Sustainable Development Goals: Analysis by Mathematics of Uncertainty [1st ed.] 9783030485221, 9783030485238

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Studies in Systems, Decision and Control 299

John N. Mordeson Sunil Mathew

Sustainable Development Goals: Analysis by Mathematics of Uncertainty

Studies in Systems, Decision and Control Volume 299

Series Editor Janusz Kacprzyk, Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

The series “Studies in Systems, Decision and Control” (SSDC) covers both new developments and advances, as well as the state of the art, in the various areas of broadly perceived systems, decision making and control–quickly, up to date and with a high quality. The intent is to cover the theory, applications, and perspectives on the state of the art and future developments relevant to systems, decision making, control, complex processes and related areas, as embedded in the fields of engineering, computer science, physics, economics, social and life sciences, as well as the paradigms and methodologies behind them. The series contains monographs, textbooks, lecture notes and edited volumes in systems, decision making and control spanning the areas of Cyber-Physical Systems, Autonomous Systems, Sensor Networks, Control Systems, Energy Systems, Automotive Systems, Biological Systems, Vehicular Networking and Connected Vehicles, Aerospace Systems, Automation, Manufacturing, Smart Grids, Nonlinear Systems, Power Systems, Robotics, Social Systems, Economic Systems and other. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution and exposure which enable both a wide and rapid dissemination of research output. ** Indexing: The books of this series are submitted to ISI, SCOPUS, DBLP, Ulrichs, MathSciNet, Current Mathematical Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and Springerlink.

More information about this series at http://www.springer.com/series/13304

John N. Mordeson Sunil Mathew •

Sustainable Development Goals: Analysis by Mathematics of Uncertainty

123

John N. Mordeson Department of Mathematics Creighton University Omaha, NE, USA

Sunil Mathew Department of Mathematics National Institute of Technology Calicut Calicut, Kerala, India

ISSN 2198-4182 ISSN 2198-4190 (electronic) Studies in Systems, Decision and Control ISBN 978-3-030-48522-1 ISBN 978-3-030-48523-8 (eBook) https://doi.org/10.1007/978-3-030-48523-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

John N. Mordeson would like to dedicate the book to climate activists. Sunil Mathew would like to dedicate the book to the victims of climate change, poverty and trafficking.

Preface

Possibly the most serious threat facing the world today is climate change. Climate change is one of the Sustainable Development Goals (SDGs). All members of the United Nations adopted Agenda 2030 and the SDGs in 2015. Climate has a negative influence on the horrible crimes of human trafficking and modern slavery. There have been many strong papers written on these issues. One paper may use linguistics such as low, medium, high to measure a country’s achievement of a various goal or target. Another research article may use colors to measure a country’s achievement. Yet another may use numbers to measure achievement. The purpose of this book is to place the study in a well established mathematical context. We use mathematics of uncertainty to accomplish this. The issues of sustainability, climate change, human trafficking, and modern slavery are good candidates for the use of mathematics of uncertainty due to the lack of accurate data available. After having placed these strong papers in a mathematical model, our main goal is to rank the countries with respect to their achievement of the SDGs. The approach involved also allows us to rank the countries with respect to their achievement in combatting human trafficking and modern slavery. The placement of sustainability in a specific mathematical model allows the issue of sustainability to be studied in the future by a wide range of mathematical techniques. The overall SDG index score and ranking is sensitive to methodological choices including the methods for aggregation and weighting. In our case, the reader should also be aware that at times we had to make adjustments for missing data. These adjustments affect the rankings. Consequently, one should not draw serious conclusions about the ranks of countries that differ a little. In Chap. 1, we present the mathematics needed to understand the rest of the book. The topics include fuzzy sets, operations on fuzzy sets, fuzzy similarity measures, fuzzy graphs, and evidence theory.

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In Chap. 2, we use the Analytical Hierarchy Process (AHP), the Guiasu method, and the Yen method to provide a ranking of countries with respect to their achievement of the SDGs. This is accomplished by constructing linear equations involving the 17 SDGs as independent variables and a measure of success as the dependent variable. The ranking is based on the point of view that for a goal or target to rank highly, it must meet all of certain criteria. Our results rely on the information provided by several strong research studies. In Chap. 3, we develop a method for measuring a country’s success in achieving the sustainable development goals that are pertinent to human trafficking. The AHP, Guiasu, and Yen methods are used in this chapter also. In Chap. 4, we use a different method as the one in Chap. 2 to provide a ranking of countries with respect to their achievement of the SDGs. As in Chap. 2, this is accomplished by constructing linear equations involving the 17 SDGs as independent variables and a measure of success as the dependent variable. The weights of the linear equation are determined by category scores given by assessors. In Chap. 5, we use the method developed in Chap. 4 to measure a country’s success in achieving the sustainable development goals that are pertinent to human trafficking. In Chap. 6, we apply a similarity measure to the ranking methods in the previous chapters. We find the similarity between the AHP-Guiasu-Yen method, the Stakeholder method, and the Sustainable Development method. The similarity measures are determined for the SDGs in general and the SDGs pertinent to human trafficking in particular. In Chap. 7, we first present factors involving climate change and their subfactors whose examination could be placed in the context of mathematics of uncertainty. We next provide measures of a country’s achievement in greenhouse gas emissions, renewable energy, and energy use. Modern slavery is next examined in terms of certain SDGs. Techniques similar to those of the previous chapter are used to rank the countries with respect to their achievement in achieving the SDGs pertinent to modern slavery. We use a similarity measure to compare the two main techniques in the rankings. In Chap. 8, we examine the deficiencies of the foster care system that lead to human trafficking. We show that an approach by the O. L. Pathy Foundation can be modified in such a way that it corresponds to the AHP, Guiasu, and Yen methods which were used in previous chapters. We use the AHP, Guiasu, and Yen methods to determine the relationship between the shortcomings of the foster care system in the United States and a child’s vulnerability to human trafficking. We also evaluate the United States’ legal approach to combatting human trafficking. Health consequences of trafficking are analyzed. We use a relatively new idea to the application of human trafficking, namely, dialectic synthesis.

Preface

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The last chapter of the book is dependent on our work published in the journal New Mathematics and Natural Computation. We thank the journal for its support of the fight against human trafficking. We also thank the journal Symmetry for their support of some of the results in Chap. 4 and the new journal, Journal of Algebraic Hyperstructures and Logical Algebra, for their support of some of the results of Chap. 3. Omaha, USA Calicut, India

John N. Mordeson Sunil Mathew

Acknowledgments The authors are grateful to the editorial board and production staff of Springer. The authors thank George and Sue Haddix for their support of mathematics of uncertainty and the organization Heart Ministry.

Contents

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1 1 3 6 9 12

2 Sustainability: Analysis Using Mathematics of Uncertainty 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Commentary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Sustainable Development Goals . . . . . . . . . . . . 2.4.2 Scoring Assessment . . . . . . . . . . . . . . . . . . . . .

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13 13 14 36 38 38 39

3 Sustainable Development Goals and 3.1 Introduction . . . . . . . . . . . . . . 3.2 Scoring Assessment . . . . . . . . 3.3 Results . . . . . . . . . . . . . . . . . . 3.4 Discussion . . . . . . . . . . . . . . . 3.5 Other Regions . . . . . . . . . . . .

Human Trafficking . . . . . . . . . . . . . .

1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Fuzzy Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Evidence Theory . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Fuzzy Similarity Measures . . . . . . . . . . . . . . . . 1.4 Implication Operations and Similarity Operations 1.5 Fuzzy Graphs . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Sustainable Development Goals: Method . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . 4.2 Weighted Average . . . . . . 4.3 Achievement Tables . . . . . 4.4 Rankings . . . . . . . . . . . . . 4.5 Other Regions . . . . . . . . .

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61 61 62 63 70 71

Analysis by the Stakeholder . . . . . .

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5 Human Trafficking Rankings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.1 Human Trafficking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 . . . . .

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117 117 124 127 128

7 Climate Change and Modern Slavery . . . . . . . . . . . 7.1 Climate Change . . . . . . . . . . . . . . . . . . . . . . . 7.2 Global Warming of 1.5  C . . . . . . . . . . . . . . . 7.3 Climate Change Performance Index . . . . . . . . . 7.4 Category Results—GHG Emissions . . . . . . . . . 7.5 Climate Change and Human Trafficking . . . . . . 7.6 Modern Slavery and Its Link to the Sustainable Development Goals . . . . . . . . . . . . . . . . . . . . . 7.7 Stakeholder . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Similarity Measure of the Rankings . . . . . . . . .

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137 138 140 143 145 149

6 Similarity Measures of Rankings 6.1 Average of Rankings . . . . . 6.2 Similarity Measures . . . . . . 6.3 Similarity Rankings . . . . . . 6.4 Human Trafficking . . . . . . .

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8 Human Trafficking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Foster Care Deficiencies and Human Trafficking . . . . 8.2 Weighted Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Foster Care and Human Trafficking . . . . . . . . . . . . . . 8.4 Foster Care Shortcomings and Human Trafficking . . . 8.5 State Report Cards for Sex Trafficking Laws . . . . . . . 8.6 Health Consequences of Trafficking Victims: A Mathematical Analysis . . . . . . . . . . . . . . . . . . . . . 8.7 Health Status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.8 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.9 Improvement Rates from Interview 1 to Interview 2 . . 8.10 Dialectic Synthesis and Human Trafficking . . . . . . . . 8.11 Fuzzy Indices with Applications to Human Trafficking

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175 175 176 179 182 187

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189 189 190 194 196 209

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

About the Authors

Dr. John N. Mordeson is Professor Emeritus of Mathematics at Creighton University. He received his B.S., M.S., and Ph.D. from Iowa State University. He is a member of Phi Kappa Phi. He has published 19 books and over 200 journal articles. He is on the editorial board of numerous journals. He has served as an external examiner of Ph.D. candidates from India, South Africa, Bulgaria, and Pakistan. He has refereed for numerous journals and granting agencies. He is particularly interested in applying mathematics of uncertainty to combat the problem of human trafficking. Dr. Sunil Mathew is a Faculty Member in the Department of Mathematics, NIT Calicut, India. He has acquired his masters from St. Joseph’s College Devagiri, Calicut, and Ph.D. from National Institute of Technology Calicut in the area of Fuzzy Graph Theory. He has published more than 100 research papers and written five books. He is a member of several academic bodies and associations. He is editor and reviewer of several international journals. He has an experience of 20 years in teaching and research. His current research topics include fuzzy graph theory, bio-computational modeling, graph theory, fractal geometry, and chaos.

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Chapter 1

Preliminaries

In this chapter, we provide the notation and concepts needed for our book. We assume the reader is familiar with basic set theory. We first consider notation. We let ∧ denote minimum or infimum and ∨ denote maximum or supremum. N denotes the positive integers. We let [0, 1] denote the closed unit interval. Let A be a subset of a set X. Then X \A (or Ac if the context is clear) denotes the complement of A in X. We let |A| denote the cardinality of A and An denote the Cartesian cross product of A n times, where n ∈ N. We let P(X ) denote the power set of X.

1.1 Fuzzy Sets In 1965, Zadeh [89] introduced the concept of a fuzzy set and a fuzzy logic. Let X be a set and A a subset of X. The characteristic function of A is the function χ of X into {0, 1} defined by χ(x) = 1 if x ∈ A and χ(x) = 0 if x ∈ / A. The characteristic function can be used to indicate either members or nonmembers of A. This notion can be generalized in a way that introduces the notion of a fuzzy subset of X. Definition 1.1.1 A fuzzy subset μ of X is a function of X into the closed interval [0, 1]. Let μ be a fuzzy subset of a set X. For all x ∈ X, μ(x) can be thought of as the degree of membership of x in μ. We sometimes use the notation μ A for a fuzzy subset of X, where A is thought of as a fuzzy set and μ A gives the grade of membership of elements of X in A. At times, A may be merely a description of a fuzzy subset μ of X. We let FP(X ) denote the fuzzy power set of X, i.e., the set of all fuzzy subsets of X. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_1

1

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1 Preliminaries

Definition 1.1.2 Let μ be a fuzzy subset of X . (1) Let α ∈ [0, 1]. Define μa = {x ∈ X |μ(x) ≥ α}. We call μα and a-cut or a α-level set. (2) The support of μ is defined to be the set Supp(μ) = {x ∈ X |μ(x) > 0}. Definition 1.1.3 Let μ and ν be fuzzy subsets of X. Define μc , μ ∩ ν, and μ ∪ ν as follows: ∀x ∈ X, μc (x) = 1 − μ(x), (μ ∩ ν)(x) = μ(x) ∧ ν(x), (μ ∪ ν)(x) = μ(x) ∨ ν(x). Then μc is called the (standard) complement of μ, μ ∩ ν the intersection of μ and ν, and μ ∪ ν the union of μ and ν. We can extend the notions of intersection and union to a family of fuzzy subsets of X. Let {μ}i∈I be a family of fuzzy subsets of X, where I is an index set. Define ∩i∈I and ∪i∈I as follows: ∀x ∈ X, (∩i∈I μi )(x) = ∧i∈I μi (x), (∪i∈I μi )(x) = ∨i∈I μi (x) Thus if I is a finite set, say I = {1, 2, . . . , n}, then (∩i∈I μi ) = μ1 ∩ μ2 ∩ · · · ∩ μn and (∪i∈I μi ) = μ1 ∪ μ2 ∪ · · · ∪ μn . In this case, we sometimes write (∩i∈I μi )(x) = μ1 (x) ∧ μ2 (x) ∧ · · · ∧ μn (x) and (∪i∈I μi )(x) = μ1 (x) ∨ μ2 (x) ∨ · · · ∨ μn (x). The intersection of two fuzzy subsets of a set is specified in general by a binary operation on the unit interval; that is, a function of the form i : [0, 1] × [0, 1] → [0, 1]. Definition 1.1.4 A fuzzy intersection (t-norm) is a binary relation i on the unit interval that satisfies the following properties: ∀a, b, d ∈ [0, 1] : (1) i(a, 1) = a (boundary condition), (2) b ≤ d implies i(a, b) ≤ i(a, d) (monotonicity), (3) i(a, b) = i(b, a) (commutativity), (4) i(a, i(b, d)) = i(i(a, b), d) (associativity). Example 1.1.5 Let a, b ∈ [0, 1]. Standard intersection: i(a, b) = a ∧ b, Algebraic product: i(a, b) = ab, Bounded difference: i(a, b) = 0 ∨ (a + b − 1). The general fuzzy union of two fuzzy subsets is specified by a function u : [0, 1] × [0, 1] → [0, 1].

1.1 Fuzzy Sets

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Definition 1.1.6 A fuzzy union(t-conorm) is a binary relation u on the unit interval that satisfies the following properties: ∀a, b, d ∈ [0, 1] : (1) u(a, 0) = a (boundary condition ); (2) b ≤ d implies u(a, b) ≤ u(a, d) (monotonicity); (3) u(a, b) = u(b, a) (commutativity); (4) u(a, u(b, d)) = u(u(a, b), d) (associativity). Example 1.1.7 Let a, b ∈ [0, 1]. Standard union: u(a, b) = a ∨ b; Algebraic sum: u(a, b) = a + b − ab; Bounded sum: u(a, b) = 1 ∧ (a + b). A special kind of aggregation operations are operations h on [0, 1] that satisfy the properties of monotonicity, commutativity, and associativity, but replace the boundary conditions of t-norms and t -conorms with weaker boundary conditions: h(0, 0) = 0 and h(1, 1) = 1. These operations are called norm operations. Example 1.1.8 Let i be a t-norm and u be a t-conorm. Let λ ∈ [0, 1]. Let h λ be the fuzzy binary relation on [0, 1] defined by for all a, b ∈ [0, 1], ⎧ ⎨ λ ∧ u(a, b) if a, b ∈ [0, λ]. h λ (a, b) = λ ∨ i(a, b) if a, b ∈ [λ, 1], ⎩ λ otherwise. Then h λ is a norm operation.

1.2 Evidence Theory Evidence theory is one of the broadest frameworks for the representation of uncertainty. Its origins lie in the works of Dempster [14, 15] and Shafer [71] and are heavily influenced by probability theory, one of the oldest uncertainty frameworks. Evidence theory encompasses belief, plausibility, necessity, possibility, and probability among a host of other measures. Here we present Evidence Theory as it was originally characterized by Shafer. Evidence theory is based on two fuzzy measures, belief measures and plausibility measures. Belief and plausibility measures can be conveniently characterized by a function m from the power set of a universal set X into the unit interval. We assume that X is finite in this section. The function m : P(X ) → [0, 1] is required to satisfy two conditions:

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1 Preliminaries

(1) m(∅) = 0;  (2) A∈P(X ) m(A) = 1. The function m is called a basic probability assignment. For each set A ∈ P(X ), the value m(A) expresses the proportion to which all available and relevant evidence supports the claim that a particular element of X belongs to the set A. This value m(A) pertains solely to one set, set A; it does not imply any additional claims regarding subsets or supersets of A. It there is some additional subset of A, say B ⊆ A, it must be expressed by another value m(B). Given a basic probability assignment, m, every set A ∈ P(X ) for which m(A) = 0 is called a focal element. The pair (F, m), where F denotes the set of all focal elements induced by m is called a body of evidence and we denote it by B = (F, m). From a basic probability assignment m, the corresponding belief measure and plausibility measure are determined for all sets A ∈ P(X ) by the formulas Bel(A) =



m(B),

B⊆A

Pl(A) =



m(B).

B∩A =∅

Thus the belief of a set A is the sum of all the evidence (basic probability) assigned to A or any subset of A. The plausibility of A is the sum of all the evidence (basic probability) that overlaps with A. It can be shown that the plausibility of an event is one minus the belief of the compliment of that event, and vice versa. That is, Bel(A) = 1 − Pl(Ac ), Pl(A) = 1 − Bel(Ac ). Since we can calculate the belief from the plausibility, and the plausibility from the belief, and both belief and plausibility can be derived from the basic probability assignment, we only need one formula to show that all three measures provide the same information. Given a belief measure Bel, the corresponding basic probability assignment m is determined for all A ∈ P(X ) by the formula  (−1)|A−B| Bel(B), m(A) = B⊂A

where |A − B| is the cardinality of the set difference of A and B, as proven by Shafer [71]. Thus each of the three functions, m, Bel, and Pl is sufficient to determine the other two.

1.2 Evidence Theory

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Total ignorance is expressed in evidence theory by m(X ) = 1 and m(A) = 0 for all A ⊂ X. Full certainty is expressed by m({x}) = 1 for one particular element x of X and m(A) = 0 for all A = {x}. Guiasu Method The Guiasu method describes the process of reaching a verdict by probabilistic weighting the available evidence. The classical rules from decision theory proposed by Hooper, Dempster, Bayes, and Jeffrey are special cases of Guiasu’s weighting process. The Guiasu method is a generalization of Dempster-Shafer theory [14, 15, 71] and makes use of fuzzy set theory. A body of information induces a probability (credibility) distribution m on P(X ), the set of all subsets of X. That is, m is a function of P(X ) into the closed interval [0, 1], written m : P(X ) → [0, 1], such that m(A) ≥ 0∀A ∈ P(X ) and A∈P(X ) m(A) = 1. The class of focal subsets of X corresponding to m is denoted by F(X ; m) = {A|A ⊆ X, m(A) > 0}. A pair of dependent bodies of information, say i and j, induce a joint probability (credibility) distribution, namely m i j : P(X ) × P(X ) → [0, 1] such that m i j (A, B) ≥ 0 and A⊆X m i j (A, B) = 1. If the bodies of information are independent, then m i j = m i m j . The corresponding class of focal pairs of subsets is F(X, X ; m i j ) = {(A, B)|A, B ⊆ X, m i j (A, B) > 0}. The weights corresponding to the body of information for which m is the probability (credibility) distribution on P(X ) are w(·|·) : P(X ) × F(X ; m) → [0, ∞). The weighted body of information provides the new probability (credibility) distribution on P(X ) given by μ(C) = A∈F (X ;m) w(C|A)m(A). We can generalize this procedure to formulate the weights wi j (·|·, ·) that are assigned to a mixed body of information inducing a joint probability (credibility) distribution induced on P(X ) by the weighted (i, j)-th body of information, i.e.,  wi j (C|A, B)m i j (A, B), C ∈ P(X ), μi j (C) = (A,B)∈F (X,X m i j )

where wi j (C|A, B) is the weight of the subset C given (A, B)  ∈ F(X, X ; m i j ). If the probability (credibility) distribution m on X is such that A∈F (X ;m) m(A) = 1 and ∀A ∈ F(X ; m), |A| = 1, then it is called probabilistic. The following discussion is explained via Sustainable Development Goals (SDGs). Given m subgoals (SDGs in this application) and n experts. Assume the experts assign numbers to each SDG a number with respect as their importance in the examination of the overarching goal (sustainability) to form a m × n matrix W = [wi j ]. When the columns of the matrix are normalized, we can consider that each column of the of the resulting matrix N to be a probability (credibility) distribution for each expert (t-norm in this application). These probability (credibility) distributions are probabilistic with the focal elements being singleton sets consisting of an SDG. The row averages provide the Guiasu weights, one for each SDG.

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Theorem 1.2.1 [47] The row averages of 1, . . . , m.

1 Preliminaries

N give the Guiasu weights wi , i =

Analytic Hierarchy Process The Analytical Hierarchy Process (AHP) is a multicriteria decision method introduced in [62] and [63]. We consider a factor to be studied by the examination of subfactors of the factor. In our case, each expert E j assigns a number, wi j , to each subfactor (SDG), G i , i = 1, . . . , m, as to its importance with respect to the overarching goal (sustainability). The row average, wi of each row of the matrix W = [wi j ] is determined to form an m × n-matrix R whose i j-th element is wi /w j . The columns of R are then normalized in orderto form the m × n-matrix N whose i j-th element m m wi /w j = wi / i=1 wi , i = 1, . . . , m. This row vector yields the is (wi /w j )/ i=1 weights for the subfactors (SDGs) for the linear equation of the overarching goal (sustainability), the dependent variable, in terms of the SDGs, the independent variables.  If the matrix W already has its columns normalized, then wi = nj=1 wi j /n, i = m m 1, . . . , m. Since i=1 wi j = 1, j = 1, . . . , n, it follows that i=1 wi = 1. Hence m wi = wi ,i.e., wi is the weight for the i-th SDG in the linear equation, wi / i=1 i = 1, . . . , m. It thus follows that if the columns of W are already normal, then the Guiasu method (with probabilistic assignments) and the analytic hierarchy process yield the same weights. However, in general, the Guiasu weights and the analytic hierarchy process can have quite different weights [47]. Yen Method Yen’s method addresses the issue of managing imprecise and vague information in evidential reasoning by combining the Dempster-Shafer theory with fuzzy set theory [87]. Several researchers have extended the Dempster-Shafer theory to deal with vague information, but their extensions did not preserve an important principle that the belief and plausibility measures are lower and upper probabilities. Yen’s method preserves this principle. Nevertheless, we use various measures of subsethood to determine belief functions. We do this to compare the results of the beliefs with Yen’s method. Yen’s method is developed under the assumption that the focal elements are normal. If the fuzzy focal elements are not normal, he normalizes them.

1.3 Fuzzy Similarity Measures In this section, we take a quick look at fuzzy similarity measures. We are interested in measuring the similarity between rankings.

1.3 Fuzzy Similarity Measures

7

Definition 1.3.1 Let S be a function of FP(X ) × FP(X ) into [0, 1]. Then S is called a fuzzy similarity measure on FP(X ) if the following properties hold: ∀μ, ν, ρ ∈ FP(X ), (1) S(μ, ν) = S(ν, μ); (2) S(μ, ν) = 1 if and only if μ = ν; (3) If μ ⊆ ν ⊆ ρ, then S(μ, ρ) ≤ S(μ, ν) ∧ S(ν, ρ); (4) If S(μ, ν) = 0, then ∀x ∈ X, μ(x) ∧ ν(x) = 0. In this section, we consider similarity measures. We apply them in a new way. Suppose that X is a finite set with n elements. Let A be a one-to-one function of X into {1, 2, . . . , n}. Then A is called a ranking of X. Define the fuzzy subset μ A of X as follows: ∀x ∈ X, μ A (x) = A(x)/n. We wish to consider the similarity of two rankings of X by the use of similarity measures. Note that (4) of Definition 1.3.1 holds vacuously for rankings. Much of our discussion is from [82]. Example 1.3.2 Let μ A and μ B be the fuzzy subsets of X associated with two rankings A and B of X, respectively. Then M, L , and S are similarity measures.  μ A (x) ∧ μ B (x) ; M(μ A , μ B ) = x∈X x∈X μ A (x) ∨ μ B (x) L(μ A , μ B ) = 1 − ∨x∈X |μ A (x) − μ B (x)|;  |μ A (x) − μ B (x)| . S(μ A , μ B ) = 1 − x∈X x∈X (μ A (x) + μ B (x)) Lemma 1.3.3 Let a, b, c ∈ [0, 1]. Then (a ∨ c) ∧ (b ∨ c) = (a ∧ b) ∨ c. Lemma 1.3.4 Let x, y, z ∈ (0, 1] be such that x ≤ y. Then (x ∨ z)/(y ∨ z) ≥ x/y.

Proof The result follows by considering the three cases z ≤ x ≤ y, x ≤ z ≤ y, and x ≤ y ≤ z.  Theorem 1.3.5 M(μ A ∪ μC , μ B ∪ μC ) ≥ M(μ A , μ B ). Proof Applying the previous lemmas, we have that

8

1 Preliminaries

 x∈X (μ A (x) ∨ μC (x)) ∧ (μ( B (x) ∨ μC (x))  μ A (x) ∨ μC (x) ∨ μ B (x) ∨ μC (x)  x∈X (μ A (x) ∧ μ B (x)) ∨ μC (x)) = x∈X μ A (x) ∨ μ B (x) ∨ μC (x)  x∈X μ A (x) ∧ μ B (x) ≥ x∈X x∈X μ A (x) ∨ μ B (x) = M(μ A , μ B ).

M(μ A ∪ μC , μ B ∪ μC ) =

 Lemma 1.3.6 Let x, y, z ∈ [0, 1]. Then |(x ∨ z) − (y ∨ z)|/(x ∨ z) + (y ∨ z) ≤ |x − y|/(x + y). Proof There is no loss in generality in assuming x ≥ y. Suppose z ≥ x ≥ y. Then (x ∨ z) − (y ∨ z) = z − z ≤ x − y. Assume x ≥ y ≥ z. Then (x ∨ z) − (y ∨ z) = x − y. Suppose x ≥ z ≥ y. Then y≤z 2x y ≤ 2x z x y − zx ≤ −x y + zx x + x y − zx − zy ≤ x 2 − x y + zx − zy 2

(x − z)(x + y) ≤ (x + z)(x − y) x−z x−y ≤ x+z x+y x∨z−y∨z x−y ≤ . x∨z+y∨z x+y  Theorem 1.3.7 S(μ A ∪ μC , μ B ∪ μC ) ≥ S(μ A , μ B ). Proof We have by the Lemma 1.3.6 that 

|(μ A (x) ∨ μC (x)) − (μ( B (x) ∨ μC (x))| μ A (x) ∨ μC (x) + μ B (x) ∨ μC (x)  x∈X |μ A (x) − μ( B (x)| ≥ 1 − x∈X x∈X μ A (x) + μ B (x) = S(μ A , μ B ).

S(μ A ∪ μC , μ B ∪ μC ) = 1 −



x∈X



1.3 Fuzzy Similarity Measures

9

Lemma 1.3.8 Let a, b, c ∈ [0, 1] be such that a ≥ b. Then (1) a − b ≥ a ∨ c − b ∨ c; (2) a − b ≥ a ∧ c − b ∧ c. Proof (1) Suppose (i)c ≥ a ≥ b. Then a ∨ c − b ∨ c = c − c ≤ a − b. Suppose (ii)a ≥ c ≥ b. Then a ∨ c − b ∨ c = a − c ≤ a − b. Suppose (iii)a ≥ b ≥ c. Then a ∨ c − b ∨ c = a − b. (2) Suppose (i)c ≥ a ≥ b. Then a ∧ c − b ∧ c = a − b. Suppose (ii)a ≥ c ≥ b. Then a ∧ c − b ∧ c = c − b ≤ a − b. Suppose (iii)a ≥ b ≥ c. Then a ∨ c − b ∨  c = c − c = 0 ≤ a − b.

Theorem 1.3.9 Let μ A , μ B , μC be fuzzy subsets of X. Then (1) L(μ A , μ B ) ≤ L(μ A ∪ μC , μ B ∪ μC ); (2) L(μ A , μ B ) ≤ L(μ A ∩ μC , μ B ∩ μC ). Proof (1) By (1) of Lemma 1.3.8, we have for all x ∈ X that |μ A (x) − μ B (x)| ≥ |μ A (x) ∨ μC (x) − μ B (x) ∨ μC (x)| 1 − |μ A (x) − μ B (x)| ≤ 1 − |μ A (x) ∨ μC (x) − μ B (x) ∨ μC (x)|. A similar argument holds for (2).



1.4 Implication Operations and Similarity Operations In this section, we define and examine similarity measures in terms of implication operators. Our discussion is from [4, 82]. Definition 1.4.1 Let I be a function of [0, 1] × [0, 1] into [0, 1] such that I(0, 0) = I(0, 1) = I(1, 1) = 1 and I(1, 0) = 0. Then I is called an implication operation. Let μ A and μ B be two fuzzy subsets of a set X. Let I be an implication operator. Then the degree to which μ A is a subset of μ B is defined to be ∧{I(μ A (x), μ B (x))|x ∈ X }. Define the fuzzy subset ⊆ I of FP(X ) × FP(X ) by ∀μ A , μ B ∈ FP(X ), ⊆ I (μ A , μ B ) = ∧{I(μ A (x), μ B (x))|x ∈ X }. Definition 1.4.2 Let I be an implication operator. Define the fuzzy subset E I of FP(X ) × FP(X ) by for all μ A , μ B ∈ FP(X ),

10

1 Preliminaries

E I (μ A , μ B ) = ∧{∧{I (μ A (x), μ B (x)) |x ∈ X }, ∧{I(μ B (x), μ A (x))|x ∈ X }}. Then is called the degree of sameness of μ A and μ B ,[4]. Let T denote a t-norm. Then there exists an implication operator IT defined by IT (x, y) = ∨{z|z ∈ [0, 1] and T (x, z) ≤ y}. The following implication operators can be determined by a suitable t-norm, [82]. Example 1.4.3 Let x, y ∈ [0, 1].



1 if x ≤ y, y otherwise.  1 if x ≤ y, (2) Goguen implication operator: I(x, y) = y/x otherwise. (1) Godel implication operator: I(x, y) =

(3) Luckasiewicz implication operator: I(x, y) = ∧{1 − x + y, 1}. Definition 1.4.4 Let I be an implication operator. Then I is called hybrid monotonous operator if I(x, ) is nondecreasing for all x ∈ [0, 1] and I( , y) is nonincreasing for all y ∈ [0, 1]. The implication operators in the previous example are hybrid monotonous. Proposition 1.4.5 [82] Let A be a finite subset of [0, 1] and b ∈ [0, 1]. Let I be a hybrid monotonous implication operator. Then (1) I(∨{a|a ∈ A}, b) = ∧{I(a, b)|a ∈ A}; (2) I(∧{a|a ∈ A}, b) = ∨{I(a, b)|a ∈ A}; (3) I(b, ∨{a|a ∈ A}) = ∨{I(a, b)|a ∈ A}; (4) I(b, ∧{a|a ∈ A}) = ∧{I(b, a)|a ∈ A}. Lemma 1.4.6 [82] Let I be the Luckasiewicz implication operator. Let a, b ∈ [0, 1]. Then I(a, b) ∧ I(b, a) = ((1 − a) ∧ (1 − b)) + a ∧ b. Proof We show that (1 − a + b) ∧ (1 − b + a) ∧ 1 = ((1 − a) ∧ (1 − b)) + a ∧ b. Suppose that a ≤ b. Then ((1 − a) ∧ (1 − b)) + a ∧ b = 1 − b + a. Clearly, 1 − a + b ≥ 1 and 1 − b + a ≤ 1. Thus (1 − a + b) ∧ (1 − b + a) ∧ 1 = 1 − b + a. The proof of the case for a ≥ b is similar. 

1.4 Implication Operations and Similarity Operations

11

Proposition 1.4.7 [4] Let I be the Luckasiewicz implication operator. Then for all μ A , μ B ∈ FP(X ), E I (μ A , μ B ) = L(μ A , μ B ). Proof We have that L(μ A , μ B ) = 1 − ∨{|μ A (x) − μ B (x)| |x ∈ X } = ∧{1 − |μ A (x) − μ B (x)| |x ∈ X } = ∧{1 − μ A (x) ∨ μ B (x) + μ A (x) ∧ μ B (x))|x ∈ X } = ∧{(1 − μ A (x)) ∧ (1 − μ B (x)) + μ A (x) ∧ μ B (x)|x ∈ X }. It suffices to show that I (μ A (x), μ B (x)) ∧ I (μ B (x), μ A (x)) = ((1 − μ A (x)) ∧ (1 − μ B (x)) + μ A (x) ∧ μ B (x).

However, this holds from Lemma 1.4.6.



We next consider the interactions between the concept of degree of sameness and fuzzy set theoretical operations. A fuzzy complement c is called involutive if for all x ∈ [0, 1], c(c(x)) = x. An implication operator I is called contrapositive (with respect to a fuzzy complement c) if ∀x, y ∈ [0, 1], I(x, y) = I(c(y), c(x)). Note that the standard complement is involutive. Proposition 1.4.8 [82] Let I be a contrapositive implication operator with respect to an involutive fuzzy complement c. Let μ, ν be fuzzy subsets of X. Then E I (μ, ν) = E I (ν c , μc ). Proof We have that E I (μ, ν) = (∧{I(μ(x), ν(x))|x ∈ X }) ∧ (∧{I(ν(x), μ(x))|x ∈ X }) = (∧{I(ν c (x), μc (x))|x ∈ X }) ∧ (∧{I(μc (x), ν c (x))|x ∈ X }) = E I (ν c , μc ).  Proposition 1.4.8 holds for Kleene-Dienes implication operator, I(x, y) = (1 − x) ∨ y and the Early Zadeh implication operator I(x, y) = (x ∧ y) ∨ (1 − x) even though these implication operators are not contrapositive, [82].

12

1 Preliminaries

1.5 Fuzzy Graphs Let V be a nonempty set. Let E denote the set of all subsets of V with cardinality 2. Let E ⊆ E. A graph is a pair (V, E). The elements of V are thought of as vertices of the graph and E as the set of edges. For {x, y} ∈ E, we let x y denote {x, y}. Then clearly x y = yx. Definition 1.5.1 [59] Let (V, E) be a graph. Then the pair (σ, μ) is called a fuzzy subgraph of (V, E) if σ is a fuzzy subset of V and μ is a fuzzy subset of E such that for all x y ∈ E, μ(x y) ≤ σ(x) ∧ σ(y). Definition 1.5.2 Let (σ, μ) be a fuzzy subgraph of the graph (V, E). Then a fuzzy subgraph (τ , ν) of (V, E) is called a partial fuzzy subgraph (σ, μ) if τ ⊆ σ and ν ⊆ μ. Definition 1.5.3 Let (σ, μ) be a fuzzy subgraph of the graph (V, E). Then a partial fuzzy subgraph (τ , ν) of (σ, μ) is said to span (σ, μ) if τ = σ. In this case, (τ , ν) is called a partial fuzzy subgraph of (σ, μ). A path P in a fuzzy graph (σ, μ) of a graph (V, E) is a sequence of distinct vertices x0 , x1 , . . . , xn (except possibly x0 and xn ) such that μ(xi−1 xi ) > 0, i = 1, . . . , n. Here n is called the length of the path. The consecutive pairs are called the edges of the path. The diameter of x, y ∈ V, written diam(x, y), is the length on the longest n μ(xi−1 xi ). The strength path joining x and y. The strength of P is defined to be ∧i=1 of connectedness between two vertices x and y is defined as the maximum of the strengths of all paths between x and y and is denoted by μ∞ (x, y) or Conn(x, y). A strongest path joining any two vertices x and y has strength μ∞ (x, y). It can be shown that if (τ , ν) is a partial fuzzy subgraph of (σ, μ), then ν ∞ ⊆ μ∞ . We call P a cycle if x0 = xn and n ≥ 3. Two vertices that are joined by a path are called connected. It follows that this notion of connectedness is an equivalence relation. The equivalence classes of vertices under this equivalence relation are called connected components of the given fuzzy subgraph. They are its maximal connected partial fuzzy subgraphs. Let G = (σ, μ) be a fuzzy graph, let x, y be distinct vertices, and let G  be the partial fuzzy subgraph of G obtained by deleting the edge x y. That is, G  = (σ, μ ), where μ (x, y) = 0 and μ = μ for all other pairs. We call x y a fuzzy bridge in G if μ∞ (u, v) < μ∞ (u, v) for some u, v in σ ∗ . In words, the deletion of x y reduces the strength of connectedness between some pair of vertices in G. Thus x y is a fuzzy bridge if and only if there exists u, v ∈ V such that x y is an edge of every strongest path between u and v.

Chapter 2

Sustainability: Analysis Using Mathematics of Uncertainty

All member states of the United Nations adopted Agenda 2030 and the Sustainable Development Goals (SDGs) in 2015. The 17 SDGs describe a universal agenda that applies to and must be implemented by all countries, both developed and developing, [67]. It is stated in [67] that sound metrics and data are critical for turning the SDGs into practical tools for problem-solving by (a) mobilizing governments, academia, civil society, and business, (b) providing a report card to track progress and ensure accountability, and (c) serving as a management tool for the transformation needed to achieve the SDGs by 2030.

2.1 Introduction The purpose of this chapter to take the metrics and data provided in [54, 67, 68] and transform them into a fuzzy logic setting. This allows for the analysis of the results in [54, 67, 68] by using techniques of mathematics of uncertainty. We first focus on countries making up the Organization for Economic Cooperation and Development (OECD). The OECD is a group of 36 democracies with market economies working with each other, as well as with more that 70 non-member economies to promote economic growth, prosperity, and sustainable development. We use the Analytic Hierarchy Process (AHP) [64, 65], the Guiasu method [24], and the Yen method [87, 88] to determine which countries are, in the opinion of the experts, the best in achieving sustainability. This is accomplished by constructing linear equations involving the 17 SDGs as independent variables and a measure of success as the dependent variable. We find that the Czech Republic, Denmark and Hungary rank the highest for the OECD. However, it is important to note that our ranking is based on the philosophy that for a goal or target to score highly, it must meet all of certain criteria. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_2

13

14

2 Sustainability: Analysis Using Mathematics of Uncertainty

An outcome of the UN Conference on Sustainable Development (Rio+20) in 2012 was international agreement to negotiate a new set of global Sustainable Development Goals (SDGs) to guide the path of sustainable development after 2015, [54]. It is stated in [54] that all of the SDGs are relevant and apply in general terms to all countries including developed countries. The report in [54] proposes a methodology for identifying which of the different goals and targets represent the biggest transformational challenges in any given implementation context. The 17 SDGs are G 1 : No Poverty, G 2 : Zero Hunger, G 3 : Good Health and Well Being, G 4 : Quality Education, G 5 : Gender Equality, G 6 : Clean Water and Sanitation, G 7 : Affordable and Clean Energy, G 8 : Decent Work and Economic Growth, G 9 : Industry, Innovation, and Infrastructure, G 10 : Reduced Inequalities, G 11 : Sustainable Cities and Communities, G 12 : Responsible Construction and Production, G 13 : Climate Action, G 14 : Life Below Water, G 15 : Life on Land, G 16 : Peace, Justice, and Strong Institutions, G 17 : Partnerships and Goals. These SDGs are discussed in more detail in [68] and Appendix 2.4.1. Goal 17 and the targets within other goals that are specifically directed towards international cooperation and development assistance responsibilities of developed countries are excluded from the analysis in [54] and consequently here also. In [54], Stakeholder Forum created a methodology or analytical tool to enable relative scores to be assigned to each of the different targets and goals according to their significance in different contexts. The method uses a number of assessors to assign their own independent scores of the significance of each of the proposed targets in the implementation context in question, according to three separate criteria. The three criteria proposed are applicability, implementability, and the transformational impact (both in the country concerned and for the world as a whole), [Ref. [54, p.10]]. The assessors’ scores are then aggregated and averaged to give an overall score for each target, and then combined to give an average score for each goal. The methodology is described in more detail in Sect. 2.2. The highest scores are given to those targets and goals which are both clearly applicable and implementable in the country in question and which represent the biggest transformational challenge. Conversely, lower scores are given to targets and goals which are less applicable or implementable in a particular country for reasons given in [54].

2.2 Results The analysis relies heavily on the concept of a t-norm. We use a t-norm since in [54], an overall score was obtained for each target by multiplying the scores given to each of the three categories. Multiplication was used in [54] to emphasize that for a goal or target to score highly, it must meet all three criteria. We use t-norms to illustrate our approach for the following reasons: Algebraic product is in keeping with the approach in [54] since multiplication is a t-norm. Standard intersection is the largest t-norm and thus is the least punitive t-norm.

2.2 Results Table 2.1 AHP method AHP Bounded difference G1 G2 G3 G4 G5 G6 G7 G8 G9 G 10 G 11 G 12 G 13 G 14 G 15 G 16 Col sum

0.10 0.05 0.09 0.19 0.11 0.26 0.78 0.24 0.14 0.36 0.16 0.78 0.88 0.48 0.15 0.18 4.95

15

Algebraic product

Standard intersection

Row avg

0.22 0.17 0.14 0.31 0.26 0.31 0.81 0.35 0.26 0.45 0.34 0.79 0.88 0.52 0.30 0.30 6.41

0.33 0.22 0.29 0.36 0.47 0.42 0.83 0.52 0.47 0.61 0.49 0.82 0.88 0.64 0.50 0.46 8.31

0.2167 0.1467 0.1733 0.2867 0.28 0.33 0.8067 0.37 0.29 0.4733 0.33 0.7967 0.88 0.5467 0.3167 0.3133 6.5567

Bounded difference is more punitive than algebraic product since 0 ∨ (a + b − 1) ≤ ab for all a, b ∈ [0, 1]. We use the AHP, Guiasu, and Yen methods to determine which countries are, in the opinion of the experts, the best in achieving sustainability. We do this by constructing a linear equation involving the first 16 goals as independent variables and a measure of success as the dependent variable. In the following, we treat the t-norms as experts. The motivation for using t-norms is given above. The entries in the following table are from the table in Appendix 2.4.2 which is determined from the table in [54, Annex 2, p. 12]. The coefficients in the following equation are determined by taking the corresponding entry in the row average column of Table 2.1 and dividing them by the sum of that column. In this way, the coefficients add to 1. G = 0.03G 1 + 0.02G 2 + 0.03G 3 + 0.04G 4 + 0.04G 5 + 0.05G 6 + 0.12G 7 +0.06G 8 + 0.04G 9 + 0.07G 10 + 0.05G 11 + 0.12G 12 + 0.13G 13 +0.08G 14 + 0.05G 15 + 0.05G 16

(2.1)

The following table is determined from Table 2.1 by dividing each member in a column by the column sum. The coefficients in the following equation are the corresponding entries of the row average column.

16

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.2 Guiasu method Guiasu Bounded difference G1 G2 G3 G4 G5 G6 G7 G8 G9 G 10 G 11 G 12 G 13 G 14 G 15 G 16 Col sum

0.02 0.01 0.02 0.04 0.02 0.05 0.16 0.05 0.03 0.07 0.03 0.16 0.18 0.10 0.03 0.04 1.01

Algebraic product

Standard intersection

Row avg

0.03 0.03 0.02 0.05 0.04 0.05 0.13 0.05 0.04 0.07 0.05 0.12 0.14 0.08 0.05 0.05 1.00

0.04 0.03 0.03 0.04 0.06 0.05 0.10 0.06 0.06 0.07 0.06 0.10 0.11 0.08 0.06 0.06 1.01

0.03 0.0233 0.0233 0.0433 0.04 0.05 0.13 0.0533 0.0433 0.07 0.0467 0.1267 0.1433 0.0867 0.0467 0.05

G  = 0.03G 1 + 0.02G 2 + 0.02G 3 + 0.04G 4 + 0.04G 5 + 0.05G 6 + 0.13G 7 +0.05G 8 + 0.04G 9 + 0.07G 10 + 0.05G 11 + 0.13G 12 + 0.14G 13 +0.09G 14 + 0.05G 15 + 0.05G 16

(2.2)

The following table is determined from Table 2.2 by dividing each column member by the maximum value of the member in the column. The coefficients in the following equation are determined by taking the corresponding entries in the row average column of Table 2.3 and dividing them by the sum of that column. G  = 0.03G 1 + 0.03G 2 + 0.03G 3 + 0.05G 4 + 0.05G 5 + 0.04G 6 + 0.13G 7 +0.05G 8 + 0.05G 9 + 0.07G 10 + 0.05G 11 + 0.13G 12 + 0.14G 13 +0.05G 14 + 0.05G 15 + 0.05G 16

(2.3)

The Organization for Economic Development (OECD) is an intergovernmental economic organization with 36 member countries, founded in 1961 to stimulate economic progress and world trade. Most OECD members are high-income economies with a very high Human Development Index (HDI) and are regarded as developed countries. We next present the degree to which the countries in the region OECD are achieving the SDGs as determined in [68, pp. 69–72] and then normalized.

2.2 Results

17

Table 2.3 Yen method Yen Bounded difference G1 G2 G3 G4 G5 G6 G7 G8 G9 G 10 G 11 G 12 G 13 G 14 G 15 G 16 Col sum

0.1111 0.0556 0.1111 0.2222 0.1111 0.0278 0.8889 0.0278 0.1667 0.3889 0.1667 0.8889 1.0 0.5556 0.1667 0.2222

Algebraic product

Standard intersection

Row avg

0.2143 0.2143 0.1429 0.3571 0.2857 0.3571 0.9286 0.3571 0.2857 0.5 0.3571 0.8571 1.0 0.5714 0.3571 0.3571

0.3636 0.2727 0.2727 0.3636 0.5455 0.4545 0.9091 0.5455 0.5455 0.6364 0.5455 0.9091 1.0 0.7273 0.5455 0.5455

0.2297 0.1809 0.1756 0.3143 0.3141 0.2798 0.9089 0.3101 0.3326 0.5084 0.3564 0.8850 1.0 0.3757 0.3564 0.3749 6.9028

The numbers in the following tables (Tables 2.4 and 2.5) are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain the following table. The rankings of the countries are the second number in the columns of the following table (Table 2.6).

0.965 0.949 0.941 0.948 0.866 0.924 0.961 0.888 0.962 0.943 0.948 0.902 0.859 0.969 0.952 0.958 0.951 0.949

0.928 0.966 0.947 0.999 0.928 0.963 0.983 0.953 0.989 0.974 0.890 0.901 0.904 0.974 0.952 0.968 0.976 0.981

0.524 0.715 0.702 0.602 0.633 0.631 0.683 0.583 0.582 0.660 0.687 0.612 0.642 0.626 0.702 0.586 0.643 0.680

Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan

0.990 0.992 0.995 0.992 0.989 0.994 0.996 0.997 0.998 0.995 0.996 0.967 0.989 0.997 0.997 0.992 0.973 0.990

G4

Table 2.4 SDGs achievement G1 G2 G3 0.789 0.791 0.839 0.804 0.705 0.711 0.848 0.753 0.892 0.865 0.770 0.626 0.641 0.855 0.731 0.752 0.712 0.585

G5 0.970 0.949 0.793 0.842 0.966 0.880 0.907 0.897 0.926 0.879 0.894 0.906 0.890 0.874 0.820 0.743 0.848 0.845

G6 0.910 0.938 0.919 0.953 0.910 0.918 0.936 0.889 0.964 0.970 0.932 0.907 0.916 0.994 0.925 0.940 0.931 0.934

G7 0.811 0.820 0.814 0.840 0.807 0.851 0.839 0.848 0.825 0.781 0.844 0.630 0.821 0.830 0.877 0.850 0.787 0.885

G8 0.842 0.802 0.759 0.744 0.492 0.632 0.881 0.615 0.837 0.736 0.804 0.499 0.496 0.755 0.672 0.775 0.638 0.799

G9 0.770 0.874 0.934 0.788 0.273 0.923 0.965 0.722 0.979 0.856 0.834 0.509 0.756 0.992 0.848 0.502 0.699 0.768

G 10 0.806 0.858 0.823 0.804 0.807 0.894 0.902 0.903 0.883 0.870 0.909 0.821 0.861 0.899 0.845 0.801 0.740 0.754

G 11 0.409 0.455 0.467 0.501 0.725 0.708 0.498 0.587 0.487 0.534 0.474 0.394 0.710 0.506 0.463 0.425 0.517 0.556

G 12 0.339 0.843 0.829 0.685 0.947 0.891 0.902 0.850 0.710 0.864 0.902 0.822 0.949 0.886 0.917 0.912 0.847 0.904

G 13 0.563 0.800 0.306 0.595 0.662 0.800 0.489 0.813 0.555 0.642 0.405 0.594 0.800 0.359 0.534 0.174 0.411 0.536

G 14 0.478 0.714 0.850 0.607 0.593 0.910 0.872 0.905 0.821 0.767 0.826 0.787 0.873 0.345 0.824 0.506 0.829 0.700

G 15

0.857 0.920 0.869 0.881 0.759 0.827 0.928 0.878 0.929 0.766 0.834 0.728 0.734 0.930 0.904 0.736 0.752 0.903

G 16

0.611 0.680 0.623 0.654 0.794 0.555 0.898 0.555 0.740 0.751 0.831 0.536 0.515 0.674 0.334 0.549 0.631 0.649

G 17

18 2 Sustainability: Analysis Using Mathematics of Uncertainty

0.924 0.845 0.846 0.964 0.819 0.964 0.946 0.979 0.876 0.921 0.880 0.927 0.954 0.978 0.978 0.836 0.945 0.895

0.958 0.957 0.987 0.944 0.926 0.942 0.981 0.999 0.944 0.955 0.838 0.966 0.954 0.993 0.919 0.937 0.994 0.893

0.639 0.702 0.721 0.746 0.774 0.815 0.847 0.877 0.711 0.807 0.689 0.753 0.827 0.889 0.822 0.453 0.813 0.734

0.779 0.604 0.585 0.624 0.547 0.654 0.631 0.570 0.612 0.560 0.688 0.646 0.562 0.633 0.626 0.558 0.664 0.660

Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. K. U. S.

0.990 0.987 0.984 0.999 0.875 0.996 1 0.995 0.999 0.987 0.982 0.997 0.981 0.990 0.999 0.995 0.997 0.989

G5

Table 2.5 SDGs achievement continued G1 G2 G3 G4 0.815 0.890 0.857 0.900 0.791 0.927 0.907 0.875 0.820 0.870 0.844 0.824 0.881 0.935 0.955 0.821 0.951 0.850

G6 0.925 0.912 0.835 0.667 0.865 0.916 0.959 0.986 0.897 0.946 0.922 0.936 0.947 0.987 0.967 0.892 0.930 0.932

G7 0.862 0.833 0.805 0.699 0.730 0.831 0.881 0.785 0.844 0.823 0.807 0.847 0.752 0.835 0.798 0.738 0.829 0.852

G8 0.837 0.493 0.454 0.694 0.363 0.823 0.739 0.800 0.549 0.561 0.497 0.610 0.681 0.917 0.933 0.465 0.814 0.833

G9 0.865 0.765 0.496 0.883 0.146 0.946 0.733 1 0.537 0.573 0.835 1 0.692 1 0.800 0.412 0.714 0.477

G 10 0.803 0.863 0.831 0.945 0.812 0.911 0.830 0.861 0.785 0.844 0.820 0.859 0.891 0.903 0.983 0.704 0.980 0.825

G 11 0.635 0.679 0.674 0.239 0.788 0.440 0.515 0.305 0.737 0.548 0.650 0.608 0.534 0.522 0.279 0.738 0.429 0.365

G 12 0.877 0.878 0.841 0.787 0.906 0.883 0.915 0.544 0.892 0.915 0.772 0.912 0.933 0.872 0.889 0.899 0.846 0.661

G 13 0.548 0.509 0.625 0.700 0.695 0.412 0.570 0.662 0.437 0.518 0.800 0.333 0.594 0.423 0.800 0.274 0.575 0.609

G 14 0.572 0.922 0.904 0.623 0.476 0.832 0.471 0.632 0.920 0.734 0.869 0.825 0.654 0.752 0.577 0.533 0.737 0.769

G 15

0.754 0.770 0.805 0.902 0.531 0.835 0.926 0.849 0.814 0.841 0.799 0.881 0.806 0.838 0.830 0.681 0.857 0.761

G 16

0.534 0.504 0.516 0.584 0.602 0.537 0.649 0.996 0.534 0.587 0.551 0.576 0.591 0.982 0.533 0.708 0.489 0.562

G 17

2.2 Results 19

20

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.6 OECD ranks Country AHP/rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. Kingdom United States

0.67699/36 0.79807/5 0.75346/24 0.74551/27 0.74936/25 0.82857/1 0.80765/2 0.79428/6 0.78596/8 0.78938/7 0.77117/18 0.69538/33 0.80449/3 0.77040/20 0.77683/13 0.69174/34 0.73497/28 0.77253/17 0.77488/15 0.77468/16 0.74636/26 0.70601/30 0.70054/33 0.77688/12 0.77598/14 0.73421/29 0.75883/22 0.75497/23 0.77705/10 0.78455/9 0.76946/21 0.80344/4 0.77901/11 0.68475/35 0.77141/19 0.70317/32

Guiasu/rank

Yen/rank

0.68144/36 0.81074/6 0.76112/25 0.75497/27 0.76507/24 0.84399/1 0.81790/3 0.80831/6 0.79525/8 0.80224/7 0.78038/20 0.70723/33 0.81944/2 0.77986/21 0.78693/13 0.69817/34 0.74465/28 0.78349/17 0.78687/14 0.78768/15 0.75960/26 0.71331/31 0.71759/30 0.78544/16 0.78730/12 0.74154/29 0.77126/22 0.76680/23 0.79162/10 0.79470/7 0.78248/18 0.81335/4 0.79060/11 0.69704/35 0.78147/19 0.71137/32

0.68970/36 0.81148/5 0.78283/22 0.76372/26 0.76517/25 0.84180/1 0.83283/2 0.80474/9 0.80641/8 0.80944/7 0.79623/15 0.70981/35 0.81396/4 0.79855/11 0.79746/13 0.72417/30 0.75893/28 0.79354/17 0.79817/12 0.79443/16 0.76196/17 0.71603/33 0.71617/32 0.80167/10 0.79687/14 0.74856/29 0.78250/23 0.77542/24 0.78710/21 0.81216/5 0.78969/20 0.83118/3 0.79183/18 0.71036/34 0.79126/19 0.71866/31

2.2 Results

21

For the OECD, Czech Republic, Denmark, Hungary, and Sweden were ranked the highest in achieving the SDGs. In [68], Czech Republic was ranked 7, Denmark 1, Hungary 25, and Sweden 2. Finland was 3 and France 4, Austria 5, and Germany 6 over all countries. It is stated in [68] that achieving the SDGs requires deep changes to policies, investments, and technologies. But success will not be possible without social activism that mobilizes stakeholders and changes norms to enable the SDGs Transformations. Our intention is not only to introduce new ideas of analysis to issues involving sustainability, but also to reinforce to the mathematics community and others the importance of achieving the SDGs. We next consider the other regions presented in [68]. The degree to which the countries of the region East and South Asia are achieving the SDGs as determined in [68, pp. 69–72] is presented next. The numbers in Table 2.7 are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain Table 2.8. We see that Thailand and China ranked the highest in achieving the SDGs for their region. The ranking in [68, p. 20] ranked China 39th and Thailand 40th over all. No other country the region was ranked higher in [68].

Bangladesh Bhutan Brunei Dar. Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor-Leste Vietnam

0.511 0.506 0.650 0.527 0.719 0.426 0.537

0.611 0.452 0.466 0.445 0.533 0.551 0.344 0.531 0.718 0.527 0.602

0.626

30.582 0.883 0.997 0.875 0.974 0.714 0.740

0.548 1 0.904 0.956 0.804 0.623 0.779 0.774 0.984 0.912 1

0.953

Table 2.7 SDGs achievement G1 G2

G3

0.748

0.498 0.790 0.911 0.657 0.538 0.587 0.502 0.589 0.950 0.795 0.767

0.596 0.688 0.803 0.626 0.811 0.588 0.629

G4

0.917

0.783 0.914 0.992 0.975 0.731 0.826 0.473 0.894 0.996 0.972 0.887

0.797 0.696 0.994 0.699 0.997 0.802 0.901

G5

0.720

0.673 0.556 0.436 0.671 0.606 0.579 0.289 0.641 0.685 0.486 0.647

0.482 0.458 0.399 0.548 0.763 0.332 0.612

G6

0.708

0.673 0.763 0.721 0.654 0.614 0.629 0.463 0.676 0.890 0.658 0.783

0.655 0.636 0.726 0.603 0.718 0.566 0.687

G7

0.824

0.447 0.900 0.969 0.553 0.497 0.627 0.724 0.699 0.947 0.647 0.829

0.560 0.758 0.803 0.429 0.769 0.654 0.734

G8

0.742

0.717 0.804 0.796 0.761 0.698 0.726 0.657 0.725 0.719 0.828 0.802

0.771 0.713 0.622 0.620 0.874 0.832 0.760

G9

0.264

0.152 0.568 0.391 0.233 0.219 0.202 0.150 0.337 0.856 0.172 0.428

0.154 0.282 0.249 0.195 0.619 0.287 0.338

G 10

0.783

0.364 0.589

0.749 0.425 0.693 0.731 0.701 0.832 0.580 0.353

0.764 0.682 0.822 0.909 0.595 0.490 0.348

G 11

0.776

0.734 0.826 0.960 0.482 0.677 0.454 0.506 0.726 0.947 0.806 0.836

0.519 0.826 0.718 0.778 0.751 0.511 0.674

G 12

0.871

0.945 0.771 0.764 0.808 0.961 0.982 0.921 0.942 0.350 0.779 0.795

0.965 0.877 0.890 0.973 0.820 0.945 0.910

G 13

0.945

0.965 0.878 0.789 0.645 0.950 0.980 0.987 0.944 0.505 0.923 0.935

0.971 0.970 0.723 0.927 0.920 0.945 0.948

G 14

0.452

0.476 0.622 0.352 0.557 0.547

0.428

0.492 0.524

0.087 0.344 0.362 0.512 0.504

0.519

G 15

0.486

0.516 0.430 0.381 0.723 0.526 0.688 0.670 0.554 0.274 0.634 0.670

0.609 0.587 0.619 0.444 0.627 0.511 0.419

G 16

0.618

0.584 0.685 0.748 0.646 0.667 0.533 0.491 0.579 0.885 0.673 0.709

0.505 0.812 0.726 0.524 0.634 0.613 0.706

G 17

0.653

0.466 0.571 0.815 0.595 0.420 0.587 0.433 0.485 0.356 0.460 0.588

0.388 0.651 0.969 0.485 0.495 0.657 0.469

22 2 Sustainability: Analysis Using Mathematics of Uncertainty

2.2 Results

23

Table 2.8 East and South Asia ranks Country AHP/rank Bangladesh Bhutan Brunei Darussaiam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

Guiasu/rank

Yen/rank

0.66777/13 0.72887/4 0.6711/12 0.6478/18 0.73292/2 0.65438/16 0.68095/9

0.68425/12 0.75011/2 0.68188/13 0.66207/18 0.74478/4 0.67074/15 0.69802/9

0.68234/14 0.73952/5 0.70209/9 0.66823/17 0.76221/1 0.66895/16 0.70116/10

0.67523/11 0.71147/6 0.72575/5 0.65402/17 0.66101/15 0.70552/7 0.6321/19 0.69075/8 0.66263/14 0.67633/10 0.74297/1

0.6952/10 0.72594/7 0.73914/5 0.66767/17 0.67701/14 0.7273/6 0.65159/19 0.70968/8 0.66785/16 0.68916/11 0.75834/1

0.68744/13 0.73143/6 0.74293/4 0.66405/18 0.68002/15 0.71895/7 0.6455/19 0.70796/8 0.68835/12 0.68982/11 0.76194/2

0.73073/3

0.74675/3

0.75434/3

We next consider the region East Europe and Central Asia. Table 2.9 provides the measure of how well the countries are achieving the SDGs as given in [68]. The numbers in Table 2.9 are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain Table 2.10. Belarus and Moldova ranked the highest in this region. Belarus ranked 23rd over all in [68] and Moldova ranked 37th over all in [68]. No other country in East Europe and Central Asia ranked higher over all in [68]. We next consider Latin America and the Caribbean. The Table 2.11 provides the measure of how well the countries are achieving the SDGs as given in [68].

0.839

0.999

0.828

Cyprus

Georgia

Kazakhstan

Kyrgz Rep.

0.996

Moldova

0.894

0.988

1

N. Macedonia

Romania

Russian Fed.

0.994

0.863

0.996

0.997

0.694

Serbia

Tajikistan

Turkmenistan

Ukraine

Uzbekistan

San Marino

0.999

Montenegro

Monaco

0.997

Malta

Liecheristan

0.984

0.999

Croatia

0.997

0.970

0.999

Belarus

Bulgaria

1

Azerbaijan

Boz. and Herz.

0.912

Armenia

0.969

0.640

0.531

0.560

0.472

0.635

0.456

0.580

0.612

0.510

0.545

0.582

0.581

0.509

0.512

0.525

0.646

0.582

0.650

0.572

0.582

0.565

0.461

Andorra

Albania

G2

0.429

Afghanistan

G1

Table 2.9 SDGs Achievement

G3

0.776

0.718

0.678

0.702

0.842

0.781

0.806

0.800

0.799

0.748

0.927

0.701

0.758

0.732

0.928

0.871

0.802

0.803

0.817

0.753

0.785

0.822

0.387

G4

0.936

0.926

0.996

0.960

0.943

0.972

0.842

0.883

0.963

0.827

0.975

0.911

0.910

0.983

0.970

0.874

0.710

0.994

0.968

0.908

0.898

0.934

0.237

G5

0.655

0.628

0.594

0.570

0.578

0.672

0.645

0.544

0.544

0.686

0.591

0.610

0.771

0.612

0.713

0.637

0.692

0.399

0.780

0.539

0.569

0.533

0.214

G6

0.571

0.800

0.562

0.568

0.757

0.890

0.780

0.752

0.744

0.740

0.864

0.679

0.748

0.756

0.701

0.825

0.780

0.726

0.922

0.628

0.669

0.772

0.394

G7

0.881

0.916

0.813

0.919

0.849

0.912

0.890

0.809

0.850

0.899

0.924

0.893

0.858

0.886

0.922

0.895

0.908

0.803

0.901

0.910

0.954

0.886

0.568

G8

0.719

0.684

0.706

0.697

0.715

0.755

0.804

0.642

0.670

0.740

0.874

0.699

0.767

0.728

0.745

0.781

0.804

0.622

0.774

0.681

0.632

0.622

0.432

G9

0.248

0.252

0.103

0.096

0.424

0.501

0.413

0.310

0.329

0.261

0.433

0.270

0.352

0.301

0.543

0.486

0.412

0.249

0.393

0.373

0.339

0.280

0.041

0.780

0.990

0.679

0.724

0.540

0.300

0.488

0.629

0.926

0.946

0.793

0.621

0.326

0.774

0.698

0.618

0.822

0.858

0.683

0.501

0.598

G 10

G 11

0.908

0.760

0.725

0.793

0.718

0.823

0.813

0.729

0.682

0.797

0.831

0.879

0.784

0.871

0.777

0.762

0.813

0.718

0.809

0.831

0.673

0.786

0.342

G 12

0.921

0.804

0.878

0.932

0.837

0.691

0.719

0.812

0.606

0.970

0.484

0.917

0.624

0.822

0.417

0.735

0.666

0.890

0.826

0.891

0.901

0.832

0.982

G 13

0.934

0.956

0.515

0.962

0.850

0.822

0.952

0.858

0.794

0.979

0.911

0.942

0.751

0.913

0.722

0.936

0.877

0.723

0.923

0.906

0.950

0.921

0.970

0.386

0.258

0.425

0.533

0.284

0.502

0.434

0.430

0.328

0.748

0.653

0.087

0.225

0.410

G 14

G 15

0.622

0.635

0.506

0.686

0.495

0.662

0.843

0.740

0.322

0.641

0.706

0.680

0.586

0.606

0.775

0.792

0.933

0.619

0.781

0.669

0.612

0.800

0.521

G 16

0.702

0.619

0.711

0.723

0.734

0.506

0.761

0.741

0.707

0.630

0.768

0.632

0.690

0.819

0.812

0.706

0.688

0.726

0.691

0.702

0.754

0.673

0.457

G 17

0.696

0.779

0.844

0.739

0.821

0.654

0.695

0.774

1

0.859

0.622

0.754

0.519

0.579

0.272

0.847

0.759

0.969

0.742

0.698

0.571

0.648

0.419

24 2 Sustainability: Analysis Using Mathematics of Uncertainty

2.2 Results

25

Table 2.10 Eastern Europe and Central Asia ranks Country AHP/rank Guiasu/rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Boznia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgz Rep. Lynchistan Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Yen/rank

0.56535/23 0.72225/13

0.59333/23 0.7383/13

0.57616/23 0.74448/14

0.74885/8 0.7191/14 0.81026/1 0.6711/20 0.74469/11 0.77554/3 0.69026/18 0.71036/16 0.68429/19 0.77146/4

0.77233/7 0.73408/15 0.8308/1 0.68188/20 0.75967/11 0.79216/4 0.69742/18 0.72627/16 0.69571/19 0.7948/3

0.76599/9 0.75035/12 0.92854/1 0.70209/20 0.75773/11 0.78913/3 0.71408/18 0.73291/15 0.70387/19 0.78652/4

0.75398/7 0.79929/2

0.76418/9 0.82302/2

0.77054/8 0.81286/2

0.64522/21 0.71672/15 0.72999/12 0.70872/17

0.65587/21 0.73599/14 0.74483/12 0.72186/17

0.66852/21 0.73023/16 0.74857/13 0.72978/17

0.74693/9 0.75702/6 0.63141/22 0.74691/10 0.7674/5

0.7659/8 0.78088/6 0.64302/22 0.76351/10 0.78947/5

0.76171/10 0.77149/6 0.6574/22 0.77062/7 0.78448/5

The numbers in Table 2.11 are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain Table 2.12. We find that Costa Rica and Ecuador ranked the highest in achieving the SDGs for this region. In [68], Costa Rica ranked 33rd and Ecuador ranked 46th over all countries. Once again no other country from the region ranked higher in [68]. We next consider the region Middle East and North Africa. Table 2.13 provides the measure of how well the countries are achieving the SDGs as given in [68]. The numbers in Table 2.13 are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain Table 2.14.

0.853

0.862

0.858

0.976

Brazil

Columbia

Costa Rica

El Salvador

Guatamalia

0.743

0.916

Ecuador

Grenada

0.950

0.882

Domini. Rep.

Dominica

Cuba

0.685

Bolivia

0.970

G1

Belize

Barbados

Bahamas, The

Argentina

Ant. and Bar.

0.433

0.487

0.478

0.556

0.649

0.524

0.569

0.621

0.533

0.599

0.608

G2

0.705

0.773

0.770

0.666

0.852

0.858

0.793

0.769

0.664

0.715

0.802

G3

Table 2.11 SDGs Achievements

0.710

0.759

0.932

0.847

0.961

0.794

0.833

0.846

0.873

0.756

0.943

G4

0.532

0.684

0.768

0.733

0.824

0.823

0.705

0.675

0.662

0.580

0.774

G5

0.693

0.720

0.734

0.723

0.737

0.754

0.769

0.794

0.677

0.728

0.802

G6

0.708

0.885

0.902

0.897

0.851

0.941

0.910

0.940

0.734

0.883

0.914

G7

0.750

0.702

0.754

0.786

0.906

0.779

0.719

0.725

0.794

0.637

0.722

G8

0.120

0.179

0.270

0.276

0.124

0.415

0.318

0.488

0.230

0.234

0.405

G9

0.202

0.389

0.355

0.328

0.347

0.217

0.256

0.486

0.397

G 10

0.843

0.890

0.904

0.800

0.561

0.902

0.816

0.783

0.822

0.726

0.835

G 11

0.856

0.864

0.848

0.860

0.906

0.827

0.848

0.787

0.876

0.765

0.791

G 12

0.949

0.906

0.939

0.889

0.567

0.934

0.907

0.917

0.944

0.873

0.921

G 13

0.411

0.278

0.702

0.678

0.601

0.662

0.749

0.632

0.312

0.399

G 14

0.530

0.659

0.601

0.752

0.683

0.672

0.564

0.609

0.731

0.439

0.530

G 15

0.451

0.690

0.808

0.599

1

0.792

0.680

0.747

0.726

0.761

0.855

G 17

(continued)

0.504

0.565

0.644

0.519

0.633

0.745

0.583

0.554

0.484

0.568

0.630

G 16

26 2 Sustainability: Analysis Using Mathematics of Uncertainty

0.577

0.860

0.851

0.935

0.949

0.899

Haiti

Honduras

Jamaica

Nicaragua

Panama

Paraguay

Peru

0.560

0.984

0.999

0.350

Suriname

Trin. and Tob.

Uruguay

Venezuela

St. Vin.& Gra.

St. Lucia

St. Kit. & Nev.

0.849

0.380

Guyana

G1

Table 2.11 (continued)

0.475

0.587

0.453

0.557

0.613

0.669

0.496

0.458

0.477

0.462

0.422

0.544

G2

0.662

0.826

0.760

0.704

0.780

0.739

0.789

0.768

0.804

0.753

0.422

0.619

G3

0.763

0.874

0.886

0.792

0.916

0.762

0.788

0.765

0.858

0.642

0.495

0.833

G4

0.690

0.738

0.679

0.665

0.681

0.666

0.653

0.823

0.700

0.650

0.399

0.619

G5

0.799

0.788

0.734

0.713

0.769

0.775

0.718

0.668

0.734

0.703

0.491

0.732

G6

0.919

0.965

0.871

0.865

0.837

0.873

0.878

0.706

0.860

0.734

0.265

0.783

G7

0.786

0.776

0.783

0.745

0.756

0.777

0.774

0.723

0.741

0.729

0.588

0.525

G8

0.247

0.383

0.305

0.239

0.329

0.238

0.293

0.147

0.317

0.138

0.073

0.148

G9

0.259

0.501

0.418

0.424

0.254

0.464

0.493

0.267

0.623

G 10

0.627

0.853

0.676

0.793

0.731

0.774

0.887

0.775

0.889

0.808

0.413

0.826

G 11

0.785

0.724

0.752

0.798

0.788

0.818

0.802

0.902

0.908

0.883

0.918

0.562

G 12

0.890

0.869

0.491

0.791

0.931

0.930

0.902

0.949

0.870

0.944

0.894

0.720

G 13

0.514

0.422

0.525

0.712

0.788

0.554

0.692

0.218

0.662

0.330

0.550

G 14

0.789

0.514

0.641

0.713

0.713

0.447

0.572

0.738

0.558

0.652

0.436

0.595

G 15

0.368

0.693

0.573

0.706

0.582

0.484

0.647

0.615

0.673

0.487

0.456

0.565

G 16

0.797

0.822

1

0.761

0.571

0.618

0.329

0.610

0.736

0.690

0.630

0.577

G 17

2.2 Results 27

28

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.12 Latin America and the Caribbean ranks Country AHP/rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatamalia Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Guiasu/rank

Yen/rank

0.71741/9

0.73242/10

0.74376/4

0.70973/13 0.71745/7 0.71714/10 0.71887/6 0.758/1 0.70288/14

0.67642/19 0.73737/6 0.73496/8 0.73789/5 0.77527/1 0.71584/13

0.69618/17 0.73038/9 0.73573/7 0.73242/8 0.77539/1 0.71883/14

0.72431/4 0.74039/2 0.68624/17

0.74303/4 0.75906/2 0.70082/16

0.73946/6 0.75582/2 0.71132/16

0.63815/21 0.62938/22 0.52445/23 0.67099/18 0.69883/16 0.71448/11 0.6996/15 0.71172/12 0.73311/3

0.65284/21 0.64519/22 0.53842/23 0.6884/17 0.71194/15 0.73206/11 0.71533/14 0.73168/12 0.75119/3

0.65447/21 0.64338/22 0.53842/23 0.68134/18 0.72744/11 0.72731/12 0.71618/15 0.72507/13 0.74517/3

0.71742/8 0.66124/20 0.71973/5 0.6636/19

0.73588/7 0.67302/20 0.73351/9 0.6802/18

0.72939/10 0.6757/20 0.74283/5 0.68002/19

Algeria Bahrain Egypt Iran Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar S. Arabia Syria Tunisia UAE Yemen Rep.

0.978

0.949

0.999

0.904 0.968 0.841 0.868

0.978

0.518 0.500 0.597 0.456 0.282 0.525 0.598 0.263

0.527 0.674 0.560 0.582 0.417 0.454 0.608 0.459

0.737 0.839 0.877 0.816 0.639 0.775 0.861 0.438

0.755 0.904 0.689 0.773 0.626 0.763 0.838 0.801

Table 2.13 SDGs achievement G1 G2 G3

G4

0.780 0.948 0.864 0.976 0.488 0.848 0.857 0.545

0.859 0.935 0.829 0.955 0.444 0.780 0.865 0.703

G5

0.429 0.358 0.576 0.391 0.343 0.527 0.563 0.104

0.511 0.500 0.461 0.426 0.382 0.427 0.512 0.425

G6

0.661 0.379 0.540 0.538 0.632 0.614 0.558 0.335

0.636 0.545 0.623 0.499 0.463 0.548 0.550 0.794

G7

0.877 0.867 0.882 0.893 0.910 0.918 0.907 0.664

0.859 0.937 0.928 0.877 0.878 0.922 0.920 0.889

G8

0.674 0.713 0.739 0.726 0.538 0.636 0.706 0.365

0.697 0.823 0.636 0.683 0.615 0.625 0.563 0.674

G9

0.324 0.449 0.569 0.575 0.124 0.312 0.608 0.072

0.298 0.432 0.323 0.398 0.209 0.417 0.489 0.423

G 10

0.766 0.612 0.859 0.741

0.615

0.895

0.374 0.647 0.944 0.559

0.887

G 11

0.722 0.775 0.355 0.406 0.477 0.625 0.760 0.504

0.666 0.520 0.597 0.761 0.597 0.752 0.488 0.599

G 12

0.825 0.698 0.580 0.567 0.854 0.867 0.409 0.955

0.865 0.721 0.829 0.806 0.906 0.858 0.222 0.756

G 13

0.924 0.711 0.499 0.597 0.967 0.907 0.334 0.984

0.943 0.457 0.978 0.890 0.900 0.948 0.502 0.774

G 14

0.482 0.651 0.455 0.557 0.300 0.594 0.625 0.637

0.419 0.578 0.566 0.732 0.285 0.279 0.424 0.366

G 15

0.756 0.517 0.596 0.473 0.476 0.656 0.454 0.510

0.632 0.548 0.634 0.680 0.394 0.902 0.410 0.566

G 16

0.690 0.749 0.813 0.685 0.535 0.703 0.813 0.358

0.724 0.671 0.702 0.647 0.652 0.745 0.770 0.659

G 17

0.759 0.747 0.690 0.734 0.624 0.802 1 0.719

0.830 0.603 0.575 0.656 0.782 0.730 1 0.579

2.2 Results 29

30

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.14 Middle East and North Africa ranks Country AHP/rank Guiasu/rank Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen Rep.

Yen/rank

0.72905/1 0.6483/10 0.69891/6 0.72687/2 0.65591/9 0.70611/5 0.62838/12 0.68651/7

0.74539/1 0.65903/10 0.71867/6 0.74536/2 0.67319/9 0.7223/5 0.63788/12 0.69961/7

0.75177/1 0.66557/10 0.71842/6 0.74243/2 0.67794/9 0.73407/4 0.65034/12 0.70514/7

0.71012/4 0.66829/8 0.61631/15 0.61923/14 0.63536/11 0.7211/3 0.62061/13 0.59744/16

0.72709/4 0.68357/8 0.6254/15 0.63114/13 0.65447/11 0.73985/3 0.62791/14 0.62257/16

0.72908/5 0.6848/8 0.63788/13 0.63612/14 0.65493/11 0.73982/3 0.63233/15 0.60751/16

The countries Bahrain, Iran, and Tunisia ranked the highest in this region. In [68], Iran ranked 58th, Tunisia 63rd and Bahrain 76th over all. No other country from the region finished higher in [68]. We now consider the region Sub-Saharan Africa. Table 2.15 provides the measure of how well the countries are achieving the SDGs as given in [68]. The numbers in Table 2.15 are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain Table 2.17.

Angola Benin Botswana Burk. Faso Burundi Cabo Verde Cameroon C. Afric. Rep. Chad Comoros Congo Congo Rep. Cote d’lvoire Djibouti Eq. Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho

0.478 0.555 0.366 0.519 0.441 0.465 0.566 0.409 0.371 0.416 0.365 0.428 0.486 0.396

0.541 0.486 0.527 0.453 0.616 0.498

0.499 0.545

0.452 0.183 0.567 0.243 0.000 0.543 0.476 0.000 0.236 0.492 0.007 0.113 0.435 0.553

0.221 0.325 0.859 0.622 0.690 0.355

0.298 0.137

0.504 0.287

0.427 0.446 0.503 0.432 0.544 0.333

0.337 0.471 0.547 0.480 0.477 0.733 0.406 0.176 0.216 0.504 0.377 0.465 0.348 0.512

Table 2.15 SDGs Achievement G1 G2 G3

0.710 0.550

0.603 0.335 0.790 0.457 0.692 0.247

0.378 0.469 0.883 0.270 0.610 0.785 0.638 0.103 0.147 0.472 0.561 0.577 0.398 0.193

G4

0.675 0.719

0.556 0.533 0.463 0.349 0.541 0.357

0.536 0.404 0.662 0.346 0.630 0.654 0.519 0.306 0.270 0.321 0.372 0.518 0.351 0.497

G5

0.539 0.581

0.561 0.394 0.617 0.600 0.528 0.437

0.460 0.466 0.606 0.441 0.543 0.691 0.525 0.480 0.408 0.666 0.427 0.383 0.519 0.415

G6

0.461 0.285

0.555 0.407 0.808 0.219 0.586 0.134

0.487 0.134 0.591 0.091 0.000 0.812 0.548 0.027 0.006 0.416 0.352 0.492 0.525 0.283

G7

0.702 0.525

0.459 0.714 0.629 0.693 0.766 0.654

0.599 0.707 0.635 0.692 0.440 0.735 0.680 0.375 0.542 0.483 0.584 0.563 0.750 0.622

G8

0.197 0.141

0.098 0.100 0.293 0.082 0.302 0.055

0.057 0.088 0.290 0.124 0.035 0.257 0.143 0.019 0.097 0.104 0.023 0.067 0.283 0.229

G9

0.363 0.003

0.000 0.673 0.470 0.443 0.586 0.825

0.572 0.362 0.000 0.781 0.671 0.366 0.432 0.193 0.533 0.508 0.589 0.294 0.467 0.532

G 10

0.622 0.675

0.827 0.606 0.540 0.646 0.500 0.560

0.460 0.526 0.825 0.578 0.565 0.810 0.397 0.278 0.394 0.762 0.482 0.579 0.589 0.705

G 11

0.931 0.921

0.897 0.980 0.911 0.936 0.973 0.949

0.937 0.949 0.674 0.937 0.966 0.918 0.949 0.961 0.959 0.839 0.947 0.931 0.962 0.952

G 12

0.948 0.662

0.527 0.957 0.947 0.935 0.965 0.992

0.909 0.973 0.715 0.908 0.993 0.899 0.979 0.994 0.763 0.974 0.992 0.952 0.994 0.909

G 13

0.581

0.628 0.604 0.510 0.708

0.283 0.153 0.593 0.546 0.149

0.455 0.502

0.478 0.497

G 14

0.592 0.621

0.633 0.567 0.855 0.749 0.754 0.781

0.650 0.847 0.717 0.821 0.739 0.547 0.686 0.899 0.807 0.412 0.654 0.911 0.760 0.370

G 15

0.572 0.462

0.537 0.516 0.531 0.601 0.697 0.483

0.417 0.490 0.657 0.570 0.489 0.744 0.460 0.495 0.322 0.676 0.311 0.530 0.522 0.675

G 16

0.601 1

0.800 0.468 0.639 0.529 0.594 0.610

0.519 0.523 0.881 0.562 0.598 0.646 0.617 0.383 0.658 0.681 0.445 0.801 0.535 0.737

G 17

2.2 Results 31

Togo Uganda Zambia Zimbabwe

Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda S.Tome& Prin. Senegal Seychelles S. Leone Somolia S. Africa S. Sudan Sudan Tanzania

0.525

0.190 0.482 0.533

0.499

0.360 0.215 0.186

0.483 0.429 0.389

0.469

0.232

0.283 0.135

0.486 0.374 0.537 0.450 0.364 0.460 0.451 0.398 0.429 0.480 0.517 0.647 0.537

0.205 0.000 0.032 0.243 0.809 0.986 0.110 0.551 0.056 0.173 0.171 0.378 0.293

0.466 0.465 0.432

0.520 0.451 0.430

0.487

0.267

0.392 0.412 0.436 0.323 0.472 0.791 0.312 0.498 0.391 0.280 0.600 0.620 0.543

0.532 0.627 0.688

0.303 0.480 0.614

0.781

0.522

0.102 0.440 0.914 0.126 0.294 0.894 0.416 0.822 0.084 0.321 0.609 0.847 0.371

Table 2.16 SDGs achievement continued G1 G2 G3 G4 G5

0.584 0.636 0.764

0.373 0.707 0.402

0.801

0.449

0.414 0.669 0.556 0.356 0.308 0.489 0.600 0.873 0.402 0.365 0.805 0.452 0.531

G6

0.411 0.526 0.524

0.330 0.465 0.468

0.670

0.389

0.480 0.387 0.763 0.560 0.543 0.570 0.411 0.590 0.404 0.535 0.562 0.666 0.578

G7

0.097 0.435 0.461

0.506 0.313 0.155

0.790

0.062

0.059 0.076 0.900 0.143 0.406 0.894 0.380 0.485 0.135 0.371 0.111 0.385 0.528

G8

0.720 0.672 0.689

0.524 0.738 0.715

0.612

0.627

0.649 0.628 0.804 0.674 0.456 0.751 0.534 0.646 0.642 0.647 0.698 0.668 0.731

G9

0.174 0.166 0.113

0.150 0.157 0.145

0.450

0.061

0.035 0.058 0.568 0.117 0.136 0.275 0.119 0.236 0.032 0.184 0.157 0.168 0.155

G 10

0.620 0.166 0.560

0.656 0.601 0.395

0.000

0.694

0.839 0.317 0.425 0.740 0.862 0.392 0.492 0.000 0.818 0.091 0.273 0.907 0.525

G 11

0.465 0.634 0.806

0.413 0.663 0.389

0.779

0.467

0.290 0.624 0.826 0.591 0.372 0.980 0.682 0.768 0.559 0.326 0.586 0.840 0.584

G 12

0.958 0.897 0.937

0.934 0.979 0.959

0.688

0.957

0.993 0.946 0.771 0.949 0.937 0.614 0.987 0.794 0.912 0.948 0.958 0.949 0.950

G 13

0.986 0.984 0.964

0.987 0.982 0.986

0.870

0.984

0.993 0.969 0.878 0.992 0.732 0.680 0.922 0.611 0.953 0.961 0.986 0.896 0.948

G 14

0.664 0.650 0.419

0.565

0.605

0.794 0.522

0.514

0.625 0.541 0.740 0.605

0.752 0.578 0.492

G 15

0.672 0.703 0.777

0.605 0.569 0.863

0.591

0.656

0.540 0.506 0.430 0.699 0.658 0.235 0.666 0.855 0.737 0.763 0.665 0.574 0.749

G 16

0.475 0.454 0.511

0.563 0.505 0.523

0.549

0.508

0.449 0.511 0.685 0.507 0.449 0.695 0.526 0.696 0.559 0.429 0.709 0.773 0.598

G 17

0.466 0.469 0.657

0.653 0.532 0.589

0.795

0.422

0.513 0.443 0.571 0.521 0.643 0.635 0.668 0.748 0.750 0.500 0.571 0.568 0.600

32 2 Sustainability: Analysis Using Mathematics of Uncertainty

2.2 Results

33

Table 2.17 Sub-Saharan Africa ranks Country AHP/rank Angola Benin Botswana Burkino Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros Congo Demo Rep. Congo Rep. Cote d’lvoire Djibouti Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal

Guiasu/rank

Yen/rank

0.57469/25 0.54958/35 0.63205/10 0.56496/29 0.66144/7 0.68561/3 0.61129/16 0.47412/42 0.45968/43 0.56428/30 0.52411/40 0.58885/20 0.62302/13 0.53773/37

0.59344/25 0.56333/35 0.64907/10 0.57956/30 0.69297/4 0.70177/3 0.63021/16 0.49577/42 0.4754/43 0.57953/31 0.53894/39 0.60825/20 0.64231/12 0.54932/37

0.58758/24 0.55866/33 0.64391/10 0.56882/30 0.67877/7 0.7056/2 0.6276/15 0.48001/42 0.46267/43 0.57972/27 0.54553/38 0.60125/18 0.63394/13 0.55748/35

0.56321/31 0.60491/17 0.69066/1 0.58706/21 0.675/4 0.58256/24

0.58293/29 0.62457/17 0.71228/1 0.60275/22 0.69224/5 0.60052/24

0.57534/28 0.61413/17 0.70675/1 0.59032/23 0.69351/5 0.58273/25

0.61786/14 0.51111/41 0.55078/34 0.53331/38 0.67351/5 0.56525/28 0.57035/27 0.63797/9 0.59091/18 0.62601/12 0.54453/36 0.53016/39 0.5868/22 0.68786/2 0.62652/11

0.63501/14 0.52833/41 0.56834/33 0.53793/40 0.69152/6 0.58341/28 0.58807/26 0.64984/9 0.61274/18 0.64054/13 0.56114/36 0.54883/38 0.60157/23 0.70522/2 0.64326/11

0.63223/14 0.5264/41 0.54775/36 0.53047/40 0.69432/3 0.56739/31 0.57338/29 0.65159/9 0.59801/21 0.63857/11 0.54735/37 0.53922/39 0.59862/20 0.69414/4 0.63797/12 (continued)

34 Table 2.17 (continued) Country Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

2 Sustainability: Analysis Using Mathematics of Uncertainty

AHP/rank

Guiasu/rank

Yen/rank

0.55143/33

0.56857/32

0.55816/34

0.6701/6

0.6896/7

0.69083/6

0.59038/19 0.61453/15 0.55366/32 0.57282/26 0.58618/23 0.6605/8

0.61085/19 0.63188/15 0.5674/34 0.5877/27 0.60558/21 0.68215/8

0.59635/22 0.624/16 0.5672/32 0.5822/26 0.599/19 0.67271/8

The countries Gabon, Cape Verde and Sao Tome Principe ranked the highest in Sub-Saharan African region. In [68], Gabon ranked 99th, Cape Verde 96th, and Sao Tome Principe 95th over all. No other country in the region ranked higher over all in [68]. We next consider the region Oceania. The Table 2.18 provides the measure of how well the countries are achieving the SDGs as given in [68]. The numbers in Table 2.18 are substituted for the G i , i = 1, . . . , 16, in the AHP, Guiasu, and Yen formulas to obtain Table 2.19. In [68], Fiji ranked 62nd, Vanuatu 118th, and Papua New Guinea 143rd over all.

Fiji 0.910 Kinbati Mar. Islands Micro., Fed. Sts. Nauru Palau P. N. Guinea 0.292 Samoa Solomon Isl. Tonga Tuvalu Vanuatu 0.587

0.463

0.686

0.276

0.503

G3

0.739

0.558

Table 2.18 SDGs achievement G1 G2

G4

0.678

0.483

0.951

G5

0.371

0.440

0.576

G6

0.775

0.275

0.766

G7

0.322

0.134

0.684

G8

0.740

0.769

0.799

G9

0.177

0.046

0.274

G 10

0.723

0.594

0.741

G 11

0.733

0.808

0.956

G 12

0.794

0.976

0.836

G 13

0.718

0.865

0.701

G 14

0.536

0.477

0.506

G 15

0.455

0.623

0.387

G 16

0.714

0.663

0.868

G 17

0.666

0.590

0.660

2.2 Results 35

36

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.19 Oceania ranks Country AHP/rank Fiji Kinbati Marshall Islands Micronesia, Fed. Sts. Nauru palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Guiasu/rank

Yen/rank

0.69534/1

0.70723/1

0.71031/1

0.55691/3

0.56911/3

0.56436/3

0.59629/2

0.60573/2

0.60069/2

2.3 Commentary OECD OECD countries are not on track for achieving the SDGs. Compared to the rest of the world OECD countries perform better on goals related to socio-economic outcomes and basic access to infrastructures. However major efforts are needed on climate mitigation and biodiversity protection. Trends on climate action and life below water are alarming in most OECD countries, [68]. East and South Asia A recent UN report warns that not a single country in Asia and the Pacific (https:// www.unidispatch.com) is on track to achieve any of the SDGs by 2030. Although progress has been made on some fronts namely, toward ending poverty (SDG10), ensuring all have access to quality education and life learning (SDG4), and delivering affordable and clean energy (SDG7), the progress is not fast enough to achieve the targets by 2030. For more than half of the goals, progress has been stagnated. But even worse, the report reveals that the situation is actually deteriorating for three of the goals: clean water and sanitation (SDG6), decent work and economic growth (SDG8), and responsible consumption and production (SDG12). Southeast Asia has made the most improvements on providing quality education, ensuring access to affordable and clean energy, and building industry, innovation and infrastructure (SDG 4, 7, and 9). The situation on decent work and economic growth (SDG 8), climate action (SDG 13), and peace, justice and strong institutions (SDG 16) is regressing though [81].

2.3 Commentary

37

Eastern Europe and Central Asia Climate change threatens to undermine development gains and efforts to eradicate poverty in Eastern Europe and Central Asia according to a report published by the UN Development Program (UNDP). Climate change impacts in the region are costing billions of dollars in lost productivity and could continue to increase, according to the report in [34]. The publication contends that tackling climate change must be central to poverty reduction and sustainable development efforts. It states that new climate risks “threaten to derail”advances and national efforts to achieve the objectives of the 2030 Agenda for Sustainable Development, the Paris Agreement on climate change and Sendic Framework for Disaster Reduction (SDR). Latin America and the Caribbean At the Rio+20 Summit, it was recognized the enormous paradox still facing countries in Latin America and the Caribbean for which development pathways are currently shaped by simultaneous concern over resource degradation, the impacts of climate change, and the need to develop a just and inclusive society. In [2], it was argued that these issues raised in Buenos Aires point to a clear need for stronger efforts toward the implementation of the SDGs. Special attention should be paid to potential synergies and trade-offs in three areas; (1) integrated policy making and budgeting, (2) securing the natural resource base, and (3) building strong and inclusive democracies. Middle East and North Africa As with most other parts of the world, the MENA region is affected by a global “multiple crisis”, which is a combination of phenomena such as climate change, mass loss of species, soil erosion, increasing social and economic divisions and instabilities, depleting fossil fuels an resources, increased forced migration and overburdened governance. Besides many other concepts, sustainable development has been created and defined as a basic strategic framework for meeting these and other challenges that threaten human well-being and livelihood and that of future generations. Apart from countries facing violent conflicts, progress in MENA has been made in ending extreme poverty SDG1, and promoting affordable and clean energy SDG7. In several sub-regions, an enhancement of energy security has been achieved due to an increase of energy efficiency and renewable energy diversifying the energy mix. Reliable and sustainable solutions have been developed to facilitate access to modern energy services among rural and remote populations. The overall goal for education SDG 4 is far from being reached, [23]. Sub-Saharan Africa Despite a widespread adoption of and progress toward the Sustainable Development Goals, Africa continues to lag behind most of the world when it comes to socioeconomic development. A recent report by the Sustainable Development Goals Center

38

2 Sustainability: Analysis Using Mathematics of Uncertainty

for Africa, Africa 2030: Sustainable Development Goals, The Year Reality Check reveals that minimal progress has been made and, in some instances, there is complete stagnation. More than half of the global poor (those who earn $1.90 PPP per day) are found in Africa. One in three Africans is at the risk of food insecurity. Oceania There is a relative lack of comparable data available for the regions to benchmark these small island countries against the rest of the world. They perform relatively well compared to the rest of the world on climate mitigation. Further discussion concerning how well regions are achieving the SDGs can be found in [68, pp. 22–23].

2.4 Appendix 2.4.1 Sustainable Development Goals G 1 : No Poverty Internationally comparable poverty lines above $1.90 PPP per day G 2 : Zero Hunger Agricultural yield gaps by cropping system Resource use efficiency (nutrients, water, energy) Food loss and food waste Greenhouse gas emissions from land use Diets and nutrient deficiencies G 3 : Good Health and Well-Being Affordability of healthcare G 4 : Quality Education Internationally comparable primary and secondary education outcomes Early childhood development G 5 : Gender Equality Gender pay gap and other empowerment measures Violence against women G 6 : Clean Water and Sanitation Water embedded in trade adjusted for environmental impact Quality of drinking water and surface water G 7 : Affordable and Clean Energy G 8 : Decent Work and Economic Growth Decent work Child labor G 9 : Industry, Innovation and Infrastructure G 10 : Reduced Inequalities Wealth inequality

2.4 Appendix

39

Vertical mobility G 11 : Sustainable Cities and Communities G 12 : Responsible Consumption and Production Environmental impact of material flows Recycling and re-use (circular economy) Chemicals G 13 : Climate Action Leading indicators for decarbonization Greenhouse gas emissions from land use Climate vulnerability metrics G 14 : Life Below Water Maximum sustainable yields Impact of high sea and cross border fishing Protected areas by level of protection G 15 : Life on Land Leading indicators on ecosystem health Trade in endangered species Protected areas by level of protection G 16 : Peace, Justice, and Strong Institutions Modern slavery and human trafficking Access to justice Financial secrecy G 17 : Partnerships and Goals Nonconcessional development finance Climate finance Unfair tax competition Development impact of trade practices.

2.4.2 Scoring Assessment The tables in Appendix 2.4.2 are determined by the table in [54, Annex 2, p.12], Results of the Scoring Assessment. In [54], the table presents the individual category scores and the overall scores for each goal and target. These were obtained by averaging the collective scores from the assessors. The scores given were out of a maximum of 2 for individual category scores and a maximum of 8 for overall scores. An overall score was then obtained for each target by multiplying the scores given to each of the three categories. Multiplication was used to emphasize that for a goal or

40

2 Sustainability: Analysis Using Mathematics of Uncertainty

target to score highly, it must meet all three criteria. Goal 17 was excluded from the analysis. We divided the individual category scores by 2 so that the resulting scores were out of a maximum of 1. The overall scores were divided by 8 so the resulting scores were out of a maximum of 1. In this way, we have placed the scoring in a fuzzy logic setting, [89]. For example, multiplication is a particular t-norm in mathematics of uncertainty. The door is now open to use other t-norms or other operators. Table 2.20 Scoring assessment Goal/Target

Applicable

Implementable Transformative Overall bounded difference

Overall algebraic product

Overall standard intersection

G1

0.53

0.94

0.33

0.1

0.22

0.33

1.1

0.0

1.0

0.0

0.0

0.0

0.0

1.2

0.85

1.0

0.5

0.35

0.425

0.5

1.3

0.65

1.0

0.5

0.15

0.325

0.5

1.4

0.5

0.85

0.15

0.0

0.064

0.15

1.5

0.65

0.85

0.5

0.0

0.276

0.5

G2

0.57

0.77

0.22

0.05

0.172

0.22

2.1

0.65

1.0

0.5

0.15

0.325

0.5

2.2

0.85

1.0

0.35

0.2

0.298

0.35

2.3

0.35

0.65

0.0

0.0

0.0

0.0

2.4

0.35

0.35

0.35

0.0

0.043

0.35

2.5

0.65

0.85

0.35

0.0

0.19

0.35

G3

0.48

0.88

0.34

0.09

0.14

0.29

3.1

0.0

1.0

0.15

0.0

0.0

0.0

3.2

0.5

0.85

0.15

0.0

0.064

0.15

3.3

0.5

0.85

0.15

0.0

0.064

0.15

3.4

0.65

0.85

0.5

0.0

0.276

0.5

3.5

1.0

1.0

0.5

0.5

0.5

0.5

3.6

0.85

0.85

0.5

0.2

0.361

0.5

3.7

0.0

0.85

0.15

0.0

0.0

0.0

3.8

0.15

1.0

0.15

0.0

0.022

0.15

3.9

0.65

0.65

0.85

0.15

0.359

0.65

G4

0.41

0.93

0.44

0.19

0.31

00.36

4.1

0.15

0.85

0.15

0.0

0.019

0.15

4.2

0.65

0.85

0.35

0.0

0.193

0.35

4.3

0.5

0.85

0.35

0.0

0.149

0.35

4.4

0.85

1.0

0.5

0.35

0.425

0.5

4.5

0.65

1.0

0.5

0.15

0.325

0.5

4.6

0.65

1.0

0.35

0.0

0.228

0.35

2.4 Appendix

41

Table 2.21 Scoring assessment continued Goal/target

Applicable

Implementable Transformative Overall bounded difference

Overall algebraic product

Overall standard intersection

4.7

1.0

1.0

0.85

0.85

0.85

0.85

G5

0.61

0.86

0.47

0.11

0.26

0.47

5.1

0.65

0.65

0.5

0.0

0.211

0.5

5.2

0.65

0.65

0.5

0.0

0.211

0.5

5.3

0.5

0.85

0.5

0.0

0.212

0.5

5.4

0.85

1.0

0.5

0.35

0.425

0.5

5.5

0.65

1.0

0.65

0.3

0.422

0.65

5.6

0.35

1.0

0.15

0.0

0.052

0.15

G6

0.475

0.95

0.45

0.26

0.31

0.42

6.1

0.15

0.85

0.15

0.0

0.019

0.15

6.2

0.0

0.85

0.0

0.0

0.0

0.0

6.3

0.65

1.0

0.85

0.5

0.552

0.65

6.4

0.85

1.0

0.5

0.35

0.425

0.5

6.5

0.35

1.0

0.35

0.0

0.122

0.35

6.6

0.85

1.0

0.85

0.7

0.722

0.85

G7

0.95

1.0

0.83

0.78

0.80

0.83

7.1

0.85

1.0

0.5

0.35

0.425

0.5

7.2

1.0

1.0

1.0

1.0

1.0

1.0

7.3

1.0

1.0

1.0

1.0

1.0

1.0

G8

0.68

0.84

0.52

0.24

0.35

0.52

8.1

0.65

0.65

0.35

0.0

0.148

0.35

8.2

0.65

0.65

0.65

0.0

0.275

0.65

8.3

0.85

0.85

0.65

0.35

0.470

0.65

8.4

1.0

1.0

1.0

1.0

1.0

1.0

8.5

0.85

0.85

0.65

0.35

0.470

0.65

8.6

1.0

0.65

0.65

0.3

0.422

0.65

8.7

0.15

1.0

0.15

0.0

0.022

0.15

8.8

0.65

1.0

0.5

0.15

0.325

0.5

42

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.22 Scoring Assessment continued Goal/target

Applicable

Implementable Transformative Overall bounded difference

Overall algebraic product

Overall standard intersection

8.9

0.65

0.85

0.5

0.2

0.276

0.5

8.10

0.35

0.85

0.15

0.0

0.045

0.15

G9

0.66

0.8

0.47

0.14

0.26

0.47

9.1

0.5

0.85

0.5

0.0

0.212

0.5

9.2

0.65

0.65

0.5

0.0

0.211

0.5

9.3

0.5

0.65

0.35

0.0

0.114

0.35

9.4

1.0

1.0

0.5

0.5

0.5

0.5

9.5

0.65

0.85

0.5

0.2

0.275

0.5

G10

0.71

0.89

0.64

0.36

0.45

0.61

10.1

0.85

1.0

0.65

0.5

0.552

0.65

10.2

0.5

0.85

0.5

0.0

0.212

0.5

10.3

0.5

0.85

0.5

0.0

0.212

0.5

10.4

1.0

1.0

0.85

0.85

0.85

0.85

10.5

0.65

1.0

0.85

0.5

0.552

0.65

10.6

1.0

1.0

0.65

0.65

0.65

0.65

10.7

0.5

0.5

0.5

0.0

0.125

0.5

G11

0.66

0.87

0.51

0.16

0.34

0.49

11.1

0.5

0.85

0.5

0.0

0.212

0.5

11.2

0.65

0.85

0.65

0.15

0.359

0.65

11.3

0.85

0.85

0.65

0.35

0.470

0.65

11.4

0.5

0.85

0.15

0.0

0.064

0.15

11.5

0.65

0.85

0.35

0.0

0.359

0.35

11.6

1.0

1.0

0.65

0.65

0.65

0.65

11.7

0.5

0.85

0.65

0.0

0.276

0.5

G12

0.98

0.94

0.79

0.78

0.79

0.82

12.1

1.0

1.0

1.0

1.0

1.0

1.0

12.2

1.0

0.85

1.0

0.85

0.85

0.85

12.3

1.0

0.85

0.85

0.7

0.722

0.85

12.4

0.85

1.0

0.5

0.35

0.425

0.5

2.4 Appendix

43

Table 2.23 Scoring assessment continued Goal/Target

Applicable

Implementable Transformative Overall bounded difference

Overall algebraic product

Overall standard intersection

12.5

1.0

0.85

1.0

0.85

0.85

0.85

12.6

1.0

1.0

1.0

1.0

1.0

1.0

12.7

1.0

1.0

0.65

0.65

0.65

0.65

12.8

1.0

1.0

0.85

0.85

0.85

0.85

G13

1.0

1.0

0.88

0.88

0.88

0.88

13.1

1.0

1.0

0.65

0.65

0.65

0.65

13.2

1.0

1.0

1.0

1.0

1.0

1.0

13.3

1.0

1.0

1.0

1.0

1.0

1.0

G14

0.89

0.48

0.7

0.48

0.52

0.64

14.1

0.85

0.65

0.85

0.35

0.470

0.65

14.2

0.85

0.85

0.85

0.55

0.614

0.85

14.3

1.0

0.65

0.85

0.5

0.552

0.65

14.4

1.0

1.0

0.85

0.85

0.85

0.85

14.5

0.85

1.0

0.65

0.5

0.552

0.65

14.6

0.85

0.85

0.85

0.55

0.614

0.85

14.7

0.85

0.5

0.0

0.0

0.0

0.0

G15

0.7

0.82

0.52

0.15

0.30

0.5

15.1

0.85

0.85

0.35

0.05

0.25

0.35

15.2

0.65

1.0

0.5

0.15

0.325

0.5

15.3

0.5

0.65

0.5

0.0

0.162

0.5

15.4

0.5

0.85

0.35

0.0

0.149

0.35

15.5

1.0

0.85

0.65

0.7

0.552

0.65

15.6

0.65

1.0

0.65

0.3

0.422

0.65

15.7

0.65

0.85

0.65

0.15

0.359

0.65

15.8

0.65

0.85

0.35

0.0

0.193

0.35

15.9

0.85

0.5

0.65

0.0

0.276

0.5

G16

0.615

0.805

0.525

0.18

0.30

0.46

16.1

0.65

0.85

0.85

0.35

0.470

0.65

16.2

0.65

0.85

0.65

0.15

0.359

0.65

44

2 Sustainability: Analysis Using Mathematics of Uncertainty

Table 2.24 Scoring assessment continued Goal/Target

Applicable

Implementable Transformative Overall bounded difference

Overall algebraic product

Overall standard intersection

16.3

0.5

0.85

0.65

0.0

0.276

0.5

16.4

0.85

0.85

0.65

0.35

0.470

0.65

16.5

0.5

0.65

0.65

0.0

0.211

0.5

16.6

0.85

0.85

0.85

0.55

0.614

0.85

16.7

0.85

0.85

0.65

0.35

0.470

0.65

16.8

0.5

0.65

0.0

0.0

0.0

0.0

16.9

0.15

0.65

0.15

0.0

0.015

0.15

16.10

0.65

1.0

0.15

0.0

0.098

0.15

Chapter 3

Sustainable Development Goals and Human Trafficking

We use mathematics of uncertainty to analyze the relationship between the sustainable development goals and human trafficking. This comes about naturally due to the use of multiplication in the determination of metrics in scoring categories by the Stakeholder Forum. We develop a method to measure how well countries are achieving the sustainable development goals that are pertinent to trafficking in humans. This method includes linear equations involving ten of the SDGs. SGD 5 (Gender Equality), 8 (Decent Work and Economic Growth), and 16 (Peace, Justice, and Strong Institutions) carry the most weight in determining these rankings.

3.1 Introduction The members of all UN’s Member States agreed to the 2030 Agenda for Sustainable Development. The 17 Sustainable Developmental Goals (SDGs) address five broad areas of critical importance: People, Planet, Prosperity, Peace, and Partnership [27]. It is stated in [27, 55], that as an over arching principle, States have a collective interest and responsibility to ensure that the most vulnerable people and populations are not left behind by economic, social, and environmental progress. This includes both migrants and refugees. Out of the 17 SDGs, trafficking in persons is specifically mentioned in three targets under three goals: 5 (Gender Equality), 8 (Decent Work and Economic Growth), and 16 (Peace, Justice, and Strong Institutions). However many other SDG targets and goals are relevant to addressing trafficking in persons. This issue is rooted in development issues at-large including poverty, education, child labor, abuse, and exploitation, gender equality and discrimination, migration and the effects of climate change, [27]. Other SDGs mentioned in [27] that contribute to combatting © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_3

45

46

3 Sustainable Development Goals and Human Trafficking

trafficking in persons are 10, 4, 17. The specific targets of the SDGs that contribute to combatting trafficking in persons are 5.2, 8.7, 16.2, 5.3, 10.7, 4.1, 4.3, 4.4, 17.18 and 17.19 [27]. It is mentioned by Professor Rochelle Dalla, Editor in-Chief of the Journal of Human Trafficking, that SDG 12 is also important in combatting trafficking persons since it is directly related to the promotion of Fair Trade production, advocacy, and market practices, an addition to consumer knowledge and choice [61]. We introduce a new approach to analyze the impact of the SDGs on trafficking in persons. Our goal is to introduce to the study of sustainability concepts from mathematics of uncertainty. This includes the important area of Dempster-Shafer theory. Fuzzy graph theory has been applied to the study of human trafficking, immigration, and modern slavery [42]. We first consider countries that are members of the Organization for Economic Cooperation and Development (OECD). The OECD is a forum where the government of 36 democracies with market economies work with each other, as well as with more than 70 non-member economies to promote economic growth, prosperity, and sustainable development. In [54], Stakeholder Forum created a methodology to enable scores to be assigned to each of the different targets and goals according to their significance in different contexts. The method uses a number of assessors to assign their own independent scores of the significance of each of the proposed targets in the implementation context in question, according to three separate criteria. The three criteria proposed are applicability, implementability, and the transformational impact (both in the country concerned and for the world as a whole) [54, p. 10]. The assessors’ scores are then aggregated and averaged to give an overall score for each target, and then combined to give an average score for each goal. The methodology is described in more detail in Sect. 3.2. The general effect is to give the highest scores to those targets and goals which are both clearly applicable and implementable in the country in question and which represent the biggest transformational challenge. Conversely, lower scores are given to targets and goals which are less applicable or implementable in a particular country for reasons given in [54]. We use the analytic hierarchy process [64, 69], the Guiasu method [24], and the Yen method [87, 88] to determine which countries are, in the opinion of the experts, the best in achieving sustainability. This is accomplished by constructing linear equations involving the SDGs as independent variables and a measure of success as the dependent variable. These methods are described in Sect. 1.2. We find that for countries from the OECD, Denmark, Finland, and Sweden rank the highest in achieving the sustainable development goals pertinent to human trafficking. Recall that we use these t-norms to illustrate our approach for the following reasons: Algebraic product (multiplication) is in keeping with the approach in [54], where the t-norm multiplication was used to emphasize that for a goal or target to score highly, it must meet all criteria. Standard intersection is the largest t-norm and thus is the least punitive t-norm. Bounded difference is more punitive than algebraic product since 0 ∨ (a + b − 1) ≤ ab for all a, b ∈ [0, 1].

3.2 Scoring Assessment

47

3.2 Scoring Assessment The Table 3.1 is determined by the table in [54, Annex 2, p. 12], Results of the Scoring Assessment. In [54], the table present the individual category scores and the overall scores for each goal and target. These were obtained by averaging the collective scores from the assessors. The scores given were out of a maximum of 2 for individual category scores and a maximum of 8 for overall scores. An overall score was then obtained for each target by multiplying the scores given to each of the three categories. Multiplication was used to emphasize that for a goal or target to score highly, it must meet all three criteria. Goals that are not immediately relevant to human trafficking are excluded from the analysis. We divided the individual category scores by 2 so that the resulting scores were out of a maximum of 1. The overall

Table 3.1 Scoring assessment Goal/target Applicable

Implementable

Transformative

Overall bounded difference

Overall algebraic product

Overall standard intersection

G1

0.53

0.94

0.33

0.1

0.22

0.33

G4

0.5

0.9

0.3333

0.12

0.20

0.33

4.1

0.15

0.85

0.15

0.0

0.019

0.15

4.3

0.5

0.85

0.35

0.0

0.149

0.35

4.4

0.85

1.0

0.5

0.35

0.425

0.5

G5

0.75

0.75

0.75

0.25

0.43

0.75

5.2

1.0

0.65

1.0

0.65

0.65

0.65

5.3

0.5

0.85

0.5

0.0

0.212

0.5

G6

0.5

0.85

0.5

0.0

0.21

0.5

6.2

0.5

0.85

0.5

0.0

0.212

0.5

G8

0.83

0.95

0.72

0.5

0.60

0.72

8.5

0.85

0.85

0.65

0.35

0.470

0.65

8.7

1.0

1.0

1.0

1.0

1.0

1.0

8.8

0.65

1.0

0.5

0.15

0.325

0.5

G 10

0.5

0.5

0.5

0.0

0.12

0.5

10.7

0.5

0.5

0.5

0.0

0.125

0.5

G 11

0.5

0.85

0.65

0.0

0.28

0.5

11.7

0.5

0.85

0.65

0.0

0.28

0.5

G 12

0.5

0.5

0.5

0.0

0.12

0.5

G 16

1.0

0.85

1.0

0.85

0.85

0.85

16.2

1.0

0.85

1.0

0.85

0.85

0.85

G 17

0.5

0.5

0.5

0.0

0.12

0.5

17.18

0.5

0.5

0.5

0.0

0.125

0.5

17.19

0.5

0.5

0.5

0.0

0.125

0.5

48

3 Sustainable Development Goals and Human Trafficking

scores were divided by 8 so the resulting scores were out of a maximum of 1. In this way, we have placed the scoring in a fuzzy logic setting [89]. For example, multiplication is a particular t-norm in mathematics of uncertainty.

3.3 Results We use the analytic hierarchy process [64, 65], the Guiasu method [24], and the Yen method, [87, 88]. These methods allow us not only to determine which countries are, in the opinion of the experts, the best in achieving sustainability, but also to construct a linear equation involving the goals as independent variables and a measure of success as the dependent variable. The entries in Table 3.2 are from Table 3.1. The coefficients in the following equation are determined by dividing the corresponding entry in the row average column by the column sum. G = 0.06G 1 + 0.06G 4 + 0.16G 5 + 0.07G 6 + 0.17G 8 +0.06G 10 + 0.07G 11 + 0.06G 12 + 0.24G 16 + 0.06G 17

(3.1)

The following table is obtained from by dividing each entry in Table 3.2 by column sum of the column it appears. Table 3.2 AHP method AHP Bounded difference G1 G4 G5 G6 G8 G 10 G 11 G 12 G 16 G 17 Column sum

0.10 0.12 0.25 0.00 0.50 0.00 0.00 0.00 0.85 0.00 1.82

Algebraic product

Standard intersection

Row average

0.22 0.20 0.43 0.21 0.60 0.12 0.28 0.12 0.85 0.12 3.15

0.33 0.33 0.75 0.50 0.72 0.50 0.50 0.50 0.85 0.50 5.48

0.2167 0.2167 0.5767 0.2367 0.6067 0.2067 0.2600 0.2067 0.8500 0.2067 3.5836

3.3 Results

49

Table 3.3 Guiasu method Guiasu Bounded difference G1 G4 G5 G6 G8 G 10 G 11 G 12 G 16 G 17 Column sum

0.05 0.07 0.14 0.00 0.27 0.00 0.00 0.00 0.47 0.00

Algebraic product Standard intersection

Row average

0.07 0.06 0.14 0.07 0.19 0.04 0.07 0.04 0.27 0.04

0.06 0.06 0.14 0.05 0.20 0.04 0.05 0.04 0.30 0.04

0.06 0.06 0.14 0.09 0.13 0.09 0.09 0.09 0.16 0.09

The coefficients in the following equation are the entries of the row average column. G  = 0.06G 1 + 0.06G 4 + 0.14G 5 + 0.05G 6 + 0.20G 8 +0.04G 10 + 0.05G 11 + 0.04G 12 + 0.30G 16 + 0.04G 17

(3.2)

The next table is determined by dividing each element in Table 3.3 by the largest element in its column. Table 3.4 Yen method Yen Bounded difference G1 G4 G5 G6 G8 G 10 G 11 G 12 G 16 G 17 Column sum

0.11 0.15 0.30 0.00 0.57 0.00 0.00 0.00 1.00 0.00

Algebraic product Standard intersection

Row average

0.26 0.22 0.52 0.26 0.70 0.15 0.26 0.15 1.00 0.15

0.25 0.25 0.56 0.27 0.69 0.24 0.27 0.24 1.00 0.24 4.01

0.38 0.38 0.87 0.56 0.81 0.56 0.56 0.56 1.00 0.56

50

3 Sustainable Development Goals and Human Trafficking

The coefficients in the following equation is determined by dividing the corresponding entries in the row average column by the column sum (Table 3.4). G  = 0.06G 1 + 0.06G 4 + 0.14G 5 + 0.07G 6 + 0.17G 8 +0.06G 10 + 0.07G 11 + 0.06G 12 + 0.25G 16 + 0.06G 17

(3.3)

We next present the degree to which the countries are achieving the SDGs as determined in [68, pp. 69–72]. Note that the following table is obtained from Table 2.4 from Chap. 2 by deleting the appropriate columns.

3.3 Results

51

Table 3.5 SDGs achievement G1 G4 G5 Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands N. Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. K. U. S.

0.990 0.992 0.995 0.992 0.989 0.994 0.996 0.997 0.998 0.995 0.996 0.967 0.989 0.997 0.997 0.992 0.973 0.990 0.990 0.987 0.984 0.999 0.875 0.996 1 0.995 0.999 0.987 0.982 0.997 0.981 0.990 0.999 0.995 0.997 0.989

0.928 0.966 0.947 0.999 0.928 0.963 0.983 0.953 0.989 0.974 0.890 0.901 0.904 0.974 0.952 0.968 0.976 0.981 0.958 0.957 0.987 0.944 0.926 0.942 0.981 0.999 0.944 0.955 0.838 0.966 0.954 0.993 0.919 0.937 0.994 0.893

0.789 0.791 0.839 0.804 0.705 0.711 0.848 0.753 0.892 0.865 0.770 0.626 0.641 0.855 0.731 0.752 0.712 0.585 0.639 0.702 0.721 0.746 0.774 0.815 0.847 0.877 0.711 0.807 0.689 0.753 0.827 0.889 0.822 0.453 0.813 0.734

G6

G8

G 10

G 11

G 12

G 16

G 17

0.970 0.949 0.793 0.842 0.966 0.880 0.907 0.897 0.926 0.879 0.894 0.906 0.890 0.874 0.820 0.743 0.848 0.845 0.815 0.890 0.857 0.900 0.791 0.927 0.907 0.875 0.820 0.870 0.844 0.824 0.881 0.935 0.955 0.821 0.951 0.850

0.811 0.820 0.814 0.840 0.807 0.851 0.839 0.848 0.825 0.781 0.844 0.630 0.821 0.830 0.877 0.850 0.787 0.885 0.862 0.833 0.805 0.699 0.730 0.831 0.881 0.785 0.844 0.823 0.807 0.847 0.752 0.835 0.798 0.738 0.829 0.852

0.770 0.874 0.934 0.788 0.273 0.923 0.965 0.722 0.979 0.856 0.834 0.509 0.756 0.992 0.848 0.502 0.699 0.768 0.865 0.765 0.496 0.883 0.146 0.946 0.733 1 0.537 0.573 0.835 1 0.692 1 0.800 0.412 0.714 0.477

0.806 0.858 0.823 0.804 0.807 0.894 0.902 0.903 0.883 0.870 0.909 0.821 0.861 0.899 0.845 0.801 0.740 0.754 0.803 0.863 0.831 0.945 0.812 0.911 0.830 0.861 0.785 0.844 0.820 0.859 0.891 0.903 0.983 0.704 0.980 0.825

0.409 0.455 0.467 0.501 0.725 0.708 0.498 0.587 0.487 0.534 0.474 0.394 0.710 0.506 0.463 0.425 0.517 0.556 0.635 0.679 0.674 0.239 0.788 0.440 0.515 0.305 0.737 0.548 0.650 0.608 0.534 0.522 0.279 0.738 0.429 0.365

0.857 0.920 0.869 0.881 0.759 0.827 0.928 0.878 0.929 0.766 0.834 0.728 0.734 0.930 0.904 0.736 0.752 0.903 0.754 0.770 0.805 0.902 0.531 0.835 0.926 0.849 0.814 0.841 0.799 0.881 0.806 0.838 0.830 0.681 0.857 0.761

0.611 0.680 0.623 0.654 0.794 0.555 0.898 0.555 0.740 0.751 0.831 0.536 0.515 0.674 0.334 0.549 0.631 0.649 0.534 0.504 0.516 0.584 0.602 0.537 0.649 0.996 0.534 0.587 0.551 0.576 0.591 0.982 0.533 0.708 0.489 0.562

The numbers in Table 3.5 are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain the first of the two numbers in the columns of the following table. The second number is the rank of the country.

52

3 Sustainable Development Goals and Human Trafficking

Table 3.6 OECD ranks Country AHP/rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands N. Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. K. U. S.

0.81659/17 0.85127/7 0.83226/12 0.83414/10 0.7788/33 0.82967/14 0.88806/1 0.8302/13 0.88414/2 0.82404/16 0.83455/9 0.70129/36 0.7733/30 0.87379/4 0.8152/18 0.7557/35 0.76711/29 0.80934/19 0.78192/28 0.79496/26 0.78299/27 0.80278/22.5 0.68781/34 0.83239/11 0.8618/5 0.85676/6 0.79001/25 0.80985/20 0.78703/24 0.84254/7 0.80276/22.5 0.88319/3 0.81384/21 0.69553/33 0.82924/15 0.75933/34

Guiasu/rank

Yen/rank

0.80524/18 0.83893/6 0.81924/11 0.82434/9 0.76315/29 0.8114/16 0.86855/1 0.81998/10 0.86649/2 0.79833/21 0.81567/14 0.68803/34 0.75451/30 0.85849/3 0.81493/15 0.74992/33 0.7529/32 0.80693/17 0.7672/28 0.77809/25 0.77354/26 0.79191/22 0.66331/36 0.8159/13 0.85417/4 0.83296/7 0.78169/24 0.80042/19 0.7714/27 0.82841/8 0.78536/23 0.8539/5 0.80014/20 0.68161/35 0.81801/12 0.75429/31

0.80938/18 0.84465/7 0.83417/12 0.82687/10 0.77229/29 0.82372/13 0.88038/1 0.82392/14 0.87559/2 0.8144/16 0.82749/9 0.69605/34 0.76782/30 0.86599/4 0.80962/17 0.74802/33 0.76039/31 0.80667/19 0.77668/27 0.78862/24 0.77662/28 0.79686/22 0.67764/36 0.82444/11 0.85412/5 0.8477/6 0.78393/25 0.80212/21 0.78124/26 0.83629/8 0.79428/23 0.87379/3 0.8057/20 0.69328/35 0.82155/15 0.75226/32

We see that for the OECD region, Denmark, Finland, and Sweden rank the highest (Table 3.6).

3.4 Discussion

53

3.4 Discussion It is stated in [30] that among the various paradigmatic changes in science and mathematics in the 20th century, one such change concerned the concept of uncertainty. In science, this change has been manifested by a gradual transition from the traditional view, which insists that uncertainty is undesirable in science and should be avoided by all possible means, to an alternative view, which is tolerant of uncertainty and insists that science cannot avoid it. Uncertainty is essential to science and has a great utility. An important point in the evolution of the modern concept of uncertainty was the publication of a seminal paper by Lotfi Zadeh [89]. Fuzzy set theory provides a methodology for carrying out approximate reasoning processes when available information is uncertain, incomplete, imprecise, or vague. This is especially true when observations are expressed in linguistic terms. The success of this methodology has been demonstrated in a variety of fields such as control of complex system, where mathematical models are difficult to specify, and in expert systems, where rules express knowledge and facts that are linguistic in nature. Due to the nature of the problem, accurate data concerning trafficking in persons is impossible to obtain. The goal of the trafficker is to be undetected. The size of the problem also makes it very difficult to obtain accurate data. There are other reasons for scarcity of data. These include the victims reluctance to report crimes or testify for fear of reprisals, disincentives, both structural and legal, for law enforcement to act against traffickers, a lack of harmony among existing data sources, and an unwillingness of some countries and agencies to share data. We next illustrate the above situation with some examples from the literature. In [76], details of the reported trafficking in persons situation of the country or territory under analysis is provided. In the left column, information is provided in terms of origin, transit and/or destination according to the citation index. Whether a country ranks (very) low, medium, or (very) high depends upon the total number of sources which made reference to this country as one of origin, transit or destination. Information provided in the right column provides further detail to information provided in the left column. If a country is reported as one of origin, information in the right column will be provided on the countries to which these victims are reportedly trafficked. Similar information is provided for transit and destination countries. A different scale was used to determine whether the related countries in the right column are ranked, high, medium, or low. If a country in the right column was mentioned by one or two sources, the related country was ranked low. If the linkage between the countries in the two columns was reported by 3–5 sources, the country in the right column was ranked medium. If more than 5 sources linked the two countries, the country in the right column was ranked high. The rankings are clearly linguistic. These rankings turned out to be useful in determining the strength of various trafficking routes [36, 39, 42, 60, 84]. These routes include trafficking networks through Mexico to the U.S and routes across the Mediterranean to Europe. This was accomplished by placing the rankings in the interval [0, 1] by assigning the num-

54

3 Sustainable Development Goals and Human Trafficking

bers 0.1, 0.3, 0, 5, 0.7.0, 9 to the measures very low, low, medium, high, very high, respectively. Another example is in [55, 68]. Here the colors red, orange, yellow and green are assigned to countries to provide a measure of their achievement for each of the 17 SDGs. These colors are assigned to the target values of each SDG. The worst two colors of a target were averaged to determine the color for its SDG. In this paper, we assigned the numbers 0.2, 0.4, 0.6, 0.8 to the colors red, orange, yellow, and green, respectively. Thus the many techniques from mathematics of uncertainty become available to examine the SDGs. There are cases where numerical data is provided for factors related to trafficking in persons or modern slavery. However the data for one factor may not be compatible to the data for another factor. The data for each factor should be normalized so that the data can be combined. In [21], a study of how governments are tackling modern slavery was undertaken. 161 countries were included in the assessment of government response. In [21], tables are provided giving measures of vulnerability to modern slavery by country with respect to four categories. Government response to human trafficking involved five categories. Tables providing measures for these five categories were also given. In [33], the data in these tables were normalized. This allowed for the use of fuzzy logic techniques to be used to compare government’s vulnerability and its response. It was shown in [45] that government response and vulnerability were opposites. Also, the vulnerability of routes could be measured by using techniques from fuzzy mathematics [33, 39, 42, 45]. Another example is in [54]. Here a table presented the individual category scores and the overall scores for each goal and target. These were obtained by averaging the collective scores from the assessors. The scores given were out of a maximum of 2 for individual category scores and a maximum of 8 for overall scores. An overall score was then obtained for each target by multiplying the scores given to each of the three categories. Multiplication was used to emphasize that for a goal or target to score highly, it must meet all three criteria. In this chapter, we normalized the data so that we could apply techniques from mathematics of uncertainty. It should be noted that expert opinion provided the rankings thus providing another reason to use mathematics of uncertainty. It should also be noted that the paper relies heavily on the notion of a t-norm because [54] used multiplication which is a t-norm. Hence we maintain the philosophy that for a goal or target to score highly, it must meet all three criteria. We note that ranking of countries in [68] is similar to ours. For example, the top three countries in achieving the SDGs were Denmark, Sweden, and Finland in [68] and Denmark, Finland, and Sweden in our work. Other related applications can be found in [39, 42].

3.5 Other Regions

55

3.5 Other Regions We recall the equations determined in Sect. 3.3. G = 0.06G 1 + 0.06G 4 + 0.16G 5 + 0.07G 6 + 0.17G 8 +0.06G 10 + 0.07G 11 + 0.06G 12 + 0.24G 16 + 0.06G 17

G  = 0.06G 1 + 0.06G 4 + 0.14G 5 + 0.05G 6 + 0.20G 8 +0.04G 10 + 0.05G 11 + 0.04G 12 + 0.30G 16 + 0.04G 17

G  = 0.06G 1 + 0.06G 4 + 0.14G 5 + 0.07G 6 + 0.17G 8 +0.06G 10 + 0.07G 11 + 0.06G 12 + 0.25G 16 + 0.06G 17

Table 3.7 East and South Asia ranks Country AHP/rank Bangladesh Bhutan Brunei Darussaiam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

Guiasu/rank

Yen/rank

0.62133/18 0.71905/8 0.72522/6 0.65197/15 0.75851/2 0.63355/17 0.69391/10

0.5993/18 0.70656/7 0.69696/9 0.61609/17 0.73993/2 0.62527/15 0.68507/11

0.61674/18 0.71801/7 0.7245/6 0/64625/15 0.74959/3 0.63304/17 0.68873/11

0.67768/13 0.72213/7 0.75235/4 0.71519/9 0.68309/12 0.65079/16 0.53476/19 0.66979/14 0.77261/1 0.69174/11 0.75489/3

0.64943/14 0.70911/6 0.73333/4 0.69796/8 0.66447/12 0.62329/16 0.52009/19 0.64982/13 0.79159/1 0.6859/10 0.73673/3

0.67006/13 0.71786/8 0.75111/2 0.70823/9 0.67764/12 0.64454/16 0.53389/19 0.66276/14 0.77567/1 0.68875/10 0.74904/4

0.74416/5

0.71328/5

0.73594/5

56

3 Sustainable Development Goals and Human Trafficking

The entries for G 1 , G 4 , G 5 , G 6 , G 8 , G 10 .G 11 , G 12 , G 16 , and G 17 are obtained from the corresponding region table (Table 2.7) in Chap. 2. The entries from columns G 2 , G 3 , G 7 , G 9 , G 13 G 14 , and G 15 are deleted to form a subtable. The numbers in the subtable are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain Table 3.7. We find that Singapore, China, and Thailand rank the highest for the region with respect to achieving the human trafficking SDGs. The entries of the suitable subtable of Table 2.9 from Chap. 2 are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain Table 3.8.

Table 3.8 Eastern Europe and Central Asia ranks Country AHP/rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Boznia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgz Rep. Liecheristan Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Guiasu/rank

Yen/rank

0.41254/23 0.70046/21

0.40968/23 0.67612/22

0.41756/23 0.69653/21

0.70036/22 0.72342/17 0.80697/1 0.72522/16 0.74741/6 0.7635/4 0.74499/10 0.74507/9 0.74697/7 0.72935/14

0.68688/20 0.70057/16 0.77291/2 0.69696/17 0.72625/11 0.73921/4 0.74298/3 0.73673/6 0.73004/8 0.6956/18

0.69652/22 0.71966/17 0.79828/1 0.7245/15 0.74045/8 0.75782/4 0.73885/9 0.74102/7 0.73845/10 0.72347/16

0.78755/2 0.76903/3

0.77309/1 0.72947/9

0.78341/2 0.76161/3

0.72226/18 0.70875/19 0.74667/8 0.70864/20

0.70068/15 0.69049/19 0.73741/5 0.67625/21

0.71845/18 0.70528/19 0.74138/6 0.70026/20

0.75258/5 0.72886/15 0.7354/13 0.74428/11 0.74066/12

0.72937/10 0.70753/13 0.73448/7 0.70672/14 0.71373/12

0.74836/5 0.72469/14 0.73815/11 0.73791/12 0.73458/13

3.5 Other Regions

57

We see that Belarus and Malta rank the highest for this region in achieving the human trafficking SDGs. The entries in the suitable table of Table 2.11 from Chap. 2 are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain Table 3.9. We see that Cuba and Costa Rica Rank the highest in achieving the human trafficking SDGs.

Table 3.9 Latin America and the Caribbean ranks Country AHP/rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatamalia Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Guiasu/rank

Yen/rank

0.74973/5

0.72011/5

0.74055/5

0.64969/18 0.69083/14 0.68448/16 0.69206/13 0.78299/2 0.78726/1

0.63745/18 0.65871/15 0.65863/16 0.66791/13 0.76216/2 0.7787/1

0.65031/18 0.68243/15 0.67652/16 0.68379/13 0.77398/2 0.78467/1

0.69711/11 0.74978/4 0.69416/12

0.67097/12 0.7227/4 0.66438/14

0.68764/11 0.74086/4 0.68613/12

0.61882/21 0.63391/20 0.51928/23 0.63412/19 0.7444/6 0.71872/8 0.69017/15 0.6775/17 0.69768/10

0.60002/21 0.62291/19 0.4948/23 0.60519/20 0.71781/7 0.69247/9 0.67935/10 0.64835/17 0.67612/11

0.61322/21 0.63349/19 0.51586/23 0.62599/20 0.73713/6 0.70841/9 0.68358/14 0.66902/17 0.68988/10

0.71849/9 0.73188/7 0.76639/3 0.6094/22

0.71562/8 0.71951/6 0.74273/3 0.57592/22

0.7195/8 0.73132/7 0.75856/3 0.59928/22

58

3 Sustainable Development Goals and Human Trafficking

The entries in the suitable subtable of Table 2.13 from Chap. 2 are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain Table 3.10. We see that Algeria and United Arab Emirates rank the highest in the region in achieving the human trafficking SDGs.

Table 3.10 Middle East and North Africa ranks Country AHP/rank Guiasu/rank Algeria Bahrain Eygpt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen Rep.

Yen/rank

0.73029/1 0.66409/9 0.64642/11 0.66967/8 0.63137/12 0.67207/7 0.5898/14 0.67417/6

0.70674/2 0.67978/4 0.63844/12 0.65308/9 0.60746/14 0.65804/8 0.61735/13 0.65297/10

0.72731/1 0.6679/8 0.64422/11 0.66762/9 0.63025/12 0.67098/7 0.59337/14 0.67226/6

0.68131/4 0.65462/10 0.6782/5 0.62144/13 0.54346/15 0.69431/3 0.72103/2 0.42204/16

0.66271/7 0.67022/6 0.70424/3 0.64001/11 0.53327/15 0.67463/5 0.73039/1 0.39805/16

0.67963/5 0.66243/10 0.68206/4 0.6274/13 0.54764/15 0.6908/3 0.72537/2 0.42813/16

3.5 Other Regions

59

The entries in the suitable subtable Table 2.15 from Chap. 2 are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain Table 3.11.

Table 3.11 Sub-Saharan Africa ranks Country AHP/rank Angola Benin Botswana Burkino Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros Congo Demo Rep. Congo Rep. Cote d’lvoire Djibouti Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger

Guiasu/rank

Yen/rank

0.52355/35 0.52103/36 0.68634/5 0.54871/26 0.56971/19 0.7087/1 0.5603/22 0.40313/43 0.42074/42 0.57519/17 0.45001/41 0.53609/28 0.55432/24 0.60368/14

0.49686/36 0.50704/33 0.67822/4 0.53077/24 0.54815/19 0.69369/2 0.53952/23 0.40424/42 0.39188/43 0.5547/17 0.42095/41 0.51466/30 0.53968/22 0.58608/13

0.517/36 0.51785/35 0.68654/5 0.54749/25 0.56756/19 0.70306/1 0.55452/22 0.40617/43 0.41856/42 0.57533/17 0.44568/41 0.53103/29 0.55252/23 0.60049/13

0.57294/18 0.56736/20 0.60958/12 0.58433/16 0.66812/6 0.53317/30

0.55049/18 0.54666/20 0.58751/12 0.57112/16 0.65848/6 0.50701/34

0.57291/18 0.56186/20 0.60563/12 0.58336/16 0.66427/6 0.53086/30

0.62007/11 0.55975/23 0.49735/39 0.53066/32 0.66405/8 0.52853/34 0.51131/37 0.69247/4 0.54991/25 0.72263/1 0.53223/31

0.60083/11 0.52524/26 0.47318/39 0.52831/25 0.65103/7 0.50483/35 0.47863/38 0.6831/3 0.52069/29 0.71509/1 0.50813/32

0.61229/11 0.54999/24 0.49356/39 0.52777/32 0.65978/8 0.52648/33 0.50964/37 0.68964/2 0.54317/26 0.71915/1 0.52978/31 (continued)

60

3 Sustainable Development Goals and Human Trafficking

Table 3.11 (continued) Country AHP/rank Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

Guiasu/rank

Yen/rank

0.4536/40 0.6529/10 0.69576/3 0.59843/15

0.44345/40 0.64128/10 0.67454/5 0.58088/14

0.45059/40 0.64389/10 0.69445/3 0.59379/15

0.52989/33

0.51162/31

0.52599/34

0.66439/7

0.64661/8

0.66026/7

0.51025/38 0.60716/13 0.53596/29 0.5627/21 0.5438/27 0.65856/9

0.49257/37 0.58066/15 0.52475/27 0.54272/21 0.52464/28 0.64348/9

0.50842/38 0.59807/14 0.53315/28 0.55577/21 0.53562/27 0.65474/9

We see that Namibia, Cape Verde, and Madagascar rank the highest in the region in achieving the human trafficking SDGs. The entries in the suitable subtable of Table 2.18 from Chap. 2 are substituted for the G i in the AHP, Guiasu, and Yen formulas to obtain Table 3.12.

Table 3.12 Oceania ranks Country AHP/rank Fiji Kinbati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Guiasu/rank

Yen/rank

0.80273/1

0.78808/1

0.79989/1

0.61216/3

0.60135/3

0.60999/3

0.66896/2

0.65276/2

0.66868/2

Chapter 4

Sustainable Development Goals: Analysis by the Stakeholder Method

In 2015, the leader of all the UN’s Member States agreed to the 2030 Agenda for Sustainable Development. The 17 Sustainable Development Goals and their 169 associated targets address five areas of critical importance: People, Planet, Prosperity, Peace, and Partnership. In this chapter we take the metrics and data provided in [68] and transform them into a fuzzy logic setting. We can then analyze the results in SDG Index and Dashboards Report 2019 by using techniques of fuzzy logic. Many of these 17 Sustainable Development Goals are related to the horrible crime of trafficking in persons. We also examine these goals in a fuzzy logic setting.

4.1 Introduction All member states of the United Nations adopted Agenda 2030 and the Sustainable Development Goals (SDGs) in 2015. The SDGs describe a universal agenda that applies to and must be implemented by all countries, both developed and developing [68]. As stated in Chap. 2, sound metrics and data are critical for turning the SDGs into practical tools for problem-solving by mobilizing governments, academias, civil society, and business, by providing a report card to track progress and ensure accountability, and by serving as a management tool for the transformation needed to achieve the SDGs by 2030. As previously stated, an outcome from the UN Conference on Sustainable Development (Rio+20) in 2012 was international agreement to negotiate a new set of global Sustainable Development Goals (SDGs) to promote sustainable development after 2015 [61]. The report in [61] proposes a methodology for identifying which of the different goals and targets represent the biggest transformational challenges in any given implementation context. The Rio+20 Outcome Document can be found in [74]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_4

61

62

4 Sustainable Development Goals …

In [61], Stakeholder Forum created a methodology to enable relative scores to be assigned to each of the different targets and goals according to their difference significance in different contexts. The method has been previously discussed. We first consider countries belonging to the Organization for Economic Cooperation and Development (OECD). The OECD is made up of 36 democracies with market economies that work with each other, as well as with more than 70 other member economies to promote economic growth, prosperity, and sustainable development. We assign numbers from the closed interval [0, 1] to the scores given a country in [68]. This places the analysis of sustainability in [54, 68] in the area of fuzzy logic. The determination of the scores can then be determined in many different ways. For example, one can use any number of norms or aggregation operators. We use a particular norm and also the aggregation operator, average. This gives three measures of how well a country is meeting each of the 17 goals. These 17 scores for a country are then averaged using a weighted average to determine a single number that measures how well a country is achieving the goals. The selection of the weights is discussed in Sect. 4.2. We must note that in [68, pp. 80–465], there are instances where some or all of the Indicators of the SDGs are missing. In these cases, we may make approximations of certain values or we do not make evaluations of the country involved.

4.2 Weighted Average In this section, we discuss the construction of the weighted average used to determine a single number that measures how well a country is doing in meeting the 17 SDGs. Goal 17 and the targets within the other goals that are specifically directed towards international cooperation and the development assistance responsibilities of developed countries are excluded from the analysis in [54]. We thus exclude G 17 also. The coefficients (or weights) in the following equation were determined as follows: Table 1 in [54, p. 6] contained the overall marks for the goals. The marks totaled 53.4. The individual goal marks were divided by 53.4 so that the new goal marks were between 0 and 1 and their total was 1. The equation is thus placed in the area of mathematics of uncertainty. S DG = 0.03G 1 + 0.04G 2 + 0.03G 3 + 0.05G 4 + 0.04G 5 + 0.05G 6 + 0.12G 7 + 0.05G 8 + 0.04G 9 + 0.07G 10 + 0.05G 11 + 0.12G 12 + 0.13G 13 + 0.08G 14 + 0.05G 15 + 0.05G 16

(4.1)

The individual goal marks in Table 1, [54, p. 6] were determined as follows: The individual category scores (determined by assessors) and the overall scores for each goal and target are presented in the tables in Annex 2 of [54]. These were obtained

4.2 Weighted Average

63

by averaging the collective scores from the assessors. The scores given are out of a maximum of 2 for individual category scores and a maximum of 8 for overall scores. Each target was assessed as to whether it is applicable, implementable, and transformative [54, p. 10]. Three independent assessors provided scores for each of the individual categories with respect to the methodology provided in [54]. An overall score was then obtained for each target by multiplying the scores given to each of the three categories. Multiplication was used to emphasize that for a goal or target to score highly, it must meet all three criteria.

4.3 Achievement Tables In Fig. 5, [68, p. 24], OECD countries are assigned colors as a ranking in their achievement of G1 through G17. The colors assigned were green, yellow, orange, and red. A green rating on the SDG Dashboard denotes achievement, and is assigned to a country on a given SDG only if all the indicators under the goal are rated green, yellow, orange and red indicate increasing distance from SDG achievement. The rankings of individual SDGs were determined by averaging the two worst ratings, e.g., green, green, yellow, red yields orange, the average of yellow and red. In order to place the analysis in a fuzzy logic setting, we assign the numbers 0.8, 0.6, 0.4, 0.2 to the colors green, yellow, orange, red, respectively. Then Fig. 5 in [68] becomes Tables 4.1 and 4.2. Recall from Chap. 1, the norm operation t : [0, 1]n → [0, 1] is defined by for all (a1 , . . . , an ) ∈ [0, 1]n , ⎧ ⎨ ∧{a1 , . . . , an } if a1 , . . . , an > λ, t (a1 , . . . , an ) = ∨{a1 , . . . , an } if a1 , . . . , an < λ, ⎩ λ otherwise. We can interpret the norm operation t in the following manner: If a1 , . . . , an > λ, then the values are at least ∧{a1 , . . . , an } and if a1 , . . . , an < λ, then the values are at most ∨{a1 , . . . , an }. In Table 4.2, we use the norm function t to determine the ratings. We let λ = 0.5. We apply this norm function to the ratings for each country in [68, pp. 96–449] to obtain Table 4.2. In Table 4.3, we obtain the average for each country in [68, pp. 96–449]. We find the average of the color ratings. For example, we obtain 0.733 for G 1 of Australia as follows: (green + green + yellow)/3 = (0.8 + 0.8 + 0.6)/3 = 0.733. Using the aggregation operator average, gives us a method of determining how well a country is doing over all in achieving the SDGs. It is not punitive as is the use of a t-norm.

G3

0.8 0.6 0.6 0.4 0.4 0.4 0.6 0.4 0.6 0.6 0.6 0.4 0.4 0.6 0.8 0.8 0.8 0.6

0.2 0.4 0.4 0.4 0.2 0.4 0.4 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.4 0.4

Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan

0.6 0.8 0.8 0.6 0.6 0.8 0.8 0.6 0.8 0.8 0.6 0.6 0.6 0.8 0.8 0.4 0.6 0.6

Table 4.1 Color values G1 G2

G4

0.6 0.4 0.4 0.8 0.2 0.4 0.6 0.6 0.8 0.4 0.6 0.2 0.2 0.4 0.6 0.2 0.4 0.8

G5 0.4 0.4 0.6 0.4 0.4 0.4 0.6 0.4 0.4 0.6 0.4 0.4 0.4 0.6 0.4 0.4 0.4 0.2

G6 0.6 0.6 0.4 0.4 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.6 0.6

G7 0.2 0.6 0.4 0.8 0.6 0.4 0.6 0.6 0.8 0.6 0.6 0.6 0.4 0.8 0.4 0.4 0.6 0.4

G8 0.4 0.6 0.4 0.6 0.4 0.6 0.6 0.6 0.6 0.4 0.6 0.4 0.6 0.6 0.4 0.6 0.4 0.6

G9 0.4 0.4 0.4 0.4 0.2 0.4 0.6 0.2 0.4 0.6 0.4 0.4 0.2 0.4 0.4 0.6 0.2 0.8

G 10 0.4 0.6 0.6 0.6 0.2 0.6 0.8 0.4 0.8 0.6 0.6 0.4 0.6 0.8 0.6 0.2 0.4 0.4

G 11 0.6 0.6 0.6 0.6 0.4 0.6 0.4 0.6 0.6 0.6 0.6 0.4 0.6 0.4 0.6 0.4 0.4 0.4

G 12 0.2 0.2 0.2 0.2 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2

G 13 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

G 14 0.4 0.5 0.2 0.4 0.4 0.5 0.2 0.4 0.4 0.4 0.2 0.4 0.5 0.2 0.4 0.2 0.2 0.4

G 15 0.4 0.6 0.6 0.4 0.4 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.8 0.4 0.6 0.2 0.6 0.6

G 16 0.6 0.8 0.6 0.6 0.2 0.8 0.8 0.4 0.6 0.4 0.6 0.4 0.4 0.8 0.6 0.2 0.6 0.6

G 17

(continued)

0.4 0.2 0.4 0.4 0.4 0.4 0.6 0.4 0.4 0.4 0.4 0.2 0.4 0.2 0.2 0.2 0.4 0.2

64 4 Sustainable Development Goals …

Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands N. Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. K U. S.

0.6 0.4 0.4 0.6 0.4 0.8 0.6 0.8 0.6 0.6 0.8 0.8 0.6 0.8 0.8 0.4 0.6 0.4

G1

Table 4.1 (continued)

G2

0.4 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.4 0.4 0.2 0.4 0.2

G3

0.4 0.4 0.4 0.8 0.4 0.6 0.6 0.8 0.6 0.6 0.6 0.6 0.6 0.8 0.6 0.4 0.6 0.4

G4

0.6 0.6 0.4 0.4 0.2 0.6 0.6 0.4 0.6 0.6 0.2 0.6 0.6 0.4 0.4 0.2 0.6 0.6

G5 0.2 0.2 0.4 0.6 0.4 0.4 0.6 0.8 0.4 0.4 0.4 0.4 0.4 0.6 0.4 0.2 0.4 0.2

G6 0.4 0.4 0.4 0.6 0.4 0.6 0.6 0.6 0.6 0.4 0.6 0.4 0.6 0.6 0.6 0.4 0.6 0.6

G7 0.4 0.6 0.4 0.2 0.2 0.4 0.8 0.8 0.4 0.8 0.4 0.8 0.6 0.8 0.8 0.4 0.4 0.4

G8 0.6 0.6 0.6 0.4 0.2 0.6 0.6 0.4 0.6 0.6 0.4 0.6 0.4 0.6 0.4 0.4 0.6 0.6

G9 0.6 0.2 0.2 0.2 0.2 0.6 0.4 0.4 0.2 0.4 0.2 0.4 0.2 0.6 0.4 0.2 0.6 0.4

G 10 0.4 0.2 0.2 0.6 0.2 0.6 0.4 0.8 0.4 0.4 0.6 0.6 0.4 0.6 0.4 0.2 0.4 0.2

G 11

0.6 0.4 0.4 0.6 0.6 0.4 0.6 0.6 0.4 0.6 0.4

0.6 0.6 0.6 0.4 0.4 0.6

G 12 0.4 0.2 0.4 0.2 0.4 0.2 0.4 0.2 0.2 0.2 0.4 0.2 0.4 0.2 0.2 0.4 0.2 0.2

G 13 0.2 0.4 0.2 0.2 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.2

G 14 0.4 0.2 0.4 0.5 0.4 0.2 0.4 0.6 0.2 0.2 0.5 0.2 0.4 0.4 0.5 0.2 0.4 0.6

G 15 0.4 0.6 0.6 0.4 0.2 0.6 0.4 0.4 0.8 0.6 0.6 0.6 0.6 0.6 0.4 0.2 0.4 0.6

G 16 0.6 0.4 0.4 0.4 0.2 0.4 0.4 0.4 0.4 0.6 0.4 0.6 0.6 0.4 0.4 0.2 0.6 0.2

G 17 0.2 0.2 0.4 0.2 0.4 0.2 0.2 0.6 0.2 0.4 0.4 0.4 0.4 0.6 0.4 0.4 0.2 0.2

4.3 Achievement Tables 65

G2

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan

0.6 0.8 0.8 0.6 0.5 0.8 0.8 0.5 0.8 0.8 0.6 0.5 0.6 0.8 0.8 0.5 0.5 0.5

Table 4.2 Norm values G1

G3

0.8 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.6 0.5 0.5 0.5 0.6 0.8 0.8 0.8 0.6

G4 0.6 0.5 0.5 0.8 0.5 0.5 0.5 0.6 0.8 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.8

G5 0.5 0.5 0.6 0.5 0.5 0.5 0.6 0.5 0.5 0.6 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5

G6 0.5 0.6 0.5 0.5 0.6 0.6 0.6 0.5 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.6

G7 0.5 06 0.5 0.8 0.6 0.5 0.6 0.5 0.8 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.6 0.5

G8 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.6 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.6

G9 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.8

G 10 0.4 0.6 0.6 0.5 0.4 0.6 0.8 0.5 0.8 0.6 0.6 0.5 0.5 0.8 0.6 0.4 0.5 0.4

G 11 0.6 0.5 0.5 0.6 0.5 0.6 0.5 0.6 0.6 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.5

G 12 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.2 0.5 0.4 0.5 0.5 0.5 0.5

G 13 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 14 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 15 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.8 0.5 0.5 0.5 0.6 0.5

G 17 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 (continued)

G 16 0.5 0.8 0.5 0.6 0.5 0.5 0.8 0.5 0.6 0.5 0.5 0.5 0.5 0.8 0.6 0.5 0.5 0.6

66 4 Sustainable Development Goals …

Korea, Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

0.5 0.5 0.5 0.6 0.5 0.8 0.6 0.8 0.6 0.6 0.8 0.8 0.5 0.8 0.8 0.5 0.6 0.5

G1

Table 4.2 (continued)

G2

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G3

0.5 0.5 0.5 0.8 0.5 0.6 0.6 0.8 0.5 0.6 0.5 0.5 0.5 0.8 0.6 0.5 0.6 0.5

G4 0.6 0.6 0.5 0.6 0.5 0.6 0.6 0.5 0.6 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.6 0.6

G5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G6 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.6 0.5 0.6 0.6

G7 0.2 0.5 0.5 0.5 0.5 0.5 0.8 0.8 0.5 0.8 0.5 0.8 0.6 0.8 0.8 0.5 0.5 0.5

G8 0.6 0.5 0.6 0.5 0.5 0.5 0.6 0.5 0.5 0.6 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.5

G9 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5

G 10 0.5 0.4 0.2 0.6 0.2 0.6 0.5 0.8 0.5 0.5 0.6 0.6 0.5 0.6 0.5 0.4 0.5 0.4

G 11 0.5 0.6 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.2

G 12

G 13 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 14 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5

G 15 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.8 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 16 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5

G 17

4.3 Achievement Tables 67

Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan

0.733 0.800 0.800 0.733 0.600 0.800 0.800 0.600 0.800 0.800 0.733 0.600 0.733 0.800 0.800 0.600 0.667 0.600

G1

Table 4.3 Averages

0.475 0.650 0.700 0.600 0.571 0.575 0.650 0.550 0.575 0.650 0.650 0.600 0.625 0.567 0.625 0.600 0.575 0.657

G2

0.800 0.776 0.775 0.741 0.654 0.729 0.788 0.694 0.765 0.765 0.776 0.725 0.725 0.775 0.800 0.800 0.800 0.776

G3

0.686 0.550 0.640 0.800 0.533 0.543 0.725 0.778 0.800 0.625 0.629 0.578 0.556 0.650 0.700 0.575 0.644 0.800

G4 0.600 0.500 0.700 0.600 0.520 0.560 0.767 0.567 0.667 0.667 0.600 0.533 0.573 0.650 0.633 0.550 0.567 0.433

G5 0.733 0.743 0.700 0.633 0.771 0.743 0.771 0.743 0.771 0.743 0.743 0.714 0.686 0.714 0.657 0.686 0.714 0.743

G6 0.500 0.750 0.550 0.810 0.750 0.600 0.750 0.650 0.800 0.650 0.650 0.650 0.650 0.800 0.550 0.600 0.700 0.600

G7 0.633 0.667 0.638 0.700 0.567 0.767 0.700 0.767 0.667 0.600 0.733 0.440 0.667 0.720 0.700 0.733 0.600 0.733

G8 0.660 0.720 0.720 0.640 0.520 0.620 0.740 0.640 0.720 0.760 0.720 0.556 0.554 0.740 0.680 0.756 0.533 0.800

G9 0.400 0.667 0.733 0.533 0.257 0.667 0.800 0.400 0.800 0.667 0.600 0.400 0.533 0.800 0.600 0.267 0.467 0.333

G 10 0.600 0.667 0.600 0.667 0.500 0.650 0.550 0.700 0.700 0.600 0.700 0.500 0.650 0.600 0.650 0.538 0.550 0.533

G 11 0.560 0.333 0.433 0.440 0.560 0.467 0.467 0.500 0.400 0.400 0.400 0.200 0.633 0.300 0.400 0.300 0.367 0.467

G 12 0.320 0.400 0.480 0.320 0.480 0.520 0.550 0.560 0.400 0.400 0.480 0.440 0.520 0.500 0.560 0.520 0.440 0.520

G 13

0.400 0.550 0.267 0.400 0.500

0.450 0.750 0.600 0.600 0.350 0.550

0.400 0.550 0.550

0.600

G 14 0.440 0.640 0.720 0.600 0.560 0.760 0.720 0.760 0.760 0.680 0.720 0.680 0.800 0.450 0.720 0.480 0.760 0.640

G 15

0.550 0.500 0.650 0.550 0.533 0.500 0.700 0.500 0.600 0.600 0.600 0.450 0.600 0.500 0.350 0.450 0.600 0.500

G 17

(continued)

0.620 0.800 0.740 0.744 0.620 0.720 0.800 0.700 0.780 0.700 0.700 0.680 0.600 0.800 0.780 0.640 0.700 0.760

G 16

68 4 Sustainable Development Goals …

Korea, Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. Kingdom United States

0.667 0.600 0.600 0.733 0.467 0.800 0.733 0.800 0.733 0.733 0.800 0.800 0.600 0.800 0.800 0.600 0.733 0.600

G1

Table 4.3 (continued)

G2

0.714 0.550 0.550 0.600 0.543 0.625 0.571 0.550 0.550 0.523 0.575 0.550 0.450 0.600 0.600 0.571 0.625 0.625

G3

0.741 0.635 0.647 0.800 0.662 0.788 0.775 0.800 0.706 0.753 0.741 0.762 0.775 0.800 0.788 0.647 0.775 0.741

G4

750 0.711 0.657 0.575 0.533 0.686 0.714 0.700 0.711 0.700 0.457 0.711 0.733 0.675 0.675 0.525 0.714 0.657

G5 0.467 0.500 0.600 0.650 0.567 0.650 0.700 0.800 0.567 0.567 0.560 0.667 0.600 0.733 0.680 0.333 0.600 0.567

G6 0.714 0.657 0.600 0.743 0.514 0.771 0.714 0.724 0.629 0.686 0.657 0.686 0.743 0.770 0.770 0.600 0.771 0.743

G7 0.600 0.700 0.650 0.560 0.500 0.550 0.800 0.800 0.550 0.800 0.650 0.800 0.750 0.800 0.800 0.535 0.600 0.600

G8 0.760 0.733 0.733 0.700 0.533 0.700 0.767 0.667 0.700 0.733 0.667 0.733 0.633 0.733 0.667 0.567 0.667 0.700

G9 0.740 0.500 0.511 0.620 0.444 0.760 0.700 0.740 0.560 0.640 0.560 0.640 0.560 0.760 0.740 0.489 0.760 0.700

G 10 0.467 0.267 0.200 0.600 0.200 0.733 0.400 0.800 0.467 0.400 0.733 0.733 0.400 0.667 0.533 0.267 0.400 0.267

G 11 0.600 0.650 0.650 0.600 0.550 0.650 0.667 0.650 0.550 0.550 0.600 0.700 0.600 0.700 0.750 0.533 0.600 0.600

G 12 0.480 0.433 0.467 0.300 0.567 0.333 0.480 0.300 0.500 0.333 0.440 0.468 0.367 0.400 0.300 0.467 0.360 0.200

G 13 0.560 0.600 0.450 0.500 0.520 0.600 0.520 0.450 0.520 0.560 0.400 0.400 0.520 0.480 0.520 0.520 0.400 0.280

G 14

0.250 0.550 0.600

0.400 0.500 0.450

0.600 0.400 0.550 0.400 0.400 0.450

0.500 0.350 0.600

G 15 0.560 0.760 0.760 0.640 0.480 0.722 0.480 0.800 0.800 0.720 0.760 0.720 0.640 0.720 0.640 0.520 0.600 0.600

G 16 0.711 0.580 0.620 0.740 0.480 0.740 0.740 0.700 0.700 0.740 0.700 0.780 0.720 0.720 0.700 0.520 0.725 0.639

G 17 0.450 0.450 0.550 0.450 0.500 0.450 0.500 0.450 0.450 0.550 0.500 0.680 0.620 0.750 0.450 0.500 0.450 0.600

4.3 Achievement Tables 69

70

4 Sustainable Development Goals …

4.4 Rankings The entries of Tables 4.1, 4.2, and 4.3 are substituted into Eq. (4.1) for the G i to determine the first entry of the corresponding column, SDG(C), SDG(N), and SDG(C), respectively, in the following table (Table 4.4). The second entry is the rank of the country determined from the first entry.

Table 4.4 OECD ranks Country SDG(C)/Rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway

0.376/29.5 0.482/5 0.404/24 0.468/7.5 0.364/33 0.468/7.5 0.506/2 0.414/22 0.514/1 0.452/11 0.444/13 0.390/26 0.424/19 0.466/9 0.412/23 0.312/34 0.398/25 0.430/16 0.420/20.5 0.386/27 0.374/31 0.376/29.5 0.306/35 0.442/14 0.446/12 0.494/3

SDG(N)/Rank

SDG(A)/Rank

0.515/26 0.548/11 0.520/24 0.569/7 0.510/27.5 0.536/13 0.574/5 0.528/18 0.601/1 0.532/16 0.525/20 0.464/35 0.523/22.5 0.576/4 0.535/14 0.502/30 0.531/17 0.538/12 0.410/36 0.508/29 0.489/31 0.524/21 0.479/33 0.534/15 0.552/10 0.587/2

0.54579/31 0.60226/14 0.59762/18 0.59793/17 0.56120/28 0.61690/10 0.65676/1 0.63030/4 0.64933/2 0.60012/15 0.59372/19 0.51811/32 0.62026/9 0.60723/13 0.60867/12 0.51649/33 0.55934/30 0.58628/22 0.59892/16 0.56425/27 0.55939/29 0.57705/24 0.50817/35 0.60976/11 0.62138/8 0.62215/7 (continued)

4.4 Rankings

71

Table 4.4 (continued) Country SDG(C)/Rank Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. Kingdom United States

0.380/28 0.426/17.5 0.426/17.5 0.456/10 0.434/15 0.492/4 0.470/6 0.290/36 0.420/20.5 0.366/32

SDG(N)/Rank

SDG(A)/Rank

0.523/22.5 0.562/8 0.526/19 0.572/6 0.510/27.5 0.580/3 0.558/9 0.485/32 0.516/25 0.472/34

0.57304/25 0.59299/20 0.58716/21 0.62511/6 0.57874/23 0.63671/3 0.62549/5 0.48291/36 0.56769/26 0.51195/34

We see that Finland, Denmark, Norway and Sweden rank the highest in achieving the SDGs for OECD. This ranking agrees roughly with their over all ranking in [68].

4.5 Other Regions Recall that rankings of individual SDGs in [68] were determined by averaging the two worst ratings, e.g., green, green, yellow, red yields orange, the average of yellow and red. In order to place the analysis in a fuzzy logic setting, we assign the numbers 0.8, 0.6, 0.4, 0.2 to the colors green, yellow, orange, red, respectively. Then Fig. 7 in [68, p. 26] yields Table 4.5. We next consider the region East and South Asia. We apply the norm function with λ = 0.5 to the ratings in Table 4.5 to obtain the following Table 4.6. We next find the averages of the G i as we did in Sect. 4.3 for the OECD countries (Table 4.7). The Table 4.8 is determined in an entirely similar manner as for the OECD countries.

G2

0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.4 0.2 0.4 0.2 0.2

Bangladesh Bhutan Brunei Darussalam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor-Leste Vietnam

0.4 0.8 0.6 0.6 0.4 0.4 0.6 0.4 0.8 0.6 0.8 0.2 0.6

0.6 0.6 0.4 0.4

0.4 0.6

Table 4.5 Color values G1

G3

0.2 0.2 0.4 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.6 0.2 0.2 0.2 0.2 0.2 0.6 0.2 0.2 0.2 0.2

G4 0.4 0.4 0.6 0.4 0.8 0.4 0.6 0.6 0.4 0.6 0.8 0.6 0.2 0.4 0.2 0.6 0.8 0.8 0.6 0.2 0.6

G5 0.4 0.2 0.4 0.2 0.4 0.2 0.4 0.6 0.4 0.2 0.2 0.4 0.4 0.2 0.2 0.4 0.4 0.2 0.4 0.2 0.4

G6 0.2 0.2 0.6 0.2 0.4 0.2 0.2 0.4 0.2 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.6 0.4 0.4 0.2 0.4

G7 0.2 0.6 0.6 0.2 0.4 0.2 0.4 0.2 0.4 0.6 0.8 0.2 0.2 0.2 0.4 9.4 0.8 0.2 0.4 0.2 0.6

G8 0.4 0.4 0.2 0.2 0.8 0.6 0.4 0.4 0.4 0.6 0.6 0.4 0.2 0.2 0.4 0.4 0.4 0.6 0.4 0.4 0.4

G9 0.2 0.2 0.4 0.2 0.4 0.2 0.2 0.2 0.2 0.6 0.4 0.2 0.2 0.2 0.2 0.2 0.8 0.2 0.4 0.2 0.2

G 10

0.2 0.2 0.6 0.4

0.4 0.2 0.4 0.4 0.4 0.6 0.2 0.2

0.6 0.2 0.2 0.2

0.4 0.4

G 11 0.2 0.6 0.8 0.2 0.4 0.2 0.4 0.4 0.2 0.6 0.6 0.2 0.2 0.2 0.2 0.4 0.6 0.4 0.4 0.4 0.4

G 12

0.8 0.4 0.4 0.4 0.8 0.8 0.6 0.6 0.2 0.4 0.4 0.8 0.6

0.8 0.6 0.4 0.6 0.4 0.6 0.6

G 13 0.6 0.6 0.2 0.6 0.2 0.6 0.6 0.4 0.6 0.2 0.4 0.2 0.4 0.6 0.6 0.4 0.2 0.6 0.2 0.6 0.4

G 14

0.4 0.4 0.2 0.4 0.2 0.4 0.2

0.4

0.4 0.2

0.4 0.2 0.2 0.4 0.4 0.2

0.4

G 15 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.4 0.4 0.2 0.4 0.6 0.4 0.4 0.6 0.4 0.4 0.4 0.4 0.6 0.2

G 16 0.2 0.6 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.2 0.2 0.2 0.2 0.2 0.6 0.2 0.4 0.2 0.2

G 17

0.2 0.4 0.6 0.4 0.2 0.4 0.2 0.2 0.4 0.2 0.4 0.4 0.6

0.2 0.6 0.6 0.4 0.4 0.6 0.2

72 4 Sustainable Development Goals …

G2

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

Bangladesh Bhutan Brunei Darussalam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor-Leste Vietnam

0.5 0.6 0.5 0.5 0.5 0.4 0.8 0.5 0.6 0.5 0.5 0.5 0.5 0.8 0.5 0.8 0.2 0.6

0.5 0.5

Table 4.6 Norm values G1

G3

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G4 0.5 0.4 0.6 0.5 0.8 0.5 0.6 0.6 0.5 0.6 0.8 0.6 0.5 0.5 0.2 0.6 0.8 0.8 0.5 0.4 0.6

G5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.5

G6 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G7 0.4 0.5 0.5 0.2 0.5 0.4 0.5 0.5 0.4 0.5 0.8 0.4 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.2 0.5

G8 0.5 0.5 0.5 0.5 0.8 0.6 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5

G9 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.6 0.5 0.5 0.5 0.4 0.5 0.5 0.8 0.5 0.5 0.4 0.5

G 10

0.2 0.2 0.6 0.4

0.6 0.2 0.2 0.2 0.5 0.4 0.2 0.4 0.4 0.4 0.6 0.2 0.2

0.4 0.4

G 11 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5

G 12 0.8 0.6 0.5 0.6 0.5 0.6 0.6 0.5 0.8 0.5 0.5 0.5 0.8 0.8 0.6 0.6 0.5 0.5 0.5 0.8 0.6

G 13 0.5 0.6 0.2 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5

G 14

0.5 0.5 0.2 0.5 0.5 0.5 0.5

0.5

0.5 0.5

0.5 0.4 0.5 0.5 0.5 0.5

0.5

G 15 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.6 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.6 0.5

G 16 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 17 0.5 0.5 0.5 0.5 0.5 0.2 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

4.5 Other Regions 73

Bangladesh Bhutan Brunei Cambodia China India Indonesia Korea Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor-Leste Vietnam

0.30 0.80 0.60 0.70 0.40 0.40 0.50 0.40 0.80 0.60 0.80 0.20 0.70

0.60 0.70 0.40 0.40

0.40 0.60

G1

Table 4.7 Averages

G2

0.46 0.52 0.47 0.46 0.63 0.46 0.51 0.60 0.53 0.43 0.43 0.37 0.54 0.54 0.37 0.51 0.68 0.51 0.60 0.51 0.54

G3

0.37 0.47 0.72 0.39 0.63 0.34 0.40 0.52 0.27 0.61 0.72 0.53 0.30 0.34 0.30 0.40 0.76 0.63 0.61 0.32 0.59

G4

0.53 0.33 0.73 0.47 0.80 0.53 0.73 0.70 0.47 0.73 0.80 0.73 0.33 0.47 0.20 0.67 0.80 0.80 0.67 0.27 0.67

G5 0.40 0.40 0.60 0.47 0.60 0.30 0.50 0.60 0.50 0.40 0.35 0.55 0.55 0.45 0.25 0.50 0.60 0.40 0.60 0.30 0.50

G6 0.52 0.44 0.75 0.44 0.56 0.44 0.52 0.55 0.48 0.60 0.60 0.48 0.44 0.48 0.36 0.48 0.68 0.52 0.68 0.44 0.56

G7 0.27 0.60 0.60 0.20 0.47 0.27 0.40 0.20 0.30 0.60 0.80 0.27 0.33 0.33 0.47 0.40 0.80 0.33 0.53 0.20 0.53

G8 0.68 0.60 0.40 0.52 0.80 0.72 0.60 0.60 0.60 0.72 0.73 0.60 0.56 0.52 0.55 0.60 0.68 0.72 0.68 0.30 0.64

G9 0.28 0.40 0.57 0.33 0.70 0.40 0.47 0.24 0.77 0.67 0.48 0.40 0.33 0.37 0.33 0.47 0.80 0.30 0.57 0.25 0.37

G 10

0.40 0.20 0.40 0.40 0.40 0.60 0.20 0.20 0.80 0.20 0.20 0.40 0.40

0.40 0.20 0.20 0.20

0.40 0.40

G 11 0.40 0.60 0.88 0.40 0.53 0.40 0.53 0.40 0.35 0.67 0.70 0.27 0.40 0.33 0.33 0.40 0.67 0.53 0.47 0.31 0.40

G 12 0.80 0.70 0.65 0.76 0.63 0.77 0.73 0.80 0.80 0.57 0.57 0.67 0.80 0.80 0.77 0.73 0.73 0.70 0.63 0.80 0.68

G 13 0.65 0.73 0.47 0.65 0.50 0.65 0.75 0.60 0.65 0.60 0.47 0.35 0.55 0.65 0.75 0.60 0.33 0.70 0.50 0.60 0.60

G 14

0.35 0.60 0.20 0.50 0.45 0.47 0.45

0.45

0.50 0.50

0.53 0.30 0.40 0.50 0.45 0.40

0.50

G 15 0.56 0.56 0.56 0.52 0.60 0.52 0.44 0.47 0.47 0.52 0.33 0.72 0.48 0.68 0.68 0.56 0.40 0.60 0.56 0.60 0.48

G 16 0.49 0.70 0.57 0.44 0.60 0.42 0.53 0.44 0.38 0.60 0.56 0.47 0.49 0.48 0.38 0.40 0.71 0.58 0.56 0.43 0.49

G 17 0.40 0.60 0.60 0.47 0.50 0.50 0.40 0.80 0.40 0.53 0.73 0.53 0.40 0.53 0.40 0.40 0.30 0.40 0.53 0.60 0.60

74 4 Sustainable Development Goals …

4.5 Other Regions

75

Table 4.8 East and South Asia ranks Country SDG(C)/Rank Bangladesh Bhutan Brunei Darussalam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

0.398/10 0.465/2 0.411/6 0.36/18 0.38/14 0.362/16 0.394/11 0.424/4 0.402/9 0.468/1 0.317/20 0.36/18 0.393/12 0.382/13 0.378/15 0.438/3 0.413/5 0.36/18 0.408/7 0.406/8

SDG(N)/Rank

SDG(A)/Rank

0.513/7.5 0.514/6 0.484/19.5 0.475/21 0.512/9 0.484/19.5 0.509/11 0.515/5 0.511/10 0.507/12 0.549/2 0.488/17.5 0.529/4 0.542/3 0.49/15 0.496/14 0.552/1 0.499/13 0.488/17 0.489/16 0.513/7.5

0.5086/15 0.571/3 0.595/2 0.4628/20 0.5446/7 0.4789/17 0.5338/10 0.512/13.5 0.512/13.5 0.5667/5 0.5695/4 0.478/18 0.4839/16 0.529/11 0.4753/19 0.5143/12 0.6255/1 0.5414/9 0.5483/6 0.4391/21 0.5443/8

We see that Singapore and Maldives rank the highest with respect to the region East and South Asia. Singapore ranked 66th over all in [68] and Maldives 47th over all. No country from their region ranked higher in [68]. We next consider the region Eastern Europe and Central Asia. Table 4.9 is determined from Fig. 9 in [68, p. 28] by replacing the color with its corresponding number as previously done for OECD and East and South Asia. Once again the norm function is used with λ = 0.5 to determine the Table 4.10 from Table 4.9. We find the averages of the G i as we did in Sect. 4.3 for the OECD countries, which are given in Table 4.11. Table 4.12 is determined in an entirely similar manner as for the OECD countries.

Afghanistan Albania Andorra Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgyz Republic Liechtenstein

Table 4.9 Color values

0.6 0.8 0.8 0.8 0.6 0.8 0.8 0.4 0.8 0.6

0.6

G1

0.2 0.2 0.6 0.2 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.4

G2

0.4 0.4 0.4 0.4 0.4 0.6 0.6 0.2 0.2 0.2

0.2 0.4

G3

0.4 0.6 0.6 0.6 0.6 0.6 0.6

0.6 0.6 0.6

0.2 0.6

G4 0.2 0.4 0.6 0.2 0.2 0.6 0.4 0.4 0.4 0.6 0.2 0.6 0.4

G5 0.2 0.4 0.8 0.4 0.4 0.6 0.4 0.6 0.6 0.2 0.4 0.4 0.4

G6 0.4 0.6 0.8 0.8 0.6 0.6 0.4 0.8 0.6 0.6 0.6 0.6 0.6

G7

0.2 0.2 0.2 0.4 0.6 0.4 0.4 0.4 0.6 0.4

0.2 0.2

G8 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.6

G9

0.2 0.4 0.6 0.6 0.2 0.4 0.4 0.2 0.2 0.6

0.2

G 10 0.2 0.4 0.6 0.2 0.6 0.4 0.4 0.4 0.4 0.4 0.6 0.6 0.6

G 11

0.6 0.6 0.6 0.6 0.2 0.4 0.2 0.6 0.2 0.6

0.8 0.6

G 12 0.6 0.4 0.2 0.6 0.4 0.4 0.2 0.4 0.4 0.2 0.6 0.2 0.6 0.6

G 13

0.2 0.4 0.6 0.2 0.2

0.2

G 14

0.2 0.6 0.4 0.4 0.6 0.6 0.4 0.8 0.6 0.6 0.4 0.4 0.4 0.4

G 15

0.2 0.4 0.6 0.4 0.6 0.6 0.8 0.6 0.8 0.2 0.4 0.4 0.6 0.6

G 17

(continued)

0.2 0.4 0.6 0.4 0.2 0.4 0.4 0.4 0.4 0.6 0.6 0.2 0.8 0.8

G 16

76 4 Sustainable Development Goals …

Malta Moldovia Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Table 4.9 (continued)

0.2 0.4

0.4 0.4 0.4 0.4

0.4 0.2 0.4 0.4 0.4

0.8 0.6 0.8 0.8

0.8 0.6 0.8 0.8 0.4

G2

0.8 0.8

G1

0.6 0.2 0.2 0.2 0.4

0.4 0.4 0.4 0.2

0.6 0.4

G3

0.6 0.6

0.6 0.6 0.6 0.6 0.6 0.6 0.6

0.6 0.4

G4

0.4 0.2 0.6 0.4 0.4

0.4 0.4 0.4 0.4

0.4 0.4

G5

0.4 0.2 0.2 0.4 0.2

0.6 0.4 0.8 0.6 0.4 0.4 0.6

G6 0.6 0.6 0.8 0.6 0.4 0.6 0.6 0.8 0.6 0.6 0.6 0.6 0.6

G7

0.4 0.2 0.2 0.4 0.4

0.4 0.4 0.6 0.4

0.6 0.4

G8 0.4 0.4 0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.2 0.4 0.2

G9

0.8 0.4

0.4 0.4

0.2 0.2 0.2 0.2

0.8 0.6

G 10

0.4 0.4 0.4 0.4 0.6

0.4 0.4 0.6 0.6

0.6 0.6

G 11

0.6 0.8 0.6 0.4 0.6

0.4 0.6 0.2 0.4

0.2 0.6

G 12 0.6 0.8 0.4 0.2 0.4 0.6 0.2 0.2 0.2 0.5 0.2 0.6 0.6

G 13

0.2

0.2 0.4 0.4

0.2

0.2

G 14

0.2 0.6 0.8 0.4 0.4 0.4 0.4 0.4 0.4 0.4

0.4 0.4

G 15

0.2 0.2 0.4 0.2 0.2 0.4 0.2 0.2 0.2 0.2

0.6 0.2

G 16

0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6

0.4 0.8 0.6

G 17

4.5 Other Regions 77

Afghanistan Albania Andorra Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgyz Republic

Table 4.10 Norm values

0.5 0.8 0.8 0.8 0.6 0.8 0.8 0.5 0.8 0.5

0.6

G1

G2

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G3

0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.5 0.5

G4

0.6 0.6 0.6 0.8 0.5 0.5 0.6 0.6 0.5 0.6

0.2 0.6

G5 0.2 0.5 0.6 0.5 0.5 0.6 0.4 0.5 0.5 0.5 0.5 0.5 0.5

G6 0.5 0.5 0.8 0.5 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.5

G7 0.4 0.6 0.8 0.8 0.5 0.5 0.5 0.8 0.5 0.5 0.6 0.5 0.6

G8 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5

G9 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.2 0.4 0.4 0.2 0.2 0.6

0.2

G 10

G 11 0.4 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 12 0.8 0.6 0.8 0.6 0.6 0.5 0.6 0.5 0.5 0.4 0.5 0.5 0.6

G 13

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6

0.4 0.5 0.2

0.2 0.5 0.5 0.5 0.5 0.6

0.5 0.4

0.5

G 14

G 15 0.5 0.6 0.5 0.5 0.5 0.6 0.5 0.8 0.6 0.5 0.5 0.5 0.5

G 16

G 17 0.5 0.5 0.6 0.5 0.5 0.6 0.8 0.6 0.8 0.5 0.5 0.5 0.6 (continued)

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.5

78 4 Sustainable Development Goals …

Liechtenstein Malta Moldovia Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Table 4.10 (continued)

0.8 0.5 0.8 0.8 0.5

0.8 0.5 0.8 0.8

0.8 0.8

G1

G2

0.8 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5

0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5

G3

G4

0.6 0.6 0.5 0.6 0.6 0.6 0.6 0.8 0.6 0.6

0.6 0.6 0.5

G5 0.2 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.2 0.5 0.5 0.5 0.5 0.5

G6 0.8 0.5 0.5 0.8 0.5 0.5 0.5 0.6 0.8 0.5 0.5 0.5 0.5 0.5

G7 0.8 0.5 0.5 0.8 0.6 0.5 0.6 0.5 0.8 0.5 0.6 0.5 0.6 0.5

G8 0.2 0.6 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.5

G9 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.8 0.4

0.4 0.4

0.2 0.2 0.2 0.2

0.8 0.6

G 10

0.5 0.5 0.5 0.5 0.5

0.5 0.6 0.8 0.5 0.5 0.6 0.6

G 11

0.6 0.8 0.6 0.5 0.6

0.5 0.6 0.5 0.5

0.4 0.6

G 12

G 13 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.2 0.5 0.6 0.2 0.5 0.5 0.4 0.5

0.5 0.5

0.2 0.4

0.5

G 14

G 15 0.5 0.5 0.5 0.2 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 16 0.8? 0.6 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 17 0.5 0.5 0.8 0.5 0.8 0.6 0.5 0.5 0.6 0.6 0.5 0.5 0.6 0.5

4.5 Other Regions 79

Afghanistan Albania Andorra Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgyz Republic Liechtenstein

Table 4.11 Averages

0.60 0.80 0.80 0.80 0.70 0.80 0.80 0.40 0.80 0.50

0.70

G1

G2

0.43 0.48 0.65 0.60 0.54 0.60 0.60 9.57 0.60 0.57 0.54 0.49 0.60 0.80

G3

0.26 0.69 0.80 0.63 0.59 0.66 0.64 0.64 0.71 0.77 0.56 0.51 0.53

G4

0.67 0.67 0.73 0.80 0.53 0.67 0.73 0.73 0.67 0.73 0.60

0.20 0.73

G5 0.25 0.60 0.70 0.50 0.45 0.70 0.35 0.60 0.50 0.60 0.50 0.65 0.50 0.20

G6 0.33 0.64 0.80 0.56 0.44 0.76 0.55 0.68 0.68 0.56 0.56 0.64 0.56 0.80

G7 0.30 0.73 0.80 0.80 0.67 0.60 0.53 0.80 0.67 0.67 0.73 0.60 0.73 0.80

G8 0.36 0.48 0.60 0.48 0.48 0.56 0.56 0.72 0.68 0.52 0.56 0.60 0.48 0.20

G9 0.23 0.43 0.50 0.48 0.48 0.56 0.40 0.53 0.67 0.67 0.43 0.53 0.36 0.70 0.20 0.40 0.40 0.60 0.20 0.40 0.20 0.20 0.20 0.60

0.20

G 10

G 11 0.27 0.47 0.70 0.40 0.53 0.53 0.40 0.60 0.53 0.60 0.60 0.53 0.53

G 12 0.80 0.70 0.80 0.70 0.72 0.65 0.70 0.53 0.53 0.27 0.60 0.52 0.75

G 13 0.60 0.60 0.20 0.75 0.55 0.50 0.50 0.55 0.65 0.40 0.55 0.50 0.70 0.50 0.20 0.53 0.65 0.33 0.40 0.60

0.40

0.40

G 14

G 15 0.48 0.76 0.44 0.60 0.68 0.76 0.68 0.80 0.72 0.70 0.60 0.60 0.68 0.60

G 16

G 17 0.40 0.60 0.70 0.47 0.60 0.67 0.80 0.73 0.80 0.33 0.53 0.53 0.67 0.60 (continued)

0.38 0.52 0.72 0.58 0.56 0.55 0.62 0.62 0.60 0.75 0.70 0.51 0.49 0.80

80 4 Sustainable Development Goals …

Malta Moldovia Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Table 4.11 (continued)

0.80 0.60 0.80 0.80 0.40

0.80 0.50 0.80 0.80

0.80 0.80

G1

0.50 0.62 0.80 0.57 0.63 0.54 0.52 0.80 0.63 0.50 0.53 0.51 0.63

G2

0.76 0.60 0.75 0.66 0.64 0.63 0.60 0.67 0.71 0.53 0.49 0.54 0.63

G3

0.73 0.67 0.60 0.73 0.70 0.73 0.73 0.80 0.73 0.73

0.73 0.53

G4 0.50 0.60 0.60 0.50 0.50 0.50 0.60 0.40 0.55 0.45 0.53 0.55 0.55

G5 0.68 0.56 0.80 0.60 0.60 0.60 0.72 0.80 0.60 0.48 0.48 0.64 0.58

G6 0.67 0.67 0.80 0.73 0.55 0.73 0.67 0.80 0.60 0.73 0.60 0.73 0.60

G7 0.75 0.60 0.20 0.56 0.56 0.68 0.56 0.30 0.56 0.48 0.56 0.48 0.60

G8 0.60 0.43 0.20 0.50 0.43 0.60 0.64 0.45 0.63 0.28 0.28 0.40 0.40

G9

0.80 0.40

0.40 0.40

0.20 0.20 0.20 0.20

0.80 0.60

G 10

0.47 0.53 0.47 0.47 0.60

0.60 0.60 0.80 0.33 0.47 0.60 0.60

G 11

0.73 0.80 0.76 0.60 0.75

0.47 0.65 0.47 0.52

0.30 0.75

G 12 0.60 0.80 0.50 0.45 0.41 0.70 0.50 0.20 0.50 0.73 0.20 0.65 0.60

G 13

0.40 0.40

0.40 0.45

0.20 0.27

0.50

G 14

0.40 0.64 0.20 0.40 0.70 0.80 0.64 0.50 0.60 0.68 0.60 0.64 0.64

G 15

0.70 0.51 0.75 0.57 0.55 0.68 0.42 0.65 0.60 0.56 0.54 0.49 0.53

G 16

0.60 0.80 0.50 0.80 0.70 0.60 0.60 0.70 0.73 0.60 0.50 0.73 0.67

G 17

4.5 Other Regions 81

82

4 Sustainable Development Goals …

Table 4.12 Eastern Europe and Central Asia ranks Country SDG(C)/Rank SDG(N)/Rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgyz Rep. Liecheristan Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

SDG(A)/Rank

0.426/16

0.47/23 0.516/12

0.5777/12

0.47/7 0.459/10 0.511/3 0.404/18.5 0.456/12 0.488/5 0.404/18.5 0.46/9 0.385/20 0.53/2

0.567/1 0.518/11 0.538/7 0.515/13 0.543/6 0.51/15 0.503/16.5 0.501/18 0.488/21 0.553/3

0.6017/6 0.5653/14 0.5742/13 0.5483/17 0.5932/10 0.6186/3 0.5189/21 0.5522/16 0.545/18 0.6233/2

0.498/4 0.537/1

0.536/8 0.55/4

0.5942/8 0.6483/1

0.38/21 0.428/15 0.458/11 0.41/17

0.497/19 0.496/20 0.52/10 0.503/16.5

0.5042/22 0.5276/19 0.5875/11 0.5537/15

0.454/13 0.445/14

0.521/9 0.56/2 0.484/22 0.547/5 0.511/14

0.596/7 0.6115/4 0.5226/20 0.6032/5 0.5936/9

0.468/8 0.472/6

The countries that rank the highest in achieving the SDGs in the region Eastern Europe and Central Asia are Moldova and Kyrgyz Rep. They ranked 37th and 48th, respectively. No other country from their region ranked higher over all in [68]. We next consider the region Latin America and the Caribbean. Table 4.13 is determined in an entirely similar manner as for the OECD countries. Table 4.14 is determined in an entirely similar manner as for the OECD countries. Table 4.15, giving averages is also determined in an entirely similar manner as for the OECD countries. Table 4.16 is determined in an entirely similar manner as for the OECD countries.

Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatemala

Table 4.13 Color values

0.4

0.6 0.6 0.6

0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.8

G1

0.2

0.4 0.2 0.4 0.4 0.4 0.2 0.4 0.4 0.2 0.4 0.4 0.2 0.2 0.4

G2

0.4 0.4 0.6 0.4 0.2 0.2 0.2 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.4 0.2

G3 0.4 0.6 0.6 0.6 0.4 0.6 0.4 0.4 0.4 0.6 0.6 0.4 0.6 0.4 0.6 0.4

G4

0.6 0.6 0.6 0.6 0.2

0.4 0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.6 0.6

G5 0.6 0.4 0.4 0.2 0.4 0.2 0.4 0.4 0.4 0.4 0.4 0.6 0.4 0.4 0.4 0.2

G6 0.6 0.6 0.8 0.8 0.6 0.4 0.8 0.6 0.8 0.6 0.8 0.6 0.6 0.6 0.6 0.4

G7

0.4

0.6 0.4 0.4

0.2 0.2 0.4 0.2 0.4 0.2 0.4 0.4 0.8

G8 0.4 0.4 0.4 0.4 0.2 0.2 0.6 0.4 0.4 0.2 0.4 0.4 0.4 0.2 0.4 0.2

G9

0.2 0.2 0.2 0.2

0.2

0.4

0.6 0.6 0.4

0.4 0.4 0.4 0.6 0.6 0.4

0.2 0.2 0.2

0.6

G 11

0.2

G 10 0.2 0.4 0.2 0.2 0.4 0.6 0.4 0.6 0.4 0.6 0.6 0.6 0.6 0.6 0.4 0.4

G 12 0.2 0.4 0.2 0.2 0.4 0.4 0.4 0.6 0.6 0.4 0.4 0.4 0.6 0.4 0.6 0.6

G 13

0.4 0.6 0.6 0.2 0.2 0.6 0.6 0.2 0.6 0.4

0.4 0.2 0.4 0.2 0.4

G 14 0.4 0.4 0.4 0.4 0.2 0.4 0.4 0.4 0.6 0.4 0.4 0.4 0.4 0.4 0.4 0.2

G 15

0.6 0.6 0.6 0.6 0.6 0.2

0.6 0.6 0.6 0.6 0.6 0.6

0.6 0.8

G 17

(continued)

0.2

0.2 0.2 0.2

0.2 0.2 0.2 0.2 0.2 0.2 0.2

0.2

G 16

4.5 Other Regions 83

Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Table 4.13 (continued)

0.2 0.8 0.8 0.2

0.6

0.4 0.2 0.2 0.6 0.4 0.6 0.6 0.6

G1

0.4 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2

G2

0.4 0.4 0.2 0.4 0.2 0.2

0.2 0.2 0.2 0.4 0.2 0.4 0.2 0.4

G3

0.6 0.6 0.4 0.6 0.6 0.4

0.4 0.6 0.4 0.4 0.4 0.6

0.4

G4

0.4 0.4 0.4 0.4 0.6 0.4

0.4 0.2 0.4 0.6 0.6 0.4 0.4 0.4

G5 0.4 0.2 0.4 0.2 0.4 0.4 0.4 0.4 0.6 0.6 0.6 0.4 0.4 0.4 0.6

G6 0.4 0.2 0.4 0.6 0.4 0.6 0.6 0.6 0.8 0.6 0.8 0.6 0.6 0.8 0.6

G7

0.4 0.4 0.4 0.6

0.2 0.2 0.2 0.2 0.4 0.6 0.4 0.4

G8 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.2 0.4 0.4 0.2

G9

0.2 0.2

0.2 0.2 0.2 0.2 0.2 0.2 0.2

G 10

0.4 0.4 0.6 0.4

0.6

0.4 0.2 0.4 0.6 0.4 0.6 0.6 0.4

G 11 0.4 0.6 0.6 0.6 0.6 0.6 0.4 0.4 0.2 0.4 0.4 0.4 0.4 0.2 0.4

G 12 0.4 0.6 0.6 0.4 0.6 0.4 0.6 0.4 0.6 0.4 0.4 0.4 0.2 0.4 0.2

G 13

0.6 0.2 0.4 0.4 0.6 0.4 0.4 0.4

0.4 0.2 0.4 0.2 0.6 0.4

G 14

0.6 0.4 0.6 0.4 0.4 0.6

0.4 0.2 0.4 0.4 0.4 0.4 0.4 0.4

G 15

0.4 0.2 0.2 0.2

0.2

0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2

G 16

0.6 0.6

0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.6 0.4 0.6 0.6

G 17

84 4 Sustainable Development Goals …

Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatemala

Table 4.14 Norm values

0.5

0.6 0.5 0.5

0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.6

G1

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G2

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G3 0.5 0.6 0.5 0.6 0.5 0.6 0.5 0.5 0.5 0.6 0.6 0.5 0.6 0.6 0.6 0.5

G4 0.5 0.6 0.5 0.4 0.5 0.5 0.5 0.5 0.6 0.6 0.2 0.5 0.6 0.5 0.6 0.5

G5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G6 0.6 0.5 0.8 0.8 0.6 0.5 0.8 0.6 0.6 0.5 0.6 0.6 0.5 0.6 0.6 0.5

G7 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.2 0.5 0.5 0.5 0.6 0.5

G8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.4 0.5 0.4

G9

0.2

0.2 0.2 0.2

0.2 0.2 0.2 0.2

0.2

G 10 0.2 0.6 0.6 0.4 0.4 0.5 0.5 0.6 0.6 0.4 0.2 0.5 0.6 0.5 0.2 0.5

G 11 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.6 0.5 0.6 0.6 0.6 0.5 0.5

G 12 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.6 0.5 0.6 0.5

G 13

0.5 0.5 0.6 0.5 0.2 0.5 0.6 0.5 0.5 0.5

0.5 0.5 0.5 0.4 0.5

G 14 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 15

0.2 0.8 0.8 0.6 0.6 0.6 0.6 0.6 0.6 0.8 0.5 0.5 0.6 0.6 0.5 0.5

G 17

(continued)

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 16

4.5 Other Regions 85

Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Table 4.14 (continued)

0.2 0.8 0.8 0.2

0.5

0.5 0.2 0.2 0.5 0.5 0.6 0.6 0.5

G1

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G2

0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5

G3

0.6 0.6 0.5 0.5 0.5 0.5

0.5 0.2 0.4 0.6 0.5 0.5 0.5 0.6

G4 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.2 0.5 0.5 0.5 0.5 0.5 0.5

G5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.5 0.5 0.5 0.5

G6 0.5 0.2 0.5 0.5 0.5 0.6 0.5 0.6 0.8 0.6 0.8 0.5 0.5 0.8 0.6

G7 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.4 0.4 0.5 0.5 0.5 0.5

G8 0.4 0.4 0.4 0.5 0.4 0.5 0.5 0.5 0.5 0.4 0.5 0.4 0.5 0.5 0.5

G9

0.2 0.2

0.5 0.2 0.2 0.2 0.2 0.2 0.2

G 10

0.5 0.2 0.5 0.2 0.6 0.5

0.6 0.6 0.5 0.6 0.5 0.6 0.5 0.5

G 11 0.5 0.5 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 12 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5

G 13

0.5 0.2 0.4 0.4 0.6 0.5 0.4 0.5

0.5 0.5 0.5 0.5 0.6 0.5

G 14

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.6 0.5 0.5 0.5

G 15

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5

G 16

0.5 0.5 0.6 0.6 0.5 0.4 0.5 0.5 0.2 0.5 0.5 0.6 0.8 0.6 0.6

G 17

86 4 Sustainable Development Goals …

Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatemala

Table 4.15 Averages

0.40

0.70 0.50 0.60

0.70 0.70 0.60 0.30 0.50 0.50 0.40 0.80

G1

G2

0.57 0.57 0.63 0.60 0.63 0.46 0.63 0.57 0.54 0.70 0.50 0.57 0.46 0.51 0.33 0.43

G3

0.65 0.63 0.65 0.69 0.53 0.40 0.60 0.66 0.64 0.67 0.51 0.46 0.59 0.61 0.67 0.53

G4 0.40 0.73 0.60 0.73 0.40 0.67 0.53 0.60 0.53 0.73 0.70 0.60 0.73 0.53 0.67 0.40

G5 0.40 0.70 0.65 0.65 0.50 0.50 0.65 0.65 0.75 0.70 0.40 0.65 0.65 0.60 0.70 0.40

G6 0.67 0.64 0.60 0.50 0.56 0.56 0.64 0.56 0.64 0.56 0.50 0.56 0.60 0.56 0.60 0.48

G7 0.70 0.67 0.80 0.80 0.70 0.40 0.80 0.73 0.80 0.53 0.80 0.73 0.67 0.73 0.70 0.40

G8 0.70 0.48 0.40 0.60 0.40 0.68 0.52 0.56 0.64 0.80 0.20 0.64 0.60 0.60 0.80 0.60

G9 0.45 0.53 0.57 0.50 0.30 0.37 0.63 0.43 0.53 0.33 0.50 0.36 0.40 0.30 0.55 0.27 0.20

0.20 0.20 0.20

0.20 0.20 0.20 0.20

0.20

G 10

G 11 0.40 0.67 0.60 0.40 0.47 0.53 0.60 0.60 0.78 0.33 0.40 0.53 0.67 0.60 0.40 0.60

G 12 0.40 0.63 0.40 0.36 0.60 0.73 0.63 0.67 0.63 0.75 0.60 0.68 0.63 0.67 0.47 0.70

G 13 0.35 0.55 0.20 0.45 0.55 0.60 0.55 0.70 0.65 0.40 0.60 0.65 0.65 0.60 0.70 0.65

G 14

0.55 0.70 0.70 0.50 0.20 0.60 0.70 0.30 0.50 0.50

0.53 0.45 0.33 0.27 0.33

G 15 0.40 0.56 0.45 0.47 0.40 0.68 0.60 0.56 0.55 0.60 0.53 0.60 0.56 0.56 0.47 0.48

G 16

G 17 0.60 0.80 0.80 0.60 0.73 0.73 0.73 0.67 0.73 0.80 0.60 0.50 0.70 0.67 0.60 0.40 (continued)

0.47 0.49 0.50 0.63 0.50 0.33 0.44 0.47 0.62 0.47 0.60 0.40 0.49 0.48 0.60 0.40

4.5 Other Regions 87

Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Table 4.15 (continued)

0.20 0.80 0.80 0.20

0.50

0.20 0.20 0.20 0.50 0.40 0.70 0.70 0.50

G1

9.40 0.40 0.40 0.49 0.40 0.49 0.66 0.54 0.60 0.55 0.60 0.54 0.46 0.57 0.46

G2

0.24 0.23 0.57 0.64 0.56 0.59 0.55 0.59 0.80 0.60 0.62 0.49 0.57 0.66 0.47

G3

0.70 0.70 0.53 0.60 0.60 047

0.20 0.20 0.47 0.67 0.40 0.47 0.47 0.67

G4 0.40 0.35 0.55 0.65 0.76 0.55 0.60 0.55 0.20 0.65 0.60 0.55 0.55 0.70 0.65

G5 0.39 0.40 0.52 0.56 0.52 0.50 0.64 0.64 0.60 0.70 0.67 0.58 0.56 0.68 0.64

G6 0.20 0.20 0.47 0.60 0.49 0.67 0.67 0.60 0.80 0.70 0.80 0.67 0.60 0.80 0.73

G7 0.52 0.40 0.56 0.52 0.64 0.68 0.64 0.64 0.60 0.30 0.30 0.55 0.64 0.60 0.60

G8 0.23 0.23 0.27 0.40 0.27 0.43 0.37 0.44 0.65 0.40 0.40 0.30 0.44 0.50 0.37

G9

0.20 0.20

0.60 0.20 0.20 0.20 0.20 0.20 0.20 0.20

G 10

0.60 0.40 0.53 0.40 0.73 0.47

0.20 0.33 0.53 0.67 0.47 0.60 0.53 0.47

G 11 0.76 0.75 0.73 0.70 0.73 0.60 0.63 0.60 0.40 0.47 0.53 0.64 0.56 0.57 0.64

G 12 0.75 0.65 0.65 0.50 0.65 0.60 0.60 0.55 0.60 0.53 0.53 0.30 0.35 0.50 0.50

G 13

0.60 0.20 0.30 0.33 0.70 0.50 0.35 0.55

0.60 0.40 0.60 0.27 0.60 0.45

G 14

0.64 0.44 0.55 0.50 0.68 0.50 0.52 0.53 0.30 0.60 0.53 0.68 0.55 0.48 0.68

G 15

0.36 0.33 0.40 0.62 0.42 0.47 0.36 0.40 0.40 0.50 0.60 0.58 0.53 0.56 0.33

G 16

0.50 0.50 0.67 0.67 0.60 0.30 0.60 0.53 0.50 0.53 0.67 0.70 0.80 0.60 0.70

G 17

88 4 Sustainable Development Goals …

4.5 Other Regions

89

Table 4.16 Latin America and Caribbean ranks Country SDG(C)/Rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

SDG(N)/Rank

SDG(A) /Rank

0.3787/23 0.4/21 0.4385/10 0.375/27 0.3785/24 0.3761/25 0.416/17.5 0.476/6 0.498/2 0.4578/9 0.4845/5 0.47/ 7 0.486/4 0.408/19 0.5183/1 0.352/29 0.3634/28 0.3053/30 0.388/22 0.416/17.5 0.428/13 0.434/11 0.4304/12 0.424/15

0.5022/14 0.496/20.5 0.5353/1 0.529/2 0.5075/11 0.4826/24 0.515/8 0.508/10 0.511/9 0.5155/7 0.4456/29 0.502/15 0.526/4 0.504/13 0.5267/3 0.475/27 0.5011/16 0.438/30 0.473/28 0.501/17 0.499/18.5 0.499/18.5 0.4804/26 0.496/20.5

0.498 /27 0.5679/9 0.5037/26 0.5263/21 0.5172/24 0.5759/6 0.529/19 0.6027/2 0.6217/1 0.602/3 0.4971/28 0.5691/7 0.5933/5 0.536/15 0.5947/4 0.5264/20 0.4966/29 0.4016/30 0.5139/25 0.5294/18 0.5354/16 0.5389/11 0.5529/10 0.5389/12

0.4682/8 0.4907/3 0.4237/9 0.4022/20 0.426/14 0.376/26

0.5054/12 0.52/6 0.486/23 0.4935/22 0.521/5 0.482/25

0.538/14 0.5349/17 0.5387/13 0.5245/23 0.5685/8 0.5262/22

Grenada, Costa Rica and Ecuador ranked the highest in achieving the SDCs for the region South America and the Caribbean. In [68], Grenada was not ranked. Costa Rica, Ecuador, Argentina, and Uruguay ranked 33rd, 46th, 45th, and 43rd, respectively. We next consider the Middle East and North Africa. The following tables were determined in an entirely similar manner as for the OECD countries (Tables 4.17, 4.18, 4.19, 4.20, 4.21, 4.22 and 4.23).

Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen, Rep.

Table 4.17 Color values

G1

0.6

0.6

0.8

0.6 0.6 0.6 0.6

0.6

G2

0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2

G3

0.4 0.6 0.2 0.2 0.2 0.2 0.4 0.2 0.4 0.2 0.4 0.6 0.4 0.2 0.2 0.6 0.2

G4

0.4 0.6 0.6 0.6 0.2 0.6 0.6 0.2

0.6 0.6 0.4 0.8 0.2 0.4 0.6 0.4

G5 0.4 0.4 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.4 0.2 0.2 0.4 0.4 0.2

G6 0.4 0.2 0.4 0.2 0.2 0.4 0.2 0.4 0.2 0.4 0.2 0.2 0.2 0.4 0.4 0.2 0.2

G7 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.2

G8 0.2 0.6 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.6 0.2 0.2 0.4 0.2

G9 0.2 0.4 0.2 0.4 0.2 0.4 0.4 0.4 0.2 0.4 0.4 0.4 0.6 0.2 0.4 0.4 0.2

G 10

0.4 0.2 0.6 0.4

0.2

0.4

0.2 0.2 0.8 0.2

0.6

G 11 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.2 0.2 0.4 0.2

G 12 0.6 0.4 0.6 0.4 0.6 0.6 0.2 0.4 0.4 0.6 0.2 0.2 0.2 0.6 0.6 0.2 0.6 0.4 0.4 0.2 0.2 0.4 0.6 0.4 0.2 0.4 0.6 0.4

0.2 0.2 0.6 0.4 0.2 0.2 0.2 0.6 0.6 0.2 0.6

G 14 0.2 0.6 0.4 0.6 0.2

G 13 0.4 0.2 0.6 0.4 0.2

G 15

0.4 0.4 0.4 0.6 0.4 0.6 0.4 0.2 0.4 0.4 0.4

0.4 0.4 0.6 0.6 0.2

G 16 0.4 0.2 0.2 0.2 0.2 0.4 0.4 0.4 0.2 0.4 0.4 0.4 0.4 0.2 0.4 0.4 0.2

G 17

0.6 0.6 0.6 0.6 0.4 0.8 0.8 0.6

0.4

0.6 0.6 0.4 0.6 0.6 0.6

90 4 Sustainable Development Goals …

Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen, Rep.

Table 4.18 Norm values

G1

0.6

0.6

0.8

0.5 0.6 0.5 0.5

0.6

G2

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5

G3

0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5

G4 0.5 0.6 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 0.6 0.5 0.5 0.5 0.4

G5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.2

G6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.2

G7 0.5 0.6 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.2

G8 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G9 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.4

G 10

0.4 0.2 0.6 0.4

0.2

0.4

0.2 0.2 0.8 0.2

0.6

G 11 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4

G 12 0.6 0.5 0.6 0.5 0.6 0.6 0.5 0.5 0.5 0.6 0.5 0.5 0.5 0.6 0.6 0.5 0.6

G 13 0.5 0.5 0.6 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.5 0.5 0.6

G 14 0.5 0.5 0.5 0.6 0.5 0.2 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 15 0.5 0.5 0.5 0.5 0.5 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 16 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G 17 0.5 0.5 0.5 0.5 0.6 0.6 0.8 0.5 0.8 0.6 0.5 0.5 0.5 0.5 0.8 0.8 0.5

4.5 Other Regions 91

Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen, Rep.

Table 4.19 Averages

G1

0.70

0.70

0.80

0.60 0.70 0.50 0.50

0.70

G2

0.49 0.56 0.51 0.60 0.43 0.46 0.57 0.46 0.30 0.49 0.49 0.60 0.46 0.24 0.51 0.57 0.31

G3

0.59 0.76 0.51 0.64 0.43 0.64 0.67 0.63 0.63 0.51 0.70 0.71 0.69 0.50 0.60 0.70 0.26

G4 0.53 0.67 0.47 0.80 0.33 0.53 0.67 0.47 0.80 0.47 0.73 0.67 0.73 0.33 0.60 0.60 0.27

G5 0.45 0.40 0.40 0.35 0.25 0.30 0.45 0.35 0.35 0.35 0.35 0.50 0.30 0.25 0.45 0.45 0.20

G6 0.52 0.56 0.56 0.40 0.36 0.52 0.56 0.60 0.48 0.52 0.40 0.56 0.48 0.52 0.52 0.56 0.24

G7 0.60 0.73 0.73 0.60 0.60 0.67 0.67 0.60 0.70 0.60 0.60 0.60 0.60 0.60 0.67 0.60 0.27

G8 0.48 0.70 0.44 0.52 0.45 0.44 0.45 0.52 0.45 0.52 0.55 0.55 0.55 0.33 0.48 0.65 0.35

G9 0.43 0.57 0.43 0.57 0.37 0.57 0.63 0.56 0.28 0.47 0.63 0.70 0.76 0.30 0.47 0.63 0.27

G 10

0.40 0.20 0.60 0.40

0.20

0.40

0.20 0.20 0.80 0.20

0.60

G 11 0.40 0.50 0.47 0.47 0.40 0.47 0.40 0.30 0.30 0.40 0.53 0.40 0.40 0.33 0.33 0.47 0.27

G 12 0.70 0.53 0.70 0.60 0.70 0.64 0.30 0.52 0.64 0.70 0.47 0.43 0.43 0.67 0.70 0.32 0.33

G 13 0.67 0.47 0.75 0.53 0.45 0.80 0.50 0.60 0.50 0.60 0.50 0.50 0.50 0.73 0.60 0.35 0.70

G 14 0.45 0.53 0.50 0.75 0.33 0.20 0.47 0.40 0.30 0.45 0.65 0.50 0.50 0.33 0.55 0.65 0.50

G 15 0.60 0.47 0.68 0.64 0.40 0.80 0.53 0.60 0.60 0.68 0.53 0.53 0.56 0.50 0.60 0.47 0.55

G 16 0.63 0.58 0.52 0.51 0.50 0.64 0.66 0.56 0.37 0.56 0.68 0.70 0.55 0.46 0.56 0.72 0.29

G 17 0.67 0.60 0.53 0.60 0.70 0.70 0.80 0.53 0.80 0.73 0.60 0.60 0.60 0.53 0.80 0.80 0.60

92 4 Sustainable Development Goals …

4.5 Other Regions

93

Table 4.20 Middle East and North Africa ranks Country SDG(C)/Rank Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen Rep.

0.436/2 0.413/4 0.426/5 0.41/7 0.37/12.5 0.411/6 0.351/16 0.37/12.5 0.386/10 0.412/5 0.364/14 0.407/8 0.356/15 0.386/10.5 0.442/1 0.394/9 0.336/17

SDG(N)/Rank

SDG(A) /Rank

0.522/3 0.528/2 0.516/5 0.505/11 0.529/1 0.521/4 0.5/12 0.497/13 0.496/14 0.494/15 0.506/9.5 0.506/9.5 0.51/7 0.51/7 0.494/16 0.51/7 0.439/17

0.5726/1 0.57/2 0.567/3 0.5494/6 0.4838/15 0.5331/8 0.521/13 0.5226/12 0.474/16 0.5227/11 0.551/5 0.549/7 0.523/10 0.492/14 0.5511/4 0.526/9 0.38/17

We find that Algeria and Bahrain rank the highest of the MENA region in achieving the SDGs. They ranked 53rd and 76th in [68]. United Arab Emirates ranked 65th in [68]. We next consider the region Sub-Saharan Africa. We find that Zimbabwe and Cabo Verde ranked the highest in achieving the SDGs for region Sub-Saharan Africa (Table 4.24). They ranked 121st and 96th, respectively in [68].

0.2

Burundi

0.4

0.4

0.4

0.2

0.2

0.2

Gabon

The Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

Ethiopia

0.2

0.2

0.2

Eswatini

0.2

0.2

0.2

0.2

0.2

0.2

0.2

Djibouti

0.2

0.2

0.2

0.2

Cote d’Ivoire

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

G3

Entrea

0.2

Cong Rep.

0.2

0.2

0.2

0.2

0.2

0.4

0.4

0.2

0.2

0.2

0.2

G2

E. Guinea

0.2

0.2

Congo

Chad

Comoros

0.2

0.2

C. African Rep.

0.2

0.2

Burkina Faso

0.2

0.2

Botswana

Cameroon

0.2

Cabo Verde

0.2

Benin

G1

Angola

Table 4.21 Color values

0.4

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.6

0.2

0.2

G4

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.4

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.4

0.2

0.4

G5

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

G6

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.6

0.2

0.2

0.2

0.2

0.2

G7

0.2

0.4

0.4

0.4

0.4

0.4

0.4

0.2

0.4

0.4

0.4

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

G8

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

G9

0.2

0.2

0.6

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.4

0.2

0.2

0.2

G 10

0.2

0.2

0.2

0.2

0.4

0.2

0.4

0.6

0.2

0.2

0.4

0.2

0.4

0.2

0.4

0.2

0.2

0.2

0.4

0.2

0.4

0.4

0.2

0.2

G 11

0.6

0.8

0.6

0.6

0.6

0.6

0.8

0.6

0.6

0.6

0.8

0.6

0.6

0.6

0.8

0.8

0.6

0.8

0.8

0.6

0.4

0.6

0.6

G 12

0.6

0.6

0.6

0.4

0.6

0.6

0.6

0.2

0.8

0.4

0.6

0.6

0.8

0.6

0.6

0.8

0.6

0.6

0.8

0.6

0.4

0.8

0.2

G 13

0.4

0.2

0.4

0.4

0.4

0.4

0.2

0.4

0.2

0.4

0.4

0.2

0.4

0.2

0.4

0.2

G 14

0.4

0.4

0.4

0.4

0.6

0.6

0.4

0.4

0.4

0.4

0.2

0.4

0.5

0.6

0.4

0.6

0.6

0.6

0.4

0.6

0.8

0.6

0.6

0.6

G 15

0.4

0.6

0.6

0.4

0.4

0.6

0.4

0.6

0.6

0.6

0.4

0.6

0.2

0.6

0.6

0.2

0.6

0.6

0.4

0.4

0.8

0.4

0.4

G 17

(continued)

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.8

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

G 16

94 4 Sustainable Development Goals …

0.4

0.2

0.2

0.2

0.2

0.2

0.2

S. Sudan

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

0.2

S. Africa

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

Somalia

0.2

0.2

Senegal

0.6

0.2

0.2

S. Torre and Prin.

0.2

0.2

0.2

Rwanda

0.2

0.2

Sierra Leone

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

G2

Seychelles

0.2

0.8

Mauritius

Nigeria

0.4

Mauritania

Niger

0.2

Mali

0.2

0.2

Malawi

0.2

0.2

Madagascar

Namibia

0.2

Mozambique

0.2

Liberia

G1

Lesotho

Table 4.21 (continued)

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

G3

0.4

0.4

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.6

0.2

0.4

0.4

0.2

0.2

0.4

0.2

0.6

0.2

0.2

0.2

0.2

0.2

0.4

G4

0.6

0.4

0.2

0.2

0.2

0.2

0.4

0.6

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.6

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.4

G5

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

G6

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.8

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.6

0.2

0.2

0.2

0.2

0.2

0.2

G7

0.4

0.2

0.4

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.4

0.2

0.2

0.4

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

G8

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

G9

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.4

0.2

0.2

0.6

0.2

0.2

0.6

0,2

0.2

0.2

0.6

0.4

0.2

0.2

0.6

0.2

G 10

0.4

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.4

9.2

0.2

0.4

0.4

0.4

0.6

0.2

0.4

0.4

0.4

0.2

0.4

G 11

0.8

0.4

0.6

0.6

0.8

0.8

0.4

0.6

0.6

0.2

0.6

0.6

0.6

0.8

0.6

0.4

0.8

0.2

0.6

0.8

0.8

0.8

0.8

0.6

G 12

0.6

0.6

0.8

0.8

0.8

0.4

0.6

0.2

0.6

0.6

0.2

0.6

0.6

0.8

0.6

0.6

0.2

0.4

0.2

0.4

0.8

0.6

0.6

0.6

0.4

G 13

0.4

0.4

0.4

0.4

0.2

0.4

0.4

0.2

0.6

0.4

0.4

0.4

0.4

0.4

0.4

0.4

G 14

0.4

0.6

0.4

0.6

0.4

0.4

0.6

0.4

0.2

0.4

0.2

0.6

0.4

0.6

0.6

0.6

0.6

0.4

0.2

0.6

0.6

0.4

0.2

0.4

0.6

G 15

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.2

G 16

0.6

0.4

0.2

0.4

0.4

0.6

0.6

0.8

0.2

0.6

0.4

0.4

0.4

0.6

0.6

0.6

0.6

0.4

0.6

0.4

0.4

0.2

0.4

0.8

G 17

4.5 Other Regions 95

0.5

0.5

0.4

0.4

0.2

0.2

0.2

0.2

Gabon

The Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.2

Ethiopia

0.5

0.5

Djibouti

0.2

0.2

0.2

Cote d’Ivoire

0.5

Eswatini

0.2

Cong Rep.

0.5

0.5

0.5

0.2

Congo, Dem. Rep.

0.5

0.2

Comoros

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

Eritrea

0.2

Chad

G2

0.5

E. Guinea

0.2

C. African Rep.

0.2

Burundi

0.2

0.5

Burkina Faso

0.2

0.2

Botswana

Cameroon

0.2

Benin

Cabo Verde

0.2

Angola

G1

Table 4.22 Norm values

G3

0.5

0.5

0.5

0.4

0.5

0.5

0.5

0.5

0.5

0.5

0.4

0.5

0.4

0.5

0.5

0.5

0.5

0.4

0.4

0.5

0.5

0.5

0.5

0.5

0.5

G4

0.4

0.4

0.2

0.2

0.4

0.2

0.5

0.4

0.5

0.4

0.5

0.2

0.4

0.4

0.2

0.2

0.2

0.2

0.5

0.5

0.5

0.5

0.6

0.5

0.2

G5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G6

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G7

0.2

0.5

0.2

0.2

0.4

0.2

0.5

0.5

0.2

0.2

0.2

0.2

0.5

0.2

0.5

0.2

0.2

0.2

0.5

0.6

0.2

0.8

0.4

0.2

0.2

G8

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G9

0.4

0.5

0.4

0.5

0.5

0.4

0.5

0.4

0.4

0.4

0.4

0.5

0.5

0.4

0.4

0.4

0.4

0.4

0.5

0.5

0.4

0.5

0.5

0.4

0.4

G 10

0.2

0.2

0.2

0.6

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

0.2

0.5

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.2

0.2

G 11

0.5

0.4

0.4

0.2

0.4

0.5

0.5

0.4

0.6

0.2

0.2

0.5

0.2

0.4

0.2

0.5

0.4

0.2

0.4

0.5

0.5

0.5

0.5

0.4

0.2

G 12

0.6

0.6

0.8

0.6

0.6

0.6

0.5

0.8

0.6

0.6

0.6

0.8

0.6

0.6

0.5

0.8

0.8

0.6

0.8

0.8

0.5

0.5

0.6

0.6

G 13

0.5

0.5

0.5

0.6

0.5

0.5

0.6

0.5

0.5

0.8

0.2

0.5

0.6

0.5

0.8

0.5

0.5

0.8

0.5

0.5

0.8

0.5

0.5

0.8

0.5

G 14

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.2

0.5

0.5

0.2

0.4

0.5

0.5

0.5

0.5

0.5

G 15

0.5

0.5

0.5

0.5

0.5

0.6

0.6

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.6

0.5

0.6

0.6

0.5

0.5

0.6

0.5

0.6

0.6

0.5

G 16

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G 17

(continued)

0.8

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.8

0.6

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.8

0.5

0.5

96 4 Sustainable Development Goals …

0.2

0.2

0.2

0.2

Niger

Nigeria

Rwanda

S. T& P.

Senegal

0.5

0.5

0.5

0.5

0.5

0.5

0.2

0.2

0.2

0.2

0.2

S. Sudan

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

0.2

0.2

S. Africa

0.5

0.5

0.5

0.5

0.5

0.4

0.5

0.5

0.5

0.2

0.2

Namibia

0.5

Somalia

0.2

Mozambique

0.5

0.5

0.2

Mauritius

0.5

0.5

0.2

0.8

Mauritania

S. Leone

0.5

Mali

0.5

0.5

0.6

0.2

Malawi

G2

0.5

Seychelles

0.2

0.2

Madagascar

0.2

Liberia

G1

Table 4.22 (continued)

G3

0.5

0.5

0.4

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.4

0.5

0.4

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G4

0.5

0.5

0.5

0.5

0.4

0.2

0.2

0.5

0.5

0.6

0.2

0.5

0.5

0.2

0.2

0.5

0.4

0.6

0.5

0.2

0.5

0.2

0.2

G5

0.6

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.4

0.5

0.4

0.5

0.4

0.5

0.5

0.5

0.6

0.5

0.5

0.4

0.5

0.5

0.4

0.5

G6

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G7

0.4

0.5

0.2

0.2

0.2.

0.5

0.2

0.5

0.2

0.2

0.8

0.2

0.2

0.2

0.2

0.2

0.2

0.5

0.4

0.2

0.2

0.2

0.2

0.2

G8

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G9

0.4

0.4

0.5

0.4

0.5

0.4

0.4

0.5

0.4

0.4

0.5

0.4

0.4

0.5

0.5

0.4

0.5

0.4

0.5

0.4

0.4

0.4

0.4

0.4

G 10

0.2

0.2

0.2

0.2

0.2

0.4

0.2

0.2

0.4

0.2

0.2

0.6

0.2

0.2

0.6

0.2

0.2

0.2

0.6

0.4

0.2

0.2

0.6

G 11

0.5

0.4

0.4

0.2

0.5

0.2

0.2

0.5

0.5

0.4

0.4

0.4

0.5

0.4

0.5

0.5

0.5

0.4

0.6

0.2

0.4

0.4

0.4

0.4

G 12

0.8

0.5

0.6

0.6

0.8

0.8

0.5

0.5

0.6

0.5

0.6

0.6

0.6

0.8

0.5

0.5

0.8

0.5

0.6

0.8

0.8

0.8

0.8

G 13

0.6

0.6

0.8

0.8

0.8

0.5

0.5

0.5

0.5

0.6

0.5

0.5

0.5

0.8

0.5

0.5

0.5

0.5

0.5

0.5

0.8

0.5

0.5

0.5

G 14

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.5

0.5

0.5

0.5

0.5

G 15

0.5

0.6

0.5

0.6

0.5

0.5

0.6

0.5

0.5

0.5

0.4

0.5

0.5

0.5

0.6

0.6

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G 16

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.5

0.5

0.5

0.5

0.5

0.5

0.5

G 17

0.5

0.5

0.5

0.5

0.5

0.5

0.2

0.8

0.8

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.6

0.6

0.5

0.5

0.5

0.5

0.5

0.5

4.5 Other Regions 97

0.40

0.50

0.30

0.30

0.20

0.20

0.20

0.20

Gabon

The Gambia

Ghana

Guinea

Guinea-Bissau

Kenya

Lesotho

0.50

0.43

0.47

0.36

0.54

0.37

0.49

0.43

0.20

0.40

Ethiopia

Djibouti

0.47

0.20

0.20

Cote d’Ivoire

0.40

0.20

0.20

Congo, Rep.

0.36

Eswatini

0.20

Congo.

0.35

0.43

0.60

0.20

Comoros

0.35

0.20

Chad

0.47

0.51

0.45

0.30

0.40

0.37

0.51

Eritrea

0.20

C. African Rep.

G2

0.46

E. Guinea

0.20

0.20

Burundi

0.20

0.20

Burkina Faso

Cameroon

0.20

Botswana

Cabo Verde

0.20

Benin

G1

0.20

Angola

Table 4.23 Averages

G3

0.24

0.30

0.25

0.24

0.30

0.27

0.29

0.29

0.26

0.35

0.22

0.27

0.21

0.29

0.29

0.34

0.24

0.21

0.23

0.51

0.32

0.29

0.39

0.30

0.26

G4

0.33

0.40

0.20

0.20

0.33

0.20

0.40

0.27

0.33

0.27

0.40

0.20

0.27

0.27

0.20

0.20

0.20

0.20

0.33

0.47

0.33

0.20

0.73

0.33

0.20

G5

0.65

0.55

0.40

0.40

0.40

0.35

0.40

0.45

0.55

0.47

0.40

0.47

0.35

0.40

0.35

0.40

0.35

0.35

0.45

0.55

0.45

0.40

0.55

0.35

0.53

G6

0.44

0.36

0.32

0.36

0.44

0.40

0.48

0.44

0.40

0.44

0.44

0.32

0.35

0.35

0.44

0.47

0.44

0.44

0.40

0.48

0.44

0.40

0.40

0.40

0.44

G7

0.20

0.27

0.20

0.20

0.27

0.20

0.47

0.40

0.20

0.20

0.20

0.20

0.33

0.20

0.40

0.20

0.20

0.20

0.40

0.40

0.20

0.20

0.27

0.20

0.20

G8

0.40

0.52

0.40

0.52

0.60

0.50

0.48

0.60

0.36

0.53

0.40

0.50

0.60

0.36

0.44

0.40

0.40

0.40

0.52

0.45

0.44

0.45

0.56

0.52

0.40

G9

0.27

0.33

0.23

0.23

0.43

0.23

0.44

0.27

0.24

0.23

0.24

0.35

0.36

0.25

0.24

0.27

0.27

0.23

0.32

0.36

0.23

0.27

0.40

0.27

0.24

G 10

0.20

0.20

0.20

0.60

0.20

0.20

0.20

0.20

0.20

0.20

0.20

0.20

0.20

0.20

0.40

0.20

0.20

0.20

0.40

0.40

0.20

0.20

0.20

G 11

0.40

0.27

0.20

0.20

0.27

0.40

0.33

0.27

0.60

0.20

0.20

0.47

0.20

0.27

0.20

0.47

0.27

0.20

0.27

0.40

0.33

0.33

0.60

0.27

0.20

G 12

0.73

0.77

0.80

0.76

0.75

0.76

0.70

0.80

0.73

0.77

0.76

0.80

0.76

0.75

0.65

0.80

0.80

0.77

0.80

0.80

0.77

0.48

0.77

0.77

G 13

0.55

0.66

0.50

0.75

0.60

0.65

0.70

0.65

0.45

0.80

0.20

0.60

0.75

0.67

0.80

0.60

0.60

0.80

0.65

0.65

0.80

0.65

0.50

0.80

0.50

G 14

0.55

0.50

0.60

0.40

0.60

0.50

0.40

0.60

0.20

0.50

0.40

0.20

0.40

0.40

0.35

0.20

0.45

G 15

0.67

0.52

0.60

0.64

0.55

0.73

0.76

0.52

0.55

0.55

0.60

0.40

0.64

0.76

0.64

0.33

0.76

0.76

0.64

0.60

0.76

0.80

0.68

0.75

0.64

G 16

G 17

0.80

0.53

0.50

0.50

0.53

0.53

0.50

0.47

0.73

0.80

0.70

0.60

0.33

0.67

0.40

0.60

0.50

0.40

0.50

0.60

0.53

0.53

0.80

0.53

0.53

(continued)

0.42

0.42

0.40

0.32

0.56

0.40

0.42

0.40

0.38

0.40

0.37

0.44

0.40

0.38

0.29

0.50

0.33

0.37

0.33

0.63

0.36

0.51

0.56

0.42

0.31

98 4 Sustainable Development Goals …

0.20

0.20

0.20

0.20

0.20

S. Sudan

Sudan

Tanzania

Togo

Uganda

Zambia

Zimbabwe

0.20

0.20

S. Africa

0.43

0.49

0.49

0.43

0.49

0.23

0.27

0.51

0.35

0.47

0.20

0.43

0.60

0.20

0.20

Senegal

Somalia

0.20

S. T & P.

0.49

0.40

0.40

0.37

0.49

0.56

0.30

0.46

S. Leone

0.20

Rwanda

0.46

0.46

0.70

0.20

Nigeria

G2

0.47

Seychelles

0.20

Niger

Mauritius

0.20

0.80

Mauritania

0.20

0.40

Mali

Namibia

0.20

Malawi

Mozambique

0.20

0.20

Madagascar

0.20

Liberia

G1

Table 4.23 (continued)

G3

0.27

0.29

0.24

0.30

0.29

0.30

0.23

0.36

0.26

0.24

0.62

0.33

0.40

0.39

0.23

0.29

0.29

0.26

0.61

0.27

0.26

0.26

0.26

0.29

G4

0.60

0.33

0.33

0.33

0.27

0.29

0.20

0.53

0.40

0.73

0.20

0.53

0.40

0.20

0.20

0.47

0.27

0.67

0.20

0.20

0.33

0.20

0.20

G5

0.73

0.50

0.45

0.35

0.55

0.35

0.45

0.70

0.27

0.35

0.20

0.45

0.30

0.60

0.35

0.35

0.75

0.45

0.40

0.25

0.36

0.50

0.50

0.35

G6

0.44

0.44

0.44

0.44

0.44

0.32

0.40

0.48

0.30

0.36

0.53

0.40

0.40

0.44

0.44

0.44

0.44

0.40

0.48

0.40

0.44

0.44

0.40

0.40

G7

0.27

0.40

0.20

0.20

0.20

0.33

0.20

0.33

0.20

0.20

0.80

0.20

0.20

0.20

0.20

0.20

0.20

0.40

0.67

0.20

0.20

0.20

0.20

0.20

G8

0.56

0.44

0.52

0.52

0.56

0.32

0.40

0.52

0.45

0.52

0.50

0.60

0.40

0.52

0.45

0.52

0.60

0.37

0.64

0.36

0.48

0.44

0.48

0.48

G9

0.27

0.30

0.37

0.30

0.33

0.28

0.24

0.53

0.23

0.23

0.44

0.27

0.28

0.30

0.37

0.27

0.33

0.27

0.43

0.28

0.27

0.27

0.28

0.23

G 10

0.20

0.20

0.20

0.20

0.20

0.20

0.20

0.20

0.40

0.20

0.20

0.60

0.20

0.20

0.60

0.20

0.20

0.20

0.60

0.40

0.20

0.20

0.60

G 11

0.47

0.27

0.27

0.20

0.33

0.20

0.20

0.47

0.33

0.27

0.40

0.27

0.40

0.27

0.73

0.47

0.40

0.27

0.67

0.20

0.33

0.33

0.33

0.27

G 12

0.80

0.67

0.76

0.77

0.80

0.80

0.57

0.70

0.77

0.50

0.77

0.73

0.73

0.80

0.73

0.60

0.80

0.40

0.77

0.80

0.80

0.80

0.80

G 13

0.75

0.75

0.80

0.80

0.80

0.60

0.60

0.45

0.65

0.75

0.35

0.65

0.67

0.80

0.65

0.65

0.50

0.55

0.40

0.60

0.80

0.65

0.65

0.70

G 14

0.40

0.65

0.55

0.55

0.40

0.60

0.47

0.45

0.65

0.53

0.65

0.60

0.55

0.55

0.50

0.65

G 15

0.60

0.68

0.60

0.70

0.56

0.60

0.75

0.56

0.45

0.68

0.27

0.65

0.53

0.60

0.75

0.72

0.75

0.60

0.35

0.67

0.64

0.60

0.48

0.52

G 16

0.38

0.42

0.38

0.35

0.40

0.40

0.40

0.55

0.37

0.42

0.63

0.42

0.53

0.60

0.36

0.35

0.60

0.40

0.63

0.33

0.40

0.44

0.36

0.36

G 17

0.60

0.47

0.40

0.53

0.40

0.50

0.50

0.80

0.80

0.40

0.50

0.53

0.60

0.52

0.50

0.60

0.73

0.67

0.53

0.50

0.47

0.60

0.60

0.47

4.5 Other Regions 99

100

4 Sustainable Development Goals …

Table 4.24 Sub-Saharan Africa ranks Country SDG(C)/Rank Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros Congo Demo Rep. Congo Rep. Cote d’Ivoire Djibouti Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique

0.276/48 0.362/20.5 0.328/39.5 0.367/16 0.409/3 0.43/1 0.336/34 0.385/10 0.357/28 0.33/36.5 0.359/26.5 0.371/15 0.36/24 0.302/46 0.259/49 0.381/12 0.293/47 0.383/11 0.394/6 0.362/21 0.326/41 0.364/18.5 0.36/24.5 0.328/39 0.338/33 0.378/13 0.35/29 0.365/17 0.411/2 0.344/31 0.364/18.5 0.334/35

SDG(N)/Rank

SDG(A)/Rank

0.412/47 0.481/14 0.465/27 0.515/4 0.526/1 0.518/2.5 0.474/19.5 0.48/15 0.447/35 0.428/45 0.468/24 0.437/42 0.46/29 0.407/48 0.432/43.5 0.49/11 0.442/38.5 0.507/6 0.497/9 0.438/41 0.466/25.5 0.454/33 0.446/36 0.472/21 0.432/43.5 0.474/19.5 0.442/38.5 0.458/31.5 0.499/8 0.46/30 0.486/12 0.492/10

0.4039/45 0.4451/32 0.4522/27 0.4445/33 0.4693/15 0.4897/6 0.4595/24 0.4465/30 0.4361/36 0.4015/48 0.4352/37 0.4245/41 0.4668/17 0.4026/46 0.4112/44 0.4716/13.5 0.4123/43 0.4752/10 0.4998/3 0.4483/29 0.4537/26 0.4675/16 0.4025/47 0.4605/23 0.4349/38 0.4732/11 0.4344/39 0.4462/31 0.4716/13.5 0.4417/34 0.5083/2 0.4552/25 (continued)

4.5 Other Regions

101

Table 4.24 (continued) Country SDG(C)/Rank Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

0.306/44 0.372/14 0.36/24 0.389/8 0.408/4 0.33/36.5 0.39/7 0.34/32 0.314/43 0.328/39 0.305/45 0.346/30 0.386/9 0.362/21 0.359/26.5 0.324/42 0.402/5

SDG(N)/Rank

SDG(A)/Rank

0.448/34 0.443/37 0.458/31.5 0.478/16 0.466/25.5 0.422/46 0.51/5 0.464/28 0.44/40 0.47/23 0.381/49 0.482/13 0.504/7 0.471/22 0.475/18 0.477/17 0.518/2

0.4627/22 0.4659/19 0.4651/20 0.483/8 0.4974/4 0.4398/35 0.4931/5 0.4776/9 0.4174/42 0.4664/18 0.356/49 0.4275/40 0.4875/7 0.4516/28 0.4628/21 0.473/12 0.5143/1

We finally consider the region Oceania (Tables 4.25, 4.26, 4.27 and 4.28). Samoa and Palau rank the highest here while Fiji ranks the highest for its region in [68].

Fiji Kiribati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Table 4.25 Color values

G1

0.4

0.2 0.6 0.2 0.6

0.6

G2

0.2

0.4 0.2 0.4 0.4

0.4

G3

0.2

0.2 0.2 0.2 0.4

0.2 0.2 0.2 0.2

0.2 0.2 0.2 0.2 0.2 0.2

0.2

0.4 0.8 0.2 0.6 0.2 0.4 0.4 0.4

G5 0.4 0.2 0.4

G4 0.6 0.6 0.6

G6

0.4

0.2 0.6 0.2

0.4 0.2 0.2

G7 0.6 0.4 0.6 0.2 0.8 0.8 0.2 0.6 0.2 0.6 0.6 0.2

G8

0.4

0.4 0.4

0.6

G9 0.4 0.2 0.2 0.2 0.4 0.2 0.2 0.2 0.2 0.2 0.2 0.2

G 10

0.2 0.2 0.4 0.4 0.4 0.4

0.2

0.4 0.4

G 11

0.4

0.4 0.4 0.4 0.6

0.6 0.4 0.4 0.4

G 12

0.6 0.8 0.8 0.4 0.4 0.8 0.4

0.8

0.4 0.8

G 13

0.6 0.8 0.6 0.4 0.4 0.4

0.4 0.6 0.4 0.6 0.6

G 14 0.6 0.6 0.4 0.2 0.2 0.6 0.2 0.4 0.4 0.4 0.6 0.4

G 15

0.2

0.4 0.4 0.2

0.2

0.2

0.2

0.2 0.6

0.2

G 16

G 17

0.6

0.6 0.6 0.8

0.6 0.8 0.8 0.6

102 4 Sustainable Development Goals …

Fiji Kiribati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Table 4.26 Norm values

G1

0.4

0.2 0.6 0.2 0.6

0.5

G2

0.5 0.5 0.2 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G3

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

G4 0.6 0.6 0.5 0.4 0.4 0.8 0.2 0.6 0.2 0.5 0.4 0.5

G5 0.5 0.2 0.5 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.2 0.5

G6 0.5 0.5 0.5 0.4 0.50 0.8 0.5 0.6 0.5 0.6 0.6 0.5

G7 0.5 0.4 0.5 0.2 0.8 0.8 0.2 0.5 0.2 0.5 0.5 0.2

G8 0.6 0.2 0.2 0.2 0.8 0.4 0.5 0.5 0.5 0.5 0.4 0.5

G9 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.4 0.4

G 10

0.2 0.2 0.4 0.4 0.4 0.4

0.2

0.4 0.4

G 11 0.6 0.5 0.5 0.5 0.2 0.8 0.4 0.5 0.5 0.5 0.8 0.5

G 12

0.5 0.8 0.8 0.5 0.5 0.8 0.5

0.5 0.8 0.8 0.8

G 13 0.5 0.5 0.5 0.5 0.5 0.2 0.5 0.8 0.5 0.5 0.5 0.5

G 14 0.5 0.6 0.5 0.2 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5

G 15 0.5 0.5 0.4 0.5 0.2 0.5 0.5 0.5 0.5 0.2 0.4 0.5

G 16 0.6 0.5 0.6 0.5 0.5 0.4 0.5 0.5 0.5 0.6 0.5 0.5

G 17 0.5 0.80 0.80 0.6 0.8 0.8 0.5 0.6 0.8 0.8 0.8 0.5

4.5 Other Regions 103

Fiji Kiribati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Table 4.27 Averages

G1

0.30

0.20 0.70 0.20 0.70

0.60

G2

0.63 0.53 0.20 0.30 0.40 0.65 0.40 0.64 0.43 0.47 0.53 0.50

G3

0.55 0.45 0.43 0.43 0.44 0.68 0.23 0.54 0.44 0.60 0.52 0.46

G4 0.70 0.70 0.60 0.40 0.40 0.80 0.20 0.73 0.20 0.53 0.30 0.47

G5 0.45 0.20 0.53 0.20 0.20 0.20 0.35 0.33 0.40 0.60 0.20 0.40

G6 0.64 0.33 0.33 0.30 0.50 0.80 0.35 0.67 0.35 0.70 0.70 0.53

G7 0.50 0.30 0.50 0.20 0.80 0.80 0.20 0.50 0.20 0.50 0.60 0.20

G8 0.67 0.20 0.20 0.20 0.80 0.40 0.55 0.47 0.50 0.50 0.40 0.53

G9 0.36 0.25 0.25 0.25 0.35 0.30 0.23 0.25 0.28 0.35 0.25 0.30

G 10

0.20 0.20 0.20 0.40 0.40 0.40

0.20

0.40 0.40

G 11 0.60 0.40 0.40 0.40 0.20 0.80 0.40 0.50 0.40 0.50 0.80 0.40

G 12

0.67 0.80 0.80 0.65 0.65 0.80 0.67

0.63 0.80 0.80 0.80

G 13 0.65 0.60 0.40 0.40 0.50 0.20 0.60 0.80 0.60 0.60 0.50 0.65

G 14 0.67 0.70 0.47 0.20 0.40 0.73 0.45 0.60 0.60 0.40 0.60 0.50

G 15 0.40 0.50 0.40 0.40 0.20 0.40 0.50 0.47 0.33 0.20 0.40 0.45

G 16 0.70 0.60 0.73 0.60 0.67 0.60 0.48 0.55 0.43 0.75 0.40 0.50

G 17 0.53 0.80 0.80 0.73 0.80 0.80 0.50 0.67 0.80 0.80 0.80 0.60

104 4 Sustainable Development Goals …

4.5 Other Regions

105

Table 4.28 Oceania ranks Country SDG(C)/Rank Fiji Kiribati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

SDG(N)/Rank

SDG(A)/Rank

0.4632/5 0.4872/4 0.4169/7 0.3942/8

0.509/5 0.499/6 0.5189/3 0.4113/11

0.5793/1 0.4981/6 0.4786/7 0.3607/11

0.6222/1 0.354/9 0.52/2 0.3305/10 0.442/6 0.511/3 0.318/11

0.5267/2 0.446/9 0.563/1 0.429/10 0.491/7 0.5186/4 0.45/8

0.5413/3 0.4241/9 0.5775/2 0.4096/10 0.5408/4 0.5331/5 0.4492/8

Chapter 5

Human Trafficking Rankings

The reason for the choice of the SDGs in the table below was discussed in Chap. 3. However, we make some additional comments. Trafficking in persons disproportionately affects women and girls;71% of all victims detected worldwide are female. The 2016 UNODC Global Report on Trafficking estimates that 51% of all detected trafficking victims are women and 20% are girls [27].

5.1 Human Trafficking The following discussion is taken from [27]. Target 5.2 advocates for the elimination of all forms of violence against all women and girls. According to the Global estimates of modern slavery: ‘Forced labour and forced Marriage’, produced by the ILO and Walk Free Foundation in partnership with IOM, 25 million people were victims of forced labor and 15 million people were victims of forced labor in 2016. Target 8.7 calls for taking immediate measures to eradicate forced labor, end modern slavery, and human trafficking. According to the 2016 UNODC Global Report, after women 61%, children remain the second largest category of detected victims of trafficking across the world. Target 16.2 calls for ending abuse, exploitation, trafficking and all forms of violence against and torture of children. Trafficking in persons is a multifaceted issue cutting across rights, rule of law, migration, labor, inequality, anti-corruption, education, gender, violence, and conflict issues. As a result, progress against many of the other targets in the SDGs

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_5

107

108

5 Human Trafficking Rankings

will contribute to preventing and eliminating human trafficking globally. The targets in Goal 5 (Gender Equality) will have a strong impact through the elimination of violence against women and girls as well as Target 5.3 specifically. Targets under Goal 8 will also have a positive impact in addressing many of the underlying conditions that lead to situations of trafficking and forced labor. Target 10.7 (Reduced Inequalities) further supports the goals of Goal 8. Further progress on combatting human trafficking will come through Goal 16’s targets to strengthen rule of law, reduce corruption, develop effective and accountable institutions, and ensure a legal identity, including birth registration for all. Beyond these goals, the outcomes of Goal 4 (Quality Education and Lifelong Learning Opportunities for all will address key factors contributing to vulnerability to trafficking faced by millions globally. Targets 4.1, 4.3, and 4.4 are particularly helpful. Targets 17.8 and 17.9 both call for enhanced capacity to collect, manage and analyze data, and will contribute to improving monitoring and accountability of action against human trafficking. What is being done to achieve these goals and what more can states do to achieve these goals can be found in [27]. Table 5.1 in [54, p. 6] contained the overall marks for the goals. The marks pertaining to the SDGs under consideration for trafficking in persons totaled 28.6. The individual goal marks were divided by 28.6 so that the new goal marks were between 0 and 1 and their total was 1. G 1 G 4 G 5 G 6 G 8 G 10 G 11 G 12 G 16 G 17 Total 1.8 2.5 2.2 2.5 2.7 3.6 2.6 6.3 2.7 1.7 28.6 The coefficients in the following equation were determined by dividing the entries in the previous table by 28.6. G = 0.06G 1 + 0.09G 4 + 0.08G 5 + 0.09G 6 + 0.09G 8 +0.13G 10 + 0.09G 11 + 0.22G 12 + 0.09G 16 + 0.06G 17 . The tables used to determine the following rankings are the appropriate subtables of Tables 4.1, 4.2, and 4.3. The subtables are determined by deleting the columns not pertaining to the SDGs in the above equation. Denmark and Finland ranked the highest in achieving the SDGs for the region OECD. We next consider the region East and South Asia. The tables used to determine the following rankings are the appropriate subtables of Tables 4.5, 4.6, and 4.7.

5.1 Human Trafficking

109

Table 5.1 OECD ranks Country Color/Rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

0.440/22.5 0.484/9 0.458/16 0.484/9 0.368/30 0.522/2.5 0.550/1 0.440/22.5 0.522/2.5 0.476/12 0.484/9 0.356/32.5 0.456/17.5 0.490/5 0.448/20 0.300/36 0.494/29 0.452/19 0.456/17.5 0.356/32.5 0.410/27.5 0.416/25.5 0.302/35 0.466/14.5 0.488/6.5 0.512/4 0.410/27.5 0.422/24 0.468/13 0.478/11 0.466/14.5 0.488/6.5 0.416/25.5 0.358/31 0.446/21 0.338/34

Norm/Rank

Average/Rank

0.502/27 0.567/6.5 0.539/16 0.560/8 0.487/30 0.558/9.5 0.610/2 0.527/19 0.611/1 0.548/12 0.546/13 0.434/35 0.515/23.5 0.570/4 0.549/11 0.487/30 0.509/25 0.541/14 0.378/36 0.505/26 0.461/32.5 0.519/22 0.461/32.5 0.558/9.5 0.524/20.5 0.581/3 0.515/23.5 0.533/18 0.540/15 0.567/5.5 0.496/28 0.564/7 0.536/17 0.487/30 0.524/20.5 0.439/34

0.5947/17 0.5864/20 0.6322/6 0.6078/13 0.5354/31 0.6203/7 0.6772/1 0.6053/15 0.6640/3 0.6062/14 0.6094/12 0.4637/36 0.6187/9 0.6136/10 0.5995/16 0.4932/33 0.5510/28 0.5679/24 0.5888/19 0.5372/30 0.5301/29 0.5692/23 0.4890/34 0.6588/4 0.6118/11 0.6187/8 0.5832/22 0.5544/27 0.5922/18 0.6653/2 0.5626/26 0.6502/5 0.5853/21 0.4771/35 0.5631/25 0.4966/32

110

5 Human Trafficking Rankings

Table 5.2 East and South Asia ranks Country HT(C)/Rank Bangladesh Bhutan Brunei Darussaiam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

0.422/11.5 0.47/2 0.459/4 0.394/15 0.44/6 0.378/19 0.388/16.5 0.43/9 0.422/11.5 0.436/7 0.48/1 0.376/20 0.386/18 0.426/10 0.33/21 0.388/16.5 0.418/13 0.453/5 0.416/14 0.432/8 0.468/3

HT(N)/Rank

HT(A)/Rank

0.553/3.5 0.5/14 0.556/2 0.535/9 0.521/12 0.474/20 0.492/17 0.527/10 0.547/6 0.506/13 0.538/8 0.493/16 0.553/4 0.579/1 0.448/21 0.492/17.5 0.552/5 0.497/15 0.479/19 0.543/7 0.524/11

0.5438/13 0.5503/11 0.6502/2 0.5253/15 0.5807/5 0.4993/19 0.5365/14 0.6347/3 0.5152/17 0.562/8 0.5903/4 0.5467/12 0.5198/16 0.551/10 0.4332/21 0.5041/18 0.6972/1 0.5555/9 0.5678/7 0.4575/20 0.568/6

Brunei Darussaiam and Maldives ranked the highest with respect achieving the human trafficking SDGs for the region East and South Asia (Table 5.2). We next consider the region Eastern Europe and Central Asia. The tables used to determine the following rankings are the appropriate subtables of Tables 4.10, 4.11 and 4.12.

5.1 Human Trafficking

111

Table 5.3 Eastern Europe and Central Asia ranks Country HT(C)/Rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Boznia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgz Rep. Liecheristan Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

HT(N)/Rank

HT(A)/Rank

0.363/23 0.43/16

0.507/14 0.498/18.5

0.4427/23 0.5616/13

0.396/21 0.464/9 0.54/2 0.53/3 0.39/22 0.484/8 0.402/20 0.456/11.5 0.406/19 0.566/1

0.54/11 0.549/9 0.563/5 0.59/1 0.491/20 0.523/12 0.501/17 0.479/22.5 0.479/22.5 0.55/8

0.5263/20 0.5716/12 0.6209/4 0.6197/5 0.5599/14 0.589/10 0.4856/22 0.5373/19 0.5377/17 0.6043/8

0.522/4 0.518/5

0.562/6 0.58/2

0.6054/7 0.639/1

0.417/18 0.442/15 0.42/17 0.446/14

0.506/15.6 0.498/18.5 0.488/21 0.506/15

0.5165/21 0.5375/18 0.5378/16 0.5451/15

0.498/6 0.46/10 0.454/13 0.488/7 0.456/11.5

0.542/10 0.562/6.5 0.577/3 0.572/4 0.518/13

0.6148/6 0.5862/11 0.6252/2 0.6247/3 0.5988/9

Moldova and Boznia and Herzegovina rank the highest in achieving the human trafficking SDGs for the region Eastern Europe and Central Asia (Table 5.3). We next consider Latin America and the Caribbean. The tables used to determine the following rankings are the appropriate subtables of Tables 4.14, 4.15, and 4.16.

112

5 Human Trafficking Rankings

Table 5.4 Latin America and the Caribbean ranks Country HT(C)/Rank

HT(N)/Rank

HT(A)/Rank

0.3741/24 0.426/13.5 0.4074/17 0.3513/27 0.3724/25.5 0.412/16 0.35/28 0.43/11 0.426/13.5 0.6828/1 0.5609/2 0.494/3 0.476/6 0.44/10 0.4852/5 0.31/30 0.3724/25.5 0.3231/29 0.382/23 0.458/8 0.416/15 0.448/9 0.386/21.5 0.386/21.5

0.4556/28 0.511/5 0.5421/3 0.5046/10 0.4966/14 0.476/21 0.467/25 0.498/12.5 0.49/17 0.5926/2 0.4148/30 0.489/18 0.515/4 0.498/12.5 0.4988/11 0.461/27 0.5103/7.5 0.464/26 0.696/1 0.507/9 0.491/16 0.47/22.5 0.467/24.5 0.47/22.5

0.4859/27 0.5815/5 0.5437/14 0.5294/18 0.5098/23 0.5497/10 0.5361/17 0.5407/15 0.6053/2 0.6532/1 0.5136/22 0.5453/13 0.5667/8 0.5469/12 0.5823/4 0.4832/28 0.4695/29 0.4104/30 0.506/25 0.5758/7 0.5279/20 0.5068/24 0.5282/19 0.5176/21

0.4615/7 0.4889/4 0.4/18.5 0.4296/12 0.4/18.5 0.392/20

0.5103/7.5 0.4778/20 0.4862/19 0.5103/6.5 0.494/15 0.449/29

0.5393/16 0.5495/11 0.561/9 0.5849/3 0.5767/6 0.4987/26

Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatamalia Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Cuba and Ecuador ranked the highest in achieving the human trafficking SDGs for Latin America and the Caribbean (Table 5.4). We next consider the region Middle East and North Africa. The tables used to determine the following rankings are the appropriate subtables of Tables 4.17, 4.18 and 4.19. Bahrain and Iraq ranked the highest in achieving the human trafficking SDGs for the region Middle East and North Africa (Table 5.5).

5.1 Human Trafficking

113

Table 5.5 Middle East North Africa ranks Country HT(C)/Rank Algeria Bahrain Eygpt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen Rep.

0.494/1 0.4148/5 0.378/9 0.364/10 0.45/2 0.408/6 0.3173/16 0.39/8 0.2939/17 0.426/4 0.3407/15 0.3605/12 0.363/11 0.3532/13 0.436/3 0.4064/7 0.3468/14

Table 5.6 Sub-Saharan Africa ranks Country Angola Benin Botswana Burkino Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros Congo Demo Rep. Congo Rep. Cote d’lvoire Djibouti

HT(N)/Rank

HT(A)/Rank

0.541/2 0.5222/5 0.483/15 0.494/13 0.559/1 0.489/14 0.5222/5 0.496/11 0.5222/5 0.495/12 0.5111/8 0.5111/8 0.5111/8 0.471/16 0.507/10 0.533/3 0.4362/17

0.5806/1 0.5679/2 0.5012/10 0.507/9 0.5336/3 0.4968/11 0.4938/14 0.4947/12 0.4574/15 0.5161/8 0.5309/5.5 0.5309/5.5 0.4938/13.5 0.4285/16 0.5301/7 0.5319/4 0.3191/17

HT(C)/Rank

HT(N)/Rank

HT(A)/Rank

0.316/40 0.3/44.5 0.368/18 0.344/34 0.37/17 0.444/2 0.312/42 0.332/37 0.356/25.5 0.348/32 0.288/47 0.384/9 0.362/22.5 0.364/20.5

0.411/46.5 0.456/34.5 0.47/23 0.467/25 0.535/2 0.509/5.5 0.456/34.5 0.464/28 0.473/21 0.455/37 0.411/46.5 0.447/40.5 0.473/21 0.438/42.5

0.4211/43 0.4418/35 0.4921/9 0.4673/19 0.4788/10 0.5127/4 0.4399/36 0.4109/45 0.4456/31 0.4326/39 0.3963/47 0.4241/42 0.4256/40 0.4525/28 (continued)

114 Table 5.6 (continued) Country Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

5 Human Trafficking Rankings

HT(C)/Rank

HT(N)/Rank

HT(A)/Rank

0.2712/48 0.3848/8 0.364/20.5 0.406/3.5 0.356/25.5 0.348/32 0.348/32 0.382/10.5 0.374/15.5 0.334/36 0.376/13.5 0.396/6 0.35/30 0.378/12 0.388/7 0.376/13.5 0.356/25.5 0.374/15.5 0.354/28 0.382/10.5 0.356/25.5 0.352/29 0.406/3.5 0.318/39 0.3412/35 0.314/41 0.322/38 0.366/19 0.2513/49 0.398/5 0.362/22.5 0.3/44.5 0.306/43 0.29/46 0.4574/1

0.5049/7 0.48/18 0.517/4 0.461/30.5 0.45/38.5 0.459/32 0.463/29 0.473/21 0.447/40.5 0.474/19 0.525/3 0.465/27 0.5/9 0.499/11 0.5/9 0.497/12.5 0.497/12.5 0.466/26 0.468/24 0.482/16 0.456/34.5 0.509/5.5 0.429/45 0.45/38.5 0.482/15.5 0.4897/14 0.461/30.5 0.3346/48 0.481/17 0.5/9 0.438/42.5 0.456/34.5 0.434/44 0.5372/1

0.5193/2 0.4727/15 0.4744/14 0.4499/29 0.442/34 0.4648/20 0.4632/22 0.4128/44 0.4605/26 0.4777/12 0.4761/13 0.4493/30 0.4682/18 0.4635/21 0.4555/27 0.5039/5 0.4441/33 0.4997/6 0.4928/8 0.4682/17 0.4785/10 0.514/3 0.4453/32 0.4607/25 0.4627/23 0.4694/16 0.4969/7 0.3179/48 0.4097/46 0.462/24 0.4328/38 0.4398/37 0.4246/41 0.5499/1

5.1 Human Trafficking

115

We just considered the region Sub-Saharan Africa. The tables used to determine the rankings are the appropriate subtables of Tables 4.22, 4.23, and 4.24. Zimbabwe ranked the highest in achieving the human trafficking SDGs for SubSaharan Africa (Tables 5.6, 5.7). We next consider the region Oceania. The tables used to determine the following rankings are the appropriate subtables of Tables 4.26, 4.27, and 4.28. Table 5.7 Oceania ranks Country HT(C)/Rank Fiji Kinbati Marshall Islands Micronesia, Fed. Sts. Nauru palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

HT(N)/Rank

HT(A)/Rank

0.442/7 0.5263/4 0.4273/8 0.548/2

0.523/7 0.5309/5 0.5815/1 0.4617/10

0.5923/3 0.5241/7 0.58/4 0.4328/10

0.658/1 0.392/9 0.5165/5 0.3363/11 0.4421/6 0.5385/3 0.378/10

0.5704/2 0.473/9 0.557/4 0.46/11 0.529/6 0.5691/3 0.481/8

0.6388/1 0.4502/9 0.5734/5 0.4302/11 0.6012/2 0.5613/6 0.5041/8

Chapter 6

Similarity Measures of Rankings

We compare the rankings of the countries in their achievement of the SDGs by using the similarity measures discussed in Chap. 1 and below in Sect. 6.2. We take the average of the three rankings using the AHP, Guiasu, and Yen method to determine one ranking. We also take the average of the three averages determined by the Stakeholder method to obtain one ranking. We then determine the ranking obtained in [68] for our third ranking. We make our comparisons in Sect. 6.3 of these three rankings. In Sect. 6.4, we compare the rankings of the countries with respect to human trafficking.

6.1 Average of Rankings In this section, we determine the averages of the rankings mentioned above. These averages appear in the first two columns of the following tables. In the third column we first provide the overall rank of the country as determined in [68]. We call the method in [68], the SD Report. We then use this ranking to determine the rank of the country in their particular region. The second entry of the column in the following table gives the rank. OECD See Table 6.1.

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_6

117

118

6 Similarity Measures of Rankings

Table 6.1 OECD combined ranks Country AHP, Guiasu, Yen average/rank Australia Austria Belgium Canada Chili Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

36/36 5 13 /5 23 23 /24.5 26 23 /27 24 23 /26 1/1 2 13 /2 7/7 8/9 7/7 17 23 /19 33 23 /34 3/3 17 13 /18 13/12 29 13 /30 28/28 17/17 13 23 /14.5 15 23 /16 23/23 31 13 /31 2 31 34 /32.5 2 12 3 /10.5 12 23 /10.5 29/29 22 13 /22 23 13 /24.5 13 23 /14.5 7/7 19 23 /21 3 23 /4 13 13 /13 34 23 /35 19/20 31 23 /32.5

Stakeholder average/rank

SD report rank (regional)

28.83/29 10/8.5 22/21 10.5/11 29.5/30 10.17/10 2 23 /2 14 23 /14 1 13 /1 14/13 22 23 /22 31/32 16.83/18 8 23 /7 16 13 /16.5 32 13 /33 24/24 16 13 /16.5 24.17/25 27 23 /28 30 13 /31 24.5/26 34 13 /35 13 13 /12 10/8.5 4/4 24.83/27 14.83/15 18.83/19 7 13 /6 21.5/20 3 13 /3 6 23 /5 34 23 /36 23.83/23 33 23 /34

38/32 5/5 16/16 20/20 31/28 7/7 1/1 10/10 3/3 4/4 6/6 50/34 25/23 14/14 19/19 49/33 30/27 15/15 18/18 24/22 32/29 34/30 78/35 9/9 11/11 8/8 29/26 26/24 27/25 12/12 21/21 2/2 17/17 79/36 13/13 35/31

6.1 Average of Rankings

119

East and South Asia See Table 6.2. Table 6.2 East and South Asia combined ranks Country AHP, Guiasu, Yen Stakeholder average rank average/rank Bangladesh Bhutan Brunei Darussalam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

SD report rank (regional)

13/13 3 23 /4 11 13 /11.5 17 23 /17 2 13 /2 15 23 /16 9 13 /9

10.83/12 3 23 /3 9.17/8.5 19 23 /19 10/10 17.5/17 10 23 /11

116/17 84/7

11 13 /11.5 6 13 /6 4 23 /5 17 23 /18 14 23 /15 6 23 /7 19/19 8/8 14/14 10 13 /10 1 13 /1

9.17/8.5 8 23 /5.5 2 13 /2 18.5/18 12 23 /13 8 23 /5.5 15 23 /16 13 23 /14.5 1 23 /1 9/7 13 23 /14.5

111/14 68/6 47/3 100/10 110/13 103/12 130/18 97/9 66/5 93/8 40/2

3/3

7.83/4

54/4

112/15 39/1 115/16 102/11

120

6 Similarity Measures of Rankings

Eastern Europe and Central Asia See Table 6.3.

Table 6.3 Eastern Europe and Central Asia combined ranks Country AHP, Guiasu, Yen Stakeholder average/rank average/rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgyz Rep. Liechtenstein Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

SD report rank (regional)

23/23 13 13 /13

13 13 /14

153/23 60/13

8/7.5 13 23 /14 1/1 20/20 11/11 3 13 /3 18/18 15 23 /16 19/19 3 23 /4

4 23 /3 11 23 /13 7 23 /7.5 16.17/16.5 9 13 /9 7 23 /7.5 18 23 /19 14 13 /15 19 23 /20 2 13 /2

75/19 59/12 23/2 69/15 36/4 22/1 61/14 73/18 77/20 48/9

8/7.5 2/2

6 23 /5.5 2/1

28/3 37/5

21/21 15/15 12 13 /12 17/17

20 23 /21 18/18 10 23 /12 16.17/16.5

87/21 70/16 42/7 55/11

9/9.5 6/6 22/22 9/9.5 5/5

9 23 /10.5 6 23 /5.5

44/8 71/17 101/22 41/6 52/10

6/4 9 23 /10.5

6.1 Average of Rankings

121

Latin America and the Caribbean See Table 6.4.

Table 6.4 Latin America and the Caribbean combined ranks continued Country AHP, Guiasu, Yen Stakeholder average/rank average/rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

SD report rank (regional)

7 23 /8

16.83/14

45/3

16 13 /16.5 7 13 /7 8 13 /9.5 6 13 /5.5 1/1 13 23 /13

19 23 /17 18 13 /16 14.83/8 6/3 4/1 6 13 /4

109/20 80/11 57/7 67/9 33/1 56/6

4 23 /4 2/2 16 13 /16.5

9 23 /6 4 13 /2 15 23 /10

64/8 46/4 89/16

21/21 22/22 23/23 17 23 /18 14/14 11 13 /11 14 23 /15 12 13 /12 3/3

25 13 /22 24 13 /19.5 30/23 25/21 17.5/15 15.83/11.5 13.5/7 16/13 15.83/11.5

122/22 114/21 156/23 107/18 74/10 82/12 90/17 86/14 51/5

8 13 /9.5 20/20 6 13 /5.5 18 23 /19

15/9 21 23 /18 9/5 24 13 /19.5

88/15 85/13 43/2 108/19

122

6 Similarity Measures of Rankings

Middle East and North Africa See Table 6.5. Table 6.5 Middle East and North Africa combined ranks Country AHP, Guiasu, Yen Stakeholder average/rank average/rank Algeria Bahrain Egypt, Arab Rep Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Republic Tunisia United Arab Emirates Yemen, Rep.

SD report rank (regional)

1/1 10/10 6/6 2/2 9/9 5/5 12/12 7/7

2/1 2 23 /2 4 13 /3 8/6 9.5/9.5 6/4 13 23 /15 12.5/14

53/1 76/6 92/10 58/2 117/14 81/7 106/13 94/11

4 13 /4 8/8 14 13 /15 13 23 /13 11/11 3/3 14/14 16/16

10 13 /11 9.5/9.5 8.17/7 10 23 /13 10.5/12 7/5 8 13 /8 17/16

72/5 83/8 91/9 98/12 123/15 63/3 65/4 133/16

Sub-Saharan Africa See Table 6.6.

Table 6.6 Sub-Saharan Africa combined ranks Country AHP, Guiasu, Yen average/rank Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros

24 23 /25 34 13 /34.5 10/10 29 23 /31 6/6 2 23 /2.5 15 23 /16 42/42 43/43 29 13 /29.5

Stakeholder average/rank

SD report rank (regional)

46 23 /42.5 22.17/16 31.17/32 17 23 /12 6 13 /4 3.17/2 25.83/24 18 13 /13 33/35 43.07/41

149/32 151/34 120/8 141/25 145/28 96/2 127/13 162/43 161/42 137/21 (continued)

6.1 Average of Rankings Table 6.6 (continued) Country Congo Democratic Rep. Congo Rep. Cote d’Ivoire Djibouti Equatorial Guinea Eritrea Eswatini Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

123

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

SD report rank (regional)

39 13 /40 19 13 /19 12 23 /13 36 23 /37

29.17/29 32 23 /34 20/14 46 23 /42.5

160/41 132/17 129/15 148/31

29 13 /29.5 17/17 1/1 22/23 4 23 /4.5 24 13 /24

42.83/40 9/7 6/3 30 13 /30 30.83/31 22.5/17

142/26 135/19 99/3 131/16 104/4 138/22

14/14 41/41 34 13 /34.5 38 13 /38 4 23 /4.5 29/28 27 13 /27 9/9 19/18 12/12 36 13 /36 39/39 21 23 /22 2 23 /2.5 11 13 /11

27 23 /27.5 38.17/38 14.5/11 35.5/37 26.5/25 7.83/6 31 23 /33 10.83/9 23 13 /19.5 33 13 /36 23 13 /19.5 25.17/23 10 23 /8 11.17/10 39.17/39

125/11 150/33 157/38 158/39 146/29 152/35 134/18 105/5 136/20 119/7 154/36 159/40 126/12 95/1 124/10

33/33

23/18

155/37

6 13 /7

26 23 /26

113/6

20/20 15 13 /15 32 23 /32 26 13 /26 21/21 8/8

27 23 /27.5 7 23 /5 23 23 /21.5 21.83/15 23 23 /21.5 2 23 /1

147/30 128/14 144/27 140/24 139/23 121/9

124

6 Similarity Measures of Rankings

Oceania See Table 6.7. Table 6.7 Oceania combined ranks Country AHP, Guiasu, Yen average/rank Fiji Kiribati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Stakeholder average/rank

SD report rank (regional)

1/1

3 23 /1

62/1

3/3

9/2.5

143/3

2/2

9/2.5

118/2

6.2 Similarity Measures In this section, we determine some useful properties of similarity measures. Let A = {a1 , . . . , an } be a set of real numbers such that a1 < · · · < an . Let π and ρ be permutations of A. We consider the following similarity relation, n π(ai ) ∧ ρ(ai ) i=1 . n i=1 π(ai ) ∨ ρ(ai ) n n Consider i=1 ai ∧ an−i+1 and i=1 ai ∨ an−i+1 . Let n be even. Then ai ∧ an−i+1 = ai and ai ∨ an−i+1 = an−i+1 for i = 1, . . . , n/2. Thus n n/2 ai ∧ an−i+1 2 i=1 ai i=1 = . n/2 n 2 i=1 an−i+1 i=1 ai ∨ an−i+1

(6.1)

That is, the numerator is twice the sum of the smallest elements while the denominator is twice the sum of the largest elements. Consequently, n n π(ai ) ∧ ρ(ai ) i=1 ai ∧ an−i+1 n . ≤ i=1 n i=1 ai ∨ an−i+1 i=1 π(ai ) ∨ ρ(ai )

(6.2)

6.2 Similarity Measures

125

Suppose n is odd. Then a similar argument shows (6.2) holds since there will be a middle element which will appear twice, once in the numerator and once on the denominator of n ai ∧ an−i+1 i=1 . n i=1 ai ∨ an−i+1 Suppose we consider n countries and they have been ranked twice 1 through n with no ties. We wish to consider the similarity of their rankings using the above fuzzy similarity operation. We can accomplish this by mapping the countries to their rank divided by n. For example, let C denote a set of n countries and if country C is ranked i, then we define the fuzzy subset μ of C by μ(C) = i/n. Let μ and ν be two such fuzzy subsets of C. Then n n nμ(Ci ) ∧ nν(Ci ) i=1 μ(C i ) ∧ ν(C i ) = i=1 . M(μ, ν) = n n i=1 μ(C i ) ∨ ν(C i ) i=1 nμ(C i ) ∨ nν(C i ) Consequently, there is no loss in generality in assuming that we are measuring the similarity of two rankings using the integers 1, . .  . , n. Hence in the following, we let ai = n n π(ai ) ∧ ρ(ai ) + i=1 π(ai ) ∨ ρ(ai ) = (n + 1)n for any i, i = 1, . . . , n. Then i=1 two permutations π and ρ. n ai ∧an−i+1 We now consider i=1 , where the ai are the integers 1 through n. Then n i=1 ai ∨an−i+1 by (6.1) (with n even) n n/2 ai ∧ an−i+1 i=1 ai i=1 = n/2 n i=1 ai ∨ an−i+1 i=1 an−i+1 = = =

(n/2 + 1)(n/2) (n + 1)n − (n/2 + 1)(n/2) 1 (n+1)n (n/2+1)(n/2)

−1

1 . 4(n + 1)/(n + 2) − 1

1 Now limn→∞ 4(n+1)/(n+2)−1 = 1/3 (letting the terms in the limit equal 1/3 if n is n

a ∧a

i n−i+1 = 1/3 (letting odd). A similar argument shows that if n is odd, limn→∞ i=1 n i=1 ai ∨an−i+1 the terms in the limit be 1/3 if n is even). Hence, we have in general that

n ai ∧ an−i+1 limn→∞ i=1 = 1/3. n i=1 ai ∨ an−i+1

(6.3)

Therefore, we have by (6.2) and (6.3), a bench mark to determine how similar two rankings are.

126

6 Similarity Measures of Rankings n ai ∧an−i+1 i=1 n i=1 ai ∨an−i+1 converge to 13 .

We next show that 1 , 3

is a decreasing sequence whose elements are

greater than but Let n be even. Then n ai ∧ an−i+1 n n n n i=1 = ( + 1)( )/[(n + 1)n − ( + 1)( )] n a ∨ a 2 2 2 2 i n−i+1 i=1 n n n+2 = ( + 1)( )/n[n + 1 − ] 2 2 4 n + 2 3n + 2 = / 4 4 n+2 = 3n + 2 Let n be odd. Let N = ( n−1 + 1)( n−1 )+ 2 2

n+1 . 2

(6.4)

Then

n ai ∧ an−i+1 n−1 n−1 n+1 n+1 i=1 = [( + 1)( )+ ]/[2( (n) − N ] n 2 2 2 2 i=1 ai ∨ an−i+1 = =

+ n+1 2 (n + 1)(n) − N n+1 n−1 2 2

(n

=

n+1 n+1 2 2 n+1 + 1)n − n+1 2 2 n+1 n+1 2 2 + 1)(n − n+1 ) 4

(n n+1 = . 3n − 1

(6.5)

n+2 k+1 = 2k+2 = 3k+1 . Let n = 2k + 1. Then in (6.5), 3n+2 6k+2 n ai ∧an−i+1 n+1 2k+2 k+1 i=1 = 6k+3−1 = 3k+1 . That is, n ai ∨an−i+1 is the same for n even and n + 1 odd. 3n−1 i=1 n ai ∧an−i+1 n+1 n+1+2 n+3 However note that for n odd, 3n−1 > 3(n+1)+2 = 3n+5 . That is, for n odd, i=1 n i=1 ai ∨an−i+1 n n ai ∧an−i+1 ai ∧an−i+1 is strictly greater than i=1 for the next even. Clearly, i=1 > 13 for n n i=1 ai ∨an−i+1 i=1 ai ∨an−i+1 n ai ∧an−i+1 all n. Thus i=1 is a decreasing sequence whose elements are greater than 31 , n i=1 ai ∨an−i+1 but converges to 31 .

Let n = 2k. Then in (6.4),

6.3 Similarity Rankings

127

6.3 Similarity Rankings We next determine the similarity of various rankings for each region, except Oceania. There isn’t sufficient data for Oceania. In the following, when a ranking has missing data for a country, we delete the country for a different ranking even if it has data for the ranking. After the deletion, we reranked. OECD Let μ denote the ranking for the AHP, Guiasu, Yen method, ν the ranking for the stakeholder, and ρ for the SD Report ranking. We first consider the similarity of the SD Report rankings and the Stakeholder ranking for OECD countries. Let C1 , . . . , C36 denote the countries making up the OECD region. We use the similarity operation 36 M(ν, ρ) = i=i 36 i=i

ν(Ci ) ∧ ρ(Ci ) ν(Ci ) ∨ ρ(Ci )

.

It follows that M(ν, ρ) = 588/744 = 0.790. By Eq. (6.4), the smallest possible similarity measure for 36 countries with no ties in the ranking can be obtained by subn+2 stituting 36 for n in the formula 3n+2 . One obtains 38/110 = 0.345. We also obtain M(μ, ρ) = 582/750 = 0.776 and M(μ, ν) = 561.5/770.5 = 0.729. East and South Asia M(ν, ρ) = 137/205 = 0.668. We find that M(μ, ρ) = 149/193 = .772. The small18+2 est the ranking can be with no ties is 54+2 = 20 = 0.357 since n = 18. We also have 56 that M(μ, ν) = 156/224 = 0.696. By Eq. (6.5), the smallest ranking with no ties is 19+1 = 20 = 0.357 since here n = 19. 57−1 56 Eastern Europe and Central Asia 207.5 We have that M(μ, ν) = 254.5 = 0.815 with 21+1 = 22 = 0.355 and M(ν, ρ) = 63−1 62 187.5 21+1 22 = 0.683 with 63−1 = 62 = 0.354 since n = 21.We found that M(μ, ρ) = 274.5 235.5 = 0.744. Here we have 23 countries and 23+1 = 24 = 0.353. 316.5 69−1 68

Latin America and the Caribbean We have that M(μ, ν) = 243 = 0.786, M(μ, ρ) = 245.5 = 0.801, and M(ν, ρ) = 309 306.5 236 23+1 24 = 0.747. Here we have 23 countries and 69−1 = 68 = 0.353. 316 Middle East and North Africa We have that M(μ, ν) = 110 = 0.679, M(μ, ρ) = 115 = 0.732, and M(ν, ρ) = 162 157 18 = 0.705. Here we have 16 countries and 16+2 = = 0.360. 48+2 50

112.5 159.5

Sub-Saharan Africa 691.5 853 We have that M(μ, ν) = 1200.5 = 0.576, M(μ, ρ) = 1039 = 0.821, and M(ν, ρ) 669.5 43+1 44 = 1222.5 = 0.548. Here we have 43 countries and 129−1 = 128 = 0.344.

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6 Similarity Measures of Rankings

6.4 Human Trafficking We next consider human trafficking. We have no ranking for the SD Report. Thus we compare only the AHP-Guiasu-Yen ranking and the Stakeholder ranking. OECD See Table 6.8.

Table 6.8 OECD combined ranks Country AHP, Guiasu, Yen average/rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands New Zealand Norway Poland

17 23 /17.5 6 23 /7 11 23 /11.5 9 23 /9 30 13 /30 14 13 /15 1/1 12 13 /13 2/2 17 23 /17.5 10 23 /10 34 23 /35 30/29 3 23 /3.5 16 23 /16 33 23 /33 30 23 /31 18 13 /19 27 23 /28 25/25 27/27 22.17/22 35 13 /36 11 23 /11.5 4 23 /5 6 13 /6 24 23 /24

Stakeholder average/rank 22.17/21.5 11.83/11 12 23 /13 10/9 30 13 /31 6 13 /6.5 1 13 /1 18.83/18 2.17/2 12 23 /13 11 13 /10 34.5/36 16 23 /17 6 13 /6.5 15 13 /15.5 33/33 27 13 /28 19/19 24.16/26 29.5/29 29 23 /30 23.5/25 33.83/35 9 13 /8 12 23 /13 5/3 24 13 /27 (continued)

6.4 Human Trafficking Table 6.8 (continued) Country Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey United Kingdom United States

129

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

20/20 25 23 /26 7 23 /8 22.83/23 3 23 /3.5 20 13 /21 34 13 /34 14/14 32 13 /32

23/24 15 13 /15.5 6.17/4.5 22.83/23 6.17/4.5 21.17/20 32/32 22.17/21.5 33 13 /34

East and South Asia See Table 6.9. Table 6.9 East and South Africa combined ranks Country AHP, Guiasu, Yen average/rank Bangladesh Bhutan Brunei Darussalam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

18/18 7 13 /8 7/6.5 15 23 /15 2 13 /2 16 13 /17 10 23 /11 13 13 /13 7/6.5 3 13 /3.5 8 23 /9 12/12 16/16 19/19 13 23 /14 1/1 10 13 /10 3 13 /3.5 5/5

Stakeholder average/rank 9.17/9 9/8 2 23 /1 13/16 7 23 /7 19 13 /10.5 15.83/18 7 13 /6 11.5/13 9 13 /10.5 4 13 /2 16/19 12 23 /15 7/5 21/21 17 13 /20 6 13 /3 9 23 /12 13 13 /17 11 23 /14 6 23 /4

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6 Similarity Measures of Rankings

Eastern Europe and Central Asia See Table 6.10. Table 6.10 Eastern Europe and Central Asia combined ranks Country AHP, Guiasu, Yen average/rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Bosnia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgyz Rep. Liechtenstein Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Stakeholder average/rank

23/23 21 13 /20.5

20/24 15.83/14

21 13 /20.5 16 23 /17 1 13 /1 16/15.5 8 13 /9.5 4/3 7 13 /7.5 7 13 /7.5 8 13 /9.5 16/15.5

17 13 /16.5 10/10.5 3 23 /3 3/2 18 23 /21 10/10.5 19 23 /23 17 13 /16.5 19.5/22 5 23 /5.5

1 23 /2 5/4

5 23 /5.5 2 23 /1

17/18 19/19 6 13 /5 20 13 /20

18.17/20 17.17/15 17 23 /18 18/19 14 23 /13 7 13 /8 9.17/9 6/7 4 23 /4 11.17/12

6 23 /6 14/14 10 13 /11 12 13 /12.5 12 13 /12.5

6.4 Human Trafficking

131

Latin America and the Caribbean See Table 6.11. Table 6.11 Latin America and the Caribbean combined ranks Country AHP, Guiasu, Yen average/rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatemala Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Stakeholder average/rank

21/21 19 13 /19 23/23 19 23 /20 6 13 /6 8 23 /9 13/13.5 17/17 10 13 /10

26 13 /28 7.83/5 11 13 /10.5 18 13 /19 20.83/22 15 23 /17 23 13 /26 12.83/14 10.83/8 1 13 /1 18/18 11 13 /10.5 6/2 11.5/12 6 23 /3 28 13 /29.5 20 23 /21 28 13 /29.5 23/25 8/6 17/17 18.5/20 21 23 /23.5 21 23 /23.5

8 13 /8 6 23 /7 3/3 22/22

10.17/7 11 23 /13 15.5/16 7.17/4 13.17/15 25/27

5/5

18/18 14 23 /15 16/16 13/13.5 2/2 1/1 11 13 /11 4/4 12 23 /12

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6 Similarity Measures of Rankings

Middle East and North Africa See Table 6.12. Table 6.12 Middle East and North Africa combined ranks Country AHP, Guiasu, Yen average/rank Algeria Bahrain Egypt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen Rep.

1 13 /1 7/6 11 13 /11 8 23 /9.5 12 23 /13 7 13 /7.5 13 23 /14 7 13 /7.5 5 13 /5 8 23 /9.5 4/4 12 13 /12 15/15 3 23 /3 1 23 /2 16/16

Stakeholder average/rank 1 13 /1 4/3 11 13 /13 10 23 /11 2/2 10 13 /9.5 11 23 /14 10 13 /9.5 12 13 /15 8/6 9.5/8 8.5/7 10.83/12 15/16 6 23 /5 4 23 /4 16/17

Sub-Saharan Africa See Table 6.13.

Table 6.13 Sub-Saharan Africa combined ranks continued Country AHP, Guiasu, Yen average/rank Angola Benin Botswana Burkina Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros

35 23 /36 34 23 /35 4 23 /5 25/25 19/19 2 13 /2 22 13 /22 42 23 /43 42 13 /42 17/17

Stakeholder average/rank 43.83/46 38 23 /42 17 13 /14 26 23 /27 10 13 /7 4.17/2 38.17/41 37 13 /40 26.5/26 36 23 /39 (continued)

6.4 Human Trafficking Table 6.13 (continued) Country Congo Democratic Rep. Congo Rep. Cote d’Ivoire Djibouti Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

133

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

41/41 29/29 23/23 14/13

47.5/48 31.17/35 28.5/30 31/34 18/15 6/4 18.5/16 7.83/5 29/32 35.5/38 28 23 /31 21.17/22 27.5/29 34.83/37 15.5/12 8/6 29 23 /33 13 23 /8.5 13 23 /8.5 17.17/13 15/11 21/21 20 23 /20 14.83/10 20.17/19 25.5/25 4 13 /3 39 13 /44 33.5/36 27.17/28 23 13 /23.5 19.5/18 49/49 23 13 /23.5 19.17/17 42 13 /45 38.83/43 44 23 /47 1 13 /1

18/18 20/20 12/12 16/16 6/6 32 23 /32 11/11 24 13 /24 39/39 29 23 /30 7 23 /8 34/34 37 13 /37 3/3 26 23 /26 1/1 31 13 /31 40/40 10/10 3 23 /4 14 23 /15 32 23 /33 7 13 /7 37 23 /38 14/14 27 23 /28 21/21 27 13 /27 9/9

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6 Similarity Measures of Rankings

Oceania See Table 6.14. Table 6.14 Oceania combined ranks Country AHP, Guiasu, Yen average/rank Fiji Kiribati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

1/1

3/3

2/2

Stakeholder average/rank 5 23 /7 5 13 /6 4 13 /3 7 13 /8 1 13 /1 9/10 4 23 /4.5 11/11 4 23 /4.5 4/2 8 23 /9

Once again let μ denote the ranking for the AHP, Guiasu, Yen method and ν the ranking for the stakeholder. We provide the similarity measure of μ and ν for the various regions. OECD We find that M(μ, ν) = 611 = 0.847. The smallest possible similarity measure for 721 36 countries with no ties in the ranking can be obtained by substituting 36 for n in n+2 . One obtains 38/110 = 0.345. the formula 3n+2 East and South Asia We have that M(μ, ν) = 0.357.

156 224

= 0.696. Here we have 19 countries and

19+1 57−1

=

20 56

=

23+1 69−1

=

24 68

=

23+1 69−1

=

24 68

=

Eastern Europe and Central Asia We have that M(μ, ν) = 0.353.

203.5 348.5

= 0.584. Here we have 23 countries and

Latin America and the Caribbean We have that M(μ, ν) = 0.353.

244.5 307.5

= 0.652. Here we have 23 countries and

6.4 Human Trafficking

135

Middle East and North Africa We have that M(μ, ν) = 0.360.

120.5 151.5

= 0.795. Here we have 16 countries and

16+2 48+2

=

18 50

=

43+1 129−1

=

Sub-Saharan Africa We have that M(μ, ν) = 44 = 0.344. 128

687 1205

= 0.570. Here we have 43 countries and

Chapter 7

Climate Change and Modern Slavery

The purpose of this chapter is to rank the countries with respect to their achievement of the SDGs that are pertinent to modern slavery. We use the AHP-Guiasu-Yen method and the Stakeholder method to obtain the rankings. We then measure the similarity of the two methods. The following discussion is taken from [11]. Climate refers to average weather conditions over many years, For example, the climate in one region may be cold and snowy part of the time, while the climate in another is warm and humid all year round. Weather, in contrast, refers to a specific event or condition that happens over a period of hours or days. For example, a thunderstorm, a snowstorm and the day’s temperature all describe weather. Climate change involves significant changes, over several decades or longer, in temperature, precipitation, wind patters, and other aspects of climate. Weather varies naturally from year to year, so one unusually cold or wet year followed by an unusually warm or dry year would not be considered a sign of climate change. Climate change involves longer-term trends, such as a gradual shift toward warmer, wetter, or drier conditions. Global warming is just one aspect of climate change. It’s a term used to describe the recent rise in the global average temperature near earth’s surface, which is caused mostly by increasing concentrations of greenhouse gases (such as carbon dioxide and methane) in the atmosphere. Global warming is only one of the ways in which climate is affected by rising concentrations of greenhouse gases. The Earth’s climate is changing. Rising temperatures are already driving changes in climate around the globe, including changes in precipitation patterns and the frequency or intensity of extreme events such as storms, floods, droughts, and heat waves. The warmer climate has also led to rising sea levels, changes in snow and ice cover, longer growing seasons, and impacts on infrastructure, public health, and ecosystems. Many of these observed changes are linked to the rising levels of carbon dioxide and other greenhouse gases on our atmosphere, caused by human activities [11]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_7

137

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Greenhouse gases, such as CO2 , methane, and nitrous oxide, act like a blanket around the planet. They trap energy in the atmosphere and cause it to warm. This phenomenon, called the greenhouse effect, is natural and necessary to support life on Earth: without it the Earth’s average temperature would be around 0 ◦ F. But scientists agree that the continuing buildup of greenhouse gases in the atmosphere— caused mainly by the burning of fossil fuels for energy—will upset the natural energy balance and change Earth’s climate, with potentially dangerous risks to human health, infrastructure, the economy, and ecosystems. In coming years millions of people will be forced to relocate due to effects of climate change, including shoreline erosion, coastal flooding or disruption of normal farming practices. When an environmental disaster occurs, people in vulnerable regions are forced to flee without legal authorization or documents. Desperate to survive and unfamiliar with the culture of the refugee community, these people are extremely vulnerable to human trafficking.

7.1 Climate Change Climate change is a serious problem. The Earth’s average temperature has risen by 1.5 ◦ F over the last century, and climate scientists estimate it will rise another 0.5 ◦ to 8.6 ◦ F by the end of the century. Relatively small changes in the planet’s average temperature can mean big changes in local and regional climate, creating risks to public health and safety, water resources, agriculture, infrastructure, and ecosystems [11]. Heat waves have become more frequent in the United States in recent decades. Climate scientists expect the number of days with temperatures above 90 ◦ F to increase in the United States as climate changes, especially toward the end of this century. In addition to heat waves, changes in precipitation patterns, including extreme precipitation events, storms, and floods, are becoming more common and more severe in many regions, and this is expected to continue. Rising temperatures will lead to an increase in heat-related deaths and illnesses. Air quality will worsen. Rising temperatures and wildfires and decreasing precipitation will lead to increases in ozone and particulate matter, elevating the risks of cardiovascular and respiratory illness and death. Higher temperatures lead to increased rates of evaporation and can lead to more rapid drying soils. Without reduction in global greenhouse gas emissions, longerterm droughts are expected to intensify in much of the Southwest, the great Plains, and the Southeast. Increases in water temperatures will alter timing and location of vibrio vulnificus growth, increasing exposure and risk of water-borne illnesses. Rising temperatures increase Salmonella prevalence in food, longer seasons and warming winters increase risk of exposures and infection. Climate change also has an impact on crops. Over the past 40 years, climate disruptions to agriculture production have increased, and this is expected to continue.

7.1 Climate Change

139

The area burned by wildfire in parts of western North America is expected to double (or more) for each 1.8 ◦ F increase in global temperature. Global sea level has risen by about eight inches since the late 1800’s and is projected to rise another 1 to 4 ft. by the end of the century. Flooding is becoming more frequent along the U. S. coastline, especially in the Mid-Atlantic region where the land is simultaneously sinking. Climate change impacts our health. Climate change is increasing our exposure to extreme temperatures, extreme weather events; degraded air quality; diseases transmitted through food, water, and insects; and stresses to mental health and wellbeing. These threats to human health are expected to increase with continued climate change. Goal 13, Climate Change, was given the highest score of all the sustainable development goals for developed countries [54]. It received 7.1 out of a maximum of 8. This goal is a crucial sustainable development objective for both developed and developing countries. Even though progress has been made in limiting greenhouse gas emissions in some countries, global emissions continue to rise and the prospects for damaging climate change are worsening. It is stated in [54] that tougher targets and more vigorous implementation will be needed, particularly from those developed and middle income countries that have been moving in the wrong direction. While all the targets under goal 13 are scored highly, Target 13.2 on integrating climate change measures into national policies and strategies and Target 13.3 on improving education, awareness and capacity of climate change are identified as the priorities for developed countries, both scoring a maximum of 8. Goal 13. Take urgent action to combat climate change and its impacts 13.1 strengthen resilience and adaptive capacity to climate related hazards and natural disasters in all countries. 13.2 integrate climate change measures into national policies, strategies, and planning. 13.3 improve education, awareness raising and human and human capacity on climate change mitigation, adaptation, impact reduction, and early warming. The following table is from [54].

Goal 13 13.1 13.2 13.3

Applicable Implementable Transformational Overall Mark 2.0 2.0 1.8 7.1 2.0 2.0 1.3 5.3 2.0 2.0 2.0 8.0 2.0 2.0 2.0 8.0

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7.2 Global Warming of 1.5 ◦ C An IPCC Special Report on the impacts of global warming of 1.5 ◦ C above preindustrial levels and related greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty is presented in [28]. The Report responds to the invitation for IPCC to provide a Special Report in 2018 on the impacts of global warming of 1.5 ◦ C above pre-industrial levels and related greenhouse gas emissions pathways contained in the Decision of the 21st Conference of Parties of the United Nations Framework Convention on Climate Change to adopt the Paris Agreement. In [28], a summary presents the key findings of the special Report, based on the assessment of the available scientific, technical and socio-economic literature relevant to global warming of 1.5◦ and for the comparison between global warming of 1.5 and 2◦ C above pre-industrial levels. The level of confidence associated with each key finding is reported using the IPCC calibrated language [28]. The underlying scientific basis of each key finding is indicated by references provided to chapter elements. In the SPM, knowledge gaps are identified associated with the underlying chapters of the Report. We next present factors and their subfactors from [28] whose examination could be placed in the context of Dempster-Shafer theory. We do not list the sub-subfactors. They can be found in [28] as well as supporting charts and figures. A. Understanding Global Warming of 1.5 ◦ C A1. Human activities are estimated to have caused approximately 1.0 ◦ C of global warming [28], above pre-industrial levels, with a likely range of 0.8–1.2 ◦ C. Global warming is likely to reach 1.5 ◦ C between 2030 and 2052 if it continues to increase at the current rate (high confidence). A2. Warming from anthropogenic emissions from the pre-industrial period to the present will persist for centuries to millennia and will continue to cause further longterm changes in the climate system, such as sea level rise, with associated impacts (high confidence), but these emissions alone are unlikely to cause global warming of 1.5 ◦ C (medium confidence). A3. Climate-related risks for natural and human systems are higher for global of 1.5 ◦ C than at present, but lower than 2 ◦ C (high confidence). These risks depend on the magnitude and rate of warming, geographic location, levels of development and vulnerability, and on the choices and implementation of adaptation and mitigation options (high confidence). B. Projected Climate Change, Potential Impacts and Associated Risks B1. Climate models project robust differences in regional climate change characteristics between present-day and global warming of 1.5 ◦ C and between 1.5 and 2 ◦ ,

7.2 Global Warming of 1.5 ◦ C

141

[28]. These differences include increases in mean temperature in most land and ocean regions (high confidence) hot extremes in most inhabited regions (high confidence), heavy precipitation in several regions (medium confidence), and the probability of drought and precipitation deficits in some regions (medium confidence). B2. By 2100, global mean sea level rise is projected to be around 0.1 metre lower with global warming of 1.5 ◦ C compared to 2 ◦ C (medium confidence). Sea level will continue to rise well beyond 2100 (high confidence), and the magnitude and rate of this depend on future emission pathways. A slower rate of seal level rise enables greater opportunities for adaptation in the human and ecological systems of small islands, low-lying coastal areas and deltas (medium confidence). B3. On land, impacts on biodiversity and ecosystems, including species loss and extinction, are projected to be lower at 1.5 ◦ C of global warming compared to 20 C. Limiting global warming to 1.5 ◦ C compared to 2 ◦ C is projected to lower the impacts on terrestrial, freshwater and coastal ecosystems and to retain more of their services to humans (high confidence). B4. Limiting global warming to 1.5 ◦ C compared to 2 ◦ C is projected to reduce increases in ocean temperatures as well as increases in ocean acidity and decreases in ocean oxygen levels (high confidence). Consequently, limiting global warming to 1.5 ◦ C is projected to reduce risks to marine biodiversity, fisheries, and ecosystems, and their functions and services to humans, as illustrated by recent changes to Arctic sea ice and warm-water coral reef ecosystems (high confidence). B5. Climate-related risks to health, livelihoods, food security, water supply, human security, and economic growth are projected to increase with global warming of 1.5◦ and increase further with 2 ◦ C. B6. Most adaptation needs will be lower for global warming of 1.5 ◦ C compared to 2 ◦ C (high confidence). There are a wide range of adaptation options that can reduce the risks of climate change (high confidence). There are limits to adaptation and adaptive capacity for some human and natural systems at global warming of 1.5 ◦ C with associated losses (medium confidence). The number and availability of adaptation options vary by sector (medium confidence). C. Emission Pathways and System Transitions Consistent with 1.5 ◦ C Global Warming C1. In model pathways with no or limited overshoot of 1.5 ◦ C, global net anthropogenic CO2 emissions decline by about 45% from 2019 levels by 2030 (40–60% interquartile range), reaching net zero around 2050 (2046–2055 interquartile range). For limiting global warming to below 2 ◦ C CO2 emissions are projected to decline

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7 Climate Change and Modern Slavery

by about 25% by 2030 in most pathways (10–30% interquartile range) and reach net zero around 2070 (2065–2080 interquartile range). Non-CO2 emissions in pathways that limit global warming to 1.5 ◦ C show deep reductions that are similar to those in pathways limiting warming to 2 ◦ C (high confidence). C2. Pathways limiting global warming to 1.5 ◦ C with no or limited overshoot would require rapid and far-reaching transitions in energy, land, urban and infrastructure (including transport and buildings), and industrial systems (high confidence). These systems transitions are unprecedented in terms of scale, but not necessarily in terms of speed, and imply deep emissions reductions in all sectors, a wide portfolio of mitigation options and a significant upscaling of investments in those options (medium confidence). C3. All pathways that limit global warming to 1.5 ◦ C with limited or no overshoot project the use of carbon dioxide removal (CDR) on the order of 100–1000 GtCO2 over the 21st century. CDR would be used to compensate for residual emissions and, in most cases, achieve net negative emissions to return global warming to 1.5 ◦ C following a peak (high confidence). CDR deployment of several hundreds of FtCO2 is subject to multiple feasibility and sustainability constraints (high confidence). Significant near-term emissions reductions and measures to lower energy and demand can limit CDR deployment to a few hundred GtCO2 without reliance on bioenergy with carbon capture and storage (high confidence). D. Strengthening the Global Response in the Context of Sustainable Development and Efforts to Eradicate Poverty D1. Estimates of the global emissions outcome of current nationally stated mitigation ambitions as submitted under the Paris Agreement would lead to global greenhouse ambitions in 2030 of 52–48 GtCO2 eq year−1 (medium confidence). Pathways reflecting these ambitions would not limit global warming to 1.5 ◦ C even if supplemented by very challenging increases in the scale and ambition of emissions reductions after 2030 (high confidence). Avoiding overshoot and reliance on future large-scale deployment of carbon dioxide removal (CDR) can only be achieved if global CO2 emissions start to decline well before 2030 (high confidence). D2. The avoided climate change impacts on sustainable development, eradication of poverty and reducing inequalities would be greater if global warming were limited to 1.5 ◦ C rather than 2 ◦ C, if mitigation and adaptation synergies are maximized while trade-offs are minimized (high confidence). D3. Adaptation options specific to national contexts, if carefully selected together with enabling conditions, will have benefits for sustainable development and poverty reduction with global warming of 2.5 ◦ C, although trade-offs are possible (high confidence).

7.2 Global Warming of 1.5 ◦ C

143

D4. Mitigation options consistent with 1.5 ◦ C pathways are associated with multiple synergies and trade-offs across the Sustainable Development Goals (SDGs). While the total number of possible synergies exceeds the number of trade-offs, their net effect will depend on the pace and magnitude of changes, the composition of the mitigation portfolio and the management of the transition (high confidence). D5. Limiting the risks from global warming of 1.5 ◦ C in the context of sustainable development and poverty eradication implies system transitions that can be enabled by an increase of adaptation and mitigation investments, policy instruments, the acceleration of technological innovation and behavior changes (high confidence). D6. Sustainable development supports, and often enables, the fundamental societal and systems transitions and transformations that help limit global warming to 1.5 ◦ . Such changes facilitate the pursuit of climate-resilient development pathways that achieve ambitious mitigation and adaptation in conjunction with poverty eradication and efforts to reduce inequalities (high confidence). D7. Strengthening the capacities for climate action of national and sub-national authorities, civil society, the private sector, indigenous peoples and local communities can support the implementation of ambitious actions implied by limiting global warming to 1.5 ◦ C (high confidence). International cooperation can provide an enabling environment for this to be achieved in all countries and for all people, in the context of sustainable development. International cooperation is a critical enabler for developing countries and vulnerable regions (high confidence).

7.3 Climate Change Performance Index The Climate Change Performance Index (CCPI) is an instrument designed to enhance transparency in international climate politics. Its aim is to put political and social ambitious action on climate protection, and to highlight those countries with the best practice climate change policies [8]. On the basis of standardized criteria, the index evaluates and compares the climate protection performance of 56 countries and the European Union (EU), which are together responsible for more than 90% of global greenhouse gas GHG) emissions. The following table is a reranking of the table in [8, p. 7]. However, it should be pointed out that in [8] no country achieved positions of one to three. We rank the countries starting with one so that we can find the similarity measure of the two rankings using techniques developed previously. Using the first similarity measure in Example 1.3.2, we find that the similarity between the overall rank and the climate policy rank is 0.67.

144

7 Climate Change and Modern Slavery Overall Climate Policy Country Rank Rank 1 Sweden 4 2 Morocco 5 3 Lithuania 8 4 Latvia 13.5 5 U. K. 16 6 Switzerland 9 7 Malta 46.5 8 India 21 9 Norway 10 10 Finland 11 11 Croatia 31 12 Denmark 30 13 EU 6 14 Portugal 1 15 Ukraine 37 16 Luxembourg 20 17 Romania 52 18 France 2 19 Brazil 25 20 Italy 27 21 Egypt 42 22 Mexico 15 23 Slovak Rep. 32 24 Germany 17 25 Netherlands 3 26 Belarus 13.5 27 Greece 51 28 Belgium 25 29 Czech Rep. 33 30 China 7 31 Argentina 12 32 Spain 40 33 Australia 55 34 Thailand 49.5 35 Indonesia 44 36 South Africa 23 37 Bulgaria 54 38 Poland 34 39 Hungary 48 40 Slovenia 49 41 New Zealand 28 42 Estonia 53 43 Cyprus 38 44 Algeria 36 45 Ireland 43 46 Japan 41 47 Turkey 56 48 Malaysia 26 49 Russian Federation 45 50 Kazakhstan 19 51 Canada 24 52 Austria 39 53 Chinese Taipei 35 54 Republic of Korea 18 55 Islamic Republic of Iran 29 56 United States 57 57 Saudi Arabia 50

7.4 Category Results—GHG Emissions

145

7.4 Category Results—GHG Emissions We next consider G20 countries. The sub-ranking results of this category are defined by a country’s aggregated performance regarding four indicators. Each reflects a different dimension and aspect of how well the country is doing in terms of GHG emissions. The evaluation looks at: (1) the current level of per capita emissions. (2) the developments in GHG emissions over the past five years in absolute terms. (3) the current levels of per capita GHG emissions compared to a country-specific well-below 2 ◦ C pathway. (4) the country’s own 2030 emissions reduction target compared to its well-blow −2 ◦ C pathway. In the table in [8, p. 9], we let Very Low, Low, Medium, High, Very High be replaced with 0.1, 0.3, 0.5, 0.7, 0.9, respectively. Under special circumstances, we allow for a rating of 1 and 0. Hence if a country received a rating of 1 in all dimensions, then the product, average, and norm operation would all equal 1. The product entries in Table 7.1 are determined by multiplying the entries of the four dimensions. The average is determined by averaging the entries of the four dimensions. The norm is determined similarly, where we let λ = 0.5. The Product, Average, and Norm columns give a measure of a country’s achievement with respect to greenhouse gas emissions. Average gives an overall measure, while product gives a measure where a country must rank high in all categories to receive a high mark. The sub-ranking results of the index category, “Renewable Energy”are defined by a country’s aggregated performance regarding four indicators. Each reflects a different dimension and aspect of how well the country is doing in terms of renewable energy. The evaluation looks at: (1) the current levels of the share of renewable energy in total primary energy supply, (2) the developments of renewable energy in the past five years in absolute terms, (3) the current levels of the share of renewable in total primary energy supply compared to a country specific well-below 2 ◦ C pathway, (4) the country’s own 2030 renewable energy target compared to its well-below 2 ◦ C pathway. The entries in Table 7.2 are determined in a similar manner as in Table 7.1. The sub-ranking results of the index category “Energy Use”are defined by a country’s aggregated performance regarding four indicators. Each reflects a different dimension and aspect of how well the country is doing in terms of energy use. The evaluation looks at: (1) the current levels of per capita energy use, (2) the developments of per capita energy use in the past five years,

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7 Climate Change and Modern Slavery

Table 7.1 Greenhouse Gas Emissions Country Product Average Norm

United Kingdom India Italy France EU (28) Brazil Indonesia Mexico Germany Turkey South Africa Russian Fed. Argentina Japan Australia China Canada United States Korea, Rep. Saudi Arabia

GHG per Capita— current level

GHG per Capita– current trend

GHG per Capita— compared to a wellbelow −2 ◦ C pathway

GHG 2030 target— compared to a wellbelow −2 ◦ C pathway

0.1225

0.60

0.5

0.5

0.7

0.5

0.7

0.0567 0.0875 0.0625 0.0375 0.0375 0.0063 0.0225 0.0135 0.075 0.0189

0.65 0.44 0.50 0.45 0.45 0.35 0.40 0.35 0.35 0.40

0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5

0.9 0.5 0.5 0.3 0.5 0.3 0.5 0.3 0.5 0.3

0.1 0.7 0.5 0.5 0.3 0.7 0.3 0.3 0.1 0.7

0.9 0.5 0.5 0.5 0.5 0.1 0.3 0.3 0.5 0.3

0.7 0.5 0.5 0.5 0.5 0.3 0.5 0.5 0.3 0.3

0.0063

0.35

0.5

0.1

0.3

0.7

0.3

0.0027 0.0009 0.0075 0.0027 0.0015 0.0005

0.25 0.20 0.35 0.25 0.25 0.20

0.3 0.3 0.5 0.3 0.3 0.5

0.3 0.3 0.1 0.3 0.1 0.1

0.3 0.3 0.5 0.3 0.5 0.5

0.1 0.1 0.3 0.3 0.1 0.1

0.3 0.1 0.5 0.1 0.3 0.1

0.0003

0.15

0.3

0.1

0.3

0.1

0.1

0.0001

0.10

0.1

0.1

0.1

0.1

0.1

(3) the current levels of per capita energy use compared to a country specific wellbelow 2 ◦ C pathway, (4) the country’s own 2030 energy use target compared to its well-below 2 ◦ C pathway (Table 7.3).

0.0189

0.0045

France

Canada

United States

0.0045

0.0015

0.0003

0.0001

0.0001

Mexico

Argentina

South Africa

Saudi Arabia

Russian Fed.

0.0045

0.0315

Indonesia

0.0045

0.0135

Korea, Rep.

Australia

0.0009

China

Japan

0.0625

0.0081

India

0.0625

Germany

0.175

0.0875

Italy

United Kingdom

0.1225

Turkey

EU (28)

0.0675

0.0675

Brazil

Product

Country

Table 7.2 Renewable energy

0.10

0.10

0.15

0.20

0.30

0.30

0.30

0.30

0.40

0.45

0.35

0.30

0.40

0.50

0.45

0.50

0.55

0.60

0.55

0.55

Average

0.1

0.1

0.3

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

Norm

0.1

0.1

0.1

0.5

0.3

0.3

0.3

0.3

0.7

0.5

0.5

0.1

0.3

0.5

0.5

0.5

0.5

0.7

0.5

0.9

GHG per Capita—current level

0.1

0.1

0.3

0.3

0.5

0.5

0.5

0.5

0.3

0.7

0.3

0.9

0.9

0.5

0.7

0.5

0.7

0.5

0.9

0.3

GHG per Capita—current trend

0.1

0.1

0.1

0.1

0.1

0.3

0.3

0.3

0.3

0.3

0.3

0.1

0.3

0.5

0.5

0.5

0.5

0.7

0.5

0.5

GHG per Capita—compared to a well-below 2 ◦ C pathway

0.1

0.1

0.1

0.1

0.3

0.1

0.1

0.1

0.3

0.3

0.3

0.1

0.1

0.5

0.1

0.5

0.5

0.5

0.3

0.5

GHG 2030 target—compared to a well-below 2◦ C pathway

7.4 Category Results—GHG Emissions 147

0.0003

0.0001

Korea, Rep.

Saudi Arabia

Australia

0.0015

0.0007

Turkey

0.0003

0.0021

China

Canada

0.0007

Russian Fed.

US

0.0063

0.0125

Japan

0.0315

0.0189

Argentina

0.0375

0.0225

EU (28)

Germany

0.0315

S. Africa

France

0.0225

Brazil

0.0018

0.0675

Italy

Indonesia

0.0875

Mexico

UK

0.0567

0.2401

India

Product

Country

Table 7.3 Energy use

0.10

0.15

0.15

0.20

0.20

0.30

0.20

0.40

0.35

0.45

0.45

0.40

0.40

0.45

0.50

0.40

0.55

0.55

0.70

0.65

Average

0.1

0.3

0.3

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.7

0.5

Norm

0.1

0.1

0.1

0.1

0.1

0.7

0.7

0.1

0.3

0.3

0.3

0.7

0.3

0.5

0.9

0.5

0.9

0.5

0.7

0.9

GHG per Capita—current level

0.1

0.3

0.3

0.5

0.7

0.1

0.1

0.5

0.7

0.5

0.7

0.3

0.5

0.7

0.1

0.3

0.3

0.7

0.7

0.1

GHG per Capita—current trend

0.1

0.1

0.1

0.3

0.1

0.3

0.1

0.5

0.3

0.5

0.3

0.3

0.5

0.3

0.7

0.3

0.5

0.5

0.7

0.9

GHG per Capita—compared to a well-below 2 ◦ C pathway

0.1

0.1

0.1

0.1

0.1

0.1

0.1

0.5

0.1

0.5

0.5

0.3

0.3

0.3

0.3

0.5

0.5

0.5

0.7

0.7

GHG 2030 target—compared to a well-below 2 ◦ C pathway

148 7 Climate Change and Modern Slavery

7.5 Climate Change and Human Trafficking

149

7.5 Climate Change and Human Trafficking The Intergovernmental Panel on Climate Change notes that in coming years millions of people will be forced to relocate due to effects of climate change, including shoreline erosion, coastal flooding or disruption of normal farming practices. Analysts predict that this crisis in making will affect 150–200 million men, women, and children by 2050, or roughly one in every 45 persons. It is well-documented that displacement leads to a considerable increase in human trafficking. The U. N. Environment Program has indicated that trafficking may increase by 20–30% during disasters [20]. The poorest countries, those least responsible for greenhouse emissions causing climate change, that are bearing the greatest burden and are the first to deal with forced migration. Moreover, as is the case in most poorest countries, those living in rural areas who depend on climate-sensitive resources such as water supplies and farming for their basic livelihoods are particularly vulnerable to climate change. When an environmental disaster occurs people are forced to flee without legal authorization or documents. Desperate to survive and unfamiliar with the culture of the refugee community, these people are extremely vulnerable to human trafficking. It is virtually impossible to estimate the number of people displaced by environmental disasters who become victims of trafficking. However data suggests that human trafficking increases by around 20–30% during disasters. (UNEP) The International Criminal Police Organization (INTERPOL) has also warned that as families are separated during environmental disasters the risk of women and children to trafficking increases. Environmental refugees are at particular risk because current international refugee agreements are such that people displaced by environmental disasters do not qualify as refugees and thus do not qualify for international aid or protection. Many forces drive environmental migration. Two of these factors are climate processes and climate events. Climate processes take place over a period of time and include desertification of land, sea-level rise, glacier melting and growing water scarcity. Climate events, on the other hand, are sudden and dramatic occurrences such as floods, storms, hurricanes and typhoons, which force people to leave their land quickly. Future changes in precipitation and temperature will make certain areas of the world less viable places to live due to unreliable supplies of food and water. An increase in the frequency and severity of floods and storms, drought, and the resulting land degradation will force migration of the world’s most vulnerable people. The rise in forced labor, sexual exploitation and other types of trafficking would be driven by many of the effects of climate change that are already well known and widely documented. Greenhouse gas emissions are making our oceans more acidic and destroying coral reefs, affecting communities’ access to fish and other food. Rising temperatures are causing the glaciers to shrink and contribute to sea-level rise, pushing people away from their homes. And intense heat waves and droughts

150

7 Climate Change and Modern Slavery

are drastically impacting the livelihoods of farmers who depend on agriculture for their survival. Collectively, these climate impacts started causing an increase in human migration, making people more vulnerable to trafficking [20]. In [20, 80], it was predicted that there could be as many as 250 million climate refugees by 2050. In [20, 80], it was warned that by the end of the century climate change would force one-eighth of the world’s population, as many as 1.54 billion people, largely from the tropics to migrate more than 620 miles from their current homes. Some of these “climate migrants,” finding themselves desperate for security and work, could become victims of human trafficking [20, 80].

7.6 Modern Slavery and Its Link to the Sustainable Development Goals Goal 8 is Decent Work and Economic Growth. Target 8.7 states ‘take immediate and effective measures to eradicate forced labor, and modern slavery and human trafficking and secure the prohibition and elimination of the worst forms of child labor, including recruitment and use of child soldiers, and by 2025 end forced labor in all forms’. The achievement of Targets of other Sustainable Development Goals are important in the achievement of Target 8.7, namely those given in Table 7.4, [70]. Their description has been given previously.

Table 7.4 Target achievements Goal/target

Applicable

Implementable

Transformative

Overall mark

Goal 1

0.85

2.0

0.5

1.65

1.1

0.0

2.0

0.0

0.0

1.2

1.7

2.0

1.0

3.3

Goal 4

0.8

1.85

0.5

1.15

4.1

0.3

1.7

0.3

0.3

4.6

1.3

2.0

0.7

2.0

Goal 5

1.15

1.5

1.0

1.7

5.2

1.3

1.3

1.0

1.7

5.3

1.0

1.7

1.0

1.7

Goal 8

1.5

1.85

1.15

3.0

8.5

1.7

1.7

1.3

3.3

8.8

1.3

2.0

1.0

2.7

Goal 10

1.0

1.0

1.0

1.0

10.7

1.0

1.0

1.0

1.0

Goal 16

1.5

1.7

1.3

4.0

16.2

1.3

1.7

1.3

3.7

16.7

1.7

1.7

1.3

4.3

7.6 Modern Slavery and Its Link to the Sustainable Development Goals Table 7.5 Targets Goal Applicable G1 G4 G5 G8 G 10 G 16

0.425 0.4 0.575 0.75 0.5 0.75

Table 7.6 AHP method AHP Bounded difference G1 G4 G5 G8 G 10 G 16 Col sum

0 0 0 0.2 0 0.25 0.45

151

Implementable

Transformative

Overall mark

1.00 0.925 0.75 0.925 0.5 0.85

0.25 0.25 0.50 0.575 0.5 0.65

0.20625 0.14375 0.2125 0.375 0.125 0.50

Algebraic product Standard intersection

Row avg

0.106 0.092 0.215 0.399 0.125 0.414 1.351

0.119 0.114 0.239 0.391 0.208 0.438 1.509

0.25 0.25 0.50 0.575 0.50 0.65 2.725

We next divide the category entries in Table 7.4 by 2 and the overall mark entries by 8 to obtain Table 7.5. We next determine the AHP table (Table 7.6) from Table 7.5 by finding the bounded difference, algebraic product, and standard intersection of the elements in the categories, Applicable, Implementable, and Transformative. The column sum of the row average column in Table 7.6 is 1.509. The coefficients of the following equation are determined by dividing the individual entries in the Row Average column by 1.509. G 8.7 = 0.08G 1 + 0.08G 4 + 0.16G 5 + 0.26G 8 + 0.14G 10 + 0.29G 16 . Table 7.7 is determined by dividing the entries in Table 7.6 by their column sum.

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7 Climate Change and Modern Slavery

Table 7.7 Guiasu method Guiasu Bounded difference G1 G4 G5 G8 G 10 G 16 Col Sum

0 0 0 0.44 0 0.56

Algebraic product Standard intersection

Row avg

0.078 0.068 0.159 0.295 0.093 0.306

0.057 0.053 0.114 0.315 0.092 0.368

0.092 0.092 0.184 0.211 0.183 0.239

The coefficients in the following equation are the entries in the Row Average column of Table 7.7. G 8.7 = 0.06G 1 + 0.05G 4 + 0.11G 5 + 0.32G 8 + 0.09G 10 + 0.37G 16 . Table 7.8 is determined by dividing the entries in Table 7.7 by the maximum entry in that column. The column sum of the row average column in Table 7.8 is 3.08. The coefficients of the following equation is determined by dividing the individual entries in the Row Average column by 3.08. G 8.7 = 0.08G 1 + 0.07G 4 + 0.14G 5 + 0.28G 8 + 0.12G 10 + 0.32G 16 . We next determine the rankings of the countries with respect to achieving the sustainable development goals pertinent to modern slavery. The rankings are with respect to the AHP, Guiasu, and Yen methods. The values used are those from the appropriate subtables of tables in Chap. 2. For example, for the countries making up the region OECD, the subtable is from Table 2.4. The subtables are obtained by deleting the columns not involving the SDGs defining G 8.7 , G 8.7 , and G 8.7 (Tables 7.9, 7.10, 7.11, 7.12, 7.13, 7.14, 7.15).

Table 7.8 Yen method Yen

Bounded difference

Algebraic product

Standard intersection

Row avg

G1

0

0.255

0.385

0.213

G4

0

0.222

0.385

0.202

G5

0

0.520

0.770

0.430

G8

0.786

0.964

0.883

0.878

G 10

0

0.304

0.766

0.357

G 16

1

1

1

1

Col sum

3.08

7.6 Modern Slavery and Its Link to the Sustainable Development Goals

153

OECD

Table 7.9 OECD ranks Country AHP/rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands N. Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. K. U. S.

0.84687/18 0.88556/8 0.88401/9 0.87213/12 0.73431/32 0.86063/13 0.91636/3 0.85266/15 0.92265/1 0.84096/21 0.85214/16 0.69578/34 0.78616/29 0.91886/2 0.88178/10 0.78168/30 0.7904/28 0.85077/17 0.82196/23 0.81482/24 0.70616/33 0.84174/20 0.63215/36 0.87609/11 0.89422/5 0.89015/7 0.79988/27 0.82257/22 0.81427/25 0.89323/6 0.81326/26 0.901/4 0.84514/19 0.67409/35 0.85339/14 0.77699/31

Guiasu/rank

Yen/rank

0.8385/18 0.87629/7 0.86541/9 0.8636/10 0.74693/32 0.84738/15 0.90088/3 0.8515/14 0.90329/1 0.81393/22 0.84268/17 0.6887/34 0.77739/31 0.90155/2 0.87927/5 0.78004/29 0.77849/30 0.85923/12 0.81026/23 0.8046/26 0.69049/33 0.82609/20 0.62715/36 0.85652/13 0.89273/4 0.86145/11 0.80494/25 0.82184/21 0.80563/24 0.87796/6 0.79867/27 0.8741/8 0.83077/19 0.68159/35 0.84558/16 0.78187/28

0.84834/18 0.8866/7 0.88143/10 0.87353/11 0.74438/32 0.86015/13 0.91489/3 0.85693/15 0.91971/1 0.8354/21 0.85306/17 0.69851/34 0.78762/29 0.91668/2 0.88534/8 0.78602/30 0.79072/28 0.85869/14 0.82216/23 0.81567/24 0.70613/33 0.84076/20 0.63502/36 0.87312/12 0.89821/4 0.88379/9 0.80678/27 0.82711/22 0.81552/25 0.89188/6 0.81256/26 0.89513/5 0.84437/19 0.68261/35 0.8552/16 0.78371/31

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7 Climate Change and Modern Slavery

East and South Asia

Table 7.10 East and South Asia ranks Country AHP/rank Bangladesh Bhutan Brunei Darussaiam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

Guiasu/rank

Yen/rank

0.64131/17 0.71594/7 0.71046/8 0.65402/15 0.77416/2 0.63709/18 0.68026/12

0.63012/17 0.72814/6 0.69505/10 0.62182/18 0.76003/2 0.65661/14 0.69251/11

0.63899/18 0.7248/7 0.71032/9 0.64601/16 0.77353/2 0.64766/15 0.68843/12

0.6748/13 0.70927/9 0.74234/6 0.74938/4 0.69281/10 0.66837/14 0.54081/19 0.64183/16 0.8274/1 0.68989/11 0.75107/3

0.65899/13 0.71584/8 0.74565/4 0.72825/5 0.68469/12 0.64678/15 0.54629/19 0.63965/16 0.8151/1 0.70351/9 0.7475/3

0.67039/13 0.71714/8 0.7482/4 0.74619/5 0.69333/11 0.6624/14 0.54657/19 0.64488/17 0.8283/1 0.69992/10 0.75479/3

0.74656/5

0.7188/7

0.74071/6

7.6 Modern Slavery and Its Link to the Sustainable Development Goals

155

Eastern Europe and Central Asia

Table 7.11 Eastern Europe and Central Asia ranks Country AHP/rank Guiasu/rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Boznia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgz Rep. Liecheristan Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

Yen/rank

0.3821/23 0.67813/22

0.4032/23 0.66534/21

0.3922/23 0.6788/22

0.68896/19 0.71514/14 0.80391/3 0.71046/17 0.7402/9 0.75608/6 0.80914/2 0.71611/13 0.76254/4 0.71276/15

0.68852/19 0.70382/15 0.77471/3 0.69505/17 0.73728/7 0.74677/5 0.79537/2 0.73214/9 0.74688/4 0.69122/18

0.69384/19 0.7163/14 0.79768/3 0.71032/17 0.74362/8 0.75744/5 0.80896/2 0.72665/12 0.76164/4 0.70853/18

0.83472/1 0.76034/5

0.82256/1 0.72981/10

0.83475/1 0.75353/6

0.71129/16 0.67933/21 0.72133/12 0.68392/20

0.70053/16 0.68116/20 0.73818/6 0.65994/22

0.71281/16 0.68493/20 0.73292/10 0.68024/21

0.74756/8 0.72299/11 0.70016/18 0.75027/7 0.73492/10

0.73591/8 0.71414/12 0.70627/14 0.71221/13 0.72051/11

0.74841/7 0.72404/13 0.71496/15 0.7409/9 0.7323/11

156

7 Climate Change and Modern Slavery

Latin America and the Caribbean

Table 7.12 Latin America and the Caribbean ranks Country AHP/rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Rep. Ecuador El Salvador Grenada Guatamalia Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

Guiasu/rank

Yen/rank

0.70288/7

0.69036/8

0.70337/7

0.62607/18 0.65884/13 0.62964/17 0.63447/16 0.74045/3 0.80494/1

0.61176/18 0.64455/13 0.62829/17 0.636/15 0.74495/2 0.77978/1

0.62391/18 0.65755/13 0.63368/17 0.63957/16 0.74704/2 0.79859/1

0.66183/12 0.7005/8 0.64427/15

0.65305/12 0.69551/7 0.63685/14

0.66343/12 0.70312/8 0.64621/14

0.5708/20 0.62087/19 0.50618/23 0.56967/21 0.70629/6 0.69225/9 0.66675/11 0.64518/14 0.67802/10

0.58326/20 0.59091/19 0.50439/23 0.57572/21 0.702/6 0.68051/9 0.67726/10 0.63418/16 0.66953/11

0.57914/20 0.61442/19 0.50623/23 0.5741/21 0.70886/6 0.69177/9 0.67562/11 0.64582/15 0.67946/10

0.71279/5 0.73022/4 0.74079/2 0.54678/22

0.70986/4 0.70396/5 0.73464/3 0.54604/22

0.71348/5 0.72546/4 0.74358/3 0.54693/22

7.6 Modern Slavery and Its Link to the Sustainable Development Goals

157

Middle East and North Africa Table 7.13 Middle East and North Africa ranks Country AHP/rank Guiasu/rank Algeria Bahrain Eygpt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunisia United Arab Emirates Yemen Rep.

Yen/rank

0.74408/3 0.72223/4 0.6337/14 0.67779/8 0.64506/13 0.65697/12 0.66769/11 0.69581/5

0.72859/3 0.72162/4 0.64332/13 0.66887/10 0.63768/14 0.66401/12 0.66427/11 0.6819/6

0.74319/3 0.72576/4 0.64249/14 0.67985/9 0.64596/13 0.6643/12 0.67034/11 0.69563/5

0.6684/10 0.68681/6 0.75537/2 0.67699/9 0.53933/15 0.68531/7 0.75895/1 0.39424/16

0.66946/9 0.69655/5 0.75747/1 0.67951/7 0.53317/15 0.67776/8 0.75406/2 0.37728/16

0.6739/10 0.69475/6 0.76025/2 0.68193/8 0.53907/15 0.68786/7 0.76058/1 0.38955/16

Sub-Saharan Africa

Table 7.14 Sub-Saharan Africa ranks Country

AHP/rank

Guiasu/rank

Yen/rank

Angola

0.50891/29

0.50243/35

0.50746/31

Benin

0.4934/35

0.51899/28

0.50223/33

Botswana

0.67157/5

0.65635/7

0.66806/6

Burkino Faso

0.55096/19

0.56877/19

0.55666/19

Burundi

0.54321/20

0.51268/31

0.5338/23

Cabo Verde

0.66898/6

0.68719/5

0.67775/5

Cameroon

0.54284/21

0.54423/21

0.54484/21

Central African Rep.

0.35355/43

0.38227/43

0.36588/43

Chad

0.38276/42

0.39176/42

0.38573/42

Comoros

0.52122/25

0.53883/24

0.52986/25

Congo Demo Rep.

0.42945/40

0.42435/41

0.42563/40

Congo Rep.

0.47932/37

0.49533/36

0.48447/38

Cote d’lvoire

0.53456/23

0.55978/20

0.54488/20

Djibouti

0.57115/15

0.59417/13

0.58133/15

(continued)

158

7 Climate Change and Modern Slavery

Table 7.14 (continued) Country

AHP/rank

Guiasu/rank

Yen/rank

Eswatini

0.49994/33

0.49466/37

0.49783/35

Ethiopia

0.56758/17

0.57485/17

0.56987/17

Gabon

0.58933/12

0.58202/16

0.59128/11

Gambia, The

0.55865/18

0.58256/16

0.57013/16

Ghana

0.68045/3

0.69126/3

0.68722/4

Guinea

0.53089/24

0.53516/26

0.53235/24

Kenya

0.58786/13

0.59658/12

0.5912/12

Lesotho

0.4409/39

0.45402/38

0.44532/39

Liberia

0.50721/30

0.51226/31

0.50758/30

Madagascar

0.48999/38

0.51215/32

0.49411/37

Malawi

0.63183/9

0.65776/6

0.6397/7

Mali

0.51235/28

0.52991/27

0.51786/28

Mauritania

0.50697/31

0.48675/38

0.50322/32

Mauritius

0.68033/4

0.6904/4

0.68964/3

Mozambique

0.49834/34

0.50318/34

0.4988/34

Namibia

0.72014/1

0.69718/1

0.713/1

Niger

0.51907/27

0.53767/25

0.52344/27

Nigeria

0.40329/41

0.44054/40

0.41677/41

Rwanda

0.61651/10

0.63952/8

0.62409/10

Sao Tome and Principe

0.69515/2

0.69615/2

0.69605/2

Senegal

0.57506/14

0.59697/11

0.58279/14

0.53966/22

0.54047/23

0.53936/22

0.63824/7

0.61107/10

0.62928/8

Sudan

0.50407/32

0.51281/29

0.50783/29

Tanzania

0.59119/11

0.59177/14

0.59014/13

Togo

0.52119/26

0.54394/22

0.5291/26

Uganda

0.57039/16

0.56977/18

0.56964/18

Zambia

0.49234/36

0.50737/33

0.49709/36

Zimbabwe

0.63371/8

0.61531/9

0.62909/9

Equatorial Guinea Eritrea

Guinea-Bissau

Seychelles Sierra Leone Somalia South Africa South Sudan

7.7 Stakeholder

159

Oceania Table 7.15 Oceania ranks Country AHP/rank Fiji Kinbati Marshall Islands Micronesia, Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Guiasu/rank

Yen/rank

0.80424/1

0.80904/1

0.81041/1

0.60777/3

0.63492/3

0.61753/3

0.66124/2

0.67598/2

0.6688/2

7.7 Stakeholder The total of the overall marks column in Table 7.4 is 12.5. The coefficients in the following equation are determined by dividing the individual overall marks by 12.5. G ∗8.7 = 0.13G 1 + 0.09G 4 + 0.14G 5 + 0.24G 8 + 0.08G 10 + 0.32G 16 We next determine the rankings of the countries with respect to achieving the sustainable development goals pertinent to modern slavery. The rankings are with respect to the Stakeholder method. The values used are those from the appropriate subtables of tables in Chap. 4. For example, for the countries making up the region OECD, the subtables are from Tables 4.1, 4.2, and 4.3. The subtables are obtained by deleting the columns not involving the SDGs defining G ∗8.7 (Tables 7.16, 7.17, 7.18, 7.19, 7.20, 7.21, 7.22).

160

7 Climate Change and Modern Slavery

OECD Table 7.16 OECD ranks Country G ∗ (C)/rank Australia Austria Belgium Canada Chile Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Isreal Italy Japan Korea Rep. Latvia Lithuania Luxembourg Mexico Netherlands N. Zealand Norway Poland Portugal Slovak Rep. Slovenia Spain Sweden Switzerland Turkey U. K. U. S.

0.508/19.5 0.644/3.5 0.56/9 0.59/7 0.328/34 0.644/3.5 0.706/1 0.492/22.5 0.632/5 0.496/21 0.572/8 0.408/31 0.472/25 0.688/2 0.55/12 0.35/33 0.49/24 0.546/13 0.528/17 0.422/30 0.432/29 0.47/26 0.254/36 0.534/16 0.52/18 0.54/15 0.492/23 0.556/10.5 0.45/28 0.598/6 0.508/19.5 0.544/14 0.452/27 0.274/35 0.556/11 0.358/32

G ∗ (N )/rank

G ∗ (A)/rank

0.514/25.5 0.643/3 0.561/13.5 0.596/7 0.514/25.5 0.571/11.5 0.697/1 0.533/20.5 0.622/4 0.561/14 0.545/18 0.5/32 0.513/27 0.673/2 0.588/8 0.492/34.5 0.5/32 0.575/10 0.533/20.5 0.501/29.5 0.5/32 0.53/22 0.476/36 0.556/15 0.546/17 0.605/6 0.522/23.5 0.578/9 0.547/16 0.612/5 0.509/28 0.571/11.5 0.539/19 0.492/34.5 0.522/23.5 0.501/29.5

0.62335/18 0.69294/13 0.70816/10 0.70001/11 0.55381/34 0.69911/12 0.76463/1 0.66748/20 0.74306/4 0.67499/18 0.68382/16 0.55984/33 0.62027/28 0.7463/2 0.72122/7 0.60883/31 0.62941/26 0.65638/24 0.66687/22 0.59487/32 0.61145/29 0.69084/14 0.48558/36 0.72018/8 0.71043/9 0.72708/5 0.66802/19 0.68239/17 0.66625/23 0.74553/3 0.64229/25 0.72705/6 0.68667/15 0.49571/35 0.66727/21 0.61035/30

7.7 Stakeholder

161

East and South Asia

Table 7.17 East and South Asia ranks Country G ∗ (C)/rank Bangladesh Bhutan Brunei Darussaiam Cambodia China India Indonesia Korea, Dem. Rep. Lao PDR Malaysia Maldives Mongolia Myanmar Nepal Pakistan Philippines Singapore Sri Lanka Thailand Timor Leste Vietnam

0.336/14.5 0.462/5 0.281/18 0.302/16 0.478/3 0.340/11 0.338/12.5 0.377/10 0.336/14.5 0.474/4 0.482/2 0.380/8.5 0.270/21 0.276/20 0.300/17 0.338/12.5 0.520/1 0.437/7 0.454/6 0.280/19 0.380/8.5

G ∗ (N )/rank

G ∗ (A)/rank

0.492/14.5 0.483/18 0.511/10 0.508/11.5 0.588/1 0.500/13 0.485/16.5 0.565/3 0.479/19 0.548/4 0.543/5 0.514/8.5 0.492/14.5 0.508/11.5 0.435/21 0.485/16.5 0.572/2 0.527/6 0.515/7 0.460/20 0.514/8.5

0.508/13 0.564/7.5 0.542/11 0.484/14.5 0.647/2 0.465/18 0.517/12 0.547/10 0.449/19 0.607/3.5 0.585/5 0.560/9 0.482/16 0.484/14.5 0.388/20 0.470/17 0.714/1 0.580/6 0.607/3.5 0.333/21 0.564/7.5

162

7 Climate Change and Modern Slavery

Eastern Europe and Central Asia

Table 7.18 Eastern Europe and Central Asia ranks Country G ∗ (C)/rank G ∗ (N )/rank Afghanistan Albania Andorra Armenia Azerbaijan Belarus Boznia and Herzegovina Bulgaria Croatia Cyprus Georgia Kazakhstan Kyrgz Rep. Liecheristan Malta Moldova Monaco Montenegro North Macedonia Romania Russian Federation San Marino Serbia Tajikistan Turkmenistan Ukraine Uzbekistan

G ∗ (A)/rank

0.200/23 0.380/22

0.413/23 0.498/21

0.3304/23 0.5383/18

0.352/19 0.330/14 0.466/3 0.475/17 0.458/9 0.470/6 0.562/2 0.438/13 0.466/4 0.588/15

0.509/18 0.548/7 0.570/5 0.560/6 0.513/17 0.531/10 0.572/2.5 0.517/13.5 0.515/15.5 0.517/13.5

0.5251/20 0.5537/15 0.6101/5 0.6058/8 0.6099/6 0.6215/4 0.6345/2 0.5621/14 0.5785/12 0.5207/22

0.614/1 0.404/5

0.628/1 0.547/8

0.7077/1 0.5909/10

0.390/16 0.364/21 0.502/12 0.390/20

0.524/11.5 0.485/22 0.515/15.5 0.524/11.5

0.5725/13 0.5217/21 0.6248/3 0.5385/17

0.470/8 0.304/11 0.361/18 0.438/7 0.354/10

0.540/9 0.501/19.5 0.572/3 0.572/3 0.501/19.5

0.6051/9 0.5331/19 0.6059/7 0.5827/11 0.5403/16

7.7 Stakeholder

163

Latin America and the Caribbean

Table 7.19 Latin America and the Caribbean ranks Country G ∗ (C)/rank Antigua and Barbuda Argentina Bahamas, The Barbados Belize Bolivia Brazil Columbia Costa Rica Cuba Dominica Dominican Ecuador El Salvador Grenada Guatamalia Guyana Haiti Honduras Jamaica Nicaragua Panama Paraguay Peru St. Kitts and Nevis St. Lucia St. Vincent and the Grenadines Suriname Trinidad and Tobago Uruguay Venezuela, RB

G ∗ (N )/rank

G ∗ (A)/rank

0.344/17 0.347/15 0.378/9 0.278/22.5 0.338/19 0.272/24 0.32/20 0.4/6 0.499/1

0.512/5 0.523/3 0.509/6 0.5/8.5 0.485/16.5 0.476/21 0.476/21 0.503/7 0.62/1

0.5427/7 0.5348/10 0.6308/2 0.4359/21 0.4801/20 0.4853/19 0.4978/17 0.6247/3 0.6406/1

0.422/2 0.392/8 0.374/10

0.489/13 0.499/10 0.485/16.5

0.5336/11 0.5385/9 0.5233/12

0.292/21 0.278/22.5 0.2/26 0.246/25 0.408/4 0.348/14 0.394/7 0.346/16 0.364/13

0.476/21 0.5/8.5 0.434/25 0.428/26 0.485/17 0.49/11 0.489/13 0.489/13 0.485/16.5

0.432/22 0.388/25 0.3106/26 0.4237/24 0.5555/6 0.4984/16 0.5399/8 0.5021/14 0.4999/15

0.371/12

0.484/19

0.4902/18

0.372/11 0.407/5 0.418/3 0.342/18

0.458/23 0.542/2 0.515/4 0.437/24

0.509/13 0.6067/4 0.5952/5 0.4249/23

164

7 Climate Change and Modern Slavery

Middle East and North Africa Table 7.20 Middle East and North Africa ranks Country G ∗ (C)/rank G ∗ (N )/rank Algeria Bahrain Eygpt, Arab Rep. Iran, Islamic Rep. Iraq Jordan Kuwait Lebanon Libya Morocco Oman Qatar Saudi Arabia Syrian Arab Rep. Tunsia United Arab Emirates Yemen Rep.

0.412/4 0.403/5 0.27/14 0.306/13 0.348/11 0.334/12 0.362/10 0.376/9 0.2/16 0.382/7 0.387/6 0.423/3 0.448/1 0.19/17 0.38/8 0.439/2 0.218/15

G ∗ (A)/rank

0.521/3 0.542/1 0.476/14.5 0.516/4 0.51/8 0.476/14.5 0.5/10.5 0.531/2 0.5/10.5 0.489/12.5 0.511/6 0.511/6 0.511/6 0.475/16 0.489/12.5 0.509/9 0.432/17

0.57/5 0.5948/3 0.4643/13 0.516/10 0.4617/14 0.4811/12 0.5601/6 0.53/7 0.3993/15 0.5/11 0.5877/4 0.6156/2 0.5262/8 0.3714/16 0.52/9 0.6338/1 0.3001/17

G ∗ (N )/rank

G ∗ (A)/rank

0.41/43.5 0.437/23.5 0.446/13 0.476/6.5 0.453/11.5 0.437/23.5 0.437/23.5 0.442/16.5 0.41/43.5 0.434/31 0.41/43.5 0.428/34.5 0.428/34.5 0.41/43.5

0.3294/45 0.3799/26 0.4983/3 0.4032/15 0.3715/30 0.4709/8 0.3651/34 0.3234/47 0.3266/46 0.372/29 0.3074/49 0.3303/44 0.3873/22 0.3866/23

Sub-Saharan Africa Table 7.21 Sub-Saharan Africa ranks Country G ∗ (C)/rank Angola Benin Botswana Burkino Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros Congo Demo Rep. Congo Rep. Cote d’lvoire Djibouti

0.228/32.5 0.2/44.5 0.312/8 0.216/36.5 0.216/36.5 0.31/9 0.2/44.5 0.2/44.5 0.2/44.5 0.2/44.5 0.2/44.5 0.392/3 0.248/22.5 0.276/14

(continued)

7.7 Stakeholder

165

Table 7.21 (continued) Country G ∗ (C)/rank Equatorial Guinea Eritrea Eswatini Ethiopia Gabon The Gambia Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauritius Mozambique Namibia Niger Nigeria Rwanda Sao Tome and Principe Senegal Seychelles Sierra Leone Somalia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

0.261/18 0.362/4 0.228/32.5 0.264/17 0.281/12 0.274/15.5 0.292/11 0.28/13 0.248/22 0.246/26 0.246/26 0.232/29.5 0.2/44.5 0.228/32.5 0.216/36.5 0.258/19 0.426/1 0.2/44.5 0.322/6 0.232/29.5 0.2/44.5 0.294/10 0.314/7 0.248/22 0.404/2 0.216/36.5 0.210/39 0.274/15.5 0.228/32.5 0.244/28 0.248/22 0.2/44.5 0.248/22 0.246/26 0.340/5

G ∗ (N )/rank

G ∗ (A)/rank

0.489/3.5 0.437/23.5 0.444/14 0.476/6.5 0.436/29.5 0.454/10 0.442/16.5 0.41/43.5 0.428/34.5 0.428/34.5 0.442/16.5 0.396/48 0.437/23.5 0.426/38.5 0.494/2 0.524/1 0.428/34.5 0.483/5 0.442/16.5 0.41/43.5 0.437/23.5 0.455/9 0.41/43.5 0.467/8 0.453/11.5 0.436/29.5 0.437/23.5 0.41/43.5 0.426/38.5 0.428/34.5 0.437/23.5 0.437/23.5 0.437/23.5 0.489/3.5

0.3878/21 0.4371/11 0.3567/37 0.4403/10 0.3966/18 0.37/32 0.4509/9 0.3752/28 0.34/42 0.4142/13 0.3931/19 0.3714/31 0.3604/35 0.3881/20 0.3696/33 0.345/41 0.5915/1 0.3461/40 0.5253/2 0.3778/27 0.3322/43 0.4788/7 0.4293/12 0.4014/17 0.4957/4 0.4022/16 0.3496/38 0.4885/6 0.347/39 0.3219/48 0.4057/14 0.3575/36 0.3811/25 0.3817/24 0.4922/5

166

7 Climate Change and Modern Slavery

Oceania Table 7.22 Oceania ranks Country G ∗ (C)/rank Fiji Kinbati Marshall Islands Micronesia, Fed. Sts. Nauru palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

G ∗ (N )/rank

G ∗ (A)/rank

0.53529/1

0.557/1

0.6208/1

0.248/5 0.4/3 0.22105/6 0.48158/2

0.41/6 0.498/3 0.426/5 0.537/2

0.3946/5 0.5077/3 0.3736/6 0.6147/2

0.308/2

0.479/4

0.4565/4

7.8 Similarity Measure of the Rankings As in Chap. 6, we find the similarity measure of the two methods of ranking. We combine the AHP, Guiasu, and Yen methods by finding their average (Tables 7.23, 7.24, 7.25, 7.26, 7.27, 7.28, 7.29). We then find the average of the three Stakeholder methods. OECD

Table 7.23 OECD combined ranks continued Country

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

Australia

18/18

21/22.5

Austria

7 23 /7.5

6.5/5

Belgium

9 13 /10

10.83/11

Canada

11/11

8 13 /6

Chili

32/32

31.17/32

Czech Rep.

13 23 /13

9/8.5

Denmark

3/3

1/1

Estonia

14 23 /15

21/22.5

Finland

1/1

4 13 /3

France

17 23 /17

Germany

21 13 /21.5 16 23 /17

Greece

34/34

32/33

14/14

(continued)

7.8 Similarity Measure of the Rankings

167

Table 7.23 (continued) Country

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

Hungary

29 23 /29.5

26 23 /27

Iceland

2/2

2/2

Ireland

7 23 /7.5

9/8.5

Isreal

29 23 /29.5

32.83/34

Italy

28 23 /28

27 13 /28

Japan

14 13 /14

15 23 /16

Korea Rep.

23/23

19.83/19

Latvia

24 23 /24.5

30.5/30.5

Lithuania

33/33

30/29

Luxembourg

20/20

20 23 /21

Mexico

36/36

36/36

Netherlands

12/12

New Zealand

4 13 /4

13/13 14 23 /15

Norway

8 13 /9

8 23 /7

Poland

21.83/24

Slovak Rep.

26 13 /26.5 21 13 /21.5 24 23 /24.5

Slovenia

6/6

4 23 /4

Spain

26 13 /26.5

24.17/26

Sweden

5 23 /5

10.5/10

Switzerland

19/19

20 13 /20

Turkey

35/35

34.83/35

United Kingdom

15 13 /16

18.5/18

United States

30/31

30.5/30.5

Portugal

12.17/12 22 13 /25

168

7 Climate Change and Modern Slavery

East and South Asia

Table 7.24 East and South Asia combined ranks Country

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

Bangladesh

17 13 /18

14/13

Bhutan

6 23 /7

10.17/10

Brunei Darussiam

9/9

13/11

Cambodia

16 13 /16.5

14/13

China

2/2

2/2

India

15 23 /15

14/13

Indonesia

11 23 /12

13 23 /12

13/13

17.5/19

8 23 /8

3.83/3

Maldives

4 23 /4.5

4/4

Mongolia

4 23 /4.5

8 23 /9

7 23 /7

Korea, Dem. Rep. Lao PDR Malaysia

Mynamar

11/11

17.17/18

Nepal

14 13 /14

15/16

Pakistan

19/19

19 23 /20

Phillipines

16 13 /16.5

15 23 /17

Singapore

1/1

1 13 /1

Sri Lanka

10/10

6 13 /6

Thailand

3/3

5.5/5

Timor Leste Vietnam

20/21 6/6

8.17/8

7.8 Similarity Measure of the Rankings

169

Eastern Europe and Central Asia

Table 7.25 Eastern Europe and Central Asia combined ranks Country

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

Afghanistan

23/23

23/23

Albania

21 23 /22

20 13 /21

Andorra Armenia

19/19

19/20

Azerbaijan

14 13 /14

12/14

Belarus

3/3

4 13 /3

Boznia and Herzegovina

17/17.5

10 13 /11

Bulgaria

8/8

10 23 /13

Croatia

5 13 /5

6 23 /4

Cyprus

2/2

2.17/2

Georgia

11 13 /12

10.17/9.5

Kazakhstan

4/4

10.5/12

Kyrgz Rep.

17/17.5

13.5/15.5

Malta

1/1

1/1

Moldova

7/6

7 23 /6

Liecherstan

Monaco Montenegro

16/16

13.5/15.5

North Macedonia

20 13 /20

21 13 /22

Romania

9 13 /9

10.17/9.5

Russian Federation

21/21

16.17/18

Serbia

7 23 /7

8 23 /7

Tajikstan

12/13

16.5/19

Turkmenistan

15 23 /15

9 13 /8

Ukraine

9 23 /10

7/5

Uzbekistan

10 23 /11

15.17/17

San Marino

170

7 Climate Change and Modern Slavery

Latin America and the Caribbean

Table 7.26 Latin America and the Caribbran combined Ranks Country

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

7 13 /7

13/12

Antigua and Barbuda Argentina Bahamas, The

9 31 /9.5

Barbados

5 23 /5 17 31 /17

Belize

18/18

Bolivia

13/13

18.5/19

Brazil

171/17

21 31 /22.5

Columbia

15 23 /16

19 31 /21

Costa Rica

2 13 /2

5 31 /4

Cuba

1/1

1/1

Dominican Rep.

12/12

8 23 /6

Ecuador

7 23 /8 14 13 /14

9/7.5

Guatamala

20/20

21 31 /22.5

Guyana

19/19

18 23 /20

Haiti

23/23

25 23 /26

Honduras

21/21

25/25

Jamaica

6/6

9/7.5

Nicaragua

9/9

13 23 /13

Panama

10 23 /11

9 31 /9.5

Paraguay

15/15

17 23 /18

Peru

10 13 /10

14.83/14

Dominican

El Salvador

12.83/11

Grenada

St. Kitts and Nevis 16 31 /16

St. Lucia St. Vincent and the Grenadines Suriname

4 23 /5

15 23 /15

Trinidad and Tobago

3 23 /2

Uruguay

4 13 /4 2 23 /3

Venezuela, RB

22/22

21 23 /24

4/3

7.8 Similarity Measure of the Rankings

171

Middle East and North Africa Table 7.27 Middle East and North Africa combined ranks Country

SAHP, Guiasu, Yen average/rank Stakeholder average/rank

Algeria

3/3

Bahrain

4/4

3/1

Egypt, Arab Rep.

13 23 /14

13.83/14.4

4/3.5

Iran, Islamic Rep.

9/9

9/9

Iraq

13 13 /13

11/12

Jordan

12/12

12.83/13

Kuwait

11/11

8.83/8

Lebanon

5 13 /5

6/7

Libya

13.83/14.5

Morocco

9 23 /10

10.17/11

Oman

5 23 /6

5 13 /6

Qatar

1 23 /2

3 23 /2

Saudi Arabia

8/8

5/5

Syrian Arab Republic

15/15

16 13 /16.5

Tunisia

9.83/10

United Arab Emirates

7 13 /7 1 13 /1

Yemen, Rep.

16/16

16 13 /16.5

4/3.5

Sub-Saharan Africa

Table 7.28 Sub-Saharan Africa Combined ranks continued Country AHP, Guiasu, Yen average/rank Angola Benin Botswana Burkino Faso Burundi Cabo Verde Cameroon Central African Rep. Chad Comoros Congo Democratic Rep. Cong Rep. Cote d’lvoire

31 23 /31 32/32 6/5 19/18 24 23 /25 5 13 /4 21/20 43/43 42/42 24 23 /25 40 13 /40 37/38 21/20

Stakeholder average/rank 40 23 /45 31 23 /34 8 13 /5 19 23 /16 26 13 /27 13.83/11.5 34 13 /35 36 13 /41 45/48 35.17/37 46/49 27.5/31 26 23 /28 (continued)

172 Table 7.28 (continued) Country Djibouti Equatorial Guinea Erita Eswatini Ethiopia Gabon Gambia, The Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Madagascar Malawi Mali Mauritania Mauttius Mozambique Nambia Niger Nigeria Rwanda Sao Tome Principe Senegal Seychelles Sierra Leone Somolia South Africa South Sudan Sudan Tanzania Togo Uganda Zambia Zimbabwe

7 Climate Change and Modern Slavery

AHP, Guiasu, Yen average/rank

Stakeholder average/rank

14 13

27.17/30 13 23 /10 6.5/6 31 13 /33 14/13 12.5/9 26/25.5 10 13 /8 19.5/15 36.17/39.5 24.83/22.5 26.83/29 26/25.5 42.83/46 25 23 /24 36.17/39.5 21/17 1/1 40/44 4 23 /2 24 23 /21 44/47 13.83/11.5 9 23 /7 27.83/32 5/4 21 23 /18 35.83/38 15 23 /14 38 23 /43 38.5/42 23.83/19.5 35/36 23.83/19.5 24.83/22.5 4.83/3

/14

35/35.5 17/16 13/12.5 16 23 /15 3 13 /2 24 23 /24 12 13 /10 38 23 /39 30 13 /30 35 23 /37 7 13 /6 27 23 /28 33 33 /33 3 23 /3 34/34 21/20 26 13 /27 40 23 /41 9 13 /9 2/1 13/12.5 22 13 /22 8 13 /7 30/29 12 23 /11 24 23 /25 17 13 /17 35/35.5 8 23 /8

7.8 Similarity Measure of the Rankings

173

Oceania Table 7.29 Oceania Combined ranks Country AHP, Guiasu, Yen average/rank Fiji Kinbati Marshall Islands Micronesia Fed. Sts. Nauru Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu

Stakeholder average/rank

1/1

1/1

3/3

5 13 /5 3/3 5 23 /6 2/2

2/2

3 13 /4

Similarity Measures In the following μ, denotes the ranking by the AHP-Guiasu-Yen method and ν the Stakeholder method. OECD n = 36, M(μ, ν) =

616 716

East and South Asia n = 19, M(μ, ν) =

168.5 211.5

= 0.860. = 0.797.

Eastern Europe and Central Asia 247.5 n = 23, M(μ, ν) = 304.5 = 0.813. Latin America and the Caribbean n = 23, M(μ, ν) = 250 = 0.781. 302 Middle East and North Africa n = 16, M(μ, ν) = 125.5 = 0.857. 146.5 Sub-Saharan Africa n = 43, M(μ, ν) =

795 1097

= 0.725.

Chapter 8

Human Trafficking

8.1 Foster Care Deficiencies and Human Trafficking It is stated in [57] that human trafficking is a danger for youth in the foster care system. Current literature has examined aspects of the link between human trafficking and foster care. However, few studies have explored the efficacy of state policy and practice in keeping children safe from being trafficked while in foster care. We apply techniques from mathematics of uncertainty to examine the problems of the foster care system in the United States and their implications to human trafficking. The following is from [51]. In recent years, the issue of human trafficking in the United States has gained increasing attention. With the passage of the Trafficking Victims Protection Act (TVPA) of 2000 and subsequent state laws, the creation of the State Department Trafficking in Persons (TIP) Report, and increased media attention, the dangers of modern-slavery in the US and abroad have been a focus for legislators and activists around the world. Even so a crucial aspect of the fight against human trafficking in the Unites States is often overlooked. This is the connection between the foster care system and human trafficking. The child welfare system is an important institution that serves to protect children whose parents are not able to take care of them. However, research has shown that anywhere from 50 to 90% of child trafficking victims have been involved in the foster care system at some point. Also, research from the National Center for Missing and Exploited Children indicates that 60% of runaways who become subject to human trafficking had run away from the custody of state welfare agencies. Human trafficking is a clear and present danger for youth in the foster care system, but little research has been done to address this issue. While a body of literature has examined aspects of the link between human trafficking and foster care, few studies have explored the efficacy of state policy and practice in keeping children safe from being trafficked while in foster care. In Sect. 8.2, we explain how the approach in [51] can be modified in such a way that it corresponds to previous approaches involving the Analytic Hierarchy Process, © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8_8

175

176

8 Human Trafficking

[66], the Guiasu method, [24], and the Yen method, [87] which have been previously discussed. In Sect. 8.3, we apply the results of Sect. 8.2 to determine how the states and the District of Columbia are doing in their anti-trafficking protections for youth within the foster care system. Using the results in [51], we find that there is a need for improvement in the policy of the United States in their anti-trafficking protections. In Sect. 8.4, we use the AHP, Guiasu, and Yen methods to determine the relationship between the shortcomings of the foster care system in the United States and a child’s vulnerability to human trafficking. We find that the U.S. foster care system is in need of improvement. In Sect. 8.5, we use the approach of Sect. 8.2, to evaluate the United States’ legal approach to combatting the human trafficking of foster care youth. The work is based on the analysis given in [7]. The next four sections are based on [57].

8.2 Weighted Factors Let F be a factor under consideration and let F1 , . . . , Fm be causal factors or subfactors of F. In [7, 51], a limit is placed on the value a subfactor, Fi , i = 1, . . . , m, can be assigned. These numbers are not necessarily from the interval [0, 1]. We show that this method is the same as assigning a number as to the importance of a subfactor and then applying a method such as the AHP or Guiasu or Yen method. This allows the approaches in [7, 51] to be placed in the area of fuzzy logic. Proposition 8.2.1 Let ai > 0 be the largest value that can be assigned to Fi , i = 1, . . . , m. Suppose that bi > 0 is assigned to Fi , i = 1, . . . , m. Let F= If Fi =

bi ai

a1 am F1 + · · · + Fm . a1 + · · · + am a1 + · · · + am

, i = 1, . . . , m, then F =

Proof We have F =

a1 a1 +···+am

  b1 a1

b1 +···+bm . a1 +···+am

+ ··· +

am a1 +···+am



bm am



=

b1 +···+bm . a1 +···+am



We next go a level deeper, where we deal with sub subfactors. Proposition 8.2.2 Let ci j > 0 be the largest value that can be assigned to Fi j , j = 1, . . . , m i , i = 1, . . . , m. Suppose that Fi j is assigned the value di j > 0, j = 1, . . . , m i , i = 1, . . . , m. Let Fi = Let Fi j =

ci1 cim i Fi1 + · · · + Fim i , i = 1, . . . , m i . ci1 + · · · + cim 1 ci1 + · · · + cim i

di j ci j

, j = 1, . . . , m i , i = 1, . . . , m. d +···+d

(1) Then Fi = ci1i1 +···+cinimmi , i = 1, . . . , m. i (2) Suppose bi = di1 + · · · + dim i and ai = ci1 + · · · + cim i , i = 1, . . . , n m i . Then m . F = ab11 +···+b +···+am

8.2 Weighted Factors

177

Proof (1) The result here follows from Proposition 8.2.1. (2) By (1), we have Fi = abii , i = 1, . . . , m i . The desired result now follows from Proposition 8.2.1.  Let F = {F1 , . . . , Fm } and E = {E 1 , . . . , E n } be the set of experts. Let M = [ai j ] denote the m × n matrix, where ai j is determined by some method from E j s opinion of the importance of Fi as a subfactorof F. For example, the method might be AHP, Guiasu, or Yen method. Let ai = n1 nj=1 ai j , i = 1, . . . , m. (ai is the row average of the ith row.) Then F=

a1 am F1 + · · · + Fm . a1 + · · · + am a1 + · · · + am

Suppose Fi is assigned the value ci , where 0 ≤ ci ≤ 1, i = 1, . . . , m. Let bi be such that ci = abii , i = 1, . . . , m. Then F=

b1 + · · · + bm . a1 + · · · + am

We next develop methods for nonfuzzy decision making that depend on the assignment of numbers to causal factors rather importance weights. These methods are similar to the AHP, Guiasu, and Yen methods. We let m ∗ denote the maximum value a factor can take on. We assume we have m causal factors F1 , . . . , Fm and n experts E 1 , . . . , E n . AHP Method Let [ai j ] denote the m × n matrix, where ai j is the value assigned to factor Fi by where 0 ≤ ai j ≤ m ∗ , i = 1, . . . , m; j = 1, . . . , n. expert E j as to its importance and 1 We consider the row averages n nj=1 ai j , i = 1, . . . , m. Then we have F=

n n 1 1 a1 j F1 + · · · + am j Fm . n j=1 n j=1

Guiasu Method m Consider the column sum C j = i=1 ai j , j = 1, . . . , n. Then [m ∗ ai j /C j ] is the ∗ m × n matrix, where m ai j /C j is the value assigned to factor Fi by expert E j and where the ai j are defined as in the AHP method. Then we have F=

n n 1 ∗ 1 ∗ (m a1 j /C1 )F1 + · · · + (m am j /Cn )Fm . n j=1 n j=1

  Let Ai∗ = n1 nj=1 ai j and G i∗ = n1 nj=1 (m ∗ ai j /C j ), i = 1, . . . , m. We next determine when the AHP coefficients are the same as the Guiasu coefficients.

178

8 Human Trafficking

Proposition 8.2.3 We have that Ai∗ = G i∗ for i = 1, . . . , m if and only if  m ∗ nj=1 ai j /C j for i = 1, . . . , m.

n j=1

ai j =

  Proof Ai∗ = G i∗ for i = 1, . . . , m ⇔ n1 nj=1 ai j = n1 nj=1 m ∗ ai j /C j for i = 1,    . . . , m ⇔ nj=1 ai j = m ∗ nj=1 ai j /C j for i = 1, . . . , m. Corollary 8.2.4 If m ∗ = C1 = · · · = Cn , then Ai∗ = G i∗ for i = 1, . . . , m.   Proof By Proposition 8.2.3, Ai∗ = G i∗ for i = 1, . . . , m ⇔ nj=1 ai j = m ∗ nj=1  n ai j /C j for i = 1, . . . , m. However j=1 ai j = m ∗ nj=1 ai j /C j since m ∗ = C1 =  · · · = Cn .  n n Let Ai = j=1 ai j / j=1 C j and G i = n1 nj=1 ai j /C j , i = 1, . . . , m. Then the coefficients for the factor Fi in Ai and the G i are, respectively, the AHP and the Guiasu m 1 n ∗ ∗ the fuzzy case, i = 1, . . . , m. Now Ai∗ = Ai ( i=1 j=1 ai j ) and G i = G i m , i = n 1, . . . , m. Theorem 8.2.5 [57] Suppose Ai = G i for i = 1, . . . , m. Then Ai∗ = G i∗ if and only 1 n ∗ if m = n j=1 C j . Proof We have that ⎛ ⎞ m n   1 Ai∗ = G i∗ for i = 1, . . . , m ⇔ Ai ⎝ ai j ⎠ = G i m ∗ for i = 1, . . . , m n i=1 j=1 ⎛ ⎞ m m n n  1 ⎠ 1  ⇔ m∗ = ⎝ ai j ⇔ m ∗ = ai j n j=1 n j=1 i=1 i=1 ⇔ m∗ =

n 1 Cj. n j=1

Thus the proof is complete.



Yen Method Let [gi j ] denote the Guiasu matrix. Then gi j = ai j /C j , i = 1, . . . , m, j = 1, . . . , n. Let m j = ∨{gi j |i = 1, . . . , m}, j = 1, . . . , n, i.e., the largest value in column j. Clearly,  0 ≤ m j ≤ 1 for j = 1, . . . , n. Let Yi∗ = n1 nj=1 (m ∗ gi j /m j ), i = 1, . . . , m. That is, Yi∗ is the coefficient of Fi , i = 1, . . . , m. Recall that ⎡

⎤ ⎡ ⎤ n m n    1 1 Yi = ⎣ gi j /m j ⎦ / ⎣ gi j /m j ⎦ n j=1 n i=1 j=1 is the Yen coefficient for Fi in the fuzzy case, i = 1, . . . , m. Then Yi∗ = m ∗ Yi  m 1 n i=1 n j=1 gi j /m j . Also,

8.2 Weighted Factors

179

⎛

⎞ n  1 G i∗ = m ∗ ⎝ gi j ⎠ n j=1 ⎛ ⎞ m n   1 = m∗Gi ⎝ gi j ⎠ . n i=1 j=1 Theorem 8.2.6 [57] Suppose m 1 = · · · = m n = q for some q ∈ [0, 1]. Then Yi∗ = 1 ∗ G , i = 1, . . . , m. q i Proof We have that ⎡

⎤ ⎡ ⎤ n m n    1 1 Yi = ⎣ gi j /m j ⎦ / ⎣ gi j /m j ⎦ n j=1 n i=1 j=1 ⎡ ⎤ ⎡ ⎤ n m n    1 1 =⎣ gi j /q ⎦ / ⎣ gi j /q ⎦ n j=1 n i=1 j=1 ⎡ ⎤ ⎡ ⎤ n m n    1 1 =⎣ gi j ⎦ / ⎣ gi j ⎦ n j=1 n i=1 j=1 = G i , i = 1, . . . , m. Thus ⎞ ⎛ m n   1 Yi∗ = m ∗ Yi ⎝ gi j /q ⎠ n i=1 j=1 ⎛ ⎞ m n   1 1 = m∗ Gi ⎝ gi j ⎠ q n i=1 j=1 =

1 ∗ G , i = 1, . . . , m. q i

Hence the proof is complete.



Results related to those in this section can be found in [44, 46].

8.3 Foster Care and Human Trafficking Let F denote the relationship between the foster care system and human trafficking. The seven main criteria (or subfactors) of F are listed below, Fi , i = 1, . . . , 7. Each criterion along with its sub-criteria, presents a challenge to creating a foster care

180

8 Human Trafficking

system that reduces the possibility of trafficking. The full criteria are listed below, along with detailed explanations of their relevance to the intersection between foster care and human trafficking. The number provided to the right of the criteria (factors) is the highest number that factor can receive. We use these numbers to determine a linear equation with F as the dependent variable and the Fi the independent variables in such a way that the coefficients of the Fi are determined by the maximum value the factors can take on. F : Foster Care Human Trafficking F1 : Aging Out Policies (6) F2 : Kinship Care (4) F3 : Relevant Foster Care Provisions (6.5) F4 : Task Forces and Law Enforcement Training (3) F5 : LGBT Youth Protections (3.5) F6 : Relevant Anti-Trafficking Provisions (7) F7 : State Reporting Laws (2). We let the maximal numbers the Fi can take on be the ai of the previous section. We see that their sum is 32. The following equation is determined by equation (8.1). 6 4 3 7 2 F1 + 32 F2 + 6.5 F + 32 F4 + 3.5 F + 32 F6 + 32 F7 F = 32 32 3 32 5 We continue in a similar manner to obtain equations for the subfactors. F11 : Age when benefits are terminated (3) F12 : Policies and benefits (3) F1 = 36 F11 + 36 F12 F21 : Level of preference given to family members (1) F22 : Financial aid (1) F23 : Legal custody (1) F24 : Policies for children of undocumented parents (1) F2 = 41 F21 + 14 F22 + 41 F23 + 41 F24 F31 : Background checks (child abuse: neglect and criminal) (1) F32 : Applicant disqualified if convicted of sex/labor trafficking (1) F33 : Applicant disqualified if on sex offender registry (1) F34 : Applicant disqualified if convicted of domestic violence (1) F35 : Foster parent training (2.5) 1 1 1 1 F31 + 6.5 F32 + 6.5 F33 + 6.5 F34 + 2.5 F F3 = 6.5 6.5 35 F41 : LE training (1) F42 : Human trafficking TF(1) F43 : Foster care task force (1) F4 = 13 F41 + 13 F42 + 13 F43 F51 : Overall equality (1.5) F52 : Non-discrimination laws for placing LGBTQ children in foster care (1) F53 : Laws to address LGBTQ youth homelessness (1) 1 1 F + 3.5 F52 + 3.5 F53 F5 = 1.5 3.5 51 F61 : State statute (1) F62 : Diversion services and immunity from punishment (adults) (1) F63 : Victim service and civil suit access (1) F64 : Lower burden of proof for minors (2)

8.3 Foster Care and Human Trafficking

181

F65 : Safe harbor laws (1) F66 : HTIC (1) F6 = 17 F61 + 17 F62 + 17 F63 + 17 F64 + 27 F65 + 17 F66 F71 : State has laws enacted that specifically address foster care children going missing from care (1) F72 : States have laws enacted that specifically address foster care system reporting victims of human trafficking from their care (1) F7 = 21 F71 + 21 F72 F121 : Voluntary reentry before 21 (1) F122 : Housing assistance (1) F123 : College tuition waiver (1) F12 = 13 F121 + 13 F122 + 13 F123 F211 : Requires giving preference (1) F212 : “Reasonable Effort”or “May Consider”(0.5) 1 F211 + 0.5 F F21 = 1.5 1.5 212 F221 : Benefits for kinship care (1) F222 : Same aid as regular foster homes (0.5) 1 F221 + 0.5 F F22 = 1.5 1.5 222 F311 : All adults cross checked in and out of state (1) F312 : All adults in-state only (0.5) 1 F311 + 0.5 F F31 = 1.5 1.5 312 F351 : Number of hours up to two (2) F352 : Require specific course training (0.5) 2 F351 + 0.5 F F35 = 2.5 2.5 352 F511 : Working towards full equality (1.5) F512 : Solidifying equality (1) F513 : Building equality (0.5) F514 : High priority for basic equality (0.5) 1 F + 3.5 F512 + 0.5 F + 0.5 F F51 = 1.5 3.5 511 3.5 513 3.5 514 F521 : Sexual orientation (0.5) F522 : Gender identity (0.5) F + 0.5 F F52 = 0.5 1 521 1 522 F611 : Sex trafficking (0.5) F612 : Labor trafficking (0.5) F + 0.5 F F61 = 0.5 1 611 1 612 F651 : Have law (1) F652 : Diversion from immunity (0.5) F653 : Victim services (0.5) F + 0.5 F F65 = 21 F651 + 0.5 2 652 2 653 In [51], a number was associated with every subfactor of every state and the District of Columbia that provided a measure of how well it was combatting human trafficking. These numbers were then totaled to find the final number for the state. For example, Illinois and Texas were given a final number of 23.5 out of 32, the highest

182

8 Human Trafficking

of all states. The total for the United States was 843. Section 8.2 shows that if we use the procedure there to determine F for the United States, we obtain 843/51 = 16.53 and so F = 16.53/32 = 0.52.

8.4 Foster Care Shortcomings and Human Trafficking In this application, we investigate the impact of the foster care system on a child’s vulnerability to human trafficking. We measure how well the foster care system is doing in the United States. We let F denote this measure. Three factors that may lead to the exploitation of children are policy gaps and lack of adequate resources (F1 ), psychological underdevelopment (F2 ), and the aging out process (F3 ), [3, 10, 86]. Under policy gaps and lack of adequate resources three different subfactors were determined: the unpreparedness of the foster families (F11 ), the deficiencies of the reunification process (F12 ), and the inadequacy of group care (F13 ), [3, 10, 17, 19]. Psychological underdevelopment was studied from four perspectives: poor academic achievement (F21 ), history of abuse or trauma (F22 ), emotional and/or behavioral problems and mental illness (F23 ) and lack of healthy and stable relationships (F24 ), [3, 10, 31, 32, 58, 86]. The aging out process had two subfactors: the greater likelihood of living in poverty (F31 ) and lack of family and social support (F32 ), [3, 32, 86]. Four experts rated the factors and subfactors on a scale of one to ten, with one being no impact and ten being a great impact. The purpose of this study is to reveal the magnitude of the impact foster care has on children and the dangers that children may be exposed to if the system’s shortcomings go unacknowledged. We next list the factors and their subfactors. F1 F11 F12 F13 F2 F21 F22 F23 F24 F3 F31 F32

: Policy Gaps and Lack of Adequate Resources : Unpreparedness of Foster Families : Deficiencies of the Reunification Process : Inadequacy of Group Home Care : Psychological Underdevelopment : Poor Academic Achievement : History of Abuse or Trauma : Emotional and/or Behavioral Problems and Mental Illnesses : Lack of Healthy and Stable Relationships : The Aging Out Process : Greater Likelihood of Living in Poverty : Lack of Family and Social Support.

We let E i denote an expert, i = 1, 2, 3, 4. The first of the methods used is the Analytic Hierarchy Process (AHP). It is derived from the four experts’ ratings.

8.4 Foster Care Shortcomings and Human Trafficking

F F1 F2 F3 Col. Sum F=

34 F 102 1

+

35 F 102 2

+

34 F 94 11

+

30 F 94 12

+

31 F 134 21

+

36 F 134 22

E3 8 8 8 24

E 4 Row Avg. 10 34/4 10 35/4 8 33/4 28 102/4

E1 6 4 5 15

E2 10 9 10 19

E3 8 7 8 23

E 4 Row Avg. 10 34/4 10 30/4 7 30/4 27 94/4

E1 5 7 6 5 23

E2 10 10 10 9 39

E3 8 9 9 8 35

E 4 Row Avg. 8 31/4 10 36/4 10 35/4 10 32/4 38 134/4

30 F . 94 13

F2 F21 F22 F23 F24 Col. Sum F2 =

E2 10 10 10 30

33 F. 102 3

F1 F11 F12 F13 Col. Sum F1 =

E1 6 7 5 18

183

+

35 F 134 23

F3 F31 F32 Col. Sum

+

E1 5 4 9

32 F . 134 24

E2 9 10 19

E3 8 8 16

E 4 Row Avg. 10 22/4 10 22/4 20 44/4

F3 = 22 F + 22 F . 44 31 44 32 To find the corresponding Guiasu equations, we take the AHP table and divide the elements by the column sum of the column they are in. We obtain F F1 F2 F3 Col. Sum We obtain F + F = 19 56 1

89 F 252 2

+

E1 E2 E3 E 4 Row Avg. 6/18 10/30 8/24 10/28 19/56 7/18 10/30 8/24 10/28 89/252 5/18 10/30 8/24 8/28 155/504 1 1 1 1 1

155 F. 504 3

184

8 Human Trafficking

In a similar manner as above, we have F1 = 0.3658F11 + 0.3129F12 + 0.3213F13 . F2 = 0.2299F21 + 0.2722F22 + 0.2613F23 + 0.2367F24 . F3 = 0.5073F31 + 0.4927F32 . To find the corresponding Yen equations, we take the Guiasu table and divide the elements by maximum entry of the column they are in. We obtain F F1 F2 F3 Col. Sum

E1 6/7 1 5/7

E2 1 1 1

E3 1 1 1

E 4 Row Avg. 1 27/28 1 1 4/5 123/140 11144/3920

We obtain F = 0.338F1 + 0.352F2 + 0.309F3 . As above, we have F1 = 0.3645F11 + 0.3136F12 + 0.3219F13 . F2 = 0.2305F21 + 0.2709F22 + 0.2613F23 + 0.2373F24 . F3 = 0.5065F31 + 0.4935F32 . Deficiencies of the foster care system The first factor was policy gaps and lack of adequate resources (F1 ). This factor was examined from three different perspectives. The first of these was unpreparedness of foster family (F11 ), [3, 10, 19]. The foster care system may not provide foster families with enough education concerning children with special needs or history of abuse or trauma. When children have mental and emotional needs that come from mental health issues and histories of abuse or trauma, lack of adequate care giving may lead the problem to get worse. The lack of ability to provide the proper care results in more placement changes and more stress on the child, [19]. In [3], it is stated that children were commonly removed because foster parents were not prepared to meet their needs often due to lack of funding, recruiting, and training by the foster system. A study in [10] stated that care givers often abdicated custody because they lacked the needed resources to address their youth’s mental health issues. A second subfactor was the deficiencies of the reunification process (F12 ). The system seems to work best in the reunification category when children removed from toxic family situations are provided temporary, nurturing care until the family crisis is resolved [3]. The lack of focus on reunification comes from issues such as: difficulty from birth parents, lack of integrative planning, and lack of stability. Birth parents may be uncooperative when they know that alternative permanent living options are being explored for the children. Flaws in planning may develop from the concurrent planning model in which reunification and adoption options are both being pursued. Working to solve the problem from both sides is a good idea in theory, but it may lead to lack of focus on either approach. Lack of stability during foster care experience and run away events were also found to have inhibited reunification, [31, 85]. Because of these issues, reentry rates back into foster care were high despite the desire to reunite the children with their families quickly. Children who remained in foster care

8.4 Foster Care Shortcomings and Human Trafficking

185

longer after the initial removal from their birth family had lower reentry rates back into the system after reunification. This may support that not enough time and effort was taken to resolve the toxic family environment. Out of those who were reunified within one year of their removal, 70% were back in foster care within a year and 57% were back within three months, [86]. A third subfactor was the inadequacy of group home care (F13 ), [10, 17]. In [10], it is suggested that group home environments do not sufficiently meet the developmental needs of younger children because of staff turnover. High rates of staff turnover do not allow younger children to create the attachments to a care giver that is needed for normative socioemotional and brain development. These conditions could lead to risks of later development problems. It is stated in [50] that the younger the children were when put into group homes, the mental health problems they had when they were older. Another reason group homes may not be as effective is because some children in the group home may be involved in risky affairs outside the home. These children may convince other children to participate in these risky affairs as well, [86]. Psychological underdevelopment F2 was the second factor. Children are at a critical stage in life where positive and stable mental development is crucial to their growth and well-being. Temporary and unstable living environments while in the foster care system take a toll on them mentally. Psychological underdevelopment was examined through four sub factors: poor academic achievement (F21 ), history of abuse or trauma (F22 ), emotional and/or behavioral problems and mental illness (F23 ), and lack of healthy and stable relationships (F24 ), [10, 19, 31, 32, 58, 86]. Consider F21 . A report in [17] stated that children who were removed from institutions displayed gains in IQ scores. More than 75 studies support their findings that children in institutions had much lower IQ scores [17]. A 2018 study stated that many children who have aged out of the system did not often attain appropriate education and were forced to live in poverty. In a previous study, almost two thirds of children in foster care experienced at least seven school changes between starting kindergarten and graduating high school [58]. The National Longitudinal Survey of Youth, 2012, stipulated there was less high school diploma achievement and lower educational attainment from foster care youth [31]. The second subfactor, F22 , is history of trauma or abuse [32, 58, 86]. Foster care youth go through many tribulations during their time in the system’s care. Developing a history of trauma is not uncommon for these youth and may result in involvement in situations that may lead to exploitation, [58]. Foster care children are at high risk of potential exploitation because they are often from abusive or negligent families, [86]. It was suggested in [32] that children who had past experiences of sexual abuse had higher chances of being exploited in the future. This was caused by an interdependent relationship between sexual exploitation and episodes of childhood trauma. The correlations in this relationship contributed to heightened likelihoods of further exploitation. Histories of trauma also caused children to have unmet mental and physical needs. Exploiters take advantage on unmet needs, and victims become reliant on the emotional and physical support provided by their perpetrators. A 2017 studied, identified sexually exploited youth, and 75% of these youth had experienced

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prior abuse and/or neglect. Over half of those studied had experienced some form of sexual abuse preceding exploitation later in life. Fewer than 30% of these youth understood they were being taken advantage of by their exploiters, [32]. The third subfactor examined, F23 , was emotional and/or behavioral problems and mental issues, [3, 10, 19, 31, 58]. Children in foster care experience many placement changes during their time in the system [3]. Repercussions from instability in their family life may lead to mental health problems associated with trauma, distress, and general discouragement in all areas of life [31]. A research study done in 2014 of foster care youth living in group homes, indicated that 55% of those studied exhibited mood disorder such as depression disorders, bipolar disorders, and other mood disorders. 39% had disruptive disorders, and 37% had attention deficit hyperactivity disorders [10]. The fourth subfactor, F24 , under psychological underdevelopment was lack of healthy and stable relationships, [3, 10, 19]. According to [3], longer time in temporary care meant more placement changes and disrupted relationships. The replacement changes made it more arduous for children to form strong and lasting healthy relationships that are the foundation for necessary positive social and emotional development. These types of relationships are integral to the early-age development in children. In [32], it was stated that without enough positive relationships there was significant emotional closeness between a victim and his or her perpetrator. It was stated in [17] that deficits in cognitive functioning and social competence correlated with a paucity of attentive, unchanging, and sensitive care giving. When children do not get stable caring environment and form positive relationships they need at an early age, they are not prepared for future relationships. Missing out on these positive relationships makes it more difficult in distinguishing appropriate interactions later in life, leaving them socially deprived, [17]. The third factor F3 was the aging out process, [3, 86]. Two sub factors were determined. The first F31 was the greater likelihood of living in poverty, [3]. When coming from foster care, there was a higher risk for homelessness, unemployment, and lack of educational achievement in adulthood, [31]. The article [31] also supports the confirmation of higher rates of homelessness in aged-out youth. There also more likely to need public assistance and experience early parenting [31]. In [3], it was written that more than 22,000 people 18–20 years of age were discharged from the foster care system in 2014. Children, aged out with no biological family nor adoptive family, are sent to face the challenges independent living on their own. These children are more likely to live in poverty than other young people. A study done in 2018 indicated episodes of homlessness between the ages of 19 and 21 years old resulted in lower chances of achieving post-secondary education or full-time employment. Homelessness is one of the most prevailing adversities former foster care youth experience, with estimates as high as 37% of these youth being homeless. Approximately 66% reported homelessness episodes within the first six months of aging out. Youth who age out of foster care earned very little per year. This is roughly half of what non foster care peers make. Homelessness rates are higher in aged-out youth than youth from low-income families. Risk factors such as placement instability and experiences of abuse affected potential homelessness significantly, [58].

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187

The second subfactor F32 was lack of family and social support [3, 32, 86]. It was stated in [32] that exploiters can be attracted to children in the foster care system in particular because many of these children have an absence of family and social support. In [3], it is stated that children are more emotionally and mentally weak due to disruptions in relationship developments. This may cause children to age out of the system without a support system in place, potentially resulting in less success in the adult world. It is said in [86] that children aging out are at particular risk of being trafficked. In [58], it is stated that reliance on their family for financial and emotional support while pursuing post-secondary education, economic independence, and independent living was common for youth aged 18–24. This is not an option for former foster care youth who age out without a support system. Based on expert opinion, we have the following ratings for the US. F11 F12 F13 F21 F22 F23 F24 F31 F32 E 0.5 0.4 0.4 0.5 0.3 0.5 0.4 0.4 0.6 Thus F1 = 0.3645(0.5) + 0.3136(0.4) + 0.3219(0.4) = 0.43655 F2 = 0.2305(0.5) + 0.2709(0.3) + 0.2613(0.5) + 0.2373(0.4) = 0.41204 F3 = 0.5065(0.4) + 0.4935(0.6) = 0.4982 F = 0.338(0.43655) + 0.352(0.41204) + 0.309(0.4982) = 0.427. We conclude that there is a need for improvement in the foster care system in the US. Other pertinent reading can be found in [26, 50, 79].

8.5 State Report Cards for Sex Trafficking Laws Let F denote the score a state receives based on the six factors described below. It is stated in [7] that domestic minor sex trafficking (DMST) is the commercial sexual exploitation of American children within U.S. borders and is synonymous with child sex slavery, child sex trafficking, prostitution of children, and commercial sexual exploitation on children (CSEC). The Toolkit is intended to be used in conjunction with the Protected Innocence Challenge materials available online at www.sharedhope.org/reportcards. These materials are organized by state and by the six areas of law of the Protected Innocence Challenge Legislative Framework. The Protected Innocence Challenge is based on the Protected Legislative Framework which was informed by research preformed by Shared Hope International and complied in “The National Report on Domestic Minor Sex Trafficking”. Domestic minor sex trafficking is the commercial sexual exploitation of American children

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under the age of 18 within U.S. borders for the purpose of prostitution, pornography, or sexual performance. Below we give the six factors of F together with the highest value they can achieve. They are described immediately below. F1 : Criminalization of Domestic Minor Sex Trafficking (10) F2 : Criminal Provision Demand (25) F3 : Criminal Provisions for Traffickers (15) F4 : Criminal Provisions for Facilitators (10) F5 : Protective Provisions for the Child Victims (27.5) F6 : Criminal Justice Tools for Investigation and Prosecution (15) As in Sect. 8.2, we arrive at the following equation for F. 10 25 15 10 27.5 15 F1 + 102.5 F2 + 102.5 F3 + 102.5 F4 + 102.5 F5 + 102.5 F6 . F = 102.5 Hence we see that if we use the procedure in Sect. 8.2 to determine F for the United States, we obtain 4100/51 = 80.39 and so F = 80.39/102.5 = 0.78. For Louisiana, we get 99.5/102.5 = 0.97, the highest of all states. The score for the other states can be determined in a similar manner, [[7], p. 27]. F1 : Criminalization of Domestic Minor Sex Trafficking The Protection Innocence Challenge Framework Sect. 1 addresses the question: Does state law specifically criminalize the exploitation of minors through sex trafficking and other that relater to the commercial sexual exploitation of children? F2 : Criminal Provision Demand The Protected Innocence Challenge Framework Sect. 2 address the question: Does state law impose criminal penalties on sex buyers who drive the commercial sex market. F3 : Criminal Provisions for Traffickers The Protection Innocence Challenge Framework Sect. 3 addresses the question: Does state law impose criminal penalties on those who traffic minors into commercial sex, including pimps, gang-members, and family members? F4 : Criminal Provisions for Facilitators The Protection Innocence Challenge Framework Sect. 4 addresses the question: Does state law impose criminal penalties on those who facilitate the sale of minors including hotels, drivers, and brothel owners. F5 : Protective Provisions for the Child Victims The Protection Innocence Challenge Framework Sect. 5 addresses the question: Does state law prevent minors from being charged with a crime if they are engaged in commercial sex acts and provide a range of services and protections, such as emergency shelter, medical and psychological services and life skills? F6 : Criminal Justice Tools for Investigation and Prosecution The Protection Innocence Challenge Framework Sect. 6 addresses the question: Does state law provide enough tools for Law Enforcement to complete the detailed investigation required for successful prosecution? We studied the problem of human trafficking with respect to the foster care system by using techniques from the area of mathematics of uncertainty. We found that there is need for improvement of the foster care system in the US. For future research, one could use a state by state analysis of the problem using techniques in Sect. 8.4.

8.6 Health Consequences of Trafficking Victims: A Mathematical Analysis

189

8.6 Health Consequences of Trafficking Victims: A Mathematical Analysis In the study described in The London School of Hygiene and Tropical Medicine, 207 women who had been recently released from a trafficking situation were interviewed while in the care of assistance organizations in destination countries, as well as transit and home country settings, [90]. Using an epidemiological approach to identify patterns of pain and illness, women were asked about their experiences of violence and about their health. A portion of the women agreed to be interviewed on three separate occasions, which offered a portrait of the changing patterns in women’s health symptoms over time. Trafficking is a severe form of violence against women and a serious violation of human rights. Women and adolescents who are trafficked suffer some of the most unspeakable acts of abuse exploitation and degradation. The damage to their health and well-being is often profound and enduring, [[90], p. 2]. In the study described in [90], 207 women who had been recently released from a trafficking situation were interviewed while in the care of assistance organizations in destination countries, as well as transit and home country settings. Using an epidemiological approach to identify patterns of pain and illness, women were asked about their experiences of violence and about their health. A portion of the women agreed to be interviewed on three separate occasions, which offered a portrait of the changing patterns in women’s health symptoms over time. We use some techniques from fuzzy logic to develop severity measures. We show using our severity measure that women reporting severe health problems declined drastically from Interview 1 to Interview 2. Our measure of upper severity also reduced drastically from Interview 1 to Interview 2.

8.7 Health Status The following table is from the chart in Fig. 6.1 of [[90], p. 44]. It concerns the perception of women’s health status over three interviews. The numbers are percentages (Table 8.1). We show next that the most improvement is attained by minimizing the number of patients that reported Fair and showed no improvement from the first interview to the second interview. Let x denote the percentage of victims that responded Poor on the first interview and Fair on the second interview.

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Table 8.1 Women’s health status % Interview 1 Very good Good Fair Poor

1.5 11.7 30.7 56.1

Interview 2

Interview 3

1.8 58.9 35.6 3.7

1.6 54.8 38.7 4.8

Let y denote the percentage of victims that responded Poor on the first interview and Good on the second interview. Let z denote the percentage of victims that responded Fair on the first interview and Good on the second interview. In the following, we assume that no victims felt that their health had deteriorated. Since Poor decreased from 56.1 to 3.7, we conclude that x + y = 56.1 − 3.7 = 52.4. Since Very Good stayed the same, no respondents who reported Good on the first interview, reported Very Good on the second interview. Thus, we conclude that y + z = 58.9 − (11.7 − 0.3) = 47.5. Now x + y + z percent reported improvement and (11.7 − 0.3) + 3.7 + 1.5 = 16.6 reported no change, we conclude that x + y + z + 16.6 + F  = 100, where F  is the percent of Fair that reported no change from the first to second interview. Solving this system of equations, we obtain x = 35.9 − F  , y = 16.5 + F  , and z = 31 − F  . Also, x + y + z + F  = 83.4.

8.8 Main Results The following factors and subfactors are from [[90], p. 47], Table 6.3. F : Physical health symptoms (severity) F1 : Fatigue and weight loss; F11 : Easily tired, F12 : Weight loss, F13 : Loss of appetite F2 : Neurological; F21 : Headaches, F22 : Dizzy spells, F23 : Difficulty remembering, F24 : Fainting F3 : Gastrological; F31 : Stomach or abdominal pain, F32 : Upset stomach, vomiting, diarrhoea, constipation F4 : Sexual & reproductive health; F41 : Urination pain, F42 : Pelvic pain, F43 : Vaginal discharge, F44 : Vaginal pain, F45 : Vaginal bleeding (not menstruation), F46 : Gynecological infection F5 : Cardiovascular; F51 : Chest/heart pain, F52 : Breathing difficulty F6 : Musculoskeletal; F61 : Back pain, F62 : Fractures/Sprains, F63 : Joint or muscular pain, F64 : Tooth pain, F65 : Facial injuries F7 : Eyes; F71 : Vision problems/Eye pain

8.8 Main Results

191

F8 : Ears, colds, flu, & sinus infections; F81 : Ear pain, F82 : Cold, flu, & sinus infections F9 : Dermatological; F91 : Rashes, itching, sores The coefficients in the following equations are importance weights that have been determined by expert opinion. Their opinions were based on the most prevalent and severe symptoms over time, [[90], p. 1] F = 0.15F1 + 0.20F2 + 0.10F3 + 0.10F4 + 0.10F5 + 0.15F6 + 0.07F7 + 0.08F8 + 0.05F9

F1 = 13 F11 + 13 F12 + 13 F12 F2 = 27 F21 + 27 F22 + 27 F23 + 17 F24 F3 = 23 F31 + 13 F32 F4 = 16 F41 + 16 F42 + 16 F43 + 16 F44 + 16 F45 + 16 F46 F5 = 21 F51 + 21 F52 F6 = 27 F61 + 17 F62 + 17 F63 + 27 F64 + 17 F65 F7 = F71 F8 = 21 F81 + 21 F82 F9 = F91 The data used in the following equations is from Table 6.3 in [[90], p. 47]. For example, 0.82 in F1 is the number representing the proportion of women reporting in Interview 1 that they were easily tired. The number is substituted for F11 . The number F = 0.4914 represents the intensity of prevalence of physical health symptoms of the group of women interviewed in the first interview. The number itself has the most meaning when compared with the corresponding numbers in Interview 1 and Interview 2. Interview 1 F1 = 13 (0.82) + 13 (0.47) + 13 (0.64) = 0.64 F2 = 27 (0.81) + 27 (0.71) + 27 (0.63) + 17 (0.22) = 0.65 F3 = 23 (0.63) + 13 (0.45) = 0.57 F4 = 16 (0.17) + 16 (0.59) + 16 (0.71) + 16 (0.24) + 16 (0.10) + 16 (0.60) = 0.40 F5 = 21 (0.5) + 21 (0.40) = 0.45 F6 = 27 (0.69) + 17 (0.12) + 17 (0.36) + 27 (0.58) + 17 (0.09) = 0.44 F7 = 0.35 F8 = 21 (0.15) + 21 (0.31) = 0.23 F9 = 0.29 F = 0.15(0.64) + 0.20(0.65) + 0.10(0.57) + 0.10(0.40) + 0.10(0.45) +0.15(0.44) + 0.07(0.35) + 0.08(0.23) + 0.05(0.29) = 0.096+0.130+0.057 + 0.040 + 0.045 + 0.066 + 0.0245 + 0.0184 + 0.0145 = 0.4914 Interview 2 F1 = 13 (0.55) + 13 (0.27) + 13 (0.37) = 0.40 F2 = 27 (0.72) + 27 (0.36) + 27 (0.42) + 17 (0.01) = 0.43 F3 = 23 (0.30) + 13 (0.18) = 0.39

192

F4 F5 F6 F7 F8 F9

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= 16 (0.08) + = 21 (0.30) + = 27 (0.32) + = 0.20 = 21 (0.08) + = 0.15

1 (0.24) + 16 (0.11) + 6 1 F (0.17) = 0.235 2 52 1 (0.08) + 17 (0.18) + 7 1 (0.14) 2

1 (0.07) 6

+ 16 (0.02) + 16 (0.20) = 0.12

2 (0.43) 7

+ 17 (0.01) = 0.21

= 0.11

F = 0.15(0.40) + 0.20(0.43) + 0.10(0.39) + 0.10(0.12) + 0.10(0.235) +0.15(0.21) + 0.07(0.20) + 0.08(0.11) + 0.05(0.15) = 0.06+0.086+0.039 + 0.012 + 0.0235 + 0.0315 + 0.014 + 0.0088 + 0.0075 = 0.2823 We see that there is a significant drop in the severity measure of health problems from Interview 1 to Interview 2, i.e., from 0.4914 to 0.2823. Interview 3 F1 = 13 (0.41) + 13 (0.19) + 13 (0.25) = 0.28 F2 = 27 (0.67) + 27 (0.38) + 27 (0.30) + 17 (0.05) = 0.39 F3 = 23 (0.33) + 13 (0.19) = 0.28 F4 = 16 (0.03) + 16 (0.17) + 16 (0.17) + 16 (0.02) + 16 (0.02) + 16 (0.10) = 0.085 F5 = 21 (0.24) + 21 (0.17) = 0.205 F6 = 27 (0.37) + 17 (0.13) + 17 (0.14) + 27 (0.24) + 17 (0.05) = 0.22 F7 = 0.10 F8 = 21 (0.6) + 21 (0.27) = 0.165 F9 = 0.19 F = 0.15(0.28) + 0.20(0.39) + 0.10(0.28) + 0.10(0.085) + 0.10(0.205) +0.15(0.22) + 0.07(0.10) + 0.08(0.165) + 0.05(0.10) = 0.042 + 0.078 + 0.028+0.0085+0.0205 + 0.033 + 0.007 + 0.0132 + 0.005 = 0.2242. Upper Severity We next consider the situation where women reported symptoms as highly sever. Let S denote severity and U S denote upper severity. We let P(S) denote the proportion of women who reported the presence of a symptom and P(U S) the proportion of women reporting upper severity. Then P(U S) = P(S)P(U S|S), where P(U S|S) means the percentage women reporting upper severity given severity. For example, consider Interview 1, Weight loss: P(S) = 0.47 and P(U S|S) = 0.70. Thus P(U S) = (0.47)(0.70) = 0.329. Interview 1 F1 = 13 ((0.75)(0.82)) + 13 ((0.70)(0.47)) + 13 ((0.59)(0.64)) = 13 (0.615) + 13 (0.329) + 13 (0.3776) = 0.44 F2 = 27 ((0.78)(.81)) + 27 ((0.68)(0.71)) + 27 ((0.70)(0.63)) + 17 ((0.41)(0.22))

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193

= 27 (0.6318) + 27 (0.4828) + 27 (0.441) + 17 (0.0968) = 0.46 F3 = 23 ((0.72)(0.63)) + 13 ((0.66)(0.45)) = 23 (0.4536) + 13 (0.297) = 0.40 F4 = 16 ((0.78)(0.17)) + 16 ((0.79)(0.59) + 16 ((0.73)(0.71)) + 16 ((0.60)(0.24)) + 1 ((0.85)(0.10)) + 16 ((0.82)(0.61)) 6 = 16 (0.1326) + 16 (0.4661) + 16 (0.5183) + 16 (0.144) + 16 (0.085) + 16 (0.5002) = 0.31 F5 = 21 ((0.58)(0.50)) + 21 ((0.54)(0.40)) = 21 (0.29) + 21 (0.216) = 0.25 F6 = 27 ((0.70)(0.69)) + 17 ((0.52)(0.12)) + 17 ((0.68)(0.36)) + 27 ((0.65)(0.58)) + 17 ((0.76)(0.09)) = 27 (0.483) + 17 (0.06) + 17 (0.2448) + 27 (0.377) + 17 (0.0684) = 0.30 F7 = (0.58)(0.35) = 0.203 F8 = 21 ((0.39)(0.15)) + 21 ((0.47)(0.31)) = 21 (0.0585) + 21 (0.1457) = 0.10 F9 = (0.59)(0.29) = 0.1711 F = 0.15(0.44) + 0.20(0.46) + 0.10(0.40) + 0.10(0.31) + 0.10(0.25) +0.15(0.30) + 0.07(0.20) + 0.08(0.10) + 0.05(0.17) = 0.066 + 0.092 + 0.04 + 0.031 + 0.025 + 0.045 + 0.014 + 0.008 + 0.0085 = 0.3295. Interview 2 F1 = 13 ((0.25)(0.55)) + 13 ((0.12)(0.27)) + 13 ((0.24)(0.37)) = 13 (0.1375) + 13 (0.0324) + 13 (0.0888) = 0.086 F2 = 27 ((0.28)(0.72)) + 27 ((0.15)(0.36)) + 27 ((0.22)(0.42)) + 17 ((0.00)(1.00)) = 27 (0.2016) + 27 (0.054) + 27 (0.0924) + 17 (0.00) = 0.099 F3 = 23 ((0.16)(0.30)) + 13 ((0.23)(0.18)) = 23 (0.048) + 13 (0.0414) = 0.046 F4 = 16 ((0.08)(0.08)) + 16 ((0.37)(0.24)) + 16 ((0.17)(0.11)) + 16 ((0.30)(0.07)) 1 + 6 ((0.50)(0.02)) + 16 ((0.27)(0.20)) = 16 (0.0064) + 16 (0.0888) + 16 (0.0187) + 16 (0.021) + 16 (0.01) + 16 (0.054) = 0.033 F5 = 21 ((0.22)(0.30)) + 21 F((0.36)(0.17)) = 21 (0.0.66) + 21 (0.0612) = 0.064 F6 = 27 ((0.17)(0.18)) + 17 ((0.53)(0.08)) + 17 ((0.17)(0.18)) + 27 ((0.20)(0.43)) 1 + 7 ((0.00)(1.00)) = 27 (0.0306) + 17 (0.0424) + 17 (0.0306) + 27 (0.086) + 17 (0.00) = 0.044 F7 = (0.31)(0.20) = 0.062 F8 = 21 ((0.25)(0.08)) + 21 ((0.52)(0.14)) = 21 (0.02) + 21 (0.0728) = 0.046 F9 = (0.21)(0.15) = 0.0315

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F = 0.15(0.086) + 0.20(0.099) + 0.10(0.046) + 0.10(0.033) + 0.10(0.064) +0.15(0.044) + 0.07(0.062) + 0.08(0.046) + 0.05(0.032) = 0.0129 + 0.0198 + 0.0046 + 0.0033 + 0.0064 + 0.0066 + 0.00434 + 0.00368 + 0.0016 = 0.0632.

As with severity, we see that there is a significant drop in the upper severity measure of health problems from Interview 1 to Interview 2, i.e., from 0.3295 to 0.0632. Interview 3 F1 = 13 ((0.28)(0.41)) + 13 ((0.25)(0.19)) + 13 ((0.32)(0.25)) = 13 (0.1148) + 13 (0.0475) + 13 (0.08) = 0.08 F2 = 27 ((0.41)(0.67)) + 27 ((0.12)(0.38)) + 27 ((0.33)(0.30)) + 17 ((0.33)(0.05)) = 27 (0.2747) + 27 (0.0456) + 27 (0.099) + 17 (0.0495) = 0.13 F3 = 23 ((0.43)(0.33)) + 13 ((0.54)(0.19)) = 23 (0.1419) + 13 (0.1026) = 0.13 F4 = 16 ((0.50)(0.03)) + 16 ((0.36)(0.17)) + 16 ((0.27)(0.17)) + 16 ((0.00)(1.00)) 1 + 6 ((1.00)(0.02)) + 16 ((0.50)(0.10)) = 16 (0.0.15) + 16 (0.0612) + 16 (0.0459) + 16 (0.00) + 16 (0.02) + 16 (0.05) = 0.03 F5 = 21 ((0.28)(0.24)) + 21 ((0.36)(0.17)) = 21 (0.192) + 21 (0.0612) = 0.13 F6 = 27 ((0.31)(0.37)) + 17 ((0.43)(0.13)) + 17 ((0.22)(0.14)) + 27 ((0.50)(0.24)) 1 + 7 ((0.00)(0.05)) = 27 (0.1147) + 17 (0.0559) + 17 ((0.0308) + 27 (0.12) + 17 (0.00) = 0.08 F7 = (0.67)(0.10) = 0.067 F8 = 21 ((0.25)(0.06)) + 21 ((0.40)(0.27)) = 21 (0.015) + 21 (0.108) = 0.06 F9 = (0.33)(0.19) = 0.0627 F = 0.15(0.08) + 0.20(0.13) + 0.10(0.13) + 0.10(0.03) + 0.10(0.13) +0.15(0.08) + 0.07(0.067) + 0.08(0.06) + 0.05(0.063) = 0.012+0.026+0.013 + 0.003 + 0.013 + 0.012+0.00469+0.0048 + 0.00315 = 0.0916

8.9 Improvement Rates from Interview 1 to Interview 2 We next present an example which could be used for future research. It deals only with weight loss of women. The following table is from [[90], p. 47]. The numbers are percentages (Table 8.2).

8.9 Improvement Rates from Interview 1 to Interview 2 Table 8.2 Weight loss Weight loss Interview 1 Any 47

195

Interview 1 Upper severity

Interview 2 Any

Interview 2 Upper severity

70

27

12

Interview 1 to Interview 2. Known Percentages Ni = % no symptom for interview i = 1, 2. Si = % symptom for interview i = 1, 2. U Si = % severe symptom for interview i = 1, 2. Unknown Percentages x = % improved from U S1 to S2 \U S2 y = % improved from U S1 to N2 z = % improved from S1 \U S1 to N2 S1 = % of S1 \U S1 that showed no improvement We assume that all victims either improved or had no change. Under this assumption, N1 representing no symptoms for Interview 1 is the sane as N1 representing no change from Interview 1 to Interview 2. Thus we have N1 is a % that reported no change and U S2 is a % that reported no change. Also x + y + z is the percent that reported an improvement. Hence (1) x + y + z + N1 + U S2 + S1 = 100. Now x + y is the % from U S1 that improved (U S2 remained the same). Thus (2) x + y = U S1 − U S2 . Also y + z is the percent that increased to N2 . Hence (3) y + z = N2 − N1 . We also have that

(4) x + S1 = S2 − U S2 .

We see that (3) + (4) = (1). We have S2 − H S2 − H S1 + H S2 ≤ S1 ≤ S1 − U S1 . Now N1 + S1 = 100 and so N1 = 53, S1 = 47, U S1 = 33 and N2 = 73, S2 = 27, U S2 = 3. (These are rounded values from P(U S) = P(S)P(U S|S). Consider

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Interview 1, Weight loss: P(S) = 0.47 and P(U S|S) = 0.70. Thus P(U S) = (0.47)(0.70) = 0.329.) Then x + y + z + 53 + 3 + S1 = 100 x+y = 30 y+z = 20 Solving, we obtain z = 14 − S1 , y = 6 + S1 , x = 24 − S1 .

8.10 Dialectic Synthesis and Human Trafficking Our purpose in this section is to use the triplet thesis-antithesis-synthesis in applications to human trafficking and modern slavery. Our work is based on [48]. We are particularly interested in a country’s government response to its vulnerability to these situations. We apply the ideas presented in [78] which developed a fuzzy logic based dialectic synthesis. Dialectic synthesis can be described as a dynamic and universal method for reasoning by means of the triplet thesis-antithesis-synthesis. This concept is attributed to Georg Wilhelm Friedrich Hegel, [72]. Hegel never used the term himself. It originated with Johann Fichte, [25, 56]. We apply these ideas to the problem of human trafficking, illegal immigration, and modern slavery. We want to stress that our approach is not the only approach that one can take. One’s approach will also depend on the choice of conjunctions, disjunctions, and fuzzy complements used. In [78], it is discussed how dialectic synthesis in a fixed universe of discourse can be generated by interpreting the terms thesis and antithesis as words naming concepts. Starting from a thesis P, there are cases in which the antithesis is its negation P  or in which it is one of the antonyms or opposites P a . It is shown in [78] how it is possible to generate a synthesis P + which is a conjecture reached from such pairs. The setting of fuzzy logic is very well-suited in which to place these notions. This is due in part to the fact that the law of the excluded middle does not hold in fuzzy logic. Also, the complexity of the problem of human trafficking makes for an interesting study of a synthesis possible. As previously discussed, accurate data concerning flow of trafficking in persons is impossible to obtain. We next briefly introduce how the notion of dialectic synthesis could be applied in a fuzzy logic setting. Let X be the universal set of discourse. Let P and P a be opposites. Let m P and m P a be fuzzy subsets of X. Let c be a fuzzy complement. Let P + have membership function m P + . If (m P ∩ m P a )  m cP + , then P + is called a dialectic synthesis for (P, P a ), with respect to c. We introduce the concept of a pair (m P (x), m P a (x)) being unacceptable, where x ∈ X. The negation P  of the imprecise word P has a membership function expressed by f P  = N P ◦ f P while those of its antonyms or opposites P a are expressed by f P a = f P ◦ s P , where N P : [0, 1] → [0, 1] and s P : X → X, [24].

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A study of how governments are combating modern slavery was presented in [21]. The assessment of government responses included 161 countries. In the following, X = {Ci |i = 1, 2, . . . , 161} denotes the set of these countries. In [21], vulnerability values and government response values were given for each country. These values were normalized in [42]. For each country Ci , we let vi denote the normalized vulnerability value and gi the normalized government response value, i = 1, . . . , 161. There are four vulnerability values and four government response values for each country given in [21]. They are as follows: Vulnerability V1 : Civil and political protections V2 : Social health and economic rights V3 : Personal security V4 : Refugee populations and conflict Government Response G 1 : Survivors of slavery are supported to exit slavery and empowered to break cycle of vulnerability G 2 : Effective criminal justice responses are in place in every jurisdiction G 3 : Effective and measurable national action plans are implemented and fully funded in every country G 4 : Laws, policies and programs address attitudes, social systems and institutions that create vulnerability an enable slavery Failure of government responses to combat modern slavery in some states There is evidence that police have not identified victims of modern slavery in past 12 months. Suspected victims do not have a choice about whether or not to remain in a shelter. Victim support services are not available for all victims of modern slavery. Foreign victims are not identified and/or are detained and deported. Criminal laws have disproportionate penalties. Specialist police units do not have necessary resources to be able to operate effectively. Judicial punishments are not appropriate to the severity of the crime and complexity of the offender. Complexity in modern slavery cases is widespread and not investigated. Patterns of abuse of labor migrants are widespread and unchecked. There are laws or policies that prevent or make it difficult for workers to leave abusive employers without punishment. Diplomatic staff are not investigated or prosecuted for alleged complicity or abuse in modern slavery cases. State-sanctioned forced labor exists. Below V stands for the average of the four vulnerabilities and G the average of the four government responses of a country. Define the fuzzy subset m P of X by m P (Ci ) = vi and m P a (Ci ) = gi , i = 1, 2, . . . , 161. Recall that ∧ denotes minimum and ∨ denotes maximum. Let e ∈ (0, 1). Let c be a fuzzy complement with equilibrium e, i.e., c(e) = e.

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Let V = P denote vulnerability of a country to human trafficking and G = P a denote a country’s response to human trafficking. Then V and G are opposites since a high government response should result in a low vulnerability. Let m p and m P a be fuzzy subsets of X , where m P denotes a country’s vulnerability to human trafficking and m P a denotes a country’s government response to human trafficking. The tables in [21] are such that a high number for V represents a high vulnerability and a high number for G represents a high government response. However, one expects a high government response to force a low vulnerability. Hence V and G are opposites. Let c be a fuzzy complement with equilibrium e. For any value in [0, 1] greater than e, we consider to be high and any number less than e to be low. Suppose there exists x ∈ X such that (m P + )c (x) = 0 and (m P ∩ m P a )(x) > 0. In this case, m P ∩ m P a  (m P + )c . Thus m P + is a dialectic synthesis for m P ∩ m P a . Our choice of c will show that both V and G being high for a particular country is not acceptable while, high, low or low, high or low, low is acceptable. We now consider 1 − V and 1 − G, the standard complements of V and G, respectively. Then the tables in [21] are such that a low number for V represents a low vulnerability and a low number for G represents a low government response. Applying the same procedure as above, we obtain both V and G being low for a particular country is not acceptable while high, low or low, high or high, high is acceptable. If we take the intersection of the results of the two procedures, we obtain both high, high and low, low are not acceptable while high, low or low, high is acceptable. In our application to human trafficking, we determine a measure of susceptibility of flow of trafficking from country to country that corresponds to the notion that H (high),L (low) for government response and vulnerability, respectively, yields the lower susceptibility. Due to the lack of data, linguistic terms are used to determine the size of the flow. In our application to modern slavery, we determine which regions confirm that V and G are opposites and which do not. A further examination of both applications should lead to interesting syntheses. Definition 8.10.1 Let e ∈ (0, 1). Let c be the fuzzy complement defined by ∀a ∈ [0, 1], ⎧ ⎨ 1 if a < e, c(a) = e if a = e, ⎩ 0 if a > e. Throughout we let ⊗ denote a t-norm and ⊕ a t-conorm. Let m P and m P a be fuzzy subsets of X, where P and P a are opposites. Set m P + = (m P ⊗ m P a ) . Definition 8.10.2 Let x ∈ X. If m P + (x) ≤ m cP + (x), then x (or m P (x), m P a (x)) is called unacceptable (with respect to c.) Let m Q and m Q a be fuzzy subsets of X, where Q and Q a are opposites. Set m Q + = (m Q ⊕ m Q a ) .

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Definition 8.10.3 Let x ∈ X. Then x (or (m Q (x), m Q a (x)) is called d-unacceptable (with respect toc) if m Q + (x) ≥ m cQ + (x). Proposition 8.10.4 (1) Suppose m cP + (x) = 1. Then x (or m P (x), m P a (x)) is unacceptable. (2) Suppose m cQ + (x) = 0. Then x (or m Q (x), m Q a (x)) is d-unacceptable. Proof Immediate from Definitions 8.10.1 and 8.10.2.



Corollary 8.10.5 (1) Suppose m P + (x) > 0. Then x is unacceptable if and only if m cP + (x) = 1 or e. (2) Suppose m ∗Q (x) < 1. Then x is d-unacceptable if and only if m Q + (x) = 0 or e. Proof (1) x is unacceptable if and only if m P + (x) ≤ m cP + (x). Suppose x is unacceptable. Since m P + (x) > 0, m cP + (x) > 0. Hence m cP + (x) = 1 or e. Conversely, suppose m cP + (x) = 1. Then m P + (x) ≤ m cP + (x). If m cP + (x) = e, then m P + (x) = e and so m P + (x) ≤ m cP + (x). (2) x is d-unacceptable if and only if m Q + (x) ≥ m cQ + (x). Suppose x is dunacceptable. Since m Q + (x) < 1, m cQ + (x) < 1. Hence m cQ + (x) = 0 or e. Conversely, suppose m cQ + (x) = 0. Then m Q + (x) ≥ m cQ + (x). If m cQ + (x) = e, then m Q + (x) = e  and so m Q + (x) ≥ m cQ + (x). Proposition 8.10.6 Suppose  has e as an equilibrium point. (1) If m P (x) ⊗ m P a (x) ≥ e, then x (or m P (x), m P a (x)) is unacceptable. (2) If m Q (x) ⊕ m Q a (x) ≤ e, then x (or m Q (x), m Q a (x)) is d-unacceptable. Proof (1) Let m p (x) ⊗ m P a (x) = t. Then t ≥ e. Now m P + (x) = (m p (x) ⊗ m P a (x)) = t  . Since e is the equilibrium point for  and t ≥ e, we have t  ≤ e. Thus m cP + (x) = 1 if t  < e by the definition of c. If t  = e, then m P + (x) = t  = e = ec = m cP + (x). (2) Let m p (x) ⊕ m P a (x) = t. Then t ≤ e. Now m Q + (x) = (m Q (x) ⊕ m Q a (x)) =  t . Since e is the equilibrium point for  and t ≤ e, we have t  ≥ e. Thus m cQ + (x) = 0 if t  > e by the definition of c. If t  = e, then m Q + (x) = t  = e = ec = m cQ + (x).  Proposition 8.10.7 Suppose  also has e for an equilibrium point. Then  ◦ c = c ◦ . Proof Suppose x > e. Then (x  )c = 1 since x  < e. Now (x c ) = 0 = 1 since x > e. Suppose x < e. Then (x  )c = 0 since x  > e. Now (x c ) = 1 = 0 since x < e.  Suppose x = e. Then (e )c = ec = e and (ec ) = e = e. Theorem 8.10.8 [48] Suppose  is involutive, i.e., a  = a for all a ∈ [0, 1]. (1) If m P (x) ⊗ m P a (x) ≥ e , then (m P (x), m P a (x)) is unacceptable. (2) If m Q (x) ⊕ m Q a (x) ≤ e , then (m Q (x), m Q a (x)) is d-unacceptable.

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Proof (1) Let m P (x) ⊗ m P a (x) = t. Then t ≥ e and so m P + (x) = t  ≤ e = e. Suppose that m P + (x) < e. Then m cP + (x) = 1. Suppose that m P + (x) = e. Then m cP + (x) = ec = e = m P + (x) and so (m P (x), m P a (x)) is unacceptable. (2) Let m Q (x) ⊕ m Q a (x) = t. Then t ≤ e and so m Q + (x) = t  ≥ e = e. Suppose that m Q + (x) > e. Then m cQ + (x)=0. Suppose that m Q + (x) = e. Then m cQ + (x) =  ec =e = m Q + (x) and so (m Q (x), m Q a (x)) is d-unacceptable. Example 8.10.9 Let  denote the standard complement. Suppose e = 0.25. Suppose that m P (x) ⊗ m P a (x) = t > 0.75. Then m P + (x) = t  < 0.75 = 0.25. Thus m cP + (x) = 1. Suppose m Q (x) ⊗ m Q a (x) = t < 0.75. Then m Q + (x) > 0.75 = 0.25. Thus m cQ + (x) = 0. Suppose e = 0.75. Suppose that m P (x) ⊗ m P a (x) = t > 0.25. Then m P = (x) = t  < 0.25 = 0.75. Thus m cP + (x) = 1. Suppose that m Q (x) ⊗ m Q a (x) < 0.25. Then m Q + (x) > 0.25 = 0.75. Thus m cQ + (x) = 0. This example leads one to consider the development of the following definitions. Suppose e = 0.5. If m cP + (x) = 1, then x is said to be unacceptable. Suppose e = 0.25. If m cP + (x) = 1, then x is said to be strongly unacceptable. Suppose e = 0.75. If m cP + (x) = 1. then x is said to be weakly unacceptable. Similar comments can be made for m Q and m Q a . Proposition 8.10.10 (1) Suppose  is such that ∀a ∈ [0, 1], a < e ⇒ a  > e. If m P (x) ∧ m P a (x) < e, then m cP + (x) = 0. (2) Suppose  is such that ∀a ∈ [0, 1], a > e ⇒ a  < e. If m P (x) ∧ m P a (x) > e, then m cP + (x) = 1. Proof (1)m P + (x) = (m P (x) ∧ m P a (x)) > e. Thus m cP + (x) = 0. (2)m P + (x) = (m P (x) ∧ m P a (x)) < e. Thus m cP + (x) = 1.



Corollary 8.10.11 If both m P (x) and m P a (x) are high. Then (m P (x), m P a (x)) is unacceptable. Proof The proof follows by (2) of Proposition 8.10.10.



Proposition 8.10.12 (1) Suppose  is such that ∀a ∈ [0, 1], a < e ⇒ a  > e. If m Q (x) ∨ m Q a (x) < e, then m cQ + (x) = 0. (2) Suppose  is such that ∀a ∈ [0, 1], a > e ⇒ a  < e. If m Q (x) ∨ m Q a (x) > e, then m cQ + (x) = 1. Proof (1)m Q + (x) = (m Q (x) ∨ m Q a (x)) > e. Thus m cQ + (x) = 0. (2)m Q + (x) = (m P (x) ∨ m Q a (x)) < e. Thus m cQ + (x) = 1.



Corollary 8.10.13 If both m Q (x) and m Q a (x) are low. Then (m Q (x), m Q a (x)) is d-unacceptable. Proof The proof follows by (1) of Proposition 8.10.12.



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Example 8.10.14 Suppose c has equilibrium e = 0.5. Let  denote the standard t-norm. Let P denote vulnerability for V1 and P a denote (average) government response. Let B denote Barbados, C denote Cuba, and D denote Dominican Republic. These countries are from the Caribbean and m P + (B) = (0.38 ∧ 0.40) = 0.62 so m cP + (B) = 0, m P + (C) = (0.57 ∧ 0.21) = 0.79 so m cP + (C) = 0, m P + (D) = (0.49 ∧ 0.63) = 0.51 so m cP + (D) = 0. Let A denote Albania. Then m P + (A) = (0.56 ∧ 0.59) = 0.44 so m cP + (A) = 1. Thus (m P (A), m P a (A)) is unacceptable. We have that m Q + (B) = (0.38 ∨ 0.40) = 0.60 so m cQ + (B) = 0. Thus (m Q (B), m Q a (B)) is d-unacceptable. Let e be such that 0 < e < 1. Let c∗ be a fuzzy complement such that c∗ (e) = e, c∗ (a) > e for all a such that 0 ≤ a < e, and c∗ (a) < e for all a such that e < a ≤ 1. Let m P , m P a , and m P + be defined as before. Let x ∈ X. Then (m P (x), m P a (x)) ∗ is said to be unacceptable with respect to c∗ if m cP = (x) ≥ e. Let m Q , m Q a , and m Q + be defined as before. Let x ∈ X. Then (m Q (x), m Q a (x)) ∗ is said to be d-unacceptable with respect to c∗ if m cQ + (x) ≤ e. Proposition 8.10.15 Let x ∈ X. (1) Then (m P (x), m P a (x)) is unacceptable with respect to c∗ if and only if (m P (x), m P a (x)) is unacceptable with respect to c. (2) Then (m Q (x), m Q a (x)) is d-unacceptable with respect to c∗ if and only if (m Q (x), m Q a (x)) is d-unacceptable with respect to c. ∗

Proof (1) Clearly, m cP + (e) = e = m cP + (e). The result follows since for all x ∈ ∗ X \{e}, m cP + (x) > e if and only if x < e if and only if m cP + (x) = 1 and also ∗ m cP + (x) = 0 if and only if x > e if and only if m cP + (x) = 0. ∗ (2) Clearly, m cQ + (e) = e = m cQ + (e). The result follows since for all x ∈ X \{e}, c∗ m Q + (x) < e if and only if x > e if and only if m cQ + (x) = 0 and also m cQ + (x) = 1 if ∗ and only if x < e if and only if m cQ + (x) > e.  We next apply our results to human trafficking. As previously discussed, accurate data concerning flow of trafficking in persons is impossible to obtain. The size of flow from country to country is taken from [77]. It is reported in linguistic terms [77]. Information is provided with respect to the reported human trafficking flow in terms of origin, transit and/or transit, destination. The method of combining linguistic data provides an ideal reason for the use of mathematics of uncertainty to study the problems of trafficking in persons. For example, by assigning numbers in the interval [0, 1] to the linguistic data, the data can be combined in a mathematical way. Consider 0.1, 0.3, 0.5, 0.7, 0.9, where we consider 0.1 and 0.3 as low and 0.7 and 0.9 as high. Let g(C) denote government response of a country C and v(C) denote 1 1 + 1−v(C) the vulnerability of C as determined in [33, 42]. We examine δ(C) = g(C)

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with respect to the pairs, (1)g(C) = high, v(C) = low or (2)g(C) = high, v(C) = high or (3)g(C) = low, v(C) = high or (4)g(C) = low, v(C) = low. Then 1 1 1 1 + 1−0.3 is the biggest g(C) + 1−v(C) can be. (1) 0.7 1 1 1 1 (2) 0.9 + 1−0.7 is the smallest g(C) + 1−v(C) can be. 1 1 1 1 + 1−0.7 is the smallest g(C) + 1−v(C) can be. (3) 0.3 1 1 1 1 (4) 0.3 + 1−0.1 is the smallest g(C) + 1−v(C) can be. We see that (1) is less than (2)(3)(4). Hence δ as measure of susceptibility of flow agrees with the notion that H, L for government response and vulnerability, respectively, yields the lower susceptibility. We illustrate this in the following table which list countries with medium flow to the United States and their average government response and average vulnerability values. G represents government response and V , the vunerability. Similar tables for other countries can be found in [33, 42]. We let c have equilibrium e = 0.5. Med. Flow Krgyzstan Latvia Lithuania Nigeria Phillipines Poland Rep. Korea G = Pa 0.38 0.59 0.29 0.55 0.63 0.64 0.27 V =P 0.35 0.24 0.23 0.69 0.50 0.22 0.29 m P+ 0.65 0.76 0.77 0.45 0.50 0.78 0.73 m cP + 0 0 0 1 0.50 0 0 m Q+ 0.62 0.41 0.71 0.31 0.37 0.36 0.71 m cQ + 0 1 0 1 1 1 0 Note that 0.65 = (0.38 ∧ 0.35) and 0.73 = (0.27 ∧ 0.29) . We see that (m P + (C), is unacceptable for C ∈ {Nigeria, Philippines} We see that (m Q + (C), m cQ + (C)) is d-unacceptable for C ∈ {Krgyzstan, Lithuania, Republic of Korea}. Application: Vulnerability and Government Response to  Modern Slavery n n In [33], the similarity relation, S(E, F) = i=1 E(ei ∧ F( f i ))/ i=1 E(ei ) ∨F( f i )) was used to measure the similarity between the averages of the vulnerabilities and the averages of the government responses as well as the complements of the vulnerabilities averages and government responses of countries, where E(ei ) is the ithe value of the n dimensional vector (e1 , . . . , en ) and F( f i ) is the ith value of the n dimensional vector ( f 1 , . . . , f n ). The reasoning to include the complements is as follows: the data is represented in such a way that a high vulnerability is represented by a large number and a high government response is represented by a large number. The goal was primarily to find the similarity between a low vulnerability and a high government since a high government response should lead to a low vulnerability. Here we wish to compare the four individual vulnerabilities with the four government responses. Matching individual vulnerabilities with individual government responses seemed not very fruitful. Thus we decided to compare the four vulnerabilities with the average of the four government responses and the four government responses with average of the vulnerabilities. Of course other similarity measures could be used as well as other techniques. m cP + (C))

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The countries that make up the following regions can be found in [42]. By breaking the study into the following regions, we can also determine in which regions the hypothesis high government response implies low vulnerability seems to hold. The vulnerabilities and government response values for the countries and the average values of the vulnerabilities and government responses for the countries can be found in [21]. Vi and G i denote the ith vulnerability and government response, respectively, i = 1, 2, 3, 4. V and G denote the average vulnerability and government response, respectively. For example, the Caribbean is made of six countries. Their V1 values are 0.38, 0.57, 0.49.0.63, 0.42, 0.32 and their average government response values are 0.40, 0.21, 0.63, 0.38, 0.67, 0.45. We obtain (0.38 + 0.21 + 0.49 + 0.38 + 0.42 + 0.32) (0.40 + 0.57 + 0.63 + 0.63 + 0.67 + 0.45) = 2.20/3.35 = 0.66.

S(V1 , G) =

We let c denote the standard complement in this section. In the following, if S(Vi , G) < S(Vic , G), then we conclude that a low vulnerability and a high government response are more similar that a high vulnerability and a high government response. A similar conclusion can be made for the situation S(Vi , G c ) > S(Vic , G c ). The following results are from [48]. Caribbean U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.66 0.45 0.67 0.23 0.74 0.56 0.75 0.51 S(U, G c ) 0.74 0.47 0.76 0.20 0.70 0.60 0.70 0.61 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 1, 3. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.74 0.60 0.56 0.62 0.56 0.60 0.53 0.62 S(V c , W ) 0.57 0.69 0.63 0.68 0.84 0.69 0.65 0.73 We have S(V, G j ) < S(V, G cj ) for no j. We have S(V c , G j ) > S(V c , G cj ) for no j. Central and South Eastern Europe U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.62 0.37 0.48 0.35 0.79 0.73 0.80 0.72 S(U, G c ) 0.70 0.50 0.65 0.50 0.65 0.54 0.59 0.53 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 1, 3.

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W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.47 0.45 0.52 0.43 0.58 0.55 0.54 0.72 S(V c , W ) 0.75 0.75 0.69 0.86 0.59 0.54 0.67 0.51 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 1, 2, 3, 4. Central America U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.73 0.68 0.73 0.49 0.87 0.81 0.83 0.62 S(U, G c ) 0.85 0.75 0.81 0.42 0.75 0.74 0.75 0.64 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 1, 2, 3. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.69 0.58 0.74 0.63 0.58 0.60 0.72 0.78 S(V c , W ) 0.57 0.73 0.77 0.85 0.81 0.58 0.79 0.65 We have S(V, G j ) < S(V, G cj ) for j = 2, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 2, 4. Commonwealth of Independent States U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.60 0.61 0.66 0.52 0.76 0.59 0.60 0.57 S(U, G c ) 0.85 0.49 0.51 0.44 0.54 0.77 0.79 0.72 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 4. We have S(Vi , G c ) > S Vic , G c for i = 1. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.65 0.67 0.49 0.73 0.59 0.69 0.53 0.76 S(V c , W ) 0.60 0.75 0.47 0.81 0.77 0.73 0.71 0.78 We have S(V, G j ) < S(V, G cj ) for j = 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 2, 4. Eastern Africa U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.54 0.42 0.63 0.61 0.68 0.59 0.55 0.51 S(U, G c ) 0.79 0.81 0.61 0.56 0.62 0.36 0.77 0.77 We have S(Vi , G) < S(V ic , G) for  i = 1, 2. We have S(Vi , G c ) > S Vic , G c for i = 1, 2.

8.10 Dialectic Synthesis and Human Trafficking

W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.51 0.55 0.45 0.70 0.73 0.73 0.66 0.83 S(V c , W ) 0.61 0.62 0.46 0.79 0.63 0.64 0.61 0.73 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 4. Eastern Asia U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.72 0.73 0.70 0.41 0.53 0.52 0.45 0.42 S(U, G c ) 0.54 0.53 0.37 0.28 0.84 0.85 0.86 0.74 We have S(Vi , G) < S(V ic , G) for  i = 4. We have S(Vi , G c ) > S Vic , G c for no i. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.63 0.74 0.64 0.71 0.46 0.41 0.39 0.48 S(V c , W ) 0.54 0.42 0.37 0.56 0.80 0.88 0.85 0.84 We have S(V, G j ) < S(V, G cj ) for no j. We have S(V c , G j ) > S(V c , G cj ) for no j. Middle Africa U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.33 0.37 0.51 0.49 0.67 0.70 0.49 0.45 S(U, G c ) 0.88 0.87 0.65 0.59 0.32 0.43 0.68 0.69 We have S(Vi , G) < S(V ic , G) for  i = 1, 2. We have S(Vi , G c ) > S Vic , G c for i = 1, 2. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.34 0.35 0.48 0.49 0.75 0.75 0.79 0.81 S(V c , W ) 0.50 0.51 0.64 0.68 0.51 0.51 0.57 0.56 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 3, 4. North America U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.10 0.29 0.12 0.53 0.85 0.88 0.86 0.75 S(U, G c ) 0.36 0.65 0.41 0.53 0.24 0.28 0.24 0.38 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 1, 2, 3, 4.

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W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.26 0.23 0.28 0.30 0.28 0.49 0.76 0.67 S(V c , W ) 0.74 0.88 0.92 0.87 0.26 0.13 0.34 0.38 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 1, 2, 3, 4. Northern Africa U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.50 0.56 0.67 0.57 0.63 0.46 0.54 0.55 S(U, G c ) 0.78 0.51 0.64 0.70 0.57 0.75 0.78 0.69 We have S(Vi , G) < S(V ic , G) for  i = 1. We have S(Vi , G c ) > S Vic , G c for i = 1. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.50 0.63 0.42 0.66 0.65 0.65 0.60 0.84 S(V c , W ) 0.49 0.54 0.39 0.83 0.68 0.74 0.64 0.70 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 4. Oceania U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.02 0.26 0.12 0.43 0.68 0.81 0.73 0.82 S(U, G c ) 0.05 0.53 0.24 0.64 0.34 0.40 0.36 0.46 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 2, 4. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.23 0.18 0.26 0.18 0.35 0.22 0.30 0.61 S(V c , W ) 0.70 0.75 0.63 0.90 0.47 0.26 0.53 0.27 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 1, 2, 3, 4. South America U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.66 0.45 0.55 0.50 0.80 0.65 0.77 0.69 S(U, G c ) 0.76 0.46 0.73 0.55 0.71 0.65 0.61 0.65 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 1, 3.

8.10 Dialectic Synthesis and Human Trafficking

W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.66 0.59 0.50 0.51 0.51 0.66 0.50 0.75 S(V c , W ) 0.53 0.76 0.61 0.86 0.80 0.68 0.62 0.54 We have S(V, G j ) < S(V, G cj ) for j = 2, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 2, 4. South Central Asia U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.77 0.89 0.61 0.59 0.86 0.77 0.76 0.59 S(U, G c ) 0.85 0.79 0.70 0.67 0.78 0.88 0.70 0.49 We have S(Vi , G) < S(V ic , G) for  i = 1, 3. We have S(Vi , G c ) > S Vic , G c for i = 1, 4. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.72 0.79 0.61 0.73 0.78 0.75 0.69 0.71 S(V c , W ) 0.71 0.76 0.71 0.77 0.78 0.78 0.59 0.67 We have S(V, G j ) < S(V, G cj ) for j = 1, 3. We have S(V c , G j ) > S(V c , G cj ) for j = 3, 4. South Eastern Asia U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.65 0.61 0.58 0.53 0.77 0.67 0.72 0.54 S(U, G c ) 0.82 0.74 0.79 0.52 0.70 0.65 0.61 0.67 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 1, 2, 3. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.56 0.63 0.64 0.61 0.70 0.74 0.76 0.76 S(V c , W ) 0.58 0.70 0.73 0.75 0.69 0.68 0.68 0.64 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 2, 3, 4. Southern Africa U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.75 0.56 0.70 0.53 0.71 0.67 0.82 0.51 S(U, G c ) 0.73 0.81 0.87 0.35 0.81 0.48 0.71 0.76 We have S(Vi , G) < S(V ic , G) for  i = 2, 4. We have S(Vi , G c ) > S Vic , G c for i = 2, 3.

207

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W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.69 0.61 0.74 0.79 0.73 0.66 0.77 0.85 S(V c , W ) 0.65 0.59 0.73 0.86 0.77 0.69 0.79 0.79 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 4. Western Africa U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.53 0.59 0.65 0.65 0.74 0.60 0.55 0.44 S(U, G c ) 0.85 0.72 0.62 0.45 0.57 0.67 0.77 0.81 We have S(Vi , G) < S(V ic , G) for  i = 1, 2. We have S(Vi , G c ) > S Vic , G c for i = 1, 2. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.54 0.66 0.65 0.66 0.64 0.66 0.70 0.74 S(V c , W ) 0.49 0.56 0.62 0.68 0.69 0.75 0.74 0.74 We have S(V, G j ) < S(V, G cj ) for j = 1, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for no j. Western Asia and Turkey U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.61 0.58 0.70 0.66 0.84 0.58 0.68 0.59 S(U, G c ) 0.86 0.47 0.65 0.53 0.65 0.75 0.79 0.78 We have S(Vi , G) < S(V ic , G) for  i = 1. We have S(Vi , G c ) > S Vic , G c for i = 1. W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.65 0.69 0.61 0.72 0.65 0.69 0.55 0.65 S(V c , W ) 0.68 0.75 0.52 0.69 0.75 0.76 0.76 0.81 We have S(V, G j ) < S(V, G cj ) for no j. We have S(V c , G j ) > S(V c , G cj ) for no j. Western Europe U V1 V2 V3 V4 V1c V2c V3c V4c S(U, G) 0.21 0.23 0.22 0.51 0.75 0.75 0.72 0.84 S(U, G c ) 0.39 0.41 0.32 0.71 0.41 0.42 0.41 0.52 We have S(Vi , G) < S(V ic , G) for  i = 1, 2, 3, 4. We have S(Vi , G c ) > S Vic , G c for i = 4.

8.10 Dialectic Synthesis and Human Trafficking

209

W G 1 G 2 G 3 G 4 G c1 G c2 G c3 G c4 S(V, W ) 0.34 0.29 0.35 0.33 0.51 0.53 0.40 0.52 S(V c , W ) 0.77 0.85 0.65 0.79 0.49 0.30 0.55 0.44 We have S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4. We have S(V c , G j ) > S(V c , G cj ) for j = 1, 2, 3, 4. We found that S(Vi , G) < S(Vic , G) for i = 1, 2, 3, 4 for the regions Caribbean, Central and South Eastern Europe, Central America, North America, Oceania, South America, South Eastern Asia, and Western Europe. We also found that S(V, G j ) < S(V, G cj ) for j = 1, 2, 3, 4 for the regions Central and South Eastern Europe, Eastern Africa, Middle Africa, North America, North Africa, Oceania, South Eastern Africa, Southern Africa, and Western Europe. We next note a possible application to fuzzy graph theory. Suppose c has equilibrium e. Let a ∈ [0, 1]. If a < e, then we say the value of a is low. If a > e, we say the value of a is high. Let G = (V, E) be a graph. Let σ be a fuzzy subset of V and μ a fuzzy subset of E. Then (σ, μ) is a fuzzy subgraph of G if ∀x, y ∈ V, μ(x y) ≤ σ (x) ∧ σ (y). Now σ  is a fuzzy subset of V, where  is a fuzzy complement with equilibrium point e. Let m P = σ  and m P a = μ. Suppose σ (x) < e and μ(x y) > e. Then m P + (x, x y) = (m P (x) ∧ m P a (x y)) < e = e. Thus m cP + (x, x y) = 1. Hence (x, x y) is unacceptable. That is, a low (< e) σ (x) and a high (> e) μ(x y) is unacceptable. A synthesis could be the assumption μ(x y) ≤ σ (x) with reasons for the assumption. Consider the following for another approach. If P denotes μ(x y) is high and P a denote σ (x) is low, then P and P a are opposites for fuzzy graphs. In this case, μ(x y) > σ (x) ∧ σ (y). If there exists t ∈ [0, 1] such that ∀x, y ∈ V, μ(x y) ∧ t ≤ σ (x) ∧ σ (y), then (σ, μ) is a fuzzy quasi-graph, [37, 42]. Hence the theory of fuzzy quasi-graphs could be a dialectic synthesis for (P, P a ). This idea could be examined in further detail. We introduced the notion of dialectic synthesis using the membership functions m P , m P a , m P + corresponding to the triplet thesis, antithesis, synthesis as an application to the problem of modern slavery. The triple m P , m P a , m P + can also be placed into the framework of neutrosophic logic thus providing a new application for this area. However, the reader should examine the important paper, [29], concerning interval-valued fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets, and neutrosophic fuzzy sets.

8.11 Fuzzy Indices with Applications to Human Trafficking Our goal here goal is to develop indices that can be used to measure the susceptibility of a route to human trafficking. The susceptibility is based on a country’s vulnerability for and its government response to human trafficking. In [22], measurements of government response and vulnerability were provided for 181 countries. The data

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was normalized using the formula (number–minimum)/(maximum–minimum) and the Pearson correlation coefficient was used to determine the correlation between five types of government response and five types of vulnerability. It was determined that comparing government response values with vulnerability values yielded a negative correlation. This is important because it shows that government response and vulnerability are opposites. The results can be found at the of the chapter. Four routes through the Americas to the United States are examined. The indices of two of the measures agree on all four routes. We use an index from [49] to provide a measure of susceptibility of a route with respect to human trafficking. We also use three other indices that provide measures of susceptibility, the Wiener [6], one based Rosenfeld’s distance function, [13, 42, 59] and one called the Country Response Vulnerability (CRV) Index, [49]. These indices are used to find the susceptibility of four main routes through the Americas to the United States. We compare the results of these measures. We find that the two indices with the government response and vulnerabilities values combined to provide the measure of susceptibility agree on all four routes. They show that the route with the highest susceptibility has the Dominican Republic as its origin country. Vulnerability Measure (1) Government issues: Includes political instability, weapons access, women’s physical security, rights for the disabled, political rights, and regulatory equality. (2) Nourishment and access: Includes call phone availability, social “security net”, undernourishment levels,access to clean water, tuberculosis rates, and the ability to borrow money. (3) Inequality: Includes confidence in judicial systems, violent crime, GINI coefficient (wealth inequality), ability to obtain emergency funds. (4) Disenfranchised groups: Includes same sex rights and acceptance of immigrants and minorities. (5) Effects of conflicts: Includes impact of terrorism, internal conflicts fought, and internally displaced persons. Variables dropped after factor analysis (insufficient variance or collinearity): refugees, political rights, civil rights, received wages in past year. GDP, literacy, child mortality, corruption, social safety net (ILO), governmental effectiveness, gender inequality index, and environmental performance index. Governmental Response (1) Support for survivors: Survivors of slavery are supported to exit slavery and empowered to break the cycle of vulnerability. (2) Criminal justice: Effective criminal justice responses are in place in every jurisdiction. (3) Coordination: Effective and measurable national action plans are implemented and fully funded in every country. (4) Response: Laws, policies, and programs address attitudes, social systems, and institutions that create vulnerability and enable slavery. (5) Supply chains: Governments stop sourcing goods or services linked to modern slavery. Let V be a nonempty finite set and E be a subset of the power set of V such that if S ∈ E, then the cardinality of S is two. If {u, v} ∈ E, we write uv for {u, v}.

8.11 Fuzzy Indices with Applications to Human Trafficking

211

Clearly, uv = vu. The pair G = (V, E) is called a graph. The elements of V are called vertices and the elements of E are called edges. A subset P of E is called a path in G if P = {x1 x2 , x2 x3 , . . . , xn−1 xn , xn xn+1 }, where n ∈ N. P is said to be of length n. Let σ be a fuzzy subset of E and let μ be a fuzzy subset E. Then (σ, μ) is called a fuzzy subgraph of G if for all x, y ∈ V, μ(x y) ≤ σ (x) ∧ σ (y). Let τ be a fuzzy subset of E and let ν be a fuzzy subset E. Then (τ, ν) is called a complementary fuzzy subgraph of G if for all x, y ∈ V, ν(x y) ≥ τ (x) ∨ τ (y). We let ⊗ denote the t-norm product and ⊕ the t-conorm algebraic sum. Let Pn (u, v) denote the set of all paths of length n from u to v, where u, v ∈ V, n ∈ N. In our applications, we let σ denote a normalized measure of a country’s government response and τ its vulnerability. Involvement Index In this section, we develop a new index that can be used to provide a measure of the susceptibility of a route to human trafficking. Let G = (V, E) be a graph and μ a fuzzy subset of E. n−1 Definition 8.11.1 Let P denote the path, x1 x2 , . . . , xn−1 xn . Define μI (P) = i=1 m i μ(xi xi+1 ), where m i is the number of subpaths of P in which μ(xi xi+1 ) is the minimum weight in the subpath, i = 1, 2, . . . , n − 1. Then μI (P) is called the μinvolvement of P. The μ-involvement of P can be used to measure the insusceptibility of P to trafficking since in the other measures, the μ-value appears in the denominator. Proposition 8.11.2 Let P denote n−1the path, x1 x2 , . . . , xn−1 xn . Let μI (P) be defined m i is the number of subpaths of P. as in Definition 8.14.1. Then i=1 Proof Every subpath of P has an edge of minimum value. Each μ(xi−1 xi ) appears at least once as the minimum value. Thus the desired result follows.  n−1 Corollary 8.11.3 i=1 m i = n(n − 1)/2. n−1 i = n(n − 1)/2.  Proof The number of subpaths of P is i=1 Define μAI (P) = -involvement of P.

2 μI (P). n(n−1)

Then μAI (P) is called the average μ

Example 8.11.4 Let V = {xi |i = 1, 2, 3, 4, 5, 6}. (1) Define μ : E → [0, 1] as follows: 1 1 1 5 3 , μ(x2 x3 ) = , ν(x3 x4 ) = , μ(x4 x5 ) = , μ(x5 x6 ) = , 8 4 2 8 4 μ(x y) = 0 elsewhere.

μ(x1 x2 ) =

Then μAI (G) =

1 (2.25). 15

Here

1 1 1 5 3 41 μI (G) = 5( ) + 4( ) + 3( ) + 2( ) + 1( ) = . 8 4 2 8 4 8

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Note that 5 + 4 + 3 + 2 + 1 = 15. (2) Now define μ : E → [0, 1] as follows: 1 3 1 5 1 , μ(x2 x3 ) = , ν(x3 x4 ) = , μ(x4 x5 ) = , μ(x5 x6 ) = , 2 4 4 8 8 μ(x y) = 0 elsewhere.

μ(x1 x2 ) =

Then μAI (G) =

1 (2.25). 15

Here

1 1 1 5 3 36 μI (G) = 5( ) + 6( ) + 2( ) + 1( ) + 1( ) = . 8 4 2 8 4 8 Note that 5 + 6 + 2 + 1 + 1 = 15 Proposition 8.11.5 Let G = (V, E) be a graph. Let Pk : x1 x2 , x2 x3 , . . . , xk−1 xk be a path in G of length k − 1. Let n i be the number of times the edge xi xi+1 appears in a subpath of Pk , i = 1, 2, . . . , k − 1. Set n k = 0. Then n i + i is the number of times xi xi+1 appears in a subpath of Pk+1 = Pk ∪ xk xk+1 , i = 1, 2, . . . , k. Proof All subpaths of Pk+1 which are not subpaths of Pk must contain xk+1 . These subpaths are xi xi+1 , xi+1 xi+2 , . . . , xk xk+1 , i = 1, 2, . . . , k. Of these subpaths, xi xi+1 appears i times i = 1, 2, . . . , k. Hence the desired result holds.  Let Pn = {x1 x2 , x2 x3 , . . . , xn−1 xn } be a path of length n − 1. Let m n,i denote the number of times xi xi+1 appears in a subpath of Pn . Example 8.11.6 Consider a path Pn . Then the m n,i are given below for various values of n. For example, m 8,1 = 7 = m 8,7 , m 8,2 = 12 = m 8,6 , m 8,3 = 15 = m 8,5 , m 8,4 = 16 = m 8,4 . n=3: n=4:

22 343

n=5: n=6: n=7:

4664 58985 6101212106

n = 8 : 71215161512 7 n = 9 : 8 14 18 20 20 18 14 8 In the following, we assume that the μ(xi xi+1 ) are distinct. We show that the smallest involvement index would occur if the edge with the smallest weight occurred in the middle of the path and the other edges would work their way to the origin and destination vertices in ascending weight order. This is illustrated in the previous example.

8.11 Fuzzy Indices with Applications to Human Trafficking

213

Lemma 8.11.7 (1) Suppose 3 ≤ i ≤ n2 . Then m n,i − m n,i−1 = m n,i−1 − m n,i−2 + 2. (2) Suppose n2 ≤ i ≤ n. Then m n,n−i − m n,n−i+1 = m n,i−i+1 − m n,n−i+2 + 2. Proof (1) Suppose m k,i − m k,i−1 = m k,i−1 − m k,i−2 + 2, the induction hypothesis. Then m k+1,i − m k+1,i−1 = m k,i + i − (m k,i−1 + i − 1) = m k,i − m k,i−1 + 1 and m k+1,i−1 − m k+1,i−2 = m k,i−1 + i − 1 − (m k,i−2 + i − 2) = m k,i−1 − m k,i−2 + 1. By the induction hypothesis, m k+1,i − m k+1,i−1 = m k+1,i−1 − m k+1,i−2 + 2. (2) The proof here follows in a similar manner.  Proposition 8.11.8 Suppose n ≥ 5. Then m n,i = m n,n−i , i = 1, 2, . . . , n − 1. Proof Suppose i ≤ n2 . Suppose n is odd. There are (n − 3)/2 differences of the form m n,i − m n,i−1 and m n,n−i − m n,n−i+1 to give n − 3 differences in all from m n,1 to m n,n−1 . Now m n,1 = n − 1 = m n,n−1 . From Lemma 8.11.7, each pair of consecutive differences differ by 2. Thus starting from the outside and working in, we have m n,1 = m n,n−1 . In fact, we obtain m n,i = m n,n−i . Suppose n is even. There are (n − 2)/2 differences of the form m n,i − m n,i−1 and m n,n−i − m n,n−i+1 to give n − 2 differences in all from m n,1 to m n,n−1 . Now m n,1 = n − 1 = m n,n−1 . From Lemma 8.11.7, each pair of consecutive differences differ by 2. Thus starting from the outside and working in, we have m n,1 = m n,n−1 . In fact, we obtain m n,i =  m n,n−i . Other Indices The following index was developed for intuitionistic fuzzy graphs in [13]. It was used to measure the susceptibility of a route with respect to illegal immigration in [21, 77]. We extend the definition to other types of fuzzy graphs. Let (σ, μ) be a fuzzy subgraph of a graph G = (V, E) and let (τ, ν) be a complementary fuzzy subgraph of G. Definition 8.11.9 Define the fuzzy subset δ of V × V by for all (u, v) ∈ V × V, δ(u, v) = ∧{

n   i=1

 1 + |Pn ∈ Pn (u, v), n ∈ N}. μ(xi−1 xi ) 1 − ν(xi−1 xi ) 1

Definition 8.11.10 [13] Define e : V → R by for all u ∈ V, e(u) = ∨{δ(u, v)|v ∈ V |}. The function e is called the eccentricity of u. Let n ∈ N. Define μn (u, v) = ∨{μ(x0 x1 ) ∧ · · · ∧ μ(xn−1 xn )|P : u = x0 , x1 , . . . , xn−1 = v is a path}. Define ν n (u, v) = ∧{ν(x0 x1 ) ∨ · · · ∨ ν (xn−1 xn ) |P : u = x0 , x1 , . . . , xn−1 = v is a path}. Let μ∞ (uv) = ∨{μn (u, v)|n ∈ N} and ν ∞ (u, v) = ∧{ν n (u, v)|n ∈ N}. We say that

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(u, v) is a strong pair if μ(uv) ≥ μ∞ (u, v) and ν(uv) ≤ ν ∞ (u, v). If (u, v) is a strong pair, then μ(uv) > 0 and ν(uv) < 1. 1 Theorem 8.11.11 [13] Suppose (u, v) is a strong pair. Then δ(u, v) = μ(uv) + 1 . 1−ν(uv) Let (σ, μ) be a fuzzy subgraph of a graph G = (V, E) and let (τ, ν) be a complementary fuzzy subgraph of G. Let u, v ∈ V. Then the pair (u, v) is called μ-effective if μ(uv) = σ (u) ∧ σ (v). The pair is called ν-effective if ν(uv) = τ (u) ∨ τ (v). The fuzzy subgraph F = (σ, μ) is called complete if (u, v) is μ-effective for all u, v ∈ V. The complementary fuzzy graph C = (τ, ν) is called complete if (u, v) is ν-effective for all u, v ∈ V. The pair (F, C) is called complete if both F and C are complete. μ is called strong if μ(uv) = σ (u) ∧ σ (v) for all uv ∈ V and ν is called strong if ν(uv) = τ (u) ∨ τ (v) for all u, v ∈ V.

Lemma 8.11.12 [13] Let (σ, μ) be a fuzzy subgraph of a graph G = (V, E) and let (τ, ν) be a complementary fuzzy subgraph of G. Then (1) If (u, v) is μ -effective, then (u, v) is μ -strong pair. (2) If (u, v) is ν-effective, then (u, v) is ν-strong pair. Proposition 8.11.13 Let (σ, μ) be a fuzzy subgraph of a graph G = (V, E) and let (τ, ν) be a complementary fuzzy subgraph of G. Suppose for all x, y, u, v ∈ V that μ(x, y) ≤ μ(u, v) if and only if ν(x, y) ≥ ν(u, v). If (F, C) is complete, then for all u ∈ V, 1 1 + . e(u) = ∧{σ (v)|v ∈ V } 1 − ∨{τ (v)|v ∈ V } Proof Since (F, C) is complete, we for all v ∈ V, v = u, that μ(uv) = σ (u) ∧ σ (v) and ν(uv) = τ (u) ∨ τ (v). Thus e(u) = ∨(δ(u, v)|v ∈ V } 1 1 = ∨{ + |v ∈ V } μ(uv) 1 − ν(uv) 1 1 + , = ∧{μ(uv)|v ∈ V } 1 − ∨{ν(uv)|v ∈ V } where the last equality holds from the hypothesis.



Definition 8.11.14 Let (σ, μ) be a fuzzy subgraph of a graph G = (V, E) and let (τ, ν) be a complementary fuzzy subgraph of G. Let V = {x1 , . . . , xm }. Then σ (x1 i ) + 1 is called the susceptibility of xi with respect to human trafficking. Let 1−τ (xi )  n  1 + 1−τ1(xi ) . P : x1 x2 , . . . , xn−1 xn be a path in G. Define C RV (P) = i=1 σ (xi ) Then C RV (P) is called the susceptibility of P with respect to human trafficking. We next discuss the Wiener Index. Let σ denote government response and τ denote vulnerability. Let u, v ∈ V. We let μ(uv) = σ (u) ⊗ σ (v) denote the measure of

8.11 Fuzzy Indices with Applications to Human Trafficking

215

success in combatting human trafficking with respect to uv and ν(uv) = τ (u) ⊕ τ (v) denote the measure of failure in combatting human trafficking with respect to uv. We use the length of route (path) defined by Rosenfeld to provide measures determining the success in combating human trafficking with respect to government n 1 . response. The definition of the μ-length of a path P : x0 , x1 , . . . , xn is i=1 μ(xi−1 xi ) We let μl(P) denote the μ-length of P. The numbers in [77] provide high numbers if the vulnerability of a country is high. The standard complement of these numbers then provides high numbers if the vulnerability is low. Consequently, we are more interested in the complement of of ν. Then the vulnerability Weuse ν c to denote the standard complement n ratings. n n 1 1 1 c c c . Note (ν ) l(P) = ν l(P) = i=1 ν c (xi−1 xi ) = i=1 i=1 1−ν c (xi−1 xi ) = 1−ν(xi−1 xi ) n 1 i=1 ν(xi−1 xi ) = νl(P). Let F = (σ, μ) be a fuzzy subgraph of the graph (V, E). Let P(u, v) = {P|P is a  path from u to v} We define the Wiener Index of F, written μW I, to be u,v∈V l(u, v), where l(u, v) = ∧{μl(P)|P ∈ P(u, v)}. Then μW I provides a measure of susceptibility of trafficking with respect to a fuzzy graph. We let μAW I denote n1 μW I, where n is the number of edges in the fuzzy graph. The indices νW I and ν c W I are defined similarly. It is important to note that the higher the success in combating trafficking with respect to a part, the lower the Wiener Indices. Application of Indices The table below is used to determine the main routes in the Americas with respect to trafficking. It was taken from [77]. In [77], details of the reported trafficking in persons situation of the country or region under analysis was provided. Information was provided with respect to the reported human trafficking in terms of origin, transit, and/or destination according to a citation index. Whether a country ranked (very) low, medium, or (very) high depended upon the total number of sources which made reference to the country as one of origin, transit, or destination. The countries heading the rows are origin countries while those heading the columns are destination countries. The measures of the amount of flow are given as L (low), M (medium), and H (high). We let t stand for transit. The tables are from [49] (Table 8.3). Routes of trafficking and the σ, μ, τ, and ν values are given in the following tables. Let (σ, μ) be a fuzzy subgraph of a graph G = (V, E) and let (τ, ν) be a complementary fuzzy subgraph of G. The σ and τ values in the following table are normalized government response and vulnerability values of [21], respectively. The μ and ν values are determined by the product of the σ values and the algebraic sum of the τ values, respectively.

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Table 8.3 Main routes Ar Be Br Ch Co CR Cu DR Ec ES Gu Ho Me Ni Pa US Ve Argentina Beliz Brazil L Chile Columbia Costa Rica Cuba Dom. Rep. M Ecuador El Salvador Guatamala Honduras Mexico Nicaragua Panama United States Venzuela

t M

L L

L M L

L L

L L M

L L M L

M

M t

M

L

L

L M

M

L

M

L t

L M M M

M L L M L

M M L H L L L

We next consider the main paths in the Americas that lead to the United States. Columbia →Costa Rica →El Salvador→ Guatemala →Mexico → US Col. Cost. El Sa. Guat. Mexi. U.S. σ 0.53 0.55 0.43 0.56 0.62 0.88 μ 0.29 0.24 0.24 0.34 0.55 τ 0.42 0.27 0.36 0.42 0.47 0.23 ν 0.57 0.53 0.63 0.69 0.59

1 1 1 1 1 1 1 + + + + + + 0.29 0.24 0.24 0.34 0.55 1 − 0.57 1 − 0.53 1 1 1 + + + 1 − 0.63 1 − 0.69 1 − 0.59 = 3.45 + 4.17 + 4.17 + 2.94 + 1.82 + 2.33 + 2.13 + 2.70 + 3.23 + 2.44

δ(Columbia, U.S.) =

= 29.38 = e(Columbia)

8.11 Fuzzy Indices with Applications to Human Trafficking

217

1 1 1 1 1 1 + + + + + 0.53 0.55 0.43 0.56 0.62 0.88 1 1 1 1 + + + + 1 − 0.42 1 − 0.27 1 − 0.36 1 − 0.42 1 1 + + 1 − 0.47 1 − 0.23 = 1.89 + 1.82 + 2.33 + 1.79 + 1.61 + 1.14 + 1.72 + 1.37 + 1.56

C RV (Columbia, U.S.) =

+1.72 + 1.89 + 1.30 = 20.20.

This path from Columbia to U.S. consists of five edges. The edges Columbia → Costa Rica and Mexico → US appear 5 times each and the other 3 edges appear 8 times in subpaths. Thus μW I = 5(3.45) + 8(4.17) + 8(4.17) + 8(2.94) + 5(1.82) = 116.59 1 μAW I = (116.59) = 33.18 5 ν c W I = 5(2.33) + 8(2.13) + 8(2.70) + 8(3.23) + 5(2.44) = 88.36 1 ν c AW I = (88.36) = 17.62 5

μI = 0.29 + 11(0.24) + 2(0.35) + 0.55 = 3.78 1 μAI = (3.78) = 0.25 15 ν c I = 0.57 + 8(0.53) + 2(0.63) + 0.69 + 3(0.59) = 8.53 1 (8.53) = 0.57 ν c AI = 15 Columbia →Ecuador →Honduras → Guatemala →Mexico → US Col. Ecu. Hond. Gua. Mexi. U.S. σ 0.53 0.51 0.39 0.56 0.62 0.88 μ 0.27 0.20 0.22 0.35 0.55 τ 0.42 0.35 0.43 0.42 0.47 0.23 ν 0.62 0.63 0.67 0.69 0.59

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1 1 1 1 1 + + + + 0.27 0.20 0.22 0.35 0.55 1 1 1 1 1 + + + + + 1 − 0.62 1 − 0.63 1 − 0.67 1 − 0.69 1 − 0.59 = 3.70 + 5.00 + 4.55 + 2.86 + 1.82

δ(Columbia, U.S.) =

+2.63 + 2.70 + 3.03 + 3.23 + 2.44 = 31.96 = e(Columbia)

1 1 1 1 1 1 + + + + + 0.53 0.51 0.39 0.56 0.62 0.88 1 1 1 1 1 1 + + + + + + 1 − 0.42 1 − 0.35 1 − 0.43 1 − 0.42 1 − 0.47 1 − 0.23 = 1.89 + 1.96 + 2.56 + 1.79 + 1.61 + 1.14 +1.72 + 1.54 + 1.75 + 1.72 + 1.89 + 1.30 C RV (Columbia, U.S.) =

= 20.86 This path from Columbia to U.S. consists of five edges. The edges Columbia → Ecuador and Mexico → US appear 5 times each and the other 3 edges appear 8 times in subpaths. Thus μW I = 5(3.70) + 8(5.00) + 8(4.55) + 8(2.86) + 5(1.82) = 126.88 1 μAW I = (126.88) = 25.38 5 ν c W I = 5(2.63) + 8(2.70) + 8(3.03) + 8(3.23) + 5(2.44) = 97.03 1 ν c AW I = (97.03) = 19.41 5

μI = 0.27 + 8(0.20) + 3(0.22) + 2(0.35) + 0.55 = 3.78 1 μAI = (3.78) = 0.25 15 ν c I = 4(0.62) + 3(0.63) + 2(0.67) + 0.69 + 5(0.59) = 9.35 1 (9.35) = 0.62 ν c AI = 15 Nicaragua →Costa Rica →El Salvador →Guatemala →Mexico → U. S.

8.11 Fuzzy Indices with Applications to Human Trafficking

219

Nica. Cost. El Sa. Gua. Mexi. U.S. σ 0.59 0.55 0.43 0.56 0.62 0.88 μ 0.32 0.24 0.24 0.35 0.55 τ 0.35 0.27 0.36 0.42 0.47 0.23 ν 0.52 0.53 0.63 0.69 0.59

1 1 1 1 1 + + + + 0.32 0.24 0.24 0.35 0.55 1 1 1 1 1 + + + + + 1 − 0.52 1 − 0.53 1 − 0.63 1 − 0.69 1 − 0.59 = 3.12 + 4.17 + 4.17 + 2.86 + 1.82 + 2.08 + 2.13 + 2.70 + 3.23 + 2.44

δ(Nicaragua, U. S.) =

= 28.89 = e(Nicaragua)

1 1 1 1 1 1 + + + + + 0.59 0.55 0.43 0.56 0.62 0.88 1 1 1 1 1 1 + + + + + + 1 − 0.35 1 − 0.27 1 − 0.36 1 − 0.42 1 − 0.47 1 − 0.23 = 1.69 + 1.82 + 2.33 + 1.79 + 1.61 + 1.14 +1.54 + 1.37 + 1.56 + 1.72 + 1.89 + 1.30 C RV (Nicaragua, U.S.) =

= 18.15 This path from Nicaragua to U.S. consists of five edges. The edges Nicaragua → Ecuador and Mexico → US appear 5 times each and the other 3 edges appear 8 times in subpaths. Thus μW I = 5(3.12) + 8(4.17) + 8(4.17) + 8(2.86) + 5(1.82) = 114.30 1 μAW I = (114.30) = 28.60 5 ν c W I = 5(2.08) + 8(2.13) + 8(2.70) + 8(3.23) + 5(2.44) = 87.08 1 ν c AW I = (87.08) = 17.56 5

μI = 0.32 + 11(0.24) + 2(0.35) + 0.55 = 4.21 1 μAI = (4.21) = 0.28 15 ν c I = 5(0.52) + 4(0.53) + 2(0.63) + 0.69 + 3(0.59) = 8.94 1 ν c AI = (8.94) = 0.60 15

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Dominican Republic →Panama →Costa Rica →El Salvador→ Guatemala →Mexico → U.S. Dom. Pan. C. R El Sa. Gua. Mex. U.S. σ 0.63 0.48 0.55 0.43 0.56 0.62 0.88 μ 0.30 0.26 0.24 0.24 0.35 0.55 τ 0.39 0.33 0.27 0.36 0.42 0.47 0.23 ν 0.59 0.51 0.53 0.63 0.69 0.59

1 1 1 1 1 1 + + + + + 0.30 0.26 0.24 0.24 0.35 0.55 1 1 1 1 1 1 + + + + + + 1 − 0.59 1 − .051 1 − 0.53 1 − 0.63 1 − 0.69 1 − 0.59 = 3.33 + 3.85 + 4.17 + 4.17 + 2.86 + 1.82

δ(Dom. Rep.,U.S.) =

+2.44 + 2.04 + 1.89 + 2.70 + 3.23 + 2.44 = 34.94 = e(Dom. Rep.)

1 1 1 1 1 1 1 + + + + + + 0.63 0.48 0.55 0.43 0.56 0.62 0.88 1 1 1 1 1 + + + + + 1 − 0.39 1 − 0.33 1 − 0.27 1 − 0.36 1 − 0.42 1 1 + + 1 − 0.47 1 − 0.23 = 1.59 + 2.03 + 1.82 + 2.33 + 1.79 + 1.61 + 1.14

C RV (Dom. Rep., U.S.) =

+1.64 + 1.49 + 1.37 + 1.56 + 1.72 + 1.89 + 1.30 = 23.21.

This path from Dominican Republic to U.S. consists of six edges. The edges Dominican Republic → Panama and Mexico → US appear 6 times each, the edges Panama → Costa Rica and Guatemala → Mexico appear 10 times, and the edges Costa Rica → El Salvador and El Salvador → Guatemala appear 12 times in subpaths. Thus μW I = 6(3.33) + 10(3.85) + 12(4.17) + 12(4.17) + 10(2.86) + 6(1.82) = 210.80 1 μAW I = (210.80) = 35.13 6 ν c W I = 6(2.44) + 10(2.04) + 12(1.89) + 12(2.70) + 10(3.23) + 6(2.44) = 137.06 1 ν c AW I = (137.06) = 22.84 6

8.11 Fuzzy Indices with Applications to Human Trafficking

221

μI = 0.30 + 2(0.26) + 15(0.24) + 2(0.35) + 0.55 = 5.67 1 (5.67) = 0.27 μAI = 21 ν c I = 4(0.59) + 10(0.51) + 4(0.53) + 2(0.63) + 0.69 = 11.53 1 ν c AI = (11.53) = 0.55 21 We have that μAI < ν c AI and μAW I > ν c AW I for all four routes. This shows that both the Wiener index and the involvement index are in agreement because the μ and ν c values appear in the denominator for the Wiener index while they appear in the numerator for the involvement index. We also have that δ(Dominican Republic,US) > δ(Columbia,US) > δ(Columbia,US) > δ(Nicaragua,US) and C RV (Dominican Republic,US) > C RV (Columbia,US) > C RV (Columbia,US) > C RV (Nicaragua,US). The correlation results between five types of government response and five types of vulnerability are given in the following tables. In the tables, G 1 denotes support for survivors, G 2 justice system, G 3 government plans for responding to slavery, G 4 laws in place, G 5 supply of potential victims, V1 denotes governance issues, V2 nourishment, V3 inequality, V4 disenfranchised, V5 conflict and Prev denotes Prevalence (estimated number of slaves per 100,000. V1 should have the strongest negative correlation with government metrics by definitions (which it does). Correlations between vulnerability measures and government response measures are present, but relatively weak This is because of how the indices were built—principal components analysis was used on a variety of global/local measurements and these were the 10 resulting components. PCA vectors are meant to explain the maximum variability in the data with the minimum inter-vector correlation. All Countries

Prev

V3 V2 0.4 V1 0.6 0.5 0.4 0.4 0.1

V4 0.4 0.4 0.4 0.2

V5 0.2 0.4 0.4 0.5 0.1

G1 −0.2 −0.2 −0.3 −0.5 −0.7 −0.4

G2 0.7 −0.2 −0.2 −0.2 −0.4 −0.6 −0.4

G3 0.6 0.6 −0.1 −0.2 −0.2 −0.4 −0.6 −0.3

G4 0.6 0.6 0.6 −0.3 −0.3 −0.2 −0.5 −0.6 −0.4

G5 0.4 0.4 0.4 0.4 −0.2 −0.3 −0.3 −0.4 −0.6 −0.1

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8 Human Trafficking

Africa

V3 V2 0.3 V1 0.3 0.3 Prev 0.4 0.3 0

V4 0.2 0.4 0.1 0.2

V5 −0.1 0.4 0.2 0.5 0.1

G2 G1 0.6 −0.1 0 0 −0.2 −0.1 −0.2 −0.1 −0.1 −0.7 −0.5 −0.4 − 0.5 −0.5

G3 0.4 0.5 0 0 −0.1 −0.1 −0.3 −0.4

G4 0.5 0.5 0.5 −0.2 0 −0.1 −0.2 −0.6 −0.4

Americas

Prev

V3 V2 0.4 V1 0.6 0.5 0.4 0.4 0.1

V4 0.4 0.4 0.4 0.2

V5 0.2 0.4 0.4 0.5 0.1

G1 −0.2 −0.2 −0.3 −0.5 −0.7 −0.4

G2 0.7 −0.2 −0.2 −0.2 −0.4 −0.6 −0.4

G3 0.6 0.6 −0.1 −0.2 −0.2 −0.4 −0.6 −0.3

G4 0.6 0.6 0.6 −0.3 −0.3 −0.2 −0.5 −0.6 −0.4

G5 0.4 0.4 0.4 0.4 −0.2 −0.3 −0.3 −0.4 −0.6 −0.1

Asia & Pacific

Prev

V3 V2 0.4 V1 0.6 0.5 0.4 0.4 0.1

V4 0.4 0.4 0.4 0.2

V5 0.2 0.4 0.4 0.5 0.1

G1 −0.2 −0.2 −0.3 −0.5 −0.7 −0.4

G2 0.7 −0.2 −0.2 −0.2 −0.4 −0.6 −0.4

G3 0.6 0.6 −0.1 −0.2 −0.2 −0.4 −0.6 −0.3

G5 0.2 0.1 0.1 0 0 0 0 −0.2 −0.1 −0.1

G4 0.6 0.6 0.6 −0.3 −0.3 −0.2 −0.5 −0.6 −0.4

G5 0.4 0.4 0.4 0.4 −0.2 −0.3 −0.3 −0.4 −0.6 −0.1

8.11 Fuzzy Indices with Applications to Human Trafficking

223

Europe & C. Asia

Prev

V3 V2 0.4 V1 0.6 0.5 0.4 0.4 0.1

V4 0.4 0.4 0.4 0.2

V5 0.2 0.4 0.4 0.5 0.1

G1 −0.2 −0.2 −0.3 −0.5 −0.7 −0.4

G2 0.7 −0.2 −0.2 −0.2 −0.4 −0.6 −0.4

G3 0.6 0.6 −0.1 −0.2 −0.2 −0.4 −0.6 −0.3

G4 0.6 0.6 0.6 −0.3 −0.3 −0.2 −0.5 −0.6 −0.4

Arab States

Prev

V3 V2 0.4 V1 0.6 0.5 0.4 0.4 0.1

V4 0.4 0.4 0.4 0.2

V5 0.2 0.4 0.4 0.5 0.1

G1 −0.2 −0.2 0.3 −0.5 −0.7 −0.4

G2 0.7 −0.2 −0.2 −0.2 −0.4 −0.6 −0.4

G3 0.6 0.6 −0.1 −0.2 −0.2 −0.4 −0.6 −0.3

G4 0.6 0.6 0.6 −0.3 −0.3 −0.2 −0.5 −0.6 −0.4

G5 0.4 0.4 0.4 0.4 −0.2 −0.3 −0.3 −0.4 −0.6 −0.1

G5 0.4 0.4 0.4 0.4 −0.2 −0.3 −0.3 −0.4 −0.6 −0.1

We applied four indices to measure the susceptibility of human trafficking routes in the Americas to the United States. Similar determinations can be found for the other four regions, Africa, Asia & Pacific, Europe and Central Asia, and Arab States.

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Index

A AHP method, 127 Algebraic product, 2, 15 Algebraic sum, 3 Analytical hierarchy process, 6 Analytic hierarchy process, 13, 46, 176 Anthropogenic emissions, 140

B Basic probability assignment, 4 Belief measure, 3 Belief of a set, 4 Biodiversity, 141 Biodiversity protection, 36 Body of evidence, 4 Bounded difference, 2, 15 Bounded sum, 3

C Cardinality, 1 Characteristic function, 1 Citation index, 53 Clean energy, 38 Climate action, 39 Climate change, 37, 137, 138, 149 Climate Change Performance Index, 143 Climate mitigation, 36 Complement, 2 Component, 12 Contrapositive operator, 11 Credibility distribution, 5 Cycle, 12

D Degree of sameness, 10 Dempster-Shafer theory, 5, 140 Dialectic synthesis, 196 Disaster reduction, 37

E Eccentricity, 213 Ecosystem, 141 Evidence theory, 3

F Focal element, 4 Forced labor, 149 Foster care, 179 Foster care deficiencies, 175 Fuzzy bridge, 12 Fuzzy complement, 197 Fuzzy logic, 1 Fuzzy power set, 1 Fuzzy set, 1 Fuzzy set theory, 53 Fuzzy similarity measure, 7 Fuzzy subgraph, 12 Fuzzy subset, 1, 125

G Gender equality, 38 Global warming, 137, 140 Governmental response, 210 Government response, 197

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 J. N. Mordeson and S. Mathew, Sustainable Development Goals: Analysis by Mathematics of Uncertainty, Studies in Systems, Decision and Control 299, https://doi.org/10.1007/978-3-030-48523-8

229

230 Greenhouse gases, 137 Guiasu method, 5, 13, 46, 48, 127, 176 H Human trafficking, 45, 128, 149, 175, 179 Hybrid monotonous, 10 I Immigration, 46 Implication operation, 9 Intersection, 2 Involutive, 11 Involutive operator, 11 Involvement index, 211

Index Sexual exploitation, 149, 187 Similarity measure, 124, 166 Similarity ranking, 127 Soil erosion, 37 Stakeholder, 159 Stakeholder method, 166 Stakeholder ranking, 127 Standard intersection, 14 Standard union, 3 Strength, 12 Strongest path, 12 Strong pair, 213 Support, 2 Susceptibility, 214 Sustainability, 5 Sustainable development goals, 5, 13, 45, 150

L Level set, 2 M Modern slavery, 46, 137, 159, 175 N Norm operations, 3 P Paris agreement, 140, 142 Partial fuzzy subgraph, 12 Path, 12 Pearson correlation coefficient, 209 Plausibility measure, 3 Poverty, 38

T t-conorm, 3 Thesis-antithesis-synthesis, 196 t-norm, 2, 14, 40, 46

U Union, 2

V Vulnerability measure, 210

W Weighted factors, 176

R Rankings, 117 Renewable energy, 145

Y Yen method, 6, 13, 46, 48, 127, 176, 178

S Scoring assessment, 39, 47

Z Zero Hunger, 38