driven by socioeconomic change, the increase in monetary losses by an order of magnitude within the last four decades cannot entirely be explained by population or economic growth only (Mills, 2005). The 4<sup>th</sup> assessment report of the IPCC found increased impacts of extremes such as cyclones and flooding due to altered intensities and frequencies of natural hazards (Parry et al., 2007), many of which are expected to increase in frequency or severity in various places in a future warmer climate (Solomon et al., 2007). Disaster impacts can be devastating, particularly in heavily exposed low- and middle-income countries, and especially the vulnerable within these countries suffer the most. During the 25 year period from 1979 to 2004 over 95% of natural disaster deaths occurred in developing countries and direct economic losses averaged US$54 billion per annum. Not only are there considerable differences in the human and economic burden, but also in insurance cover. In the richest countries average total losses during this period amounted to 3.7% as measured in gross national income with about 30% of the losses insured. In low-income countries, total losses amounted to 12.9% with 99% of these losses uninsured (Munich Re, 2005).<sup>2</sup> It should be emphasized that these disaster statistics do not (for the most part) reflect medium to long-term indirect losses, which can be very significant, particularly in countries with little capacity to cope with extremes, yet are generally very difficult to parcel out from other effects.

There are many ways for absorbing the financial burdens of disasters, with market-based insurance being one, albeit prominent, option. Households often use informal mechanisms relying on family and relatives abroad; governments may simply rely on their tax base or international assistance. Yet, it is a fact that in the face of large and covariate risks, such ad hoc mechanisms often break down and a severe shortfall, a resource gap, remains (see LinneroothBayer and Mechler, 2007).

> 1 The literature does often not clearly distinguish between _losses_ and _damages_ . We refer to damages as the physical impacts, and losses as the monetized values (direct losses) or the economic follow on effects (indirect losses).

> 2 These losses are mostly _direct_ losses of productive assets and property ( _stocks_ ); only to a minor extent are _indirect_ losses of value added ( _flows_ ), such as business interruption losses, accounted for and insured.

3
# _Financing the resource gap_

The seriousness of the post-disaster resource gap, as well as the emergence of novel insurance instruments for pricing and transferring catastrophe risks to the global financial markets, has motivated developing country governments, as well as development institutions, NGOs and other donor organizations, to consider pre-disaster financial instruments as an important component of disaster risk management (Linnerooth-Bayer et al., 2005). Donor-supported pilot insurance programs are already demonstrating their potential to pool economic losses and smooth incomes of the poor facing weather variability, climate extremes and geophysical disasters. These schemes provide insurance to farmers, property owners and small businesses, as well as transfer the risks facing governments to the global capital markets. Particularly, the latter risk management options, where donors or governments cede risk, are particularly interesting. A few examples shown in box 1 may serve to illustrate the issues.

## **Box 1: Novel mechanisms for sharing extreme event risks in developing countries**

##  **_The Ethiopian weather derivative_**

To supplement and partly replace their traditional food-aid response to famine, the World Food Programme (WFP) designed an index-based insurance system to provide extra capital in the case of extreme drought, the amount being based on contractually specified catastrophic shortfalls in precipitation measured in terms of the Ethiopia Drought Index (EDI). Rainfall data is taken from 26 weather stations representing the various agricultural areas of Ethiopia. In 2006, WFP successfully obtained an insurance contract based on the EDI through AXA Re, a Paris-based reinsurer (Wiseman and Hess, 2007).

##  **_The Mexican catastrophe bond_**

In 2006, the Mexican government chose to insure its catastrophe reserve fund, FONDEN, against major earthquakes with a mix of reinsurance and a catastrophe bond. The resulting contract is linked to a parametric trigger in terms of magnitude and depth of seismicity for the three-year period 2007-09. The catastrophe bond provides cover of US$160 million out of a total cover of $450 million for a premium/interest totalling $26 million. The major reinsurance company, Swiss Re, issued the bond, which pays an interest of 230 basis points if payment is not triggered. Mexico received substantial technical assistance from the World Bank and Inter American Development Bank over the years, but ,as a middle-income developing country and member of the OECD, it financed the transaction out of its own means. (Cardenas, et al., 2007)

##  **_The Caribbean Catastrophe Risk Insurance Facility (CCRIF)_**

The _Caribbean Island States_ in 2007 formed the world’s first multi-country catastrophe insurance pool, reinsured in the capital markets, to provide governments with immediate liquidity in the aftermath of hurricanes or earthquakes. 16 Caribbean countries contribute resources ranging from US$200 thousand to US$ 4 million depending on the exposure of their specific country to earthquakes and hurricanes. This better-diversified portfolio is expected to result in a substantial reduction in premium cost of about 45 – 50% for the participating countries. The fund covers up to 20% of the estimated loss, and claims will be paid depending on an index for hurricanes (wind speed) and earthquakes (ground shaking). Initial funding by donor organizations provided support for start-up costs and helped capitalize the pool. The facility transfers the risks it cannot retain to the international financial markets through reinsurance or through other financial instruments (for example, catastrophe bonds). In addition, donors are adding to the reserves. The governments of Bermuda, Canada, France, the United Kingdom, as well as the Caribbean Development Bank, the World Bank and the EU recently pledged in excess of US $50 million to the CCRIF (Ghesquiere, et al., 2006; World Bank, 2007) 4
<!-- Start of picture text --> Insurance Pillar<br>500 year<br>Tier 1 Very extreme losses<br>A Climate Insurance igo<br>Pool pieita 100 year<br>Tier 2 Medium losses<br>Support for insurance ptreinie<br>instruments that | |——> Low losses<br>middlerovide coverer risksfor eaeF<br>y more effective) 10 year<br><!-- End of picture text -->
<!-- Start of picture text -->
| Very extreme losses:<br>500 year Residual risk unprotected<br>2] | as not effective to reduce or transfer risks<br>2 100 year . 9<br>® Medium-sized to extreme losses;<br>= Risk financing more<br>8 effective<br>oO<br>Low to medium-<br>sized losses:<br>10 year Risk reduction more effective<br><!-- End of picture text -->
The opposite is generally true for costly risk-financing instruments, like insurance, catastrophe bonds and contingent credit arrangements As catastrophe insurance premiums and the cost of other instruments fluctuate widely and are often substantially higher than the pure risk premium (average expected loss), mainly because the insurer’s cost of back-up capital is reflected in the premium, it is generally advisable to use those instruments for lower probability hazards, which may have debilitating consequences. To put it differently, risk financing is generally used to smooth the _variability_ of losses, while risk reduction reduces ( _expected_ ) risk. It would not be economically rational to implement insurance for frequent, but low loss events, which may be covered domestically or be reduced easily. Finally, most individuals and governments find it too costly to insure against very extreme risks occurring less frequently than say every 500 years.

_Financial vulnerability and the resource gap: The rationale for financing disaster risk_ According to an early theorem by Arrow and Lind (1970) governments should not insure if they are not averse to risks, i.e. if financial risks faced by the government can be absorbed without major difficulty. According to Arrow and Lind a government may

- Pool risks as it possesses a large number of independent assets and infrastructure so that aggregate risk is negligible, or

- Spread risk over the population base, so that per-capita risk to risk-averse households is negligible.

In theory, thus, governments are not advised to incur the extra costs of transferring their disaster risks if they carry a large portfolio of independent assets and/or they can spread the losses of the disaster over a large population. Because of their ability to spread and diversify risks, Priest (1996) refers to governments as "the most effective insurance instrument of society." Furthermore, the extra costs of insurance can be significant; for example Froot (2001) reports cost up to seven times greater than the expected loss, due to high transaction costs, uncertainties inherent in risk assessment, the limited size of risk transfer markets and the large volatility of losses. Consequently, according to Arrow and Lind governments should behave risk-neutrally and evaluate their investments only through the expected net present (social) value. The Arrow and Lind theorem serves as the basis for government strategies for dealing with risk. In practice, most governments neglect catastrophic risks in decision making, thus implicitly or explicitly they behave _risk-neutrally_ (Guy Carpenter, 2000 _)._ The case for sovereign self-insurance, however, may not hold for highly exposed developing country governments, especially those that are not sufficiently diversified or cannot spread losses over the tax-paying public. Already in 1991, the Organization of American States' primer on natural disasters states that the risk neutral proposition is valid only up to certain point and that the reality in developing countries suggests that some governments cannot afford to be risk-neutral: The reality of developing countries suggests otherwise. Government decisions should be based on the opportunity costs to society of the resources invested in the project and on the loss of economic assets, functions and products. In view of the responsibility vested in the public sector for the administration of scarce resources, and considering issues such as fiscal debt, trade balances, income distribution, and a wide range of other economic and social, and political concerns, governments should not act risk-neutral (OAS, 1991).

7
<!-- Start of picture text --> Human Interference CLIMATEf 5 CHANGEwar<br>including: Variability<br>| Fox posure<br>1 8<br>re]<br>Tnitéal Impacts. =<br>MITIGATION re elBICES a<br>of Climate. Change via4 Q= } . a<<br>GHG Sources_ and Sinkssh == Ruianansens———— eI<br>Adaptations =<br>1 =<br>Residual or Planned<br>Net Impacts ADAPTATION<br>to the Impacts and Wulnerahilities<br><!-- End of picture text -->
A narrower definition that focuses only on the affected system is common in the risk/hazards and vulnerability communities. Turner et al. (2003) define vulnerability as the degree to which a system or subsystem is likely to experience harm due to exposure to a hazard, either as a perturbation or stressor. In this framework, multiple hazards can be caused or aggravated by global-change phenomena, where vulnerability is a function of the _exposure, sensitivity and resilience_ of the system in question. Risk, alternatively, is a function of the _hazard (likelihood and severity) and its potential consequences (exposure, sensitivity)_ , but usually stops short of considering the coping capacity and resilience of the exposed system.

We consider governments financially vulnerable to disasters if they cannot access sufficient funding after a disaster to cover their liabilities in terms of reconstructing public infrastructure and providing assistance to households and businesses in need. We operationalize this concept by the term _resource gap,_ which we define the net loss associated with a disaster event after exhausting all possible ex-pots and ex ante financing sources. Such a _resource gap_ is a useful measure of sovereign financial vulnerability. The repercussions of a resource gap can be substantial. The inability of a government to repair infrastructure in a timely manner and provide adequate support to low-income households can result in adverse long-term socio-economic impacts. As a case in point Honduras experienced extreme difficulties in repairing public infrastructure and assisting the recovery of the private sector following Hurricane Mitch in 1998. Five years after Mitch’s devastation the GDP of Honduras was 6% below pre-disaster projections.<sup>3</sup> When considering whether Honduras and other highly exposed countries should protect themselves against resource gaps and associated long-term negative consequences, it is important to keep in mind that risk management measures have associated opportunity costs, which means that they can reduce GDP by diverting financial resources from other public sector objectives, such as investments into social or infrastructure projects.

In the following, we outline a methodology based on the CATSIM model (Hochrainer and Mechler, 2009) to calculate this resource gap, and derive a global country-level estimate for the most hazard-exposed countries. Figure 3 shows schematically how we combine risk estimates with financial resilience to lead to an estimate of financial vulnerability.

> 3 Own calculations.

9
<!-- Start of picture text --> People, assets Vulnerability<br>2. Financial<br>Resilience<br>1. Direct Risk + Expost sources<br>* Produced capital * Excante sources<br>* Himan capital<br>+ Environmental capital<br>3. Financial Vulnerability :<br>Inability to finance relief and Risk Management<br><!-- End of picture text --> <!-- Start of picture text -->
| EconomicreconstructionRisk | |<br><!-- End of picture text -->
Table 1: Government liabilities and disaster risk

|**Liabilities**|**Direct:**obligation in any event|**Contingent:**obligation if a particular<br>event occurs|
|---|---|---|
|**Explicit**<br>Government liability<br>recognized by law or<br>contract|<br>Foreign and domestic sovereign<br>borrowing, Expenditures by<br>budget law and budget<br>expenditures|State guarantees for nonsovereign<br>borrowing and public and private<br>sector entities,**reconstruction of**<br>**public infrastructure**|
|**Implicit**<br>A "moral" obligation<br>of the government|Future recurrent costs of public<br>investment projects, pension and<br>health care expenditure|Default of subnational government and<br>public or private entities,**disaster**<br>**relief**|

Source: Modified After Schick and Polackova Brixi, 2004 In this first CATSIM step the risk of direct losses is assessed in terms of the probability of asset losses in the relevant country or region. Consistent with general practice, risk is modeled as a function of hazard (frequency and intensity), the elements exposed to those hazards and their physical vulnerability (Burby, 1991; Swiss Re, 2000).<sup>4</sup> In more detail,

1. Natural _hazards_ , such as hurricanes, or floods, are described by their intensity (e.g. peak flows for floods) and recurrency (such as a 1 in 100 year events, i.e. with a probability of 1%). We focus on sudden-onset climate-related events only such as tropical cyclones, floods and winterstorms. Generally, for the sudden-onset events analysts generally equate given loss and risk data with asset losses.<sup>5</sup>

Our estimates, i.e. probability of given events and corresponding losses on the country level, are based on available data as reported in Cummins and Mahul (2009) as well as own estimates based on past losses and using extreme value theory. While Cummins and Mahul (2009) present risks based on catastrophe model approaches, for countries where such data were not available, we used reported loss data from CRED (2009) and Munich Re (2009) to estimate a Generalized Pareto distribution using either Maximum likelihood optimization methods if more data points existed (i.e., usually more than 10 observations) or Minimum-Distance methods if only a few data points were available. Based on the estimated parameters the selected loss return periods were calculated. The historically observed cumulated yearly relative losses (losses in percent of GDP) were taken as the basic data. A Generalized Pareto distribution was fitted to the tails (i.e. to the data exceeding the 80%-quantile) by minimizing the integrated square distance between the empirical distribution and the estimated distribution. For countries with more than 25 data points, the maximum likelihood estimates of the parameters were calculated alternatively. The higher quantiles (90%, 95%, 98%, 99%, 99.6%, 99.8%) of the fitted distribution as well as the

> 4 In the hazards and risk community, “sensitivity” is referred to as “vulnerability”, and often exposure is included in the sensitivity component; thus, risk is defined by hazard and vulnerability. In catastrophe models carried out for insurance purposes, the contract specifications of the underwritten and exposed portfolios are added as a fourth component (eg.  Swiss Re, 2000).

> 5 An indication that this assumption can be maintained is the fact that loss data are usually relatively quickly available after a catastrophe, which indicates that flow-indirect impacts emanating over months to years, are usually not considered.

11
<!-- Start of picture text --> Allocation Function Distribution Function<br>other market failure:<br>fe) RBK 1<br>Regulation/<br>premia<br>“Government is insurer<br>oflast resort”<br><!-- End of picture text -->
<!-- Start of picture text --> 51%<br><!-- End of picture text --> <!-- Start of picture text --> 18%<br><!-- End of picture text --> <!-- Start of picture text --> 31%<br><!-- End of picture text --> <!-- Start of picture text --> 58%<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br>2%<br><!-- End of picture text --> <!-- Start of picture text --> 44%<br><!-- End of picture text --> <!-- Start of picture text --> 48%<br>8%<br><!-- End of picture text --> <!-- Start of picture text --> 87%<br>13%<br><!-- End of picture text --> Fig. 5: Insurance and government assistance for selected disasters as a percentage of direct losses (Source: Linnerooth-Bayer and Mechler, 2007) 3. Physical vulnerability describes the degree of damage to the capital stock due to a natural hazard event. The method commonly used here are fragility curves setting the degree of damage in relation to the intensity of a hazard.

Based on data on the return period and losses in percent of capital stock, CATSIM generates loss frequency distributions describing the probability of specified losses occurring, such as a 100-year event causing a loss of 200 million USD, a 50-year event causing a 40 million USD loss, and so on.<sup>6</sup> It should be kept in mind that top-down estimates at this broad scale are necessarily rough. Since most disasters are rare events, there is often little in terms of historical data; furthermore it is difficult to include dynamic changes in the system, for example, population and capital movements and climate change. To improve the data information, bottom-up assessments can be undertaken that involve a detailed analysis of the occurrence of hazards in certain areas, the exposed elements and vulnerabilities of structures on a more microscale level.

As already indicated risk and potential losses are summarized by means of loss-frequency distributions, which relate probabilities of loss to assets destroyed. For example, figure 6 shows a cumulative loss-frequency distribution for flood risk in a hypothetical country. The horizontal axis shows the fraction of capital stock destroyed by a disaster, and the vertical axis represents the probability that losses will _not_ exceed a given level. For example, with a probability of 0.9 (90%) flood losses will not exceed 250 million LCU; inversely, there is a 0.1 (10% chance) that such a loss and larger will occur.

> 6 It is standard practice to refer to 20-, 50-, 100-, 500- and 1000-year events.

13
<!-- Start of picture text --> Probability of losses not exceeding a certain level<br>1.00<br>0.98<br>0.96<br>0.94<br>0.92<br>0.90<br>0 250 500 750 1000 1250 1500<br>Government liabilities (million LCU)<br><!-- End of picture text --> Fig. 6: Risk of losses as measured by a cumulative loss-frequency distribution An important summary measure of this distribution is the annual expected losses, or the losses to be expected on average every year. The annual expected loss is the sum of all losses weighted by the probability of occurrence. Graphically, the expected losses are represented by the area above the cumulative distribution curve. However, it has to be kept in mind that disasters are not average events, rather they are extreme events occurring very rarely. Over a specified time period, like 100 years, catastrophes may occur, and the losses suffered over this period will be close to the sum of annual expected losses over these years.Based on available information, potential losses due to earthquake events in terms of percent of capital stock lost can be established for a country, state or region.

# **_3.2 Step 2: Estimation of the public sector’s financial resilience_**

Given limited resources to reduce human and economic losses, governments need to be financially resilient, or be able to provide sufficient funds to finance reconstruction of public capital, provide relief to households and support business in their recovery efforts. Sources of funding for reconstruction include aid, budget diversion as well as multilateral and international lending. However, these are not infinite and come at a cost.

Based on the information on direct risks to the government portfolio, financial resilience can be evaluated by assessing the government’s ability to finance its obligations for the specified disaster scenarios. Financial resilience is directly affected by the general conditions prevailing in an economy, i.e., changes in tax revenue have important implications on a country’s financial capacity to deal with disaster losses. The specific question underlying the CATSIM tool is whether a government is financially prepared to repair damaged infrastructure and provide adequate relief and support to the private sector for the estimated damages of 10- 50- 100- and 200-year events? For this assessment, it is necessary to examine the government’s sources, including sources that will be relied on (probably in an ad hoc manner) after the disaster and sources put into place before the disaster (ex ante financing). These sources are described below (based on Mechler, 2004 and Hochrainer, 2006).

14
# _Ex post financing sources_

The government can raise funds _after_ a disaster by accessing international assistance, diverting funds from other budget items, imposing or raising taxes, taking a credit from the Central Bank (which either prints money or depletes its foreign currency reserves), borrowing by issuing domestic bonds, borrowing from the IFIs and issuing bonds on the international market (Benson, 1997 a,b,c; Fisher and Easterly, 1990). Each of these financing sources can be characterized by costs to the government as well as factors that constrain its availability, which are assessed by this CATSIM module (see table 2).

_Aid inflows_ from abroad after a catastrophe include private and public donations from private institutions, government agencies and inter-governmental agencies in the form of relief, technical assistance, grants, commodities and money (Albala-Bertrand 1993). The amount of aid is as much dependent on the event as on the will of the donors to grant assistance. Thus there is considerable uncertainty as regards the amount of aid obtained post-catastrophe necessitating a case by case examination. As discussed, a value of 10.4% of direct losses for this parameter was estimated. It is assumed that all aid inflows will be divided up between the public and the private sector in relation to their share of infrastructure (government) and non-infrastructure (private sector) in total capital stock. As there is uncertainty whether aid will in fact be made available, the availability of aid is assumed to be constrained in three scenarios: 0, 50, 100% made available, i.e. 0% of losses are financed by aid, 5.2% and 10.4%. These scenarios will be looked at in combination with the scenarios on the availability of foreign borrowing as is explained below.

Table 2: Ex Post and ex-ante financing sources for relief and reconstruction

|**Type**|**Source**|**Considered in model**|
|---|---|---|
|**Ex-post sources**|||
|Decreasing government<br>expenditures|Diversion from budget|x|
|Raising government revenues|Taxation|-|
||Central Bank credit|-|
|Deficit financing<br>_Domestic_|Foreign reserves|-|
||Domestic bonds and credit|X|
|Deficit financing|Multilateral borrowing|X|
|_External_|International borrowing|X|
||Aid|X|
|**Ex-ante sources**|||
|Reserve funds||X|
|Insurance||X|
|Contingent Credit||X|

_Budget diversion_ means using funds that were earmarked for other purposes and thus implies foregoing the returns and benefits of these projects. As well, there is often high political cost to diversion when money is taken from ministries. It is assumed that the government is able to 15
divert some funds from government spending to reconstruction activities. In recent research maximum diversion post-disaster for the four Latin American countries Bolivia, Colombia, Dominican Republic and El Salvador was estimated at 5-10% of current expenditure (government spending) (Freeman et al. 2002b: 35). For this report, we use an average value of 7.5% for both Honduras and Argentina.

Establishing additional _taxes_ after a catastrophe will decrease private savings when consumption is to stay constant and exert additional depressionary effects on the economy. Furthermore, disaster taxes are expensive to administer. For this reason, no additional tax revenue is assumed.

Given a budget deficit, _deficit financing_ options are accessing credit from the Central Bank or the private sector (commercial banks and private households), tapping into the foreign reserves of the central bank, obtaining loans from IFIs or selling bonds abroad (Benson 1997c).

_Central bank credit_ is usually granted by selling government bonds to the Central Bank resulting in money creation which is potentially inflationary if money growth is not held in proportion to real GDP growth (Fischer and Easterley, 1990). Using _foreign exchange reserves_ of the central bank creates the potential for a balance-of-payment crisis due to the lack of needed reserves for imports. The sources reserves and central bank credit are generally considered to be particularly problematic, e.g. an assessment of a World Bank and IMF team on reconstruction financing options in El Salvador after the earthquakes in 2001 stated: Under any monetary system, a country needs to maintain a strong underlying fiscal position and a sound credit policy, with an adequate cushion of net international reserves, to preserve macroeconomic stability. Expanding the money supply or reducing the central bank’s net international reserves are never optional sources of financing for reconstruction costs. (IMF and World Bank, 2001: 5).

Central Bank credit and tapping into reserves are used in practice as deficit financing sources, but from a normative planning point of view, they should not be considered in the case study countries in Latin America where inflation and external debt issues are important policy issues (Ferranti, 2000: 61). For these reasons, these two sources will not be considered as viable sources for ex-post catastrophe finance in this report.

_Borrowing domestically_ also incurs costs: domestically, credit may be compressed particularly so in shallow credit markets resulting in a rise of the interest rate and a crowding-out of domestic investment. Borrowing from the private sector via issuing domestic government bonds is another option. However, it is a common characteristic that in developing countries domestic bond or financial markets are rather shallow (Ferranti 2000). We assume 10% additional government borrowing from the private sector, which seems an optimistic assumption given the post disaster crunch and shallow domestic financial markets in most of the disaster vulnerable countries studied.

A major source of a country’s ex-post disaster funding is _foreign borrowing_ . The importance of (foreign) borrowing for reconstruction is demonstrated by the following statement that also came from the post-earthquake IMF and World Bank mission to El Salvador.

From the standpoint of macroeconomic policy, the key question is how much and how rapidly can the government afford to borrow to finance the reconstruction costs, while keeping fiscal policy on a sustainable path (IMF and World Bank, 2001).

16
We consider borrowing to be constrained by the existing country debt. CATSIM assumes that the sum of all loans cannot exceed the so-called _credit buffer_ for the country. In the Highly Indebted Poor Countries Initiative (HIPC) the credit buffer is defined as 150% of the typical export value of this country minus the present value of existing loans (HIPC, 2002).

# _Ex ante financing sources_

In addition to accessing ex post sources, a government can arrange for financing before a disaster occurs. Ex ante financing options include reserve funds, traditional insurance instruments (public or private), alternative insurance instruments, such as catastrophe bonds, or arranging a contingent credit. The government can create a reserve fund, which accumulates in years without catastrophes. In the case of an event, the accumulated funds can be used to finance reconstruction and relief. A catastrophe bond (cat bond) is an instrument whereby the investor receives an abovemarket return when a specific catastrophe does not occur, but shares the insurer’s or government’s losses by sacrificing interest or principal following the event. Contingent credit arrangements call for the payment of a fee for the option of securing a loan with pre-arranged conditions after a disaster. Insurance and other risk-transfer arrangements provide indemnification against losses in exchange for a premium payment. Risk is transferred from an individual to a (large) pool of risks. These ex-ante options can involve substantial annual payments and opportunity costs; statistically the purchasing government will pay more with a hedging instrument than if it absorbs the loss directly. While a number of countries have reserve funds implemented (albeit generally with low nominal amounts), insurance and contingent credit options are only currently being considered with prime examples being Mexico, Colombia and the countries participating the in the Caribbean pool. Table 3 graphically shows the ex post and ex ante instruments that can be accessed to finance post-disaster needs. Another critical point suggested in this chart is the time dimension, which generally is in favour of ex ante instruments releasing financing rather quickly.

17
Table 3: Ex-post vs. ex ante financing instruments.

||Immediate<br>hours/days|Short term<br>1-3 months|Medium term<br>3-9 months|Long term<br>Over 9 months|
|---|---|---|---|---|
|**Financial needs for**<br>**post disaster**<br>**operations**|||||
|Relief|||||
|Recovery|||||
|Reconstruction|||||
|Financing tools|||||
|Ex-post financing|Budget<br>contingencies|Relief<br>Budget<br>reallocation|Domestic/<br>external credit|Donor assistance<br>Tax increase|
|Ex-ante financing|Reserve fund|Parametric<br>RT,<br>Contingent<br>debt|Traditional RT||

Source: Cummins and Mahul, 2009.

# **_3.3 Step 3:  Assessment of financial vulnerability and the “resource gap”_**

Using the information on direct risks to the government portfolio and financial resilience, financial vulnerability can be evaluated. We define financial vulnerability as the (probabilistic) availability of government finances for paying for relief and reconstruction. The resource gap is the difference between the cost of a disaster (step 1), and the funds available to the government to rebuild and provide relief and assistance with recovery efforts.<sup>7</sup> Figure 7 illustrates the calculation of this metric for a hypothetical case.

Given losses due to a certain event, such as the 100 year event (public sector loss of 4,000 local currency units (LCU)), the algorithm evaluates the sources for funding these losses. An implicit ordering of these sources is assumed according to the availability and marginal opportunity costs of the sources: grants from donors and international financial institutions (IFIs) would have the least costs associated as these are donations; thus they would be used first. Second, diversions from the budget could be used, then domestic credit, followed by borrowing from IFIs and the international markets (bonds). While in this illustration a 100 year event could be financed, for a 200 year (public sector loss of 10,000 LCU), there would be lack of (ex-post) sources and consequently a resource gap. Ghesquiere and Mahul (2007) added another important dimension related to the timing of resource flows.

> 7 The term resource or financing/resource gap has been heavily used in the economic growth modeling literature as the difference between required investments in an economy and the actual available resources. In this report, this tradition is followed and the financing gap is understood as the lack of financial resources to restore assets lost due to natural disasters and continue with development as planned.

18
<!-- Start of picture text --> Loss function: financing needs<br>0.1 10 year event<br>0.08<br>0.06<br>0.04<br>0.02 100 year event 200 year event<br>0<br>0 2,000 4,000 6,000 8,000 10,000 12,000<br>Losses in LCU<br>Financing sources: financing supply<br>12<br>International bonds<br>10<br>Borrowing<br>from IFIs<br>8<br>resource<br>6 Domestic bonds gap<br>and credit<br>4<br>Diversion<br>2<br>Grants<br>0<br>Amount available<br>Probability<br>Marginal cost<br><!-- End of picture text --> Fig. 7: Illustration for calculating the disaster resource gap As shown in figure 7, while enough funding may be available over time, yet there may be a sporadic resource gap, as generally in the aftermath of a disaster event, urgent expenditure needs are high and immediately available financial resources often very limited. As an example how the resource gap is calculated here we refer to box 3.

Due to the focus of this work in step 3, we do not go into more detail on steps 4 and 5 in this report but rather present a small overview for the sake of completeness of the CATSIM methodology.

# **_3.4 Step 4: Illustrating the developmental consequences of a resource gap_**

Financial vulnerability can have serious repercussions on the national or regional economy and the population. If the government cannot replace or repair damaged infrastructure, for example, roads and hospitals, or provide assistance to those in need after a disaster, this will have longterm consequences. The consequences on long-term economic development can be illustrated by the CATSIM tool. Generally, economic welfare will be higher on average if the government does not allocate its resources to catastrophe insurance or other risk management, but the economy has fewer extremes and is more stable with public sector insurance. Investing in the risk financing instruments can thus be viewed as a trade-off between economic growth and stability. Budgetary resources allocated to catastrophe reserve funds, insurance and contingent credit (as well as to preventive loss-reduction measures) reduce the potential resource gap, and thus can ensure a more stable development path. On the other hand, ex ante financing and prevention measures come at a price in terms of other investments foregone and will inevitably have an adverse impact on the growth path of an economy.

19
<!-- Start of picture text --> Jo) eats Apt =lelxi<br>aoa p. first kos Seyret) Seyret (i) 1Oyrevi() Syrevt(Sy) 1000-yreve (i<br>Haz_G Low | ol 104 ma wz 05 51 brio<br>Wesibence file: Diversion (2) Drom, Creel fedinlRF (bn 3) Creel butter (bev. $1 prop dre (26) Assistance ('®) Ratio Mla Taxation (3%)<br>Kase Load ? ot o 02 fl 14 a5 0<br>Se IndalCap (bn) damage_yetio ret.on iene. (38)<br>Ex Lesa | [?: [os [ww<br>T<br>0.45 Loss Financing Strategies<br>Ex-post Ordering in ' ' ' ' \ ‘<br>o4 HE Diversion<br>Assit fr [a Tanaton<br>Broce [2 a5 i!HR BondDornesticMFI creditcredit<br>We BEE Financing gap<br>Taeneinn [a a O25<br><!-- End of picture text -->

<!-- Start of picture text --> Dremnstin Creat [a Bo<br>MFIBored CreteCrest [s: 0.15<br>a1<br>0.05 J<br>oO FI] Bt) 40 a oo 70 oo<br>yearevents<br>Fisenciigionn gharis'al ihe Loge Financing Portfolio for the year evant<br>eT ‘years event 2<br>sail Posie } HE sistance:<br>” cal HE DiversionTansation<br>1 Oomestic credit<br>_ teins | MBer bond credit<br>o O08 1 O15 o2 oO o3 os 4<br>Million USD, lass finaneing for T=chasen yoar event 2 critical year event<br><!-- End of picture text -->
assessment of the resource gap and potential economic consequences, CATSIM illustrates the pros and cons of strategies for building financial resilience using ex-ante financial instruments. Below, we suggest how risk financing instruments may be supported. Due to the scope of this report, we here do not go into more detail on steps 4 and 5, which would merit separate discussions (see Mechler et al., 2006, Hochrainer, 2006).

## _Uncertainty_

Uncertainty is inherent in every estimation procedure, some of these uncertainties can be quantified some of them not. Also in this report, large uncertainties around the estimates still remain, mostly due to lack of additional data for recalibration and back-testing. This is a quite usual thing for low frequency but high consequence events today and will only get better in the future if loss reports of disaster events are based on a holistic methodological approach which can be applied for in all areas of the world for as much hazard types as possible. In this report, the losses and corresponding probabilities are based on models or past data; in the latter case due to the usually small number of data points, the parameter estimates have large confidence bounds. In this report it was decided to use the mean estimates and neglect the confidence bounds because of violation of some assumptions like minimum amount of data points necessary for variance calculations and so on, which would lead to biases in the uncertainty bounds. Also for the exposure estimates, data were used that were based on simplified versions of capital stock estimation techniques, such as relying on the Penn World tables and selecting a specific depreciation rate. However, for such a large number of countries, and the necessity to have capital stock estimates for all countries, only such approaches can be used. The government liabilities are also taken to be the same in percentage for all countries, an assumption which is not valid if only a country specific approach is taken. However, here it serves well, also because such assumptions can be found empirically in developed as well as developing countries after catastrophe events. Furthermore, the estimation of the resilience of the government is also very difficult, either because data is not there or it is difficult to estimate the different financing sources in each case. Also here, average results are used were appropriate or estimated via econometric methods using databases such as the World Bank. Last but not least, because the loss financing situation for each country is also time dependent, e.g. a disaster within a strong growth period might be less disastrous then in a recession, the loss estimates are only valid for a certain time period. All in all, the numbers presented should be regarded as ballpark numbers and not taken for granted forever or exact without mentioning the uncertainties in it. They numbers however should serve as a starting point at which scale we have a problem and how it could be possibly handled within a generic risk management approach.

# **_3.6 The data sample_**

We calculate financial vulnerability for the 73 most weather prone, vulnerable countries that have had at least one large sudden-onset natural disaster event over the last 30 years but also selected some countries where it was feasible to estimate risks based on observed losses. A large event is defined by a threshold of a ratio of (asset) losses expressed per GDP of larger than 1%. For those countries we define risk, loss distributions and financial exposure. As a next step, we assess financial vulnerability. Financial vulnerability is indicated in terms of resource gap recurrence, i.e. it indicates an event and recurrence of this event, for which government financial 21
funds would be insufficient to cover government disaster liabilities caused by the event. Table 1 lists data sources employed for our estimation.

Table 4: Data used in the assessment

|**Variable**|**Data source**|
|---|---|
|Disaster losses (USD)|EMDAT, Munich Re databases|
|Risk estimates|1. Cummins and Mahul (2009) based on various sources.<br>2. IIASA estimates|
|Capital stock data|Sanderson and Striessnig (2009)|
|Socioeconomic data|World Bank, 2009|

The disaster loss sample is based on information from two databases and was compiled by Okuyama (2009) with the threshold for a large event defined arbitrarily by a loss exceeding 1 percent of GDP.<sup>8</sup> One database is the open-source EMDAT disaster database (CRED, 2009) maintained by the Centre for Research on the Epidemiology of Disasters at the Université Catholique de Louvain. The other database is the proprietary Munich Re NatCat Service database, which mainly serves to inform insurance and reinsurance pricing. Probabilistic risk estimates are based on two sources: (i) a recent report by Cummins and Mahul (2009), who report such estimates from various sources for a number of countries, (ii) IIASA estimates of risk using extreme value statistics. Capital stock data were used from a new global database also generated IIASA by Sanderson and Striessnig (2009). Finally, other socioeconomic data were taken from the World Development Indicators (World Bank, 2009).

> 8 In order to define the “event set” the threshold of stock losses is set as a share (1%) of flow effects (GDP). It would have been more systematic to define an asset based threshold, yet we responded to the larger intuitive appeal of using GDP as a denominator.

22
# **4 Findings and discussion**

We find that a number of countries are highly financially vulnerable. As shown in Figure 8, the majority of the disaster prone countries in our sample experience a disaster financing problem, a resource gap, below a 50 year event.

<!-- Start of picture text --> Count rie s Experienc ing Init ial Re sourc e Gap Bet w ee n<br>S pec ified Ret urn Periods<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>50 Y e ars or 51 t o 100 101 t o 250 No Gap W it hin<br>Le ss Y ea rs Y ea rs Periods<br>Ret urn Periods<br>Count rie<br><!-- End of picture text --> Fig. 8: Number of countries experiencing a resource gap for some return periods Figure 9 shows how risk as measured in expected losses translates into an estimate of financial vulnerability based on assessments of financial and economic resilience. No clear relationship is discernible between the losses as percent of GDP and the resource gap (in terms of a probabilistic return period) and the resource gap for a 100 year event, which shows that the translation is a more complex one and GDP as a mere indicator of vulnerability may not suffice.

<!-- Start of picture text --> 500 250%<br>400 200%<br>300 150%<br>200 100%<br>100 50%<br>- 0%<br>0% 2% 4% 6% 8% 10% 0% 2% 4% 6%<br>Expected losses as % GDP Expected losses as % GDP<br>Finance gap year<br>100 year finance gap<br><!-- End of picture text --> Fig. 9: Risk vs. financial vulnerability 23
<!-- Start of picture text --> ye £ ’ 5 . ian : a f = 2<br>.<br>fy = n<br>‘Y wa | \ 2 ys SR an"Z<br>P aa ee ~'<br>ey vy<br>_ ¢ ~ < ‘ .<br>Resource Gap Year (years)<br>MM <20 451-100 9) 151-200 MEM 251 - 500<br>ME 21 - 50 101-150 (0) 201-250 MMM> 500<br><!-- End of picture text -->
<!-- Start of picture text -->
- ~ ;<br>bese a ‘<br>* - J / 7, ‘ ae m<br>; Se, ise,ee<br>Wr: oF<br>S. —> = or ee<br>50 Year Events Gaps (USD, millions)<br>ME No Resource Gap<br>M<10 M1.1-15 M16-20 2.1-2.5<br>) 2.6-3.0 M31-35 MMB36-40 M41-45<br><!-- End of picture text -->

<!-- Start of picture text --> 5 x ( -<br>. » f ’ . * \) out, -* t<br>5 : S 8 .<br>i ; °<br>100 Year Events Gaps (USD, millions)<br>ME No Resource Gap<br>MM <40 Ml 4.1-8.0 8.1- 12.0<br>M8 12.1-16.0 MMM 16.1-20.0 MMMM 20.1-24.0<br><!-- End of picture text -->
<!-- Start of picture text --> .<br>WAS : z ‘<br>, a ot WS a7<br>;!a;g:.x:<br>na<br>OY Ee.<br>“ES. ’<br>4 at<br>Q ‘ : wo . oc<br>250 Year Events Gaps (USD, millions)<br>ME No Resource Gap<br>Ml <6.0 Ml 6.1 - 12.0 12.1 - 18.0<br> 18.1-24.0 MEM 24.1-30.0 MMM 30.1 - 36.7<br><!-- End of picture text -->
governments with immediate liquidity in the aftermath of hurricanes or earthquakes, donors and IFIs have provided significant capital to the extent of 50 million USD. This funding helps to back up the pool in its early years when accumulated country contributions are insufficient to render this scheme robust to withstand major events such as hurricanes.

# **_How much money would a pool require to fill the funding gap post-disaster for all disaster prone countries?_**

Our estimates may also inform decisions pertaining to a “climate insurance fund,” which would fund “high level” country losses that exceed the ability of any given country to pay in the case of an extreme event. Figure 14 shows the funding requirement for different layers of disaster event recurrence. If for example, funding would be set aside to cover resource gaps for more frequent events with a return period of 50 to the 100 year events, than about 1.4 billion USD would be required annually. Covering more infrequent losses as well (such as up to a 250 year event), would mean that more funding would be required, up to 2.6 billion USD. Also, other layers, such as 100-250 year resource gap layers, could be considered to be covered with associated funding requirements. Bundling many risks in a portfolio leads to a diversification effect and thus lower funding requirements. We took the simplifying assumption that risks would be independent. Thus the estimates of the funding requirements have to be considered a lower bound. Furthermore, we only looked at average costs. However, it would be important to consider the whole range of possible costs and associated probabilities, i.e. a global cost curve would be needed. Hence, one research direction for the future is to come up with such a cost curve to incorporate the variability in more detail and accordingly to introduce risk functionals (Pflug and Römisch, 2007).

<!-- Start of picture text --> 50 - 100<br>50 - 250<br>100 - 250<br>100 - 500<br>250 - 500<br>0 0.5 1 1.5 2 2.5 3<br>Global annual cost (billions 2008 USD)<br>Return Period in years<br><!-- End of picture text --> Fig. 14: Funding requirements to cover resource gaps for different layers of return periods Our estimates can be used to gauge the support needed by vulnerable countries to buffer against extremes and the scale of funding necessary when implementing a global fund for this purpose.

27
Our findings may also be used for identifying the scope of the problem of managing climate variability today and in the future, and may thus be interpreted as a sort of baseline estimate for what may be to come, if we do not manage emission reductions and adaptation to climatic risks better. Comparing our estimates of the costs of financing high loss layers of sudden onset events with the at least as uncertain numbers currently available regarding the costs of adaptation to both slow and sudden onset climate variability and change, we arrive at the lower end of the estimates of the prominent UNFCCC (2007) report. For example, for adapting infrastructure alone to climate change impacts (including extreme events), the UNFCCC calculates a broad range of 8 –130 billion USD necessary in 2030 (in 2030 values) (UNFCCC, 2007). Others have even put their estimates a magnitude higher, yet essentially these, as well as our numbers are associated with uncertainty, and remain imprecise. However, as societies have found many ways for coping with uncertainty, such quantitative estimates may be sufficiently precise to at least start a process for thinking about appropriate options and courses of action for absorbing current and future extreme event risks.

28