metadata: authors: 0: author: Buys, Piet
1: author: Dasgupta, Susmita
2: author: Meisner, Craig
3: author: Pand
docna: 0: docna: Measuring up - new directions for
            environmental programs at the World Bank
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Pollution Management & Control,Air Quality & Clean Air,Hydrology,Brown Issues and Health,Inequality,Global Environment
teratopic: Water Resources
abstracts: cdata!: The World Bank's new environment
            strategy advocates cost-effective reduction of air and water
            pollutants that are most harmful to human health. In
            addition, it addresses threats to the livelihood of over one
            billion people who live on fragile lands-lands that are
            steeply sloped, arid, or covered by natural forests. The new
            approach will require accurate information about
            environmental threats to health and livelihood, as well as
            an appropriate resource-allocation strategy. Drawing on
            recent research at the World Bank and elsewhere, this paper
            attempts to apply an optimal investment approach. It
            develops a rule for optimal cross-country resource
            allocation that reflects the Bank's investment policy.
            Using this rule, the paper estimates optimal country shares
            of the Bank's environmental investments from two sets
            of variables: threats from outdoor air pollution, water
            pollution, and fragile lands; and estimates of the
            likelihood that Bank projects will succeed. The paper
            combines the country shares with the Bank's investment
            data to estimate optimal country allocations for each
            environmental problem. Finally, it aggregates the country
            results to allocations for the major regions in which the
            Bank operates. Combining optimal investments for pollution
            and fragile lands, it finds that the largest share of total
            investment goes to East Asia (44 percent), followed by South
            Asia (21 percent) and Sub-Saharan Africa (19 percent). Other
            regions get significantly lower shares.
repnme: repnme: Measuring up - new directions for
            environmental programs at the World Bank
display_title: Measuring up - new directions for
            environmental programs at the World Bank
listing_relative_url: /research/2003/07/2475063/measuring-up-new-directions-environmental-programs-world-bank
topicv3: Water m1326522 137,Poverty m1326687 695,Air Pollution m1327122 1791,Environment, Natural Resources and Blue
            Economy m1327391 566,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Water Resources
origu: Development Research Group (DECRG)
no_of_pages: 48
model_extractions: | POLICY RESEARCH WORKING PAPER 3097

# Abstract

The World Bank's new environment strategy advocates cost-effective reduction of air and water pollutants that are most harmful to human health. In addition, it addresses threats t- .'e livelihood of over one billion people who live on fragile lands-lands that are steeply sloped, arid, or covered, i-" nntur:, forests. The new approach will require accurate information about environmental threats to health and livelihood, as well as an appropriate resource-allocation strategy.

Drawing on recent research at the World Bank and elsewhere, this paper attempts to apply an optimal investment approach. It develops a rule for optimal cross-country resource allocation that reflects the Bank's investment policy. Using this rule, the paper estimates optimal country shares of the Bank's environmental investments from two sets of variables: threats from outdoor air pollution, water pollution, and fragile lands; and estimates of the likelihood that Bank projects will succeed. The paper combines the country shares with the Bank's investment data to estimate optimal country allocations for each environmental problem. Finally, it aggregates the country results to allocations for the major regions in which the Bank operates.

Combining optimal investments for pollution and fragile lands, it finds that the largest share of total investment goes to East Asia (44 percent), followed by South Asia (21 percent) and Sub-Saharan Africa (19 percent). Other regions get significantly lower shares.

This paper-a joint product of Infrastructure and Environment, Development Research Group, and the Environment Department-is part of a larger effort to implement the World Bank's new environment strategy. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Yasmin D'Souza, room MC2-205, telephone 202-473-1449, fax 202-522-3230, email address ydsouza@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pbuys@worldbank.org, sdasgupta@worldbank.org, cmeisner@worldbank.org, kpandey@worldbank.org, dwheeler@worldbank.org, kbolt@worldbank.org, khamilton@worldbank.org, or lwangl@worldbank.org. July 2003. (39 pages) _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 view of the World Bank, its Executive Directors, or the countries they represent._ Produced by Partnerships, Capacity Building, and Outreach
# Measuring Up

New Directions for Environmental Programs at the World Bank* Piet Buys, Susmita Dasgupta, Craig Meisner, Kiran Pandey, David Wheeler Development Research Group Katharine Bolt, Kirk Hamilton, Limin Wang Environment Department

## World Bank

* For valuable comments and suggestions, our thanks to Kristalina Georgieva, Roberto Zagha, Ede Jorge Ijjasz-Vasquez, Magda Lovei, Ken Chomitz, and the members of the World Bank's Environment Sector Board. The research summarized in this paper has been jointly supported by the World Bank's Environment Department and Development Research Group.
# Executive Summary

The World Bank's new Environment Strategy focuses on environmental programs that will improve the well-being of poor people in developing countries. The strategy advocates cost-effective reduction of air and water pollutants that are most harmful to human health. In addition, it addresses threats to the livelihood of over one billion people who live on fragile lands (i.e., lands that are steeply sloped, arid, or covered by natural forests).

The new approach will require accurate information about environmental threats to health and livelihood, as well as an appropriate resource-allocation strategy. Drawing on recent research at the World Bank and elsewhere, this paper attempts to contribute in three ways. First, we develop a rule for optimal cross-country resource allocation that reflects the Bank's investment policy. Using this rule, we estimate optimal country shares of Bank environmental investments from two sets of variables: threats from outdoor air pollution, water pollution and fragile lands; and estimates of the likelihood that Bank projects will succeed. We combine the country shares with Bank investment data to estimate optimal country allocations. Finally, we aggregate our country results to allocations for the major regions in which the Bank operates.

We find that the largest share of total optimal investment goes to East Asia (44%), followed by South Asia (21%) and Sub-Saharan Africa (19%). Other regions get significantly lower shares (respectively 6%, 5% and 5% for Latin America and the Caribbean, Eastern Europe and Central Asia, and North Africa and the Middle East). Within sectors, optimal investment patterns vary significantly. Sub-Saharan Africa gets a large allocation for safe water (34%), exceeded only by East Asia (38%), while South Asia gets 15%. Africa's allocation for cleaner air is strikingly lower (6%), in the same range as the lowest-investment regions, while East Asia (largely China) commands 50% and South Asia 24%. The allocation for natural resource management is close to the overall allocation, with East Asia receiving 44%, South Asia 24%, Sub-Saharan Africa 15%, and the other regions much lower shares.

It would be lucky indeed if the Bank's current investment allocation matched the optimal allocation, for several reasons: The Bank is pursuing a new strategy; we have just developed appropriate environmental threat indices; new measures of project success likelihood have just become available; and the Bank's Environment Department has recently completed its first comprehensive accounting of the Bank's environment portfolio. In a subsequent paper, we will compare our results with the Bank's current portfolio, and explore the implications for resource allocation. Future work may also extend the optimal investment approach to indoor air pollution and biodiversity conservation.

We recognize that the optimal investment approach cannot capture the full complexity of environmental decision-making in the Bank, and we do not claim that our results will provide a comprehensive blueprint for adjustment. Nevertheless we hope that they will make a useful contribution to the discussion of new environmental priorities.

1
# 1. Introduction

The World Bank's commitment to the Millennium Development Goals and its renewed focus on poverty alleviation have had significant impacts on its environment strategy. The new strategy focuses particularly on programs for pollution control and resource conservation that will improve the health and livelihood of the poor in developing countries. Among air pollutants, the scientific consensus attributes most health damage to fine particulate matter (diameter 2.5 microns or less) produced by indoor and outdoor combustion (Holgate, et al., 1999). Among water pollutants, the consensus attributes most health damage to waterbome pathogens (WRI, 1999). Recent research has also identified the vulnerability of people on fragile lands (i.e., land that is steeply-sloped, arid, or covered by natural forest) as a major determinant of rural poverty and natural resource degradation in developing countries (WDR, 2003).

The attribution of so much damage to so few sources may have important strategic implications for the Bank's environmental portfolio. In this paper, we explore the implications by narrowing the focus of decision-making to three critical problems: outdoor fine-particulate air pollution', waterborne pathogens, and the vulnerability of poor people on fragile lands. We develop the analysis in several stages. First, we derive a resource-allocation rule from budget-constrained maximization of an objective function that reflects the Bank's approach to investment. In our results, optimal country investment shares depend on both the scale of environmental problems and the probability of project success.

> ' Indoor fine-particulate air pollution is clearly a major problem as well, but cross-country estimates of its severity and impact are not yet available. Current research at the World Bank is addressing this problem.

2
Second, we develop indices of environmental threats. For air pollution, our measure of problem scale is attributable daly's (disability-adjusted life-year losses from health damage). Our estimates come from recent collaborative research by the World Bank and the World Health Organization (Pandey, et al., 2003). For water pollution, our measure of problem scale is preventable deaths from unsafe water and poor sanitation. The country estimates have been produced by recent research at the World Bank (Wang, et al., 2003). Quantitative studies of poverty-environment links on fragile lands are less advanced, but policymakers and researchers agree that people are particularly vulnerable in such areas. For this reason, our measure of problem scale is the total rural population living on fragile lands. To estimate the affected population, we apply GIS (Geographic Information Systems) techniques to spatial overlays of demographic, topographical, climatic and natural resource information.

Third, we develop country estimates of project success probability. Our information source is a database of over 3,000 project outcome ratings maintained by the Bank's Operations Evaluation Department (OED). For each country, we use the proportion of projects judged satisfactory by OED as an estimate of success probability.

We use our environmental threat measures and success probabilities to compute optimal country investment shares on two bases: Problem scale unadjusted for project success probability, and problem scale adjusted by the OED ratings. To obtain country allocations, we multiply the investment shares by the Environment Department's most recent estimates of total investments for pollution control and natural resource conservation since 1990. We obtain overall optimal allocations by summing the optimal allocations for reducing air pollution, water pollution, and threats to fragile lands.

3
The remainder of the paper is organized as follows. We develop the conceptual model and implied allocation rule in Section 2. In Sections 3 and 4, we introduce<sup>the</sup> measures of problem scale and success probability that are needed to implement<sup>the</sup> model. Section 5 presents our results at the country and regional levels, while Section<sup>6</sup> summarizes the paper.

# **2. Optimal Allocation of Environmental Investments**

We model the welfare impact of World Bank investments as a function of their levels and distributions across countries. Inevitably, the Bank must strike a balance between country representation and global welfare maximization in its resource allocation decisions. We cannot realistically characterize its objective function as<sup>linear</sup> (infinite elasticity of substitution across countries), because sole allocation to one country is infeasible, whatever the relative scale of its problems. Some representation for many countries is implied by the Bank's charter. At the same time, the Bank's objective function is not purely fixed-coefficient (zero elasticity of substitution across countries), because nothing forces it to maintain cross-country parity in per-capita allocation. This is a good thing for the Bank's environment program, since the distribution of environmental problems across countries does not necessarily reflect the distribution of population.

We adopt an intermediate assumption: that the Bank's objective function is characterized by unit-elastic substitution across countries. A unit-elastic (Cobb-Douglas) welfare function permits tailoring of programs to a country's circumstances, while encouraging portfolio diversification through the operation of diminishing returns. Expected welfare gains from Bank investments are related to both the scale of<sup>a country's</sup> environmental problems and the probability that projects will be successful under<sup>local</sup> 4
conditions. The Bank assigns the same opportunity values to human life, health and natural resource savings in all of its partner countries.

For each area of concern (outdoor air pollution, water pollution, fragile lands), we specify the Bank's objective function for damage abatement as: _N_ (1) _W=tCorH A,'_ **i=l** where Ai = Environmental damage abatement in country i cod = Poverty weight assigned to country i For each country, we specify the relevant damage abatement function as:

- (2) _Ai = aoBi (al > 0)_

where Bi = Scale of Bank activity in country i Di = Scale of damage in country i pi = Probability of project success in country i Equation (2) incorporates scale economies: The abatement productivity of Bank activity rises with the scale of existing environmental damage. However, productivity is also sensitive to local conditions that affect project success. To capture this effect, we multiply the base output elasticity of Bank activity (a,Di) by pi.

For the sector (or problem area) in question, the Bank faces a fixed budget constraint and differential unit costs of operating in different countries:<sup>2</sup>

- _N_

- (3) _CiBi IT_

where c; = Unit cost of Bank activity in country i IT = Total sectoral budget

> **2** In this paper, we assume that allocations for specific enviromnental problems are exogenous. In future work, we hope to address the cross-sectoral allocation question, both among environment sectors, and between environment and other sectors.

_5_
Substitution from (2) into (1) yields the following welfare function:

> _(4) W = wo a°Bi" i=,_

> Maximization of W subject to the overall budget constraint yields the following

ratio of optimal Bank allocations to countries i and j: Since co is a poverty weight, we can specify it as a function of income per capita: (6) _wo = 009y (Oi_ < O)

> We also allow for the possibility that project success probability is itself a function of the

> level of development. For the model, we use per capita income as a proxy:

_(7) pi =_ **_, 0yf'_** _(Si >_ 0) Substituting (6) and (7) into (5), we obtain: For country i, we obtain two formulations of the optimal budget share from (5) and (8): In _(5'),_ the country's budget share is equal to the product of its poverty weight (o,i), environmental damage (Di), and project success probability (pi), divided by the sum of products for all of the Bank's partner countries. In (8'), the budget share is equal to the 6
product of environmental damage and the appropriate exponential of per-capita income, divided by the sum of products for all partner countries.

Equations _(5')_ and (8') lend themselves to a variety of uses and interpretations, For example, _(5')_ can be applied to the Bank's loan portfolio, in which case si* is each country's optimal investment share. It can also be applied to the Bank's policy dialogue and technical assistance activities, in which case si* is each country's annual budget share; Bi* is a relevant measure of Bank activity (staff time, etc.), and ci is a countryspecific cost index. Poverty weights ((oi) can be assigned explicitly, or simply assumed to be the same across countries (implying that the Bank assigns equal value at the margin to damage abatement in any country, ceteris paribus).

In equation (8'), the optimal country share depends on environmental damage (Di) and income per capita (yj). If the income elasticities of the poverty weight (0) and project success probability (o1) are equal in absolute value (while opposite in sign), then the optimal budget share in (8') is simply the country's share of total environmental damage.

# 3. **Measures of Environmental Problems (DI)**

# 3.1 **Health Damage from Water Pollution**

Our estimates of health damage from water pollution are based on recent econometric work by Wang, et al. (2003). This approach models health outcomes (measured by the under-five mortality rate) as a function of income; social and environmental variables (female education, immunization coverage, and access to safe water); and policy variables (e.g., share of public health expenditure to GDP). To project lives lost, the econometric estimates are combined with country-level demographic data 7
and estimates of the proportion of the population without access to safe water. For countries where access data are not available, the model uses the population-weighted average level of access for the income groups to which the countries belong. Figure 1 displays the results, which suggest that the greatest number of preventable deaths are in Sub-Saharan Africa, South Asia and East Asia. Countries with relatively low incidence of this problem include Russia and several East European states.

# **3.2 Health Damage From Outdoor Air Pollution**

We attribute health damage from outdoor air pollution to atmospheric contamination by fine particulates. Over time, health research has narrowed its focus from total suspended particulate matter (SPM) to small particles less than 10 microns in diameter (PMIo) and, most recently, to particles whose diameters are less than 2.5 microns (PM2.5). Small particles are likely to be more dangerous because they can be inhaled deeply into the lungs, and because their constituent elements tend to be more chemically active (WHO, 2000; WRI, 1999; Holgate, 1999). At present, atmospheric monitoring in developing countries is limited to SPM and PM10 .

Our health damage estimates come from a recent collaborative project with WHO, which is described in Pandey, et al. (2003). Using an econometrically-estimated model of particulate pollution, we project ambient PM1 O concentrations for 3,226 cities. We use recently-estimated "dose-response" relations to compute the associated mortality and morbidity probabilities by age-sex group for each city; multiply these probabilities by numbers of people in each group; convert the results to daly's, and sum across cities, groups and damage categories in each country to obtain our estimates of total health damage. Figure 2 displays our estimates of total daly losses by country. Health damage 8
is heaviest in the populous countries of South and East Asia and a few African countries.

All regions display a broad pattern of variation.

# **3.3 Vulnerable Populations on Fragile Lands**

> The Bank's World Development Report 2003 has identified the vulnerability of human populations on fragile lands as a critical poverty-environment problem. Approximately 1.4 billion people live on fragile lands that are steeply-sloped, arid, or forested, and many of these people are very poor (WDR, 2003). Research on povertyenvironment links in this context is not highly developed, but policymakers and researchers generally agree that people on fragile lands bear a high risk of natural resource degradation and impoverishment. For this study, we highlight the overall problem by computing total population on fragile lands using GIS techniques. Figure 3 displays the results, which indicate a particularly heavy incidence of this problem in East and South Asia. With some visible exceptions, vulnerable populations are generally smaller in Eastern Europe, Sub-Saharan Africa, and Latin America.

# **4. A Measure of Project Success Probability (pi)**

A country's optimal environmental investment share depends on the probability that a project or program will succeed, as well as the scale of its environmental problems. To estimate country success probabilities, we have drawn on a large database maintained by the World Bank's Operations Evaluation Department (OED). Since 1990, OED has rated the outcomes of over 3,000 World Bank projects in 146 countries. OED rates projects in eight categories: Highly satisfactory, satisfactory, moderately satisfactory, marginally satisfactory, marginally unsatisfactory, moderately unsatisfactory, unsatisfactory, and highly unsatisfactory. We interpret the first four ratings as "successful" for our 9
> probability calculation. Of 3,075 projects rated by OED since 1990, about 70% have

achieved one of the four satisfactory ratings;3 Figure 4 and Table 4.1 display our estimates of project success probability.

Although the estimated probabilities are generally highest in Eastern Europe/Central Asia and lowest in Sub-Saharan Africa, countries in all Bank regions except South Asia exhibit a very wide range of variation. Country estimates are presented in Appendix Table A.6.

**Table 4.1: Distribution of Country Probabilities of Project Success, by Region**

|**Region**|**Mn**|**Median**|Max|
|---|---|---|---|
|Sub-Saharan Africa|**0**|<br>64|100|
|MiddleEast,NorthAfrica|0|<br>71|100|
|SouthAsia|**69**|<br>**71**|100|
|EastAsia,Pacific|33|76|100|
|LatinAmerican,Caribbean|0|<br>76|100|
|EastEurope, CentralAsia|0|<br>83|100|

# **5. Optimal Environmental Investment by Country**

> We develop two estimates of optimal investment for each environmental problem.

In the first (case 1), optimal country investment shares are based entirely on our measures of environmental problems (Di for outdoor air pollution, water pollution, and vulnerable populations on fragile lands). In equation _(5'),_ this is equivalent to assuming that pollution abatement in all countries is given equal weight by the Bank (constant co; across countries) and future projects have the same probability of success in all countries. For

> 3 For six countries with no OED project ratings, we have substituted predictions from a regression of our OED success probability estimates on the World Bank's internal rating of the countries' policy and

> institutional effectiveness. The regression fit is quite good (t-statistic over 7.0), so we have reasonable confidence in the six adjunct estimates. We prefer this approach to exclusion of the six countries, since the

> objective of this exercise is a comprehensive view of investment priorities.

10
convenience we assume that this probability is 1, but any constant probability will yield the same result. In equation (8'), case 1 is equivalent to assuming that the income elasticities of country poverty weights and project success probabilities are equal in magnitude but opposite in sign. This amounts to assuming that institutional and administrative difficulties in poorer countries are counterbalanced by higher weights assigned to abatement, so that only damage matters in the allocation of resources.

The second approach (case 2) uses more information, by incorporating our OEDbased estimate of success probability as the measure of pi in equation _(5')._ For this case, we assume that the Bank assigns equal weight to damage abatement in all partner countries (i.e. all income weights co, are the same).

Recently, the World Bank's Environment Department has completed a detailed accounting of the Bank's environmental projects by sector. We have used this information to estimate current Bank investments by country for air pollution control, improved water and sanitation, and natural resource management. Using our estimated optimal shares, we have distributed total Bank investments in the three problem categories across all developing countries.

Since Figures 1, 2 and 3 represent quintile ranges for the environmental problem measures across countries, they also display the relative size of optimal investment shares for case I (optimal shares equal to shares of environmental problems). Figures 5, 6 and 7 provide the same information by problem for case 2. Figure 8 displays the results when we sum across the three environment sectors to obtain estimates of total optimal environmental investment in case 2. Full results for both cases are reported in Appendix A.
In general, our results suggest that investment orders of magnitude and country rankings are not highly sensitive to our assumptions about pi. For pi = OED (the OED success rate), cross-regional variation is considerably greater than within-region variation. The consequence is general similarity in optimal investment rankings and relative magnitudes for p1=1<sup>4</sup> and pi = OED. The greatest water and sanitation investments are concentrated in Sub-Saharan Africa, South Asia and East Asia. For air pollution reduction, the greatest investments are in South Asia and China. The pattern for natural resource management is more diverse, with some large country investments indicated for all regions except Eastern Europe and Central Asia.

Differences in OED success probabilities are, however, reflected in some patterns of cross-regional allocation. Many African countries, for example, get substantially higher allocations when policy doesn't matter (pi = 1),5 since there is no countervailing weight for poverty in case 2. In contrast, China's allocation is significantly higher when pi = OED.

Figure 8 summarizes the results for total optimal investment when pi = OED. The largest indicated investments are in South and East Asia, Sub-Saharan Africa, and the two largest countries in Latin America (Brazil and Mexico). Lower levels are generally (although not always) indicated for Eastern Europe and Central Asia, Northwest Africa, Western South America, and Central America.

# 6. **Summary and Conclusions**

In this paper, we use several new datasets and a model of World Bank decisionmaking to estimate optimal. environmental investments for the Bank across countries and

> 4 We have chosen **pi** = **I** for expositional clarity, but any constant probability will yield the same result. 5 Examnples are Nigeria, Congo, Somalia, Central African Republic, Congo (DR), and Cameroon.

12
regions. We focus on three environmental problems that have been identified as critical for poor people in developing countries: health damage from outdoor particulate air pollution; health damage from waterbome pathogens; and vulnerability of rural populations on fragile lands.

We base our optimization exercise on a welfare function that makes three basic assumptions about the Bank's decision environment: the desirability of some representation for all partner countries; the importance of relative enviromnental damage across countries; and equal valuation of damage abatement across countries. In the first exercise (case 1), countries' optimal investment shares are simply their shares of total environmental damage. In case 2, we maintain equal valuation of abatement across countries but drop the assumption that all countries have equal likelihood of project success. We estimate country success probabilities from thousands of actual cases reviewed by OED. In case 2, each country's optimal share of total investment is determined by the product of its environmental damage and project success probability.

**Table 6.1: Optimal Investment Shares (%) by Sector (Case 2)**

|**Region**|**Total**|**Water**|**Air**|**Fragile**<br>**Lands**|
|---|---|---|---|---|
|EastAsia,Pacific|44|38|_50_|44|
|SouthAsia|21|15|24|24|
|Sub-SaharanAfrica|19|34|6|15|
|Latin America,Caribbean|6|7|6|_5_|
|East Europe,CentralAsia|_5_|3|9|4|
|North Africa,MiddleEast|_5_|3|5|7|

Our overall results are summarized by region in Table 6.1. We provide a detailed presentation of our country data and results in Appendix A. We find that the largest share of total optimal investment goes to East Asia (44%), followed by South Asia (21%) and Sub-Saharan Africa (19%). Other regions get significantly lower shares (respectively 13
6%, 5% and 5% for Latin America and the Caribbean, Eastern Europe and Central Asia, and North Africa and the Middle East). Within sectors, optimal investment patterns vary significantly. Sub-Saharan Africa gets a large allocation for safe water (34%), exceeded only by East Asia (38%), while South Asia gets 15%. Africa's allocation for cleaner air is strikingly lower (6%), in the same range as the lowest-investment regions, while East Asia (largely China) commands 50% and South Asia 24%. The allocation for natural resource management is close to the overall allocation, with East Asia receiving 44%, South Asia 24%, Sub-Saharan Africa 15%, and the other regions much lower shares.

To illustrate the consequences of introducing project success probabilities, Table 6.2 provides the same regional breakdown for case 1 (project success probabilities assumed to be equal across countries). Table 6.3 shows the change in regional allocations induced by moving from case 1 to case 2. It is clear that the major result of introducing project success probabilities is a net shift from Sub-Saharan Africa to the East-Asia Pacific region.

**Table 6.2: Optimal Investment Shares (%) by Sector (Case 1)**

|**Region**|**Total**|**Water**|**Air**|**Fragile**<br>**Lands**|
|---|---|---|---|---|
|EastAsia,Pacific|37|30|44|37|
|Sub-SaharanAfrica|25|43|9|21|
|SouthAsia|22|15|26|25|
|LatinAmerica,Caribbean|6|7|6|_5_|
|Eastern Europe, CentralAsia|6|3|10|_5_|
|MiddleEast,North Africa|_5_|2|5|8|

14
**Table 6.3: Change in Regional % Shares***

|**Region**|**Total**|**Water**|**Air**|**Fragile**<br>**Lands**|
|---|---|---|---|---|
|Sub-SaharanAfrica|-6|-9|-3|-5|
|EasternEurope, CentralAsia|-1|0|-1|-1|
|SouthAsia|-1|0|-2|-1|
|LatinAmerica,Caribbean|0|0|0|0|
|MiddleEast,NorthAfrica|0|0|0|0|
|East Asia, Pacific|7|8|6|7|

* Some columns do not add to zero because of rounding

In a subsequent paper, we will compare our results with the Bank's current portfolio, and explore the implications for resource allocation. It would be lucky indeed if the Bank's current investment allocation matched the optimal allocation, for several reasons: The Bank is pursuing a new strategy; we have just developed appropriate environmental threat indices; new measures of project success likelihood have just become available; and the Bank's Environment Department has recently completed its first comprehensive accounting of the Bank's environment portfolio. We also recognize that the optimal investment approach cannot capture the full complexity of environmental decision-making in the Bank, and we do not claim that our results will provide a comprehensive blueprint for adjustment. Nevertheless we hope that they will make a useful contribution to the discussion of new environmental priorities.

15
# 8. **References**

Holgate, S., Samet, J., Koren, H., Maynard, R. (ed.), 1999, _Air Pollution and Health,_ San Diego, California: Academic Press.

Pandey, K. D., Bolt, K., Deichmann, U., Hamilton, K., Ostro, B., Wheeler, D., 2003 (forthcoming), "The Human Cost of Air Pollution: New Estimates for Developing Countries," _World Bank Development Research Group Working Paper,_ Washington, DC.

Wang, L., Bolt, K., Hamilton, K., 2003 (forthcoming), "Estimating Potential Lives Saved from Improved Environmental Infrastructure," Environment Department, The World Bank, Washington, D.C.

World Bank, 2003, _World Development Report 2003: Sustainable Development in a Dynamic World,_ Washington: World Bank/Oxford University Press.

World Health Organization (WHO), 2000, _Guidelines for Air Quality,_ WHO, Geneva. Available at <http://www.who.int/peh/air/Airqualitygd.htm>).

World Resources Institute (WRI), 1999, _World Resources 1998-1999: A Guide to the Global Environment: Environmental Change and Human Health,_ New York, NY: Oxford University Press.

16
# Appendix A: Data and Results Tables*

- Al: Optimal Environmental Investments by Region and Country

- A2: Optimal Water and Sanitation Investments by Region and Country

- A3: Optimal Air Pollution-Related Investments by Region and Country

- A4: Optimal Natural Resource Management Investments by Region and Country

- A5: Environmental Problem Indices by Region and Country

- A6: OED Success Rates by Region and Country

- Missing data have led to some missing values in the optimal investment tables; Investments below.$.09 million are entered as 0.0.

17
**Table A.1: Optimal Environmental Investment by Region and Country ($Million)**

|**Sub-Saharan**||**]_,**|**Latin**|**P**|**East**|**Europe,**|]||
|---|---|---|---|---|---|---|---|---|
|**Africa**|**OED**|**1**|**Caribbean**|**OED**|**1**|**CentralAsia**|**OED**|**1**|
|Ethiopia|758.5|810.5|Mexico|361.2|318.0|Turkey|256.5|295.8|
|Nigeria|688.5|1,083.4|Brazil|296.9|282.6|Ukraine|172.9|153.6|
|Tanzania|287.7|254.0|Argentina|183.2|182.1|Russia|163.9|238.5|
|Uganda|203.0|207.6|Peru|122.0|108.5|Poland|80.3|77.2|
|<br>Sudan|164.9|206.2|Colombia|56.9|64.9|Uzbekistan|70.6|103.8|
|South Africa|163.5|151.1|Ecuador|52.1|44.3|Romania|52.9|48.0|
|Congo (DR)|157.9|555.7|Chile|51.0|43.6|Georgia|48.6|37.0|
|Angola|145.7|164.4|Bolivia|49.8|42.5|Bulgaria|45.7|45.7|
|Mozambique|135.3|117.9|Guatemala|29.0|33.2|Kazakhstan|45.6|40.0|
|Kenya|129.0|226.9|Haiti|28.9|56.8|Azerbaijan|35.3|39.3|
|Madagascar|123.7|126.3|ElSalvador|26.4|21.9|Armenia|30.1|25.0|
|<br>Chad|108.0|103.1|Dom.Rep.|23.8|25.6|Kyrgyz Republic|27.7|22.0|
|BurkinaFaso|99.6|86.9|Venezuela|19.3|35.0|Tajikistan|27.5|27.9|
|Niger<br>|98.9|120.5|Uruguay|19.2|14.8|Yugoslavia|19.4|19.3|
|Ghana|94.5|93.4|Nicaragua|17.9|15.5|Hungary|14.5|12.4|
|Mali|85.6|93.1|Honduras|17.0|17.6|Bosnia-Herz.|13.8|10.6|
|Zimbabwe|75.4|65.0|Paraguay|13.4|19.3|Lithuania|10.3|8.9|
|Coted'lvoire|72.4|88.0|CostaRica|11.9|8.9|Albania|10.2|8.5|
|Senegal|71.3|66.7|Panama|10.5|7.6|CzechRepublic|9.2|6.6|
|<br>Eritrea|64.6|46.0|Jamaica|8.7|10.4|<br>Moldova|7.6|8.7|
|Guinea|62.0|74.1|Guyana|2.0|2.1|Slovakia|6.1|4.5|
|Zambia|55.1|73.5|Trin.,Tobago|1.9|2.1|Croatia|5.5|6.1|
|Malawi|54.8|72.1|Belize|1.0|0.9|Latvia|4.8|*3.6|
|Benin|45.5|41.7|St.Vinc.,Gren.|0.4|0.3|Cyprus|4.2|3.7|
|Rwanda|37.9|82.9|Bahamas|0.3|0.5|Estonia|3.3|2.4|
|SierraLeone|37.0|64.7|Grenada|0.3|0.2|FYR Macedonia|3.0|.<br>3.3|
|Cameroon|36.3|114.3|St.Lucia|0.2|0.2|Slovenia|2.9|2.6|
|Mauritania|32.0|34.2|Dominica|0.0|0.0|Belarus|0.7|0.8|
|Burundi|23.3|30.2|Barbados|0.0|0.0|Turkmenistan|0.0|30.1|
|Somalia|19.3|68.9|St.Kitts,Nevis|0.0|0.0|**Total**|**1,173.1**|**1,286.9**|
|Togo|17.7|33.7|Total|1,405.2|1,360.4||||
|GuineaBissau|11.2|11.5|||||||
|Namibia|10.1|10.2|||||||
|Gambia|7.7|10.1|||||||
|Swaziland|7.3|5.2|||||||
|Lesotho|7.1|9.0|||||||
|Cen.Afr. Rep.|7.0|24.9|||||||
|Botswana|4.9|5.2|||||||
|Congo|3.5|38.3|||||||
|Gabon|2.8|4.8|||||||
|CapeVerde|1.7|1.2|||||||
|Equatorial Guinea|1.5|4.1|||||||
|<br>Comoros|0.8|1.7|||||||
|Djibouti|0.5|0.5|||||||
|<br>SaoTome, Principe|0.4|0.6|||||1||
|<br>Seychelles|0.1|0.1|||||_||
|<br>Liberia|0.0|16.0|||||||
|Mauritius|0.0|0.0|||||||
|**Total**|**4,215.5**|**5,501.4**|||||||

18
**Table A.1: Optimal Environmental Investment by Region and**<sup>**Country ($Million)**</sup>

|**EastAsia,**<br>|**P**<br>|**i**<br>|**South**<br>|**Pi**<br>|<br>|**North Africa,**<br>|**P**<br>|**i**<br>|
|---|---|---|---|---|---|---|---|---|
|**Pacific**|**OED**|**I**<br>|**Asia**<br>|**OED**<br>|**1**<br>|**MiddleEast**<br>|**OED**<br>|**1**<br>|
|China|7,344.9|5,993.5|India<br>|3,568.6<br>|3,740.5<br>|Egypt<br>|371.1<br>|394.2<br>|
|Indonesia|903.1|864.2|Pakistan|708.9<br>|741.3<br>|Iran<br>|260.4<br>|227.5<br>|
|Vietnam|532.2|379.8|Bangladesh|175.5|170.5|Yemen|140.6<br>|149.0<br>|
|Thailand|195.8|156.5|Nepal|82.0|84.1|Morocco|99.9|114.7|
|Myanmar|193.7|216.0|SriLanka|46.0|48.3|Algeria|73.9<br>|117.2<br>|
|Philippines|170.8|171.3|Bhutan|9.3|6.6|Tunisia<br>|36.5<br>|33.3<br>|
|Korea|148.2|124.9|Maldives|0.3|0.3|Oman|27.2|19.3|
|Cambodia|114.0|99.0|Total|4,590.6|4,792.6|Jordan<br>|19.6<br>|16.3<br>|
|Malaysia|67.3|51.9||||WestBank,Gaza|10.4|7.1|
|PapuaNewGuinea|27.9|41.6|.|||Lebanon<br>|0.0<br>|7.3<br>|
|Lao PDR|19.2|20.3||||WesternSahara<br>|0.0<br>|0.0<br>|
|Mongolia|12.6|10.3||||**Total**|**1,039.6**|**1,086.9**|
|Fiji|5.1|3.5|____._|_|||,|,|
|Solomon Islands|1.7|2.5|||||||
|Kiribati|0.4|0.4|||||||
|Vanuatu|0.3|0.7|||||||
|Samoa|0.0|0.0|||_||||
|EastTimor|0.0|0.0|_._||.||||
|Micronesia|0.0|0.0_|<br>_||||||
|MarshallIslands|0.0|0.0|||||||
|Tonga|0.0|0.0|||||||
|**Total**|**9,737.2**|**8,137.4**|||||||

19
**Table A.2: Optimal Water and Sanitation Investment by Region and Country ($Million)**

|**Sub-Saharan**|||**Latin**|**P**|**i**|**EastEurope,**|**P**|**i**|
|---|---|---|---|---|---|---|---|---|
|**Africa**|**OED**|**1**|**Camribben**|**OED**|**1**|**CentralAsia**|OE7D|1|
|Ethiopia|613.6|644.0|Brazil|142.9|128.7|Turkey|65.3|69.1|
|<br>Nigeria|423.1|639.6|Mexico|135.9|111.9|Ukraine|38.3|31.0|
|<br>Tanzania|212.4|183.1|Peru|51.2|42.8|Romania|32.8|28.6|
|Uganda|151.9|152.3|Argentina|47.2|42.7|Uzbekistan|19.8|27.1|
|<br>Congo(DR)|110.0|376.6|<br>Ecuador|32.5|26.7|Poland|17.2|15.0|
|<br>Angola|104.2|114.1|Colombia|24.9|27.0|Kyrgyz Republic|9.9|7.5|
|Madagascar|99.5|99.8|Haiti|21.0|40.2|Tajikistan|9.9|9.5|
|Mozambique|99.2|84.1|Bolivia|20.7|16.6|Azerbaijan|9.1|9.4|
|<br>Kenya|90.3|154.5|ElSalvador|16.3|13.0|Yugoslavia|8.2|7.7|
|<br>Chad|79.2|73.6|Venezuela|14.9|26.6|<br>Kazakhstan|7.2|5.8|
|Sudan|68.0|80.4|Dom.Rep.|12.1|12.4|Georgia|5.2|3.6|
|Ghana|65.4|62.9|Nicaragua|11.8|9.9|CzechRepublic|4.9|3.4|
|Niger|57.2|67.1|<br>Guatemala|8.5|9.1|Albania|4.7|3.7|
|South Africa|54.2|47.2|Paraguay|7.5|10.2|Bulgaria|4.4|4.0|
|Mali|49.7|52.0|<br>Honduras|6.3|6.2|<br>Armenia|4.2|3.2|
|Malawi|46.6|60.4|Chile|6.2|4.8|Bosnia-Herz.|4.2|3.0|
|BurkinaFaso|43.0|35.6|Jamaica|4.4|5.0|Russia|2.8|3.7|
|Guinea|39.5|45.5|Panama|3.5|2.4|Lithuania|2.7|2.2|
|Eritrea|36.7|25.1|Trin.,Tobago|0.8|0.8|Latvia|1.6|1.1|
|Coted'lvoire|34.6|39.9|CostaRica|0.7|0.5|Croatia|0.7|0.7|
|Zambia|34.4|44.0|Belize|0.6|0.5|Estonia|0.7|0.4|
|Benin|30.8|27.4|Uruguay|0.5|0.3|Hungary|0.4|0.3|
|Rwanda|30.1|64.8|<br>Guyana|0.3|0.3|<br>FYRMacedonia|0.1|0.1|
|SierraLeone|28.0|47.9|St.Vinc.,Gren.|0.1|0.0|Turkmenistan|0.0|14.6|
|Senegal|26.8|23.5|<br>Grenada,|0.1|0.0|Belarus|0.0|0.0|
|Zimbabwe|21.4|17.2|St.Lucia|0.0|0.0|Moldova|0.0|0.0|
|Cameroon|21.0|63.2|St.Kitts,Nevis|0.0|0.0|Slovakia|0.0|0.0|
|Mauritania|20.3|20.8|Barbados|0.0|0.0|Cyprus|||
|Burundi|16.1|20.3|Bahamas|||Slovenia|||
|Togo|12.9|23.9|Dominica|||**Total**|**254.3**|**255.7**|
|Somalia|9.4|32.1|Total|570.9|539.6||||
|Guinea Bissau|7.8|7.7|_||||||
|Gambia|4.7|6.0|||||||
|Cen.Afr.Rep.|4.6|15.9||||_|||
|Namibia|4.4|4.2|||||||
|Swaziland|3.3|2.2|||||||
|Gabon|2.3|3.9|||||||
|Botswana|1.9|2.0|1||||||
|Congo|1.8|18.8|||||||
|Cape Verde|1.7|1.2|||||||
|Lesotho<br>|1.6|1.8|||||||
|EquatorialGuinea|1.1|31|||||||
|<br>Sao Tome,Principe|<br>0.3|.<br>0.4|||||||
|Comoros|0.1|0.2|||||||
|<br>Seychelles|<br>0.1|0.1|||||||
|<br>Liberia|0.0|10.0|||||||
|Djibouti|0.0|0.0|||||||
|Mauritius|0.0|0.0|||||||
|**Total**|**2,765.2**|**3,501.4**|||||||

20
Table A.2: Optimal Water and Sanitation Investment by Region and Country ($Million)

|EastAsia,||P|South|**P**|**i**|NorthAfrica,|**P**|**i**|
|---|---|---|---|---|---|---|---|---|
|Pacific|OED|1|Asia|OED|1|MiddleEast|OED|1|
|China|1,958.2|1,476.7|India|974.1|946.2|Yemen|63.3|63.9|
|Indonesia|376.9|339.6|Pakistan|177.1|171.0|Morocco|35.0|38.0|
|Vietnam|307.1|210.2|Nepal|44.0|43.4|Iran|26.1|20.8|
|Myanmar|109.3|116.3|Bangladesh|36.3|32.2|Egypt|25.1|24.2|
|Cambodia|95.2|81.5|SriLanka|17.5|17.2|Oman|19.0|13.0|
|Philippines|84.9|80.4|Bhutan|5.0|3.4|Tunisia|11.9|_<br>10.2|
|Thailand|80.7|60.6|Maldives|0.0|0.0|WestBank,Gaza|10.4|7.1|
|Malaysia|29.7|21.8|**Total**|**1,254.0**|**1,214.4**|Algeria|9.5|13.8|
|Korea|20.0|15.3||||Jordan|2.3|1.8|
|Papua New Guinea|19.3|28.1||||Lebanon|0.0|0.0|
|Mongolia|7.8|6.1|_|||WesternSahara|||
|<br>LaoPDR|5.9|5.9||||Total|**202.6**|**193.8**|
|Fiji|4.4<br>|3.0<br>|||||||
|SolomonIslands|1.1|_1.5_|||||||
|Kiribati|0.4|0.4|||||||
|Vanuatu|0.1|0.2|||||||
|Samoa<br>l|0.0|0.0|||||||
|Tonga|0.0|0.0|||||||
|<br>EastTimor|||||||||
|Micronesia__|||||||||
|MarshallIslands|||||||||
|Total|**3,101.0**|**2,448.6**|||||||

21
**Table A.3: Optimal Air Pollution-Related Investment by Region and Country ($MiHlion)**

|**Sub-Saharan**<br>|<br>|**P**<br>|**LatinAmerica,**<br>|<br>|**P**|<br>|**EastEurope,**<br>||**I**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**Africa**|**OED**|**I**|**Caribbean**|**OED**||**i**|**CentralAsia**|**OED**|**1**|
|Nigeria|129.2|221.2|Argentina|129.9||133.3|Turkey|148.5|177.9|
|Ethiopia|36.6|43.6|Mexico|129.4||120.7|Russia|123.5|181.9|
|Sudan|32.6|43.7|Brazil|83.7||85.4|Ukraine|117.1|107.3|
|Senegal|22.9|22.7|Chile|38.7||33.7|Poland|61.0|60.2|
|SouthAfrica|22.4|22.1|Peru|38.0||36.0|Bulgaria|38.7|39.2|
|Angola<br>|20.2<br>|25.0<br>|Uruguay<br>|18.5<br>||14.3<br>|Georgia<br>|37.0<br>|28.7<br>|
|Cote d'lvoire|18.4|24.0|Bolivia|15.6||14.2|Uzbekistan|21.7|33.6|
|Zimbabwe<br>|17.4<br>|15.9<br>|Colombia<br>|7.7||9.5<br>|Armenia<br>|20.3<br>|17.3|
|Tanzania|i7.1|16.7|Guatemala|4.4||5.4|Azerbaijan|16.1|18.8|
|Mozambique|16.3|15.6|Ecuador|4.2||3.9|Kazakhstan|13.4|12.3|
|BurkinaFaso|14.0|13.1|Dom.Rep.|3.9||4.5|Romania|10.5|10.3|
|Zambia|13.4|19.3|Paraguay|3.3||5.2|Hungary|9.7|8.4|
|Chad|12.1|12.8|CostaRica|3.1||2.4|Lithuania|5.7|5.2|
|Congo(DR)|11.5|44.7|ElSalvador|2.7||2.5|Bosnia-Herz.|4.0|3.3|
|Mali|9.6|11.4|Panama|2.6||2.0|Yugoslavia|4.0|4.2|
|Niger|8.2|10.9|Honduras|2.6||2.9|Moldova|3.9|4.6|
|Guinea|7.9|10.3|Haiti|1.9||4.1|Tajikistan|3.0|3.2|
|Kenya|7.4|14.3|Nicaragua|1.6||1.5|CzechRepublic|3.0|2.3|
|Mauritania|7.3|8.5|Jamaica|1.4||1.8|KyrgyzRepublic|2.6|2.2|
|Cameroon|7.0|23.9|Venezuela|0.4||0.7|Latvia|2.3|1.8|
|Madagascar|6.6|7.5|Bahamas|0.3||0.4|Cyprus|2.3|2.0|
|Ghana|6.3|6.8|Guyana|0.3||0.3|Croatia|2.2|2.6|
|Eritrea|5.7|4.4|<br>St.Lucia|0.1||0.1|Slovakia|1.4|**1.1**|
|Benin|4.4|4.4|St.Vinc.,Gren.|0.0||0.0|FYRMacedonia|1.4|1.5|
|Malawi|2.5|3.7|Grenada|0.0||0.0|Estonia|0.8|0.6|
|Sierra Leone<br>|2.2<br>|4.3<br>|Trin.,Tobago<br>|0.0<br>||0.0<br>|Slovenia<br>|0.7<br>|0.7<br>|
|GuineaBissau|2.1|2.4|Belize|0.0||0.0|Albania|0.6|0.5|
|Gambia|_1.5_|2.1|Barbados|0.0||0.0|Turkmenistan|0.0|4.0|
|Lesotho|1.4|1.9|Dominica|0.0||0.0|Belarus|0.0|0.0|
|Congo|1.4|16.5|St.Kitts,Nevis|0.0||0.0|**Total**|**655.4**|**736.7**|
|Togo|1.2|2.6<br>|**Total**|**494.3**||**485.8**||||
|Namibia|0.9|1.0||||||||
|Cen.Afr. Rep.|0.8|3.3||||||||
|Somalia|0.8|3.1||||||||
|Burundi|0.6|0.9||||||||
|Rwanda|0.3|0.8||||||||
|Swaziland|0.3|0.2||||||||
|Gabon|0.2|0.4||||||||
|Sao Tome, Principe|0.1|0.1||||||||
|Uganda|0.1|0.1||=||||||
|Comoros|0.1|0.2||_||||||
|Liberia|0.0|_1.5_||||||||
|Botswana||||||||||
|CapeVerde||||||||||
|Djibouti<br>||||||||||
|EquatorialGuinea<br>|||||||_|||
|Mauritius<br>||||_||||||
|Seychelles||||||||||
|**Total**|**471.0**|**688.9**||||||||

22
**Table A.3: Optimal Air Pollution-Related Investment by Region and Country ($Million)**

|**EastAsia,**|**P**||**South**|**P**||**Middle East,**|**P**||
|---|---|---|---|---|---|---|---|---|
|**Pacific**|**OED**|**I**|**Asia**|**OED**|**I**|**NorthAfrica**|**OED**|**I**|
|China|3,206.8|2,739.2|India|1,388.7|1,527.9|Egypt|183.9|200.9|
|Indonesia|256.9|262.2|Pakistan|303.7|332.2|Iran|105.2|95.2|
|Korea|99.2|85.7|Bangladesh|98.1|98.7|Algeria|28.6|47.2|
|Vietnam|84.0|65.2|SriLanka|12.3|13.8|Yemen|16.5|18.9|
|Thailand|62.9|53.5|Nepal|3.0|3.3|Morocco|12.1|14.9|
|Philippines|58.0|62.3|Maldives|0.3|0.3|Jordan|9.8|8.4|
|Myanmar|44.2|53.3|Bhutan|0.2|0.1|Tunisia|6.8|6.6|
|Malaysia|4.2|3.5|**Total**|**1,806.3**|**1977.3**|Oman|6.0|4.7|
|Cambodia|3.0|2.9||,|,|Lebanon|0.0|5.7|
|Mongolia|2.8|2.5||||West Bank,Gaza|||
|LaoPDR|1.7|1.9||||WesternSahara|||
|Fiji|_0.3|0.3||||**Total**|**368.9**|**403.5**|
|PapuaNewGuinea|0.2|0.3|||||||
|Solomon Islands|0.0|0.1|||||||
|Vanuatu|0.0|0.0|||||||
|EastTimor|||_||||||
|Micronesia||.|||||||
|Kiribati|||||||||
|MarshallIslands|||||||||
|Samoa|||||||||
|Tonga|||||||||
|**Total**|**3,824.2**|**3,333.9**|||||_||

23
**Table A.4: Optimal Natural Resource Management Investment by Region and Country ($Million)**

|**Sub-Saharan**<br>**Africa**<br>|<br>|**PI**<br>|**Latin**<br>**America,**<br>|**P**<br>|**i**<br>|**EastEurope,**<br>**CentralAsia**|**P**<br>|**i**<br>|
|---|---|---|---|---|---|---|---|---|
|**Africa**|**OED**|**1**|<br>**Caribbean**|**OED**|**1**||**OED**|**1**|
|Nigeria|136.2|222.6|Mexico|95.9|85.4|Turkey|42.7|48.8|
|Ethiopia|108.2|122.8|Brazil|70.3|68.5|Russia|37.6|52.9|
|South Africa|86.9|81.8|Peru|32.8|29.7|Uzbekistan|29.2|43.2|
|Sudan|64.2|82.1|Colombia|24.2|28.4|Kazakhstan|25.0|21.9|
|Tanzania|58.2|54.3|Guatemala|16.1|18.7|Ukraine|17.4|15.2|
|Uganda|51.0|55.3|Ecuador|15.4|13.7|KyrgyzRepublic|15.1|12.3|
|BurkinaFaso|42.6|38.2|Bolivia|13.5|11.7|Tajikistan|14.6|15.1|
|Zimbabwe|36.6|31.9|Honduras|8.1|8.6|<br>Azerbaijan|10.0|11.1|
|Congo(DR)<br>|36.3<br>|134.4<br>|CostaRica|8.1<br>|6.0|Romania<br>|9.7|9.1|
|Niger<br>|33.5<br>|42.5<br>|Dom.Rep.<br>|7.9|8.7<br>|Yugoslavia<br>|7.2<br>|7.3|
|Kenya|31.4|58.1|ElSalvador|7.4|6.4|Georgia|6.4|4.7|
|Mali|26.3|29.8|Argentina|6.2|6.0|Bosnia-Herz.|5.6|4.4|
|Ghana|22.8|23.7|Chile|6.1|5.1|Armenia|5.6|4.6|
|Eritrea|22.3|16.5|Haiti|6.0|12.5|Albania|5.0|4.3|
|Senegal|21.6|20.5|Nicaragua|4.5|4.1|Slovakia|4.7|3.5|
|Angola|21.3|25.2|Panama|4.3|3.2|Hungary|4.5|3.7|
|Mozambigue|19.9|18.2|Venezuela|4.0|7.7|Moldova|3.7|4.1|
|Coted'lvoire|19.4|24.1|Jamaica|2.9|3.6|Bulgaria|2.6|2.5|
|Madagascar|17.6|19.1|Paraguay|2.6|3.8|Croatia|2.6|2.8|
|Chad<br>|16.7<br>|16.7<br>|Guyana<br>|1.4<br>|1.5<br>|Slovenia<br>|2.1<br>|1.9<br>|
|Guinea|14.6|18.3|Trin.,Tobago|1.2|1.3|Poland|2.1|1.9|
|Benin<br>|10.3<br>|9.9<br>|Belize<br>|0.4<br>|0.4<br>|Cyprus<br>|1.9<br>|1.6<br>|
|Somalia|9.1|33.7|St.Vinc.,Gren.|0.3|0.2|Lithuania|1.8|1.6|
|Cameroon|8.3|27.2|Grenada|0.2|0.1|Estonia|1.8|1.3|
|Rwanda|7.4|17.3|Uruguay|0.2|0.1|FYRMacedonia|1.5|1.6|
|Zambia|7.4|10.2|St.Lucia|0.1|0.1|CzechRepublic|1.3|1.0|
|Sierra Leone|6.7|12.4|Bahamas|0.1|0.1|Latvia|0.9|0.7|
|Burundi|6.5|8.9|Dominica|0.0|0.0|Belarus|0.7|0.8|
|Malawi<br>|5.7|8.0<br>|St.Kitts,Nevis|0.0|0.0|Turkmenistan|0.0|11.6|
|Namibia|4.8|5.0|Barbados|0.0|0.0|**Total**|**263.3**|**296.5**|
|Mauritania<br>|4.4<br>|4.9<br>|**Total**|**340.2**|**336.6**||||
|Lesotho|4.1|5.2|||||||
|Swaziland|3.8|2.8|||||||
|Togo|3.6|7.2|||||||
|Botswana|2.9|3.2|||||||
|Cen.Afr. Rep.|1.5|5.7|||||||
|Gambia|1.5|2.0|||||||
|GuineaBissau|1.3|1.4|||||||
|Comoros|0.6|1.3|||||||
|Djibouti|0.5|0.5|||||||
|EquatorialGuinea|0.3|1.0|||||||
|Gabon|0.3|0.5|||||||
|Congo|0.3|3.0|||||||
|SaoTome,Principe|0.1|0.1|||||||
|Liberia|0.0|4.5|||||||
|CapeVerde|||||||||
|Mauritius|||||||||
|Seychelles|||||||||
|**Total**|**979.0**|**1,313.0**|||||||

**Table A.4: Optimal Natural Resource Management Investment**

24
# **by Region and Country ($Mfltion)**

|**EastAsia,**|**P**|**i**|**South**|**P**|**i**|**NorthAfrica,**|**Pi**||
|---|---|---|---|---|---|---|---|---|
|**Pacific**|**OED**|**1**|**Asia**|**OED**|**1**|**MiddleEast**|**OED**|**1**|
|China|2,180.0|1,777.5|India|1,205.8|1,266.4|Egypt|162.1|169.1|
|Indonesia|269.3|262.4|Pakistan|228.0|238.1|Iran|129.1|111.5|
|Vietnam|141.1|104.5|Bangladesh|41.1|39.5|Yemen|60.7|66.2|
|Thailand|52.3|42.4|Nepal|35.0|37.3|Morocco|52.8|61.8|
|Myanmar|40.2|46.3|SriLanka|16.2|17.3|Algeria|35.8|56.3|
|Malaysia|33.5|26.6|Bhutan|4.1|3.1|Tunisia|17.7|16.4|
|Korea|29.1|24.0|Maldives|||Jordan|7.5|6.2|
|Philippines|27.9|28.6|**Total**|**1,530.2**|1,602.7|Oman|2.1|1.6|
|Cambodia|15.8|14.6||,||Lebanon|0.0|1.6|
|LaoPDR|11.5|12.4||||WestBank,Gaza|||
|PapuaNewGuinea|8.4|13.3||||WesternSahara|||
|Mongolia|2.0|1.7||||**Total**|**467.8**|**491.7**|
|SolomonIslands|0.6|0.9|||||||
|Fiji|0.3|0.2||_|||||
|Vanuatu|0.2|0.5|||||||
|EastTimor|||||||||
|Micronesia|||||||||
|Kiribati|||||||||
|Marshall Islands|||||||||
|Samoa|||||||||
|Tonga|||||||||
|**Total**|**2,812.2**|**2,356.9**|||||||

25
**Table A.5: OED Project Success Rates by Region and Country**

|**Sub-Saharan**<br>|**OED**<br>|**LatinAmerica,**<br>|**OED**<br>|**East Europe,**<br>|**OED**<br>|
|---|---|---|---|---|---|
|**Africa**|**Rate**|**Caribbean**|**Rate**|**CentralAsia**|**Rate**|
|CapeVerde|100.0|Costa Rica|100.0|Estonia|100.0|
|Eritrea|100.0|St.Vinc.,Gren.|100.0|CzechRepublic|100.0|
|Swaziland|100.0|Uruguay|100.0|Latvia|100.0|
|Zimbabwe|85.0|<br>Grenada|100.0|Slovakia|100.0|
|BurkinaFaso<br>|82.6|Panama<br>|100.0|Georgia<br>|100.0|
|Mozambique|80.8|Dominica|100.0|Bosnia-Herz.|95.7|
|Tanzania|79.4|Chile|88.9|Armenia|90.9|
|SouthAfrica|78.6|ElSalvador|85.7|KyrgyzRepublic|90.9|
|Senegal<br>|78.1<br>|Bolivia<br>|85.3<br>|Hungary<br>|89.2<br>|
|Benin<br>|76.9<br>|Belize<br>|83.3<br>|Cyprus<br>|87.5<br>|
|Djibouti|75.0|Ecuador|83.3|Albania|86.4|
|Chad|73.7|Mexico|83.1|Lithuania|85.7|
|Ghana|71.2|Peru|81.8|Ukraine|84.6|
|Namibia|70.7|Nicaragua|81.8|Kazakhstan|84.6|
|GuineaBissau<br>|68.8<br>|<br>Brazil<br>|76.0<br>|Slovenia<br>|83.3<br>|
|Uganda<br>|68.3<br>|Argentina<br>|75.5<br>|Poland<br>|78.6<br>|
|Madagascar|68.3|Honduras|70.0|Romania|78.6|
|Botswana|66.7|Guyana|70.0|Bulgaria|76.5|
|Mauritania|66.7|Trin., Tobago|66.7|Yugoslavia|72.7|
|Seychelles|66.7|St.Lucia|66.7|Tajikistan|71.4|
|Mali|65.4|Barbados|66.7|FYRMacedonia|70.0|
|Ethiopia|65.2|Dom. Rep.|66.7|Croatia|66.7|
|Mauritius<br>|63.6<br>|Guatemala<br>|63.6<br>|Moldova<br>|66.7<br>|
|Angola|62.5|Colombia|63.2|Azerbaijan|66.7|
|Coted'lvoire|59.4|Jamaica|60.0|Turkey|64.7|
|Guinea|59.4|Paraguay|50.0|<br>Belarus|60.0|
|Lesotho|58.3|Bahamas|50.0|Russia|52.6|
|Niger|58.3|Venezuela|38.5|Uzbekistan|50.0|
|Sudan|57.9|Haiti|35.7|Turkmenistan|0.0|
|Burundi|54.2|St.Kitts,Nevis|0.0|**Average**|**77.7**|
|Gambia<br>|53.8<br>|Cuba<br>||||
|Zambia|53.6|Suriname||||
|Malawi|52.8|Ant.,Barbuda||||
|SaoTome, Principe|50.0|Aruba||||
|Nigeria|45.3|Anguilla||||
|Kenya|40.0|Neth.Antilles||||
|SierraLeone|40.0|Bermuda||||
|Gabon|40.0|Cayman Islands||||
|Togo|36.8|FalklandIs.||||
|Comoros|33.3|Guadeloupe||||
|Rwanda|31.8|French Guiana||||
|EquatorialGuinea|25.0|Montserrat||||
|Cameroon|22.7|Martinique||||
|Congo(DR)|20.0|PuertoRico||||
|Cen.Afr.Rep.|20.0|USVirginIs.||||
|Somalia|20.0|**Average**|**73.1**|||
|Congo|6.7|||||
|Liberia|0.0|||||
|Mayotte||||||
|Reunion||||||
|SaintHelena||||.||
|**Average**|**57.4**|||||

26
**Table A.5: OED Project Success Rates by Region and Country**

|**EastAsia,**|**OED**|**South**|**OED**|**MiddleEast,**|**OED**|
|---|---|---|---|---|---|
|**Pacific**|**Rate**|**Asia**|**Rate**|**North Africa**|**Rate**|
|EastTimor|100.0|Bhutan|100.0|Oman|100.0|
|Samoa|100.0|Maldives|100.0|W.Bank,Gaza|100.0|
|Vietnam|100.0|Bangladesh|77.0|Jordan|90.3|
|Fiji|100.0|Pakistan|70.9|Iran|85.7|
|Malaysia|93.1|India|70.5|Tunisia|80.0|
|Thailand|91.2|SriLanka|69.4|Egypt|71.0|
|China|90.8|Nepal|69.4|Yemen|67.9|
|Korea, Republicof|89.7|Afghanistan||WesternSahara|66.7|
|Mongolia|87.5|**Average**|**79.6**|Morocco|63.2|
|Cambodia|80.0|||Algeria|47.1|
|Indonesia|76.0|||Lebanon|0.0|
|Philippines|72.2|||Iraq||
|LaoPDR|68.8|||SaudiArabia||
|Myanmar|64.3|||Syria||
|Marshall Islands|59.1|||Unit.ArabEmir.||
|Micronesia|58.6|||Israel||
|Kiribati|57.2|||Kuwait||
|Tonga|50.0|||Qatar||
|SolomonIslands|50.0|||Bahrain||
|PapuaNewGuinea|47.1|||Libya||
|<br>Vanuatu|33.3|||Malta||
|Korea(DR)||||HolySee||
|Singapore|___|||**Average**|**70.2**|
|BruneiDarussalam||||_||
|HongKong||||||
|AmericanSamoa||||||
|Australia||||||
|CookIslands||||||
|Guam|||_|||
|Japan|||||_|
|Macau||||||
|Midway Islands|||||_|
|N.MarianaIslands|_______|||||
|NewCaledonia|||||_|
|Nauru||||||
|NewZealand||||||
|Pitcaimr||||||
|Palau||__<br>__||||
|FrenchPolynesia||||||
|Taiwan,China||||||
|**Average**|**74.7**|||||

27
# **Table A.6: Environmental Problem Index by Region and Country**

|**Sub-Saharan**<br>**Africa**|**Air**|**Water**|Pop.**On**<br>**FLrangds**<br>|i**Latin**<br>**eCaribbean**|**Air**|**Water**|Pop.**On**<br>**FLrangd**|**EastEurope,i**<br>**iseCentralAsia**|**Air**|**Water**|Pop.**On**<br>**FLrangdise**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|
||||**(million)**<br>||||**(million)**<br>||||**(million)**|
|Nigeria<br>|170,493<br>|145,893<br>|43.82<br>|Argentina<br>|102,771<br>|9,749<br>|1.19<br>|Russia<br>|140,164<br>|844|10.42|
|Congo(DR)<br>|34,448<br>|85,899<br>|26.47<br>|Mexico<br>|93,018<br>|25,526<br>|16.82<br>|Turkey|137,105|15,758|9.61|
|Sudan<br>|33,685<br>|18,337<br>|16.17<br>|Brazil<br>|65,786<br>|29,360<br>|13.48<br>|Ukraine|82,725|7,070|3.00|
|Ethiopia<br>Al|33,578<br>|146,897<br>|24.19<br>|Peru<br>|27,732<br>|9,770<br>|5.84<br>|Poland<br>|46,414<br>|3,427<br>|0.38<br>|
|ngoa<br> '|19,299<br>|26,023<br>|4.97<br>|Chile<br>|26,011<br>|1,088<br>|1.00<br>|Bulgaria<br>|30,250<br>|907|0.49|
|Cotedlvoire<br>|18,502<br>|9,101<br>|4.75<br>|Uruguay<br>|11,046<br>|75|0.03|Uzbekistan|25,898|6,174|8.50|
|Cameroon<br>|18,427<br>|14,419<br>|5.35<br>|Bolivia<br>|10,936<br>|3,782<br>|2.31|Georgia|22,110|814|0.93|
|Senegal<br>Shfi|17,510<br>|5,365<br>|4.03<br>|Colombia<br>|7,331<br>|6,147<br>|5.59<br>|Azerbaijan<br>|14,460<br>|2,141|2.19|
|outArca<br>|17,066<br>|10,768<br>|16.10<br>|Guatemala<br>|4,174|2,081|3.68|Armenia|13,318|722|0.90|
|Zambia<br>|14,899<br>|10,031<br>|2.00<br>|Paraguay<br>|3,995<br>|2,336|0.76|Kazakhstan|9,455|1,330|4.31|
|Tanzania<br>|12,853<br>|41,752<br>|10.69<br>|Dom.Rep.<br>|3487<br>|2,825<br>|1.72<br>|Romania<br>|7,968|6,520|1.79|
|Congo<br>|12,748<br>|4,280<br>|0.59<br>|Haiti<br>|3,143<br>|9,179<br>|2.45|Hungary|6,470|66|0.73|
|Zimbabwe<br>|12,235<br>|3,929<br>|6.28<br>|Ecuador<br>|3,040<br>|6,085|2.70|Lithuania|3,973|495|0.31|
|Mozambique<br>|12,026<br>|19,183<br>|3.58<br>|Cuba<br>|2,632<br>|490<br>|1.10<br>|Moldova<br>|3,512|0|0.81|
|Kenya<br>|11,042<br>|35,229<br>|11.44<br>|Honduras<br>|2,213<br>|1,407<br>|1.69<br>|Yugoslavia<br>|3,249|1,767|1.44|
|BurkinaFaso<br>|10,104<br>|8,126<br>|7.52<br>|ElSalvador<br>|1,895|2,970|1.25|Turkmenistan|3,066|3,326|2.28|
|Chad<br>|9,851<br>|16,790<br>|3.29<br>|CostaRica<br>|1,850<br>|112|1.18|Bosnia-Herz.|2,517|678|0.86|
|Mali<br>|8,758<br>|11,864<br>|5.86<br>|Panama<br>|1,557<br>|549<br>|0.63<br>|Tajikistan<br>|2,480|2,168|2.98|
|Niger<br>|8,401<br>|15,297<br>|8.37<br>|Jamaica<br>|1,349<br>|1,144<br>|0.71|Croatia|1,992|158|0.56|
|Guinea<br>|7,918<br>|10,385<br>|3.59<br>|Nicaragua<br>|1,182|2,252|0.80|CzechRepublic|1,763|769|0.19|
|Mauritania<br>|6,555<br>|4,755<br>|0.96<br>|Venezuela<br>|551|6,057|1.52|KyrgyzRepublic|1,701|1,707|2.43|
|Madagascar<br>|5,745<br>|22,755<br>|3.75<br>|Suriname<br>|369<br>|33|0.07|Cyprus|1,561||0.32|
|Ghana<br>|5,275<br>|14,352<br>|4.66<br>|Bahamas<br>|307||0.02|Latvia|1,390|252|0.13|
|Benin<br>|3,402<br>|6,256<br>|1.95<br>|Guyana<br>|218|71|0.29|FYR Macedonia|1,194|20|0.32|
|Eritrea<br>|3,381<br>|5,729<br>|3.25<br>|St.Lucia<br>|77<br>|4<br>|0.02|Slovakia|841|0|0.68|
|Sierra Leone<br>|3,352<br>|10,934<br>|2.44|Trin.,Tobago|23|176|0.25|Slovenia|535||0.37|
|Malawi<br>|2,862<br>|13,775<br>|1.58<br>|St.Vinc.,Gren.<br>|20|11|0.04|Estonia|490|102|0.26|
|Cen.Afr.Rep.<br>|2,519<br>|3,629<br>|1.13<br>|Grenada<br>|19<br>|9<br>|0.03|Albania|397|848|0.84|
|Somalia<br>|2,378<br>|7,312<br>|6.64<br>|Barbados<br>|19|0|0.00|Belarus|0|0|0.17|
|Togo<br>|2,024<br>|5,451<br>|1.42<br>|Belize<br>|16|108|0.08|||||
|GuineaBissau<br>|1,868<br>|1,765<br>|0.27<br>|Dominica<br>|12||0.00|||||
|Gambia<br>|1,630<br>|1,367|0.40|St.Kitts,Nevis|5|1|0.00|||||
|Lesotho<br>|1,457|418|1.03|Ant.,Barbuda|1|8|0.00|||||
|Liberia<br>|1,136|2,289|0.89|Aruba|||0.50|||||
|Namibia|747|962|0.99|Anguilla|||0.06|||||
|Burundi<br>|703<br>|4,641<br>|1.76<br>|Neth.Antilles<br>|||0.00|_||||
|Rwanda<br>|621<br>|14,787<br>|3.40<br>|Bermuda<br>||||||||
|Gabon<br>|325<br>|881<br>|0.10<br>|CaymanIslands<br>||||||||
|Swaziland|155|511|0.55|Falkland Islandsl||||||||
|Comoros|119|51|0.26|Guadeloupe||||||||

28
<!-- Start of picture text --> Sub-Saharan Pop. On LatinPop. On East Europe, Pop. On<br>Africa Air Water Fragile America, Water Frangile Fetro OraEge<br>Lands Caribbean Air Waer Fagils Central Asia Air Water Fragile<br>(million) (million) (million)<br>Sao Tome, Pfincipe 109 80 0.02 French Guiana<br>Uganda 69 34,731 10.88 Montserrat .<br>Botswana 453 0.64 Martinique<br>Cape Verde 269 0.19 Puerto Rico 532<br>Djibouti 0 0.10 US Virgin Is. 0.00<br>Equatorial Guinea 709<br>Mauritius 0<br>Mayotte _<br>Reunion<br>Saint Helena<br>Seychelles 13<br><!-- End of picture text --> 29
# **Table A.6: Environmental Problem Index by Region and Country**

|**EastAsia**||**Di**|Pop**On**|**South**||**Di**|**P**O|**NthAfi**||**Di**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**,**<br>|||. <br>|<br>|||**op.**n|**orrca,**|||Pop.On|
|**Pacific**|**Air**|**Water**|**Fragile**<br>**Lands**<br>|**Asia**|**Air**|**Water**|**Fragile**<br>**Lands**|**MiddleEast**|**Air**|**Water**|<br>**Fragile**<br>**Lands**|
||||**(million)**<br>||||**(million)**<br>||||**(million)**|
|China<br>Idi|2,111,123<br>202063|336,819<br>|349.98<br>|India<br>|1,177,555<br>|215,815<br>|249.35<br>|Egypt<br>|154,843<br>|5,526<br>|33.29<br>|
|nonesa<br>|,<br>|77,461<br>|51.66<br>|Pakistan<br>|256,046<br>|39,012<br>|46.87<br>|Iraq<br>|100,533<br>|7,574|4.04|
|Korea, Republic<br>Kr(DR)|66,040<br>54338|3,480<br>6702|4.72<br>|Bangladesh<br>|76,067<br>|7,355<br>|7.78<br>|Iran<br>|73,354<br>|4,752<br>|21.95<br>|
|oea <br>Vit|,<br>50219|,<br>|5.26<br>|SriLanka<br>|10,611<br>|3,933<br>|3.40<br>|SaudiArabia<br>|37,658<br>|2,341<br>|2.11<br>|
|enam<br>|,<br>|47,939<br>|20.57<br>|Afghanistan<br>|9,058<br>|73,257<br>|14.94<br>|Algeria<br>|36,345|3,148|11.08|
|Philippines<br>|48,001<br>|18,343<br>|5.63<br>|Nepal<br>|2,559<br>|9,900<br>|7.35|Syria<br>|22,534<br>|6,365|6.66|
|Thailand<br>M|41,197<br>|13,820<br>|8.35<br>|Maldives<br>|208<br>|0<br>||Yemen<br>|14,572<br>|14,569<br>|13.04|
|yanmar<br>Mli|41,107<br>|26,536<br>|9.12<br>|Bhutan|101|785|0.60|Morocco<br>|11,485<br>|8,660|12.18<br>|
|aaysa<br>|2,669<br>|4,974|5.25<br>|||||Unit.ArabEmi.<br>|11,470<br>||0.37|
|Singapore<br>Cbdi|2,506<br>||0.00<br>|||||Israel<br>|8,253<br>||0.52|
|amoa<br>Mli|2,207<br>|18,587<br>|2.88<br>||_|||Kuwait<br>|7,281<br>||0.04|
|ongoa<br>LPDR|1,896<br>|1,385<br>|0.34<br>|||||Jordan<br>|6,457<br>|404<br>|1.22|
|ao <br>|1,490<br>|1,345<br>|2.45<br>|||||Tunisia<br>|5,110|2,331|3.23|
|Papua New Guinea<br>Fii|205<br>|6,401<br>|2.61<br>|||||Lebanon<br>|4,405<br>|0<br>|0.32|
|j<br>|195<br>|690<br>|0.05<br>|||||Oman<br>|3,612<br>|2,970|0.31|
|SolomonIslands<br>Bi Dl|48<br>|336|0.18<br>|||||Qatar<br>|1,675<br>|,<br>|0.04<br>|
|rune arussaam<br>|41<br>||0.05<br>|||||Bahrain<br>|591|126|0.04|
|Vanuatu<br>|_15__<br>|_0_|0.09<br>|||||WestemSahara<br>||||
|HongKong<br>|0<br>_||0.00|||||Libya<br>||2,620|0.53|
|AmericanSamoa<br>||||||||Malta<br>||0|0.03|
|Australia<br>|||1.53|||||HolySee<br>||||
|CookIslands<br>||||||||W.Bank,Gaza||1,628||
|Micronesia.,<br>||||||||||||
|Guam<br>||||||||||||
|Japan<br>Kifibti|||19.50|||||||||
|a<br>M||99||||||||||
|acau<br>|||0.00|||||||||
|MarshallIslands<br>||||||||||||
|MidwayIslands<br>|||||||||_|||
|N.MarianaIslands<br> li||||||||||||
|NewCaedona<br>|||0.02|||||||||
|Nauru<br>||||||||||||
|NewZealand<br>|||0.11|||||||||
|Pitcaim<br>Palau<br>||||||||||||
|FrenchPolynesia<br>||||||||||||
|EastTimor<br>Tonga<br>||l<br>0||||||||||
|Taiwan,China|<br> <br>|||||||||||
|<br>Samoa|_<br>_<br>__|3|||||||||_|

30
# **Text Figures**

## **Figures 1 - 3: Environmental Problem Indices**

- **1:** Mortality From Waterbome Disease

- 2: DALY's From Outdoor Air Pollution

- 3: Fragile Lands: Population at Risk

white: Lower Impact light gray: Intermediate Impact dark gray: Higher Impact

**Figure** 4: **OED Ratings: Probability of Project**<sup>**Success**</sup>

white: Low Probability light gray: Intermediate Probability dark gray: High Probability

**Figures** 5 - 8: **Optimal Investment Levels**

- 5: Water and Sanitation

- 6: Air Pollution-Related

- 7: Natural Resource Management

- 8: Total

white: Lower Investment Level light gray: Intermediate Investment Level dark gray: Higher Investment Level 31
**Figure**<sup>**1: Mortality From Waterborne Disease**</sup>

<!-- Start of picture text --> _^~~~~~~-,<br>X<br>0 -84<br>843 - 2340--~4~ -<br>2340 -134<br>- ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~-<br>18343 - 73257<br>_73257 - 336819<br>.n a. .<br>Source: Wang, et al. (2003)<br><!-- End of picture text --> 32
Figure 2: DALY's From Outdoor Air Pollution <!-- Start of picture text --> 4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~<br>Ar Dalys<br>0- 2480<br>2506 -26011<br>27732 - 137105<br>140164<br>154843-2111123<br>*- nla.<br><!-- End of picture text -->

> Source: Pandey, et al. (2003)

33
Figure 3: Fragile Lands: Population at Risk <!-- Start of picture text -->
-<br>ss-  - - - ,  Iz<br>r  - r~~~~~~~~ i  -<br>- v~~~~~~~~~~~~~~~~I<br>Rural  Population on  Fragile Lands<br>0-  1001693<br>1029588  - 5323851<br>5352795-  10416502<br>10686560  -40585050<br>_  43819400-  349975712<br>- n.a.<br><!-- End of picture text -->

> Source: World Development Report (2003)

34
Figure<sup>4: OED Ratings:Probabilityof Project Success</sup> <!-- Start of picture text --> OED Ratings: Probability of Project Success<br>0 - 50<br>53 - 60<br>63 - 76<br>77 - 89<br>_ 90- 100<br>- n.a.<br><!-- End of picture text --> Source:<sup>World Bank, OED</sup> 35
# Figure 5: Optimal Water and Sanitation Investment

<!-- Start of picture text --> 0 - 2<br>4-20<br>2 - 4<br>n.a.<br>X, N '.<br>Optimal Water Investment<br>0 -20<br>20 - 142<br>__ 142- 1959<br><!-- End of picture text --> 36
Figure 6: Optimal<sup>Air Pollution-Related Investment</sup> <!-- Start of picture text --> 7 - ~ ~ ~ ~ ~ ~ 7<br>.t~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. .......<br>0-2~~~~~~~~~~F<br>2- 38<br>_38-1'I17<br>_ 117- 129<br>129 - 3207<br>n.a.<br><!-- End of picture text --> 37
Figure 7: Optimal Natural Resource Management<sup>Investment</sup> <!-- Start of picture text -->
- ;. --<br>,- /  t A-<br>0  - 6<br>6 -35<br>-35  - 36<br>-36  - 70<br>70-  2180<br>n.a.<br><!-- End of picture text -->

_38_
Figure 8: Total Environmental<sup>Investment</sup> <!-- Start of picture text --> ~~~~ - ~ ~ ~ ~ ~ ~ ;<br>-<br>* 4 ---} W<br>x - 19- v<br>39 s<br>n.a. ~ ~ ~ ~ ~ ~ ~~~~~~~~~~a<br>Total Environmental Investment<br>0- 19<br>19 -54<br>_54 -163<br>_163 -903<br>903 - 7345<br>. n.a.<br><!-- End of picture text --> 39
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