Plane Thermoelastic Waves in Infinite Half-Space Caused Operational Research in Engineering Sciences: Theory and Applications Vol. 5, Issue 2, 2022, pp. 117-151 ISSN: 2620-1607 eISSN: 2620-1747 DOI: https://doi.org/10.31181/oresta060722090g * Corresponding author. hakangokhangundogdu@anadolu.edu.tr (H. G. Gündoğdu), ahmetaytekin@artvin.edu.tr (A. Aytekin) EFFECTS OF SUSTAINABLE GOVERNANCE TO SUSTAINABLE DEVELOPMENT Hakan Gökhan Gündoğdu 1, Ahmet Aytekin 2* 1 Department of Political Science and Public Administration, Faculty of Economics, Anadolu University, Turkey 2 Department of Business Administration, Faculty of Hopa Economics and Administrative Sciences, Artvin Çoruh University, Turkey Received: 06 April 2022 Accepted: 20 June 2022 First online: 06 July 2022 Research paper Abstract: Sustainable development advocates effective and efficient planning of both present and future use of resources. Governance, on the other hand, is based on the joint and coordinated management of multidimensional variables, which is the basis of the sustainability approach. This study aims to determine how much sustainable governance influences the fulfillment of multidimensional sustainable development. Multiple regression analysis was used to determine the variables that reveal the impact of governance on development in terms of sustainability while the gray relational analysis method was used to rank the countries. The results reveal that increases in the number of people using the internet in society, as well as in the levels of developments in e-government and human development, environmental performance, and political reform, all assist countries achieve their SDGs. Furthermore, it was found that governance has a positive and significant impact on SDGs. In addition, an MCDM model consisting of BWM and gray relational analysis was used to evaluate countries based on their performance in sustainable development, the economic, governance and environment. The gray relational analysis results, on the other hand, revealed that developed and wealthy countries ranked first, while underdeveloped countries experiencing instability, such as war and conflict, ranked last. The Nordic countries outperform other countries in terms of governance and sustainability, depending on the strength of their democracy and executive capacity. Key words: Sustainable Development, Sustainable Governance, Best Worst Method, Gray Relational Analysis. 1. Introduction Production and consumption needs have become more prominent as development resources because of the rise of industrialization, excessive resource Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 118 use, and environmental degradation have been criticized as the main culprits (Caradonna, 2014). The considerable rate of economic expansion experienced during the "Golden Age of Capitalism" (Marglin & Schor, 1991; Middleton, 2000; Skidelsky, 2009), particularly following the Second World War, underscored the necessity to strike a balance between development and the environment (Caradonna, 2014). The development of sustainability and governance initiatives has been accelerated by factors such as increased competitiveness and development on a global, regional, and local scale, diversification of commercial and public sector service provision, and the avoidance of climate change and pollution. Sustainable development has become a key idea while addressing issues in different fields, and governance indicators have been used as solution tools (Meadowcroft, 2007). The global ecosystem, on the other hand, is negatively impacted by global population growth and the resulting increase in production and consumption requirements. For example, the world population, which was around four billion in 1975, has almost doubled to 8 billion by 2021 (Worldometer, 2021). This massive increase has several negative consequences for the environment, including global warming and climate change. Therefore, the importance of future population, production, and consumption control and transformation into planned sustainable development has resurfaced. There are also global obstacles such as education and health issues, poverty, inequality, and the recent COVID-19 pandemic, all of which have a negative impact on the development of all countries. To achieve the Sustainable Development Goals (SDGs), it is essential to solve these problems and use resources wisely. Moreover, it has once again become apparent that countries must collaborate and coordinate their efforts to attain these goals (Barbier & Burgess, 2020). The study focuses on the impact of governance on the sustainability of development, which is linked to systematic and planned development (sustainable development). Furthermore, the variables presented in this study are used to assess the relationship and change of sustainable governance to sustainable development. As a result, the purpose of this study is to examine how the independent variables connected to sustainable governance affect the variable of sustainable development. In this study, regression analysis will be used to determine the ones that are effective on sustainable development among sustainable governance indicators. Regression models, on the other hand, reflect the existence and degree of relationships between variables, but they cannot reveal the superiority of the countries, which are the study's units, over one another. A multi-criteria decision model will be used to assess countries' performance in terms of both sustainable governance and sustainable development in this context. It will be possible to provide policy suggestions as a result of the multi-criteria decision analysis by determining the positive features of the prominent countries and the negative features of the remaining countries. As a result of the application of the regression model and the multi-criteria decision model, a holistic evaluation will be provided. To determine the causality and effect levels between the variables, multiple regression analysis (MRA) will be used. Gray Relational Analysis (GRA) will be used to rank countries' performance in terms of sustainable development and governance. GRA was selected for the study because it provides a comparable solution to the references to be determined in the criteria. In addition, the Best-Worst Method (BWM) was chosen to determine the weight values of the criteria because it provides consistency with fewer pairwise comparisons than other methods in the literature. Effects of Sustainable Governance to Sustainable Development 119 There is a positive and statistically significant association between sustainable governance and sustainable development, according to the literature. To put it another way, as countries' levels of sustainable governance increase, so do their degree of sustainable development. It is critical for countries to concentrate on sustainable governance policies to achieve long-term sustainable development. Studies on sustainable development, which fall under the category of quantitative analysis, have mostly been the focus of investigations1 in domains such as economics, business, the environment, and energy. However, no research has been found that analyzes the link between sustainable governance and sustainable development. Some studies have specifically explored the relationship between governance and sustainable development (Meadowcroft, 2007; Kardos, 2012; Stojanović et al., 2016; Davis, 2017; Güney, 2017; Omri & Ben Mabrouk, 2020). Others have investigated the link between one facet of sustainable development and the quality of governance (Rajkumar & Swaroop, 2008; Farag et al., 2013; Jindra & Vaz, 2019). None of these studies focused on the role of sustainable governance in achieving sustainable development and evaluated them from the perspective of public administration. Accordingly, this study offers significant contributions to the empirical literature on the interaction between environmental, economic, social, political, and technological variables, which are the components of sustainable governance, and sustainable development. The study seeks to explore the existence of a connection between economic, technological, human, and legal development in sustainable governance and sustainable development. We also look at the variables which may be considered to have a substantial effect among the variables often considered in this area. Do the rankings determined using GRA differ between developed, emerging, and underdeveloped (high, medium, and low level) countries? This study is primarily based on the responses to these two questions, as well as the related evaluations. In addition, the study incorporated data from 149 high, middle, and low-income nations from a variety of international agencies. The data for the study's independent variables were compiled by merging current data from international institutions. In this regard, the study stands out for its inclusiveness and for contributing to the field in a current manner. According to the results of the research, individuals using the internet in society and in e-government development contribute to SDGs. Similarly, human development, environmental performance, and political transformation have all had a favorable impact on the SDGs. Governance, in addition to all these variables, has been shown to have a substantial impact on SDGs. First, the background of sustainable development will be examined in the chapters of this study and a theoretical framework will be developed for the link between sustainable governance and sustainable development. Second, information on the dimensions affecting sustainable development and sustainable governance will be provided. Then, the research method, research findings, and their interpretation are included. Finally, the research findings are evaluated. 1 For these studies, see: (Stojanović et al., 2016; Davis, 2017; Güney, 2017; Glass & Newig, 2019; Jindra & Vaz 2019; Omri & Ben Mabrouk, 2020). Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 120 2. Literature Following the 1970s, the topic of sustainability has been a major topic in a variety of sectors, particularly in the environment and economy. Meeting the requirements emerging from rapid population expansion, utilizing resources evenly, and safeguarding the natural environment have all been recognized as issues of research in the context of sustainable development (Harborth, 1991). The substantial increase in consumption, rapid population expansion, and economic growth in this period had severe consequences on the natural environment, causing environmental problems to reach a worldwide scale (Meadows et al., 1972; Turner, 2008). As a consequence, the necessity for a balanced interaction between development and the natural environment has emerged, prompting solution proposals for "sustainable future planning." Sustainable development has been frequently used as a solution tool in this context since the 1980s. This notion has been evaluated in particular by associating it with economic progress in the face of global difficulties, efficient use of natural resources, and resolving social and environmental challenges. In this context, the literature will be discussed in the study in several subsections. 2.1. The evolution of the sustainable development concept In the literature, there is no one, agreed-upon definition for the terms sustainability and sustainable development. The way researchers approach the subject may differ in both concepts. However, sustainability can be defined as the continuing of something that already exists (Meadowcroft, 1997). Sustainability is conceptually linked to a wide range of themes in the literature. In this context, studies for the welfare of future generations, equality policies for the fair distribution of incomes across generations, studies for global environmentalism, and biodiversity policies for maintaining the ecological balance are some of these issues (Basiago, 1999). Sustainability research has been performed in a wide variety of fields, including economic (Jackson, 2009), financial (Quayes, 2012), environmental (Goodland, 1995; Morelli, 2011), social (Torjman, 2000), political (Patashnik, 2003), socio-cultural (Chiu, 2004), corporate (Bansal, 2005), digital (Funk, 2015; Gouvea et al., 2018) and urban (Alberti, 1996). Moreover, the studies on the relationship between digitalization, or technological transformation, and sustainability (Funk, 2015; Gouvea et al., 2018; Kostoska & Kocarev, 2019; del Río Castro et al., 2020) have exploded in popularity in recent years. In this context, a group of academics has drawn attention to the link between digital transformation, big data, and sustainable society, and have proposed the "Digital Transformation and Sustainability" model for achieving sustainable development (Pappas et al., 2018). Furthermore, while some researchers proposed models for studies in various fields related to sustainability (Boulanger & Bréchet, 2005; Bebbington et al., 2007), others drew attention to criticisms on various issues related to sustainable development (De Graaf et al., 1996; Marcuse, 1998; Robinson, 2004). Sustainable development has been characterized in the literature as a crucial concept that "solves all problems" (Fischer-Kowalski & Haberl, 1998), and various scientific studies have been conducted on this subject (Barbier, 1987; Harborth, 1991; Harris, 2000; Ciegis et al., 2009). Economic, social, and environmental/ecological policies are all evaluated equally and simultaneously at all Effects of Sustainable Governance to Sustainable Development 121 stages of sustainable development in this framework (Basiago, 1999; Harris, 2000; Bell & Morse, 2003; Ciegis et al., 2009). To put it another way, research on sustainable development often emphasizes that it is not possible to achieve sustainable development solely through economic efficiency (Garrod & Fyall, 1998; Harris, 2000; Ciegis et al., 2009; Morelli, 2011). In this context, sustainable development attempts to construct a multidimensional and socioeconomic system that considers factors like income, education, living standards, and health (Ciegis et al., 2009). On the other hand, there is a discussion of strong and weak sustainability in terms of the fact that resources can be substituted or not substituted according to their original forms in appropriate situations. The key topic of discussion in this context is the contrasts in sustainability between the environment and the economy (Ayres et al., 2001). As a result, the concept of sustainable development is a fundamental concept that may be applied to a wide range of fields and different perspectives. The notion of sustainable development was used particularly in terms of industrialized countries' ability to achieve balanced growth and effective resource management in all sectors, including the environment, the economy, and security (McKenzie, 2004). The UN World Commission on Environment and Development's report "Our Common Future2" in 1987 provided the most comprehensive and widely acknowledged explanation of the idea of sustainable development (Basiago, 1999). The notion of sustainable development is defined in the report as "development that seeks to meet the needs of the present without compromising the ability of future generations to meet their own needs" (WCED, 1987). Besides, after the publication of this report, the idea of "sustainable development" has become a contentious and vital topic in the public debate (Mitcham, 1995). Another key feature of the aforementioned report is that it emphasizes the significance of establishing justice (equality between generations) between present and future generations, not merely on the basis of economic efficiency in-country growth or development (Garrod & Fyall, 1998). As a result, rather than focusing on a one-dimensional and limited view of growth, a multidimensional and inclusive development model was highlighted. On the issues of environment and development, the "UN Conference on Environment and Development," also known as the "Rio Conference," was held in 1992. In 1993, the United Nations Commission on Sustainable Development was founded. Various conferences, summits, and forums were organized in the following years to discuss decisions on sustainable development and environmental protection. The "Millennium Development Goals" (MDGs), which support country development and were implemented between 2000 and 2015, were one of the most important moves done in recent years in terms of sustainable development. In the period 2001-2015, the MDGs made some progress in developing countries. Developed and underdeveloped countries, on the other hand, painted a picture of development that fell far short of expectations throughout the same time period (Sachs, 2012). SDGs that broaden the scope and limitations of the MDGs has come to the fore in the UN as of the end of this period (Biermann et al., 2017). In contrast to 2 The Report, commonly known as the Brundtland Report, addresses worldwide problems and solutions for the common future. Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 122 previous development goals, SDGs include more comprehensive and holistic aims as well as a vision of progress (Le Blanc, 2015; Fukuda-Parr, 2016). In 2015, the "UN Sustainable Development Summit3" on sustainable development took hold in addition to all of these events. At this Summit, 17 SDGs were adopted, with all member states committing to achieving them between 2015 and 2030. These goals are made up of 17 major goals and 169 sub-goals that have been endorsed as an urgent call to action by all UN member states. In this framework, it aims to overcome global challenges on important issues such as education, poverty, inequality, climate change, global warming, environmental degradation, economic growth and innovation, peace, and justice, which are relevant to all countries and should be implemented4 (UN General Assembly, 2015). In addition to these issues, the COVID- 19 outbreak is still affecting humans worldwide as of 2020. During the pandemic process, all countries' ability to reach their 2030 goals, especially economic growth, has been interrupted. 2.2. The sustainable governance and sustainable development: What's the connection? The term "governance" refers to multidimensional management involving formal and informal actors (Huther & Shah, 1998; Hyden et al., 2004; Gündoğdu, 2020). The concept was officially used for the first time in the World Bank's 1989 report on Africa's development. This report emphasized the importance of under-developed and developing countries having proper governance processes or mechanisms to develop by creating a link between development and governance (World Bank, 1989). Moreover, the notion of governance was used to relate to the concepts of accountability, openness, and transparency in a 1992 report from the same agency (World Bank, 1992). Several international agencies, including the UN, the OECD, and the IMF, have used the concept of governance in the years afterward. The UN's "MDGs" and research on the issue underline the relevance of the idea of "governance" (UN, 2007). Some researchers have attempted to explain the definition of governance in the literature (e.g., Huther & Shah, 1998; Pierre, 2000; Hyden et al., 2004; Benz, 2007; Treib, Bähr & Falkner, 2007; Bevir, 2009; Osborne, 2010). Treib, Bähr, and Falkner (2007) define governance as a multidimensional notion that incorporates various actors, processes, structures, and agencies engaged in political decision-making and execution. To put it another way, in order to comprehend governance, the government must be viewed as a "cooperative state," and decision-making procedures must be developed in collaboration with the public, private sector (market), non-governmental organizations, and citizens (Benz, 2007; Osborne, 2010). In this regard, governance emphasizes the coordination, cooperation, and harmony of actors at all levels (Pierre, 2000). Besides, Gündoğdu (2019) stresses that multi-level governance and participatory democracy will evolve as a collaborative strategy involving numerous actors. 3 Every year, the UN General Secretariat also publishes a "report" on the SDGs, which covers current progress. 4 In addition, there is a sustainable development index/indicator that ranks and evaluates countries based on the SDGs (Kroll, 2015). Effects of Sustainable Governance to Sustainable Development 123 The concept of sustainable governance is defined as “socio-political governance processes that contribute to the realization of sustainable development” (Meadowcroft, 2007). As a result, sustainable governance plays a significant role in the sustainable management of various actors (Awuzie & Monyane, 2020) as well as the achievement of countries' long-term goals (Aytekin & Gündodu, 2021). Governance, in particular, is critical to achieving the SDGs and overcoming global issues (UN, 2012). The "sustainable governance index" is a crucial tool for measuring a country's level of sustainable governance. The sustainable governance index and the SDGs are complementary in this context. For example, in order to achieve strong and sustainable governance, countries have to overcome issues such as economic globalization, social inequality, climate change, resource scarcity, and demographic transition (Brusis & Siegmund, 2011). For the SDGs, a similar explanation applies. In this context, an answer is sought to the extent to which countries are successful in economic, social, and environmental policies, both in the sustainable governance indicators and in the SDGs. There have been studies that show that there is a theoretical link between sustainable development and governance (Kemp, Parto & Gibson, 2005; Sachs, 2012). Several studies have concluded that using a sustainable governance approach to natural catastrophes and crisis management is critical in this context (Ahrens & Rudolph, 2006; Ansell et al., 2010; Tierney, 2012). Some studies, according to Rothstein and Teorell (2008), underline that there is a significant relationship between economic growth and governance, which they regard as a critical component of development. Similarly, in previous research on the ties between sustainable development and sustainable governance, economic, social, and ecological factors, as well as relationships between official and non-official agencies, have been mentioned (Spangenberg, 2002; Meadowcroft et al., 2005). Various studies have been conducted examining the impact of governance on development outcomes. In this context, it has been discovered that in countries with a high level of governance, it has a regulatory and considerable effect on public health and primary education expenditures (Rajkumar & Swaroop, 2008; Farag et al., 2013). According to certain studies, there is a strong link between a country's per capita income and its degree of governance quality (Campos & Nugent, 1999, Kaufmann et al., 1999; Kaufmann et al., 2010; Fayissa & Nsiah, 2013). As a result, it has been stressed that governance is critical to a country's development and attainment of higher wealth levels (Oster, 2009). Another study concluded that, in the next years, sustainability will make more positive development if governance worldwide improves (Joshi et al., 2015). In other studies, the relationship between corruption prevention, which is a component of governance, and sustainable development has been investigated, and it has been discovered that there is a negative relationship between increased corruption and sustainable development (Aidt, 2009; Bentzen, 2012). Additionally, Lennan and Ngoma (2004) stressed the significance of institutional capacity building to support good governance and sustainable development. The literature-based on quantitative analysis (Rajkumar & Swaroop, 2008; Stojanović et al., 2016; Davis 2017; Güney, 2017; Glass & Newig, 2019; Omri & Ben Mabrouk, 2020) emphasizes that there is a multidimensional relationship between Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 124 sustainable development and governance. In this context, Stojanović et al. (2016) used the World Bank governance indicator data set to establish the relationship between sustainable development and governance. Davis (2017) has examined the associations between good governance and human development indicators in Sub- Saharan Africa. Güney (2017), on the other side, used the Adjusted Net Saving indicator to examine the relationship between sustainable development and governance in 121 countries using data spanning the years 1996 to 2012. Moreover, Jindra and Vaz (2019) discussed the relationship between multidimensional poverty prevention, which is a component of sustainable development, and governance quality. Glass and Newig (2019) used multiple governance indicators (participation, policy coherence, reflexivity, adaptation, and democratic institutions) to examine SDG achievement in 41 high and upper-middle-income countries. Finally, Omri and Ben Mabrouk (2020) analyzed data on governance and sustainable development from 1996 to 2014 to study countries in 20 MENA (Middle East and North Africa-) areas. As can be seen from all these studies, it is desirable to analyze and quantify the institutional development and governance quality of countries based on certain variables in studies where sustainable development and governance indicators are accepted as data. Ultimately, it has been underlined that the relationship between sustainable development and governance is multidimensional and interdependent. 3. Methodology The study's aim is to obtain a comprehensive evaluation based on the results of two different models. The MRA will be used to investigate the relationship between sustainable development and sustainable governance in this context. Countries are the units considered in the MRA. The BWM-GRA multi-criteria decision model will be used to assess the countries' sustainable development and governance performance. Figure 1 depicts the methodology used in the study. Figure 1. The scheme of methodology Effects of Sustainable Governance to Sustainable Development 125 Different variables or indicators are used to measure sustainability goals and the dimensions associated with these goals, as shown by the literature (Munda and Nardo, 2005; Gasparatos et al., 2008; Wu and Wu, 2012; Diaz-Balteiro et al., 2017; Croissant and Pelke, 2022). However, the relative superiority, validity, and reliability of these various indicators and data are debatable. Because of this problem, researchers have looked for a single variable/criterion from sources that measure the same variable/criterion with different units. For this reason, data collected from various sources with the aim of measuring the same variable were standardized and integrated. As a result, data that was comparable and clear of measurement differences were created. The study was done using data collected from a variety of sources (BTI, 2021; Data World Bank, 2021; Freedom House, 2021; Human Development Reports, 2021; SDGs Database, 2021; SGI, 2021; The Economist Intelligence Unit, 2021; Worldometer, 2021; WVS, 2021; WJP, 2021). In this context, Political Transformation, Political Participation, Rule of Law, Quality of Democracy, Political Integration, Economic Transformation, and Governance variables were created using various indicators, and data sources. The normalization process was used to eliminate data measurement differences and create a one-dimensional data frame that was comparable. Eq. (1) has been applied in this context. * ij j ij j j x x z x x − − − = − (1) In Eq. (1), the best value in the related indicator is * j x , while the worst value is j x − . Because of the normalization process, the best value is 1 and the worst value is 0 in the indicators. In the variables formed by integrating more than one indicator, the arithmetic average of the normalized values of the relevant indicators was used. Table 1 shows the indicators that were used to form the variables. Table 1. Indicators and variables Notation Variables/Criteria Indicators C1 SDG SDG Index C2 Political Transformation BTI-Stateness, SGI-Executive Capacity, WGI- Political Stability and Absence of Violence/Terrorism, WGI-Government Effectiveness, WGI-Regulatory Quality C3 Political Participation Freedom House-Freedom Index, BTI-Political Participation, SGI- Citizens' Participatory Competence C4 Rule of Law WGI-Rule of Law, WJP- Rule of Law Index, BTI- Rule of Law, SGI-Rule of Law C5 Quality of Democracy BTI- Stability of Democratic Institutions, SGI- Quality of Democracy C6 Political Integration BTI-Political and Social Integration, SGI- Social Policies C7 Economic Transformation BTI- Economic Performance, SGI-Economic Policies C8 Governance BTI-Governance Index, SGI-Governance Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 126 C9 HDI HDI C10 Democracy Index Democracy Index C11 CPI CPI Score C12 E-government E-Government Index C13 EPI EPI C14 CO2 Emissions CO2 emissions (metric tons per capita) C15 GDP Growth GDP growth (annual %) The effects of governance variables on the SDG will be investigated using regression analysis in this study. The variables C1-C8 in Table 1 will be used in the regression analysis in this context. Also, countries will be ranked according to their levels of sustainable development and sustainable governance using Gray Relational Analysis, one of the multi-criteria decision-making methods. C1-C15 criteria will be considered in GRA evaluations. As a result, it aims to provide a more comprehensive assessment. In the following part, it will be given some explanatory information about MRA, and GRA used in this study. 3.1. Multiple Regression Analysis The regression analysis is a collection of procedures that uses one or more independent variables to explain changes in a dependent variable. At the end of this process, the model specified in Eq. (2) is obtained where dependent variable is Y, independent variables are i X , constant term is 0  , regression coefficient of p variables are p  , error term is  , and p=1,…,k (Tabachnick & Fidell, 2013; Kalaycı, 2014; İslamoğlu & Alnıaçık, 2014). 0 p p Y X  = + + (2) In Eq. (2),  denotes the error caused by variables that were not included in the analysis for various reasons, whereas 0  represents the value of the dependent variable when all the independent variables regression coefficient values in the model are zero. The null hypothesis that all regression coefficients for the p independent variable are equal to zero and the alternative hypothesis that at least one regression coefficient is different from zero are both tested in multiple linear regression analysis. The t-test is used to determine the singular significance of the specified parameters, while the F-test is used to determine the model's overall significance. The assumptions of normal distribution, linearity, zero mean of error terms, constant variance, no autocorrelation, and no multiple correlations must all be met in multiple linear regression analysis. Additionally, the level of explanation of the change in the dependent variable of the independent variables included in the model can be calculated as a percentage using the coefficient of determination, 2 R . If the model contains many independent variables, the adjusted coefficient of determination, 2 R , is used instead of 2 R (Tabachnick & Fidell, 2013; Kalaycı, 2014; İslamoğlu & Alnıaçık, 2014). Among the recent studies in which the MRA has been used, we can indicate financial risk measurement and prediction (Valaskova et al., 2018), evaluating the impact of corporate social sustainability culture on financial success (Schönborn et al., 2019), the influence of different aspects of governance, namely participation, policy coherence, reflexivity, adaptation and democratic institutions on SDG Effects of Sustainable Governance to Sustainable Development 127 achievement (Glass & Newig, 2019), determining the factors influencing the integration of sustainability indicators into a company’s performance management system (Zharfpeykan & Akroyd, 2022), and investigating the factors attracting the population (Kokubun, 2022). 3.2. Best-Worst Method Weighting processes are used to determine the importance levels of the criteria on the solution of multi-criteria decision problems. There are numerous methods for determining criteria weighting based on the data structure of a decision matrix or subjective evaluations of experts/decision-makers. Subjective weighting techniques based on pairwise comparisons are frequently used in this context. BWM, one of the pairwise comparison methods, will be used in this study. In general, for n criteria, n(n-1)/2 comparisons are usually required in the pairwise comparison-based techniques. The large number of pairwise comparisons appears to be a significant impediment to effective weighting, especially in problems with a large number of criteria. When compared to the commonly used AHP (Analytic Hierarchy Process), which provides weighting with pairwise comparisons, BWM allows the weighting process to be completed with fewer pairwise comparisons. FUCOM (Full Consistency Method), which has a similar structure to BWM, prevents inconsistency in expert evaluation and ensures complete consistency. However, BWM allows for pairwise criteria comparisons in the context of the most and least important criteria. As a result, BWM was preferred because it allows comparison with both the least important and most important criteria, making the expert feel at ease with the evaluations. Furthermore, BWM, like FUCOM, allows for the measurement of consistency analysis using a mathematical programming model and reduces pairwise comparison inconsistency (Rezai, 2015; 2016; Aytekin, 2020). On the other hand, BWM provides weighting based on the subjective assessments of experts or decision makers. As a result, it is lacking in objectivity. BWM also employs Saaty's 1-9 Fundamental Scale. Criticisms of the Saaty Fundamental Scale are valid for BWM. In the study, BWM will be used to obtain criteria weight values based on expert judgments in a way that minimizes inconsistency. BWM has recently been used to solve decision problems such as wagons for the internal transport (Stević et al., 2017), evaluating financial performance of companies (Aytekin, 2020), off-road vehicle selection (Pamučar & Savin, 2020), supplier selection for biofuel companies (Kazemitash et al., 2021), analyzing barriers to industrial sharing economy (Govindan et al., 2020). Implementation steps of BWM are outlined below (Rezai, 2015; Rezai, 2016; Aytekin, 2020). Step 1: Determine the criteria to be used: The criteria that will be used to solve multi-criteria decision-making problems are identified. Step 2: Determine the most important and the least important criteria: Among the criteria, the most important (the best) and least important (the worst) criteria are determined. B denotes the most important criterion, while K denotes the least important criterion. Step 3: Make pairwise comparisons of criteria based on the most important one: The Saaty 1-9 Fundamental Scale is used to determine the importance level of the most important criterion in relation to other criteria, and the vector in Eq. (3) is created. Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 128 ( )1 2, , ,B B B BnA a a a= K (3) When using Saaty's 1-9 Fundamental Scale to determine the importance level of B according to the j criterion, a value of 1 indicates equal importance, a value of 2 indicates very little importance, and a value of 3 indicates a little more importance. Similarly, a value of 4 denotes more than a little importance, a value of 5 denotes strong importance, a value of 6 denotes slightly more than strong importance, a value of 7 denotes very strong importance, a value of 8 denotes more than very strong importance, and a value of 9 denotes absolute importance (Saaty, 1977; Aytekin & Durucasu, 2020). Step 4: Make pairwise comparisons based on the least important criteria: The Saaty 1-9 Fundamental Scale is used to determine the importance levels of the criteria other than the least important criteria in relation to the least important criteria, and the vector in Eq. (4) is created. ( )1 2, , , T K K K nK A a a a= K (4) Step 5: Calculate the optimal criteria weight values: The weight values of the criteria are determined using the linear programming model in Eq. (4). The aim of this process is to make the largest of the B Bj j w a w − and the smallest of j jK K w a w − differences for each j criterion. The purpose of Equation (5) is to find criterion weights that minimize the value of ξ. 1 to , , 1 0, B Bj j j jK K n j j j Min Subject w a j w w a j w w w j    = −   −   =    (5) Step 6: Check consistency: In this step, the consistency of pairwise comparisons of criteria is determined. ξ shows the model's inconsistency in Eq. (5). As a result, it is tried to achieve high consistency criterion weight values. Rezai (2016) proposed the consistency index (CI) in Table 2 for the control of consistency in the context of the importance level of the most important criterion relative to the least important criterion (aBK). Table 2. Consistency Index aBK 1 2 3 4 5 6 7 8 9 CI (enb ξ) 0,00 0,44 1,00 1,63 2,30 3,00 3,73 4,47 5,23 Effects of Sustainable Governance to Sustainable Development 129 Table 2 shows the maximum acceptable ξ values based on the number of criteria. The fact that the objective function ξ value obtained from solving the model in Equation (4) is less than the value in Table 1 indicates that the comparisons are consistent. Also, the consistency ratio (CR, or ξ*) given in Eq. (6) can also be used for consistency analysis. CR CI  = (6) While the CR value is between 0 and 1, it is important to note that consistency increases as it approaches 0, and inconsistency increases as it approaches 1. BWM is said to produce more consistent and reliable results than other weighting techniques (Rezai, 2015; Rezai, 2016). 3.3. Grey Relational Analysis Julong (1989) proposed Gray System Theory to solve problems with insufficient or uncertain information. Gray System Theory is based on the idea that understanding a system is insufficient to construct a relational analysis or a model to characterize it. Gray is employed to express uncertain or incomplete information in this theory. White denotes the possession of certain/complete information, while black denotes the absence of such information. Systems analysis, data processing, modeling, forecasting, decision making, and control are all fields where Gray Theory is applied. Gray Relational Analysis (GRA) is a form of quantitative analysis that involves the evaluation of alternatives and is used in the field of decision making. At this point, Gray Theory, like Fuzzy Set Theory, has a mathematical structure that can process weak information (Julong, 1989; Wu, 2002; Lin & Liu, 2004; Sallehuddin et al., 2008; Tzeng & Huang, 2011). As previously stated, the data used in the evaluation of countries was compiled from various sources and normalized. The values of the indicators in such data are difficult to interpret. In other words, as the values of an indicator rise, the level of sustainability rises or falls, but there is no direct equivalent of this value. As a result, the Gray Relational Analysis method, which allows for the creation of a comparability series known as a reference series, was chosen for the study by taking into account the performances of the alternatives with incomplete information. The reference series is used to calculate the gray relational coefficient values for the alternatives. Finally, the gray relationship degrees are calculated using these values. If an alternative has the highest gray relational degree with the reference series, it means that the corresponding alternative is the most similar to the reference series and will be the best choice (Liu et al., 2013; Biswas et al., 2014). However, problems can arise when using the normalization operation, which is commonly used in GRA, in decision matrices containing some data structures, such as the reference series value being 0 or greater than the values in the decision matrix (Aytekin, 2021a). Different normalization techniques can be used in this case to generate a comparable decision matrix. GRA, which is widely used in the field of multi-criteria decision making, provides a solution by defining the ideal values (points) for each criterion in the decision matrix and using this reference series to measure the relational degree of the alternatives. As a result, the alternative with the highest degree of relation is chosen Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 130 as the solution. GRA might be called a reference-based method because of this basic feature. Furthermore, many integrated decision models and fuzzy derivatives where GRA is used in combination with other multi-criteria decision-making methods can be found in the literature. Supplier selection (Yang & Chen, 2006), determination of the most appropriate parameters in the drilling process (Tosun, 2006), wastewater treatment method selection (Zeng et al., 2007), facility layout (Kuo et al., 2008), sustainable electricity generation planning (Malekpoor et al., 2018), identification of factors affecting Taiwan's economic growth (Huang et al., 2020), and evaluation of countries' climates are examples of decision problems to which GRA is applied (Niazi et al., 2021). Among the recent studies in which GRA has been used, we can indicate evaluation of healthcare service quality factor (Aydemir & Şahin, 2019), measurement of city sustainability (Yi et al., 2021), investigation of life cycle assessment barriers for sustainable development (Kaswan & Rathi, 2021), evaluation of water quality (Tao et al., 2022), and sustainable industrialization performance evaluation of European Union countries (Candan & Cengiz Toklu, 2022). The GRA process steps can be summarized as follows (Wu, 2002; Tzeng & Huang, 2011): Step 1. Construct the decision matrix: The decision matrix X indicated in Eq. (7) is constructed where i=1,…,m alternatives and j=1,…,n criteria. 11 1 1 n m mn x x X x x     =       K M O M L (7) Step 2. Create the reference series: The ideal values for each criterion are determined to generate a reference series ( 0 j x ). The reference series can be assigned independently of the decision matrix by the decision maker. The values in the decision matrix, on the other hand, are primarily considered in the GRA implementation, and the best ones are determined as a reference. The reference series is obtained with Eq. (8) if the best values in the decision matrix are used as a reference. ( ) ( ) 0 max , minj ij ij ii x x j J x j J + − =   (8) J + denotes for benefit-oriented criterion, while J − shows for cost-oriented criteria in Eq. (8). Step 3. Construct the normalized decision matrix: The normalized matrix is constructed using Eq.s (4-5), depending on how the ideal values are derived. When a reference is decided in the context of the decision matrix's values, Eq. (9) is used, and when a reference is determined independently of the decision matrix, Eq. (10) is used. * max max min , max , max min ij ij j ij ij jj ij ij ij j ij ij jj x x x x j J x x x j J x x + + −  −  =  −   − (9) Effects of Sustainable Governance to Sustainable Development 131 0* 0 max ij j ij ij j j x x x x x − = − (10) Other reference-based normalization techniques can be used if the operation specified in Eq. (10) does not provide effective normalization under certain decision matrices (Aytekin, 2021a). Step 4. Calculate the distances between the alternatives from the references: Eq. (11) is used to compute the distances of the alternatives from the reference series using the normalized values, where * 0 j x is the normalized reference value for the criterion j. * * 0ij j ij x x = − (11) ij  represents the distance between the alternative i and the reference series in criterion j in Eq. (11). As a result, the distance matrix Δ will be constructed according to Eq. (12). 11 1 1 n m mn       =       K M O M L (12) Step 5. Calculate gray relational coefficients: To calculate gray relational coefficients, first determine the largest and smallest values in the Δ matrix, as well as the discriminant coefficient (ζ). The largest and smallest values in the Δ matrix are determined using Eq.s (13-14). max max max ij i j  =  (13) min min min ij i j  =  (14) The ζ coefficient regulates the relationship between min  and max  values by taking a value in the range [0,1]. To put it another way, the range of the ζ coefficient and gray relationship coefficient can be increased or compressed. The ζ coefficient is generally defines as 0.5 for averaging. After determining the ζ, min  and max  values, Eq. (15) is used to derive the gray relational coefficients ( ij  ). min max max ij ij     +  =  +  (15) Step 6. Calculate the gray relational degrees: Eq. (16) is used to determine the gray relational degree (Γi), which is a measure of how similar the alternatives are to the reference series. It takes into consideration weighting of criteria. 1 1 1 , the criteria are not weighted , the criteria are weighted n ij j i n ij j j n w   = =     =       (16) Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 132 Alternative’s closeness to the reference series representing ideal solutions is measured by the Γi value. As a result, the problem's solution is finished by ordering the alternatives from largest to smallest based on the Γi values. 4. Results MRA is used to examine causality relationships in this study, which is discussed in the context of sustainable governance and development. The countries were then evaluated using the GRA in terms of sustainable development and governance. Four different models were used in the MRA analyses, which took into consideration the relationships between independent variables. These models have been used to examine the relationships between various dimensions of sustainable governance and development. Table 3 summarizes the models and analysis findings. Table 3. Results of MRA Dependent Variable SDG SDG SDG SDG Technology, Economy, Environment, Social Political, Economy, Environment, Social Rule of Law, Economy, Environment, Social Governance, Economy, Environment, Social Independent Variables Model 1 Model 2 Model 3 Model 4 Constant 43.487*** 23.732*** 48.112*** 48.835*** Individuals using the Internet (% of population) .116*** Population growth (annual %) -.746* -2.399*** -3.031* GDP per capita (current US$) 2.893E-5 -1,011E-5 -5.784E-5 GDP growth (annual %) .439*** CO2 emissions (metric tons per capita) -.455*** -.373*** Human Development Index (HDI) 64.527*** Political Transformation 20.419*** Political Participation -3.183 Political Integration -1.291 2.943 E-Government Index 31.406*** Governance 6.312* Quality of Democracy -3.884 Economist Democracy Index -.018 Environmental Performance Index .303*** .381*** ∆R2 0.827 0.863 0.765 0.74 F 142.460*** 156.569*** 81.178*** 106.088*** Note: *, **, and *** indicate the significance at 10%, 5%, and 1% levels, respectively. It is obvious that the relationship between sustainable governance and sustainable development. By focusing on management and governance, the scope of this research has been narrowed. The effects of the economy, environment, and social policies, which are the foundations of sustainable development, are included as dependent variables in all four models in this context. In addition, the independent Effects of Sustainable Governance to Sustainable Development 133 variables were analyzed for the meaning of technology influence in Model 1, political influence in Model 2, rule of law and democracy effect in Model 3, and governance effect covering all of these in Model 4. The increase in the number of people utilizing the internet in the country, as well as the value of the E-Government Index, had a positive impact on the SDG in Model 1. Countries that have advanced in ICTs (for example, the increase in internet usage rates of countries and the spread of e- participation policies) have also achieved a certain level in terms of sustainable development. Individuals' increased Internet access, in particular, has an impact on their policymaker's ability to be more transparent, democratic, and accountable in front of the public. As a result, citizens' demands for information, consultation, and active participation in the delivery of public services are on the rise. Some of these expectations are being met by the public agencies, particularly through their websites (Gündoğdu, 2021). Indeed, people's expectations for the development of e- government and e-participation opportunities have risen as they increasingly use the internet, smartphones, and social media (ITU-International Telecommunication Union, 2020). As a result, the global expansion of ICTs has had an impact on e- government and digitalization in public administration (Sandoval-Almazan & Gil- Garcia, 2012). In this regard, the findings of the research are consistent with those of other studies that have concluded that digitization has a favorable impact on sustainability (Funk, 2015; Gouvea et al., 2018; Pappas et al., 2018; del Río Castro et al., 2020). Another finding made possible by this model is that increased carbon emissions have a negative effect on the SDGs. To put it another way, countries' sustainable development and technological progress are effective in lowering carbon emissions. In this aspect, the research findings gained are similar to Funk's (2015) and Omri and Ben Mabrouk's research findings (2020). The increase in the Human Development Index (HDI) as an independent variable and the increase in SDGs are exactly related in Model 2, which we derived by adding the political element influence on the primary components of sustainable development. The results are consistent with previous research (Garrod & Fyall, 1998; Harris, 2000; Ciegis et al., 2009; Morelli, 2011) that emphasizes that evaluating development solely by economic growth is insufficient. The HDI, in particular, is based on three fundamental components: health, knowledge, and income level. These elements emphasize the importance of fulfilling social, economic, and political goals in human development. As a result, sustainable development helps to create a diverse socio-economic system that includes income, education, living standards, and health (Ciegis et al., 2009). The findings support the link between political (political participation + political transformation + political integration) and social factors (HDI). In addition, this finding indicates that studies dealing with the subject of sustainability in political (Patashnik, 2003) and social (Torjman, 2000; McKenzie, 2004) dimensions may be related to each other. Another result of this model is the prediction that as the population grows, sustainable development would decline. The major goal of the sustainable development issue is to come up with answers to the problems that will arise as the world's population grows. As a result, population increase has an impact on many aspects of a country, including production, consumption, social, and environmental variables. There is a direct link between a country's sustainable development and population planning in this context. The relationship between the rule of law and the SDGs, as well as independent variables, was investigated in Model 3 developed as part of the research. In this Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 134 context, it has been determined that countries with democratic, free, and independent judicial systems have greatly improved environmental and, in particular, political performance. It has been discovered that there is a positive and significant relationship between political transformation (stateness, political participation, rule of law, democratic institutions, political and social integration) and sustainable development, particularly in these countries. Other research analyzing the relationship between judicial independence and democracy and sustainable development (Stojanović et al., 2016; Güney, 2017; Glass & Newig, 2019; Omri & Ben Mabrouk, 2020) used the rule of law, SDGs, and governance variables. As a result, countries with legitimacy and democratic governance are more likely to achieve the SDGs. Finally, in Model 4, it was discovered that governance indicators and SDGs had a directly proportional relationship. While achieving the SDGs, it is critical for governments to develop solution policies that analyze the interactions between goals with a broad and holistic governance perspective. Policymakers can solve development problems by implementing a multi-level governance process that includes all relevant stakeholders and follows a transparent, responsible, and effective governance strategy. As a result, governments are advised to develop integrated and coordinated sustainable policies. In this regard, the study, like others (Stojanović et al., 2016; Davis, 2017; Güney, 2017; Jindra & Vaz, 2019; Omri & Ben Mabrouk, 2020), has confirmed that governance has a favorable impact on sustainable development through quantitative analysis. In reality, like Güney's research (2017), the findings of this study demonstrated that as the quality of governance rises, so does the level of sustainable development in both developed and developing countries. The research's original finding is that it indicates a link between several variable groups and sustainable development and governance. The level of governance, on the other hand, should be questioned considering each country's particular characteristics. A multi-criteria decision-making model was used to evaluate countries in terms of sustainable development and governance. BWM was used to weight criteria in this model. The criteria weights obtained by the BWM method are shown in Table 4. Table 4. Results of BWM Criteria C1 C2 C3 C4 C5 C6 C7 C8 Weights 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 Importance Rankings 1 9 5 5 5 14 2 2 Criteria C9 C10 C11 C12 C13 C14 C15 Weights 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 Importance Rankings 12 5 13 15 4 10 10 According to the BWM weighting results in Table 3, the most important criterion was SDG, while e-government was the least important criterion. Also, the CR value specified in Eq. (6) is very close to zero for these comparisons (CR=0.06). Thus, it can be said that a high level of consistency is achieved. Economic, social, and environmental dimensions are the basis of sustainable development (Basiago, 1999; Mitcham, 1995). There are economic, social, and environmental components to the relationship between sustainable development and sustainable governance, as well Effects of Sustainable Governance to Sustainable Development 135 as certain related institutional dimensions (Spangenberg, 2002; Bansal, 2005; Meadowcroft et al., 2005). Therefore, sustainable development was used as the best criterion in BWM, and the economic, social, and environmental criteria (Economic Transformation, Governance, and EPI) that directly affect the SDG were weighted as criteria near to the best. Other criteria used within the scope of the study were correlated according to their importance. GRA was used to evaluate countries in terms of sustainable development and governance, and to identify leading and behind countries and make comparisons. The analysis included 149 countries with no missing data in the criteria used in the study. The weight values of the criteria obtained using BWM are included in the GRA processes. Table 5 shows the ranking results obtained by GRA. Table 5. Results of GRA Rank Country Rank Country Rank Country Rank Country 1 Sweden 41 Ghana 81 Nepal 121 Saudi Arabia 2 Denmark 42 Greece 82 Gambia 122 Mauritania 3 Norway 43 Jamaica 83 Côte d'Ivoire 123 Laos 4 Finland 44 Hungary 84 Kuwait 124 Oman 5 Switzerland 45 Romania 85 Bosnia and Her. 125 Myanmar 6 New Zealand 46 Bulgaria 86 Morocco 126 Iraq 7 Germany 47 India 87 Thailand 127 Nigeria 8 Estonia 48 Peru 88 Belarus 128 Nicaragua 9 Uruguay 49 Argentina 89 Rwanda 129 Cameroon 10 United Kingdom 50 Malaysia 90 Burkina Faso 130 Mozambique 11 Ireland 51 Montenegro 91 Kenya 131 Pakistan 12 Netherlands 52 Colombia 92 Jordan 132 Angola 13 Austria 53 Georgia 93 Malawi 133 Afghanistan 14 Canada 54 Armenia 94 Tanzania 134 Eswatini 15 Iceland 55 Brazil 95 P.N. Guinea 135 Congo, Dem. Rep. 16 Czechia 56 North Macedonia 96 Cambodia 136 Iran 17 Australia 57 Albania 97 Sierra Leone 137 Zimbabwe 18 France 58 Serbia 98 Guinea 138 Congo, Rep. 19 Slovenia 59 Dominican Rep. 99 Turkey 139 Eritrea 20 Lithuania 60 UAE 100 Uganda 140 Haiti 21 Costa Rica 61 Ukraine 101 Algeria 141 Cent. Afr. Rep. 22 Belgium 62 El Salvador 102 Niger 142 Burundi 23 Latvia 63 Paraguay 103 Kazakhstan 143 Chad 24 Korea, Rep. 64 Indonesia 104 Guinea-Bissau 144 Venezuela 25 Mauritius 65 Vietnam 105 Honduras 145 Syrian Ar. Rep. 26 Japan 66 Philippines 106 Azerbaijan 146 Libya 27 Spain 67 Sri Lanka 107 Uzbekistan 147 Yemen 28 Chile 68 Ecuador 108 Egypt 148 Sudan 29 Malta 69 Tunisia 109 Ethiopia 149 South Sudan 30 Portugal 70 Senegal 110 Russian Fed. 31 Slovak Rep. 71 Benin 111 Madagascar 32 Israel 72 Mongolia 112 Djibouti 33 United States 73 China 113 Guatemala 34 Botswana 74 South Africa 114 Gabon 35 Poland 75 Moldova 115 Mali 36 Panama 76 Bolivia 116 Zambia 37 Italy 77 Namibia 117 Togo 38 Croatia 78 Mexico 118 Tajikistan 39 Bhutan 79 Bangladesh 119 Liberia 40 Cyprus 80 Kyrgyzstan 120 Lebanon When looked at the findings in Table 5, it's clear that Sweden is in top place and South Sudan is in worst place. Denmark, Norway, Finland, Switzerland, New Zealand, Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 136 Germany, Estonia, Uruguay, and the United Kingdom also include the top ten countries. Zambia, Togo, Tajikistan, Liberia, Lebanon, Syrian Arab Republic, Libya, Yemen, Sudan and South Sudan include the bottom ten. Sweden's focus on environmental integration and welfare policies in social and political terms might be seen as the cause for its ranking in first place in terms of sustainable development and governance. This country also has adaptable and effective action plans that are economically, environmentally, and socially viable (Government Offices of Sweden, 2021). According to Table 5, several leading European countries (Denmark, Norway, Finland, Switzerland, Germany, and England) have enacted sustainability policies that are similar to Sweden's. The GRA results showed that developed and wealthy countries were first, while underdeveloped countries experiencing instability, such as war and conflict, were last. Also, the countries in the first place are those that are at the top of several international institutions and organizations' indices of economic and democratic development levels. Northern European and Scandinavian countries do better in terms of governance and sustainability than other countries, depending on the strength of their democracy and executive capacity. It should also be stated unequivocally that the economic and social problems caused by the COVID-19 pandemic have severely harmed several countries' political, administrative, and reform capacities. 4.1. Validation of Results and Sensitivity Analysis Sensitivity analysis is commonly used to evaluate the effects of parameter changes, the reliability, and the validity of multi-criteria decision analysis solutions. Sensitivity analysis can be performed using various approaches, such as changing the weighting coefficients of the criteria, changing the units of measurement in which the values of the alternatives are expressed, changing the scales presenting the linguistic criteria, changing the type of criteria (cost/benefit), and comparing the results obtained by various methods. Most studies, however, conduct a sensitivity analysis based on changes in the weighting coefficients of the criteria and compares similar MCDA methods’ results (Biswas, 2020; Durmić et al., 2020; Božanić et al., 2021; Puška et al., 2021; Biswas et al., 2021a; Biswas et al., 2021b; Aytekin, 2022). For this reason, changing criteria weight coefficients and comparing similar MCDA methods’ results are used to make sensitivity analysis for the validation of results. The sensitivity analysis on the criterion weight values is used to assess the impact of the most influential criterion on the ranking performance of the proposed model. In this context, to investigate changes in criterion weighting coefficients, fourteen different sets were created. The weight values of the other criteria were changed only once for each criterion to create these sets (Aytekin, 2022). These sets, which include new criterion weight coefficients, are shown in Table 6. Effects of Sustainable Governance to Sustainable Development 137 Table 6. The Sets for Changing Criteria Weight Coefficients C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 SET 0 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 SET 1 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 SET 2 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 SET 3 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 SET 4 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 SET 5 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 SET 6 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 SET 7 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 SET 8 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 SET 9 0.0637 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 SET 10 0.0477 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 SET 11 0.0101 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 SET 12 0.0955 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 SET 13 0.0637 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 SET 14 0.0637 0.1307 0.0637 0.0637 0.0637 0.0637 0.0318 0.0955 0.0955 0.0477 0.0637 0.0477 0.0101 0.0955 0.0637 Set 0 in Table 6 represents the original weight values obtained using BWM in this study. Table 7 shows the Spearman rank correlation (r s) results of the ranking results obtained with the sets created with the criterion weight values in Table 6. Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 138 Table 7. The values of the Spearman’s rank coefficient Set 0 Set 1 Set 2 Set 3 Set 4 Set 5 Set 6 Set 7 Set 8 Set 9 Set 10 Set 11 Set 12 Set 13 Set 14 Set 0 1 0.994 0.982 0.997 0.990 0.988 0.994 0.987 0.986 0.996 0.992 0.991 0.992 0.992 0.993 Set 1 0.994 1 0.976 0.991 0.986 0.979 0.988 0.987 0.983 0.994 0.987 0.986 0.983 0.989 0.987 Set 2 0.982 0.976 1 0.987 0.965 0.985 0.963 0.951 0.996 0.980 0.983 0.985 0.975 0.971 0.992 Set 3 0.997 0.991 0.987 1 0.990 0.993 0.991 0.982 0.989 0.993 0.995 0.995 0.994 0.993 0.996 Set 4 0.990 0.986 0.965 0.990 1 0.987 0.995 0.995 0.968 0.981 0.990 0.987 0.994 0.996 0.986 Set 5 0.988 0.979 0.985 0.993 0.987 1 0.983 0.975 0.985 0.979 0.991 0.995 0.993 0.990 0.996 Set 6 0.994 0.988 0.963 0.991 0.995 0.983 1 0.994 0.970 0.989 0.986 0.986 0.993 0.994 0.985 Set 7 0.987 0.987 0.951 0.982 0.995 0.975 0.994 1 0.958 0.979 0.981 0.977 0.985 0.992 0.976 Set 8 0.986 0.983 0.996 0.989 0.968 0.985 0.970 0.958 1 0.986 0.984 0.989 0.977 0.977 0.993 Set 9 0.996 0.994 0.980 0.993 0.981 0.979 0.989 0.979 0.986 1 0.985 0.987 0.986 0.984 0.988 Set 10 0.992 0.987 0.983 0.995 0.990 0.991 0.986 0.981 0.984 0.985 1 0.992 0.993 0.994 0.992 Set 11 0.991 0.986 0.985 0.995 0.987 0.995 0.986 0.977 0.989 0.987 0.992 1 0.994 0.992 0.995 Set 12 0.992 0.983 0.975 0.994 0.994 0.993 0.993 0.985 0.977 0.986 0.993 0.994 1 0.995 0.992 Set 13 0.992 0.989 0.971 0.993 0.996 0.990 0.994 0.992 0.977 0.984 0.994 0.992 0.995 1 0.989 Set 14 0.993 0.987 0.992 0.996 0.986 0.996 0.985 0.976 0.993 0.988 0.992 0.995 0.992 0.989 1 Table 7 shows that the Spearman's rank correlation coefficients of the sets have a very high correlation degree (r s ≥ 0.95). These results show that changes in the criterion weighting coefficients have no significant effect on the model. On the other hand, a comparative analysis of the stability of the obtained results using GRA was executed throughout the application of other methods. The proposed model was compared to recent techniques such as CRADIS (Compromise Ranking of Alternatives from Distance to Ideal Solution) (Puška et al., 2021), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis) (Pamučar et al., 2014; 2017). REF-I (Nearest Solution to References-I) (Aytekin and Durucasu, 2021), REF-II (Aytekin, 2021b), WASPAS (Weighted Aggregated Sum Product Assessment) (Zavadskas et al., 2012), PSI (Preference Selection Index) (Maniya and Bhatt, 2010), MABAC (Multi-Attributive Border Approximation Area Comparison) (Pamučar and Ćirović, 2015). The t-score conversion (Aytekin, 2022) was determined for those affected by negative values, λ=0.5 in WASPAS, and reference values in REF-I and REF-II were determined depending on the optimization aspect of the criteria in the applications performed with these methods. Figure 2 depicts the obtained results in the form of a ray graph. Effects of Sustainable Governance to Sustainable Development 139 Figure 2 Comparative analysis of ranking results using different methods and GRA Figure 2 shows the reliability of the GRA rankings. As shown in Figure 2, all methods produced remarkably similar results. The rank correlation coefficients of the methods also shed light on the ranking's similarity and validity. As a result, the GRA method produces strong rank coefficients when compared to the ranking results of CRADIS (rs=0.996), MAIRCA (rs=0.993), REF-I (rs=0.988), REF-II (rs=0.986), WASPAS (rs =0.981), and MABAC (rs =0.993). GRA ranking results are valid and reliable for the nature of the problem determined. 5. Conclusions The subject of sustainable development and sustainable governance has universal characteristics in that it contains SDGs and sustainable governance indexes that apply to a wide range of disciplines. This study, which considers variables connected to governance, has investigated the effect of sustainable governance on sustainable development. The link between sustainable governance and sustainable development in a sample of 149 countries was discovered. In this context, we have determined that, despite some variances, sustainable governance has an impact on sustainable development. The study's most notable feature is that it uses multiple Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 140 regression analysis to find the sustainable governance variables that influence sustainable development. In addition, the BWM-GRA multi-criteria decision model is used to classify and evaluate the countries included in the study based on their performance. As a result, by combining two quantitative analytic methods, this study is able to draw a thorough conclusion regarding the research topic. Also, it was limited by considering the criteria/variables, datasets, and countries, and studies relating to this issue were used to determine the variables. In addition, the data diversity has been extended by incorporating data sets from a variety of international agencies and organizations concerned with sustainable governance. Multiple regression analysis was utilized in this study to investigate the change and relationship between sustainable governance and sustainable development, and four models were estimated. Significant findings were obtained as a result of the established models. It has been established that there is a link between several variable groups and sustainable development and governance. According to the results obtained in the study, variables like the number of people utilizing the internet in a country, the E-Government Index, HDI, the population growth, the rule of law with political transformations, and governance influence sustainable development. The findings are consistent with previous research (Stojanović et al., 2016; Davis, 2017; Güney, 2017; Omri & Ben Mabrouk, 2020). In addition, we also observed that the population growth rate is the strong control variable in analyzing the relationship between sustainable governance and sustainable development indicators. In conclusion, there is an inverse relationship between population increase and sustainable development. This outcome is consistent with the characteristics of the sustainable development paradigm. On the one hand, components of good governance such as democracy, rule of law, and accountability have a favorable impact on the implementation of sustainability policies. Political polarization and unilateral policies that are not inclusive, on the other hand, have a detrimental impact on sustainability. There is a similarity between the development level indicators of many international agencies and organizations and the GRA results acquired in the study in terms of sustainable development and governance. According to the MCDM findings, countries at the forefront, such as Sweden, Denmark, Norway, and Finland, are also ahead in terms of sustainable development and governance policies. The GRA results obtained for the countries in the study also confirm the literature in this regard. Sustainable development is also on the rise in some developed and developing countries with average or above-average sustainable governance. The situation in low-income or undeveloped countries where governance quality is below average has a detrimental impact on development sustainability. We confirm that governance has a good and significant impact on SDGs, indicating that this idea will continue to play a unifying and auxiliary role today and in the future. As a matter of fact, reports from international institutions and extraordinary events like the present COVID-19 epidemic demonstrate that systems based on governance, coordination, and cooperation among stakeholders have regained prominence. By properly implementing the rule of law, an independent judiciary, democracy, and related governance features, rules, and regulations, countries can help ensure that present resource use is at a level that is least damaging to future resource use. Additionally, the impact of ICT-related advancements such as the Internet continues to have an impact on governance and development sustainability. Therefore, we suggest that Effects of Sustainable Governance to Sustainable Development 141 when developing methods to address global concerns, the above-mentioned variables be considered. We believe that it is important to explore the sustainability relationship between governance and development from the perspective of management science and to determine which variables differ from developed and developing countries. In this regard, we suggest a deeper investigation into the nature of the connection between development and governance, ideally by regions and cities. The diverse sets of indicators that can measure the relationship between governance and development can be used to produce unique conclusions and analyses. References Ahrens, J., & Rudolph, P. M. (2006). The Importance of Governance in Risk Reduction and Disaster Management. Journal of Contingencies and Crisis Management, 14(4), 207-220. https://doi.org/10.1111/j.1468-5973.2006.00497.x Aidt, T. S. (2009). Corruption, Institutions, and Economic Development. Oxford Review of Economic Policy, 25(2), 271-291. https://doi.org/10.1093/oxrep/grp012 Alberti, M. (1996). Measuring Urban Sustainability. Environmental Impact Assessment Review, 16, 381-424. https://doi.org/10.1016/S0195-9255(96)00083-2 Ansell, C., Boin, A., & Keller, A. (2010). Managing Transboundary Crises: Identifying the Building Blocks of an Effective Response System. Journal of Contingencies and Crisis Management, 18(4), 195-207. https://dx.doi.org/10.1111/j.1468- 5973.2010.00620.x Awuzie, B., & Monyane, T. G. (2020). Conceptualizing Sustainability Governance Implementation for Infrastructure Delivery Systems in Developing Countries: Success Factors. Sustainability, 12(3), 961; 1-13. https://doi.org/10.3390/su12030961 Aydemir, E., & Sahin, Y. (2019). Evaluation of healthcare service quality factors using grey relational analysis in a dialysis center. Grey Systems: Theory and Application. https://doi.org/10.1108/GS-01-2019-0001 Ayres, R. U., Van den Bergh, J. C. M., & Gowdy, J. M. (2001). Strong versus Weak Sustainability: Economics, Natural Sciences, and ‘‘Consilience’’. Environmental Ethics, 23, 155-168. https://doi.org/10.5840/enviroethics200123225 Aytekin, A. (2020). Türkiye’de önde gelen şirketlerin etkinlik, farklılık ve performans ölçümü. Anadolu Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 21(4), 19- 35. https://doi.org/10.6084/m9.figshare.16652089.v1 Aytekin, A., & Durucasu, H. (2020). Çok kriterli karar problemlerine yönelik yeni bir ölçek: Aralıklı ve Aşamalı Tercih-Önem Ölçeği. E. Sarıkaya (Ed.), Sosyal ve Beşerî Bilimlerde Teori ve Araştırmalar- Cilt 2 içinde (453-474 ss.). Ankara: Gece Kitaplığı. https://doi.org/10.6084/m9.figshare.16651900.v1 Aytekin, A. & Gündoğdu, H. G. (2021). OECD ve AB Üyesi Ülkelerin Sürdürülebilir Yönetişim Düzeylerine Göre SWARA Tabanlı TOPSIS-SORT-B ve WASPAS Yöntemleriyle İncelenmesi. Öneri Dergisi, 16(56), 943- 971. https://doi.org/10.14783/maruoneri.862996 Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 142 Aytekin, A. (2021a). Comparative analysis of the normalization techniques in the context of MCDM problems. Decision Making: Applications in Management and Engineering, 4(2), 1-25. https://doi.org/10.31181/dmame210402001a Aytekin A, Durucasu H. (2021). Nearest solution to references method for multicriteria decision-making problems. Decision Science Letters; 10: 111–128. http://dx.doi.org/10.5267/j.dsl.2020.11.007 Aytekin (2021b). Efficiency and performance analyses of food companies via IDOCRIW, REF-II, and OCRA methods. In: Business Studies and New Approaches. Lyon: Livre de Lyon, pp. 7–24. https://doi.org/10.6084/m9.figshare.16669432.v1 Aytekin, A. (2022). Energy, environment, and sustainability: A multi-criteria evaluation of countries. Strategic Planning for Energy and the Environment, 281-316. https://doi.org/10.13052/spee1048-5236.4133 Bansal, P. (2005). Evolving Sustainably: A Longitudinal Study of Corporate Sustainable Development. Strategic Management Journal, 26(3), 197-218. https://doi.org/10.1002/smj.441 Barbier, E. B. (1987). The concept of sustainable development. Environmental Conservation, 14, 101–110. https://doi.org/10.1017/S0376892900011449 Barbier, E. B., & Burgess, J. C. (2020). Sustainability and development after COVID-19. World Development, 135. 105082. https://doi.org/10.1016/j.worlddev.2020.105082 Basiago, A. D. (1999). Economic, Social and Environmental Sustainability in Development Theory and Urban Planning Practice. The Environmentalist, 19(2), 145-161. https://doi.org/10.1023/A:1006697118620 Bebbington, J., Brown, J., & Frame, B. (2007). Accounting technologies and sustainability assessment models. Ecological Economics, 61(2-3), 224-236. https://doi.org/10.1016/j.ecolecon.2006.10.021 Bell, S., & Morse, S. (2003). Measuring Sustainability: Learning by Doing. London: Earthscan. Bentzen, J. S. (2012). How Bad is Corruption? Cross-country Evidence of the Impact of Corruption on Economic Prosperity. Review of Development Economics, 16(1), 167-184. https://doi.org/10.1111/j.1467-9361.2011.00653.x Benz, A. (2007). Governance in Connected Arenas – Political Science Analysis of Coordination and Control in Complex Rule Systems, in D. Jansen (ed.), New Forms of Governance in Research Organizations. Disciplinary Approaches, Interfaces and Integration (pp. 3-22), Dordrecht: Springer. https://doi.org/10.1007/978-1-4020- 5831-8 Bevir, M. (2009). Key Concepts in Governance, London: Thousand Oaks, SAGE. Biermann, F., Kanie, N., & Kim, R. E. (2017). Global governance by goal-setting: The novel approach of the UN Sustainable Development Goals. Current Opinion in Environmental Sustainability, 26-27, 26-31. https://doi.org/10.1016/j.cosust.2017.01.010 Biswas, P., Pramanik, S., & Giri, B. C. (2014). Entropy Based Grey Relational Analysis Method for Multi-Attribute Decision Making under Single Valued Neutrosophic Assessments. Neutrosophic Sets and Systems, 102. Biswas, S. (2020). Measuring performance of healthcare supply chains in India: A comparative analysis of multi-criteria decision making methods. Decision Making: Effects of Sustainable Governance to Sustainable Development 143 Applications in Management and Engineering, 3(2), 162-189. https://doi.org/10.31181/dmame2003162b Biswas, S., Majumder, S., Pamucar, D., & Dawn, S. K. (2021a). An Extended LBWA Framework in Picture Fuzzy Environment Using Actual Score Measures Application in Social Enterprise Systems. International Journal of Enterprise Information Systems (IJEIS), 17(4), 37-68. https://doi.org/10.31181/dmame2003162b Biswas, S., Majumder, S., & Dawn, S. K. (2021b). Comparing the socioeconomic development of G7 and BRICS countries and resilience to COVID-19: An entropy– MARCOS framework. Business Perspectives and Research, 22785337211015406. https://doi.org/10.1177%2F22785337211015406 Boulanger, P. M., & Bréchet, T. (2005). Models for policy-making in sustainable development: The state of the art and perspectives for research. Ecological Economics, 55(3), 337-350. https://doi.org/10.1016/j.ecolecon.2005.07.033 Božanić, D., Milić, A., Tešić, D., Salabun, W., & Pamučar, D. (2021). D numbers– FUCOM–fuzzy RAFSI model for selecting the Group of construction machines for enabling mobility. Facta Universitatis, Series: Mechanical Engineering, 19(3), 447- 471. DOI:10.22190/fume210318047b Brusis, M., & Siegmund, J. (2011). Designing Sustainable Governance Indicators 2011: Criteria and Methodology. Bertelsmann Stiftung. BTI (2021). The Bertelsmann Stiftung Transformation Index. https://www.bertelsmann-stiftung.de/de/startseite Campos, N. F., & Nugent, J. B. (1999). Development performance and the institutions of governance: Evidence from East Asia and Latin America. World Development, 27(3), 439-452. https://doi.org/10.1016/S0305-750X(98)00149-1 Candan, G., & Cengiz Toklu, M. (2022). Sustainable industrialization performance evaluation of European Union countries: an integrated spherical fuzzy analytic hierarchy process and grey relational analysis approach. International Journal of Sustainable Development & World Ecology, 1-14. https://doi.org/10.1080/13504509.2022.2027293 Caraddona, J. (2014). Sustainability: A History. New York: Oxford University Press. Chiu, R. L. H. (2004). Socio-Cultural Sustainability of Housing: A Conceptual Exploration. Housing, Theory and Society, 21(2), 65-76. https://doi.org/10.1080/14036090410014999 Ciegis, R., Ramanauskiene, J., & Martinkus, B. (2009). The Concept of Sustainable Development and its Use for Sustainability Scenarios. Engineering Economics, 62(2), 28-37. Croissant, A., & Pelke, L. (2022). Measuring policy performance, democracy, and governance capacities: a conceptual and methodological assessment of the sustainable governance indicators. European Policy Analysis. https://doi.org/10.1002/epa2.1141 Data World Bank (2021). World Bank Open Data. https://data.worldbank.org/ Davis, T. J. (2017). Good governance as a foundation for sustainable human development in sub-Saharan Africa. Third World Quarterly, 38(3), 636-654. https://doi.org/10.1080/01436597.2016.1191340 https://www.bertelsmann-stiftung.de/de/startseite Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 144 De Graaf, H. J., Musters, C. J. M., & Ter Keurs, W. J. (1996). Sustainable development: Looking for new strategies. Ecological Economics, 16, 205-216. https://doi.org/10.1016/0921-8009(95)00088-7 del Río Castro, G., González-Fernández, M. C., & Uruburu-Colsa, A. (2020). Unleashing the convergence amid digitalization and sustainability towards pursuing the Sustainable Development Goals (SDGs): A holistic review. Journal of Cleaner Production, 280: 122204. https://doi.org/10.1016/j.jclepro.2020.122204 Diaz-Balteiro, L., González-Pachón, J., & Romero, C. (2017). Measuring systems sustainability with multi-criteria methods: A critical review. European Journal of Operational Research, 258(2), 607-616. https://doi.org/10.1016/j.ejor.2016.08.075 Farag, M., Nandakumar, A. K., Wallack, S., Hodgkin, D., Gaumer, G., & Erbil, C. (2013). Health expenditures, health outcomes and the role of good governance. International Journal of Health Care Finance and Economics, 13(1), 33-52. https://doi.org/10.1007/s10754-012-9120-3 Fayissa, B., & Nsiah, C. (2013). The impact of governance on economic growth in Africa. The Journal of Developing Areas, 47(1), 91-108. DOI: 10.1353/jda.2013.0009 Fischer-Kowalski, M., & Haberl, H. (1998). Sustainable Development: Socio-Economic Metabolism and Colonization of Nature. International Social Science Journal, 50(158), 573-587. https://doi.org/10.1111/1468-2451.00169 Freedom House (2021). Freedom Index. https://freedomhouse.org/countries/freedom-world/scores Fukuda-Parr, S. (2016). From the Millennium Development Goals to the Sustainable Development Goals: shifts in purpose, concept, and politics of global goal setting for development. Gender & Development, 24(1), 43-52. https://doi.org/10.1080/13552074.2016.1145895 Funk, J. L. (2015). IT and sustainability: New strategies for reducing carbon emissions and resource usage in transportation. Telecommunications Policy, 39 (10), 861-874. https://doi.org/10.1016/j.telpol.2015.07.007 Garrod, B., & Fyall, A. (1998). Beyond the rhetoric of sustainable tourism?. Tourism Management, 19(3), 199-212. https://doi.org/10.1016/S0261-5177(98)00013-2 Gasparatos, A., El-Haram, M., & Horner, M. (2008). A critical review of reductionist approaches for assessing the progress towards sustainability. Environmental impact assessment review, 28(4-5), 286-311. https://doi.org/10.1016/j.eiar.2007.09.002 Glass, L. M., & Newig, J. (2019). Governance for achieving the Sustainable Development Goals: How important are participation, policy coherence, reflexivity, adaptation and democratic institutions?. Earth System Governance, 2, 100031. https://doi.org/10.1016/j.esg.2019.100031 Goodland, R. (1995). The Concept of Environmental Sustainability. Annual Review of Ecology and Systematics, 26(1), 1-24. Gouvea, R., Kapelianis, D., & Kassicieh, S. (2018). Assessing the Nexus of Sustainability and Information & Communications Technology. Technological Forecasting & Social Change, 130, 39-44. https://doi.org/10.1016/j.techfore.2017.07.023 Government Offices of Sweden (2021). Voluntary National Review 2021-Sweden: Report on the implementation of the 2030 Agenda for Sustainable Development, Sweden. Effects of Sustainable Governance to Sustainable Development 145 https://sustainabledevelopment.un.org/content/documents/279582021_VNR_Repo rt_Sweden.pdf Govindan, K., Shankar, K. M., & Kannan, D. (2020). Achieving sustainable development goals through identifying and analyzing barriers to industrial sharing economy: A framework development. International Journal of Production Economics, 227, 107575. https://doi.org/10.1016/j.ijpe.2019.107575 Gündoğdu, H. G. (2019). The Importance of the Participatory Democracy and the Multilevel Governance in the Solution of the Problems of Representative Democracy, in T. U. Uysal & C. Aldemir (ed.), Multi-Level Governance in Developing Economies, (pp. 215-239), Hershey PA: IGI Global Publisher. DOI: 10.4018/978-1-5225-5547- 6.ch009 Gündoğdu, H. G. (2020). Türkiye'de Kamu Yönetiminde Koordinasyon, Ankara: Nobel Bilimsel Eserler. Gündoğdu, H. G. (2021). Web Sitelerinin e-Katılım Düzeyleri Üzerine Bir Araştırma: Türkiye Büyükşehir Belediyeleri Örneği. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 10(28), 338-367. Güney, T. (2017). Governance and sustainable development: How effective is governance?. The Journal of International Trade & Economic Development, 26(3), 316-335. https://doi.org/10.1080/09638199.2016.1249391 Harborth, H. J. (1991). The debate about sustainable development: Starting point for an environment-oriented international development policy. Economics, 44, 7-31. Harris, J. M. (2000). Basic Principles of Sustainable Development. Global Development and Environmental Institute Working Paper: 00-04, USA: Tufts University. Human Development Reports (2021). https://hdr.undp.org/en/content/latest- human-development-index-ranking Huang, C.-Y., Hsu, C.-C., Chiou, M.-L., & Chen, C.-I. (2020). The main factors affecting Taiwan’s economic growth rate via dynamic grey relational analysis. Plos one, 15(10), e0240065. https://doi.org/10.1371/journal.pone.0240065 Huther, J., & Shah, A. (1998). Applying a Simple Measure of Good Governance to the Debate on Fiscal Decentralization. Policy Research Working Paper No. 1894. Washington, D.C: World Bank Publication. Hyden, G., Court, J., & Mease, K. (2004). Making Sense of Governance: Empirical Evidence from Sixteen Developing Countries. Boulder, CO: Lynne Rienner. https://doi.org/10.1515/9781626373839 ITU-International Telecommunication Union (2020). Measuring digital development Facts and figures 2020. Geneva: ITU Publications. İslamoğlu, A. H., & Alnıaçık, Ü. (2014). Sosyal bilimlerde araştırma yöntemleri. İstanbul: Beta Yayınevi. Jackson, T. (2009). Prosperity Without Growth? The Transition to a Sustainable Economy. London: Sustainable Development Commission. Joshi, D. K., Hughes, B. B., & Sisk, T. D. (2015). Improving Governance for the Post- 2015 Sustainable Development Goals: Scenario Forecasting the Next 50 Years. World Development, 70, 286-302. https://doi.org/10.1016/j.worlddev.2015.01.013 Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 146 Julong D. (1989). Introduction to grey system theory. The Journal of grey system. 1(1), 1-24. Kalaycı, Ş. (2014). SPSS uygulamalı çok değişkenli istatistik teknikleri (6th edition). Ankara: Asil Yayın Dağıtım. Kardos, M. (2012). The Reflection of Good Governance in Sustainable Development Strategies. Procedia-Social and Behavioral Sciences, 58(1), 1166-1173. https://doi.org/10.1016/j.sbspro.2012.09.1098 Kaswan, M. S., & Rathi, R. (2021). Investigation of life cycle assessment barriers for sustainable development in manufacturing using grey relational analysis and best worst method. International Journal of Sustainable Engineering, 14(4), 672-685. https://doi.org/10.1080/19397038.2021.1929550 Kaufmann, D., Kraay, A., & Zoido-Lobatón, P. (1999). Governance matters. The World Bank Policy Research Working Paper, 2196, 1-61. Kaufmann, D., Kraay, A., & Mastruzzi, M. (2010). The worldwide governance indicators: Methodology and analytical issues. The World Bank Policy Research Working Paper, 5430, 1-29. Kazemitash, N., Fazlollahtabar, H., & Abbaspour, M. (2021). Rough best-worst method for supplier selection in biofuel companies based on green criteria. Operational Research in Engineering Sciences: Theory and Applications, 4(2), 1-12. https://doi.org/10.31181/oresta20402001k Kemp, R., Parto, S., & Gibson, R. (2005). Governance for sustainable development: moving from theory to Practice, International Journal for Sustainable Development, 8(1/2), 12-30. https://dx.doi.org/10.1504/IJSD.2005.007372 Kokubun, K. (2022). Factors That Attract the Population: Empirical Research by Multiple Regression Analysis Using Data by Prefecture in Japan. Sustainability, 14(3), 1595. https://doi.org/10.3390/su14031595 Kostoska, O., Kocarev, L., (2019). A Novel ICT Framework for Sustainable Development Goals. Sustainability, 11 (7), 1961. https://doi.org/10.3390/su11071961 Kuo Y, Yang T, & Huang, G-W. (2008). The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & industrial engineering. 55(1), 80-93. https://doi.org/10.1016/j.cie.2007.12.002 Le Blanc, D. (2015). Towards Integration at Last? The Sustainable Development Goals as a Network of Targets. Sustainable Development, 23, 176-187. https://doi.org/10.1002/sd.1582 Lennan, A. M., & Ngoma, W. Y. (2004). Quality Governance for Sustainable Development. Progress in Development Studies, 4(4), 279-293. https://doi.org/10.1191%2F1464993404ps091oa Lin, Y., & Liu, S. (Ed.) (2004). A historical introduction to grey systems theory, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583). Doi: 10.1109/ICSMC.2004.1400689 Liu, G., Baniyounes, A. M., Rasul, M. G., Amanullah, M. T. O., & Khan, M. M. K. (2013). General sustainability indicator of renewable energy system based on grey relational analysis. International journal of energy research, 37(14), 1928-1936. https://doi.org/10.1002/er.3016 Effects of Sustainable Governance to Sustainable Development 147 Malekpoor H, Chalvatzis K, Mishra N, Mehlawat MK, Zafirakis D, Song M. (2018). Integrated grey relational analysis and multi objective grey linear programming for sustainable electricity generation planning. Annals of Operations Research. 269(1), 475-503. https://doi.org/10.1007/s10479-017-2566-4 Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision- making method: Preference selection index method. Materials & Design, 31(4), 1785- 1789. https://doi.org/10.1016/j.matdes.2009.11.020 Marcuse, P. (1998). Sustainability is not Enough. Environment and Urbanization, 10(2), 103-111. https://doi.org/10.1177/095624789801000201 Marglin, S. A., & Schor, J. B. (eds.) (1991). The Golden Age of Capitalism: Reinterpreting the Postwar Experience. Oxford: Oxford University Press. McKenzie, S. (2004). Social Sustainability: Towards Some Definitions. Hawke Research Institute Working Paper Series No. 27, University of South Australia Magill. Meadowcroft, J. (1997). Planning, Democracy and the Challenge of Sustainable Development. International Political Science Review, 18(2), 167-190. https://doi.org/10.1177/019251297018002004 Meadowcroft, J., Farrell, K. N., & Spangenberg, J. H. (2005). Developing a framework for sustainability governance in the European Union. International Journal of Sustainable Development, 8(1/2), 3-11. https://doi.org/10.1504/IJSD.2005.007371 Meadowcroft, J. (2007). Who is in Charge here? Governance for Sustainable Development in a Complex World. Journal of Environmental Policy & Planning, 9(3 - 4), 299-314. https://doi.org/10.1080/15239080701631544 Meadows, D., Meadows, D., Randers, J., & Behrens, W. (1972). The Limits to Growth: A Report for the Club of Rome’s Project on the Predicament of Mankind. Earth Island: London. Middleton, R. (2000). The British Economy Since 1945, London: Palgrave Macmillan. Mitcham, C. (1995). The Concept of Sustainable Development: Its Origins and Ambivalence. Technology in Society, 17(3), 311-326. https://doi.org/10.1016/0160- 791X(95)00008-F Morelli, J. (2011). Environmental Sustainability: A Definition for Environmental Professionals. Journal of Environmental Sustainability, 1(1), 1-9. DOI: 10.14448/jes.01.0002 Munda, G., & Nardo, M. (2005). Non-compensatory composite indicators for ranking countries: A defensible setting. EUR Report, EUR, 21833. Niazi AAK, Qazi TF, Basit A, Shaukat MZ. (2021). Evaluation of Climate of Selected Sixty-six Countries using Grey Relational Analysis: Focus on Pakistan. Journal of Business and Social Review in Emerging Economies. 7(1), 51-62. https://doi.org/10.26710/jbsee.v7i1.1533 Omri A., & Ben Mabrouk, N. (2020). Good governance for sustainable development goals: Getting ahead of the pack or falling behind?. Environmental Impact Assessment Review, 83, 106388. https://doi.org/10.1016/j.eiar.2020.106388 Osborne, Stephen P. (2010). The New Public Governance?: Emerging Perspectives on the Theory and Practice of Public Governance, London; New York: Routledge. https://doi.org/10.4324/9780203861684 Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 148 Oster, E. (2009). Does increased access increase equality? Gender and child health investments in India. Journal of Development Economics, 89(1), 62-76. https://doi.org/10.1016/j.jdeveco.2008.07.003 Pamučar, D., Vasin, L., & Lukovac, L. (2014, October). Selection of railway level crossings for investing in security equipment using hybrid DEMATEL-MARICA model. In XVI international scientific-expert conference on railway, railcon (pp. 89- 92). Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation Area Comparison (MABAC). Expert systems with applications, 42(6), 3016-3028. https://doi.org/10.1016/j.eswa.2014.11.057 Pamučar, D., Mihajlović, M., Obradović, R., & Atanasković, P. (2017). Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model. Expert Systems with Applications, 88, 58-80. https://doi.org/10.1016/j.eswa.2017.06.037 Pamučar, D. S., & Savin, L. M. (2020). Multiple-criteria model for optimal off-road vehicle selection for passenger transportation: BWM-COPRAS model. Vojnotehnički glasnik, 68(1), 28-64. https://doi.org/10.5937/vojtehg68-22916 Pappas, I.O., Mikalef, P., Giannakos, M. N., Krogstie, J., & Lekakos, G. (2018). Big data and business analytics ecosystems: paving the way towards digital transformation and sustainable societies. Information Systems and e-Business Management, 16, 479- 491. https://doi.org/10.1007/s10257-018-0377-z Patashnik, E. (2003). After the Public Interest Prevails: The Political Sustainability of Policy Reform. Governance, 16(2), 203-234. https://doi.org/10.1111/1468- 0491.00214 Pierre, J. (2000). Debating Governance: Authority, Steering, and Democracy. Oxford: Oxford University Press. Puška, A., Nedeljković, M., Hashemkhani Zolfani, S., & Pamučar, D. (2021). Application of interval fuzzy logic in selecting a sustainable supplier on the example of agricultural production. Symmetry, 13(5), 774. https://doi.org/10.3390/sym13050774 Puška A, Stević Ž, Pamučar D. (2021). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environ Dev Sustain; 1–31. https://doi.org/10.1007/s10668-021-01902-2 Quayes, S. (2012). Depth of outreach and financial sustainability of microfinance institutions. Applied Economics, 44, 3421-3433. https://doi.org/10.1080/00036846.2011.577016 Rajkumar, A. S., & Swaroop, V. (2008). Public spending and outcomes: Does governance matter?. Journal of Development Economics, 86(1), 96-111. https://doi.org/10.1016/j.jdeveco.2007.08.003 Robinson J. (2004). Squaring the Circle? Some Thoughts on the Idea of Sustainable Development. Ecological Economics, 48, 369-384. https://doi.org/10.1016/j.ecolecon.2003.10.017 Rothstein, B., & J. Teorell. J. (2008). What is Quality of Government: A Theory of Impartial Political Institutions. Governance, 21(2), 165-190. https://doi.org/10.1111/j.1468-0491.2008.00391.x Effects of Sustainable Governance to Sustainable Development 149 Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. https://doi.org/10.1016/0022- 2496(77)90033-5 Sachs, J. D. (2012). From Millennium Development Goals to Sustainable Development Goals. The Lancet, 379(9832), 2206-2211. https://doi.org/10.1016/S0140- 6736(12)60685-0 Sallehuddin, R., Shamsuddin, S. M. H., & Hashim, S. Z. M. (2008, November). Application of grey relational analysis for multivariate time series. In 2008 Eighth International Conference on Intelligent Systems Design and Applications (Vol. 2, pp. 432-437). IEEE. Doi: 10.1109/ISDA.2008.181 Sandoval-Almazan, R., & Gil-Garcia, J. R. (2012). Are Government İnternet Portals Evolving Towards More İnteraction, Participation, and Collaboration? Revisiting The Rhetoric of E-Government Among Municipalities. Government Information Quarterly, 29, 72-81. https://doi.org/10.1016/j.giq.2011.09.004 Schönborn, G., Berlin, C., Pinzone, M., Hanisch, C., Georgoulias, K., & Lanz, M. (2019). Why social sustainability counts: The impact of corporate social sustainability culture on financial success. Sustainable Production and Consumption, 17, 1-10. https://doi.org/10.1016/j.spc.2018.08.008 SDGs Database (2021). SDG Indicators Database. https://unstats.un.org/sdgs/dataportal SGI (2021). Sustainable Governance Indicators. https://www.sgi-network.org/2020/ Skidelsky, R. (2009). Keynes: The return of the Master, London: Penguin books. Spangenberg, J. H. (2002). Environmental space and the prism of sustainability: frameworks for indicators measuring sustainable development. Ecological Indicators, 2(3), 295-309. https://doi.org/10.1016/S1470-160X(02)00065-1 Stević, Ž., Pamučar, D., Kazimieras Zavadskas, E., Ćirović, G., & Prentkovskis, O. (2017). The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods. Symmetry, 9(11), 264. https://doi.org/10.3390/sym9110264 Stojanović, I., Ateljević, J., & Stević, R. S. (2016). Good Governance as a Tool of Sustainable Development. European Journal of Sustainable Development, 5(4), 558- 573. https://doi.org/10.14207/ejsd.2016.v5n4p558 Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics, 6th Edn. Northridge. CA: California State University. Tao, J., Sun, X. H., Cao, Y., & Ling, M. H. (2022). Evaluation of water quality and its driving forces in the Shaying River Basin with the grey relational analysis based on combination weighting. Environmental Science and Pollution Research, 29(12), 18103-18115. https://doi.org/10.1007/s11356-021-16939-z The Economist Intelligence Unit (2021). Democracy Index. https://www.eiu.com/n/campaigns/democracy-index-2020/ Tierney, K. (2012). Disaster Governance: Social, Political, and Economic Dimensions. Annual Review of Environment and Resources, 37(1), 341-363. https://doi.org/10.1146/annurev-environ-020911-095618 Torjman, S. (2000). The Social Dimension of Sustainable Development. Toronto: Caledon Institute of Social Policy. Gündoğdu & Aytekin/Oper. Res. Eng. Sci. Theor. Appl. 5(2) 2022 117-151 150 Tosun N. (2006). Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. The International Journal of Advanced Manufacturing Technology, 28(5), 450-455. https://doi.org/10.1007/s00170-004-2386-y Turner, G. M. (2008). A comparison of “The Limits to Growth” with 30 years of reality. Global Environmental Change, 18(3), 397–411. https://doi.org/10.1016/j.gloenvcha.2008.05.001 Tzeng, G.-H., & Huang. J.-J. (2011). Multiple attribute decision making: methods and applications: CRC press. https://doi.org/10.1201/b11032 UN (2007). Public Governance Indicators: A Literature Review. New York: United Nations. UN (2012). Realizing the Future We Want for All: Report to the Secretary-General. New York: UN System Task Team on the Post-2015 Development Agenda. UN General Assembly (2015). Transforming Our World: The 2030 Agenda for Sustainable Development. New York: United Nations. Valaskova, K., Kliestik, T., Svabova, L., & Adamko, P. (2018). Financial risk measurement and prediction modelling for sustainable development of business entities using regression analysis. Sustainability, 10(7), 2144. https://doi.org/10.3390/su10072144 Van de Walle, S. (2005). Peut-on mesurer la qualité des administrations publiques grâce aux indicateurs de gouvernance ?. Revue française d'administration publique, 115(3), 435-461. https://doi.org/10.3917/rfap.115.0435 WCED (1987). Our Common Future: The World Commission on Environment and Development, Oxford: Oxford University Press. World Bank (1989). Sub-Saharan Africa: From Crisis to Sustainable Development. Washington, D.C: World Bank Publication. World Bank (1992). Governance and Development. Washington, D.C: World Bank Publication. https://doi.org/10.1596/0-8213-2094-7 Worldometer (2021). World Population: Past, Present, and Future. https://www.worldometers.info/world-population/ (Date of access: 06.10.2021). WJP (2021). World Justice Project. https://worldjusticeproject.org/rule-of-law- index/(Date of access: 06.10.2021). WVS (2021). World Values Survey. https://www.worldvaluessurvey.org/wvs.jsp Wu, H-H. (2002). A comparative study of using grey relational analysis in multiple attribute decision making problems. Quality Engineering. 15(2), 209-217. https://doi.org/10.1081/QEN-120015853 Wu, J., & Wu, T. (2012). Sustainability indicators and indices: an overview. Handbook of sustainability management, 65-86. https://doi.org/10.1142/9789814354820_0004 Yang. C.-C., & Chen, B.-S. (2006). Supplier selection using combined analytical hierarchy process and grey relational analysis. Journal of Manufacturing Technology Management. 17(7), 926-941. https://doi.org/10.1108/17410380610688241 Yi, P., Dong, Q., Li, W., & Wang, L. (2021). Measurement of city sustainability based on the grey relational analysis: The case of 15 sub-provincial cities in China. Sustainable Cities and Society, 73, 103143. https://doi.org/10.1016/j.scs.2021.103143 https://www.worldometers.info/world-population/ Effects of Sustainable Governance to Sustainable Development 151 Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6. https://doi.org/10.5755/j01.eee.122.6.1810 Zeng, G., Jiang, R., Huang, G., Xu, M., & Li, J. (2007). Optimization of wastewater treatment alternative selection by hierarchy grey relational analysis. Journal of environmental management, 82(2), 250-259. https://doi.org/10.1016/j.jenvman.2005.12.024 Zharfpeykan, R., & Akroyd, C. (2022). Factors influencing the integration of sustainability indicators into a company's performance management system. Journal of Cleaner Production, 331, 129988. https://doi.org/10.1016/j.jclepro.2021.129988 © 2022 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).