POLICY RESEARCH WORKING PAPER 3 097 Measuring Up New Directions for Environmental Programs at the World Bank Piet Buys Susmita Dasgupta Craig Meisner Kiran Pandey David Wheeler Katharine Bolt Kirk Hamilton Limin Wang The World Bank Development Research Group Infrastructure and Environment and Environment Department July 2003 | POLICY RESEARCH WORKING PAPER 3097 Abstract The World Bank's new environment strategy advocates optimal country shares of the Bank's environmental cost-effective reduction of air and water pollutants that investments from two sets of variables: threats from are most harmful to human health. In addition, it outdoor air pollution, water pollution, and fragile lands; addresses threats t- .'e livelihood of over one billion and estimates of the likelihood that Bank projects will people who live on fragile lands-lands that are steeply succeed. The paper combines the country shares with the sloped, arid, or covered, i-" nntur:, forests. The new Bank's investment data to estimate optimal country approach will require accurate information about allocations for each environmental problem. Finally, it environmental threats to health and livelihood, as well as aggregates the country results to allocations for the major an appropriate resource-allocation strategy. regions in which the Bank operates. Drawing on recent research at the World Bank and Combining optimal investments for pollution and elsewhere, this paper attempts to apply an optimal fragile lands, it finds that the largest share of total investment approach. It develops a rule for optimal investment goes to East Asia (44 percent), followed by cross-country resource allocation that reflects the Bank's South Asia (21 percent) and Sub-Saharan Africa (19 investment policy. Using this rule, the paper estimates 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 the measures of problem scale and success probability that are needed to implement the model. Section 5 presents our results at the country and regional levels, while Section 6 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 linear (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 a country's environmental problems and the probability that projects will be successful under local 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:2 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: (5) fLK= _ iDipi cjB; a)jDjpj 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: (8) i ; +Y, (91 +5, -0) c B; D j1y51 For country i, we obtain two formulations of the optimal budget share from (5) and (8): _ 0 B oiDipi (5,) S; =B Dj N IT EwjDjp i=, (8') s -D N N i=l 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 country- specific 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 PM1O 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 poverty- environment 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 64 100 Middle East, North Africa 0 71 100 South Asia 69 71 100 East Asia, Pacific 33 76 100 Latin American, Caribbean 0 76 100 East Europe, Central Asia 0 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 OED- based 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=14 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 decision- making 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) Fragile Region Total Water Air Lands East Asia, Pacific 44 38 50 44 South Asia 21 15 24 24 Sub-Saharan Africa 19 34 6 15 Latin America, Caribbean 6 7 6 5 East Europe, Central Asia 5 3 9 4 North Africa, Middle East 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) Fragile Region Total Water Air Lands East Asia, Pacific 37 30 44 37 Sub-Saharan Africa 25 43 9 21 South Asia 22 15 26 25 Latin America, Caribbean 6 7 6 5 Eastern Europe, Central Asia 6 3 10 5 Middle East, North Africa 5 2 5 8 14 Table 6.3: Change in Regional % Shares* Fragile Region Total Water Air Lands Sub-Saharan Africa -6 -9 -3 -5 Eastern Europe, Central Asia -1 0 -1 -1 South Asia -1 0 -2 -1 Latin America, Caribbean 0 0 0 0 Middle East, North Africa 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 ). 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 PEast Europe, ] Africa OED 1 Caribbean OED 1 Central Asia 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 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 El Salvador 26.4 21.9 Armenia 30.1 25.0 Chad 108.0 103.1 Dom. Rep. 23.8 25.6 Kyrgyz Republic 27.7 22.0 Burkina Faso 99.6 86.9 Venezuela 19.3 35.0 Tajikistan 27.5 27.9 Niger 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 Cote d'lvoire 72.4 88.0 Costa Rica 11.9 8.9 Albania 10.2 8.5 Senegal 71.3 66.7 Panama 10.5 7.6 Czech Republic 9.2 6.6 Eritrea 64.6 46.0 Jamaica 8.7 10.4 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 Sierra Leone 37.0 64.7 Grenada 0.3 0.2 FYR Macedonia 3.0 . 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 Guinea Bissau 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 Cape Verde 1.7 1.2 Equatorial Guinea 1.5 4.1 Comoros 0.8 1.7 Djibouti 0.5 0.5 Sao Tome, Principe 0.4 0.6 1 Seychelles 0.1 0.1 _ 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 Country ($Million) East Asia, Pi South Pi North Africa, Pi Pacific OED I Asia OED 1 Middle East OED 1 China 7,344.9 5,993.5 India 3,568.6 3,740.5 Egypt 371.1 394.2 Indonesia 903.1 864.2 Pakistan 708.9 741.3 Iran 260.4 227.5 Vietnam 532.2 379.8 Bangladesh 175.5 170.5 Yemen 140.6 149.0 Thailand 195.8 156.5 Nepal 82.0 84.1 Morocco 99.9 114.7 Myanmar 193.7 216.0 Sri Lanka 46.0 48.3 Algeria 73.9 117.2 Philippines 170.8 171.3 Bhutan 9.3 6.6 Tunisia 36.5 33.3 Korea 148.2 124.9 Maldives 0.3 0.3 Oman 27.2 19.3 Cambodia 114.0 99.0 Total 4,590.6 4,792.6 Jordan 19.6 16.3 Malaysia 67.3 51.9 West Bank, Gaza 10.4 7.1 Papua New Guinea 27.9 41.6 . Lebanon 0.0 7.3 Lao PDR 19.2 20.3 Western Sahara 0.0 0.0 Mongolia 12.6 10.3 Total 1,039.6 1,086.9 Fiji 5.1 3.5 ____.__ , , Solomon Islands 1.7 2.5 Kiribati 0.4 0.4 Vanuatu 0.3 0.7 Samoa 0.0 0.0 _ East Timor 0.0 0.0 _ ._ . Micronesia 0.0 0.0 _ _ Marshall Islands 0.0 0.0 Tonga 0.0 0.0 Total 9,737.2 8,137.4 19 Table A.2: Optimal Water and Sanitation Investment by Region and Country ($Million) Sub-Saharan Latin Pi East Europe, Pi Africa OED 1 Camribben OED 1 Central Asia OE7D 1 Ethiopia 613.6 644.0 Brazil 142.9 128.7 Turkey 65.3 69.1 Nigeria 423.1 639.6 Mexico 135.9 111.9 Ukraine 38.3 31.0 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 Congo (DR) 110.0 376.6 Ecuador 32.5 26.7 Poland 17.2 15.0 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 Kenya 90.3 154.5 El Salvador 16.3 13.0 Yugoslavia 8.2 7.7 Chad 79.2 73.6 Venezuela 14.9 26.6 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 Czech Republic 4.9 3.4 Niger 57.2 67.1 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 Honduras 6.3 6.2 Armenia 4.2 3.2 Malawi 46.6 60.4 Chile 6.2 4.8 Bosnia-Herz. 4.2 3.0 Burkina Faso 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 Cote d'lvoire 34.6 39.9 Costa Rica 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 Guyana 0.3 0.3 FYR Macedonia 0.1 0.1 Sierra Leone 28.0 47.9 St. Vinc., Gren. 0.1 0.0 Turkmenistan 0.0 14.6 Senegal 26.8 23.5 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 1.6 1.8 Equatorial Guinea 1.1 3.1 Sao Tome, Principe 0.3 0.4 Comoros 0.1 0.2 Seychelles 0.1 0.1 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 ($Million) East Asia, P South Pi North Africa, Pi Pacific OED 1 Asia OED 1 Middle East OED 1 China 1,958.2 1,476.7 India 974.1 946.2 Yemen 63.3 63.9 Indonesia 376.9 339.6 Pakistan 177.1 171.0 Morocco 35.0 38.0 Vietnam 307.1 210.2 Nepal 44.0 43.4 Iran 26.1 20.8 Myanmar 109.3 116.3 Bangladesh 36.3 32.2 Egypt 25.1 24.2 Cambodia 95.2 81.5 Sri Lanka 17.5 17.2 Oman 19.0 13.0 Philippines 84.9 80.4 Bhutan 5.0 3.4 Tunisia 11.9 _ 10.2 Thailand 80.7 60.6 Maldives 0.0 0.0 West Bank, Gaza 10.4 7.1 Malaysia 29.7 21.8 Total 1,254.0 1,214.4 Algeria 9.5 13.8 Korea 20.0 15.3 Jordan 2.3 1.8 Papua New Guinea 19.3 28.1 Lebanon 0.0 0.0 Mongolia 7.8 6.1 _ Western Sahara Lao PDR 5.9 5.9 Total 202.6 193.8 Fiji 4.4 3.0 Solomon Islands 1.1 1.5 Kiribati 0.4 0.4 Vanuatu 0.1 0.2 Samoa l 0.0 0.0 Tonga 0.0 0.0 East Timor Micronesia__ Marshall Islands Total 3,101.0 2,448.6 21 Table A.3: Optimal Air Pollution-Related Investment by Region and Country ($MiHlion) Sub-Saharan P Latin America, P East Europe, I Africa OED I Caribbean OED i Central Asia 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 South Africa 22.4 22.1 Peru 38.0 36.0 Bulgaria 38.7 39.2 Angola 20.2 25.0 Uruguay 18.5 14.3 Georgia 37.0 28.7 Cote d'lvoire 18.4 24.0 Bolivia 15.6 14.2 Uzbekistan 21.7 33.6 Zimbabwe 17.4 15.9 Colombia 7.7 9.5 Armenia 20.3 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 Burkina Faso 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 Costa Rica 3.1 2.4 Lithuania 5.7 5.2 Congo (DR) 11.5 44.7 El Salvador 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 Czech Republic 3.0 2.3 Mauritania 7.3 8.5 Jamaica 1.4 1.8 Kyrgyz Republic 2.6 2.2 Cameroon 7.0 23.9 Venezuela 0.4 0.7 Latvi a 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 St. Lucia 0.1 0.1 Slovakia 1.4 1.1 Benin 4.4 4.4 St. Vinc., Gren. 0.0 0.0 FYR Macedonia 1.4 1.5 Malawi 2.5 3.7 Grenada 0.0 0.0 Estonia 0.8 0.6 Sierra Leone 2.2 4.3 Trin., Tobago 0.0 0.0 Slovenia 0.7 0.7 Guinea Bissau 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 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 Cape Verde Djibouti Equatorial Guinea _ Mauritius _ Seychelles Total 471.0 688.9 22 Table A.3: Optimal Air Pollution-Related Investment by Region and Country ($Million) East Asia, P South P Middle East, P Pacific OED I Asia OED I North Africa OED I China 3,206.8 2,739.2 India 1,388.7 1,527.9 Egypt 183.9 200.9 Indonesia 256.9 262.2 Pakistan 303.7 332.2 Iran 105.2 95.2 Korea 99.2 85.7 Bangladesh 98.1 98.7 Algeria 28.6 47.2 Vietnam 84.0 65.2 Sri Lanka 12.3 13.8 Yemen 16.5 18.9 Thailand 62.9 53.5 Nepal 3.0 3.3 Morocco 12.1 14.9 Philippines 58.0 62.3 Maldives 0.3 0.3 Jordan 9.8 8.4 Myanmar 44.2 53.3 Bhutan 0.2 0.1 Tunisia 6.8 6.6 Malaysia 4.2 3.5 Total 1,806.3 1977.3 Oman 6.0 4.7 Cambodia 3.0 2.9 , , Lebanon 0.0 5.7 Mongolia 2.8 2.5 West Bank, Gaza Lao PDR 1.7 1.9 Western Sahara Fiji _ 0.3 0.3 Total 368.9 403.5 Papua New Guinea 0.2 0.3 Solomon Islands 0.0 0.1 Vanuatu 0.0 0.0 East Timor _ Micronesia . Kiribati Marshall Islands Samoa Tonga Total 3,824.2 3,333.9 _ 23 Table A.4: Optimal Natural Resource Management Investment by Region and Country ($Million) Sub-Saharan PI Latin Pi East Europe, Pi Africa America, Central Asia Africa OED 1 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 Kyrgyz Republic 15.1 12.3 Burkina Faso 42.6 38.2 Bolivia 13.5 11.7 Tajikistan 14.6 15.1 Zimbabwe 36.6 31.9 Honduras 8.1 8.6 Azerbaijan 10.0 11.1 Congo (DR) 36.3 134.4 Costa Rica 8.1 6.0 Romania 9.7 9.1 Niger 33.5 42.5 Dom. Rep. 7.9 8.7 Yugoslavia 7.2 7.3 Kenya 31.4 58.1 El Salvador 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 Cote d'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 16.7 16.7 Guyana 1.4 1.5 Slovenia 2.1 1.9 Guinea 14.6 18.3 Trin., Tobago 1.2 1.3 Poland 2.1 1.9 Benin 10.3 9.9 Belize 0.4 0.4 Cyprus 1.9 1.6 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 FYR Macedonia 1.5 1.6 Zambia 7.4 10.2 St. Lucia 0.1 0.1 Czech Republic 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 5.7 8.0 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 4.4 4.9 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 Guinea Bissau 1.3 1.4 Comoros 0.6 1.3 Djibouti 0.5 0.5 Equatorial Guinea 0.3 1.0 Gabon 0.3 0.5 Congo 0.3 3.0 Sao Tome, Principe 0.1 0.1 Liberia 0.0 4.5 Cape Verde Mauritius Seychelles Total 979.0 1,313.0 Table A.4: Optimal Natural Resource Management Investment 24 by Region and Country ($Mfltion) East Asia, Pi South Pi North Africa, Pi Pacific OED 1 Asia OED 1 Middle East 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 Sri Lanka 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 Lao PDR 11.5 12.4 West Bank, Gaza Papua New Guinea 8.4 13.3 Western Sahara Mongolia 2.0 1.7 Total 467.8 491.7 Solomon Islands 0.6 0.9 Fiji 0.3 0.2 _ Vanuatu 0.2 0.5 East Timor 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 OED Latin America, OED East Europe, OED Africa Rate Caribbean Rate Central Asia Rate Cape Verde 100.0 Costa Rica 100.0 Estonia 100.0 Eritrea 100.0 St. Vinc., Gren. 100.0 Czech Republic 100.0 Swaziland 100.0 Uruguay 100.0 Latvia 100.0 Zimbabwe 85.0 Grenada 100.0 Slovakia 100.0 Burkina Faso 82.6 Panama 100.0 Georgia 100.0 Mozambique 80.8 Dominica 100.0 Bosnia-Herz. 95.7 Tanzania 79.4 Chile 88.9 Armenia 90.9 South Africa 78.6 El Salvador 85.7 Kyrgyz Republic 90.9 Senegal 78.1 Bolivia 85.3 Hungary 89.2 Benin 76.9 Belize 83.3 Cyprus 87.5 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 Guinea Bissau 68.8 Brazil 76.0 Slovenia 83.3 Uganda 68.3 Argentina 75.5 Poland 78.6 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 FYR Macedonia 70.0 Ethiopia 65.2 Dom. Rep. 66.7 Croatia 66.7 Mauritius 63.6 Guatemala 63.6 Moldova 66.7 Angola 62.5 Colombia 63.2 Azerbaijan 66.7 Cote d'lvoire 59.4 Jamaica 60.0 Turkey 64.7 Guinea 59.4 Paraguay 50.0 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 53.8 Cuba Zambia 53.6 Suriname Malawi 52.8 Ant., Barbuda Sao Tome, Principe 50.0 Aruba Nigeria 45.3 Anguilla Kenya 40.0 Neth. Antilles Sierra Leone 40.0 Bermuda Gabon 40.0 Cayman Islands Togo 36.8 Falkland Is. Comoros 33.3 Guadeloupe Rwanda 31.8 French Guiana Equatorial Guinea 25.0 Montserrat Cameroon 22.7 Martinique Congo (DR) 20.0 Puerto Rico Cen. Afr. Rep. 20.0 US Virgin Is. Somalia 20.0 Average 73.1 Congo 6.7 Liberia 0.0 Mayotte Reunion Saint Helena . Average 57.4 26 Table A.5: OED Project Success Rates by Region and Country East Asia, OED South OED Middle East, OED Pacific Rate Asia Rate North Africa Rate East Timor 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 Sri Lanka 69.4 Egypt 71.0 China 90.8 Nepal 69.4 Yemen 67.9 Korea, Republic of 89.7 Afghanistan Western Sahara 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 Lao PDR 68.8 Saudi Arabia Myanmar 64.3 Syria Marshall Islands 59.1 Unit. Arab Emir. Micronesia 58.6 Israel Kiribati 57.2 Kuwait Tonga 50.0 Qatar Solomon Islands 50.0 Bahrain Papua New Guinea 47.1 Libya Vanuatu 33.3 Malta Korea (DR) Holy See Singapore __ _ Average 70.2 Brunei Darussalam _ Hong Kong American Samoa Australia Cook Islands Guam _ Japan _ Macau Midway Islands _ N. Mariana Islands _______ New Caledonia _ Nauru New Zealand Pitcaimr Palau __ _ _ French Polynesia Taiwan, China Average 74.7 27 Table A.6: Environmental Problem Index by Region and Country Sub-Saharan Pop. On i Latin Pop. On East Europe,i Pop. On Africa Air Water FLrangdse Caribbean Air Water FLrangdise Central Asia Air Water FLrangdise (million) (million) (million) Nigeria 170,493 145,893 43.82 Argentina 102,771 9,749 1.19 Russia 140,164 844 10.42 Congo (DR) 34,448 85,899 26.47 Mexico 93,018 25,526 16.82 Turkey 137,105 15,758 9.61 Sudan 33,685 18,337 16.17 Brazil 65,786 29,360 13.48 Ukraine 82,725 7,070 3.00 Ethiopia 33,578 146,897 24.19 Peru 27,732 9,770 5.84 Poland 46,414 3,427 0.38 Angola 19,299 26,023 4.97 Chile 26,011 1,088 1.00 Bulgaria 30,250 907 0.49 Cote d'lvoire 18,502 9,101 4.75 Uruguay 11,046 75 0.03 Uzbekistan 25,898 6,174 8.50 Cameroon 18,427 14,419 5.35 Bolivia 10,936 3,782 2.31 Georgia 22,110 814 0.93 Senegal 17,510 5,365 4.03 Colombia 7,331 6,147 5.59 Azerbaijan 14,460 2,141 2.19 South Africa 17,066 10,768 16.10 Guatemala 4,174 2,081 3.68 Armenia 13,318 722 0.90 Zambia 14,899 10,031 2.00 Paraguay 3,995 2,336 0.76 Kazakhstan 9,455 1,330 4.31 Tanzania 12,853 41,752 10.69 Dom. Rep. 3 487 2,825 1.72 Romania 7,968 6,520 1.79 Congo 12,748 4,280 0.59 Haiti 3,143 9,179 2.45 Hungary 6,470 66 0.73 Zimbabwe 12,235 3,929 6.28 Ecuador 3,040 6,085 2.70 Lithuania 3,973 495 0.31 Mozambique 12,026 19,183 3.58 Cuba 2,632 490 1.10 Moldova 3,512 0 0.81 Kenya 11,042 35,229 11.44 Honduras 2,213 1,407 1.69 Yugoslavia 3,249 1,767 1.44 Burkina Faso 10,104 8,126 7.52 El Salvador 1,895 2,970 1.25 Turkmenistan 3,066 3,326 2.28 Chad 9,851 16,790 3.29 Costa Rica 1,850 112 1.18 Bosnia-Herz. 2,517 678 0.86 Mali 8,758 11,864 5.86 Panama 1,557 549 0.63 Tajikistan 2,480 2,168 2.98 Niger 8,401 15,297 8.37 Jamaica 1,349 1,144 0.71 Croatia 1,992 158 0.56 Guinea 7,918 10,385 3.59 Nicaragua 1,182 2,252 0.80 Czech Republic 1,763 769 0.19 Mauritania 6,555 4,755 0.96 Venezuela 551 6,057 1.52 Kyrgyz Republic 1,701 1,707 2.43 Madagascar 5,745 22,755 3.75 Suriname 369 33 0.07 Cyprus 1,561 0.32 Ghana 5,275 14,352 4.66 Bahamas 307 0.02 Latvia 1,390 252 0.13 Benin 3,402 6,256 1.95 Guyana 218 71 0.29 FYR Macedonia 1,194 20 0.32 Eritrea 3,381 5,729 3.25 St. Lucia 77 4 0.02 Slovakia 841 0 0.68 Sierra Leone 3,352 10,934 2.44 Trin., Tobago 23 176 0.25 Slovenia 535 0.37 Malawi 2,862 13,775 1.58 St. Vinc., Gren. 20 11 0.04 Estonia 490 102 0.26 Cen. Afr. Rep. 2,519 3,629 1.13 Grenada 19 9 0.03 Albania 397 848 0.84 Somalia 2,378 7,312 6.64 Barbados 19 0 0.00 Belarus 0 0 0.17 Togo 2,024 5,451 1.42 Belize 16 108 0.08 Guinea Bissau 1,868 1,765 0.27 Dominica 12 0.00 Gambia 1,630 1,367 0.40 St. Kitts, Nevis 5 1 0.00 Lesotho 1,457 418 1.03 Ant., Barbuda 1 8 0.00 Liberia 1,136 2,289 0.89 Aruba 0.50 Namibia 747 962 0.99 Anguilla 0.06 Burundi 703 4,641 1.76 Neth. Antilles 0.00 _ Rwanda 621 14,787 3.40 Bermuda Gabon 325 881 0.10 Cayman Islands Swaziland 155 511 0.55 Falkland Islands l Comoros 119 51 0.26 Guadeloupe 28 Sub-Saharan Pop. On LatinPop. On East Europe, Pop. On Africa Air Water Fragile America, Water Frangile Fetro OraEge Lands Caribbean Air Waer Fagils Central Asia Air Water Fragile (million) (million) (million) Sao Tome, Pfincipe 109 80 0.02 French Guiana Uganda 69 34,731 10.88 Montserrat . Botswana 453 0.64 Martinique Cape Verde 269 0.19 Puerto Rico 532 Djibouti 0 0.10 US Virgin Is. 0.00 Equatorial Guinea 709 Mauritius 0 Mayotte _ Reunion Saint Helena Seychelles 13 29 Table A.6: Environmental Problem Index by Region and Country Di Di Di East Asia, Pop. On South Pop. On North Africa, Pop. On Pacific Air Water Fragile Asia Air Water Fragile Middle East Air Water Fragile Lands Lands Lands (million) (million) (million) China 2,111,123 336,819 349.98 India 1,177,555 215,815 249.35 Egypt 154,843 5,526 33.29 Indonesia 202,063 77,461 51.66 Pakistan 256,046 39,012 46.87 Iraq 100,533 7,574 4.04 Korea, Republic 66,040 3,480 4.72 Bangladesh 76,067 7,355 7.78 Iran 73,354 4,752 21.95 Korea (DR) 54,338 6,702 5.26 Sri Lanka 10,611 3,933 3.40 Saudi Arabia 37,658 2,341 2.11 Vietnam 50,219 47,939 20.57 Afghanistan 9,058 73,257 14.94 Algeria 36,345 3,148 11.08 Philippines 48,001 18,343 5.63 Nepal 2,559 9,900 7.35 Syria 22,534 6,365 6.66 Thailand 41,197 13,820 8.35 Maldives 208 0 Yemen 14,572 14,569 13.04 Myanmar 41,107 26,536 9.12 Bhutan 101 785 0.60 Morocco 11,485 8,660 12.18 Malaysia 2,669 4,974 5.25 Unit.Arab Emi. 11,470 0.37 Singapore 2,506 0.00 Israel 8,253 0.52 Cambodia 2,207 18,587 2.88 _ Kuwait 7,281 0.04 Mongolia 1,896 1,385 0.34 Jordan 6,457 404 1.22 Lao PDR 1,490 1,345 2.45 Tunisia 5,110 2,331 3.23 Papua New Guinea 205 6,401 2.61 Lebanon 4,405 0 0.32 Fiji 195 690 0.05 Oman 3,612 2,970 0.31 Solomon Islands 48 336 0.18 Qatar 1,675 , 0.04 Brunei Darussalam 41 0.05 Bahrain 591 126 0.04 Vanuatu 15_50 0.09 Westem Sahara Hong Kong 0 _ 0.00 Libya 2,620 0.53 American Samoa Malta 0 0.03 Australia 1.53 Holy See Cook Islands W. Bank, Gaza 1,628 Micronesia., Guam Japan 19.50 Kifibati 99 Macau 0.00 Marshall Islands Midway Islands _ N. Mariana Islands New Caledonia 0.02 Nauru New Zealand 0.11 Pitcaim Palau French Polynesia East Timor Tonga l 0 Taiwan, China _ _ __ _ Samoa 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 Success 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 1: Mortality From Waterborne Disease 0 -84 843 - - 2340--~4~ _^~~~~~~-, 2340 -134 X - ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~~~~~~~~~~~~- 18343 - 73257 _73257 - 336819 . . n.a. Source: Wang, et al. (2003) 32 Figure 2: DALY's From Outdoor Air Pollution 4~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ar Dalys 0- 2480 2506 -26011 27732 - 137105 140164 154843-2111123 *- nla. Source: Pandey, et al. (2003) 33 Figure 3: Fragile Lands: Population at Risk - r~~~~~~~~ ss- - - - , Iz r - i - - v~~~~~~~~~~~~~~~~I Rural Population on Fragile Lands 0- 1001693 1029588 - 5323851 5352795- 10416502 10686560 - 40585050 _ 43819400- 349975712 - n.a. Source: World Development Report (2003) 34 Figure 4: OED Ratings: Probability of Project Success OED Ratings: Probability of Project Success 0 - 50 53 - 60 63 - 76 77 - 89 _ 90- 100 - n.a. Source: World Bank, OED 35 Figure 5: Optimal Water and Sanitation Investment 0 - 2 2 - 4 4-20 n.a. X, N '. Optimal Water Investment 0 -20 20 - 142 __ 142- 1959 36 Figure 6: Optimal Air Pollution-Related Investment .t~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~. ....... 0-2~~~~~~~~~~F 7 - ~ ~ ~ ~ ~ ~ 7 2- 38 _38-1'I17 _ 117- 129 129 - 3207 n.a. 37 Figure 7: Optimal Natural Resource Management Investment - -- ;. 0 - 6 ,- / t A- 6 -35 -35 - 36 -36 - 70 70- 2180 n.a. 38 Figure 8: Total Environmental Investment ~~~~ - ~ ~ ~ ~ ~ ~ ; * 4 4---} W - x - 19- v n.a.~ ~ ~ ~ ~ ~ ~ ~~~~~~~~~~a 39 s Total Environmental Investment 0- 19 19 -54 _54 -163 _163 -903 903 - 7345 . n.a. 39 Policy Research Working Paper Series Contact Title Author Date for paper WPS3071 Survey Techniques to Measure and Ritva Reinikka June 2003 H. Sladovich Explain Corruption Jakob Svensson 37698 WPS3072 Diversity Matters: The Economic Somik V. Lall June 2003 V. Soukhanov Geography of Industry Location in Jun Koo 35721 India Sanjoy Chakravorty WPS3073 Metropolitan Industrial Clusters: Sanjoy Chakravorty June 2003 V. 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