From fatalism to resilience: reducing disaster ... - Wiley Online Library

doi:10.1111/j.1467-7717.2011.01256.x

From fatalism to resilience: reducing disaster impacts through systematic investments Harvey Hill Manager, Climate Decision Support and Adaptation Unit, National Agroclimate Information Service, Agriculture and Agri-Food Canada, Canada, John Wiener Research Associate, Environment and Society Program, Institute of Behavioral Science, University of Colorado, United States, and Koko Warner Academic Officer, Head of Section, Institute for Environment and Human Security, United Nations University, Germany

This paper describes a method for reducing the economic risks associated with predictable natural hazards by enhancing the resilience of national infrastructure systems. The three-step generalised framework is described along with examples. Step one establishes economic baseline growth without the disaster impact. Step two characterises economic growth constrained by a disaster. Step three assesses the economy’s resilience to the disaster event when it is buffered by alternative resiliency investments. The successful outcome of step three is a disaster-resistant core of infrastructure systems and social capacity more able to maintain the national economy and development post disaster. In addition, the paper considers ways to achieve this goal in data-limited environments. The method provides a methodology to address this challenge via the integration of physical and social data of different spatial scales into macroeconomic models. This supports the disaster risk reduction objectives of governments, donor agencies, and the United Nations International Strategy for Disaster Reduction. Keywords: adaptation, adaptive capacity, climate, disaster, economic growth, geographic information systems (GIS), Hyogo, infrastructure systems, resiliency investment, risk reduction

Introduction An increasing number of indicators have emerged in recent years that support the argument that climate change has already influenced the frequency and intensity of natural catastrophes (Hoeppe and Gurenko, 2006; Stern, 2006; Solomon et al., 2007). If the scientific global climate models are accurate, the present problems will be magnified in the near future, increasing the need for effective risk reduction (Warner et al., 2009a). These models suggest that one should expect: • a rise in the number and severity of atmospheric extreme events, such as bush fires, droughts, flash floods, hailstorms, heat waves, storm surges, tornados, and tropical and extra tropical cyclones, in many parts of the world, and; • more extensive impacts on economies, the environment and society due to weatherrelated disasters. Disasters, 2012, 36(2): 175−194. © 2012 The Author(s). Disasters © Overseas Development Institute, 2012 Published by Blackwell Publishing, 9600 Garsington Road, Oxford, OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA

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Harvey Hill, John Wiener and Koko Warner

Box 1 Vulnerable countries and the poor suffer the most In the past 25 years, 95 per cent of deaths due to natural disasters occurred in developing countries, and direct economic losses (averaging USD 100 billion per annum in the past decade) in relation to national income were more than twice as high in low-income countries as opposed to high-income countries. These disaster statistics do not, for the most part, reflect long-term indirect losses, which can be very significant, particularly in countries with little capacity to respond and recover. There are considerable differences not only with respect to the human and economic burden of disasters in developed versus developing countries, but also in relation to insurance cover. In the richest countries, approximately 30 per cent of losses in 1980–2004 (totalling around 3.7 per cent of gross national product (GNP)) were insured; in low-income countries, only about one per cent of losses (amounting to 12.9 per cent of GNP) were insured (Warner and Spiegel, 2009).

Even if climate change does not unfold as forecast, a significant proportion of recent economic losses caused by natural hazards can be attributed to droughts, floods, windstorms and other climate-related hazards (Höppe and Grimm, 2008)—the second major source being geophysical disasters (UNISDR, 2007). This trend can be largely attributed to changes in land use and the growing concentration of people and capital in vulnerable areas, such as coastal regions exposed to windstorms and in fertile river basins exposed to floods (Mileti, 1999). As a result, the exposure of infrastructure systems (IS) to geophysical and weather-related hazards is increasing. These mounting hazard-related risks—associated with both climate change and social and economic developments—place a substantial additional burden on sustainable development agendas (Warner et al., 2009b). These risks demand attention, particularly since the negative consequences of climate change could hinder progress towards critical social needs and achievement of the Millennium Development Goals (Stern, 2006). The links between disasters and reduced economic growth are well established. Two recent examples from geophysical events illustrate this: 1. A non-government aid worker described the impacts of the earthquake that struck Port-Au-Prince, Haiti on 12 January 2010: I have never seen anything like this. It is extremely difficult to bring aid into Port-au-Prince and then distribute it to the people in need. The damage sustained by the airport is limiting incoming aid flights, the closure of the port means aid shipments are being redirected, and ground transportation is taking at least twice as long as normal from the Dominican Republic. All [sic] these infrastructure challenges mean that it is taking much longer to get aid into Haiti and ultimately to those who need it most (Scherrer, 2010). 2. The much stronger earthquake that struck Chile on 27 February 2010 demonstrated similarities and significant differences. The similarities included extensive damage to infrastructure:

From fatalism to resilience

This will be a major blow to the country’s infrastructure; there has been major damage to roads, airports, which are now suspended, ports, industry, and also in housing (Saavedra, 2010). Advanced preparation, however, reduced the impact significantly: While Haiti is the poorest country in the Western hemisphere, Chile has been one of Latin America’s better-performing economies for years. That wealth has enabled it to invest in transport and health infrastructure that far surpasses Haiti’s’ (The Economist, 2010). As noted earlier, members of the scientific community are concerned that hydrometeorological-related extreme events may increase in the future because of seasonal variability, long-term climate trends and development patterns (Lamb, 1981; Hewitt, 1983, 1997; Blaikie et al., 1994; Comfort et al., 1999; IPCC, 2007). There is a growing recognition, therefore, that without proactive investments, countries chronically vulnerable to natural hazards will face significant barriers to short-term recovery and long-term development (Freeman and Warner, 2001; Hallegatte, Hourcade and Dumas, 2006). Without coherent risk reduction and resiliency investing, such as the earthquake adaptation policies observed in countries like Chile, economic growth rates in countries vulnerable to disasters will decline (Freeman, 2000). For example, Hurricane Mitch, a Force 5 event that struck Honduras directly in October 1998, affected at least 1.5 million Hondurans, and claimed the lives of thousands of people as well as destroying some 60 per cent of the transportation infrastructure (Diaz and Pulwarty, 1997; Inter-American Development Bank, 2000; Pielke and Pielke, 2001). It is estimated that economic production losses due to Hurricane Mitch in Honduras still amount to USD 170 million annually (Diaz and Pulwarty, 1997; Inter-American Development Bank, 2000; Pielke and Pielke, 2001). If a similar hurricane were to hit the cities of Houston, Texas, or New Orleans, Louisiana, much of the oil-refining capacity of the United States could be damaged or destroyed. The consequent domestic and international economic consequences would be considerable, affecting areas far beyond the direct damage zone (Guihou et al., 2006). Wealthier countries have made significant investments in disaster response and building resilience, but there is still a significant gap in all countries in the area of IS resiliency investment (RI) (Little, 2003). As Chilean President Sebastián Piñera noted shortly after the devastating earthquake in 2010, the destruction to Chile’s infrastructure systems was a significant blow to the economy even though the country had done better than most in terms of disaster resilience investments (The Economist, 2010; Saavedra, 2010). Governments are aware of these systemic risks yet often they seem to be paralysed by the problem. The impact of Hurricane Katrina (August 2005) on New Orleans appears to have been greater than necessary because of a failure to prepare for foreseeable risks.

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One reason why it is difficult to enhance IS is that the impact that natural disasters have on them is not well appreciated, even though the destruction of these complex systems is increasing (White and Haas, 1975; Mileti, 1999; Yezer, 2000; Pelling, Ozerdem and Barakat, 2002; Little, 2003). This problem is even more critical for developing countries due to their limited infrastructure system capacity. Fortunately, IS vulnerability to disasters often is foreseeable. Proactively assessing disaster vulnerability and identifying and implementing viable RI options can significantly diminish the amount of funds that have to be redirected from economic development and services to disaster recovery. Effective proactive adaptation and disaster risk reduction mitigation can reduce poverty levels and losses of life and human capital (Bendimerad, 2001). This paper describes a method of evaluating systematically the vulnerability of jurisdictions’ IS to a natural hazard and pinpoints the benefits of alternative RI options. It addresses a key risk management challenge concerning identification of the tradeoff between investment in IS resilience versus transference of risk to a third party (Shukla, Kapshe and Garg, 2004). The term ‘resiliency investment’ denotes investments that would help to maintain the economic growth of a region or country at an acceptable level after a disaster (as well as serving other development purposes) (IPCC, 2007; World Economic Forum, World Bank and the United Nations International Strategy for Disaster Reduction, 2008).

Methodology and rationale Our methodology within the RI framework was developed as a special kind of investment, focusing on the economic impacts of disruptions to infrastructure systems. The generalised framework can be adapted to various practical applications. It involves three steps (see Figure 1): step one establishes an economic growth baseline without a disaster impact event; step two defines economic growth with a disaster event; and step three assesses the economy’s resilience to the disaster event due to alternative resiliency investments. The logic is also a potential basis for identifying adaptation investments even where the data for formal modelling is unavailable; in such cases, decisions still must be made transparently and with a rationale that supports action. This methodology is simple and flexible. It allows for pure public goods (Samuelson, 1954) or club goods (toll roads and other forms of private investment) (Buchanan, 1965; Cornes and Sandler, 1986), or any combination of the two investment philosophies. In addition, it recognises that all decisions do not require sophisticated modelling. Consequently it is designed to utilise datasets and models of differing levels of complexity. The method can accommodate the valuing of ecosystem maintenance investments as sources of commercial activity, such as in water systems, adding value for water quality maintenance as well as physical barriers to storm surges and floods (Western Water Policy Review Advisory Commission, 1998; Colorado Water Conservation Board, 2004, 2007).


Figure 1 Generic steps to identify vulnerability and resiliency to natural disasters Step 1. Establish an economic baseline without an extreme event shock

Step 2. Establish an economic baseline with an extreme event shock by:

Step 2.1. Identify critical economic and demographic-environmental regions and sectors within the target country or region.

Step 2.2. Identify vulnerable regions based on the probability of a threshold extreme event occurring.

Step 2.3. Physically model the regions of importance and those vulnerable to the natural hazard or utilise some form of expert opinion.

Step 2.4. Estimate damage to infrastructure by linking the GIS-based physical model results to a GIS infrastructure layer (or best available spatial/mapping approach).

Step 2.5. Estimate the economic impact of the extreme event on the economy in terms of lower expected baseline production.

Step 3. Assess the economic impact of alternative resiliency adaptation investments in terms of projected improved economic growth.

In data-limited cases, this framework is valuable even if ordinal rankings are used as it provides a means of establishing, comparing and applying transparent and easily updated costs, benefits and decision processes. Non-market valuations for cultural, historical and environmental interests may require the use of ordinal rankings to define credible trade-off options. Such rankings also may be useful for transparency where assets or conditions have values related to equity or other social goals. The details associated with each step are: Step 1: establish an economic baseline without an extreme event shock In step one, the economy is modelled as developing without a disaster. Its gross domestic product (GDP) growth path establishes the upper boundary of economic growth. The baseline may be modelled deterministically or stochastically, stochastically being preferable as it provides a more realistic estimate of the uncertainty associated with the impact of an event. This example illustrates the framework used to consider the issue. It assumes that a small country’s GDP is being modelled as: GDPB = ƒ ((∑(Pitb,* Qitb)) - ((∑wgjtb* lgjtb) + (∑ngatb*kgatb) - TEV) + σgt

[1]

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where: GDPB = baseline GDP with no disaster event (B represents baseline). Pit = the price of the ith output of the country for period t. Qit = the quantity of the ith output of the country for period t. wgt = the wage for labour for region g for skill set j in period t. lgjt = the quantity of labour for region g for skill set j in period t. ngat = the cost in region g of capital input a in period t. kgat = the quantity in region g of capital input a in period t. S = the costs of vulnerability reduction, safety (that is, infrastructure investment, maintenance, emergency preparedness, and insurance). E = the costs of investment in ecosystem maintenance. σgt = the random error term of the model (if the model is stochastic). Constraints Total expenditures on vulnerability reduction = TEV, where: TEV = S + E ≤ the budget set by national government without extra RI expenditures. EP ≤ total and planned energy production capital over the planning horizon of national or regional energy production capacity plus access to international sources. ET ≤ total electrical transmission system, pipelines and anticipated over the planning horizon, etc. X ≤ total functioning roads, airports, railway lines, and other transportation networks in the country and alternative routes in neighbouring countries or regions. ∑kgat ≤ total of all other capital available in the country or region = K+EP+EN+X ∑lgjt ≤ total labour available in the country or region and labour accessible in other countries or regions. The model in Equation 1 can estimate both the first scenario, a deterministic growth path (see Figure 2a), and a stochastic growth path ensemble for GDP growth (see Figure 2b). For simplicity, further illustrations will portray the deterministic example. In the Chilean, Haiti or Honduras examples this step would estimate the anticipated economic development for each country’s economy without the impact of the disaster of concern. The planning horizon is subjective but usually is calculated for a period between 25 and 30 years. A successful modelling approach captures disaster impacts in a spatially coherent manner that integrates physical processes into economic and social infrastructure. Examples include the Sequential Inter-industry Model (SIM), which has modelled the economic consequences of disasters (Levine and Romanoff, 1989; Angelsen and Kaimowitz, 1999; Yezer, 2000; Okuyama and Lim, 2002). The models link the landscape and infrastructure in a spatially coherent manner although early efforts were


Figure 2a Example of a deterministic growth path without a disaster shock modelled for a 30-year planning horizon

Figure 2b Example of an expected stochastic ensemble growth path without a shock modelled for a 30-year planning horizon

limited by rudimentary economic elements (Angelsen, Kaimowitz 1998). Another important effort, the Environment Explorer, analyses social, economic and environmental policies dynamically, temporally and spatially at the regional and national level (Engelen, White and Nijs, 2003; Sarkar et al., 2006). The Computable General Equilibrium (CGE) Model describes equilibrium resource allocations and growth paths (Bergman and Henrekson, 2003). A CGE has been used to model present and future climate impacts on an economy (Roson, Calzadilla and Pauli, 2006). The method also has been applied as a spatially-specified CGE multi-regional infrastructure model to compare alternative investments in a South Korean highway network (Kim and Hewings (2003).

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Where data and modelling issues prevent formal employment of these approaches, thoughtful scenario development with attention to each term may be useful as a means of providing a comprehensive qualitative view of the economic importance of different elements of the national or regional infrastructure. Step 2: establish an economic baseline with an extreme event shock To account for the impact of a disaster, one must estimate the effects on human resources, physical (built) capital, infrastructure, land endowments (natural capital), and productivity (Roson, Calzadilla and Pauli, 2006; Wiener, 2009). This accounting comprises three components: • identify sectors or regions of the country that contribute to economic growth; • estimate the disruptive potential of the disaster on the IS of key sectors or regions; and • modify the economic model by translating the estimated damage into reduced capacity or increased constraints.

Step 2.1: identify critical economic and demographic-environmental regions and sectors within the target country or region In step 2.1, economic models are used to pinpoint which regions contribute the most economic value to the economy. These areas can be ranked in order to identify the regions of greatest economic importance currently and to make such predictions for the future. It is vital to clarify that economic productivity is only one of the measures of values that are served by infrastructure; access to medical services may be equally or even more important. Such values should be openly identified and considered in the final decision-making process, but should not be confused with economic productivity per se.

Step 2.2: identify vulnerable regions based on the probability of a threshold extreme event occurring The baseline economic output scenario(s) identify regions of economic value and rank them in order of relative importance to the economy (current and anticipated). In addition, areas considered important to ensure the preservation of habitat, life, property and species are highlighted for consideration. In this step, regions and infrastructure systems of economic importance that are vulnerable to a hazard are identified. Vulnerabilities associated with regional equity, protection of life and property, and ecosystem maintenance are included. Where no data exists, a useful economic valuation of ecosystem goods can be obtained by estimating the ‘next best’ means of acquisition of goods, services or conditions. The classic example is: ‘what would it cost to get this kind of water treatment, storage and biological outputs if that wetland did not exist?’ (National Research Council, 2005).


Another estimation problem arises when there are high value cultural assets or areas of ecological worth that may be important for non-economic reasons, or important in terms of future applications. In these instances, the analyst can call on decision-makers and policymakers, scholars in the fields of the human and social sciences, existing studies, and political weightings. What is most important is clear specification of the values, however non-quantifiable they may be, so that policy can be well-informed and transparent. Ranking the regions and sectors by socio-economic importance identifies which ones are accorded priority in this analysis. Completion of this step requires pinpointing the intersection of the prioritised regions and sectors and the probability of reaching a threshold where an extreme event becomes a disaster event. Understanding how antecedent conditions interact with the extreme event is critical to quantifying vulnerability (Hewitt, 1983, 1997; Blaikie et al., 1994; Turner et al., 2003). Extreme event examples are 10-, 50-, 100- and 500-year probabilities of the occurrence of an event. As noted, the disaster’s impact varies depending on the conditions prevailing at the time of the event. It is important to consider the probability of different antecedent conditions existing before the anticipated extreme event. This is important because the soil moisture condition, existing levels in rivers, and other factors at the time of the event can either mute or exacerbate the effects of a natural hydro-meteorological event. The landslide in northeast Venezuela in 1999, for instance, was a function of an intense storm preceded by weeks of precipitation that made the region’s soils unstable (Comfort et al., 1999). One way to obtain this information is to employ a stochastic sub-sampling method developed by Clark et al. (2004) for hydro-climate events, or an equivalent procedure that preserves the spatial and temporal statistical properties that could be adapted to the impact assessment tool developed by Palmieri et al. (2006). At the least, a qualified judgement can support resource allocation responses based on this assessment of the risks. The resulting rankings, even if they are only qualitative judgements, have important implications for investments related to resilience. The process is valuable as it explicitly identifies vulnerable economic activities and vulnerable non-economic factors of worth. This comparison allows for a richer decision-making process. One example is the trade-off between enhanced safety and rescue capacity and an investment supporting the resilience of an industry or sector prior to an event.

Step 2.3: physically model the regions of importance and those vulnerable to the natural hazard or utilise some form of expert opinion The collection of physical information in step 2.2 closely overlaps with step 2.3. For example, in the case of a hydro-climate disaster, the availability of digital elevation maps may be a vital asset (or a critical limitation). Such data is not always available, particularly in emerging and developing countries. Fortunately, the analysis allows for digital elevation maps that have lower resolution as long as they provide reasonable descriptions of the water flows. In very flat areas that require more precise digital elevation maps, decision analysis may be needed.

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Examples of geographic information systems (GIS)-based hydrologic tools include the operational storm warning system for Central America developed by Georgakakos (2006) or the Soil and Water Integrated Model (SWIM) developed by Krysanova, Müller-Wohlfeil and Becker (1998). Examples of GIS-based hazards tools include the Hazards United States (HAZUS) series developed by the US Federal Emergency Management Agency (FEMA). As its name implies, HAZUS has been designed for the infrastructure realities of the US. Europe also has made significant investments in this area, although like the HAZUS approach, the work has not extended so far as to address the macroeconomic questions raised in this paper. The impact of trends in land use patterns and demographic movement on the vulnerability of infrastructure is another element of the analysis. Infrastructure systems, for instance, built for an expected flood water flow or earthquake are now vulnerable to increased risks due to land use changes and urbanisation,1 as well as possibly changing probabilities of antecedent thresholds because of factors such as climate change. Where possible, analysis also should consider the changes in sectoral and regional conditions that can have effects of this type. For instance, agricultural intensification in some places has taken land out of traditional rotation patterns with consequent changes in erosion, fertility, sediment transport, and aquatic conditions. This may be foreseeable if government policy or market pressures are identifiable and are incorporated in considerations of IS sustainability (IAASTD, 2009).

Step 2.4: estimate damage to infrastructure by linking the geographic information systems (GIS)-based physical model results with a GIS infrastructure layer (or the best available spatial/mapping approach) Linking the impact of the disaster to infrastructure follows a causal sequence as described in Figure 3 and utilises a decision analysis approach. It requires a set of rules to ensure the estimated cost of the impact can be aggregated or reflected as constraints in the economic model. The less-supported analysis can follow this sequential approach to consider the range of infrastructure even if employing only an ordinal ranking for a given region or sector. With the damage estimate for the selected regions completed, the constraints of the economic model can be modified. For example, if the disaster is expected to affect a transportation artery, then the distance to market is altered using consequent changes in cost per mile. In addition, the carrying capacity of roads may be reduced, causing the cost per mile to rise. Access to key inputs may be constrained, raising the cost of the inputs. Such modifications are made for each region included in the analysis. The information is incorporated in the economic model as modified constraints, that is, for instance, reduced productive capacity and modified conversion parameters. Where GIS capacity and spatially-distributed hydrologic modelling capacity are limited, it is valuable to recall that basic GIS can be simulated with more effort through the use of traditional cartography and overlays. The hydrologic modelling is, however, harder to apply in a non-computerised fashion.


Figure 3 Causal sequence of events to assess the impact of a disaster on infrastructure

Source: Christiansen, Sparks and Kostuk (2005).

If the analysis is stochastic, an ensemble of scenarios is created. This is translated into a probabilistic distribution of impacts on the constraints of the economic model. For example, if a specific region is identified as the most economically important region in the set, with the highest probability of being degraded or destroyed, then in this step, the probable range of damage to infrastructure is estimated for that region. Note that the ensemble of scenarios would be temporally and spatially coherent across all regions in the analysis. This is translated, as noted earlier, into the constraints of the economic model via a probability distribution (Clark et al., 2004; Breuer et al., 2005).

Step 2.5: estimate the economic impact of the extreme event on the economy in terms of lower expected baseline production As stated, the estimation process can be deterministic or an ensemble of impacts that can be estimated based on the constraint scenarios generated iteratively via a subsampling procedure. The changes in additional costs and changed production constraints after a disaster event are described as: GDPd = ƒ((∑Pitd,* Qitd) - (∑wgjtd*lgjtd+∑ngatd* kgatd) - TEV + σgt

[2]

where GDPd = GDP after a disaster. Total expenditure on vulnerability reduction = TEV, where: TEV = S + E ≤ the budget set by national government without extra RI expenditure. σgt = the random error term of the model (if the model is stochastic). EPd ≤ total and planned energy production capacity over the planning horizon plus access to international sources after the disaster. ETd ≤ total energy transmission capacity including: existing electrical transmission system, pipelines and planned transmission capacity over the planning horizon.

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Figure 4 Relative modelled baseline growth with and without a natural disaster impact

Xd ≤ total functioning roads, airports, railway lines, and other capacity transportation networks in the country and alternative routes in neighbouring countries or regions after the disaster. kgatd ≤ total of all other capital available in the country or region + EPd + ETd + Xd. lg jtd ≤ total labour available in the country or region and labour accessible in other countries or regions after the disaster. All other variables respond to the changes in capital and labour. The results of such an analysis would appear to be similar to Figure 4. This information allows all of the parties to understand better and to anticipate the short- and long-term impacts of the disaster. Even where there are substantial uncertainties, the gap between the paths may be helpful in illustrating the economic shocks due to disasters. In this context, the Chilean, Haitian and Honduran examples would reflect the reduction in their airport, port and road facilities and the consequent changes in the constraints on their infrastructure system (Diaz and Pulwarty, 1997; InterAmerican Development Bank, 2000; Pielke and Pielke, 2001; Scherrer, 2010). The changes then translate into higher costs and lost productive capacity. The destroyed residential and institutional buildings in the model are reflected in changes to the constraints affecting available labour, productivity and human capital. Analysts may wish to be explicit about the migration implications of lost infrastructure and economic impacts in different regions, even if estimated only qualitatively. Step 3: Assess the economic impact of alternative resiliency adaptation investments in terms of projected improved economic growth The process is repeated, now integrating the infrastructure system and landscape adaptations and policies identified by decision-makers as RI options. Examples include investment in bridges, drainage structures and heavier grade roads, reforestation, land contouring, and green cover programmes. Changes in land management policies,


such as zoning, may reduce disaster event impacts to an economically acceptable level while meeting other socio-environmental objectives. Investment in insurance and disaster bonds can be considered to gauge the relative benefits of differing ratios of investment in RI versus financial risk management tools. Examples of landscape resiliency investments and policies that can improve the economic and soil viability also have environmental services that can diminish threats to the larger economy by protecting infrastructure systems. Improvements in soil conservation can reduce excessive flooding. Policies to enhance agricultural productivity and access to roads can increase off-farm labour opportunities, thereby reducing pressure on the landscape (Jansen et al., 2006). In semi-arid areas, community-based land restoration for improved water management and environmental services also is an option (Kerr, 2002). Here, as noted, it may be important as well to consider weighting based on judgement of the local importance of an outcome, such as prevention of impoverishment, or the distribution of losses, to compare explicitly and transparently that variable to the net welfare measures. The results are ranked in terms of greatest benefit relative to the cost of and the potential to reduce risks to life, property, habitat and animals. By comparing the alternative RI options, it becomes possible to identify which investment or combination of investments can maintain or enhance the core economic capacity of the nation or the region’s GDP growth, with minimum diversion of funds from other priorities. This is the reward for all of the work invested in this exercise: the process provides a transparent and explicit rationale for a comparison of alternative, or a combined set of, investments. GDPa = ƒ((∑Pita,*Qita) - (∑wgjta*lgjta+∑ngata*kgata) - TEVa) + σgt

[3]

where: GDPa = GDP with RI. Total expenditure on vulnerability reduction = TEVa, where TEVa = S + E + ΣRIz + CB ≤ budget set by national government with its international along with additional planned adaptation investments by donor or private sector partners. RIz is z = 1 to total number resiliency investments. CB = catastrophe bonds.

Constraints EPa ≤ Total and planned national or regional energy production capacity over the planning horizon along with access to international sources after the disaster. ETa ≤ total electrical transmission system, pipelines and planned energy transmission capacity over the planning horizon, etc. after the disaster with resiliency investments. Xa ≤ total functioning roads, airports, railway lines, and other capacity transportation networks in the country and alternative routes in neighbouring countries or regions after the disaster with resiliency investments.

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Figure 5 Example of expected growth paths with a disaster shock modelled for a 25-year planning horizon with a resiliency response scenario

kgata ≤ total of all other capital available in the country or region after a disaster with resiliency investments + EPa + ETa + Xa. lgjta ≤ total labour available in the country or region and labour accessible in other countries or regions after the disaster with resiliency investments. All other variables respond to the changes in capital and labour. The results would appear graphically in a manner similar to Figure 5. The costs and benefits of differing thresholds of resiliency allow decision-makers to understand better the trade-offs between relative levels of investment, such as enhancing infrastructure resiliency to withstand better impacts, land and riparian management to reduce the risks, and catastrophe bond purchases for the rebuilding of infrastructure systems not deemed critical. The balance between investments and insurance is identified as the RI that is not generating further marginal increases in GDP up to or beyond the baseline relative to the marginal cost of the investment required. Figure 6 illustrates such a comparison. The marginal benefit is calculated as the difference between the GDP path affected by a disaster event shock with the respective RI option or combination of options versus the GDP growth path affected by a disaster without the respective RI option. The marginal cost is defined as the difference between the RI under the adaptation scenario versus the RI under the baseline scenario. This approach is for illustrative purposes as the actual analysis is probabilistic in nature. We define the resiliency investment equilibrium (RIE) as: RIE = MBa- MCa = 0

[4]

MBa = ∑t=1,30 (GDPat - GDPbt)

[5]

MCa = ∑t=1,30 (TEVat - TEVt )

[6]


Figure 6 A simple example using cost–benefit analysis to discover the potential equilibrium resilience investment costs versus resilience investment benefits

where MBa is the average marginal benefit of the resilience investment discounted over the planning horizon and MCa is the average marginal cost of the resilience investment discounted over the planning horizon (30 years in this example). The costs include resiliency investments in IS to support economic activity, costs related to human and property safety, ecosystem maintenance, and the costs associated with purchasing the catastrophe bonds. Where detailed economic modelling is infeasible, careful efforts to follow the methodology using estimated ranges of costs and benefits can be a useful guide to help identify the sensitivity of outcomes. An example is estimating the relative value of subsistence economic outputs (such as feeding a family) versus more easily measured economic outputs (such as wages deemed as adequate for feeding a family in an urban setting). Similarly, the impacts of infrastructural resilience or vulnerability can be measured partly by evaluating such things as the costs of public health ramifications where policy requires that they do not fall below a certain level of service. The application of such an approach to the Chilean, Haitian and Honduran examples would allow national governments, donor agencies, non-governmental organisations (NGOs), the private sector, and citizens to understand the relative values and costs of alternative RI to maintain the development paths of their countries while increasing the resilience of the country and its region to the natural hazards to which they are vulnerable. For donor agencies and institutions such as the United Nations International Strategy for Disaster Reduction (UNISDR), the approach would support efforts to implement proactive disaster reduction at the regional and national scale.

Conclusion The direct and indirect costs to national economies of infrastructure systems destruction due to predictable disasters are becoming too great to ignore. In addition to the more obvious impacts on budgets, the hidden costs of infrastructure damage have

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been noted in recent literature. Emerging evidence related to human displacement and extreme events (Renaud et al., 2007), particularly with respect to Hurricane Katrina, illustrates the tragic consequences of failed infrastructure—as well as policy steps that could be taken to avoid such failures. The Hyogo Framework for Action 2005–15 and the establishment of the UNISDR are explicit recognition of this fact. Translation of proactive disaster risk reduction policy into cost-effective applications are becoming increasingly plausible as physical analytical methodologies to make such assessments, including the Global Facility for Disaster Risk Reduction’s Central American Probabilistic Risk Assessment (CAPRA) initiative (GFDRR, 2011), are developed in combination with the use of cost–benefit and economic tools such as the one described in this paper. The method discussed, if implemented in full or at least attempted conscientiously, quantifies or estimates transparently the socioeconomic risks associated with natural disasters. Unlike many impact assessments, though, it provides a means to compare adaptation options in terms of benefits and costs. It does so by using an integrated approach to address the increasingly complex interactions between economies and natural disasters (Changnon et al., 2000; Pielke and Pielke, 2000). An initial concern raised about such an approach is its dependence on the technical quality of modelling and quality control of input information and data. This concern is not unmerited, but should not be a reason to cease such efforts. The reality is that data are often limited, as is the capacity to model rigorously. Decisions will be made despite the scarcity of data. Consequently, there are benefits even when each step in the described methodology is taken only with the best available expert judgement. A second concern is that the approach may be logical in theory, but too complex to apply, regardless of which approach is taken to address the problem of limited data. There is no question that a great deal still needs to be done. Advances have been made in a number of disciplines that are moving the concept from the theoretical realm to the applied sphere at the local scale. An example of its application at the regional level in Canada was presented at the Fourth Caribbean Conference on Comprehensive Disaster Management in December 2009 (Hill, 2009). The CAPRA) methodology that is being applied in Latin American and other regions of the world is rapidly becoming a strong candidate to estimate the physical impacts of an extreme event on infrastructure, population, and the landscape. It and other similar tools could provide a great deal of information required to conduct this economic analysis. The approach presented provides decision-makers with a common, transparent and integrated process with which to assess vulnerabilities and to pinpoint viable RI options. This approach to enhancing resilience supports proactive preparation for disasters in order to support sustainable development. As noted, it supports as well UNISDR objectives. In addition, it can facilitate the identification and resolution of socio-ecological resilience issues raised by the adaptive capacity community (Folke et al., 2002; Ostrom, 2007). Ultimately, the value of this approach is not in providing a perfectly calibrated answer, but rather, in providing a path with which to compare the relative costs and


benefits of various RI, given the best information available. The methodology is independent of any single economic modelling process to assess the costs and benefits of alternative scenarios. Furthermore, it is not restricted to a specific economic or spatial extent. This flexibility allows for ongoing adaptation as data, modelling and disaster risk reduction goals evolve.

Acknowledgements The authors would like to thank Dr James Mjelde and Dr Denys Yemshanov as well as Collette Gaucher, Marie Hill and Priya Montgomery for their comments and insights. The opinions expressed in this paper are solely those of the authors and do not represent the opinions of Agriculture and Agri-Food Canada or the Government of Canada.

Correspondence Harvey Hill, Agriculture and Agri-Food Canada, 11 Innovation Boulevard, Saskatoon, Saskatchewan, S7N 3H5, Canada. Telephone: +1 306 374 9630; e-mail: [email protected]

Endnotes 1

Examples include the links between deforestation and landslides, sedimentation, habitation in vulnerable settings, urbanisation, and extensive drainage of agricultural regions.

References Angelsen, A. and D. Kaimowitz (1999) ’Rethinking the causes of deforestation: lessons from economic models’. The World Bank Research Observer. 14 February. pp. 73–98. Bendimerad, F. (2001) ’Disaster risk reduction and sustainable development’. http://info.worldbank. org/etools/docs/library/114715/istanbul03/docs/istanbul03/05bendimerad3-n%5B1%5D.pdf (accessed on 16 August 2011). Bergman, L. and M. Henrekson (2003) ’CGE Modeling of Environmental Policy and Resource Management’. Second advanced course on computable general equilibrium (CGE) modelling and the environment. http://users.ictp.it/~eee/workshops/smr1533/Bergman%20-%20Handbook-1.doc. Blaikie, P., T. Cannon, I. Davies and B. Wisner (1994) At Risk: Natural Hazards, People’s Vulnerability, and Disasters. Routledge, London. Breuer, L. et al. (2005) ‘Ensemble model prediction of the effects of land use change on hydrology’. Geophysical Research Abstracts. 7. http://meetings.copernicus.org/www.cosis.net/abstracts/EGU05/ 07924/EGU05-J-07924.pdf (accessed on 7 September 2011). Buchanan, J.M. (1965). ‘An Economic Theory Of Clubs’. Economica. 32(1). pp. 1–14. Changnon S.A., R.A. Pielke, Jr., D. Changnon, R.T. Sylves and R. Pulwarty (2000) ‘Human factors explain the increased losses from weather and climate extremes’. Bulletin of the American Meteorological Society. 81(3). pp. 437–442.

191

192


Christensen, P.N., G.A. Sparks and K.J. Kostuk (2005) ’A method-based survey of life cycle costing literature pertinent to infrastructure design and renewal’. Canadian Journal of Civil Engineering. 32(1). pp. 250–259. Clark, M., S. Gangopadhyay, L. Hay, B. Rajagopalan and R. Wilby (2004) ‘The Schaake shuffle: a method for reconstructing space–time variability in forecasted precipitation and temperature fields’. Journal of Hydrometerology. February. pp. 243–262. Colorado Water Conservation Board (2004) ‘Statewide Water Supply Initiative, Report.’ http:// cwcbweblink.state.co.us/weblink/0/doc/144066/Electronic.aspx?searchid=2c16c041-d0b24ec5-ac42-8b95aa0c04e3 (accessed on 7 September 2011). Colorado Water Conservation Board (2007) ‘Colorado’s Water Supply Future: Statewide Water Supply Initiative – Phase 2’, Department of Natural Resources, Colorado Water Conservation Board, Denver, CO. http://cwcbweblink.state.co.us/weblink/0/doc/144066/Electronic.aspx? searchid=2c16c041-d0b2-4ec5-ac42-8b95aa0c04e3 (accessed on 7 September 2011). Comfort, L. et al. (1999) ‘Reframing disaster policy: the global evolution of vulnerable communities’. Environmental Hazards. 1. pp. 39–44. Cornes, R. and T. Sandler. (1986) ‘The Theory of Externality, Public Goods, and Club Goods’. Cambridge University Press, New York, NY. Diaz, H.F. and R.S. Pulwarty (1997) Hurricanes: Climate and Socioeconomic Impacts. Springer, New York, NY. Engelen, G., R. White and T. Nijs (2003) ‘Environment Explorer: spatial support system for the integrated assessment of socio-economic and environmental policies in the Netherlands’. Integrated Assessment. 4(2). pp. 97–105. Folke, C. et al. (2002) ‘Resilience and sustainable development: building adaptive capacity in a world of transformation’. Amibio. 31(5). pp. 437–440. Freeman, P. (2000) Estimating Chronic Risk from Natural Disasters in Developing Countries: A Case Study of Honduras. Paper presented at the Annual World Bank Conference on Development Economics in Europe, Paris, France, 26–28 June. Freeman, P. and K. Warner (2001) Vulnerability of Infrastructure to Variability: How does this Affect Infrastructure Lending Policies? Report commissioned by the Disaster Management Facility of the World Bank and the ProVention Consortium, Washington, DC. Georgakakos, K.P. (2006) ‘Analytical results for operational flash flood guidance’. Journal of Hydrology. 317(1–2). pp. 81–103. Guihou, X., P. Lagadec, D. Madet and J.P. Roche (2006) Non-conventional Crises and Critical Infrastructure, Katrina. Report-back Mission, New Orleans, Gulfport (Mississippi), 19–25 February 2006, and Washington, DC, 13–15 March 2006. EDF Risk Management Division. http://www.patrick lagadec.net/fr/pdf/EDF-Katrina-Report-31.pdf (accessed on 16 August 2011). GFDRR (Global Facility for Disaster Risk Reduction) (2011) ‘Disaster risk assessment and monitoring. GFDRR, Washington, DC. http://gfdrr.org/gfdrr/node/65 (accessed on 7 September 2011). Hallegatte, S., J.C. Hourcade and B. Dumas (2007) ‘Why economic dynamics matter in assessing change damages: illustration on extreme events’. Ecological Economics. 62(2). pp. 330–340. Hewitt, K. (ed.) (1983) Interpretations of Calamity: From the Viewpoint of Human Ecology. Allen and Unwin, London. Hewitt, K. (ed.) (1997) Regions of Risk: A Geographical Introduction to Disasters. Addison Wesley Longman Ltd., Harlow. Hill, H. (2009) The Landscape and Infrastructure Resiliency Assessment Methodology (LIRA): Is it Relevant to Disaster Adaptation and Preparedness in the Caribbean? Paper presented at the Fourth Caribbean Conference on Comprehensive Disaster Management, Montego Bay, Jamaica, 9 December. Hoeppe, P. and E.N. Grurenko (2006) ‘Scientific and economic rationales for innovative climate insurance solutions’. Climate Policy. 6(6). pp. 607–620.


Höppe, P. and T. Grimm (2009) ‘Rising natural catastrophe losses: what is the role of climate change?’ In B. Hansjürgens and R. Antes (eds.) Economics and Management of Climate Change: Risks, Mitigation and Adaptation. Springer, New York, NY. pp. 13–22. Inter-American Development Bank (2000) Central America after Hurricane Mitch: The Challenge of Turning a Disaster into an Opportunity. Consultative Group for the Reconstruction and Transformation of Central America. http://www.iadb.org/regions/re2/consultative_group/backgrounder2.htm (accessed on 16 August 2011). IAASTD (International Assessment of Agricultural Knowledge) (2009) Synthesis Report: A Synthesis of the Global and Sub-Global IAASTD Reports. Island Press (for the United Nations Development Programme), Washington, DC. Jansen, H.G.P., J. Pender, A. Damon and R. Schipper (2006) Rural Development Policies and Sustainable Land Use in Hillside Areas of Honduras. Research Report. 147. International Food Policy Research Institute, Washington, DC. Kerr, J. (2002) ‘Watershed development, environmental services, and poverty alleviation in India’. World Development. 30(8). pp. 1387–1400. Kim, E. and J.D. Hewings (2003) An Application of Integrated Transport Network – Multiregional CGE Model II: Calibration of Network Effects of Highway. Paper presented at the 42 nd Southern Regional Science Association, Louisville, KY, 10–12 April. Krysanova, V., D.I. Müller-Wohlfeil and A. Becker (1998) ‘Development and test of a spatially distributed hydrological/water quality model for mesoscale watersheds’. Ecological Modelling. 106(2–3). pp. 261–289. Lamb, P.J. (1981) ‘Do we know what we should be trying to forecast—climatically?’ Bulletin of the American Meteorological Society. 62. pp. 1000–1001. Levine, S.H. and E. Romanoff (1989) ’Economic impact dynamics of complex engineering project scheduling’. IEEE Transactions on Systems, Man, and Cybernetics. 19(2). pp. 232–240. Little, R.G. (2003) Toward More Robust Infrastructure: Observations on Improving the Resilience and Reliability of Critical Systems. Proceedings of the 36 th Annual Hawaii International Conference on Systems Science, Hilton Waikoloa Village, Island of Hawaii (Big Island), HI, 6–9 January. Mileti, D.S. (1999) Disasters by Design – The Changing Risk Landscape: Implications for Insurance Risk Management. Proceedings of a Conference Sponsored by the Aon Group Australia Ltd. http://www. aonline-aon.com/public/intelligence/disasters_by_design.pdf (accessed on 16 August 2011). National Research Council (2005) Valuing Ecosystem Services: Toward Better Environmental Decision-making. National Academies Press, Washington, DC. Okuyama, Y. and H. Lim (2002) Linking Economic Model and Engineering Model: Application of Sequential Interindustry Model (SIM). Paper presented at the 49th North American Meeting of the Regional Science Association International, San Juan, Puerto Rico, 14–16 November. Research Paper #2002-12. Ostrom, E. (2007) ‘A diagnostic approach for going beyond panaceas’. Proceedings of the National Academy of Sciences of the United States of America. 104(39). pp. 15181–15187. Palmieri, S., L. Teodonio, A.M. Siani and G. Casle. (2006) ‘Tropical storm impact in Central America’. Meteorological Applications. 13. pp. 21–28. Pelling, M, A. Ozerdem and S. Barakat (2002) ‘The macro-economic impact of disasters’. Progress in Development Studies. 2(4). pp. 283–305. Pielke, R.A., Jr. and R. Pielke, Sr. (2001) ‘Hurricanes: their nature and impacts on society’. Climatic Change. 49(3). pp. 367–369. Renaud, F.G., J.J. Bogardi, K. Warner and O. Dun (2007) ‘Control, adapt or flee: how to face environmental migration?’ InterSections. 5/2007. United Nations University, Bonn. Roson, R., A. Calzadilla and F. Pauli (2006) Change Change and Extreme Events: An Assessment of Economic Implications. Working Paper 18. Department of Economics, University of Venice, Venice.

193

194


Saavedra, J.L. (2010) ‘Massive earthquake hits Chile, 214 dead’. Reuters. 27 February. http://www. reuters.com/article/idUSTRE61Q0S920100227 (accessed on 16 August 2011). Samuelson, P.A. (1954) ‘The Pure Theory of Public Expenditure’. The Review of Economics and Statistics. 36(4). pp. 387–389. Sarkar, S. et al. (2006) ‘Biodiversity conservation planning tools: present status and challenges for the future’. Annual Review of Environment and Resources. 31. pp. 123–159. Scherrer, V. (2010) ‘Haiti earthquake – update’. http://www.cbmcanada.org/default.htm (accessed on 16 August 2011). Shukla, P.R., M. Kapshe and A. Garg (2004) Development and Climate: Impacts and Adaptation for Infrastructure Assets in India. Environment Directorate, Environment Policy Committee, Organisation for Economic Co-operation and Development, Paris. Solomon, S. et al. (2007) Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge and New York, NY. Stern, N. (2006) Stern review on the Economics of Climate Change. Cambridge University Press, Cambridge. The Economist (2010) ‘Destruction in Chile’. The Economist. 28 February. http://www.economist. com/world/americas/displaystory.cfm?story_id =15591569&source = features_box_main (accessed on 16 August 2011). Turner, B.L. et al. (2003) ‘Illustrating the coupled human–environment system for vulnerability analysis: Three case studies’. Proceedings of the National Academy of Sciences of the United States of America. 100(14). pp. 8080–8085. UNISDR (United Nations International Strategy for Disaster Reduction) (2007) Annual Disaster Statistical Review: Numbers and Trends 2007. http://www.unisdr.org/we/inform/publications/2796 (accessed on 16 August 2011). Warner, K. and A. Spiegel (2009) Climate Change and Emerging Markets: The role of the insurance industry in climate risk management. The Geneva Reports: Risk and Insurance Research. Institute for the Environment and Human Security, United Nations University, Bonn. pp. 83–94. Warner, K. et al. (2009a) Adaptation to Climate Change: Linking Disaster Risk Reduction and Insurance. Policy paper prepared for the 2009 climate negotiations, Bonn, Germany. Produced in collaboration with the United Nations International Strategy for Disaster Reduction and the United Nations University, Bonn. Warner, K. et al. (2009b) Vulnerable Countries and People: How Disaster Risk Reduction and Insurance can Help Manage the Risks of Climate Change. Policy Brief. United Nations University, Bonn. Western Water Policy Review Advisory Commission (1998) Water in the West. National Technical Information Service, Port Royal, VA. White, G.F. and J.E. Haas (1975) Assessment of Research on Natural Hazards. Massachusetts Institute of Technology Press, Cambridge, MA. Wiener, J. (2009) ’Suppose a Long View: A Framework for Sustainability Using Climate Information Applications’. Seventh Climate Prediction Applications Science Workshop, March 2009, Norman, OK. http://climate.ok.gov/cpasw/presentations.php (PowerPoint) and http://climate.ok.gov/cpasw/ presentations.php (PowerPoint plus audio) (accessed on 7 September 2011). World Economic Forum, World Bank and the United Nations International Strategy for Disaster Reduction (2008) Building Resilience to Natural Disasters: A Framework for Private Sector Engagement. The World Economic Forum, Geneva. Yezer, A.M. (2000) The Economics of Natural Disasters. Center for Economic Research Discussion Paper No. 98-11. Department of Economics, George Washington University, Washington, DC. http:// www.unisdr.org/europe/publications/v.php?id=1392 (accessed on 7 September 2011).