WPS4300 Policy ReseaRch WoRking PaPeR 4300 Country Stakes in Climate Change Negotiations: Two Dimensions of Vulnerability Piet Buys Uwe Deichmann Craig Meisner Thao Ton That David Wheeler The World Bank Development Research Group Sustainable Rural and Urban Development Team August 2007 Policy ReseaRch WoRking PaPeR 4300 Abstract Using a comprehensive geo-referenced database of dimensions. The findings show clear differences in indicators relating to global change and energy, the the factors that determine likely negotiating positions. paper assesses countries' likely attitudes with respect to This analysis and the resulting detailed, country level international treaties that regulate carbon emissions. information help to explain the incentives required to The authors distinguish between source and impact make the establishment of such agreements more likely. vulnerability and classify countries according to these This paper--a product of the Sustainable Rural and Urban Development Team, Development Research Group--is part of a larger effort in the department to understand the implications and impacts of climate change. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Yvonne Edwards, room MC3-428, telephone 202-473-6308, fax 202-522-1151, email address yedwards@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at udeichmann@ worldbank.org. August 2007. (95 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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Country Stakes in Climate Change Negotiations: Two Dimensions of Vulnerability Piet Buys* Uwe Deichmann* Craig Meisner* Thao Ton That** David Wheeler*** *Development Research Group, World Bank, Washington D.C. **UN Environment Programme, Geneva ***Center for Global Development, Washington D.C. Ken Chomitz, Ariel Dinar and Franck Lecocq provided helpful comments on an earlier draft of this paper. The views expressed here are the authors' and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent. 1. Introduction Concern about global warming is escalating rapidly, and the international community has agreed to stabilize the concentration of greenhouse gases at a level that will prevent dangerous climate change. The next round of global negotiations seems likely to include developing countries. For their policymakers and negotiators, the sudden emergence of this issue raises several critical questions: Which countries are most vulnerable to climate change? How will the distribution of energy resources affect countries' willingness and ability to accept restrictions on their greenhouse emissions? Are renewable energy resources sufficient for a rapid transition from fossil fuel use? What are the implications of this transition for employment and the welfare of the poor? This paper marshals the available data to provide tentative answers to these questions. We recognize that better information would be helpful, and that our results depend on a host of assumptions and estimates that can and should be challenged. To facilitate discussion, we provide a detailed appendix on data sources, assumptions, and computations. Despite these caveats, our results seem sufficiently robust that further refinement is unlikely to change two main conclusions. First, countries have very different stakes in this issue, regardless of their development levels. Their differences partly reflect their vulnerability to climate change impacts (we term this "impact vulnerability"): weather events and sea level rise. They also reflect sources of differential vulnerability to emissions reduction mandates ("source vulnerability"): access to fossil fuels and renewable energy sources, options for sequestering greenhouse emissions, and the potential size of employment and income shocks. Our results suggest that some countries have modest impact and source vulnerability, some have great vulnerability in both dimensions, and some are relatively vulnerable in one dimension but not the other. Logically, countries with high impact vulnerability and low source vulnerability should be most inclined to support greenhouse emissions limits. Conversely, countries with high source vulnerability and low impact vulnerability should be most resistant to such limits. Our second major conclusion seems paradoxical, in light of the first. We find that potential supplies of clean energy are abundant, and that no physical or technical barrier prevents a transition from fossil fuels to renewable energy sources. Our results imply 2 that the fundamental transition problems are political and institutional, rooted in the unfortunate magnitude of cross-country differences in impact and source vulnerability. We believe that a successful transition to clean energy sources will require recognition of differing country stakes and negotiation of compensation in two dimensions: transition support for countries with high source vulnerability, and adaptation support for countries with high impact vulnerability. The rest of the paper is organized as follows. Section 2 frames the issue by analyzing cross-country differences in greenhouse emissions intensity and identifying the two critical dimensions of vulnerability. Section 3 explores two major elements of source vulnerability: access to fossil fuels and the structure of employment. In Section 4, we broaden the assessment of source vulnerability to include potential access to clean energy sources: solar, wind, hydro, geothermal and biofuels. In Section 5, we consider the potential role of sequestration options ­ reduced deforestation and underground storage -- in reducing source vulnerability. Section 6 considers the two primary dimensions of impact vulnerability: sea level rise and weather-related damage. Section 7 combines our evidence on source and impact vulnerability in an overall assessment of country stakes in climate change negotiations. In Section 8, we provide a summary and conclusions. 2. Carbon Emissions, Global Restrictions, and Two Dimensions of Vulnerability Concern about global warming is relatively new, so most countries' greenhouse emissions intensities (emissions/output) are historical artifacts. The four basic determinants of intensity are energy efficiency (greenhouse emissions come disproportionately from energy production), the sectoral composition of the economy (some sectors are much more emissions-intensive than others), the fuel mix (fuels vary greatly in greenhouse intensity per unit of energy), and the collateral effects of local pollution regulation.1 In developed economies, emissions intensity is reduced (ceteris paribus) by greater energy efficiency and the economic dominance of generally low- polluting service activities. However, this partial effect may be offset by other factors. Within many sectors (e.g. mining, manufacturing, agriculture (including land-clearing), 1Regulation of local pollution (e.g., SO2, NOx) reduces global pollution by shifting production and consumption away from fuels whose combustion is CO2-intensive. 3 and transport), the activity mix and pollution intensity vary greatly with differences in subsectoral comparative advantage and politically-determined subsidies. Local factors also affect the emissions intensity of the fuel mix. Since fossil fuels are bulky and costly to transport, their delivered costs are strongly affected by relative proximity to extraction sites. This creates a strong incentive for fossil fuel use in countries with large, economically-exploitable deposits of coal, oil and natural gas. The same economic logic encourages greater use of clean, renewable energy sources (solar, wind, hydro, and geothermal) where their supply is plentiful. As Table 1 shows, these factors produce great variations in country-level emissions intensities (tons of CO2/$10,000) in all income groups. Whatever their income levels, most countries fall within a common, broad range: 1.6 - 18.3 tons/$10,000.2 This point is reinforced by Table 2, which presents selected country matches by intensity in the lowest and highest income groups: Yemen/US (5.8 tons/$10,000), Cote d'Ivoire/Finland (4.1), Pakistan/UK (3.9); Nigeria/Netherlands (3.2) and Kenya/Norway (3.1/3.2). For high- income countries, this pattern has clear political significance: It is no accident that the Kyoto Protocol has not been ratified by the United States and Australia, which have the highest emissions intensities among industrialized countries. Table 4 identifies an important source of their resistance: Australia ranks highest among all countries in coal resources relative to current energy consumption, and the US is the only other high- income economy in the top 10. The ultimate significance of variations in national emissions intensities will depend on the pace of rising concern about global warming and the scope of a future protocol to limit emissions. Limiting overall emissions in a general protocol confronts all countries with an emissions "shadow price" in some form: an explicit tax, or an opportunity cost associated with a quantitative emissions limit. Relative emissions intensities provide robust indicators of country vulnerability in this context, because countries with higher emissions intensities per unit of output also face higher taxes or opportunity costs per unit of output. However, other protocols are possible. A plausible alternative would reduce global emissions by focusing on the largest emitters, regardless of their emissions intensities. Table 3 illustrates the potential for strategic targeting among developed and 2 These are the highest minimum (Group 3) and lowest maximum (Group 4) observations, respectively. 4 developing countries. It displays total CO2 emissions from countries that are above and below the international median per capita income. Overall, above-median countries emit 74% of the global total (16 billion tons), while below-median countries emit 26% (5.7 billion tons). Both groups have highly-skewed emissions patterns, particularly the below-median countries. China alone accounts for 49% of emissions in this group and India another 19%, while the top 10 emitters collectively account for 90%. Among above-median countries the largest emitter, the US, produces 33% of total emissions, while the top 10 emitters produce 69%. Overall, the pattern is clear: CO2 emissions are so concentrated that major reductions in a few countries would effectively address the global problem. Despite the abstract appeal of a targeted protocol, we do not believe that it is politically feasible. If it penalizes emissions, the largest emitters will refuse to absorb all the abatement costs while other economies remain untouched. Conversely, smaller emitters will reject the competitive implications of a protocol that promotes clean technology development only for the larger emitters. The Kyoto Protocol illustrates the force of this parity principle, since its binding provisions apply to both major emitters (e.g., Russia, Germany) and countries whose emissions make a negligible contribution to the total (e.g., Luxembourg). The parity principle seems likely to apply to future agreements as well, although large emitters may come under greater compliance scrutiny than small ones. In this paper, we therefore assume that a future international protocol to limit greenhouse emissions will apply to all countries. We characterize the onset of this global protocol as a "shadow price shock," because an opportunity cost accompanies sudden limitation of access to a previously-free resource (the global atmosphere). Countries experience the shock via quantity restrictions, emissions taxes, or mandated support of cross subsidies (e.g., sequestration payments from emissions-intensive to low-polluting economies). The most vulnerable countries will have high dependence on domestic fossil fuel resources and few options for clean energy development. If the protocol includes sequestration payments, countries' vulnerability will be lower if they have high potential to sequester carbon. Conversely, vulnerability will be greater, particularly for the poor, if emissions-intensive economic sectors are also primary sources of employment. 5 These elements ­ emissions intensity, clean energy options, sequestration potential, and employment structure -- are all factors in determining one dimension of country vulnerability, which we term source vulnerability. As we will demonstrate in this paper, countries at all income levels exhibit great variation in all dimensions of source vulnerability. Another type of vulnerability is, of course, the potential impact of climate change itself. Icecap melting and sea-level rise will disproportionately affect countries whose populations and economic activities are concentrated in coastal lowlands. Agriculturally- dependent economies will suffer particularly heavy losses as droughts increase. Some countries are prone to storm-related wind and flood damage, and storm severity is predicted to increase. All of these factors are components of the second dimension, which we label impact vulnerability. We have no prior reason to believe that country vulnerabilities in the source and impact dimensions are significantly correlated (we will test this proposition later in the paper). We believe that information about both dimensions is useful for several reasons. First, the near-term possibility of a shadow-price shock has raised the stakes for national policymakers. They want to understand the sources and significance of their vulnerability in each dimension. Policy researchers are most interested in exploring high- vulnerability cases, and those involving the poor may be particularly compelling. Our two-dimensional typology may also be useful for assessing the stakes for international climate change negotiations. Resistance to a new global protocol should be greatest from countries with low impact vulnerability and high source vulnerability. Conversely, the most support should come from countries with low source vulnerability and high impact vulnerability. Positioning countries in the two dimensions may provide useful insights about the levels and types of incentives needed to bring all countries to the international bargaining table. 6 3. Source Vulnerability 3.1 Hydrocarbons Fossil fuels enjoy a natural economic advantage in economies with ample local supplies, because these fuels are costly to transport. In many cases, they may also be primary sources of export earnings. For both reasons, a global emissions reduction protocol will impose a greater shadow price shock on countries with significant hydrocarbon resources. Countries with large coal deposits have the highest sensitivity, since coal is the most emissions-intensive fossil fuel. Emissions intensities from oil and natural gas are about 75% and 60% of coal's intensity, respectively.3 To index the relative vulnerability of different countries to a shadow price shock, we divide their total estimated reserves of coal, oil, and natural gas by their total annual energy consumption (both in mtoe ­ millions of tons of oil equivalent). We believe that this normalization produces a reasonable proxy for relative dependence on fossil fuels (i.e., local coal reserves of 20 mtoe have more significance for an economy with 1 mtoe of annual energy consumption than for an economy with 500 mtoe). We present results for the highest- vulnerability countries in Table 4, including a weighted total that assigns fuels weights equal to their relative greenhouse emissions intensities.4 Countries are sorted in decreasing order for each fuel. For coal, the highest-vulnerability region comprises seven ex-COMECON economies (Kazakhstan, Ukraine, Russia, Poland, Czech Republic, Hungary, Bulgaria). Coal deposits are also high relative to energy consumption in countries of Oceania (Australia, New Zealand), North America (US, Canada), Southern Africa (South Africa, Zimbabwe), South Asia (India, Pakistan), and East Asia (China, Indonesia). In contrast, Latin America has only two high-vulnerability economies (Colombia, Venezuela), and in Western Europe, only Germany and the UK have even modest coal reserves relative to domestic energy consumption. Oil deposits are highly concentrated, with the majority of vulnerable economies in the Middle East and North Africa (13 countries - Kuwait, Iraq, UAE, Saudi Arabia, 3 Source: International Energy Agency data. 4 Complete estimates for countries are presented in the tables below. 7 Libya, Qatar, Iran, Yemen, Oman, Sudan, Algeria, Syria, Tunisia), West Africa (6 countries - Congo, Gabon, Angola, Equatorial Guinea, Nigeria, Chad), the former USSR (3 countries -- Kazakhstan, Azerbaijan, Russia), and Andean and Central America (5 countries - Venezuela, Ecuador, Trinidad and Tobago, Peru, Mexico). Norway and Brunei also have significant supplies relative to their energy consumption. Gas follows a similar pattern: Middle East and North Africa (11 - Qatar, Iran, UAE, Algeria, Yemen, Iraq, Oman, Libya, Kuwait, Saudi Arabia, Egypt), Andean America and the Caribbean (4 - Bolivia, Venezuela, Trinidad and Tobago, Peru), the former USSR (5 - Turkmenistan, Azerbaijan, Russia, Kazakhstan, Uzbekistan), Southeast Asia (4 - Brunei, Malaysia, Myanmar, Indonesia), Western Europe (2 ­ Norway, Netherlands), and a few countries in other regions (Nigeria, Australia, Bangladesh). At current prices, exploitable reserves also include natural bitumen (tar sands, heavy crude) and oil shale. As Table 4 shows, known reserves are very large and highly concentrated in a few countries in the Western Hemisphere (Venezuela, Canada, the US, Brazil), the Middle East, and North Africa (Jordan, Morocco, Israel). Our weighted total hydrocarbon vulnerability measure reflects the concentrated pattern of vulnerability for the individual hydrocarbon sources. Under a global emissions reduction protocol, highly-ranked countries would face relatively high opportunity costs (domestically and via foregone export earnings) in the transition to clean energy use. This would be particularly true for coal, the most emissions-intensive fossil fuel. 3.2 Employment Employment is another potentially-important dimension of vulnerability to a shadow price shock. In this paper, employment vulnerability also serves as a proxy for the distributional implications of a global emissions protocol. To estimate differential employment vulnerability, we combine information from several sources. We use UN data to compute GDP for 2002 (in constant 1990 $US) in six composite sectors: Mining and Energy, Manufacturing, Construction, Services, Transport and Other. Using data from the International Energy Agency, we aggregate estimated CO2 emissions from 64 subsectors into the six composite sectors. We divide sectoral emissions by sectoral GDP to obtain greenhouse emissions intensities. Using employment data from the ILO, we 8 compute the shares of the composite sectors in total employment. We then compute the employment-weighted average emissions intensity for each country, using sectoral employment shares as weights. This provides a first-order estimate of employment vulnerability, since countries with relatively high emissions intensities in high- employment sectors are likely to be most affected by a shadow-price shock. Tables 5 and 6 summarize our results for four groups of countries: low income, ex- COMECON (Eastern Europe, Central Asia), middle income and OECD high income. For ease of comparison, we normalize so that the maximum intensity is equal to 100. The results indicate that employment vulnerability is heavily concentrated in low-income and ex-COMECON countries. The median value of the employment vulnerability index (EVI) drops by 50% from low- to middle-income countries, and by over 50% again from middle- to high-income countries. In the overall distribution, China joins India among countries whose vulnerability puts them in the highest quartile. As Figure 6 shows, the EVI distributions do not even overlap for the low- and high-income groups. Although data scarcity prevents us from calculating EVI's for all countries, our results indicate that a shadow price shock is likely to have the most pronounced impact on employment in the world's poorest and most populous countries. 4. Clean Energy Potential 4.1 Solar, Wind, Hydro, Geothermal We have used the best available data to develop estimates of potential energy from solar, wind, hydro and geothermal sources in the medium term.5 While our assumptions about convertibility are optimistic, we believe that they are feasible. In addition, for solar, wind and geothermal we adopt low, intermediate and high scenarios to incorporate uncertainties about these technologies. For solar, we assume collection by photovoltaic (PV) cells on shares of each country's land area ranging from 0.05 to 0.18%.6 For conversion to electric power, we use alternative efficiency levels (15-20%). For wind, 5See Figures 1-3 for illustrations of the global distributions of solar, wind and geothermal energy. 6Note that we do not include solar water heating in cold climates or power generation from concentrated solar plants in regions with high solar insolation. Our estimates for total solar energy production may thus be conservative. 9 we include estimates at the 80m hub height for both onshore and offshore potential. We employ standard engineering parameters for wind-energy generation with current technology. For onshore potential, we adopt Germany's current siting density as our benchmark, since the German program is well-advanced. Turbine siting assumptions range from Germany's current intensity (as a high) to 60% of that potential (low). For offshore wind, we estimate the potential for turbine siting 0-15 km from shore and with bathymetric depths of 0-20m. For hydro, we employ standard estimates of technically- exploitable potential by country. Our geothermal calculations are based on exploitation of subsurface heat potential at an upper intensity limit comparable to Switzerland's current program (15% of potentially exploitable areas), and at a low of 5% of that potential.7 We also allow for future efficiencies 10-30% higher than current levels. Appendix 1 includes a detailed presentation of our data sources, assumptions and calculations. For the purposes of simulation, we use the intermediate renewable energy potential for solar, wind and geothermal. Figures 4 and 5 show total estimated renewable energy potential (medium estimate) versus current energy consumption for all countries as well as for World Bank regions separately. Complete results are presented in the tables in Appendix 2. Table 7 presents our basic results for the countries with the greatest renewable resources, relative to their current energy consumption levels. We normalize by energy consumption to index local significance as an option for clean energy conversion (i.e., annual solar energy potential of 20 mtoe has much greater significance for an economy with 1 mtoe of annual energy consumption than for an economy with 500 mtoe). Any ratio greater than 1.0 for a renewable energy source implies that it is potentially sufficient to meet a country's current energy demand. Cross-country patterns differ greatly by energy source but solar is clearly the largest, even though we assume that PV cells cover only one-thousandth of the available land area. Of the top 35 countries overall, 17 are in Sub-Saharan Africa, 7 in Latin America, 2 in the Middle East and North Africa, 3 from the Former Soviet Union, 4 in East Asia, and 3 in the Pacific region. Developing countries that dominate the solar measure are generally in high-insolation regions, with 7We do not include potential heating or cooling energy from shallow geothermal systems which could add considerable that could replace conventional fossil-fuel powered HVAC systems. 10 large areas relative to their economic size (and energy consumption). The most dominant solar group is in Sub-Saharan Africa and, within that region, in the Sahel. Mauritania, Chad, Niger, Mali and CAR are all in the top 10 countries by this measure. Among the high-income economies, only a few enter the top 35 for any renewable category: Australia (overall, solar, wind), Iceland (overall, wind, hydro, geothermal), Norway (wind, hydro), New Zealand (geothermal) and Canada (wind). Mongolia, some of the Sahelian states and Namibia rank highest in total and solar categories, and Mongolia and Bolivia rank highest in wind and geothermal. Three African states (Congo, Gabon, Congo Democratic Republic) have top-5 rankings for hydro, along with Tajikistan and Papua New Guinea. In addition to Mongolia and Bolivia, the top-5 countries for geothermal include Namibia, Kyrgyzstan and Tajikistan. Table 8 summarizes representation across World Bank regions for countries in the top 35 of each renewable category. In all categories, developing and middle-income clients of the World Bank account for at least 30 of the 35 top-ranking countries. In each case, Sub-Saharan Africa dominates the group. 4.2 Biofuels Biofuels include biogas from wastes (principally animal manure), biodiesel from oil-producing plants, and ethanol from plant biomass. In this paper, we attempt to estimate potential energy from biofuels under realistic assumptions about availability and conversion efficiency. Appendix 1 includes a detailed presentation of our data sources, assumptions and calculations. Complete results are presented in Appendix 2. For estimation of biogas potential from manure, we use parameters from existing conversion facilities and develop estimates of collectible manure from FAO livestock data and standard collection efficiency studies. For biodiesel and ethanol, potential yields differ greatly across field crops. Since we are interested in feasible output, we estimate the highest potential yield from crops suitable for cultivation under different agro- climatic conditions. For field crops, we estimate potential yield from 10% of existing suitable land that is not currently forested. The highest ethanol yields per hectare come from sugar-producing plants (sugar cane, sorghum and sugar beets, respectively in three broad latitude belts: tropical, sub-tropical and temperate). We obtain data on suitable 11 lands from the FAO Terrastat database, and use standard yield estimates to derive potential energy production. In addition, we estimate the potential ethanol yield from native tallgrasses in temperate savanna areas that are currently not used for agriculture. Recent experiments have indicated that potential yields from tallgrasses are very high. The best-known example is switchgrass, a tallgrass native to the North American northern plains, but kindred species grow in similar areas worldwide. Using current estimates of ethanol yield per hectare, we estimate tallgrass potential from a digital map of world ecoregions, developed by WWF International, which explicitly identifies tallgrass savanna areas (specifically, "Temperate Grasslands, Savannas and Shrublands"). Drawing on recent digital mapping work at IFPRI (the International Food Policy Research Institute), we mask out agricultural areas in the tallgrass savanna zones as well as urbanized areas and estimate potential production for a share of the remaining suitable areas in each country. Finally, we estimate potential biodiesel production from jatropha curcas, a hedge plant of Latin American origin that thrives on marginal lands in many arid and semiarid tropical regions of the world. Biodiesel can be extracted directly from jatropha seeds with high yields per hectare, and cultivation does not have to compete with food crops for land because jatropha thrives on marginal lands under widely-varying conditions. To estimate potential biodiesel production from jatropha, we draw on work by WWF and IFPRI to identify appropriate non-agricultural and non-urban areas in two world vegetation regions: Tropical-Subtropical Grasslands, Savannas and Shrublands; and Non-Montane Xeric Shrublands. We use recent studies of jatropha's biodiesel yield per hectare to develop our potential energy estimates. Tables 9a and 9b summarize our results for the highest-ranking countries, after normalizing each country's resources for annual domestic energy consumption. While biogas from manure plays a relatively minor role, sugar crops, tallgrass and jatropha all make large contributions. Sub-Saharan Africa dominates the top-35 rankings, with 25 countries overall, 26 for sugar crops, 29 for jatropha and 17 for biogas. Latin America / Caribbean ranks second in all categories. African strength in sugar crops is distributed across climate zones, while jatropha potential is particularly concentrated in the Sahel (Mauritania, Chad, CAR, Mali, Niger) and semi-arid regions of southern Africa 12 (Namibia, Botswana, Angola). Tallgrass potential, on the other hand, is heavily concentrated in 14 countries in the savanna grasslands region of Europe and Central Asia. Table 10 summarizes our estimates for clean energy resources. With the exception of a few small states (Equatorial Guinea, Rwanda, Cape Verde, Comoros), all countries in Sub-Saharan Africa have a clean energy potential that at least matches their current domestic energy consumption. Most have potentials that are many times their current consumption. The same pattern holds in Latin America and the Caribbean, South Asia, the Middle East and North Africa and the developing countries of Europe and Central Asia. In the East Asia / Pacific region, the ratio of clean energy potential to current energy consumption is 60% or higher for all countries except Samoa, Tonga and Korea. Remarkably, we find that even China and India have estimated clean energy potentials that exceed or nearly match current domestic energy consumption (120% for China, 90% for India). Worldwide, the countries whose clean energy potential is at least 50 times current annual energy consumption include Mongolia (515), Namibia (101) and five Sahelian states in Africa: Central African Republic (91), Mauritania (86), Chad (77), Mali (58) and Niger (50). We conclude that in the medium term, clean energy resources can provide most developing countries with abundant options for damping a shadow price shock from restrictions on global emissions. 5. Sequestration Potential Countries have two options for sequestering carbon that are likely to be recognized by a global protocol. The first is reduction of emissions from deforestation and other land-clearing. Currently, the World Resources Institute estimates that these activities account for about 19% of total greenhouse emissions. We index the scope of country potential in this dimension by dividing emissions from deforestation and land clearing by total emissions. Table 11 presents the results by region, for countries whose emissions from clearing and deforestation exceed 1% of total emissions. Clearly, reduction on this margin would be the primary task for many developing countries in all regions. Emissions from this source are over 50% of all greenhouse emissions for 37 developing countries: 20 in Sub-Saharan Africa, 10 in Latin America, 5 in East Asia/Pacific, and 2 in South Asia. 13 The second option is underground sequestration. Drawing on the estimates of Hendriks, et al. (2004; see Appendix 1) for 18 world regions, we develop country estimates of CO2 capture potential in four classes: oil fields, natural gas fields, coal fields, and saline aquifers (the latter mostly offshore). Then we normalize by current energy consumption. Appendix 1 provides a detailed explanation of our methodology. Table 12 summarizes the results, which indicate that this is an important option for a large number of countries. Among 31 developing countries with 50 or more years of sequestration potential, Sub-Saharan Africa has 12 (including Nigeria and South Africa), East Asia 2 (China and Vietnam), Eastern Europe 3 (Russia, Albania, Croatia), Latin America 4 (Venezuela, Suriname, Chile, Bolivia), the Middle East and North Africa 9 (including Iran and Egypt), and South Asia 1 (Bangladesh). 6. Impact Vulnerability 6.1 Sea Level Rise The anticipated impact of sea-level rise (SLR) has three elements: Natural expansion as the ocean warms; higher ocean levels from icecap melting; and higher storm surges. Recent evidence suggests that the polar caps are melting more quickly than anticipated, and forecasts of a 1-meter rise in this century no longer appear implausible. If melting accelerates further, a 3-meter forecast will become plausible.8 A recent study by DECRG (Dasgupta et al., 2007) has used high-resolution digital mapping to estimate country impacts of SLR in the range of 1-5 meters. Using GIS overlays, the study computes the percentages of total population, agriculture and general economic activity on land that will be covered by a 1 and 3-meter SLR. For this paper, we index the potential impact using the average 3-meter percent coverage for GDP. The study focuses on developing countries that are not small islands, so we do not have comparative SLR impact measures for most islands. However, it is clear that many small islands will be seriously impacted by SLR. We have only one comparable island economy, the Bahamas, whose estimated coverage area at 3-meter SLR accounts for 14.5% of current 8 Total melting of the Greenland icecap would raise sea level by approximately 7 meters. Total melting of the West Antarctic ice sheet would also raise sea level by about 7 meters. 14 GDP. This is one of the highest estimated impacts in the world. To account for this factor in our comparative assessment, we arbitrarily assign other small islands a 15% estimate for GDP coverage. Table 13 provides a summary of SLR estimates that we use for this study. With the exception of island states, coverage is reasonably complete for World Bank client countries. Non-island states with measured impacts9 include 27 of 42 countries in Sub-Saharan Africa, 10 of 11 in East Asia / Pacific, 7 of 28 in Europe / Central Asia, 21 of 23 in Latin America / Caribbean, 9 of 14 in Middle East / North Africa, and 4 of 7 in South Asia. Table 14 presents impact estimates for specific countries. Besides island states, where significant impacts are likely in most cases, the distribution of potential impacts for a 3-meter SLR is strongly skewed in all regions. East Asia / Pacific registers most strongly, with 8 of 10 measured non-island countries experiencing an impact of 1% or more. These are large, populous countries, so the implications of SLR for this region (which also includes many islands) are clearly serious. Vietnam is particularly striking (24.2%, reflecting heavy impacts in the Mekong and Red River Deltas), and China's estimated impact (5.6%) is huge in absolute value. Sub-Saharan Africa has a lower incidence, with impacts over 1% in 10 of 27 measured non-island countries. However, four states experience heavy impacts: Mauritania (17.5%), Benin (14.8%), Senegal (8.1%) and Gambia (7.6%). Among European and Central Asian countries 4 have impacts above 1% of GDP: Georgia (2%), Ukraine (1.5%), Estonia (1.5%) and Turkey (1.1%). In Latin America, 12 countries have impacts in a comparable range, while Middle East./North Africa has four: Egypt (12.1%), Tunisia (4.9%), Libya (2.4%) and Oman (1.4%). In summary, our assessment for a 3-meter SLR suggests that GDP impact percentages greater than 5% will be mostly limited to islands and small coastal states. However, there are important exceptions to this pattern in Sub-Saharan Africa, North Africa and East Asia. In South Asia and Latin America as well, relatively "modest" impacts for states such as India, Bangladesh, Brazil and Mexico translate to very large absolute numbers. 9 These include landlocked countries, where direct SLR impact is zero. 15 6.2 Weather Damage Regional forecasts of climate change remain uncertain, although there is general agreement that variability will increase, and existing weather conditions are likely to be exacerbated. At present, it seems most reasonable to assume that future weather conditions in each country will reflect historical conditions, but with more extreme events. To index expected damage, we draw on the Emergency Disasters Database (EM- DAT) maintained by the Center for Research on the Epidemiology of Disasters at the Université Catholique de Louvain in Brussels. We develop our country indices from all recorded disasters during the period 1960-2002 that are attributed to weather-related events: droughts, extreme temperatures, floods, wild fires and wind storms. Since estimates of economic damage are extremely spotty, we develop a weighted damage measure from population impacts in three categories: killed (weight 1000), homeless (10) and affected (1). Then we divide by population for 1980 (the midpoint of the period) to develop the final index: population impact relative to population size. Table 15 summarizes the results by region, while Table 16 presents country indices. Both tables illustrate two striking facets of these data. First, there are very large regional differences in human vulnerability to weather events. For South Asia and East Asia / Pacific, the median indices for population impact relative to size are over twice the indices for Sub-Saharan Africa and Latin America / Caribbean. These are, in turn, at least four times the index for Middle East / North Africa and 14 times the index for Eastern Europe and Central Asia. Second, intraregional distributions are quite skewed, and at least one country in several regions has extreme vulnerability relative to the others. Clear outlier countries include Ethiopia, Mozambique, Sudan, Honduras, Iran and Bangladesh. Among countries with large populations, those with vulnerability indices above 200 are Ethiopia (1,809), Philippines (392), Vietnam (235), China (223), Bangladesh (1,940) and India (566). Island states with indices above 200 also figure prominently in the Pacific (Tonga, Somoa, Solomons, Vanuatu, Fiji) and the Caribbean (Antigua and Barbuda, Haiti, St. Lucia). 16 7. Country Stakes in Climate Change Negotiations For a comparative assessment of country stakes in climate change negotiations, we focus on summary measures of source and impact vulnerability. We simplify by computing aggregative indices from variables whose measures are compatible. The first index is the weighted sum of nonrenewable energy sources (coal, oil, gas, natural bitumen, oil shale), measured in year-equivalents of current domestic energy consumption and weighted by relative CO2 intensity (1.0, 0.75, 0.60, 0.75, 0.75, respectively). The second index aggregates renewable energy sources (solar, wind, hydro, geothermal, biogas, sugar ethanol, tallgrass ethanol, jatropha ethanol), again in year-equivalents of domestic energy consumption. Our two sequestration variables (potential for reduced deforestation and carbon storage) are not measured in compatible units, so we leave them separate. For the same reason, we leave the two impact variables (sea level rise, weather damage) separate. 7.1 Overall Correlations Our indices all have highly skewed distributions, so we compute rank correlations to obtain robust estimates of their relationships. Table 17 presents the results, with starred coefficients denoting significance at the 95% level. We are particular interested in assessing the interaction of two broad dimensions: source vulnerability (related to factors affecting the response to emissions limits ­ nonrenewables, renewables, employment risk and sequestration options) and impact vulnerability (sea level rise and weather damage). Part of our interest lies in determining whether correlations suggest reinforcing or offsetting effects within the two vulnerability groups. We are also interested in the overall direction of the relationship between the two dimensions, because this has implications for successful negotiation of a global protocol. In general, countries with high impact vulnerability and low source vulnerability should be the strongest supporters of a protocol, while the converse should be true for countries with low impact vulnerability and high source vulnerability. Country postures in the other two cases (both vulnerabilities high or low) would depend on the relative strength of the two effects. 17 Within the source vulnerability group, Table 17 indicates that four correlations are significant and relatively large, and that all four moderate overall vulnerability via offsetting effects. Countries with plentiful non-renewable energy resources (relative to energy demand) also tend to have significantly less sequestration storage potential. Countries with more sources of renewable energy tend to have slightly less non- renewable energy. Countries with relatively high employment vulnerability also tend to have less renewable energy resource options (again, relative to energy demand) and less potential for sequestration through reduced deforestation. As a statistical corollary, countries with plentiful renewable energy sources also tend to have high potential for sequestration through reduced deforestation; and those with higher sequestration (storage) opportunities tend to have less potential for sequestration through reduced deforestation. Within the impact vulnerability group, the two dimensions have a small but significant negative correlation: Countries with large potential impacts from sea level rise tend to have smaller potential damage from weather events. Across the two dimensions, overall results are mixed. Countries vulnerable to sea level rise tend to have weaker options for renewable energy and sequestration via reduced deforestation, higher employment vulnerability and greater options for sequestration via storage. Countries vulnerable to weather damage also tend to have lower employment vulnerability. However, they also have greater renewable and non-renewable energy options, less potential for storage sequestration and greater potential for sequestration via reduced deforestation. To summarize, our correlation results suggest that countries with high source vulnerability in some dimensions tend to have lower vulnerability in others. The same is true for impact vulnerability. Between source and impact vulnerability, the evidence is mixed. While these general results are of some interest, none of the observed correlations is very high. By implication, country cases should be our principal focus because they tend to be unique. 18 7.2 Country Cases For a composite view, we combine all of our vulnerability measures into a general index that reflects countries' ability and willingness to participate in an international protocol. We construct an index with high values for low source vulnerability and high impact vulnerability, and low values for the converse case. Again, we ensure robust estimates by using ranks rather than numerical values. We also normalize ranks to the range 1-100 to prevent distortion from differences in data structure.10 Our analysis considers seven dimensions that affect country orientation. Five dimensions promote a positive orientation toward a protocol: Three reflect source vulnerability (renewable energy resources and both dimensions of sequestration (deforestation reduction and storage)), and two relate to impact vulnerability (sea level rise, weather damage). Ceteris paribus, the higher a country's measure in any of these dimensions, the greater the relative attraction of a global protocol. Two dimensions, both source vulnerability elements, reflect negative factors: nonrenewable energy resources and employment vulnerability. Ceteris paribus, the greater a country's measure in either dimension, the lower the relative attraction of a global protocol. To develop an overall orientation index, we compute standard ranks (rank 1 for the largest value) for the five positive dimensions and inverse ranks (rank 1 for the smallest value) for the two negative dimensions. We compute one sequestration measure by averaging ranks for deforestation and storage potentials. We normalize all six remaining rank measures to the range 0-100 and select the set of non-island states that have complete measures for all variables.11 This yields a computation set of 120 countries. We compute orientation indices as weighted averages of dimensional ranks, with total weights constrained to one (Table 18). We test for robustness by computing indices with widely-varying weights to reflect relative emphasis on energy resources (renewable and nonrenewable), impact vulnerability (sea level rise; weather damage); neutrality (equal 10 Almost all countries have a non-zero measure for weather damage, but measures in other dimensions have several missing values or zeros. Arbitrary assignment of rank numbers by standard algorithms can lead to misleading comparisons in such cases. Remapping dimensional ranks to the range 1-100 neutralizes this potential distortion. The appropriate transformation is: Y = 1+99*[(X-Min(X))/(Max(x)-Min(x)]. 11Although they are numerous, small island states represent a special case because of their particular vulnerability to sea level rise. 19 weights); positive source factors (renewable energy; sequestration potential) and negative source factors (nonrenewable energy, employment vulnerability). Table 19 reports the results for countries, by World Bank region. For each weighting scheme, we compute index values for all 120 countries and divided the results into three equal groups with high (1), medium (2) and low (3) values. Then we tabulate the results for each country. We assign countries to the High orientation category if at least three of five index values are 1's and the rest are 2's. We assign them to the Low orientation category if at least two of five values are 3's. Intermediate cases are assigned to the Middle category. Tables 20, 21 and 22 provide useful information about general relationships. Table 20 indicates that correlations among the six dimensional indices are all relatively low, except between renewable energy sources and employment vulnerability and weather damage. Nevertheless, Table 21 shows that alternatively-weighted combinations of these indices are highly correlated, with one exception: high weights for positive source vulnerability factors (index D: renewable energy; sequestration potential) vs. high weights for negative source vulnerability factors (index E: nonrenewable energy, employment vulnerability). Table 22 shows that many countries retain their orientations as the index weights change. Overall, 35 of 120 countries rate `High' because they display positive orientations across all five weighting schemes. Conversely, 48 countries are consistently `Low'. The results suggest that the Latin America / Caribbean region has the strongest regional orientation toward a protocol, with 16 countries scoring `High', 5 `Middle' and 1 `Low'. However, sub-regions are distinctly different. Non-Andean South America and Central America are almost completely in the `High' Category, while the Andean countries (except Peru and Ecuador, which are `High') are `Middle' and the Caribbean is mixed. In contrast, Eastern Europe / Central Asia (except Georgia) and South Asia are predominantly `Low' regions: 18 of 21 ECA countries and 2 of 4 SAR countries, including India, are in the `Low' category. The Middle East / North Africa region is also unbalanced in this direction, with 2 `High', 3 `Middle' and 4 `Low'. Sub-Saharan Africa and East Asia / Pacific are more evenly balanced. In Sub- Saharan Africa, 12 states are `High', 14 `Middle' and 8 `Low'. Within the region, 20 however, sub-regions differ markedly. Among Coastal West African states, 8 are `High', 1 `Middle' and 2 Low. In contrast, all Central African states are `Middle' (5) or `Low' (3). The Sahelian, Eastern and Southern Africa sub-regions exhibit more diversity. Unfortunately, our evidence also suggests that, among the World Bank's partner countries, total emissions are much greater from the 48 `Low' states than from the 35 `High' states. Overall, `High' states account for 21.0 % of emissions, while `Low' states account for 50.1% (Table 23). China and India, both `Low' states, account for nearly 80% of that group's emissions. However, even among states other than the four greatest emitters (China, Indonesia, Brazil, India), `Low' states account for the greatest share of emissions. This unfortunate result provides a suggestive indicator of potential difficulty in attempting to extend an emissions-reduction protocol to low- and middle-income countries in the next round. To summarize, our results suggest that the geographic distribution of protocol orientation is far from random. Among 35 states with `High' scores, almost two-thirds are in three sub-regions: non-Andean South America (8), Central America (6), and Coastal West Africa (8). States with `Low' scores also display some concentration, particularly in Eastern Europe and Central Asia. Unfortunately, carbon emissions are also heavily concentrated in World Bank states with `Low' scores: China (4.9 Gt), India (1.8 Gt) and 46 others (4.2 Gt) (Table 23). 7.3 Two Dimensions of Country Vulnerability It is also useful to position countries two-dimensionally, according to their relative source and impact vulnerability. We focus on non-island states, and for this exercise we include states with some incomplete data. Using normalized ranks (1 ­ 100), we compute source and impact vulnerability indices as means of the available dimensional indices for each country (four for source vulnerability (nonrenewable energy, renewable energy, sequestration, employment); two for impact vulnerability (sea level rise, weather damage)). Then we divide the source and impact vulnerability index values into equal `Low', `Medium' and `High' groups. Table 24 and Figure 7 present our results for non-island states. Ceteris paribus, `High' source vulnerability should discourage participation in a global protocol, while 21 `High' impact vulnerability should encourage it. The most positively-oriented states should therefore have `Low' source vulnerability and `High' impact vulnerability. In Table 24, 26 states are in this category. All are low- or middle-income countries, and they are heavily concentrated in three regions: Latin America, Sub-Saharan Africa and Southeast Asia. Table 25 indicates that these 26 states account for about 11% of tabulated emissions. Table 24 identifies 32 states with the greatest negative orientation (`High' source vulnerability, `Low' impact vulnerability). These are middle- and high-income countries, concentrated in three regions: Eastern Europe, Central Asia and the Middle East. However, a number of these negatively-oriented countries are major emitters. Table 25 shows that together, they account for about 11% of total emissions (dominated by CO2 emissions from the Russian Federation, see Table 3). Table 25 shows that two groups with the greatest volume of emissions are subject to conflicting pulls in the two dimensions of vulnerability. The countries with `High' source vulnerability and `High' impact vulnerability account for about 19% of all emissions. Of particular interest in the `High'/'High' group are China and India, which together account for 68% of the greenhouse emissions from countries in the lower half of the international income distribution (Table 3). The `High'/'Medium' group, accounting for 27% of emissions in Table 25, is dominated by the United States and the Ukraine. 8. Summary and Conclusions In this paper, we report results from a broad survey of country stakes in the control of greenhouse emissions. We identify source and impact vulnerability as two major dimensions of the problem. Sub-dimensions of source vulnerability include renewable energy sources, potential for carbon sequestration, nonrenewable energy sources, and employment vulnerability. The sub-dimensions of impact vulnerability are sea-level rise and weather-related damage. In our typology, four sub-dimensions have positive effects on countries' orientation toward a global emissions protocol: renewable energy sources, sequestration potential, sea level rise, and weather damage. Conversely, two dimensions have negative effects: Nonrenewable (fossil fuel) energy sources and employment vulnerability. 22 To index these broad dimensions, we assemble the best available information on components and standardize their measurement for aggregation. Within the nonrenewable energy category, we develop estimates of total resource deposits for coal, oil, natural gas, natural bitumen and oil shale. To make aggregation possible, we translate the estimates to millions of tons of oil equivalent (mtoe). Similarly, we develop mtoe-equivalent estimates for many renewable energy resources: solar, wind, hydro, geothermal, and four biofuel sources: biogas from manure; ethanol from sugar crops (which have the highest ethanol yield among food crops), savanna tallgrass (in land not cleared for agriculture), and biodiesel from jatropha (in land not cleared for agriculture). Our other dimensions are measured in diverse units. For employment vulnerability we develop a weighted average of sectoral emissions intensities, where the weights are shares of total employment. For sequestration, we separately index the potential contribution of reduced deforestation [(annual CO2 emissions from deforestation)/(total annual CO2 emissions)] and underground storage. To index storage potential, we divide total annual CO2 emissions into total CO2 storage capacity in existing coal, oil and gas fields, plus saline aquifers that extend into territorial waters. We measure two dimensions of impact vulnerability: sea level rise and weather damage. Our index for sea level rise is the estimated percentage of GDP accounted for by areas that will be covered by a 3-meter rise. For weather damage, we develop an index of population affected by climate-related events since 1960 and divide it by total population in 1980. After indexing the basic dimensions of the problem, we assess the structure of the data. We find generally low correlations among dimensions, except for carbon sequestration and nonrenewable energy resources. The low correlations imply that each dimension adds significant independent information about country circumstances, making it difficult to generalize about "typical" conditions confronting countries subject to an international emissions-reduction protocol. We extend the analysis by combining information from the six dimensions into weighted indices. We test for the existence and robustness of country clusters by computing five indices with very different weights. We find large numbers of countries that have persistently high or low index values, implying a high likelihood that many countries do in fact have very different stakes in a global 23 protocol. Countries with positively-oriented states are highly clustered in Latin America and West Africa, while Eastern Europe and Central Asia have a large number of countries with unfavorable stakes. Other regions have mixed conditions, but individual countries within those regions often have persistently favorable or unfavorable stakes. Unfortunately, countries with unfavorable stakes (by our composite measure) include two of the largest emitters ­ India and China. Many other significant emitters have unfavorable stakes as well. Together, the states with unfavorable stakes account for almost half of all CO2 emissions from World Bank partner countries. In contrast, countries with favorable stakes account for about 19% of total emissions. We perform an alternative assessment of protocol orientation by positioning countries according to their source and impact vulnerability. Among countries in the two most extreme categories, we find clear regional differences. Those with the most positive orientation to a global protocol (`Low' source vulnerability, `High' impact vulnerability) are all low- or middle-income states, and are heavily concentrated in Latin America, Sub- Saharan Africa and Southeast Asia. Conversely, countries with the most negative orientation (`High' source vulnerability, `Low' impact vulnerability) are almost all middle- or high-income states in Eastern Europe, Central Asia and the Middle East. We draw four broad conclusions from this exercise. First, with six dimensions of vulnerability affecting country stakes, even neighboring states in the same region can have very different orientations toward a global protocol. Policy analysis and dialogue should therefore be tailored to specific conditions in each country. Second, despite significant country-level variation in each region, our analysis does indicate sufficient regional clustering to warrant some attention to regional strategies. Third, even with good information and programs tailored to country conditions, our results suggest that many countries will resist a global protocol unless they are compensated for disadvantages associated with source vulnerability. Many countries have persistently unfavorable stakes in emissions reduction, no matter how we index their relative vulnerability. As a group, furthermore, they account for almost half of all CO2 emissions from the World Bank's partner countries. Ultimately, we believe that successful negotiation of a global protocol will require the design of compensation and cross- 24 subsidy mechanisms that reflect the dimensions of vulnerability that we identify in this paper. Having said this, we should conclude on a note of optimism. Although individual countries have very different stakes in climate change negotiations, the global stakes are much clearer and more positive. Our assessment of renewable energy alternatives suggests that the world community can draw on enormous clean energy sources to ease the transition to global sustainability. The ultimate keys to negotiating a global protocol are neither technical nor economic, but institutional and political. Once the international community becomes convinced that we face a climate crisis, it should be possible to organize the politics of compensation and cross-subsidy that will have to accompany a truly global protocol for greenhouse emissions reduction. 25 Table 1: Distribution of CO2 Intensities by Income Group (164 Countries ­ Intensities in tons/$10,000) Group # Income Group Min Q1 Median Q3 Max 1 $ 460 - $ 2,000 0.188 0.895 2.040 3.173 31.750 2 $ 2,000 - $ 4,999 0.682 2.295 4.364 8.144 20.314 3 $ 5,000 - $12,499 1.581 2.781 4.890 6.932 22.849 4 $12,500 - $56,300 0.297 2.847 3.629 4.911 18.339 Total 0.188 1.987 3.470 5.761 31.750 Source: World Bank: World Development Indicators, 2005 Table 2: CO2 Intensities and Income Per Capita ($US PPP 2000): Selected Low- and High-Income Countries CO2 CO2 Low-Income Intensity Income Per High-Income Intensity Income Per Countries (2000) Capita Countries (2000) Capita Kenya 3.10 1,002 Norway 3.16 35,132 Nigeria 3.24 878 Netherlands 3.20 27,229 Pakistan 3.94 1,925 United Kingdom 3.91 24,675 Cote d'Ivoire 4.18 1,585 Finland 4.11 25,141 Yemen, Rep. 5.84 826 United States 5.82 34,114 Source: World Bank: World Development Indicators, 2005 Table 3: Largest CO2 Emitters by Income Group (CO2 in `000 tons/year) Low and Low- Cum. High-Middle and Cum. Middle Income CO2 Percent Percent High Income CO2 Percent Percent China 2,790,451 49.2 49.2 United States 5,601,509 32.9 32.9 India 1,070,859 18.9 68.1 Russian 1,435,057 8.4 41.3 Federation Ukraine 342,771 6.0 74.1 Japan 1,184,502 7.0 48.3 Indonesia 269,568 4.8 78.8 Germany 785,510 4.6 52.9 Egypt, Arab Rep. 142,226 2.5 81.4 United Kingdom 567,843 3.3 56.2 Kazakhstan 121,275 2.1 83.5 Canada 435,858 2.6 58.8 Uzbekistan 118,626 2.1 85.6 Italy 428,171 2.5 61.3 Pakistan 104,805 1.8 87.4 Korea, Rep. 427,014 2.5 63.8 Philippines 77,530 1.4 88.8 Mexico 423,972 2.5 66.3 Belarus 59,152 1.0 89.8 Saudi Arabia 374,344 2.2 68.5 France 362,432 2.1 70.6 Subtotal 5,097,262 89.8 Australia 344,760 2.0 72.7 South Africa 327,280 1.9 74.6 Overall Total 5,673,953 100.0 Iran, Islamic Rep. 310,301 1.8 76.4 Brazil 307,520 1.8 78.2 26 Poland 301,346 1.8 80.0 Spain 282,934 1.7 81.6 Turkey 221,555 1.3 82.9 Thailand 198,647 1.2 84.1 Korea, Dem. Rep. 188,857 1.1 85.2 Venezuela, RB 157,750 0.9 86.1 Malaysia 144,413 0.8 87.0 Netherlands 138,866 0.8 87.8 Argentina 138,188 0.8 88.6 Czech Republic 118,772 0.7 89.3 Belgium 102,244 0.6 89.9 Greece 89,603 0.5 90.4 Algeria 89,416 0.5 91.0 Romania 86,280 0.5 91.5 Iraq 76,336 0.4 91.9 Israel 63,098 0.4 92.3 Austria 60,848 0.4 92.6 Portugal 59,833 0.4 93.0 Chile 59,500 0.3 93.3 Singapore 59,045 0.3 93.7 Subtotal 15,953,603 93.7 Overall Total 15,868,476 100.0 Source: World Bank, World Development Indicators, 2005 27 549 328 58 51 50 29 10 4 3 Shale 4,478 a d ntry cco e ouC Jordan Moro ustraliA SAU razilB Ukrain Israel Thailan Turkey Albania 7 2 0 700 181 Bitumen tryn nia ada Cou Venezuela Can Jordan USA Roma 180 157 155 150 142 136 134 106 105 99 83 82 72 72 70 64 58 53 49 49 43 36 33 32 24 20 19 16 16 Gas 1,911 a Repc Guine bago To a stan n n ds New stan & esh trynu Islami a kh ad Arabi a ria kista lanr sia rkmeni ssi Co Qatar Iran, Tu Bolivia UAE Brunei Papua Algeria Azerbaija Yemen Iraq Oman Norway Venezuela Libya Ru Kuwait Kaza Trinid Saudi Nige Malaysia Myanmar Egypt Uzbe Peru Australi Banglad Nethe Indone Oil 630 535 359 287 273 272 213 190 164 146 138 117 97 91 82 79 71 70 55 55 52 51 49 22 17 12 12 11 11 10 no a Repc inea Rep bago Gu To n Consumpti Arabi stan al & Arab a ad a ntry go n Islami kh or d iar Energycti Cou Kuwait Iraq UAE Saudi Con Libya Venezuela Gabo Qatar Iran, Angola Kaza Equatori Yemen Azerbaija Ecuad Brunei Oman Cha Sudan Algeria igeN orwayN yrianS ssiuR Trinid eruP exico M unisiT alaysia M 6 5 5 4 2 1 1 0 0 Coal 382 343 242 157 136 117 80 68 60 50 47 33 27 21 20 20 20 19 18 14 12 Domes Total mod ofs p nd a stan States Re we n a sia a d King Rep ntry kh Africa iab e a gary Zeala ce any Year ssi w ada an 1)x in Cou Australi Kaza South Colom Ukrain Ru India Poland United Czech Chin Zimbab Hun Pakista Brazil Indone Ne Bulgari Gree Can Turkey Venezuela Germ Thailan Mexico Vietnam Spain United Jap Korea, ts,si ndiep 734 643 511 465 461 412 360 247 245 242 217 205 178 171 164 160 142 138 131 125 119 107 104 102 98 93 86 83 Ap Depo Total 3,364 1,270 (see Fuel ssil a Repc Fo:4 n stan a stan WEC,PB States ntry kh Africa cco Arabi Islami e a bia go n ble ssi ada urces: Ta Cou Jordan Qatar Venezuela Australi Kuwait Kaza Iraq Moro UAE Libya Saudi South Iran, Con Ukrain Ru Colom Can Gabo Brunei Yemen Azerbaija Algeria United Angola Oman Turkmeni Bolivia Norway India So Table 5: Employment Vulnerability Indices (EVI) by Group Eastern Europe / OECD Low-Income EVI Central Asia EVI Middle Income EVI High Income EVI Tanzania 100 Poland 39 China 29 Korea, Rep 10 Zimbabwe 43 Ukraine 39 Iran, Islamic Rep 26 Greece 10 India 43 Bulgaria 36 Ecuador 23 Portugal 9 Togo 27 Serbia and Montenegro 36 Trinidad Tobago 18 Belgium 8 Congo 24 Lithuania 34 Oman 17 Canada 7 Nicaragua 23 Moldova, Rep 33 Egypt 17 Netherlands 7 Yemen 20 Slovakia 31 Guatemala 16 Australia 5 Ethiopia 12 Czech Rep 25 Colombia 15 Finland 5 Russian Federation 20 Philippines 11 Norway 5 Georgia 20 Peru 11 New Zealand 5 Latvia 16 Brazil 9 Germany 5 Hungary 14 Mexico 9 France 4 Kazakhstan 14 Dominican Rep 9 Spain 4 Turkey 9 Panama 8 Italy 4 Slovenia 6 Argentina 5 UK 4 Albania 5 Costa Rica 4 Ireland 4 Croatia 4 Japan 4 Sweden 3 Denmark 3 Switzerland 3 Luxembourg 2 Iceland 2 Table 6: Employment Vulnerability Index Statistics by Group Group Min Q1 Median Q3 Max Low Income 12 22 26 43 100 Eastern Europe / Central Asia 4 14 20 34 39 Middle Income 4 9 13 18 29 OECD High Income 2 4 5 7 10 Total 2 5 10 23 100 rmal 2.37 0.65 0.33 0.32 0.25 0.25 0.16 0.14 0.10 0.09 0.08 0.08 0.08 0.06 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 Geothe a Rep p Guine n stan nd Re n n a a Dem tans stan a cco d na d New a sia a s ntry ssi go, Zeala kista kh w a gary ouc Mongoli Bolivia Kyrgyzsta Namibi Tajiki Moro Peru Turkmeni Icelan Ru Botswa Con Ne Indone Kenya Armenia Latvia Moldova, Zambia Thailan Uzbe Azerbaija Estonia Tanzania Kaza Cub Sudan Egypt Lithuani Bulgari Papua Mexico Spain Hun Cypru ses)ac 6.50 4.33 4.03 4.02 3.47 3.12 2.66 2.38 2.34 2.17 2.13 2.07 1.74 1.73 1.50 1.48 1.40 1.29 1.04 0.97 0.83 0.82 0.78 0.68 0.62 0.62 0.61 0.60 0.58 0.52 0.44 0.44 0.39 0.38 0.35 ce Hydro sour tea a ovina Rep Guine r e ndsa n ca Herzeg Isl ue (Intermedi Dem New asca a on no tryn tans a & go n uay d go, PDR la ro odia Ri s eonL bia mbiq cou Tajiki Con Gabo Con apuaP sta Kyrgyzsta Lao Guyana Bolivia Madag Georgia Mongoli Parag Peru Icelan Nep Came Camb Ethiopia Co Myanmar Angola Cypru Sierra Brazil Albania Namibi Norway Colom Chile Bosnia Solomon Venezuela Moza Zambia 94 56 30 52 25 Consumpti 9.27 7.86 6.98 3. 2.82 2. 2.41 2. 1.86 1.78 1.42 0.90 0.86 0.71 0.69 0.68 0.63 0.57 0.53 0. 0.50 0.49 0.39 0.36 0.36 0.32 0.32 0.26 0. 0.24 0.22 0.22 0.20 0.13 0.12 gy Wind Ener a Domestic n dn r Barbud Repc ratione a n e Antillessd tal a nia Fed d uay Verd asca & ark gua n To tryn ia tans Zeala ali e ada d sta ay a lanr Islami an ofs jiki rgyzsta w Lucia ssi Cou Mongoli Boliv Icelan Maurita Chile Parag Ta Argentin Ky Ne Georgia Sudan Ireland Azerbaija Cap Austra Madag Denm Yemen Can Antigua Armenia Nicara Cha Libya Belize Paki Norw Chin Kenya St. Vanuatu Nethe Iran, Ru ar Ye in 1)x Solar 52.48 27.21 26.17 22.63 19.05 16.74 13.78 12.47 9.31 8.71 6.32 5.84 5.58 5.47 5.46 5.34 5.30 5.02 4.13 4.09 3.99 3.82 3.80 3.48 3.42 3.24 3.13 3.11 3.07 2.88 2.78 2.67 2.66 2.65 2.44 ndi pe urces,o a Rep Ape Res n Guine ndsa Rep u Isl ue r e so sas (se.al a a nia Africa New na Dem asca Energy tryn eonL a Fa on et d r uay -Bia ro go go, n PDR mbiq na Cou Mongoli Namibi Maurita Cha Nige Mali Central Con Bolivia Guyana Sudan Papua Yemen Botswa Solomon Angola Con Gabo Zambia Peru Lao Libya Moza Madag Belize Algeria Sierra Parag Vanuatu Australi Burki Eritrea Guine Came Tanzania ack,lloP, Renewable Total 66.19 30.11 28.14 23.00 20.15 19.08 16.88 16.80 13.92 11.10 10.42 9.39 9.33 9.05 9.01 7.41 7.35 7.34 6.65 6.27 6.15 6.11 5.97 5.91 5.55 4.52 4.49 4.30 4.21 4.18 4.17 4.05 3.81 3.74 3.59 a Annual WEC,ASAN 7: Rep n Rep Guine r ndsa n ue e urces: Table a ian Isl a Africa Dem asca na a on tryn ro eonL a So d r go tans New go, n d uay PDR mbiq Cou Mongoli Maurita Namibi Cha Bolivia Nige Mali Con Central Guyana Tajiki Con Papua Gabo Icelan Parag Kyrgyzsta Sudan Lao Madag Angola Yemen Peru Solomon Botswa Zambia Chile Argentin Georgia Libya Moza Came Sierra Belize Australi Table 8: Top 35 Countries for Solar, Wind, Hydro, Geothermal by World Bank Region (by Years of Domestic Energy Consumption) Region Total Solar Wind Hydro Geothermal Sub-Saharan Africa 17 21 6 11 7 East Asia / Pacific 4 5 3 6 4 Europe / Central Asia 3 0 6 5 14 Latin America / Caribbean 7 5 8 9 3 Middle East / North Africa 2 3 3 0 2 South Asia 0 0 1 1 0 All World Bank Regions 33 34 27 32 30 Table 9a: Top 35 Countries for Biofuels, by World Bank Region (by Years of Domestic Energy Consumption) Sugar Savanna Region Total Crops Tallgrass Jatropha Biogas Sub-Saharan Africa 25 26 0 29 17 East Asia / Pacific 1 3 2 0 5 Europe / Central Asia 3 0 14 0 0 Latin America / Caribbean 5 6 3 3 9 Middle East / North Africa 0 0 5 2 1 South Asia 0 0 0 0 3 All World Bank Regions 34 35 24a 34 35 a of 27 total countries with significant savanna tallgrass potential 0.21 0.17 0.12 0.11 0.11 0.10 0.10 0.10 0.10 0.08 0.08 0.08 0.08 0.07 0.07 0.06 0.06 0.06 0.06 0.06 0.05 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 Biogas Rep n u so sas r a Fa Africa nia asca esh on a tryn uay a na -Bia n or odia guay d PDR la gua iab r ro cco Cou Uru Mongoli Mali Cha Sudan Parag Bolivia Namibi Burki Central Ethiopia Lao Maurita Guine Madag Senegal Nep Myanmar Banglad Eritrea Nicara Tanzania Nige Vanuatu Brazil Came Colom Benin Pakista Ecuad Camb Argentin Guyana Kenya Moro pha 65.05 55.43 38.28 35.32 32.56 28.96 15.39 13.83 13.53 13.25 10.55 8.31 8.08 7.70 7.28 6.75 6.65 6.47 5.61 5.51 4.93 4.28 4.08 3.82 3.61 3.58 3.50 3.49 3.33 3.22 3.22 3.09 2.87 2.47 2.42 Jatro Rep. Rep u ue so sas e a nia Afr. na Dem Fa we on a tryn eonL d r go go, mbiq na -Bia a uay ro uti n d'Ivoire guayu Cou Namibi Maurita Cha Central Mali Nige Botswa Angola Sudan Con Zambia Con Moza Ur Burki Guine Senegal Eritrea Benin Tanzania Yemen Zimbab Guine Parag Togo Came Kenya Ethiopia Bolivia Australi Djibo Gabo Côte Sierra Malawi 35 llgrass 448.51 24.24 17.80 14.87 12.81 4.67 3.23 2.86 1.87 1.73 1.59 1.53 1.49 1.36 1. 1.33 1.16 0.94 0.88 0.83 0.66 0.41 0.33 0.30 0.27 0.14 0.02 Ta umption Cons gyr Rep p Ene n nd Re n Repc a a n stan stan tryn zsta kh tans a Arab Zeala a a ada kista ein States nia ed a Islami eni ongoli w ssi Domestic Cou M Kyrgy Argentin Kaza Tajiki Armenia Australi Syrian Ne Can Moldova, Ru Oman Jordan Georgia Uzbe Ukra Chile Turkey aqIr Unit Roma Chin Iran, Azerbaija Turkm Bulgari taloT 91 51 ofs Sugar 41.63 21.38 15.91 13.92 13.50 11.11 10.09 8.81 8.20 7. 7.73 7.24 6.97 6.64 6.60 6.23 5.98 5.76 5.01 4.85 4.81 4.76 4.72 3.85 3.82 3.38 3.26 2.87 2.73 2.53 2. 2.41 2.36 2.28 2.15 ar Ye in Rep Rep u ue r so sas e ndsa inea Gu Afr. a Dem asca Fa Isl on we tryn mbiq n uay na ro d'Ivoire -Bia a eonL a a odia guay d go go, New r Resources, Cou Central Uru Cha Bolivia Con Moza Zambia Mali Gabo Angola Parag Namibi Con Sudan Guyana Madag Tanzania Burki Came Benin Cote Guine Argentin Togo Sierra Solomon Senegal Guine Malawi Ghan Pap. Camb Nige Brazil Zimbab ofuel 1x Bi ndi Total 448.68 77.03 72.39 56.09 54.30 41.49 31.37 29.29 26.76 24.27 23.15 21.75 20.65 20.28 19.20 17.35 16.86 15.29 15.20 13.14 12.92 11.65 11.58 11.53 11.30 10.50 9.97 8.63 8.30 8.24 7.73 7.70 7.48 6.98 6.86 pe Ap Annual u See 9b: n ue so sas r a Afr.Rep a nia a na DR stan Fa on asca a urces: Table tryn d r uay a-Bi ro a guay go mbiq go, kh na tans n d'Ivoire So Cou Mongoli Central Namibi Maurita Cha Mali Nige Uru Con Kyrgyzsta Argentin Angola Zambia Sudan Moza Bolivia Botswa Con Kaza Burki Tajiki Parag Guine Tanzania Gabo Benin Senegal Came Madag Guyana Australi Ce Togo Guine Eritrea taloT 2.8 1.9 1.2 1.1 0.9 a she Asia la lad Lank S. Nep Pakistan Sri Bang India taloT 11.1 6.3 5.7 4.6 3.9 3.8 3.3 2.7 2.0 1.2 0.9 0.1 Afr N ai n on ME, emenY yaibL outi lgerA Djib manO orocco M airyS ordaJ unisiaT ptygE an ranI Leb taloT 37.5 31.7 27.5 19.3 19.1 6.7 6.4 5.5 4.4 3.8 3.8 2.6 2.6 2.2 1.9 1.8 1.3 1.1 1.1 0.7 0.7 0.5 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 da p Carib/ Re Nevis y na a ua yau a a aib aug a Barbu n a dor obagoT uel or Ric a/ ica cai a a dos Biofuels Amer Kitts/ yana e e duras Salva Lucia Vincent Lat. Bolivi Urug Argenti Gu Parag Peru Brazil Chil Colom Nicara Beliz Venez Ecuad Hon Panam Costa Guatemal Mexico Haiti Antigu Domin El St. Domin Jamaic Saint St. Grenad Barba inidad/rT taloT 31.6 23.3 16.9 6.6 6.0 3.2 3.1 2.3 1.9 1.7 1.7 1.5 1.4 1.4 1.4 1.4 1.3 1.3 0.9 0.9 0.7 0.6 0.6 0.5 0.4 0.4 0.4 0.3 Geothermal, o, iasA gov. egro Hydr RYF 33 n n Rep nd,i a a ant a, e ija a y W, Central ia oni yzsta ova, a Herze/ai n ey su nia nia ani a Monten/a epR aria nd ian lar ma gar Eur., rgyK jikistanaT Kazakhsta Georgi Armeni Mold Russi Latvia Ukrain Uzbekis Bosn rkuT Azerba Be Alba Ro Lithu rkmenistanuT Croati Serbi Hun Estonia Bulg Pola Slovak Czech Slove Maced Solar ialt taloT 6 5 1 514.9 12.6 9.3 8.8 4.9 3.9 3.3 1.5 1.2 1.2 0.8 0.7 0. 0.6 0. 0.2 0. tenoP umption): a ine rgy Cons Pac./ Is. p a Gu a a oli w on R odi ines a Re Ene gyr PD Asia Ne marnay atu a esi pp ysi Ene E. Mong P. Solom Lao Camb M Vanu Fiji Chin Indon Vietnam hailandT Phili Mala Samoa ongaT Korea, Renewable taloT 9.6 9.5 9.0 8.9 8.5 8.0 6.5 6.4 5.7 3.1 2.7 2.2 2.0 1.6 1.4 1.3 0.9 0.9 0.7 0.2 100.5 90.9 86.2 77.3 58.4 50.4 43.6 27.9 27.6 25.2 24.7 23.4 22.4 20.3 15.9 14.6 14.2 14.1 12.7 12.5 12.5 10.1 taloT Domestic of Africa epR u nea 10:elbaT Years an Rep. e Gui e on d (by ia Afr.l ian Dem euq ew gascar a o ial d go la n go, anaw soaF a -Bissaa gal eonL n Ivoired' ia iw id nda ia Africa Verd da e 10: Sub-Sahar Namib Centra Maurita Cha Mali Niger Con Ango Suda mbiaaZ ay Con Mozambi Bots Gabon Burkin Mada Guine anzaniaT Camero Sene Beni Sierra Côte Eritrea Guine ogoT Ethiop imbabZ Ken Mala Ghana Uga Gambia Burun Niger anilzawS Lesoth South Equator Cap anwR Comoros Table % 79.6 51.2 28.4 10.4 n ka n ia al As Lan S. epN riS fghanistaA akistaP % 11.3 4.3 3.2 2.2 1.7 1.7 1.6 1.1 a Repc Afric N./ a cco non Islami ME Tunisi Moro Leba Algeria Egypt Iran, Yemen Libya % 94.7 90.2 80.5 80.1 72.8 72.5 68.0 61.7 58.4 56.6 44.2 44.1 39.8 37.6 26.5 21.3 16.9 16.7 16.0 15.9 a ca a gua or Ri bia Carib./ emal uay ac duras LA elizeB uyanaG icaraN anamaP sta eruP Guat oliviaB razilB cuadE Salvador Hon Co Parag Colom Venezuela El Haiti Chile Jamai Argentin Mexico Clearing: % 28.9 17.4 17.2 8.8 6.6 6.2 5.5 5.4 2.8 ndaL a CA s enia a Other Eur., Latvia lbaniaA Lithuani stoniaE elaruB lovakiaS Turkey lovS ssiuR and % 94.3 83.7 83.6 80.8 76.4 50.0 45.0 41.6 33.3 15.3 2.9 1.8 tation fores ficic ea ndsa De Pa/ s Isl Guin sia d a ia odia from PDR As New E. P. Myanmar Indone Malaysia Lao Samoa Camb Philippine Solomon Thailan Fiji Mongoli sions Emissions Emis taloT % 94.5 93.0 86.0 85.8 83.0 80.4 76.9 71.9 70.2 66.4 65.8 63.8 59.3 59.3 59.0 58.6 57.4 56.1 53.1 50.2 44.1 38.4 36.7 34.4 26.7 24.1 24.0 19.7 18.5 17.6 16.4 16.0 12.6 5.4 2.8 of nhousee Afr. Rep inea u e r Rep Gre Percent ue sas so Dem on 11: a Gula a asca we na Afr. a Fa go, d'Ivoire eonL ro di nda go a a ria mbiq -Bia n d r na Table Sub-Saharan Liberi Zambia Con Côte Benin Malawi Sierra Came Burun Rwand Madag Equatori Togo Uga Zimbab Con Ghan Botswa Guine Nige Central Moza Guine Angola Gabo Sudan Mali Tanzania Kenya Namibi Cha Senegal Ethiopia Nige Burki 202 68 58 24 esh ka n Asia Lan S. Banglad Sri Pakista India 459 428 369 353 329 270 96 95 56 48 6 Afr N cco a non ME, Yemen Iran Iraq Libya Oman Algeria Egypt Moro Tunisi Syria Leba 165 112 99 60 41 39 36 35 34 21 19 15 12 10 9 7 7 6 4 2 1 Carib/ Rep e ca a n a or Ri bia ca ca gua Amer Lat. Venezuela Surinam Chile Bolivia Mexico Ecuad tasoC duras rgentinA aitiH olomC amaiJ eruP Salvador Domini Hon El Brazil Guyana Panama Nicara Guatemal Belize 184 62 58 26 17 11 8 7 5 3 2 1 Asia issions ral negro mE nte p Cent a a a nia Mo Re rent ssi atia Cur Europe, Ru Albania Cro Poland Bulgari Latvia stoniaE omaR Turkey Serbia Lithuani Czech ofs 54 26 24 22 13 8 6 3 Year 1,678 ntial:teoP ea s no Pacific/ Guin d sia Rep a ati Asia New E. Vietnam Chin P. Thailan Malaysia Indone Myanmar Philippine Korea, Sequestr l 917 406 268 182 156 106 101 75 63 50 49 42 38 27 25 15 14 11 7 5 4 4 3 9,939 7,582 1 ndix inea Africa u Appe Underground an sas Gu e ue r See 12: a Tome -Bia al n Africa eonL ria go d mbiq a a a asca uti d'Ivoire Table Sub-Sahar Gambia Sao Namibi Guine Equatori Gabo Angola Senegal Nige Con Cha South Sierra Moza Guine Ghan Liberi Madag Djibo Tanzania Côte Togo Sudan Kenya Benin Source: Table 13: Regional Distribution of Information on the Impact of Sea-Level Rise Coastal Coastal, Mean Non-Island Impact Impact Countries Island Landlocked Not Region (GDP %) Impacted Countries Countries Estimated Total Sub-Saharan Africa 2.5 27 5 15 47 East Asia / Pacific 5.6 10 9 2 1 22 Eastern Europe / Central Asia 1.1 7 0 14 7 28 Latin America / Caribbean 2.9 21 8 2 0 31 Middle East / North Africa 2.6 9 0 0 5 14 South Asia 1.8 4 1 3 0 8 Total 2.6 77 23 36 14 150 % .843 1.57 1.57 0.33 Asia esh ka n Lan South Banglad Sri India Pakista % 12.13 4.87 2.41 1.36 0.74 0.65 0.59 0.59 0.48 st ca Ea Afri a tiu cco Middle North Egypt Tunisi Libya Oman Djibo Algeria Yemen Iran Moro % 19.86 14.47 5.56 4.37 3.20 1.71 1.50 1.38 1.29 1.22 1.16 1.03 0.76 0.60 0.55 0.48 0.40 0.30 0.29 0.18 0.07 ac an Rep Ameri bbe e a n ca a or ac bia ca gua Ri Cari guay duras Latin Salvador sta Surinam Guyana Belize Ecuad Uru Panama Venezuela Argentin Mexico Brazil Jamai Peru Colom El Domini Nicara Hon Haiti Co Chile Guatemal % 17 24. 7.46 5.59 4.95 4.94 3.97 2.12 1.75 0.60 0.11 ea ia Ast ci s cifaP d odia sia ne Rep Guin Eas a anmar New GDP Vietnam Thailan Chin Camb Indone My Philippi Malaysia Korea, P. of % s:tea % 1.99 1.54 1.53 1.10 0.85 0.56 0.19 / Estimt Asia e nia a pac Eastern Europe Im Central Georgia Ukrain Estonia Turkey Poland Roma Bulgari Rise % 17.48 14.76 8.12 7.60 3.31 1.83 1.58 1.37 1.25 1.21 0.96 0.93 0.76 0.67 0.42 0.37 0.30 0.23 0.21 0.19 0.19 0.11 0.10 0.06 0.05 0.04 0.03 a-Level Se n 14: ra u inea Rep ha a e ue r Table Sa-b Afric ian sas a -Bia Gula on Dem a ria a a asca d'Ivoire eonL mbiq n ro Africa Su go go, Maurita Benin Senegal Gambia Guine Guine Nige Ghan Liberi Côte Sierra Moza Angola Madag Gabo Ethiopia Kenya Togo Equatori Somalia Came Tanzania Con Sudan Con South Namibi Table 15: Weather Damage Index: Regional Distributions Region Min Q1 Median Q3 Max South Asia 64.5 78.9 161.5 441.6 1,940.2 East Asia / Pacific 0.6 67.9 193.5 339.5 698.2 Sub-Saharan Africa 0.4 21.4 88.4 194.7 1,809.0 Latin America / Caribbean 1.3 30.7 68.6 191.0 818.5 Middle East / North Africa 0.0 11.1 17.6 29.3 585.9 Eastern Europe / Central Asia 0.1 1.0 4.7 17.1 140.0 Table 16: Weather Damage Index (WDI) by Region SS Africa WDI E. Asia / Pacific WDI Lat. Am./Carib. WDI MENA WDI South Asia WDI Ethiopia 1,809.0 Tonga 698.2 Honduras 818.5 Iran 183.1 Bangladesh 1,940.2 Mozambique 1,133.8 Samoa 589.0 Antigua Barb. 387.0 Jordan 32.9 India 565.5 Sudan 999.2 Lao PDR 572.8 Belize 384.7 Tunisia 29.3 Sri Lanka 317.8 Djibouti 585.9 Solomon Islands 416.0 Haiti 253.7 Yemen 27.5 Pakistan 172.0 Botswana 536.2 Philippines 391.6 Nicaragua 241.5 Syria 18.4 Maldives 151.1 Somalia 496.8 Vanuatu 339.5 Venezuela 215.1 Algeria 17.6 Nepal 84.4 Mauritania 432.9 Fiji 310.4 St. Lucia 212.2 Oman 14.5 Afghanistan 73.5 Malawi 411.2 Vietnam 234.8 Dominican Rep 191.0 Morocco 13.3 Bhutan 64.5 Zimbabwe 393.6 China 222.6 Dominica 181.9 Iraq 11.1 Swaziland 351.7 Cambodia 213.4 Bolivia 124.1 Lebanon 5.6 Chad 260.1 Marshall Islands 193.5 Jamaica 117.3 Egypt 3.7 Benin 196.9 Mongolia 187.7 Guyana 99.4 Libya 0.0 Niger 194.7 Kiribati 150.9 Costa Rica 76.7 Comoros 186.3 Micronesia 121.3 St. Kitts Nevis 74.1 Madagascar 162.6 Thailand 106.0 Peru 73.0 Gambia 158.4 P. New Guinea 67.9 Argentina 68.6 Senegal 155.5 Korea, Rep 64.1 Paraguay 68.0 Zambia 137.5 Myanmar 27.2 Brazil 61.8 Mauritius 125.0 Indonesia 19.9 Chile 54.5 Lesotho 112.6 Malaysia 11.7 Ecuador 49.3 Rwanda 107.3 Timor-Leste 0.6 Colombia 41.7 Sao Tome 104.5 El Salvador 36.7 Kenya 102.3 St. Vincent 34.2 Eritrea 89.5 Barbados 30.7 Burkina Faso 87.2 Guatemala 29.5 Namibia 77.4 Mexico 26.1 Tanzania 69.7 Panama 23.0 Mali 64.0 Grenada 8.0 Angola 62.2 Trinidad Tobago 7.6 Togo 58.5 Uruguay 5.8 Liberia 55.2 Suriname 1.3 Ghana 32.9 Burundi 28.5 Uganda 25.3 Seychelles 21.8 CAR 21.4 Guinea- Bissau 18.5 Nigeria 15.1 Cape Verde 13.7 South Africa 10.4 Sierra Leone 9.1 Guinea 5.7 Congo 5.7 Cameroon 5.7 Congo,, DR 5.0 Gabon 1.4 Cote d'Ivoire 0.4 Table 17: Correlations Between Country Indicators Non- Renewable Employment Sequestration Sequestration Sea Level Renewable Energy Vulnerability (Storage) (Reduced Rise Energy Deforestation) Impact Renewable Energy -0.07* Employment -0.02 -0.45* Vulnerability Sequestration -0.56* 0.03 0.07 (Storage) Reduced 0.04 0.49* -0.35* -0.16* Deforestation Sea Level Rise Impact 0.08* -0.42* 0.33* 0.35* -0.30* Weather Impact 0.20* 0.36* -0.30* -0.13* 0.29* -0.13* * Denotes significance at 95% Table 18: Alternative Orientation Index Weights Non- Renewable Renewable Energy Sequestration Sea Level Weather Energy Employment Weighting Emphasis Resources Potential Rise Damage Resources Vulnerability Energy Resources 0.3 0.1 0.1 0.1 0.3 0.1 Impact Vulnerability 0.1 0.1 0.3 0.3 0.1 0.1 Neutral 0.167 0.167 0.167 0.167 0.167 0.167 Positive Source Factors 0.3 0.3 0.1 0.1 0.1 0.1 Negative Source Factors 0.1 0.1 0.1 0.1 0.3 0.3 39 Table 19: Country Orientation Toward a Global Protocol: Robustness in Different Scenarios Counts Index Values Region Subregion Country Group 1 2 3 I1 I2 I3 I4 I5 AFR East Africa Ethiopia High 5 0 0 1 1 1 1 1 AFR East Africa Madagascar High 4 1 0 1 1 1 1 2 AFR Southern Africa Mozambique High 4 1 0 1 1 1 1 2 AFR Sahelian Africa Mauritania High 4 1 0 1 1 1 1 2 AFR Coastal West Africa Benin High 5 0 0 1 1 1 1 1 AFR Coastal West Africa Senegal High 5 0 0 1 1 1 1 1 AFR Coastal West Africa Guinea-Bissau High 4 1 0 1 1 1 1 2 AFR Coastal West Africa Ghana High 4 1 0 1 1 1 1 2 AFR Coastal West Africa Gambia High 4 1 0 1 1 1 1 2 AFR Coastal West Africa Togo High 3 2 0 1 2 1 1 2 AFR Coastal West Africa Guinea High 3 2 0 1 2 1 1 2 AFR Coastal West Africa Sierra Leone High 3 2 0 1 2 1 1 2 AFR Central Africa Zambia Middle 2 3 0 1 2 2 1 2 AFR Central Africa Congo, Dem Rep Middle 2 2 1 1 3 2 1 2 AFR Central Africa Angola Middle 1 3 1 2 2 2 1 3 AFR Central Africa Central African Rep Middle 1 3 1 1 3 2 2 2 AFR Central Africa Cameroon Middle 1 3 1 1 3 2 2 2 AFR East Africa Tanzania Middle 2 3 0 1 2 2 1 2 AFR East Africa Sudan Middle 2 2 1 2 1 2 1 3 AFR East Africa Kenya Middle 1 4 0 1 2 2 2 2 AFR East Africa Malawi Middle 1 4 0 1 2 2 2 2 AFR Sahelian Africa Niger Middle 1 4 0 1 2 2 2 2 AFR Sahelian Africa Mali Middle 1 3 1 1 3 2 2 2 AFR Sahelian Africa Burkina Faso Middle 1 3 1 1 3 2 2 2 AFR Sahelian Africa Chad Middle 1 3 1 2 2 2 1 3 AFR Coastal West Africa Côte d'Ivoire Middle 2 3 0 1 2 2 1 2 AFR Central Africa Congo Low 1 0 4 3 3 3 1 3 AFR Central Africa Burundi Low 0 2 3 2 3 3 2 3 AFR Central Africa Rwanda Low 0 0 5 3 3 3 3 3 AFR East Africa Uganda Low 0 2 3 2 3 3 2 3 AFR Southern Africa Zimbabwe Low 0 3 2 2 2 3 2 3 AFR Southern Africa Lesotho Low 0 1 4 2 3 3 3 3 AFR Coastal West Africa Nigeria Low 0 2 3 3 2 3 2 3 AFR Coastal West Africa Equatorial Guinea Low 0 0 5 3 3 3 3 3 EAP Southeast Asia Philippines High 4 1 0 1 1 1 2 1 EAP Southeast Asia Thailand Middle 2 3 0 2 1 1 2 2 EAP Southeast Asia Indonesia Middle 1 4 0 2 1 2 2 2 EAP China China Low 1 2 2 3 1 2 2 3 EAP Northeast Asia Korea, Rep Low 1 2 2 3 2 2 3 1 ECA Eastern Europe Georgia High 4 1 0 1 1 1 2 1 ECA Middle East Turkey Middle 0 5 0 2 2 2 2 2 ECA Western Asia Tajikistan Middle 1 4 0 1 2 2 2 2 ECA Eastern Europe Moldova, Rep Low 0 3 2 2 3 3 2 2 ECA Eastern Europe Armenia Low 0 3 2 2 3 3 2 2 ECA Eastern Europe Belarus Low 0 2 3 2 3 3 3 2 ECA Eastern Europe Romania Low 0 1 4 2 3 3 3 3 ECA Eastern Europe Macedonia, FYR Low 0 0 5 3 3 3 3 3 ECA Eastern Europe Ukraine Low 0 0 5 3 3 3 3 3 ECA Eastern Europe Poland Low 0 0 5 3 3 3 3 3 Serbia and ECA Eastern Europe Montenegro Low 0 0 5 3 3 3 3 3 ECA Eastern Europe Hungary Low 0 0 5 3 3 3 3 3 ECA Eastern Europe Bulgaria Low 0 0 5 3 3 3 3 3 ECA Eastern Europe Slovakia Low 0 0 5 3 3 3 3 3 40 Counts Index Values Region Subregion Country Group 1 2 3 I1 I2 I3 I4 I5 ECA Eastern Europe Czech Rep Low 0 0 5 3 3 3 3 3 ECA Eastern Europe Estonia Low 0 0 5 3 3 3 3 3 ECA Western Asia Kyrgyzstan Low 1 2 2 1 3 3 2 2 ECA Western Asia Azerbaijan Low 0 0 5 3 3 3 3 3 ECA Western Asia Kazakhstan Low 0 0 5 3 3 3 3 3 ECA Western Asia Uzbekistan Low 0 0 5 3 3 3 3 3 ECA Western Asia Turkmenistan Low 0 0 5 3 3 3 3 3 LCR Andean South America Peru High 5 0 0 1 1 1 1 1 LCR Andean South America Ecuador High 3 2 0 2 1 1 1 2 LCR Central America Panama High 5 0 0 1 1 1 1 1 LCR Central America Belize High 5 0 0 1 1 1 1 1 LCR Central America Honduras High 5 0 0 1 1 1 1 1 LCR Central America Costa Rica High 5 0 0 1 1 1 1 1 LCR Central America Nicaragua High 5 0 0 1 1 1 1 1 LCR Central America Mexico High 3 2 0 2 1 1 2 1 LCR Caribbean Islands Dominican Rep High 3 2 0 2 1 1 2 1 LCR Caribbean Islands Jamaica High 3 2 0 2 1 1 2 1 LCR Northern South America Guyana High 5 0 0 1 1 1 1 1 LCR Northern South America Brazil High 3 2 0 2 1 1 1 2 LCR Southern South America Argentina High 5 0 0 1 1 1 1 1 LCR Southern South America Chile High 5 0 0 1 1 1 1 1 LCR Southern South America Paraguay High 4 1 0 1 2 1 1 1 LCR Southern South America Uruguay High 3 2 0 1 2 1 2 1 LCR Andean South America Bolivia Middle 2 3 0 2 2 1 1 2 LCR Central America Guatemala Middle 1 4 0 2 2 2 2 1 LCR Central America El Salvador Middle 1 4 0 2 2 2 2 1 LCR Caribbean Islands Haiti Middle 2 3 0 2 1 1 2 2 LCR Northern South America Venezuela Middle 3 1 1 3 1 1 1 2 LCR Andean South America Colombia Low 0 3 2 3 2 2 2 3 MNA East Africa Djibouti High 5 0 0 1 1 1 1 1 MNA North Africa Tunisia High 3 2 0 2 1 1 1 2 MNA Middle East Yemen Middle 1 3 1 2 2 2 1 3 MNA Middle East Oman Middle 0 4 1 3 2 2 2 2 MNA North Africa Egypt Middle 0 5 0 2 2 2 2 2 MNA North Africa Algeria Low 0 3 2 3 2 2 2 3 Libyan Arab MNA North Africa Jamahiriya Low 0 1 4 3 3 3 2 3 MNA North Africa Morocco Low 0 1 4 3 3 3 2 3 MNA Western Asia Iran, Islamic Rep Low 0 2 3 3 2 3 2 3 SAR Southern Asia Bangladesh Middle 1 3 1 2 1 2 2 3 SAR Western Asia Pakistan Middle 0 4 1 2 2 2 2 3 SAR Southern Asia Nepal Low 0 2 3 2 3 3 2 3 SAR India India Low 0 1 4 3 2 3 3 3 OTH AustraliaNZ New Zealand High 5 0 0 1 1 1 1 1 OTH AustraliaNZ Australia High 3 2 0 2 1 1 1 2 OTH Western Europe Denmark High 3 2 0 2 1 1 2 1 OTH North America Canada Middle 1 3 1 3 2 2 1 2 OTH Northeast Asia Japan Middle 2 2 1 2 1 2 3 1 OTH Western Europe Netherlands Middle 3 1 1 2 1 1 3 1 OTH Western Europe Spain Middle 2 2 1 2 1 2 3 1 OTH Western Europe Ireland Middle 1 4 0 2 2 2 2 1 OTH Western Europe France Middle 1 3 1 2 2 2 3 1 OTH Western Europe Portugal Middle 1 3 1 2 2 2 3 1 OTH Western Europe Italy Middle 1 3 1 2 2 2 3 1 OTH Western Europe Finland Middle 1 3 1 2 2 2 3 1 OTH Middle East Qatar Low 0 0 5 3 3 3 3 3 41 Counts Index Values Region Subregion Country Group 1 2 3 I1 I2 I3 I4 I5 OTH Middle East Kuwait Low 0 0 5 3 3 3 3 3 OTH Middle East United Arab Emirates Low 0 0 5 3 3 3 3 3 OTH North America United States Low 0 2 3 3 2 3 3 2 OTH Southeast Asia Brunei Darussalam Low 0 0 5 3 3 3 3 3 OTH Western Europe Belgium Low 1 2 2 3 2 2 3 1 OTH Western Europe Sweden Low 1 2 2 2 3 2 3 1 OTH Western Europe Germany Low 1 2 2 3 2 2 3 1 OTH Western Europe United Kingdom Low 1 2 2 3 2 2 3 1 OTH Western Europe Luxembourg Low 1 0 4 3 3 3 3 1 OTH Western Europe Switzerland Low 1 0 4 3 3 3 3 1 OTH Western Europe Austria Low 1 0 4 3 3 3 3 1 OTH Western Europe Greece Low 0 2 3 3 2 3 3 2 42 1 E 1 D 0.3865 1 s C 1 0.8377 0.7881 ation 1 Employment Vulnerability Correl B 0.9333 0.7508 0.6373 - elb e se wa rc 1 1 1 Non neeR Energ sou A Re -0.084 0.7019 0.8498 0.7836 0.6885 er ge 1 0 A B C D E Index Weath Dama 0.1562 -0.310 1 9 9 Level e 0.1 0.1 0.167 0.1 0.3 Ris Sea -0.104 -0.093 0.3471 Employment Vulnerability n - se 1 3 8 Non wablee rc 0.3 0.1 0.167 0.1 0.3 stratio Energy sou Re 20)1 Potential 0.1801 0.2736 Ren -0.283 -0.236 Seque (N= 20)1 er nso elb ge se (N= 1 4 9 0.1 0.3 0.167 0.1 0.1 5 9 4 ati wa rc hts Weath Dama Total 34 21 22 25 120 neeR Energy sou Re 0.3831 -0.237 0.4490 0.0330 -0.472 Weights Weig Correl 8 2 1 4 2 s Index Level e stlu Low 18 13 48 Ris 0.1 0.3 0.167 0.1 0.1 Res s urce Alternative Sea Dimensions: cer Reso ns:o n ofyra 14 2 2 5 3 2 9 37 ati Middle Resou Change Energy stratio Summ Correl 0.1 0.1 0.167 0.3 0.1 nergyE Potential 1 1 2 0 3 n se mage ble Vulnerability Potential onali Seque High 12 16 35 Climate Ri 20: stratio Dare wa ndexI Reg: 21: R ewable Level Rene- se rc 22e on Table Ren Seque Sea Weath Non Employment Table wablee Energy sou 0.3 0.1 0.167 0.3 0.1 blaT Regi AFR EAP ECA LCR MNA SAR OTHE Total Ren Re Table 23: Total CO2 Emissions in 2000 (Mt) High Medium Low China 4,890.4 Indonesia 3,065.6 Brazil 2,223.2 India 1,843.8 Other 2,379.3 3,268.8 4,243.4 Total 4,602.5 6,325.4 10,977.6 Percent 21.0 28.9 50.1 Source: World Resources Institute, Climate Analysis Indicators Tool (CAIT). Total GHG Emissions, including land-use change http://cait.wri.org/cait.php Table 24: Countries by Source and Impact Vulnerability Impact Vulnerability Source Vulnerability High Medium Low Bangladesh Afghanistan Andorra China Bhutan Azerbaijan Greece Iraq Bahrain India Korea, Rep Bosnia and Herzegovina Iran, Islamic Rep Lesotho Brunei Darussalam Jordan Morocco Bulgaria Liberia Nigeria Burundi Pakistan Poland Czech Rep Somalia Rwanda Equatorial Guinea Sudan Swaziland Estonia Ukraine Greenland United States Hungary Zimbabwe Israel Kazakhstan Kuwait High Liechtenstein Macedonia, FYR Monaco Qatar Romania Russian Federation San Marino Saudi Arabia Serbia and Montenegro Singapore Slovakia South Africa Taiwan Turkmenistan United Arab Emirates Uzbekistan West Bank and Gaza Impact Vulnerability Source Vulnerability High Medium Low Australia Algeria Armenia Canada Angola Austria Cuba Belgium Belarus Ecuador Burkina Faso Central African Rep Egypt Chad Congo Eritrea Colombia Lebanon Finland France Libyan Arab Jamahiriya Gambia Germany Luxembourg Haiti Italy Norway Indonesia Kenya Slovenia Medium Jamaica Malawi Sweden Japan Malaysia Switzerland Korea, Dem People's Rep Mali Uganda Lithuania Moldova, Rep Mauritania Nepal Netherlands Niger Spain Oman Thailand Portugal Venezuela Syrian Arab Rep Vietnam Tanzania United Kingdom Yemen Albania Bolivia Cameroon Argentina Chile Congo, Dem Rep Belize Costa Rica Croatia Benin Cyprus Côte d'Ivoire Botswana El Salvador Gabon Brazil Guatemala Iceland Cambodia Guinea Kyrgyzstan Denmark Guinea-Bissau Latvia Djibouti Ireland Turkey Dominican Rep Mexico Ethiopia Mongolia Georgia Namibia Ghana Panama Low Guyana Papua New Guinea Honduras Paraguay Lao People's Dem Rep Sierra Leone Madagascar Suriname Mozambique Tajikistan Myanmar Togo New Zealand Uruguay Nicaragua Zambia Peru Philippines Senegal Sri Lanka Tunisia Table 25: Emissions (Mt) by Source and Impact Vulnerability Impact High Medium Low Total High 7,964 8,586 4,384 20,934 (19.4%) (21.0%) (10.7%) (51.1%) Source Medium 7,556 4,779 548 12,884 (18.4%) (11.7%) (1.3%) (31.5%) Low 4,491 1,634 1,022 7,146 (11.0%) (4.0%) (2.5%) (17.4%) Total 20,010 14,999 5,954 (45.9%) (39.5%) (14.6%) 40,963 45 Figure 1: Global Distribution of Geothermal Resources (Pollack et al. 1993) (Heat flow at measurement points in mW/m2) Figure 2: Global Distribution of Solar Resources (NASA) (Yearly average insolation in kwh/m2/day) 46 Figure 3: Global Distribution of Wind Resources (Archer and Jacobson, 2005) (Yearly average wind speed at 80 meters (m/s)) 47 Figure 4: Estimated Potential Renewable Energy Production versus Current Consumption Broken lines indicate 10%, 50%, 200% and 1000% of current use. Current consumption / estimated potential renewable energy in mtoe/year. 48 Figure 5: Estimated Potential Renewable Energy Production versus Current Consumption (World Bank Regions) Broken lines indicate 10%, 50%, 200% and 1000% of current use. Current consumption / estimated potential renewable energy in mtoe/year. Figure 6: Distribution of Employment Vulnerability Indices, by Income Group x* de 40 In yti bil 30 era ulnVt 20 menyo 10 Empl 0 Low Income ECA Middle Income High Income Income Group * Top value (100) dropped from EVI 49 Figure 7: Countries by Source and Impact Vulnerability 50 Appendix 1: Data sources and references Notes: Mtoe = million tones of oil equivalent Energy conversion factors from http://www.energymarkets.eu.com/ documents/Conversionfactors_000.xls 1. Natural Gas: Proved Reserves at end 2005 Source: BP Statistical Review of World Energy, 2006 (http://www.bp.com/productlanding.do?categoryId=91&contentId=7017990) Method: Reserves tabulated in trillion cubic meters Conversion to Mtoe via current production ratio (Mtoe/cubic meters) for each country. 2. Oil: Proved Reserves at end 2005 Source: BP Statistical Review of World Energy, 2006 (http://www.bp.com/productlanding.do?categoryId=91&contentId=7017990) Method: Direct transcription from BP spreadsheet (Converted to Mtoe) 3. Coal: Proved reserves at end 2005 Source: BP Statistical Review of World Energy, 2006 (http://www.bp.com/productlanding.do?categoryId=91&contentId=7017990) Method: Reserves tabulated in millions of tons Conversion to Mtoe via current production ratio (Mtoe/mt coal prod.) for each country. 4. Natural Bitumen, Oil Shale Source: World Energy Council. 2001. Survey of Energy Resources http://www.worldenergy.org/wec-geis/publications/reports/ser/shale/shale.asp Method: Oil Shale: Proved recoverable reserves plus estimated additional reserves (Mtoe). Table 3.1 Oil shale: resources, reserves and production at end-1999 Note: Only economically recoverable reserves are used in the calculation since the technology is relatively experimental (and costly) with few examples of actual production-scale implementation. Source: World Energy Council. 2001. Survey of Energy Resources http://www.worldenergy.org/wec-geis/publications/reports/ser/bitumen/bitumen.asp Method: Natural Bitumen: Proved amount in place plus estimated additional reserves (Mtoe). Table 4.1 Natural bitumen: resources, reserves and production at end-1999 5. Geothermal Potential Energy Data Source: Pollack, Henry N.; Hurter, Suzanne J.; Johnson, Jeffrey R. 1993. "Heat flow from the earth's interior - Analysis of the global data set." Reviews of Geophysics. vol. 31, no. 3, p. 267-280. a) Initial screen of potentially exploitable sites from the above database: - Remove sites where the temperature gradient is larger than 110K/m (volcanoes, etc) (leaving 10703 sites out of 14238). 51 - Calculate the minimum temperature gradient necessary to produce electricity: - Depth = dT/TGradient = 150K/TGradient - If no temperature gradient was given, the following calculation was made: Depth = 150K*conductivity/heat flow - If no conductivity value was given: an arbitrary conductivity of 2.5 was chosen (Granite) - Keep sites with location depths of <6 km (leaving 8040 sites out of 10703) b) Calculate spacing requirements for exploitation: - Site observations in mW/m2 (milliwatts/sq. meter) by country - Calculate total number of observation sites per country - Divide into area to get sq. km./ site - Set maximum spacing at 25000 sq. km./site (Spacing procedure incorporates area represented by site) Calculate a "reasonable" coverage extrapolation area for one sample drilling. Rationale: When mapped, the data points clearly reflect greater drilling frequency in countries that are believed to have more thermal resources. Average heat flow cannot be simply calculated from the sample points and multiplied by national area since this would clearly overestimate the total for countries where sampling is sparse (e.g., Niger). After looking at average area per sample for all the countries, and focusing where coverage is near-total (e.g., US, South Africa), we arrive at 25,000 sq. km. per sample drilling as a reasonable estimate. For countries with smaller sample areas per sample (which we call spacing), we use the actual number, otherwise, we truncate at 25,000). c) Generate estimated heat (power) flow associated with each site as follows: - Factors multiplied together to obtain heat energy associated with each site Heatkw=heatflow/1,000,000 (1 kW = 106 mW): Conversion to kW/m2 Spacing (Area in sq. km.) x 1,000,000 (Conversion to m2) [1 sq. km. = 103 meters x 103 meters] [After cancellation, heatkw = heatflow x spacing] - Heatkw totaled across sites for each country to get country kW. - Conversion to heat potential in annual mtoe: Heat potential = Heatkw x 24 (hours/day) x 365.25 (days/year) x 8e-11 (kWh => mtoe) d) Assumptions: Current Capacity Factor: Country-specific capacity factor from Lund, Freeston and Boyd (2005) below Sensitivity ranges: Low: 10% greater capacity factor (max. 100%) High: 30% greater capacity factor (max. 100%) Conversion potential: Low: 5% of total heat potential High: 15% of total heat potential; set at current Swiss conversion percent, on the assumption that Swiss exploitation is near the current technical limit] [Exception for Turkey, which is currently 0.68] Data Source: Lund, J., D. Freeston and T. Boyd. 2005. "Direct Application of Geothermal Energy: 2005 Worldwide Review," Geothermics 34: 691-727 (Table 1). e) Final calculation: 52 Heatmtoe = Capacity Factor x Conversion potential x heat potential 5. Potential Wind Power Onshore wind potential: Data Sources: NASA Surface meteorology and Solar Energy: Global Data Monthly Averaged Wind Speed At 50 m Above The Surface Of The Earth (m/s) Site URL: http://eosweb.larc.nasa.gov/sse/ Archer, C. and M. Jacobson (2005) Evaluation of global wind power, Journal of Geophysical Research, Vol. 110, D12110, doi:10.1029/2004JD005462. http://www.stanford.edu/group/efmh/winds/global_winds.html Urban areas subtracted as they are probably not suitable for large turbine siting. Urban areas GIS data: Global Rural- Urban Mapping Project (GRUMP): Urban/Rural Extents Center for International Earth Science Information Network (CIESIN), Columbia University; International Food Policy Research Institute (IPFRI); the World Bank; and Centro Internacional de Agricultura Tropical (CIAT);2004. Palisades, NY: CIESIN, Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw/ a) Wind speed area calculation: i) Calculation of wind speed at 80m elevation from NASA 50m Use of the Shear expression: http://www.windpower.org/en/tour/wres/shear.htm v = vref ln(z/z0)/ln(zref/z0) where v = wind speed at height z above ground level vref = reference speed, i.e. a wind speed we already know at height zref z = height above ground level for the desired velocity, v (i.e., 80 m) z0 = roughness length in the current wind direction. Roughness lengths may be found in the Reference Manual at http://www.windpower.org/en/tour/wres/shear.htm. zref = reference height, i.e. the height where we know the exact wind speed vref Roughness class = 1.55 and roughness length = 0.055 corresponding to following landscape type: Agricultural land with some houses and 8 meters tall sheltering hedgerows with a distance of approximately 1250 meters http://www.windpower.org/en/stat/unitsw.htm#roughness ii) Merge with Archer and Jacobson 80m wind speed data iii) Spatial interpolation to raster using: Inverse Distance Weighted (IDW): Neighbors: 3, at 0.1 degree resolution (approx 11 km at the equator). iv) Re-classification into 7 wind classes (meters/second): (Archer and Jacobsen (2005)): Class 1 < 5.9 5.9 < Class 2 < 6.9 6.9 < Class 3 < 7.5 53 7.5 < Class 4 < 8.1 8.1 < Class 5 < 8.4 8.4 < Class 6 < 9.4 9.4 < Class 7 v) Intersection with the country land area to get total area by wind classes in each country vi) Re-projection to equal area projection to get the km2 of each wind class for each country Steps in calculation of global wind potential: b) Drop areas that are wind class < 3.0 (not feasible) c) Assumption: technically feasible rotor density: High: Assume technical feasibility at current wind rotor density (rotors/sq. km.) for Germany (which has the most installations: 17,574 as of 2005). Low: 60% of Germany's current density Source: German Wind Energy Institute, Statistics end of 2005 http://www.dewi.de/dewi_neu/englisch/index.html d) Calculation of power production per standard wind rotor (POSR): Source: Wind Energy Reference Manual, Danish Wind Industry Association http://www.windpower.org/en/stat/unitsw.htm#anchor1345942 Wind power input: 0.5*1.225*(average wind speed per power class cubed) [Watts/ m2] [The formula for the power per m2 in Watts = 0.5 * 1.225 * v3, where v is the wind speed in m/s.] Adjustment for Weibull distribution of wind speed: Multiply by 2 [Watts/ m2] Expected power output: Multiply by 0.3 [Watts/ m2] Convert to yearly kWh: Multiply by (24 x 365.25/1000) [kWh/ m2/year] e) Assumption: Standard 80m wind rotor swept area: Multiply by 4656 m2 [kWh/ m2/year] f) Calculate total power output: Total power output (kWh/year) = POSR x technically feasible rotor density x feasible area g) Adjustment for power losses (10%) = 0.90 x Total power output [Due to wind turbine wakes, blade soiling, Operations & Management] h) Convert to mtoe: Total adjusted power output x 8e-11 Offshore wind potential: Most of the offshore wind locations are between 5 to15 meters in depth and a few kilometres to 15km from the shore. For each country we estimate the total area within its EEZ that is potentially suitable for offshore turbine installations. Data Sources: 54 Wind speed at 80m elevation using same method as above for onshore; and Two-minute gridded global relief for both ocean and land areas (combined bathymetry and topography) in the ETOPO2v2 (2006), NOAA: http://www.ngdc.noaa.gov/mgg/global/global.html EEZ: Exclusive economic zones: The Global Maritime Boundaries Database http://www.maritimeBoundaries.com Coastline: SRTM Water Body Dataset (SWBD) http://edc.usgs.gov/products/elevation/swbd.html Coastal delimitation based on the digital coastlines from the SRTM Water Body Dataset (shuttle radar topography mission), 90m resolution. http://edcsns17.cr.usgs.gov/srtmbil/ a) Wind power calculation: same as steps b) through h) above. 6. Potential Solar Power Data Source: NASA Surface Meteorology and Solar Energy: Global Data Site URL: http://eosweb.larc.nasa.gov/sse/ a) Average Monthly Insolation (AMI): Monthly Averaged Insolation Incident On A Horizontal Surface (kWh/m2/day) Monthly average for July 1983 - June 1993 b) Assumptions: Low: Solar PV collectors cover 0.05% of total land area High: Solar PV collectors cover 0.18% of total land area High estimate is equivalent to the country area share assumed in estimates for Germany in: Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit (2004), Ökologisch optimierter Ausbau der Nutzung erneuerbarer Energien in Deutschland, Berlin. http://www.bmu.de/erneuerbare/energien/doc/5650.php (English summary available at this site) c) Assumed conversion efficiency range (insolar => PV): Low: 15% High: 20% Sources for conversion efficiency estimate: - U.S. National Renewable Energy Laboratory. 2006. "High Performance Photovoltaic Project ­ Overview." http://www.nrel.gov/highperformancepv/overview.html - Green, M.A. 1998. "Photovoltaic Solar Energy Conversion: An Update." Australian Academy of Technological Sciences and Engineering, ATSE Focus No 102, May/June. http://www.atse.org.au/index.php?sectionid=391 d) Annual Energy Generation (AEG - mtoe/year): APG = 0.15 or 0.20 (conversion efficiency) x 8e-11 (mtoe/kWh) x AMI (kWh/m2/day) x 365.25 (days/year) x .001 (% of land area) x land area (sq. km.) x 106 (m2 / sq. km.) 55 7. Potential Hydro Power Data Source: World Energy Council. 2001. Survey of Energy Resources ­ Hydro: Technically-Exploitable Capability (TEC - TWh/year) http://www.worldenergy.org/wec-geis/publications/reports/ser/hydro/hydro.asp a) Conversion to mtoe/year: TEC (mtoe/year) = 0.08 (mtoe/TWh) x TEC (TWh/year) 8. CO2 Storage Potential Data Source: Hendriks, Chris, Graus, Wina, and Frank van Bergen. 2004. "Global Carbon Dioxide Storage Potential and Costs." Ecofys / Netherlands Institute of Applied Geoscience. EEP-02001. Table 20. CO2 storage potentials for the 18 world regions. a) Initial Estimates, 18 World Regions: USA, Central America, South America, Northern Africa,Western Africa, Eastern Africa, Southern Africa, Western Europe, Eastern Europe, Former Soviet Union, Middle East, Southern Asia, Eastern Asia, South East. Asia, Oceania, Japan, Greenland "Best Estimates" chosen from [Low, Best, High] for CO2 capture potential in four classes: Oil fields, natural gas fields, coal fields, and saline aquifers (mostly offshore) b) Country allocation of regional totals: Country shares for natural gas, oil and coal fields calculated as: Sum natural gas, oil and coal reserves by region from Sections 1-3 above Compute country shares by region for natural gas, oil and coal Apply country shares to "Best" estimated CO2 capture potentials by region c) Country shares for saline aquifer capture potential: Sum extended economic zone (EEZ) areas by region Compute country EEZ shares by region Apply country shares to "Best" estimated CO2 saline aquifer capture potentials by region. d) Multiply country shares by "Best" regional totals to obtain country CO2 capture potential for natural gas, oil and coal fields, and for saline aquifers. Sum to obtain total CO2 capture potential (units: Gigatonnes) e) Calculate storage as multiple of annual country CO2 emissions: Annual CO2 Emissions (including land use) ­ thousand tonnes / year Data Source: World Resources Institute ­ CAIT Database, 2006. Ratio: Total CO2 capture potential (Gt) x 106 (kt/Gt) / Annual CO2 emissions (kt) 9. Employment Vulnerability to a Global CO2 Shadow Price Shock a) Calculation of Sectoral Emissions Intensities: i) Sectoral Emissions by IEA Industry Classification, 2002. Data Source: International Energy Agency CO2 Emissions Database 56 http://www.iea.org/Textbase/publications/index.asp ii) GDP Breakdown by Sector, 2002. Data Source: GDP and its breakdown at constant 1990 prices in US Dollars UN National Accounts Main Aggregates Database at: http://unstats.un.org/unsd/snaama/dnllist.asp UN/IEA Sector Concordance: Developed by the authors b) Calculation of Sectoral Employment Shares: Data Source: ILO LABORSTA Online Database: Paid Employment by Economic Activity at: http://laborsta.ilo.org/, Yearly Data. ILO/UN/IEA Sector Concordance: Developed by authors Sector shares of paid employment computed by year Utilized shares: Complete set (six composite sectors) only; most recent year. c) Computation of Employment Vulnerability Index: Weighted Combination of Sectoral CO2 Emissions Intensities (2002; exception Estonia (1998)) Weights are sector employment shares from b) 10. Sequestration Potential via Reduced Deforestation Data Source: World Resources Institute ­ CAIT Database, 2006 http://cait.wri.org/ CED: Annual CO2 emissions attributable to deforestation and land clearing (ktonnes/yr) CEG: Total annual CO2 emissions (ktonnes/yr). a) Calculation of sequestration potential index: CED / CEG 11. Sea Level Impact Data Source: DECRG Spatial Analysis Unit and computations from the project, "A Comparative Analysis of the Impacts of Sea-Level Rise in Developing Countries", 2007, Forthcoming Policy Research Working Paper, World Bank: Washington, DC. SRTM :Shuttle Radar Topography Mission: http://edcsns17.cr.usgs.gov/srtmbil/ Population: Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3): Population Grids. Palisades, Columbia University. http://sedac.ciesin.columbia.edu/gpw GDP: http://www.worldbank.org/data/wdi2000 Agriculture: PAGE Global Agricultural Extent version 2, IFPRI http://www.ifpri.org/data/PAGE01.htm a) Impact index: Average % impacted for population, GDP and agriculture at 1- and 3-meter SLR. b) Assumptions: 57 Sensitivity range: maximum 50km inland impact zone Low: 1-meter impact Intermediate: 2-meter impact High: 3-meter impact 12. Climate-Induced Damage Index Data source: EM-DAT: Emergency Disasters Database CRED (Centre for Research on the Epidemiology of Disasters) Université Catholique de Louvain, Brussels, Belgium http://www.em-dat.net/ Indicators: Deaths (D), population rendered homeless (H) and population affected (A) For disasters in weather-related categories: Drought, Extreme Temperature, Flood, Wild Fire and Wind Storm Time period: 1960 ­ 2002 b) Assumption: Weights placed on impacts: 1000 for deaths, 10 for homeless and 1 for "affected". c) Calculation: Damage Index = (1000*D + 10*H + A) / Population 1980 (midpoint population) 13. Biogas From Livestock Manure a) Data on live animals: Data source: FAOSTAT database, (2005): Cattle, buffalo, sheep, pigs, chickens, ducks http://faostat.fao.org/faostat/form?collection=Production.Livestock.Stocks&Domain=Production&servlet=1&hasbulk=&v ersion=ext&language=EN b) Assumption: Manure generation by species, daily dry dung production Source: Woods, J., and D.O. Hall. 1994. "Bioenergy for development - Technical and environmental dimensions." FAO, Environment and energy paper No. 13. Dung production/animal/day: Cattle 3 kg, buffalo 4 kg, sheep 0.5 kg, pigs 0.6 kg, chickens 0.1 kg, ducks 0.1 kg c) Assumptions (from Woods and Hall): 50% of dung is potentially harvestable; 25% of potentially harvestable dung is recoverable; Therefore, 12.5% (1/4 of ˝) of total production is recoverable. d) Conversion to energy (from Woods and Hall): Vester Hjermitslev plant, Denmark, has a digester capacity of 1,500 m3 (approx. 50 tons manure per day) designed to produce 3,500 m3/day biogas. 58 e) Assumption: Energy content assumption for biogas: 24.4 MJ/m3 Source: Wheeldon, Ian, Caners, Chris and Kunal Karan. 2005. "Anaerobic Digester Produced Biogas and Solid Oxide Fuel Cells: An Alternative Energy Source for Ontario Wastewater Treatment Facilities." Conference presentation, BIOCAP Canada. http://www.biocap.ca/images/pdfs/conferencePosters/Wheeldon_I_P1.pdf f) Calculation of Biogas energy potential, based on Vester Hjermitslev and BIOCAP Canada: Biogasmtoe: 0.125 x (total dung (tonnes)/50) x 3500 (m3/50 tons) x 24.4 (MJ/m3) x 1.6e-10 (mtoe/MJ) 15. Land Ethanol Potential ­ Sugar Crops Sources: Sugar crop yields: Table 2-2. Ethanol and Biodiesel Yield per Acre from Selected Crops, in Lester R. Brown, Plan B 2.0: Rescuing a Planet Under Stress and a Civilization in Trouble (NY: W.W. Norton & Co., 2006). Table 2.2 compiled by Earth Policy Institute from the following sources: FAO, U.N. Food and Agriculture Organization (FAO), FAOSTAT Statistics Database, at apps.fao.org, updated 14 July 2005; Manitoba Department of Energy, Science, and Technology, "Ethanol FAQ," Energy Development Initiative Web site, www.gov.mb.ca/est/energy/ethanol/ ethanolfaq.html, viewed 5 August 2005; Renewable Fuels Association, Renewable Fuels Association, Homegrown Homeland for the Ethanol Industry Outlook 2005 (Washington, DC: 2005), pp. 2, 14­15; Nandini Nimbkar and Anil Rajvanshi, "Sweet Sorghum Ideal for Biofuel," Seed World, vol. 14, no. 8 (November 2003); Ellen I. Burnes et al., Ethanol in California: A Feasibility Framework (Modesto, CA: Great Valley Center, 2004), p. 18; Berg, op. cit. note 43; DOE, Biofuels from Switchgrass: Greener Energy Pastures (Oak Ridge, TN: Oak Ridge National Laboratory, 1998). a) Sugar crops: Calculation of Ethanol Energy Potential (EEP ­ in Mtoe) per `000 hectares Sugar Crop Yields in gallons of ethanol/acre (higher than any other rated farm crop (e.g. corn, cassava): Sugar beet (France) 714 Sugar cane (Brazil) 662 Sweet Sorghum (India) 374 a.1) Conversion to gasoline equivalent (gal./acre): Multiply by 0.67 (Source: Brown, op. cit.) a.2) Conversion to energy equivalent (MJ/acre): Multiply by 130.88 (MJ/gallon) Energy in 1 gallon of gasoline: 130.88 MJ Source: U.S. Department of Energy http://www.eia.doe.gov/kids/energyfacts/science/energy_calculator.html#oilcalc a.3) Conversion to MJ/hectare: Divide by 0.405 (1 acre = 0.405 hectares) a.4) Conversion to Mtoe/hectare: Multiply by 1.6e-10 (Mtoe/MJ) a.5) Conversion to Mtoe/1000 hectares: Multiply by 1000 Final calculation: EEP = Crop Yield x 0.67 x 130.88 / 0.405 x 1.6e-10 x 1000 b) Area suitable for cultivation of each sugar crop, by country: 59 Data Source: FAO ­ Global Agro-Ecological Zone Assessment http://www.fao.org/AG/agl/agll/gaez/index.htm Datasets: Suitability for 27 crops under rain-fed conditions Mixed-input case employed: Determination of total suitable land (TSL) and total suitable land under forest ecosystems (TSLFE) as follows: a) Determine all land very suitable and suitable at high level of inputs; b) Of the balance of land after a), determine all land very suitable, suitable or moderately suitable at intermediate level of inputs, and c) Of the balance of land after a) and b), determine all suitable land (i.e. "very suitable, suitable, moderately or marginally suitable land") at low level of inputs; d) Of the balance of land after a), determine all land very suitable, suitable, moderately suitable or marginally suitable land in areas dominantly under forest ecosystems. Suitable land for production (SLP) (`000 hectares): [TSL] - [TSLFE] c) Final calculation of ethanol energy potential from 10% suitable land for each sugar crop in each country: Potential (Mtoe) = 0.10 x [Ethanol Energy Potential (/'000 hectares)] x SLP 16: Land Ethanol Potential ­ Switchgrass a) Assumption: Ethanol Yield from Switchgrass (EYS): 2800 liters/hectare Source: "Switchgrass : a living solar battery for the prairies" http://www.eap.mcgill.ca/MagRack/SF/Fall%2091%20L.htm Roger Samson Ecological Agriculture Projects McGill University (Macdonald Campus), Ste-Anne-de-Bellevue, Quebec Yield in gallons per acre: EYS / 3.79 (gallons/liter) / 0.405 (hectares/acre) x 0.67 (gasoline equivalent of ethanol) = 1222.19 (gallons/liter) Convert to Mtoe/'000 hectares: (same as steps a.2) to a.5) above) = 1222.19 / 0.405 x 1.6e-10 x 1000 b) Calculation of tallgrass savanna area (TSA - `000 hectares) in each country: Data Source: WWF International: Terrestrial Ecoregions of the World http://www.worldwildlife.org/science/data/terreco.cfm Area data provided to DECRG in GIS format by WWF International Agricultural land masked out. Vegetation class used for tallgrass savanna: "Temperate Grasslands, Savannas and Shrublands" Urban areas masked out based on GRUMP (see sources for 5. above). c) Final calculation of switchgrass ethanol potential production (Mtoe) on 10% of total savanna for each country: Potential (Mtoe) = 0.10 x [Tallgrass savanna area (`000 hectares)] x [Ethanol Yield from Switchgrass (Mtoe/'000 hectares)] 60 17. Land Biodiesel Potential ­ Jatropha Curcas a) Assumption: Biodiesel Yield from Jatropha: 2000 liters of biodiesel/hectare Source: Worldwatch Institute, German Federal Ministry of Food, Agriculture and Consumer Protection. 2006. "Biofuels for Transportation: Global Potential and Implications for Sustainable Agriculture and Energy in the 21st Century." Washington, D.C., June 7. b) Calculation of potential cultivation area (sq. km.) in each country: Data Source: WWF International: Terrestrial Ecoregions of the World http://www.worldwildlife.org/science/data/terreco.cfm Area data provided to DECRG in GIS format by WWF International Agricultural land masked out. Vegetation classes used: "Deserts and Xeric Shrublands" ­ Non-montane shrublands only "Tropical-Subtropical Grasslands, Savannas and Shrublands" Urban areas masked out based on GRUMP (see sources for 5. above). c) Assumption: Energy content of biodiesel: 34 MJ/liter Source: Oak Ridge National Laboratory, Bioenergy Feedstock Development Programs http://bioenergy.ornl.gov/papers/misc/energy_conv.html d) Final calculation: Jatropha biodiesel potential (Mtoe) from 10% of potential cultivable land: Potential (Mtoe) = 0.10 x [Potential area (sq km)] x 100 (hectares/sq km) x 2000 (liters/hectare) x 34 (MJ/liter) x 1.6e-10 (Mtoe/MJ) 61 Appendix 2: Country level estimates Table A2.1: Non-Renewable Energy Resources and Annual Energy Consumption (Millions of tons of oil equivalent) Annual Natural Energy Region Subregion Country Coal Oil Gas Oil Shale Bitumen Consumption AFR Central Africa Angola 0 1,219 0 0 0 8.8 AFR Central Africa Burundi 0 0 0 0 0 1.6 AFR Central Africa Cameroon 0 0 0 0 0 6.6 AFR Central Africa Central African Rep 0 0 0 0 0 1.7 AFR Central Africa Congo 0 252 0 0 0 0.9 AFR Central Africa Congo, Dem Rep 0 0 0 0 0 15.4 AFR Central Africa Gabon 0 302 0 0 0 1.6 AFR Central Africa Rwanda 0 0 0 0 0 3.4 AFR Central Africa Zambia 0 0 0 0 0 6.5 AFR Coastal West Africa Benin 0 0 0 0 0 2.2 AFR Coastal West Africa Cape Verde 0 0 0 0 0 0.7 AFR Coastal West Africa Côte d'Ivoire 0 0 0 0 0 6.6 AFR Coastal West Africa Equatorial Guinea 0 240 0 0 0 2.5 AFR Coastal West Africa Gambia 0 0 0 0 0 0.8 AFR Coastal West Africa Ghana 0 0 0 0 0 8.3 AFR Coastal West Africa Guinea 0 0 0 0 0 5.2 AFR Coastal West Africa Guinea-Bissau 0 0 0 0 0 0.4 AFR Coastal West Africa Liberia 0 0 0 0 0 AFR Coastal West Africa Nigeria 0 4,842 4,702 0 0 95.7 AFR Coastal West Africa Sao Tome & Principe 0 0 0 0 0 AFR Coastal West Africa Senegal 0 0 0 0 0 3.2 AFR Coastal West Africa Sierra Leone 0 0 0 0 0 0.8 AFR Coastal West Africa Togo 0 0 0 0 0 1.5 AFR East Africa Djibouti 0 0 0 0 0 0.7 AFR East Africa Eritrea 0 0 0 0 0 1.6 AFR East Africa Ethiopia 0 0 0 0 0 19.9 AFR East Africa Kenya 0 0 0 0 0 15.3 AFR East Africa Malawi 0 0 0 0 0 3.2 AFR East Africa Somalia 0 0 0 0 0 AFR East Africa Sudan 0 864 0 0 0 15.9 AFR East Africa Tanzania 0 0 0 0 0 14.3 AFR East Africa Uganda 0 0 0 0 0 15.3 AFR Indian Ocean Islands Comoros 0 0 0 0 0 0.5 AFR Indian Ocean Islands Mauritius 0 0 0 0 0 6.0 AFR Indian Ocean Islands Seychelles 0 0 0 0 0 AFR Madagascar Madagascar 0 0 0 0 0 6.6 AFR Sahelian Africa Burkina Faso 0 0 0 0 0 4.1 AFR Sahelian Africa Chad 0 129 0 0 0 2.4 AFR Sahelian Africa Mali 0 0 0 0 0 3.1 AFR Sahelian Africa Mauritania 0 0 0 0 0 1.6 AFR Sahelian Africa Niger 0 0 0 0 0 2.9 62 Annual Natural Energy Region Subregion Country Coal Oil Gas Oil Shale Bitumen Consumption AFR Southern Africa Botswana 0 0 0 0 0 3.8 AFR Southern Africa Lesotho 0 0 0 0 0 1.1 AFR Southern Africa Mozambique 0 0 0 0 0 8.0 AFR Southern Africa Namibia 0 0 0 0 0 1.2 AFR Southern Africa South Africa 27,470 0 0 0 0 113.5 AFR Southern Africa Swaziland 0 0 0 0 0 1.4 AFR Southern Africa Zimbabwe 326 0 0 0 0 9.8 EAP China China 57,914 2,191 2,115 0 0 1,228.6 EAP Northeast Asia Korea, Rep 37 0 0 0 0 203.5 EAP Northeast Asia Mongolia 0 0 0 0 0 0.9 EAP Pacific Islands Fiji 0 0 0 0 0 0.9 EAP Pacific Islands Kiribati 0 0 0 0 0 EAP Pacific Islands Marshall Islands 0 0 0 0 0 EAP Pacific Islands Micronesia, Fed States 0 0 0 0 0 EAP Pacific Islands Pacific Islands (Palau) 0 0 0 0 0 EAP Pacific Islands Samoa 0 0 0 0 0 0.2 EAP Pacific Islands Solomon Islands 0 0 0 0 0 0.2 EAP Pacific Islands Timor-Leste 0 0 EAP Pacific Islands Tonga 0 0 0 0 0 0.1 EAP Pacific Islands Vanuatu 0 0 0 0 0 0.1 EAP Southeast Asia Cambodia 0 0 0 0 0 5.2 EAP Southeast Asia Indonesia 3,055 595 2,484 0 0 156.1 EAP Southeast Asia Lao People's Dem Rep 0 0 0 0 0 1.9 EAP Southeast Asia Malaysia 0 537 2,236 0 0 51.8 EAP Southeast Asia Myanmar 0 0 450 0 0 12.6 EAP Southeast Asia Papua New Guinea 0 0 385 0 0 2.8 EAP Southeast Asia Philippines 0 0 0 0 0 42.0 EAP Southeast Asia Thailand 377 69 319 810 0 83.3 EAP Southeast Asia Viet Nam 84 426 208 0 0 42.6 ECA Eastern Europe Albania 0 0 0 5 0 1.9 ECA Eastern Europe Armenia 0 0 0 0 0 1.9 ECA Eastern Europe Belarus 0 0 0 0 0 24.8 ECA Eastern Europe Bosnia and Herzegovina 0 0 0 0 0 4.3 ECA Eastern Europe Bulgaria 367 0 0 0 0 19.0 ECA Eastern Europe Croatia 0 0 0 0 0 8.2 ECA Eastern Europe Czech Rep 2,104 0 0 0 0 41.7 ECA Eastern Europe Estonia 0 0 0 0 0 4.5 ECA Eastern Europe Georgia 0 0 0 0 0 2.6 ECA Eastern Europe Hungary 699 0 0 0 0 25.4 ECA Eastern Europe Latvia 0 0 0 0 0 4.3 ECA Eastern Europe Lithuania 0 0 0 0 0 8.6 ECA Eastern Europe Macedonia, FYR 0 0 0 0 0 4.3 ECA Eastern Europe Moldova, Rep 0 0 0 0 0 3.0 ECA Eastern Europe Poland 6,030 0 99 0 0 89.2 ECA Eastern Europe Romania 0 0 0 0 4 37.0 ECA Eastern Europe Russian Federation 72,182 10,478 43,038 0 0 617.8 63 Annual Natural Energy Region Subregion Country Coal Oil Gas Oil Shale Bitumen Consumption ECA Eastern Europe Serbia and Montenegro 0 0 0 0 0 16.2 ECA Eastern Europe Slovakia 0 0 0 0 0 18.5 ECA Eastern Europe Slovenia 0 0 0 0 0 7.0 ECA Eastern Europe Ukraine 17,753 0 993 6,500 0 130.7 ECA Middle East Turkey 868 0 0 269 0 75.4 ECA Western Asia Azerbaijan 0 959 1,241 0 0 11.7 ECA Western Asia Kazakhstan 15,929 5,427 2,694 0 0 46.5 ECA Western Asia Kyrgyzstan 0 0 0 0 0 2.5 ECA Western Asia Tajikistan 0 0 0 0 0 3.2 ECA Western Asia Turkmenistan 0 75 2,609 0 0 16.6 ECA Western Asia Uzbekistan 0 81 1,664 0 0 51.7 LCR Andean South America Bolivia 0 0 669 0 0 4.3 LCR Andean South America Colombia 4,295 196 101 0 0 27.4 LCR Andean South America Ecuador 0 711 0 0 0 9.0 LCR Andean South America Peru 0 148 292 0 0 12.0 LCR Caribbean Islands Antigua and Barbuda 0 0 0 0 0 0.3 LCR Caribbean Islands Barbados 0 0 0 0 0 1.6 LCR Caribbean Islands Dominica 0 0 0 0 0 0.2 LCR Caribbean Islands Dominican Rep 0 0 0 0 0 8.2 LCR Caribbean Islands Grenada 0 0 0 0 0 0.3 LCR Caribbean Islands Haiti 0 0 0 0 0 2.1 LCR Caribbean Islands Jamaica 0 0 0 0 0 3.9 LCR Caribbean Islands Saint Kitts and Nevis 0 0 0 0 0 0.2 LCR Caribbean Islands St. Lucia 0 0 0 0 0 0.3 LCR Caribbean Islands St. Vincent & Grenadines 0 0 0 0 0 0.2 LCR Caribbean Islands Trinidad and Tobago 0 116 491 0 0 9.3 LCR Central America Belize 0 0 0 0 0 0.2 LCR Central America Costa Rica 0 0 0 0 0 3.6 LCR Central America El Salvador 0 0 0 0 0 4.3 LCR Central America Guatemala 0 0 0 0 0 7.4 LCR Central America Honduras 0 0 0 0 0 3.4 LCR Central America Mexico 581 1,733 371 0 0 157.3 LCR Central America Nicaragua 0 0 0 0 0 2.9 LCR Central America Panama 0 0 0 0 0 3.0 LCR Northern South America Brazil 3,903 1,693 277 9,646 0 190.7 LCR Northern South America Guyana 0 0 0 0 0 0.8 LCR Northern South America Suriname 0 0 0 0 0 LCR Northern South America Venezuela 349 11,488 3,897 0 37,800 54.0 Southern South LCR America Argentina 0 317 455 0 0 56.3 Southern South LCR America Chile 0 0 0 0 0 24.7 Southern South LCR America Paraguay 0 0 0 0 0 3.9 Southern South LCR America Uruguay 0 0 0 0 0 2.5 MNA Middle East Iraq 0 15,520 2,859 0 0 29.0 MNA Middle East Jordan 0 0 0 24,000 40 5.4 64 Annual Natural Energy Region Subregion Country Coal Oil Gas Oil Shale Bitumen Consumption MNA Middle East Lebanon 0 0 0 0 0 5.4 MNA Middle East Oman 0 756 898 0 0 10.8 MNA Middle East Syrian Arab Rep 0 389 281 0 0 18.1 MNA Middle East West Bank and Gaza 0 0 0 0 0 MNA Middle East Yemen 0 374 432 0 0 4.1 MNA North Africa Algeria 0 1,589 4,121 0 0 30.8 MNA North Africa Egypt 0 538 1,705 0 0 52.4 MNA North Africa Libyan Arab Jamahiriya 0 5,095 1,338 0 0 18.7 MNA North Africa Morocco 0 0 0 5,900 0 10.8 MNA North Africa Tunisia 0 89 0 0 0 8.3 MNA Western Asia Iran, Islamic Rep 0 19,562 24,066 0 0 134.0 OTHER Atlantic Islands Channel Islands 0 0 0 0 0 OTHER Atlantic Islands Faeroe Islands 0 0 0 0 0 OTHER Atlantic Islands Greenland 0 0 0 0 0 OTHER Atlantic Islands Iceland 0 0 0 0 0 3.4 OTHER Atlantic Islands Isle of Man 0 0 0 0 0 OTHER AustraliaNZ Australia 43,023 515 2,269 36,985 0 112.7 OTHER AustraliaNZ New Zealand 351 0 0 0 0 18.0 OTHER Caribbean Islands Aruba 0 0 0 0 0 OTHER Caribbean Islands Bahamas 0 0 0 0 0 2.0 OTHER Caribbean Islands Bermuda 0 0 0 0 0 OTHER Caribbean Islands Cayman Islands 0 0 0 0 0 OTHER Caribbean Islands Cuba 0 0 0 0 0 14.2 OTHER Caribbean Islands Netherlands Antilles 0 0 0 0 0 1.5 OTHER Caribbean Islands Puerto Rico 0 0 0 0 0 32.4 OTHER Caribbean Islands Virgin Islands 0 0 0 0 0 OTHER Indian Ocean Islands Mayotte 0 0 0 0 0 OTHER Middle East Bahrain 0 0 81 0 0 6.9 OTHER Middle East Israel 0 0 0 600 0 21.0 OTHER Middle East Kuwait 0 13,981 1,410 0 0 22.2 OTHER Middle East Qatar 0 1,996 23,234 0 0 12.2 OTHER Middle East Saudi Arabia 0 36,279 6,215 0 0 126.4 OTHER Middle East United Arab Emirates 0 12,954 5,426 0 0 36.1 OTHER North America Canada 3,465 2,314 1,426 0 45,300 250.0 OTHER North America United States 138,231 3,617 4,908 132,000 4,231 2,290.4 OTHER Northeast Asia Japan 196 0 0 0 0 516.9 OTHER Northeast Asia Korea, Dem People's Rep 0 0 0 0 0 19.5 OTHER Northeast Asia Taiwan 0 0 0 0 0 OTHER Pacific Islands American Samoa 0 0 0 0 0 OTHER Pacific Islands Cook Islands 0 0 OTHER Pacific Islands French Polynesia 0 0 0 0 0 1.3 OTHER Pacific Islands Guam 0 0 0 0 0 OTHER Pacific Islands Nauru 0 0 0 0 0 OTHER Pacific Islands New Caledonia 0 0 0 0 0 1.1 OTHER Pacific Islands Niue 0 0 OTHER Pacific Islands Northern Mariana Islands 0 0 0 0 0 65 Annual Natural Energy Region Subregion Country Coal Oil Gas Oil Shale Bitumen Consumption OTHER Pacific Islands Tuvalu 0 0 0 0 0 OTHER Southeast Asia Brunei Darussalam 0 153 306 0 0 2.2 OTHER Southeast Asia Singapore 0 0 0 0 0 25.3 OTHER Western Europe Andorra 0 0 0 0 0 OTHER Western Europe Austria 0 0 0 0 0 30.4 OTHER Western Europe Belgium 0 0 0 0 0 56.9 OTHER Western Europe Cyprus 0 0 0 0 0 2.5 OTHER Western Europe Denmark 0 164 61 0 0 19.7 OTHER Western Europe Finland 0 0 0 0 0 35.6 OTHER Western Europe France 5 0 0 0 0 265.9 OTHER Western Europe Germany 1,768 0 168 0 0 346.4 OTHER Western Europe Gibraltar 0 0 0 0 0 OTHER Western Europe Greece 522 0 0 0 0 29.0 OTHER Western Europe Ireland 0 0 0 0 0 15.3 OTHER Western Europe Italy 0 98 151 0 0 172.7 OTHER Western Europe Liechtenstein 0 0 0 0 0 OTHER Western Europe Luxembourg 0 0 0 0 0 4.0 OTHER Western Europe Malta 0 0 0 0 0 0.9 OTHER Western Europe Monaco 0 0 0 0 0 OTHER Western Europe Netherlands 0 0 1,264 0 0 77.9 OTHER Western Europe Norway 0 1,298 2,165 0 0 26.5 OTHER Western Europe Portugal 0 0 0 0 0 26.4 OTHER Western Europe San Marino 0 0 0 0 0 OTHER Western Europe Spain 174 0 0 0 0 131.6 OTHER Western Europe Sweden 0 0 0 0 0 51.0 OTHER Western Europe Switzerland 0 0 0 0 0 27.1 OTHER Western Europe United Kingdom 133 533 478 0 0 226.5 SAR Indian Ocean Islands Maldives 0 0 0 0 0 SAR Southern Asia Bangladesh 0 0 393 0 0 21.0 SAR Southern Asia Bhutan 0 0 0 0 0 SAR Southern Asia India 43,294 836 992 0 0 538.3 SAR Southern Asia Nepal 0 0 0 0 0 8.5 SAR Southern Asia Sri Lanka 0 0 0 0 0 8.2 SAR Western Asia Afghanistan 0 0 0 0 0 SAR Western Asia Pakistan 1,394 0 866 0 0 65.8 66 5.9 0.0 0.0 0.0 0.4 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 .60 .00 6.8 0.0 High .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 0.3 0.0 .00 .00 ndi um W Offshore Medi 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .10 .00 0.0 0.0 w Lo .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .60 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .60 .64 .00 54.0 17.9 .00 .00 High 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 3.7 0.0 ndi um 43.2 14.3 0.0 0.0 W Onshore Medi ndi .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .30 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .30 .72 .00 w 32.4 10.7 .00 .00 We Lo Offshor 78.4 1.3 29.0 39.4 19.2 135.9 13.3 1.3 45.0 7.2 0.3 19.6 1.4 0.6 14.1 15.2 1.7 5.6 63.9 0.1 12.8 4.3 3.4 1.5 7.1 67.0 38.6 5.7 46.2 167.0 58.3 12.7 High and 67 2 um 47.1 .80 17.4 23.6 11.5 81.6 .08 .80 27.0 .34 .20 11.8 .90 .40 .58 .19 .01 .43 38.3 .00 .77 .62 .02 .90 .34 40.2 23.1 .43 27.7 100. 35.0 .67 Onshore Solar Medi 3 0 4 5 0 Solar, w 16. 0.3 6. 8.2 4.0 28.3 2.8 0.3 9. 1.5 0.1 4.1 0.3 0.1 2.9 3.2 0.4 1.2 13.3 0.0 2.7 0.9 0.7 0.3 1. 14. 8.0 1.2 9.6 34.8 12.1 2.6 Lo sources:eR e Rep equivalent) epR nea u inciprP e re Gui & e oil Renewable of ytrn la id Africanl Dem ial da Verd go go, n e d'Ivoi a -Bissaa riae ia gal eonL a iw n tons Cou Ango Burun Cameroon Centra Con Con Gabon anwR meoT outi nda From Zambia Beni Cap Côte Equator Gambia Ghana Guine Guine Lib Niger Sao Sene Sierra ogoT Djib Eritrea Ethiopia Keny Mala Somalia Suda anzaniaT Uga of Energy Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa (Millions ste ste ste ste ste ste ste ste ste Annual niog ste ste Africal Africal Africa Africal Africal Africal Africal Africal Africal Wl Wl West Wl Wl Wl Wl Wl Wl Westl West Wl Wl Wl Africa Africa Africa Africa Africa Africa Africa Africa Africa Subre Centra Centra Central Centra Centra Centra Centra Centra Centra Coasta Coasta Coastal Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coastal Coasta Coasta Coasta East East East East East East East East East A2.2a: on R R R R R R R R R R R R R R R R R R R R R Table Regi AF AF AFR AFR AF AF AF AF AFR AF AF AFR AF AF AF AF AF AF AFR AFR AF AF AF AFR AFR AFR AFR AF AFR AF AF AF 0.2 0.3 0.5 High 20.5 0.0 0.0 0.0 0.6 0.0 0.0 0.0 14.2 1.2 1.9 0.0 0.0 23.1 0.1 0.0 3.8 0.4 0.1 0.0 0.6 0.0 5.8 0.0 0.0 1.0 3.4 0.0 134.7 36.2 18.8 21.8 .00 .00 .00 .30 .00 .00 .00 .30 .00 .00 .00 .00 .00 .00 .00 .00 0.6 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 ndi um W Offshore Medi 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 w Lo .00 .00 .00 .84 .00 .11 .00 .47 .00 .00 .00 .00 .00 .10 .00 .00 .00 .99 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 381.1 High 0.0 0.0 0.0 3.9 0.0 0.9 0.0 5.9 0.0 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 7.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ndi um 304.9 W Onshore Medi .00 .00 .00 .92 .00 .60 .00 .44 .00 .00 .00 .00 .00 .10 .00 .00 .00 .95 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 w 228.6 Lo 0 0.1 0.1 0.0 38.5 18.9 89.4 85.6 68.3 91.0 34.3 1.6 50.9 53.9 75.9 0.9 23.0 4.5 523. 74.5 1.1 0.0 0.0 0.0 0.0 0.2 1.6 1.0 0.0 0.7 9.9 126.0 12.6 19.8 37.4 27.6 High 68 .10 .10 .00 um 23.1 11.3 53.6 51.4 41.0 54.6 20.6 .01 30.5 32.3 45.5 .50 13.8 .72 313.8 44.7 .70 .00 .00 .00 .00 .10 .01 .60 .00 .40 .95 75.6 .67 11.9 22.4 16.5 Solar Medi 2 3 2 8 5 2 1 0.0 0.0 0.0 8.0 3.9 w 18.6 17.8 14.2 19.0 7. 0. 10.6 11. 15.8 0.2 4. 0.9 109.0 15. 0.2 0.0 0.0 0.0 0.0 0.0 0.3 0.2 0.0 0.1 2.1 26. 2.6 4. 7.8 5.7 Lo Rep States d (Palau) s meD Fe Guinea Island a le's w ytrn s euq d Islands ia, lles soaF op Ne hecy gascar a ian o ai Africa ew Rep on a esi sia rkin d anaw Cou Comoros Mauritiu Se Mada Bu Cha Mali Maurita Niger Bots Lesoth Mozambi Namib South anilzawS Islandsc Pe mbabiZ Chin Korea, Mongolia Fiji Kiribati Marshall Micrones Pacifi Samoa Solom mor-LesteiT mbodia ongaT anmary Vanuatu Ca Indon Lao Malay M Papua sd sd sd lansI lansI lansI sd sd sd Asia Asia niog Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Asia Asia Asia Asia Asia Asia Ocean Ocean Ocean n n n n n n n n n n n Islan Islands Islands Islan Islands Islands Islands Islands Islan Islands ast ast n n n gascar lia lia lia lia a Subre India India India Mada Sahelian Sahe Sahe Sahe Sahe Souther Souther Souther Souther Souther Souther Souther Chin Northeast Northeast Pacific Pacific Pacific Pacific Pacific Pacific Pacific Pacific Pacific Pacific Southeast Southe Southe Southeast Southeast Southeast on R R R R R R R R R R R R R Regi AF AF AF AF AFR AF AFR AFR AF AF AF AF AF AF AF AF EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP High 49.0 24.6 21.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 .36 0.0 0.0 0.0 0.0 0.0 .00 0.0 0.0 0.0 0.0 0.0 .00 0.6 0.0 0.0 .00 .00 .10 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 1.7 .00 .00 .00 .00 .00 0.0 .00 .00 .00 .00 .00 0.0 .30 .00 .00 ndi um W Offshore Medi 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .60 0.0 0.0 0.0 0.0 0.0 .00 0.0 0.0 0.0 0.0 0.0 .00 0.1 0.0 0.0 w Lo .90 .00 .90 .00 .21 .00 .40 .00 .00 .83 .00 .54 .10 .20 .30 .00 .00 .60 .20 92.9 .00 .11 .00 .00 .00 10.4 .00 .95 .89 .00 .00 42.3 .13 .00 .00 High 0.7 0.0 0.7 0.0 1.0 0.0 0.3 0.0 0.0 3.0 0.0 3.6 0.0 0.2 0.2 0.0 0.0 0.5 0.1 ndi um 74.3 0.0 0.9 0.0 0.0 0.0 8.3 0.0 4.7 7.8 0.0 0.0 33.9 2.5 0.0 0.0 W Onshore Medi .50 .00 .60 .00 .70 .00 .30 .00 .00 .32 .00 .72 .00 .10 .20 .00 .00 .40 .10 w 55.7 .00 .70 .00 .00 .00 .26 .00 .53 .95 .00 .00 25.4 .81 .00 .00 Lo 18.6 29.7 18.4 1.3 1.2 6.4 2.0 4.8 2.4 2.4 1.3 2.8 3.4 2.0 2.0 1.1 1.2 10.4 9.3 4.0 1.6 0.7 705.3 23.3 41.0 3.6 8.6 6.8 123.3 23.5 20.3 66.8 70.9 18.7 81.9 High 69 um 11.2 17.8 11.1 .80 .70 .93 .21 .92 .41 .41 .80 .71 .02 .21 .21 .60 .70 .36 .65 .42 .01 .40 423.2 14.0 24.6 .22 74.0 .25 .14 14.1 12.2 40.1 42.5 11.2 49.1 Solar Medi 8 5 3 6 7 4 9 8 7 4 9 2 9 1 3.9 6.2 3. 0.3 0.2 1.3 0.4 1.0 0. 0.5 0. 0. 0. 0. 0.4 0.2 0.2 2.2 1.9 0.8 0.3 0.1 4. 8.6 0. w 146.9 25. 1.8 1. 4. 4. 13. 14.8 3.9 17. Lo ina gro egov on rz He RYF ene ati d a, epR Mont ederF n n n an ytrn a ija aib or ppines Nam nia su an a y a and a epR a oni n ia aria gar ani nia nd ian yzsta a Cou Phili hailandT Viet Alba Armeni Belar Bosni Bulg Croati Czech Estonia Georgi Hun Latvia Lithu Maced Moldova, Pola Roma Russia Serbia Slovak Slove Ukraine rkeyuT Azerba Kazakhsta rgyK jikistanaT rkmenistanuT Uzbekist Bolivi Colom Ecuad Peru ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep America America America America Asia Asia Asia uth uth uth uth niog ast Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Europe Euro Euro Euro Europe Euro Euro Europe Asia Asia Asia Asia Asia Asia East So So So So e an an an an sterne sterne sterne sterne sterne sterne Subre Southeast Southe Southeast Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Middl W W W W W W Ande Ande Ande Ande on Regi EAP EAP EAP ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA LCR LCR LCR LCR 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0 0.0 High 29.0 0.0 0.0 1.8 23.1 11.0 .00 3.1 0.0 0.0 0.0 0.1 0.0 0.0 0.1 0.0 .10 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .10 .50 .00 0.2 .00 .00 .90 5.2 8.4 0.0 .00 .00 .00 .00 .10 .00 .00 .00 .00 ndi um W Offshore Medi 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 .00 0.0 0.0 0.3 .32 .72 .00 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 w Lo .10 .00 .00 .50 .00 .00 .00 .00 .10 .00 .00 .10 .00 .00 .00 .00 .42 .90 .00 10.7 .00 .00 .73 155.2 76.6 12.5 .10 .00 .00 .00 .90 .00 .00 .72 .00 High 2 3 0.1 0.0 0.0 0.4 0.0 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.9 0.7 0.0 8.6 0.0 0.0 3.0 ndi um 124. 61. 10.0 0.1 0.0 0.0 0.0 0.8 0.0 0.0 2.1 0.0 W Onshore Medi .00 .00 .00 .30 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .41 .50 .00 .46 .00 .00 .22 w 93.1 46.0 .57 .10 .00 .00 .00 .60 .00 .00 .61 .00 Lo 0.0 0.0 0.0 3.1 0.0 1.8 0.7 0.0 0.0 0.0 0.3 1.4 3.2 1.3 6.6 6.9 7.3 4.2 125.3 567.7 12.2 9.9 59.9 170.4 46.8 20.3 8.5 25.3 5.3 0.6 20.8 10.2 0.4 38.2 166.6 High 70 .00 .00 .00 .91 .00 .11 .40 .00 .00 .00 .20 .80 .91 .80 .93 .14 2 5 9 um 75.2 .44 .52 .37 .95 340.6 36.0 102.3 28.1 12.2 .15 15. .23 .40 12. .16 .20 22. 100.0 Solar Medi 4 3 1 9 3 7 0.0 0.0 0.0 0.6 0.0 0. 0.1 0.0 0.0 0.0 0.1 0. 0.7 0.3 1.4 1.4 w 26. 1.5 0. 2.5 2.1 118. 12.5 35.5 9. 4.2 1.8 5.3 1.1 0.1 4.3 2.1 0.1 8.0 34.7 Lo ines da go p Nevis enad Gaza Barbu Gr obaT p d Re and & d Re and an n a a b an ytrn a dos cai cai a a dor Kitts Ric aug a a uel na e yau y ua n on Ara Bank a Lucia Vincent duras mea Salva yana e an Cou Antigu Barba Domin Domin Grenad Haiti Jamaic Saint St. St. inidadrT Beliz Costa El Guatemal Hon Mexico Nicara Panam Brazil Gu Surin Venez Argenti Chil Parag Urug Iraq Jorda Leb Oman anriyS ste W Yemen Algeri America America America America nds nds nds nds nds nds nds nds nds nds nds a a a a a a a a America America America America h h h h Isla Isla Isla Isla Isla Isla Isla Isla Isla Isla Isla niog South South South South Sout Sout Sout Sout aneb aneb aneb aneb aneb aneb aneb aneb aneb aneb aneb Americl Americl Americl Americl Americl Americl Americl Americl n n n n n n n n East East East East East East East e e e e e e e Africa Subre Carib Carib Carib Carib Carib Carib Carib Carib Carib Carib Carib Centra Centra Centra Centra Centra Centra Centra Centra Norther Norther Norther Norther Souther Souther Souther Souther Middl Middl Middl Middl Middl Middl Middl North on Regi LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR MNA MNA MNA MNA MNA MNA MNA MNA 0.0 0.0 0.0 0.0 .00 0.0 0.0 .10 .00 0.0 .79 0.1 0.2 0.0 0.0 0.0 0.1 0.2 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 High 135.3 24.6 17.1 17.0 0.0 2.4 0.0 0.0 0.0 .00 .00 .00 .00 0.0 .00 .00 0.1 0.0 .00 6.2 2.0 .00 .10 .00 .00 .00 .10 .10 .10 .00 .00 .00 .00 .00 .00 .00 ndi um 13.1 8.3 .20 .00 .10 .00 .00 .00 W Offshore Medi 0.0 0.0 0.0 0.0 .00 0.0 0.0 .00 .00 0.0 .02 .90 0.0 0.1 0.0 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 .74 .62 0.1 0.0 0.1 0.0 0.0 0.0 w Lo .75 .48 .40 .50 21.6 .00 .50 95.7 29.7 .10 87.5 37.4 .10 .10 .00 .00 .00 .30 .10 .00 .00 .00 .00 .00 .00 .00 .00 .44 .00 .21 .00 .00 .10 145.4 186.3 High 3 0 4.5 6.7 0.3 0.4 ndi um 17. 0.0 0.4 76.5 23.7 0.1 70.0 30.0 0.1 0.1 0.0 0.0 0.0 0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.5 0.0 1.0 0.0 0.0 0.1 116.3 149. W Onshore Medi .43 .05 .30 .30 w 12.9 .00 .30 57.4 17.8 .10 52.5 22.5 .10 .10 0.0 .00 .00 .20 .10 .00 .00 .00 .00 .00 .00 .00 .00 87.3 .62 .00 .70 .00 0.0 .00 111.8 Lo 69.9 119.0 26.7 9.7 96.7 0.0 0.0 13.0 2.4 0.0 541.4 13.8 0.0 0.6 0.0 6.9 0.1 0.6 0.0 0.0 0.0 1.3 1.0 0.7 5.5 146.3 354.0 567.5 17.7 5.2 1.8 0.0 0.3 High 71 um 42.0 71.4 16.0 .85 58.0 .00 .00 .87 .51 .00 .38 .00 .40 .00 .14 .00 .30 .00 .00 .00 .80 .60 .40 324.9 87.8 .33 212.4 340.5 10.6 .13 .11 .00 .20 Solar Medi 7 9 0 3 2 5 7 w 14.6 24.8 5.6 2.0 20.2 0.0 0.0 2. 0.5 0.0 2. 0.0 0.1 0.0 1.4 0.0 0.1 0.0 0.0 0. 0. 0. 0.1 112.8 30. 1.1 73.8 3. 1.1 0.4 0.0 0.1 118.2 Lo yari es mahi Ja Rep sdn s tilles irat oa b nds d and mE anl o a iaseny nds ytrn Ara Islal Isl Ansd R Sam Isla and Man a as a ands n Pol tp nay Islamic d n lan Ric Arab States DP nne of Zea Isl in Isla w yma a Arabii d yotte ada d n Cou Egy Lib Morocco unisiaT itaw Iran, Cha Faeroe Greenl Icelan Isle Australi Ne Aruba Baham Bermud Ca Cub Nether Puerto Virgin Ma Bahra Israel Ku Qatar Saud Unite Can Unite Japa Korea, nawiaT America Cook enchrF sd sd sd sd sd sd nds nds nds nds nds nds nds nds lansI a a Isla Isla Isla Isla Isla Isla Isla Isla sd sd sd niog Asia Asia Asia Asia Islan Islan Islan Islan Islan Africa Africa Africa Africa aNZ aNZ aneb aneb aneb aneb aneb aneb aneb aneb Ocean East East East East East East ast ast Islan Islan Islan n e e e e e e Americ Americ ast sterne Subre North North North North W Atlantic Atlantic Atlantic Atlantic Atlantic Australi Australi Carib Carib Carib Carib Carib Carib Carib Carib India Middl Middl Middl Middl Middl Middl North North Northe Northe Northe Pacific Pacific Pacific on HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER Regi MNA MNA MNA MNA MNA OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT 0.0 0.0 0.4 0.0 0.0 0.0 1.7 0.2 0.0 0.0 0.5 0.0 7.5 0.0 .91 1.2 0.0 0.4 .02 0.1 0.0 0.0 0.0 0.0 3.7 0.2 0.0 0.0 0.3 0.4 0.0 .05 0.1 4.9 0.0 High .00 .00 .00 .00 .00 .00 .00 .00 .00 .00 .30 .00 .83 .00 0.7 .50 .00 .30 1.0 .00 .00 .00 .00 .00 .91 .10 .00 .00 .10 .10 .00 2.3 .00 .00 .00 ndi um W Offshore Medi 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.0 1.0 0.0 .20 0.1 0.0 0.1 .40 0.0 0.0 0.0 0.0 0.0 0.6 0.0 0.0 0.0 0.1 0.0 0.0 .90 0.0 0.0 0.0 w Lo .00 .00 .00 .10 .00 .00 .00 .00 .00 .00 .00 .00 .29 .00 13.3 .46 .00 .40 15.1 .90 .00 .00 .00 .00 .33 .48 .10 .00 .24 .01 .20 23.0 .00 .00 .40 High 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.4 0.0 ndi um 10.7 5.1 0.0 0.3 12.1 0.7 0.0 0.0 0.0 0.0 2.7 6.7 0.1 0.0 3.3 0.8 0.1 18.4 0.0 0.0 0.3 W Onshore Medi .00 0.0 .00 0.0 0.0 0.0 .00 .00 .00 .00 .00 .00 .55 .00 .08 .83 0.0 .20 .19 .60 .00 .00 .00 0.0 .02 .15 .10 .00 .52 .60 .10 w 13.8 0.0 .00 .20 Lo 0.0 1.1 .00 0.3 0.0 0.0 2.9 1.0 0.5 1.4 9.2 25.9 11.8 7.4 2.1 15.6 0.0 0.1 0.0 1.2 8.9 4.8 0.0 30.4 13.4 1.4 8.1 6.9 2.6 High 72 .00 .70 0.0 .20 .00 .00 .71 .60 .30 .80 .55 um 15.6 .17 .54 .31 .39 .00 .00 .00 .70 .45 .92 .00 18.3 .08 .80 .94 .14 .51 Solar Medi 0.0 0.2 .00 6 4 0 3 8 0.1 0.0 0.0 0.6 0.2 0.1 0.3 1.9 5.4 2.5 1. 0. 3.2 0.0 0.0 0. 0.2 1.9 1.0 0.0 6. 2. 0.3 1.7 1.4 0.5 w Lo sd Islan na ia amal m in g do ytrn donle Maria e sd no nd n u Ca Darussi y a bour o lan l she ark King apor ium nd d es w y htenste Mari n rlae d lad Cou Guam Naur Ne Niue Norther valuuT Brune Sing Andorr Austria Belg prusyC xem yaw Denm inlaF ancerF German Gibraltar Greece Irelan Ital Liec Lu Malta Monac Nether Nor Portuga San Spai endewS itzwS Unite Maldiv Bang Bhutan sd e e e e e e e e e e e e e e e e e e e e e e e e sd sd sd sd sd sd lansI Asia Asia niog Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Asia Asia Islan Islan Islan Islan Islan Islan ast ast Ocean n n n sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne Subre Pacific Pacific Pacific Pacific Pacific Pacific Southe Southe W W W W W W W W W W W W W W W W Western W W W W W Western Western India Souther Souther on HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER Regi OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT SAR SAR SAR High 44.0 0.0 6.4 .00 .11 0.0 .00 .10 0.0 0.0 ndi um W Offshore Medi .00 0.0 0.0 .00 .00 w Lo 29.7 .10 .80 53.8 26.2 High ndi um 23.8 0.0 0.6 43.0 20.9 W Onshore Medi w 17.8 .00 .50 32.3 15.7 Lo 8.0 3.9 177.3 37.9 45.1 High 73 4 .84 .32 um 106. 22.8 27.1 Solar Medi 8 w 36.9 1.7 0. 7.9 9.4 Lo a ytrn istan la Lank Cou India Nep Sri Afghan Pakistan niog Asia Asia Asia n n n Asia Asia sterne sterne Subre Souther Souther Souther W W on Regi SAR SAR SAR SAR SAR Table A2.2b: Annual Energy From Renewable Resources: Hydro and Geothermal (Millions of tons of oil equivalent) Region Subregion Country Hydro Geothermal Low Medium High AFR Central Africa Angola 7.2 0.0 0.0 0.0 AFR Central Africa Burundi 0.1 0.0 0.0 0.0 AFR Central Africa Cameroon 9.2 0.0 0.0 0.0 AFR Central Africa Central African Rep 0.2 0.0 0.0 0.0 AFR Central Africa Congo 4.0 0.0 0.0 0.0 AFR Central Africa Congo, Dem Rep 61.9 0.6 1.2 1.8 AFR Central Africa Gabon 6.4 0.0 0.0 0.0 AFR Central Africa Rwanda 0.0 0.0 0.0 0.0 AFR Central Africa Zambia 2.3 0.1 0.3 0.4 AFR Coastal West Africa Benin 0.1 0.0 0.0 0.0 AFR Coastal West Africa Cape Verde 0.0 0.0 0.0 0.0 AFR Coastal West Africa Côte d'Ivoire 1.0 0.0 0.0 0.0 AFR Coastal West Africa Equatorial Guinea 0.0 0.0 0.0 0.0 AFR Coastal West Africa Gambia 0.0 0.0 0.0 0.0 AFR Coastal West Africa Ghana 0.9 0.0 0.0 0.0 AFR Coastal West Africa Guinea 1.5 0.0 0.0 0.0 AFR Coastal West Africa Guinea-Bissau 0.0 0.0 0.0 0.0 AFR Coastal West Africa Liberia 0.9 0.0 0.0 0.0 AFR Coastal West Africa Nigeria 2.6 0.0 0.1 0.1 AFR Coastal West Africa Sao Tome & Principe 0.0 0.0 0.0 0.0 AFR Coastal West Africa Senegal 0.3 0.0 0.0 0.0 AFR Coastal West Africa Sierra Leone 0.6 0.0 0.0 0.0 AFR Coastal West Africa Togo 0.2 0.0 0.0 0.0 AFR East Africa Djibouti 0.0 0.0 0.0 0.0 AFR East Africa Eritrea 0.0 0.0 0.0 0.0 AFR East Africa Ethiopia 20.8 0.0 0.0 0.0 AFR East Africa Kenya 0.7 0.4 0.8 1.3 AFR East Africa Malawi 0.5 0.0 0.0 0.0 AFR East Africa Somalia 0.1 0.1 0.1 0.2 AFR East Africa Sudan 1.5 0.2 0.3 0.5 AFR East Africa Tanzania 1.6 0.2 0.4 0.6 AFR East Africa Uganda 0.6 0.0 0.0 0.0 AFR Indian Ocean Islands Comoros 0.0 0.0 0.0 0.0 AFR Indian Ocean Islands Mauritius 0.0 0.0 0.0 0.0 AFR Indian Ocean Islands Seychelles 0.0 0.0 0.0 0.0 AFR Madagascar Madagascar 14.4 0.0 0.0 0.0 AFR Sahelian Africa Burkina Faso 0.0 0.0 0.0 0.0 AFR Sahelian Africa Chad 0.0 0.0 0.0 0.0 AFR Sahelian Africa Mali 0.4 0.0 0.0 0.0 AFR Sahelian Africa Mauritania 0.0 0.0 0.0 0.0 AFR Sahelian Africa Niger 0.1 0.0 0.0 0.0 AFR Southern Africa Botswana 0.0 0.2 0.3 0.5 AFR Southern Africa Lesotho 0.2 0.0 0.0 0.0 AFR Southern Africa Mozambique 3.0 0.0 0.0 0.0 AFR Southern Africa Namibia 0.7 0.2 0.4 0.6 74 Region Subregion Country Hydro Geothermal Low Medium High AFR Southern Africa South Africa 0.9 0.1 0.2 0.3 AFR Southern Africa Swaziland 0.1 0.0 0.0 0.0 AFR Southern Africa Zimbabwe 1.4 0.0 0.1 0.1 EAP China China 153.6 4.7 10.2 16.6 EAP Northeast Asia Korea, Rep 2.1 0.1 0.2 0.4 EAP Northeast Asia Mongolia 1.8 1.0 2.0 3.0 EAP Pacific Islands Fiji 0.1 0.0 0.0 0.0 EAP Pacific Islands Kiribati 0.0 0.0 0.0 0.0 EAP Pacific Islands Marshall Islands 0.0 0.0 0.0 0.0 EAP Pacific Islands Micronesia, Fed States 0.0 0.0 0.0 0.0 EAP Pacific Islands Pacific Islands (Palau) 0.0 0.0 0.0 0.0 EAP Pacific Islands Samoa 0.0 0.0 0.0 0.0 EAP Pacific Islands Solomon Islands 0.1 0.0 0.0 0.0 EAP Pacific Islands Timor-Leste 0.0 0.0 0.0 0.0 EAP Pacific Islands Tonga 0.0 0.0 0.0 0.0 EAP Pacific Islands Vanuatu 0.0 0.0 0.0 0.0 EAP Southeast Asia Cambodia 6.6 0.0 0.0 0.0 EAP Southeast Asia Indonesia 32.2 4.5 9.9 16.1 EAP Southeast Asia Lao People's Dem Rep 5.0 0.0 0.0 0.0 EAP Southeast Asia Malaysia 9.8 0.1 0.2 0.3 EAP Southeast Asia Myanmar 10.4 0.0 0.0 0.0 EAP Southeast Asia Papua New Guinea 9.8 0.0 0.0 0.1 EAP Southeast Asia Philippines 1.6 0.1 0.2 0.3 EAP Southeast Asia Thailand 1.5 1.6 3.1 4.7 EAP Southeast Asia Viet Nam 8.0 0.0 0.0 0.0 ECA Eastern Europe Albania 1.2 0.0 0.0 0.0 ECA Eastern Europe Armenia 0.6 0.0 0.1 0.2 ECA Eastern Europe Belarus 0.2 0.0 0.0 0.1 ECA Eastern Europe Bosnia and Herzegovina 1.9 0.0 0.0 0.0 ECA Eastern Europe Bulgaria 1.2 0.1 0.3 0.5 ECA Eastern Europe Croatia 0.7 0.0 0.0 0.0 ECA Eastern Europe Czech Rep 0.3 0.0 0.1 0.1 ECA Eastern Europe Estonia 0.0 0.1 0.2 0.2 ECA Eastern Europe Georgia 5.4 0.0 0.0 0.0 ECA Eastern Europe Hungary 0.4 0.1 0.3 0.5 ECA Eastern Europe Latvia 0.5 0.1 0.2 0.3 ECA Eastern Europe Lithuania 0.2 0.1 0.1 0.2 ECA Eastern Europe Macedonia, FYR 0.5 0.0 0.0 0.0 ECA Eastern Europe Moldova, Rep 0.1 0.1 0.1 0.2 ECA Eastern Europe Poland 1.1 0.1 0.3 0.5 ECA Eastern Europe Romania 2.9 0.0 0.0 0.0 ECA Eastern Europe Russian Federation 133.6 26.2 57.1 92.8 ECA Eastern Europe Serbia and Montenegro 2.2 0.0 0.0 0.0 ECA Eastern Europe Slovakia 0.6 0.1 0.2 0.3 ECA Eastern Europe Slovenia 0.7 0.0 0.0 0.0 ECA Eastern Europe Ukraine 1.9 0.5 1.0 1.6 ECA Middle East Turkey 17.3 0.0 0.0 0.0 ECA Western Asia Azerbaijan 1.3 0.2 0.4 0.6 75 Region Subregion Country Hydro Geothermal Low Medium High ECA Western Asia Kazakhstan 5.0 0.6 1.2 1.8 ECA Western Asia Kyrgyzstan 7.9 0.4 0.8 1.3 ECA Western Asia Tajikistan 21.1 0.4 0.8 1.2 ECA Western Asia Turkmenistan 0.4 1.1 2.3 3.4 ECA Western Asia Uzbekistan 2.2 0.9 1.9 2.8 LCR Andean South America Bolivia 10.1 1.4 2.8 4.2 LCR Andean South America Colombia 16.0 0.0 0.0 0.0 LCR Andean South America Ecuador 2.6 0.0 0.1 0.1 LCR Andean South America Peru 20.8 0.9 1.9 3.0 LCR Caribbean Islands Antigua and Barbuda 0.0 0.0 0.0 0.0 LCR Caribbean Islands Barbados 0.0 0.0 0.0 0.0 LCR Caribbean Islands Dominica 0.0 0.0 0.0 0.0 LCR Caribbean Islands Dominican Rep 0.7 0.0 0.0 0.0 LCR Caribbean Islands Grenada 0.0 0.0 0.0 0.0 LCR Caribbean Islands Haiti 0.1 0.0 0.0 0.0 LCR Caribbean Islands Jamaica 0.0 0.0 0.0 0.0 LCR Caribbean Islands Saint Kitts and Nevis 0.0 0.0 0.0 0.0 LCR Caribbean Islands St. Lucia 0.0 0.0 0.0 0.0 LCR Caribbean Islands St. Vincent & Grenadines 0.0 0.0 0.0 0.0 LCR Caribbean Islands Trinidad and Tobago 0.0 0.0 0.0 0.0 LCR Central America Belize 0.0 0.0 0.0 0.0 LCR Central America Costa Rica 3.4 0.0 0.0 0.0 LCR Central America El Salvador 0.4 0.0 0.0 0.0 LCR Central America Guatemala 1.8 0.0 0.0 0.0 LCR Central America Honduras 0.5 0.0 0.0 0.0 LCR Central America Mexico 5.1 1.0 2.1 3.4 LCR Central America Nicaragua 0.8 0.0 0.0 0.0 LCR Central America Panama 1.0 0.0 0.0 0.0 LCR Northern South America Brazil 119.0 0.1 0.3 0.5 LCR Northern South America Guyana 2.0 0.0 0.0 0.0 LCR Northern South America Suriname 1.0 0.0 0.0 0.0 LCR Northern South America Venezuela 20.9 0.0 0.0 0.0 LCR Southern South America Argentina 10.4 0.0 0.0 0.0 LCR Southern South America Chile 13.0 0.1 0.3 0.4 LCR Southern South America Paraguay 6.8 0.0 0.0 0.0 LCR Southern South America Uruguay 0.8 0.0 0.0 0.0 MNA Middle East Iraq 7.2 0.0 0.0 0.0 MNA Middle East Jordan 0.0 0.0 0.0 0.1 MNA Middle East Lebanon 0.1 0.0 0.0 0.0 MNA Middle East Oman 0.0 0.0 0.0 0.0 MNA Middle East Syrian Arab Rep 0.3 0.0 0.0 0.0 MNA Middle East West Bank and Gaza 0.0 0.0 0.0 0.0 MNA Middle East Yemen 0.0 0.0 0.0 0.0 MNA North Africa Algeria 0.4 0.0 0.0 0.0 MNA North Africa Egypt 4.0 0.4 1.0 1.5 MNA North Africa Libyan Arab Jamahiriya 0.0 0.0 0.0 0.0 MNA North Africa Morocco 0.4 1.4 2.7 4.1 MNA North Africa Tunisia 0.0 0.0 0.0 0.0 76 Region Subregion Country Hydro Geothermal Low Medium High MNA Western Asia Iran, Islamic Rep 7.0 0.1 0.1 0.2 OTHER Atlantic Islands Channel Islands 0.0 0.0 0.0 0.0 OTHER Atlantic Islands Faeroe Islands 0.0 0.0 0.0 0.0 OTHER Atlantic Islands Greenland 1.1 0.0 0.0 0.0 OTHER Atlantic Islands Iceland 5.1 0.2 0.4 0.6 OTHER Atlantic Islands Isle of Man 0.0 0.0 0.0 0.0 OTHER AustraliaNZ Australia 2.4 0.5 1.1 1.7 OTHER AustraliaNZ New Zealand 6.2 0.6 1.4 2.2 OTHER Caribbean Islands Aruba 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Bahamas 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Bermuda 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Cayman Islands 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Cuba 0.2 0.2 0.3 0.5 OTHER Caribbean Islands Netherlands Antilles 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Puerto Rico 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Virgin Islands 0.0 0.0 0.0 0.0 OTHER Indian Ocean Islands Mayotte 0.0 0.0 0.0 0.0 OTHER Middle East Bahrain 0.0 0.0 0.0 0.0 OTHER Middle East Israel 2.8 0.0 0.1 0.1 OTHER Middle East Kuwait 0.0 0.0 0.0 0.0 OTHER Middle East Qatar 0.0 0.0 0.0 0.0 OTHER Middle East Saudi Arabia 0.0 0.1 0.2 0.3 OTHER Middle East United Arab Emirates 0.0 0.0 0.0 0.0 OTHER North America Canada 76.1 1.1 2.4 3.9 OTHER North America United States 42.3 3.9 8.6 13.9 OTHER Northeast Asia Japan 10.9 0.6 1.4 2.3 OTHER Northeast Asia Korea, Dem People's Rep 0.0 0.0 0.0 0.0 OTHER Northeast Asia Taiwan 1.1 0.0 0.0 0.0 OTHER Pacific Islands American Samoa 0.0 0.0 0.0 0.0 OTHER Pacific Islands Cook Islands 0.0 0.0 0.0 OTHER Pacific Islands French Polynesia 0.0 0.0 0.0 0.0 OTHER Pacific Islands Guam 0.0 0.0 0.0 0.0 OTHER Pacific Islands Nauru 0.0 0.0 0.0 0.0 OTHER Pacific Islands New Caledonia 0.0 0.0 0.0 0.0 OTHER Pacific Islands Niue 0.0 0.0 0.0 0.0 OTHER Pacific Islands Northern Mariana Islands 0.0 0.0 0.0 0.0 OTHER Pacific Islands Tuvalu 0.0 0.0 0.0 0.0 OTHER Southeast Asia Brunei Darussalam 0.0 0.0 0.0 0.0 OTHER Southeast Asia Singapore 0.0 0.0 0.0 0.0 OTHER Western Europe Andorra 0.0 0.0 0.0 0.0 OTHER Western Europe Austria 4.5 0.0 0.1 0.1 OTHER Western Europe Belgium 0.0 0.0 0.0 0.0 OTHER Western Europe Cyprus 1.9 0.0 0.0 0.0 OTHER Western Europe Denmark 0.0 0.0 0.1 0.2 OTHER Western Europe Finland 1.6 0.0 0.0 0.0 OTHER Western Europe France 5.8 1.1 2.3 3.7 OTHER Western Europe Germany 2.1 0.2 0.4 0.7 OTHER Western Europe Gibraltar 0.0 0.0 0.0 0.0 77 Region Subregion Country Hydro Geothermal Low Medium High OTHER Western Europe Greece 1.2 0.1 0.2 0.3 OTHER Western Europe Ireland 0.1 0.0 0.1 0.1 OTHER Western Europe Italy 8.4 0.4 0.8 1.3 OTHER Western Europe Liechtenstein 0.0 0.0 0.0 0.0 OTHER Western Europe Luxembourg 0.0 0.0 0.0 0.0 OTHER Western Europe Malta 0.0 0.0 0.0 0.0 OTHER Western Europe Monaco 0.0 0.0 0.0 0.0 OTHER Western Europe Netherlands 0.0 0.0 0.0 0.0 OTHER Western Europe Norway 16.0 0.1 0.2 0.3 OTHER Western Europe Portugal 2.0 0.0 0.0 0.0 OTHER Western Europe San Marino 0.0 0.0 0.0 0.0 OTHER Western Europe Spain 5.6 0.8 1.7 2.8 OTHER Western Europe Sweden 10.4 0.0 0.0 0.0 OTHER Western Europe Switzerland 3.3 0.0 0.1 0.1 OTHER Western Europe United Kingdom 0.2 0.1 0.2 0.4 SAR Indian Ocean Islands Maldives 0.0 0.0 0.0 0.0 SAR Southern Asia Bangladesh 0.2 0.0 0.0 0.0 SAR Southern Asia Bhutan 5.6 0.0 0.0 0.0 SAR Southern Asia India 52.8 0.0 0.1 0.1 SAR Southern Asia Nepal 12.6 0.0 0.0 0.0 SAR Southern Asia Sri Lanka 0.6 0.0 0.0 0.0 SAR Western Asia Afghanistan 0.0 0.0 0.0 0.0 SAR Western Asia Pakistan 10.4 0.0 0.0 0.0 78 Table A2.3: Annual Renewable Energy: Biofuels (Millions of tons of oil equivalent) Sugar Savanna Cropsa Manure Tallgrassb Jatropha Region Subregion Country (Ethanol) (Biogas) (Ethanol) (Biodiesel) AFR Central Africa Angola 69.7 0.2 0.0 121.9 AFR Central Africa Burundi 0.6 0.0 0.0 2.0 AFR Central Africa Cameroon 32.9 0.3 0.0 23.5 AFR Central Africa Central African Rep 71.4 0.1 0.0 60.5 AFR Central Africa Congo 12.5 0.0 0.0 12.2 AFR Central Africa Congo, Dem Rep 107.4 0.1 0.0 128.0 AFR Central Africa Gabon 13.0 0.0 0.0 4.9 AFR Central Africa Rwanda 0.1 0.0 0.0 1.5 AFR Central Africa Zambia 66.0 0.1 0.0 69.1 AFR Coastal West Africa Benin 10.8 0.1 0.0 12.5 AFR Coastal West Africa Cape Verde 0.0 0.0 0.0 0.0 AFR Coastal West Africa Côte d'Ivoire 31.5 0.1 0.0 18.8 AFR Coastal West Africa Equatorial Guinea 1.4 0.0 0.0 0.0 AFR Coastal West Africa Gambia 0.8 0.0 0.0 1.1 AFR Coastal West Africa Ghana 21.1 0.1 0.0 17.2 AFR Coastal West Africa Guinea 14.9 0.2 0.0 21.2 AFR Coastal West Africa Guinea-Bissau 1.8 0.0 0.0 2.6 AFR Coastal West Africa Liberia 8.6 0.0 0.0 0.0 AFR Coastal West Africa Nigeria 67.0 0.9 0.0 81.2 AFR Coastal West Africa Sao Tome & Principe 0.0 0.0 0.0 0.0 AFR Coastal West Africa Senegal 10.4 0.2 0.0 21.2 AFR Coastal West Africa Sierra Leone 3.2 0.0 0.0 2.0 AFR Coastal West Africa Togo 5.9 0.0 0.0 5.6 AFR East Africa Djibouti 0.0 0.0 0.0 2.1 AFR East Africa Eritrea 0.5 0.1 0.0 10.3 AFR East Africa Ethiopia 36.7 1.6 0.0 69.6 AFR East Africa Kenya 17.6 0.5 0.0 53.6 AFR East Africa Malawi 8.8 0.1 0.0 7.8 AFR East Africa Somalia 2.6 0.0 59.2 AFR East Africa Sudan 105.2 1.8 0.0 214.5 AFR East Africa Tanzania 85.7 0.7 0.0 79.0 AFR East Africa Uganda 19.0 0.3 0.0 20.2 AFR Indian Ocean Islands Comoros 0.0 0.0 0.0 0.0 AFR Indian Ocean Islands Mauritius 0.0 0.0 0.0 0.0 AFR Indian Ocean Islands Seychelles 0.0 0.0 0.0 0.0 AFR Madagascar Madagascar 41.4 0.4 0.0 13.3 AFR Sahelian Africa Burkina Faso 23.5 0.4 0.0 29.7 AFR Sahelian Africa Chad 37.7 0.3 0.0 90.7 AFR Sahelian Africa Mali 27.0 0.4 0.0 99.9 AFR Sahelian Africa Mauritania 0.9 0.1 0.0 86.7 AFR Sahelian Africa Niger 6.8 0.1 0.0 83.0 AFR Southern Africa Botswana 5.5 0.1 0.0 58.0 AFR Southern Africa Lesotho 0.4 0.0 0.0 0.0 AFR Southern Africa Mozambique 89.4 0.1 0.0 65.0 AFR Southern Africa Namibia 8.6 0.1 0.0 77.3 79 Sugar Savanna Cropsa Manure Tallgrassb Jatropha Region Subregion Country (Ethanol) (Biogas) (Ethanol) (Biodiesel) AFR Southern Africa South Africa 27.3 0.8 0.0 77.6 AFR Southern Africa Swaziland 0.7 0.0 0.0 0.8 AFR Southern Africa Zimbabwe 21.0 0.2 0.0 41.8 EAP China China 228.7 16.5 401.8 0.0 EAP Northeast Asia Korea, Rep 6.4 0.3 0.0 0.0 EAP Northeast Asia Mongolia 0.0 0.1 381.9 0.0 EAP Pacific Islands Fiji 0.7 0.0 0.0 0.0 EAP Pacific Islands Kiribati 0.0 0.0 0.0 0.0 EAP Pacific Islands Marshall Islands 0.0 0.0 0.0 EAP Pacific Islands Micronesia, Fed States 0.0 0.0 0.0 0.0 EAP Pacific Islands Pacific Islands (Palau) 0.0 0.0 0.0 EAP Pacific Islands Samoa 0.0 0.0 0.0 0.0 EAP Pacific Islands Solomon Islands 0.6 0.0 0.0 0.0 EAP Pacific Islands Timor-Leste 0.6 0.0 0.0 0.0 EAP Pacific Islands Tonga 0.0 0.0 0.0 0.0 EAP Pacific Islands Vanuatu 0.0 0.0 0.0 0.0 EAP Southeast Asia Cambodia 12.4 0.2 0.0 0.0 EAP Southeast Asia Indonesia 59.4 2.3 0.0 0.9 EAP Southeast Asia Lao People's Dem Rep 3.9 0.1 0.0 0.0 EAP Southeast Asia Malaysia 10.0 0.3 0.0 0.0 EAP Southeast Asia Myanmar 15.8 0.7 0.0 0.0 EAP Southeast Asia Papua New Guinea 7.1 0.0 0.0 2.0 EAP Southeast Asia Philippines 13.0 0.5 0.0 0.0 EAP Southeast Asia Thailand 36.7 0.7 0.0 0.0 EAP Southeast Asia Viet Nam 13.3 0.9 0.0 0.0 ECA Eastern Europe Albania 0.7 0.0 0.0 0.0 ECA Eastern Europe Armenia 0.1 0.0 9.1 0.0 ECA Eastern Europe Belarus 31.0 0.2 0.0 0.0 ECA Eastern Europe Bosnia and Herzegovina 3.9 0.0 0.0 0.0 ECA Eastern Europe Bulgaria 5.9 0.1 0.3 0.0 ECA Eastern Europe Croatia 5.4 0.0 0.0 0.0 ECA Eastern Europe Czech Rep 11.7 0.1 0.0 0.0 ECA Eastern Europe Estonia 1.9 0.0 0.0 0.0 ECA Eastern Europe Georgia 2.7 0.1 3.5 0.0 ECA Eastern Europe Hungary 15.2 0.1 0.0 0.0 ECA Eastern Europe Latvia 7.9 0.0 0.0 0.0 ECA Eastern Europe Lithuania 9.4 0.0 0.0 0.0 ECA Eastern Europe Macedonia, FYR 0.1 0.0 0.0 0.0 ECA Eastern Europe Moldova, Rep 3.9 0.0 4.8 0.0 ECA Eastern Europe Poland 32.7 0.5 0.0 0.0 ECA Eastern Europe Romania 26.1 0.3 15.3 0.0 ECA Eastern Europe Russian Federation 298.3 1.5 947.3 0.0 ECA Eastern Europe Serbia and Montenegro 9.8 0.1 0.0 0.0 ECA Eastern Europe Slovakia 4.8 0.0 0.0 0.0 ECA Eastern Europe Slovenia 1.3 0.0 0.0 0.0 ECA Eastern Europe Ukraine 79.5 0.5 151.9 0.0 ECA Middle East Turkey 5.8 0.9 66.6 0.0 ECA Western Asia Azerbaijan 1.3 0.2 3.2 0.0 80 Sugar Savanna Cropsa Manure Tallgrassb Jatropha Region Subregion Country (Ethanol) (Biogas) (Ethanol) (Biodiesel) ECA Western Asia Kazakhstan 9.7 0.3 690.8 5.1 ECA Western Asia Kyrgyzstan 0.0 0.1 61.5 0.0 ECA Western Asia Tajikistan 0.3 0.1 41.6 0.0 ECA Western Asia Turkmenistan 0.5 0.2 2.3 1.5 ECA Western Asia Uzbekistan 0.9 0.3 68.6 3.1 LCR Andean South America Bolivia 60.0 0.4 0.0 14.4 LCR Andean South America Colombia 38.5 1.1 0.0 19.7 LCR Andean South America Ecuador 8.3 0.3 0.0 0.8 LCR Andean South America Peru 8.0 0.4 0.0 0.0 LCR Caribbean Islands Antigua and Barbuda 0.0 0.0 0.0 0.0 LCR Caribbean Islands Barbados 0.0 0.0 0.0 0.0 LCR Caribbean Islands Dominica 0.0 0.0 0.0 0.0 LCR Caribbean Islands Dominican Rep 2.3 0.1 0.0 0.0 LCR Caribbean Islands Grenada 0.0 0.0 0.0 0.0 LCR Caribbean Islands Haiti 1.0 0.1 0.0 0.0 LCR Caribbean Islands Jamaica 0.3 0.0 0.0 0.0 LCR Caribbean Islands Saint Kitts and Nevis 0.0 0.0 0.0 0.0 LCR Caribbean Islands St. Lucia 0.0 0.0 0.0 0.0 LCR Caribbean Islands St. Vincent & Grenadines 0.0 0.0 0.0 0.0 LCR Caribbean Islands Trinidad and Tobago 0.0 0.0 0.0 0.0 LCR Central America Belize 0.0 0.0 0.0 0.0 LCR Central America Costa Rica 0.9 0.1 0.0 0.0 LCR Central America El Salvador 0.9 0.1 0.0 0.0 LCR Central America Guatemala 3.4 0.1 0.0 0.3 LCR Central America Honduras 2.9 0.1 0.0 0.0 LCR Central America Mexico 48.0 1.9 0.0 35.3 LCR Central America Nicaragua 4.7 0.2 0.0 0.0 LCR Central America Panama 2.3 0.1 0.0 0.0 LCR Northern South America Brazil 434.0 9.0 0.0 314.3 LCR Northern South America Guyana 5.5 0.0 0.0 1.3 LCR Northern South America Suriname 1.9 0.0 0.0 0.1 LCR Northern South America Venezuela 43.8 0.8 0.0 37.2 LCR Southern South America Argentina 266.0 2.1 1001.9 33.3 LCR Southern South America Chile 1.7 0.3 23.3 0.0 LCR Southern South America Paraguay 30.2 0.4 0.0 14.9 LCR Southern South America Uruguay 53.7 0.5 0.0 19.3 MNA Middle East Iraq 1.9 24.0 21.1 MNA Middle East Jordan 0.1 0.0 7.3 3.9 MNA Middle East Lebanon 0.1 0.0 0.0 0.0 MNA Middle East Oman 0.0 0.0 16.1 12.9 MNA Middle East Syrian Arab Rep 1.0 0.2 51.7 0.0 MNA Middle East West Bank and Gaza 0.0 0.0 0.0 0.0 MNA Middle East Yemen 0.0 0.1 0.0 20.2 MNA North Africa Algeria 4.4 0.3 0.0 69.6 MNA North Africa Egypt 0.0 0.5 0.0 13.3 MNA North Africa Libyan Arab Jamahiriya 2.4 0.1 0.0 37.5 MNA North Africa Morocco 12.7 0.4 0.0 8.7 MNA North Africa Tunisia 3.5 0.1 0.0 6.9 81 Sugar Savanna Cropsa Manure Tallgrassb Jatropha Region Subregion Country (Ethanol) (Biogas) (Ethanol) (Biodiesel) MNA Western Asia Iran, Islamic Rep 1.2 1.0 40.6 0.3 OTHER Atlantic Islands Channel Islands 0.0 0.0 0.0 OTHER Atlantic Islands Faeroe Islands 0.0 0.0 0.0 0.0 OTHER Atlantic Islands Greenland 0.0 0.0 0.0 0.0 OTHER Atlantic Islands Iceland 0.0 0.0 0.0 0.0 OTHER Atlantic Islands Isle of Man 0.0 0.0 0.0 OTHER AustraliaNZ Australia 142.4 1.8 364.4 363.1 OTHER AustraliaNZ New Zealand 5.1 0.6 33.7 0.0 OTHER Caribbean Islands Aruba 0.0 0.0 0.0 OTHER Caribbean Islands Bahamas 2.3 0.0 0.0 0.0 OTHER Caribbean Islands Bermuda 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Cayman Islands 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Cuba 14.8 0.2 0.0 0.3 OTHER Caribbean Islands Netherlands Antilles 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Puerto Rico 0.0 0.0 0.0 0.0 OTHER Caribbean Islands Virgin Islands 0.0 0.0 0.0 OTHER Indian Ocean Islands Mayotte 0.0 0.0 0.0 OTHER Middle East Bahrain 0.0 0.0 0.0 0.0 OTHER Middle East Israel 0.7 0.1 0.0 1.1 OTHER Middle East Kuwait 0.0 0.0 0.0 0.1 OTHER Middle East Qatar 0.0 0.0 0.0 0.7 OTHER Middle East Saudi Arabia 0.0 0.2 0.0 159.7 OTHER Middle East United Arab Emirates 0.0 0.0 0.1 6.2 OTHER North America Canada 47.7 0.9 432.3 0.0 OTHER North America United States 604.2 6.5 1522.1 124.8 OTHER Northeast Asia Japan 15.9 0.6 0.0 0.0 OTHER Northeast Asia Korea, Dem People's Rep 7.5 0.1 0.0 0.0 OTHER Northeast Asia Taiwan 0.0 0.0 0.0 OTHER Pacific Islands American Samoa 0.0 0.0 0.0 0.0 OTHER Pacific Islands Cook Islands 0.0 0.0 0.0 0.0 OTHER Pacific Islands French Polynesia 0.0 0.0 0.0 0.0 OTHER Pacific Islands Guam 0.0 0.0 0.0 0.0 OTHER Pacific Islands Nauru 0.0 0.0 0.0 0.0 OTHER Pacific Islands New Caledonia 0.0 0.0 0.0 0.0 OTHER Pacific Islands Niue 0.0 0.0 0.0 0.0 OTHER Pacific Islands Northern Mariana Islands 0.0 0.0 0.0 OTHER Pacific Islands Tuvalu 0.0 0.0 0.0 0.0 OTHER Southeast Asia Brunei Darussalam 0.0 0.0 0.0 0.0 OTHER Southeast Asia Singapore 0.0 0.0 0.0 0.0 OTHER Western Europe Andorra 0.0 0.0 0.0 OTHER Western Europe Austria 6.0 0.1 0.0 0.0 OTHER Western Europe Belgium 3.1 0.2 0.0 0.0 OTHER Western Europe Cyprus 0.0 0.0 0.0 0.0 OTHER Western Europe Denmark 5.2 0.2 0.0 0.0 OTHER Western Europe Finland 0.4 0.1 0.0 0.0 OTHER Western Europe France 63.4 1.2 0.0 0.0 OTHER Western Europe Germany 44.8 0.8 0.0 0.0 OTHER Western Europe Gibraltar 0.0 0.0 0.0 82 Sugar Savanna Cropsa Manure Tallgrassb Jatropha Region Subregion Country (Ethanol) (Biogas) (Ethanol) (Biodiesel) OTHER Western Europe Greece 3.7 0.1 0.0 0.0 OTHER Western Europe Ireland 4.6 0.3 0.0 0.0 OTHER Western Europe Italy 18.5 0.5 0.0 0.0 OTHER Western Europe Liechtenstein 0.0 0.0 0.0 0.0 OTHER Western Europe Luxembourg 0.0 0.1 0.0 0.0 OTHER Western Europe Malta 0.0 0.0 0.0 0.0 OTHER Western Europe Monaco 0.0 0.0 0.0 OTHER Western Europe Netherlands 0.6 0.3 0.0 0.0 OTHER Western Europe Norway 0.1 0.1 0.0 0.0 OTHER Western Europe Portugal 6.3 0.1 0.0 0.0 OTHER Western Europe San Marino 0.0 0.0 0.0 OTHER Western Europe Spain 8.8 0.7 0.0 0.0 OTHER Western Europe Sweden 3.3 0.1 0.0 0.0 OTHER Western Europe Switzerland 2.0 0.1 0.0 0.0 OTHER Western Europe United Kingdom 16.3 0.8 0.0 0.0 SAR Indian Ocean Islands Maldives 0.0 0.0 0.0 SAR Southern Asia Bangladesh 17.9 1.2 0.0 0.0 SAR Southern Asia Bhutan 0.0 0.0 0.0 0.0 SAR Southern Asia India 249.0 12.9 0.0 64.5 SAR Southern Asia Nepal 3.4 0.5 0.0 2.5 SAR Southern Asia Sri Lanka 6.1 0.1 0.0 0.2 SAR Western Asia Afghanistan 0.8 2.2 28.9 SAR Western Asia Pakistan 10.1 2.6 0.0 54.3 aSugar cane, sugar beets, sorghum bUS species: switchgrass 83 n 00 00 00 00 00 0 00 00 00 00 00 00 00 0 00 00 00 0 0 00 00 00 00 00 0 0 00 00 0 0 0 00 latio 0 9,00 3,00 8,00 695,0 289,0 641,0 793,0 89,00 7,00 2,00 7,00 1,00 6,90 198 ,048,07 ,130,04 ,754,08 ,313,02 ,804,01 27,90 ,163,05 ,738,05 ,459,03 8,194,0 11,04 ,461,04 ,876,01 71,14 ,538,05 ,236,03 ,519,02 300,0 ,382,02 37,71 16,63 ,183,06 ,487,06 335,0 19,36 18,58 12,80 Popu 0 82 02 00 2 0 00 0 37 43 10 05 37 66 22 00 00 0 15 00 50 57 00 5 0 5 60 00 6 0 61 0 196 401,0 51,94 81,50 919,0 10,00 ,6007 ,0007 861,2 974,0 231,0 102,9 93,00 6,91 1, 6,02 6,70 0,43 65,00 Affected ,909,93 1,154,7 ,231,15 ,863,77 ,816,93 1,005,0 ,629,53 ,822,87 210,0 705,4 ,257,71 ,973,01 25 77, 16,42 23,05 ,054,05 30,51 11,54 ,639,62 Events,re 0 6 6 0 0 0 athe 500 59 0 0 0 00 00 0 20 0 0 0 0 0 9 00 00 00 00 2 0 0 Wyb sse 32,00 3,200 43,22 ,0002 37,05 18,00 ,0004 2,000 3,200 2002- 289,2 10, 242,5 635,0 57,50 76,50 32,00 15,65 205,8 131,3 480,0 87,05 30,73 50,00 ,254,71 Homel 2 0 4 0 3 3 1 Affected n 152 18 69 11 119 128 28 100 32 28 54 229 25 10 744 215 86 180 18 562 34 545 292 59 d 1,057 latio 602,9 22,37 150,4 Kille Popu 0.76 0.19 0.10 0.05 0.42 14.76 1.21 0.21 7.60 1.37 3.31 1.83 1.25 1.58 8.12 0.96 0.23 0.74 0.37 0.30 0.19 0.06 0.11 SLR meters) (3 54 0.47 0.14 0.05 0.04 0.27 Affected 10.20 15.00 1.16 0.11 4.17 1.11 1.82 1.13 1.16 0.91 15.00 4.63 0.57 0.19 0. 0.00 0.22 0.14 0.04 0.09 15.00 84 SLR Events meters) GDP (2 % 08 12 64 23 14 15 0.17 0. 0.00 0.02 0. 5. 1.11 0.00 0.74 0.84 0.32 0.43 1.07 0. 1. 0.18 0. 0.34 0.35 0.14 0.09 0.02 0.06 Weather SLR meter) and (1 Rise e Level Rep n epR nea u Princip Sea e Gui ssa & e ytrn la id on Africal Dem da Verd go go, n e oireIvd' ial a -Bia a riae ia meoT gal eonL ia outi ay iw n pacts: Cou Ango Burun Camero Centra Con Con Gabon anwR nda mbiaaZ Beni Cap Côte Equator Gambia Ghana Guine Guine Lib Niger Sao Sene Sierra ogoT Djib Eritrea Ethiop Ken Mala Somali Suda anzaniaT Uga Comoros Im sd Change Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa lansI ate ste ste ste niog ste ste ste ste ste ste ste ste ste ste ste Africal Africal Africal Africal Africal Africal Africal Africal Africal Wl Wl Wl Wl Wl Wl Wl Wl Wl Wl Wl Wl Wl Wl Ocean Clim Africa Africa Africa Africa Africa Africa Africa Africa Africa n Subre Centra Centra Centra Centra Centra Centra Centra Centra Centra Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta Coasta East East East East East East East East East India A2.4: on R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R Table Regi AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF AFR AF AF AF AF AF AF AF AF AF n 00 0 00 00 00 00 00 00 00 00 0 00 0 00 00 00 0 00 00 0 00 00 00 0 06 00 00 00 0 0 00 0 latio 0 966,0 64,40 5,00 6,00 906,0 565,0 35,0 4,00 634,0 58,100 31,000 71,00 155,0 229,0 581,0 94,00 115,0 03,0 3,00 5,00 5,00 198 ,873,08 ,962,06 ,477,04 ,590,06 ,609,01 ,586,05 ,277,01 12,09 ,018,01 27,57 ,133,07 38,12 ,663,01 ,613,06 ,205,03 ,086,03 Popu 981,2 148,3 13,76 33,70 48,03 0 94 21 42 12 81 57 96 61 03 6 00 60 00 0 96 82 00 60 0 0 00 03 12 55 6 2 09 78 27 00 5 196 7,925 2,67 5,00 2,508 0,75 3,59 5,07 Affected ,014,81 012,9,9 ,904,85 236,7,7 10 788,2 90,4 84,700 30,10 ,723,93 ,688,46 ,826,09 ,486,74 1,386,7 29, ,055,11 1,429,0 27,28 ,862,17 ,720,02 ,227,91 352,0 216,5 141,3 180,6 15 44 10, 12, ,707,97 842,0 905,6,2 896,0 84 72, Events,re ,649,21 0 athe 110 05 2 34 0 2 1 0 05 0 5 0 0 500 35 0 2 0 0 0 0 0 5 400 50 53 00 0 98 00 68 Wyb sse 12,50 12,23 44,85 24,25 94,78 34,00 1,020 34,23 66,00 7,82 20,00 46,77 ,0006 2002- 398,8 119,8 570,5 28,00 61,30 50,20 10,89 285,8 202,5 26,00 421,6 100,5 Homel 48,99 1,064,6 1,017,6 ,612,88 Affected 68 5 44 42 35 0 4 3 0 1 1 8 n 104 31 41 12 553 130 202 272 52 281 123 13 101 474 508 193 d 1,422 3,208 1,482 5,917 1,104 2,052 latio 102,3 44,66 14,99 29,14 Kille Popu 0.67 17.48 0.93 0.03 0.04 5.59 0.60 4.95 4.94 1.75 3.97 0.11 2.12 SLR meters) (3 60 41 00 Affected 15.00 15.00 0.45 13.42 0. 0.03 0.03 4.00 0. 15. 15.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00 3.01 3.41 1.15 2.47 0.09 1.37 85 SLR meters) GDP (2 % 35 0.23 9. 0.27 0.03 0.01 2.40 0.21 1.06 1.88 0.55 0.96 0.06 0.62 SLR meter) (1 Rep a States d (Palau) sd meD ine Fe Gu d p nds Islan a le's w ytrn s euq landsIs ia, a lles soaF o ai Africa Re a op Ne hecy gascar a ian d anaw Cou Mauritiu Se Mada Burkin Cha Mali Maurita Niger Bots Lesoth Mozambi Namib South anilzawS ew oli i Isla on ines a atu odi esi Pe aisy a pp mbabiZ Chin Korea, Mong Fiji Kiribat Marshall Micrones Pacific Samoa Solom mor-LesteiT ongaT marnay Vanu Camb Indon Lao Mala M Papu Phili sd sd lansI lansI sd sd sd sd sd sd sd niog Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Africa Asia Asia Asia Asia Asia Asia Asia Asia Asia Ocean Ocean n n n n n n n n n n n n Islan Islands Islands Islan Islands Islan Islan Islan Islan Islan ast ast ast ast ast ast ast n n gascar ast ast lia lia lia lia lia a Subre India India Mada Sahe Sahe Sahe Sahe Sahe Souther Souther Souther Souther Souther Souther Souther Chin Northe Northe Pacific Pacific Pacific Pacific Pacific Pacific Pacific Pacific Pacific Pacific Southe Southe Southe Southe Southe Southe Southe on R R R R R R R R R R R R R R R Regi AF AF AF AF AF AF AF AF AF AF AF AF AF AF AF EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP EAP 0 0 n 00 00 00 00 00 00 0 00 0 00 00 00 00 0 00 00 0 0 00 0 00 00 00 0 00 0 00 0 0 latio 0 8,00 0,00 2,00 7,00 8,00 10,0 3,00 4,00 3,50 2,00 7,00 4,00 61,00 198 46,71 53,70 ,671,02 ,096,03 ,643,09 ,092,04 8,862,0 4,588,0 10,23 ,073,05 10,70 2,544,0 ,413,03 1,889,0 ,002,04 35,57 ,984,34 ,166,06 ,632,03 ,966,03 2,861,0 ,355,05 ,961,07 Popu 139,0 50,04 44,48 14,87 15,95 28,44 17,32 0 7 9 92 00 5 0 52 00 17 0 00 80 009 01 7 00 665 00 86 0 96 300 00 12 98 70 08 00 196 7,07 4,92 82 304,0 63,04 10,00 5,500 ,2001 3,000 287,7 220,2 144,8 780,0 46,05 Affected 42,95 66, 3,459,1 1, ,628,62 199, ,515,02 456,4,2 114, ,577,81 644,1 385,5,3 1,100,0 ,692,94 ,045,47 ,031,12 099,7,7 152,0 Events,re athe 07 04 144 300 09 0 3 0 0 9 4 2 0 000 200 150 850 00 0 7 0 57 36 63 00 Wyb sse 120 1,340 1, 2,254 1,697 7,728 ,5067 2002- 271,3 11,97 26,34 63,82 41,53 24,34 ,866,23 156,0 69,81 134,8 299,1 115,4 295,8 Homel 9 Affected 89 4 7 0 17 41 47 1 0 0 9 n 434 21 58 15 60 936 62 41 834 16 125 12 602 743 d 3,859 20,58 2,275 1,467 1,818 2,596 latio Kille Popu 17 7.46 24. 0.19 1.53 1.99 0.85 0.56 1.54 1.10 0.76 4.37 1.03 SLR meters) (3 79 40 84 4.44 Affected 17.19 0.16 1.42 1.72 0. 0.53 1. 0.90 0.48 3.52 0. 15.00 86 SLR meters) GDP (2 % 44 66 1.42 10.21 0.13 1.30 1. 0.72 0.51 1.26 0.70 0.20 2. 0.64 SLR meter) (1 ina gro da on rzegov He RYF ati ntene d a, epR Barbu ederF Mo d n n n d an ytrn a an Nam nia su an a y a oni nia an ia e ija a epR a n enistan aib or a aria onia gar ani ova, nd a yzsta a Cou hailandT Viet Alba Armeni Belar Bosni Bulg Croati Czech Est Georgi Hun Latvia Lithu Maced Mold Pola Roma Russia Serbi Slovak Slovenia Ukrain rkeyuT Azerba Kazakhsta rgyK jikistanaT rkmuT Uzbekist Bolivi Colom Ecuad Peru Antigu ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep ep America America America America nds Asia Asia uth uth uth uth Isla niog ast ast Euro Euro Euro Euro Euro Euro Euro Europe Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Euro Europe Euro Asia Asia Asia Asia Asia Asia East So So So So e an an an an aneb sterne sterne sterne sterne sterne sterne Subre Southe Southe Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Eastern Middl W W W W W W Ande Ande Ande Ande Carib on Regi EAP EAP ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA ECA LCR LCR LCR LCR LCR n 00 0 00 0 00 00 0 00 0 00 00 00 00 00 00 0 00 00 00 00 00 0 0 0 00 00 0 00 05 00 00 00 0 0 latio 0 249,1 73,35 90,10 44,40 115,5 97,80 0,00 146,0 16,0 1,00 4,00 7,00 7,00 9,17 5,00 198 ,695,05 ,353,05 ,133,02 ,082,01 ,284,02 ,586,04 ,820,06 ,567,03 67,57 ,921,02 ,950,01 761,0 355,0 ,114,03 ,914,02 ,181,02 ,002,33 1,101,0 ,704,08 ,538,08 Popu 121,6 15,09 28,09 11,14 13,00 18,66 40,87 0 5 200 59 75 09 0 0 2 0 00 10 05 40 24 06 38 77 6 00 30 0 12 07 3 00 00 00 00 01 29 066 196 84,28 1,210 12,88 73,60 21,44 51,20 0,14 4,600 2,40 5,050 Affected ,694,03 ,361,44 ,765,21 237,6 ,210,41 607,3 457,2 ,895,04 513,0,3 197,4 656,2 582,4 978,4 83,26 ,274,62 59,13 13,09 ,564,51 850,0 351,7 102,0 828,0 148,5 501,2 192, Events,re athe 315 00 0 56 2 0 700 10 0 7 9 000 270 04 9 82 0 0 00 88 43 00 0 0 800 05 02 0 Wyb sse ,3307 52,72 ,4001 450 2, 4, 9,170 6, 2,500 4,000 1,500 2002- 479,0 101,7 10,35 37,62 58,33 795,2 72,16 10, 153,3 539, 305,1 100,0 60,00 75,00 164,6 129,4 53,20 1,036,9 Homel 3 Affected 46 6 6 5 9 0 0 4 0 n 209 68 31 324 166 159 786 140 19 340 25 140 27 550 799 d 2,391 8,204 1,056 1,510 23,71 6,165 4,059 5,614 30,34 1,458 1,481 latio Kille Popu 0.55 0.30 1.16 5.56 0.29 0.60 0.07 0.40 1.29 0.48 1.71 1.22 14.47 19.86 1.50 1.38 0.18 3.20 1.36 0.59 0.65 12.13 SLR meters) (3 19 24 92 10 54 59 29 Affected 15.00 15.00 0.35 15.00 0. 0.92 15.00 15.00 15.00 15.00 3.81 0.20 0.44 0.05 0. 0. 0.29 1.28 0.90 9.56 13.14 1.16 0.85 0.13 2.34 1. 0. 0. 9. 87 SLR meters) GDP (2 % 15 06 02 0. 0.08 0.67 2. 0.11 0.28 0. 0.08 0.54 0.10 0.85 0.58 4.64 6.42 0.81 0.31 0.08 1.48 0.84 0.49 0.52 6.44 SLR meter) (1 ines go p Nevis Gaza Grenad obaT p Re Re n and & d and an a a b dor ytrn dos ica ica a a Kitts Ric aug a a e Bank a Lucia Vincent duras Salva yana mean uel na yau y ua n on Ara e an Cou Barba Domin Domin Grenad Haiti Jamaic Saint St. St. inidadrT Beliz Costa El Guatemal Hon Mexico Nicara Panam Brazil Gu Suri Venez Argenti Chil Parag Urug Iraq Jorda Leb Oman anriyS ste ypt W Yemen Algeri Eg nds America America America America America nds nds nds nds nds nds nds nds nds a a a a a a a a America America America h h h h Isla Isla Isla Isla Isla Isla Isla Isla Isla Isla niog South South South South Sout Sout Sout Sout aneb aneb aneb aneb aneb aneb aneb aneb aneb aneb Americl Americl Americl Americl Americl Americl Americl Americl n n n n n n n n East East East East East East East e e e e e e e Africa Africa Subre Carib Carib Carib Carib Carib Carib Carib Carib Carib Carib Centra Centra Centra Centra Centra Centra Centra Centra Norther Norther Norther Norther Souther Souther Souther Souther Middl Middl Middl Middl Middl Middl Middl North North on Regi LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR LCR MNA MNA MNA MNA MNA MNA MNA MNA MNA n 00 0 00 0 00 0 00 00 0 00 00 00 0 00 00 00 0 00 00 0 00 00 latio 0 2,00 4,00 2,00 228,0 210,0 54,00 174,0 97,00 3,00 25,0 82,0 6,00 198 3,043,0 19,38 ,384,06 39,12 14,69 ,113,03 ,710,09 ,206,03 151,0 107,0 3,878,0 1,375,0 9,372,0 24,59 Popu 227,2 116,7 17,19 0 0 46 95 1 5 7 0 280 58 0 46 0 200 004 41 10 70 4 196 0,20 0,33 892,4 446,6 99 24,70 1,200 40,00 ,8501 1,000 5,000 11,44 Affected 65, 15,79 ,475,09 121,6 640, 883,7,3 224,4,6 ,724,99 Events,re 0 7 0 athe 05 0 4 0 0 42 0 0 0 0 714 000 63 69 24 0 2 Wyb sse 39,54 50,50 3,200 5, 4,330 2002- 194,7 23,56 290,9 10,00 417,2 188,6 699,5 15,60 Homel 0 0 0 2 2 6 7 Affected n 917 66 10 633 11 19 71 175 770 21 d 1,298 3,715 1,159 1,948 13,23 5,606 latio Kille Popu 2.41 0.48 4.87 0.59 4.95 7.45 14.53 4.21 5.73 1.82 4.31 0.30 3.24 8.90 3.04 4.63 .420 4.31 SLR meters) (3 97 31 80 1. 0. 3.90 0.42 Affected 15.00 15.00 15.00 4.25 6.92 15.00 9.64 15.00 15.00 2.33 15.00 3.63 15.00 15.00 1.49 3.06 0.23 2.63 6.69 2.50 3. .260 3.06 15.00 15.00 15.00 15.00 88 SLR meters) GDP (2 % 1.53 0.14 2.93 0.24 3.56 6.39 4.74 0.45 1.53 1.15 1.81 0.15 2.01 4.49 1.96 2.98 .100 1.81 SLR meter) (1 ya Reps le' mahiri Ja Rep sd s irates oa b nds d o a mE eopP iaseny nds ytrn Ara Islanl Isla Island ands n nay and a as a Pol Islamic d Man nne of lanaeZ Antillessd Sam n lan Ric Isl in Arab States Dem Isla w yma a yotte itaw Arabii d ada d n Cou Lib Morocco unisiaT Iran, Cha aeroeF Greenl Icelan Isle Australi Ne Aruba Baham Bermud Ca Cub Nether Puerto Virgin Ma Bahra Israel Ku Qatar Saud Unite Can Unite Japa Korea, nawiaT America Cook enchrF Guam sd sd sd sd sd sd nds nds nds nds nds nds nds nds lansI a a Isla Isla Isla Isla Isla Isla Isla Isla sd sd sd sd niog Asia Asia Asia Asia Islan Islan Islan Islan Islan Africa Africa Africa aNZ aNZ aneb aneb aneb aneb aneb aneb aneb aneb Ocean East East East East East East Islan Islan Islan Islan n e e e e e e Americ Americ ast ast ast sterne Subre North North North W Atlantic Atlantic Atlantic Atlantic Atlantic Australi Australi Carib Carib Carib Carib Carib Carib Carib Carib India Middl Middl Middl Middl Middl Middl North North Northe Northe Northe Pacific Pacific Pacific Pacific on HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER Regi MNA MNA MNA MNA OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT n 00 00 00 00 00 00 0 0 00 00 0 00 0 00 00 0 00 00 0 00 0 08 00 latio 0 0,00 3,00 4,00 0,00 6,00 0,00 8,00 143,0 32,0 198 ,553,07 611,0 9,847,0 5,123,0 4,780,0 53,88 78,30 ,643,09 364,9 3,401,0 56,43 14,15 4,091,0 ,766,09 158,0 487,8 37,38 8,310,0 6,319,0 56,33 85,43 Popu 687,3 0 0 0 0 0 00 0 06 0 00 0 421 07 300 48 0 58 196 2,000 60,30 ,5755 ,0003 199 000 575, 13,04 ,8003 6,100 Affected 3,891, 470,8,1 264,0 39,95 811,4 489,5 87,75 65,60 74,2 6, Events,re 352, ,105,22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 7 0 athe Wyb sse 406 390 14,95 10,52 6,000 23,84 3,66 1,000 3,13 2002- Homel 70,75 17,26 9 Affected 41 22 60 19 0 0 6 1 0 n 508 123 38 887 606 12 20 294 33 239 38 d 1,283 1,380 latio 597,5 Kille 1,608,8 Popu 0 84 0.00 7.81 100.0 26.29 1.87 3.37 3.55 2.04 3.81 52.52 3.34 3.32 1.92 3.44 3. 1.57 SLR meters) (3 0 37 84 Affected 15.00 15.00 15.00 15.00 15.00 0.00 6.11 100.0 26.38 1.53 2.96 3.10 1.74 3. 15.00 48.04 2.88 2. 1.45 2.58 15.00 2.39 1.08 89 SLR meters) GDP (2 % 0 56 66 43 59 0.00 4.42 100.0 26.48 1.20 2. 2. 1.43 2.93 43.57 2. 2.35 0.98 1.72 0.94 0. SLR meter) (1 sd Islan na ia alam m in g do ytrn donle Maria russ e sd no nd n u Ca Dai a k y bour o lan l she ar King apor ria ium d es w y htenste Mari n rlae d lad Cou Naur Ne Niue Norther valuuT Brune Sing Andorr Aust Belg prusyC nda xem yaw Denm inlF ancerF German Gibraltar Greece Irelan Ital Liec Lu Malta Monac Nether Nor Portuga San Spai endewS itzwS Unite Maldiv Bang Bhutan India sd e e e e e e e e e e e e e e e e e e e e e e e e sd sd sd sd sd lansI Asia Asia niog Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Europ Asia Asia Asia Islan Islan Islan Islan Islan ast ast Ocean n n n n sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne sterne Subre Pacific Pacific Pacific Pacific Pacific Southe Southe W W W W W W W W W W W W W W W W W W W W W W W W India Souther Souther Souther on HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER HER Regi OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT OT SAR SAR SAR SAR 0 0 0 0 n latio 0 9,00 3,00 0,00 0,33 198 s.ama 14,55 14,60 15,95 82,73 Popu Bah the 0 512 7 447 8 fort 196 8,12 9,28 tha Affected ,861,5 16,27 ,524,8 03 to 33, ilar Events,re sim athe 05 10 0 28 is Wyb sse 2002- 123,3 76,55 2,847,6 ,934,28 which Homel SLR 2m 6 Affected at n d 5,188 1,651 2,429 19,88 entc latio Kille per 15 Popu 57 33 ofeul 1. 0. vaa SLR meters) (3 ssumea oref 1.01 0.22 Affected ereht 90 SLR meters) GDP (2 % We.dect 0.45 0.10 affe SLR meter) GDP (1 of estimation allow not a ytrn istan la did Lank Cou Nep Sri Afghan Pakistan dataeblali ava,s atest niog Asia Asia n n Asia Asia nda isl sterne sterne allms Subre Souther Souther W W stom on For Regi SAR SAR SAR SAR Note: Table A2.5: Sequestration Potential (Reduction of Deforestation, Storage) Annual CO2 Storage CO2 Emissions (Mt) Potential / Region Subregion Country Deforestation Total Annual Emissions AFR Central Africa Angola 17.8 51.7 156.934 AFR Central Africa Burundi 7.3 10.4 0.000 AFR Central Africa Cameroon 77.1 107.2 0.721 AFR Central Africa Central African Rep 9.0 20.4 0.000 AFR Central Africa Congo 9.9 16.9 53.624 AFR Central Africa Congo, Dem Rep 317.3 369.0 0.012 AFR Central Africa Gabon 3.6 13.5 182.262 AFR Central Africa Rwanda 7.5 11.3 0.000 AFR Central Africa Zambia 235.5 253.2 0.000 AFR Coastal West Africa Benin 36.2 43.6 3.167 AFR Coastal West Africa Cape Verde 0.0 0.1 AFR Coastal West Africa Côte d'Ivoire 91.2 106.3 7.456 AFR Coastal West Africa Equatorial Guinea 4.4 6.9 282.696 AFR Coastal West Africa Gambia -0.3 1.1 9939.023 AFR Coastal West Africa Ghana 27.9 48.6 27.400 AFR Coastal West Africa Guinea 10.4 19.6 38.082 AFR Coastal West Africa Guinea-Bissau 1.1 3.0 406.341 AFR Coastal West Africa Liberia 39.4 41.7 24.645 AFR Coastal West Africa Nigeria 194.8 388.3 101.104 AFR Coastal West Africa Sao Tome & Principe 0.0 0.1 7582.267 AFR Coastal West Africa Senegal 3.6 22.5 105.614 AFR Coastal West Africa Sierra Leone 13.3 17.3 48.764 AFR Coastal West Africa Togo 8.6 14.5 4.723 AFR East Africa Djibouti 0.0 1.7 14.307 AFR East Africa Eritrea 0.0 0.6 AFR East Africa Ethiopia 8.5 67.4 0.000 AFR East Africa Kenya 12.0 64.7 4.047 AFR East Africa Malawi 26.7 33.2 0.000 AFR East Africa Somalia AFR East Africa Sudan 30.5 126.8 4.188 AFR East Africa Tanzania 14.5 73.7 10.737 AFR East Africa Uganda 39.3 66.3 0.000 AFR Indian Ocean Islands Comoros 0.0 0.4 AFR Indian Ocean Islands Mauritius 0.0 4.0 AFR Indian Ocean Islands Seychelles 0.0 0.6 AFR Madagascar Madagascar 60.3 91.7 14.991 AFR Sahelian Africa Burkina Faso 0.6 21.6 0.000 AFR Sahelian Africa Chad 3.5 21.3 81.024 AFR Sahelian Africa Mali 8.0 33.4 0.000 AFR Sahelian Africa Mauritania 0.0 13.6 AFR Sahelian Africa Niger 0.7 12.9 0.000 AFR Southern Africa Botswana 19.7 35.1 0.000 Annual CO2 Storage CO2 Emissions (Mt) Potential / Region Subregion Country Deforestation Total Annual Emissions AFR Southern Africa Lesotho 0.0 2.9 0.000 AFR Southern Africa Mozambique 9.3 24.2 41.732 AFR Southern Africa Namibia 2.2 12.5 917.112 AFR Southern Africa South Africa 1.7 419.1 50.123 AFR Southern Africa Swaziland -1.7 1.5 AFR Southern Africa Zimbabwe 47.4 80.4 0.000 EAP China China -47.3 4890.4 53.543 EAP Northeast Asia Korea, Rep 1.2 522.1 2.694 EAP Northeast Asia Mongolia 0.5 28.3 0.000 EAP Pacific Islands Fiji 0.1 3.5 0.000 EAP Pacific Islands Kiribati 0.0 0.1 EAP Pacific Islands Marshall Islands EAP Pacific Islands Micronesia, Fed States EAP Pacific Islands Pacific Islands (Palau) 0.0 0.2 EAP Pacific Islands Samoa 0.1 0.2 0.000 EAP Pacific Islands Solomon Islands 0.2 0.6 0.000 EAP Pacific Islands Timor-Leste EAP Pacific Islands Tonga 0.0 0.3 0.000 EAP Pacific Islands Vanuatu 0.0 0.9 0.000 EAP Southeast Asia Cambodia 56.2 124.8 0.534 EAP Southeast Asia Indonesia 2563.0 3065.6 13.679 EAP Southeast Asia Lao People's Dem Rep 23.6 30.9 0.000 EAP Southeast Asia Malaysia 699.0 865.2 22.034 EAP Southeast Asia Myanmar 425.4 508.1 7.788 EAP Southeast Asia Papua New Guinea 146.0 154.8 25.639 EAP Southeast Asia Philippines 94.8 227.8 6.462 EAP Southeast Asia Thailand 47.7 312.2 21.075 EAP Southeast Asia Viet Nam -48.7 85.3 1743.894 ECA Eastern Europe Albania 0.8 4.6 62.344 ECA Eastern Europe Armenia 0.0 6.6 ECA Eastern Europe Belarus 5.6 84.3 0.000 ECA Eastern Europe Bosnia and Herzegovina 0.0 16.6 0.014 ECA Eastern Europe Bulgaria -2.0 59.8 16.861 ECA Eastern Europe Croatia -0.2 25.9 58.026 ECA Eastern Europe Czech Rep 0.0 143.0 1.291 ECA Eastern Europe Estonia 2.2 24.9 7.914 ECA Eastern Europe Georgia 0.0 10.2 ECA Eastern Europe Hungary -0.7 75.0 0.947 ECA Eastern Europe Latvia 3.9 13.5 10.738 ECA Eastern Europe Lithuania 3.1 18.0 1.742 ECA Eastern Europe Macedonia, FYR 0.0 11.2 ECA Eastern Europe Moldova, Rep 0.0 10.9 ECA Eastern Europe Poland -1.8 378.8 26.165 ECA Eastern Europe Romania -1.4 123.3 6.758 92 Annual CO2 Storage CO2 Emissions (Mt) Potential / Region Subregion Country Deforestation Total Annual Emissions ECA Eastern Europe Russian Federation 54.2 1969.4 184.646 ECA Eastern Europe Serbia and Montenegro 0.1 60.4 3.160 ECA Eastern Europe Slovakia 3.0 48.4 0.000 ECA Eastern Europe Slovenia 1.1 20.2 0.294 ECA Eastern Europe Ukraine 0.0 481.9 ECA Middle East Turkey 20.8 376.2 4.990 ECA Western Asia Azerbaijan 0.0 55.4 ECA Western Asia Kazakhstan 0.0 161.0 ECA Western Asia Kyrgyzstan 0.0 7.1 ECA Western Asia Tajikistan 0.0 8.2 ECA Western Asia Turkmenistan 0.0 64.1 ECA Western Asia Uzbekistan 0.0 180.8 LCR Andean South America Bolivia 83.8 123.2 50.290 LCR Andean South America Colombia 106.1 266.4 21.291 LCR Andean South America Ecuador 58.9 100.8 39.057 LCR Andean South America Peru 187.2 257.0 18.022 LCR Caribbean Islands Antigua and Barbuda 0.0 1.3 LCR Caribbean Islands Barbados 0.0 1.5 LCR Caribbean Islands Dominica 0.0 0.2 LCR Caribbean Islands Dominican Rep 0.0 30.8 11.751 LCR Caribbean Islands Grenada 0.0 0.3 LCR Caribbean Islands Haiti 2.0 9.4 34.402 LCR Caribbean Islands Jamaica 2.6 15.6 18.799 LCR Caribbean Islands Saint Kitts and Nevis 0.0 0.1 LCR Caribbean Islands St. Lucia 0.0 0.5 LCR Caribbean Islands St. Vincent & Grenadines 0.0 0.2 LCR Caribbean Islands Trinidad and Tobago 0.0 24.9 LCR Central America Belize 21.4 22.6 1.487 LCR Central America Costa Rica 9.9 22.4 35.609 LCR Central America El Salvador 4.1 15.5 9.257 LCR Central America Guatemala 56.6 78.1 1.600 LCR Central America Honduras 17.6 31.1 10.459 LCR Central America Mexico 96.9 608.7 40.338 LCR Central America Nicaragua 53.7 66.7 4.212 LCR Central America Panama 47.5 59.3 5.880 LCR Northern South America Brazil 1372.1 2223.2 7.367 LCR Northern South America Guyana 34.9 38.7 6.962 LCR Northern South America Suriname 0.0 3.4 111.568 LCR Northern South America Venezuela 144.1 383.6 169.167 LCR Southern South America Argentina 55.1 344.5 30.757 LCR Southern South America Chile 15.5 91.9 98.527 LCR Southern South America Paraguay 20.6 46.7 0.000 LCR Southern South America Uruguay -24.4 1.1 MNA Middle East Iraq 0.2 101.1 368.695 93 Annual CO2 Storage CO2 Emissions (Mt) Potential / Region Subregion Country Deforestation Total Annual Emissions MNA Middle East Jordan 0.1 23.9 0.029 MNA Middle East Lebanon 0.6 18.7 5.642 MNA Middle East Oman 0.0 29.2 329.315 MNA Middle East Syrian Arab Rep 0.1 66.8 40.215 MNA Middle East West Bank and Gaza MNA Middle East Yemen 0.4 25.8 458.871 MNA North Africa Algeria 2.8 128.8 271.094 MNA North Africa Egypt 3.0 180.5 96.663 MNA North Africa Libyan Arab Jamahiriya 0.7 62.5 350.286 MNA North Africa Morocco 2.6 59.8 95.252 MNA North Africa Tunisia 3.9 34.6 53.348 MNA Western Asia Iran, Islamic Rep 8.1 488.4 423.841 OTHER Atlantic Islands Channel Islands OTHER Atlantic Islands Faeroe Islands OTHER Atlantic Islands Greenland OTHER Atlantic Islands Iceland 0.0 2.8 OTHER Atlantic Islands Isle of Man OTHER AustraliaNZ Australia 4.2 495.1 145.099 OTHER AustraliaNZ New Zealand 3.2 76.1 281.966 OTHER Caribbean Islands Aruba OTHER Caribbean Islands Bahamas 0.0 2.0 341.840 OTHER Caribbean Islands Bermuda OTHER Caribbean Islands Cayman Islands OTHER Caribbean Islands Cuba -8.9 41.2 14.392 OTHER Caribbean Islands Netherlands Antilles OTHER Caribbean Islands Puerto Rico OTHER Caribbean Islands Virgin Islands OTHER Indian Ocean Islands Mayotte OTHER Middle East Bahrain 0.0 16.6 34.330 OTHER Middle East Israel 0.1 77.4 1.979 OTHER Middle East Kuwait 0.0 69.1 347.268 OTHER Middle East Qatar 0.0 39.7 3585.906 OTHER Middle East Saudi Arabia 0.0 340.6 243.650 OTHER Middle East United Arab Emirates 0.0 117.2 OTHER North America Canada 64.5 744.7 72.956 OTHER North America United States -402.9 6525.2 14.579 OTHER Northeast Asia Japan 4.3 1321.0 1.649 OTHER Northeast Asia Korea, Dem People's Rep 1.0 112.8 6.353 OTHER Northeast Asia Taiwan 0.0 230.4 OTHER Pacific Islands American Samoa OTHER Pacific Islands Cook Islands 0.0 0.0 0.000 OTHER Pacific Islands French Polynesia OTHER Pacific Islands Guam OTHER Pacific Islands Nauru 0.0 0.2 94 Annual CO2 Storage CO2 Emissions (Mt) Potential / Region Subregion Country Deforestation Total Annual Emissions OTHER Pacific Islands New Caledonia OTHER Pacific Islands Niue 0.0 0.0 OTHER Pacific Islands Northern Mariana Islands OTHER Pacific Islands Tuvalu OTHER Southeast Asia Brunei Darussalam 0.0 7.3 OTHER Southeast Asia Singapore 0.1 55.9 0.007 OTHER Western Europe Andorra OTHER Western Europe Austria -0.8 79.6 0.000 OTHER Western Europe Belgium 0.0 148.2 OTHER Western Europe Cyprus 0.1 8.0 9.768 OTHER Western Europe Denmark -0.1 66.4 38.008 OTHER Western Europe Finland -0.8 67.7 1.056 OTHER Western Europe France -6.1 507.3 2.692 OTHER Western Europe Germany 0.0 1009.4 3.214 OTHER Western Europe Gibraltar OTHER Western Europe Greece -3.0 117.0 6.175 OTHER Western Europe Ireland -1.7 64.1 7.903 OTHER Western Europe Italy -3.0 528.1 6.537 OTHER Western Europe Liechtenstein OTHER Western Europe Luxembourg 0.0 9.2 OTHER Western Europe Malta 0.0 2.4 17.981 OTHER Western Europe Monaco OTHER Western Europe Netherlands -0.1 215.0 88.731 OTHER Western Europe Norway -3.1 50.7 1043.731 OTHER Western Europe Portugal -5.8 73.4 20.732 OTHER Western Europe San Marino OTHER Western Europe Spain -8.7 372.4 2.706 OTHER Western Europe Sweden -0.1 61.8 2.355 OTHER Western Europe Switzerland -0.4 51.1 0.000 OTHER Western Europe United Kingdom -1.7 652.0 18.370 SAR Indian Ocean Islands Maldives 0.0 0.6 SAR Southern Asia Bangladesh -9.3 113.1 177.582 SAR Southern Asia Bhutan 0.0 1.9 0.000 SAR Southern Asia India -40.3 1843.8 24.146 SAR Southern Asia Nepal 123.5 155.1 0.000 SAR Southern Asia Sri Lanka 29.5 57.6 68.121 SAR Western Asia Afghanistan 8.8 31.0 0.000 SAR Western Asia Pakistan 33.0 318.4 60.496 95 Policy Research Working Paper Series Title Author Date Contact for paper WPS4280GovernanceMattersVI:Aggregate DanielKaufmann July2007 R.Bonfield andIndividualGovernance: AartKraay 31248 Indicators,1996-2006 MassimoMastruzzi WPS4281CreditGrowthInEmergingEurope: SophieSirtaine July2007 S.Sirtaine ACauseForStabilityConcerns? IliasSkamnelos 87006 WPS4282AreCashTransfersMadetoWomen NorbertSchady July2007 I.Hafiz SpentLikeOtherSourcesofIncome JoséRosero 37851 WPS4283InnovationShortfalls WilliamMaloney July2007 V.Cornago AndrésRodríguez-Clare 84039 WPS4284CustomerMarketPowerandthe NeeltjeVanHoren July2007 M.Gamboa ProvisionofTradeCredit: 34847 EvidencefromEasternEuropeand CentralAsia WPS4285PovertyAnalysisUsingAn J.A.L.Cranfield July2007 P.Flewitt InternationalCross-CountryDemand PaulV.Preckel 32724 System ThomasW.Hertel WPS4286InstitutionalEffectsasDeterminants JesúsÁlvarez July2007 S.Baksh ofLearningOutcomes:Exploring VicenteGarcíaMoreno 31085 StateVariationsinMexico HarryAnthonyPatrinos WPS4287ACross-CountryAnalysisofPublic MartinMeleckyy July2007 M.Rosenquist DebtManagementStrategies 82602 WPS4288ActualCropWaterUseinProject RobinaWahaj July2007 P.Kokila CountriesASynthesisatthe FlorentMaraux 33716 RegionalLevel GiovanniMunoz WPS4289SensitivityofCroppingPatternsin AlexanderLotsch July2007 P.Kokila AfricatoTransientClimateChange 33716 WPS4290TheImpactsofClimateChange KennethStrzepek July2007 P.Kokila onRegionalWaterResources AlyssaMcCluskey 33716 andAgricultureinAfrica WPS4291AnEmpiricalEconomic SumanJain July2007 P.Kokila AssessmentOfImpactsOfClimate 33716 ChangeOnAgricultureInZambia WPS4292AssessmentoftheEconomic RenethMano July2007 P.Kokila ImpactsofClimateChangeon CharlesNhemachena 33716 AgricultureinZimbabwe: ARicardianApproach WPS4293AssessingtheEconomicImpacts HelmyM.Eid July2007 P.Kokila ofClimateChangeonAgriculturein SamiaM.El-Marsafawy 33716 Egypt:ARicardianApproach SamihaA.Ouda WPS4294Scarperation:AnEmpiricalInquiry ShlomiDinar July2007 P.Kokila intoTheRoleofScarcityinFostering ArielDinar 33716 CooperationBetweenInternational PradeepKurukulasuriya RiverRiparian WPS4295EconomicBenefitofTuberculosis RamananLaxminarayan August2007 M.Elias Control EiliKlein 82175 ChristopherDye KatherineFloyd SarahDarley OlusojiAdeyi Policy Research Working Paper Series Title Author Date Contact for paper WPS4296 WhenDoCreditorRightsWork? MehnazSafavian August2007 S.Narsiah SiddharthSharma 88768 WPS4297 BigDragon,LittleDragons: SjamsuRahardja August2007 K.Shaw China'sChallengetotheMachinery 81307 ExportsofSoutheastAsia WPS4298 YieldImpactofIrrigation SushenjitBandyopadhyay August2007 A.Sears ManagementTransfer:ASuccess PriyaShyamsundar 82819 StoryfromthePhilippines MeiXie WPS4299 BalancingExpendituresonMitigation FranckLecocq August2007 P.Kokila ofandAdaptationtoClimateChange ZmarakShalizi 33716 AnExplorationofIssuesRelevantto DevelopingCountries