metadata: authors: 0: author: Maruyama Rentschler,Jun Erik
1: author: Avner,Paolo
2: author: Marconcini,Mattia
3: author: Su,Rui
4: author: Strano,Emanuele
5: author: Bernard,Louise Alice Karine
6: author: Riom,Capucine Anne Veronique
7: author: Hallegatte,Stephane
count: World
docna: 0: docna: Rapid Urban Growth in Flood Zones : Global
            Evidence since 1985
docty: Policy Research Working Paper
abstracts: cdata!: As countries rapidly urbanize,
            settlements are expanding into hazardous flood zones. This
            study provides a global analysis of spatial urbanization
            patterns and the evolution of flood exposure between 1985
            and 2015. Using high-resolution annual data, it shows that
            settlements across the world grew by 85 percent to over 1.28
            million square kilometers. In the same period, settlements
            exposed to the highest flood hazard level increased by 122
            percent. In many regions, risky growth is outpacing safe
            growth, particularly in East Asia, where high-risk
            settlements have expanded 60 percent faster than safe ones.
            Developing countries are driving the recent growth of flood
            exposure: 36,500 square kilometers of settlements were built
            in the world&#x2019;s highest-risk zones since
            1985&#x2013;82 percent of which are in low- and
            middle-income countries. In comparison, recent growth in
            high-income countries has been relatively slow and safe.
            These results document a divergence in countries&#x2019;
            exposure to flood hazards. Rather than adapting their
            exposure to climatic hazards, many countries are actively
            increasing their exposure.
display_title: Rapid Urban Growth in Flood Zones :
            Global Evidence since 1985
listing_relative_url: /research/2022/04/33791974/rapid-urban-growth-flood-zones-global-evidence-1985
topicv3: Spatial Planning and Land Use Management
            m1326256 1721,Urbanization and Growth m1326437 796,Flood m1503315 3216,Climate Resilience m1504411 4184
publishtoextweb_dt: 2022-04-21T00:00:00Z
dois: 10.1596/1813-9450-1014
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1217979
1: author_key: 1256219
2: author_key: NA
3: author_key: 1954360
4: author_key: 1691718
5: author_key: 1295353
6: author_key: 2061540
7: author_key: 1103816
model_extractions: Policy Research Working Paper 10014 Rapid Urban Growth in Flood Zones Global Evidence since 1985 _Jun Rentschler Paolo Avner Mattia Marconcini Rui Su Emanuele Strano Louise Bernard Capucine Riom Stephane Hallegatte_ Sustainable Development Chief Economist Office April 2022
Policy Research Working Paper 10014

# **Abstract**

As countries rapidly urbanize, settlements are expanding into hazardous flood zones. This study provides a global analysis of spatial urbanization patterns and the evolution of flood exposure between 1985 and 2015. Using high-resolution annual data, it shows that settlements across the world grew by 85 percent to over 1.28 million square kilometers. In the same period, settlements exposed to the highest flood hazard level increased by 122 percent. In many regions, risky growth is outpacing safe growth, particularly in East Asia, where high-risk settlements have expanded 60 percent faster than safe ones. Developing countries are driving the recent growth of flood exposure: 36,500 square kilometers of settlements were built in the world’s highest-risk zones since 1985–82 percent of which are in low- and middle-income countries. In comparison, recent growth in high-income countries has been relatively slow and safe. These results document a divergence in countries’ exposure to flood hazards. Rather than adapting their exposure to climatic hazards, many countries are actively increasing their exposure.

This paper is a product of the Sustainable Development Chief Economist Office. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at jrentschler@worldbank.org, pavner@worldbank.org, shallegatte@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Rapid Urban Growth in Flood Zones: Global Evidence since 1985

**Jun Rentschler**<sup>**1**</sup> **, Paolo Avner**<sup>**1**</sup> **, Mattia Marconcini**<sup>**2,3**</sup> **, Rui Su**<sup>**1**</sup> **, Emanuele Strano**<sup>**3**</sup> **, Louise Bernard**<sup>**4**</sup> **, Capucine Riom**<sup>**4**</sup> **, Stephane Hallegatte**<sup>**1**</sup>

1 The World Bank, Washington DC, USA 2 German Aerospace Center (DLR), Munich, Germany 3 Mindearth, Biel, Switzerland 4 London School of Economics and Political Science, London, UK

**Key words:** Urbanization, floods, exposure, adaptation, climate change **JEL classification:** R12, Q54, O18

**Acknowledgments:** This study has benefited from feedback and inputs by Richard Damania, Vivien Deparday, Keith Garrett, Caroline Gevaert, Niels Holm-Nielsen, Brenden Jongman, Nancy Lozano Gracia, Srilakshmi Ramesh, Lucy Southwood, and Michalis Vousdoukas. It was supported by the Global Facility for Disaster Reduction and Recovery (GFDRR).
# 1. Introduction

Disaster losses are rising, and evidence is mounting that climate change is driving up the probability of extreme natural shocks<sup>1,2,3,4</sup> . Yet, it has also proven politically expedient to invoke climate change as an exogenous force that supposedly places disasters beyond the influence of local planners and authorities<sup>5,6</sup> . However, locally determined patterns of urbanization and spatial development are key factors to people’s exposure and vulnerability to climatic shocks<sup>7</sup> . In this study we show that since 1985 human settlements around the world – from rural villages to large cities – have expanded continuously and rapidly into present flood zones. Globally, settlement growth in high-risk zones is outpacing growth in safe zones by a large margin. This provides systematic evidence that rather than adapting to increasingly frequent climatic shocks, countries around the world are actively increasing their exposure to flood hazards.

Around the world, urban populations are growing rapidly, as people from agricultural peripheral regions move into cities searching for economic opportunities. As a consequence, cities continue to grow, in terms of population and spatial footprint. Urbanization and economic development have traditionally gone hand in hand as cities enable agglomeration economies<sup>8</sup> ; for instance, they enable matchmaking between employers and job seekers, sellers and buyers, and capital and projects; support expensive productivityenhancing infrastructure such as public transport systems; and favor the circulation of ideas<sup>9,10</sup> .

However, on the downside, rapid urban growth can cause congestion effects; for instance, by increasing exposure to natural hazards and pressure on public services and infrastructure. This is particularly relevant in developing countries, which often lack the capacity for comprehensive urban planning and implementation, and the financial resources to invest in transport and protection infrastructure. These challenges are often compounded by fast demographic growth and internal migration, which can mean that risk-informed urban and infrastructure planning cannot keep pace. Moreover, as safe spaces are increasingly occupied, the resulting land scarcity can drive new developments disproportionately into previously avoided areas such as riverbeds, flood plains, or wetlands (figure 1).

This study offers a systematic and global analysis of spatial urbanization patterns and their evolving exposure to flood hazards. It leverages the 2019 high resolution global flood hazard layers by Fathom and the World Settlement Footprint Evolution (WSF-Evo) data set, developed by the German Aerospace Center (DLR) in 2021, which offers high-precision measurements of yearly settlement extents from 1985 to 2015 at 30 by 30 meters spatial resolution and with global coverage (figure 2)<sup>11</sup> . These data allow us to track the speed and shape of urban expansion in large cities through to small rural settlements and estimate flood exposure with high precision, and therefore understand what share of countries’ built-up land is exposed to high flood hazards and how this evolves over time. We show that settlements across the world grew by 85 percent between 1985 and 2015, from 693,000 to over 1.28 million square kilometers. In the same period, settlements exposed to the highest flood hazard level increased by 122 percent – thus highlighting that in many places, risky growth is outpacing safe growth.

2
<!-- Start of picture text --> eas.<br>REE<br>rae ae Se<br>ans Z<br>Gereak ee odin at<br>Oza——tepH °‘ >ae" pea\esao,wetas aeaeme aDn: <Taeea% “Seeoe~ pe. weeree.Tee VeeBRACS.92 SINS eeNoes=ck SeeR ;»<br>” fezCi MT)aeee Te wale3 » Wyla?NYSva>ieehhh.BO x3.besaoreskos. | as = >J<br>3 x _& 2 t<br>YZ<br>— < (o> sieey tye |<br>ait aoe sae]<br>- te<br>. = ees ae Sey c<br>a. Le<br>ar PSY: cm<br>uit SS e ee EN<br>Tir iw<br>yal ues ~mY4 hme aft 2 Zoe<br>ee | usiee BS<br>ay.le em 2 HA!fTle ge%y e ><br>eae ter Ie eeeee<br>00th ia Ly Piero by : oF<br>a ee<br>p > ~<br>aBe ©<br>a<br>BSE: Pomme, a<br>—<br><!-- End of picture text -->

<!-- Start of picture text --> 2 eee<br>: ELed Sess: Se<br>Bessre oatHarr Lees-ae ¥ at: hetLnWsrasis ists|<br> ae| (LbeeoeA egyee Ge SOAS Te<br> i<br>Haypee a vied J ae 2 ot, “Se<br>‘Saag Pee ag ee Te ae<br>apo eet SSE eet eee re ee :<br>togAS Sesear eaeMe Op: SA echTak aeSe<br>rete <_<fp \ BAYiGon sh 3% peBe eefo 3= SdBY.=?ee. AASShoe Ae.% NSPa ;<br> Befoo oe<br>oe oor Mee c.<br>IS‘ SAP haiDEES oS} \eeORbrent Face UN oes<br>ee AN > oe Cred a: wx<br>gePee TRO Vs Sees ker<br>a igee<br>m<br>Bs<br>/; ae te SCA “Sp<br>es RSKOE Sy , 4 eee a<br>. / WIcas Px Pe up aes M<br>1g P< ae<br>S eoae CS ON<br>GHEEGy 9 i, os th =<br><!-- End of picture text -->

<!-- Start of picture text --> : }<br>akWY I<br>Sa ar<br>Pea ry SaoeSas oern s as|PROTASi SeemaSat SREeegePERS.Me,ne<br>‘<<ae oan 3 “i sb e. e es ageSatesLEStom B e etse a OR a<br>RLYfay7 OLfe ~ Be: ee Eee 3% a Ls<br>Ne od ba<br>Oo MS, i<br>Sige) ESeee eeeGEA<br>Oaceap yesnaea<br>GamePeLOEgoesGS Ze aEWee S SSESeS<br><9 OGY Res Ss<br>raRS & > a,Bmieeg v<br>ae é tins. ; BesSars0 &RaKSSo SPSBota e a)algaPER, TARARenPao2 :<br>‘ Zz,ODIBEAaah ShSpe SED<br>"Get.SAI Se> pew. ARO R<br>7 eonWe Paes)St aie<br>BR % POESn<br>| Cape "og eS<br>See<br>EB, Bin MS<br>aeie.hae iit a See«Ly<br>he<br>AN<br><!-- End of picture text -->

<!-- Start of picture text --> ED aD<br>bi cites 8 wey 5A aes<br>UETet26.eas oe0 ¥yonei AE eeeSET ARess Gls68 Fennyes i QELS BileSO<br>SS hg aa seo PRES SP FS ge<br> sees,hy7 “fe fe te 5m “SANAae At, Cn Peg!AaaVon geaco igseeeG Gta<br> Sant? ee, GE<br>paeae aeEM pad<br>. eeaan he S Siete & ¢ ie<br>4 Pe Hag tA he Lav Si IYO<br>Og FG 4¢ MRK<br> aby Oahy &e ipie Sir LL mos pei<br> Bes(aes: 44 "4OYs %, oanKae Koe sRE)a AY,Ry RaSOVA EeOeSEIOg PTEBF * Me ivfLy yen be<br> OF9 - om) 4 Bt Ape at Ag Ee<br>er alleee; Oe,i. ‘ bpBSPSa hti BLteesare One’it * odPawi. LapaBaUgPict’Ce,F<br>y<br>POM LS OF we, Hi oft, Hs<br>C863reeKe Me.tteey+s de ae aeoBi) yi hegleWeey fat: na<br>eesae! 4 %ed rs Sstr, ae SF Br 2Sar 24sras,<br>; MOE SE !<br>a Tea Mes Te = dads Ww 6<br>(pi Ms 1 BeSo<br>UE ie<br>oS PENAL P Pat tr Sey Fe he 4<br>BEF be arias Pt as<br>i=ee me, mhse & ok<br>W@,/. be TigMe hay tShe Lpl e hesaa ‘ ,.SF ap AG7)iF a<br>I5 PSY nat SOS :EMM os Thee“De i 7<br><!-- End of picture text -->
# 2. Existing evidence

When safe spaces are already occupied, new developments can disproportionately occur in previously avoided areas such as riverbeds, flood plains, or wetlands. When making locational decisions, households and businesses often trade off job accessibility and market potential with disaster risks, and settling in flood-prone areas can become a rational choice<sup>12,13</sup> . Behavioral biases, market failures, and information constraints can also exacerbate excessive risk taking<sup>14,15,16</sup> , while inefficient land markets<sup>17</sup> can allow safe land to sit idle while risky areas are developed. Risk levels in urban development depend on geography, socioeconomic trends, and institutional and regulatory factors, such as planning restrictions.

Case study evidence illustrates these mechanisms. For example, in India, low-income households move to Mumbai from rural peripheral regions, searching for economic opportunities. The high density of settlements and land price differentials in the city force new arrivals into previously avoided areas, such as high-risk flood zones near riverbeds, and informal settlements grow without planning or public infrastructure<sup>18</sup> . Similarly, in Vietnam, a flood risk assessment for Ho Chi Minh City finds that informal poor settlements are systematically exposed to higher flood risks than the rest of the city<sup>19</sup> , while evidence from the United States documents how, in recent decades, new construction on the Atlantic coast avoided flood-prone areas in sparse locations but took place in the ‘least-bad’ flood-prone areas in dense locations<sup>20</sup> . New Orleans, first established on higher lands, expanded into lower-lying flood plains as the port’s growing role spurred economic activity<sup>21</sup> .

The case of Vietnam illustrates the scale and dynamics of evolving flood exposure. Almost one-third of Vietnam’s coastline is occupied by towns and built-up settlements, and its 28 coastal provinces are home to 46.6 million people<sup>22</sup> . With the safest and most productive locations already occupied, new developments are increasingly forced onto hazardous land and previously avoided areas that are further from regional hubs or more exposed to natural hazards. Risks are exacerbated when urban planning fails to prioritize densification of safe areas to avoid expanding into risky zones. Evidence from satellite-based nightlight imagery has confirmed that areas in Vietnam with high urban and economic growth face significantly higher flood risk than low-growth areas<sup>22</sup> . About 27 percent of areas with low urban and economic growth are estimated to be exposed to a 100-year flood, compared to some 50 percent of highgrowth areas.

Despite the rising interplay between rapid urbanization trends and flood disasters, these risks are mostly documented in anecdotal evidence and case studies. Previous global flood risk assessments provide only limited evidence of the long-term evolution of global settlement expansion and flood exposure.

The lack of high-resolution flood hazard maps and annual settlement footprint data has been a key factor preventing systematic analysis of flood exposure trends over time. Although satellite imagery is now widely used to understand the spatial patterns of urban development across the world, most data sets only cover the most recent years. The Global Human Settlements Layers were seminal as they produced global urban footprints for four discrete time steps—1975, 1990, 2000, and 2014<sup>23</sup> ; yet inaccuracies and large time gaps between observations limit the insights into the rapid dynamics of urbanization patterns.<sup>24</sup> Global settlement footprint data sets with high spatiotemporal resolution have become available recently, but have thus far not been used in global flood hazard assessments<sup>25,26</sup> .

By using global historical inventories of recorded flood events, such as EM-DAT, studies have estimated exposure indicators at the country level<sup>27</sup> . Yet, the lack of data on the spatial distribution and coincidence of flood hazards and populations means that continuously evolving urban shapes cannot be taken into 4
account accurately, thus making flood exposure difficult to compare over time<sup>27,28,29</sup> . Another study based on satellite data from 2000 to 2018 documents increasing flood exposure, but omits at-risk populations who were unaffected during the study period and events that were not detected by satellite observations<sup>29</sup> .

Due to data limitations, past studies also focus on certain types of flood, rather than assessing the combined risks from all flood types (that is, fluvial, pluvial and coastal flooding)<sup>30,31,32,33,34</sup> . Others assess risks for a subset of countries only, falling short of full global coverage<sup>35,36</sup> . Similarly, studies that use relatively coarse spatial resolution flood hazard data tend to inaccurately represent major fluvial floodplains. They do not capture pluvial flood hazards and flooding along secondary rivers, drastically underestimating exposure<sup>35,37,38</sup> . One study using coarse flood data projected that the global number of flood-exposed people will reach 1.3 billion by 2050<sup>3937</sup> but a more recent high-resolution study showed that this threshold has already been exceeded by at least 39% in 2020<sup>39</sup> . This illustrates the importance of using high-resolution data to capture the highly localized nature of flood hazards and people’s tendency to avoid settling in the most hazardous locations<sup>355</sup> .

This study contributes to the literature in several ways: First, by considering flood exposure trends with annual frequency, we demonstrate the value of annual monitoring of evolving flood exposure. The study documents significant variation in year-on-year growth rates, thus highlighting the need for spatial planners to be highly responsive. Second, it explicitly distinguishes the growth dynamics of safe versus flood-exposed spatial development. This allows us to document a divergence in flood exposure – regions that are either increasing or decreasing their flood exposure as they urbanize over time. Third, rather than focusing on a certain flood hazard type, we combine different flood types (fluvial, pluvial, coastal floods) and assess overall flood exposure. Fourth, by using global data sets, we document trends with complete global coverage and within one consistent methodology. Finally, while past studies (both global and country-specific) have documented a general increase in flood exposure, this study corroborates and expands these findings based on data with high spatio-temporal resolution.

# 3. Data and methods

## 3.1. Global flood hazard and settlement data

### Flood hazard data

To obtain complete estimates of urban exposure to flood hazards, this study considers the three most common flood types:

- _Fluvial flooding:_ when intense or excessive precipitation or snow melt causes rivers to overflow

- _Pluvial flooding:_ when surface water builds up beyond the absorptive capacity of soil, due to extended precipitation and insufficient drainage

- _Coastal flooding:_ due to tidal or storm surges, or sea level rise.

Country-level pluvial and fluvial flood data are based on the 2019 global Fathom flood hazard data set<sup>40,41</sup> . These provide gridded information on flood extents and depths at a 3-arcsecond resolution (equivalent to 90 meters at the equator), simulating 5, 20, 50, 100, 250, and 500-year flood events, and are available for all countries. The maps are based on the DEM MERIT elevation model that corrects for multiple errors, including absolute bias, stripe noise, speckle noise, and tree and building height biases<sup>42</sup> . We consider flooding with a 100-year return period.

5
For coastal flood hazards, we use a global coastal flood map with 3-arcsecond resolution, generated using the LISFLOOD-FP hydrological model<sup>43</sup> , with DEM MERIT as an input<sup>42</sup> . Coastal flood simulations are forced by extreme sea level scenarios derived from reanalyzing waves (using the <mark>WAVEWATCH-III mode</mark><sup>44</sup> <mark>)</mark> and storm surges (using the DFLOW-FM model<sup>31</sup> ), combined with tidal information<sup>45,46</sup> . As with fluvial and pluvial floods, we consider 100-year events.

Global flood maps do not incorporate the effects of artificial flood protection structures like dikes. This data limitation is pervasive in the literature, as there is no complete global inventory of flood defenses. Ongoing initiatives, such as the FLOPROS database, could eventually fulfill this need, but are still falling short of comprehensive coverage<sup>47</sup> . The use of undefended flood maps is likely to result in overestimating exposure in locations where flood protection systems defend against 100-year floods (or higher). Case studies and World Bank country risk assessments suggest that the vast majority of flood-exposed people in LICs and LMICs have no protection from a 100-year flood. Many LICs lack even basic drainage systems to manage light flooding. It is likely that only HICs and some UMICs offer such flood protection standards to a significant share of their populations; however, frequent flood disasters in these countries also demonstrate that coverage is far from complete.

This study considers a 1-in-100 year return flood intensity to reflect relatively rare and intense disasters. However, the concept of return periods is easily misunderstood and the probability underestimated. A 1- in-100 year flood has, on average, a 1 percent probability of occurrence in any given year, which translates to a 10 percent probability in a decade, or 50 percent in a lifetime (68 years). These are significant probabilities that lie well within government planning horizons. For comparison, the Dutch flood protection system protects against events up to 1-in-10,000 years. Further, these probabilities apply independently to any given river basin or microclimate (and we consider hundreds of thousands of locations for this study). This means that, globally, hundreds of 1-in-100 year flood events happen every year.

# Global settlement footprint data since 1985

Considering the rapid urbanization patterns observed in the recent past, annual high-resolution settlement footprint data are crucial for monitoring the evolution of people’s exposure to natural hazards. In this study, we use the WSF-Evo data set developed by DLR<sup>11,48</sup> , which enables us to track the flood exposure of new urban development in cities and small rural settlements alike (figure 2). WSF-Evo outlines the global settlement extent from 1985 to 2015 on a yearly basis at about 30x30-meter resolution based on archived Landsat imagery<sup>49</sup> . For each year, we gather all available Landsat-5 and -7 scenes acquired over the given area of interest and extract key statistics—temporal mean, standard deviation, minimum, maximum—for different spectral indices after performing cloud and cloud-shadow masking. Among others, these include: the normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and modified normalized difference water index (MNDWI). Temporal features prove generally robust if computed over at least 7 clear cloud-/cloud-shadow-free observations. Accordingly, if this constraint is not satisfied for a given pixel in the target year, we enlarge the time frame backwards (at 1-year steps) until the condition is met.

The 2015 edition of WSF-Evo, generated by jointly exploiting both optical (Landsat-8) and radar (Sentinel1) multitemporal satellite imagery, outlines the global settlement extent at 10x10-meter resolution.<sup>50</sup> Starting backwards from 2015 (using the WSF2015 as a reference), we iteratively extract settlement and 6
<!-- Start of picture text --> ¢ 34 >{ en rEbay veoy Weekae a= eh4aet =Ff<br>ars & (> ara ‘ae pat, oo<br>mY L. j 1 ee er |<br>woayaeas eee, Pree tifae lee he osut weal:peSeNi} geLti]al eo\ ti 4<br>4 if po ~ a i eter fi tot<br>- thy «ee Tone Se ie 1 aan |<br>px e RAS GG Rs eee 5<br>i wt Mgrhe Pad he Tg a be ; hb memati lf<br>LGR e eh ae Ne RRgeyn, Saylige mnmeTe ELSf 4 FSi<br>ek ¥ eas VAs eb eg) bs pw a ae 5<br>SC ALE Cie 3. ne Ber SS ie zy"<br>Ears Sapag are ae Pee!ee 4 he YSa Bs tS Gs:eS AREBANGBas<br>—Bs 5 ATre oe ee, Tae 20, rey / we ee ee “1<br>;ea < ie RP oeae ia se paGR fonShaitbe fy; eeasSeex cin f<br>‘ paki ie pe<br>Aire Oe GOL ops. Se Buk5 he,es ea oe ” thet02 R. i<br>eit<br><!-- End of picture text -->

<!-- Start of picture text --> WsFevolution2015 3125 oe cs x fg‘ i<br>mole2013<br>2012 A 123 ae ots i<br>2011 eet bot | a<br>2010 J , j Ay idwe ee<br>2009 iZ<br>20082007 . ; we a +oo i<br>i 2005 ' i} i Mees Aig t's . |<br>2004 ne js ee Le ae ba fam)” i<br>I 2003 ‘] ‘ t ‘ i = oy Uh |<br>WH19992000 es VAMS Fe eeseeReeered e aege ME Be)<br>1995 ES eras ee SAL eee? Ope tae<br>BH 1992isee 4 NawRT aTes seme cma PAT 5 Weea Aer ROEors TeSA<br>1987 . en See, ee Sa ~ sen “aa oat aes ‘<br>1996 SOE gee aE eee is Fn eek we a Ae<br>Mjaximum meFoFkeeneoat weetwe res3 Bree ict ieBn gtiaa Dts Y<br>Water Depthx SRR SERRE Sant atiy te ss |<br>a benif) SOE See if Bee<br>Osboet som Ne [oo<br>BH 0<xs0.15m<br><!-- End of picture text -->
- **Step 2.** We assign each settlement pixel to one of the five considered flood hazard categories defined in line with risk to lives and livelihoods (table 1). We repeat this step for all the world’s settlements and each year.

- **Step 3.** On a yearly basis, we compute the total settlement area per flood hazard category for each subnational administrative unit (such as state or province) and calculate its share relative to the corresponding overall settlement area. To address possible limitations of settlement over/under-estimation due to poor Landsat-5 data availability before 2000 in some parts of the world, we apply SPM (see above). To obtain wider trends, we further aggregate exposure estimates to national and regional levels and compute the long-term trends of settlement exposure to flood hazards.

**Table 1.** Flood hazard categorization applied in this study

|**Category**|**Hazard**<br>**class**|**Flood depth**<br>**(meters)**|**Description**|
|---|---|---|---|
|0|None|0|Unaffected during a 1-in-100 year flood|
|1|Low|0 – 0.15|No significant risk to life or economic activity|
|2<br>3<br>4|Moderate<br>High<br>Very high|0.15 – 0.5<br>0.5 – 1.5<br>Over 1.5|Disruptions to livelihoods and economic activity; some risk to life for<br>select populations, especially vulnerable groups such as children and<br>disabled people (including through water-borne diseases)<br>A significant share of the affected population is expected to face risk<br>to life, especially if flood waters have a current; major disruptions to<br>livelihoods<br>Most affected people could face substantial risk to life and severe and<br>prolonged disruptions to livelihoods|

# 4. Results

## 4.1. Substantial flood hazard exposure in all regions

Estimates from this study show that in 2015, at least 11.3 percent of all built-up areas globally face high or very high flood hazards; i.e. inundation depths of at least 50 cm during 1-in-100 year flood events (figure 3). Exposure is lowest in Sub-Saharan Africa (4.6 percent) and North America (4.5 percent), and highest in the East Asia and Pacific region (18.4 percent). These figures confirm that flood exposure levels are substantial across all global regions and income groups. While the normalized shares of exposed settlements (Figure 3) illustrate the relative proportions of safe and at-risk settlements over time, they hide that total settlement extents have expanded significantly in the considered period. This is assessed further in the following.

8
<!-- Start of picture text --> East Asia & Pacific World South Asia Sub-Saharan Africa<br>1.00<br>0.75<br>0.50<br>0.00 ——— eee<br>Latin America & Caribbean Europe & Central Asia Middle East & North Africa North America<br>1.00<br>0.75<br>0.50<br>0.25<br>i Ss<br>1985 1995 2005 20151985 1995 2005 20151985 1995 2005 20151985 1995 2005 2015<br>Risk levels None Low || Moderate | | High || Very high<br><!-- End of picture text -->

<!-- Start of picture text --> Upper-middle-income countries — Lower-middle-income countries Low-income countries High-income countries<br>1.00<br>0.75<br>0.50<br>0.25<br>1985 1995 2005 20151985 1995 2005 20151985 1995 2005 20151985 1995 2005 2015<br>Risk levels None Low | | Moderate || High || Very high<br><!-- End of picture text -->
<!-- Start of picture text --> 1000<br>500<br>;——<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 125<br>100<br>75<br>50<br>25<br>0<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>(0)<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 3.0<br>Riski levels<br>25<br>. None<br>Low<br>== Moderate<br>— High<br>2.0 — Very high<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> 140<br>120<br>100<br>80<br>60<br>40<br>20<br>(e)<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> <!-- Start of picture text --> .<br>Risk levels<br>None<br>Low<br>— Moderate<br>= High<br>— Very high<br><!-- End of picture text -->
<!-- Start of picture text --> East Asia & Pacific World South Asia Sub-Saharan Africa<br>150<br>100 et<br>50 Pees<br>ween<br>0 eee”<br>Latin America & Caribbean Europe & Central Asia Middle East & North Africa North America<br>150<br>100<br>0<br>1985 1995 2005 20151985 1995 2005 20151985 1995 2005 20151985 1995 2005 2015<br>Risk levels None Low Moderate — High — Very high<br><!-- End of picture text --> <!-- Start of picture text --> Upper-middle-income countries Lower-middle-income countries Low-income countries High-income countries<br>150<br>100<br>.0 _— _——"<br>1985 1995 2005 20151985 1995 2005 20151985 1995 2005 20151985 1995 2005 2015<br>Risk levels None Low Moderate — High — Very high<br><!-- End of picture text -->
<!-- Start of picture text --> East Asia & Pacific World South Asia Sub-Saharan Africa<br>1<br>Latin America & Caribbean Europe & Central Asia Middle East & North Africa North America<br>3<br>; SS<br><!-- End of picture text --> 1985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 2015 Risk levels None Low —— Moderate —- High — Very high <!-- Start of picture text --> Upper-middle-income countries Lower-middle-income countries Low-income countries High-income countries<br>150<br>100<br>;0 aa —<br>1985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 20151985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> Risk levels None Low —— Moderate — High — Very high
<!-- Start of picture text --> 175°W)Ss«150°W Ss 125°W)Ss«100°W__75°W 50°w 25°W 0° 25°E 50°E 75°E 100°9E =:125°E_-~—s«150°E<br>a en | : a =| B<br>a F |” am = = 75°N<br>= - = = * E<br>io -—| Swe. ee !<br>— = ae 5 Pe,ob eePoe lead wx + = i 50°N<br>~~ we Stet oo ae<br>ee rt a es Pe<br>A oe a we<br>;<br>a ty | if z Ca,” |<br>ay. = , a ty o> oO = ; ae +. |<br>‘yCoe. ereeee~ peeweeLe eeba ‘ WASai AkfeifsWheeoAle = -ar| 0 3<br>HI -100 - -50 % tr.‘ers ae | -—a 25°S<br>-50- -10 % WB ey<br>MM -10-10%10-50% . _<br>mm > 50% x - | 5°5<br><!-- End of picture text -->
<!-- Start of picture text --> See<br>ba Am iil me<br>“i oese<br>ee<br>India 22 eT<br>Ukraine oo 6<br>stn TM Seria | Dee BL<br>ae a Bann58<br>| a rg<br>le ee”<br><!-- End of picture text -->

<!-- Start of picture text --> ~~<br>Arab<br>of Egypt<br>=<br>ee | ET<br>Vietnam ieeeeoe:<br>innnee<br>India fae mee meere<br><!-- End of picture text -->

<!-- Start of picture text --> — oe<br>RepublicArab Pakistan<br>se ofiEgypt aS<br>reo Bee<br>a<br>Turkey an ena<br>gases LI<br>_ [|<br>inci pice— a|<br>Mae sss!<br><!-- End of picture text -->

<!-- Start of picture text --> ') Low income<br>Lower middle income<br>_<br><!-- End of picture text --> ') Low income Lower middle income _ ©) Upper middle income
expansion of high-risk settlement. Vietnam and Bangladesh also stand out as LMICs with large settlement areas exposed to the highest flood hazard category (figure 8b).

Several HICs have large settlement areas in high-risk flood zones (figure 8b). While they have seen relatively slow and safe growth in the past 30 years, many HICs, including Japan, the United States, and the Netherlands, already had a significant number of at-risk settlements in 1985 and have invested heavily in protecting them. In the latter, sea dikes protect against up to 1-in-10,000-year storm surges. Nevertheless, even in HICs, many settlements are not protected from the 1-in-100-year flood hazards considered in this study. Recent flooding disasters—in the United States, United Kingdom, Belgium, Germany, and other countries—illustrate that flood hazards continue to pose substantial risks to lives, livelihoods, and assets.

# 4.6. Some European and Asian countries face disproportionately high exposure

Considering the share of high-risk settlements relative to overall settlements can help us identify smaller countries that face disproportionately high exposure. In three-quarters of all countries, 4 percent or less of overall settlements are estimated to be high-risk, but in several countries, exposure shares are significantly higher. Figure 9 presents the top 20 countries in terms of flood-exposed settlement shares. In the Netherlands, over 35 percent of all settlements are in high-risk zones in terms of hydrology and elevation, though these risks are mitigated through advanced (but not infallible) protection infrastructure. The same is not true in most developing countries, such as the Lao People’s Democratic Republic (34 percent) and Vietnam (25.4 percent), where many settlements are highly exposed without strong protection systems. Flood exposure is particularly high for countries where settlements concentrate along river valleys and basins (for example, Bhutan, the Arab Republic of Egypt, and Bangladesh), and coastal areas (such as Fiji and Vietnam).

15
<!-- Start of picture text --> Netherlands Vietnam Laos Bangladesh Fiji<br>es ee es ee ee<br>Bhutan Japan Switzerland Egypt China<br>ee es Gees pees<br>North Korea Austria Bosnia & Herzegovina Croatia Montenegro<br>a a a a ee<br>Sudan Slovenia South Korea Myanmar Albania<br>a es es<br><!-- End of picture text -->
<!-- Start of picture text --> 400,000<br>Y 350,000<br>& Pluvial<br>& 300,000<br>=<br>&£ 250,000<br>z<br>B<br>3 200,000<br>5<br>& 150,000<br>°<br>z<br>3100,000 Fluvial<br>& 50,000 Coastal<br>0<br>0 0.1 02 03 040506070809 1 11 1.2 1.3 14 1.5 16<br>Inundation depth (m)<br><!-- End of picture text --> <!-- Start of picture text --> 7<br>6<br>5<br>vo 4<br>8<br>Q 3<br>=]<br>1<br>0<br>1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->
<!-- Start of picture text --> 0.60 ———————————— o—<br>eea A en<br>a — _ ey ee ,<br>O38 ee ee———; ee —_____d<br>0.00<br>1990 1995 2000 2005 2010 2015<br>-—O-WSF evolution -—Q@—GAUD -@—GAIA -@-GISA -@—GHSL<br><!-- End of picture text -->
<!-- Start of picture text -->
1.00<br>0.20 EE<br>0.10<br>1990 1995 2000 2005 2010 2015<br>-~O-WSF evolution -Q—-GAUD -Q-GAIA -Q-—GISA -@Q—GHSL<br><!-- End of picture text -->

<!-- Start of picture text --> _<br>1.00<br>wada re ee<br>1990 1995 2000 2005 2010 2015<br>—©@-WSF evolution —Q@—-GAUD -@-GAIA -@—GISA -—@Q—GHSL<br><!-- End of picture text -->
Globally, this study shows that overall settlements expanded by 85 percent between 1985 and 2015 on average, while those exposed to the highest flood hazard level increased by 122 percent. These differences suggest the influence of a host of underlying causes for the increasing flood exposure of urbanization, including geographic and climatic features, socioeconomic trends, planning practices, and institutions.

These findings carry concrete implications for urban planners and policy makers. In areas, where flood exposure is already high, investments in disaster preparedness and protection are crucial to mitigate losses; for instance, through early warning systems, evacuation protocols, insurance, social protection, and retrofitting of infrastructure. In areas, where flood exposure is still low but increasing rapidly, revision of land use and urbanization plans will be essential, along with reviewing and reforming risk-informed building codes and infrastructure masterplans. While land scarcity and geographic constraints may mean that settling in flood zones cannot always be avoided, this necessitates careful planning of flood protection systems and disaster preparedness measures to enable resilient socio-economic development.

In an age of increasing disaster losses due to flood events, effective flood risk mitigation efforts must be based on a robust understanding of the different contributing factors to flood risk. These can include intensifying hazards (for instance, due to climate change), increasing exposure to hazards (due to expanding at-risk settlements), and rising vulnerability to shocks (for instance, due to inadequate building standards or social protection systems). This study shows with high geographic granularity that rising exposure is key to understanding the global rise in flood disaster losses.

# **References**

> 1 <mark>Otto, F. The art of attribution.</mark> _<mark>Nature Clim Change</mark>_ <mark>6, 342–343 (2016).</mark>

> 2 Schiermeier, Q. Droughts, heatwaves and floods: How to tell when climate change is to blame. _<mark>Nature</mark>_ <mark>560, 20-22 (2018)</mark>

> 3 <mark>Otto, F., van Oldenborgh, G., Eden, J.</mark> _<mark>et al.</mark>_ <mark>The attribution question.</mark> _<mark>Nature Clim Change</mark>_ <mark>6, 813–816 (2016).</mark>

> 4 <mark>Philip, S., Kew, S., van Oldenborgh, G. J., Otto, F., Vautard, R., van der Wiel, K., King, A., Lott, F., Arrighi, J., Singh, R., and van Aalst, M.: A protocol for probabilistic extreme event attribution analyses, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 177–203 (2020)</mark>

> 5 <mark>Raju, E., Boyd, E. & Otto, F. Stop blaming the climate for disasters.</mark> _<mark>Commun Earth Environ</mark>_ <mark>3, 1 (2022).</mark>

> 6 <mark>Lahsen, M., & Ribot, J. (2022). Politics of attributing extreme events and disasters to climate change.</mark> _<mark>Wiley Interdisciplinary Reviews: Climate Change</mark>_ <mark>, 13( 1), e750.</mark>

7 Hallegatte, S., Vogt-Schilb, A., Bangalore, M., Rozenberg, J. Unbreakable : Building the Resilience of the Poor in the Face of Natural Disasters. Washington, DC: World Bank (2017) 8 Henderson, J.V., Cities and development. Journal of Regional Science, 50: 515-540. (2010)

> 9 Rosenthal, S. S. & Strang, W.C. Evidence on the nature and sources of agglomeration economies. _Handbook of Regional and Urban Economics_ , **4** , 2119–71, (Elsevier, 2004).

> 10 Duranton, G. & Puga, D. Micro-foundations of urban agglomeration economies. _Handbook of Regional and Urban Economics_ **4** , 2063–2117 (2004).

11 DLR(2021) https://geoservice.dlr.de/web/maps/eoc:wsfevolution

> 12 Hallegatte, S. _An Exploration of the Link between Development, Economic Growth, and Natural Risk_ (World Bank, Washington DC, 2012).

> 13 Hallegatte, S. _How Economic Growth and Rational Decisions Can Make Disaster Losses Grow Faster Than Wealth_ (World Bank, Washington DC, 2011).

> 14 Kuhnreuter, H. Mitigating disaster losses through insurance. _J. Risk Uncertain._ **12** , 171–181 (1996).

20
> 15 Erwann, M-K. & Kunreuther, H. Redesigning flood insurance. _Science_ **333** , 6041 (2011).

> 16 Kunreuther, H. Disaster Mitigation and Insurance: Learning from Katrina. _Ann. Am. Acad. Pol. Soc. Sci._ **604** , 1 (2006).

> 17 World Bank. _Planning, Connecting, and Financing Cities Now: Priorities for City Leaders_ (World Bank, Washington DC, 2013).

> 18 Hallegatte, S. et al. _Shock Waves: Managing the Impacts of Climate Change on Poverty_ (World Bank, Washington DC, 2016).

> 19 Bangalore, M., Smith, A. & Veldkamp, T. Exposure to floods, climate change, and poverty in Vietnam. _Economics of Disasters and Climate Change_ **3** , 7–99 (2019).

> 20 Lin, Yatang, McDermott, T.K.J. & Michaels, G. _Cities and the Sea Level_ (CESifo Working Papers Series 8997, 2021).

> 21 Hallegatte, S. _An Exploration of the Link between Development, Economic Growth, and Natural Risk_ (World Bank, Washington DC, 2012).

> 22 Rentschler, J. et al. _Resilient Shores: Vietnam’s Coastal Development Between Opportunity and Disaster Risk_ (World Bank, Washington DC, 2020).

> 23 Corbane, C., Florczyk, A., Pesaresi, M., Politis, P. & Syrris, V. _GHS Built-up Grid, Derived from Landsat, Multitemporal_ (1975-1990-2000-2014), R2018A. (European Commission, Joint Research Centre, 2018).

> 24 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020). 25 DLR(2021) https://geoservice.dlr.de/web/maps/eoc:wsfevolution

> 26 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020).

> 27 <mark>Peduzzi, P., Dao, H., Herold, C. & Mouton, F. Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index.</mark> _<mark>Nat. Hazards Earth Syst. Sci.</mark>_ **<mark>9</mark>** <mark>, 1149–1159 (2009).</mark>

> 28 de Bruijn, J. A. et al. A global database of historic and real-time flood events based on social media. _Sci. Data_ **6,** 311 (2019).

> 29 Tellman, B. et al. Satellite imaging reveals increased proportion of population exposed to floods. _Nature_ **596** , 80– 86 (2021).

> 30 <mark>Arnell, N. W. & Gosling, S. N. The impacts of climate change on river flood risk at the global scale.</mark> _<mark>Clim. Chang.</mark>_ **<mark>134</mark>** <mark>(3), 387–401 (2016).</mark>

> 31 <mark>Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. & Ward, P. J. A global reanalysis of storm surges and extreme sea levels.</mark> _<mark>Nat. Commun.</mark>_ **<mark>7</mark>** <mark>, 1–12 (2016).</mark>

> 32 <mark>Alfieri, L. et al. Global projections of river flood risk in a warmer world.</mark> _<mark>Earth's Future</mark>_ **<mark>5</mark>** <mark>, 171–182 (2017).</mark>

> 33 <mark>Dottori, F. et al. Increased human and economic losses from river flooding with anthropogenic warming.</mark> _<mark>Nat. Clim. Chang.</mark>_ **<mark>8,</mark>** <mark>781–786 (2018).</mark>

> 34 <mark>Neumann, B., Vafeidis, A. T., Zimmermann, J. & Nicholls, R. J. Future coastal population growth and exposure to sea-level rise and coastal flooding-a global assessment.</mark> _<mark>PloS one</mark>_ <mark>,</mark> **<mark>10</mark>** <mark>, e0118571 (2015).</mark>

> 35 <mark>Smith, A. et al. New estimates of flood exposure in developing countries using high-resolution population data.</mark> _<mark>Nat. Commun.</mark>_ **<mark>10</mark>** <mark>1–7 (2019).</mark>

> 36 Kocornik-Mina, A., McDermott, T. K. J., Michaels, G., and Rauch, F. Flooded Cities. _Am. Econ. J. Appl. Econ._ **12** , 2, 35–66 (2020).

> 37 <mark>Jongman, B., Ward, P. & Aerts, J. Global exposure to river and coastal flooding: long term trends and changes.</mark> _<mark>Glob. Environ. Chang.</mark>_ **<mark>22</mark>** <mark>, 823–835 (2012).</mark>

38 Tiggeloven, T. et al. Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. _Nat. Hazards Earth Syst. Sci._ **20** , 1025–1044 (2020).

39 Rentschler, J., Salhab, M., Jafino, B. A., Flood exposure and poverty in 188 countries. Preprint at https://www.researchsquare.com/article/rs-965657/v1

> 40 Sampson, C. C. et al. A high-resolution global flood hazard model. _Water Resour. Res._ **51** , 7358–7381 (2015).

> 41 Smith, A., Sampson, C. & Bates, P. Regional flood frequency analysis at the global scale. _Water Resour. Res._ **51** , 539–553 (2015).

> 42 Yamazaki D. et al. A high-accuracy map of global terrain elevations. _Geophys. Res. Lett_ . **44** , 5844–5853 (2017).

21
> 43 Koks, E.E. et al. A global multi-hazard risk analysis of road and railway infrastructure assets. _Nat. Commun._ **10** , 2677 (2019).

> 44 Tolman, H. L. _User Manual and System Documentation of WAVEWATCH III_ TM version 3.14. Technical note, MMAB Contribution 276, 220 (2009).

45 Vousdoukas, M.I. et al. Global probabilistic projections of extreme sea levels show intensification of coastal flood hazard. _Nat. Commun._ **9** , 2360 (2018).

46 Vousdoukas, M. I. et al. Developments in large-scale coastal flood hazard mapping, _Nat. Hazards Earth Syst. Sci_ . **16** , 1841–1853 (2016).

> 47 Scussolini et al. FLOPROS: an evolving global database of flood protection standards. _Nat. Hazards Earth Syst. Sci._ , 16, 1049–1061 (2016).

48 Marconcini, M., Metz-Marconcini, A., Esch, T. & Gorelick, N. Understanding current trends in global urbanisation – the World Settlement Footprint suite _. GI_Forum,_ **9** (1), 33–38 (2021).

49 The WSF-Evo dataset and its technical documentation are available from DLR at <u>https://www.dlr.de/content/de/artikel/news/2021/04/20211111_blick_aus_dem_weltraum_so_entwickeln_sich_ staedte.html</u> 50 Marconcini, M., Metz-Marconcini, A., Esch, T. & Gorelick, N. Understanding current trends in global urbanisation – the World Settlement Footprint suite _. GI_Forum,_ **9** (1), 33–38 (2021).

> 51 Liu, X., Huang, Y., Xu, X. _et al._ High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. _Nat Sustain_ **3,** 564–570 (2020).

52 Fang, J., Zhang, C., Fang, J., Liu, M. & Luan, Y. Increasing exposure to floods in China revealed by nighttime light data and flood susceptibility mapping. _Environ. Res. Lett._ **16** , 104044 (2021).

> 53 Ying, X., Bing, Z., Bo-Tao, Z., Si-Yan, D., Li, Y, and Rou-Ke, L. Projected flood risks in China based on CMIP5. _Adv. Clim. Chang. Res._ **5** (2), 57–65 (2014).

> 54 D'Errico, J. _SLM - Shape Language Modeling_ (MATLAB Central File Exchange, 2021).

https://www.mathworks.com/matlabcentral/fileexchange/24443-slm-shape-language-modeling 55 Dilling, L., Prakash, A., Zommers, Z., Ahmad, F., Singh, N., de Wit, S., Nalau, J., Daly, M., Bowman, K. Is adaptation success a flawed concept?. Nat. Clim. Chang. **9,** 572–574 (2019).

Economy-level summary statistics 22
This table provides an economy-level breakdown of results. Results show total built-up areas, i.e. settlement extent (SE). Safe SE refers to areas that are not estimated to experience any flooding during a 1-in-100 year flood event. High risk (or very high) SE denotes areas that are estimated to experience inundation depths over 0.5 meters (or 1.5 meters) during a 1-in-100 year flood event.

|||**Total SE**|||**Safe SE**|||**High risk SE**||**V**|**ery high risk**|**SE**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**1985**<br>_(km_<sup>_2_</sup>_)_|**2015**<br>_(km_<sup>_2_</sup>_)_|**Change**<br>_(%)_|**1985**<br>_(km_<sup>_2_</sup>_)_|**2015**<br>_(km_<sup>_2_</sup>_)_|**Change**<br>_(km_<sup>_2_</sup>_)_|**1985**<br>_(km_<sup>_2_</sup>_)_|**2015**<br>_(km_<sup>_2_</sup>_)_|**Change**<br>_(%)_|**1985**<br>_(km_<sup>_2_</sup>_)_|**2015**<br>_(km_<sup>_2_</sup>_)_|**Change**<br>_(%)_|
|**World**|**692,629.5**|**1,284,036.0**|**85.4**|**445,001.8**|**799,493.3**|**79.7**|**68,188.8**|**144,631.7**|**112.1**|**29,999.2**|**66,477.0**|**121.6**|
|**China**|100,806.6|270,440.2|168.3|61,190.2|148,952.5|143.4|15,708.7|50,693.3|222.7|8,142.7|26,561.6|226.2|
|**United States**|90,361.7|155,721.3|72.3|36,571.5|64,527.7|76.4|4,412.8|6,847.3|55.2|2,026.4|2,963.4|46.2|
|**India**|43,901.1|80,726.0|83.9|28,195.3|51,973.4|84.3|4,744.7|8,183.0|72.5|1,401.5|2,330.3|66.3|
|**Russia**|29,108.3|53,714.5|84.5|23,687.9|42,954.0|81.3|1,267.4|2,714.2|114.2|445.7|970.0|117.7|
|**Japan**|29,532.2|41,020.1|38.9|17,114.1|23,848.0|39.3|6,266.9|8,551.0|36.4|3,661.1|4,938.6|34.9|
|**Indonesia**|22,117.9|37,149.1|68.0|12,777.0|21,536.3|68.6|2,495.1|4,466.8|79.0|719.0|1,375.9|91.4|
|**Brazil**|19,086.4|32,750.4|71.6|15,865.2|26,979.9|70.1|1,170.7|2,125.2|81.5|513.1|913.9|78.1|
|**France**|19,859.8|31,061.5|56.4|14,758.9|23,347.5|58.2|2,116.5|3,069.3|45.0|1,068.7|1,540.7|44.2|
|**Germany**|19,694.6|29,890.3|51.8|14,199.7|21,578.2|52.0|1,883.4|2,925.6|55.3|751.8|1,225.6|63.0|
|**Thailand**|12,256.3|24,488.4|99.8|7,184.6|13,215.8|83.9|1,711.4|4,205.5|145.7|552.6|1,310.0|137.1|
|**Italy**|15,486.0|22,907.1|47.9|9,995.7|14,381.4|43.9|1,374.4|2,225.8|61.9|506.9|839.6|65.6|
|**Ukraine**|13,014.2|22,695.5|74.4|10,935.3|18,749.2|71.5|516.4|1,046.0|102.6|181.9|385.7|112.0|
|**Mexico**|12,001.6|20,591.8|71.6|7,272.7|12,332.8|69.6|758.2|1,417.8|87.0|204.3|380.5|86.3|
|**Nigeria**|9,401.2|19,321.1|105.5|7,235.8|14,772.0|104.2|499.7|1,020.3|104.2|134.0|278.4|107.7|
|**Vietnam**|5,251.4|16,617.8|216.4|2,241.2|6,239.1|178.4|1,895.4|7,077.9|273.4|1,332.5|5,189.9|289.5|
|**Poland**|8,154.2|15,924.4|95.3|6,462.5|12,410.4|92.0|385.4|853.1|121.3|79.0|185.6|134.9|
|**UK**|10,522.4|15,222.0|44.7|8,648.3|12,412.5|43.5|720.2|1,081.2|50.1|275.6|421.9|53.1|
|**Canada**|9,223.9|15,132.8|64.1|5,369.5|8,948.2|66.7|490.7|791.2|61.2|186.0|294.5|58.4|
|**Australia**|9,227.6|14,132.4|53.2|7,615.8|11,500.4|51.0|396.9|664.8|67.5|133.8|221.2|65.3|
|**Spain**|8,396.7|13,960.7|66.3|6,540.3|10,705.7|63.7|494.6|876.9|77.3|189.5|334.0|76.3|
|**Turkey**|6,884.9|13,408.1|94.7|4,569.0|8,702.1|90.5|474.3|1,021.2|115.3|142.4|294.0|106.5|
|**Iran**|7,258.2|12,657.9|74.4|4,187.1|7,265.7|73.5|405.9|773.5|90.6|147.8|276.2|86.9|
|**South Africa**|7,619.6|12,279.3|61.2|6,428.2|10,297.8|60.2|135.4|246.0|81.7|35.7|74.5|108.8|
|**Pakistan**|5,936.9|10,567.3|78.0|3,245.8|5,728.8|76.5|894.6|1,548.1|73.1|131.2|247.8|88.9|
|**Bangladesh**|5,362.0|10,217.4|90.6|2,051.0|3,878.1|89.1|1,994.3|3,985.1|99.8|1,173.4|2,392.2|103.9|
|**Argentina**|6,209.5|9,441.9|52.1|3,879.3|5,749.4|48.2|234.9|429.9|83.0|37.6|72.3|92.1|
|**Uzbekistan**|5,719.1|9,292.7|62.5|3,671.8|5,860.2|59.6|249.0|403.6|62.1|44.7|73.3|63.9|
|**Korea, Rep.**|4,112.0|8,777.8|113.5|2,685.1|5,754.3|114.3|598.6|1,311.7|119.1|285.1|623.0|118.5|
|**Romania**|5,147.9|8,711.8|69.2|3,367.2|5,650.5|67.8|522.4|900.7|72.4|129.9|226.0|74.0|
|**Malaysia**|4,340.4|8,286.8|90.9|2,684.4|5,147.5|91.8|644.1|1,247.9|93.7|257.8|497.4|93.0|
|**Philippines**|4,355.9|6,717.7|54.2|2,333.4|3,590.7|53.9|510.6|818.6|60.3|124.5|200.4|61.0|
|**Saudi Arabia**|3,536.0|6,708.7|89.7|2,916.9|5,449.2|86.8|114.9|231.8|101.8|26.4|53.8|103.5|
|**Egypt**|4,198.1|6,278.3|49.6|2,459.7|3,677.0|49.5|1,196.8|1,798.9|50.3|452.2|673.1|48.8|

23
|**Kazakhstan**|3,293.4|5,584.6|69.6|2,237.5|3,694.9|65.1|163.3|323.5|98.1|23.2|52.4|125.8|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Portugal**|3,171.4|5,325.1|67.9|2,780.8|4,633.9|66.6|96.1|175.5|82.7|38.0|70.3|85.1|
|**Myanmar**|3,413.2|5,280.3|54.7|2,112.2|3,208.2|51.9|486.8|826.1|69.7|217.4|369.9|70.2|
|**Hungary**|3,826.3|5,244.9|37.1|2,791.5|3,771.9|35.1|217.8|336.8|54.7|43.5|73.4|68.7|
|**Tanzania**|2,248.6|4,779.2|112.5|1,799.5|3,738.0|107.7|57.2|127.2|122.4|11.8|25.4|114.8|
|**Czech Rep.**|3,105.9|4,764.6|53.4|2,240.6|3,445.7|53.8|289.3|451.5|56.1|97.3|152.8|57.0|
|**Algeria**|2,734.3|4,711.7|72.3|2,118.1|3,682.1|73.8|104.7|168.5|61.0|24.5|40.1|63.8|
|**Netherlands**|2,845.8|4,706.8|65.4|1,196.9|1,767.1|47.6|1,226.5|2,243.1|82.9|1,002.7|1,869.6|86.5|
|**Belarus**|2,634.8|4,678.2|77.6|2,358.9|4,123.0|74.8|44.6|99.2|122.3|9.3|21.2|128.7|
|**Ghana**|2,462.2|4,540.4|84.4|2,064.5|3,781.3|83.2|101.7|193.7|90.5|30.6|61.7|102.0|
|**Taiwan,**<br>**China**|2,934.5|4,463.6|52.1|1,619.5|2,296.8|41.8|408.5|670.8|64.2|163.6|276.6|69.0|
|**Belgium**|2,676.1|4,438.3|65.8|1,927.4|3,191.1|65.6|306.1|506.4|65.4|169.5|286.2|68.9|
|**Venezuela**|2,883.3|4,353.3|51.0|1,830.3|2,696.8|47.3|196.4|322.3|64.1|59.8|96.9|62.1|
|**Iraq**|2,604.1|4,329.0|66.2|1,406.9|2,311.0|64.3|570.5|899.1|57.6|150.3|251.7|67.4|
|**Greece**|2,803.7|4,289.9|53.0|1,852.0|2,743.2|48.1|156.0|264.2|69.4|27.6|48.9|77.3|
|**Congo, Dem.**<br>**Rep.**|2,664.1|4,135.3|55.2|2,307.4|3,523.8|52.7|108.6|196.3|80.7|62.8|115.1|83.3|
|**Zambia**|1,619.5|3,816.3|135.6|1,303.9|2,947.2|126.0|21.9|72.4|230.9|6.2|22.6|266.5|
|**Sweden**|2,487.1|3,815.9|53.4|2,065.9|3,179.2|53.9|129.2|189.3|46.6|46.7|67.9|45.4|
|**Syria**|2,360.7|3,551.0|50.4|1,739.8|2,566.1|47.5|88.1|148.6|68.7|18.3|34.3|87.7|
|**Austria**|1,976.5|3,541.9|79.2|1,134.6|2,077.9|83.1|382.0|666.2|74.4|186.8|332.0|77.7|
|**Bulgaria**|2,305.5|3,199.5|38.8|1,520.4|2,113.1|39.0|187.4|268.6|43.3|50.6|76.6|51.3|
|**Colombia**|2,019.3|3,061.1|51.6|1,228.8|1,800.9|46.6|263.7|432.5|64.0|117.7|197.1|67.4|
|**Mozambique**|1,079.7|3,049.5|182.4|932.1|2,582.9|177.1|38.8|97.2|150.3|11.1|27.4|147.0|
|**Denmark**|1,912.5|3,028.1|58.3|1,621.6|2,532.2|56.2|77.5|142.6|84.0|28.2|55.0|95.0|
|**Azerbaijan**|1,590.9|3,009.5|89.2|973.8|1,791.4|84.0|93.8|159.8|70.5|15.8|26.2|66.3|
|**Côte d'Ivoire**|1,619.4|2,894.9|78.8|1,416.6|2,526.9|78.4|61.9|112.7|82.0|27.9|49.1|76.1|
|**Serbia**|1,747.1|2,786.8|59.5|1,179.3|1,852.4|57.1|159.7|281.9|76.5|48.0|89.6|86.8|
|**Mali**|972.6|2,754.6|183.2|492.7|1,445.3|193.3|129.0|313.3|142.9|43.1|99.1|129.8|
|**Morocco**|1,605.5|2,739.1|70.6|1,161.2|1,951.7|68.1|62.8|108.0|72.0|15.5|27.0|74.4|
|**Finland**|1,459.7|2,664.7|82.6|1,245.1|2,264.7|81.9|64.2|112.4|75.1|21.3|36.2|70.3|
|**Chile**|1,482.3|2,616.0|76.5|861.4|1,549.4|79.9|79.1|144.8|83.1|20.1|38.7|92.3|
|**Tunisia**|1,442.6|2,555.2|77.1|1,105.9|1,950.7|76.4|47.4|79.7|68.0|9.6|15.9|65.9|
|**Slovak Rep.**|1,733.5|2,443.3|40.9|944.4|1,328.9|40.7|307.0|445.1|45.0|87.2|131.6|51.0|
|**Kenya**|1,499.0|2,438.7|62.7|1,266.2|2,012.7|59.0|41.6|76.8|84.5|12.5|22.2|77.7|
|**Sudan**|1,561.9|2,396.3|53.4|849.8|1,385.5|63.0|331.7|425.5|28.3|150.8|191.0|26.7|
|**Malawi**|1,366.6|2,376.4|73.9|1,155.6|1,968.6|70.3|25.0|50.4|101.4|5.6|12.2|116.9|
|**Uganda**|1,094.6|2,355.5|115.2|1,023.6|2,168.0|111.8|15.7|39.3|150.9|4.6|11.5|149.6|
|**Switzerland**|1,439.0|2,354.9|63.6|905.1|1,484.4|64.0|237.3|402.5|69.6|145.6|254.4|74.7|
|**Cameroon**|1,651.3|2,354.8|42.6|1,372.2|1,916.3|39.7|79.3|126.3|59.3|27.7|44.5|60.7|
|**Peru**|1,508.6|2,184.2|44.8|1,168.7|1,624.9|39.0|90.4|158.1|74.8|47.2|84.0|78.2|

24
|**Sri Lanka**|1,244.7|2,169.4|74.3|910.6|1,586.8|74.3|120.2|218.9|82.1|54.9|103.5|88.7|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Ethiopia**|963.3|2,038.5|111.6|784.0|1,666.7|112.6|32.4|69.0|113.1|9.5|21.0|121.2|
|**Croatia**|1,230.7|2,033.6|65.2|802.9|1,310.2|63.2|194.7|336.5|72.8|94.9|172.5|81.7|
|**Cambodia**|544.1|1,927.2|254.2|303.6|1,124.7|270.5|84.4|286.6|239.5|32.9|115.5|250.9|
|**Tajikistan**|1,160.3|1,917.2|65.2|591.8|981.0|65.8|81.2|136.3|67.7|25.3|44.6|76.6|
|**New Zealand**|1,463.9|1,852.6|26.6|1,006.2|1,266.3|25.8|110.6|141.1|27.5|32.4|41.6|28.6|
|**Libya**|1,142.9|1,843.1|61.3|897.1|1,437.2|60.2|51.0|84.9|66.4|12.3|22.5|82.7|
|**Moldova**|1,327.8|1,830.0|37.8|1,126.4|1,546.3|37.3|56.8|85.8|51.1|17.3|27.5|58.8|
|**Burkina Faso**|607.3|1,804.5|197.1|406.0|1,183.3|191.4|14.4|43.1|198.7|0.8|3.1|287.9|
|**Benin**|779.8|1,798.8|130.7|605.1|1,399.9|131.3|17.8|42.7|140.0|3.8|9.3|144.7|
|**UAE**|694.8|1,765.6|154.1|559.9|1,405.6|151.0|31.4|121.4|286.6|6.7|34.4|412.7|
|**Ecuador**|1,040.1|1,672.5|60.8|679.8|1,028.5|51.3|113.0|229.8|103.4|39.6|80.2|102.5|
|**Ireland**|770.6|1,663.1|115.8|610.7|1,311.0|114.7|44.9|98.8|120.2|16.2|34.2|111.5|
|**Angola**|1,072.3|1,649.7|53.8|936.9|1,415.8|51.1|21.1|43.6|106.3|3.5|9.3|165.9|
|**Madagascar**|936.7|1,630.4|74.1|732.2|1,279.9|74.8|84.3|140.9|67.2|41.7|67.9|63.0|
|**Cuba**|1,152.4|1,574.9|36.7|845.9|1,135.4|34.2|42.8|64.7|51.1|14.6|21.3|45.8|
|**Lithuania**|922.3|1,571.6|70.4|797.4|1,349.3|69.2|26.3|46.4|76.2|8.2|13.8|69.0|
|**Guinea**|752.9|1,569.8|108.5|642.2|1,299.4|102.3|31.1|76.1|144.5|11.4|28.4|150.5|
|**Kyrgyz Rep.**|826.8|1,561.4|88.9|403.0|760.6|88.7|41.9|84.0|100.6|11.7|25.9|120.8|
|**Turkmenistan**|1,063.6|1,560.5|46.7|623.0|905.8|45.4|147.9|221.8|50.0|20.7|32.7|57.9|
|**Israel**|1,029.5|1,536.7|49.3|908.1|1,316.1|44.9|17.1|32.8|91.3|2.4|5.1|109.8|
|**Norway**|807.7|1,520.7|88.3|655.3|1,261.5|92.5|67.4|109.2|62.0|43.4|68.3|57.3|
|**Botswana**|697.2|1,488.4|113.5|510.8|1,069.4|109.4|22.2|46.8|110.9|2.7|7.5|175.9|
|**Georgia**|948.2|1,415.2|49.3|560.5|830.4|48.2|80.9|121.5|50.2|30.0|43.3|44.3|
|**Guatemala**|980.4|1,394.2|42.2|750.8|1,025.0|36.5|53.7|86.5|61.0|22.2|34.3|54.4|
|**North Korea**|765.5|1,302.3|70.1|467.4|803.2|71.8|144.4|255.3|76.7|66.6|124.3|86.6|
|**Afghanistan**|723.5|1,228.0|69.7|401.7|669.7|66.7|44.1|78.6|78.1|10.0|21.0|109.3|
|**Bolivia**|671.9|1,217.0|81.1|395.3|682.1|72.5|38.0|73.6|93.6|8.7|19.3|121.7|
|**Senegal**|621.4|1,205.7|94.0|447.7|852.2|90.3|37.9|70.7|86.3|13.8|21.1|52.7|
|**Puerto Rico**<br>**(U.S.) **|850.1|1,174.8|38.2|640.3|892.5|39.4|44.6|60.1|34.8|11.3|16.1|41.9|
|**Lebanon**|793.6|1,137.6|43.4|681.1|958.0|40.6|20.2|32.1|59.5|5.8|9.4|61.9|
|**Nepal**|512.9|1,084.9|111.5|286.6|586.9|104.8|45.9|106.2|131.1|8.8|25.2|186.1|
|**Dominican Rep.**|548.4|1,080.8|97.1|368.3|723.3|96.4|25.6|53.3|108.5|5.6|12.2|118.7|
|**Laos**|380.1|1,041.8|174.1|170.7|501.7|193.8|161.7|389.2|140.7|129.1|299.4|131.9|
|**Zimbabwe**|666.4|1,033.9|55.1|580.8|882.1|51.9|12.6|22.9|82.0|4.8|8.1|69.0|
|**Jordan**|789.8|1,001.1|26.8|711.3|891.9|25.4|11.7|15.0|27.4|3.2|3.8|17.0|
|**Bosnia and**<br>**Herzegovina**|426.6|979.2|129.6|242.4|560.8|131.3|85.3|191.1|124.0|39.0|85.8|120.2|
|**Oman**|763.1|947.3|24.1|590.0|745.2|26.3|17.7|21.3|20.4|2.2|2.6|18.5|
|**Paraguay**|471.3|941.9|99.8|409.9|808.7|97.3|8.1|22.9|181.8|2.8|9.1|228.4|
|**Yemen**|532.9|852.8|60.0|406.6|658.7|62.0|12.6|21.1|67.7|1.6|2.9|81.1|

25
|**Armenia**|574.6|827.7|44.0|355.3|503.8|41.8|40.3|60.4|49.8|12.7|18.7|47.3|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Slovenia**|468.1|756.7|61.7|299.7|481.3|60.6|71.6|120.8|68.7|33.2|55.4|66.8|
|**Honduras**|429.7|747.5|74.0|254.7|429.8|68.7|34.2|62.6|83.0|8.9|14.9|66.9|
|**Costa Rica**|443.5|743.4|67.6|301.6|493.5|63.6|29.9|53.4|78.6|9.1|16.3|78.6|
|**Latvia**|479.0|735.5|53.5|373.1|567.9|52.2|49.6|77.5|56.4|22.2|36.8|65.5|
|**Uruguay**|479.1|723.0|50.9|420.0|627.0|49.3|7.8|14.7|87.6|2.3|4.2|83.1|
|**Albania**|338.2|707.2|109.1|182.0|367.0|101.7|60.6|125.6|107.2|23.1|46.5|101.6|
|**West Bank**<br>**and Gaza**|590.7|707.0|19.7|548.5|652.0|18.9|6.5|8.5|31.4|1.7|2.1|23.6|
|**Togo**|417.5|685.3|64.1|347.9|568.7|63.4|9.5|17.2|81.0|1.6|3.4|116.8|
|**Sierra Leone**|331.6|678.0|104.4|269.7|549.1|103.6|24.9|49.8|100.0|12.2|24.2|97.7|
|**Cyprus**|369.4|613.5|66.1|267.6|441.8|65.1|10.3|19.1|84.9|1.9|4.0|107.8|
|**Jamaica**|373.8|597.9|60.0|263.4|433.9|64.8|17.4|27.5|57.9|5.1|8.6|67.9|
|**Rwanda**|326.5|585.5|79.3|282.6|517.5|83.1|9.7|15.3|57.0|3.2|5.2|62.4|
|**Panama**|328.7|569.1|73.1|263.5|446.3|69.4|16.0|32.9|105.7|4.7|10.4|119.4|
|**Kuwait**|452.0|560.1|23.9|378.7|444.9|17.5|19.7|33.8|72.0|5.6|8.2|46.2|
|**El Salvador**|335.9|555.3|65.3|220.1|358.6|62.9|20.9|35.3|69.1|6.6|10.7|62.4|
|**Liberia**|295.4|552.2|86.9|194.7|384.8|97.6|34.0|59.9|76.1|8.2|16.9|107.0|
|**Central**<br>**African Rep.**|324.1|529.1|63.3|262.6|421.8|60.6|25.3|40.6|60.8|18.1|27.7|52.7|
|**Chad**|329.0|526.0|59.8|137.0|225.7|64.8|85.8|135.9|58.3|18.2|27.8|52.8|
|**Estonia**|336.0|523.5|55.8|297.3|455.0|53.0|5.2|10.6|104.9|0.8|2.0|152.7|
|**Haiti**|291.0|517.1|77.7|145.6|258.9|77.8|22.4|38.4|71.3|2.8|5.3|89.3|
|**North**<br>**Macedonia**|349.7|517.1|47.9|175.4|250.1|42.6|45.3|71.8|58.5|12.9|22.7|76.0|
|**Niger**|244.4|495.4|102.7|156.0|332.5|113.2|32.5|57.6|77.3|11.2|19.0|70.8|
|**Nicaragua**|292.8|475.5|62.4|194.5|309.0|58.9|12.6|23.3|84.7|3.7|6.9|85.2|
|**Congo, Rep.**|373.8|473.4|26.6|309.3|382.6|23.7|19.0|28.8|51.2|9.1|14.6|59.8|
|**Kosovo**|196.0|455.7|132.6|121.0|277.4|129.1|17.5|39.3|124.5|5.3|11.3|112.9|
|**Trinidad and**<br>**Tobago**|366.5|435.5|18.8|223.1|264.7|18.7|21.8|27.6|26.5|2.8|3.5|22.0|
|**Qatar**|169.6|434.9|156.4|169.3|430.1|154.0|0.1|3.2|2209.5|0.0|1.2|15616.6|
|**Gabon**|294.7|387.3|31.4|226.8|291.2|28.4|20.9|30.3|45.1|11.8|16.4|39.3|
|**Burundi**|191.3|334.5|74.9|143.7|256.3|78.4|9.8|16.3|66.9|2.8|4.8|72.3|
|**Singapore**|279.1|329.6|18.1|197.6|236.3|19.6|13.7|16.2|17.6|1.9|2.5|34.7|
|**Somalia**|150.4|318.4|111.6|99.3|201.2|102.7|17.5|33.5|91.7|3.7|6.7|82.3|
|**Gambia, The**|152.7|300.5|96.9|115.6|223.9|93.7|3.4|6.5|94.4|1.2|1.6|37.7|
|**Namibia**|160.9|283.5|76.2|115.0|196.0|70.5|8.6|19.6|128.9|1.5|4.4|194.1|
|**Mongolia**|127.7|265.7|108.1|82.0|176.2|114.8|3.8|10.1|166.5|0.1|0.3|113.4|
|**Brunei**<br>**Darussalam**|159.1|253.4|59.3|106.8|166.8|56.1|13.3|26.9|101.7|2.7|5.6|110.7|
|**Mauritania**|49.3|245.0|397.0|33.0|186.5|464.4|6.0|16.5|176.0|2.5|4.9|92.5|
|**Suriname**|145.8|228.5|56.8|57.9|90.6|56.5|20.2|34.5|70.7|2.5|4.3|76.9|
|**Guadalupe**|152.7|227.5|48.9|128.6|192.1|49.4|5.6|8.0|41.7|1.4|2.1|52.6|

26
|**Guyana**|197.1|223.1|13.2|89.4|101.6|13.6|27.0|31.1|15.0|4.7|6.0|27.5|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Réunion (Fr.)**|60.4|208.0|244.2|60.4|208.0|244.2|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Luxembourg**|120.8|206.6|71.0|92.0|161.5|75.6|12.8|19.7|53.1|6.7|10.2|51.3|
|**Guinea-**<br>**Bissau**|104.4|206.0|97.4|98.5|191.3|94.2|0.3|1.2|258.8|0.1|0.3|409.0|
|**South Sudan**|72.2|203.9|182.3|50.3|137.5|173.3|6.5|24.4|274.1|2.1|9.2|333.9|
|**Martinique**<br>**(Fr.)**|137.8|199.3|44.6|122.0|176.9|45.0|5.2|7.4|43.3|1.5|2.2|51.3|
|**Hong Kong**<br>**SAR, China**|105.4|195.9|85.8|76.2|128.9|69.0|14.7|36.0|144.5|8.7|23.4|167.7|
|**Fiji**|91.9|189.3|105.9|62.0|123.1|98.5|18.6|40.1|114.9|11.1|24.1|116.0|
|**Bahrain**|111.5|183.8|64.8|105.2|166.2|58.0|0.3|1.6|521.2|0.0|0.1|21915.8|
|**Equatorial**<br>**Guinea**|78.8|181.1|129.9|59.3|122.3|106.4|6.0|16.4|175.1|2.5|5.8|129.7|
|**Montenegro**|94.6|176.3|86.5|54.1|98.2|81.5|15.4|30.5|98.0|7.2|14.1|96.2|
|**Papua New**<br>**Guinea**|133.1|172.1|29.2|96.9|120.2|24.0|6.8|11.4|66.1|2.4|4.3|75.4|
|**Mauritius**|109.8|158.1|43.9|71.1|99.8|40.4|3.4|5.4|60.2|0.5|1.0|95.0|
|**Lesotho**|82.4|151.9|84.3|71.9|134.9|87.5|2.4|3.7|53.9|1.1|1.6|39.5|
|**Bahamas,**<br>**The**|46.3|118.1|155.3|26.9|71.0|164.0|1.9|7.3|275.6|0.5|2.5|412.6|
|**Barbados**|95.6|114.7|20.0|78.4|93.5|19.3|1.0|1.3|31.2|0.2|0.2|24.5|
|**Eswatini**|48.4|105.6|118.3|45.2|97.5|115.9|1.2|2.5|105.6|0.6|1.2|84.2|
|**Malta**|87.4|101.9|16.6|81.1|94.0|16.0|1.2|1.5|19.5|0.2|0.3|25.8|
|**Eritrea**|45.6|99.9|119.0|39.1|84.5|116.5|0.5|1.5|187.4|0.0|0.3|566.3|
|**Iceland**<br>|52.5|99.6|89.6|41.8|76.1|81.9|5.4|10.5|93.4|3.5|6.4|84.9|
|**New**<br>**Caledonia**<br>**(Fr.)**|71.5|89.6|25.3|56.8|70.8|24.6|4.8|5.9|22.8|1.8|2.1|16.9|
|**Guam (U.S.)**|83.4|89.4|7.3|64.7|69.8|8.0|5.1|5.3|4.2|2.7|2.8|3.4|
|**Azores**<br>**Islands(Por.)**|74.0|85.1|14.9|74.0|85.1|14.9|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Belize**|37.9|82.9|119.1|28.6|62.3|117.8|1.7|3.8|126.6|0.2|0.6|179.5|
|**Madeira**<br>**Islands(Por.)**|56.5|78.7|39.2|50.9|70.6|38.7|1.0|1.5|53.8|0.2|0.4|78.8|
|**Timor-Leste**|46.1|69.8|51.4|20.0|30.7|53.5|6.7|9.3|38.8|1.2|1.7|39.6|
|**Comoros**|61.4|68.3|11.3|27.6|31.8|15.0|1.5|1.6|10.9|0.2|0.2|9.6|
|**French**<br>**Guiana(Fr.)**|54.3|68.2|25.5|40.1|48.5|21.0|4.5|7.4|64.0|1.7|3.5|108.1|
|**Virgin Islands**<br>**(U.S.)**|52.1|65.9|26.5|42.5|54.4|27.9|0.5|0.6|27.1|0.1|0.1|25.1|
|**Curaçao**<br>**(Neth.)**|17.0|55.9|228.8|6.3|21.6|241.8|0.0|0.1|375.3|0.0|0.0|n.a.|
|**Saint Lucia**|29.2|52.8|81.3|25.2|45.4|80.4|1.1|2.3|108.4|0.3|0.7|175.3|
|**Aruba (Neth.)**|40.0|49.4|23.4|32.1|39.7|23.7|0.5|0.7|22.2|0.1|0.1|59.6|
|**Cabo Verde**|27.8|44.7|60.6|24.2|39.2|62.2|0.6|0.9|33.5|0.1|0.1|29.7|
|**Djibouti**|8.5|41.0|382.5|7.6|36.2|375.3|0.1|0.7|627.6|0.0|0.1|472.7|
|**Cayman**<br>**Islands(U.K.)**|20.2|37.8|87.0|12.1|24.0|98.8|2.5|4.0|58.5|0.7|0.9|33.7|

27
|**Saint Vincent**<br>**& Grenadines**|27.1|32.3|19.3|19.2|22.9|19.4|1.9|2.3|19.9|0.6|0.8|20.6|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Jersey (U.K.)**|18.7|30.4|62.0|15.6|25.8|65.7|1.6|2.0|30.8|1.0|1.4|37.3|
|**Solomon**<br>**Islands**|25.4|28.3|11.5|19.2|20.9|8.7|2.3|2.7|18.2|1.2|1.4|18.1|
|**Grenada**|19.8|26.5|33.9|16.9|22.5|33.0|0.7|1.0|38.5|0.2|0.3|41.2|
|**Maldives**|25.0|25.7|2.8|23.5|24.1|2.5|0.1|0.1|0.0|0.0|0.0|0.0|
|**Antigua and**<br>**Barbuda**|19.6|25.0|27.4|16.2|20.4|25.7|0.7|1.1|42.9|0.2|0.4|53.4|
|**Vanuatu**|19.4|24.8|27.7|15.9|20.1|26.4|1.0|1.3|26.3|0.4|0.5|21.1|
|**Isle of Man**<br>**(U.K.)**|13.3|24.1|80.8|11.2|19.7|76.5|1.1|1.8|60.5|0.7|1.0|52.3|
|**Guernsey**<br>**(U.K.)**|18.0|24.1|33.5|13.0|17.7|36.1|3.2|3.9|22.5|2.5|3.1|21.3|
|**Dominica**|12.6|19.5|55.6|9.7|15.2|56.6|0.4|0.9|104.0|0.1|0.3|109.8|
|**Saint Kitts**<br>**and Nevis**|13.9|18.7|35.0|9.2|12.8|38.5|0.1|0.1|24.7|0.0|0.0|n.a.|
|**Bhutan**|5.0|15.1|200.2|2.2|7.6|238.9|1.0|3.0|207.8|0.6|1.9|232.4|
|**Sint Maarten**<br>**(Neth.)**|12.2|13.8|13.3|10.0|11.3|13.1|0.7|0.8|12.6|0.2|0.2|12.9|
|**Liechtenstein**|7.1|13.5|91.1|3.5|6.8|92.2|2.5|4.8|91.2|1.6|3.4|115.6|
|**Saint-Martin**<br>**(Fr.)**|10.2|12.6|23.0|6.8|8.6|25.8|1.1|1.2|13.7|0.1|0.2|17.0|
|**British Virgin**<br>**Islands(U.K.)**|8.6|11.5|34.4|7.4|10.0|34.9|0.0|0.0|143.9|0.0|0.0|n.a.|
|**Mayotte (Fr.)**|10.6|11.3|7.3|10.5|11.3|7.2|0.0|0.1|28.5|0.0|0.0|16.7|
|**San Marino**|8.2|11.2|36.6|7.3|9.9|35.9|0.5|0.7|40.2|0.2|0.2|39.0|
|**Anguilla**<br>**(U.K.)**|8.1|9.6|17.7|7.4|8.6|17.5|0.3|0.3|14.3|0.1|0.1|14.7|
|**Macau, China**|5.8|8.6|48.6|5.7|7.9|39.7|0.0|0.4|1106.3|0.0|0.3|n.a.|
|**Bonaire**<br>**(Neth.)**|2.0|7.2|260.1|1.1|4.6|309.6|0.1|0.2|43.0|0.0|0.0|56.9|
|**Andorra**|1.4|4.6|233.8|0.8|2.1|179.2|0.4|1.7|315.5|0.3|1.3|281.9|
|**Saint**<br>**Barthélemy**<br>**(Fr.)**|3.2|3.7|15.6|3.1|3.5|15.3|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Saint-Pierre-**<br>**et-Miquelon**<br>**(Fr.)**|2.3|3.0|26.7|1.8|2.3|31.8|0.3|0.3|9.7|0.1|0.1|25.4|
|**Gibraltar**<br>**(U.K.)**|2.3|2.6|10.2|1.7|1.8|5.7|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Monaco**|1.5|1.7|13.8|1.5|1.7|13.9|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Turks &**<br>**Caicos Islands**<br>**(U.K.)**|0.4|1.5|255.2|0.4|1.3|240.7|0.0|0.0|366.1|0.0|0.0|n.a.|
|**Sint Eustatius**<br>**(Neth.)**|1.3|1.4|10.0|1.3|1.4|9.6|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Montserrat**<br>**(U.K.)**|0.8|0.9|22.9|0.7|0.9|22.3|0.0|0.0|n.a.|0.0|0.0|n.a.|
|**Vatican City**|0.3|0.4|7.1|0.3|0.4|7.3|0.0|0.0|0.0|0.0|0.0|n.a.|
|**Saba (Neth.)**|0.3|0.3|18.9|0.2|0.3|22.3|0.0|0.0|n.a.|0.0|0.0|n.a.|

28