\clearpage

\renewcommand\thefigure{A.\arabic{figure}}
\setcounter{figure}{0}

\renewcommand\thetable{A.\arabic{table}}
\setcounter{table}{0}

\onecolumn
\section{Appendix}
\label{sec:Appendix}

\tocless\subsection{Abbreviations}
\begin{tabularx}{\textwidth}{p{2cm} X}
	F\textsubscript{collection} & The fraction of MSW\textsubscript{T} that is collected by a waste collection system. \\
	F\textsubscript{dumps} & Fraction of MSW\textsubscript{C} that is disposed in dumps. \\
	F\textsubscript{landfills} & Fraction of MSW\textsubscript{C} that is disposed in landfills. \\
	F\textsubscript{plastic} & Fraction of plastic in MSW\textsubscript{C}. \\
	F\textsubscript{recycled} & Fraction of MSW\textsubscript{C} that is recycled.  \\
	MLM & Machine learning models used to estimate MSW\textsubscript{C}. \\
	MSW & Municipal solid waste. \\
	MSW\textsubscript{C} & Amount of MSW that is collected by a waste collection system. \\
	MSW\textsubscript{T} & Total amount of MSW that is produced. \\
	NNET & Neural network model used to predict MSW\textsubscript{C}.\\
	P\textsubscript{E} & Amount of plastic that leaked into the environment. \\
	P\textsubscript{O} & Amount of plastic that leaks into the oceans. \\
	P\textsubscript{T} & Total amount of plastic waste that is produced. \\
	PV & Predictor variables used in the MLM. \\
	RF & Random Forest model used to predict MSW\textsubscript{C}. \\
	WFM & Waste flow model used to estimate how much of P\textsubscript{E} \\
\end{tabularx}

\newpage
\tocless\subsection{Glossary}
Following, the most important key words used in this thesis will be briefly explained and clarified.\\
\\
\begin{tabularx}{\columnwidth}{@{} p{3.1cm} X @{}}
	Municipal solid waste (MSW) & Waste from households, usually including non-hazardous streams of commercial and industrial waste \cite{McKinseyCenter2015}. The composition varies strongly among and also within countries, including organic material, paper, glass, metal, plastic and other compounds. \cite{Hoornweg2012}\\
	Sanitary landfill & Government operated facility to where MSW is deposited. At a selected and controlled location, placement is registered, waste is compacted and covered and measures for leachates, final top cover and closure exist. Energy recovery from gases possible \cite{Hoornweg2012}. \\
	Controlled Landfill & Similar to sanitary landfills but with overall lesser quality. Sites may be less selectively chosen and the control and treatment of leachate and gases might be reduced \cite{Hoornweg2012}. \\
	Controlled Dump & Non government operated facility where MSW is deposited. This includes site selection, controlled access and sometimes compaction of waste. Neither leachages or gases are monitored or treated. Often includes landfills operated by private agencies \cite{Hoornweg2012}. \\
	Uncontrolled dumps & Illegal sites where MSW is regularly and directly deposited by both the local population or from within the waste collection system. No engineering measures or controls \cite{Hoornweg2012,McKinseyCenter2015}. \\
	Incineration & Burning of MSW to reduce the volume by up to 90\%. Controlled incineration controls the emissions and generates energy. Uncontrolled incineration (open burning) creates high air pollution due to the low temperature combustion. Due to the waste composition, addition of fuel might be necessary (high fraction of organic material). Residuals require the use of a sanitary landfill \cite{Hoornweg2012}. \\
	Recycling & There are two types of recycling: Closed loop recycling, where the waste materials are reused for the same or similar-quality application and cascaded recycling, where the waste products are used in lower-value applications. Recycling rates vary among the waste types: 80-90\% for iron and steel, 58\% for paper, \texttildelow50\% for PET plastics (whereas 7\% are used in a closed loop recycling), 14\% for plastic packaging but only 5\% for all plastics combined \cite{EllenMacArthurFoundation2016}. \\
	Composting & Aerobic process to treat organic waste compounds. Preferably it avoids forming methane formed under aerobic conditions (as opposed to landfills and dumps) \cite{Hoornweg2012}. \\
	\\
	Plastic & 	\begin{tabular}{@{} p{3cm} p{6.5cm} >{\raggedleft\arraybackslash}p{1.3cm} >{\raggedleft\arraybackslash}p{1.6cm}  @{}}
		\multicolumn{4}{@{} p{13.7cm}}{Plastic consists of hundreds of materials with different properties. All of them are based on organic material, either on fossil fuel or on renewable bio materials such as cellulose or vegetable oils \cite{PlasticsEurope2016,PlasticsEurope2017}. As plastics from renewable sources only make up 1\% (4Mt) of the total plastic production in 2015 \cite{Geyer2017} they are neglected in this work. The most common plastics produced and their use, share of the demand in 2016 in the EU28 plus Norway and Switzerland and density is as follows:}\\
		\\
		\textbf{Type} & \textbf{Usage} \cite{PlasticsEurope2017,EllenMacArthurFoundation2016}& \textbf{Share} \cite{PlasticsEurope2017,EllenMacArthurFoundation2016}& \textbf{Density (g/ml)} \cite{Moret-Ferguson2010} \\
		PET & Drink bottles and food containers & 7.4\% & 1.38-1.41 \\
		PE-HD and PE-MD & Milk bottles, freezer bags & 12.3\% & 0.94-0.97 \\
		PE-LD and PE-LLD & Cling wrap, rubbish bags, reusable bags & 17.5\% & 0.89-0.93 \\
		PVC & Cosmetic containers, pipes, window frames & 10.0\% & 1.16-1.41 \\
		PP & Microwave dishes, sweet and snack wrappers, automotive parts & 19.3\% & 0.85-0.92 \\
		PS and PS-E & Plastic cutlery, egg trays (PS) and foamed polystyrene used for take-away food containers and protective packaging (PS-E) & 6.7\% & 1.04-1.08 \\
		Others & water cooler bottles, flexible films, optical fiber. & 19.3\% & \\
	\end{tabular} \\
\end{tabularx}

\newpage
\tocless\subsection{Scatterplots of the Predictor Variables versus MSW\textsubscript{C}}
\begin{figure}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\textwidth]{img/scatterplot1.pdf}
	\caption{Continued on next page.}
\end{figure}
\begin{figure}[htbp]
	\ContinuedFloat
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\textwidth]{img/scatterplot2.pdf}
	\caption{Continued on next page.}
\end{figure}
\begin{figure}[htbp]
	\ContinuedFloat
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\textwidth]{img/scatterplot3.pdf}
	\caption{Continued on next page.}
\end{figure}
\begin{figure}[htbp]
	\ContinuedFloat
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\textwidth]{img/scatterplot4.pdf}
	\caption{Scatterplots of MSW\textsubscript{C} versus the PVs. The left column includes all 1'394 available data points while the right column includes only the 95\% quantile of MSW\textsubscript{C}. The red line represents a linear fit and the corresponding r\textsuperscript{2} and p-values are indicated in the subtitle of each scatterplot. The red data points represent countries whose prediction error of the RF model is \textgreater30\%.}
	\label{fig:Scatterplots}
\end{figure}

\newpage
\tocless\subsection{Scatterplots Among the Predictor Variables}
\begin{minipage}{\linewidth}
	\makebox[\linewidth]{
		\includegraphics[width=\textwidth]{img/scatterplot_PVs.pdf}
	}
	\captionof{figure}{Scatterplots of the PVs. The red data points represent countries whose prediction error of the RF model is \textgreater30\%. See figure \ref{fig:Scatterplots} for scatterplots of the PVs versus the target variable MSW\textsubscript{C}.}
	\label{fig:ScatterplotsPV}
\end{minipage}

\newpage
\tocless\subsection{Correlation Dendrogram of the PVs and MSW\textsubscript{C}}
\begin{figure}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\textwidth]{img/correlation_dendrogram.pdf}
	\caption{Dendrogram of the correlations among the target variable MSW\textsubscript{C} and the PVs. A) Dendrogram using the Pearson correlations, B) Dendrogram using the Spearman correlations. The PVs in bold font were selected for the MLM as described in section \ref{sec:PredictorVariableSelection}.}
	\label{fig:CorrelationDendrogram}
\end{figure}

\newpage
\tocless\subsection{Numbers of GDP-clusters}
\begin{figure}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\textwidth]{img/clusters_error.pdf}
	\caption{Differences between the sum of squared errors of the trajectory of each country and the respective cluster trajectory mean for different numbers of clusters (2-15). This was used to determine the number of GDP-clusters as described in section \ref{sec:Clustering}. These clusters are then used to estimate missing data in the PVs of countries that have no data points available.}
	\label{fig:ClusterError}
\end{figure}

\tocless\subsection{NNET Architecture Optimization}
\begin{figure}[H]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\columnwidth]{img/NNET_architecture_all.pdf}
	\caption{Summary of the average MSE for the all different layer and node combinations that were tested as possible model architectures for the NNET. The first number indicates the amount of predictor variables, the second number shows the number of nodes in the hidden layer and the third number stands for the number of outputs.}
	\label{fig:NNETArchitecture_all}
\end{figure}

\newpage
\tocless\subsection{Number of Trees in RF versus MSE}
\begin{figure}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=8.5cm]{img/RF_error_ntree.pdf}
	\caption{MSE of the RF versus the number of trees. We chose a number of 500 as the MSE would not decrease by increasing the tree number. This is used in the RF optimization process described in section \ref{sec:MLMOptimization}.}
	\label{fig:RF_error_ntree}
\end{figure}

\newpage
\tocless\subsection{Data Modification}
\begin{table*}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\rowcolors{30}{}{color2!10}
	\centering
	\caption{Overview of the data completeness of each variable and the measures taken to modify the data. If a country has missing data for some but not all years, the data of this variable was inter- and/or extrapolated using either a polynomial (GDP) or linear fit (all others). We indicate the corresponding r\textsubscript{2} and p-vlaues as both mean and median. If the country lacked data in a predictor variable for all years, the average of its corresponding GDP-cluster was applied. The column \textit{N complete} indicates how many of the 173 countries have data available for all years in the time period 1990-2015. The predictor variables \textit{F\textsubscript{landfills}}, \textit{F\textsubscript{dums}} and \textit{F\textsubscript{plastic}} do only contain values for the year 2012. Abbreviations: N complete: number of countries with 100\% available data data from 1990-2015, N cluster: number of countries where the data was completed using the corresponding GDP-cluster averages.}
	\begin{tabularx}{\textwidth}{p{3cm} p{2.3cm} *{2}{p{1cm}} p{2cm} p{3cm} X }
		\toprule
		\multirow{2}{*}{\textbf{Predictor variable}} & \multirow{2}{*}{\textbf{N complete}} & \multicolumn{4}{l}{\textbf{Inter- or extrapolation of data}} & \multirow{2}{*}{\textbf{N cluster}} \\ \cline{3-6}
		& & N & Type & r\textsuperscript{2} (mean/median) & p (mean/median) & \\ \toprule
		Total population & 173 & 0 & & & & 0 \\
		Urban population & 173 & 0 & & & & 0 \\
		Young population & 164 & 1 & Lin & 0.76 / 0.76 & $1.5\cdot10^{-7}$ / $1.5\cdot10^{-7}$ & 8 \\
		Electricity access & 0 & 170 & Lin & 0.92 / 0.96 & $1.8\cdot10^{-4}$ / $4.8\cdot10^{-17}$ & 3 \\
		GDP & 144 & 29 & Poly & 0.90 / 0.92 & $4\cdot10^{-5}$ / $1.6\cdot10^{-13}$ & 0 \\ 
		GNI & 173 & 0 & & & & 0 \\
		Energy consumption & 34 & 112 & Lin & 0.57 / 0.62 & $9.5\cdot10^{-2}$ / $1.4\cdot10^{-4}$ & 27 \\ 
		Greenhouse gas & 0 & 162 & Lin & 0.42 / 0.39 & $1.1\cdot10^{-1}$ / $1.6\cdot10^{-3}$ & 11 \\  
		F\textsubscript{collection} & 0 & 78 & Lin & 0.70 / 0.74 & $8.4\cdot10^{-2}$ / $2.2\cdot10^{-3}$& 95 \\  
		F\textsubscript{recycled} & 0 & 60 & Lin & 0.68 / 0.74 & $8.8\cdot10^{-2}$ / $4.6\cdot10^{-6}$& 113 \\ 
		F\textsubscript{landfills}, F\textsubscript{dumps} & 68 (2012 only) & & & & & 105* \\
		F\textsubscript{plastic} & 95 (2012 only) & & & & & 78 \\ \bottomrule
		\multicolumn{7}{l}{\textit{* Missing data was not derived from cluster averages but from the corresponding values from \cite{Hoornweg2012} based on the GNI.}}
	\end{tabularx} 
	\label{tab:DataModification}
\end{table*}


\begin{landscape}
	\tocless\subsection{Data Completeness}
	\rowcolors{2}{}{color2!10}
	\begin{longtable}{p{1.8cm} p{1cm} p{0.5cm} *{2}{p{1.2cm}} *{4}{p{1.1cm}} p{0.9cm} *{4}{p{1.1cm}} p{1cm} p{0.8cm} }
	\caption{Overview of the data completeness of each dataset for each country. The years in the column header indicate the period of data availability. Each cell shows haw many data points were available in the whole period (number before the brackets) as well as in the selected period 1990-2015 (in the brackets). Countries with a bold formatting were grouped and those with a cursive formatting were dropped (compare table \ref{tab:DroppedCountries}). Note that the variable \textit{energy access} was not available for the year 2015. The variables \textit{GNI}, \textit{Landfills, Dumps} and \textit{MSW\textsubscript{plastic}} are only available for one year and it is thus indicated if the data was available or not. Table titles: Cl. = GDP-cluster, Tot. pop. = total population, Urb. pop. = urban population, Y. pop. = young population, E. acc. = electricity access, Collected = MSW\textsubscript{collected}, En. cons. = energy consumption, Gh. gas = greenhouse gas, collection = MSW\textsubscript{collection}, Recyc. = {MSW\textsubscript{recycled}, Lf, Dp = Landfills and dumps, Plastic = MSW\textsubscript{plastic}}}\\ \toprule
	\textbf{Country} & \textbf{Region} & \textbf{Cl.} & \textbf{Tot. pop. (1950-2050)} \cite{UnitedNations2018Pop} & \textbf{Urb. pop. (1950-2050)} \cite{UnitedNations2018Urb} & \textbf{Y. pop. (1960-2016)} \cite{TheWorldBank2014Young} & \textbf{El. acc. (1990-2014)} \cite{TheWorldBank2018Ele} & \textbf{Collec-ted (1990-2015)} \cite{UnitedNations2018MSWcollected} & \textbf{GDP (1960-2015)} \cite{TheWorldBank2018GDP} & \textbf{GNI (1960-2016)} \cite{TheWorldBank2018GNI} & \textbf{En. cons. (1990-2015)} \cite{TheWorldBank2018Ene} & \textbf{Gh. gas (1970-2012)} \cite{TheWorldBank2018GHG} & \textbf{Collec-tion (1990-2015)} \cite{UnitedNations2018MSWcollection} & \textbf{Recyc. (1990, 1995-2013)} \cite{UnitedNations2018MSWrecycled} & \textbf{Lf, Dp (2012)} \cite{Hoornweg2012} & \textbf{Plastic (2012)} \cite{Hoornweg2012} \\ \toprule 
	\endfirsthead
	\multicolumn{14}{l}{{\bfseries \sffamily \small \tablename\ \thetable\ } -- Continued from previous page}\\ \toprule
	\textbf{Country} & \textbf{Region} & \textbf{Cl.} & \textbf{Tot. pop.} & \textbf{Urb. pop.} & \textbf{Y. pop.} & \textbf{El. acc.} & \textbf{Collec-ted} & \textbf{GDP} & \textbf{GNI} & \textbf{En. cons.} & \textbf{Gh. gas} & \textbf{Collec-tion} & \textbf{Recyc.} & \textbf{Lf, Dumps} & \textbf{Plastic} \\ \toprule
	\endhead
		Country & Continent & Cluster & tot pop (1950-2050) & urb\_pop(1950-2050) & young pop(1960-2016) & elec acc (1960-2014) & MSW collected & GDP (1960-2016) & GNI & energy consumption (1990-2015) & GHG (1970-2012) & MSW collection & MSW recycled & Dumps & Plastic \\ \hline
			Burundi & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 11 (11) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Comoros & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 37 (26) & L & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Djibouti & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 30 (26) & LM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Eritrea & Africa & 1 & 101 (26) & 101 (26) & 52 (22) & 25 (25) & 0 (0) & 20 (20) & L & 20 (20) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Ethiopia & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 36 (26) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 2 \\ \hline
			Kenya & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 5 (5) & 0 (0) & No data & 0 \\ \hline
			Madagascar & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 2 (2) & 57 (26) & L & 0 (0) & 43 (23) & 3 (3) & 3 (3) & 0 / 97 & 1 \\ \hline
			Malawi & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Mauritius & Africa & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 13 (13) & 41 (26) & UM & 25 (25) & 24 (4) & 6 (6) & 3 (3) & 0 / 91 & 9 \\ \hline
			\textit{Mayotte} & Africa & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Mozambique & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 37 (26) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 10 \\ \hline
			\textit{Réunion} & Africa & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 8 (8) & 0 (0) & No data & 0 (0) & 0 (0) & 4 (4) & 0 (0) & No data & No data \\ \hline
			Rwanda & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Seychelles & Africa & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & H & 5 (5) & 36 (16) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Somalia & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 23 (23) & 0 (0) & 4 (3) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			South Sudan & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 13 (13) & 0 (0) & 8 (8) & L & 3 (3) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Uganda & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 29 (26) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & 0 / 100 & 1 \\ \hline
			United Republic of Tanzania & Africa & 1 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 27 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Zambia & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 2 (2) & 57 (26) & LM & 25 (25) & 32 (18) & 4 (4) & 0 (0) & No data & 5 \\ \hline
			Zimbabwe & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & L & 24 (24) & 43 (23) & 0 (0) & 0 (0) & No data & 20 \\ \hline
			Angola & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 32 (26) & UM & 25 (25) & 43 (23) & 1 (1) & 0 (0) & No data & 0 \\ \hline
			Cameroon & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 4 (4) & 57 (26) & LM & 25 (25) & 43 (23) & 2 (2) & 4 (4) & 95 / 0 & 5 \\ \hline
			Central African Republic & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Chad & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Congo & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Democratic Republic of the Congo & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 56 (25) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Equatorial Guinea & Africa & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 53 (26) & UM & 4 (4) & 40 (20) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Gabon & Africa & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Sao Tome and Principe & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 16 (15) & No data & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Algeria & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 57 (26) & UM & 25 (25) & 43 (23) & 4 (4) & 1 (1) & 97 / 0 & 5 \\ \hline
			Egypt & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 4 (4) & 52 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 3 (3) & No data & 12 \\ \hline
			Libya & Africa & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 22 (22) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Morocco & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 1 (1) & 95 / 1 & 4 \\ \hline
			Sudan & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 8 (8) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Tunisia & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 6 (6) & 52 (26) & LM & 25 (25) & 43 (23) & 4 (4) & 2 (2) & 58 / 42 & 11 \\ \hline
			\textit{Western Sahara} & Africa & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Botswana & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Lesotho & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 17 (17) & 0 (0) & 57 (26) & LM & 4 (4) & 43 (23) & 1 (1) & 0 (0) & No data & 0 \\ \hline
			Namibia & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 37 (26) & UM & 24 (24) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			South Africa & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 27 (26) & UM & 24 (24) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Swaziland & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 57 (26) & LM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Benin & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 7 \\ \hline
			Burkina Faso & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 14 (14) & 57 (26) & L & 0 (0) & 43 (23) & 6 (6) & 0 (0) & No data & 0 \\ \hline
			Cabo Verde & Africa & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 11 (11) & 37 (26) & LM & 5 (5) & 38 (18) & 11 (11) & 0 (0) & No data & 0 \\ \hline
			Côte d'Ivoire & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Gambia & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 51 (26) & L & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Ghana & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 4 \\ \hline
			Guinea & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 31 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 4 \\ \hline
			Guinea-Bissau & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 11 (11) & 0 (0) & 47 (26) & L & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Liberia & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 12 (12) & 0 (0) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 13 \\ \hline
			Mali & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 0 (0) & 50 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 2 \\ \hline
			Mauritania & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textit{Niger} & Africa & No data & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 8 (8) & 0 (0) & L & 15 (15) & 43 (23) & 0 (0) & 8 (8) & 0 / 64 & 2 \\ \hline
			Nigeria & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 18 \\ \hline
			\textit{Saint Helena} & Africa & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Senegal & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 57 (26) & L & 25 (25) & 43 (23) & 8 (8) & 0 (0) & No data & 3 \\ \hline
			Sierra Leone & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & L & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Togo & Africa & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 3 (3) & 57 (26) & L & 25 (25) & 43 (23) & 0 (0) & 2 (2) & No data & 10 \\ \hline
			China & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 13 (13) & 57 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			China, Hong Kong SAR & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 16 (16) & 57 (26) & H & 25 (25) & 43 (23) & 16 (16) & 16 (16) & No data & No data \\ \hline
			China, Macao SAR & Asia & 6 & 101 (26) & 101 (26) & 57 (26) & 21 (21) & 16 (16) & 35 (26) & H & 0 (0) & 14 (2) & 16 (16) & 30 (30) & No data & No data \\ \hline
			Dem. People's Republic of Korea & Asia & No data & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 0 (0) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Japan & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 24 (24) & 57 (26) & H & 26 (26) & 43 (23) & 2 (2) & 20 (20) & 0 / 3 & 9 \\ \hline
			Mongolia & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 36 (26) & LM & 25 (25) & 41 (21) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Republic of Korea & Asia & 5 & 101 (26) & 101 (26) & 57 (26) & 20 (20) & 24 (24) & 57 (26) & H & 26 (26) & 43 (23) & 1 (1) & 18 (18) & No data & No data \\ \hline
			Kazakhstan & Asia & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 5 (5) & 27 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textit{Kyrgyzstan} & Asia & No data & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 19 (19) & 0 (0) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Tajikistan & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 27 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Turkmenistan & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 30 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Uzbekistan & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 26 (26) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Afghanistan & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 16 (16) & 0 (0) & 38 (15) & L & 0 (0) & 42 (22) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Bangladesh & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 7 \\ \hline
			Bhutan & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 1 (1) & 37 (26) & LM & 5 (5) & 43 (23) & 1 (1) & 1 (1) & No data & 13 \\ \hline
			India & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 2 \\ \hline
			Iran (Islamic Republic of) & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 55 (24) & UM & 25 (25) & 38 (18) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Maldives & Asia & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 16 (16) & 37 (26) & UM & 5 (5) & 20 (5) & 5 (5) & 0 (0) & No data & 0 \\ \hline
			Nepal & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 2 (2) & 57 (26) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 3 \\ \hline
			Pakistan & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 18 \\ \hline
			Sri Lanka & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 57 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 6 \\ \hline
			Brunei Darussalam & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 7 (7) & 52 (26) & H & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 2 \\ \hline
			Cambodia & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 24 (24) & 4 (4) & 39 (23) & LM & 20 (20) & 43 (23) & 0 (0) & 0 (0) & 100 / 0 & 10 \\ \hline
			Indonesia & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 50 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 10 \\ \hline
			Lao People's Democratic Republic & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 33 (26) & LM & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Malaysia & Asia & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 16 (16) & 57 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 12 \\ \hline
			Myanmar & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 17 (16) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 16 \\ \hline
			Philippines & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 57 (26) & LM & 25 (25) & 43 (23) & 3 (3) & 0 (0) & No data & 14 \\ \hline
			Singapore & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 57 (26) & H & 25 (25) & 11 (11) & 20 (20) & 19 (19) & 0 / 15 & 12 \\ \hline
			Thailand & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & 33 / 59 & 14 \\ \hline
			Timor-Leste & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 17 (16) & LM & 4 (4) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Viet Nam & Asia & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 48 (18) & LM & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Armenia & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 19 (19) & 27 (26) & LM & 25 (25) & 43 (23) & 19 (19) & 16 (16) & 0 / 100 & 10 \\ \hline
			Azerbaijan & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 13 (13) & 27 (26) & UM & 25 (25) & 42 (22) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Bahrain & Asia & 5 & 101 (26) & 101 (26) & 57 (26) & 14 (14) & 0 (0) & 37 (26) & H & 25 (25) & 38 (18) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Cyprus & Asia & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 42 (26) & H & 25 (25) & 41 (21) & 14 (14) & 19 (19) & 0 / 87 & 11 \\ \hline
			Georgia & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 27 (26) & UM & 25 (25) & 43 (23) & 9 (9) & 0 (0) & No data & 3 \\ \hline
			Iraq & Asia & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 7 (7) & 41 (13) & UM & 25 (25) & 43 (23) & 7 (7) & 0 (0) & No data & 0 \\ \hline
			Israel & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 19 (19) & 57 (26) & H & 26 (26) & 43 (23) & 0 (0) & 10 (10) & 0 / 90 & 13 \\ \hline
			Jordan & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 6 (6) & 52 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & 0 / 85 & 16 \\ \hline
			Kuwait & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 13 (13) & 52 (26) & H & 22 (22) & 41 (21) & 19 (19) & 18 (18) & No data & 0 \\ \hline
			Lebanon & Asia & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 4 (4) & 29 (26) & UM & 25 (25) & 35 (17) & 0 (0) & 4 (4) & 37 / 46 & 7 \\ \hline
			\textit{State of Palestine} & Asia & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 15 (15) & 0 (0) & No data & 0 (0) & 0 (0) & 11 (11) & 24 (24) & No data & No data \\ \hline
			Oman & Asia & 5 & 101 (26) & 101 (26) & 57 (26) & 8 (8) & 0 (0) & 52 (26) & H & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Qatar & Asia & 6 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 5 (5) & 47 (26) & H & 25 (25) & 37 (17) & 5 (5) & 2 (2) & No data & 0 \\ \hline
			Saudi Arabia & Asia & 5 & 101 (26) & 101 (26) & 57 (26) & 11 (11) & 0 (0) & 49 (26) & H & 25 (25) & 28 (13) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Syrian Arab Republic & Asia & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 48 (18) & LM & 25 (25) & 42 (23) & 2 (2) & 0 (0) & No data & No data \\ \hline
			Turkey & Asia & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 22 (22) & 57 (26) & UM & 26 (26) & 43 (23) & 15 (15) & 20 (20) & 66 / 30 & 9 \\ \hline
			United Arab Emirates & Asia & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 6 (6) & 42 (26) & H & 25 (25) & 43 (23) & 1 (1) & 3 (3) & No data & 0 \\ \hline
			\textit{West Bank and Gaza} & Asia & No data & 0 (0) & 0 (0) & 27 (26) & 25 (25) & 0 (0) & 23 (22) & LM & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Yemen & Asia & 1 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 14 (14) & 17 (16) & LM & 15 (15) & 0 (0) & 11 (11) & 0 (0) & No data & 0 \\ \hline
			Belarus & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 15 (15) & 27 (26) & UM & 25 (25) & 43 (23) & 17 (17) & 0 (0) & 0 / 96 & 10 \\ \hline
			Bulgaria & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 37 (26) & UM & 25 (25) & 43 (23) & 15 (15) & 31 (31) & 0 / 83 & 0 \\ \hline
			Czech Republic & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 27 (26) & H & 26 (26) & 43 (23) & 12 (12) & 27 (27) & 0 / 80 & 4 \\ \hline
			Hungary & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 26 (25) & H & 26 (26) & 43 (23) & 17 (17) & 22 (22) & 0 / 90 & 17 \\ \hline
			Poland & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 25 (25) & 27 (26) & H & 26 (26) & 43 (23) & 7 (7) & 27 (27) & 0 / 92 & 10 \\ \hline
			Republic of Moldova & Europe & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 18 (18) & 22 (21) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Romania & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 30 (26) & UM & 25 (25) & 43 (23) & 14 (14) & 29 (29) & 0 / 75 & 3 \\ \hline
			Russian Federation & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 2 (2) & 28 (26) & UM & 25 (25) & 43 (23) & 0 (0) & 4 (4) & No data & No data \\ \hline
			Slovakia & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 22 (22) & 27 (26) & H & 26 (26) & 43 (23) & 9 (9) & 18 (18) & No data & 7 \\ \hline
			Ukraine & Europe & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 12 (12) & 57 (26) & LM & 0 (0) & 43 (23) & 0 (0) & 1 (1) & No data & 0 \\ \hline
			\textit{Channel Islands} & Europe & No data & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 10 (10) & H & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Denmark & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 57 (26) & H & 26 (26) & 43 (23) & 15 (15) & 21 (21) & 0 / 5 & 1 \\ \hline
			Estonia & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 22 (21) & H & 26 (26) & 43 (23) & 15 (15) & 29 (29) & No data & 0 \\ \hline
			Faroe Islands & Europe & 4 & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 18 (18) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Finland & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 57 (26) & H & 26 (26) & 43 (23) & 16 (16) & 19 (19) & No data & 10 \\ \hline
			Iceland & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 19 (19) & 57 (26) & H & 26 (26) & 43 (23) & 17 (17) & 16 (16) & 0 / 72 & 17 \\ \hline
			Ireland & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 18 (18) & 57 (26) & H & 26 (26) & 43 (23) & 1 (1) & 26 (26) & 0 / 66 & 11 \\ \hline
			Isle of Man & Europe & 6 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 21 (21) & H & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Latvia & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 22 (21) & H & 25 (25) & 43 (23) & 15 (15) & 13 (13) & 60 / 40 & 0 \\ \hline
			Lithuania & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 22 (21) & H & 25 (25) & 43 (23) & 3 (3) & 22 (22) & 0 / 44 & 0 \\ \hline
			Norway & Europe & 6 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 24 (24) & 57 (26) & H & 26 (26) & 43 (23) & 16 (16) & 21 (21) & 0 / 26 & 9 \\ \hline
			Sweden & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 22 (22) & 57 (26) & H & 26 (26) & 43 (23) & 6 (6) & 20 (20) & 0 / 5 & 2 \\ \hline
			United Kingdom & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 57 (26) & H & 26 (26) & 43 (23) & 14 (14) & 21 (21) & 0 / 64 & 0 \\ \hline
			Albania & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 10 (10) & 33 (26) & UM & 25 (25) & 43 (23) & 5 (5) & 0 (0) & No data & 8 \\ \hline
			Andorra & Europe & 4 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 18 (18) & 47 (26) & H & 0 (0) & 0 (0) & 22 (22) & 18 (18) & No data & 14 \\ \hline
			Bosnia and Herzegovina & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 8 (8) & 23 (22) & UM & 25 (25) & 43 (23) & 8 (8) & 0 (0) & No data & 0 \\ \hline
			Croatia & Europe & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 14 (14) & 22 (21) & H & 25 (25) & 43 (23) & 9 (9) & 9 (9) & 0 / 70 & 12 \\ \hline
			\textit{Gibraltar} & Europe & No data & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 0 (0) & H & 25 (25) & 29 (18) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Greece & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 57 (26) & H & 26 (26) & 43 (23) & 18 (18) & 20 (20) & 0 / 92 & 9 \\ \hline
			\textit{Holy See} & Europe & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Italy & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 23 (23) & 57 (26) & H & 26 (26) & 43 (23) & 14 (14) & 20 (20) & 0 / 54 & 5 \\ \hline
			\textit{Kosovo} & Europe & No data & 0 (0) & 0 (0) & 57 (26) & 25 (25) & 0 (0) & 35 (26) & LM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Malta & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 47 (26) & H & 25 (25) & 38 (18) & 15 (15) & 23 (23) & 0 / 88 & 0 \\ \hline
			Montenegro & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 10 (10) & 17 (16) & UM & 10 (10) & 0 (0) & 2 (2) & 0 (0) & No data & 0 \\ \hline
			Portugal & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 24 (24) & 57 (26) & H & 26 (26) & 43 (23) & 17 (17) & 19 (19) & 0 / 64 & 11 \\ \hline
			San Marino & Europe & 6 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 18 (17) & H & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Serbia & Europe & 2 & 101 (26) & 101 (26) & 27 (26) & 25 (25) & 10 (10) & 22 (21) & UM & 25 (25) & 0 (0) & 10 (10) & 7 (7) & No data & 12 \\ \hline
			Slovenia & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 22 (21) & H & 26 (26) & 43 (23) & 11 (11) & 26 (26) & 0 / 86 & 0 \\ \hline
			Spain & Europe & 5 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 20 (20) & 57 (26) & H & 26 (26) & 43 (23) & 5 (5) & 6 (6) & 0 / 52 & 12 \\ \hline
			TFYR Macedonia & Europe & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 9 (9) & 27 (26) & UM & 25 (25) & 37 (17) & 8 (8) & 4 (4) & No data & No data \\ \hline
			Austria & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 25 (25) & 57 (26) & H & 26 (26) & 43 (23) & 16 (16) & 22 (22) & 0 / 7 & 11 \\ \hline
			Belgium & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 57 (26) & H & 26 (26) & 43 (23) & 19 (19) & 20 (20) & 0 / 12 & 5 \\ \hline
			France & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 22 (22) & 57 (26) & H & 26 (26) & 43 (23) & 16 (16) & 20 (20) & No data & 9 \\ \hline
			Germany & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 47 (26) & H & 26 (26) & 43 (23) & 16 (16) & 26 (26) & No data & 22 \\ \hline
			Liechtenstein & Europe & 7 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 22 (22) & 46 (26) & H & 0 (0) & 0 (0) & 22 (22) & 19 (19) & No data & 0 \\ \hline
			Luxembourg & Europe & 6 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 25 (25) & 57 (26) & H & 26 (26) & 43 (23) & 19 (19) & 19 (19) & 0 / 19 & 1 \\ \hline
			Monaco & Europe & 7 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 21 (21) & 42 (22) & H & 0 (0) & 0 (0) & 22 (22) & 33 (33) & 0 / 27 & 0 \\ \hline
			Netherlands & Europe & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 24 (24) & 57 (26) & H & 26 (26) & 43 (23) & 15 (15) & 20 (20) & 0 / 2 & 19 \\ \hline
			Switzerland & Europe & 6 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 25 (25) & 47 (26) & H & 26 (26) & 43 (23) & 20 (20) & 20 (20) & 0 / 1 & 15 \\ \hline
			\textit{Anguilla} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 8 (8) & 0 (0) & No data & 0 (0) & 0 (0) & 8 (8) & 9 (9) & No data & No data \\ \hline
			\textbf{Antigua and Barbuda} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 11 (11) & 40 (26) & H & 5 (5) & 31 (21) & 11 (11) & 15 (15) & 0 / 99 & 0 \\ \hline
			\textbf{Aruba} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 18 (18) & H & 0 (0) & 18 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Bahamas}, The & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 57 (26) & H & 5 (5) & 33 (23) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Barbados} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 15 (15) & 0 (0) & 27 (26) & H & 5 (5) & 34 (14) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{British Virgin Islands} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 8 (8) & 55 (25) & H & 24 (24) & 43 (23) & 1 (1) & 8 (8) & No data & 0 \\ \hline
			\textbf{Cayman Islands} & North America & 3 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 2 (2) & H & 0 (0) & 18 (2) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Cuba} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 14 (14) & 46 (26) & UM & 25 (25) & 43 (23) & 14 (14) & 12 (12) & 0 / 100 & 10 \\ \hline
			\textbf{Dominica} & North America & 3 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 5 (5) & 40 (26) & UM & 5 (5) & 43 (23) & 5 (5) & 0 (0) & 0 / 100 & 0 \\ \hline
			\textbf{Dominican Republic} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 25 (25) & 43 (23) & 9 (9) & 0 (0) & No data & 36 \\ \hline
			\textbf{Grenada} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 40 (26) & UM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & 0 / 90 & 0 \\ \hline
			\textit{Guadeloupe} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 6 (6) & 0 (0) & No data & 0 (0) & 0 (0) & 4 (4) & 1 (1) & No data & No data \\ \hline
			\textbf{Haiti} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 26 (25) & L & 25 (25) & 43 (23) & 0 (0) & 0 (0) & 13 / 59 & 0 \\ \hline
			\textbf{Jamaica} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 7 (7) & 57 (26) & UM & 25 (25) & 41 (23) & 2 (2) & 0 (0) & 0 / 100 & 18 \\ \hline
			\textit{Martinique} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 6 (6) & 0 (0) & No data & 0 (0) & 0 (0) & 10 (10) & 1 (1) & No data & No data \\ \hline
			\textit{Montserrat} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textit{Sint Maarten (Dutch part)} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textit{Caribbean Netherlands} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textit{Curaçao} & North America & No data & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 0 (0) & H & 25 (25) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Puerto Rico} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 13 (13) & 0 (0) & 54 (24) & H & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Saint Kitts and Nevis} & North America & 3 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 57 (26) & H & 5 (5) & 20 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Saint Lucia} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 12 (12) & 40 (26) & UM & 5 (5) & 41 (21) & 12 (12) & 8 (8) & No data & No data \\ \hline
			\textit{St. Martin (French part)} & North America & No data & 0 (0) & 0 (0) & 0 (0) & 25 (25) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Saint Vincent and the Grenadines} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 27 (26) & UM & 24 (24) & 43 (23) & 1 (1) & 2 (2) & No data & No data \\ \hline
			\textbf{Trinidad and Tobago} & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 9 (9) & 57 (26) & H & 25 (25) & 16 (14) & 1 (1) & 0 (0) & 43 / 0 & 24 \\ \hline
			\textit{Turks and Caicos Islands} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 0 (0) & H & 0 (0) & 25 (5) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{United States Virgin Islands} & North America & 3 & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & H & 0 (0) & 22 (3) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Belize & North America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 2 (2) & 57 (26) & UM & 5 (5) & 43 (23) & 16 (16) & 0 (0) & 0 / 100 & 5 \\ \hline
			Costa Rica & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 57 (26) & UM & 25 (25) & 43 (23) & 1 (1) & 0 (0) & 22 / 72 & 18 \\ \hline
			El Salvador & North America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 52 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			Guatemala & North America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 25 (25) & 43 (23) & 4 (4) & 0 (0) & 49 / 11 & 13 \\ \hline
			Honduras & North America & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 57 (26) & LM & 25 (25) & 43 (23) & 3 (3) & 0 (0) & No data & 0 \\ \hline
			Mexico & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 22 (22) & 57 (26) & UM & 26 (26) & 43 (23) & 2 (2) & 18 (18) & 0 / 97 & 6 \\ \hline
			Nicaragua & North America & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 28 (26) & LM & 25 (25) & 43 (23) & 0 (0) & 0 (0) & 34 / 28 & 0 \\ \hline
			Panama & North America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 21 (21) & 57 (26) & UM & 25 (25) & 43 (23) & 8 (8) & 0 (0) & 20 / 56 & 11 \\ \hline
			Argentina & South America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 5 (5) & 55 (26) & UM & 25 (25) & 43 (23) & 2 (2) & 0 (0) & No data & 14 \\ \hline
			Bolivia (Plurinational State of) & South America & 1 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 17 (17) & 57 (26) & LM & 25 (25) & 43 (23) & 11 (11) & 0 (0) & No data & No data \\ \hline
			Brazil & South America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 11 (11) & 57 (26) & UM & 25 (25) & 43 (23) & 18 (18) & 1 (1) & No data & 15 \\ \hline
			Chile & South America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 12 (12) & 57 (26) & H & 26 (26) & 43 (23) & 0 (0) & 0 (0) & 0 / 100 & 10 \\ \hline
			Colombia & South America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 9 (9) & 57 (26) & UM & 25 (25) & 43 (23) & 6 (6) & 7 (7) & 54 / 46 & 10 \\ \hline
			Ecuador & South America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 2 (2) & 57 (26) & UM & 25 (25) & 43 (23) & 9 (9) & 2 (2) & No data & 0 \\ \hline
			\textit{Falkland Islands (Malvinas)} & South America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textit{French Guiana} & South America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 9 (9) & 0 (0) & No data & 0 (0) & 0 (0) & 3 (3) & 0 (0) & No data & No data \\ \hline
			Guyana & South America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & 37 / 59 & 10 \\ \hline
			Paraguay & South America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 52 (26) & UM & 25 (25) & 43 (23) & 18 (18) & 0 (0) & 42 / 44 & 0 \\ \hline
			Peru & South America & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 57 (26) & UM & 25 (25) & 43 (23) & 6 (6) & 1 (1) & 19 / 66 & 4 \\ \hline
			Suriname & South America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 9 (9) & 57 (26) & UM & 15 (15) & 41 (21) & 3 (3) & 0 (0) & 100 / 0 & 0 \\ \hline
			Uruguay & South America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 3 (3) & 30 (26) & H & 25 (25) & 43 (23) & 0 (0) & 3 (3) & 32 / 3 & 11 \\ \hline
			Venezuela (Bolivarian Republic of) & South America & 3 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 5 (5) & 42 (22) & 0 (0) & 0 (0) & No data & No data \\ \hline
			Bermuda & North America & 6 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 12 (12) & 54 (24) & H & 0 (0) & 37 (20) & 12 (12) & 12 (12) & No data & 0 \\ \hline
			Canada & North America & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & H & 26 (26) & 43 (23) & 1 (1) & 0 (0) & 0 / 42 & 3 \\ \hline
			Greenland & North America & 4 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 46 (26) & H & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textit{Saint Pierre and Miquelon} & North America & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			United States of America & North America & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 23 (23) & 57 (26) & H & 25 (25) & 43 (23) & 2 (2) & 19 (19) & No data & No data \\ \hline
			Australia & Oceania & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 4 (4) & 57 (26) & H & 26 (26) & 43 (23) & 0 (0) & 3 (3) & 0 / 70 & 4 \\ \hline
			New Zealand & Oceania & 4 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 11 (11) & 56 (26) & H & 26 (26) & 43 (23) & 0 (0) & 0 (0) & 0 / 85 & 8 \\ \hline
			\textbf{Fiji} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & UM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 8 \\ \hline
			\textbf{New Caledonia} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 20 (20) & 0 (0) & 36 (11) & H & 0 (0) & 9 (4) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Papua New Guinea} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 57 (26) & LM & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Solomon Islands} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 23 (23) & 0 (0) & 49 (26) & LM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 17 \\ \hline
			\textbf{Vanuatu} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 32 (26) & LM & 24 (24) & 43 (23) & 0 (0) & 0 (0) & No data & 8 \\ \hline
			\textbf{Guam} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 20 (20) & 0 (0) & 15 (14) & H & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Kiribati} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 47 (26) & LM & 5 (5) & 27 (17) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Marshall Islands} & Oceania & 2 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 1 (1) & 36 (26) & UM & 4 (4) & 20 (0) & 1 (1) & 1 (1) & 33 / 59 & 15 \\ \hline
			\textbf{Micronesia (Fed. States of)} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 32 (26) & No data & 0 (0) & 27 (7) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Nauru} & Oceania & 2 & 101 (26) & 101 (26) & 0 (0) & 15 (15) & 0 (0) & 10 (9) & H & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Northern Mariana Islands} & Oceania & 2 & 101 (26) & 101 (26) & 0 (0) & 20 (20) & 0 (0) & 15 (14) & H & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{Palau} & Oceania & 2 & 101 (26) & 101 (26) & 0 (0) & 25 (25) & 0 (0) & 27 (26) & UM & 1 (1) & 0 (0) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textbf{American Samoa} & Oceania & 2 & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 15 (14) & UM & 0 (0) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textit{Cook Islands} & Oceania & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{French Polynesia} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 1 (1) & 36 (11) & H & 0 (0) & 21 (11) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textit{Niue} & Oceania & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Samoa} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 38 (26) & LM & 5 (5) & 43 (23) & 0 (0) & 0 (0) & No data & 0 \\ \hline
			\textit{Tokelau} & Oceania & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textbf{Tonga} & Oceania & 2 & 101 (26) & 101 (26) & 57 (26) & 25 (25) & 0 (0) & 42 (26) & LM & 5 (5) & 42 (22) & 0 (0) & 0 (0) & No data & 5 \\ \hline
			\textit{Tuvalu} & Oceania & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & UM & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \hline
			\textit{Wallis and Futuna Islands} & Oceania & No data & 101 (26) & 101 (26) & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & 0 (0) & 0 (0) & 0 (0) & 0 (0) & No data & No data \\ \bottomrule
	\label{tab:DataCompleteness}	
	\end{longtable}
\end{landscape}

\tocless\subsection{Predictor Variable Distribution of Erroneous Countries}
\begin{figure}[H]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\columnwidth]{img/3d_predictors.pdf}
	\caption{Three dimensional representation of the three predictors that were used in the NNET and RF models to estimate MSW\textsubscript{collected}. The countries in red have an prediction error \textgreater30\%. The prediction of countries with a high fraction of population \textless14 years and a low fraction of urban population or with a steep gradient between these variables is often erroneous. Only three countries do not show these traits: Argentina Russia and Ukraine}
	\label{fig:3DPredictors}
\end{figure}
\begin{figure}[H]
	\renewcommand{\arraystretch}{1.2}
	\centering
	\includegraphics[width=\columnwidth, angle=270]{img/predictor_comparison_erroneous_countries.pdf}
	\caption{Scatterplot matrix of the three predictors that were used in the NNET and RF models and MSW\textsubscript{collected}. The coloured lines represent countries countries that have a prediction error \textgreater30\%.}
	\label{fig:PredictorComparison}
\end{figure}

\newpage
\tocless\subsection{Predictions of P\textsubscript{O} from coastal sources and in total}
\begin{table}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\rowcolors{32}{}{color2!10}
	\caption{Summary of the modelled coastal plastic input to the ocean in 1990 and 2015 for the income classes, the continents and worldwide. See section \ref{sec:PredictedPO} for more details.}
	\begin{tabularx}{\columnwidth}{>{\centering}p{0.3cm} X *{2}{>{\raggedleft\arraybackslash}p{1.2cm}} }
		\toprule 
		& \multirow{2}{5cm}{\textbf{Subset}} & \multicolumn{2}{c}{\begin{minipage}{2.8cm}\textbf{Total marine plastic input [kt/y]}\end{minipage}} \\ \cline{3-4}
		& & 1990 & 2015 \\
		\toprule
		\cellcolor{white} & High-income & 1'299 & 1'640\\
		\cellcolor{white} & Upper middle-income & 9'318 & 9'762 \\
		\cellcolor{white} & Lower middle-income & 2'309 & 6'117 \\
		\cellcolor{white} \multirow{-4}{0.3cm}{\rotatebox[origin=rigin=c]{90}{Income}} & Low-income & 44 & 173 \\ \hline
		\cellcolor{white} & Africa & 279 & 728 \\
		\cellcolor{white} & Asia & 11'2162 & 15'015 \\
		\cellcolor{white} & Europe & 900 & 733 \\
		\cellcolor{white} & North America \& Caribbean & 331 & 587 \\
		\cellcolor{white} & South America & 213 & 593\\
		\cellcolor{white} \multirow{-6}{0.3cm}{\rotatebox[origin=rigin=c]{90}{Continents}} & Oceania & 29 & 36 \\ \hline
		\cellcolor{white} & World & 12'972 & 17'693 \\ \bottomrule
	\end{tabularx}
	\label{tab:ocean_flux_shore}
\end{table}

\begin{table}[htbp]
	\renewcommand{\arraystretch}{1.2}
	\rowcolors{32}{}{color2!10}
	\caption{Summary of the modelled total plastic input to the ocean in 1990 and 2015 for the income classes, the continents and worldwide. See section \ref{sec:PredictedPO} for more details.}
	\begin{tabularx}{\columnwidth}{>{\centering}p{0.3cm} X *{2}{>{\raggedleft\arraybackslash}p{1.2cm}} }
		\toprule 
		& \multirow{2}{5cm}{\textbf{Subset}} & \multicolumn{2}{c}{\begin{minipage}{2.8cm}\textbf{Total marine plastic input [kt/y]}\end{minipage}} \\ \cline{3-4}
		& & 1990 & 2015 \\
		\toprule
		\cellcolor{white} & High-income & 1'372 & 1'735\\
		\cellcolor{white} & Upper middle-income & 9'410 & 10'054 \\
		\cellcolor{white} & Lower middle-income & 2'604 & 6'755 \\
		\cellcolor{white} \multirow{-4}{0.3cm}{\rotatebox[origin=rigin=c]{90}{Income}} & Low-income & 66 & 208 \\ \hline
		\cellcolor{white} & Africa & 340 & 864 \\
		\cellcolor{white} & Asia & 11'442 & 15'754 \\
		\cellcolor{white} & Europe & 1'063 & 847 \\
		\cellcolor{white} & North America \& Caribbean & 359 & 637 \\
		\cellcolor{white} & South America & 220 & 615\\
		\cellcolor{white} \multirow{-6}{0.3cm}{\rotatebox[origin=rigin=c]{90}{Continents}} & Oceania & 31 & 37 \\ \hline
		\cellcolor{white} & World & 13'455 & 18'754 \\ \bottomrule
	\end{tabularx}
	\label{tab:ocean_flux_total}
\end{table}

\clearpage

%\includepdf[addtotoc={1,subsection,2,Declaration of Originality,1}, pagecommand={\pagestyle{fancy}}]{Declaration-Originality}
\includepdf[pagecommand={\pagestyle{fancy}}]{Declaration-Originality}