metadata: authors: 0: author: Dasgupta,Susmita
1: author: Yan,Jianping
2: author: Laplante,Benoit
3: author: Meisner,Craig M.
4: author: Wheeler, David
count: World
docna: 0: docna: The impact of sea level rise on developing
            countries : a comparative analysis
docty: Policy Research Working Paper
subtopic: Adaptation to Climate Change,Science of Climate Change,Climate Change and Health,Climate Change and Environment,Wetlands,Hydrology,Coastal and Marine Resources,Food Security,Ecosystems and Natural Habitats,Energy and Natural Resources
teratopic: Environment,Health, Nutrition and Population,Water Resources
abstracts: cdata!: Sea level rise (SLR) due to climate
            change is a serious global threat. The scientific evidence
            is now overwhelming. Continued growth of greenhouse gas
            emissions and associated global warming could well promote
            SLR of 1m-3m in this century, and unexpectedly rapid breakup
            of the Greenland and West Antarctic ice sheets might produce
            a 5m SLR. In this paper, the authors have assessed the
            consequences of continued SLR for 84 developing countries.
            Geographic Information System (GIS) software has been used
            to overlay the best available, spatially-disaggregated
            global data on critical impact elements (land, population,
            agriculture, urban extent, wetlands, and GDP) with the
            inundation zones projected for 1-5m SLR. The results reveal
            that hundreds of millions of people in the developing world
            are likely to be displaced by SLR within this century, and
            accompanying economic and ecological damage will be severe
            for many. At the country level, results are extremely
            skewed, with severe impacts limited to a relatively small
            number of countries. For these countries (such as Vietnam,
            A. R. of Egypt, and The Bahamas), however, the consequences
            of SLR are potentially catastrophic. For many others,
            including some of the largest (such as China), the absolute
            magnitudes of potential impacts are very large. At the other
            extreme, many developing countries experience limited
            impacts. Among regions, East Asia and the Middle East and
            North Africa exhibit the greatest relative impacts. To date,
            there is little evidence that the international community
            has seriously considered the implications of SLR for
            population location and infrastructure planning in
            developing countries. The authors hope that the information
            provided in this paper will encourage immediate planning for adaptation.
repnme: repnme: The impact of sea level rise on developing
            countries : a comparative analysis
display_title: The impact of sea level rise on
            developing countries : a comparative analysis
listing_relative_url: /research/2007/02/7383552/impact-sea-level-rise-developing-countries-comparative-analysis
topicv3: Agriculture and Food Security m1326205 435,Water m1326522 137,Climate Change m1326829 567,Wetlands m1327363 594,Climate Change Adaptation m1327767 574
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Health, Nutrition and Population,Water Resources
no_of_pages: 51
author_keys: 0: author_key: 95558
1: author_key: 790125
2: author_key: 97490
3: author_key: 128399
model_extractions: WPS4136

# **The Impact of Sea Level Rise on Developing Countries: A Comparative Analysis**

By Susmita Dasgupta<sup>*</sup> Benoit Laplante<sup>**</sup> Craig Meisner<sup>*</sup> David Wheeler<sup>***</sup> and Jianping Yan<sup>**</sup>

## World Bank Policy Research Working Paper 4136, February 2007

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* Development Research Group, World Bank.

** Independent consultants, Canada.

*** Senior Fellow, Center for Global Development

Correspondence should be addressed to: Dr. Susmita Dasgupta, World Bank, 1818 H Street, NW, Washington, DC 20433, <u>sdasgupta@worldbank.org.</u> Acknowledgements: Funding for this project was provided by the Canadian Trust Fund (TF030569) sponsored by the Canadian International Development Agency (CIDA). We would also like to extend our special thanks to Piet Buys, Uwe Deichmann and Jillian Kingston for their guidance and valuable help.
# **Abstract**

Sea level rise (SLR) due to climate change is a serious global threat: The scientific evidence is now overwhelming . Continued growth of greenhouse gas emissions and associated global warming could well promote SLR of 1m-3m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 5m SLR. In this paper, we have assessed the consequences of continued SLR for 84 developing countries. Geographic Information System (GIS) software has been used to overlay the best available, spatially-disaggregated <u>global</u> data on critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5m SLR. Our results reveal that hundreds of millions of people in the developing world are likely to be displaced by SLR within this century; and accompanying economic and ecological damage will be severe for many. At the country level, results are extremely skewed, with severe impacts limited to a relatively small number of countries. For these countries (e.g., Vietnam, A.R. of Egypt, and The Bahamas), however, the consequences of SLR are potentially catastrophic. For many others, including some of the largest (e.g., China), the absolute magnitudes of potential impacts are very large. At the other extreme, many developing countries experience limited impacts. Among regions, East Asia and Middle East/North Africa exhibit the greatest relative impacts. To date, there is little evidence that the international community has seriously considered the implications of SLR for population location and infrastructure planning in developing countries. We hope that the information provided in this paper will encourage immediate planning for adaptation.

2
# **I. Introduction**

As noted by the International Panel on Climate Change (IPCC, 2001b), climate change will have many negative effects, including greater frequency of heat waves; increased intensity of storms, floods and droughts; rising sea levels; a more rapid spread of disease; and loss of biodiversity. Sea level rise (SLR) poses a particular threat to countries with heavy concentrations of population and economic activity in coastal regions.

Until recently, studies of SLR typically predicted a 0-1 meter rise during the 21<sup>st</sup> century (Church et al. 2001, IPCC Third Assessment, 2001). The three primary contributing factors have been cited as: (i) ocean thermal expansion; (ii) glacial melt from Greenland and Antarctica (plus a smaller contribution from other ice sheets); and (iii) change in terrestrial storage. Among these, ocean thermal expansion was expected to be the dominating factor behind the rise in sea level. However, new data on rates of deglaciation in Greenland and Antarctica suggest greater significance for glacial melt, and a possible revision of the upper-bound estimate for SLR in this century. Since the Greenland and Antarctic ice sheets contain enough water to raise the sea level by almost 70 m (Table 1), small changes in their volume would have a significant effect.<sup>1</sup>

**Table 1** : Physical characteristics of ice on Earth.

||**Glaciers**|**Ice caps**|**Glaciers and**<br>**ice caps ***|**Greenland**<br>**ice sheet**|**Antarctic ice**<br>**sheet**|
|---|---|---|---|---|---|
|Number<br><br>|> 160,000|70||||
|Area (10<br>6km<br>2)<br><br>|0.43|0.24|0.68|1.71|12.37|
|Volume (10<br>6km<br>3)|0.08|0.10|0.18 ± 0.04|2.85|25.71|
|Sea-level rise equivalent (m)|0.24|0.27|0.50 ± 0.10|7.2|61.1|
|Accumulation<br>(sea-level equivalent,mm/yr)|||1.9 ± 0.3|1.4 ± 0.1|5.1 ± 0.2|

Source: Church _et al_ . (2001), Table 11.3 Data sources: Meier and Bahr (1996), Warrick _et al_ . (1996), Reeh _et al_ . (1999), Huybrechts _et al_ . (2000). * - does not include Greenland and Antarctic ice sheets (represented in the next columns) Since the IPCC Third Assessment Report in 2001, there has been an increased effort to improve measures of mass loss for the Greenland ice sheet and its contribution to SLR. Using satellite interferometry observations, Ringot and Kanagaratnam (2006) detected widespread glacier flow acceleration in the lower latitudes between 1996 and 2000, and rapid extension to higher latitudes by 2005. When combined with surface loss estimates

> 1 If the Greenland ice sheet were to melt completely, it would raise average sea level by approximately 7 meters (Church et al. 2001).

3
by Hanna _et al_ . (2005), they calculated a total loss double that in the previous decade. Comparing this rate of contribution of Greenland’s ice sheet to SLR with the IPCC estimate for the 20<sup>th</sup> century, the new measures are roughly two to five times greater. In another study of mass loss for Greenland using repeat altimetry, Krabill _et al_ . (2004) found that between 1993-1994 and 1998-1999, the ice sheet was losing 54 ± 14 gigatons of ice per year (Gt/yr). In contrast, net mass loss over the 1997-2003 interval averaged 74 ± 11 Gt/yr. At these rates of net mass loss, the contribution of the Greenland ice sheet to SLR is roughly double the rate assumed in the IPCC Third Assessment (2001) report<sup>2</sup> .

In Antarctica, using the Gravity Recovery and Climate Experiment (GRACE) satellites, Velicogna and Wahr (2006) have determined mass variations of the entire Antarctic ice sheet during 2002-2005.<sup>3</sup> Their results indicate that the mass of the ice sheet decreased significantly, at a rate of 152 ± 80 cubic kilometers of ice per year; most of this loss came from the West Antarctic ice sheet (WAIS). This rate is several times greater than that assumed in the IPCC Third Assessment, and the IPCC admitted that its final estimate did not take into account the dynamic changes in the WAIS. Increasing concern also attaches to the stability of the WAIS, which currently rests on bedrock below sea level. Mercer (1978) speculated that human-induced global warming could cause the WAIS to be released into the ocean by a sliding mechanism (also referred to as WAIS collapse). This would cause a rapid rise in sea level, since it would be triggered solely through a displacement of the WAIS without its having to melt. Were the WAIS to collapse, it would raise average sea level by approximately 5 to 6 meters (Tol et al., 2006).

While there remains considerable uncertainty about the above scenarios, and the time horizon over which they may unfold, recent research and expert opinion indicate that significant SLR may occur earlier than previously thought.<sup>4</sup> This has prompted a number of researchers to model the estimated impact of significant increases in SLR (these are sometimes termed ‘extreme climate scenarios’). A number of studies have provided estimates of the potential impacts for specific developed countries (e.g. France, the

> 2 360 gigatons of ice correspond approximately to 1 mm of sea level.

> 3 The GRACE result for total Antarctic ice mass change includes complete contributions from such regions as the EAIS coastline and the circular cap south of 82°S, which has not been completely surveyed with other techniques.

> 4 See Vaughan and Spouge, 2002.

4
Netherlands, Poland, Singapore and the United States)<sup>5</sup> ; developing countries (e.g. Bangladesh, Benin, China, Nigeria, and Senegal)<sup>6</sup> ; or specific areas of individual countries (e.g. deltas of the Nile and Bengal; Rhine Delta, Thames Estuary and Rhone Delta)<sup>7</sup> . Only a limited number of studies have assessed the impacts of SLR on a broader regional or world scale. Such studies include: Darwin and Tol (1999), Hoozemans et al. (1993), Nicholls and Mimura (1998), Nicholls et al. (2004), Nicholls and Lowe (2006), and Nicholls and Tol (2006). Some of these studies examine the impact of ‘extreme climate scenarios’ such as a 5 meter SLR (e.g. Nicholls et al., 2004). However, while indicators of impacts generally include land loss, population affected, capital loss value and wetlands loss, different studies have used different subsets of indicators or regions, making it difficult to compare the relative magnitude of impacts across countries or regions.<sup>8</sup> This paper provides a broader comparison, by assessing the impacts of SLR for all developing countries using a homogeneous set of indicators, and for multiple SLR scenarios. To our knowledge, this is the first such exercise. Mendelsohn et al. (2006) provide complementary evidence, by examining the market impacts of climate change on rich and poor countries for a number of different climate scenarios. However, their work does not assess the impact of SLR on multiple physical and economic indicators.

For this study, we group 84 coastal developing countries into 5 regions (corresponding to the 5 regional departments of the World Bank):<sup>9</sup> Latin America and the Caribbean (25 countries); Middle East and North Africa (13); Sub-Saharan Africa (29); East Asia (13); and South Asia (4). For each country and region, we assess the impact of SLR using the following 6 indicators: land, population, gross domestic product (GDP), urban extent, agricultural extent, and wetlands. Finally, these impacts are calculated for SLR scenarios ranging from 1 to 5 meters.

> 5 See Baarse et al. (1994), Bijlsma et al. (1996), Mendelsohn and Neumann (1999), Ng and Mendelsohn (2005), Olsthoorn et al. (2002), and Zeidler (1997).

> 6 Adam (1995), Dennis et al. (1995), French et al. (1995), Han et al. (1995), and Warrick et al. (1996).

> 7 Tol et al. (2005), Yim (1995).

> 8 For example, the regional assessments presented in Nicholls and Mimura (1998) cover four regions: Europe; West Africa; South, South-East, and East Asia; and the Pacific Small Islands. It does not include Latin America and the Caribbean or other regions of Africa.

> 9 Hoozemans et al. (1993) divided the globe (including developed countries) into 20 regions.

5
At the outset, we acknowledge that this analysis has limitations. First, we do not assess the likelihood of alternative SLR scenarios. We take each scenario as given, and assess the impacts using our 6 indicators for each of the 84 developing countries and 5 regions. Second, we assess the impacts of SLR using existing populations, socio-economic conditions and patterns of land use, rather than attempting to predict their future states. Since human activity is generally increasing more rapidly in coastal areas, our approach undoubtedly _underestimates_ the future impacts of SLR in most cases. This underestimation will be greatest for SLR impacts on population and GDP in absolute terms (number of people impacted or $ of GDP impacted), Third, our study is conservative because we do not consider storm surge augmentation. Even a small increase in sea level can significantly magnify the impact of storm surges, which occur regularly and with devastating consequences in some coastal areas.

Despite these limitations, we believe that our comprehensive baseline estimates of SLR impacts can assist policymakers and international development institutions in allocating resources for adaptation to climate change. In particular, we believe that our specific estimates, based on existing coastal conditions, are more likely to interest decisionmakers than estimates based on projections of future coastal population, economic activity, etc.

In the next section, we describe the methodology and data sources used to estimate the impact of SLR in developing countries. We present our results in Section III, at the global, regional and country levels. Section IV provides a summary and conclusions.

# **II. Methodology and data sources**

# **II.1 Data Sources**

We employed geographic information system (GIS) software to overlay the critical impact elements (land, population, agriculture, urban extent, wetlands, and GDP) with the inundation zones projected for 1-5 m. SLR. We used the best available, spatiallydisaggregated data sets from various public sources, including the Center for Environmental Systems Research (CESR), the Center for International Earth Science Information Network (CIESIN), the International Centre for Tropical Agriculture (CIAT), 6
the International Food Policy Research Institute (IFPRI), the National Aeronautics and Space Administration (NASA), the National Oceanographic and Atmospheric Administration (NOAA), and the World Bank. Table 2 summarizes the data sources for assessments of inundation zones and impacts.

# **Table 2 Summary of Data Sources**

|**Dimension**|**Dataset**<br>**Name**|**Unit**|**Resolution**|**Source(s)**|
|---|---|---|---|---|
|Coastline and<br>country<br>boundary|WVS||1:250,000|NOAA/NASA|
|Elevation|SRTM 90m<br>DEM V2|km<sup>2</sup>|90m|CIAT|
|Population|GPW-3|Population<br>counts|1km|CIESIN|
|Economic<br>activity|GDP2000|million US<br>dollars|5km|World Bank, based on<br>Sachs_et al._(2001)|
|Urban extent|GRUMP V1|km<sup>2</sup>|1km|CIESIN|
|Agricultural<br>extent|GAE-2|km<sup>2</sup>|1km|IFPRI|
|Wetlands|GLWD-3|km<sup>2</sup>|1km|CESR, Lehner, B. and<br>Döll, P.(2004)|

# **II.2 Methodology**

The country indicator database was developed by following the six-step procedure described below.

# **II.2.1** Preparing country boundaries and coastlines

Country coastlines were built by sub-setting polygons from the World Vector Shoreline (polygon), a standard National Geospatial Intelligence Agency (formerly Defense Mapping Agency) product at a nominal scale of 1:250,000. It contains worldwide coverage of shorelines and international boundaries. The subset country coastlines were also used as a mask for calculating country totals for the selected exposure indicators.

7
# **II.2.2** Building coastal terrain models (DTM)

Coastal terrain models were derived from the CIAT SRTM 90 meters digital elevation model (DEM) data (Version 2), released in 2005.<sup>10</sup> Zipped data files were downloaded from the CIAT website, and then converted into raster format, and mosaiced in terms of country boundaries in the ArcGIS environment.

# **II.2.3** Identifying inundation zones

Inundation zones were derived from the coastal terrain model (DTM) by setting the value of pixels in the DTM to 1 for the different SLR scenarios examined in this study. Pixels that are apparently not connected to coastlines, such as inland wetlands and lakes, were masked out manually.

# **II.2.4** Calculating exposure indicators

Estimates for each indicator were calculated by overlaying the inundation zone with the appropriate exposure surface dataset (land area, GDP, population, urban extent, agriculture extent, and wetland). Exposure surface data were collected from various public sources. Unless otherwise indicated, latitude and longitude are specified in decimal degrees. The horizontal datum used is the World Geodetic System 1984 (WGS 1984). For area calculation, all units are projected to World Equal Area.

For the exposure grid surfaces, two GIS models were built for calculating the exposed value. Because the values of the pixels in GDP and population surfaces are respectively in millions of US dollars and number of people, the exposure is calculated by multiplying the exposure surface with the inundation zone and then summing up by multiplying grid count and value. Exposure indicators, such as land surface, urban extent, agriculture extent and wetland are measured in square kilometers.

# **II.2.5** Adjusting absolute exposure indicators

For exposure indicators such as land area, population and GDP, which have measured country totals available, the exposed value is adjusted to reflect its real value by using the following formula:

> 10 Shuttle Radar Topographic Mission.

8
where _Vadj_ – Exposed value adjusted; _Vcal_ – Exposed value calculated from exposure grid surfaces; _CTmea_ – Country total obtained based on statistics; _CTcal_ – Country total calculated from exposure grid surface.

**II.2.6** Conducting data quality assurance and control

Quality control was conducted to adjust for errors caused by overlaying grid surfaces of different resolutions, such as the 90-meter resolution inundation zone with 1-kilometer or 5-kilometer exposure grid surfaces. The following procedure was employed:

- 1) Calculate the country total from the grid surface using the country boundary;

- 2) Calculate the aspect exposure that is under 5-meter SLR;

- 3) Calculate the aspect exposure that is over 5-meter SLR;

- 4) Compare the country total with the sum of both aspect exposures. If the difference is less that 5%, the calculated aspect exposure was considered within the error tolerance. If not, the exposure calculation was reviewed and estimates revised until the 5% difference threshold was reached.

A more detailed description of each dataset is provided in Appendix 1.

# **III. Results**

In the first sub-section below, we present results at the global level for the 84 developing countries included in this analysis. In sub-section III.2, we present the results for each of the 5 regions and, individually, for each of the 84 countries. Our results indicate that for a number of countries, even a 1-meter SLR would have a very significant impact.

# **III.1 Global results**

Table 3 indicates that approximately 0.3% (194,000 km<sup>2</sup> ) of the territory of the 84 developing countries would be impacted by a 1-meter SLR. This would increase to 1.2% 9
in a 5m SLR scenario. Though this remains relatively small in percentage terms, approximately 56 million people (or 1.28% of the population) of these countries would be impacted under a 1m SLR scenario. This would increase to 89 million people for 2m SLR (2.03%), and 245 million people (5.57%) for 5m SLR. The impact of SLR on GDP is slightly larger than the impact on population, because GDP per capita is generally above average for coastal populations and cities. Wetlands would experience significant impact even with a 1m SLR. Up to 7.3% of wetlands in the 84 countries would be impacted by a 5m SLR.

As shown in the next section, these impacts are not uniformly distributed across the regions and countries of the developing world. The impacts are particularly severe in a limited number of countries.

**Table 3 Impacts of sea level rise: Global level**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (T**<br>|**otal= 63,332,5**<br>|**30 sq. km.)**<br>|||
|Impacted area|194,309|305,036|449,428|608,239|768,804|
|% of total area|0.31|0.48|0.71|0.96|1.21|
||**Populati**|**on (Total= 4,4**|**14,030,000)**|||
|Impacted population|56,344,110|89,640,441|133,049,836|183,467,312|245,904,401|
|% of total population|1.28|2.03|3.01|4.16|5.57|
|<br>|**GDP (Tota**|**l= 16,890,948**|**million USD)**|||
|Impacted GDP (USD)|219,181|357,401|541,744|789,569|1,022,349|
|% oftotalGDP|1.30|2.12|3.21|4.67|6.05|
||**Urban exte**<br>|**nt (Total= 1,43**<br>|**4,712 sq. km**<br>|**.)**<br>||
|Impacted area|14,646|23,497|35,794|50,742|67,140|
|% of total area|1.02|1.64|2.49|3.54|4.68|
||**Agricultural ex**|**tent (Total= 1**|**7,975,807 sq.**|**km.)**||
|Impacted area|70,671|124,247|196,834|285,172|377,930|
|% oftotalarea|0.39|0.69|1.09|1.59|2.10|
||**Wetlands ar**<br>|**ea (Total= 4,7**<br>|**44,149 sq. km**<br>|**.)**<br>||
|Impacted area|88,224|140,355|205,697|283,009|347,400|
|% of total area|1.86|2.96|4.34|5.97|7.32|

10
# **III.2 Regional results**

In this sub-section, we examine results for Latin America and the Caribbean, Middle East and North Africa, Sub-Saharan Africa, East Asia, and South Asia.<sup>11</sup> To facilitate the reading of these results, we follow a similar structure of presentation for all regions.

# _(i) Latin America and the Caribbean region_

As shown in Table 4, the impact of SLR in Latin America and the Caribbean is relatively similar to the impact noted earlier for all developing countries insofar as land area, agriculture and wetlands are concerned. However, a much smaller percentage of the region’s population and GDP would be impacted (0.57% and 0.54% respectively for 1m SLR, vs. 1.28% and 1.30% respectively worldwide). The same holds for the impact on urban infrastructure.

# **Table 4 Impacts of sea level rise: Latin America & Caribbean region**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (To**<br>|**tal= 18,806,59**<br>|**8 sq. km.)**<br>|||
|Impacted area|64,632|101,736|149,183|193,786|234,117|
|% of total area|0.34|0.54|0.79|1.03|1.24|
||**Populat**|**ion (Total= 50**|**1,550,000)**|||
|Impacted population|2,873,505|4,732,734|7,247,905|10,268,489|13,472,827|
|% of total population|0.57|0.94|1.45|2.05|2.69|
||**GDP (Tota**<br>|**l= 3,649,731 m**<br>|**illion USD)**<br>|||
|Impacted GDP (USD)|19,663|36,201|49,447|67,403|86,850|
|% of total GDP|0.54|0.99|1.35|1.85|2.38|
||**Urban exte**<br>|**nt (Total= 505**<br>|**,477 sq. km.**<br>|**)**<br>||
|Impacted area|3,080|5,212|8,090|11,614|15,294|
|% of total area|0.61|1.03|1.60|2.30|3.03|
||**Agricultural ex**<br>|**tent (Total= 4,**<br>|**889,156 sq.**<br>|**km.)**<br>||
|Impacted area|16,104|29,514|47,003|66,330|85,959|
|% of total area|0.33|0.60|0.96|1.36|1.76|
||**Wetlands ar**<br>|**ea (Total= 1,65**<br>|**1,735 sq. km**<br>|**.)**<br>||
|Impacted area|22,314|38,782|60,876|85,734|108,476|
|% oftotalarea|1.35|2.35|3.69|5.19|6.57|

> 11 These differ from the 20 coastal regions used in Nicholls and Nomura (1998). Our selection of these 5 regions is simply explained by the fact that those regions correspond to the 5 regional departments of the World Bank.

11
When the results are examined at the country level, one notes very significant differences within the region. As is starkly revealed in Figure 1a, The Bahamas would experience the largest percentage of impacted land: Even with a 1m SLR, approximately 11% of the land area of The Bahamas would be impacted. This percentage reaches in excess of 60% under a 5m SLR scenario. Cuba and Belize would also experience significant impacts, albeit at a much reduced scale when compared with The Bahamas.

**Figure 1a Latin America & Caribbean: Country area impacted**<sup>**12**</sup>

<!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>50<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Cuba Belize<br><!-- End of picture text --> <!-- Start of picture text --> Nicaragua Jamaica<br><!-- End of picture text --> <!-- Start of picture text --> Mexico Honduras<br><!-- End of picture text --> <!-- Start of picture text --> Uruguay Haiti<br><!-- End of picture text --> <!-- Start of picture text --> R. B. de Venezuela Panama<br><!-- End of picture text --> <!-- Start of picture text --> El Salvador Costa Rica<br><!-- End of picture text --> <!-- Start of picture text --> French Guiana (Fr) Argentina Colombia<br><!-- End of picture text --> <!-- Start of picture text --> Brazil Guatemala Peru<br><!-- End of picture text --> Figures 1b and 1c show the impact of SLR on population. With a 1m SLR, the populations of Suriname, Guyana, French Guiana, and The Bahamas would be most severely impacted (as a percentage of national population): 7.0%, 6.3%, 5.4% and 4.5% respectively. These percentages increase rapidly, reaching 30% in Suriname and 25% in Guyana for a 3m SLR. Approximately half of the population of these countries would be impacted with a 5m SLR.

In terms of economic activity (Figure 1d), the impact of a 1m SLR on Suriname, Guyana, and The Bahamas’ GDP is expected to reach approximately 5%. With a 3m SLR, impacted GDP reaches 20% in Suriname, and approximately 15% in both Guyana and

> 12 Note that Puerto Rico is officially a Territory, and not a country.

12
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# **Figure 1c Latin America & Caribbean: Population impacted**

<!-- Start of picture text --> 60<br><!-- End of picture text --> <!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 2 meter<br><!-- End of picture text --> <!-- Start of picture text --> 5 meter<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>40<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Guyana The Bahamas Belize<br><!-- End of picture text --> <!-- Start of picture text --> French Guiana (Fr) Puerto Rico<br><!-- End of picture text --> <!-- Start of picture text --> Ecuador Jamaica Cuba<br><!-- End of picture text --> <!-- Start of picture text --> Argentina Panama Brazil<br><!-- End of picture text --> <!-- Start of picture text --> R.B. de Venezuela Dominican Republic Mexico<br><!-- End of picture text --> <!-- Start of picture text --> Colombia Haiti<br><!-- End of picture text --> <!-- Start of picture text --> Honduras Nicaragua<br><!-- End of picture text --> <!-- Start of picture text --> Peru Chile<br><!-- End of picture text -->

# **Figure 1d Latin America & Caribbean: GDP impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> 40<br>35<br>30<br>25<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>15<br>10<br><!-- End of picture text --> <!-- Start of picture text --> Cuba Uruguay Panama<br><!-- End of picture text --> <!-- Start of picture text --> 5<br>0<br>Suriname<br><!-- End of picture text --> <!-- Start of picture text --> The Bahamas Guyana French Guiana (Fr)<br><!-- End of picture text --> <!-- Start of picture text --> Belize Puerto Rico Ecuador<br><!-- End of picture text --> <!-- Start of picture text --> Brazil Jamaica<br><!-- End of picture text --> <!-- Start of picture text --> Mexico R.B. de Venezuela<br><!-- End of picture text --> <!-- Start of picture text --> Peru Honduras Colombia<br><!-- End of picture text --> <!-- Start of picture text --> Haiti El Salvador<br><!-- End of picture text --> <!-- Start of picture text --> Costa Rica Chile<br><!-- End of picture text --> 14
<!-- Start of picture text --> ea ee<br>ooeesesea a ososes |||——- |<br>oe Cc ———- |<br>Beles a Ed eee“| Fo fo<br><!-- End of picture text -->
# **Figure 1f Latin America & Caribbean: Agricultural extent impacted**

<!-- Start of picture text --> 1 meter 2 meter 3 meter 4 meter 5 meter<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Figure 1g<br>Latin America & Caribbean: Wetlands impacted<br>1 meter 2 meter 3 meter 4 meter 5 meter<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>% Impact (Agriculture)<br>The Bahamas Suriname Argentina Jamaica Belize Guyana Cuba Mexico Nicaragua Ecuador Puerto Rico R. B. de Venezuela Honduras Colombia Panama Costa Rica El Salvador Uruguay French Guiana ( Fr) Dominican Republic Guatemala Haiti Brazil Chile Peru<br>% Impact (Wetland)<br>The Bahamas Belize Cuba Jamaica Mexico Uruguay Honduras Haiti Dominican Republic Suriname Nicaragua Guyana Panama French Guiana (Fr) Argentina Costa Rica Colombia R.B. de Venezuela Brazil Ecuador El Salvador Guatemala Chile Peru Puerto Rico<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>20<br>15<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br>0<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>60<br>50<br>40<br><!-- End of picture text --> <!-- Start of picture text --> 30<br>20<br>10<br>0<br><!-- End of picture text --> 16
# _(ii) Middle East and North Africa_

Table 5 reveals that, while the land area of the Middle East and North Africa region would be less impacted by SLR than the developing world generally (0.25% vs. 0.31% with a 1m SLR), all other indicators suggest more severe impacts of SLR in this region. In particular, with a 1m SLR, 3.2% of its population would be impacted (vs. 1.28% worldwide), 1.49% of its GDP (vs. 1.30% worldwide), 1.94% of its urban population (vs. 1.02% worldwide), and 3.32% of its wetlands (vs. 1.86% worldwide). Except for land area, the impacts of SLR are much more severe in this region than in Latin America and the Caribbean.

# **Table 5 Impacts of sea level rise: Middle East and North Africa region**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (T**<br>|**otal= 10,050,5**<br>|**56 sq. km.)**<br>|||
|Impacted area|24,654|33,864|43,727|53,615|63,120|
|% of total area|0.25|0.34|0.44|0.53|0.63|
||**Popula**|**tion (Total= 25**|**9,396,000)**|||
|Impacted population|8,307,472|10,912,744|13,684,993|16,454,655|19,439,678|
|% of total population|3.20|4.21|5.28|6.34|7.49|
||**GDP (Tot**|**al= 1,404,470**|**million USD)**|||
|Impacted GDP (USD)|20,870|30,365|39,037|46,209|54,853|
|% of total GDP|1.49|2.16|2.78|3.29|3.91|
||**Urban ext**<br>|**ent (Total= 190**<br>|**,030 sq. km.)**<br>|<br>||
|Impacted area|3,679|5,037|6,529|7,951|9,384|
|% oftotalarea|1.94|2.65|3.44|4.18|4.94|
||**Agricultural e**|**xtent (Total=**|**354,294 sq. k**|**m.)**||
|Impacted area|4,086|6,031|8,007|9,819|11,451|
|% of total area|1.15|1.70|2.26|2.77|3.23|
||**Wetlands a**<br>|**rea (Total= 34**<br>|**2,185 sq. km.**<br>|**)**<br>||
|Impacted area|11,361|14,758|18,224|21,417|24,277|
|% oftotalarea|3.32|4.31|5.33|6.26|7.09|

When examined at the country level, only Qatar’s land area would experience a significant impact (Figure 2a).

17
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<!-- Start of picture text --> Nl a<br>rae ‘<br>‘Qata<br><!-- End of picture text --> <!-- Start of picture text --> in<br>\;<br>mmPopulation<1%1-5%Exposed (% of country tota‘<br>emI 5-10-15%10%<br>Hl >1520% - 20% ’<br><!-- End of picture text -->
**Figure 2c Middle East and North Africa region: Population impacted**

<!-- Start of picture text --> 2 meter<br><!-- End of picture text --> <!-- Start of picture text --> 3 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> Qatar Tunisia<br><!-- End of picture text --> <!-- Start of picture text --> A.R. Egypt United Arab Emirates<br><!-- End of picture text --> <!-- Start of picture text --> Saudi Arabia<br><!-- End of picture text --> <!-- Start of picture text --> Oman<br><!-- End of picture text --> <!-- Start of picture text --> Morocco Kuwait<br><!-- End of picture text --> <!-- Start of picture text --> Rep. of Yemen I.R. Iran<br><!-- End of picture text --> <!-- Start of picture text --> Algeria<br><!-- End of picture text -->

**Figure 2d Middle East and North Africa: GDP impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> 18<br>16<br>14<br>12<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>8<br>6<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2<br>0<br><!-- End of picture text --> <!-- Start of picture text --> A.R. Egypt Qatar<br><!-- End of picture text --> <!-- Start of picture text --> Tunisia<br><!-- End of picture text --> <!-- Start of picture text --> Kuwait<br><!-- End of picture text --> <!-- Start of picture text --> Libya Oman<br><!-- End of picture text --> <!-- Start of picture text --> Morocco<br><!-- End of picture text --> <!-- Start of picture text --> I.R. Iran Rep. of Yemen<br><!-- End of picture text --> <!-- Start of picture text --> Algeria<br><!-- End of picture text --> <!-- Start of picture text --> Saudi Arabia Former Spanish Sahara<br><!-- End of picture text --> The urban extent of the region would also be significantly impacted (Figure 2e). In the A.R. of Egypt, Libya, United Arab Emirates, and Tunisia, the impact reaches approximately 5% with a 1m SLR, 6 to 7% with a 2m SLR, and approximately 10% with a 5m SLR. The wetlands of Qatar, and to a lesser extent Kuwait, Libya, and United Arab Emirates would be significantly impacted by SLR (Figure 2g).

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# **Figure 2e Middle East and North Africa: Urban extent impacted**

<!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> Rep. of Yemen Morocco<br><!-- End of picture text --> <!-- Start of picture text --> United Arab Emirates A.R. Egypt<br><!-- End of picture text --> <!-- Start of picture text --> Libya<br><!-- End of picture text --> <!-- Start of picture text --> Qatar<br><!-- End of picture text --> <!-- Start of picture text --> Saudi Arabia<br><!-- End of picture text --> <!-- Start of picture text --> Kuwait<br><!-- End of picture text --> <!-- Start of picture text --> Former Spanish Sahara<br><!-- End of picture text --> <!-- Start of picture text --> I.R. Iran Algeria<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2f<br><!-- End of picture text --> <!-- Start of picture text --> 1 meter 2 meter 3 meter 4 meter 5 meter<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>% Impact (Agriculture)<br>A.R. Egypt Tunisia Algeria Libya Oman Morocco I.R. Iran Rep. of Yemen Saudi Arabia Kuwait Qatar United Arab Emirates Former Spanish Sahara<br><!-- End of picture text --> 21
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# **Table 6 Impacts of sea level rise: Sub-Saharan Africa**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (T**<br>|**otal= 16,137,43**<br>|**8 sq. km.)**<br>|||
|Impacted area|18,641|28,083|42,645|59,661|77,253|
|% of total area|0.12|0.17|0.26|0.37|0.48|
||**Popula**<br>|**tion (Total= 46**<br>|**3,121,000)**<br>|||
|Impacted population|2,098,795|3,651,629|4,303,289|8,471,790|11,040,978|
|% of total population|0.45|0.79|0.93|1.83|2.38|
|<br>|**GDP (To**<br>|**tal= 963,974 m**<br>|**illion USD)**<br>|||
|Impacted GDP (USD)|2,228|4,368|7,143|10,897|13,722|
|% of total GDP|0.23|0.45|0.74|1.13|1.42|
||**Urban ext**<br>|**ent (Total= 109**<br>|**,372 sq. km.**<br>|**)**<br>||
|Impacted area|430|742|1,268|1,853|2,449|
|% of total area|0.39|0.68|1.16|1.69|2.24|
||**Agricultural e**<br>|**xtent (Total= 4,**<br>|**236,159 sq.**<br>|**km.)**<br>||
|Impacted area|1,646|3,404|6,595|11,231|16,145|
|% of total area|0.04|0.08|0.16|0.27|0.38|
||**Wetlands a**|**rea (Total= 805**|**,030 sq. km**|**.)**||
|Impacted area|8,902|13,551|20,625|29,078|37,864|
|% oftotalarea|1.11|1.68|2.56|3.61|4.70|

Within the region, the countries with greatest land area impact would be The Gambia and, to a lesser extent, Guinea-Bissau (reference Figure 3a). However, even in these two countries, the impact remains well below 4% with a 2m SLR, and it never raises above 10% except for The Gambia with a SLR of 4m or greater.

# **Figure 3a Sub-Saharan Africa: Country area impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 2 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter 5 meter<br><!-- End of picture text --> <!-- Start of picture text --> Congo<br><!-- End of picture text --> <!-- Start of picture text --> 14<br>12<br>10<br>8<br>% Impact (Area)<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2<br><!-- End of picture text --> <!-- Start of picture text --> Cameroon Sudan D.R. Congo<br><!-- End of picture text --> <!-- Start of picture text --> The Gambia Guinea-Bissau<br><!-- End of picture text --> <!-- Start of picture text --> Senegal Sierra Leone Mozambique<br><!-- End of picture text --> <!-- Start of picture text --> Sao Tome and Principe Benin Mauritania<br><!-- End of picture text --> <!-- Start of picture text --> Guinea Madagascar Ghana Eritrea<br><!-- End of picture text --> <!-- Start of picture text --> Djibouti Gabon Nigeria<br><!-- End of picture text --> <!-- Start of picture text --> Togo Equatorial Guinea Liberia<br><!-- End of picture text --> <!-- Start of picture text --> Ivory Coast Somalia Kenya Namibia<br><!-- End of picture text --> <!-- Start of picture text --> Angola South Africa Tanzania, United Republic of<br><!-- End of picture text --> 23
<!-- Start of picture text --> ‘J<br>The Gambia :<br>Guinea-Bissatgine a Djibouti |<br>Libe :<br>Togo a<br>ao ee<br>Equatorial Ga J<br>-<br>Jo Fea<br>P ae ao ae<br><!-- End of picture text -->

<!-- Start of picture text --> pa } |<br>Population <) 0z2 bique +<br> Exposed (% of country total) | ><br>— Fe. South Africa<br>Ml 15 - 20%<br>ll > 20%<br><!-- End of picture text -->
# **Figure 3c Sub-Saharan Africa: Population impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> 1 meter 2 meter 3 meter 4 meter 5 meter<br>25<br>20<br>15<br>10<br>5<br>0<br>Figure 3d<br>Sub-Saharan Africa: GDP impacted<br>1 meter 2 meter 3 meter 4 meter 5 meter<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>% Impact (Total Population)<br>The Gambia Mauritania Guinea-Bissau Liberia Benin Senegal Djibouti Mozambique Guinea Gabon Togo Nigeria Sierra Leone Ivory Coast Ghana Namibia Madagascar Somalia Sao Tome and Principe Angola Equatorial Guinea Congo Cameroon Sudan Tanzania, United Republic of Eritrea South Africa Kenya D.R. Congo<br>% Impact (GDP)<br>Mauritania The Gambia Benin Guinea-Bissau Senegal Mozambique Guinea Ivory Coast Nigeria Ghana Sierra Leone Liberia Angola Madagascar Djibouti Cameroon Gabon Togo Congo Kenya Somalia Tanzania Equatorial Guinea South Africa Sudan Eritrea Namibia D.R. Congo Sao Tome and Principe<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>15<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>The Gambia<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>20<br>15<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>5<br>0<br><!-- End of picture text --> 25
# **Figure 3e Sub-Saharan Africa: Urban extent impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 2 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> 1 meter 2 meter 3 meter 4 meter 5 meter<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Figure 3f<br>Sub-Saharan Africa: Agricultural extent impacted<br>1 meter 2 meter 3 meter 4 meter 5 meter<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>% Impact (Urban Extent)<br>Mauritania The Gambia Liberia Senegal Guinea Guinea-Bissau Djibouti Benin Mozambique Togo Somalia Sierra Leone Ivory Coast Madagascar Gabon Angola Sao Tome and Principe Nigeria Eritrea Ghana Equatorial Guinea Namibia Congo Tanzania, United Republic of D.R. Congo Cameroon South Africa Sudan Kenya<br>% Impact (Agriculture)<br>The Gambia Guinea-Bissau Mauritania Senegal Sierra Leone Mozambique Somalia Benin Madagascar Guinea Gabon Togo Ghana Equatorial Guinea Kenya Nigeria Angola Liberia Ivory Coast Cameroon Congo Tanzania, United Republic of South Africa D.R. Congo Sudan Namibia Eritrea Sao Tome and Principe Djibouti<br><!-- End of picture text --> <!-- Start of picture text --> 35<br>30<br><!-- End of picture text --> <!-- Start of picture text --> 25<br>20<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br>5<br><!-- End of picture text --> <!-- Start of picture text --> 0<br>Mauritania<br><!-- End of picture text --> <!-- Start of picture text --> 10<br>8<br>6<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>0<br><!-- End of picture text --> 26
# **Figure 3g Sub-Saharan Africa: Wetlands impacted**

<!-- Start of picture text --> 1 meter<br><!-- End of picture text --> <!-- Start of picture text --> 2 meter<br><!-- End of picture text --> <!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br><!-- End of picture text --> <!-- Start of picture text --> Guinea-Bissau Mauritania Ivory Coast<br><!-- End of picture text --> <!-- Start of picture text --> 15<br>10<br>5<br>0<br>The Gambia<br><!-- End of picture text --> <!-- Start of picture text --> Senegal Benin Guinea<br><!-- End of picture text --> <!-- Start of picture text --> Liberia Madagascar Togo<br><!-- End of picture text --> <!-- Start of picture text --> Ghana Mozambique Equatorial Guinea Gabon<br><!-- End of picture text --> <!-- Start of picture text --> Nigeria Eritrea South Africa<br><!-- End of picture text --> <!-- Start of picture text --> Kenya Somalia Djibouti<br><!-- End of picture text --> <!-- Start of picture text --> Cameroon Congo Sudan<br><!-- End of picture text --> <!-- Start of picture text --> Namibia Tanzania, United Republic of D.R. Congo Sao Tome and Principe<br><!-- End of picture text -->

# _(iv) East Asia_

East Asia would be significantly impacted by SLR. At 5m SLR, East Asia is the most severely impacted region in the developing world. For 1m to 5m SLR, the impacted population is 2% to 8.6%, while impacted GDP is 2.09% to 10.2%. Urban extent and wetlands are also significantly impacted by SLR (Table 7).

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# **Table 7 Impacts of sea level rise: East Asia**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (To**<br>|**tal= 14,140,7**<br>|**67 sq. km.)**<br>|||
|Impacted area|74,020|119,370|178,177|248,970|325,089|
|% of total area|0.52|0.84|1.26|1.76|2.30|
||**Populati**|**on (Total= 1,8**|**83,407,000)**|||
|Impacted population|37,193,866|60,155,640|90,003,580|126,207,275|162,445,397|
|% of total population|1.97|3.19|4.78|6.70|8.63|
||**GDP (Tot**|**al= 7,577,206**|**million USD)**|||
|Impacted GDP(USD)|158,399|255,510|394,081|592,598|772,904|
|% of total GDP|2.09|3.37|5.20|7.82|10.20|
||**Urban exte**<br>|**nt (Total= 388**<br>|**,054 sq. km.)**<br>|<br>||
|Impacted area|6,648|11,127|17,596|25,725|34,896|
|% of total area|1.71|2.87|4.53|6.63|8.99|
||**Agricultural ex**|**tent (Total= 5**|**,472,581 sq.**|**km.)**||
|Impacted area|45,393|78,347|121,728|174,076|229,185|
|% oftotalarea|0.83|1.43|2.22|3.18|4.19|
||**Wetlands ar**<br>|**ea (Total= 1,3**<br>|**66,069 sq. km**<br>|**.)**<br>||
|Impacted area|36,463|56,579|79,984|110,671|130,780|
|% of total area|2.67|4.14|5.86|8.10|9.57|

As shown in Figure 4a, Vietnam is the most seriously impacted by SLR: Up to 16% of its area would be impacted by a 5m SLR, making it second only to The Bahamas among countries analyzed for this paper. Most of this impact is in the Mekong and Red River Deltas. Note in Figure 4b that most of Vietnam’s land area southwest of Ho Chi Minh City would be severely impacted by SLR.

Large percentages of Vietnam’s population and economic activity are located in these two river deltas. As shown in Figures 4c and 4d, 10.8% of Vietnam’s population would be impacted by a 1m SLR. This is the largest percentage of impacted population among all 84 countries (A.R. of Egypt follows with 10.56%). Vietnam’s impacted population would reach 35% with a 5m SLR. The impacts of SLR on Vietnam’s GDP (Figure 4e) and urban extent (Figure 4f) closely follow the impact on its population.

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eyett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced<br>PIT Eee Beerere] Feed al eS) ocean fen<br>Faasfetedstfetedststfetedstststfetedstststst printieeeeeieeeeeeeieeeeeeeeeieeeeeeeeeee eerererseee:eee::eee:::eee:::: ceceere|EeeEeeEeeEee eed poe secacese|s5e5e535ARARs5e5e535ARARARARs5e5e535ARARARARARARs5e5e535ARARARARARARARAR e5e5255BASSwien,BASSwien,wien,BASSwien,wien,wien,BASSwien,wien,wien,wien,BASSwien,wien,wien,wien,wien, =Fiedsessiedsesssessiedsesssesssessiedsesssesssesssess eeceekHpelpelpelpel =<br><!-- End of picture text -->

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cedeedcedcedcedcedcedcedcedeedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedeedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedced<br>eyett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedced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Eee Beerere] Feed al eS) ocean fen<br>Faasfetedstfetedststfetedstststfetedstststst printieeeeeieeeeeeeieeeeeeeeeieeeeeeeeeee eerererseee:eee::eee:::eee:::: ceceere|EeeEeeEeeEee eed poe secacese|s5e5e535ARARs5e5e535ARARARARs5e5e535ARARARARARARs5e5e535ARARARARARARARAR e5e5255BASSwien,BASSwien,wien,BASSwien,wien,wien,BASSwien,wien,wien,wien,BASSwien,wien,wien,wien,wien, =Fiedsessiedsesssessiedsesssesssessiedsesssesssesssess eeceekHpelpelpelpel =<br><!-- End of picture text -->

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cedeedcedcedcedcedcedcedcedeedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedeedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedcedced<br>eyett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedett[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ected[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedced[ectedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedettettetaceceeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedettetaceceeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedcedcedcedcedcedcedcedceceeesteteecedeedcedcedcedcedcedcedcedeesteteecedeedcedcedcedcedcedcedcedetaceceeesteteecedeedced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Eee Beerere] Feed al eS) ocean fen<br>Faasfetedstfetedststfetedstststfetedstststst printieeeeeieeeeeeeieeeeeeeeeieeeeeeeeeee eerererseee:eee::eee:::eee:::: ceceere|EeeEeeEeeEee eed poe secacese|s5e5e535ARARs5e5e535ARARARARs5e5e535ARARARARARARs5e5e535ARARARARARARARAR e5e5255BASSwien,BASSwien,wien,BASSwien,wien,wien,BASSwien,wien,wien,wien,BASSwien,wien,wien,wien,wien, =Fiedsessiedsesssessiedsesssesssessiedsesssesssesssess eeceekHpelpelpelpel =<br><!-- End of picture text -->

<!-- Start of picture text --> secacese|s5e5e535ARARs5e5e535ARARARARs5e5e535ARARARARARARs5e5e535ARARARARARARARAR e5e5255BASSwien,BASSwien,wien,BASSwien,wien,wien,BASSwien,wien,wien,wien,BASSwien,wien,wien,wien,wien,<br><!-- End of picture text -->

<!-- Start of picture text --> e5e5255BASSwien,BASSwien,wien,BASSwien,wien,wien,BASSwien,wien,wien,wien,BASSwien,wien,wien,wien,wien, =Fiedsessiedsesssessiedsesssesssessiedsesssesssesssess<br><!-- End of picture text --> <!-- Start of picture text --> Faasfetedstfetedststfetedstststfetedstststst printieeeeeieeeeeeeieeeeeeeeeieeeeeeeeeee<br><!-- End of picture text --> <!-- Start of picture text --> eerererseee:eee::eee:::eee::::<br><!-- End of picture text --> <!-- Start of picture text --> ceceere|EeeEeeEeeEee<br><!-- End of picture text -->

<!-- Start of picture text --> poe<br><!-- End of picture text --> <!-- Start of picture text --> eeceekHpelpelpelpel =<br><!-- End of picture text -->
<!-- Start of picture text --> er 1 meter aes , 2 meter<br>rite rh<br>Hanoi Haiphong Hanoi Haiphong<br><!-- End of picture text --> <!-- Start of picture text --> 1 HoChiMinh 1 HoChiMinh<br>" 3 meter 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> SEY, -thy<br>Hanot HaiPhong Hanoi HaiPhong<br><!-- End of picture text --> <!-- Start of picture text --> ee<br>1 HoChiMinh 1 HoChiMinh<br><!-- End of picture text --> <!-- Start of picture text -->
- 5 meter<br><!-- End of picture text -->

<!-- Start of picture text --> 1 HoChiMinh<br><!-- End of picture text -->
<!-- Start of picture text --> tens -<br>:<br>Z o o<br>Bal<br>ss China - or<br>fs Emirates <\ rt<br><!-- End of picture text --> <!-- Start of picture text --> PI i pines<br>: ey ‘ "yi Brunei; ath ' :<br>Gs .<br>s) ms oa ; .<br>: %ie |; an £ Se soe<br>‘N - ORR ip<br>Population : Indonesia : va ua New Guinea<br><1% Exposed (% of country total) : beeen*. “Timor-Leste7 le 2 oo: SS<br>1-5% ane ~ . |<br>ly 5-10 -10% 15% : : |<br>lm 15 - 20%<br>a > 20% x<br><!-- End of picture text -->

<!-- Start of picture text -->
[=<br><!-- End of picture text --> <!-- Start of picture text --> 8<br><!-- End of picture text --> <!-- Start of picture text -->
--<br><!-- End of picture text -->
<!-- Start of picture text --> Fees esses<br><!-- End of picture text --> <!-- Start of picture text --> eeepacedeeeBa eaenee==aaeeeae<br>Dbl pe e aeeet e el<br><!-- End of picture text --> <!-- Start of picture text --> aressaid<br><!-- End of picture text --> <!-- Start of picture text --> parser]eset<br><!-- End of picture text --> <!-- Start of picture text -->
[resee e t<br><!-- End of picture text --> <!-- Start of picture text --> peear r erece<br><!-- End of picture text --> <!-- Start of picture text --> Eeeereedee wee<br><!-- End of picture text --> <!-- Start of picture text --> PeeeceBaaleeeReeReneEeeelpeste]eee| crane_——<br>BAAS eee Es | peer ee] eeaeee Bey Ferd pee _— =<br><!-- End of picture text -->

<!-- Start of picture text -->
|<br>BaaleeeReeReneEeeelpeste]eee|eeeReeReneEeeelpeste]eee|ReeReneEeeelpeste]eee|ReneEeeelpeste]eee|Eeeelpeste]eee|peste]eee|eee| _——<br>PeeeceBaaleeeReeReneEeeelpeste]eee| crane_——<br>BAAS eee Es | peer ee] eeaeee Bey Ferd pee _— =<br><!-- End of picture text -->

<!-- Start of picture text --> eee<br><!-- End of picture text --> <!-- Start of picture text --> BAAS<br><!-- End of picture text --> <!-- Start of picture text -->
| peer<br><!-- End of picture text -->

<!-- Start of picture text --> ee]<br><!-- End of picture text --> <!-- Start of picture text --> Bey<br><!-- End of picture text --> <!-- Start of picture text --> Ferd pee<br><!-- End of picture text --> <!-- Start of picture text --> _—<br><!-- End of picture text --> <!-- Start of picture text --> =<br><!-- End of picture text -->
# **Figure 4g East Asia: Agricultural extent impacted**

<!-- Start of picture text --> 4 meter<br><!-- End of picture text --> <!-- Start of picture text --> Vietnam Myanmar<br><!-- End of picture text --> <!-- Start of picture text --> Indonesia<br><!-- End of picture text --> <!-- Start of picture text --> Cambodia Taiwan China<br><!-- End of picture text --> <!-- Start of picture text --> Thailand<br><!-- End of picture text --> <!-- Start of picture text --> Malaysia<br><!-- End of picture text --> <!-- Start of picture text --> D.P.R. Korea Philippines<br><!-- End of picture text --> <!-- Start of picture text --> Rep. of Korea<br><!-- End of picture text --> <!-- Start of picture text --> Brunei<br>Guinea<br>Papua New<br><!-- End of picture text -->

# **Figure 4h East Asia: Wetlands impacted**

<!-- Start of picture text --> 1 meter 2 meter 3 meter 4 meter 5 meter<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>% Impact (Wetland)<br>Vietnam Myanmar Thailand Taiwan China Philippines Rep. of Korea D.P.R. Korea Cambodia Indonesia Malaysia China Brunei Papua New Guinea<br><!-- End of picture text --> 33
# _(v) South Asia_

For a 1m SLR, the impacts of SLR in South Asia resemble those noted in Sub-Saharan Africa, albeit slightly higher. Except for land area, the impacts of SLR in South Asia are smaller than worldwide impacts (Table 8).

# **Table 8 Impacts of sea level rise: South Asia**

||**1m**|**2m**|**3m**|**4m**|**5m**|
|---|---|---|---|---|---|
||**Area (**<br>|**Total= 4,197,17**<br>|**1 sq. km.)**<br>|||
|Impacted area|12,362|21,983|35,696|52,207|69,225|
|% of total area|0.29|0.52|0.85|1.24|1.65|
||**Populat**|**ion (Total= 1,3**|**06,556,000)**|||
|Impacted population|5,870,472|10,187,694|17,810,069|22,065,103|39,505,521|
|% oftotalpopulation|0.45|0.78|1.36|1.69|3.02|
||**GDP (To**<br>|**tal= 3,295,567**<br>|**million USD)**<br>|||
|Impacted GDP (USD)|18,021|30,957|52,036|72,462|94,020|
|% of total GDP|0.55|0.94|1.58|2.20|2.85|
||**Urban ext**<br>|**ent (Total= 241**<br>|**,779 sq. km.**<br>|**)**<br>||
|Impacted area|809|1,379|2,311|3,599|5,117|
|% of total area|0.33|0.57|0.96|1.49|2.12|
||**Agricultural e**<br>|**xtent (Total= 3**<br>|**,023,617 sq.**<br>|**km.)**<br>||
|Impacted area|3,442|6,951|13,501|23,716|35,190|
|% of total area|0.11|0.23|0.45|0.78|1.16|
||**Wetlands**<br>|**area (Total= 57**<br>|**9,130 sq. km**<br>|**.)**<br>||
|Impacted area|9,184|16,685|25,988|36,109|46,003|
|% oftotalarea|1.59|2.88|4.49|6.24|7.94|

Within South Asia, Bangladesh would experience the largest percentage share of land area impacted (Figure 5a). However, this impact exceeds 5% only when SLR is in excess of 3m. With a 1m SLR, the populations of Bangladesh and Sri Lanka experience similar percentage impacts (Figures 5b and 5c). However, as the sea level rises, the impact on Bangladesh’s population becomes more important. A similar situation prevails for GDP (Figure 5d).

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**Figure 5d South Asia: GDP impacted**

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**Table 9 Summary of world and regional impacts**

||**World**|**LA**|**MENA**|**SSA**|**EA**|**SA**|
|---|---|---|---|---|---|---|
|**Indicators**|||||||
||||**1m S**<br>|**LR**<br>|||
|**Area**|0.31|0.34|0.25|0.12|0.52|0.29|
|**Population**|1.28|0.57|3.20|0.45|1.97|0.45|
|**GDP**|1.30|0.54|1.49|0.23|2.09|0.55|
|**Urban extent**|1.02|0.61|1.94|0.39|1.71|0.33|
|**Ag. extent**|0.39|0.33|1.15|0.04|0.83|0.11|
|**Wetlands**|1.86|1.35|3.32|1.11|2.67|1.59|
||||**5m S**<br>|**LR**<br>|||
|**Area**|1.21|1.24|0.63|0.48|2.30|1.65|
|**Population**|5.57|2.69|7.49|2.38|8.63|3.02|
|**GDP**|6.05|2.38|3.91|1.42|10.2|2.85|
|**Urban extent**|4.68|3.03|4.94|2.24|8.99|2.72|
|**Ag. extent**|2.10|1.76|3.23|0.38|4.19|1.16|
|**Wetlands**|7.30|6.57|7.09|4.70|9.57|7.94|

LA: Latin America and Caribbean; MENA: Middle East and North Africa; SSA: Sub-Saharan Africa; EA: East Asia; SA: South Asia.

# _(ii) Most impacted countries_

Figures 6a to 6f summarize our results for each indicator by presenting the top 10 impacted countries (as a percentage of their national values). For this purpose, we use the 1m SLR scenario. For land area (Figure 6a), The Bahamas is by far the most impacted country, with close to 12% of its area affected. Around 10% of Vietnam’s and the A.R. of Egypt’s populations would be impacted with a 1m SLR (Figure 6b). Vietnam would also see 10% of its GDP (Figure 6c) and urban extent (Figure 6d) impacted. Mauritania’s GDP would be significantly impacted. The A.R. of Egypt’s agricultural extent would experience the largest percentage impact, reaching approximately 13%. Finally, nearly 28% of Vietnam, Jamaica and Belize’s wetlands would be impacted by a 1m SLR. Among all of the indicators used in this paper, _Vietnam_ ranks among the top 5 most impacted countries, with the A.R. of Egypt, Suriname and The Bahamas consistently ranking among the highest.

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**Figure 6a Most impacted countries: Land area**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>The Bahamas Vietnam Qatar BelizePuerto Rico CubaTaiwan ChinaThe Gambia JamaicaBangladesh<br>% land area impacted<br><!-- End of picture text -->

**Figure 6b Most impacted countries: Population**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Vietnam A.R. Egypt Mauritania Suriname GuyanaFrench Guiana (Fr) United Arab EmiratesTunisia The Bahamas Benin<br>% population impacted<br><!-- End of picture text --> 41
# **Figure 6c Most impacted countries: GDP**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Vietnam MauritaniaA.R. Egypt Suriname BeninThe Bahamas GuyanaFrench Guiana (Fr) Tunisia Ecuador<br>% GDP impacted<br><!-- End of picture text -->

**Figure 6d Most impacted countries: Urban extent**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Vietnam GuyanaFrench Guiana (Fr) MauritaniaA.R. Egypt United Arab EmiratesLibya Tunisia SurinameThe Bahamas<br>% urban area impacted<br><!-- End of picture text --> 42
# **Figure 6e Most impacted countries: Agriculture extent**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>A.R. Egypt Vietnam SurinameThe Bahamas Argentina Jamaica Mexico Myanmar GuyanaTaiwan China<br>% agr area impacted<br><!-- End of picture text -->

**Figure 6f Most impacted countries: Wetlands**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Vietnam Jamaica Belize QatarThe Bahamas Libya Uruguay Mexico BeninTaiwan China<br>% wetlands impacted<br><!-- End of picture text --> 43
# **IV. Conclusions**

Even if greenhouse gas (GHG) emissions were stabilized in the near future, thermal expansion and deglaciation would continue to raise the sea level for many decades. Continued growth of GHG emissions and associated global warming could well promote SLR of 1m-3m in this century, and unexpectedly rapid breakup of the Greenland and West Antarctic ice sheets might produce a 5m SLR. In this paper, we have assessed the consequences of continued SLR for 84 developing countries. Our results are extremely skewed, with severe impacts limited to a relatively small number of countries. For these countries (e.g., Vietnam, A.R. of Egypt, The Bahamas), however, the consequences of SLR are potentially catastrophic. For many others, including some of the largest (e.g., China), the absolute magnitudes of potential impacts are very large. At the other extreme, many developing countries experience limited impacts. Among regions, East Asia and Middle East/North Africa exhibit the greatest relative impacts.

In this conclusion, we would like to highlight two important implications of our findings. First, the overall magnitudes for the developing world are sobering: Within this century, hundreds of millions of people are likely to be displaced by SLR; accompanying economic and ecological damage will be severe for many. The world has not previously faced a crisis on this scale, and planning for adaptation should begin immediately. Second, international resource allocation strategies should recognize the skewed impact distribution that we have documented in this paper. Some countries will be little-affected by SLR, while others will be so heavily impacted that their national integrity may be threatened. Given the scarcity of available resources, it would seem sensible to allocate aid according to degree of threat.

Under the provisions of the United Nations Framework Convention on Climate Change (UNFCCC), some work has begun on National Adaptation Programmes of Action (NAPAs). These are intended to facilitate the identification of priority activities, including adaptation to SLR, for the least-developed countries. To date however, only 8 countries have developed comprehensive NAPAs: Bangladesh, Bhutan, Comoros, Djibouti, Malawi, Mauritania, Niger and Samoa. Of these, five are coastal states with potential SLR problems. For comparison, our summary above reveals 10 poor countries that will be very severely impacted by SLR. With the exceptions of Bangladesh and Mauritania, 44
none are included in the NAPA list above: Benin, Guyana, Suriname, A.R. of Egypt, The Gambia, Guinea-Bissau, Mauritania, Vietnam, Bangladesh and Sri Lanka. Many other developing countries will also experience significant impacts.

We should reiterate that these results are not speculative: The current atmospheric concentration of GHG’s is sufficient to drive global warming well into the next century, and much higher concentrations will undoubtedly be reached before any global agreement can be implemented. For precautionary planning purposes, SLR in the range of 1m – 3m should therefore be regarded as realistic. To date, however, there is little evidence that the international community has seriously considered the implications for population location and infrastructure planning in many developing countries. We hope that the information provided in this paper will encourage more rapid action on this front.

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