metadata: authors: 0: author: Maruyama Rentschler,Jun Erik
1: author: Leonova,Nadia
count: World
docna: 0: docna: Air Pollution and Poverty : PM2.5 Exposure
            in 211 Countries and Territories
docty: Policy Research Working Paper
abstracts: cdata!: Air pollution is one of the leading
            causes of death worldwide, especially affecting poorer
            people who tend to be more exposed and vulnerable. This
            study contributes (i) updated global exposure estimates for
            the World Health Organizations's 2021 revised fine
            particulate matter (PM2.5) thresholds, and (ii) estimates of
            the number of poor people exposed to unsafe PM2.5
            concentrations. It shows that 7.28 billion people, or 94
            percent of the world population, are directly exposed to
            unsafe average annual PM2.5 concentrations. Low- and
            middle-income countries account for 80 percent of people
            exposed to unsafe PM2.5 levels. Moreover, 716 million poor
            people (living on less than $1.90 per day) live in areas
            with unsafe air pollution. Around half of them are located
            in just three countries: India, Nigeria, and the Democratic
            Republic of Congo. Air pollution levels are particularly
            high in lower-middle-income countries, where economies tend
            to rely more heavily on polluting industries and
            technologies. The findings are based on high-resolution air
            pollution and population maps with global coverage, as well
            as subnational poverty estimates based on harmonized
            household surveys.
display_title: Air Pollution and Poverty : PM2.5
            Exposure in 211 Countries and Territories
listing_relative_url: /research/2022/04/33788309/air-pollution-poverty-pm25-exposure-211-countries-territories
topicv3: Environmental Sustainability m1325984 1786,Air Pollution m1327122 1791,Contracting for Health Risk m1583356 4686
publishtoextweb_dt: 2022-04-18T00:00:00Z
dois: 10.1596/1813-9450-10005
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1217979
model_extractions: Policy Research Working Paper 10005 Air Pollution and Poverty PM2.5 Exposure in 211 Countries and Territories _Jun Rentschler Nadia Leonova_ Sustainable Development Chief Economist Office April 2022
Policy Research Working Paper 10005

# **Abstract**

Air pollution is one of the leading causes of death worldwide, especially affecting poorer people who tend to be more exposed and vulnerable. This study contributes (i) updated global exposure estimates for the World Health Organizations’s 2021 revised fine particulate matter (PM2.5) thresholds, and (ii) estimates of the number of poor people exposed to unsafe PM2.5 concentrations. It shows that 7.28 billion people, or 94 percent of the world population, are directly exposed to unsafe average annual PM2.5 concentrations. Low- and middle-income countries account for 80 percent of people exposed to unsafe PM2.5 levels. Moreover, 716 million poor people (living on less than $1.90 per day) live in areas with unsafe air pollution. Around half of them are located in just three countries: India, Nigeria, and the Democratic Republic of Congo. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. The findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.

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.

_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
# Air Pollution and Poverty: PM2.5 Exposure in 211 Countries and Territories

**Jun Rentschler**<sup>**1**</sup> **, Nadia Leonova**<sup>**1**</sup>

**JEL codes:** Q53, Q56, I14, O44 **Keywords:** air pollution, PM2.5, poverty, exposure, health

**Acknowledgments:** This working paper contributes to the World Bank’s 2022 flagship report on environmentally harmful subsidies. It has benefited from helpful comments, feedback, and inputs by Mattia Amadio, Esteban Balseca, Samira Barzin, Lander Bosch, Richard Damania, Ira Dorband, Xinming Du, Bramka Jafino, Kichan Kim, Christoph Klaiber, Helena Naber, Jason Russ, Melda Salhab, Ernesto Sanchez-Triana, Margaret Triyana, and Esha Zaveri.
# **1. Introduction**

Air pollution has wide-ranging and profound impacts on human health and well-being. Poor air quality has been shown to be responsible for over 4 million death each year from outdoor pollutants, another 2.3 million from indoor air pollution, a wide range of cardiovascular, respiratory, and neurological diseases, as well as reductions in cognitive abilities (WHO, 2018). Studies show that the vast majority of the world’s population face unsafe air pollution levels. Yet, our understanding of what exact air pollution levels should be considered _unsafe_ is still evolving. Based on the latest medical evidence the WHO updated its air quality guidelines in 2021, significantly increasing the stringency of its 2005 guidelines.

A growing evidence base also highlights that the exposure to and impact of air pollution are not equally distributed. As health and productivity suffer, evidence from the US has shown that air pollution reinforces socio-economic inequalities. Especially poor and marginalized groups are often exposed to higher pollution levels. In addition, these groups also tend to be more vulnerable to the impacts of pollution, since low-paying jobs are more likely to require physical and outdoor labor, meaning that people are faced with heightened exposure. Pollution sources, such as industrial plants or transport corridors, are disproportionately placed in low-income neighborhoods. And as they increase air pollution further, this drives down housing prices, which in turn reinforces their status as low-income neighborhoods. Constraints in terms of the accessibility, availability and quality of health care provision further increase air pollution related mortality among low-income groups.

In short, socio-economic marginalization makes people more exposed and vulnerable to air pollution. Yet, apart from substantial evidence for the US, little evidence exists documenting the global scale of poor people’s exposure to harmful air pollution. A better understanding of the interplay between air pollution and poverty could be crucial for several reasons. Studies from high-income countries on the mortality and morbidity associated with air pollution may not be directly transferable to low-income countries and communities, where the nature of occupations and health care differ substantially. The health and productivity implications of unsafe air pollution will also impact the socio-economic development prospects of low- and middle-income countries. This is especially pertinent in low-income countries, which – as this study shows – still tend to have relatively low pollution levels compared to more industrialized middle-income countries. In these countries, it will be important to ensure that development progress does not come hand in hand with intensifying air pollution and the associated adverse effects.

Against this context, this study explores the global prevalence of unsafe outdoor air pollution, and how it interacts with poverty. It uses global high-resolution data on ambient air pollution (outdoor concentrations of PM2.5), population distribution, and poverty to provide aggregate exposure estimates (see Section 3.1 on data sources). It also shows that pollution levels are most hazardous in middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. This study does not cover indoor air pollution, which is another pervasive risk to health and well-being, especially in developing countries.

Overall, this study contributes to the literature in two ways. It offers the first global population PM2.5 exposure estimates based on the WHO’s recent guidelines, revised in September 2021, with detailed national and subnational estimates. It also offers the first global estimates of PM2.5 exposure and its interaction with national and subnational poverty levels.

The rest of this study is organized as follows. Section 2 reviews the existing evidence on air pollution, health impacts and development outcomes. Section 3 summarizes the data sets and analytical methods used in this study. Section 4 presents results, and section 5 concludes.

2
# **2. Existing evidence**

## **Air pollution is pervasive, caused by a wide range of economic activities.**

<mark>Air pollution refers to the heightened concentration of a range of different pollutants, most notably particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and sulfur dioxide (SO2). While also occurring naturally, hazardous concentration levels of these pollutants are predominantly the result of human activities, including transport, fossil fuel combustion, and agriculture. This also means that their occurrence has high spatial correlation. For instance, major urban centers typically have increased concentrations of all of the above-mentioned pollutants. Using remotely sensed carbon dioxide (CO2) measurements from over 1,200 cities in 138 countries, Dasgupta et al (2021) show that emission levels correlate strongly with population size and economic development levels, though the presence of public transport systems (subways) can reduce local emissions.</mark> The vast majority of the world’s population faces air pollution levels that are considered unsafe by the <mark>WHO. For PM2.5, one of the most pervasive pollutants, the WHO’s 2005 guidelines recommend for average annual concentrations not to exceed 10 μg/m</mark><sup>3</sup> <mark>(WHO, 2005). Southerland et al (2022) study PM2.5 concentration trends over two decades for over 10,000 urban centers, and show that 86% (2.5 billion inhabitants) of urban inhabitants lived in urban areas that exceeded the WHO’s 2005 guideline threshold of 10 μg/m</mark><sup>3</sup> <mark>for PM2.5. The WHO’s (2021) Global Air Quality Guidelines further revise the 2005 guideline threshold down to 5 μg/m</mark><sup>3</sup> <mark>, meaning that an even higher percentage of the world’s population is exposed to unsafe concentrations.</mark>

## **Exposure to unsafe levels of particle concentration has a wide range of health impacts.**

<mark>The WHO’s revised threshold reflects a growing body of evidence on the wide-ranging global burden of disease associated with air pollution. There is strong evidence on the causal relationship between air pollution – especially PM, O3, NO2, SO2 – and all-cause mortality, as summarized by the WHO (2021). Heightened air pollution exposure has been shown to increase the risk of chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, as well as chronic and acute respiratory diseases such as asthma (Cohen et al., 2017; WHO, 2018). Besides cardiovascular and respiratory diseases, growing evidence exists on the role of PM2.5 exposure in increasing the risk of type II diabetes and neurological diseases such as Alzheimer’s (Peters et al., 2019; GBD 2019 Risk Factors Collaborators, 2020). Evidence also suggests that air pollution exposure increases neonatal mortality due to low birth weight and short gestation (GBD 2019 Risk Factors Collaborators, 2020; Patel, et al. 2021). Cumulative exposure to air</mark> pollution can also result in a range of health issues that lower long-term quality of life (O’Neill, et al. 2003).

<mark>Estimates of the number of people affected by air pollution related diseases are staggering. The WHO (2018) estimated that ambient (i.e. outdoor) air pollution caused 4.2 million premature deaths in 2016. Of these deaths, 58 percent were due to ischemic heart disease and stroke, 18 percent were due to chronic obstructive pulmonary disease and acute lower respiratory infections, and 6 percent due to lung cancer. Such ambient air pollution is driven mainly by fossil fuel combustion, transport and industry. In addition, the WHO highlights the serious health risks due to indoor air pollution faced by about 2.6 billion people who rely on coal and biomass for cooking and heating their homes. In 2016, the WHO attributed about 3.8 million premature deaths to such indoor air pollution. The 2019 Global Burden of Disease study (GBD) estimates that 4.5 million premature deaths were due to ambient air pollution and another 2.3 million premature deaths due to indoor air pollution. PM2.5 is identified as the pollutant responsible for</mark> the vast majority of air pollution deaths.

<mark>Focusing specifically on PM2.5 exposure in urban areas, Southerland et al (2022) show that average pollution levels have decreased in most regions between 2000 and 2019. Nevertheless, PM2.5 attributable deaths have increased in the same period in all regions except Europe, and North and Latin</mark> 3
<mark>America – mostly driven by changes in population sizes, shifting age structures, and pre-existing disease prevalence. They estimate that 1.8 million premature deaths in urban areas can be attributed to PM2.5 exposure in 2019. These numbers suggest that meaningful reductions in air pollution deaths can only be</mark> achieved through rapid pollution reduction and improved access to affordable high-quality health care.

<mark>Besides mortality and morbidity effects, air pollution also reduces cognitive ability and productivity of affected populations. Empirical evidence from brain-training experiments shows that PM2.5 exposure impairs cognitive abilities of adults, while these effects are largest for those in prime working age and those with low ability (La Nauze and Severini, 2021). There is even evidence that short-term exposure to air pollution negatively affects the performance of highly skilled workers – professional baseball umpires have been documented to make more incorrect calls when exposed to high levels of carbon monoxide (Archsmith et al 2018). Such evidence on the relationship between air quality and brain health explains how air pollution can negatively impact productivity and exacerbate inequalities (Peeples 2020).</mark>

# **Poor people tend to be more exposed and more vulnerable to air pollution.**

While global estimates of air pollution deaths differ somewhat from study to study, the evidence is in strong agreement that air pollution is one of the leading causes of deaths (Lelieveld et al. 2020). Several factors are compounding so that this burden is disproportionately borne by low- and middle-income countries. Especially in the developing countries of South and East Asia, large populations are located in densely populated urban areas. Less stringent air quality regulations, the prevalence of older polluting machinery and vehicles, subsidized fossil fuels, congested urban transport systems, rapidly developing industrial sectors, and cut-and-burn practices in agriculture are all contributing to heightened concentration levels. In addition, high proportions of physical and outdoor labor mean that more people are faced with heightened exposure. Constraints in terms of the accessibility, availability and quality of health care provision further increase air pollution related mortality in developing countries (Lelieveld, et al. 2020). Evidence on air pollution in India suggests that a one GW increase in coal-fired capacity corresponds to a 14 percent increase in infant mortality rates in districts near versus far from the plant site (Barrows et al 2019). This effect is 2-3 times larger than estimates from the developed world.

The burden of air pollution not only falls disproportionately on poorer countries; within countries poorer and more marginalized communities also tend to be exposed to higher pollution levels (Jbaily et al. 2022; Bell et al 2013). This inequality in exposure is well documented in the United States, where data on socioeconomic characteristics is available with high spatial disaggregation (Fann et al, 2018; Bell et al, 2013; Colmer et al, 2020; Kioumourtzoglou et al 2016; Bell & Ebisu, 2012; Mikati et al. 2018).

Patel et al. (2021) show that in the US, industrial planning policies have disproportionately placed polluting industries in areas with large Black, Hispanic and Asian populations. As a result, these minority groups face systematically higher pollution levels. Tessum et al. (2021) show that the disproportionate exposure to high PM2.5 exposure by racial-ethnic minorities in the US is not limited to industrial emitters. The study documents that the disproportionate pollution exposure is consistent across all major emission categories, across states, urban and rural areas, income levels, and exposure levels. Studying the period from 2004 to 2016, Jbaily et al (2022) show that in addition to ethnic minorities, low-income populations also face systematically higher PM2.5 pollution levels – and these exposure disparities have been increasing over time. Air quality is also documented to influence real estate prices, so that lower-income households tend to locate into cheaper, more polluted neighborhoods (Chay & Greenstone, 2005).

Outside the US, evidence on the inequality in air pollution exposure is more limited – often due to a lack of socio-economic data with high spatial disaggregation. Hajat et al. (2015) offer a systematic review of the literature on socio-economic disparities and air pollution exposure, compiling country- and city-level evidence from all regions. They confirm the strong evidence of unequal exposure in the US, but find mixed 4
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<mark>constructed by combining Aerosol Optical Depth (AOD) satellite retrievals from the NASA MODIS, MISR, and SeaWIFS instruments with the GEOS-Chem chemical transport model, and subsequently calibrating to global ground-based observations using a geographically weighted regression. The resolution of 0.01 degree (equivalent to about 1.1 kilometer at the equator) is well suited to capture regional variation in</mark> concentrations, though not granular local variations.

<mark>As a global modeled data set, some uncertainty is to be expected, though sensitivity tests suggest good agreement with ground measurements (van Donkelaar et al, 2021). For more spatially nuanced analyses, for instance at the neighborhood or street level, alternative data based on local measures would be required. Moreover, it should be noted that the chemical composition of PM2.5 particles can differ by pollution source (Thurston, et al 2022). PM2.5 particles associated with the combustion of fossil fuels are more toxic due to higher acidity levels (e.g. sulfuric particulate matter from coal burning). The global PM2.5 can inform on total particle concentration, but not the spatial variation in the chemical composition (i.e. acidity) of particles.</mark>

**Population density:** We estimate the location of people using the WorldPop Global High Resolution Population data set (WPGP), produced by the University of Southampton, the World Bank, and other partners. It offers global coverage and is available yearly from 2000 to 2020. While WorldPop provides several data sets (including poverty, demographics, and urban change mapping), we use the population density map (WorldPop-PPP-2020). In a raster format, this data set provides the number of inhabitants per cell, with a resolution of 3 arcseconds, thus specifying the distribution of population. This information is based on administrative or census-based population data, disaggregated to grid cells based on distribution and density of built-up area, which is derived from satellite imagery (Freire et al., 2016, 2020).

The choice of population density map is important for estimating people’s exposure to natural hazards. Smith et al. (2019) provide a sensitivity analysis for flood exposure assessments using different population density maps, including WorldPop (3 arcsecond). They show that high-resolution population density maps perform best in capturing local exposure distribution, particularly the High-Resolution Settlement Layer (HRSL) with 1-arcsecond, or ~30 m resolution, produced jointly by Facebook, Columbia University and the World Bank. While HRSL is only available for a limited number of countries, WorldPop is shown to perform better than alternatives with global coverage, such as LandScan data (30-arcsecond, ~900 m resolution) (Bright et al., 2015).

**Subnational poverty rates:** For 1,755 of the 2,227 subnational units, the World Bank’s Global Subnational Poverty Atlas offers several poverty estimates, which are all derived from the latest available Living Standards Measurement Survey (LSMS) for the respective country (World Bank, 2021). Areas where no poverty estimates are available tend to be high-income countries and small island states. For the purpose of this study, the standard World Bank definitions of poverty are used to determine the number of poor people in a given subnational administrative unit. Specifically, poverty is defined by the daily expenditure thresholds of $1.90, $3.20, and $5.50.

**Administrative boundaries:** The definition of national administrative boundaries follows the standard World Bank global administrative map. However, national boundaries are further disaggregated into subnational units for all countries where World Bank household surveys are available with subnational representativeness. These subnational units are typically provinces or states (i.e. admin1) but can also include custom groupings of subnational regions determined by the sampling strategy of household surveys. Overall, this study covers 211 countries, which are disaggregated into 2,183 subnational units.

6
# **3.2. Methodology and step-wise computational process**

To estimate the number of people who are exposed to unsafe air pollution levels, this study follows a computational process in four main steps, which are outlined in this section. For further details, refer to Rentschler and Malhab (2020), who offer a detailed description of the stepwise computational process that is replicated in this study.

_Resample the PM2.5 data:_ The air pollution map is resampled to ensure that pixels align with the gridded population density map.

_Define air pollution risk categories:_ While the air pollution map offers average annual PM2.5 concentration levels along a continuous scale, the values are aggregated into six risk categories as summarized in Table 1. These risk categories are defined in line with the air quality guideline (AQG) level by the WHO (2021). The WHO recommends an annual PM2.5 AQG level of up to 5 μg/m. For countries with PM2.5 concentrations above this threshold, it recommends interim targets at 10, 15, 25, and 35 μg/m<sup>3</sup> , which correspond to a linearly increasing mortality rate (Table 1). At higher concentrations, the concentration-response function of mortality may no longer be linear. The epidemiological evidence underpinning these AQG thresholds for PM2.5 is discussed in a systematic review by Chen & Hoek (2020). Each 1 degree cell of each country is assigned one of the six risk categories. This is repeated for the world’s landmass of 149 million square kilometers, which implies the processing of about 300 million data points.

**Table 1. PM2.5 concentration thresholds**

|**Risk**|**classification**|**PM2.5 concentration**|**Mortality**|
|---|---|---|---|
|**Safe**|**No/minor risk**|≤ 5μg/m<sup>3</sup>|_baseline_|
||**Low**|5 – 10μg/m<sup>3</sup>|4% increased mortality rate|
||**Moderate**|10 – 15 μg/m<sup>3</sup>|8% increased mortality rate|
|**Unf**|**High**|15 – 25 μg/m<sup>3</sup>|16% increased mortality rate|
|**sae**|**Very high **|25 - 35μg/m<sup>3</sup>|24% increased mortality rate|
||**Hazardous**|over 35μg/m<sup>3</sup>|> 24% increased mortality rate|

_Assign air pollution risk categories to population headcounts at the pixel level and aggregate to the administrative unit:_ As the air pollution and population density maps are converted into the same spatial resolution, each population map cell can be assigned a unique air pollution risk classification – these cells can then be aggregated to the administrative unit level (e.g. province or district level). This allows the calculation of population headcounts for each risk category and for each (sub-)national administrative unit. This process yields an estimate of the number and share of people exposed to no-, low, moderate, high, very high and hazardous air pollution concentrations throughout the year. We offer these exposure headcount maps as global gridded layers with a resolution of 90 meters. In addition, we aggregate them to administrative units, including for each country and subnational unit. These estimates are further aggregated to yield regional and global estimates.

_Compute the number of poor people exposed to air pollution risk:_ While poverty estimates are not available at the pixel level, the World Bank’s Global Subnational Poverty Atlas (GSPA) of harmonized household surveys provides them at the subnational level for at least 153 countries. These poverty shares are multiplied with the population headcount that is estimated to be exposed to unsafe air pollution, in order to obtain an estimate of the number of poor people in each administrative unit exposed to air pollution risk.

7
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Moreover, this study estimates that 716 million poor people (living on under $1.90 per day) live in areas with unsafe air pollution. At least 405 million people in Sub-Saharan Africa are estimated to live in both extreme poverty (using a $1.90 a day definition) and unsafe air pollution – thus making them particularly vulnerable to prolonged adverse impacts on livelihoods and well-being. For them, the same air pollution level is likely to imply more severe health risks than for higher income households. Among low-income population groups, high proportions of physical and outdoor labor mean that they are faced with heightened exposure and intake of pollutants. Constraints in terms of the accessibility, availability and quality of health care provision further increase air pollution related mortality among poor people. For instance, a study on air pollution and infant mortality suggests that mortality risks are 2 to 3 times as large in India compared to high-income countries. Moreover, exposure to indoor air pollution – not covered in this study – also affects poor people disproportionately, as they are more dependent on the use of polluting low-cost fuels such as charcoal, kerosene, or firewood for cooking and lighting.

Air pollution is one of the leading causes of death worldwide, especially affecting poorer people who tend to be more exposed and vulnerable. The estimates provided in this study affirm the case for implementing targeted measures to reduce the pollution intensity of economic growth – for instance, supporting the uptake of less polluting technologies in industry and infrastructure, or facilitating the transition towards cleaner fuels (in particular electrification). In addition, measures are warranted to directly address the disproportionate exposure of poor people highlighted in this study. Expanding the provision of affordable and adequate health care in large urban centers in low- and middle-income countries can help reduce mortality, bringing it closer to the levels experienced in higher income countries. Mandating transparent accounting for environmental and health externalities in planning decisions can help to steer pollution sources (e.g. industrial zones or power plants) away from low-income communities. Finally, removing incentives that perpetuate the over-consumption of fossil fuels can yield a double dividend for poor people; e.g. fossil fuel subsidies are well documented to confer disproportionate monetary benefits to richer households, but the air pollution externalities associated with subsidized fossil fuel consumption are also a burden that is borne disproportionately by poorer households. Addressing these policy distortions can be pro-poor both in terms of fiscal and health benefits.

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# **<mark>Annex 1. Country level estimates</mark>**

||_Empty cells ref_<br>|_lect the la_|_ck of i_<br>**Sa**<br>**(< 5** **μ**|_nput da_<br>**fe**<br>**g/m**<sup>**3**</sup>**)**|_ta._<br>**Unsa**<br>**(> 5** **μg/**|**fe**<br>**m**<sup>**3**</sup>**)**|**Hazard**<br>**(> 35** **μg**|**ous**<br>**/m**<sup>**3**</sup>**)**|**Poor**<br>**and u**<br>**(> 5** **μ**|**($1.9)**<br>**nsafe**<br>**g/m**<sup>**3**</sup>**)**|**Poor (**<br>**and u**<br>**(> 5** **μ**|**$3.2)**<br>**nsafe**<br>**g/m**<sup>**3**</sup>**)**|**Poor (**<br>**and un**<br>**(>5** **μg**|**$5.5)**<br>**safe**<br>**/m**<sup>**3**</sup>**)**|**Poor (**<br>**and haz**<br>**(> 35** **μ**|**$5.5)**<br>**ardous**<br>**g/m**<sup>**3**</sup>**)**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Economy/**<br>**territory**|**Pop. (m)**|**(m)**|**(%)**|**(m)**|**(%)**|**(m)**|**(%)**|**(m)**|**(%)**|**(m)**|**(%)**|**(m)**|**(%)**|**(m)**|**(%)**|
|1|China|1,431.97|18.54|1.3|1,413.43|98.7|764.9|53.4|21.3|1.5|||||||
|2|India|1,377.06|17.22|1.3|1,359.85|98.8|1,326.6|96.3|202.5|14.7|716.8|52.1|1,135.1|82.4|1,109.2|80.6|
|3|United States|<br>329.86|<br>24.98|7.6|<br>304.88|92.4|<br>0.0|0.0|3.0|0.9|3.8|1.1|<br>5.3|1.6|<br>0.0|0.0|
|4|Indonesia|269.68|18.28|6.8|251.40|93.2|84.1|31.2|9.0|3.4|53.8|19.9|133.2|49.4|41.9|15.5|
|5|Pakistan|220.52|1.87|0.8|218.66|99.2|205.2|93.1|3.3|1.5|50.5|22.9|139.2|63.1|130.6|59.2|
|6|Nigeria|205.63|2.47|1.2|203.16|98.8|116.9|56.9|79.7|38.7|141.0|68.6|181.4|88.2|103.3|50.3|
|7|Bangladesh|162.83|3.42|2.1|159.41|97.9|159.4|97.9|12.4|7.6|64.4|39.5|123.6|75.9|123.6|75.9|
|8|<br>Brazil|212.30|54.14|25.5|158.15|74.5|0.0|0.0|5.0|2.3|10.7|5.0|25.1|11.8|0.0|0.0|
|9|Russia|145.60|8.59|5.9|137.00|94.1|0.0|0.0|0.0|0.0|0.5|0.3|4.9|3.4|0.0|0.0|
|10|Mexico|128.79|8.68|6.7|120.11|93.3|0.0|0.0|2.1|1.6|7.9|6.1|27.5|21.4|0.0|0.0|
|11|Japan|123.47|7.82|6.3|115.65|93.7|0.0|0.0|0.8|0.7|1.1|0.9|1.4|1.1|0.0|0.0|
|12|<br>Ethiopia|114.83|0.78|0.7|114.05|99.3|0.0|0.0|27.0|23.5|68.4|59.6|100.3|87.3|0.0|0.0|
|13|<br>Egypt, Arab Rep.|102.22|4.77|4.7|97.45|95.3|0.0|0.0|3.9|3.8|29.4|28.8|72.1|70.6|0.0|0.0|
|14|Vietnam|96.80|3.32|3.4|93.48|96.6|15.6|16.2|1.7|1.7|5.9|6.1|20.1|20.8|2.4|2.5|
|15|Congo, Dem. Rep.|89.41|0.83|0.9|88.57|99.1|11.5|12.9|65.9|73.7|80.1|89.6|86.3|96.6|10.8|12.1|
|16|Germany|83.70|0.61|0.7|83.10|99.3|0.0|0.0|0.1|0.1|0.2|0.2|0.4|0.5|0.0|0.0|
|17|<br>Iran, Islamic Rep.|83.76|1.21|1.4|82.55|98.6|0.1|0.1|0.3|0.4|2.7|3.2|12.1|14.4|0.0|0.0|
|18|Turkey|82.46|4.22|5.1|78.24|94.9|0.0|0.0|0.0|0.0|1.1|1.3|6.7|8.1|0.0|0.0|
|19|Thailand|69.68|2.18|3.1|67.51|96.9|0.0|0.0|0.0|0.0|0.3|0.5|5.0|7.2|0.0|0.0|
|20|Philippines|104.57|41.07|39.3|63.50|60.7|0.0|0.0|2.3|2.2|12.4|11.8|31.6|30.3|0.0|0.0|
|21|<br>United Kingdom|67.23|4.26|6.3|62.97|93.7|0.0|0.0|0.1|0.2|0.2|0.3|0.3|0.5|0.0|0.0|
|22|<br>France|64.64|1.74|2.7|62.90|97.3|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.0|
|23|Italy|60.02|4.13|6.9|55.89|93.1|0.0|0.0|0.8|1.3|1.0|1.6|1.7|2.8|0.0|0.0|
|24|Tanzania|57.89|4.78|8.3|53.11|91.7|0.0|0.0|26.0|44.9|40.5|70.0|48.5|83.7|0.0|0.0|
|25|Myanmar|53.56|0.90|1.7|52.66|98.3|0.2|0.4|0.5|0.9|6.4|11.9|26.0|48.5|0.1|0.2|
|26|South Africa|59.29|9.43|15.9|49.85|84.1|0.0|0.0|9.1|15.4|18.3|30.8|28.1|47.4|0.0|0.0|
|27|Colombia|50.78|1.43|2.8|49.35|97.2|1.0|1.9|1.9|3.7|5.1|10.1|13.1|25.8|0.3|0.6|
|28|Korea, Rep.|50.01|1.30|2.6|48.71|97.4|0.0|0.0|0.1|0.2|0.2|0.5|0.5|1.0|0.0|0.0|
|29|<br>Kenya|53.72|6.30|11.7|47.42|88.3|0.0|0.0|16.2|30.2|30.3|56.5|40.7|75.8|0.0|0.0|
|30|Uganda|45.60|0.88|1.9|44.72|98.1|0.0|0.0|17.8|39.1|30.7|67.3|38.9|85.4|0.0|0.0|
|31|Sudan|43.72|0.24|0.6|43.48|99.4|0.0|0.0|5.6|12.7|19.4|44.4|34.3|78.4|0.0|0.0|
|32|Ukraine|43.61|1.02|2.3|42.59|97.7|0.0|0.0|0.0|0.0|0.2|0.4|1.5|3.5|0.0|0.0|
|33|Algeria|43.75|3.81|8.7|39.94|91.3|0.0|0.0|0.1|0.3|0.9|2.1|8.4|19.1|0.0|0.0|
|34|Iraq|40.15|0.40|1.0|39.75|99.0|0.1|0.2|0.6|1.4|5.3|13.2|19.7|49.1|0.0|0.1|
|35|Afghanistan|38.82|0.14|0.3|38.69|99.7|11.5|29.5|||||||||
|36|Spain|45.70|7.62|16.7|38.09|83.3|0.0|0.0|0.3|0.6|0.4|0.9|0.8|1.7|0.0|0.0|
|37|<br>Poland|37.81|0.26|0.7|37.55|99.3|0.0|0.0|0.1|0.3|0.2|0.5|0.4|1.1|0.0|0.0|
|38|Argentina|44.74|10.68|23.9|34.06|76.1|0.0|0.0|||||||||
|39|<br>Saudi Arabia|34.60|0.71|2.0|33.90|98.0|1.1|3.2|||||||||
|40|Uzbekistan|33.44|0.36|1.1|33.07|98.9|1.4|4.2|4.0|12.1|14.2|42.5|26.3|78.5|1.1|3.2|
|41|Canada|36.84|4.46|12.1|32.39|87.9|0.0|0.0|0.1|0.2|0.2|0.4|0.2|0.6|0.0|0.0|
|42|Angola|32.67|2.02|6.2|30.66|93.8|0.0|0.0|13.6|41.7|20.7|63.5|26.6|81.5|0.0|0.0|
|43|Peru|32.82|2.40|7.3|30.42|92.7|8.5|25.9|0.8|2.5|2.6|7.9|6.8|20.9|0.6|1.7|
|44|Morocco|36.78|6.47|17.6|30.31|82.4|0.0|0.0|0.2|0.5|1.6|4.4|7.5|20.3|0.0|0.0|
|45|Ghana|30.99|1.20|3.9|29.79|96.1|19.3|62.3|3.5|11.5|8.3|26.8|15.8|51.1|8.4|27.2|
|46|Malaysia|31.81|2.02|6.4|29.79|93.6|2.7|8.5|0.0|0.0|0.0|0.1|0.5|1.5|0.0|0.0|
|47|<br>Nepal|29.03|0.15|0.5|28.87|99.5|28.0|96.4|1.7|5.8|9.7|33.4|20.3|70.0|19.6|67.6|
|48|<br>Yemen|29.68|1.03|3.5|28.65|96.5|0.0|0.2|14.9|50.3|23.3|78.5|27.0|91.1|0.0|0.2|
|49|Cameroon|26.38|0.30|1.1|26.09|98.9|4.0|15.2|5.8|22.0|11.1|42.1|17.4|66.0|2.4|9.1|
|50|Côte d'Ivoire|26.37|0.63|2.4|25.73|97.6|7.4|28.0|6.0|22.6|13.3|50.4|20.1|76.1|4.6|17.3|
|51|North Korea|25.40|0.80|3.2|24.59|96.8|0.4|1.6|||||||||
|52|Niger|24.10|0.68|2.8|23.42|97.2|0.0|0.0|10.1|42.1|17.9|74.1|21.9|90.8|0.0|0.0|
|53|Mozambique|31.09|9.06|29.1|22.03|70.9|0.0|0.0|13.3|42.8|17.7|56.9|20.1|64.7|0.0|0.0|
|54|Venezuela|28.06|6.20|22.1|21.86|77.9|0.0|0.0|||||||||
|<br>55|<br>Taiwan, China|<br>23.68|<br>1.85|<br>7.8|<br>21.83|<br>92.2|<br>0.0|<br>0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|

23
|56<br>|Burkina Faso<br>|20.86<br>|0.09<br>|0.4<br>|20.77<br>|99.6<br>|0.0<br>|0.0<br>|6.9<br>|32.9<br>|14.2<br>|68.3<br>|18.4<br>|88.3<br>|0.0<br>|0.0<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|57<br>58|Sri Lanka<br>Romania|21.16<br>19.21|1.94<br>0.05|9.2<br>0.3|19.22<br>19.16|90.8<br>99.7|0.0<br>0.0|0.0<br>0.0|0.1<br>0.4|0.7<br>2.3|1.9<br>0.9|8.9<br>4.9|7.6<br>2.0|36.0<br>10.2|0.0<br>0.0|0.0<br>0.0|
|<br>59|<br>Malawi|<br>19.13|<br>0.69|<br>3.6|<br>18.44|<br>96.4|<br>0.0|<br>0.0|<br>12.5|<br>65.4|<br>16.2|<br>84.9|<br>17.8|<br>92.9|<br>0.0|<br>0.0|
|60|Zambia|1837|018|10|1818|990|00|00|106|578|136|741|159|868|00|00|
|<br>61|<br>Mali|.<br>20.23|.<br>2.19|.<br>10.8|.<br>18.04|.<br>89.2|.<br>0.0|.<br>0.0|.<br>7.7|.<br>38.2|.<br>13.3|.<br>65.8|.<br>16.7|.<br>82.5|.<br>0.0|.<br>0.0|
|<br>62|<br>Kazakhstan|<br>18.72|<br>0.94|<br>5.0|<br>17.78|<br>95.0|<br>0.0|<br>0.0|<br>0.0|<br>0.0|<br>0.1|<br>0.3|<br>1.0|<br>5.4|<br>0.0|<br>0.0|
|63|Guatemala|17.89|0.30|1.7|17.59|98.3|0.0|0.0|1.3|7.0|3.8|21.0|7.8|43.8|0.0|0.0|
|64|Syria|17.30|0.30|1.7|17.01|98.3|0.0|0.0|7.5|43.6|13.0|74.9|15.7|91.0|0.0|0.0|
|65|<br>Ecuador|17.58|0.59|3.4|16.99|96.6|0.0|0.0|0.5|3.0|1.5|8.8|3.9|22.4|0.0|0.0|
|66|Cambodia|16.62|0.25|1.5|16.37|98.5|0.0|0.0|||||||||
|67|Netherlands|16.98|0.65|3.8|16.33|96.2|0.0|0.0|0.0|0.2|0.0|0.3|0.1|0.3|0.0|0.0|
|68|Chad|16.41|0.23|1.4|16.18|98.6|0.0|0.0|6.4|39.0|10.9|66.3|14.1|85.7|0.0|0.0|
|69|Chile|18.98|3.63|19.2|15.34|80.8|0.0|0.0|0.0|0.2|0.1|0.5|0.5|2.5|0.0|0.0|
|70|Zimbabwe|14.86|0.12|0.8|14.74|99.2|0.0|0.0|4.6|30.6|8.6|57.9|11.7|78.5|0.0|0.0|
|71|Guinea|12.90|0.44|3.4|12.46|96.6|0.0|0.0|2.9|22.7|7.2|55.9|10.9|84.4|0.0|0.0|
|72|Rwanda|12.91|0.69|5.3|12.22|94.7|0.0|0.0|6.4|49.7|9.6|74.1|11.1|86.2|0.0|0.0|
|73|Senegal|16.41|4.56|27.8|11.84|72.2|0.0|0.0|4.1|25.2|7.7|46.7|10.3|62.6|0.0|0.0|
|74|<br>Bolivia|11.65|0.07|0.6|11.58|99.4|0.0|0.0|0.5|4.3|1.2|10.0|2.5|21.4|0.0|0.0|
|75|Belgium|11.56|0.17|1.5|11.39|98.5|0.0|0.0|0.0|0.1|0.0|0.2|0.0|0.3|0.0|0.0|
|76|<br>Burundi|11.86|0.68|5.7|11.18|94.3|0.0|0.0|8.9|74.9|10.4|87.4|11.0|92.3|0.0|0.0|
|77|South Sudan|11.18|0.04|0.3|11.14|99.7|0.0|0.0|9.5|84.8|10.6|94.8|11.0|98.7|0.0|0.0|
|78|Benin|12.06|0.99|8.2|11.06|91.8|6.8|56.3|5.3|43.9|8.3|69.2|10.2|84.5|6.1|50.8|
|79|Tunisia|11.75|0.96|8.1|10.80|91.9|0.0|0.0|0.0|0.4|0.5|4.3|2.6|22.2|0.0|0.0|
|80<br>81|Somalia<br>Czech Republic|15.81<br>10.70|5.04<br>0.04|31.8<br>0.4|10.78<br>10.66|68.2<br>99.6|0.0<br>0.0|0.0<br>0.0|0.0|0.1|0.0|0.1|0.0|0.4|0.0|0.0|
|82|<br>Jordan|10.19|0.19|1.8|10.01|98.2|0.0|0.0|0.0|0.2|0.3|2.7|2.3|22.4|0.0|0.0|
|83|Cuba|11.25|1.27|11.3|9.98|88.7|0.0|0.0|||||||||
|84|Hungary|9.64|0.09|0.9|9.56|99.1|0.0|0.0|0.1|0.5|0.1|1.1|0.3|2.7|0.0|0.0|
|85|Tajikistan|9.53|0.11|1.2|9.42|98.8|0.2|2.6|0.2|2.3|1.2|12.2|3.9|40.8|0.1|1.1|
|86|Honduras|986|045|46|941|954|00|00|||||||||
|<br>87|<br>Belarus|.<br>9.44|.<br>0.14|.<br>1.5|.<br>9.30|.<br>98.5|.<br>0.0|.<br>0.0|0.0|0.0|0.0|0.0|0.0|0.4|0.0|0.0|
|88|Austria|8.98|0.05|0.5|8.94|99.5|0.0|0.0|0.0|0.3|0.0|0.4|0.1|0.7|0.0|0.0|
|89<br>|United Arab Emirates|9.42|0.66|7.0|8.76|93.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|90|Serbia|8.74|0.01|0.2|8.72|99.8|0.0|0.0|0.0|0.0|0.1|0.7|0.5|5.7|0.0|0.0|
|<br>91|<br>Greece|<br>9.94|<br>1.28|<br>12.9|<br>8.66|<br>87.1|<br>0.0|<br>0.0|<br>0.1|<br>0.8|<br>0.1|<br>1.4|<br>0.4|<br>3.9|<br>0.0|<br>0.0|
|92|Haiti|11.25|2.64|23.5|8.61|76.5|0.0|0.0|2.3|20.1|4.6|40.5|6.9|61.4|0.0|0.0|
|93<br>|Dominican Republic|10.78|2.40|22.3|8.38|77.7|0.0|0.0|||||||||
|<br>94|<br>Azerbaijan|8.54|0.24|2.8|8.30|97.2|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|95|<br>Sweden|9.92|1.66|16.7|8.27|83.3|0.0|0.0|0.0|0.2|0.0|0.2|0.0|0.5|0.0|0.0|
|96|Switzerland|861|036|42|825|958|00|00|||||||||
|<br>97|<br>Togo|.<br>8.13|.<br>0.29|.<br>3.6|.<br>7.84|.<br>96.4|.<br>5.5|.<br>67.4|3.5|43.3|5.6|68.5|7.0|85.6|4.7|57.6|
|98|<br>Israel|8.34|1.01|12.1|7.33|87.9|0.0|0.0|0.0|0.2|0.0|0.6|0.2|2.2|0.0|0.0|
|<br>99|<br>Australia|<br>24.72|<br>17.42|<br>70.5|<br>7.30|<br>29.5|<br>0.0|<br>0.0|<br>0.0|<br>0.2|<br>0.1|<br>0.2|<br>0.1|<br>0.2|<br>0.0|<br>0.0|
|100|Lao PDR|7.23|0.03|0.4|7.19|99.6|0.0|0.0|0.6|7.9|2.4|33.9|5.1|70.6|0.0|0.0|
|101|Paraguay|7.11|0.09|1.2|7.03|98.8|0.0|0.0|0.1|1.4|0.4|5.7|1.1|15.6|0.0|0.0|
|102|<br>Sierra Leone|7.86|0.90|11.4|6.97|88.6|0.0|0.0|3.3|42.2|5.6|71.6|6.6|84.1|0.0|0.0|
|103|Bulgaria|6.89|0.08|1.2|6.81|98.8|0.0|0.0|0.1|1.3|0.2|3.0|0.5|7.3|0.0|0.0|
|104|<br>Kyrgyz Republic|6.50|0.10|1.5|6.40|98.5|0.2|3.0|0.0|0.6|0.7|10.6|3.5|53.7|0.1|1.9|
|105|<br>El Salvador|6.47|0.19|2.9|6.28|97.1|0.0|0.0|0.1|1.5|0.5|7.6|1.6|25.3|0.0|0.0|
|106|Nicaragua|662|035|53|627|947|00|00|02|28|07|112|21|315|00|00|
|<br>107|<br>Turkmenistan|.<br>6.02|.<br>0.24|.<br>4.1|.<br>5.78|.<br>95.9|.<br>0.0|.<br>0.0|.<br>0.1|.<br>1.0|.<br>0.6|.<br>10.1|.<br>1.9|.<br>31.9|.<br>0.0|.<br>0.0|
|108|Liba|684|113|165|571|835|00|00|||||||||
|<br>109|y<br>Slovak Republic|.<br>5.45|.<br>0.01|.<br>0.3|.<br>5.44|.<br>99.7|.<br>0.0|.<br>0.0|||||||||
|110|<br>Portugal|9.64|4.26|44.1|5.38|55.9|0.0|0.0|0.0|0.2|0.0|0.3|0.1|0.9|0.0|0.0|
|111|<br>Denmark|5.75|0.44|7.6|5.31|92.4|0.0|0.0|||||||||
|112|Congo, Rep.|5.45|0.20|3.6|5.26|96.4|2.7|50.4|2.9|53.1|3.9|72.2|4.7|85.7|2.4|43.8|
|113|Hong Kong SAR,<br>Chi|6.04|0.93|15.4|5.11|84.6|0.0|0.0|||||||||
|114|na<br>Papua New Guinea|8.83|3.78|42.8|5.06|57.2|0.0|0.0|1.5|16.5|2.8|31.5|4.0|45.4|0.0|0.0|
|115|Singapore|5.71|0.85|14.9|4.86|85.1|4.9|85.1|||||||||
||Central African||||||||||||||||
|116|Republic|4.79|0.06|1.2|4.73|98.8|0.7|15.6|3.4|71.7|4.1|85.8|4.5|93.7|0.7|13.6|
|117|<br>Lebanon<br>|6.33|1.66|26.2|4.67|73.8|0.0|0.0|0.0|0.0|0.0|0.0|0.1|1.3|0.0|0.0|
|118|West Bank and|492|033|67|459|933|00|00|00|08|02|41|10|200|00|00|
||Gaza|.|.|.|.|.|.|.|.|.|.|.|.|.|.|.|

24
|119<br>Finland<br> <br>|5.52<br>|1.09<br>|19.7<br>|4.43<br>|80.3<br>|0.0<br>|0.0<br>|0.0<br>|0.1<br>|0.0<br>|0.1<br>|0.0<br>|0.1<br>|0.0<br>|0.0<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|120<br>Costa Rica|5.08|0.67|13.3|4.40|86.7|0.0|0.0|0.1|1.2|0.1|2.8|0.5|8.9|0.0|0.0|
|121<br>Oman|500|064|129|436|871|02|45|||||||||
|<br> <br>122<br>Liberia|.<br>5.03|.<br>0.72|.<br>14.3|.<br>4.31|.<br>85.7|.<br>0.0|.<br>0.0|2.1|41.8|3.4|68.3|4.1|81.2|0.0|0.0|
|<br> <br>123<br>Moldova|<br>4.01|<br>0.05|<br>1.2|<br>3.96|<br>98.8|<br>0.0|<br>0.0|<br>0.0|<br>0.0|<br>0.0|<br>0.8|<br>0.5|<br>11.3|<br>0.0|<br>0.0|
|124<br>Croatia|4.00|0.27|6.9|3.73|93.1|0.0|0.0|0.0|0.5|0.0|1.0|0.1|3.0|0.0|0.0|
|125<br>Georgia<br> <br>|3.98<br>|0.37<br>|9.3<br>|3.61<br>|90.7<br>|0.0<br>|0.0<br>|0.2|3.8|0.5|13.3|1.5|36.6|0.0|0.0|
|126<br>Eritrea<br>127<br>P|3.52<br>426|0.05<br>094|1.5<br>220|3.47<br>333|98.5<br>780|0.0<br>00|0.0<br>00|00|08|01|29|03|77|00|00|
|<br>anama<br>128<br>Kosovo|.<br>3.35|.<br>0.04|.<br>1.1|.<br>3.32|.<br>98.9|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.3|.<br>0.1|.<br>2.6|.<br>0.7|.<br>20.6|.<br>0.0|.<br>0.0|
|129<br>Kuwait|4.14|0.87|21.0|3.27|79.0|2.0|47.7|||||||||
|130 Bosnia-Herzegovina|3.25|0.02|0.7|3.23|99.3|0.0|0.0|0.0|0.1|0.0|0.2|0.1|2.0|0.0|0.0|
|<br>131<br>Armenia|2.96|0.05|1.8|2.91|98.2|0.0|0.0|0.0|1.3|0.3|8.9|1.2|41.0|0.0|0.0|
|132<br>Albania|2.87|0.14|5.0|2.73|95.0|0.0|0.0|0.0|1.0|0.2|6.6|0.8|28.9|0.0|0.0|
|133<br>Norway|5.27|2.55|48.4|2.72|51.6|0.0|0.0|0.0|0.1|0.0|0.2|0.0|0.2|0.0|0.0|
|<br> <br>134<br>Lithuania|<br>2.72|<br>0.03|<br>1.2|<br>2.68|<br>98.8|<br>0.0|<br>0.0|||||||||
|135<br>Qatar|2.87|0.29|10.0|2.58|90.0|1.7|58.9|||||||||
|136<br>Jamaica|2.89|0.36|12.5|2.53|87.5|0.0|0.0|0.0|1.6|0.2|8.2|0.8|26.2|0.0|0.0|
|137<br>Mongolia|3.28|0.86|26.1|2.42|73.9|0.0|0.0|0.0|0.4|0.1|3.4|0.6|18.0|0.0|0.0|
|138<br>Botswana|2.35|0.05|2.3|2.29|97.7|0.0|0.0|0.3|13.7|0.8|35.0|1.4|58.1|0.0|0.0|
|139<br>Lesotho|2.14|0.07|3.3|2.07|96.7|0.0|0.0|0.6|27.1|1.0|48.7|1.5|71.4|0.0|0.0|
|140<br>Macedonia|2.08|0.02|1.2|2.06|98.8|0.0|0.0|0.1|4.1|0.2|8.0|0.4|17.8|0.0|0.0|
|141<br>Gambia, The|2.38|0.34|14.4|2.04|85.6|0.0|0.0|0.2|8.6|0.8|32.7|1.5|62.9|0.0|0.0|
|142<br>Slovenia|2.06|0.03|1.3|2.03|98.7|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.1|0.0|0.0|
|143<br>Gabon|2.17|0.15|7.1|2.02|92.9|0.0|0.2|0.1|3.9|0.3|13.7|0.8|36.7|0.0|0.1|
|144<br>Latvia|1.86|0.07|4.0|1.79|96.0|0.0|0.0|0.0|0.7|0.0|1.3|0.1|3.0|0.0|0.0|
|145<br>Guinea-Bissau|1.85|0.11|5.9|1.74|94.1|0.0|0.0|1.1|58.8|1.4|77.5|1.6|87.2|0.0|0.0|
|146<br>Namibia|2.54|0.87|34.2|1.67|65.8|0.0|0.0|0.3|13.3|0.7|26.6|1.1|41.9|0.0|0.0|
|147<br>Madagascar|27.42|26.17|95.5|1.25|4.5|0.0|0.0|0.9|3.1|1.1|3.9|1.2|4.3|0.0|0.0|
|<br>148<br>Equatorial Guinea|1.39|<br>0.16|<br>11.2|<br>1.23|<br>88.8|<br>0.0|<br>0.0|||||||||
|149<br>Eswatini<br>150<br>Estonia|1.16<br>1.32|0.01<br>0.23|0.7<br>17.4|1.15<br>1.09|99.3<br>82.6|0.0<br>0.0|0.0<br>0.0|0.3|28.2|0.6|51.3|0.8|70.8|0.0|0.0|
|151<br>Bahrain|1.43|0.38|26.6|1.05|73.4|0.7|50.0|||||||||
|152<br>Timor-Leste|1.31|0.26|20.1|1.04|80.0|0.0|0.0|0.2|16.4|0.7|51.4|1.0|73.0|0.0|0.0|
|153<br>Cyprus<br> <br>|1.14<br>|0.18<br>|15.9<br>|0.96<br>|84.1<br>|0.0<br>|0.0<br>|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|154<br>Ireland<br>155<br>Djibouti|4.91<br>097|4.03<br>022|81.9<br>222|0.89<br>076|18.1<br>778|0.0<br>00|0.0<br>00|02|208|04|380|06|583|00|00|
|<br> <br>156<br>Bhutan|.<br>0.75|.<br>0.01|.<br>1.2|.<br>0.74|.<br>98.8|.<br>0.4|.<br>47.2|.<br>0.0|.<br>0.7|.<br>0.1|.<br>8.0|.<br>0.2|.<br>31.3|.<br>0.1|.<br>19.9|
|157<br>Mauritania<br> <br>|4.62<br>|4.00<br>|86.4<br>|0.63<br>|13.6<br>|0.0<br>|0.0<br>|0.1|1.9|0.2|5.1|0.5|10.4|0.0|0.0|
|158<br>Luxembourg<br>159<br>Montenegro|0.62<br>062|0.01<br>002|1.5<br>38|0.61<br>059|98.5<br>962|0.0<br>00|0.0<br>00|00|07|00|57|01|140|00|00|
|<br> <br>160<br>Suriname|.<br>0.58|.<br>0.02|.<br>4.0|.<br>0.56|.<br>96.0|.<br>0.0|.<br>0.0|.<br>0.1|.<br>18.6|.<br>0.2|.<br>28.7|.<br>0.3|.<br>44.5|.<br>0.0|.<br>0.0|
|161<br>Guyana|0.78|0.22|28.2|0.56|71.8|0.0|0.0|0.0|3.3|0.1|8.3|0.1|18.4|0.0|0.0|
|<br> <br>162<br>Belize|<br>0.39|<br>0.02|<br>5.0|<br>0.37|<br>95.0|<br>0.0|<br>0.0|<br>0.0|<br>11.1|<br>0.1|<br>23.2|<br>0.2|<br>44.5|<br>0.0|<br>0.0|
|163<br>Brunei Darussalam|0.43|0.07|16.7|0.36|83.3|0.0|0.0|||||||||
|<br> <br>164<br>Uruguay|<br>3.45|<br>3.13|<br>90.6|<br>0.32|<br>9.4|<br>0.0|<br>0.0|||||||||
|<br>165<br>Malta|0.42|0.11|25.4|0.32|74.6|0.0|0.0|0.0|0.1|0.0|0.2|0.0|0.2|0.0|0.0|
|<br> <br>166<br>Macau, China|<br>0.44|<br>0.16|<br>35.4|<br>0.29|<br>64.6|<br>0.0|<br>0.0|||||||||
|167<br>São Tomé & Príncie|021|009|406|012|594|00|00|00|211|01|388|01|513|00|00|
|<br>p<br>168<br>Andorra|.<br>0.08|.<br>0.01|.<br>9.2|.<br>0.07|.<br>90.8|.<br>0.0|.<br>0.0|.|.|.|.|.|.|.|.|
|169<br>New Zealand|2.63|2.57|97.6|0.06|2.4|0.0|0.0|||||||||
|<br> <br>170<br>Isle of Man|<br>0.08|<br>0.04|<br>49.1|<br>0.04|<br>50.9|<br>0.0|<br>0.0|||||||||
|171<br>San Marino|003|000|11|003|989|00|00|||||||||
|<br> <br>172<br>Liechtenstein|.<br>0.03|.<br>0.01|.<br>19.7|.<br>0.03|.<br>80.3|.<br>0.0|.<br>0.0|||||||||
|173<br>Cayman Islands|0.06|0.06|94.2|0.00|5.8|0.0|0.0|||||||||
|<br> <br>174<br>Puerto Rico<br>175<br>Trinidad & Tobao|<br>2.86<br>138|<br>2.86<br>138|<br>100.0<br>1000|<br>0.00<br>000|<br>0.0<br>00|<br>0.0<br>00|<br>0.0<br>00|00|00|00|00|00|00|00|00|
|<br>g<br>176<br>Mauritius|.<br>1.26|.<br>1.26|.<br>100.0|.<br>0.00|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|
|177<br>Comoros|082|082|1000|000|00|00|00|00|00|00|00|00|00|00|00|
|<br> <br>178<br>Fiji|.<br>0.79|.<br>0.79|.<br>100.0|.<br>0.00|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|
|179<br>Solomon Islands|059|059|1000|000|00|00|00|00|00|00|00|00|00|00|00|
|<br> <br>180<br>Cabo Verde|.<br>0.52|.<br>0.52|.<br>100.0|.<br>0.00|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|.<br>0.0|
|181<br>Bahamas|037|037|1000|000|00|00|00|||||||||
|<br> <br>182<br>Iceland<br>183<br>Bbd|.<br>0.33<br>028|.<br>0.33<br>028|.<br>100.0<br>1000|.<br>0.00<br>000|.<br>0.0<br>00|.<br>0.0<br>00|.<br>0.0<br>00|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|
|<br>araos<br>184<br>Vanuatu|.<br>0.27|.<br>0.27|.<br>100.0|.<br>0.00|.<br>0.0|.<br>0.0|.<br>0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|

25
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