metadata: authors: 0: author: Blankespoor,Brian
1: author: Dasgupta,Susmita
2: author: Laplante,Benoit
3: author: Wheeler, David
count: World
docna: 0: docna: Adaptation to climate extremes in developing
            countries : the role of education
docty: Policy Research Working Paper
subtopic: Adaptation to Climate Change,Science of Climate Change,Climate Change and Health,Climate Change and Environment,Population & Development,Educational Sciences,Natural Disasters
teratopic: Environment,Macroeconomics and Economic Growth,Urban Development,Health, Nutrition and Population
abstracts: cdata!: Global climate models predict a rise in
            extreme weather in the next century. To better understand
            future interactions among adaptation costs, socioeconomic
            development, and climate change in developing countries,
            observed losses of life from floods and droughts during
            1960-2003 are modeled using three determinants: weather
            events, income per capita, and female education. The
            analysis reveals countries with high female education
            weathered extreme weather events better than countries with
            equivalent income and weather conditions. In that case, one
            would expect resilience to increase with economic growth and
            improvements in education. The relationship between
            resilience in the face of extreme weather events and
            increases in female education expenditure holds when
            socioeconomic development continues but the climate does not
            change, and socioeconomic development continues with weather
            paths driven by "wet" and "dry" Global
            Climate Models. Educating young women may be one of the best
            climate change disaster prevention investments in addition
            to high social rates of return in overall sustainable
            development goals.
repnme: repnme: Adaptation to climate extremes in
            developing countries : the role of education
display_title: Adaptation to climate extremes in
            developing countries : the role of education
listing_relative_url: /research/2010/06/12447159/adaptation-climate-extremes-developing-countries-role-education
topicv3: Climate Change m1326829 567,Education m1327048 470,Climate Change Adaptation m1327767 574,Flood m1503315 3216,Life Expectancy m1504363 2666
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Macroeconomics and Economic Growth,Urban Development,Health, Nutrition and Population
no_of_pages: 44
author_keys: 0: author_key: 957135
1: author_key: 95558
2: author_key: 97490
model_extractions: WPS5342 Policy Research Working Paper 5342 Adaptation to Climate Extremes in Developing Countries The Role of Education _Brian Blankespoor Susmita Dasgupta Benoit Laplante David Wheeler_ The World Bank Development Research Group Environment and Energy Team June 2010
Policy Research Working Paper 5342

# **Abstract**

Global climate models predict a rise in extreme weather in the next century. To better understand future interactions among adaptation costs, socioeconomic development, and climate change in developing countries, observed losses of life from floods and droughts during 1960–2003 are modeled using three determinants: weather events, income per capita, and female education. The analysis reveals countries with high female education weathered extreme weather events better than countries with equivalent income and weather conditions. In that case, one would expect resilience to increase with economic growth and improvements in education. The relationship between resilience in the face of extreme weather events and increases in female education expenditure holds when socioeconomic development continues but the climate does not change, and socioeconomic development continues with weather paths driven by “wet” and “dry” Global Climate Models. Educating young women may be one of the best climate change disaster prevention investments in addition to high social rates of return in overall sustainable development goals.

This paper—a product of the Environment and Energy Team, Development Research Group—is part of a larger effort in the department to understand the economics of adaptation to climate change. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at bblankespoor@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
Adaptation to Climate Extremes in Developing Countries: The Role of Education Brian Blankespoor Susmita Dasgupta Benoit Laplante David Wheeler* World Bank Authors’ names are in alphabetical order. The authors are respectively Environmental Specialist, Lead Environmental Economist, consultant, World Bank, and Senior Fellow, Center for Global Development. We are thankful to Tim Essam for his assistance with database construction. Thanks also to Sergio Margulis, Urvashi Narain, Gordon Hughes and Michele de Nevers for useful comments and suggestions.

The views expressed here are the authors’, and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

Corresponding author: Brian Blankespoor, MC3-419A, World Bank, 1818 H Street, N.W., Washington, DC 20433; E-mail: bblankespoor@worldbank.org; telephone: 202473-1536; fax: 202-522-1151.
# **1.  Introduction**

Without international assistance, developing countries will adapt to climate change as best they can. Part of the cost will be absorbed by households and part by the public sector. Adaptation costs will themselves be affected by socioeconomic development, which will also be affected by climate change. Without a better understanding of these interactions, it will be difficult for climate negotiators and donor institutions to determine the appropriate levels and modes of adaptation assistance. This paper attempts to contribute by assessing the economics of adaptation to extreme weather events. We address several questions that are relevant for the international discussion: How will climate change alter the incidence of these events, and how will their impact be distributed geographically? How will future socioeconomic development affect the vulnerability of affected communities? And, of primary interest to negotiators and donors, how much would it cost to neutralize the threat of additional losses in this context?

From a narrow technical perspective, it might be desirable to address the latter question with a detailed engineering cost analysis of specific disaster prevention measures. However, as we show in the paper, existing cross-country information about relevant emergency preparedness programs is far too sparse to support systematic analysis and projection. And in any case, we believe that the effectiveness of such measures is contingent on the characteristics of the communities that employ them. We therefore adopt an alternative approach in this paper, focusing on the role of socioeconomic development in increasing climate resilience.

In principle, a full analysis of climate vulnerability should consider a broad range of losses, including deaths, injuries and economic damage. Our analysis relies on the most comprehensive data on weather-related losses, which are maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université Catholique de Louvain, Brussels. These data provide a detailed accounting of deaths and persons affected by weather-related disasters, but their coverage of economic 2
losses is extremely sparse. Our empirical analysis therefore focuses on deaths and persons affected.

Our analysis builds on empirical work and case studies that have documented the role of socioeconomic development in reducing vulnerability to climate shocks. Horwich (2000), Tol and Leek (1993), Burton, et al. (1993) and Kahn (2005) have focused on the effect of rising income per capita: As communities get richer, they have greater willingness and ability to pay for preventive measures. Kahn (2005) finds that the institutional improvement that accompanies economic development also plays a significant role, through enhanced public-sector capability to organize disaster prevention and relief.

Other work focuses on the role of political and human development. Albala-Bertrand (1993) finds that politically-disenfranchised communities exhibit greater vulnerability to natural disasters. Toya and Skidmore (2005) find a significant role for education in reducing vulnerability, through better choices in areas ranging from safe construction practices to assessment of potential risks. Recently, Oxfam International (2008) has drawn on extensive evidence from South Asia to highlight the particular vulnerability of women, who often suffer substantially higher death and injury rates than men in natural disasters: _Nature does not dictate that poor people, or women, should be the first to die. Cyclones do not hand-pick their victims. Yet, history consistently shows that vulnerable groups end up suffering from such events disproportionately ... In the 1991 Bangladesh cyclone, for example, four times more women died than men ... Disasters are therefore an issue of unsustainable and unequal development at all levels ...(Oxfam (2008), p. 1)_ A logical inference from Oxfam (2008), Albala-Bertrand (1993) and Toya and Skidmore (2005) is that empowering women through improved education may be a critical factor in reducing families’ vulnerability to death and injury in weather-related disasters. This would also be consistent with the extensive literature that documents the powerful effect of female education on community-level social capital and general welfare measures such as life expectancy (King and Mason, 2001).

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To the best of our knowledge, no empirical research has focused on female education as a potentially-critical determinant of vulnerability to extreme weather events. Assessing its importance and implications is a core feature of this paper. Drawing on an econometric analysis of panel data, we address two key questions: As climate change increases potential vulnerability to extreme weather events, can expanding female education neutralize this increased vulnerability? If so, how much would it cost?

The remainder of the paper is organized as follows. Section 2 provides a summary of losses from extreme weather events in developing countries during the period 1960-2006. In Section 3, we review recent projections of climate impacts, economic growth and demographic change. We focus particularly on projections by integrated assessment models that incorporate links between climate change and economic activity. Section 4 specifies a set of risk equations for weather-related disasters and estimates them by fixedeffects. In Section 5, we develop country-specific projections for female education. Section 6 uses our econometric results and education projections to forecast future risks under alternative assumptions about climate change. In Section 7, we use these projections to estimate the cost of reducing future weather-related risks through more intensive investment in female education. Section 8 summarizes and concludes the paper.

# **2.  Losses from Extreme Weather Since 1960**

The most comprehensive data on weather-related losses are maintained by the Centre for Research on the Epidemiology of Disasters (CRED) at the School of Public Health of the Université Catholique de Louvain, Brussels.<sup>1</sup> For developing countries<sup>2</sup> , the CRED data provide a sobering view of weather-related losses since 1960. Table 1 presents the

> 1  To be entered in CRED’s EM-DAT database, a natural disaster must involve at least 10 people reported killed; 100 people reported affected; the declaration of a state of emergency; or a call for international assistance.  Recorded deaths include persons confirmed as dead and persons missing and presumed dead. Total affected persons include people suffering from disaster-related physical injuries, trauma or illness requiring medical treatment; people needing immediate assistance for shelter; or people requiring other forms of immediate assistance, including displaced or evacuated people.

> 2  We define these as countries identified by the World Bank as Low Income or Lower Middle Income.

4
numbers of people killed and affected by floods and droughts by decade. Flood-related deaths rose steadily from 17,000 in the 1960s to over 58,000 in the 1990s. Droughts killed far more people, although their impact was heaviest in the 1960s (1.5 million deaths) and basically absent in the 1990s (800 deaths). Aside from those killed, floods and droughts affected huge numbers of people who were injured, made homeless, or forced to seek emergency assistance. Trends for persons affected parallel the trends for deaths: rising for floods, but not for droughts.

To understand this phenomenon better, it is useful to separate losses into their two components: risk of loss (total losses/population) and population. The latter component increased rapidly in developing countries, from 2.2 billion in 1961 to 4.7 billion in 2000. Figures 1a-d provide graphical evidence on the risk component. For floods, the trends in risk of being killed or affected are clearly positive, while no trend is apparent for droughts. Figures 1a-d show that the risk series for floods and droughts are very different. Floods exhibit roughly continuous behavior through time, while droughts have exhibited huge, rare pulses that have not recurred since the early 1980s. The sources of these pulses are provided by Table 2, which presents the worst nine cases of droughtrelated death since 1960. Truly catastrophic losses of life in droughts in previous decades have been limited to four countries: India (1.5 million total), Ethiopia (600,000), Sudan (150,000) and Mozambique (100,000).

To summarize, losses from flooding have increased markedly since 1960 for two reasons: The risk of loss has increased significantly, and the population subject to risk has more than doubled. Droughts present a very different case, with risks dominated by catastrophic events in a few countries decades ago, and no clear overall trend. Even with rapidly-increasing population, there is no clear upward trend in persons affected by drought (Figure 2). Nevertheless, as Table 1 and Figure 2 show, the number of people affected by droughts continues to be huge.

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# **3. What Lies Ahead**

Our approach to impact forecasting incorporates projected socioeconomic and demographic trends as well as climate change. This requires us to adopt internallyconsistent assumptions about future changes in emissions, economies, human development levels and populations. The emissions scenario leads to a forecast of greenhouse gas concentration in the atmosphere, which is related to changes in global and local climate through one of a large number of global circulation models (GCMs). For the economic forecast, we draw on a recent summary of integrated assessment models by Hughes (2009), who draws on a critical assessment of the IPCC’s SRES scenarios by Tol, et al. (2005). Hughes develops a consensus economic projection by taking an average growth rate from three major integrated assessment models:<sup>3</sup> Climate Framework for Uncertainty, Negotiation, and Distribution (Anthoff and Tol, 2008); PAGE2002 (Hope, 2006); and Regional Dynamic Integrated Model of Climate and the Economy (Nordhaus and Boyer, 2000). We use Hughes’ constant-dollar GDP series, because our econometric risk estimation requires the use of data extending back to 1960.

For the population forecast (which includes projections of life expectancies and total fertility rates), we use the UN’s Medium Variant Projection (2006 Revision). Because our economic projections incorporate interactions with projected climate change, they are moderate in their view of future prospects. Overall, the economic and demographic projections we employ are relatively close to those in the SRES A2 Scenario.<sup>4</sup> We attempt to bound the set of reasonable expectations about future climate using GCMs with strongly-contrasting predictions. Within the SRES A2 scenario, the GCMs provide a relatively uniform view of future increases in temperature. However, this is not the

> 3  Using an average growth rate for the three major integrated assessment models greatly simplifies the econometric simulation work in this paper, which would otherwise require comparison of complex runs for three different macro-scenarios.  Use of the average also ensures consistency with the IPCC A2 scenario which, as we note above, incorporates a nearly-identical growth rate.

> 4  IPCC (2000) characterizes A2 as follows: “A very heterogeneous world, characterized by self-reliance and preservation of local identities. Fertility patterns across regions converge very slowly, which results in high population growth. Economic development is primarily regionally oriented and per capita economic growth and technological change are more fragmented and slower than in other scenarios.”

6
case for precipitation which, as we will show in the following section, is closely related to losses from floods and droughts. To provide a sense of what is possible from the current scientific perspective, our projections use two GCMs that are the wettest and driest of approximately 20 available GCMs at the global level.<sup>5</sup> The driest overall is CSIRO’s Mk 3.0 model, which was transmitted to the IPCC’s data collection center in 2005.<sup>6</sup> The driest is NCAR’s Community Climate System Model (CCSM), version 3.0, which was released to the public in 2004.<sup>7</sup> To illustrate the implications of the forecasts, Figure 3 provides historical and projected data for India for income per capita, population, life expectancy, fertility, mean annual rainfall, and maximum (monthly) annual temperature. We include both the CSIRO and NCAR weather projections. It is immediately clear that the India of 2050 bears little resemblance to present-day India, even in our relatively moderate scenario. GDP grows at an annual rate of 4.4%, increasing GDP per capita (constant $US 2000) from $450 in 2000 to $4,300 in 2050. Life expectancy increases from 63 to 76 years and the total fertility rate declines from 3.25 to 1.85 (below replacement). In 2050, India is an upper middle income country by current World Bank standards, closely resembling the Chile of 2000 in income per capita, life expectancy and fertility. But not, of course, in population: By 2050, India has 1.66 billion people in this scenario.

The climate projections provide a sense of the disparities in GCM predictions associated with the IPCC A2 scenario. From a monthly average of 88 mm in 2000, precipitation increases 8% by 2050 to 95 mm in the NCAR scenario, and decreases by 8% to 81 mm in the CSIRO scenario. Thus, the total difference attributable to GCM variation within the same IPCC scenario is 16% -- a very large number in this context. The two scenarios are

> 5  In general, the IPCC’s SRES scenarios project higher variance in precipitation events with increases in the atmospheric concentration of greenhouse gases.  Although the SRES A2 scenario anticipates a rapid increase in atmospheric concentration, the A1 scenario anticipates even faster growth and, by implication, higher variance in precipitation (i.e., more heavy rainfall events and serious droughts).  The A2-based results reported in this paper are therefore likely to be more “moderate” than A1-based results would be. 6  CSIRO is Australia’s Commonwealth Scientific and Industrial Research Organization (www.csiro.au/) 7  NCAR is the US National Center for Atmospheric Research.  For a detailed description of the CCSM program, see http://www.ccsm.ucar.edu/about/.

7
much closer on mean monthly temperature. After an increase of 1° C. since 1970, both predict another increase of 1° C. by 2050.

This illustration serves to make one point very clearly: In scenario A2, the GCMs concur that India’s temperature will rise steadily as greenhouse gases accumulate in the atmosphere. They also predict significant impacts on precipitation, but not consistently, and with different implications for extreme events. A wetter India will experience more floods, while a drier India will have more droughts. But in either case, it is critical to note that a changed climate in the full scenario is associated with a changed country. By 2050, India has become a middle income country, whose per capita income, human resources and institutional capabilities give it much greater resilience in the face of weather changes.

The Indian case reflects a more general and under-appreciated feature of scenario A2, as well as the other SRES scenarios: Anticipated future emissions increases reflect the expectation that development will also continue. By implication, many of the countries currently termed Low Income or Lower Middle Income by the World Bank will have departed that status by 2050.

Tables 3a and 3b, calculated from the Hughes’ (2009) projections, provide a striking indication of this shift for 94 countries identified as Low Income and Lower Middle Income by the World Bank in 2000. Table 3a tracks the status of 56 Low Income Countries through the succeeding decades. By 2050, 27 have moved to Lower Middle Income Status or higher. Table 3b provides the same information for 38 countries identified as Lower Middle Income in 2000. By 2050, only one has not advanced beyond that group.

Table 4 provides related information on changes in life expectancy, which are linked to the population projections. Life expectancy information is not available for two of the countries tabulated in Tables 3a and 3b. During the present decade, 14 countries still have life expectancies between 41 and 50 years and 18 have life expectancies between 51 8
and 60 years, while 29 have life expectancies above 70 years. By 2050, the tabulation has reversed. From 32 countries with life expectancies between 41 and 60 years in 20002010, the number has dropped to 7. And the number of countries with life expectancies greater than 70 has increased from 29 to 64.

In summary, even our moderately optimistic projections entail very large changes for developing countries during the next four decades. And, more importantly for this analysis, these changes are directly tied to the emissions scenario that generates the climate change problem. Countries with much higher incomes and life expectancies will also have much greater willingness and ability to pay for protection from extreme weather events. They will also have more highly-educated populations with much greater ability to avoid losses from adverse weather. This turns out to have major consequences for our assessment of future risk, as we will see in the next section.

# **4.  Development and Vulnerability to Extreme Weather Events**

As we noted in Section 2, extreme weather events have killed over 170,000 people and seriously affected billions since 1960. A changing climate may carry even greater risks in the future. But, as we noted above, changing development levels will also affect resilience in the face of weather shocks. To determine the balance between worsening weather and growing resilience over time, we specify a model of weather-related impact risk and estimate it using panel data for the period 1960-2002. Panel estimation allows for relatively clear interpretation of results, because it absorbs many sources of potentially-misleading cross-sectional correlation into estimated country effects. At the same time, however, the need for lengthy time series limits the estimation variables to a sparse set.

Our specification of the risk model incorporates three effects: economic development, weather, and education. We focus on female education, for reasons that we explained in the introduction. The formal specification is as follows for country i in period t: 9
- where R = Impact risk  (death from floods; affected by floods and droughts)

- L = Total loss (persons killed or affected)

- P = Population

- G = GDP per capita

- E = Female educational enrollment rate

- R = Precipitation

- T = Temperature ε = A random error term

Event-related losses are drawn from the CRED database; data on population, GDP per capita (in constant $US 2000) and education are drawn from the World Bank’s World Development Indicators. We use constant-dollar GDP data to maximize the sample size for years prior to 1980. Data on net female primary and secondary enrollment rates are limited to the period since 1990. To extend the series for panel estimation, we “backcast” them using panel regressions that relate enrollment ratios to income per capita, life expectancy and the total fertility rate (see Section 5). These associational regressions are a transformation of the conventional fertility equation, in which the total fertility rate is a function of income per capita, life expectancy and female schooling.

We specify the education rates as flogs, to ensure that predictions are restricted to the interval 0-100%. For a probability p between 0 and 1, the flog is defined as log [p/(1-p)]. This is an appropriate specification for the regressions in any case, since it is consistent with natural lower and upper bounds for net enrollment rates. These are, in effect, firststage regressions, with the total fertility rate, life expectancy and per capita income playing the role of instruments for second-stage estimation of the climate impact regressions. Life expectancy is not significantly affected by deaths from floods, which are minuscule by comparison with deaths from other causes. Therefore, use of this variable as a first-stage instrument should not lead to biased estimates for schooling in the regression for death from floods.

Table 5 presents the fixed-effects estimation results, which are extremely robust in both regressions for the total fertility rate and life expectancy. Per capita income is not a 10
significant determinant of net female primary enrollment, but it has great explanatory power in the secondary enrollment regression.

Prior experimentation has indicated that the appropriate functional form for equation (1) is log-linear, and that some climate indicators are much more robust than others. In every case, mean rainfall is far more robust than either maximum or minimum rainfall, as well as other possible transformations (max/min ratio, etc.). No measure of temperature (mean, maximum, minimum, max/min ratio, etc.) is significant in any of the three risk equations.

Table 6 presents results for the risk of being killed or affected by floods, and affected by droughts. Footnote 1 provides a detailed explanation of the criteria for determining persons affected. Our instrumental variables approach creates high collinearity for primary and secondary enrollment ratios, so we estimate separate equations for primary and secondary schooling. This serves our primary objective – inferring the schooling needed to neutralize future climate impacts – but raises the risk of upward bias in the separately-estimated impact of each schooling variable. Such bias would lead to an ultimate under-estimate of schooling required to neutralize climate change (the higher the estimated effect of schooling on risk, the lower the schooling needed to neutralize additional risk from worsening weather). However, we compensate for this by using the two sets of econometric results to calculate demands for primary and secondary education separately. This introduces something akin to double-counting, thereby reducing or eliminating the effect of upward bias in the regressions themselves.

The panel estimation results in Table 6 are quite robust for flooding risk, with all variables highly significant and all parameter signs consistent with prior expectations. Flood risk rises significantly as mean precipitation rises and falls significantly as per capita income rises. The two schooling variables have highly-significant impacts of approximately equal magnitude: Flood risk falls as female enrollment increases. The results are much weaker for drought risk, partly because the smaller sample size reduces degrees of freedom for estimation. GDP per capita is insignificant and has a perverse 11
sign when it is included in these regressions, so we have excluded it from the estimates. Education and rainfall retain the correct signs, although only female primary enrollment is significant at 5%. These are the maximum likelihood estimates in any case, and we need to project drought effects, so we retain the two drought equations in Table 6.

To explore the implications of our results, we compare actual historical losses with a counterfactual case in which countries at each World Bank development level are assigned the same female primary enrollment ratio as the “best practice” country in the same World Bank class in the same year (e.g., all Low Income countries in 1985 are assigned the highest female primary enrollment ratio among Low Income countries in 1985). We perform this experiment to see how much difference feasible policy changes could have made for extreme weather risk. As Table 7 and Figure 4 show, our results strongly indicate that more progressive policies would have made an enormous difference. From 1970 to 1999, the CRED database records 153,079 deaths from floods in low income and lower middle income countries. In our “best practice” counterfactual, by contrast, flood deaths number 91,541: 61,538 fewer people lose their lives. For numbers affected, the estimated differences are very large. From 1960 to 1999, the CRED database indicates that 2.12 billion people in developing countries were affected by floods. In the counterfactual best practice case, this falls by 465 million to 1.65 billion. For droughts, the number affected falls by about 667 million – from 1.34 billion to 676 million.

We conclude that a huge number of weather-related tragedies could have been averted if more developing countries had focused on progressive but feasible female education policies. Countries that focused on female education suffered far fewer losses from extreme weather events than less-progressive countries with equivalent income and weather conditions. It seems reasonable to assert that what has been true in the past will also be true in the future. Given the significance of income and female education in determining vulnerability to extreme weather events, we would expect countries’ future resilience to increase with economic growth and improvements in education.

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# **5.  Projecting Baseline Changes in Female Education**

The panel estimation results in Table 5 provide a reasonable basis for projecting the future paths of female primary and secondary education in each country, given our exogenous projections of income and population. Our projections for life expectancy and the total fertility rate are taken directly from the UN’s Medium Variant population forecast. Given the paths of the three variables (life expectancy, total fertility rate, income per capita), we use the fixed-effects results reported in Table 5 to plot the paths of future net female primary and secondary enrollment rates. Here it is worth repeating that we estimate both equations in Table 5 using flog transformations on net enrollment rates, which insure that projections are bounded in the range 0-100.

To illustrate the implications of our approach, Table 8 presents projected future schooling rates by region. We compute these rates in several steps. First, we estimate the panel regressions, incorporating subregional dummies as well as country effects.<sup>8</sup> Then we combine the results with country projections of GDP per capita, life expectancy and the total fertility rate to predict future net female primary and secondary enrollment rates. We combine these rates with appropriate UN Medium Variant female population cohort data to calculate the actual number of females enrolled in primary and secondary school. Finally, we total enrolled females and relevant cohort females by region for primary and secondary schooling, and form the ratios to project regional primary and secondary enrollment ratios.

Although our economic and demographic projections are “moderate” by current standards, they nevertheless entail continued rapid progress in female education. By 2050, Sub-Saharan Africa increases its net female primary enrollment rate from 54.9 to 93.5, and its net female secondary enrollment rate from 19.7 to 78.0. South Asia also makes rapid progress, moving its female primary and secondary enrollment rates from 69.5 to 92.4 and 42.0 to 90.8, respectively. East Asia/Pacific, Latin American/Caribbean and Middle East/North Africa also move upward, but proportionately less because they start from higher bases. While educational progress is quite noteworthy in this scenario,

> 8  We include fixed effects for 25 subregions.

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it still falls well short of the Millennium Development Goals. For example, Sub-Saharan Africa only approaches the MDG for female primary education by 2050.

# **6.  Climate Change, Development and Future Vulnerability**

Now we turn our attention to simulating the future. Our approach is identical to the counterfactual approach in Section 4, except that all of the righthand variables are projected for this exercise. To get a clear sense of the stakes, we introduce several variants: We estimate impacts with and without climate change, for both GCM climate projections, and with and without improvements in income and female education. We introduce the latter variant to highlight the stark difference in results when only future emissions are counted, not the economic and human development that accompany those emissions. As we have noted in Section 3, forty years hence many of today’s low and lower middle income countries will have experienced major growth in income and female education. Our panel results in Table 6 suggest that the latter changes will have a significant impact on climate vulnerability. To assess the relevant magnitudes, we develop a detailed illustration for India that incorporates variations in the GCMs and development conditions.

We focus on computed risks, or incidence probabilities, because they provide clear insight into the impact of model variables on projected future losses. In each case, the relevant probability is multiplied by population to provide an overall loss estimate. India’s population is projected to continue growing to about 1.66 billion people by 2050. Since the population is growing, even constant risk (measured as a loss probability) will translate to more losses when it is multiplied by the growing population.

In Figure 5, our historical baseline for India is set at income per capita and life expectancy in 2000, and mean precipitation during the period 1995-2000. The associated annual loss probabilities are .62 per million for being killed by a flood; 0.0039 for being affected by a flood; and .0509 for being affected by a drought. From this baseline, we forecast the impact of GCM-projected changes in mean precipitation while holding income and life expectancy at their 2000 levels. The results, labeled “Static” in Figure 5, 14
show the magnitudes of the projected impacts, as well as their directions. NCAR is the wettest global scenario, and this is reflected in the Indian projections. In the static NCAR case, the risk of death from flooding rises from 0.62 in 2000 to 0.66 in 2050. Conversely, CSIRO, which produces the driest scenario, projects a drop in mean precipitation and an associated fall in flood-related death risk: from 0.62 in 2000 to 0.57 in 2050.

Thus, holding economic and social development constant at 2000 levels, precipitation variations across GCMs include a range of about .09 per million in flood death risk. In all cases, the deviation from current death risk is sufficiently small to be dominated by population growth in the assessment of losses. Table 9 presents the relevant projections. In the Historical case, projected annual deaths increase from 626 to 1,023 (the risk of death remains constant, while population continues growing). Projected annual deaths from flooding rise to 1,092 for NCAR, the wettest scenario, and fall to 950 for CSIRO, the driest. Clearly, the differences are quite small in absolute terms: By 2050, climate is responsible for 69 additional flood-related deaths per year in the NCAR scenario, and 73 fewer deaths in CSIRO. For the entire fifty-year period, continuation of the historical climate pattern in a static India (at 2000 income per capita and educational enrollment rates) would yield 44,038 expected deaths from flooding. In the static case, this rises to 45,458 for the NCAR climate change scenario and falls to 41,325 for CSIRO.

In comparison to these relatively small mortality effects, the number of people affected by floods is larger by one order of magnitude and people affected by droughts by two orders of magnitude. For floods, the baseline probability of being affected (in the staticIndia case with no climate change) is .0039. This rises to .0043 for NCAR climate change and falls to .0035 for CSIRO. These are much larger fractions than the death risks, and they translate to large absolute numbers. Projected people affected annually rises from 4.0 million to 6.5 million in the baseline case (constant risk, population growth), rises to 7.2 million for NCAR (in a static India) and falls to 5.7 million for CSIRO. Thus, the range of impacts is between 700,000 more people and 800,000 fewer people affected by floods. The associated totals and differences in Table 9 are quite large: A fifty-year increase from the baseline of 15 million for NCAR (293.6 vs. 278.6 15
million), and a decrease of 27.7 million for NCAR. The numbers are larger by another order of magnitude for drought, but in the opposite direction. For annual numbers affected by drought, the baseline case rises from 51.6 million in 2000 to 84.4 million in 2050. The numbers rise less rapidly in the NCAR (wet) case for a static India, to 77.1 million and more rapidly for CSIRO (dry), to 93.4 million. Translated to fifty-year totals, the differences are huge: 337 million more people affected for CSIRO, and 153 million fewer for NCAR.

All of the cases discussed above have elements in common: In an India that experiences no change in income and life expectancy, population growth alone (with constant loss risk) ensures that losses from extreme weather events increase substantially, even if there is no climate change. The projected range of climate changes will alter the forecast, making it lower in some cases and higher in others. But in the static-India case, all the scenarios project greater future losses, and climate effects that are smaller proportionally than anticipated effects from population change.

Of course, all of the projections above are unrealistic, because they assume a static India that bears no resemblance to the India in the climate change forecasts. As we have seen in a previous section, that India is quite close to present-day Chile in income and education levels by 2050. Despite their unreality, we have included the static-India forecasts because we believe that they reflect the implicit assumptions in many current climate-impact analyses.

For an instructive contrast, we now turn to projections that also utilize the fixed-effects estimates for equation (1), but incorporate our income and education projections for India. Although we will review the numbers in some detail, the basic results are made graphically clear by the Development scenarios in Figure 5. For flooding, in both NCAR and CSIRO scenarios, the probabilities of being killed or affected plunge so sharply that they dominate rising population in the calculation of total losses. The result is many fewer deaths and people affected by floods in 2050, although there is still a climate effect 16
at a much lower level. A rapid fall is also evident for risk in the NCAR scenario for drought, although much less so for CSIRO.

When we translate these risks into total losses, the results are quite striking. The India of 2050 has annual flooding deaths of about 461 for NCAR and CSIRO, vs. 1,023 in the baseline. It has 3.6 million people affected by floods in NCAR and 2.8 million in CSIRO, vs. 6.5 million in the baseline. In the case of droughts, 62.5 million are affected in NCAR and 75.7 million in CSIRO, vs. 84.4 million in the baseline.

We draw two conclusions from these results. First, it still makes sense to discuss financial support for adaptation to climate change in this context, because risks and losses can still be greater with climate change than without it. But second, and perhaps more important, our results strongly indicate that the India of 2050 will suffer fewer losses from extreme weather after four more decades of climate change than present-day India suffers.

For global perspective, we have included the same projection comparisons for all developing countries in Table 10. Although the magnitudes are larger, they replicate the patterns that we have just discussed. The developing world of 2050 may well suffer more losses with climate change than without it (the impact depends on whether the wet or dry scenario dominates), but the available evidence makes it very likely that it will suffer far fewer losses than presently in either case. And our results for female education in equation (1) reinforce a fundamental point: If we are really interested in reducing losses from climate events, assistance for greater resiliency now can make a huge difference.

# **7.  Estimating the Cost of Adapting to Extreme Weather Events**

# **7.1  Data on Weather Emergency Preparedness Costs**

Systematic work on the cost of adaptation to extreme weather events has been hindered by scanty data on the cost of measures for emergency preparedness. A study of this type would be aided considerably by country-specific cost information for measures targeted on floods or droughts. However, the representative information in Table 11 illustrates 17
why we have not been able to employ such information. Its entries have been extracted from country reports by the Asian Disaster Reduction Center. The reports generally focus on summary information rather than specific information for emergency preparedness by type of disaster (e.g., floods, droughts). In the case of Japan, for example, much of the $34 billion expenditure is clearly for earthquake-related measures. The listed funds for Bangladesh are more than twice China’s and four times Indonesia’s, and they include both emergency food assistance and disaster management. Much of the Indonesian fund undoubtedly relates to geologic disasters (earthquakes, volcanic eruptions, tsunamis) as well as weather-related disasters.

Most reports do not provide time series information, nor do they go beyond reporting for single funds or national-level agencies.<sup>9</sup> Local expenditures are not included.

In summary, the available data are far too spotty, non-specific, non-standardized, and temporally limited to permit estimation of cost functions that could be used for projection. In addition, they cover relatively few countries. There is simply no way to construct a reasonable cost analysis from such information.

# **7.2  The Education Alternative**

Since direct cost measures cannot be derived from the available data, we turn to an indirect approach. As the panel results in Table 6 show, improvements in female education are powerfully associated with reductions in disaster risks once changes in weather and income are accounted for. In this section, we exploit this relationship to address the adaptation cost question indirectly.

Our approach applies straightforward algebra to equation (1). Given an anticipated change in precipitation, we calculate the increase in education that will be just sufficient to restore the risk level prior to the precipitation change. With subscripts B for the baseline case and N for the risk-neutralizing case, we impose the following constraint on the relevant elements of equation (1) (the others cancel because they remain unchanged):

> 9  Our thanks to Tim Essam for his help with gathering this information.

18
This yields the change in the educational enrollment rate that will neutralize the change in risk introduced by deviation of rainfall from the baseline case: For each education level, we calculate ∆ Ei for persons killed by floods, persons affected by floods, and persons affected by droughts. Adopting a conservative approach, we only consider positive ∆ Ei.<sup>10</sup> We compute the 50-year sum of positive ∆ Ei for each of the three risks and, in keeping with our conservative approach, we choose the risk for which the sum is largest.

# **7.3  The Cost of Climate Change Neutralization**

Now we are ready to compute the cost of neutralizing the risk impact of more extreme weather events via increased schooling for young women. Once we have chosen the appropriate ∆ Ei for each schooling level in each country, computing the associated incremental cost involves two steps. First, we obtain the number of new students by multiplying ∆ Ei (as a percent) by the number of females in the appropriate age cohort.<sup>11</sup> Then we multiply by projected expenditure per pupil. To compute projected unit expenditures, we have drawn on the World Bank’s World Development Indicators to estimate panel regressions for primary and secondary expenditures per student as a proportion of gdp per capita. After extensive experimentation with available and plausible righthand variables (e.g., per capita income, size of student population, time trend), we find significance only for country, subregional and regional fixed effects. We use all three sets of fixed effects to get the most accurate estimates for countries that have

> 10  In a wetter climate regime the number of people killed and affected by floods will rise, but the number affected by droughts will fall.  The converse is true for a dryer regime.  A complete accounting would therefore involve calculation of net impacts (losses from flooding vs. losses from droughts)  But in the case of floods, such an exercise would require assigning relative weights to being killed and being affected (injured, rendered homeless, or requiring temporary assistance).  It would also require the assignment of relative weights to the affects of floods and droughts.  Rather than adopt arbitrary weights, we take the conservative approach and use the greatest change in enrollment ratio across the three risk categories. 11 Our primary-school cohort is young women in age group 5-9, plus half of young women 10-14.  Our secondary cohort is half the age group 10-14, plus the age group 15-19.

19
no unit expenditure data in the WDI. Then we apply the country estimates to projected per capita income to obtain predicted expenditures per primary and secondary student by year. We combine these with the calculated numbers of primary and secondary students required for “climate change neutralization” to obtain our estimate of the public cost.<sup>12</sup> Table 12 provides a representative set of results for three countries: Republic of Congo, Nepal and Nicaragua. For ease of interpretation, we present results at ten-year intervals, beginning in 2010. The results highlight the global diversity that interacts with the GCM projections. Overall costs are higher for CSIRO in Congo and Nepal, and for NCAR in Nicaragua; we focus on the higher-cost scenario for each country. Here it is useful to recall that these are adjustments from a baseline in which the countries continue their socioeconomic development. Weather impact risks decline as income and life expectancy increase. The numbers in Table 12 reflect the deviations from this baseline, which assumes no climate change.

In Congo, neutralizing the effect of climate change in the CSIRO scenario requires 4.700 additional females in primary school in 2010, along with 4,100 additional female students in secondary school. The associated annual schooling costs for primary and secondary students are $97 and $233, respectively. When these are applied to the schooling increments, the result is an additional expenditure of $1.4 million in 2010. The numbers increase steadily through 2040. In that year, the addition to schooling is 32,900 primary students and 32,400 secondary students which, at projected unit costs of $218 and $524, yields a total expenditure of $24.2 million. Projected short-term moderation of climate impact after 2040 reduces the numbers in 2050.

In Nepal, the number of needed additional students in the CSIRO (higher-cost) scenario is far greater than for the Congo and the unit costs substantially lower. When combined, the two factors yield climate-neutralizing costs that increase from $5.9 million in 2010 to $27.2 million in 2040, then fall to $26.5 million in 2050. The NCAR scenario is more

> 12  Our estimate of costs if quite conservative, of course, because we do not incorporate the substantial cobenefits of female education.

20
potentially-damaging for Nicaragua than CSIRO. Neutralizing the impact of NCARprojected change requires the addition of 35,900 young women to primary schooling and 69,400 to secondary schooling in 2010. The total cost is $5.9 million in 2010, increasing to $13.5 million in 2050.<sup>13</sup> Table 13 summarizes our results at the regional level. Here the scale of effort needed for climate neutralization becomes apparent. In Sub-Saharan Africa, the overall impacts of CSIRO and NCAR are roughly similar. For both GCM scenarios, the requisite annual expenditure rises from about $200 million in 2010 to over $2 billion in 2050. By the latter date, climate neutralization in CSIRO requires 1.7 million additional primary school students and 3.5 million additional secondary students. In the NCAR case, these numbers rise to 3.0 million primary students and 7.1 million secondary students. The Sub-Saharan case points to another feature of geographic diversity that has implications for the cost of climate neutralization. Climate changes in the two scenarios have different geographic distributions. In the African case, by happenstance, the countries most adversely affected in NCAR have significantly lower unit schooling costs than the countries with the greatest effects in CSIRO. As a result, climate-neutralizing expenditure is slightly lower in NCAR, even though the number of additional students is substantially higher.

South Asia is also not far from cost parity in the two climate scenarios. Although the expenditure difference is large in 2010 – $529 million in CSIRO vs. $266 million in NCAR – by 2050, the numbers are proportionally much closer ($5.4 billion and $4.9 billion, respectively). Other regions exhibit disparities, but with different patterns. East Asia and the Pacific Islands are dominated by China, whose rising prosperity generates steadily-increasing schooling costs. Costs in the CSIRO scenario dominate until 2040, when a projected climate shift significantly moderates climate stress during the same period that it increases in NCAR. The result is a reversal for CSIRO, as regional expenditures fall from $2.1 billion in 2040 to $796 million in 2050, while NCAR

> 13 Like many countries in Latin America, Nicaragua is reported by the World Development Indicators as spending more per capita on primary students than on secondary students.  The results for Nicaragua in Table 12 reflect this disparity.

21
expenditures continue expanding, from $1.7 billion to $2.6 billion. In the remaining three regions, NCAR dominates expenditures in varying degrees.

Table 14 summarizes the annual results, which tell a story of impressive magnitudes. Overall, annual expenditures are remarkably close for CSIRO and NCAR until 2040. Climate-neutralizing educational expenditure is about $1.6 billion for both in 2010. By 2040, NCAR is slightly ahead ($9.5 billion vs. $9.2 billion). Projected short-run climate shifts and a host of other factors shift the balance by 2050, and NCAR finishes well ahead of CSIRO ($13.6 billion annually, vs. $10.9 billion). In both scenarios, the implications for climate-neutralizing female education are massive. By 2050, neutralizing CSIRO requires 7 million additional young women in primary school and 11.3 million in secondary school. The corresponding numbers for NCAR are 8.3 million and 14.9 million.

Table 15 provides a final summary by totaling annual expenditures for the period 20022050<sup>14</sup> at varying discount rates. Overall, at a 0 discount rate, impact-neutralizing expenditures for additional female education are $279.4 billion for CSIRO and $288.1 billion for NCAR. The totals fall sharply as the discount rate increases. At 7%, present values in 2002 are $40.2 billion for CSIRO and $39.5 billion for NCAR. Among the world’s regions, it is both clear and unsurprising that the largest climate-neutralizing expenditures are in the areas whose low incomes and schooling rates are associated with higher climate impact risks. South Asia has the greatest expenditure in both climate scenarios, with CSIRO much more costly than NCAR ($121.2 billion vs. $89.7 billion for a zero discount rate). Sub-Saharan Africa and East Asia/Pacific Islands are in the next rank, with rough balance across the two scenarios: Around $46 billion for Sub-Saharan Africa and $55 billion for East Asia/Pacific. In the next rank, both Eastern Europe/Central Asia and Latin America/Caribbean have NCAR expenditures about twice as high as CSIRO expenditures. The pattern reverses for Middle East/North Africa, where expenditures are $15.7 billion for CSIRO and $12.9 billion for NCAR.

> 14  A few missing value problems prevent generation of fully-comparable numbers for 2000 and 2001.

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# **8.  Summary and Conclusions**

In this paper, we have addressed several questions that are relevant for the international discussion of adaptation to climate change: How will climate change alter the incidence of these events, and how will their impact be distributed geographically? How will future socioeconomic development affect the vulnerability of affected communities? And, of primary interest to negotiators and donors, how much would it cost to neutralize the threat of additional losses in this context?

From a narrow technical perspective, it might be desirable to address the latter question with a detailed engineering cost analysis of specific disaster prevention measures. However, as we show in the paper, existing cross-country information about relevant emergency preparedness programs is far too sparse to support systematic analysis and projection. And in any case, we believe that the effectiveness of such measures is contingent on the characteristics of the communities that employ them. We therefore adopt an alternative approach in this paper, focusing on the role of socioeconomic development in increasing climate resilience. Drawing on extensive research, our approach highlights the importance of female education and empowerment in reducing weather-related loss risks. Our cost analysis asks two key questions: As climate change increases potential vulnerability to extreme weather events, how many additional young women would have to be educated to neutralize this increased vulnerability? And how much would it cost?

Our study relies heavily on fixed-effects estimation of risk equations that link losses from floods and droughts during the period 1960-2003 to three basic determinants: weather events that increase potential losses, income per capita, and female education. We estimate separate equations for the risk of death from a flood, the risk of being affected by a flood, and the risk of being affected by a drought (the data are too sparse to support estimation for death from droughts).

Our analysis combines the estimated risk equations with projections of economic growth and population change, along with accompanying changes in primary and secondary 23
schooling. We develop three scenarios: A baseline in which socioeconomic development continues but the climate does not change, and two scenarios with the same baseline development path but alternative weather paths driven by particularly “wet” and “dry” GCMs. For each GCM scenario, we calculate the associated changes in the risks of death from floods and being affected by floods or droughts. Then, choosing the worstcase risk, we calculate the increase in female schooling that would neutralize this additional risk. We multiply the results by expenditures per student to estimate the total educational investment required to neutralize the additional weather risk posed by climate change.

Our approach is conservative, in the sense that it is very unlikely to underestimate the required investment. First, we base our cost assessment on general preparedness via increased education, rather than more narrowly-targeted investment in emergency preparedness. Second, we base our cost calculation on worst-case risk scenarios, which require the greatest increase in schooling to neutralize. Third, we incorporate only projected increases in vulnerability, not decreases. As an alternative, for example, we could perform a net impact analysis for a wet climate scenario that would subtract expected decreased losses from drought from expected increased losses from flooding. Fourth, our analysis employs the two GCMs (among approximately twenty) that generate the wettest and driest scenarios at the global scale. Other GCMs would generate more moderate intermediate results. Finally, we do not average across the two GCMs, which would have the effect of neutralizing their extreme signals.

In summary, we believe that our approach is sufficiently conservative to create a strong upward bias in our cost estimation. It is certainly possible that the “true” cost of adaptation to extreme weather events is lower than our estimates, but we very much doubt that it is higher.

At the same time, our approach offers significant co-benefits because female education has a much broader sphere of potential influence than direct investment in emergency preparedness. As the development literature has noted for many years, educating young 24
women is one of the major determinants (indeed, some would argue, _the_ major determinant) of sustainable development. A disaster-prevention approach that focuses on investment in female education therefore has an expected social rate of return on other margins that probably warrants the exercise, even if the expected benefits in reduced disaster vulnerability are overstated (and in fact, we believe the opposite to be true).

Our analysis has generated a set of estimates for required female schooling and associated costs by GCM scenario, country and year. Variations in projected climatic, socioeconomic and demographic variables are more than sufficient to produce wide disparities in outcomes by 2050, even among countries within the same region. At the country and regional levels, neither climate scenario dominates in all cases. The “wet” scenario generates higher risk-neutralizing expenditure on female schooling in come countries and regions; the “dry” scenario is more costly in others. Among regions, South Asia requires the most expenditure in both climate scenarios, followed by Sub-Saharan Africa and East Asia, and then more distantly by the other regions.

At both regional and global levels, we find an impressive scale for the requisite increases in female education expenditure. By mid-century, neutralizing the impact of extreme weather events requires educating an additional 18 to 23 million young women at a cost of $11 to $14 billion annually. For the period 2000-2050 as a whole, both GCM scenarios entail about $280 billion in additional expenditure. The present value of these expenditures is substantially reduced by time-discounting, even at modest rates, but the basic result stands: In the developing world, neutralizing the impact of worsening weather over the coming decades will require educating a large new cohort of young women at a cost that will steadily escalate to several billions of dollars annually. However, it will be enormously worthwhile on other margins to invest in education for millions of young women who might otherwise be denied its many benefits.

25
# **References:**

Albala-Bertrand, J. 1993. Political Economy of Large Natural Disasters. New York: Oxford University Press.

Anthoff, D., and R.S.J. Tol. 2008. “The Impact of Climate Change on the Balanced Growth Equivalent.” Working Paper 228. Economic and Social Research Institute, Dublin, Ireland.

Burton, K., R. Kates and G. White. 1993. The Environment as Hazard, 2<sup>nd</sup> Edition. New York: Guilford Press Hope, C. 2006. “The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC’s Five Reasons for Concern.” Integrated Assessment 6(1): 19–56.

Horwich, G. 2000. Economic Lessons From the Kobe Earthquake. Economic Development and Cultural Change. 48, 521-542.

Hughes, Gordon. 2009. The Economics of Adaptation to Climate Change: Global Estimates for Infrastructure. Washington, DC: World Bank Kahn, Matthew. 2005. The Death Toll From Natural Disasters: The Role of Income, Geography, and Institutions. The Review of Economics and Statistics, 87(2) 271-284.

King, Elizabeth and Andrew Mason. 2001. Engendering Development. World Bank Policy Research Report. January Nordhaus, William and Joseph Boyer. 2000. Warming the World: Economics Models of Global Warming. Cambridge, MA: MIT Press.

Oxfam International. 2008. Rethinking Disasters: Why Death and Destruction is not Nature's Fault but Human Failure. South Asia Regional Centre, Oxfam (India) Trust, New Delhi.

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Tol, Richard S.J., Brian O’Neill, and Detlef P. van Vuuren. 2005. A Critical Assessment of the IPCC SRES Scenarios. Research Unit, Sustainability and Global Change, Hamburg University. March Tol, R. and F. Leek. 1999. Economic Analysis of Natural Disasters. In T. Downing, A. Oisthoorn and R. Tol, eds. Climate Change and Risk. London: Rutledge Toya, H. and M. Skidmore, Economic development and the impacts of natural disasters, Economics Letters, 94 (1) 20-25.

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Table 1: Losses From Extreme Weather Events: Developing Countries, 1960-1999

||De<br>(‘0<br>|aths<br>00)<br>|Number<br> (‘00<br>|Affected<br>0)<br>|
|---|---|---|---|---|
|Period|Floods|Droughts|Floods|Droughts|
|1960-69|17.0|1,510.1|34,256|110,000|
|1970-79|46.4|319.1|200,000|460,000|
|1980-89|50.3|556.9|480,000|700,000|
|1990-99|58.5|0.8|1,400,000|190,000|
|Total|172.2|2,386.9|2,114,256|1,460,000|

# Source: CRED (EM-DAT)

Table 2: Catastrophic Death Tolls From Droughts, 1960-2002

|Country|Year|Deaths|
|---|---|---|
|India|1965|500,000|
|India|1966|500,000|
|India|1967|500,000|
|Ethiopia|1984|300,000|
|Ethiopia|1974|200,000|
|Sudan|1984|150,000|
|Mozambique|1984|100,000|
|Ethiopia|1973|100,000|
|Somalia|1974|19,000|

Source: CRED (EM-DAT) 28
Table 3a: Year 2000 Low Income Countries Through 2050

|Decade|Low<br>Income|Lower<br>Middle<br>Income|Higher|Total|
|---|---|---|---|---|
|2001-2010|56|0|0|56|
|2011-2020|49|7|0|56|
|2021-2030|42|14|0|56|
|2031-2040|34|22|0|56|
|2041-2050|29|24|3|56|

Table 3b: Year 2000 Lower Middle Income Countries Through 2050

|Decade|Lower<br>Middle<br>Income|Higher|Total|
|---|---|---|---|
|2001-2010|38|0|38|
|2011-2020|26|12|38|
|2021-2030|11|27|38|
|2031-2040|7|31|38|
|2041-2050|1|37|38|

Table 4: Year 2000 Low Income and Lower Middle Income Countries: Changes in Life Expectancy Through 2050

|Decade|41-50|<br>51-60|Life Expect<br>61-70|ancyat Bir<br>71-80|th<br>81-90|Total|
|---|---|---|---|---|---|---|
|2001-2010|14|18|31|29|0|92|
|2011-2020|8|18|31|35|0|92|
|2021-2030|4|18|20|50|0|92|
|2031-2040|0|14|22|55|1|92|
|2041-2050|0|7|21|60|4|92|

29
# Table 5:  Determinants of Net Female Enrollment Ratios

Dependent Variable: Flog Net Enrollment Rate

||(1)<br>Primary|(2)<br>Secondary|
|---|---|---|
|Total Fertility Rate|-0.349<br>(7.35)**|-0.555<br>(14.87)**|
|Life Expectancy|0.068<br>(6.04)**|0.043<br>(6.51)**|
|Log GDP Per Capita|0.139<br>(1.34)|0.893<br>(10.67)**|
|Constant|-1.774|-5.213|
||(1.39)|(8.14)**|
|Observations|1849|1025|
|R-squared|0.89|0.98|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 30
# Table 6:  Weather Risk Model: Fixed-Effects Estimates

# Dependent Variables [Log(Variable/Population)]

||(1)<br>Floods<br>Killed|(2)<br>Floods<br>Killed|(3)<br>Floods<br>Affected|(4)<br>Floods<br>Affected|(5)<br>Droughts<br>Affected|(6)<br>Droughts<br>Affected|
|---|---|---|---|---|---|---|
|Female Primary|-0.018||-0.017||-0.020||
|Enrollment Rate|(4.67)**||(2.70)**||(2.05)*||
|Female Secondary||-0.017||-0.015||-0.011|
|Enrollment Rate||(5.36)**||(2.87)**||(1.16)|
|GDP Per Capita|-0.137|-0.122|-0.120|-0.107|||
|($’000)|(6.22)**|(5.46)**|(2.91)**|(2.53)*|||
|Precipitation|0.010|0.010|0.016|0.016|-0.013|-0.014|
|(mm.)|(3.28)**|(3.23)**|(3.61)**|(3.60)**|(1.75)|(1.79)|
|Constant|-12.833|-13.438|-6.630|-7.297|-0.883|-1.752|
||(28.38)**|(36.28)**|(9.29)**|(12.92)**|<br>(1.08)|(2.67)**|
|Observations|933|929|1051|1047|323|322|
|Countries|120|120|134|134|83|82|
|R-squared|0.46|0.46|0.41|0.42|0.54|0.53|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1% 31
Table 7: Historical and Simulated “Best Practice” Weather-Related Losses 1970-2000

|RiskCategory|Historical|Best<br>Practice|Difference|%<br>Difference|
|---|---|---|---|---|
|Flood Deaths|153,079|91,541|61,538|40.2%|
|Floods Affected('000)|2,116,243|1,651,065|465,178|22.0%|
|Droughts Affected('000)|1,342,337|675,797|666,540|49.7%|

Table 8: Projected Net Female Primary and Secondary Enrollment Ratios by Region

|Region|Sub-Sa<br>Afr|haran<br>ica|East A<br>Pacific|sia and<br>Islands|Latin A<br>and Ca|merica<br>ribbean|Middle<br>North|East and<br>Africa|South|Asia|
|---|---|---|---|---|---|---|---|---|---|---|
|Year|P|S|P|S|P|S|P|S|P|S|
|2000|54.9|19.7|95.0|80.5|90.8|61.3|80.5|56.0|69.5|42.0|
|2010|68.7|28.5|96.9|86.5|93.3|73.1|89.1|67.4|81.3|60.5|
|2020|78.0|43.8|97.8|89.9|94.9|81.1|92.9|77.6|85.8|73.5|
|2030|86.0|59.4|98.2|93.0|96.0|85.8|94.9|84.7|88.8|81.9|
|2040|90.8|70.9|98.6|94.8|96.9|89.6|96.1|89.3|90.7|87.2|
|2050|93.5|78.0|98.8|96.2|97.4|92.1|96.9|92.1|92.4|90.8|

# Table 9:  Alternative Scenarios for India, 2000-2050

||Year||FloodDeat|hs|Af|fected by<br>(Millio|Floods<br>n)|Affe|cted by Dr<br>(Million)|oughts<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|||CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|
|2000||626.1|626.1|626.1|4.0|4.0|4.0|51.6|51.6|51.6|
|2050|Static|950.4|1,092.3|1,023.4|5.7|7.2|6.5|93.4|77.1|84.4|
|2050|Devel|461.0|461.0|1,023.4|2.8|3.6|6.5|75.7|62.5|84.4|
|Totals|||||||||||
|2000-50|Static|41,325.2|45,457.6|44,038.4|250.9|293.6|278.6|3,967.4|3,477.1|3,630.2|
|2000-50|Devel|27,768.1|30,269.3|44,038.4|176.7|204.1|278.6|3,362.6|2,956.4|3,630.2|

Table 10: Alternative Scenarios for Developing Countries, 2000-2050

||Year||FloodDeath|s|Aff|ected by F<br>(Million|loods<br>)|Aff|ected by Dro<br>(Million)|ughts|
|---|---|---|---|---|---|---|---|---|---|---|
|||CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|CSIRO|NCAR|Historical|
|2000||5,520|5,520|5,520|16.4|16.4|16.4|142.7|142.7|142.7|
|2050|Static|10,861|11,018|10,871|24.6|27.1|25.5|262.2|241.6|250.5|
|2050|Devel|3,425|3,464|10,871|8.3|9.3|25.5|184.8|169.2|250.5|
|Totals|||||||||||
|2000-50|Static|419,849|427,755|423,185|1,054|1,140|1,102|10,690|10,116|10,304|
|2000-50|Devel|231,330|234,889|423,185|645|693|1,102|8,516|8,055|10,304|
Table 11: Disaster Preparedness and Management Data

|Country|Agency|Year|Annual<br>Equivalent<br>(Million$US)|
|---|---|---|---|
|Armenia|EmergencyManagement Administration|2006|7.0|
|Bangladesh|Food and Disaster Management Budget|Annual|500.0|
|China|Various agencies|2005|217.7|
|Indonesia|Contingencybudget for disaster response|Annual|125.8|
|India|CalamityRelief Fund|2000-2005|5.1|
|Japan|Budget for disaster risk reduction|Annual|34,000.0|
|Kazakhstan|For debris flows|1999|200.0|
|Republic of Korea|National EmergencyManagement Agency|Annual|300.0|
|Thailand|Department of Disaster Prevention and Mitigation|2003|25.6|
|Thailand|Department of Disaster Prevention and Mitigation|2006|63.9|
|Thailand|Department of Disaster Prevention and Mitigation|2005|46.0|
|Thailand|Department of Disaster Prevention and Mitigation|2004|32.4|
|Mongolia|Total Buget|2006|12.5|
|Malaysia|Disaster Relief Fund|Annual|15.5|
|Nepal|EmergencyFund|2006|0.015|
|Pakistan|Ten Year Perspective Development Plan|2001-2011|18.8|
|Philippines|National CalamityFund|2005|12.8|
|Russian Federation|Fund for prevention and elimination of emergency<br>situations|2003|687.4|
|Tajikistan|activities for disaster management|Annual|5.5|

Source: Asian Disaster Reduction Center, Country Reports <u>http://www.adrc.asia/</u> 33
Table 12: Climate-Neutralizing Female Education - Students and Costs Republic of Congo, Nepal and Nicaragua

|Year|CSIRO<br>Total<br>Cost<br>($’000)|NCAR<br>Total<br>Cost<br>($’000)|CSIRO<br>New<br>Primary<br>Students<br>(‘000)|CSIRO<br>New<br>Secondary<br>Students<br>(‘000)|NCAR<br>New<br>Primary<br>Students<br>(‘000)|NCAR<br>New<br>Secondary<br>Students<br>(‘000)|Cost Per<br>Primary<br>Student<br>($US)|Cost Per<br>Secondary<br>Student<br>($US)|
|---|---|---|---|---|---|---|---|---|
|||||Republic|of Congo||||
|2010|1,399|0|4.7|4.1|0.0|0.0|97|233|
|2020|4,341|0|10.5|9.8|0.0|0.0|128|307|
|2030|11,868|4,729|21.9|20.9|8.7|8.3|164|395|
|2040|24,179|16,055|32.9|32.4|21.8|21.5|218|524|
|2050|23,501|23,501|24.8|24.7|24.8|24.7|280|672|
|||||Ne|pal||||
|2010|5,935|0|93.6|88.4|0.0|0.0|32|33|
|2020|15,059|0|182.5|183.7|0.0|0.0|40|42|
|2030|27,796|500|274.9|262.4|4.9|4.7|51|53|
|2040|27,237|9,382|198.8|204.6|68.5|70.5|66|69|
|2050|26,494|14,319|151.4|154.8|81.8|83.7|85|88|
|||||Nicar<br>|agua<br>||||
|2010|0|5,889|0.0|0.0|35.9|69.4|89|39|
|2020|0|12,343|0.0|0.0|37.0|155.3|118|51|
|2030|1,788|12,488|6.1|11.8|27.7|116.9|159|69|
|2040|9,425|12,911|21.6|46.0|21.6|81.4|226|99|
|2050|3,695|13,549|6.4|12.4|17.8|58.3|313|137|

34
Table 13: Climate-Neutralizing Female Education - Students and Costs Developing Regions

||CSIRO<br>Total<br>Cost<br>|NCAR<br>Total<br>Cost<br>|CSIRO<br>New<br>Primary<br>Students<br>|CSIRO<br>New<br>Secondary<br>Students<br>|NCAR<br>New<br>Primary<br>Students<br>|NCAR<br>New<br>Secondary<br>Students<br>|
|---|---|---|---|---|---|---|
||($’000)|($’000)|(‘000)|(‘000)|(‘000)|(‘000)|
|Year|||Sub-Saha|ran Africa|||
|2010|179,036|211,757|800|880|981|974|
|2020|562,746|672,806|1,847|2,258|2,422|2,508|
|2030|1,001,736|1,044,923|2,117|2,843|3,038|4,262|
|2040|1,680,756|1,623,282|2,038|3,311|3,383|6,481|
|2050|2,294,642|2,203,969|1,708|3,488|2,967|7,053|
||||Sout|h Asia|||
|2010|528,691|266,339|2,264|2,603|990|1,020|
|2020|1,567,993|784,403|4,354|5,277|1,961|2,024|
|2030|2,983,226|1,771,123|5,129|7,143|2,960|3,139|
|2040|4,101,156|3,502,443|4,470|6,040|3,752|4,056|
|2050|5,446,895|4,853,930|4,277|5,357|3,539|3,681|
|||E|ast Asia and|Pacific Islands|||
|2010|339,783|397,749|1,100|1,237|872|1,276|
|2020|883,470|1,019,321|1,423|2,580|1,561|1,984|
|2030|1,352,575|1,312,094|990|2,681|1,130|1,635|
|2040|2,104,735|1,723,739|768|3,367|820|1,554|
|2050|795,767|2,636,736|241|1,315|780|2,307|
|||Eas|tern Europe|and Central As|ia||
|2010|112,192|150,593|200|246|214|313|
|2020|339,834|479,968|419|436|459|561|
|2030|417,795|727,301|301|359|375|561|
|2040|476,751|868,335|216|265|311|429|
|2050|524959|1554609|148|156|345|447|
||,|,,<br>M|iddle East a|nd North Afric|a||
|2010|60,080|32,367|114|147|46|93|
|2020|171,062|95,127|269|318|101|205|
|2030|327,340|226,553|255|339|135|259|
|2040|573,419|443,764|232|322|181|312|
|2050|891,987|958,291|219|323|244|372|
|||Lat|in America a|nd the Caribbe|an||
|2010|396,497|513,496|465|597|619|959|
|2020|1,016,825|1,266,047|555|1,486|929|1,928|
|2030|617,302|1,187,125|412|833|700|1,687|
|2040|218,728|1,370,353|200|644|526|1,445|
|2050|962,013|1,396,755|365|703|407|1,032|

35
Table 14: Climate-Neutralizing Female Education Global Totals, 2010-2050

|Year|CSIRO<br>Total<br>Cost<br>($Million)|NCAR<br>Total<br>Cost<br>($Million)|CSIRO<br>New<br>Primary<br>Students<br>(‘000)|CSIRO<br>New<br>Secondary<br>Students<br>(‘000)|NCAR<br>New<br>Primary<br>Students<br>(‘000)|NCAR<br>New<br>Secondary<br>Students<br>(‘000)|
|---|---|---|---|---|---|---|
|2010|1,616,279|1,572,299|4,943|5,710|3,721|4,634|
|2020|4,541,929|4,317,672|8,867|12,355|7,433|9,209|
|2030|6,699,975|6,269,119|9,203|14,199|8,339|11,542|
|2040|9,155,545|9,531,917|7,923|13,948|8,973|14,278|
|2050|10,900,000|13,600,000|6,959|11,341|8,282|14,892|

Table 15: Climate-Neutralizing Female Education, 2002-2050 Global and Regional Costs: Selected Discount Rates ($US Billion)

|**Discount**<br>**Rate (%)**|**Global**<br>**Total**|**Global**<br>**Total**|**Sub-Sa**<br>**Afr**|**haran**<br>**ica**|**East A**<br>**Pacific**|**sia and**<br>**Islands**|**Eastern**<br>**and Cen**|**Europe**<br>**tral Asia**|**Latin A**<br>**and Car**|**merica**<br>**ibbean**|**Middle E**<br>**North**|**ast and**<br>**Africa**|**South**|**Asia**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**CSIRO**|**NCAR**|**CSIRO**|**NCAR**|**CSIRO**|**NCAR**|**CSIRO**|**NCAR**|**CSIRO**|**NCAR**|**CSIRO**|**NCAR**|**CSIRO**|**NCAR**|
|**0**|279.4|288.1|46.4|47.0|53.8|57.7|15.8|30.2|26.5|50.6|15.7|12.9|121.2|89.7|
|**3**|110.3|111.1|17.1|17.8|21.7|23.2|6.6|11.7|13.0|22.3|5.7|4.4|46.1|31.7|
|**5**|64.5|64.1|9.5|10.1|12.8|13.8|4.0|6.8|8.8|14.0|3.2|2.3|26.2|17.1|
|**7**|40.2|39.5|5.7|6.1|8.0|8.8|2.6|4.2|6.2|9.3|1.9|1.3|15.8|9.8|

36
# Figure 1: Trends in Risks From Extreme Events, 1960-2000

(Source: CRED: EM-DAT)

- 1a: Risk of Death From Flooding (per Million – Log Scale)

<!-- Start of picture text --> 10.00<br>1.00<br>0.10<br>1960 1970 1980 1990 2000<br><!-- End of picture text -->

- 1b:  Risk of Being Affected by Flooding (Per Million – Log Scale)

<!-- Start of picture text --> 100,000.00<br>10,000.00<br>1,000.00<br>100.00<br>1960 1970 1980 1990 2000<br><!-- End of picture text --> 37
1c: Risk of Death From Drought (Per Million – Log Scale) <!-- Start of picture text --> 1,000.00<br>100.00<br>10.00<br>1.00<br>0.10<br>0.01<br>1960 1970 1980 1990 2000<br><!-- End of picture text -->

- 1d: Risk of Being Affected by Drought (Per Million – Log Scale)

<!-- Start of picture text --> 100,000.00<br>10,000.00<br>1,000.00<br>100.00<br>10.00<br>1.00<br>1960 1970 1980 1990 2000<br><!-- End of picture text --> 38
Figure 2: Number Affected by Droughts in Developing Countries, 1960-2002 (Log Scale) <!-- Start of picture text --> 1,000,000,000<br>100,000,000<br>10,000,000<br>1,000,000<br>100,000<br>10,000<br>1,000<br>100<br>10<br>1<br>1960 1970 1980 1990 2000<br><!-- End of picture text --> Source: CRED: EM-DAT 39
# Fig 3:  India – Historical and Projected Data

<!-- Start of picture text --> GDP Per Capita Life Expectancy<br>5000 80<br>4000<br>70<br>3000<br>2000<br>60<br>1000<br>0 50<br>1970 1990 2010 2030 2050 1970 1990 2010 2030 2050<br>Population Total Fertility Rate<br>1700 6<br>1500 5<br>1300<br>4<br>1100<br>3<br>900<br>700 2<br>500 1<br>1970 1990 2010 2030 2050 1970 1990 2010 2030 2050<br>Mean Monthly Maximum Monthly<br>Precipitation (mm.) Temperature (°C)<br>110 26.5<br>26.0<br>100 NCAR<br>NCAR 25.5<br>90 25.0 CSIRO<br>24.5<br>80<br>CSIRO 24.0<br>70 23.5<br>1970 1990 2010 2030 2050 1970 1990 2010 2030 2050<br><!-- End of picture text --> 40
Figure 4: Extreme Weather Losses, 1970-1999 Historical and Best Practice

# **Killed by Floods**

<!-- Start of picture text --> Historical<br>Best Practice<br><!-- End of picture text --> <!-- Start of picture text --> 30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>1970 1980 1990 2000<br>Affected by Floods (‘000)<br>300,000<br>250,000<br>200,000<br>150,000<br>100,000<br>50,000<br>0<br>1970 1980 1990 2000<br><!-- End of picture text -->

# **Affected by Droughts (‘000)**

<!-- Start of picture text --> 300,000<br>250,000<br>200,000<br>150,000<br>100,000<br>50,000<br>0<br>1970 1980 1990 2000<br>41<br><!-- End of picture text --> \
Figure 5: Loss Risks in India, 2000-2050

# **Killed by Floods (Per Million)**

<!-- Start of picture text --> NCAR<br>Static<br>0. 7<br>Historical<br>0. 6<br>CSIRO<br>0. 5<br>Static<br>0. 4<br>NCAR<br>0. 3 CSIRO Development<br>Development<br>0. 2<br>2000 2010 2020 2030 2040 2050<br>Affected by Floods<br>NCAR<br>Static<br>0.0045<br>Historical<br>0.0035<br>CSIRO<br>Static<br>NCAR<br>0.0025<br>Development<br>CSIRO<br>Development<br>0.0015<br>2000 2010 2020 2030 2040 2050<br>Affected by Droughts<br>CSIRO<br>Static<br>0.055<br>Historical<br>NCAR<br>0.045 Static<br>CSIRO<br>Development<br>NCAR<br>Development<br>0.035<br>2000 2010 2020 2030 2040 2050<br>42<br><!-- End of picture text -->